A Conceptual Evaluation of Sustainable Variable-Rate Agricultural Residue Removal

Agricultural residues have near-term potential as a feedstock for bioenergy production, but their removal must be managed carefully to maintain soil health and productivity. Recent studies have shown that subfield scale variability in soil properties (e.g., slope, texture, and organic matter content) that affect grain yield significantly affect the amount of residue that can be sustainably removed from different areas within a single field. This modeling study examines the concept of variable-rate residue removal equipment that would be capable of on-the-fly residue removal rate adjustments ranging from 0 to 80%. Thirteen residue removal rates (0% and 25–80% in 5% increments) were simulated using a subfield scale integrated modeling framework that evaluates residue removal sustainability considering wind erosion, water erosion, and soil carbon constraints. Three Iowa fields with diverse soil, slope, and grain yield characteristics were examined and showed sustainable, variable-rate agricultural residue removal that averaged 2.35, 7.69, and 5.62 Mg ha−1, respectively. In contrast, the projected sustainable removal rates using rake and bale removal for the entire field averaged 0.0, 6.40, and 5.06 Mg ha−1, respectively. The modeling procedure also projected that variable-rate residue harvest would result in 100% of the land area in all three fields being managed in a sustainable manner, whereas Field 1 could not be sustainably managed using rake and bale removal, and only 83 and 62% of the land area in Fields 2 and 3 would be managed sustainably using a rake and bale operation for the entire field. In addition, it was found that residue removal adjustments of 40 to 65% are sufficient to collect 90% of the sustainably available agricultural residue

Developing Multiple Diverse Potential Designs for Heat Transfer Utilizing Graph Based Evolutionary Algorithms

This paper examines the use of graph based evolutionary algorithms (GBEAs) to find multiple acceptable solutions for heat transfer in engineering systems during the optimization process. GBEAs are a type of evolutionary algorithm (EA) in which a topology, or geography, is imposed on an evolving population of solutions. The rates at which solutions can spread within the population are controlled by the choice of topology. As in nature geography can be used to develop and sustain diversity within the solution population. Altering the choice of graph can create a more or less diverse population of potential solutions. The choice of graph can also affect the convergence rate for the EA and the number of mating events required for convergence. The engineering system examined in this paper is a biomass fueled cookstove used in developing nations for household cooking. In this cookstove wood is combusted in a small combustion chamber and the resulting hot gases are utilized to heat the stove’s cooking surface. The spatial temperature profile of the cooking surface is determined by a series of baffles that direct the flow of hot gases. The optimization goal is to find baffle configurations that provide an even temperature distribution on the cooking surface. Often in engineering, the goal of optimization is not to find the single optimum solution but rather to identify a number of good solutions that can be used as a starting point for detailed engineering design. Because of this a key aspect of evolutionary optimization is the diversity of the solutions found. The key conclusion in this paper is that GBEA’s can be used to create multiple good solutions needed to support engineering design

An Integrated Modeling and Data Management Strategy for Cellulosic Biomass Production Decisions

Emerging cellulosic bioenergy markets can provide land managers with additional options for crop production decisions. For example, integrating dedicated bioenergy crops such as perennial grasses and short rotation woody species within the agricultural landscape can have positive impacts on several environmental processes including increased soil organic matter in degraded soils, reduced sediment and nutrient loading in watersheds, and lower green house gas fluxes. Implementing this type of diverse bioenergy production system to maximize the potential environmental benefits requires a detailed understanding of the many interwoven aspects of environmental landscapes. This paper presents a dynamic framework-based integrated modeling and data management strategy that can design sustainable bioenergy cropping systems within the existing row crop production landscape of the Midwestern U.S. Critical environmental processes— including soil erosion from wind and water, and soil organic matter changes—are quantified by this integrated model to determine sustainable removal rates of agricultural residues for bioenergy production at the sub-field scale. Seven land management options for a 59 ha Iowa field are examined using the integrated model. These include a baseline case of sustainable residue removal and various incorporations of rye cover cropping and switchgrass use in marginal land. Relative to the baseline metrics, the adoption of rye cover crops with sustainable residue removal increases the total biomass sustainably available for biofuel production by 289% and reduces soil loss by 42%. Combining rye cover crops while displacing less productive and at-risk areas of the field with switchgrass increases the sustainable biomass available by 436% and decreases soil loss by 64%

Integrated ag landscapes for profit and risk management

The emerging cellulosic bioenergy and bioproduct industries can provide several agronomic opportunities. New biomass markets give land managers additional choices that can create revenue, mitigate operational risks, and proactively manage soil quality

Sustainable agricultural residue removal for bioenergy: A spatially comprehensive US national assessment

This study provides a spatially comprehensive assessment of sustainable agricultural residue removal potential across the United States for bioenergy production. Earlier assessments determining the quantity of agricultural residue that could be sustainably removed for bioenergy production at the regional and national scale faced a number of computational limitations. These limitations included the number of environmental factors, the number of land management scenarios, and the spatial fidelity and spatial extent of the assessment. This study utilizes integrated multi-factor environmental process modeling and high fidelity land use datasets to perform the sustainable agricultural residue removal assessment. Soil type represents the base spatial unit for this study and is modeled using a national soil survey database at the 10–100 m scale. Current crop rotation practices are identified by processing land cover data available from the USDA National Agricultural Statistics Service Cropland Data Layer database. Land management and residue removal scenarios are identified for each unique crop rotation and crop management zone. Estimates of county averages and state totals of sustainably available agricultural residues are provided. The results of the assessment show that in 2011 over 150 million metric tons of agricultural residues could have been sustainably removed across the United States. Projecting crop yields and land management practices to 2030, the assessment determines that over 207 million metric tons of agricultural residues will be able to be sustainably removed for bioenergy production at that time. This biomass resource has the potential for producing over 68 billion liters of cellulosic biofuels

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

LANDSCAPE MANAGEMENT FOR SUSTAINABLE SUPPLIES OF BIOENERGY FEEDSTOCK AND ENHANCED SOIL QUALITY

Agriculture can simultaneously address global food, feed, fiber, and energy challenges provided our soil, water, and air resources are not compromised in doing so. As we embark on the 19th Triennial Conference of the International Soil and Tillage Research Organization (ISTRO), I am pleased to proclaim that our members are well poised to lead these endeavors because of our comprehensive understanding of soil, water, agricultural and bio-systems engineering processes. The concept of landscape management, as an approach for integrating multiple bioenergy feedstock sources, including biomass residuals, into current crop production systems, is used as the focal point to show how these ever-increasing global challenges can be met in a sustainable manner. Starting with the 2005 Billion Ton Study (BTS) goals, research and technology transfer activities leading to the 2011 U.S. Department of Energy (DOE) Revised Billion Ton Study (BT2) and development of a residue management tool to guide sustainable crop residue harvest will be reviewed. Multi-location USDA-Agricultural Research Service (ARS) Renewable Energy Assessment Project (REAP) team research and on-going partnerships between public and private sector groups will be shared to show the development of landscape management strategies that can simultaneously address the multiple factors that must be balanced to meet the global challenges. Effective landscape management strategies recognize the importance of nature’s diversity and strive to emulate those conditions to sustain multiple critical ecosystem services. To illustrate those services, the soil quality impact of harvesting crop residues are presented to show how careful, comprehensive monitoring of soil, water and air resources must be an integral part of sustainable bioenergy feedstock production systems. Preliminary analyses suggest that to sustain soil resources within the U.S. Corn Belt, corn (Zea mays L.) stover should not be harvested if average grain yields are less than 11 Mg ha-1 (175 bu ac-1) unless more intensive landscape management practices are implemented. Furthermore, although non-irrigated corn grain yields east and west of the primary Corn Belt may not consistently achieve the 11 Mg ha-1 yield levels, corn can still be part of an overall landscape approach for sustainable feedstock production. Another option for producers with consistently high yields (> 12.6 Mg ha-1 or 200 bu ac-1) that may enable them to sustainably harvest even more stover is to decrease their tillage intensity which will reduce fuel use, preserve rhizosphere carbon, and/or help maintain soil structure and soil quality benefits often attributed to no-till production systems. In conclusion, I challenge all ISTRO scientists to critically ask if your research is contributing to improved soil and crop management strategies that effectively address the complexity associated with sustainable food, feed, fiber and fuel production throughout the world