Location: Northwest Sustainable Agroecosystems Research
2024 Annual Report
Objectives
The purpose of the project is to provide information relevant to growers, agribusiness, and the USDA Climate Hub and Long-Term Agroecosystem Research (LTAR) networks, that includes the development and evaluation of: (1) cropping system diversification and intensification options; (2) practices that enhance soil health and nutrient use efficiencies, and mitigate greenhouse gas emissions; and (3) remote and proximal sensing technologies that diagnose economic and environmental.
This research will be conducted via the following objectives and sub-objectives:
Objective 1: Assess management impacts on soil degradation and link measures of soil health to agroecosystem performance in order to provide science-based decision support.
Sub-objective 1A: Evaluate linkages between agroecosystem efficiencies and greenhouse gas production.
Sub-objective 1B: Evaluate the net climate footprint of Palouse grain-fallow and annual cropping systems through life cycle assessment by further developing the CropSyst-LCA greenhouse gas accounting tool.
Sub-objective 1C: Identify field-scale drivers of soil acidification in a LTAR aspirational cropping system.
Sub-objective 1D: Develop and evaluate management practices to mitigate soil acidification in the Palouse region.
Objective 2: Link remote and proximal sensing technologies and precision agroecology concepts to quantify and diagnose ecosystem service outcomes and to inform decisions regarding agricultural practices and systems.
Sub-objective 2A: Use spatiotemporal (ST) modeling with remote and proximal sensing data to assess agroecosystem performance.
Sub-objective 2B: Assess abiotic crop stressors using above-ground visual and thermal imagery.
Objective 3: Develop cropping systems that advance intensification and diversification and further enable mitigation and adaptation to emerging weather extremes and climate change.
Sub-objective 3A: Leverage partnerships with growers and researchers throughout CAF-LTAR to study co-innovation strategies and on-farm research methodology.
Sub-objective 3B: Compare the yield and rhizosphere microbiomes of grain legumes and canola grown in intercropped stands and determine any impact on the following wheat crop.
Sub-objective 3C: Conduct a long-term trial of the perennial grain crop Kernza (Thinopyrum intermedium) in the annual, transitional, and fallow agroecological classes of the iPNW focusing on changes in seed yield performance and impact on soil water content.
Approach
Hypothesis 1A: Farming practices for site-specific locations can be designed to lower gaseous nitrogen (N) emissions while meeting N performance goals. The LTAR site (37 ha) at the CAF will be used where four N performance classes have been developed and related to nitrous oxide (N2O) and carbon dioxide (CO2) emissions.
Hypothesis 1B: Annual cropping systems have higher overall greenhouse gas emissions than grain-fallow systems. The Organic Farming Footprint model will be expanded to include more farming operations and emissions factors.
Goal 1C: Legacy soil pH data from 184 locations at the CAF will be used to assess the Very Simple Dynamic model (VSD+). Model outputs will be compared to assess N transformations, base-cation leaching, and nutrient cycling as drivers of acidification. VSD+ may be coupled with HYDRUS-1D or CropSyst-MicroBasin.
Goal 1D: Treatments with and without lime will be tested at four landscape positions at the CAF and assessed with mixed-effects ANOVA.
Goal 2A-i: The models will use Bayesian spatiotemporal modeling framework using existing modeling tools to provide estimates of predictive distributions of key variables.
Goal 2A-ii: The models developed in 2A-ii will be used to evaluate new prescription maps based on predicted N performance within a desired risk tolerance.
Goal 2A-iii: CAF datasets for soil pH, and BH method described in 2A-i, a multivariate linear model for pH will be fit on predictor variables using topographic, crop type, and remote sensing indices to find optimal soil sampling schemes.
Goal 2B-i: This work will focus on CAF Eddy Covariance (EC) tower fetches. The system will consist of low-cost thermal/RGB cameras taking hourly imagery. Predictions will be compared to EC measured ET at CAF and lysimeter data from Bushland, Texas.
Goal 2B-ii: Monthly sampling will include an LAI reading from each replicate from Sub-objective 1D. These will be coordinated with RGB imaging over the same area and resulting images will be orthomosaiced using OpenDroneMap to train a machine learning algorithm (ANN or RF) to predict LAI.
Goal 2B-iii: Measurements will be linked to the LAI sequence over the season, derived from imagery in 2B-ii of the liming test plots from 1D. A multivariate model will be developed for soil pH, fit and tested on data from Subobjective 1D.
Goal 3A-I, 3A-ii: The farmer network will be engaged using an established co-innovation storyboard agenda to quantify stakeholder reactions to ongoing research and document a consensus to direct future experimental trials. Spatio-temporal maps will be used to design and determine optimal areas to deploy large field plot studies.
Hypothesis 3B, 3Bii: Two experimental locations will be set at the PCFS. Soil water, bulk density, C, N, rhizosphere and rhizoplane sampling will assess intercrop performance and the efficiency of the through calculation of LER.
Hypothesis 3Ci, 3Cii, 3Ciii: Three experimental locations will be established at PCFS, WSU Wilke Farm, and the Horse Heaven Hills to assess Kernza establishment, yield and related resource use. A modified staggered-start design will be used to investigate the potential to grow Kernza.
Progress Report
This report documents FY 2024 progress for project 2090-11000-010-000D, “Advancing Soil Health and Agricultural Performance to Promote Sustainable Intensification and Resilience of Northwest Dryland Cropping Systems”, which began in October 2022.
In support of Objective 1, a peer-reviewed paper is under review that assesses field-scale soil profile carbon sequestration over two decades at the Cook Agronomy Farm Long-Term Agroecosystem Research Site. In addition, field studies initiated in year one continue at the Cook Agronomy Farm and other locations to assess mitigation of soil acidification and to assess greenhouse gas emissions from various agricultural management practices. Long-term data at the Cook Agronomy Farm were used to assess filed-scale outcomes of the nitrogen balance index, a field measure of how efficient nitrogen fertilizers are used. This assessment will serve to identify field locations where greenhouse gas emissions will be monitored in the future. Also, in support of Objective 1, nitrogen stabilizer research to assess green-house gas and efficiency outcomes was initiated, and long-term data on soil acidification at the Cook Agronomy Farm were modeled using soil, crop, management, and terrain variables and applied to a cooperative farmer’s field to target field sampling to assess soil acidity.
For Objective 2, spatio-temporal modeling was used to assess and publish long-term yield patterns as well as N use efficiency metrics at the Cook Agronomy Farm. In addition, remote and proximal sensing methods and equipment were field tested in relation to crop senescence, weed pressure, and other field outcomes.
Under Objective 3, over ten dryland agricultural farmers were identified and supported to establish and assess trials of alternative crops including sorghum, millet, and Kernza, a variety of perennial wheat. In addition, intercropping field trials were completed involving canola with peas and canola with garbanzo beans and monitored for yield, effects on the soil microbiome, water use and other performance outcomes.
Accomplishments
1. Developed Fluid Lime Injector for Conservation Tillage Systems. Deep-banding ammonia and urea-based nitrogen fertilizers under continuous no-tillage can result in stratified acidification at the fertilizer-injection depth that negatively impacts crop yields. Currently, producers address this adverse soil acidification through primary tillage operations that mix the soil and dilute the acidification but increase the hazard of soil erosion to intolerable levels. Typically, lime is required to ameliorate soil acidity; however, surface-applied lime does not immediately address subsurface acidity, and often tillage is used to incorporate lime and correct acidified layers created through deep-banded fertilizer placement under no-tillage. ARS researchers in Pullman, Washington, designed, developed, and tested a subsurface applicator for fluid lime that can target specific soil depths and rates while meeting soil conservation goals. This technology will be of considerable interest to farmers, federal agencies, and other agricultural professionals interested in soil conservation and liming.
Review Publications
Opdahl, L.J., Hansen, J.C., Strawn, D.G., Sanguinet, K.A. 2024. The fate and biostimulant potential of metal lactates in silt loam soil. Soil Science Society of America Journal. 88(4):1200-1215. https://doi.org/10.1002/saj2.20693.
Casanova, J.J., Heineck, G.C., LeTourneau, M.K., Hansen, J.C., Carlson, J.L., Huggins, D.R. 2024. Soil and crop effects of a subsurface fluid lime applicator. Applied Engineering in Agriculture. 40(3):351-362. https://doi.org/10.13031/aea.15939.
Davis, A., Huggins, D.R., Reganold, J. 2023. Linking soil health and ecological resilience to achieve sustainable agricultural intensification under climate change. Frontiers in Ecology and the Environment. 21(3):131-139. https://doi.org/10.1002/fee.2594.
Naasko, K., Pan, W., Reganold, J., Huggins, D.R., Madsen, I., Sullivan, T., Wills, S.A., Tao, H. 2023. Soil profile health in the Palouse soil series: Carbon, nitrogen, nutrients, and aggregates. Agrosystems, Geosciences & Environment. 6(4). Article e20421. https://doi.org/10.1002/agg2.20421.
Lockhart, S., Keller, K., Evans, D., Carpenter-Boggs, L., Huggins, D.R. 2023. Soil CO2 in organic and no-till agroecosystems. Agriculture, Ecosystems and Environment. 349. Article 108442. https://doi.org/10.1016/j.agee.2023.108442.
Casanova, J.J., Heineck, G.C., Huggins, D.R. 2024. A comparison of yield prediction approaches using long-term multi-crop site-specific data. Journal of the ASABE. 67(3):601-615. https://doi.org/10.13031/ja.15216.
