Research Project: Improving Resiliency of Semi-Arid Agroecosystems and Watersheds to Change and Disturbance through Data-Driven Research, AI, and Integrated Models

Location: Water Management and Systems Research

2024 Annual Report

Objectives
Objective 1: Improve biophysical and ecohydrologic components of crop and ecosystem services models ranging in spatial scale from sub-field areas to watersheds by linking process-based modeling, data assimilation, and artificial intelligence (AI). Sub-objective 1.A: Develop ecophysiological model components for croplands. Sub-objective 1.B: Enhance modeling of crop phenology, yield, and ET in semi-arid conditions at daily to seasonal and plot to watershed scales. Objective 2: Inform precision agriculture (water and nutrient management in crop systems) and precision conservation within fields, across farms, and at regional watershed scales, using high-resolution process modeling and machine learning. Sub-objective 2.A: Improve the Agricultural Ecosystems Services (AgES) process model using components from Objective 1; develop and test a subdaily version of AgES. Sub-objective 2.B: Apply AgES to simulate on-farm precision conservation; train surrogate models for users; publish long-term data and results. Objective 3: Quantify current and future impacts of climate variability, land-use change, land disturbance (e.g., wildfire, insect infestation), and rehabilitation on water resources from source-water catchments in snow-dominated agricultural watersheds. Sub-objective 3.A: Develop and implement snow-process model components and ecosystem × hydrometeorology interactions. Sub-objective 3.B: Develop geospatial methods to analyze and model hydrologic function and response to change, from “fire to farm”. Sub-objective 3.C: Predict the ecohydrological impact of precision-conservation treatments in source-area catchments. Objective 4. Develop management practices incorporating the latest technology developments for a field-size aspirational four-year dryland crop rotation system with precision nutrient, agrichemical, and weed control and crop population management. 211 C1 PS1C, C3 PS3A. Objective 5. Compare yields, economic returns, and environmental impacts of the aspirational dryland rotation system to the system that is currently used by producers of the region. 211 C1 PS1C, C3 PS3A.

Approach
Agricultural productivity and ecosystem services are inextricably linked to water resources that are facing dual pressures of decreasing supply and increasing demand. In the western US, water resources are predominantly derived from the melting of seasonal high-elevation snowpack where disturbance (e.g., wildfire, insect infestation) and hydrologic and ecosystem functioning can directly impact water availability for agricultural production, i.e., “fire to farm”. Additionally, shifting precipitation patterns and increasing air temperatures are resulting in smaller and earlier peak snowpack water equivalents and advancing the timing of snowmelt and peak streamflow. Subsequent impacts of these changes to moisture availability affect natural ecosystem functioning, plant ecophysiological responses, and vegetation contributions to water cycling, in turn affecting ecosystem services and downstream water availability and quality. This project aims to improve the understanding of ecohydrological processes in semi-arid western US agricultural watersheds by considering the continuum of water resources from streamflow generation in the mountains through ecosystem controls of water cycling to impacts of farm-level water limitations on crop growth and productivity. To address these needs, we will use a variety of data-assimilation tools, process-based models, and artificial intelligence (AI) to better characterize the soil-plant-atmosphere pathway across landscape types. Model components and improvements resulting from this research will inform precision agriculture and conservation across spatiotemporal scales and improve quantification of water supply responses to climate, disturbance, and management (Fig. 1). By focusing on agricultural watersheds, this project will develop holistic tools and build broad and spatially resolved datasets that will improve crop production under limited water, operational forecasting of water supplies and ecosystem services, precision conservation of water quality, and broader earth-systems research to inform land surface models.

Progress Report
Objective 1a: Extensive instrumentation has been installed and paired with remote sensing data at the Limited Irrigation Research Farm (LIRF; Greeley, Colorado), and at multiple sites within high-elevation source-water catchments to measure plant responses to climate variability and to parameterize ecophysiological models. Data collection at LIRF has focused on measuring plant transpiration using sapflow, remotely sensed vegetation indices, and micrometeorology. Instrumentation at source-water locations has focused on quantifying the transport and storage of water within tree stems, which has the potential to represent a substantial portion of total water within a watershed. These data will be analyzed and used to develop model components in the Ages hydrologic model. Objective 1b: The Ages model with the Unified Plant Growth Model crop component has been used to simulate soil-water dynamics and crop phenology in experimental plots near Greeley, Colorado. Data include replicated irrigation treatments at full evaporative demand and limited irrigation for corn, wheat, sorghum, dry bean, and sunflower. Simulated soil moisture was calibrated to fit measured data at multiple depths. The calibrated model for soil hydrology was then used to refine crop phenology parameters at multiple developmental stages. Two manuscripts are in progress for submission to journals. Objective 2a: Development of tools to support modeling with Ages was presented at the International Congress on Environmental Modelling and Software, East Lansing, Michigan, June 24-28, 2024. These tools include the Catchment areas delineation (Cadel) software with web interface and a new Multi-Group Particle Swarm Optimization (MG-PSO) web service for parallelized model calibration. The development team, including ARS and university partners, also held a training workshop at the conference using a new MG-PSO graphical interface that was developed as a deliverable of an ARS Innovation Fund award. Objective 2b: Previous simulations of streamflow and nitrate loads have been extended in time to address temporal changes in wastewater discharges to the Big Dry Creek in Colorado. Reduced concentrations of point sources due to improved water treatment are changing the relative importance of agricultural non-point sources, which makes simulation of precision management more impactful. The model is also being improved to account for direct pumping from the stream and groundwater. New methods of model calibration will fit statistical distributions of flow and load in comparison to previous calibrations to daily timeseries data. The new results will be presented to the Big Dry Creek Watershed Association. Objective 3a: To improve understanding of spatial variability in snowpack processes, ten monitoring locations and sensor networks have been established across an elevation gradient (5000-11,200 ft) to monitor hydrometeorology and streamflow within the Cache la Poudre and Big Thompson river basins in Colorado. These data have been paired with field measurements of snowpack water equivalent to parameterize and calibrate the Ages hydrologic model. In addition, 4 km modeled climate data (Weather Research and Forecasting model) and 30 m spatially explicit modeled snowpack data (SnowModel) have been used as calibration data for Ages in the Blue River Basin in central Colorado, identifying climatic factors driving snowpack dynamics and streamflow from high-elevation watersheds. Cosmic ray sensors were installed at two sites (persistent and transitional snowpack) to detect both snow water equivalent and soil moisture where other sensors have been installed, including multiple sensors for spatial variability of soil moisture on a steep burned slope. A new laser flow velocity sensor was installed downstream of the burned transitional snow site to improve estimation of rapidly changing stream flow rates. Objective 3b: Wildfire severity plays an important role in river basin functioning, to better understand factors driving wildfire burn severity a uniquely large and comprehensive dataset has been developed of landscape, climate, and forest structure predictors associated with pixel-level burn severity for all wildfires within in the Southern Rockies ecoregion from 1990-2022. This dataset was analyzed using a multivariate machine learning model to determine which predictors have the greatest influence on burn severity, finding that forest structure (canopy height, canopy density), and climate (minimum daily air temperature, relative humidity) are key to predicting burn severity across various forest types within a large regional area. Objective 3b: To reduce risk from future wildfire, land management agencies commonly perform fuel reduction treatments within forests; to improve understanding of fuel treatment effects, researchers have compiled a vast dataset of 284 fuel reduction treatments paired with untreated controls completed over 10 years ago. These locations have been sampled for the recovery of fuel and vegetation structure before and after treatments and will be analyzed to assess climate and landscape factors driving fuel treatment effectiveness and longevity. This is a departure from original research objectives to establish hydrometeorological monitoring stations and future fuel treatment locations due to complexities in organizing instrumentation and treatment timing with land managers. Objective 4a: The data of four years from Aspirational (ASP) and Business-As-Usual (BAU) were compiled. The regarding yield in relation to different management practices and weather patterns was regarding Ages model of soil water in ASP-BAU fields. Data was presented for the scientific communities at the European Geosciences Union (EGU). At the meeting, ideas were exchanged regarding the site management, crop rotations, and nutrient management to cope with climate change. Objective 4b: Included assessment of precision management (PM) zones in ASP. Additional data collection (year 2) for the split-N PM study was completed, and year 3 data collection is ongoing (need for year 4 will be assessed after year 3 is complete). Dryland PM fertilizer requirements (based on yield projection) are driven by water availability, so seasonal weather (timing and amount of in-season soil moisture as it relates to crop phenology) is a critical factor in the experimental design. Objective 4c: Year 1 of the corn population study has been completed. Year 2 was postponed to fiscal year 2025 due to ARS and Colorado State University (CSU)-collaborator staffing limitations as well as irrigation well aquifer limitations. Objective 4e: Collected aerial Normalized Differenced Vegetation Index (NDVI) data concurrent with soil moisture data along topographical field transects in 2023, and data collection continues in 2024. Additional data are needed to develop a robust NDVI-yield relationship covering a range of in-season soil water availability conditions (as discussed in Obj. 4b). Objective 5a: Data for both Aspirational (ASP) and Business as Usual (BAU)(4-year) as well as long-term Alternative Crop Rotation (ACR, 30-year) studies have been compiled for inclusion in the economic analyses, which has been expanded to include data from all 196 plots over the entire period of record. Preliminary data analyses, in collaboration with CSU economists, were presented at two Customer Focus Group meetings and a Field Day. Objective 5b: Analyses for several soil chemical properties were included in the. More comprehensive analysis was partially accomplished and is ongoing. The analysis will resume after July 15, 2024, with the new full-time technician.

Accomplishments
1. Land management affects soil structural stability. Soil structural stability impacts water holding capacity, soil productivity, and land sustainability; however, the impacts of agricultural best management practices (such as cropping intensity, tillage, and nitrogen source) are not well understood. ARS researchers in Fort Collins, Colorado, evaluated the effectiveness of multiple soil aggregate indices to quantify soil structural development using long-term data from three locations within the central Great Plains. Tillage treatments include no-tillage, reduced tillage, conventional tillage, and moldboard plow. At 0–15 cm, the reduction in soil structural stability ranged from 8-77% with tillage compared with no-till and reduced till treatments. The increase in soil structural stability ranged from 33-72% with manure addition compared to commercial fertilizer treatments. The soil ability to withstand its structural stability due to anthropogenic or environmental sources increased by approximately 6% with manure addition due to increased soil organic matter. Land sustainability and productivity can be improved using best management processes guided by this study’s classification of soil structure and aggregate stability.

2. Tillage and nitrogen rate effects on winter wheat yield in wheat-sorghum rotation. Winter wheat is grown throughout the central Great Plains region of the United States, in which water and nitrogen are most often the limiting factors in wheat yields. Pressures on water resources and global increases in fertilizer prices require best management practices that ensure sustainable production and enhance yield with low input costs in these dryland systems. ARS researchers in Fort Collins, Colorado, quantified how tillage practices and nitrogen (N) fertilizer rates affected wheat yield and nutrient use efficiency in a winter wheat-sorghum-fallow rotation over the 2015-2018 growing seasons. Averaged across years and tillage practices, winter wheat yield increased by 18.4 pounds of grain per pound of N fertilizer. Yields were 7-9% greater for conventional tillage than no-till or reduced tillage specifically in years with low precipitation, which is partially attributed to poor plant stands and inadequate control of herbicide tolerant weed in no-till compared with conventional till plots. Nevertheless, nitrogen use efficiency increased with N rate that ranged from 40-80 lb N/ac and decreased thereafter, indicating that N addition (beyond 80 lb/ac) was unnecessary. Wheat yield increased by about 5 lb/ac for every 0.1-inch increase in precipitation during the fallow period. We concluded that wheat yield response to N rates, in this study site, was highly dependent on the growing season environmental conditions and water stored during the fallow period. The long-term benefits of no-till in increasing soil organic matter, enhancing soil structural stability, reducing soil erosion, and increasing water holding capacity could have a potential to compensate for extra fertilizer and may offset the short-term yield loss.

3. Assessing impacts of aerial mulching on post-fire water quality impacts and debris flow risks. Wood strand mulch is commonly applied via helicopters to post-wildfire burn areas to improve precipitation infiltration and reduce risk for catastrophic debris flows and flash floods. In the two years after the 2020 Cameron Peak wildfire in northern Colorado, two such events claimed the lives of seven individuals and caused millions of dollars in property damage. ARS researchers in Fort Collins, Colorado, in collaboration with scientists at the U.S. Forest Service and Colorado State University, recently submitted a comprehensive report to water districts, land managers, and non-profits detailing the findings of a multi-year study investigating the influence of aerial mulching on streamflow, sediment production, and water quality at sites throughout the Cameron Peak and East Troublesome fires, the first and second largest wildfires in Colorado history (208k and 193k acres burned, respectively). Between these two fires, over $50 million in aerial mulching operations were conducted on roughly 50,000 acres of areas identified as high risk of debris flows and flash flooding. The report found inconsistent and limited efficacy of mulching treatments on streamflow responses and sediment yield when compared to un-mulched controls, but with some positive effects on water quality. The report concluded that focusing mulching resources more densely onto smaller and more targeted areas may improve mulching effectiveness in the future. Given an important need to mitigate the impacts of post-wildfire impacts on water quality, private property, and human health, these findings will guide resource management and emergency response actions following wildfire across the western United States.

Review Publications
Aula, L., Easterly, A.C., Mikha, M.M., Creech, C. 2024. Tillage practices in long-term winter wheat-fallow affect soil fertility. Soil Science Society of America Journal. 88(2):498-509. https://doi.org/10.1002/saj2.20628.
Mankin, K.R., Patel, R.P. 2023. Wildfire burn severity affects postfire shifts in evapotranspiration in subalpine forests. Journal of Natural Resources and Agricultural Ecosystems. 1(1):1-11. https://doi.org/10.13031/jnrae.15438.
Ramírez, P.B., Calderón, F.J., Vigil, M.F., Mankin, K.R., Poss, D.J., Fonte, S.J. 2023. Dryland winter wheat production and its relationship to fine-scale soil carbon heterogeneity - A case study in the US Central High Plains. Agronomy. 13(10). Article e2600. https://doi.org/10.3390/agronomy13102600.
Majrashi, M.A., Obour, A.K., Moorberg, C.J., Lollato, R.P., Holman, J.D., Du, J., Mikha, M.M., Assefa, Y. 2023. Tillage and nitrogen rate effects on winter wheat yield in a wheat-sorghum rotation. Canadian Journal of Soil Science. 103(4):671-683. https://doi.org/10.1139/CJSS-2023-0028.
Mikha, M.M., Green, T.R., Untiedt, T.J., Hergret, G.W. 2023. Land management affects soil structural stability: Multi-index principal component analyses of treatment interactions. Soil and Tillage Research. 235. Article e105890. https://doi.org/10.1016/j.still.2023.105890.
Jones-Diamond, S., Mason, E., Asfeld, E., Meyer, R., Mattes, M., Larson, K., Mankin, K.R., Pettinger, B., Nachappa, P., Brummer, J., Talbert, C., Stromberg, J., Roberts, R., Wardle, E., Trujillo, W., Pottorff, L., West, M., Osterholzer, A., Erker, B., Brown, A. 2023. 2022 Colorado winter wheat variety performance trials. Colorado State University Technical Report. TR23-3:1-58.
Higuera, P.E., Cook, M.C., Balch, J.K., Stavros, E.N., Mahood, A.L., St. Denis, L.E. 2023. Shifting social-ecological fire regimes explain increasing structure loss from Western wildfires. Proceedings of the National Academy of Sciences-Nexus. 2(3). Article epgad005. https://doi.org/10.1093/pnasnexus/pgad005.
Mankin, K.R., Modala, N.R. 2022. Integrating streambank erosion with overland and ephemeral gully models improves stream sediment yield simulation. Journal of the ASABE. 65(4):763-778. https://doi.org/10.13031/ja.14840.
Mankin, K.R., Rumsey, C.A., Sexstone, G.A., Ivahnenko, T.I., Houston, N.A., Chavarria, S.B., Senay, G.B., Foster, L., Thomas, J.V., Flickinger, A.K., Galanter, A.E., Moeser, C.D., Welborn, T., Pedraza, D.E., Lamber, P.M. 2022. Upper Rio Grande Basin water-resource status and trends: Focus area study review and synthesis. Journal of the ASABE. 65(4):881-901. https://doi.org/10.13031/ja.14964.
Tatarko, J., Presley, D., Mankin, K.R. 2022. Wind erosion potential from stover harvest in the Central Plains: Measurements and simulations. Soil & Tillage Research. 224. Article e105486. https://doi.org/10.1016/j.still.2022.105486.
Elias, E.H., Tsegaye, T.D., Hapeman, C.J., Mankin, K.R., Kleinman, P.J., Cosh, M.H., Peck, D.E., Coffin, A.W., Archer, D.W., Alfieri, J.G., Anderson, M.C., Baffaut, C., Baker, J.M., Bingner, R.L., Bjorneberg, D.L., Bryant, R.B., Gao, F.N., Gao, S., Heilman, P., Knipper, K.R., Kustas, W.P., Leytem, A.B., Locke, M.A., McCarty, G.W., McElrone, A.J., Moglen, G.E., Moriasi, D.N., OShaughnessy, S.A., Reba, M.L., Rice, P.J., Silber-Coats, N., Wang, D., White, M.J., Dombrowski, J.E. 2023. A vision for integrated, collaborative solutions to critical water and food challenges. Journal of Soil and Water Conservation. 78(3):63A-68A. https://doi.org/10.2489/jswc.2023.1220A.
Castro-Bolinaga, C., Mittelstet, A., Mankin, K.R. 2023. Perspective: Current and future research directions in understanding streambank erosion phenomena. Journal of the ASABE. 66(5):1223-1228. https://doi.org/10.13031/ja.15613.
Mankin, K.R., Edmunds, D.A., McMaster, G.S., Fox, F.A., Wagner, L.E., Green, T.R. 2023. Winter wheat crop models improve growth simulation by including phenological response to water-deficit stress. Environmental Modeling and Assessment. 29:235-248. https://doi.org/10.1007/s10666-023-09939-5.
Gleason, S.M., Stewart, J.J., Allen, B.S., Polutchko, S.K., McMahon, J.E., Barnard, D.M., Spitzer, D.B. 2024. Development and application of an inexpensive open-source dendrometer for detecting xylem water potential and radial stem growth at high spatial and temporal resolution. AoB Plants. 16(2). Article eplae009. https://doi.org/10.1093/aobpla/plae009.