A Computational Strategy for Design and Implementation of Equipment That Addresses Sustainable Agricultural Residue Removal at the Subfield Scale

Agricultural residues are the largest potential near term source of biomass for bioenergy production. Sustainable use of agricultural residues for bioenergy production requires consideration of the important role that residues play in maintaining soil health and productivity. Innovation equipment designs for residue harvesting systems can help economically collect agricultural residues while mitigating sustainability concerns. A key challenge in developing these equipment designs is establishing sustainable reside removal rates at the sub-field scale. Several previous analysis studies have developed methodologies and tools to estimate sustainable agricultural residue removal by considering environmental constraints including soil loss from wind and water erosion and soil organic carbon at field scale or larger but have not considered variation at the sub-field scale. This paper introduces a computational strategy to integrate data and models from multiple spatial scales to investigate how variability of soil, grade, and yield within an individual cornfield can impact sustainable residue removal for bioenergy production. This strategy includes the current modeling tools (i.e., RUSLE2, WEPS, and SCI), the existing data sources (i.e., SSURGO soils, CLIGEN, WINDGEN, and NRCS managements), and the available high fidelity spatial information (i.e., LiDAR slope and crop yield monitor output). Rather than using average or representative values for crop yields, soil characteristics, and slope for a field, county, or larger area, the modeling inputs are based on the same spatial scale as the precision farming data available. There are three challenges for developing an integrated model for sub-field variability of sustainable agricultural residue removal—the computational challenge of iteratively computing with 400 or more spatial points per hectare, the inclusion of geoprocessing tools, and the integration of data from different spatial scales. Using a representative field in Iowa, this paper demonstrates the computational algorithms used and establishes key design parameters for an innovative residue removal equipment design concept

Developing an Integrated Model Framework for the Assessment of Sustainable Agricultural Residue Removal Limits for Bioenergy Systems

Agricultural residues have significant potential as a feedstock for bioenergy production, but removing these residues from the land can have negative impacts on soil health. Because of this computational tools are needed that can help guide decisions on the amount of agricultural residue that can be sustainably removed. Models and datasets that can support decisions about sustainable agricultural residue removal are available; however, no tools currently exist that are capable of simultaneously addressing all of the environmental factors that can limit the availability of residue for bioenergy production. This paper presents an integrated framework of models and data that provide a coupled a set of environmental process models and databases that can support agricultural residue removal decisions. Specifically the RUSLE2, WEPS, and Soil Conditioning Index models have been integrated together with the disparate set of databases providing the soils, climate, and management practice data required. The integrated system has been demonstrated for two example cases. In the first case the potential impact of agricultural residue removal is explored. In the second case an aggregate assessment of the agricultural residues available bioenergy production in the state of Iowa is performed

A Computational Strategy for Design and Implementation of Equipment That Addresses Sustainable Agricultural Residue Removal at the Subfield Scale

Agricultural residues are the largest potential near term source of biomass for bioenergy production. Sustainable use of agricultural residues for bioenergy production requires consideration of the important role that residues play in maintaining soil health and productivity. Innovation equipment designs for residue harvesting systems can help economically collect agricultural residues while mitigating sustainability concerns. A key challenge in developing these equipment designs is establishing sustainable reside removal rates at the sub-field scale. Several previous analysis studies have developed methodologies and tools to estimate sustainable agricultural residue removal by considering environmental constraints including soil loss from wind and water erosion and soil organic carbon at field scale or larger but have not considered variation at the sub-field scale. This paper introduces a computational strategy to integrate data and models from multiple spatial scales to investigate how variability of soil, grade, and yield within an individual cornfield can impact sustainable residue removal for bioenergy production. This strategy includes the current modeling tools (i.e., RUSLE2, WEPS, and SCI), the existing data sources (i.e., SSURGO soils, CLIGEN, WINDGEN, and NRCS managements), and the available high fidelity spatial information (i.e., LiDAR slope and crop yield monitor output). Rather than using average or representative values for crop yields, soil characteristics, and slope for a field, county, or larger area, the modeling inputs are based on the same spatial scale as the precision farming data available. There are three challenges for developing an integrated model for sub-field variability of sustainable agricultural residue removal—the computational challenge of iteratively computing with 400 or more spatial points per hectare, the inclusion of geoprocessing tools, and the integration of data from different spatial scales. Using a representative field in Iowa, this paper demonstrates the computational algorithms used and establishes key design parameters for an innovative residue removal equipment design concept.This proceeding is from ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Vol. 2: 32nd Computers and Information in Engineering Conference, Parts A and B. (2012): 1287–1294, doi:10.1115/DETC2012-71430. Posted with permission.</p

A Computational Strategy for Design and Implementation of Equipment That Addresses Sustainable Agricultural Residue Removal at the Subfield Scale

Agricultural residues are the largest potential near term source of biomass for bioenergy production. Sustainable use of agricultural residues for bioenergy production requires consideration of the important role that residues play in maintaining soil health and productivity. Innovation equipment designs for residue harvesting systems can help economically collect agricultural residues while mitigating sustainability concerns. A key challenge in developing these equipment designs is establishing sustainable reside removal rates at the sub-field scale. Several previous analysis studies have developed methodologies and tools to estimate sustainable agricultural residue removal by considering environmental constraints including soil loss from wind and water erosion and soil organic carbon at field scale or larger but have not considered variation at the sub-field scale. This paper introduces a computational strategy to integrate data and models from multiple spatial scales to investigate how variability of soil, grade, and yield within an individual cornfield can impact sustainable residue removal for bioenergy production. This strategy includes the current modeling tools (i.e., RUSLE2, WEPS, and SCI), the existing data sources (i.e., SSURGO soils, CLIGEN, WINDGEN, and NRCS managements), and the available high fidelity spatial information (i.e., LiDAR slope and crop yield monitor output). Rather than using average or representative values for crop yields, soil characteristics, and slope for a field, county, or larger area, the modeling inputs are based on the same spatial scale as the precision farming data available. There are three challenges for developing an integrated model for sub-field variability of sustainable agricultural residue removal—the computational challenge of iteratively computing with 400 or more spatial points per hectare, the inclusion of geoprocessing tools, and the integration of data from different spatial scales. Using a representative field in Iowa, this paper demonstrates the computational algorithms used and establishes key design parameters for an innovative residue removal equipment design concept.This proceeding is from ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Vol. 2: 32nd Computers and Information in Engineering Conference, Parts A and B. (2012): 1287–1294, doi:10.1115/DETC2012-71430. Posted with permission.</p

Modeling Sustainable Agricultural Residue Removal at the Subfield Scale

This study developed a computational strategy that utilizes data inputs from multiple spatial scales to investigate how variability within individual fields can impact sustainable residue removal for bioenergy production. Sustainable use of agricultural residues for bioenergy production requires consideration of the important role that residues play in limiting soil erosion and maintaining soil C, health, and productivity. Increased availability of subfield-scale data sets such as grain yield data, high-fidelity digital elevation models, and soil characteristic data provides an opportunity to investigate the impacts of subfield-scale variability on sustainable agricultural residue removal. Using three representative fields in Iowa, this study contrasted the results of current NRCS conservation management planning analysis with subfield-scale analysis for rake-and-bale removal of agricultural residue. The results of the comparison show that the field-average assumptions used in NRCS conservation management planning may lead to unsustainable residue removal decisions for significant portions of some fields. This highlights the need for additional research on subfield-scale sustainable agricultural residue removal including the development of real-time variable removal technologies for agricultural residue.This article is from Agronomy Journal 104 (2012): 970–981, doi:10.2134/agronj2012.0024. Posted with permission.</p

Developing an Integrated Model Framework for the Assessment of Sustainable Agricultural Residue Removal Limits for Bioenergy Systems

Agricultural residues have significant potential as a feedstock for bioenergy production, but removing these residues from the land can have negative impacts on soil health. Because of this computational tools are needed that can help guide decisions on the amount of agricultural residue that can be sustainably removed. Models and datasets that can support decisions about sustainable agricultural residue removal are available; however, no tools currently exist that are capable of simultaneously addressing all of the environmental factors that can limit the availability of residue for bioenergy production. This paper presents an integrated framework of models and data that provide a coupled a set of environmental process models and databases that can support agricultural residue removal decisions. Specifically the RUSLE2, WEPS, and Soil Conditioning Index models have been integrated together with the disparate set of databases providing the soils, climate, and management practice data required. The integrated system has been demonstrated for two example cases. In the first case the potential impact of agricultural residue removal is explored. In the second case an aggregate assessment of the agricultural residues available bioenergy production in the state of Iowa is performed.This proceeding is from ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (pp. 259-268). Posted with permission.</p

Developing an Integrated Model Framework for the Assessment of Sustainable Agricultural Residue Removal Limits for Bioenergy Systems

Agricultural residues have significant potential as a feedstock for bioenergy production, but removing these residues can have negative impacts on soil health. Models and datasets that can support decisions about sustainable agricultural residue removal are available; however, no tools currently exist capable of simultaneously addressing all environmental factors that can limit availability of residue. The VE-Suite model integration framework has been used to couple a set of environmental process models to support agricultural residue removal decisions. The RUSLE2, WEPS, and Soil Conditioning Index models have been integrated. A disparate set of databases providing the soils, climate, and management practice data required to run these models have also been integrated. The integrated system has been demonstrated for two example cases. First, an assessment using high spatial fidelity crop yield data has been run for a single farm. This analysis shows the significant variance in sustainably accessible residue across a single farm and crop year. A second example is an aggregate assessment of agricultural residues available in the state of Iowa. This implementation of the integrated systems model demonstrates the capability to run a vast range of scenarios required to represent a large geographic region