Progress on reducing nutrient loss from annual croplands has been hampered by perceived conflicts between short-term profitability and long-term stewardship, but these may be overcome through strategic integration of perennial crops. Perennial biomass crops like switchgrass can mitigate nitrate-nitrogen (NO 3 -N) leaching, address bioenergy feedstock targets, and -as a lower-cost management alternative to annual crops (i.e., corn, soybeans) -may also improve farm profitability. We analyzed publicly available environmental, agronomic, and economic data with two integrated models: a subfield agroecosystem management model, Landscape Environmental Assessment Framework (LEAF), and a process-based biogeochemical model, DeNitrification-DeComposition (DNDC). We constructed a factorial combination of profitability and NO 3 -N leaching thresholds and simulated targeted switchgrass integration into corn/soybean cropland in the agricultural state of Iowa, USA. For each combination, we modeled (i) area converted to switchgrass, (ii) switchgrass biomass production, and (iii) NO 3 -N leaching reduction. We spatially analyzed two scenarios: converting to switchgrass corn/soybean cropland losing >US$ 100 ha À1 and leaching >50 kg ha À1 ('conservative' scenario) or losing >US$ 0 ha À1 and leaching >20 kg ha À1 ('nutrient reduction' scenario). Compared to baseline, the 'conservative' scenario resulted in 12% of cropland converted to switchgrass, which produced 11 million Mg of biomass and reduced leached NO 3 -N 18% statewide. The 'nutrient reduction' scenario converted 37% of cropland to switchgrass, producing 34 million Mg biomass and reducing leached NO 3 -N 38% statewide. The opportunity to meet joint goals was greatest within watersheds with undulating topography and lower corn/soybean productivity. Our approach bridges the scales at which NO 3 -N loss and profitability are usually considered, and is informed by both mechanistic and empirical understanding. Though approximated, our analysis supports development of farm-level tools that can identify locations where both farm profitability and water quality improvement can be achieved through the strategic integration of perennial vegetation.
Development of a productive advanced biofuels economy will require a suite of lignocellulosic feedstocks, including both agricultural residues and dedicated energy crops. This research utilizes precision conservation and multicriteria decision analysis (MCDA) techniques to model the integration of switchgrass (Panicum virgatum L.), a perennial bioenergy crop, into a corn (Zea mays L.)-producing field in Iowa, United States. The impacts of energy crop integration are quantified in terms of productivity, economics, and environmental performance. Management areas identified using a multi-objective optimization method are modeled using the Landscape Environmental Assessment Framework (LEAF) to calculate biomass availability and impacts to soil health, while the Water Quality Index for Agricultural Lands (WQIag) is used to assess the risk to surface water quality. The results show that subfield management zones optimized to reduce economic losses and maximize environmental performance are capable of improving the field's annual rate of soil organic carbon (SOC) gain by 69%, reducing annual soil erosion by 63%, and increasing sustainable biomass availability by 35%. Environmental improvements are valued at US$158 ha -1 (US$64 ac -1 ), making the integrated management system an effective financial loss mitigation strategy compared to conventional corn production when feedstock prices are greater than US$107 Mg -1 (US$97 tn -1 ). The results of this work demonstrate that the production of bioenergy crops integrated with commodity row crops can be a tenable means to improve the overall production of a field, improve the profitability of row crop farming, and preserve or improve water and soil resources.
Integrated landscape management has emerged in recent years as a methodology to integrate the environmental impacts of various agricultural practices along with yield and profitability in a variety of cropping systems. In this study, the Landscape Environmental Assessment Framework (LEAF), a decision support toolset for use in integrated landscape management and developed at Idaho National Laboratory, was used to evaluate the profitability of grain producing subfields, to determine the efficacy of sustainably harvesting residual biomass after grain harvest, and to determine the efficacy of integrating bioenergy crops into grain-producing landscapes to enhance farmer profitability. Three bioenergy crops, sorghum, switchgrass, and miscanthus, were integrated into non-profitable subfields in four U.S. counties. The manuscript describes in detail the material and methods used to define crop rotations, land management units and practices, subfield units and productivity, grain profitability, sustainability criteria, energy crop integration, and feedstock cost estimation. With the integration of bioenergy crops, the overall annual biomass production rates in the four counties could be increased by factors ranging from 0.8 to 21, depending on the energy crop and county, over the annual residue biomass production rates.By modeling the harvesting of residual biomass and energy crops using geo-referenced, precision harvesting equipment and optimal harvesting paths on individual sub-fields, the average logistics costs including harvesting of both residual biomass and energy crops were observed to fall well below US DOE's 2017 goals for biomass feedstock price of US$84/ton or US$92.6/dry Mg. Miscanthus, grown in counties in Ohio and Kansas, provided the maximum potential, among the three energy crops considered, for increment in biomass production and also posed maximum threat to the grain production. Considerable variability was observed in the harvesting and total costs because of the size, shape, and productivity of individual subfields. It was shown that variability in the harvesting costs could be used to down-select non-profitable farms with low harvesting costs and high residue and bioenergy crop yields and to reduce the negative impacts of bioenergy crop integration into croplands on grain production. The results of the assessment suggest that (1) the potential to produce biomass is considerably enhanced when non-profitable grain-producing subfields are replaced by bioenergy crops, (2) the subfield-scale integrated landscape assessment provides a defensible methodology to directly address individual farmer's profitability, sustainability, and environmental stewardship.
Objectives Implementing Climate smart agriculture (CSA) agricultural practices in cropping systems can help to mitigate and even offset negative environmental impacts that contribute to climate change, soil erosion, and nutrient loss. A modeling approach was developed to provide a scalable, geographically-explicit accounting framework for quantifying greenhouse gas (GHG) emissions reductions associated with adoption of CSA practices in cropping systems. Methods A model-based GHG accounting framework was developed to quantify spatially-explicit GHG reductions associated with the adoption of specific CSA practices. To enable analysis of large geographic regions, the framework uses a cloud-based computational infrastructure that deploys the DNDC 9.5 biogeochemistry process model to quantify carbon and nitrogen impacts of CSA practice scenarios. Results Specific practices included in the model were; conversion to reduced and no-tillage, adoption of cereal and legume cover crops, and alternative N-fertilizer application timing. In total, 648 management scenarios were simulated across all fields. Transitioning to no-tillage had the most significant effect on GHG emissions. Regional scale impacts associated with a transition from conventional- to reduced- or no-tillage indicated a GHG reduction of 262.7 and 2015.6 kg ha−1 yr−1 of CO2e (carbon dioxide equivalent), respectively. Additional GHG emissions reductions were identified for other practices such as cover cropping and improved fertilizer management. Conclusions Widespread adoption of CSA practices has the potential to greatly reduce GHG emissions associated with agriculture, improving the sustainability of food production. Potential impacts of such practices depend on localized weather and soil condition which vary both temporally and geographically. Capturing the effects of spatial and temporal variability with the above modeling framework are needed to identify and strategically target the integration of CSA practices to specific areas where the practices are most impactful and cost-effective. Funding Sources Model framework development and multi-state analysis were partially funded by 2016 NRCS Conservation Innovation Grant.
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