ACS Publications. Most Trusted. Most Cited. Most Read
Spatially and Temporally Explicit Life Cycle Environmental Impacts of Soybean Production in the U.S. Midwest
More

OR SEARCH CITATIONS

My Activity
Publications

Figure 1Loading Img
Download Hi-Res ImageDownload to MS-PowerPointCite This:Environ. Sci. Technol. 2020, 54, 8, 4758-4768
  • Free to Read
Policy Analysis

Spatially and Temporally Explicit Life Cycle Environmental Impacts of Soybean Production in the U.S. Midwest
Click to copy article linkArticle link copied!

  • Xiaobo Xue Romeiko*
    Xiaobo Xue Romeiko
    Department of Environmental Health Sciences, University at Albany, State University of New York, One University Place, Rensselaer, New York 12144, United States
    *Email: [email protected]
    More by Xiaobo Xue Romeiko
    Orcidhttp://orcid.org/0000-0001-5579-2111
  • Eun Kyung Lee
    Eun Kyung Lee
    Department of Environmental Health Sciences, University at Albany, State University of New York, One University Place, Rensselaer, New York 12144, United States
    More by Eun Kyung Lee
  • Yetunde Sorunmu
    Yetunde Sorunmu
    Department of Environmental Health Sciences, University at Albany, State University of New York, One University Place, Rensselaer, New York 12144, United States
    More by Yetunde Sorunmu
  • Xuesong Zhang
    Xuesong Zhang
    Joint Global Change Research Institute, Pacific Northwest National Laboratory, 5825 University Research Court, College Park, Maryland 20740, United States
    Earth System Sciences Interdisciplinary Center, 5825 University Research Court, Suite 4001 College Park, Maryland 20740, United States
    More by Xuesong Zhang
Open PDFSupporting Information (1)

Environmental Science & Technology

Cite this: Environ. Sci. Technol. 2020, 54, 8, 4758–4768
Click to copy citationCitation copied!
https://doi.org/10.1021/acs.est.9b06874
Published March 23, 2020

Copyright © 2020 American Chemical Society. This publication is available under these Terms of Use.

Request reuse permissions

Abstract

Click to copy section linkSection link copied!
High Resolution Image
Download MS PowerPoint Slide

Understanding spatially and temporally explicit life cycle environmental impacts is critical for designing sustainable supply chains for biofuel and animal sectors. However, annual life cycle environmental impacts of crop production at county scale across mutiple years are lacking. To address this knowledge gap, this study used a combination of Environmental Policy Integrated Climate and process-based life cycle assessment models to quantify life cycle global warming (GWP), eutrophication (EU) and acidification (AD) impacts of soybean production in nearly 1000 Midwest counties yr–1 over 9 years. Sequentially, a machine learning approach was applied to identify the top influential factors among soil, climate, and farming practices, which drive the spatial and temporal heterogeneity of life cycle environmental impacts. The results indicated that significant variations existed in life cycle GWP, EU, and AD among counties and across years. Life cycle GWP impacts ranged from −11.4 to 22.0 kg CO2-eq kg soybean–1, whereas life cycle EU and AD impacts varied by factors of 302 and 44, respectively. Nitrogen application rates, temperature in March and soil texture were the top influencing factors for life cycle GWP impacts. In contrast, soil organic content and nitrogen application rate were the top influencing factors for life cycle EU and AD impacts.

This publication is licensed for personal use by The American Chemical Society.

Copyright © 2020 American Chemical Society

Subjects

what are subjects

1. Introduction

Click to copy section linkSection link copied!
Soybean farming plays an important role in ensuring food and energy security and promoting economic prosperity. Processed soybean is the world’s largest source of animal protein feed and the second largest source of vegetable oil. (1) The United States (U.S.) alone produced 4.5 billion bushels of soybean in 2018. (2) The soybean planting area in the U.S. is estimated to expand to 91.5 million acres in 2028, in order to meet the continuously increasing global market of soybean meal and soybean oil. (2) Meanwhile, soybean farming is associated with a range of negative environmental consequences such as global warming and nutrient pollution. (3−8) Nutrient releases from soybean production in the Midwest rank as an important contributor to riverine and coastal eutrophication. (9,10) Quantifying the environmental impacts of soybean production is required for understanding and mitigating the negative environmental impacts of soybean and its derived products.
Life cycle assessment (LCA) is a systematic method for quantifying both on-farm and supply chain environmental impacts of a product, process, or system. (11) In recent years, in a bid to reduce agricultural related environmental emissions, LCA has been used globally for quantifying the life cycle impacts of various crops such as corn, (9,12,13) wheat, (14,15) rice, (16) food cereal, (17) and soybean. (3,4,6,8,9,18−25) However, the quantitative analyses on spatially and temporally explicit life cycle environmental impacts of crop production remain limited.
First, life cycle environmental inventories of soybean production at county scale across multiple years are lacking. Life cycle environmental impacts of soybean production are inherently spatially and temporally heterogeneous, due to different weather, soil, and farming practices across counties and years. (26) A majority of the existing studies on soybean have examined the life cycle environmental impacts of soybean production either at a coarse spatial scale (such as region and country scales) (18,21−24) or for a few specific counties. (20,27) Despite being valuable, these studies are limited in quantifying spatially and temporally explicit life cycle environmental impacts of soybean production. The coarse scale assessment cannot capture the spatial heterogeneity in life cycle environmental impacts among counties, particularly for regions with spatially distinct soil, weather, and farming practices. Furthermore, the existing LCAs of soybean production were either temporally homogeneous (28) or focused on a single year. (8,23,24) Additionally, recent efforts in coupling LCAs with agent based approaches evaluated the important influences of stakeholders’ (i.e., farmers and policy makers) decisions on life cycle environmental impacts of crop production under various climate and geographical contexts. (29,30) Despite the value, these coupled approaches often rely on spatially coarse on-farm release inventory from the ecoinvent database. (29,30) Overall, LCAs of soybean production at finer spatial scales (i.e., county scale) across multiple years are necessary for accurate biophysical accounting, supply chain management, and policy aiding.
Furthermore, the relative influences of weather, soil, and farming practices on life cycle environmental impacts of soybean production are unknown. Some studies (23,27,31) used scenario comparisons to identify the influences of farming locations and practices on life cycle impacts of soybean production. Other studies (9,32) evaluated the variability of life cycle impacts from soybean production by utilizing Monte Carlo assessment. These traditional scenario or variability assessment approaches usually assess small sets of scenarios or input parameters and are often infeasible to systematically identify the relative influences of weather, soil, and farming practice parameters for large spatially and temporally heterogeneous data sets. Such large data sets include life cycle environmental impacts of soybean production over thousands of counties across multiple years. In contrast, machine learning (ML) capable of efficiently computing big data sets have surfaced recently, creating new opportunities to analyze large spatially and temporally heterogeneous data sets. (33) ML has been used in discovering the driving factors for spatial and temporal trends in transportation, public health, and other disciplines. (34−36) The existing applications of ML in LCA focused on either spatially and temporally homogeneous inventory (37−40) or chemical toxicity estimates. (41,42) To our best knowledge, no existing LCA studies have applied ML to identify the relative influences of weather, soil, and farming practices on spatially and temporally explicit environmental impacts from soybean production.
To fill in these knowledge gaps, this study assessed the spatially and temporally explicit life cycle environmental impacts of soybean production in the Midwest region of the U.S. by fusing a biogeochemistry model and process-based LCA model. Furthermore, this study used a ML approach to identify the leading influential factors among weather, soil, and farming practice parameters, which affect the spatially and temporally explicit life cycle environmental impacts. Additionally, this study discussed the implications of spatial and temporally explicit life cycle assessment for biophysical accounting, supply chain management, and environmental remediation.

2. Methodology

Click to copy section linkSection link copied!
LCA method framework was applied to analyze the life cycle GWP, EU, and AD impacts of soybean production in the 12 U.S. Midwest states. Following the International Organization for Standardization (ISO) standards, an internationally recognized technical evaluation standards for environmental impact assessments (ISO standards: 14040 and 14044), (11,43) four stages of LCA including (i) goal and scope definition, (ii) life cycle inventory, (iii) life cycle impact assessment, and (iv) life cycle interpretation are described in detail. Additionally, machine learning was applied to identify leading variables (i.e., weather, soil characteristics, farming practices) that contribute to life-cycle environmental impacts associated with soybean production in the U.S. Midwest (Figure S2 in the Supporting Information (SI)).

2.1. Goal and Scope Definition

The goals of this study were (1) to quantify the life cycle environmental impacts of soybean production in the U.S. Midwest states at county scale over multiple years, and (2) to identify the most influential factors among weather, soil, and farming practices affecting spatially and temporally explicit life cycle environmental impacts of soybean production. Additionally, this study discusses the potential use of spatially and temporally explicit life cycle environmental impacts for biophysical accounting, supply chain management, and environmental remediation.
This study focused on soybean production in 12 U.S. Midwest states: Indiana, Illinois, Iowa, Kansas, Missouri, Minnesota, North Dakota, South Dakota, Ohio, Wisconsin, Michigan, and Nebraska from 2000 through 2008. These states produced 79% of total national soybean in 2018. (44) The system boundary of this study consisted of foreground and background subsystems, as depicted in Figure 1 and SI Figure S1. The foreground subsystem included all on-farm agronomic operations, such as tillage, application of fertilizers, pesticides, limestone, water irrigation, and their associated on-field carbon (C), nitrogen (N), and phosphorus (P) emissions. The background subsystem consisted of all supply chain activities including the production of soybean seeds, agrochemicals (i.e., pesticides, fertilizers, and limestone), fuel, electricity, and agricultural machinery (i.e., tractors and harvesting equipment) and their associated environmental releases.

Figure 1

Figure 1. Stages of life cycle assessment (LCA) of soybean production in the U.S. Midwest. The diagram shows the four stages of LCA: (1) Goal and Scope, (2) Life Cycle Inventory, (3) Life Cycle Impact Assessment, and (4) Interpretation.

High Resolution Image
Download MS PowerPoint Slide
This study focused on the impact categories of GWP, EU, and AD, mainly because of the emergent needs of mitigating these environmental impacts from soybean production and the data availability of spatially and temporally explicit on-farm releases. The functional unit for the analysis was 1 kg of soybean. This functional unit was chosen, because it has been widely used in previous soybean LCA studies and facilitates a fair comparison with other studies. (9,45,46)

2.2. Life Cycle Inventory (LCI)

The total LCI included on-farm inventory from foreground subsystem and supply chain inventory from background subsystem. Environmental Policy Integrated Climate (EPIC) model was used to assess the spatially and temporally explicit on-farm inventory. Process-based LCI approach was used to quantify the supply chain inventory. The LCI of both foreground and background subsystems were combined to obtain the total LCI associated with soybean production (SI Figure S2).

2.2.1. On-Farm LCI from Foreground Subsystem

The on-farm C, N, and P releases were quantified by the EPIC model. The EPIC model is capable of simulating key biophysical and biogeochemical processes such as plant growth and development, water balance, soil erosion, C, and nutrient cycling and associated greenhouse-gas emissions and aqueous nutrient releases, as influenced by weather, soil, and farming practices. (47,48) The EPIC model, which is based on geospatial modeling framework, was adapted to quantify on-farm greenhouse-gas emissions and aqueous nutrient releases at the county level. Daily weather data (i.e., temperature, precipitation, relative humidity, etc.), soil conditions (soil organic carbon and soil types), fertilizers application and tillage practices were overlaid to the county-level land use data based on hydrologic unit and political boundary. The linked files were then processed to generate on-farm soil C change, N2O, NH3, P, and N leaching and runoff (SI Figure S3). While daily weather information (e.g., daily temperature and precipitation) was obtained from the National Oceanic and Atmospheric Administration, (49) soil characteristics (e.g., soil types and soil organic matter) were extracted from the USDA’s Soil Survey Geographic (SSURGO) database. (50) The county-level tillage information (fraction of conventional tillage and no tillage) and state-level fertilizer application rates were provided by the USDA. (51,52) The spatially explicit EPIC modeling system has previously been developed and tested to estimate yields and environmental impacts of major crop systems in the U.S. Midwest. (53,54)SI Table S1 provides the details about the input parameters for EPIC modeling.

2.2.2. Supply Chain LCI from Background Subsystem

The supply chain processes included the production and provision of agrochemicals, seedlings, electricity, and irrigation water. Consistent with EPIC’s data sources, agrochemical application rates for fertilizers, herbicides and lime, and soybean yields were obtained from the USDA National Agricultural Survey Statistics (NASS) at the county scale. (52) Aligned with previous soybean LCA studies, (9,55) this study included the most commonly used herbicides – Glyphosate and 2,4-D. The diesel use was obtained from previous literature. (56) The irrigation requirement for soybean production (in gallons per acres) was obtained from the United States Geological Survey (USGS) at the county level for year 2005, since this information is collected in 5-year intervals. (57) County-level water usage was estimated based on soybean planted area obtained at the county-level from USDA. (52,57) The percentages of groundwater and surface water as irrigation sources were obtained from USDA Farm and Ranch Irrigation Survey (FRIS) and soybean crop planted by acres. (58) The environmental release profiles for these supply chain activities, such as production of agrochemicals and diesel and provision of irrigation water, were obtained from the Ecoinvent v3.1 database (shown in SI Figure S2 and Table S3). (59)

2.3. Life Cycle Impact Assessment (LCIA)

The characterization factors reported by the Tool for Reduction and Assessment of Chemicals and other environmental Impacts (TRACI) assessment model version 2.1 were used to estimate the life cycle environmental impacts. TRACI, developed by the U.S. Environmental Protection Agency, integrates environmental transport fate and toxicity models and provides characterization factors specifically for the North American region. (60)
The total life cycle environmental impacts were estimated by the summation of both on-farm and supply chain impacts (see SI eqs S1–S7). (61,62) On-farm environmental impacts were quantified by multiplying the EPIC modeling estimates (detailed in Section 2.2.1) with the corresponding characterization factors, as shown in SI eqs S1 and S2 as well Table S2. Soil carbon fluxes/sequestration and nitrous oxide (N2O) were included to estimate on-farm life cycle GWP. While nutrients leaching and runoff for both N and P were included for on-farm life cycle EU, ammonia (NH3) was included for on-farm life cycle AD. Similarly, supply chain impacts were determined by the sums of products of supply chain inventory values and corresponding characterization factors (SI eqs S1–S7 and Table S3). Life cycle GWP, EU, AD impacts of soybean production in this study are expressed in the units of kg CO2-eq kg soybean–1, g N-eq kg soybean–1, and g SO2-eq kg soybean–1, respectively.

2.4. Life Cycle Interpretation

This study examined the spatial and temporal variability of life cycle GWP, EU, and AD impacts of soybean production across U.S. Midwest counties over 9 years, and assessed the relative contributions of on-farm and supply chain processes across those counties and years. Additionally, the boosted regression tree (BRT) model was used to identify the leading contributors among soil, weather, and farming practice parameters affecting the life cycle impacts.
BRT is a nonparametric and regression tree based approach. (34,63) Regression trees recursively partition data sets to generate a decision tree structure. BRT is the sum of a large number of boosted regression trees to characterize the overall relationship of the response variable to a collection of predictor variables. BRT is capable of capturing both variable interactions and nonlinear relationships. BRT also presents unique advantages of obtaining robust estimates in the presence of colinearity issues, missing data or outliers, particularly for large spatially, and temporally heterogeneous data sets. Due to these strengths, BRT has been utilized to identify top contributing predictors for spatial and temporal processes in ecology, public health, and other fields. (35,64,65) Extending the scope of previous studies, this study applied BRT to identify the top contributing predictors for spatially and temporally explicit life cycle impacts.
To build the BRT model, the following three parameters are required including learning rates (lr), tree complexity (tc), and bag fraction (bf). The lr shrinks the contribution of each tree until obtaining the final best model, whereas the tc determines the depth of interaction between the predictor variables. The bf determines the proportion of samples that are randomly selected to build the model. To maximize the predictive accuracy, lr and bf were set at 0.005 and 0.75, respectively. Hastie et al. suggested that 4–8 splits are sufficient in most cases. (66) Consistent with their suggestions, tc was set to 5 and the number of trees ranged from 0 to 10 000.
Furthermore, to identify the relative influences among weather, soil, and farming practices for life cycle impacts, 31 predictor variables were included in the BRT model. They consist of climate factors (monthly precipitation and monthly temperature), soil characteristics (proportion of soil organic content and types of soil), and farming practices (tillage and N fertilizers). These predictor variables were chosen based on previous studies examining similar environmental impacts associated with crop production. (55,67−71) The response variables were life cycle GWP, EU, and AD impacts calculated based on EPIC and process-based LCA models throughout 2.2 and 2.3. The summary of predicator and response variables is in Table 1. Based on the combination of model parameters, a 10-fold cross validation was conducted to obtain the optimal number of trees (SI Table S4). Additionally, based on the best predictive model, partial dependence plots of life cycle GWP, EU, and AD impacts against predictor variables were created to demonstrate the relative contributions of predictor variables (in percentages) with the summary function in R (shown in Figure 5 and SI Figure S5).
Table 1. Data Sources for the Boosted Regression Modela
predictor variablesdata descriptiondata sourcesspatial resolutiontime resolution
nitrogen fertilizer application ratesoybean fertilizer application rate (lbs acre–1)USDA NASS (44)stateannual
soil organic content (%)percentage of soil organic content measured in soil depth up to 6 mUSDA SSURGO (50)countymultiyear average
soil type: clay, sand, silt (%)percentage of soil typesUSDA SSURGO (50)countymultiyear average
temperature (°C)monthly mean temperatureNOAA (49)countymonthly
precipitation (mm)monthly mean precipitationNOAA (49)countymonthly
farming practices (NT and CT)farming practices in fractionsUSDA (44)countyannual
Response Variables
life cycle GWP, EU, and AD valueslife cycle GWP (kg CO2-eq kg soybean–1), EU (g N-eq kg soybean–1), AD (g SO2-eq kg soybean–1)estimated in this study (2.2 and 2.3)countyannual
a

Abbreviations: CT, conventional tillage; NT, no tillage.

3. Results and Discussion

Click to copy section linkSection link copied!

3.1. Magnitudes of Soybean’S Life Cycle Impacts in the Midwest Counties for Nine Years

The life cycle GWP impacts varied significantly across the U.S. Midwest counties, spanning from −11.4 kg CO2-eq to 22.0 kg CO2-eq kg soybean–1 (Figure 2 and SI Figure S6). This large variability was mainly driven by the differences in soil carbon fluxes, nitrous oxide emission, and irrigation, as shown in SI Figure S7. For example, Stanton County of Kansas State in year 2000 (SI Figures S6 and S8), exhibiting the highest GWP impact among all Midwest counties across 9 years, had significantly high on-farm soil carbon emission of 2.1 kg CO2-eq kg soybean–1 and moderate on-farm nitrous oxide emission of 0.44 kg CO2-eq kg soybean–1. Additionally, irrigation-related supply chain GHG emissions in Stanton County in year 2000 reached up to 19.4 kg CO2-eq kg soybean–1. In contrast, Crawford County of Kansas State in year 2000, exhibiting the lowest GWP impact among all Midwest counties across 9 years, showed substantial soil carbon sequestration of 13.5 kg CO2-eq kg soybean–1, slightly high nitrogen oxide emission of 1.5 kg CO2-eq kg soybean–1, and minimal irrigation-related GHG emissions as low as 0.006 kg CO2-eq kg soybean–1. The carbon emissions from soil was 15.6 kg CO2-eq kg soybean–1 less in Crawford County than in Stanton County. Moreover, the irrigation related GHG emissions for Crawford County was less than 1% of the irrigation related GHG emissions for Stanton County. Stanton County with 19.4 gallons acre–1 of irrigated cropland required a much higher amount of irrigation water than Crawford County with only 0.006 gallons acre–1 of irrigated land for soybean production (mean: 0.015 gallons acre–1 of irrigation among all counties in Kansas). Furthermore, soybean yield in year 2000 was significantly higher in Crawford County (1412.3 kg ha–1) compared to Stanton County (672.5 kg ha–1).

Figure 2

Figure 2. Maps showing county-level life cycle environmental impacts of soybean production in the U.S. Midwest for years 2002, 2004, and 2006: (A) global warming potential, (B) eutrophication, and (C) acidification.

High Resolution Image
Download MS PowerPoint Slide
Life cycle EU impacts among Midwest counties varied from 2.6 to 785.7 g N-eq kg soybean–1. While Rusk County of Wisconsin State in year 2002 displayed the highest EU impact of 785.7 g N-eq kg soybean–1, Adams County of Nebraska State in year 2003 presented the lowest life cycle EU impact of 2.6 g N-eq kg soybean–1 (SI Figures S6 and S8). The differences in on-farm phosphorus leaching and runoff among counties led to this significant spatial disparity. For example, phosphorus leaching and runoff in Rusk County of Wisconsin State reached 738.5 and 34.2 g N-eq kg soybean–1, respectively. In contrast, phosphorus leaching and runoff in Adams County of Nebraska State were as low as 0.2 and 0.8 g N-eq kg soybean–1, respectively.
Life cycle AD impacts across the Midwest counties spanned from 2.8 in Guernsey County of Ohio State in year 2007 to 124.7 g SO2-eq kg soybean–1 in Stanton County of Kansas State in year 2000 (SI Figure S8). Such large spatial variability was mainly caused by the differences in on-farm NH3 emissions and irrigation requirement. Guernsey County, exhibiting the lowest life cycle AD impact, showed very low on-farm NH3 emission of 2.14 g SO2-eq kg soybean–1 and no irrigation related emissions. In contrast, Stanton County, exhibiting the highest life cycle AD impacts, had 10.7 g SO2-eq kg soybean–1 from on-farm NH3 emission and 112.5 g SO2-eq kg soybean–1 from irrigation process.

3.2. Spatial and Temporal Variability in Life Cycle Impacts of Soybean Production among States Across 9 Years

This study suggested that the estimates of life cycle environmental releases at state scale cannot capture the spatial heterogeneity of life cycle environmental releases among counties. The differences in median life cycle impacts among 12 states were much smaller than the differences in life cycle impacts among counties within a state (Figure 3). Utilizing median values of life cycle impacts of soybean production in a Midwest state to represent life cycle impacts of soybean production in a county of that state can lead to large errors. For example, the median life cycle GWP of Kansas (median: −0.46 kg CO2-eq kg soybean–1) in 2000 was nearly 10.9 kg CO2-eq kg soybean–1 higher than the life cycle GWP of −11.4 kg CO2-eq kg soybean–1 in Crawford County in Kansas (county exhibiting the lowest life cycle GWP impacts) during that same year. The median life cycle EU of Wisconsin (median: 39.8 g N-eq kg soybean–1) in 2002 was nearly 19.7 times smaller than the life cycle EU in Rusk County in Wisconsin (county exhibiting the highest life cycle EU impacts) in that same year (785.7 g N-eq kg soybean–1). The median life cycle AD Kansas (median: 15.4 g SO2-eq kg soybean–1) in 2000 was 8.1 times lower than the life cycle AD impacts in Stanton County (county exhibiting the highest life cycle AD impacts) in Kansas in 2000 (124.7 g SO2-eq kg soybean–1).

Figure 3

Figure 3. Statistical distribution of life cycle environmental impacts of soybean production in 12 US Midwest states (2000–2008) state-by-state: (A) Life cycle GWP (kg CO2-eq kg soybean–1), (B) life cycle EU (g N-eq kg soybean–1), and (C) life cycle AD (g SO2-eq kg soybean–1). The error bars represent county-level min and max values and the lines in box plots show 25th, 50th, and 75th percentiles.

High Resolution Image
Download MS PowerPoint Slide
Moreover, substantial temporal variability existed for life cycle impacts of producing 1 kg soybean. For example, the median life cycle GWP of Crawford County in Kansas state over 9 years was 0.74 kg CO2-eq kg soybean–1, which was 16.4 times larger than the life cycle GWP of the same county in year 2000 (−11.4 kg CO2-eq kg soybean–1). Similarly, the median life cycle EU of Rusk County in Wisconsin state over 9 years was 39.0 g N-eq kg soybean–1, which was 20.1 times lower than the life cycle EU of the same county in year 2002 (785.7 g N-eq kg soybean–1). It was also interesting to note that the differences in life cycle impacts of soybean production among different counties for a single year were more substantial than the differences in the median life cycle impacts of Midwest soybean production over multiple years (Figure 4). For example, the difference in median life cycle EU impacts of Midwest counties across 9 years was less than 46.5 g N-eq kg soybean–1, which was much less than the difference in life cycle EU of Midwest counties in year 2002 at 782.4 g N-eq kg soybean–1.

Figure 4

Figure 4. Statistical distribution of life cycle environmental impacts of soybean production in 12 US Midwest states, 2000–2008 year-by-year: (A) Life cycle GWP (kg CO2-eq kg soybean–1), (B) life cycle EU (g N-eq kg soybean–1), and (C) life cycle AD (g SO2-eq kg soybean–1). The error bars represent county-level min and max values and the lines in box plots show 25th, 50th, and 75th percentiles.

High Resolution Image
Download MS PowerPoint Slide
Additionally, life cycle EU exhibited greater variability than life cycle GWP and AD impacts (SI Figure S8). The life cycle EU impacts across Midwest counties varied by a factor of 302. In contrast, the life cycle AD impacts across Midwest counties varied by a factor of 44.5. Overall, LCA at county level across multiple years is necessary to reveal the spatial and temporal heterogeneity of life cycle emissions. Caution should be taken, when median values of LCIs for a state are used to approximate the LCIs of a county in that state, particularly for life cycle nutrient releases.

3.3. The Relative Influences of Weather, Soil and Farming Practices on Life Cycle Impacts

The spatial and temporal differences in life cycle impacts of soybean production were caused by the variations in top influencing factors among weather, soil, and farming practices. The application rates of nitrogen fertilizers ranked as the most influential variable for life cycle GWP impacts of producing 1 kg of soybean. Nitrogen application rates influence both on-farm N2O emission and supply chain GHGs from nitrogen fertilizer production (Figure 5a). Following nitrogen application rates, March temperature ranked as the second most influential factor. March temperature influences shift of N2O:N2 ratio (72) and increases N2O emissions during freeze–thaw cycle. (73) Furthermore, July temperature and clay soil content were important influencing predictors to life cycle GWP impacts. Warmer temperatures in July have been shown to enhance N2O emissions by accelerating the soil microbial nitrification–denitrification processes. (74) Furthermore, soil texture played a significant role in the magnitude of GHGs. For instance, finer textured soil (i.e., clay soil) tend to retain more water than coarse textured soil (i.e., sandy soil) and enhance denitrification, therefore resulting in higher N2O emission. (75)

Figure 5

Figure 5. Relative influences of predictor variables to life cycle environmental impacts of: (A) Global warming potential, (B) eutrophication, and (C) acidification of soybean production in 12 U.S. Midwest states from 2000 to 2008. The bars show the relative contributions of predictor variables (x-axis) in percentages.

High Resolution Image
Download MS PowerPoint Slide
The spatial and temporal variability of life cycle EU from soybean production was mainly driven by the differences in soil organic content and nitrogen application rates. Soil organic content appeared to be the leading predictor to life cycle EU impacts (Figure 5b). Greater proportion of soil organic matter exhibited higher N losses due to faster decomposition of nutrients, increasing the availability of nutrient losses through leaching and runoff. (76−78) Nitrogen fertilizer application rate was identified as the second most important predictor variable. Consistent with our findings, previous studies (71,79) exhibited the strong association between nitrogen fertilizer application rate and nitrogen leaching and runoff. A higher nitrogen fertilizer application rate resulted in a higher amount of aqueous nitrogen available for leaching and runoff. Additionally, temperature was also positively associated with life cycle EU impacts. Higher temperature promoted soil nitrogen mineralization, consequently increasing nitrogen runoff from soil. (80,81)
The nitrogen fertilizer application rate was the leading predictor to life cycle AD impacts (Figure 5c). This finding was expected since nitrogen fertilizers are well-known to be the primary sources of NH3 emissions from agricultural production. In addition, precipitation in the month of December showed inverse relationships with life cycle AD impacts (SI Figure S5). Based on field measurements conducted in European regions, (82) low precipitation decreased diffusion of NH3 in soil, thereby increasing volatilization losses by 13.1% as in the case of urea. Furthermore, soil organic content (SOC) was the third top predictor variable to life cycle AD impacts. Previous studies (83,84) based on field samples indicated that higher proportions of SOC were associated with increases in urea hydrolyses rates and volatilization. The higher rates of hydrolyses and volatilization were potentially driven by the increase of urease enzymes (more abundant in organic soil) produced by soil microorganisms, consequently enhancing greater N losses from nitrogen fertilizers. (85)

3.4. Comparison of Existing Soybean LCA Literature

Although this study homogenized the system boundary of all 12 studies by excluding the impacts of soybean transportation and processing, large discrepancies existed among reported LCA values for soybean production. This study calculated life cycle GWP to range from −11.4 to 22.1 kg CO2-eq kg soybean–1 in the Midwest, with a median value of 0.56 kg CO2-eq kg soybean–1. The majority of the existing studies estimated median life cycle GWP impacts lower than 0.75 kg CO2-eq kg soybean–1. (8,9,12,13,18,21,23,24,86−88) In contrast, two studies authored by Reijnders and Huijbregts (21) and Castanheira and Freire (81) found significantly higher median life cycle GWP impacts, which were 4.4 and 11.6 kg CO2-eq kg soybean–1, respectively. In particular, Castanheira and Freire’s study (81) indicated the median life cycle GWP of soybean production in Brazil was almost 20 times higher in life cycle GWP impacts than the median life cycle GWP value of this study. Moreover, life cycle EU impacts varied substantially from 0.04 to 70.2 g N-eq kg soybean–1. (8,9,18,23,24) Additionally, life cycle AD impacts changed from 0.002 to 107.4 g SO2-eq kg soybean–1 (Figure 6). (8,9,18,23,24) These reported values for life cycle EU and AD resided in the estimated ranges of this study.

Figure 6

Figure 6. Comparison of existing literature in life cycle analysis of soybean production: (A) global warming potential, (B) eutrophication, and (C) acidification. The orange bars represent the median values of the life cycle environmental impacts of the present study and the blue bars represent the median values of life cycle environmental impacts associated with soybean production in the existing literature. The asterisks below the bars indicate studies focusing on U.S. Midwest regions and the error bars represent min and max values of life cycle environmental impacts for studies providing those values.

High Resolution Image
Download MS PowerPoint Slide
These differences in LCA estimates resulted from different choices in spatial and temporal scales, system scopes and LCI/LCIA modeling approaches. Primarily, the majority of the existing studies were based on regional inventory for a single year and did not assess impacts across different counties or years. This study is the first study that comprehensively assesses life cycle GWP, EU, and AD impacts of soybean in approximately 1000 counties across 9 years. Moreover, the studied geographic scopes were diverse. While three studies including this study, Kim and Dale, (12) and Landis et al. (9) focused on Midwest region in the U.S., other studies investigated soybean farming in Pennsylvania in the U.S., (23,86) Brazil, (21,24,89) Nigeria, (18) Iran, (88) and Argentina. (8) Furthermore, soil carbon change was not always included in the system scope. While this study and two other studies (27,89) included soil carbon change for life cycle GWP, the remaining studies (23,88,89) did not account for soil carbon change. It is worth noting that soil carbon change is one of top contributors to the total life cycle GWP. Additionally, different modeling approaches were utilized to generate on-farm LCI. This study used EPIC, a well-recognized process-based biogeochemical model, to capture spatially and temporally heterogeneous on-farm emissions. Other studies used DAYCENT, (27) GREET, (9,87) or literature based values (88) to account for on-farm environmental releases. Although process-based biogeochemistry models such as EPIC and DAYCENT are more complex than other models, they are uniquely capable of simulating the spatially and temporally explicit environmental releases influenced by weather, soil, and farming practices. Various impact assessment tools were utilized to compute life cycle EU and AD impacts of soybean. This study and Xue et al. (23) used characterization factors from TRACI 2.0 for life cycle EU and AD. In contrast, Zortea (24) and Panicelli (8) used characterization factors from CML for life cycle EU and AD. While Landis et al. only reported life cycle inventory, this study computed their life cycle EU and AD impact based on TRACI 2.0.

3.5. Implication for Biophysical Accounting and Supply Chain Management

Utilizing the life cycle impact intensities of soybean production at coarse scales (i.e., state or region scales) for estimating the total life cycle environmental impacts of soybean production can yield large errors (SI Figures S11, S12, and S13). For example, the total life cycle GHGs from soybean production in the Midwest was estimated to be 3.95 × 109 kg CO2-eq in 2003, by multiplying the median life cycle GWP intensity at state scale and the corresponding soybean production in the same state. This value is only a third of the actual total life cycle environmental impacts in 2003, calculated by multiplying the life cycle GHG intensity at county scale and the corresponding soybean production in the same county. Similarly, utilizing the median life cycle EU intensity of soybean production at state scale would underestimate the total life cycle EU of soybean production in the Midwest by 19% in 2001. Relying on the median life cycle AD intensity of soybean production at state scale would underestimate the total life cycle AD of soybean production in the Midwest by 27%. It is necessary to capture spatial heterogeneity at fine spatial scales (such as county) in order to yield reliable biophysical accounting for agricultural life cycle impacts over a large region such as the Midwest.
The spatial hotspots of life cycle GWP, EU, and AD were not consistent, revealing location-specific and impact-specific strategies are required to mitigate the life cycle environmental impacts of soybean production. For counties and states where soybean production presented high life cycle global warming impact, mitigation strategies effectively reducing on-farm N2O emission and promoting soil carbon sequestration are needed. In contrast, for counties and states where soybean production caused high life cycle EU impacts, mitigation strategies, such as buffers and low-release fertilizers, capable of decreasing on-farm aqueous P and N releases are required. (32,90,91) Furthermore, for counties and states where soybean production showed high life cycle AD impacts, mitigation strategies capable of decreasing on-farm NH3 releases are necessary. Additionally, the BRT analyses highlighted that the life cycle GWP, EU, and AD were influenced by nitrogen fertilizer application rate and soil organic carbon in addition to temperature and precipitation. These findings suggest the importance of improving soil organic carbon and nitrogen fertilizer use for reducing life cycle impacts of soybean. Overall, spatially targeted strategies are required for effectively mitigating life cycle environmental impacts of soybean production.
The spatially explicit life cycle impact estimates yielded from this study improve the transparency of life cycle environmental performances of soybean supply chains and serve as the scientific basis for designing environmentally friendly sourcing strategies. Sourcing soybean from producing counties with the lowest life cycle environmental impacts is capable of mitigating life cycle environmental impacts of these soybean derived products such as soybean-based biodiesel, milk, or meat from animal operations utilizing soybean as feed. For example, if biorefineries utilize soybeans from counties with the lowest life cycle EU impacts to produce biodiesel, the life cycle EU of soybean-derived biodiesel would be reduced by at least 54%. This estimate was based on three observations, including (1) soybean farming contributes to 70% of total life cycle EU of soybean derived biodiesel; (8,23) (2) switching soybean suppliers from counties with median life cycle EU to the counties with the lowest life cycle EU would decrease life cycle EU of soybean farming stage for biodiesel production by 80% (this study); and (3) Life cycle EU of transporting soybean is insignificant, compared with life cycle EU of soybean farming. (23) Overall, the spatially and temporally explicit LCA estimates of soybean production serve as critical basis for designing effective remediation strategies and environmentally friendly supply chains for soybean derived products.

Supporting Information

Click to copy section linkSection link copied!

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.9b06874.

  • Figures showing the system boundary, EPIC/LCA/BRT model integration, life cycle environmental impacts of soybean for Midwest counties over nine years, stage contributions at county and state scales, statistical distributions of life cycle GWP with/out net carbon exchange, the improvements on biophysical accounting with county scale estimates, and sensitivity analyses; Tables showing the detailed data sources for EPIC, impact intensities for supply chain LCI, characterization factors for LCIA, and highest/lowest values of life cycle impacts of soybean in the Midwest; Equations utilized to estimate life cycle impacts (PDF)

Terms & Conditions

Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

Click to copy section linkSection link copied!
  • Corresponding Author
  • Authors
    • Eun Kyung Lee - Department of Environmental Health Sciences, University at Albany, State University of New York, One University Place, Rensselaer, New York 12144, United States
    • Yetunde Sorunmu - Department of Environmental Health Sciences, University at Albany, State University of New York, One University Place, Rensselaer, New York 12144, United States
    • Xuesong Zhang - Joint Global Change Research Institute, Pacific Northwest National Laboratory, 5825 University Research Court, College Park, Maryland 20740, United States;  Earth System Sciences Interdisciplinary Center, 5825 University Research Court, Suite 4001 College Park, Maryland 20740, United States
  • Notes
    The authors declare no competing financial interest.

Acknowledgments

Click to copy section linkSection link copied!

We acknowledge the Presidential Innovation Fund for Research and Scholarship Award and the Faculty Research Award from the State University of New York at Albany for sponsoring this work. X.Z. gratefully acknowledges support from NASA (NNH13ZDA001N, NNX17AE66G, and 18-CMS18-0052) and NSF (1639327). We also thank the anonymous reviewers for their comments that improved the quality of the manuscript.

References

Click to copy section linkSection link copied!

This article references 91 other publications.

  1. 1
    USDA Soybeans & Oil Crops. https://www.ers.usda.gov/topics/crops/soybeans-oil-crops/ (2019/10//1).
    Google Scholar
    There is no corresponding record for this reference.
  2. 2
    Webster, J. C. Forecast for U.S. Soybean Demand: Strong and Stronger; American Soybean Magazine Jan 30, 2018, 2018.
    Google Scholar
    There is no corresponding record for this reference.
  3. 3
    Cavalett, O.; Ortega, E. Integrated environmental assessment of biodiesel production f rom soybean in Brazil. J. Cleaner Prod. 2010, 18 (1), 55– 70,  DOI: 10.1016/j.jclepro.2009.09.008
    Google Scholar
    There is no corresponding record for this reference.
  4. 4
    Maciel, V. G.; Zortea, R. B.; Grillo, I. B.; Ugaya, C. M. L.; Einloft, S.; Seferin, M. Greenhouse gases assessment of soybean cultivation steps in southern Brazil. J. Cleaner Prod. 2016, 131, 747– 753,  DOI: 10.1016/j.jclepro.2016.04.100
    Google Scholar
    4
    Greenhouse gases assessment of soybean cultivation steps in southern Brazil
    Maciel, Vinicius Goncalves; Zortea, Rafael Batista; Grillo, Igor Barden; Lie Ugaya, Cassia Maria; Einloft, Sandra; Seferin, Marcus
    Journal of Cleaner Production (2016), 131 (), 747-753CODEN: JCROE8; ISSN:0959-6526. (Elsevier Ltd.)
    This study presents a greenhouse gases emissions assessment of soybean cultivation in southern Brazil based on life cycle inventory. Although there are currently some studies on this topic, it is focused in the country level. Nevertheless, there are differences among the producing regions and it's estd. that for each 20 kg of soybeans produced in Brazil, one is produced in Rio Grande do Sul state. In a previous study, a life cycle inventory of soybean cultivated in this Brazilian state was developed, nevertheless, the influence of land use change along the life cycle was not taken into account. Therefore, the current study discusses the influence of direct land use change over the greenhouse gas emissions. The functional unit (FU) employed was 1 kg of soybean harvested for a cradle to gate study. For the soybean cultivation, in the scenario related to no land use change (scenario 1), 0.352 kg CO2/FU was emitted. This value increases up to 205% in scenario 2 (in this case, the actual scenario was that 15.4% of soybean cropland area replaced grassland) and 892% in scenario 3 (all land transformation was over forest). In scenario 1, soybean cultivation was responsible for the higher share of the greenhouse gases emissions (42%). The highest contributions in soybean cultivation for greenhouse gases emissions were: liming (37%), fertilization (19%) and seeding (9%).
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XnvFWls7o%253D&md5=8bf85e8d5973bb4e21a639bc0a0a2ce8
  5. 5
    Dalgaard, R.; Schmidt, J.; Halberg, N.; Christensen, P.; Thrane, M.; Pengue, W. A. LCA of soybean meal. Int. J. Life Cycle Assess. 2008, 13 (3), 240,  DOI: 10.1065/lca2007.06.342
    Google Scholar
    5
    LCA of soybean meal
    Dalgaard, Randi; Schmidt, Jannick; Halberg, Niels; Christensen, Per; Thrane, Mikkel; Pengue, Walter A.
    International Journal of Life Cycle Assessment (2008), 13 (3), 240-254CODEN: IJLCFF; ISSN:0948-3349. (Ecomed Publishers)
    Soybean meal is an important protein input to the European livestock prodn., with Argentina being an important supplier. The area cultivated with soybeans is still increasing globally, and so are the no. of LCAs where the prodn. of soybean meal forms part of the product chain. In recent years there has been increasing focus on how soybean prodn. affects the environment. The purpose of the study was to est. the environmental consequences of soybean meal consumption using a consequential LCA approach. The functional unit is 'one kg of soybean meal produced in Argentina and delivered to Rotterdam Harbor'. Soybean meal has the co-product soybean oil. In this study, the consequential LCA method was applied, and co-product allocation was thereby avoided through system expansion. In this context, system expansion implies that the inputs and outputs are entirely ascribed to soybean meal, and the product system is subsequently expanded to include the avoided prodn. of palm oil. Presently, the marginal vegetable oil on the world market is palm oil but, to be prepd. for fluctuations in market demands, an alternative product system with rapeseed oil as the marginal vegetable oil has been established. EDIP97 (updated version 2.3) was used for LCIA and the following impact categories were included: Global warming, eutrophication, acidification, ozone depletion and photochem. smog. Two soybean loops were established to demonstrate how an increased demand for soybean meal affects the palm oil and rapeseed oil prodn., resp. The characterized results from LCA on soybean meal (with palm oil as marginal oil) were 721 g CO2 eq. for global warming potential, 0.3 mg CFC11 equiv. for ozone depletion potential, 3.1 g SO2 eq. for acidification potential, -2 g NO3 eq. for eutrophication potential and 0.4 g ethene eq. for photochem. smog potential per kg soybean meal. The av. area per kg soybean meal consumed was 3.6 m2year. Attributional results, calcd. by economic and mass allocation, are also presented. Normalized results show that the most dominating impact categories were: global warming, eutrophication and acidification. The 'hot spot' in relation to global warming, was 'soybean cultivation', dominated by N2O emissions from degrdn. of crop residues (e.g., straw) and during biol. nitrogen fixation. In relation to eutrophication and acidification, the transport of soybeans by truck is important, and sensitivity analyses showed that the acidification potential is very sensitive to the increased transport distance by truck. The potential environmental impacts (except photochem. smog) were lower when using rapeseed oil as the marginal vegetable oil, because the avoided prodn. of rapeseed contributes more neg. compared with the avoided prodn. of palm oil. Identification of the marginal vegetable oil (palm oil or rapeseed oil) turned out to be important for the result, and this shows how crucial it is in consequential LCA to identify the right marginal product system (e.g., marginal vegetable oil). Consequential LCAs were successfully performed on soybean meal and LCA data on soybean meal are now available for consequential (or attributional) LCAs on livestock products. The study clearly shows that consequential LCAs are quite easy to handle, even though it has been necessary to include prodn. of palm oil, rapeseed and spring barley, as these prodn. systems are affected by the soybean oil co-product. We would appreciate it if the International Journal of Life Cycle Assessment had articles on the developments on, for example, marginal protein, marginal vegetable oil, marginal electricity (related to relevant markets), marginal heat, marginal cereals and, likewise, on metals and other basic commodities. This will not only facilitate the work with consequential LCAs, but will also increase the quality of LCAs.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXmtFyrsLo%253D&md5=123f12931d7b1ad942e19fce5256ce0c
  6. 6
    Lehuger, S.; Gabrielle, B.; Gagnaire, N. Environmental impact of the substitution of imported soybean meal with locally-produced rapeseed meal in dairy cow feed. J. Cleaner Prod. 2009, 17 (6), 616– 624,  DOI: 10.1016/j.jclepro.2008.10.005
    Google Scholar
    6
    Environmental impact of the substitution of imported soybean meal with locally-produced rapeseed meal in dairy cow feed
    Lehuger, Simon; Gabrielle, Benoit; Gagnaire, Nathalie
    Journal of Cleaner Production (2009), 17 (6), 616-624CODEN: JCROE8; ISSN:0959-6526. (Elsevier Ltd.)
    Growing public concerns about the traceability, safety and environmental-friendliness of food products provide an incentive for shorter supply chains in agricultural prodn. Here, we assessed the environmental impacts of the substitution of imported soybean meal with locally-produced rapeseed meal in French dairy prodn. systems, using a life-cycle approach. Two feeding rations based on either French-produced rapeseed meal or Brazilian-produced soy meal as concs., were compared for nine impact categories, including global warming, ecotoxicity and eutrophication. Crop prodn. was the main contributor to most impacts, while overseas transport of soy meal only had a marginal effect. The soybean ration appeared more environmentally-efficient than the rapeseed ration because it involved less intensive management practices, in particular regarding synthetic fertilizers consumption. However, land use changes brought about by soybean cultivation should also be examd.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXovFOiurw%253D&md5=a0df1960717f759715def322bd569ca2
  7. 7
    Özilgen, M.; Sorgüven, E. Energy and exergy utilization, and carbon dioxide emission in vegetable oil production. Energy 2011, 36 (10), 5954– 5967,  DOI: 10.1016/j.energy.2011.08.020
    Google Scholar
    There is no corresponding record for this reference.
  8. 8
    Panichelli, L.; Dauriat, A.; Gnansounou, E. Life cycle assessment of soybean-based biodiesel in Argentina for export. Int. J. Life Cycle Assess. 2009, 14 (2), 144– 159,  DOI: 10.1007/s11367-008-0050-8
    Google Scholar
    8
    Life cycle assessment of soybean-based biodiesel in Argentina for export
    Panichelli, Luis; Dauriat, Arnaud; Gnansounou, Edgard
    International Journal of Life Cycle Assessment (2009), 14 (2), 144-159CODEN: IJLCFF; ISSN:0948-3349. (Springer)
    Regional specificities are a key factor when analyzing the environmental impact of a biofuel pathway through a life cycle assessment (LCA). Due to different energy mixes, transport distances, agricultural practices and land use changes, results can significantly vary from one country to another. The Republic of Argentina is the first exporter of soybean oil and meal and the third largest soybean producer in the world, and therefore, soybean-based biodiesel prodn. is expected to significantly increase in the near future, mostly for exportation. Moreover, Argentinean biodiesel producers will need to evaluate the environmental performances of their product in order to comply with sustainability criteria being developed. However, because of regional specificities, the environmental performances of this biofuel pathway can be expected to be different from those obtained for other countries and feedstocks previously studied. This work aims at analyzing the environmental impact of soybean-based biodiesel prodn. in Argentina for export. The relevant impact categories account for the primary non-renewable energy consumption (CED), the global warming potential (GWP), the eutrophication potential (EP), the acidification potential (AP), the terrestrial ecotoxicity (TE), the aquatic ecotoxicity (AE), the human toxicity (HT) and land use competition (LU). The paper tackles the feedstock and country specificities in biodiesel prodn. by comparing the results of soybean-based biodiesel in Argentina with other ref. cases. Emphasis is put on explaining the factors that contribute most to the final results and the regional specificities that lead to different results for each biodiesel pathway. The Argentinean (AR) biodiesel pathway was modelled through an LCA and was compared with ref. cases available in the ecoinvent 2.01 database, namely, soybean-based biodiesel prodn. in Brazil (BR) and the United States (US), rapeseed-based biodiesel prodn. in the European Union (EU) and Switzerland (CH) and palm-oil-based biodiesel prodn. in Malaysia (MY). In all cases, the systems were modelled from feedstock prodn. to biodiesel use as B100 in a 28 t truck in CH. Furthermore, biodiesel pathways were compared with fossil low-sulfur diesel produced and used in CH. The LCA was performed according to the ISO stds. The life cycle inventory and the life cycle impact assessment (LCIA) were performed in Excel spreadsheets using the ecoinvent 2.01 database. The cumulative energy demand (CED) and the GWP were estd. through the CED for fossil and nuclear energy and the IPCC 2001 (climate change) LCIA methods, resp. Other impact categories were assessed according to CML 2001, as implemented in ecoinvent. As the product is a fuel for transportation (service), the system was defined for one vehicle kilometer (functional unit) and was divided into seven unit processes, namely, agricultural phase, soybean oil extn. and refining, transesterification, transport to port, transport to the destination country border, distribution and utilization. The Argentinean pathway results in the highest GWP, CED, AE and HT compared with the ref. biofuel pathways. Compared with the fossil ref., all impact categories are higher for the AR case, except for the CED. The most significant factor that contributes to the environmental impact in the Argentinean case varies depending on the evaluated category. Land provision through deforestation for soybean cultivation is the most impacting factor of the AR biodiesel pathway for the GWP, the CED and the HT categories. While nitrogen oxide emissions during the fuel use are the main cause of acidification, nitrate leaching during soybean cultivation is the main factor of eutrophication. LU is almost totally affected by arable land occupation for soybean cultivation. Cypermethrin used as pesticide in feedstock prodn. accounts for almost the total impact on TE and AE. Discussion The sensitivity anal. shows that an increase of 10% in the soybean yield, while keeping the same inputs, will reduce the total impact of the system. Avoiding deforestation is the main challenge to improve the environmental performances of soybean-based biodiesel prodn. in AR. If the soybean expansion can be done on marginal and set-aside agricultural land, the neg. impact of the system will be significantly reduced. Further implementation of crops' successions, soybean inoculation, reduced tillage and less toxic pesticides will also improve the environmental performances. Using ethanol as alc. in the transesterification process could significantly improve the energy balance of the Argentinean pathway. The main explaining factors depend on regional specificities of the system that lead to different results from those obtained in the ref. cases. Significantly different results can be obtained depending on the level of detail of the input data, the use of punctual or av. data and the assumptions made to build up the LCA inventory. Further improvement of the AR biodiesel pathways should be done in order to comply with international sustainability criteria on biofuel prodn. Due to the influence of land use changes in the final results, more efforts should be made to account for land use changes others than deforestation. More data are needed to det. the part of deforestation attributable to soybean cultivation. More efforts should be done to improve modeling of interaction between variables and previous crops in the agricultural phase, future transesterification technologies and market prices evolution. In order to assess more accurately the environmental impact of soybean-based biodiesel prodn. in Argentina, further considerations should be made to account for indirect land use changes, domestic biodiesel consumption and exportation to other regions, prodn. scale and regional georeferenced differentiation of prodn. systems.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXivVersrY%253D&md5=6322b8cedc1e84c39281b5c2391e61c1
  9. 9
    Landis, A. E.; Miller, S. A.; Theis, T. L. Life Cycle of the Corn-Soybean Agroecosystem for Biobased Production. Environ. Sci. Technol. 2007, 41, 1457– 1464,  DOI: 10.1021/es0606125
    Google Scholar
    9
    Life Cycle of the Corn-Soybean Agroecosystem for Biobased Production
    Landis, Amy E.; Miller, Shelie A.; Theis, Thomas L.
    Environmental Science & Technology (2007), 41 (4), 1457-1464CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    Bio-based product life cycle assessments (LCA) have largely focused on energy (fossil fuel) use and greenhouse gas emissions during agriculture and prodn. stages. This work compiled a more comprehensive life cycle inventory (LCI) for use in future bio-product LCA which rely on corn or soybean crops as feedstocks. The inventory includes energy, C, N, P, major pesticides, and USEPA criteria air pollutants resulting from processes such as fertilizer prodn., energy prodn., and on-farm chem. and equipment use. Agro-ecosystem material flows were modeled using a combination of GREET (Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation model), a linear fractionation model which describes P biogeochem. cycling, and Monte Carlo anal. Results showed that dominant air emissions resulted from crop farming, fertilizers, and on-farm N flows (e.g., N2O and NO). Seed prodn. and irrigation provided ≤0.002% to any inventory emission or energy flow and may be neglected in future LCA of corn or soybeans as feedstocks from the US corn belt. Lime contributed significantly (17% of total emissions) to air emissions and should not be neglected in bio-product LCA.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXlt1Kjsw%253D%253D&md5=6001d514994c9e492295af69cfa813f0
  10. 10
    Smil, V. Phosphorus in the environment: Natural Flows and Human Interferences. Annu. Rev. Energy Environ. 2000, 25, 53– 88,  DOI: 10.1146/annurev.energy.25.1.53
    Google Scholar
    There is no corresponding record for this reference.
  11. 11
    ISO. International Organization for Standardization (ISO) standards 14044: Environmental management and Life cycle assessment: Requirements and guidelines. 2006.
    Google Scholar
    There is no corresponding record for this reference.
  12. 12
    Kim, S.; Dale, B. E.; Jenkins, R. Life cycle assessment of corn grain and corn stover in the United States. Int. J. Life Cycle Assess. 2009, 14 (2), 160– 174,  DOI: 10.1007/s11367-008-0054-4
    Google Scholar
    12
    Life cycle assessment of corn grain and corn stover in the United States
    Kim, Seungdo; Dale, Bruce E.; Jenkins, Robin
    International Journal of Life Cycle Assessment (2009), 14 (2), 160-174CODEN: IJLCFF; ISSN:0948-3349. (Springer)
    The goal of this study is to est. the county-level environmental performance for continuous corn cultivation of corn grain and corn stover grown under the current tillage practices for various corn-growing locations in the US Corn Belt. The environmental performance of corn grain varies with its farming location because of climate, soil properties, cropping management, etc. Corn stover, all of the above ground parts of the corn plant except the grain, would be used as a feedstock for cellulosic ethanol. Two cropping systems are under investigation: corn produced for grain only without collecting corn stover (referred to as CRN) and corn produced for grain and stover harvest (referred to as CSR). The functional unit in this study is defined as dry biomass, and the ref. flow is 1 kg of dry biomass. The system boundary includes processes from cradle to farm gate. The default allocation procedure between corn grain and stover in the CSR system is the system expansion approach. County-level soil org. carbon dynamics, nitrate losses due to leaching, and nitrogen oxide and nitrous oxide emissions are simulated by the DAYCENT model. Life cycle environmental impact categories considered in this study are total fossil energy use, climate change (referred to as greenhouse gas emissions), acidification, and eutrophication. Sensitivities on farming practices and allocation are included. Simulations from the DAYCENT model predict that removing corn stover from soil could decrease nitrogen-related emissions from soil (i.e., N2O, NO x , and NO3 - leaching). DAYCENT also predicts a redn. in the annual accumulation rates of soil org. carbon (SOC) with corn stover removal. Corn stover has a better environmental performance than corn grain according to all life cycle environmental impacts considered. This is due to lower consumption of agrochems. and fuel used in the field operations and lower nitrogen-related emissions from the soil. Discussion The primary source of total fossil energy assocd. with biomass prodn. is nitrogen fertilizer, accounting for over 30% of the total fossil energy. Nitrogen-related emissions from soil (i.e., N2O, NO x , and NO3 - leaching) are the primary contributors to all other life cycle environmental impacts considered in this study. The environmental performance of corn grain and corn stover varies with the farming location due to crop management, soil properties, and climate conditions. Several general trends were identified from this study. Corn stover has a lower impact than corn grain in terms of total fossil energy, greenhouse gas emissions, acidification, and eutrophication. Harvesting corn stover reduces nitrogen-related emissions from the soil (i.e., N2O, NO x , NO3 -). The accumulation rate of soil org. carbon is reduced when corn stover is removed, and in some cases, the soil org. carbon level decreases. Harvesting only the cob portion of the stover would reduce the neg. impact of stover removal on soil org. carbon sequestration rate while still bringing the benefit of lower nitrogen-related emissions from the soil. No-tillage practices offer higher accumulation rates of soil org. carbon, lower fuel consumption, and lower nitrogen emissions from the soil than the current or conventional tillage practices. Planting winter cover crops could be a way to reduce nitrogen losses from soil and to increase soil org. carbon levels. County-level modeling is more accurate in estg. the local environmental burdens assocd. with biomass prodn. than national- or regional-level modeling. When possible, site-specific exptl. information on soil carbon and nitrogen dynamics should be obtained to reflect the system more accurately. The allocation approach between corn grain and stover significantly affects the environmental performance of each. The preferred allocation method is the system expansion approach where incremental fuel usage, addnl. nutrients in the subsequent growing season, and changes in soil carbon and nitrogen dynamics due to removing corn stover are assigned to only the collected corn stover.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXivVersrg%253D&md5=1b4d089d5a1dfb0879df9f306c222a02
  13. 13
    Tabatabaie, S. M. H.; Bolte, J. P.; Murthy, G. S. A regional scale modeling framework combining biogeochemical model with life cycle and economic analysis for integrated assessment of cropping systems. Sci. Total Environ. 2018, 625, 428– 439,  DOI: 10.1016/j.scitotenv.2017.12.208
    Google Scholar
    13
    A regional scale modeling framework combining biogeochemical model with life cycle and economic analysis for integrated assessment of cropping systems
    Tabatabaie, Seyed Mohammad Hossein; Bolte, John P.; Murthy, Ganti S.
    Science of the Total Environment (2018), 625 (), 428-439CODEN: STENDL; ISSN:0048-9697. (Elsevier B.V.)
    The goal of this study was to integrate a crop model, DNDC (DeNitrification-Decompn.), with life cycle assessment (LCA) and economic anal. models using a GIS-based integrated platform, ENVISION. The integrated model enables LCA practitioners to conduct integrated economic anal. and LCA on a regional scale while capturing the variability of soil emissions due to variation in regional factors during prodn. of crops and biofuel feedstocks. In order to evaluate the integrated model, the corn-soybean cropping system in Eagle Creek Watershed, Indiana was studied and the integrated model was used to first model the soil emissions and then conduct the LCA as well as economic anal. The results showed that the variation in soil emissions due to variation in weather is high causing some locations to be carbon sink in some years and source of CO2 in other years. In order to test the model under different scenarios, two tillage scenarios were defined: (1) conventional tillage (CT) and (2) no tillage (NT) and analyzed with the model. The overall GHG emissions for the corn-soybean cropping system was simulated and results showed that the NT scenario resulted in lower soil GHG emissions compared to CT scenario. Moreover, global warming potential (GWP) of corn ethanol from well to pump varied between 57 and 92 g CO2-eq./MJ while GWP under the NT system was lower than that of the CT system. The cost break-even point was calcd. as $3612.5/ha in a two year corn-soybean cropping system and the results showed that under low and medium prices for corn and soybean most of the farms did not meet the break-even point.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhvVKmsw%253D%253D&md5=a2af1c768d6424c3b7c1277513878a83
  14. 14
    Brentrup, F., Life cycle assessment of crop production. In Green Technologies in Food Production and Processing; Boye, J. I., Arcand, Y., Eds.; Springer US: Boston, MA, 2012; pp 61– 82.
    Google Scholar
    There is no corresponding record for this reference.
  15. 15
    Brentrup, F.; Küsters, J.; Kuhlmann, H.; Lammel, J. Environmental impact assessment of agricultural production systems using the life cycle assessment methodology: I. Theoretical concept of a LCA method tailored to crop production. Eur. J. Agron. 2004, 20 (3), 247– 264,  DOI: 10.1016/S1161-0301(03)00024-8
    Google Scholar
    There is no corresponding record for this reference.
  16. 16
    Mohammadi, A.; Rafiee, S.; Jafari, A.; Keyhani, A.; Dalgaard, T.; Knudsen, M. T.; Nguyen, T. L. T.; Borek, R.; Hermansen, J. E. Joint Life Cycle Assessment and Data Envelopment Analysis for the benchmarking of environmental impacts in rice paddy production. J. Cleaner Prod. 2015, 106, 521– 532,  DOI: 10.1016/j.jclepro.2014.05.008
    Google Scholar
    There is no corresponding record for this reference.
  17. 17
    Noya, I.; González-García, S.; Bacenetti, J.; Arroja, L.; Moreira, M. T. Comparative life cycle assessment of three representative feed cereals production in the Po Valley (Italy). J. Cleaner Prod. 2015, 99, 250– 265,  DOI: 10.1016/j.jclepro.2015.03.001
    Google Scholar
    17
    Comparative life cycle assessment of three representative feed cereals production in the Po Valley (Italy)
    Noya, Isabel; Gonzalez-Garcia, Sara; Bacenetti, Jacopo; Arroja, Luis; Moreira, Maria Teresa
    Journal of Cleaner Production (2015), 99 (), 250-265CODEN: JCROE8; ISSN:0959-6526. (Elsevier Ltd.)
    The cultivation of three different cereals - wheat, triticale and maize (five classes: 300, 400, 500, 600 and 700) - dedicated to grain prodn. for feed purposes was assessed to quantify their environmental profiles and identify the most sustainable crop from an environmental perspective. The most crit. processes throughout the life cycle of the cropping systems were also identified. These cereals were chosen because they are the most widespread cereal crops in the Po Valley (Lombardy region), the most important agricultural area in Italy.The std. framework of the Life Cycle Assessment (LCA) was followed to assess the environmental performance of the different cropping systems. Several impact categories were evaluated, including climate change (CC), ozone depletion (OD), terrestrial acidification (TA), freshwater eutrophication (FE), marine eutrophication (ME), human toxicity (HT), photochem. oxidant formation (POF), terrestrial ecotoxicity (TEC), freshwater ecotoxicity (FEC), marine ecotoxicity (MEC), water depletion (WD), fossil depletion (FD) as well as land use as an indicator.The results showed that the maize class 300 was the cereal with the worst environmental profile in the base case, considering economic allocation and no environmental burdens related with digestate prodn. This scenario presented the most intensive agricultural practices and the lowest biomass yield in comparison with the other crops. In contrast, the maize classes 600 and 700 were the cereal crops with the best environmental profiles in most impact categories. The lower requirements of fertilizer (and thus, fertilization activities) as well as the higher biomass yield were responsible of these favorable results.However, according to the environmental results, the selection of the best biomass source depends on several methodol. assumptions such as the functional unit and the allocation criteria considered (between the grain and the straw) as base for the calcns. Thus, the results of a sensitivity anal. showed that the choice of a mass allocation instead of economic one caused lower environmental impacts in all the categories. Moreover, the consideration or not of the environmental burdens related to the digestate prodn. (the main org. fertilizer used) was also a crit. step in the environmental evaluations. The inclusion of environmental loads related to digestate prodn. caused a notable increase in the impact of all the cropping systems regardless the cereal and the impact category. This conclusion could be extrapolated to other systems that exclude the addnl. burdens allocated to the prodn. of org. fertilizers.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXksFSqsL8%253D&md5=6fdd67fff8b591bcd9ba6bb38305a059
  18. 18
    Jekayinfa, S. O.; Olaniran, J. A.; Sasanya, B. F. Life cycle assessment of soybeans production and processing system into soy oil using solvent extraction process. International Journal of Product Lifecycle Management 2013, 6 (4), 311– 321,  DOI: 10.1504/IJPLM.2013.063203
    Google Scholar
    There is no corresponding record for this reference.
  19. 19
    Kim, S.; Dale, B. Regional variations in greenhouse gas emissions of biobased products in the United States—corn-based ethanol and soybean oil. Int. J. Life Cycle Assess. 2009, 14, 540– 546,  DOI: 10.1007/s11367-009-0106-4
    Google Scholar
    19
    Regional variations in greenhouse gas emissions of biobased products in the United States-corn-based ethanol and soybean oil
    Kim, Seungdo; Dale, Bruce E.
    International Journal of Life Cycle Assessment (2009), 14 (6), 540-546CODEN: IJLCFF; ISSN:0948-3349. (Springer)
    Background, aim, and scope Regional variations in the environmental impacts of plant biomass prodn. are significant, and the environmental impacts assocd. with feedstock supply also contribute substantially to the environmental performance of biobased products. Thus, the regional variations in the environmental performance of biobased products are also significant. This study scrutinizes greenhouse gas (GHG) emissions assocd. with two biobased products (i.e., ethanol and soybean oil) whose feedstocks (i.e., corn and soybean) are produced in different farming locations. Methods We chose 40 counties in Corn Belt States in the United States as biorefinery locations (i.e., corn dry milling, soybean crushing) and farming sites, and estd. cradle-to-gate GHG emissions of ethanol and of soybean oil, resp. The ests. are based on 1 kg of each biobased product (i.e., ethanol or soybean oil). The system boundary includes biomass prodn., the biorefinery, and upstream processes. Effects of direct land use change are included in the greenhouse gas anal. and measured as changes in soil org. carbon level, while the effects of indirect land use change are not considered in the baseline calcns. Those indirect effects however are scrutinized in a sensitivity anal. Results GHG emissions of corn-based ethanol range from 1.1 to 2.0 kg of CO2 equivalent per kg of ethanol, while GHG emissions of soybean oil are 0.4-2.5 kg of CO2 equivalent per kg of soybean oil. Thus, the regional variations due to farming locations are significant (by factors of 2-7). The largest GHG emission sources in ethanol prodn. are N2O emissions from soil during corn cultivation and carbon dioxide from burning the natural gas used in corn dry milling. The second largest GHG emission source groups in the ethanol prodn. system are nitrogen fertilizer (8-12%), carbon sequestration by soil (-15-2%), and electricity used in corn dry milling (7-16%). The largest GHG emission sources in soybean oil prodn. are N2O emissions from soil during soybean cultivation (13-57%) and carbon dioxide from burning the natural gas used in soybean crushing (21-47%). The second largest GHG emission source groups in soybean oil prodn. are carbon sequestration by soil (-29-24%), diesel used in soybean cultivation (4-24%), and electricity used in the soybean crushing process (10-21%). The indirect land use changes increase GHG emissions of ethanol by 7-38%, depending on the fraction of forest converted when newly converted croplands maintain crop cultivation for 100 years. Conclusions, recommendations, and perspectives Farming sites with higher biomass yields, lower nitrogen fertilizer application rates, and less tillage are favorable to future biorefinery locations in terms of global warming. For existing biorefineries, farmers are encouraged to apply a site-specific optimal nitrogen fertilizer application rate, to convert to no-tillage practices and also to adopt winter cover practices whenever possible to reduce the GHG emissions of their biobased products. Current practices for estg. the effects of indirect land use changes suffer from large uncertainties. More research and consensus about system boundaries and allocation issues are needed to reduce uncertainties related to the effects of indirect land use changes.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXhtV2ntLfF&md5=36bc616a8392f17341df8ccc182bb493
  20. 20
    Moeller, D.; Sieverding, H.; Stone, J. Comparative Farm-Gate Life Cycle Assessment of Oilseed Feedstocks in the Northern Great Plains. Biophysical Economics and Resource Quality 2017, 2, 2,  DOI: 10.1007/s41247-017-0030-3
    Google Scholar
    There is no corresponding record for this reference.
  21. 21
    Reijnders, L.; Huijbregts, M. Biogenic greenhouse gas emissions linked to the life cycles of biodiesel derived from European rapeseed and Brazilian soybeans. J. Cleaner Prod. 2008, 16, 1943– 1948,  DOI: 10.1016/j.jclepro.2008.01.012
    Google Scholar
    21
    Biogenic greenhouse gas emissions linked to the life cycles of biodiesel derived from European rapeseed and Brazilian soybeans
    Reijnders, L.; Huijbregts, M. A. J.
    Journal of Cleaner Production (2008), 16 (18), 1943-1948CODEN: JCROE8 ISSN:. (Elsevier Ltd.)
    Biogenic emissions of carbonaceous greenhouse gases and N2O turn out to be important determinants of life cycle emissions of greenhouse gases linked to the life cycle of biodiesel from European rapeseed and Brazilian soybeans. For biodiesel from European rapeseed and for biodiesel from Brazilian soybeans grown for up to 25 years with no tillage on arable soil for which tropical rainforest or Cerrado (savannah) have been cleared, the life cycle emissions of greenhouse gases are estd. to be worse than for conventional diesel. Improving agricultural practices should be an important focus for cleaner prodn. of biodiesel. These may include increasing soil carbon stocks by, e.g., conservation tillage and return of harvest residues and improving N-efficiency by precision agriculture and/or improved irrigation practices.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXhtlGisb%252FF&md5=9940ad8c55ae04757ce2a8dc11236bb0
  22. 22
    United Soybean Board. Life cycle impact of soybean production and soy industrial products. 2013.
    Google Scholar
    There is no corresponding record for this reference.
  23. 23
    Xue, X.; Collinge, W. O.; Shrake, S. O.; Bilec, M. M.; Landis, A. E. Regional life cycle assessment of soybean derived biodiesel for transportation fleets. Energy Policy 2012, 48, 295– 303,  DOI: 10.1016/j.enpol.2012.05.025
    Google Scholar
    There is no corresponding record for this reference.
  24. 24
    Zortea, R. B.; Maciel, V. G.; Passuello, A. Sustainability assessment of soybean production in Southern Brazil: A life cycle approach. Sustainable Production and Consumption 2018, 13, 102– 112,  DOI: 10.1016/j.spc.2017.11.002
    Google Scholar
    There is no corresponding record for this reference.
  25. 25
    Dalgaard, R.; Schmidt, J.; Halberg, N.; Christensen, P.; Thrane, M.; Pengue, W. A. LCA of soybean meal. Int. J. Life Cycle Assess. 2008, 13 (3), 240,  DOI: 10.1065/lca2007.06.342
    Google Scholar
    25
    LCA of soybean meal
    Dalgaard, Randi; Schmidt, Jannick; Halberg, Niels; Christensen, Per; Thrane, Mikkel; Pengue, Walter A.
    International Journal of Life Cycle Assessment (2008), 13 (3), 240-254CODEN: IJLCFF; ISSN:0948-3349. (Ecomed Publishers)
    Soybean meal is an important protein input to the European livestock prodn., with Argentina being an important supplier. The area cultivated with soybeans is still increasing globally, and so are the no. of LCAs where the prodn. of soybean meal forms part of the product chain. In recent years there has been increasing focus on how soybean prodn. affects the environment. The purpose of the study was to est. the environmental consequences of soybean meal consumption using a consequential LCA approach. The functional unit is 'one kg of soybean meal produced in Argentina and delivered to Rotterdam Harbor'. Soybean meal has the co-product soybean oil. In this study, the consequential LCA method was applied, and co-product allocation was thereby avoided through system expansion. In this context, system expansion implies that the inputs and outputs are entirely ascribed to soybean meal, and the product system is subsequently expanded to include the avoided prodn. of palm oil. Presently, the marginal vegetable oil on the world market is palm oil but, to be prepd. for fluctuations in market demands, an alternative product system with rapeseed oil as the marginal vegetable oil has been established. EDIP97 (updated version 2.3) was used for LCIA and the following impact categories were included: Global warming, eutrophication, acidification, ozone depletion and photochem. smog. Two soybean loops were established to demonstrate how an increased demand for soybean meal affects the palm oil and rapeseed oil prodn., resp. The characterized results from LCA on soybean meal (with palm oil as marginal oil) were 721 g CO2 eq. for global warming potential, 0.3 mg CFC11 equiv. for ozone depletion potential, 3.1 g SO2 eq. for acidification potential, -2 g NO3 eq. for eutrophication potential and 0.4 g ethene eq. for photochem. smog potential per kg soybean meal. The av. area per kg soybean meal consumed was 3.6 m2year. Attributional results, calcd. by economic and mass allocation, are also presented. Normalized results show that the most dominating impact categories were: global warming, eutrophication and acidification. The 'hot spot' in relation to global warming, was 'soybean cultivation', dominated by N2O emissions from degrdn. of crop residues (e.g., straw) and during biol. nitrogen fixation. In relation to eutrophication and acidification, the transport of soybeans by truck is important, and sensitivity analyses showed that the acidification potential is very sensitive to the increased transport distance by truck. The potential environmental impacts (except photochem. smog) were lower when using rapeseed oil as the marginal vegetable oil, because the avoided prodn. of rapeseed contributes more neg. compared with the avoided prodn. of palm oil. Identification of the marginal vegetable oil (palm oil or rapeseed oil) turned out to be important for the result, and this shows how crucial it is in consequential LCA to identify the right marginal product system (e.g., marginal vegetable oil). Consequential LCAs were successfully performed on soybean meal and LCA data on soybean meal are now available for consequential (or attributional) LCAs on livestock products. The study clearly shows that consequential LCAs are quite easy to handle, even though it has been necessary to include prodn. of palm oil, rapeseed and spring barley, as these prodn. systems are affected by the soybean oil co-product. We would appreciate it if the International Journal of Life Cycle Assessment had articles on the developments on, for example, marginal protein, marginal vegetable oil, marginal electricity (related to relevant markets), marginal heat, marginal cereals and, likewise, on metals and other basic commodities. This will not only facilitate the work with consequential LCAs, but will also increase the quality of LCAs.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXmtFyrsLo%253D&md5=123f12931d7b1ad942e19fce5256ce0c
  26. 26
    Godde, C. M.; Thorburn, P. J.; Biggs, J. S.; Meier, E. A. Understanding the Impacts of Soil, Climate, and Farming Practices on Soil Organic Carbon Sequestration: A Simulation Study in Australia. Front. Plant Sci. 2016, 7, 661,  DOI: 10.3389/fpls.2016.00661
    Google Scholar
    26
    Understanding the Impacts of Soil, Climate, and Farming Practices on Soil Organic Carbon Sequestration: A Simulation Study in Australia
    Godde Cecile M; Thorburn Peter J; Biggs Jody S; Meier Elizabeth A
    Frontiers in plant science (2016), 7 (), 661 ISSN:1664-462X.
    Carbon sequestration in agricultural soils has the capacity to mitigate greenhouse gas emissions, as well as to improve soil biological, physical, and chemical properties. The review of literature pertaining to soil organic carbon (SOC) dynamics within Australian grain farming systems does not enable us to conclude on the best farming practices to increase or maintain SOC for a specific combination of soil and climate. This study aimed to further explore the complex interactions of soil, climate, and farming practices on SOC. We undertook a modeling study with the Agricultural Production Systems sIMulator modeling framework, by combining contrasting Australian soils, climates, and farming practices (crop rotations, and management within rotations, such as fertilization, tillage, and residue management) in a factorial design. This design resulted in the transposition of contrasting soils and climates in our simulations, giving soil-climate combinations that do not occur in the study area to help provide insights into the importance of the climate constraints on SOC. We statistically analyzed the model's outputs to determinate the relative contributions of soil parameters, climate, and farming practices on SOC. The initial SOC content had the largest impact on the value of SOC, followed by the climate and the fertilization practices. These factors explained 66, 18, and 15% of SOC variations, respectively, after 80 years of constant farming practices in the simulation. Tillage and stubble management had the lowest impacts on SOC. This study highlighted the possible negative impact on SOC of a chickpea phase in a wheat-chickpea rotation and the potential positive impact of a cover crop in a sub-tropical climate (QLD, Australia) on SOC. It also showed the complexities in managing to achieve increased SOC, while simultaneously aiming to minimize nitrous oxide (N2O) emissions and nitrate leaching in farming systems. The transposition of contrasting soils and climates in our simulations revealed the importance of the climate constraints on SOC.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2s%252Flt1Gitw%253D%253D&md5=3c6cd6959731b90face8fe39ad9e8141
  27. 27
    Kim, S.; Dale, B. Regional variations in greenhouse gas emissions of biobased products in the United States—corn-based ethanol and soybean oil. Int. J. Life Cycle Assess. 2009, 14, 540– 546,  DOI: 10.1007/s11367-009-0106-4
    Google Scholar
    27
    Regional variations in greenhouse gas emissions of biobased products in the United States-corn-based ethanol and soybean oil
    Kim, Seungdo; Dale, Bruce E.
    International Journal of Life Cycle Assessment (2009), 14 (6), 540-546CODEN: IJLCFF; ISSN:0948-3349. (Springer)
    Background, aim, and scope Regional variations in the environmental impacts of plant biomass prodn. are significant, and the environmental impacts assocd. with feedstock supply also contribute substantially to the environmental performance of biobased products. Thus, the regional variations in the environmental performance of biobased products are also significant. This study scrutinizes greenhouse gas (GHG) emissions assocd. with two biobased products (i.e., ethanol and soybean oil) whose feedstocks (i.e., corn and soybean) are produced in different farming locations. Methods We chose 40 counties in Corn Belt States in the United States as biorefinery locations (i.e., corn dry milling, soybean crushing) and farming sites, and estd. cradle-to-gate GHG emissions of ethanol and of soybean oil, resp. The ests. are based on 1 kg of each biobased product (i.e., ethanol or soybean oil). The system boundary includes biomass prodn., the biorefinery, and upstream processes. Effects of direct land use change are included in the greenhouse gas anal. and measured as changes in soil org. carbon level, while the effects of indirect land use change are not considered in the baseline calcns. Those indirect effects however are scrutinized in a sensitivity anal. Results GHG emissions of corn-based ethanol range from 1.1 to 2.0 kg of CO2 equivalent per kg of ethanol, while GHG emissions of soybean oil are 0.4-2.5 kg of CO2 equivalent per kg of soybean oil. Thus, the regional variations due to farming locations are significant (by factors of 2-7). The largest GHG emission sources in ethanol prodn. are N2O emissions from soil during corn cultivation and carbon dioxide from burning the natural gas used in corn dry milling. The second largest GHG emission source groups in the ethanol prodn. system are nitrogen fertilizer (8-12%), carbon sequestration by soil (-15-2%), and electricity used in corn dry milling (7-16%). The largest GHG emission sources in soybean oil prodn. are N2O emissions from soil during soybean cultivation (13-57%) and carbon dioxide from burning the natural gas used in soybean crushing (21-47%). The second largest GHG emission source groups in soybean oil prodn. are carbon sequestration by soil (-29-24%), diesel used in soybean cultivation (4-24%), and electricity used in the soybean crushing process (10-21%). The indirect land use changes increase GHG emissions of ethanol by 7-38%, depending on the fraction of forest converted when newly converted croplands maintain crop cultivation for 100 years. Conclusions, recommendations, and perspectives Farming sites with higher biomass yields, lower nitrogen fertilizer application rates, and less tillage are favorable to future biorefinery locations in terms of global warming. For existing biorefineries, farmers are encouraged to apply a site-specific optimal nitrogen fertilizer application rate, to convert to no-tillage practices and also to adopt winter cover practices whenever possible to reduce the GHG emissions of their biobased products. Current practices for estg. the effects of indirect land use changes suffer from large uncertainties. More research and consensus about system boundaries and allocation issues are needed to reduce uncertainties related to the effects of indirect land use changes.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXhtV2ntLfF&md5=36bc616a8392f17341df8ccc182bb493
  28. 28
    Castanheira, É. G.; Grisoli, R.; Coelho, S.; Anderi da Silva, G.; Freire, F. Life-cycle assessment of soybean-based biodiesel in Europe: comparing grain, oil and biodiesel import from Brazil. J. Cleaner Prod. 2015, 102, 188– 201,  DOI: 10.1016/j.jclepro.2015.04.036
    Google Scholar
    28
    Life-cycle assessment of soybean-based biodiesel in Europe: comparing grain, oil and biodiesel import from Brazil
    Castanheira, Erica Geraldes; Grisoli, Renata; Coelho, Suani; Anderi da Silva, Gil; Freire, Fausto
    Journal of Cleaner Production (2015), 102 (), 188-201CODEN: JCROE8; ISSN:0959-6526. (Elsevier Ltd.)
    The purpose of this article is to present a life-cycle assessment of soybean Me ester addressing three alternative pathways: biodiesel totally produced in Brazil and exported to Portugal; biodiesel produced in Portugal using soybean oil and soybean imported from Brazil. Soybean cultivation was assessed for four states in Brazil: Mato Grosso; Goi´as; Paran´a and Rio Grande do Sul. A life-cycle inventory and model of biodiesel was implemented, including land-use change, soybean cultivation, oil extn. and refining, transesterification and biodiesel transport. A sensitivity anal. of alternative multifunctionality procedures for dealing with co-products was performed. The lowest environmental impacts were calcd. for mass allocation and the highest for price or energy allocation. Biodiesel produced in Portugal with imported soybean grain had the lowest impacts for all categories and soybean cultivation locations for mass allocation. For price or energy allocation, the pathway with the lowest environmental impacts was detd. by the cultivation location. Land-use change had a high influence on the greenhouse gas intensity of biodiesel, while soybean cultivation and transport contributed most to the remaining impact categories. Soybean Me ester (SME) used in Portugal has the lowest impacts when produced with oil or grain imported from Brazil, instead of importing directly SME. The environmental impacts of biodiesel can be reduced by avoiding land-use change, improving soybean yield and optimizing soybean transportation routes in Brazil.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXnsl2kur8%253D&md5=8ad80fd31a0a39623d8a90b729c40253
  29. 29
    Lan, K.; Yao, Y. Integrating Life Cycle Assessment and Agent-Based Modeling: A Dynamic Modeling Framework for Sustainable Agricultural Systems. J. Cleaner Prod. 2019, 238, 117853,  DOI: 10.1016/j.jclepro.2019.117853
    Google Scholar
    There is no corresponding record for this reference.
  30. 30
    Navarrete Gutiérrez, T.; Rege, S.; Marvuglia, A.; Benetto, E., Sustainable Farming Behaviours: An Agent Based Modelling and LCA Perspective. In Agent-Based Modeling of Sustainable Behaviors; Alonso-Betanzos, A.; Sánchez-Maroño, N.; Fontenla-Romero, O.; Polhill, J. G.; Craig, T.; Bajo, J.; Corchado, J. M., Eds.; Springer International Publishing: Cham, 2017; pp 187– 206.
    Google Scholar
    There is no corresponding record for this reference.
  31. 31
    Prudêncio da Silva, V.; van der Werf, H. M. G.; Spies, A.; Soares, S. R. Variability in environmental impacts of Brazilian soybean according to crop production and transport scenarios. J. Environ. Manage. 2010, 91 (9), 1831– 1839,  DOI: 10.1016/j.jenvman.2010.04.001
    Google Scholar
    There is no corresponding record for this reference.
  32. 32
    Xue, X.; Pang, Y.; Landis, A. E. Evaluating agricultural management practices to improve the environmental footprint of corn-derived ethanol. Renewable Energy 2014, 66, 454– 460,  DOI: 10.1016/j.renene.2013.12.026
    Google Scholar
    There is no corresponding record for this reference.
  33. 33
    Liakos, K.; Busato, P.; Moshou, D.; Pearson, S.; Bochtis, D. Machine learning in agriculture: A review. Sensors 2018, 18 (8), 2674,  DOI: 10.3390/s18082674
    Google Scholar
    There is no corresponding record for this reference.
  34. 34
    Elith, J.; Leathwick, J. Boosted Regression Trees for Ecological Modeling, 2013.
    Google Scholar
    There is no corresponding record for this reference.
  35. 35
    Zhang, W.; Du, Z.; Zhang, D.; Yu, S.; Hao, Y. Boosted regression tree model-based assessment of the impacts of meteorological drivers of hand, foot and mouth disease in Guangdong, China. Sci. Total Environ. 2016, 553, 366– 371,  DOI: 10.1016/j.scitotenv.2016.02.023
    Google Scholar
    35
    Boosted regression tree model-based assessment of the impacts of meteorological drivers of hand, foot and mouth disease in Guangdong, China
    Zhang, Wangjian; Du, Zhicheng; Zhang, Dingmei; Yu, Shicheng; Hao, Yuantao
    Science of the Total Environment (2016), 553 (), 366-371CODEN: STENDL; ISSN:0048-9697. (Elsevier B.V.)
    Hand, foot and mouth disease (HFMD) is a common childhood infection and has become a major public health issue in China. Considerable research has focused on the role of meteorol. factors in HFMD development. Nonlinear relationship, delayed effects and collinearity problems are key issues for achieving robust and accurate estns. in this kind of weather-health relationship explorations. The current study was designed to address these issues and assess the impact of meteorol. factors on HFMD in Guangdong, China. Case-based HFMD surveillance data and daily meteorol. data collected between 2010 and 2012 was obtained from China CDC and the National Meteorol. Information Center, resp. After a preliminary variable selection, for each dataset boosted regression tree (BRT) models were applied to det. the optimal lag for meteorol. factors at which the variance of HFMD cases was most explained, and to assess the impacts of these meteorol. factors at the optimal lag. Variance of HFMD cases was explained most by meteorol. factors about 1 wk ago. Younger children and those from the Pearl-River Delta Region were more sensitive to weather changes. Temp. had the largest contribution to HFMD epidemics (28.99-71.93%), followed by pptn. (6.52-16.11%), humidity (3.92-17.66%), wind speed (3.84-11.37%) and sunshine (6.21-10.36%). Temp. between 10 °C and 25 °C, as well as humidity between 70% and 90%, had a facilitating effect on the epidemic of HFMD. Sunshine duration above 9 h and wind speed below 2.5 m/s also contributed to an elevated risk of HFMD. The pos. relationship between HFMD and pptn. reversed when the daily amt. of rainfall exceeded 25 mm. This study indicated significantly facilitating effects of five meteorol. factors within some range on the epidemic of HFMD. Results from the current study were particularly important for developing early warning and response system on HFMD in the context of global climate change.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XksVWnsL8%253D&md5=99ef66e679502d793bf0b266087e367c
  36. 36
    Bhavsar, P.; Safro, I.; Bouaynaya, N.; Polikar, R.; Dera, D. Chapter 12 - Machine Learning in Transportation Data Analytics. In Data Analytics for Intelligent Transportation Systems; Chowdhury, M.; Apon, A.; Dey, K., Eds.; Elsevier, 2017; pp 283– 307.
    Google Scholar
    There is no corresponding record for this reference.
  37. 37
    Cashman, S. A.; Meyer, D. E.; Edelen, A. N.; Ingwersen, W. W.; Abraham, J. P.; Barrett, W. M.; Gonzalez, M. A.; Randall, P. M.; Ruiz-Mercado, G.; Smith, R. L. Mining Available Data from the United States Environmental Protection Agency to Support Rapid Life Cycle Inventory Modeling of Chemical Manufacturing. Environ. Sci. Technol. 2016, 50 (17), 9013– 9025,  DOI: 10.1021/acs.est.6b02160
    Google Scholar
    37
    Mining Available Data from the United States Environmental Protection Agency to Support Rapid Life Cycle Inventory Modeling of Chemical Manufacturing
    Cashman, Sarah A.; Meyer, David E.; Edelen, Ashley N.; Ingwersen, Wesley W.; Abraham, John P.; Barrett, William M.; Gonzalez, Michael A.; Randall, Paul M.; Ruiz-Mercado, Gerardo; Smith, Raymond L.
    Environmental Science & Technology (2016), 50 (17), 9013-9025CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    Demands for quick, accurate life cycle assessments create a need for methods to rapidly generate reliable life cycle inventories (LCI). Data mining is a suitable tool for this purpose, particularly given the large amt. of available governmental data. These data are typically applied to LCI on a case-by-case basis. As linked open data becomes more prevalent, it may be possible to automate LCI using data mining by establishing a reproducible approach to identify, ext., and process the data. This paper discusses a method to standardize and eventually automate discovery and use of publicly available data at the USEPA for chem. manufg. LCI. The method was developed using acetic acid as a case study. Data quality and gap analyses for the generated inventory detd. that selected data sources can provide information with equal or better reliability and representativeness on air, water, hazardous waste, on-site energy use, and prodn. vols., but with key data gaps: material inputs, water use, purchased electricity, and transportation requirements. A comparison of the generated LCI with existing data showed the data mining inventory agreed reasonably well with existing data and may provide a more comprehensive inventory of air emissions and water discharges. The case study highlighted challenges for current data management practices which must be overcome to successfully automate the method using semantic technol. Method benefits are that openly available data can be compiled in a standardized, transparent approach which supports potential automation with flexibility to incorporate new data sources as needed.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XhtlaktbjN&md5=00d1ca2a546f66dc55655489c8a25457
  38. 38
    Wernet, G.; Hellweg, S.; Fischer, U.; Papadokonstantakis, S.; Hungerbühler, K. Molecular-Structure-Based Models of Chemical Inventories using Neural Networks. Environ. Sci. Technol. 2008, 42 (17), 6717– 6722,  DOI: 10.1021/es7022362
    Google Scholar
    38
    Molecular-Structure-Based Models of Chemical Inventories using Neural Networks
    Wernet, Gregor; Hellweg, Stefanie; Fischer, Ulrich; Papadokonstantakis, Stavros; Hungerbuhler, Konrad
    Environmental Science & Technology (2008), 42 (17), 6717-6722CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    Chem. synthesis is a complex and diverse procedure, and prodn. data are often scarce or incomplete. A detailed inventory anal. of all mass and energy flows necessary for the prodn. of chems. is often costly and time-intensive. Therefore only few chem. inventories exist, even though they are essential for process optimization and the environmental assessment of many products. This paper introduces a new type of model to provide ests. for inventory data and environmental impacts of chem. prodn. based on the mol. structure of a chem. and without a priori knowledge of the prodn. process. These mol.-structure-based models offer inventory data for users in process design and optimization, screening life cycle assessment (LCA), and supply chain management. They can be applied even if the producer is unknown or the prodn. process is not documented. We assessed the capabilities of linear regression and neural network models for this purpose. All models were generated with a data set of inventory data on 103 chems. Different input sets were chosen as ways to transform the chem. structure into a numerical vector of descriptors and the effectiveness of the different input sets was analyzed. The results show that a correctly developed neural network model can perform on an acceptable level for many purposes. The models can assist process developers to improve energy efficiency in all design stages and aid in LCA and supply chain management by filling data gaps.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXpt1yns74%253D&md5=2608ca86a5b094dd2b27ea92b7aea016
  39. 39
    Mittal, V. K.; Bailin, S. C.; Gonzalez, M. A.; Meyer, D. E.; Barrett, W. M.; Smith, R. L. Toward Automated Inventory Modeling in Life Cycle Assessment: The Utility of Semantic Data Modeling to Predict Real-World Chemical Production. ACS Sustainable Chem. Eng. 2018, 6 (2), 1961– 1976,  DOI: 10.1021/acssuschemeng.7b03379
    Google Scholar
    39
    Toward Automated Inventory Modeling in Life Cycle Assessment: The Utility of Semantic Data Modeling to Predict Real-World Chemical Production
    Mittal, Vinit K.; Bailin, Sidney C.; Gonzalez, Michael A.; Meyer, David E.; Barrett, William M.; Smith, Raymond L.
    ACS Sustainable Chemistry & Engineering (2018), 6 (2), 1961-1976CODEN: ASCECG; ISSN:2168-0485. (American Chemical Society)
    A set of coupled semantic data models, i.e., ontologies, are presented to advance a methodol. toward automated inventory modeling of chem. manufg. in life cycle assessment. The cradle-to-gate life cycle inventory for chem. manufg. is a detailed collection of the material and energy flows assocd. with a chem.'s supply chain. Thus, there is a need to manage data describing both the lineage (or synthesis pathway) and processing conditions for a chem. To this end, a Lineage ontol. is proposed to reveal all the synthesis steps required to produce a chem. from raw materials, such as crude oil or biomaterials, while a Process ontol. is developed to manage data describing the various unit processes assocd. with each synthesis step. The two ontologies are coupled such that process data, which is the basis for inventory modeling, is linked to lineage data through key concepts like the chem. reaction and reaction participants. To facilitate automated inventory modeling, a series of SPARQL queries, based on the concepts of ancestor and parent, are presented to generate a lineage for a chem. of interest from a set of reaction data. The proposed ontologies and SPARQL queries are evaluated and tested using a case study of nylon-6 prodn. Once a lineage is established, the process ontol. can be used to guide inventory modeling based on both data mining (top-down) and simulation (bottom-up) approaches. The ability to generate a cradle-to-gate life cycle for a chem. represents a key achievement toward the ultimate goal of automated life cycle inventory modeling.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhvFehsL3O&md5=229ea4fe85c9c2f522618e670c298b73
  40. 40
    Nabavi-Pelesaraei, A.; Rafiee, S.; Mohtasebi, S. S.; Hosseinzadeh-Bandbafha, H.; Chau, K.-w. Integration of artificial intelligence methods and life cycle assessment to predict energy output and environmental impacts of paddy production. Sci. Total Environ. 2018, 631, 1279– 1294,  DOI: 10.1016/j.scitotenv.2018.03.088
    Google Scholar
    40
    Integration of artificial intelligence methods and life cycle assessment to predict energy output and environmental impacts of paddy production
    Nabavi-Pelesaraei, Ashkan; Rafiee, Shahin; Mohtasebi, Seyed Saeid; Hosseinzadeh-Bandbafha, Homa; Chau, Kwok-wing
    Science of the Total Environment (2018), 631-632 (), 1279-1294CODEN: STENDL; ISSN:0048-9697. (Elsevier B.V.)
    Prediction of agricultural energy output and environmental impacts play important role in energy management and conservation of environment as it can help us to evaluate agricultural energy efficiency, conduct crops prodn. system commissioning, and detect and diagnose faults of crop prodn. system. Agricultural energy output and environmental impacts can be readily predicted by artificial intelligence (AI), owing to the ease of use and adaptability to seek optimal solns. in a rapid manner as well as the use of historical data to predict future agricultural energy use pattern under constraints. This paper conducts energy output and environmental impact prediction of paddy prodn. in Guilan province, Iran based on two AI methods, artificial neural networks (ANNs), and adaptive neuro fuzzy inference system (ANFIS). The amts. of energy input and output are 51,585.61 MJ kg-1 and 66,112.94 MJ kg-1, resp., in paddy prodn. Life Cycle Assessment (LCA) is used to evaluate environmental impacts of paddy prodn. Results show that, in paddy prodn., in-farm emission is a hotspot in global warming, acidification and eutrophication impact categories. ANN model with 12-6-8-1 structure is selected as the best one for predicting energy output. The correlation coeff. (R) varies from 0.524 to 0.999 in training for energy input and environmental impacts in ANN models. ANFIS model is developed based on a hybrid learning algorithm, with R for predicting output energy being 0.860 and, for environmental impacts, varying from 0.944 to 0.997. Results indicate that the multi-level ANFIS is a useful tool to managers for large-scale planning in forecasting energy output and environmental indexes of agricultural prodn. systems owing to its higher speed of computation processes compared to ANN model, despite ANN's higher accuracy.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXltFOqtrw%253D&md5=44df2728c72c318744bb93be186bb671
  41. 41
    Hou, P.; Jolliet, O.; Zhu, J.; Xu, M. Estimate ecotoxicity characterization factors for chemicals in life cycle assessment using machine learning models. Environ. Int. 2020, 135, 105393,  DOI: 10.1016/j.envint.2019.105393
    Google Scholar
    41
    Estimate ecotoxicity characterization factors for chemicals in life cycle assessment using machine learning models
    Hou, Ping; Jolliet, Olivier; Zhu, Ji; Xu, Ming
    Environment International (2020), 135 (), 105393CODEN: ENVIDV; ISSN:0160-4120. (Elsevier Ltd.)
    In life cycle assessment, characterization factors are used to convert the amt. of the chems. and other pollutants generated in a product's life cycle to the std. unit of an impact category, such as ecotoxicity. However, as a widely used impact assessment method, USEtox (version 2.11) only has ecotoxicity characterization factors for a small portion of chems. due to the lack of lab. expt. data. Here we develop machine learning models to est. ecotoxicity hazardous concns. 50% (HC50) in USEtox to calc. characterization factors for chems. based on their phys.-chem. properties in EPA's CompTox Chem. Dashborad and the classification of their mode of action. The model is validated by ten randomly selected test sets that are not used for training. The results show that the random forest model has the best predictive performance. The av. root mean squared error of the estd. HC50 on the test sets is 0.761. The av. coeff. of detn. (R2) on the test set is 0.630, meaning 63% of the variability of HC50 in USEtox can be explained by the predicted HC50 from the random forest model. Our model outperforms a traditional quant. structure-activity relationship (QSAR) model (ECOSAR) and linear regression models. We also provide ests. of missing ecotoxicity characterization factors for 552 chems. in USEtox using the validated random forest model.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXisVGgsL%252FI&md5=13897f26f18f46913301ef9ba67ba6d6
  42. 42
    Marvuglia, A.; Kanevski, M.; Benetto, E. Machine learning for toxicity characterization of organic chemical emissions using USEtox database: Learning the structure of the input space. Environ. Int. 2015, 83, 72– 85,  DOI: 10.1016/j.envint.2015.05.011
    Google Scholar
    42
    Machine learning for toxicity characterization of organic chemical emissions using USEtox database: Learning the structure of the input space
    Marvuglia, Antonino; Kanevski, Mikhail; Benetto, Enrico
    Environment International (2015), 83 (), 72-85CODEN: ENVIDV; ISSN:0160-4120. (Elsevier Ltd.)
    Toxicity characterization of chem. emissions in Life Cycle Assessment (LCA) is a complex task which usually proceeds via multimedia (fate, exposure and effect) models attached to models of dose-response relationships to assess the effects on target. Different models and approaches do exist, but all require a vast amt. of data on the properties of the chem. compds. being assessed, which are hard to collect or hardly publicly available (esp. for thousands of less common or newly developed chems.), therefore hampering in practice the assessment in LCA. An example is USEtox, a consensual model for the characterization of human toxicity and freshwater ecotoxicity. This paper places itself in a line of research aiming at providing a methodol. to reduce the no. of input parameters necessary to run multimedia fate models, focusing in particular to the application of the USEtox toxicity model. By focusing on USEtox, in this paper two main goals are pursued: 1) performing an extensive exploratory anal. (using dimensionality redn. techniques) of the input space constituted by the substance-specific properties at the aim of detecting particular patterns in the data manifold and estg. the dimension of the subspace in which the data manifold actually lies; and 2) exploring the application of a set of linear models, based on partial least squares (PLS) regression, as well as a nonlinear model (general regression neural network - GRNN) in the seek for an automatic selection strategy of the most informative variables according to the modelled output (USEtox factor). After extensive anal., the intrinsic dimension of the input manifold has been identified between three and four. The variables selected as most informative may vary according to the output modelled and the model used, but for the toxicity factors modelled in this paper the input variables selected as most informative are coherent with prior expectations based on scientific knowledge of toxicity factors modeling. Thus the outcomes of the anal. are promising for the future application of the approach to other portions of the model, affected by important data gaps, e.g., to the calcn. of human health effect factors.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtFSqtL%252FF&md5=b33c58315f03623dd77d8baeb24318c4
  43. 43
    ISO International Organization for Standardization (ISO) standards 14040: Environmental management and Life cycle assessment: Principles and framework. https://www.iso.org/standard/37456.html.
    Google Scholar
    There is no corresponding record for this reference.
  44. 44
    USDA Statistics by subject: crop & plants - field crops - corn field. https://www.nass.usda.gov/Quick_Stats/ (2017/10/10).
    Google Scholar
    There is no corresponding record for this reference.
  45. 45
    Kim, S.; Dale, B. E. Life cycle assessment of various cropping systems utilized for producing biofuels: Bioethanol and biodiesel. Biomass Bioenergy 2005, 29 (6), 426– 439,  DOI: 10.1016/j.biombioe.2005.06.004
    Google Scholar
    There is no corresponding record for this reference.
  46. 46
    Castanheira, É. G.; Grisoli, R.; Coelho, S.; da Silva, G. A.; Freire, F. Life-cycle assessment of soybean-based biodiesel in Europe: comparing grain, oil and biodiesel import from Brazil. J. Cleaner Prod. 2015, 102, 188– 201,  DOI: 10.1016/j.jclepro.2015.04.036
    Google Scholar
    46
    Life-cycle assessment of soybean-based biodiesel in Europe: comparing grain, oil and biodiesel import from Brazil
    Castanheira, Erica Geraldes; Grisoli, Renata; Coelho, Suani; Anderi da Silva, Gil; Freire, Fausto
    Journal of Cleaner Production (2015), 102 (), 188-201CODEN: JCROE8; ISSN:0959-6526. (Elsevier Ltd.)
    The purpose of this article is to present a life-cycle assessment of soybean Me ester addressing three alternative pathways: biodiesel totally produced in Brazil and exported to Portugal; biodiesel produced in Portugal using soybean oil and soybean imported from Brazil. Soybean cultivation was assessed for four states in Brazil: Mato Grosso; Goi´as; Paran´a and Rio Grande do Sul. A life-cycle inventory and model of biodiesel was implemented, including land-use change, soybean cultivation, oil extn. and refining, transesterification and biodiesel transport. A sensitivity anal. of alternative multifunctionality procedures for dealing with co-products was performed. The lowest environmental impacts were calcd. for mass allocation and the highest for price or energy allocation. Biodiesel produced in Portugal with imported soybean grain had the lowest impacts for all categories and soybean cultivation locations for mass allocation. For price or energy allocation, the pathway with the lowest environmental impacts was detd. by the cultivation location. Land-use change had a high influence on the greenhouse gas intensity of biodiesel, while soybean cultivation and transport contributed most to the remaining impact categories. Soybean Me ester (SME) used in Portugal has the lowest impacts when produced with oil or grain imported from Brazil, instead of importing directly SME. The environmental impacts of biodiesel can be reduced by avoiding land-use change, improving soybean yield and optimizing soybean transportation routes in Brazil.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXnsl2kur8%253D&md5=8ad80fd31a0a39623d8a90b729c40253
  47. 47
    Williams, J.; Dagitz, S.; Magre, M.; Meinardus, A.; Steglich, E.; Taylor, R. The EPIC model. In Computer Models of Watershed HydrologySingh, V. P., Ed.; Water Resources Publications: Highlands Ranch, CO, pp 909– 1000. In 1995.
    Google Scholar
    There is no corresponding record for this reference.
  48. 48
    Izaurralde, R. C.; Williams, J. R.; McGill, W. B.; Rosenberg, N. J.; Jakas, M. C. Q. Simulating soil C dynamics with EPIC: Model description and testing against long-term data. Ecol. Modell. 2006, 192 (3), 362– 384,  DOI: 10.1016/j.ecolmodel.2005.07.010
    Google Scholar
    There is no corresponding record for this reference.
  49. 49
    NOAA National Climatic Data Center. https://www.ncdc.noaa.gov/cdo-web/search (2017/11/1).
    Google Scholar
    There is no corresponding record for this reference.
  50. 50
    USDA Natural Resources Conservation Service: Web Soil Survey. https://websoilsurvey.sc.egov.usda.gov/App/WebSoilSurvey.aspx (2017/10/10).
    Google Scholar
    There is no corresponding record for this reference.
  51. 51
    USGS. County-level estimates of nutrient inputs to the land surface of the conterminous United States, 1982–2001. In Scientific Investigations Report 2006–5012 , 2006.
    Google Scholar
    There is no corresponding record for this reference.
  52. 52
    USDA USDA National Agricultural Statistics Service: Quick Stats- Crop yield data. https://quickstats.nass.usda.gov/ (2017/10/10).
    Google Scholar
    There is no corresponding record for this reference.
  53. 53
    Zhang, X.; Izaurralde, R. C.; Manowitz, D.; West, T. O.; Post, W. M.; Thomas, A. M.; Bandaru, V. P.; Nichols, J.; Williams, J. R. An integrative modeling framework to evaluate the productivity and sustainability of biofuel crop production systems. GCB Bioenergy 2010, 2, 258– 277,  DOI: 10.1111/j.1757-1707.2010.01046.x
    Google Scholar
    There is no corresponding record for this reference.
  54. 54
    Zhang, X.; Izaurralde, R. C.; Manowitz, D. H.; Sahajpal, R.; West, T. O.; Thomson, A. M.; Xu, M.; Zhao, K.; LeDuc, S. D.; Williams, J. R. Regional scale cropland carbon budgets: evaluating a geospatial agricultural modeling system using inventory data Ecological Modelling. Environmental Modelling & Software 2015, 63, 199– 216,  DOI: 10.1016/j.envsoft.2014.10.005
    Google Scholar
    There is no corresponding record for this reference.
  55. 55
    Taghavifar, H.; Mardani, A. Prognostication of energy consumption and greenhouse gas (GHG) emissions analysis of apple production in West Azarbayjan of Iran using Artificial Neural Network Ecological Modelling. J. Cleaner Prod. 2015, 87, 159– 167,  DOI: 10.1016/j.jclepro.2014.10.054
    Google Scholar
    There is no corresponding record for this reference.
  56. 56
    Pradhan, A., Energy Life-Cycle Assessment of Soybean Biodiesel Revisited. Transactions of the ASABE 2011, 54 (3), 1031–1039.
    Google Scholar
    There is no corresponding record for this reference.
  57. 57
    USGS Water use in the United States: Estimated use of water in the United States county-level data for 2005. https://water.usgs.gov/watuse/data/2005/index.html (October 18, 2017).
    Google Scholar
    There is no corresponding record for this reference.
  58. 58
    USDA USDA Economic Research Service: Irrigation & Water Use. https://www.ers.usda.gov/topics/farm-practices-management/irrigation-water-use/ (2017/10/17).
    Google Scholar
    There is no corresponding record for this reference.
  59. 59
    Wernet, G.; Bauer, C.; Steubing, B.; Reinhard, J.; Moreno-Ruiz, E.; Weidema, B. The ecoinvent database version 3 (part I): overview and methodology. Int. J. Life Cycle Assess. 2016, 21 (9), 1218– 1230,  DOI: 10.1007/s11367-016-1087-8
    Google Scholar
    There is no corresponding record for this reference.
  60. 60
    Bare, J. C., The Tool for the Reduction and Assessment of Chemical and other environmental Impacts. In Clean Technologies and Environmental Policy; Springer-Verlag: New York, NY, 2011; Vol. 13 (5), pp 687– 696.
    Google Scholar
    There is no corresponding record for this reference.
  61. 61
    Huang, T.; Gao, B.; Christie, P.; Ju, X. Net global warming potential and greenhouse gas intensity in a double-cropping cereal rotation as affected by nitrogen and straw management. Biogeosciences 2013, 10 (12), 7897– 7911,  DOI: 10.5194/bg-10-7897-2013
    Google Scholar
    61
    Net global warming potential and greenhouse gas intensity in a double-cropping cereal rotation as affected by nitrogen and straw management
    Huang, T.; Gao, B.; Christie, P.; Ju, X.
    Biogeosciences (2013), 10 (12), 7897-7911, 15 pp.CODEN: BIOGGR; ISSN:1726-4189. (Copernicus Publications)
    The effects of nitrogen and straw management on global warming potential (GWP) and greenhouse gas intensity (GHGI) in a winter wheat-summer maize double-cropping system on the North China Plain were investigated. We measured nitrous oxide (N2O) emissions and studied net GWP (NGWP) and GHGI by calcg. the net exchange of CO2 equiv (CO2-equiv) from greenhouse gas emissions, agricultural inputs and management practices, as well as changes in soil org. carbon (SOC), based on a long-term field expt. established in 2006. The field expt. includes six treatments with three fertilizer N levels (zero N (control), optimum and conventional N) and straw removal (i.e. N0, Nopt and Ncon) or return (i.e. SN0, SNopt and SNcon). Optimum N management (Nopt, SNopt) saved roughly half of the fertilizer N compared to conventional agricultural practice (Ncon, SNcon), with no significant effect on grain yields. Annual mean N2O emissions reached 3.90 kg N2O-N ha-1 in Ncon and SNcon, and N2O emissions were reduced by 46.9% by optimizing N management of Nopt and SNopt. Straw return increased annual mean N2O emissions by 27.9%. Annual SOC sequestration was 0.40-1.44 Mg C ha-1 yr-1 in plots with N application and/or straw return. Compared to the conventional N treatments the optimum N treatments reduced NGWP by 51%, comprising 25% from decreasing N2O emissions and 75% from reducing N fertilizer application rates. Straw return treatments reduced NGWP by 30% compared to no straw return because the GWP from increments of SOC offset the GWP from higher emissions of N2O, N fertilizer and fuel after straw return. The GHGI trends from the different nitrogen and straw management practices were similar to the NGWP. In conclusion, optimum N and straw return significantly reduced NGWP and GHGI and concomitantly achieved relatively high grain yields in this important winter wheat-summer maize double-cropping system.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXjvFaht7o%253D&md5=6fa75de490ba0d4a6c500863b15227ee
  62. 62
    Kim, S.; Dale, B. E. Cumulative Energy and Global Warming Impact from the Production of Biomass for Biobased Products. J. Ind. Ecol. 2003, 7 (3–4), 147– 162,  DOI: 10.1162/108819803323059442
    Google Scholar
    There is no corresponding record for this reference.
  63. 63
    Friedman, J. H. Greedy function approximation: A gradient boosting machine. Ann. Statist. 2001, 29 (5), 1189– 1232,  DOI: 10.1214/aos/1013203451
    Google Scholar
    There is no corresponding record for this reference.
  64. 64
    Williams, G. J.; Aeby, G. S.; Cowie, R. O.; Davy, S. K. Predictive modeling of coral disease distribution within a reef system. PLoS One 2010, 5 (2), e9264  DOI: 10.1371/journal.pone.0009264
    Google Scholar
    There is no corresponding record for this reference.
  65. 65
    Elith, J.; Leathwick, J. R.; Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 2008, 77 (4), 802– 13,  DOI: 10.1111/j.1365-2656.2008.01390.x
    Google Scholar
    65
    A working guide to boosted regression trees
    Elith J; Leathwick J R; Hastie T
    The Journal of animal ecology (2008), 77 (4), 802-13 ISSN:.
    1. Ecologists use statistical models for both explanation and prediction, and need techniques that are flexible enough to express typical features of their data, such as nonlinearities and interactions. 2. This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fit a single parsimonious model. Boosted regression trees combine the strengths of two algorithms: regression trees (models that relate a response to their predictors by recursive binary splits) and boosting (an adaptive method for combining many simple models to give improved predictive performance). The final BRT model can be understood as an additive regression model in which individual terms are simple trees, fitted in a forward, stagewise fashion. 3. Boosted regression trees incorporate important advantages of tree-based methods, handling different types of predictor variables and accommodating missing data. They have no need for prior data transformation or elimination of outliers, can fit complex nonlinear relationships, and automatically handle interaction effects between predictors. Fitting multiple trees in BRT overcomes the biggest drawback of single tree models: their relatively poor predictive performance. Although BRT models are complex, they can be summarized in ways that give powerful ecological insight, and their predictive performance is superior to most traditional modelling methods. 4. The unique features of BRT raise a number of practical issues in model fitting. We demonstrate the practicalities and advantages of using BRT through a distributional analysis of the short-finned eel (Anguilla australis Richardson), a native freshwater fish of New Zealand. We use a data set of over 13 000 sites to illustrate effects of several settings, and then fit and interpret a model using a subset of the data. We provide code and a tutorial to enable the wider use of BRT by ecologists.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD1cvgsFOqsQ%253D%253D&md5=71c5a86d5b3715450b5fa8d2862f6629
  66. 66
    Hastie, T.; Tibshirani, R.; Friedman, J. H. The Elements of Statistical Learning: Data Mining, Inference, And Prediction; New York, 2009; Vol. 2nd ed..
    Google Scholar
    There is no corresponding record for this reference.
  67. 67
    Khoshnevisan, B.; Rafiee, S.; Omid, M.; Yousefi, M.; Movahedi, M. Modeling of energy consumption and GHG (greenhouse gas) emissions in wheat production in Esfahan province of Iran using artificial neural networks. Energy (Oxford, U. K.) 2013, 52, 333– 338,  DOI: 10.1016/j.energy.2013.01.028
    Google Scholar
    There is no corresponding record for this reference.
  68. 68
    Nabavi-Pelesaraei, A.; Rafiee, S.; Hosseinzadeh-Bandbafha, H.; Shamshirband, S. Modeling energy consumption and greenhouse gas emissions for kiwifruit production using artificial neural networks. J. Cleaner Prod. 2016, 133, 924– 931,  DOI: 10.1016/j.jclepro.2016.05.188
    Google Scholar
    There is no corresponding record for this reference.
  69. 69
    Pahlavan, R.; Omid, M.; Akram, A. Energy input-output analysis and application of artificial neural networks for predicting greenhouse basil production. Energy 2012, 37 (1), 171– 176,  DOI: 10.1016/j.energy.2011.11.055
    Google Scholar
    69
    Energy input-output analysis and application of artificial neural networks for predicting greenhouse basil production
    Pahlavan, Reza; Omid, Mahmoud; Akram, Asadollah
    Energy (Oxford, United Kingdom) (2012), 37 (1), 171-176CODEN: ENEYDS; ISSN:0360-5442. (Elsevier Ltd.)
    Various Artificial Neural Networks (ANNs) were developed to est. the prodn. yield of greenhouse basil in Iran. For this purpose, the data collected by random method from 26 greenhouses in the region during 4 periods of plant cultivation in 2009-2010. The total input energy and energy ratio for basil prodn. were 14,308,998 MJ ha-1 and 0.02, resp. The developed ANN was a multilayer perceptron (MLP) with 7 neurons in the input layer, one, 2 and 3 hidden layer(s) of various nos. of neurons and one neuron (basil yield) in the output layer. The input energies were human labor, diesel fuel, chem. fertilizers, farm yard manure, chems., electricity and transportation. Results showed, the ANN model having 7-20-20-1 topol. can predict the yield value with higher accuracy. So, this 2 hidden layer topol. was selected as the best model for estg. basil prodn. of regional greenhouses with similar conditions. For the optimal model, the values of the models outputs correlated well with actual outputs, with coeff. of detn. (R2) of 0.976. For this configuration, RMSE and MAE values were 0.046 and 0.035, resp. Sensitivity anal. revealed that chem. fertilizers are the most significant parameter in the basil prodn.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38Xjsl2hsw%253D%253D&md5=2c91482440c8f43ebf0f3a64d327284c
  70. 70
    Perlman, J.; Hijmans, R. J.; Horwath, W. R. A metamodelling approach to estimate global N2O emissions from agricultural soils. Global Ecology and Biogeography 2014, 23 (8), 912– 924,  DOI: 10.1111/geb.12166
    Google Scholar
    There is no corresponding record for this reference.
  71. 71
    Lee, E. K.; Zhang, X.; Adler, P. R.; Kleppel, G. S.; Romeiko, X. X. Spatially and temporally explicit life cycle global warming, eutrophication, and acidification impacts from corn production in the U.S. Midwest. J. Cleaner Prod. 2020, 242, 118465,  DOI: 10.1016/j.jclepro.2019.118465
    Google Scholar
    71
    Spatially and temporally explicit life cycle global warming, eutrophication, and acidification impacts from corn production in the U.S. Midwest
    Lee, Eun Kyung; Zhang, Xuesong; Adler, Paul R.; Kleppel, Gary S.; Romeiko, Xiaobo Xue
    Journal of Cleaner Production (2020), 242 (), 118465CODEN: JCROE8; ISSN:0959-6526. (Elsevier Ltd.)
    The demand for biobased products, such as food, fuel, and chems., has been continuously increasing. Meanwhile, agricultural prodn., serving as the primary stage of biobased products, is one of the largest contributors to greenhouse gas (GHG) emissions and nutrient releases. Environmental impacts of agricultural prodn. influenced by farming practices, soil properties, and climate conditions, are often site-specific and time dependent. Although assessing spatially and temporally explicit environmental releases and impacts are required to inform a sustainable trajectory for agricultural prodn., such analyses are largely lacking. This study provides site-specific anal. of on-farm and supply chain emissions from corn prodn. to demonstrate the spatio-temporal variability of environmental impacts in the U. S. Midwest states. Using process-based life cycle assessment (LCA) and the phys.-based Environmental Policy Integrated Climate (EPIC) agroecosystem model, we estd. county-level life cycle environmental release inventories from corn prodn. in 12 U. S. Midwest states for the period of 2000-2008. Based on the Tool for Redn. and Assessment of Chems. and Other Environmental Impacts (TRACI) impact assessment model, we quantified the corresponding life cycle global warming (GW), eutrophication (EU) and acidification (AD) impacts of corn. The results show that life cycle GW, EU and AD of corn prodn. varied by factors of 4.2, 83.7 and 10.6, resp., across the Midwest counties over the nine-year span (2000-2008). Life cycle GW impacts of producing 1 kg of corn ranged from -6.4 in Franklin County, Illinois to 20.2 kg CO2-eq. in Perkins County, South Dakota. The life cycle EU impacts also spanned over a wide range of 0.99 g in Morton County, Kansas to 82.9 g N-eq. in Leelanau County, Michigan, whereas life cycle AD impacts ranged from 1.3 in Clermont County, Ohio to 100.7 g SO2-eq. in Perkins County, South Dakota. Moreover, trade-offs existed among life cycle GW, EU and AD impact categories for corn prodn. The spatial variation analyses showed that key contributors were the different soil types, pptn., elevation and the amts. of fertilizers applied. These findings provided crit. insight into spatio-temporal variations of life cycle environmental impacts of corn prodn. and identified spatial hotspots and top contributors for improving environmental performances of corn prodn.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXhvVarsrvE&md5=3f32487fbf699db4005d3766e1e9a928
  72. 72
    Butterbach-Bahl, K.; Dannenmann, M. Denitrification and associated soil N2O emissions due to agricultural activities in a changing climate. Current Opinion in Environmental Sustainability 2011, 3 (5), 389– 395,  DOI: 10.1016/j.cosust.2011.08.004
    Google Scholar
    There is no corresponding record for this reference.
  73. 73
    Congreves, K. A.; Wagner-Riddle, C.; Si, B. C.; Clough, T. J. Nitrous oxide emissions and biogeochemical responses to soil freezing-thawing and drying-wetting. Soil Biol. Biochem. 2018, 117, 5– 15,  DOI: 10.1016/j.soilbio.2017.10.040
    Google Scholar
    73
    Nitrous oxide emissions and biogeochemical responses to soil freezing-thawing and drying-wetting
    Congreves, K. A.; Wagner-Riddle, C.; Si, B. C.; Clough, T. J.
    Soil Biology & Biochemistry (2018), 117 (), 5-15CODEN: SBIOAH; ISSN:0038-0717. (Elsevier B.V.)
    A review concerning the systematic identification of similarities and differences in soil freeze/thaw (FT) and dry/wet (DW) cycles and their effect on timing and magnitude of N2O emissions from agricultural systems, including strategic research areas required to improve understanding of FT and DW processes leading to N2O emissions, are discussed. Topics covered include: introduction; soil physics behind soil FT and DW cycles; transport processes involved in N2O prodn. and emissions (gas soly., gas transport, phys. trapping); microbial and biogeochem. processes involved in N2O prodn. and emissions (anaerobic and redox conditions and substrate for, microbial C use and community compn.); and conclusions.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhsl2qtb3M&md5=094ffc87bb0d4627a8897c2901460b0c
  74. 74
    Parton, W. J.; Gutmann, M. P.; Merchant, E. R.; Hartman, M. D.; Adler, P. R.; McNeal, F. M.; Lutz, S. M. Measuring and mitigating agricultural greenhouse gas production in the US Great Plains, 1870–2000. Proc. Natl. Acad. Sci. U. S. A. 2015, 112 (34), E4681– E4688,  DOI: 10.1073/pnas.1416499112
    Google Scholar
    74
    Measuring and mitigating agricultural greenhouse gas production in the US Great Plains, 1870-2000
    Parton, William J.; Gutmann, Myron P.; Merchant, Emily R.; Hartman, Melannie D.; Adler, Paul R.; McNeal, Frederick M.; Lutz, Susan M.
    Proceedings of the National Academy of Sciences of the United States of America (2015), 112 (34), E4681-E4688CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
    The US Great Plains region is an agricultural prodn. center for the global market and, as such, an important source of greenhouse gas (GHG) emissions. This paper uses historical agricultural census data and ecosystem models to est. the magnitude of annual GHG fluxes from all agricultural sources (e.g., cropping, livestock raising, irrigation, fertilizer prodn., tractor use) in the Great Plains from 1870 to 2000. It showed that C released during native grassland plow-out was the largest source of GHG emissions before 1930; livestock prodn., direct energy use, and soil N2O emissions are currently the largest sources. Climate factors mediate these emissions: cool, wet weather promotes C sequestration; hot, dry weather increase GHG release. This anal. demonstrated long-term ecosystem consequences of historical and current agricultural activities, indicating adoption of available alternative management practices could substantially mitigate agricultural GHG fluxes, from a 34% redn. with a 25% adoption rate to as much as complete elimination with possible net C sequestration when a greater proportion of farmers adopt new agricultural practices.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXht1ygt7jI&md5=d67c4203c4f0df6f92afce287a4c6b78
  75. 75
    Adler, P.; Del Grosso, S.; Inman, D.; Jenkins, R. E.; Spatari, S.; Zhang, Y. Mitigation Opportunities for Life-Cycle Greenhouse Gas Emissions during Feedstock Production across Heterogeneous Landscapes. Managing Agricultural Greenhouse Gases 2012, 203– 219,  DOI: 10.1016/B978-0-12-386897-8.00012-7
    Google Scholar
    There is no corresponding record for this reference.
  76. 76
    Kopáček, J.; Hejzlar, J.; Posch, M. Factors Controlling the Export of Nitrogen from Agricultural Land in a Large Central European Catchment during 1900–2010. Environ. Sci. Technol. 2013, 47 (12), 6400– 6407,  DOI: 10.1021/es400181m
    Google Scholar
    76
    Factors Controlling the Export of Nitrogen from Agricultural Land in a Large Central European Catchment during 1900-2010
    Kopacek, Jiri; Hejzlar, Josef; Posch, Maximilian
    Environmental Science & Technology (2013), 47 (12), 6400-6407CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    Using an empirical model, we quantified the N export from agricultural land in a large central European catchment (upper Vltava River, Czech Republic, ∼13,000 Km2) over the 1959-2010 period. The catchment witnessed a rapid socio-economic shift from a planned to a market economy in the 1990s, resulting in an abrupt (∼50%) redn. in N fertilization rates at otherwise relatively stable land-use practices. This large-scale expt. enabled disentangling and quantification of individual effects of N fertilization and drainage on N leaching. The model is based on a 2-step regression between annual N export and 3 independent variables: (1) annual av. discharge in the 1st step and (2) net anthropogenic N inputs (NANI) and proportion of drained agricultural land in the 2nd step. Results show that N export was more related to mineralization of soil org. N pools due to drainage and tillage than to external N sources (NANI). The model, together with other reconstructed N sources in the catchment (leaching from forests, wastewaters, and atm. deposition) and extrapolated back to 1900, explained 77% of the obsd. variability in N concns. in the Vltava River during the 1900-2010 period.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXnsFSgs7o%253D&md5=c5f6012dc20cbbd2f6c4f7074a03a9d5
  77. 77
    Vagstad, N.; Eggestad, H. O.; Hoyas, T. R. Mineral nitrogen in agricultural soils and nitrogen losses: relation to soil properties, weather conditions, and farm practices. Ambio 1997, 26 (5), 266– 272
    Google Scholar
    There is no corresponding record for this reference.
  78. 78
    SARE Organic Matter: Organic Matter and Natural Cycles. https://www.sare.org/Learning-Center/Books/Building-Soils-for-Better-Crops-3rd-Edition/Text-Version/Organic-Matter-What-It-Is-and-Why-It-s-So-Important/Why-Soil-Organic-Matter-Is-So-Important (accessed 2019/5/18).
    Google Scholar
    There is no corresponding record for this reference.
  79. 79
    Howarth, R. W.; Marino, R. Nitrogen as the limiting nutrient for eutrophication in coastal marine ecosystems: Evolving views over three decades. Limnol. Oceanogr. 2006, 51 (1part2), 364– 376,  DOI: 10.4319/lo.2006.51.1_part_2.0364
    Google Scholar
    79
    Nitrogen as the limiting nutrient for eutrophication in coastal marine ecosystems: evolving views over three decades
    Howarth, Robert W.; Marino, Roxanne
    Limnology and Oceanography (2006), 51 (1, Pt. 2), 364-376CODEN: LIOCAH; ISSN:0024-3590. (American Society of Limnology and Oceanography)
    A review. The first special vol. of Limnol. and Oceanog., published in 1972, focused on whether phosphorus (P) or carbon (C) is the major agent causing eutrophication in aquatic ecosystems. Only slight mention was made that estuaries may behave differently from lakes and that nitrogen (N) may cause eutrophication in estuaries. In the following decade, an understanding of eutrophication in estuaries proceeded in relative isolation from the community of scientists studying lakes. National water quality policy in the United States was directed almost solely toward P control for both lakes and estuaries, and similarly, European nations tended to focus on P control in lakes. Although bioassay data indicated N control of eutrophication in estuaries as early as the 1970s, this body of knowledge was treated with skepticism by many freshwater scientists and water-quality managers, because bioassay data in lakes often did not properly indicate the importance of P relative to C in those ecosystems. Hence, the bioassay data in estuaries had little influence on water-quality management. Over the past two decades, a strong consensus has evolved among the scientific community that N is the primary cause of eutrophication in many coastal ecosystems. The development of this consensus was based in part on data from whole-ecosystem studies and on a growing body of evidence that presented convincing mechanistic reasons why the controls of eutrophication in lakes and coastal marine ecosystems may differ. Even though N is probably the major cause of eutrophication in most coastal systems in the temperate zone, optimal management of coastal eutrophication suggests controlling both N and P, in part because P can limit primary prodn. in some systems. In addn., excess P in estuaries can interact with the availability of N and silica (Si) to adversely affect ecol. structure. Redn. of P to upstream freshwater ecosystems can also benefit coastal marine ecosystems through mechanisms such as increased Si fluxes.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28XhsFaqtbo%253D&md5=e74e91b7617b5e4868054d28ad46a0b1
  80. 80
    Yang, X.-e.; Wu, X.; Hao, H.-l.; He, Z.-l. Mechanisms and assessment of water eutrophication. J. Zhejiang Univ., Sci., B 2008, 9 (3), 197– 209,  DOI: 10.1631/jzus.B0710626
    Google Scholar
    80
    Mechanisms and assessment of water eutrophication
    Yang, Xiao-e; Wu, Xiang; Hao, Hu-lin; He, Zhen-li
    Journal of Zhejiang University, Science, B (2008), 9 (3), 197-209CODEN: JZUSAM; ISSN:1673-1581. (Zhejiang University Press)
    A review is given. Water eutrophication has become a worldwide environmental problem in recent years, and understanding the mechanisms of water eutrophication will help for prevention and remediation of water eutrophication. Recent advances in current status and major mechanisms of water eutrophication, assessment and evaluation criteria, and the influencing factors are presented. Water eutrophication in lakes, reservoirs, estuaries and rivers is widespread all over the world and the severity is increasing, esp. in the developing countries like China. The assessment of water eutrophication has been advanced from simple individual parameters like total P, total N, etc., to comprehensive indexes like total nutrient status index. The major influencing factors on water eutrophication include nutrient enrichment, hydrodynamics, environmental factors such as temp., salinity, CO2, element balance, etc., and microbial and biodiversity. The occurrence of water eutrophication is actually a complex function of all the possible influencing factors. The mechanisms of algal blooming are not fully understood and need to be further investigated.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXksVaqsb0%253D&md5=747a21fe0a10465f5e8ce0b4f9e99713
  81. 81
    Guntiñas, M. E.; Leirós, M. C.; Trasar-Cepeda, C.; Gil-Sotres, F. Effects of moisture and temperature on net soil nitrogen mineralization: A laboratory study. Eur. J. Soil Biol. 2012, 48, 73– 80,  DOI: 10.1016/j.ejsobi.2011.07.015
    Google Scholar
    81
    Effects of moisture and temperature on net soil nitrogen mineralization: A laboratory study
    Guntinas, M. E.; Leiros, M. C.; Trasar-Cepeda, C.; Gil-Sotres, F.
    European Journal of Soil Biology (2012), 48 (), 73-80CODEN: EJSBE2; ISSN:1164-5563. (Elsevier Masson SAS)
    Climate change will lead to changes in soil moisture and temp., thereby affecting org. matter mineralization and the cycling of biophilic elements such as nitrogen. However, very few studies have considered how the sensitivity of the rate of net nitrogen mineralization to temp. and/or moisture content may be modified by changes in these parameters. To investigate how changes in temp. and moisture content affect net nitrogen mineralization (as regards both the mineralization rate and the sensitivity of the mineralization rate to changes in temp. and moisture content), a lab. expt. was carried out in which three soils under different types of use (Forest, Grassland, Cropland) were incubated for 42 days under different moisture conditions (between 40 and 100% field capacity) and temps. (between 10 and 35 °C); total inorg. nitrogen levels were detd. at different times throughout the expt. The rate of mineralization was detd. at each temp. and moisture level considered, by use of the mono-compartmental model developed by Stanford and Smith (1972). For all soils, changes in the rate of mineralization with temp. followed the pattern described by the Q 10 model, while the models used to det. the effect of moisture content on the net rate of mineralization (linear, semilogarithmic, partial parabolic and complete parabolic) were only verified for the Forest soil. In general, the sensitivity to temp. was maximal at 25 °C, and the optimal moisture content for nitrogen mineralization was between 80% and 100% of field capacity. A relatively simple model that included the temp.-moisture-time interaction was also tested. This model provided a significant fit for the three soils under study, in contrast with the other models tested. In any case, further studies are necessary in order to address the extent to which changes in the quality of org. matter, caused by land use, affect any modifications to soil nitrogen that may be generated by climate change.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXhsF2qs73J&md5=8bb3eba6de5f4f4022a501ea556bba1e
  82. 82
    Bouwman, A. F.; Boumans, L. J. M.; Batjes, N. H. Estimation of global NH3 volatilization loss from synthetic fertilizers and animal manure applied to arable lands and grasslands. Global Biogeochemical Cycles 2002, 16 (2), 8.1– 8.14,  DOI: 10.1029/2000GB001389
    Google Scholar
    There is no corresponding record for this reference.
  83. 83
    Kissel, D. E.; Cabrera, M. L., Factors affecting urea hydrolysis. 87-98-J. In Ammonia Volatilization from Urea Fertilizers. Bulletin Y-206. Tennessee Valley Authority; Kansas Agricultural Experiment Station. Dept. of Agronomy. Kansas State University: Muscle Shoals, Alabama., 1988.
    Google Scholar
    There is no corresponding record for this reference.
  84. 84
    Torello, W. A.; Wehner, D. J. Urease activity in a Kentucky bluegrass turf. Agron. J. 1983, 75, 654– 656,  DOI: 10.2134/agronj1983.00021962007500040018x
    Google Scholar
    84
    Urease activity in a Kentucky bluegrass turf
    Torello, W. A.; Wehner, D. J.
    Agronomy Journal (1983), 75 (4), 654-6CODEN: AGJOAT; ISSN:0002-1962.
    The extent of urease [9002-13-5] activity assocd. with turfgrass tissue, thatch, and the underlying soil was detd. Because a turfgrass stand frequently possesses an extensive thatch layer that may serve as the primary plant growth medium, addnl. objectives included: detg. the effects of air-drying and seasonal variation on the activity of ureases in thatch; detg. the variability in thatch urease activity by analyzing multiple field samples; and detg. the variation of urease activity within a thatch profile. Turfgrass clippings, thatch, and underlying Flanagan silt loam soil (Aquic Arguidoll) samples were taken from a field-grown Kentucky bluegrass (Poa pratensis) turf in either Sept. 1980 or Mar. 1981. On a dry wt. basis, urease activity was 18-30 times higher from turfgrass clippings and thatch than from soil. Air-drying thatch increased urease activity by 20% over moist samples, whereas air-drying soil samples had no apparent effect. Greenhouse incubation of winter-dormant thatch samples increased urease activity by 40%, presumably in response to the duration of increased temp. Thatch urease activity varied between sampling sites but still remained extremely high compared to soil activity. Within each thatch sample (1 × 1 × 2 cm), urease activity was highest in the upper 1.0 cm of the profile. Thus, thatch urease activity was variable in nature depending on seasonal conditions, in sharp contrast with extremely stable soil urease activities. These findings suggest that, because of the high level of urease in thatch, NH3 volatilization will occur from most urea-treated turfgrass stands, regardless of the type of underlying soil, unless the urea is thoroughly washed into the soil.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaL3sXlt1Whtbo%253D&md5=cbc2ee07ea1e4c4abae2eeed30a4c795
  85. 85
    Acres Soil organic matter: Tips for responsible nitrogen management. http://www.ecofarmingdaily.com/soil-organic-matter-tips-nitrogen-management/?cn-reloaded=1 (accessed 2018/4/30).
    Google Scholar
    There is no corresponding record for this reference.
  86. 86
    Adler, P. R.; Hums, M. E.; McNeal, F. M.; Spatari, S. Evaluation of environmental and cost tradeoffs of producing energy from soybeans for on-farm use. J. Cleaner Prod. 2019, 210, 1635– 1649,  DOI: 10.1016/j.jclepro.2018.11.019
    Google Scholar
    There is no corresponding record for this reference.
  87. 87
    Huo, H.; Wang, M.; Bloyd, C.; Putsche, V. Life-cycle assessment of energy use and greenhouse gas emissions of soybean-derived biodiesel and renewable fuels. Environ. Sci. Technol. 2009, 43 (3), 750– 6,  DOI: 10.1021/es8011436
    Google Scholar
    87
    Life-Cycle Assessment of Energy Use and Greenhouse Gas Emissions of Soybean-Derived Biodiesel and Renewable Fuels
    Huo, Hong; Wang, Michael; Bloyd, Cary; Putsche, Vicky
    Environmental Science & Technology (2009), 43 (3), 750-756CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    The authors used Argonne National Lab.'s Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model to assess the life-cycle energy and greenhouse gas (GHG) emission impacts of 4 soybean-derived fuels: biodiesel fuel produced via transesterification, 2 renewable diesel fuels (I and II) produced from different hydrogenation processes, and renewable gasoline produced from catalytic cracking. Five approaches were employed to allocate the coproducts: a displacement approach; 2 allocation approaches, one based on the energy value and the other based on the market value; and 2 hybrid approaches that integrated the displacement and allocation methods. The relative rankings of soybean-based fuels in terms of energy and environmental impacts were different under the different approaches, and the reasons were analyzed. Results from the 5 allocation approaches showed that although the prodn. and combustion of soybean-based fuels might increase total energy use, they could have significant benefits in reducing fossil energy use (>52%), petroleum use (>88%), and GHG emissions (>57%) relative to petroleum fuels. This study emphasized the importance of the methods used to deal with coproduct issues and provide a comprehensive soln. for conducting a life-cycle assessment of fuel pathways with multiple coproducts.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXhsFejurrI&md5=f407189121b7b0c767605d0dd647d6b2
  88. 88
    Rajaeifar, M. A.; Ghobadian, B.; Safa, M.; Heidari, M. D. Energy life-cycle assessment and CO2 emissions analysis of soybean-based biodiesel: a case study. J. Cleaner Prod. 2014, 66, 233– 241,  DOI: 10.1016/j.jclepro.2013.10.041
    Google Scholar
    88
    Energy life-cycle assessment and CO2 emissions analysis of soybean-based biodiesel: a case study
    Rajaeifar, Mohammad Ali; Ghobadian, Barat; Safa, Majeed; Heidari, Mohammad Davoud
    Journal of Cleaner Production (2014), 66 (), 233-241CODEN: JCROE8; ISSN:0959-6526. (Elsevier Ltd.)
    In this study the energy consumption and CO2 emissions of biodiesel prodn. from soybean in Golestan province of Iran were studied. For this purpose, the life-cycle process of biodiesel was considered as five stages of agricultural soybean prodn., soybean transportation, soybean crushing, biodiesel conversion, and its transportation. The results indicated that the total fossil energy consumption with coproduct allocation was 8617.7 MJ ha-1 and the renewable energy output content (biodiesel as the final outcome) was estd. as 16,991.4 MJ ha-1. The net energy gain (NEG) and the fossil energy ratio (FER) were calcd. as 8373.7 MJ ha-1 and 1.97, resp., which show soybean is a suitable energy crop for biodiesel prodn. Agricultural soybean prodn. stage ranked the first in energy consumption among the five main stages where it consumed 50.56% of total fossil energy consumption in the biodiesel life-cycle process. The greenhouse gas (GHG) emissions data anal. revealed that the total GHG emission was 1710.3 kg CO2eq ha-1 which biodiesel prodn. life-cycle was only account for 311.96 kg CO2eq ha-1 if the mass allocation is considered. Overall, biodiesel prodn. from soybean in Iran can be considered as a way to increase energy security in the near future. Also, soybean cultivation must be considered along with other common oilseeds cultivation in order to prevent food competition between biodiesel feedstocks and food prodn. in Iran.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhvVSltrrM&md5=45bd0b2f28639add36f1878ef88ad7c1
  89. 89
    Castanheira, É. G.; Freire, F. Greenhouse gas assessment of soybean production: implications of land use change and different cultivation systems. J. Cleaner Prod. 2013, 54, 49– 60,  DOI: 10.1016/j.jclepro.2013.05.026
    Google Scholar
    89
    Greenhouse gas assessment of soybean production: implications of land use change and different cultivation systems
    Castanheira, Erica Geraldes; Freire, Fausto
    Journal of Cleaner Production (2013), 54 (), 49-60CODEN: JCROE8; ISSN:0959-6526. (Elsevier Ltd.)
    The increase in soybean prodn. as a source of protein and oil is being stimulated by the growing demand for livestock feed, food and numerous other applications. Significant greenhouse gas (GHG) emissions can result from land use change due to the expansion and cultivation of soybean. However, this is complex to assess and the results can vary widely. The main goal of this article is to investigate the life-cycle GHG balance for soybean produced in Latin America, assessing the implications of direct land use change emissions and different cultivation systems. A life-cycle model, including inventories for soybean produced in three different climate regions, was developed, addressing land use change, cultivation and transport to Europe. A comprehensive evaluation of alternative land use change scenarios (conversion of tropical forest, forest plantations, perennial crop plantations, savannah and grasslands), cultivation (tillage, reduced tillage and no-tillage) and soybean transportation systems was undertaken. The main results show the importance of land use change in soybean GHG emissions, but significant differences were obsd. for the alternative scenarios, namely 0.1-17.8 kg CO2eq kg-1 soybean. The original land choice is a crit. issue in ensuring the lowest soybean GHG balance and degraded grassland should preferably be used for soybean cultivation. The highest GHG emissions were calcd. for tropical moist regions when rainforest is converted into soybean plantations (tillage system). When land use change is not considered, the GHG intensity varies from 0.3 to 0.6 kg CO2eq kg-1 soybean. It was calcd. that all tillage systems have higher GHG emissions than the corresponding no-tillage and reduced tillage systems. The results also show that N2O emissions play a major role in the GHG emissions from cultivation, although N2O emission calcns. are very sensitive to the parameters and emission factors adopted.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXpt12iu70%253D&md5=f62179bb5010d514185f3750b205821b
  90. 90
    Costello, C.; Griffin, W. M.; Landis, A. E.; Matthews, H. S. Impact of biofuel crop production on the formation of hypoxia in the Gulf of Mexico. Environ. Sci. Technol. 2009, 43, 7985– 7991,  DOI: 10.1021/es9011433
    Google Scholar
    90
    Impact of Biofuel Crop Production on the Formation of Hypoxia in the Gulf of Mexico
    Costello, Christine; Griffin, W. Michael; Landis, Amy E.; Matthews, H. Scott
    Environmental Science & Technology (2009), 43 (20), 7985-7991CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    Many studies have compared corn-based ethanol to cellulosic ethanol on a per unit basis and have generally concluded that cellulosic ethanol will result in fewer environmental consequences, including NO3- output. This study takes a system-wide approach in considering the NO3- output and the relative areal extent of hypoxia in the Northern Gulf of Mexico (NGOM) due to the introduction of addnl. crops for biofuel prodn. We stochastically est. NO3- loading to the NGOM and use these results to approx. the areal extent of hypoxia for scenarios that meet the Energy Independence and Security Act of 2007's biofuel goals for 2015 and 2022. Crops for ethanol include corn, corn stover, and switchgrass; all biodiesel is assumed to be from soybeans. Our results indicate that moving from corn to cellulosics for ethanol prodn. may result in a 20% decrease (based on mean values) in NO3- output from the Mississippi and Atchafalaya River Basin (MARB). This decrease will not meet the EPA target for hypoxic zone redn. An aggressive nutrient management strategy will be needed to reach the 5000-Km2 areal extent of hypoxia in the NGOM goal set forth by the Mississippi River/Gulf of Mexico Watershed Nutrient Task Force even in the absence of biofuels, given current prodn. to meet food, feed, and other industrial needs.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXpslCltrc%253D&md5=b31dd1b952c9751028e8439d59d49305
  91. 91
    Dosskey, M. G. Toward quantifying water pollution abatement in response to installing buffers on crop land. Environ. Manage. 2001, 28 (5), 577– 98,  DOI: 10.1007/s002670010245
    Google Scholar
    91
    Toward quantifying water pollution abatement in response to installing buffers on crop land
    Dosskey M G
    Environmental management (2001), 28 (5), 577-98 ISSN:0364-152X.
    The scientific research literature is reviewed (i) for evidence of how much reduction in nonpoint source pollution can be achieved by installing buffers on crop land, (ii) to summarize important factors that can affect this response, and (iii) to identify remaining major information gaps that limit our ability to make probable estimates. This review is intended to clarify the current scientific foundation of the USDA and similar buffer programs designed in part for water pollution abatement and to highlight important research needs. At this time, research reports are lacking that quantify a change in pollutant amounts (concentration and/or load) in streams or lakes in response to converting portions of cropped land to buffers. Most evidence that such a change should occur is indirect, coming from site-scale studies of individual functions of buffers that act to retain pollutants from runoff: (1) reduce surface runoff from fields, (2) filter surface runoff from fields, (3) filter groundwater runoff from fields, (4) reduce bank erosion, and (5) filter stream water. The term filter is used here to encompass the range of specific processes that act to reduce pollutant amounts in runoff flow. A consensus of experimental research on functions of buffers clearly shows that they can substantially limit sediment runoff from fields, retain sediment and sediment-bound pollutants from surface runoff, and remove nitrate N from groundwater runoff. Less certain is the magnitude of these functions compared to the cultivated crop condition that buffers would replace within the context of buffer installation programs. Other evidence suggests that buffer installation can substantially reduce bank erosion sources of sediment under certain circumstances. Studies have yet to address the degree to which buffer installation can enhance channel processes that remove pollutants from stream flow. Mathematical models offer an alternative way to develop estimates for water quality changes in response to buffer installation. Numerous site conditions and buffer design factors have been identified that can determine the magnitude of each buffer function. Accurate models must be able to account for and integrate these functions and factors over whole watersheds. At this time, only pollutant runoff and surface filtration functions have been modeled to this extent. Capability is increasing as research data is produced, models become more comprehensive, and new techniques provide means to describe variable conditions across watersheds. A great deal of professional judgment is still required to extrapolate current knowledge of buffer functions into broadly accurate estimates of water pollution abatement in response to buffer installation on crop land. Much important research remains to be done to improve this capability. The greatest need is to produce direct quantitative evidence of this response. Such data would confirm the hypothesis and enable direct testing of watershed-scale prediction models as they become available. Further study of individual pollution control functions is also needed, particularly to generate comparative evidence for how much they can be manipulated through buffer installation and management.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD3MritF2rsA%253D%253D&md5=7eb88f959eee3fce7bf2210e98372d56

Cited By

Click to copy section linkSection link copied!

This article is cited by 34 publications.

  1. Tania Itzel Serrano-Arévalo, Carlos Antonio Padilla-Esquivel, Luis Germán Hernández-Pérez, Felipe A. Díaz-Alvarado, César Ramírez-Márquez, José María Ponce-Ortega. Green Ammonia Production: The Performance of Global Systems in Eco-Indicator 99 and Circular Economy Metrics. ACS Sustainable Chemistry & Engineering 2024, 12 (33) , 12652-12669. https://doi.org/10.1021/acssuschemeng.4c05181
  2. Shuai Li, Hongwei Lu, Xiaodong Li, Yanan Shao, Yifan Tang, Gaojie Chen, Zuo Chen, Ziqian Zhu, Jiesong Zhu, Lin Tang, Jie Liang. Toward Low-Carbon Rice Production in China: Historical Changes, Driving Factors, and Mitigation Potential. Environmental Science & Technology 2024, 58 (13) , 5772-5783. https://doi.org/10.1021/acs.est.4c00539
  3. Peiyuan Liu, Zhongming Ren, Wenhui Ding, Deqiang Kong, Slav W. Hermanowicz, Yuxiong Huang. Comparative Environmental Impact Assessment of Copper-Based Nanopesticides and Conventional Pesticides. ACS Agricultural Science & Technology 2023, 3 (7) , 593-600. https://doi.org/10.1021/acsagscitech.3c00118
  4. Yize Liu, Weiyi Gu, Beibei Liu, Chao Zhang, Chun Wang, Yi Yang, Minghao Zhuang. Closing Greenhouse Gas Emission Gaps of Staple Crops in China. Environmental Science & Technology 2022, 56 (13) , 9302-9311. https://doi.org/10.1021/acs.est.2c01978
  5. Bu Zhao, Chenyang Shuai, Ping Hou, Shen Qu, Ming Xu. Estimation of Unit Process Data for Life Cycle Assessment Using a Decision Tree-Based Approach. Environmental Science & Technology 2021, 55 (12) , 8439-8446. https://doi.org/10.1021/acs.est.0c07484
  6. Shuo Wang, Tianzuo Zhang, Ziheng Li, Kang Wang, Jinglan Hong. Interpretable machine learning method empowers dynamic life cycle impact assessment: A case study on the carcinogenic impact of coal power generation. Environmental Impact Assessment Review 2025, 112 , 107837. https://doi.org/10.1016/j.eiar.2025.107837
  7. Shivani Chauhan, Preeti Solanki, Chayanika Putatunda, Abhishek Walia, Arvind Keprate, Arvind Kumar Bhatt, Vijay Kumar Thakur, Ravi Kant Bhatia. Recent advancements in biomass to bioenergy management and carbon capture through artificial intelligence integrated technologies to achieve carbon neutrality. Sustainable Energy Technologies and Assessments 2025, 73 , 104123. https://doi.org/10.1016/j.seta.2024.104123
  8. Martin Popowicz, Nicolas J. Katzer, Moritz Kettele, Josef-Peter Schöggl, Rupert J. Baumgartner. Digital technologies for life cycle assessment: a review and integrated combination framework. The International Journal of Life Cycle Assessment 2024, 31 https://doi.org/10.1007/s11367-024-02409-4
  9. Yoel Gebrai, Gideon Danso-Abbeam, Kebreab Ghebremichael, James R. Mihelcic. Sustainability assessment of products of the tropical tree moringa in Ghana with a focus on small-scale producers. Sustainable Production and Consumption 2024, 52 , 253-267. https://doi.org/10.1016/j.spc.2024.10.017
  10. Xiaobo Xue Romeiko, Wangjian Zhang, Xuesong Zhang, Jun-Ki Choi. Spatially Explicit Life Cycle Global Warming and Eutrophication Potentials of Confined Dairy Production in the Contiguous US. Environments 2024, 11 (11) , 230. https://doi.org/10.3390/environments11110230
  11. Tianhong Mu, Beth Feingold, Akiko Hosler, Christine Bozlak, Jiacheng Chen, Roni Neff, Mariana Torres Arroyo, Peter Crasto-Donnelly, Natasha Pernicka, Stacy Pettigrew, Victor Russak, Peyton Yourch, Xiaobo Xue Romeiko. Comparing life cycle environmental impacts of food access and consumption pre- and during COVID 19 in New York State's Capital Region. Science of The Total Environment 2024, 949 , 175037. https://doi.org/10.1016/j.scitotenv.2024.175037
  12. Weiyi Gu, Guosong Ma, Rui Wang, Laura Scherer, Pan He, Longlong Xia, Yuyao Zhu, Jun Bi, Beibei Liu. Climate adaptation through crop migration requires a nexus perspective for environmental sustainability in the North China Plain. Nature Food 2024, 5 (7) , 569-580. https://doi.org/10.1038/s43016-024-01008-8
  13. Bowei Ouyang, Qinmei Zhong, Peng Ouyang, Yue Yuan, Xian Wu, Sheng-Tao Yang. Graphene quantum dots enhance the biological nitrogen fixation by up-regulation of cellular metabolism and electron transport. Chemical Engineering Journal 2024, 487 , 150694. https://doi.org/10.1016/j.cej.2024.150694
  14. Chelsea Wentworth, Mariana Torres Arroyo, Rafael Cavalcanti Lembi, Beth J. Feingold, Darcy Freedman, Steven Gray, Jennifer Hodbod, Becca B.R. Jablonski, Kathryn M. Janda-Thomte, Pablo Lemoine, Aida Nielsen, Xiaobo Xue Romeiko, Deborah Salvo, Laura Schmitt Olabisi, Alexandra E. van den Berg, Owusua Yamoah. Navigating community engagement in participatory modeling of food systems. Environmental Science & Policy 2024, 152 , 103645. https://doi.org/10.1016/j.envsci.2023.103645
  15. Fan Chen, Shujun Li, Lingyi Hao, Yi An, Lili Huo, Lili Wang, Yutong Li, Xiaoyu Zhu. Research Progress on Soil Security Assessment in Farmlands and Grasslands Based on Bibliometrics over the Last Four Decades. Sustainability 2024, 16 (1) , 404. https://doi.org/10.3390/su16010404
  16. Rupert J. Baumgartner, Katharina Berger, Josef-Peter Schöggl. Digital Technologies for Sustainable Product Management in the Circular Economy. 2024, 121-143. https://doi.org/10.1007/978-3-031-61749-2_7
  17. Jie Yan, Zhaoyang Kong, Yize Liu, Ning Li, Xiaolin Yang, Minghao Zhuang. A high-resolution energy use efficiency assessment of China’s staple food crop production and associated improvement potential. Renewable and Sustainable Energy Reviews 2023, 188 , 113789. https://doi.org/10.1016/j.rser.2023.113789
  18. Gabriela Giusti, Nadya Regina Galo, Rodrigo Pinheiro Tóffano Pereira, Diogo Aparecido Lopes Silva, Viachaslau Filimonau. Assessing the impact of drought on carbon footprint of soybean production from the life cycle perspective. Journal of Cleaner Production 2023, 425 , 138843. https://doi.org/10.1016/j.jclepro.2023.138843
  19. Xuan Wang, Lei Xiao, Zhenyan Fan, Yueyuan Chen, Bo Wang, Zhifang Cui. Impact-oriented assessment of carbon, water and energy footprints for maize production: a case study in Shandong, China. International Journal of Sustainable Development & World Ecology 2023, 3 , 1-12. https://doi.org/10.1080/13504509.2023.2263852
  20. Sneha Kumari, V. G. Venkatesh, Felix Ter Chian Tan, S. Vijayakumar Bharathi, M. Ramasubramanian, Yangyan Shi. Application of machine learning and artificial intelligence on agriculture supply chain: a comprehensive review and future research directions. Annals of Operations Research 2023, 14 https://doi.org/10.1007/s10479-023-05556-3
  21. Aimi Izyana Ismail, Ahmad Khairul Muhamad, Mohd Ikram Mohammad, Masnindah Malahubban. Natural rubber serum protein as a potential feed ingredient for growing broiler chickens. Journal of Rubber Research 2023, 26 (3) , 193-204. https://doi.org/10.1007/s42464-023-00219-1
  22. Maximilian Andreas Storz, Maria Brommer, Mauro Lombardo, Gianluca Rizzo. Soy Milk Consumption in the United States of America: An NHANES Data Report. Nutrients 2023, 15 (11) , 2532. https://doi.org/10.3390/nu15112532
  23. Aditya Velidandi, Pradeep Kumar Gandam, Madhavi Latha Chinta, Srilekha Konakanchi, Anji reddy Bhavanam, Rama Raju Baadhe, Minaxi Sharma, James Gaffey, Quang D. Nguyen, Vijai Kumar Gupta. State-of-the-art and future directions of machine learning for biomass characterization and for sustainable biorefinery. Journal of Energy Chemistry 2023, 81 , 42-63. https://doi.org/10.1016/j.jechem.2023.02.020
  24. Faezeh Mohammadi Kashka, Zeinolabedin Tahmasebi Sarvestani, Hemmatollah Pirdashti, Ali Motevali, Mehdi Nadi, Mohammad Valipour. Sustainable Systems Engineering Using Life Cycle Assessment: Application of Artificial Intelligence for Predicting Agro-Environmental Footprint. Sustainability 2023, 15 (7) , 6326. https://doi.org/10.3390/su15076326
  25. Zewei Jiang, Shihong Yang, Pete Smith, Qingqing Pang. Ensemble machine learning for modeling greenhouse gas emissions at different time scales from irrigated paddy fields. Field Crops Research 2023, 292 , 108821. https://doi.org/10.1016/j.fcr.2023.108821
  26. Ehouman J.M, Zougranaa Y, Lamoussa O, Ghislain A.Y, Ziao N. Assessment of Soap and Cosmetic Effluents quality in Metallic Trace Elements from Yopougon Industrial Area, Abidjan, Ivory Coast. International Research Journal of Multidisciplinary Technovation 2023, , 13-20. https://doi.org/10.54392/irjmt2312
  27. Zhaozhuo Yu, Haoxiang Ma, Xiangjun Liu, Miao Wang, Junqi Wang. Review in life cycle assessment of biomass conversion through pyrolysis-issues and recommendations. Green Chemical Engineering 2022, 3 (4) , 304-312. https://doi.org/10.1016/j.gce.2022.08.002
  28. Gerald C Shurson, Rylie E O Pelton, Zhaohui Yang, Pedro E Urriola, Jennifer Schmitt. Environmental impacts of eco-nutrition swine feeding programs in spatially explicit geographic regions of the United States. Journal of Animal Science 2022, 100 (12) https://doi.org/10.1093/jas/skac356
  29. Rakhi Bajpai Dixit, Uma Shankar Sagaram, Chandra Gocher, G. Raja Krishna Kumar, Santanu Dasgupta. Biomolecular characterisation of marine microalga in comparison to fishmeal and soymeal as an alternative feed ingredient. Phytochemical Analysis 2022, 33 (3) , 365-372. https://doi.org/10.1002/pca.3094
  30. Ali Ghoroghi, Yacine Rezgui, Ioan Petri, Thomas Beach. Advances in application of machine learning to life cycle assessment: a literature review. The International Journal of Life Cycle Assessment 2022, 27 (3) , 433-456. https://doi.org/10.1007/s11367-022-02030-3
  31. N. Prioux, R. Ouaret, G. Hetreux, J.-P. Belaud. Environmental assessment coupled with machine learning for circular economy. Clean Technologies and Environmental Policy 2022, 251 https://doi.org/10.1007/s10098-022-02275-4
  32. Mochen Liao, Yuan Yao. Applications of artificial intelligence‐based modeling for bioenergy systems: A review. GCB Bioenergy 2021, 13 (5) , 774-802. https://doi.org/10.1111/gcbb.12816
  33. L. Reijnders. Life Cycle Assessment of Biofuels. 2021, 53-67. https://doi.org/10.1007/978-1-0716-1323-8_4
  34. Zoë Plakias. Cost-benefit analysis as a tool for measuring economic impacts of local food systems: Case study of an institutional sourcing change. Journal of Agriculture, Food Systems, and Community Development 2021, , 1-25. https://doi.org/10.5304/jafscd.2021.103.011
Download PDF
Go to Environmental Science & Technology
Get e-Alerts
Get e-Alerts

Environmental Science & Technology

Cite this: Environ. Sci. Technol. 2020, 54, 8, 4758–4768
Click to copy citationCitation copied!
https://doi.org/10.1021/acs.est.9b06874
Published March 23, 2020

Copyright © 2020 American Chemical Society. This publication is available under these Terms of Use.

Request reuse permissions

Article Views

4947

Altmetric

-

Citations

34
Learn about these metrics

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.

Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.

The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.

  • Abstract

    High Resolution Image
    Download MS PowerPoint Slide

    Figure 1

    Figure 1. Stages of life cycle assessment (LCA) of soybean production in the U.S. Midwest. The diagram shows the four stages of LCA: (1) Goal and Scope, (2) Life Cycle Inventory, (3) Life Cycle Impact Assessment, and (4) Interpretation.

    High Resolution Image
    Download MS PowerPoint Slide

    Figure 2

    Figure 2. Maps showing county-level life cycle environmental impacts of soybean production in the U.S. Midwest for years 2002, 2004, and 2006: (A) global warming potential, (B) eutrophication, and (C) acidification.

    High Resolution Image
    Download MS PowerPoint Slide

    Figure 3

    Figure 3. Statistical distribution of life cycle environmental impacts of soybean production in 12 US Midwest states (2000–2008) state-by-state: (A) Life cycle GWP (kg CO2-eq kg soybean–1), (B) life cycle EU (g N-eq kg soybean–1), and (C) life cycle AD (g SO2-eq kg soybean–1). The error bars represent county-level min and max values and the lines in box plots show 25th, 50th, and 75th percentiles.

    High Resolution Image
    Download MS PowerPoint Slide

    Figure 4

    Figure 4. Statistical distribution of life cycle environmental impacts of soybean production in 12 US Midwest states, 2000–2008 year-by-year: (A) Life cycle GWP (kg CO2-eq kg soybean–1), (B) life cycle EU (g N-eq kg soybean–1), and (C) life cycle AD (g SO2-eq kg soybean–1). The error bars represent county-level min and max values and the lines in box plots show 25th, 50th, and 75th percentiles.

    High Resolution Image
    Download MS PowerPoint Slide

    Figure 5

    Figure 5. Relative influences of predictor variables to life cycle environmental impacts of: (A) Global warming potential, (B) eutrophication, and (C) acidification of soybean production in 12 U.S. Midwest states from 2000 to 2008. The bars show the relative contributions of predictor variables (x-axis) in percentages.

    High Resolution Image
    Download MS PowerPoint Slide

    Figure 6

    Figure 6. Comparison of existing literature in life cycle analysis of soybean production: (A) global warming potential, (B) eutrophication, and (C) acidification. The orange bars represent the median values of the life cycle environmental impacts of the present study and the blue bars represent the median values of life cycle environmental impacts associated with soybean production in the existing literature. The asterisks below the bars indicate studies focusing on U.S. Midwest regions and the error bars represent min and max values of life cycle environmental impacts for studies providing those values.

    High Resolution Image
    Download MS PowerPoint Slide

  • References

    This article references 91 other publications.

    1. 1
      USDA Soybeans & Oil Crops. https://www.ers.usda.gov/topics/crops/soybeans-oil-crops/ (2019/10//1).
      There is no corresponding record for this reference.
    2. 2
      Webster, J. C. Forecast for U.S. Soybean Demand: Strong and Stronger; American Soybean Magazine Jan 30, 2018, 2018.
      There is no corresponding record for this reference.
    3. 3
      Cavalett, O.; Ortega, E. Integrated environmental assessment of biodiesel production f rom soybean in Brazil. J. Cleaner Prod. 2010, 18 (1), 55– 70,  DOI: 10.1016/j.jclepro.2009.09.008
      There is no corresponding record for this reference.
    4. 4
      Maciel, V. G.; Zortea, R. B.; Grillo, I. B.; Ugaya, C. M. L.; Einloft, S.; Seferin, M. Greenhouse gases assessment of soybean cultivation steps in southern Brazil. J. Cleaner Prod. 2016, 131, 747– 753,  DOI: 10.1016/j.jclepro.2016.04.100
      4
      Greenhouse gases assessment of soybean cultivation steps in southern Brazil
      Maciel, Vinicius Goncalves; Zortea, Rafael Batista; Grillo, Igor Barden; Lie Ugaya, Cassia Maria; Einloft, Sandra; Seferin, Marcus
      Journal of Cleaner Production (2016), 131 (), 747-753CODEN: JCROE8; ISSN:0959-6526. (Elsevier Ltd.)
      This study presents a greenhouse gases emissions assessment of soybean cultivation in southern Brazil based on life cycle inventory. Although there are currently some studies on this topic, it is focused in the country level. Nevertheless, there are differences among the producing regions and it's estd. that for each 20 kg of soybeans produced in Brazil, one is produced in Rio Grande do Sul state. In a previous study, a life cycle inventory of soybean cultivated in this Brazilian state was developed, nevertheless, the influence of land use change along the life cycle was not taken into account. Therefore, the current study discusses the influence of direct land use change over the greenhouse gas emissions. The functional unit (FU) employed was 1 kg of soybean harvested for a cradle to gate study. For the soybean cultivation, in the scenario related to no land use change (scenario 1), 0.352 kg CO2/FU was emitted. This value increases up to 205% in scenario 2 (in this case, the actual scenario was that 15.4% of soybean cropland area replaced grassland) and 892% in scenario 3 (all land transformation was over forest). In scenario 1, soybean cultivation was responsible for the higher share of the greenhouse gases emissions (42%). The highest contributions in soybean cultivation for greenhouse gases emissions were: liming (37%), fertilization (19%) and seeding (9%).
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XnvFWls7o%253D&md5=8bf85e8d5973bb4e21a639bc0a0a2ce8
    5. 5
      Dalgaard, R.; Schmidt, J.; Halberg, N.; Christensen, P.; Thrane, M.; Pengue, W. A. LCA of soybean meal. Int. J. Life Cycle Assess. 2008, 13 (3), 240,  DOI: 10.1065/lca2007.06.342
      5
      LCA of soybean meal
      Dalgaard, Randi; Schmidt, Jannick; Halberg, Niels; Christensen, Per; Thrane, Mikkel; Pengue, Walter A.
      International Journal of Life Cycle Assessment (2008), 13 (3), 240-254CODEN: IJLCFF; ISSN:0948-3349. (Ecomed Publishers)
      Soybean meal is an important protein input to the European livestock prodn., with Argentina being an important supplier. The area cultivated with soybeans is still increasing globally, and so are the no. of LCAs where the prodn. of soybean meal forms part of the product chain. In recent years there has been increasing focus on how soybean prodn. affects the environment. The purpose of the study was to est. the environmental consequences of soybean meal consumption using a consequential LCA approach. The functional unit is 'one kg of soybean meal produced in Argentina and delivered to Rotterdam Harbor'. Soybean meal has the co-product soybean oil. In this study, the consequential LCA method was applied, and co-product allocation was thereby avoided through system expansion. In this context, system expansion implies that the inputs and outputs are entirely ascribed to soybean meal, and the product system is subsequently expanded to include the avoided prodn. of palm oil. Presently, the marginal vegetable oil on the world market is palm oil but, to be prepd. for fluctuations in market demands, an alternative product system with rapeseed oil as the marginal vegetable oil has been established. EDIP97 (updated version 2.3) was used for LCIA and the following impact categories were included: Global warming, eutrophication, acidification, ozone depletion and photochem. smog. Two soybean loops were established to demonstrate how an increased demand for soybean meal affects the palm oil and rapeseed oil prodn., resp. The characterized results from LCA on soybean meal (with palm oil as marginal oil) were 721 g CO2 eq. for global warming potential, 0.3 mg CFC11 equiv. for ozone depletion potential, 3.1 g SO2 eq. for acidification potential, -2 g NO3 eq. for eutrophication potential and 0.4 g ethene eq. for photochem. smog potential per kg soybean meal. The av. area per kg soybean meal consumed was 3.6 m2year. Attributional results, calcd. by economic and mass allocation, are also presented. Normalized results show that the most dominating impact categories were: global warming, eutrophication and acidification. The 'hot spot' in relation to global warming, was 'soybean cultivation', dominated by N2O emissions from degrdn. of crop residues (e.g., straw) and during biol. nitrogen fixation. In relation to eutrophication and acidification, the transport of soybeans by truck is important, and sensitivity analyses showed that the acidification potential is very sensitive to the increased transport distance by truck. The potential environmental impacts (except photochem. smog) were lower when using rapeseed oil as the marginal vegetable oil, because the avoided prodn. of rapeseed contributes more neg. compared with the avoided prodn. of palm oil. Identification of the marginal vegetable oil (palm oil or rapeseed oil) turned out to be important for the result, and this shows how crucial it is in consequential LCA to identify the right marginal product system (e.g., marginal vegetable oil). Consequential LCAs were successfully performed on soybean meal and LCA data on soybean meal are now available for consequential (or attributional) LCAs on livestock products. The study clearly shows that consequential LCAs are quite easy to handle, even though it has been necessary to include prodn. of palm oil, rapeseed and spring barley, as these prodn. systems are affected by the soybean oil co-product. We would appreciate it if the International Journal of Life Cycle Assessment had articles on the developments on, for example, marginal protein, marginal vegetable oil, marginal electricity (related to relevant markets), marginal heat, marginal cereals and, likewise, on metals and other basic commodities. This will not only facilitate the work with consequential LCAs, but will also increase the quality of LCAs.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXmtFyrsLo%253D&md5=123f12931d7b1ad942e19fce5256ce0c
    6. 6
      Lehuger, S.; Gabrielle, B.; Gagnaire, N. Environmental impact of the substitution of imported soybean meal with locally-produced rapeseed meal in dairy cow feed. J. Cleaner Prod. 2009, 17 (6), 616– 624,  DOI: 10.1016/j.jclepro.2008.10.005
      6
      Environmental impact of the substitution of imported soybean meal with locally-produced rapeseed meal in dairy cow feed
      Lehuger, Simon; Gabrielle, Benoit; Gagnaire, Nathalie
      Journal of Cleaner Production (2009), 17 (6), 616-624CODEN: JCROE8; ISSN:0959-6526. (Elsevier Ltd.)
      Growing public concerns about the traceability, safety and environmental-friendliness of food products provide an incentive for shorter supply chains in agricultural prodn. Here, we assessed the environmental impacts of the substitution of imported soybean meal with locally-produced rapeseed meal in French dairy prodn. systems, using a life-cycle approach. Two feeding rations based on either French-produced rapeseed meal or Brazilian-produced soy meal as concs., were compared for nine impact categories, including global warming, ecotoxicity and eutrophication. Crop prodn. was the main contributor to most impacts, while overseas transport of soy meal only had a marginal effect. The soybean ration appeared more environmentally-efficient than the rapeseed ration because it involved less intensive management practices, in particular regarding synthetic fertilizers consumption. However, land use changes brought about by soybean cultivation should also be examd.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXovFOiurw%253D&md5=a0df1960717f759715def322bd569ca2
    7. 7
      Özilgen, M.; Sorgüven, E. Energy and exergy utilization, and carbon dioxide emission in vegetable oil production. Energy 2011, 36 (10), 5954– 5967,  DOI: 10.1016/j.energy.2011.08.020
      There is no corresponding record for this reference.
    8. 8
      Panichelli, L.; Dauriat, A.; Gnansounou, E. Life cycle assessment of soybean-based biodiesel in Argentina for export. Int. J. Life Cycle Assess. 2009, 14 (2), 144– 159,  DOI: 10.1007/s11367-008-0050-8
      8
      Life cycle assessment of soybean-based biodiesel in Argentina for export
      Panichelli, Luis; Dauriat, Arnaud; Gnansounou, Edgard
      International Journal of Life Cycle Assessment (2009), 14 (2), 144-159CODEN: IJLCFF; ISSN:0948-3349. (Springer)
      Regional specificities are a key factor when analyzing the environmental impact of a biofuel pathway through a life cycle assessment (LCA). Due to different energy mixes, transport distances, agricultural practices and land use changes, results can significantly vary from one country to another. The Republic of Argentina is the first exporter of soybean oil and meal and the third largest soybean producer in the world, and therefore, soybean-based biodiesel prodn. is expected to significantly increase in the near future, mostly for exportation. Moreover, Argentinean biodiesel producers will need to evaluate the environmental performances of their product in order to comply with sustainability criteria being developed. However, because of regional specificities, the environmental performances of this biofuel pathway can be expected to be different from those obtained for other countries and feedstocks previously studied. This work aims at analyzing the environmental impact of soybean-based biodiesel prodn. in Argentina for export. The relevant impact categories account for the primary non-renewable energy consumption (CED), the global warming potential (GWP), the eutrophication potential (EP), the acidification potential (AP), the terrestrial ecotoxicity (TE), the aquatic ecotoxicity (AE), the human toxicity (HT) and land use competition (LU). The paper tackles the feedstock and country specificities in biodiesel prodn. by comparing the results of soybean-based biodiesel in Argentina with other ref. cases. Emphasis is put on explaining the factors that contribute most to the final results and the regional specificities that lead to different results for each biodiesel pathway. The Argentinean (AR) biodiesel pathway was modelled through an LCA and was compared with ref. cases available in the ecoinvent 2.01 database, namely, soybean-based biodiesel prodn. in Brazil (BR) and the United States (US), rapeseed-based biodiesel prodn. in the European Union (EU) and Switzerland (CH) and palm-oil-based biodiesel prodn. in Malaysia (MY). In all cases, the systems were modelled from feedstock prodn. to biodiesel use as B100 in a 28 t truck in CH. Furthermore, biodiesel pathways were compared with fossil low-sulfur diesel produced and used in CH. The LCA was performed according to the ISO stds. The life cycle inventory and the life cycle impact assessment (LCIA) were performed in Excel spreadsheets using the ecoinvent 2.01 database. The cumulative energy demand (CED) and the GWP were estd. through the CED for fossil and nuclear energy and the IPCC 2001 (climate change) LCIA methods, resp. Other impact categories were assessed according to CML 2001, as implemented in ecoinvent. As the product is a fuel for transportation (service), the system was defined for one vehicle kilometer (functional unit) and was divided into seven unit processes, namely, agricultural phase, soybean oil extn. and refining, transesterification, transport to port, transport to the destination country border, distribution and utilization. The Argentinean pathway results in the highest GWP, CED, AE and HT compared with the ref. biofuel pathways. Compared with the fossil ref., all impact categories are higher for the AR case, except for the CED. The most significant factor that contributes to the environmental impact in the Argentinean case varies depending on the evaluated category. Land provision through deforestation for soybean cultivation is the most impacting factor of the AR biodiesel pathway for the GWP, the CED and the HT categories. While nitrogen oxide emissions during the fuel use are the main cause of acidification, nitrate leaching during soybean cultivation is the main factor of eutrophication. LU is almost totally affected by arable land occupation for soybean cultivation. Cypermethrin used as pesticide in feedstock prodn. accounts for almost the total impact on TE and AE. Discussion The sensitivity anal. shows that an increase of 10% in the soybean yield, while keeping the same inputs, will reduce the total impact of the system. Avoiding deforestation is the main challenge to improve the environmental performances of soybean-based biodiesel prodn. in AR. If the soybean expansion can be done on marginal and set-aside agricultural land, the neg. impact of the system will be significantly reduced. Further implementation of crops' successions, soybean inoculation, reduced tillage and less toxic pesticides will also improve the environmental performances. Using ethanol as alc. in the transesterification process could significantly improve the energy balance of the Argentinean pathway. The main explaining factors depend on regional specificities of the system that lead to different results from those obtained in the ref. cases. Significantly different results can be obtained depending on the level of detail of the input data, the use of punctual or av. data and the assumptions made to build up the LCA inventory. Further improvement of the AR biodiesel pathways should be done in order to comply with international sustainability criteria on biofuel prodn. Due to the influence of land use changes in the final results, more efforts should be made to account for land use changes others than deforestation. More data are needed to det. the part of deforestation attributable to soybean cultivation. More efforts should be done to improve modeling of interaction between variables and previous crops in the agricultural phase, future transesterification technologies and market prices evolution. In order to assess more accurately the environmental impact of soybean-based biodiesel prodn. in Argentina, further considerations should be made to account for indirect land use changes, domestic biodiesel consumption and exportation to other regions, prodn. scale and regional georeferenced differentiation of prodn. systems.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXivVersrY%253D&md5=6322b8cedc1e84c39281b5c2391e61c1
    9. 9
      Landis, A. E.; Miller, S. A.; Theis, T. L. Life Cycle of the Corn-Soybean Agroecosystem for Biobased Production. Environ. Sci. Technol. 2007, 41, 1457– 1464,  DOI: 10.1021/es0606125
      9
      Life Cycle of the Corn-Soybean Agroecosystem for Biobased Production
      Landis, Amy E.; Miller, Shelie A.; Theis, Thomas L.
      Environmental Science & Technology (2007), 41 (4), 1457-1464CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      Bio-based product life cycle assessments (LCA) have largely focused on energy (fossil fuel) use and greenhouse gas emissions during agriculture and prodn. stages. This work compiled a more comprehensive life cycle inventory (LCI) for use in future bio-product LCA which rely on corn or soybean crops as feedstocks. The inventory includes energy, C, N, P, major pesticides, and USEPA criteria air pollutants resulting from processes such as fertilizer prodn., energy prodn., and on-farm chem. and equipment use. Agro-ecosystem material flows were modeled using a combination of GREET (Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation model), a linear fractionation model which describes P biogeochem. cycling, and Monte Carlo anal. Results showed that dominant air emissions resulted from crop farming, fertilizers, and on-farm N flows (e.g., N2O and NO). Seed prodn. and irrigation provided ≤0.002% to any inventory emission or energy flow and may be neglected in future LCA of corn or soybeans as feedstocks from the US corn belt. Lime contributed significantly (17% of total emissions) to air emissions and should not be neglected in bio-product LCA.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXlt1Kjsw%253D%253D&md5=6001d514994c9e492295af69cfa813f0
    10. 10
      Smil, V. Phosphorus in the environment: Natural Flows and Human Interferences. Annu. Rev. Energy Environ. 2000, 25, 53– 88,  DOI: 10.1146/annurev.energy.25.1.53
      There is no corresponding record for this reference.
    11. 11
      ISO. International Organization for Standardization (ISO) standards 14044: Environmental management and Life cycle assessment: Requirements and guidelines. 2006.
      There is no corresponding record for this reference.
    12. 12
      Kim, S.; Dale, B. E.; Jenkins, R. Life cycle assessment of corn grain and corn stover in the United States. Int. J. Life Cycle Assess. 2009, 14 (2), 160– 174,  DOI: 10.1007/s11367-008-0054-4
      12
      Life cycle assessment of corn grain and corn stover in the United States
      Kim, Seungdo; Dale, Bruce E.; Jenkins, Robin
      International Journal of Life Cycle Assessment (2009), 14 (2), 160-174CODEN: IJLCFF; ISSN:0948-3349. (Springer)
      The goal of this study is to est. the county-level environmental performance for continuous corn cultivation of corn grain and corn stover grown under the current tillage practices for various corn-growing locations in the US Corn Belt. The environmental performance of corn grain varies with its farming location because of climate, soil properties, cropping management, etc. Corn stover, all of the above ground parts of the corn plant except the grain, would be used as a feedstock for cellulosic ethanol. Two cropping systems are under investigation: corn produced for grain only without collecting corn stover (referred to as CRN) and corn produced for grain and stover harvest (referred to as CSR). The functional unit in this study is defined as dry biomass, and the ref. flow is 1 kg of dry biomass. The system boundary includes processes from cradle to farm gate. The default allocation procedure between corn grain and stover in the CSR system is the system expansion approach. County-level soil org. carbon dynamics, nitrate losses due to leaching, and nitrogen oxide and nitrous oxide emissions are simulated by the DAYCENT model. Life cycle environmental impact categories considered in this study are total fossil energy use, climate change (referred to as greenhouse gas emissions), acidification, and eutrophication. Sensitivities on farming practices and allocation are included. Simulations from the DAYCENT model predict that removing corn stover from soil could decrease nitrogen-related emissions from soil (i.e., N2O, NO x , and NO3 - leaching). DAYCENT also predicts a redn. in the annual accumulation rates of soil org. carbon (SOC) with corn stover removal. Corn stover has a better environmental performance than corn grain according to all life cycle environmental impacts considered. This is due to lower consumption of agrochems. and fuel used in the field operations and lower nitrogen-related emissions from the soil. Discussion The primary source of total fossil energy assocd. with biomass prodn. is nitrogen fertilizer, accounting for over 30% of the total fossil energy. Nitrogen-related emissions from soil (i.e., N2O, NO x , and NO3 - leaching) are the primary contributors to all other life cycle environmental impacts considered in this study. The environmental performance of corn grain and corn stover varies with the farming location due to crop management, soil properties, and climate conditions. Several general trends were identified from this study. Corn stover has a lower impact than corn grain in terms of total fossil energy, greenhouse gas emissions, acidification, and eutrophication. Harvesting corn stover reduces nitrogen-related emissions from the soil (i.e., N2O, NO x , NO3 -). The accumulation rate of soil org. carbon is reduced when corn stover is removed, and in some cases, the soil org. carbon level decreases. Harvesting only the cob portion of the stover would reduce the neg. impact of stover removal on soil org. carbon sequestration rate while still bringing the benefit of lower nitrogen-related emissions from the soil. No-tillage practices offer higher accumulation rates of soil org. carbon, lower fuel consumption, and lower nitrogen emissions from the soil than the current or conventional tillage practices. Planting winter cover crops could be a way to reduce nitrogen losses from soil and to increase soil org. carbon levels. County-level modeling is more accurate in estg. the local environmental burdens assocd. with biomass prodn. than national- or regional-level modeling. When possible, site-specific exptl. information on soil carbon and nitrogen dynamics should be obtained to reflect the system more accurately. The allocation approach between corn grain and stover significantly affects the environmental performance of each. The preferred allocation method is the system expansion approach where incremental fuel usage, addnl. nutrients in the subsequent growing season, and changes in soil carbon and nitrogen dynamics due to removing corn stover are assigned to only the collected corn stover.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXivVersrg%253D&md5=1b4d089d5a1dfb0879df9f306c222a02
    13. 13
      Tabatabaie, S. M. H.; Bolte, J. P.; Murthy, G. S. A regional scale modeling framework combining biogeochemical model with life cycle and economic analysis for integrated assessment of cropping systems. Sci. Total Environ. 2018, 625, 428– 439,  DOI: 10.1016/j.scitotenv.2017.12.208
      13
      A regional scale modeling framework combining biogeochemical model with life cycle and economic analysis for integrated assessment of cropping systems
      Tabatabaie, Seyed Mohammad Hossein; Bolte, John P.; Murthy, Ganti S.
      Science of the Total Environment (2018), 625 (), 428-439CODEN: STENDL; ISSN:0048-9697. (Elsevier B.V.)
      The goal of this study was to integrate a crop model, DNDC (DeNitrification-Decompn.), with life cycle assessment (LCA) and economic anal. models using a GIS-based integrated platform, ENVISION. The integrated model enables LCA practitioners to conduct integrated economic anal. and LCA on a regional scale while capturing the variability of soil emissions due to variation in regional factors during prodn. of crops and biofuel feedstocks. In order to evaluate the integrated model, the corn-soybean cropping system in Eagle Creek Watershed, Indiana was studied and the integrated model was used to first model the soil emissions and then conduct the LCA as well as economic anal. The results showed that the variation in soil emissions due to variation in weather is high causing some locations to be carbon sink in some years and source of CO2 in other years. In order to test the model under different scenarios, two tillage scenarios were defined: (1) conventional tillage (CT) and (2) no tillage (NT) and analyzed with the model. The overall GHG emissions for the corn-soybean cropping system was simulated and results showed that the NT scenario resulted in lower soil GHG emissions compared to CT scenario. Moreover, global warming potential (GWP) of corn ethanol from well to pump varied between 57 and 92 g CO2-eq./MJ while GWP under the NT system was lower than that of the CT system. The cost break-even point was calcd. as $3612.5/ha in a two year corn-soybean cropping system and the results showed that under low and medium prices for corn and soybean most of the farms did not meet the break-even point.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhvVKmsw%253D%253D&md5=a2af1c768d6424c3b7c1277513878a83
    14. 14
      Brentrup, F., Life cycle assessment of crop production. In Green Technologies in Food Production and Processing; Boye, J. I., Arcand, Y., Eds.; Springer US: Boston, MA, 2012; pp 61– 82.
      There is no corresponding record for this reference.
    15. 15
      Brentrup, F.; Küsters, J.; Kuhlmann, H.; Lammel, J. Environmental impact assessment of agricultural production systems using the life cycle assessment methodology: I. Theoretical concept of a LCA method tailored to crop production. Eur. J. Agron. 2004, 20 (3), 247– 264,  DOI: 10.1016/S1161-0301(03)00024-8
      There is no corresponding record for this reference.
    16. 16
      Mohammadi, A.; Rafiee, S.; Jafari, A.; Keyhani, A.; Dalgaard, T.; Knudsen, M. T.; Nguyen, T. L. T.; Borek, R.; Hermansen, J. E. Joint Life Cycle Assessment and Data Envelopment Analysis for the benchmarking of environmental impacts in rice paddy production. J. Cleaner Prod. 2015, 106, 521– 532,  DOI: 10.1016/j.jclepro.2014.05.008
      There is no corresponding record for this reference.
    17. 17
      Noya, I.; González-García, S.; Bacenetti, J.; Arroja, L.; Moreira, M. T. Comparative life cycle assessment of three representative feed cereals production in the Po Valley (Italy). J. Cleaner Prod. 2015, 99, 250– 265,  DOI: 10.1016/j.jclepro.2015.03.001
      17
      Comparative life cycle assessment of three representative feed cereals production in the Po Valley (Italy)
      Noya, Isabel; Gonzalez-Garcia, Sara; Bacenetti, Jacopo; Arroja, Luis; Moreira, Maria Teresa
      Journal of Cleaner Production (2015), 99 (), 250-265CODEN: JCROE8; ISSN:0959-6526. (Elsevier Ltd.)
      The cultivation of three different cereals - wheat, triticale and maize (five classes: 300, 400, 500, 600 and 700) - dedicated to grain prodn. for feed purposes was assessed to quantify their environmental profiles and identify the most sustainable crop from an environmental perspective. The most crit. processes throughout the life cycle of the cropping systems were also identified. These cereals were chosen because they are the most widespread cereal crops in the Po Valley (Lombardy region), the most important agricultural area in Italy.The std. framework of the Life Cycle Assessment (LCA) was followed to assess the environmental performance of the different cropping systems. Several impact categories were evaluated, including climate change (CC), ozone depletion (OD), terrestrial acidification (TA), freshwater eutrophication (FE), marine eutrophication (ME), human toxicity (HT), photochem. oxidant formation (POF), terrestrial ecotoxicity (TEC), freshwater ecotoxicity (FEC), marine ecotoxicity (MEC), water depletion (WD), fossil depletion (FD) as well as land use as an indicator.The results showed that the maize class 300 was the cereal with the worst environmental profile in the base case, considering economic allocation and no environmental burdens related with digestate prodn. This scenario presented the most intensive agricultural practices and the lowest biomass yield in comparison with the other crops. In contrast, the maize classes 600 and 700 were the cereal crops with the best environmental profiles in most impact categories. The lower requirements of fertilizer (and thus, fertilization activities) as well as the higher biomass yield were responsible of these favorable results.However, according to the environmental results, the selection of the best biomass source depends on several methodol. assumptions such as the functional unit and the allocation criteria considered (between the grain and the straw) as base for the calcns. Thus, the results of a sensitivity anal. showed that the choice of a mass allocation instead of economic one caused lower environmental impacts in all the categories. Moreover, the consideration or not of the environmental burdens related to the digestate prodn. (the main org. fertilizer used) was also a crit. step in the environmental evaluations. The inclusion of environmental loads related to digestate prodn. caused a notable increase in the impact of all the cropping systems regardless the cereal and the impact category. This conclusion could be extrapolated to other systems that exclude the addnl. burdens allocated to the prodn. of org. fertilizers.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXksFSqsL8%253D&md5=6fdd67fff8b591bcd9ba6bb38305a059
    18. 18
      Jekayinfa, S. O.; Olaniran, J. A.; Sasanya, B. F. Life cycle assessment of soybeans production and processing system into soy oil using solvent extraction process. International Journal of Product Lifecycle Management 2013, 6 (4), 311– 321,  DOI: 10.1504/IJPLM.2013.063203
      There is no corresponding record for this reference.
    19. 19
      Kim, S.; Dale, B. Regional variations in greenhouse gas emissions of biobased products in the United States—corn-based ethanol and soybean oil. Int. J. Life Cycle Assess. 2009, 14, 540– 546,  DOI: 10.1007/s11367-009-0106-4
      19
      Regional variations in greenhouse gas emissions of biobased products in the United States-corn-based ethanol and soybean oil
      Kim, Seungdo; Dale, Bruce E.
      International Journal of Life Cycle Assessment (2009), 14 (6), 540-546CODEN: IJLCFF; ISSN:0948-3349. (Springer)
      Background, aim, and scope Regional variations in the environmental impacts of plant biomass prodn. are significant, and the environmental impacts assocd. with feedstock supply also contribute substantially to the environmental performance of biobased products. Thus, the regional variations in the environmental performance of biobased products are also significant. This study scrutinizes greenhouse gas (GHG) emissions assocd. with two biobased products (i.e., ethanol and soybean oil) whose feedstocks (i.e., corn and soybean) are produced in different farming locations. Methods We chose 40 counties in Corn Belt States in the United States as biorefinery locations (i.e., corn dry milling, soybean crushing) and farming sites, and estd. cradle-to-gate GHG emissions of ethanol and of soybean oil, resp. The ests. are based on 1 kg of each biobased product (i.e., ethanol or soybean oil). The system boundary includes biomass prodn., the biorefinery, and upstream processes. Effects of direct land use change are included in the greenhouse gas anal. and measured as changes in soil org. carbon level, while the effects of indirect land use change are not considered in the baseline calcns. Those indirect effects however are scrutinized in a sensitivity anal. Results GHG emissions of corn-based ethanol range from 1.1 to 2.0 kg of CO2 equivalent per kg of ethanol, while GHG emissions of soybean oil are 0.4-2.5 kg of CO2 equivalent per kg of soybean oil. Thus, the regional variations due to farming locations are significant (by factors of 2-7). The largest GHG emission sources in ethanol prodn. are N2O emissions from soil during corn cultivation and carbon dioxide from burning the natural gas used in corn dry milling. The second largest GHG emission source groups in the ethanol prodn. system are nitrogen fertilizer (8-12%), carbon sequestration by soil (-15-2%), and electricity used in corn dry milling (7-16%). The largest GHG emission sources in soybean oil prodn. are N2O emissions from soil during soybean cultivation (13-57%) and carbon dioxide from burning the natural gas used in soybean crushing (21-47%). The second largest GHG emission source groups in soybean oil prodn. are carbon sequestration by soil (-29-24%), diesel used in soybean cultivation (4-24%), and electricity used in the soybean crushing process (10-21%). The indirect land use changes increase GHG emissions of ethanol by 7-38%, depending on the fraction of forest converted when newly converted croplands maintain crop cultivation for 100 years. Conclusions, recommendations, and perspectives Farming sites with higher biomass yields, lower nitrogen fertilizer application rates, and less tillage are favorable to future biorefinery locations in terms of global warming. For existing biorefineries, farmers are encouraged to apply a site-specific optimal nitrogen fertilizer application rate, to convert to no-tillage practices and also to adopt winter cover practices whenever possible to reduce the GHG emissions of their biobased products. Current practices for estg. the effects of indirect land use changes suffer from large uncertainties. More research and consensus about system boundaries and allocation issues are needed to reduce uncertainties related to the effects of indirect land use changes.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXhtV2ntLfF&md5=36bc616a8392f17341df8ccc182bb493
    20. 20
      Moeller, D.; Sieverding, H.; Stone, J. Comparative Farm-Gate Life Cycle Assessment of Oilseed Feedstocks in the Northern Great Plains. Biophysical Economics and Resource Quality 2017, 2, 2,  DOI: 10.1007/s41247-017-0030-3
      There is no corresponding record for this reference.
    21. 21
      Reijnders, L.; Huijbregts, M. Biogenic greenhouse gas emissions linked to the life cycles of biodiesel derived from European rapeseed and Brazilian soybeans. J. Cleaner Prod. 2008, 16, 1943– 1948,  DOI: 10.1016/j.jclepro.2008.01.012
      21
      Biogenic greenhouse gas emissions linked to the life cycles of biodiesel derived from European rapeseed and Brazilian soybeans
      Reijnders, L.; Huijbregts, M. A. J.
      Journal of Cleaner Production (2008), 16 (18), 1943-1948CODEN: JCROE8 ISSN:. (Elsevier Ltd.)
      Biogenic emissions of carbonaceous greenhouse gases and N2O turn out to be important determinants of life cycle emissions of greenhouse gases linked to the life cycle of biodiesel from European rapeseed and Brazilian soybeans. For biodiesel from European rapeseed and for biodiesel from Brazilian soybeans grown for up to 25 years with no tillage on arable soil for which tropical rainforest or Cerrado (savannah) have been cleared, the life cycle emissions of greenhouse gases are estd. to be worse than for conventional diesel. Improving agricultural practices should be an important focus for cleaner prodn. of biodiesel. These may include increasing soil carbon stocks by, e.g., conservation tillage and return of harvest residues and improving N-efficiency by precision agriculture and/or improved irrigation practices.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXhtlGisb%252FF&md5=9940ad8c55ae04757ce2a8dc11236bb0
    22. 22
      United Soybean Board. Life cycle impact of soybean production and soy industrial products. 2013.
      There is no corresponding record for this reference.
    23. 23
      Xue, X.; Collinge, W. O.; Shrake, S. O.; Bilec, M. M.; Landis, A. E. Regional life cycle assessment of soybean derived biodiesel for transportation fleets. Energy Policy 2012, 48, 295– 303,  DOI: 10.1016/j.enpol.2012.05.025
      There is no corresponding record for this reference.
    24. 24
      Zortea, R. B.; Maciel, V. G.; Passuello, A. Sustainability assessment of soybean production in Southern Brazil: A life cycle approach. Sustainable Production and Consumption 2018, 13, 102– 112,  DOI: 10.1016/j.spc.2017.11.002
      There is no corresponding record for this reference.
    25. 25
      Dalgaard, R.; Schmidt, J.; Halberg, N.; Christensen, P.; Thrane, M.; Pengue, W. A. LCA of soybean meal. Int. J. Life Cycle Assess. 2008, 13 (3), 240,  DOI: 10.1065/lca2007.06.342
      25
      LCA of soybean meal
      Dalgaard, Randi; Schmidt, Jannick; Halberg, Niels; Christensen, Per; Thrane, Mikkel; Pengue, Walter A.
      International Journal of Life Cycle Assessment (2008), 13 (3), 240-254CODEN: IJLCFF; ISSN:0948-3349. (Ecomed Publishers)
      Soybean meal is an important protein input to the European livestock prodn., with Argentina being an important supplier. The area cultivated with soybeans is still increasing globally, and so are the no. of LCAs where the prodn. of soybean meal forms part of the product chain. In recent years there has been increasing focus on how soybean prodn. affects the environment. The purpose of the study was to est. the environmental consequences of soybean meal consumption using a consequential LCA approach. The functional unit is 'one kg of soybean meal produced in Argentina and delivered to Rotterdam Harbor'. Soybean meal has the co-product soybean oil. In this study, the consequential LCA method was applied, and co-product allocation was thereby avoided through system expansion. In this context, system expansion implies that the inputs and outputs are entirely ascribed to soybean meal, and the product system is subsequently expanded to include the avoided prodn. of palm oil. Presently, the marginal vegetable oil on the world market is palm oil but, to be prepd. for fluctuations in market demands, an alternative product system with rapeseed oil as the marginal vegetable oil has been established. EDIP97 (updated version 2.3) was used for LCIA and the following impact categories were included: Global warming, eutrophication, acidification, ozone depletion and photochem. smog. Two soybean loops were established to demonstrate how an increased demand for soybean meal affects the palm oil and rapeseed oil prodn., resp. The characterized results from LCA on soybean meal (with palm oil as marginal oil) were 721 g CO2 eq. for global warming potential, 0.3 mg CFC11 equiv. for ozone depletion potential, 3.1 g SO2 eq. for acidification potential, -2 g NO3 eq. for eutrophication potential and 0.4 g ethene eq. for photochem. smog potential per kg soybean meal. The av. area per kg soybean meal consumed was 3.6 m2year. Attributional results, calcd. by economic and mass allocation, are also presented. Normalized results show that the most dominating impact categories were: global warming, eutrophication and acidification. The 'hot spot' in relation to global warming, was 'soybean cultivation', dominated by N2O emissions from degrdn. of crop residues (e.g., straw) and during biol. nitrogen fixation. In relation to eutrophication and acidification, the transport of soybeans by truck is important, and sensitivity analyses showed that the acidification potential is very sensitive to the increased transport distance by truck. The potential environmental impacts (except photochem. smog) were lower when using rapeseed oil as the marginal vegetable oil, because the avoided prodn. of rapeseed contributes more neg. compared with the avoided prodn. of palm oil. Identification of the marginal vegetable oil (palm oil or rapeseed oil) turned out to be important for the result, and this shows how crucial it is in consequential LCA to identify the right marginal product system (e.g., marginal vegetable oil). Consequential LCAs were successfully performed on soybean meal and LCA data on soybean meal are now available for consequential (or attributional) LCAs on livestock products. The study clearly shows that consequential LCAs are quite easy to handle, even though it has been necessary to include prodn. of palm oil, rapeseed and spring barley, as these prodn. systems are affected by the soybean oil co-product. We would appreciate it if the International Journal of Life Cycle Assessment had articles on the developments on, for example, marginal protein, marginal vegetable oil, marginal electricity (related to relevant markets), marginal heat, marginal cereals and, likewise, on metals and other basic commodities. This will not only facilitate the work with consequential LCAs, but will also increase the quality of LCAs.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXmtFyrsLo%253D&md5=123f12931d7b1ad942e19fce5256ce0c
    26. 26
      Godde, C. M.; Thorburn, P. J.; Biggs, J. S.; Meier, E. A. Understanding the Impacts of Soil, Climate, and Farming Practices on Soil Organic Carbon Sequestration: A Simulation Study in Australia. Front. Plant Sci. 2016, 7, 661,  DOI: 10.3389/fpls.2016.00661
      26
      Understanding the Impacts of Soil, Climate, and Farming Practices on Soil Organic Carbon Sequestration: A Simulation Study in Australia
      Godde Cecile M; Thorburn Peter J; Biggs Jody S; Meier Elizabeth A
      Frontiers in plant science (2016), 7 (), 661 ISSN:1664-462X.
      Carbon sequestration in agricultural soils has the capacity to mitigate greenhouse gas emissions, as well as to improve soil biological, physical, and chemical properties. The review of literature pertaining to soil organic carbon (SOC) dynamics within Australian grain farming systems does not enable us to conclude on the best farming practices to increase or maintain SOC for a specific combination of soil and climate. This study aimed to further explore the complex interactions of soil, climate, and farming practices on SOC. We undertook a modeling study with the Agricultural Production Systems sIMulator modeling framework, by combining contrasting Australian soils, climates, and farming practices (crop rotations, and management within rotations, such as fertilization, tillage, and residue management) in a factorial design. This design resulted in the transposition of contrasting soils and climates in our simulations, giving soil-climate combinations that do not occur in the study area to help provide insights into the importance of the climate constraints on SOC. We statistically analyzed the model's outputs to determinate the relative contributions of soil parameters, climate, and farming practices on SOC. The initial SOC content had the largest impact on the value of SOC, followed by the climate and the fertilization practices. These factors explained 66, 18, and 15% of SOC variations, respectively, after 80 years of constant farming practices in the simulation. Tillage and stubble management had the lowest impacts on SOC. This study highlighted the possible negative impact on SOC of a chickpea phase in a wheat-chickpea rotation and the potential positive impact of a cover crop in a sub-tropical climate (QLD, Australia) on SOC. It also showed the complexities in managing to achieve increased SOC, while simultaneously aiming to minimize nitrous oxide (N2O) emissions and nitrate leaching in farming systems. The transposition of contrasting soils and climates in our simulations revealed the importance of the climate constraints on SOC.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2s%252Flt1Gitw%253D%253D&md5=3c6cd6959731b90face8fe39ad9e8141
    27. 27
      Kim, S.; Dale, B. Regional variations in greenhouse gas emissions of biobased products in the United States—corn-based ethanol and soybean oil. Int. J. Life Cycle Assess. 2009, 14, 540– 546,  DOI: 10.1007/s11367-009-0106-4
      27
      Regional variations in greenhouse gas emissions of biobased products in the United States-corn-based ethanol and soybean oil
      Kim, Seungdo; Dale, Bruce E.
      International Journal of Life Cycle Assessment (2009), 14 (6), 540-546CODEN: IJLCFF; ISSN:0948-3349. (Springer)
      Background, aim, and scope Regional variations in the environmental impacts of plant biomass prodn. are significant, and the environmental impacts assocd. with feedstock supply also contribute substantially to the environmental performance of biobased products. Thus, the regional variations in the environmental performance of biobased products are also significant. This study scrutinizes greenhouse gas (GHG) emissions assocd. with two biobased products (i.e., ethanol and soybean oil) whose feedstocks (i.e., corn and soybean) are produced in different farming locations. Methods We chose 40 counties in Corn Belt States in the United States as biorefinery locations (i.e., corn dry milling, soybean crushing) and farming sites, and estd. cradle-to-gate GHG emissions of ethanol and of soybean oil, resp. The ests. are based on 1 kg of each biobased product (i.e., ethanol or soybean oil). The system boundary includes biomass prodn., the biorefinery, and upstream processes. Effects of direct land use change are included in the greenhouse gas anal. and measured as changes in soil org. carbon level, while the effects of indirect land use change are not considered in the baseline calcns. Those indirect effects however are scrutinized in a sensitivity anal. Results GHG emissions of corn-based ethanol range from 1.1 to 2.0 kg of CO2 equivalent per kg of ethanol, while GHG emissions of soybean oil are 0.4-2.5 kg of CO2 equivalent per kg of soybean oil. Thus, the regional variations due to farming locations are significant (by factors of 2-7). The largest GHG emission sources in ethanol prodn. are N2O emissions from soil during corn cultivation and carbon dioxide from burning the natural gas used in corn dry milling. The second largest GHG emission source groups in the ethanol prodn. system are nitrogen fertilizer (8-12%), carbon sequestration by soil (-15-2%), and electricity used in corn dry milling (7-16%). The largest GHG emission sources in soybean oil prodn. are N2O emissions from soil during soybean cultivation (13-57%) and carbon dioxide from burning the natural gas used in soybean crushing (21-47%). The second largest GHG emission source groups in soybean oil prodn. are carbon sequestration by soil (-29-24%), diesel used in soybean cultivation (4-24%), and electricity used in the soybean crushing process (10-21%). The indirect land use changes increase GHG emissions of ethanol by 7-38%, depending on the fraction of forest converted when newly converted croplands maintain crop cultivation for 100 years. Conclusions, recommendations, and perspectives Farming sites with higher biomass yields, lower nitrogen fertilizer application rates, and less tillage are favorable to future biorefinery locations in terms of global warming. For existing biorefineries, farmers are encouraged to apply a site-specific optimal nitrogen fertilizer application rate, to convert to no-tillage practices and also to adopt winter cover practices whenever possible to reduce the GHG emissions of their biobased products. Current practices for estg. the effects of indirect land use changes suffer from large uncertainties. More research and consensus about system boundaries and allocation issues are needed to reduce uncertainties related to the effects of indirect land use changes.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXhtV2ntLfF&md5=36bc616a8392f17341df8ccc182bb493
    28. 28
      Castanheira, É. G.; Grisoli, R.; Coelho, S.; Anderi da Silva, G.; Freire, F. Life-cycle assessment of soybean-based biodiesel in Europe: comparing grain, oil and biodiesel import from Brazil. J. Cleaner Prod. 2015, 102, 188– 201,  DOI: 10.1016/j.jclepro.2015.04.036
      28
      Life-cycle assessment of soybean-based biodiesel in Europe: comparing grain, oil and biodiesel import from Brazil
      Castanheira, Erica Geraldes; Grisoli, Renata; Coelho, Suani; Anderi da Silva, Gil; Freire, Fausto
      Journal of Cleaner Production (2015), 102 (), 188-201CODEN: JCROE8; ISSN:0959-6526. (Elsevier Ltd.)
      The purpose of this article is to present a life-cycle assessment of soybean Me ester addressing three alternative pathways: biodiesel totally produced in Brazil and exported to Portugal; biodiesel produced in Portugal using soybean oil and soybean imported from Brazil. Soybean cultivation was assessed for four states in Brazil: Mato Grosso; Goi´as; Paran´a and Rio Grande do Sul. A life-cycle inventory and model of biodiesel was implemented, including land-use change, soybean cultivation, oil extn. and refining, transesterification and biodiesel transport. A sensitivity anal. of alternative multifunctionality procedures for dealing with co-products was performed. The lowest environmental impacts were calcd. for mass allocation and the highest for price or energy allocation. Biodiesel produced in Portugal with imported soybean grain had the lowest impacts for all categories and soybean cultivation locations for mass allocation. For price or energy allocation, the pathway with the lowest environmental impacts was detd. by the cultivation location. Land-use change had a high influence on the greenhouse gas intensity of biodiesel, while soybean cultivation and transport contributed most to the remaining impact categories. Soybean Me ester (SME) used in Portugal has the lowest impacts when produced with oil or grain imported from Brazil, instead of importing directly SME. The environmental impacts of biodiesel can be reduced by avoiding land-use change, improving soybean yield and optimizing soybean transportation routes in Brazil.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXnsl2kur8%253D&md5=8ad80fd31a0a39623d8a90b729c40253
    29. 29
      Lan, K.; Yao, Y. Integrating Life Cycle Assessment and Agent-Based Modeling: A Dynamic Modeling Framework for Sustainable Agricultural Systems. J. Cleaner Prod. 2019, 238, 117853,  DOI: 10.1016/j.jclepro.2019.117853
      There is no corresponding record for this reference.
    30. 30
      Navarrete Gutiérrez, T.; Rege, S.; Marvuglia, A.; Benetto, E., Sustainable Farming Behaviours: An Agent Based Modelling and LCA Perspective. In Agent-Based Modeling of Sustainable Behaviors; Alonso-Betanzos, A.; Sánchez-Maroño, N.; Fontenla-Romero, O.; Polhill, J. G.; Craig, T.; Bajo, J.; Corchado, J. M., Eds.; Springer International Publishing: Cham, 2017; pp 187– 206.
      There is no corresponding record for this reference.
    31. 31
      Prudêncio da Silva, V.; van der Werf, H. M. G.; Spies, A.; Soares, S. R. Variability in environmental impacts of Brazilian soybean according to crop production and transport scenarios. J. Environ. Manage. 2010, 91 (9), 1831– 1839,  DOI: 10.1016/j.jenvman.2010.04.001
      There is no corresponding record for this reference.
    32. 32
      Xue, X.; Pang, Y.; Landis, A. E. Evaluating agricultural management practices to improve the environmental footprint of corn-derived ethanol. Renewable Energy 2014, 66, 454– 460,  DOI: 10.1016/j.renene.2013.12.026
      There is no corresponding record for this reference.
    33. 33
      Liakos, K.; Busato, P.; Moshou, D.; Pearson, S.; Bochtis, D. Machine learning in agriculture: A review. Sensors 2018, 18 (8), 2674,  DOI: 10.3390/s18082674
      There is no corresponding record for this reference.
    34. 34
      Elith, J.; Leathwick, J. Boosted Regression Trees for Ecological Modeling, 2013.
      There is no corresponding record for this reference.
    35. 35
      Zhang, W.; Du, Z.; Zhang, D.; Yu, S.; Hao, Y. Boosted regression tree model-based assessment of the impacts of meteorological drivers of hand, foot and mouth disease in Guangdong, China. Sci. Total Environ. 2016, 553, 366– 371,  DOI: 10.1016/j.scitotenv.2016.02.023
      35
      Boosted regression tree model-based assessment of the impacts of meteorological drivers of hand, foot and mouth disease in Guangdong, China
      Zhang, Wangjian; Du, Zhicheng; Zhang, Dingmei; Yu, Shicheng; Hao, Yuantao
      Science of the Total Environment (2016), 553 (), 366-371CODEN: STENDL; ISSN:0048-9697. (Elsevier B.V.)
      Hand, foot and mouth disease (HFMD) is a common childhood infection and has become a major public health issue in China. Considerable research has focused on the role of meteorol. factors in HFMD development. Nonlinear relationship, delayed effects and collinearity problems are key issues for achieving robust and accurate estns. in this kind of weather-health relationship explorations. The current study was designed to address these issues and assess the impact of meteorol. factors on HFMD in Guangdong, China. Case-based HFMD surveillance data and daily meteorol. data collected between 2010 and 2012 was obtained from China CDC and the National Meteorol. Information Center, resp. After a preliminary variable selection, for each dataset boosted regression tree (BRT) models were applied to det. the optimal lag for meteorol. factors at which the variance of HFMD cases was most explained, and to assess the impacts of these meteorol. factors at the optimal lag. Variance of HFMD cases was explained most by meteorol. factors about 1 wk ago. Younger children and those from the Pearl-River Delta Region were more sensitive to weather changes. Temp. had the largest contribution to HFMD epidemics (28.99-71.93%), followed by pptn. (6.52-16.11%), humidity (3.92-17.66%), wind speed (3.84-11.37%) and sunshine (6.21-10.36%). Temp. between 10 °C and 25 °C, as well as humidity between 70% and 90%, had a facilitating effect on the epidemic of HFMD. Sunshine duration above 9 h and wind speed below 2.5 m/s also contributed to an elevated risk of HFMD. The pos. relationship between HFMD and pptn. reversed when the daily amt. of rainfall exceeded 25 mm. This study indicated significantly facilitating effects of five meteorol. factors within some range on the epidemic of HFMD. Results from the current study were particularly important for developing early warning and response system on HFMD in the context of global climate change.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XksVWnsL8%253D&md5=99ef66e679502d793bf0b266087e367c
    36. 36
      Bhavsar, P.; Safro, I.; Bouaynaya, N.; Polikar, R.; Dera, D. Chapter 12 - Machine Learning in Transportation Data Analytics. In Data Analytics for Intelligent Transportation Systems; Chowdhury, M.; Apon, A.; Dey, K., Eds.; Elsevier, 2017; pp 283– 307.
      There is no corresponding record for this reference.
    37. 37
      Cashman, S. A.; Meyer, D. E.; Edelen, A. N.; Ingwersen, W. W.; Abraham, J. P.; Barrett, W. M.; Gonzalez, M. A.; Randall, P. M.; Ruiz-Mercado, G.; Smith, R. L. Mining Available Data from the United States Environmental Protection Agency to Support Rapid Life Cycle Inventory Modeling of Chemical Manufacturing. Environ. Sci. Technol. 2016, 50 (17), 9013– 9025,  DOI: 10.1021/acs.est.6b02160
      37
      Mining Available Data from the United States Environmental Protection Agency to Support Rapid Life Cycle Inventory Modeling of Chemical Manufacturing
      Cashman, Sarah A.; Meyer, David E.; Edelen, Ashley N.; Ingwersen, Wesley W.; Abraham, John P.; Barrett, William M.; Gonzalez, Michael A.; Randall, Paul M.; Ruiz-Mercado, Gerardo; Smith, Raymond L.
      Environmental Science & Technology (2016), 50 (17), 9013-9025CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      Demands for quick, accurate life cycle assessments create a need for methods to rapidly generate reliable life cycle inventories (LCI). Data mining is a suitable tool for this purpose, particularly given the large amt. of available governmental data. These data are typically applied to LCI on a case-by-case basis. As linked open data becomes more prevalent, it may be possible to automate LCI using data mining by establishing a reproducible approach to identify, ext., and process the data. This paper discusses a method to standardize and eventually automate discovery and use of publicly available data at the USEPA for chem. manufg. LCI. The method was developed using acetic acid as a case study. Data quality and gap analyses for the generated inventory detd. that selected data sources can provide information with equal or better reliability and representativeness on air, water, hazardous waste, on-site energy use, and prodn. vols., but with key data gaps: material inputs, water use, purchased electricity, and transportation requirements. A comparison of the generated LCI with existing data showed the data mining inventory agreed reasonably well with existing data and may provide a more comprehensive inventory of air emissions and water discharges. The case study highlighted challenges for current data management practices which must be overcome to successfully automate the method using semantic technol. Method benefits are that openly available data can be compiled in a standardized, transparent approach which supports potential automation with flexibility to incorporate new data sources as needed.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XhtlaktbjN&md5=00d1ca2a546f66dc55655489c8a25457
    38. 38
      Wernet, G.; Hellweg, S.; Fischer, U.; Papadokonstantakis, S.; Hungerbühler, K. Molecular-Structure-Based Models of Chemical Inventories using Neural Networks. Environ. Sci. Technol. 2008, 42 (17), 6717– 6722,  DOI: 10.1021/es7022362
      38
      Molecular-Structure-Based Models of Chemical Inventories using Neural Networks
      Wernet, Gregor; Hellweg, Stefanie; Fischer, Ulrich; Papadokonstantakis, Stavros; Hungerbuhler, Konrad
      Environmental Science & Technology (2008), 42 (17), 6717-6722CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      Chem. synthesis is a complex and diverse procedure, and prodn. data are often scarce or incomplete. A detailed inventory anal. of all mass and energy flows necessary for the prodn. of chems. is often costly and time-intensive. Therefore only few chem. inventories exist, even though they are essential for process optimization and the environmental assessment of many products. This paper introduces a new type of model to provide ests. for inventory data and environmental impacts of chem. prodn. based on the mol. structure of a chem. and without a priori knowledge of the prodn. process. These mol.-structure-based models offer inventory data for users in process design and optimization, screening life cycle assessment (LCA), and supply chain management. They can be applied even if the producer is unknown or the prodn. process is not documented. We assessed the capabilities of linear regression and neural network models for this purpose. All models were generated with a data set of inventory data on 103 chems. Different input sets were chosen as ways to transform the chem. structure into a numerical vector of descriptors and the effectiveness of the different input sets was analyzed. The results show that a correctly developed neural network model can perform on an acceptable level for many purposes. The models can assist process developers to improve energy efficiency in all design stages and aid in LCA and supply chain management by filling data gaps.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXpt1yns74%253D&md5=2608ca86a5b094dd2b27ea92b7aea016
    39. 39
      Mittal, V. K.; Bailin, S. C.; Gonzalez, M. A.; Meyer, D. E.; Barrett, W. M.; Smith, R. L. Toward Automated Inventory Modeling in Life Cycle Assessment: The Utility of Semantic Data Modeling to Predict Real-World Chemical Production. ACS Sustainable Chem. Eng. 2018, 6 (2), 1961– 1976,  DOI: 10.1021/acssuschemeng.7b03379
      39
      Toward Automated Inventory Modeling in Life Cycle Assessment: The Utility of Semantic Data Modeling to Predict Real-World Chemical Production
      Mittal, Vinit K.; Bailin, Sidney C.; Gonzalez, Michael A.; Meyer, David E.; Barrett, William M.; Smith, Raymond L.
      ACS Sustainable Chemistry & Engineering (2018), 6 (2), 1961-1976CODEN: ASCECG; ISSN:2168-0485. (American Chemical Society)
      A set of coupled semantic data models, i.e., ontologies, are presented to advance a methodol. toward automated inventory modeling of chem. manufg. in life cycle assessment. The cradle-to-gate life cycle inventory for chem. manufg. is a detailed collection of the material and energy flows assocd. with a chem.'s supply chain. Thus, there is a need to manage data describing both the lineage (or synthesis pathway) and processing conditions for a chem. To this end, a Lineage ontol. is proposed to reveal all the synthesis steps required to produce a chem. from raw materials, such as crude oil or biomaterials, while a Process ontol. is developed to manage data describing the various unit processes assocd. with each synthesis step. The two ontologies are coupled such that process data, which is the basis for inventory modeling, is linked to lineage data through key concepts like the chem. reaction and reaction participants. To facilitate automated inventory modeling, a series of SPARQL queries, based on the concepts of ancestor and parent, are presented to generate a lineage for a chem. of interest from a set of reaction data. The proposed ontologies and SPARQL queries are evaluated and tested using a case study of nylon-6 prodn. Once a lineage is established, the process ontol. can be used to guide inventory modeling based on both data mining (top-down) and simulation (bottom-up) approaches. The ability to generate a cradle-to-gate life cycle for a chem. represents a key achievement toward the ultimate goal of automated life cycle inventory modeling.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhvFehsL3O&md5=229ea4fe85c9c2f522618e670c298b73
    40. 40
      Nabavi-Pelesaraei, A.; Rafiee, S.; Mohtasebi, S. S.; Hosseinzadeh-Bandbafha, H.; Chau, K.-w. Integration of artificial intelligence methods and life cycle assessment to predict energy output and environmental impacts of paddy production. Sci. Total Environ. 2018, 631, 1279– 1294,  DOI: 10.1016/j.scitotenv.2018.03.088
      40
      Integration of artificial intelligence methods and life cycle assessment to predict energy output and environmental impacts of paddy production
      Nabavi-Pelesaraei, Ashkan; Rafiee, Shahin; Mohtasebi, Seyed Saeid; Hosseinzadeh-Bandbafha, Homa; Chau, Kwok-wing
      Science of the Total Environment (2018), 631-632 (), 1279-1294CODEN: STENDL; ISSN:0048-9697. (Elsevier B.V.)
      Prediction of agricultural energy output and environmental impacts play important role in energy management and conservation of environment as it can help us to evaluate agricultural energy efficiency, conduct crops prodn. system commissioning, and detect and diagnose faults of crop prodn. system. Agricultural energy output and environmental impacts can be readily predicted by artificial intelligence (AI), owing to the ease of use and adaptability to seek optimal solns. in a rapid manner as well as the use of historical data to predict future agricultural energy use pattern under constraints. This paper conducts energy output and environmental impact prediction of paddy prodn. in Guilan province, Iran based on two AI methods, artificial neural networks (ANNs), and adaptive neuro fuzzy inference system (ANFIS). The amts. of energy input and output are 51,585.61 MJ kg-1 and 66,112.94 MJ kg-1, resp., in paddy prodn. Life Cycle Assessment (LCA) is used to evaluate environmental impacts of paddy prodn. Results show that, in paddy prodn., in-farm emission is a hotspot in global warming, acidification and eutrophication impact categories. ANN model with 12-6-8-1 structure is selected as the best one for predicting energy output. The correlation coeff. (R) varies from 0.524 to 0.999 in training for energy input and environmental impacts in ANN models. ANFIS model is developed based on a hybrid learning algorithm, with R for predicting output energy being 0.860 and, for environmental impacts, varying from 0.944 to 0.997. Results indicate that the multi-level ANFIS is a useful tool to managers for large-scale planning in forecasting energy output and environmental indexes of agricultural prodn. systems owing to its higher speed of computation processes compared to ANN model, despite ANN's higher accuracy.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXltFOqtrw%253D&md5=44df2728c72c318744bb93be186bb671
    41. 41
      Hou, P.; Jolliet, O.; Zhu, J.; Xu, M. Estimate ecotoxicity characterization factors for chemicals in life cycle assessment using machine learning models. Environ. Int. 2020, 135, 105393,  DOI: 10.1016/j.envint.2019.105393
      41
      Estimate ecotoxicity characterization factors for chemicals in life cycle assessment using machine learning models
      Hou, Ping; Jolliet, Olivier; Zhu, Ji; Xu, Ming
      Environment International (2020), 135 (), 105393CODEN: ENVIDV; ISSN:0160-4120. (Elsevier Ltd.)
      In life cycle assessment, characterization factors are used to convert the amt. of the chems. and other pollutants generated in a product's life cycle to the std. unit of an impact category, such as ecotoxicity. However, as a widely used impact assessment method, USEtox (version 2.11) only has ecotoxicity characterization factors for a small portion of chems. due to the lack of lab. expt. data. Here we develop machine learning models to est. ecotoxicity hazardous concns. 50% (HC50) in USEtox to calc. characterization factors for chems. based on their phys.-chem. properties in EPA's CompTox Chem. Dashborad and the classification of their mode of action. The model is validated by ten randomly selected test sets that are not used for training. The results show that the random forest model has the best predictive performance. The av. root mean squared error of the estd. HC50 on the test sets is 0.761. The av. coeff. of detn. (R2) on the test set is 0.630, meaning 63% of the variability of HC50 in USEtox can be explained by the predicted HC50 from the random forest model. Our model outperforms a traditional quant. structure-activity relationship (QSAR) model (ECOSAR) and linear regression models. We also provide ests. of missing ecotoxicity characterization factors for 552 chems. in USEtox using the validated random forest model.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXisVGgsL%252FI&md5=13897f26f18f46913301ef9ba67ba6d6
    42. 42
      Marvuglia, A.; Kanevski, M.; Benetto, E. Machine learning for toxicity characterization of organic chemical emissions using USEtox database: Learning the structure of the input space. Environ. Int. 2015, 83, 72– 85,  DOI: 10.1016/j.envint.2015.05.011
      42
      Machine learning for toxicity characterization of organic chemical emissions using USEtox database: Learning the structure of the input space
      Marvuglia, Antonino; Kanevski, Mikhail; Benetto, Enrico
      Environment International (2015), 83 (), 72-85CODEN: ENVIDV; ISSN:0160-4120. (Elsevier Ltd.)
      Toxicity characterization of chem. emissions in Life Cycle Assessment (LCA) is a complex task which usually proceeds via multimedia (fate, exposure and effect) models attached to models of dose-response relationships to assess the effects on target. Different models and approaches do exist, but all require a vast amt. of data on the properties of the chem. compds. being assessed, which are hard to collect or hardly publicly available (esp. for thousands of less common or newly developed chems.), therefore hampering in practice the assessment in LCA. An example is USEtox, a consensual model for the characterization of human toxicity and freshwater ecotoxicity. This paper places itself in a line of research aiming at providing a methodol. to reduce the no. of input parameters necessary to run multimedia fate models, focusing in particular to the application of the USEtox toxicity model. By focusing on USEtox, in this paper two main goals are pursued: 1) performing an extensive exploratory anal. (using dimensionality redn. techniques) of the input space constituted by the substance-specific properties at the aim of detecting particular patterns in the data manifold and estg. the dimension of the subspace in which the data manifold actually lies; and 2) exploring the application of a set of linear models, based on partial least squares (PLS) regression, as well as a nonlinear model (general regression neural network - GRNN) in the seek for an automatic selection strategy of the most informative variables according to the modelled output (USEtox factor). After extensive anal., the intrinsic dimension of the input manifold has been identified between three and four. The variables selected as most informative may vary according to the output modelled and the model used, but for the toxicity factors modelled in this paper the input variables selected as most informative are coherent with prior expectations based on scientific knowledge of toxicity factors modeling. Thus the outcomes of the anal. are promising for the future application of the approach to other portions of the model, affected by important data gaps, e.g., to the calcn. of human health effect factors.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtFSqtL%252FF&md5=b33c58315f03623dd77d8baeb24318c4
    43. 43
      ISO International Organization for Standardization (ISO) standards 14040: Environmental management and Life cycle assessment: Principles and framework. https://www.iso.org/standard/37456.html.
      There is no corresponding record for this reference.
    44. 44
      USDA Statistics by subject: crop & plants - field crops - corn field. https://www.nass.usda.gov/Quick_Stats/ (2017/10/10).
      There is no corresponding record for this reference.
    45. 45
      Kim, S.; Dale, B. E. Life cycle assessment of various cropping systems utilized for producing biofuels: Bioethanol and biodiesel. Biomass Bioenergy 2005, 29 (6), 426– 439,  DOI: 10.1016/j.biombioe.2005.06.004
      There is no corresponding record for this reference.
    46. 46
      Castanheira, É. G.; Grisoli, R.; Coelho, S.; da Silva, G. A.; Freire, F. Life-cycle assessment of soybean-based biodiesel in Europe: comparing grain, oil and biodiesel import from Brazil. J. Cleaner Prod. 2015, 102, 188– 201,  DOI: 10.1016/j.jclepro.2015.04.036
      46
      Life-cycle assessment of soybean-based biodiesel in Europe: comparing grain, oil and biodiesel import from Brazil
      Castanheira, Erica Geraldes; Grisoli, Renata; Coelho, Suani; Anderi da Silva, Gil; Freire, Fausto
      Journal of Cleaner Production (2015), 102 (), 188-201CODEN: JCROE8; ISSN:0959-6526. (Elsevier Ltd.)
      The purpose of this article is to present a life-cycle assessment of soybean Me ester addressing three alternative pathways: biodiesel totally produced in Brazil and exported to Portugal; biodiesel produced in Portugal using soybean oil and soybean imported from Brazil. Soybean cultivation was assessed for four states in Brazil: Mato Grosso; Goi´as; Paran´a and Rio Grande do Sul. A life-cycle inventory and model of biodiesel was implemented, including land-use change, soybean cultivation, oil extn. and refining, transesterification and biodiesel transport. A sensitivity anal. of alternative multifunctionality procedures for dealing with co-products was performed. The lowest environmental impacts were calcd. for mass allocation and the highest for price or energy allocation. Biodiesel produced in Portugal with imported soybean grain had the lowest impacts for all categories and soybean cultivation locations for mass allocation. For price or energy allocation, the pathway with the lowest environmental impacts was detd. by the cultivation location. Land-use change had a high influence on the greenhouse gas intensity of biodiesel, while soybean cultivation and transport contributed most to the remaining impact categories. Soybean Me ester (SME) used in Portugal has the lowest impacts when produced with oil or grain imported from Brazil, instead of importing directly SME. The environmental impacts of biodiesel can be reduced by avoiding land-use change, improving soybean yield and optimizing soybean transportation routes in Brazil.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXnsl2kur8%253D&md5=8ad80fd31a0a39623d8a90b729c40253
    47. 47
      Williams, J.; Dagitz, S.; Magre, M.; Meinardus, A.; Steglich, E.; Taylor, R. The EPIC model. In Computer Models of Watershed HydrologySingh, V. P., Ed.; Water Resources Publications: Highlands Ranch, CO, pp 909– 1000. In 1995.
      There is no corresponding record for this reference.
    48. 48
      Izaurralde, R. C.; Williams, J. R.; McGill, W. B.; Rosenberg, N. J.; Jakas, M. C. Q. Simulating soil C dynamics with EPIC: Model description and testing against long-term data. Ecol. Modell. 2006, 192 (3), 362– 384,  DOI: 10.1016/j.ecolmodel.2005.07.010
      There is no corresponding record for this reference.
    49. 49
      NOAA National Climatic Data Center. https://www.ncdc.noaa.gov/cdo-web/search (2017/11/1).
      There is no corresponding record for this reference.
    50. 50
      USDA Natural Resources Conservation Service: Web Soil Survey. https://websoilsurvey.sc.egov.usda.gov/App/WebSoilSurvey.aspx (2017/10/10).
      There is no corresponding record for this reference.
    51. 51
      USGS. County-level estimates of nutrient inputs to the land surface of the conterminous United States, 1982–2001. In Scientific Investigations Report 2006–5012 , 2006.
      There is no corresponding record for this reference.
    52. 52
      USDA USDA National Agricultural Statistics Service: Quick Stats- Crop yield data. https://quickstats.nass.usda.gov/ (2017/10/10).
      There is no corresponding record for this reference.
    53. 53
      Zhang, X.; Izaurralde, R. C.; Manowitz, D.; West, T. O.; Post, W. M.; Thomas, A. M.; Bandaru, V. P.; Nichols, J.; Williams, J. R. An integrative modeling framework to evaluate the productivity and sustainability of biofuel crop production systems. GCB Bioenergy 2010, 2, 258– 277,  DOI: 10.1111/j.1757-1707.2010.01046.x
      There is no corresponding record for this reference.
    54. 54
      Zhang, X.; Izaurralde, R. C.; Manowitz, D. H.; Sahajpal, R.; West, T. O.; Thomson, A. M.; Xu, M.; Zhao, K.; LeDuc, S. D.; Williams, J. R. Regional scale cropland carbon budgets: evaluating a geospatial agricultural modeling system using inventory data Ecological Modelling. Environmental Modelling & Software 2015, 63, 199– 216,  DOI: 10.1016/j.envsoft.2014.10.005
      There is no corresponding record for this reference.
    55. 55
      Taghavifar, H.; Mardani, A. Prognostication of energy consumption and greenhouse gas (GHG) emissions analysis of apple production in West Azarbayjan of Iran using Artificial Neural Network Ecological Modelling. J. Cleaner Prod. 2015, 87, 159– 167,  DOI: 10.1016/j.jclepro.2014.10.054
      There is no corresponding record for this reference.
    56. 56
      Pradhan, A., Energy Life-Cycle Assessment of Soybean Biodiesel Revisited. Transactions of the ASABE 2011, 54 (3), 1031–1039.
      There is no corresponding record for this reference.
    57. 57
      USGS Water use in the United States: Estimated use of water in the United States county-level data for 2005. https://water.usgs.gov/watuse/data/2005/index.html (October 18, 2017).
      There is no corresponding record for this reference.
    58. 58
      USDA USDA Economic Research Service: Irrigation & Water Use. https://www.ers.usda.gov/topics/farm-practices-management/irrigation-water-use/ (2017/10/17).
      There is no corresponding record for this reference.
    59. 59
      Wernet, G.; Bauer, C.; Steubing, B.; Reinhard, J.; Moreno-Ruiz, E.; Weidema, B. The ecoinvent database version 3 (part I): overview and methodology. Int. J. Life Cycle Assess. 2016, 21 (9), 1218– 1230,  DOI: 10.1007/s11367-016-1087-8
      There is no corresponding record for this reference.
    60. 60
      Bare, J. C., The Tool for the Reduction and Assessment of Chemical and other environmental Impacts. In Clean Technologies and Environmental Policy; Springer-Verlag: New York, NY, 2011; Vol. 13 (5), pp 687– 696.
      There is no corresponding record for this reference.
    61. 61
      Huang, T.; Gao, B.; Christie, P.; Ju, X. Net global warming potential and greenhouse gas intensity in a double-cropping cereal rotation as affected by nitrogen and straw management. Biogeosciences 2013, 10 (12), 7897– 7911,  DOI: 10.5194/bg-10-7897-2013
      61
      Net global warming potential and greenhouse gas intensity in a double-cropping cereal rotation as affected by nitrogen and straw management
      Huang, T.; Gao, B.; Christie, P.; Ju, X.
      Biogeosciences (2013), 10 (12), 7897-7911, 15 pp.CODEN: BIOGGR; ISSN:1726-4189. (Copernicus Publications)
      The effects of nitrogen and straw management on global warming potential (GWP) and greenhouse gas intensity (GHGI) in a winter wheat-summer maize double-cropping system on the North China Plain were investigated. We measured nitrous oxide (N2O) emissions and studied net GWP (NGWP) and GHGI by calcg. the net exchange of CO2 equiv (CO2-equiv) from greenhouse gas emissions, agricultural inputs and management practices, as well as changes in soil org. carbon (SOC), based on a long-term field expt. established in 2006. The field expt. includes six treatments with three fertilizer N levels (zero N (control), optimum and conventional N) and straw removal (i.e. N0, Nopt and Ncon) or return (i.e. SN0, SNopt and SNcon). Optimum N management (Nopt, SNopt) saved roughly half of the fertilizer N compared to conventional agricultural practice (Ncon, SNcon), with no significant effect on grain yields. Annual mean N2O emissions reached 3.90 kg N2O-N ha-1 in Ncon and SNcon, and N2O emissions were reduced by 46.9% by optimizing N management of Nopt and SNopt. Straw return increased annual mean N2O emissions by 27.9%. Annual SOC sequestration was 0.40-1.44 Mg C ha-1 yr-1 in plots with N application and/or straw return. Compared to the conventional N treatments the optimum N treatments reduced NGWP by 51%, comprising 25% from decreasing N2O emissions and 75% from reducing N fertilizer application rates. Straw return treatments reduced NGWP by 30% compared to no straw return because the GWP from increments of SOC offset the GWP from higher emissions of N2O, N fertilizer and fuel after straw return. The GHGI trends from the different nitrogen and straw management practices were similar to the NGWP. In conclusion, optimum N and straw return significantly reduced NGWP and GHGI and concomitantly achieved relatively high grain yields in this important winter wheat-summer maize double-cropping system.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXjvFaht7o%253D&md5=6fa75de490ba0d4a6c500863b15227ee
    62. 62
      Kim, S.; Dale, B. E. Cumulative Energy and Global Warming Impact from the Production of Biomass for Biobased Products. J. Ind. Ecol. 2003, 7 (3–4), 147– 162,  DOI: 10.1162/108819803323059442
      There is no corresponding record for this reference.
    63. 63
      Friedman, J. H. Greedy function approximation: A gradient boosting machine. Ann. Statist. 2001, 29 (5), 1189– 1232,  DOI: 10.1214/aos/1013203451
      There is no corresponding record for this reference.
    64. 64
      Williams, G. J.; Aeby, G. S.; Cowie, R. O.; Davy, S. K. Predictive modeling of coral disease distribution within a reef system. PLoS One 2010, 5 (2), e9264  DOI: 10.1371/journal.pone.0009264
      There is no corresponding record for this reference.
    65. 65
      Elith, J.; Leathwick, J. R.; Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 2008, 77 (4), 802– 13,  DOI: 10.1111/j.1365-2656.2008.01390.x
      65
      A working guide to boosted regression trees
      Elith J; Leathwick J R; Hastie T
      The Journal of animal ecology (2008), 77 (4), 802-13 ISSN:.
      1. Ecologists use statistical models for both explanation and prediction, and need techniques that are flexible enough to express typical features of their data, such as nonlinearities and interactions. 2. This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fit a single parsimonious model. Boosted regression trees combine the strengths of two algorithms: regression trees (models that relate a response to their predictors by recursive binary splits) and boosting (an adaptive method for combining many simple models to give improved predictive performance). The final BRT model can be understood as an additive regression model in which individual terms are simple trees, fitted in a forward, stagewise fashion. 3. Boosted regression trees incorporate important advantages of tree-based methods, handling different types of predictor variables and accommodating missing data. They have no need for prior data transformation or elimination of outliers, can fit complex nonlinear relationships, and automatically handle interaction effects between predictors. Fitting multiple trees in BRT overcomes the biggest drawback of single tree models: their relatively poor predictive performance. Although BRT models are complex, they can be summarized in ways that give powerful ecological insight, and their predictive performance is superior to most traditional modelling methods. 4. The unique features of BRT raise a number of practical issues in model fitting. We demonstrate the practicalities and advantages of using BRT through a distributional analysis of the short-finned eel (Anguilla australis Richardson), a native freshwater fish of New Zealand. We use a data set of over 13 000 sites to illustrate effects of several settings, and then fit and interpret a model using a subset of the data. We provide code and a tutorial to enable the wider use of BRT by ecologists.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD1cvgsFOqsQ%253D%253D&md5=71c5a86d5b3715450b5fa8d2862f6629
    66. 66
      Hastie, T.; Tibshirani, R.; Friedman, J. H. The Elements of Statistical Learning: Data Mining, Inference, And Prediction; New York, 2009; Vol. 2nd ed..
      There is no corresponding record for this reference.
    67. 67
      Khoshnevisan, B.; Rafiee, S.; Omid, M.; Yousefi, M.; Movahedi, M. Modeling of energy consumption and GHG (greenhouse gas) emissions in wheat production in Esfahan province of Iran using artificial neural networks. Energy (Oxford, U. K.) 2013, 52, 333– 338,  DOI: 10.1016/j.energy.2013.01.028
      There is no corresponding record for this reference.
    68. 68
      Nabavi-Pelesaraei, A.; Rafiee, S.; Hosseinzadeh-Bandbafha, H.; Shamshirband, S. Modeling energy consumption and greenhouse gas emissions for kiwifruit production using artificial neural networks. J. Cleaner Prod. 2016, 133, 924– 931,  DOI: 10.1016/j.jclepro.2016.05.188
      There is no corresponding record for this reference.
    69. 69
      Pahlavan, R.; Omid, M.; Akram, A. Energy input-output analysis and application of artificial neural networks for predicting greenhouse basil production. Energy 2012, 37 (1), 171– 176,  DOI: 10.1016/j.energy.2011.11.055
      69
      Energy input-output analysis and application of artificial neural networks for predicting greenhouse basil production
      Pahlavan, Reza; Omid, Mahmoud; Akram, Asadollah
      Energy (Oxford, United Kingdom) (2012), 37 (1), 171-176CODEN: ENEYDS; ISSN:0360-5442. (Elsevier Ltd.)
      Various Artificial Neural Networks (ANNs) were developed to est. the prodn. yield of greenhouse basil in Iran. For this purpose, the data collected by random method from 26 greenhouses in the region during 4 periods of plant cultivation in 2009-2010. The total input energy and energy ratio for basil prodn. were 14,308,998 MJ ha-1 and 0.02, resp. The developed ANN was a multilayer perceptron (MLP) with 7 neurons in the input layer, one, 2 and 3 hidden layer(s) of various nos. of neurons and one neuron (basil yield) in the output layer. The input energies were human labor, diesel fuel, chem. fertilizers, farm yard manure, chems., electricity and transportation. Results showed, the ANN model having 7-20-20-1 topol. can predict the yield value with higher accuracy. So, this 2 hidden layer topol. was selected as the best model for estg. basil prodn. of regional greenhouses with similar conditions. For the optimal model, the values of the models outputs correlated well with actual outputs, with coeff. of detn. (R2) of 0.976. For this configuration, RMSE and MAE values were 0.046 and 0.035, resp. Sensitivity anal. revealed that chem. fertilizers are the most significant parameter in the basil prodn.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38Xjsl2hsw%253D%253D&md5=2c91482440c8f43ebf0f3a64d327284c
    70. 70
      Perlman, J.; Hijmans, R. J.; Horwath, W. R. A metamodelling approach to estimate global N2O emissions from agricultural soils. Global Ecology and Biogeography 2014, 23 (8), 912– 924,  DOI: 10.1111/geb.12166
      There is no corresponding record for this reference.
    71. 71
      Lee, E. K.; Zhang, X.; Adler, P. R.; Kleppel, G. S.; Romeiko, X. X. Spatially and temporally explicit life cycle global warming, eutrophication, and acidification impacts from corn production in the U.S. Midwest. J. Cleaner Prod. 2020, 242, 118465,  DOI: 10.1016/j.jclepro.2019.118465
      71
      Spatially and temporally explicit life cycle global warming, eutrophication, and acidification impacts from corn production in the U.S. Midwest
      Lee, Eun Kyung; Zhang, Xuesong; Adler, Paul R.; Kleppel, Gary S.; Romeiko, Xiaobo Xue
      Journal of Cleaner Production (2020), 242 (), 118465CODEN: JCROE8; ISSN:0959-6526. (Elsevier Ltd.)
      The demand for biobased products, such as food, fuel, and chems., has been continuously increasing. Meanwhile, agricultural prodn., serving as the primary stage of biobased products, is one of the largest contributors to greenhouse gas (GHG) emissions and nutrient releases. Environmental impacts of agricultural prodn. influenced by farming practices, soil properties, and climate conditions, are often site-specific and time dependent. Although assessing spatially and temporally explicit environmental releases and impacts are required to inform a sustainable trajectory for agricultural prodn., such analyses are largely lacking. This study provides site-specific anal. of on-farm and supply chain emissions from corn prodn. to demonstrate the spatio-temporal variability of environmental impacts in the U. S. Midwest states. Using process-based life cycle assessment (LCA) and the phys.-based Environmental Policy Integrated Climate (EPIC) agroecosystem model, we estd. county-level life cycle environmental release inventories from corn prodn. in 12 U. S. Midwest states for the period of 2000-2008. Based on the Tool for Redn. and Assessment of Chems. and Other Environmental Impacts (TRACI) impact assessment model, we quantified the corresponding life cycle global warming (GW), eutrophication (EU) and acidification (AD) impacts of corn. The results show that life cycle GW, EU and AD of corn prodn. varied by factors of 4.2, 83.7 and 10.6, resp., across the Midwest counties over the nine-year span (2000-2008). Life cycle GW impacts of producing 1 kg of corn ranged from -6.4 in Franklin County, Illinois to 20.2 kg CO2-eq. in Perkins County, South Dakota. The life cycle EU impacts also spanned over a wide range of 0.99 g in Morton County, Kansas to 82.9 g N-eq. in Leelanau County, Michigan, whereas life cycle AD impacts ranged from 1.3 in Clermont County, Ohio to 100.7 g SO2-eq. in Perkins County, South Dakota. Moreover, trade-offs existed among life cycle GW, EU and AD impact categories for corn prodn. The spatial variation analyses showed that key contributors were the different soil types, pptn., elevation and the amts. of fertilizers applied. These findings provided crit. insight into spatio-temporal variations of life cycle environmental impacts of corn prodn. and identified spatial hotspots and top contributors for improving environmental performances of corn prodn.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXhvVarsrvE&md5=3f32487fbf699db4005d3766e1e9a928
    72. 72
      Butterbach-Bahl, K.; Dannenmann, M. Denitrification and associated soil N2O emissions due to agricultural activities in a changing climate. Current Opinion in Environmental Sustainability 2011, 3 (5), 389– 395,  DOI: 10.1016/j.cosust.2011.08.004
      There is no corresponding record for this reference.
    73. 73
      Congreves, K. A.; Wagner-Riddle, C.; Si, B. C.; Clough, T. J. Nitrous oxide emissions and biogeochemical responses to soil freezing-thawing and drying-wetting. Soil Biol. Biochem. 2018, 117, 5– 15,  DOI: 10.1016/j.soilbio.2017.10.040
      73
      Nitrous oxide emissions and biogeochemical responses to soil freezing-thawing and drying-wetting
      Congreves, K. A.; Wagner-Riddle, C.; Si, B. C.; Clough, T. J.
      Soil Biology & Biochemistry (2018), 117 (), 5-15CODEN: SBIOAH; ISSN:0038-0717. (Elsevier B.V.)
      A review concerning the systematic identification of similarities and differences in soil freeze/thaw (FT) and dry/wet (DW) cycles and their effect on timing and magnitude of N2O emissions from agricultural systems, including strategic research areas required to improve understanding of FT and DW processes leading to N2O emissions, are discussed. Topics covered include: introduction; soil physics behind soil FT and DW cycles; transport processes involved in N2O prodn. and emissions (gas soly., gas transport, phys. trapping); microbial and biogeochem. processes involved in N2O prodn. and emissions (anaerobic and redox conditions and substrate for, microbial C use and community compn.); and conclusions.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhsl2qtb3M&md5=094ffc87bb0d4627a8897c2901460b0c
    74. 74
      Parton, W. J.; Gutmann, M. P.; Merchant, E. R.; Hartman, M. D.; Adler, P. R.; McNeal, F. M.; Lutz, S. M. Measuring and mitigating agricultural greenhouse gas production in the US Great Plains, 1870–2000. Proc. Natl. Acad. Sci. U. S. A. 2015, 112 (34), E4681– E4688,  DOI: 10.1073/pnas.1416499112
      74
      Measuring and mitigating agricultural greenhouse gas production in the US Great Plains, 1870-2000
      Parton, William J.; Gutmann, Myron P.; Merchant, Emily R.; Hartman, Melannie D.; Adler, Paul R.; McNeal, Frederick M.; Lutz, Susan M.
      Proceedings of the National Academy of Sciences of the United States of America (2015), 112 (34), E4681-E4688CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
      The US Great Plains region is an agricultural prodn. center for the global market and, as such, an important source of greenhouse gas (GHG) emissions. This paper uses historical agricultural census data and ecosystem models to est. the magnitude of annual GHG fluxes from all agricultural sources (e.g., cropping, livestock raising, irrigation, fertilizer prodn., tractor use) in the Great Plains from 1870 to 2000. It showed that C released during native grassland plow-out was the largest source of GHG emissions before 1930; livestock prodn., direct energy use, and soil N2O emissions are currently the largest sources. Climate factors mediate these emissions: cool, wet weather promotes C sequestration; hot, dry weather increase GHG release. This anal. demonstrated long-term ecosystem consequences of historical and current agricultural activities, indicating adoption of available alternative management practices could substantially mitigate agricultural GHG fluxes, from a 34% redn. with a 25% adoption rate to as much as complete elimination with possible net C sequestration when a greater proportion of farmers adopt new agricultural practices.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXht1ygt7jI&md5=d67c4203c4f0df6f92afce287a4c6b78
    75. 75
      Adler, P.; Del Grosso, S.; Inman, D.; Jenkins, R. E.; Spatari, S.; Zhang, Y. Mitigation Opportunities for Life-Cycle Greenhouse Gas Emissions during Feedstock Production across Heterogeneous Landscapes. Managing Agricultural Greenhouse Gases 2012, 203– 219,  DOI: 10.1016/B978-0-12-386897-8.00012-7
      There is no corresponding record for this reference.
    76. 76
      Kopáček, J.; Hejzlar, J.; Posch, M. Factors Controlling the Export of Nitrogen from Agricultural Land in a Large Central European Catchment during 1900–2010. Environ. Sci. Technol. 2013, 47 (12), 6400– 6407,  DOI: 10.1021/es400181m
      76
      Factors Controlling the Export of Nitrogen from Agricultural Land in a Large Central European Catchment during 1900-2010
      Kopacek, Jiri; Hejzlar, Josef; Posch, Maximilian
      Environmental Science & Technology (2013), 47 (12), 6400-6407CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      Using an empirical model, we quantified the N export from agricultural land in a large central European catchment (upper Vltava River, Czech Republic, ∼13,000 Km2) over the 1959-2010 period. The catchment witnessed a rapid socio-economic shift from a planned to a market economy in the 1990s, resulting in an abrupt (∼50%) redn. in N fertilization rates at otherwise relatively stable land-use practices. This large-scale expt. enabled disentangling and quantification of individual effects of N fertilization and drainage on N leaching. The model is based on a 2-step regression between annual N export and 3 independent variables: (1) annual av. discharge in the 1st step and (2) net anthropogenic N inputs (NANI) and proportion of drained agricultural land in the 2nd step. Results show that N export was more related to mineralization of soil org. N pools due to drainage and tillage than to external N sources (NANI). The model, together with other reconstructed N sources in the catchment (leaching from forests, wastewaters, and atm. deposition) and extrapolated back to 1900, explained 77% of the obsd. variability in N concns. in the Vltava River during the 1900-2010 period.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXnsFSgs7o%253D&md5=c5f6012dc20cbbd2f6c4f7074a03a9d5
    77. 77
      Vagstad, N.; Eggestad, H. O.; Hoyas, T. R. Mineral nitrogen in agricultural soils and nitrogen losses: relation to soil properties, weather conditions, and farm practices. Ambio 1997, 26 (5), 266– 272
      There is no corresponding record for this reference.
    78. 78
      SARE Organic Matter: Organic Matter and Natural Cycles. https://www.sare.org/Learning-Center/Books/Building-Soils-for-Better-Crops-3rd-Edition/Text-Version/Organic-Matter-What-It-Is-and-Why-It-s-So-Important/Why-Soil-Organic-Matter-Is-So-Important (accessed 2019/5/18).
      There is no corresponding record for this reference.
    79. 79
      Howarth, R. W.; Marino, R. Nitrogen as the limiting nutrient for eutrophication in coastal marine ecosystems: Evolving views over three decades. Limnol. Oceanogr. 2006, 51 (1part2), 364– 376,  DOI: 10.4319/lo.2006.51.1_part_2.0364
      79
      Nitrogen as the limiting nutrient for eutrophication in coastal marine ecosystems: evolving views over three decades
      Howarth, Robert W.; Marino, Roxanne
      Limnology and Oceanography (2006), 51 (1, Pt. 2), 364-376CODEN: LIOCAH; ISSN:0024-3590. (American Society of Limnology and Oceanography)
      A review. The first special vol. of Limnol. and Oceanog., published in 1972, focused on whether phosphorus (P) or carbon (C) is the major agent causing eutrophication in aquatic ecosystems. Only slight mention was made that estuaries may behave differently from lakes and that nitrogen (N) may cause eutrophication in estuaries. In the following decade, an understanding of eutrophication in estuaries proceeded in relative isolation from the community of scientists studying lakes. National water quality policy in the United States was directed almost solely toward P control for both lakes and estuaries, and similarly, European nations tended to focus on P control in lakes. Although bioassay data indicated N control of eutrophication in estuaries as early as the 1970s, this body of knowledge was treated with skepticism by many freshwater scientists and water-quality managers, because bioassay data in lakes often did not properly indicate the importance of P relative to C in those ecosystems. Hence, the bioassay data in estuaries had little influence on water-quality management. Over the past two decades, a strong consensus has evolved among the scientific community that N is the primary cause of eutrophication in many coastal ecosystems. The development of this consensus was based in part on data from whole-ecosystem studies and on a growing body of evidence that presented convincing mechanistic reasons why the controls of eutrophication in lakes and coastal marine ecosystems may differ. Even though N is probably the major cause of eutrophication in most coastal systems in the temperate zone, optimal management of coastal eutrophication suggests controlling both N and P, in part because P can limit primary prodn. in some systems. In addn., excess P in estuaries can interact with the availability of N and silica (Si) to adversely affect ecol. structure. Redn. of P to upstream freshwater ecosystems can also benefit coastal marine ecosystems through mechanisms such as increased Si fluxes.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28XhsFaqtbo%253D&md5=e74e91b7617b5e4868054d28ad46a0b1
    80. 80
      Yang, X.-e.; Wu, X.; Hao, H.-l.; He, Z.-l. Mechanisms and assessment of water eutrophication. J. Zhejiang Univ., Sci., B 2008, 9 (3), 197– 209,  DOI: 10.1631/jzus.B0710626
      80
      Mechanisms and assessment of water eutrophication
      Yang, Xiao-e; Wu, Xiang; Hao, Hu-lin; He, Zhen-li
      Journal of Zhejiang University, Science, B (2008), 9 (3), 197-209CODEN: JZUSAM; ISSN:1673-1581. (Zhejiang University Press)
      A review is given. Water eutrophication has become a worldwide environmental problem in recent years, and understanding the mechanisms of water eutrophication will help for prevention and remediation of water eutrophication. Recent advances in current status and major mechanisms of water eutrophication, assessment and evaluation criteria, and the influencing factors are presented. Water eutrophication in lakes, reservoirs, estuaries and rivers is widespread all over the world and the severity is increasing, esp. in the developing countries like China. The assessment of water eutrophication has been advanced from simple individual parameters like total P, total N, etc., to comprehensive indexes like total nutrient status index. The major influencing factors on water eutrophication include nutrient enrichment, hydrodynamics, environmental factors such as temp., salinity, CO2, element balance, etc., and microbial and biodiversity. The occurrence of water eutrophication is actually a complex function of all the possible influencing factors. The mechanisms of algal blooming are not fully understood and need to be further investigated.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXksVaqsb0%253D&md5=747a21fe0a10465f5e8ce0b4f9e99713
    81. 81
      Guntiñas, M. E.; Leirós, M. C.; Trasar-Cepeda, C.; Gil-Sotres, F. Effects of moisture and temperature on net soil nitrogen mineralization: A laboratory study. Eur. J. Soil Biol. 2012, 48, 73– 80,  DOI: 10.1016/j.ejsobi.2011.07.015
      81
      Effects of moisture and temperature on net soil nitrogen mineralization: A laboratory study
      Guntinas, M. E.; Leiros, M. C.; Trasar-Cepeda, C.; Gil-Sotres, F.
      European Journal of Soil Biology (2012), 48 (), 73-80CODEN: EJSBE2; ISSN:1164-5563. (Elsevier Masson SAS)
      Climate change will lead to changes in soil moisture and temp., thereby affecting org. matter mineralization and the cycling of biophilic elements such as nitrogen. However, very few studies have considered how the sensitivity of the rate of net nitrogen mineralization to temp. and/or moisture content may be modified by changes in these parameters. To investigate how changes in temp. and moisture content affect net nitrogen mineralization (as regards both the mineralization rate and the sensitivity of the mineralization rate to changes in temp. and moisture content), a lab. expt. was carried out in which three soils under different types of use (Forest, Grassland, Cropland) were incubated for 42 days under different moisture conditions (between 40 and 100% field capacity) and temps. (between 10 and 35 °C); total inorg. nitrogen levels were detd. at different times throughout the expt. The rate of mineralization was detd. at each temp. and moisture level considered, by use of the mono-compartmental model developed by Stanford and Smith (1972). For all soils, changes in the rate of mineralization with temp. followed the pattern described by the Q 10 model, while the models used to det. the effect of moisture content on the net rate of mineralization (linear, semilogarithmic, partial parabolic and complete parabolic) were only verified for the Forest soil. In general, the sensitivity to temp. was maximal at 25 °C, and the optimal moisture content for nitrogen mineralization was between 80% and 100% of field capacity. A relatively simple model that included the temp.-moisture-time interaction was also tested. This model provided a significant fit for the three soils under study, in contrast with the other models tested. In any case, further studies are necessary in order to address the extent to which changes in the quality of org. matter, caused by land use, affect any modifications to soil nitrogen that may be generated by climate change.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXhsF2qs73J&md5=8bb3eba6de5f4f4022a501ea556bba1e
    82. 82
      Bouwman, A. F.; Boumans, L. J. M.; Batjes, N. H. Estimation of global NH3 volatilization loss from synthetic fertilizers and animal manure applied to arable lands and grasslands. Global Biogeochemical Cycles 2002, 16 (2), 8.1– 8.14,  DOI: 10.1029/2000GB001389
      There is no corresponding record for this reference.
    83. 83
      Kissel, D. E.; Cabrera, M. L., Factors affecting urea hydrolysis. 87-98-J. In Ammonia Volatilization from Urea Fertilizers. Bulletin Y-206. Tennessee Valley Authority; Kansas Agricultural Experiment Station. Dept. of Agronomy. Kansas State University: Muscle Shoals, Alabama., 1988.
      There is no corresponding record for this reference.
    84. 84
      Torello, W. A.; Wehner, D. J. Urease activity in a Kentucky bluegrass turf. Agron. J. 1983, 75, 654– 656,  DOI: 10.2134/agronj1983.00021962007500040018x
      84
      Urease activity in a Kentucky bluegrass turf
      Torello, W. A.; Wehner, D. J.
      Agronomy Journal (1983), 75 (4), 654-6CODEN: AGJOAT; ISSN:0002-1962.
      The extent of urease [9002-13-5] activity assocd. with turfgrass tissue, thatch, and the underlying soil was detd. Because a turfgrass stand frequently possesses an extensive thatch layer that may serve as the primary plant growth medium, addnl. objectives included: detg. the effects of air-drying and seasonal variation on the activity of ureases in thatch; detg. the variability in thatch urease activity by analyzing multiple field samples; and detg. the variation of urease activity within a thatch profile. Turfgrass clippings, thatch, and underlying Flanagan silt loam soil (Aquic Arguidoll) samples were taken from a field-grown Kentucky bluegrass (Poa pratensis) turf in either Sept. 1980 or Mar. 1981. On a dry wt. basis, urease activity was 18-30 times higher from turfgrass clippings and thatch than from soil. Air-drying thatch increased urease activity by 20% over moist samples, whereas air-drying soil samples had no apparent effect. Greenhouse incubation of winter-dormant thatch samples increased urease activity by 40%, presumably in response to the duration of increased temp. Thatch urease activity varied between sampling sites but still remained extremely high compared to soil activity. Within each thatch sample (1 × 1 × 2 cm), urease activity was highest in the upper 1.0 cm of the profile. Thus, thatch urease activity was variable in nature depending on seasonal conditions, in sharp contrast with extremely stable soil urease activities. These findings suggest that, because of the high level of urease in thatch, NH3 volatilization will occur from most urea-treated turfgrass stands, regardless of the type of underlying soil, unless the urea is thoroughly washed into the soil.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaL3sXlt1Whtbo%253D&md5=cbc2ee07ea1e4c4abae2eeed30a4c795
    85. 85
      Acres Soil organic matter: Tips for responsible nitrogen management. http://www.ecofarmingdaily.com/soil-organic-matter-tips-nitrogen-management/?cn-reloaded=1 (accessed 2018/4/30).
      There is no corresponding record for this reference.
    86. 86
      Adler, P. R.; Hums, M. E.; McNeal, F. M.; Spatari, S. Evaluation of environmental and cost tradeoffs of producing energy from soybeans for on-farm use. J. Cleaner Prod. 2019, 210, 1635– 1649,  DOI: 10.1016/j.jclepro.2018.11.019
      There is no corresponding record for this reference.
    87. 87
      Huo, H.; Wang, M.; Bloyd, C.; Putsche, V. Life-cycle assessment of energy use and greenhouse gas emissions of soybean-derived biodiesel and renewable fuels. Environ. Sci. Technol. 2009, 43 (3), 750– 6,  DOI: 10.1021/es8011436
      87
      Life-Cycle Assessment of Energy Use and Greenhouse Gas Emissions of Soybean-Derived Biodiesel and Renewable Fuels
      Huo, Hong; Wang, Michael; Bloyd, Cary; Putsche, Vicky
      Environmental Science & Technology (2009), 43 (3), 750-756CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      The authors used Argonne National Lab.'s Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model to assess the life-cycle energy and greenhouse gas (GHG) emission impacts of 4 soybean-derived fuels: biodiesel fuel produced via transesterification, 2 renewable diesel fuels (I and II) produced from different hydrogenation processes, and renewable gasoline produced from catalytic cracking. Five approaches were employed to allocate the coproducts: a displacement approach; 2 allocation approaches, one based on the energy value and the other based on the market value; and 2 hybrid approaches that integrated the displacement and allocation methods. The relative rankings of soybean-based fuels in terms of energy and environmental impacts were different under the different approaches, and the reasons were analyzed. Results from the 5 allocation approaches showed that although the prodn. and combustion of soybean-based fuels might increase total energy use, they could have significant benefits in reducing fossil energy use (>52%), petroleum use (>88%), and GHG emissions (>57%) relative to petroleum fuels. This study emphasized the importance of the methods used to deal with coproduct issues and provide a comprehensive soln. for conducting a life-cycle assessment of fuel pathways with multiple coproducts.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXhsFejurrI&md5=f407189121b7b0c767605d0dd647d6b2
    88. 88
      Rajaeifar, M. A.; Ghobadian, B.; Safa, M.; Heidari, M. D. Energy life-cycle assessment and CO2 emissions analysis of soybean-based biodiesel: a case study. J. Cleaner Prod. 2014, 66, 233– 241,  DOI: 10.1016/j.jclepro.2013.10.041
      88
      Energy life-cycle assessment and CO2 emissions analysis of soybean-based biodiesel: a case study
      Rajaeifar, Mohammad Ali; Ghobadian, Barat; Safa, Majeed; Heidari, Mohammad Davoud
      Journal of Cleaner Production (2014), 66 (), 233-241CODEN: JCROE8; ISSN:0959-6526. (Elsevier Ltd.)
      In this study the energy consumption and CO2 emissions of biodiesel prodn. from soybean in Golestan province of Iran were studied. For this purpose, the life-cycle process of biodiesel was considered as five stages of agricultural soybean prodn., soybean transportation, soybean crushing, biodiesel conversion, and its transportation. The results indicated that the total fossil energy consumption with coproduct allocation was 8617.7 MJ ha-1 and the renewable energy output content (biodiesel as the final outcome) was estd. as 16,991.4 MJ ha-1. The net energy gain (NEG) and the fossil energy ratio (FER) were calcd. as 8373.7 MJ ha-1 and 1.97, resp., which show soybean is a suitable energy crop for biodiesel prodn. Agricultural soybean prodn. stage ranked the first in energy consumption among the five main stages where it consumed 50.56% of total fossil energy consumption in the biodiesel life-cycle process. The greenhouse gas (GHG) emissions data anal. revealed that the total GHG emission was 1710.3 kg CO2eq ha-1 which biodiesel prodn. life-cycle was only account for 311.96 kg CO2eq ha-1 if the mass allocation is considered. Overall, biodiesel prodn. from soybean in Iran can be considered as a way to increase energy security in the near future. Also, soybean cultivation must be considered along with other common oilseeds cultivation in order to prevent food competition between biodiesel feedstocks and food prodn. in Iran.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhvVSltrrM&md5=45bd0b2f28639add36f1878ef88ad7c1
    89. 89
      Castanheira, É. G.; Freire, F. Greenhouse gas assessment of soybean production: implications of land use change and different cultivation systems. J. Cleaner Prod. 2013, 54, 49– 60,  DOI: 10.1016/j.jclepro.2013.05.026
      89
      Greenhouse gas assessment of soybean production: implications of land use change and different cultivation systems
      Castanheira, Erica Geraldes; Freire, Fausto
      Journal of Cleaner Production (2013), 54 (), 49-60CODEN: JCROE8; ISSN:0959-6526. (Elsevier Ltd.)
      The increase in soybean prodn. as a source of protein and oil is being stimulated by the growing demand for livestock feed, food and numerous other applications. Significant greenhouse gas (GHG) emissions can result from land use change due to the expansion and cultivation of soybean. However, this is complex to assess and the results can vary widely. The main goal of this article is to investigate the life-cycle GHG balance for soybean produced in Latin America, assessing the implications of direct land use change emissions and different cultivation systems. A life-cycle model, including inventories for soybean produced in three different climate regions, was developed, addressing land use change, cultivation and transport to Europe. A comprehensive evaluation of alternative land use change scenarios (conversion of tropical forest, forest plantations, perennial crop plantations, savannah and grasslands), cultivation (tillage, reduced tillage and no-tillage) and soybean transportation systems was undertaken. The main results show the importance of land use change in soybean GHG emissions, but significant differences were obsd. for the alternative scenarios, namely 0.1-17.8 kg CO2eq kg-1 soybean. The original land choice is a crit. issue in ensuring the lowest soybean GHG balance and degraded grassland should preferably be used for soybean cultivation. The highest GHG emissions were calcd. for tropical moist regions when rainforest is converted into soybean plantations (tillage system). When land use change is not considered, the GHG intensity varies from 0.3 to 0.6 kg CO2eq kg-1 soybean. It was calcd. that all tillage systems have higher GHG emissions than the corresponding no-tillage and reduced tillage systems. The results also show that N2O emissions play a major role in the GHG emissions from cultivation, although N2O emission calcns. are very sensitive to the parameters and emission factors adopted.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXpt12iu70%253D&md5=f62179bb5010d514185f3750b205821b
    90. 90
      Costello, C.; Griffin, W. M.; Landis, A. E.; Matthews, H. S. Impact of biofuel crop production on the formation of hypoxia in the Gulf of Mexico. Environ. Sci. Technol. 2009, 43, 7985– 7991,  DOI: 10.1021/es9011433
      90
      Impact of Biofuel Crop Production on the Formation of Hypoxia in the Gulf of Mexico
      Costello, Christine; Griffin, W. Michael; Landis, Amy E.; Matthews, H. Scott
      Environmental Science & Technology (2009), 43 (20), 7985-7991CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      Many studies have compared corn-based ethanol to cellulosic ethanol on a per unit basis and have generally concluded that cellulosic ethanol will result in fewer environmental consequences, including NO3- output. This study takes a system-wide approach in considering the NO3- output and the relative areal extent of hypoxia in the Northern Gulf of Mexico (NGOM) due to the introduction of addnl. crops for biofuel prodn. We stochastically est. NO3- loading to the NGOM and use these results to approx. the areal extent of hypoxia for scenarios that meet the Energy Independence and Security Act of 2007's biofuel goals for 2015 and 2022. Crops for ethanol include corn, corn stover, and switchgrass; all biodiesel is assumed to be from soybeans. Our results indicate that moving from corn to cellulosics for ethanol prodn. may result in a 20% decrease (based on mean values) in NO3- output from the Mississippi and Atchafalaya River Basin (MARB). This decrease will not meet the EPA target for hypoxic zone redn. An aggressive nutrient management strategy will be needed to reach the 5000-Km2 areal extent of hypoxia in the NGOM goal set forth by the Mississippi River/Gulf of Mexico Watershed Nutrient Task Force even in the absence of biofuels, given current prodn. to meet food, feed, and other industrial needs.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXpslCltrc%253D&md5=b31dd1b952c9751028e8439d59d49305
    91. 91
      Dosskey, M. G. Toward quantifying water pollution abatement in response to installing buffers on crop land. Environ. Manage. 2001, 28 (5), 577– 98,  DOI: 10.1007/s002670010245
      91
      Toward quantifying water pollution abatement in response to installing buffers on crop land
      Dosskey M G
      Environmental management (2001), 28 (5), 577-98 ISSN:0364-152X.
      The scientific research literature is reviewed (i) for evidence of how much reduction in nonpoint source pollution can be achieved by installing buffers on crop land, (ii) to summarize important factors that can affect this response, and (iii) to identify remaining major information gaps that limit our ability to make probable estimates. This review is intended to clarify the current scientific foundation of the USDA and similar buffer programs designed in part for water pollution abatement and to highlight important research needs. At this time, research reports are lacking that quantify a change in pollutant amounts (concentration and/or load) in streams or lakes in response to converting portions of cropped land to buffers. Most evidence that such a change should occur is indirect, coming from site-scale studies of individual functions of buffers that act to retain pollutants from runoff: (1) reduce surface runoff from fields, (2) filter surface runoff from fields, (3) filter groundwater runoff from fields, (4) reduce bank erosion, and (5) filter stream water. The term filter is used here to encompass the range of specific processes that act to reduce pollutant amounts in runoff flow. A consensus of experimental research on functions of buffers clearly shows that they can substantially limit sediment runoff from fields, retain sediment and sediment-bound pollutants from surface runoff, and remove nitrate N from groundwater runoff. Less certain is the magnitude of these functions compared to the cultivated crop condition that buffers would replace within the context of buffer installation programs. Other evidence suggests that buffer installation can substantially reduce bank erosion sources of sediment under certain circumstances. Studies have yet to address the degree to which buffer installation can enhance channel processes that remove pollutants from stream flow. Mathematical models offer an alternative way to develop estimates for water quality changes in response to buffer installation. Numerous site conditions and buffer design factors have been identified that can determine the magnitude of each buffer function. Accurate models must be able to account for and integrate these functions and factors over whole watersheds. At this time, only pollutant runoff and surface filtration functions have been modeled to this extent. Capability is increasing as research data is produced, models become more comprehensive, and new techniques provide means to describe variable conditions across watersheds. A great deal of professional judgment is still required to extrapolate current knowledge of buffer functions into broadly accurate estimates of water pollution abatement in response to buffer installation on crop land. Much important research remains to be done to improve this capability. The greatest need is to produce direct quantitative evidence of this response. Such data would confirm the hypothesis and enable direct testing of watershed-scale prediction models as they become available. Further study of individual pollution control functions is also needed, particularly to generate comparative evidence for how much they can be manipulated through buffer installation and management.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD3MritF2rsA%253D%253D&md5=7eb88f959eee3fce7bf2210e98372d56

  • Supporting Information

    Supporting Information

    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.9b06874.

    • Figures showing the system boundary, EPIC/LCA/BRT model integration, life cycle environmental impacts of soybean for Midwest counties over nine years, stage contributions at county and state scales, statistical distributions of life cycle GWP with/out net carbon exchange, the improvements on biophysical accounting with county scale estimates, and sensitivity analyses; Tables showing the detailed data sources for EPIC, impact intensities for supply chain LCI, characterization factors for LCIA, and highest/lowest values of life cycle impacts of soybean in the Midwest; Equations utilized to estimate life cycle impacts (PDF)


    Terms & Conditions

    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.