Location: Water Management and Systems Research
2023 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: Work has focused on model selection and suitability for integration/parallelization with the Agricultural & ecosystems services (Ages) hydrologic model. We have shifted focus onto post-fire recovery sites at mountain locations versus crop production systems because (1) plant physiological models have already been tested widely in cropping systems, and (2) post-fire models of vegetation recovery using demography and physiology are absent from the literature. Literature review of plant physiological and plant demographic models have been completed and ranked according to suitability. Field data collection on plant functional traits will be completed summer of FY 2023 at multiple field sites. A postdoc has been selected (starting January 2024) to lead the vegetation model development and testing.
Objective 1b: The Unified Plant Growth Model (UPGM), a crop growth submodel within Ages, explicitly simulates important developmental events accounting for both temperature and water stress effects, but the proposed parameter sets had not been rigorously calibrated. Field data (2008-2016) collected at the USDA-ARS Limited Irrigation Research Farm, Greeley, Colorado, for phenology of corn, sorghum, wheat, sunflower, and dry bean under fully irrigated and limited-irrigated treatments were used to simulated daily water stress (using Ages) and calibrate UPGM parameters. Calibration improved phenological simulation compared to expert-estimated baseline parameters. Calibration generally decreased (growing degree days; faster) for wheat and increased GDDs (slower) for other crops compared to baseline parameters. In addition, water-stressed (GS) conditions resulted in slower development than non-stressed (GN) for corn and sorghum, whereas GS was faster than GN for sunflower and wheat.
Objective 2a: ARS scientists in Fort Collins, Colorado, are working with university collaborators to deploy new cloud computing services for agricultural and environmental modeling. We developed a method that calibrates groups model parameters using a Particle Swarm Optimization (PSO) method. The multi-group PSO software reduces the real time for model calibration by 10 times or more. ARS Innovations Funds are enabling researchers to develop a graphical interface for users. The team also developed a method for model sensitivity analysis (ranking the parameters with the most influence on model results) that leverages parallel computing. The development team is working on documentation and plans to present these tools at an international conference on environmental modeling and software.
Objective 2b: The Ages model was previously calibrated and evaluated using historical data for streamflow and nitrate concentrations in the Big Dry Creek near Denver, Colorado. Big Dry Creek Watershed encompasses multiple land uses, including dryland and irrigated crops, rangelands for cattle and wildlife, and peri-urban development. We are working with university collaborators to explore possible benefits of conservation management practices (reduced tillage, fertilizer applications, and sprinkler irrigation) on the simulated nitrogen loads to Big Dry Creek. Preliminary model results are being analyzed. Results will also be shared with the Big Dry Creek Watershed Association to assist planning. Finally, this work provides a foundation for new research with university collaborators and state agricultural extension agents.
Objective 3a: Over 50 new relative humidity and air temperature (RHT) sensors have been installed at six sites throughout northern Colorado, southern Colorado, and northern New Mexico to capture spatial variability in RHT and develop 30m resolution maps of microclimate (Headquarters funded postdoc) for use in Ages modeling. In addition, we have installed new meteorological stations, two new precipitation gauges, 24 soil moisture sensors, and collected continuous snowpack data at one new site in a mid-elevation post-fire lodgepole stand. We have continued data collection of snowpack characteristics at a high-elevation site which is co-located with a network of meteorological instrumentation as part of a project with Colorado State University collaborators. We will install soil moisture sensors and a cosmic ray sensor (soil moisture and snow water equivalent) at an additional high-elevation subalpine site this summer as part of a collaboration with the U.S. Forest Service.
Objective 3b: Effects of fire were assessed using remote-sensing data (1985-2019) of actual evapotranspiration (ETa) using 30-m resolution Landsat-based Simplified Surface Energy Balance model (SSEBop) in the upper Rio Grande basin of southern Colorado and northern New Mexico. Results showed step reductions in ETa after fire across all burn severities in all four fire areas studied. Prior to fire, ETa was generally higher in high-severity burn areas, indicative of higher fuel loads, although high variability indicated the influence of additional factors. All areas had greater response (decreased ETa) to fire in high-severity areas and lesser response as burn severity decreased. The decrease in ETa after high-severity fire ranged from 42% to 63%, compared to after low-severity fire, which ranged from 24% to 44%. None of the four burn areas demonstrated postfire ETa recovery after 17 to 23 years.
Objective 3c: We attended meetings with stakeholders and resource managers (Larimer Conservation District, Boulder Valley, and Longmont Conservation District, National Resource Conservation Service, and the Northern Colorado Fireshed Cooperative) to discuss and better understand management needs for decision making in fuels and forest conservation treatments. We have identified three candidate locations for this research project and one location for a pilot study to be completed summer of 2023. We will select a final site from the three candidate sites once the land owners and conservation district agree when, and to what extent, treatments will be completed such that they represent the most ideal conditions and treatment responses to address our research questions. These treatment decisions are expected from the land owners and conservation district in Fall of 2023.
Objective 4a: Soil samples were collected from Aspirational (ASP) and Business-As-Usual (BAU) treatments and are being analyzed for nutrient content. These are critical samples representing the ending of the previous project plan (2019-2022) and beginning of the new project plan (2023-2027). Statistical analysis is underway on yield data collected throughout 2019-2022. Multiple presentations were made for scientific communities (American Society of Agronomy (ASA) and Soil and Water Conservation Society (SWCS)), farmers/producers, Universities, and Natural Resource Conservation Service (NRCS) through Field Days and customer focus groups.
Objective 4b: Preliminary studies in 2022 and 2023 have been conducted to explore experimental design, unmanned aerial vehicle (UAV) data collection, data analysis, and data interpretation of a split application, variable nitrogen rate, field-scale study on corn. Primary data collection for this objective is planned for 2024.
Objective 4c: A factorial study of plant population vs. precipitation (via irrigation) was conducted in 2021 (field was idle in 2022 and 2023 to reset soil-water uniformity). Planting densities were 20, 30, 40, and 50 thousand plants/ha and irrigation amounts (in addition to 68 mm June-September growing-season rainfall) were 0, 76, 152, and 229 mm, corresponding to 1, 15, 56, and 87 percentile growing seasons for Akron, Colorado. Both planting density and water availability affected grain yield. Yield plateaued with population (40-50k/ha) at higher water levels (above 50 percentile) but did not exhibit a yield threshold for more water-limited conditions. Thus, the maximum-yield threshold may be at lower populations under drought conditions. Yield per shoot biomass was relatively unchanged, suggesting crowding did not add additional stress beyond water limitations on growth. The study will be repeated in 2024.
Objective 4d: All Fourier transform infrared (FTIR) spectroscopy data were compiled and archived on the local server.
Objective 4e: Unmanned aerial vehicle (UAV) red, green, blue (RGB) and Normalized Difference Vegetation Index (NDVI) images were collected in 2022 instead of multispectral and thermal due to delayed delivery of the newly purchased platform. Image processing hasn’t been completed with the departure of the UAV pilot. UAV data collection, data analysis and interpretation will be continued in 2023 and 2024.
Objective 5a: Data for yield and production costs were compiled to assess net return after variable costs for individual crops (wheat, corn, millet) under different rotations (wheat-fallow, business as usual, wheat-corn-millet-fallow, aspirational) and N application zones (High-Medium-Low in ASP) from field-scale data for 2019-2022. Plot-scale data from the long-term (30-year) alternative crop rotation study at Akron, Colorado are being compiled to augment yield data over a longer period of climate conditions.
Objective 5b: Soil samples were collected from Aspirational (ASP) and Business-As-Usual (BAU). The samples are being processed for soil health assessment. Multiple presentations were made to scientific communities (national meeting), farmers/producers, universities, and NRCS through field days and customer focus group.
Accomplishments
1. Soil organic matter distribution under different management practices. Soil organic matter (OM) is a complex mixture of multiple fractions of soil organic carbon (SOC) that is considered one of the important soil health parameters and can be influenced by management decisions. ARS scientists in Akron, Colorado, evaluated the soil OM distribution within different fractions in moldboard plow (MP) and no-tillage (NT) systems with three rates (0, 100, and 200 kg nitrogen (N) ha–1) of limestone ammonium nitrate (28–0–0) in dryland in Lesotho, Africa. The Lesotho scientist was directly engaged in this research, providing samples from that study site for analysis. The N-rate of 200 kg N ha–1 increased particulate OM by 28.8% for MP and 22.6% for NT than the100 kg N ha–1. The high percentage of SOC was observed within fine particulate OM fraction (48%) in MP and within mineral-associated OM fraction (49%) in NT, making the SOC susceptible to wind erosion. The C/N ratio of 7.2 was observed with mineral-associated OM at both tillage practices which made it vulnerable to microbial decomposition. These findings suggest the need for SOC conservation efforts, globally, to reduce SOC losses through decomposition/wind erosion and improve/sustain soil health that may ultimately contribute to enhancing land productivity. This research benefits producers that are farming on land susceptible to soil OM loss through erosion and decomposition.
2. Wheat yield stability under different tillage practices. Grain yield stability is vital for achieving yield consistency across a broad range of environments. The significance of this is well documented in crop genetic studies and may equally be relevant for tillage practices used in croplands. An ARS scientist in Fort Collins, Colorado, in collaboration with a scientist from the University of Nebraska-Lincoln, used the yield stability approach using a 38 years (1972-2010) study site located in Sidney, Nebraska, Wheat yield stability throughout the 38 years under different tillage practices (no tillage (NT), stubble mulch (SM), and moldboard plow (MP)) were evaluated. The results suggest that SM had a more stable yield under different environments when compared with NT and MP. The study concluded that using minimum tillage, such as SM, that maintains residues on the soil surface could contribute to yield resiliency across different environments and enhance land sustainability in dryland cropping systems. This research benefits farmers and producers trying to maintain land sustainability and economic return while mitigating the climate change challenges.
3. Streamflow threshold responses to rainfall change with urbanization near Denver, Colorado. Urbanization increases the fraction of impervious areas and their connectivity, which tends to increase surface runoff after rainfall events. A field study across a rural to urban gradient in the semi-arid area of Denver, Colorado, showed changes in the threshold of rainfall that produces runoff events. The data comprised 8 years of instantaneous streamflow data in 21 watersheds ranging in size from 0.8 to 90 km2 and with impervious areas ranging from 1% to 47%. Watersheds with greater than 10% impervious area needed only 1–2 mm/hour of rainfall to produce a streamflow response, whereas watersheds with <10% impervious area needed to exceed 4–36 mm/hour to see runoff. On average, streamflow responses had shorter duration and higher peak flows in watersheds with more impervious surface cover. These alterations in streamflow response to rainfall point to the need for local adaptation of stormwater management to mitigate the effects of streamflow changes with urbanization.
Review Publications
Mahood, A.L., Koontz, M.J., Balch, J.K. 2022. Fuel connectivity, burn severity, and seedbank survivorship drive ecosystem transformation in a semiarid shrubland. Ecology. 4(3). Article e3968. https://doi.org/10.1002/ecy.3968.
Fusco, E.J., Mahood, A.L., Beaury, E.M., Bradley, B.A., Cox, M., Jarnevich, C.S., Nagy, R.C., Nietupski, T., Halofsky, J.E. 2023. The invasive plant data landscape: A synthesis of spatial data and applications for research and management in the United States. Landscape Ecology. https://doi.org/10.1007/s10980-023-01623-z.
Mikha, M.M., Marake, M.V. 2022. Soil organic matter fractions and carbon distribution under different management in Lesotho, Southern Africa. Soil Science Society of America Journal. 81(1):140-155. https://doi.org/10.1002/saj2.20471.
Aula, L., Mikha, M.M., Easterly, A.C., Creech, C.F. 2022. Winter wheat grain yield stability under different tillage practices. Agronomy Journal. 115(2):1006-1014. https://doi.org/10.1002/agj2.21236.
Wilson, S., Bhaskar, A.S., Choat, B., Kampf, S.K., Green, T.R., Hopkins, K.G. 2022. Urbanization of grasslands in the Denver area affects streamflow responses to rainfall events. Hydrological Processes. 36(10). Article e14720. https://doi.org/10.1002/hyp.14720.
Hopmans, J., Green, T.R., Young, M. 2022. Western U.S. multistate research project on “water movement in soils”: A retrospective. Vadose Zone Journal. 22(1). Article e20245. https://doi.org/10.1002/vzj2.20245.
Barnard, D.M., Green, T.R., Mankin, K.R., DeJonge, K.C., Rhoades, C.C., Kampf, S., Giovando, J., Wilkins, M., Mahood, A.L., Sears, M., Comas, L.H., Gleason, S.M., Zhang, H., Fassnacht, S.R., Harmel, R.D., Altenhofen, J. 2023. Wildfire and climate change amplify knowledge gaps linking mountain source-water systems and agricultural water supply in the western United States. Agricultural Water Management. 286. Article e108377. https://doi.org/10.1016/j.agwat.2023.108377.
