Project Number: 2090-11000-010-000-D
Project Type: In-House Appropriated
Start Date: Oct 18, 2022
End Date: Oct 17, 2026
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.
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.