Research Project: Innovative Cropping System Solutions for Sustainable Production on Spatially Variable Landscapes

Location: Cropping Systems and Water Quality Research

2024 Annual Report

Objectives
Objective 1: Develop state-of-the-art, climate-adaptive cropping systems for improved ecosystem services and productivity in the Midwest. Sub-objective 1A: Evaluate reduced tillage, expanded crop rotations, cover crops, and perennialization for improved soil health and ecosystem services. Sub-objective 1B: Develop and evaluate cropping systems that include cover crops for improved nitrogen management and sustainability. Sub-objective 1C. Enhance growing season utilization through crop diversity and intensity for improved climate resilience. Objective 2: Develop precision agriculture techniques and advanced analytic tools for resilient and sustainable cropping systems. Sub-objective 2A: Develop and improve fertility management tools for enhanced cropping system sustainability and resilience. Sub-objective 2B: Develop and improve proximal soil sensing tools for rapid and inexpensive soil property estimation. Sub-objective 2C: Expand the SHAPE scoring curve framework for improved soil health interpretation.

Approach
In this project, our interdisciplinary team will address challenges faced by Midwestern U.S. farmers by developing and evaluating novel cropping systems and advanced analytic tools for enhanced decision support. In the first objective, state-of-the-art, climate-adaptive cropping systems will be developed and evaluated for sustainability, including yield, profitability, and multiple ecosystem services across a range of crops, soils, and climate conditions. Specifically, we will develop and evaluate aspirational grain cropping systems that incorporate multiple conservation practices including variable-rate nitrogen application, reduced tillage, expanded rotations, cover crops, and semi-perennial hay crops (1A). We will incorporate cover crops for improved nitrogen use efficiency in corn production systems (1B), and we will develop cropping systems that are resilient to changing climate conditions and take advantage of altered growing seasons for enhanced productivity (1C). In addition to our multiple, long-term experimental sites, management systems will be evaluated through collaborative, on-farm research with active participation by crop producers and crop advisors. In the second objective, tools to support management decisions to increase crop production, profitability, and soil health will be developed. These tools will improve precision agriculture techniques and advanced analytic tools for resilient and sustainable cropping systems. Specifically, we will improve Missouri grain crop nitrogen management and soil fertility recommendations by including cover crop and landscape information and we will improve sensor decision support for in-season variable-rate grain crop nitrogen management (2A). Further, sensor information will be used to improve estimates of soil properties and planting-time decision support (2B). Finally, a national scale soil health assessment tool will be improved to provide producers with a regionally relevant soil health interpretation (2C). The outcomes of this project will include innovative cropping systems designed for resilience to climate and landscape variability, and crop and soil sensor technology to determine best planting and fertilization practices for increased nitrogen use efficiency and profitability. Together, these results and products will provide producers and other stakeholders with the knowledge and tools to effectively implement and manage more sustainable cropping systems.

Progress Report
This is the first report for this new project which began in October 2023 and continues research from the previous project, 5070-12610-005-000D, “Sustainable Intensification of Cropping Systems on Spatially Variable Landscapes and Soils”. In support of Objective 1, “Develop state-of-the-art, climate-adaptive cropping systems for improved ecosystem services and productivity in the Midwest,” progress has been made on all sub-objectives. For Sub-objective 1A, “Evaluate reduced tillage, expanded crop rotations, cover crops, and perennialization for improved ecosystem services”, a preliminary analysis of the Bradford Rotation and Cover Crop (BRCC) experiment found that conservation management practices increased soil health after only five years. The BRCC yield analysis is ongoing to quantify the relationship between improved soil health and productivity outcomes for corn, soybean, and alfalfa. These results will be presented at a conference in November 2024. Soil samples were collected from the long-term Soil Productivity Assessment for Renewable energy and Conservation (SPARC) plots in 2023 to compare a traditional corn-soybean rotation with perennial switchgrass and miscanthus, and plant tissue analyses are underway. Soil health laboratory analyses, including organic carbon, total protein, active carbon, potentially mineralizable nitrogen, soil respiration, and aggregate stability, have been completed and the statistical analysis was initiated. A manuscript highlighting the legacy effects of annual production systems on soil health following long-term perennial restoration efforts was published, and an article on the relationship between plant diversity and productivity in grasslands was published. A separate analysis linking long-term water use efficiency (WUE) and yield with management practices on the SPARC plots is underway using historical data from 2010 through 2022. Plant tissue analysis for nitrogen from these plots was completed for a companion publication evaluating long-term nitrogen use efficiency (NUE). A manuscript demonstrating the relationship between plant sap flow measurements, topsoil depth, soil water content, and yield was published. To assess the impact of management practices on biological stressors (e.g., plant pathogen pressure) and aboveground plant biomass and crop productivity, field data collection is ongoing. To better understand water dynamics and soil health outcomes in claypan soils under variable management practices, new depth to claypan (topsoil depth) measurements were collected by hand at the sub-plot level for the SPARC site in March 2024. For Sub-objective 1B (Develop and evaluate cropping systems that include cover crops for improved nitrogen management and sustainability), an analysis of NUE using Goodwater Creek Experimental Watershed (GCEW) plot data from 2011 through 2024 proceeded as planned, with grain nitrogen (N) analysis completed and the final measurements for 2024 on target. To understand N fertilizer utilization in corn fields via on-farm experiments, soil, cover crop biomass, and nitrogen management data from 11 field studies (five concluded in 2023 and six currently underway for 2024) was aggregated ahead of schedule. In Sub-objective 1C, “Enhance growing season utilization through crop diversity and intensity for improved climate resilience”, the experiment representing eight treatments with all rotation phases was established at the Fisher Delta Research Extension and Education Center at Portageville Missouri, and the 2024 agronomic measurements, including plant biomass and plant tissue and grain nutrient content, were collected as planned. In support of Objective 2, “Develop precision agriculture techniques and advanced analytic tools for resilient and sustainable cropping systems,” progress has been made on all sub-objectives. For Sub-objective 2A, “Develop and improve fertility management tools for enhanced cropping system sustainability and resilience”), four locations in central Missouri were identified through the Missouri Fertilizer Control Board Initiative, and soil and agronomic data collection proceeded as planned. Treatments included a cereal rye cover crop (with and without), nitrogen fertilizer rates, and landscape position. The nitrogen fertilizer rates included a no-N control and five N rates. To optimize fertilizer N rates at the sub-field scale to improve NUE and profitability, a preliminary analysis examining the interaction of spatial N management with crop N response using 2022 data was completed and results are pending. New data to support this sub-objective was collected as planned at the plot and field scale at the GCEW experimental site using multiple sensor tools. Two articles assessing nitrogen and phosphorus fertilizer recommendation tools for corn were published. Sub-objective 2B, “Develop and improve proximal soil sensing tools for rapid and inexpensive soil property estimation”, made substantial progress. Deep core sensor data and soil samples were collected from the SPARC and Lone Tree sites. Laboratory analysis of the deep cores is ongoing, and analysis of the associated shallow soil health samples was completed, including biological, chemical, and physical soil health indicators. The effort to develop proximal soil sensing methods for rapid estimation of soil carbon stocks also made substantial progress. Samples were collected from two plot-level experiments (SPARC and Lone Tree) and three fields, and a data pipeline was established to process and analyze the proximal soil sensor data from the sensor probe, including visible and near-infrared spectra, apparent electrical conductivity, and soil strength. Field and laboratory data collection proceeded as planned to develop a robust soil carbon and sensor dataset. A manuscript illustrating the utility of eddy flux tower measurements for estimation of changes in soil carbon stocks was published. In addition, field work to estimate seed-zone soil properties on sites in Missouri and Illinois is ongoing, and a presentation of initial results is planned for October 2024. An article quantifying the use of remote sensing data to estimate planting uniformity for determining optimal seeding rates was published. Progress on Sub-objective 2C, “Expand the Soil Health Assessment Protocol and Evaluation (SHAPE) scoring curve framework for improved soil health interpretation”, includes completion of a comparison of three aggregate stability protocols and 11 variations of four enzyme activity assays. The data is currently undergoing statistical analysis to determine final recommended protocols. An article describing the new SHAPE scoring curves and benchmarking approach for five additional soil health indicators and an article on the sensitivity of soil respiration to short-term cover crop changes were published.

Accomplishments
1. Developed regionally relevant soil health scoring curves and benchmarks to assess the benefits of conservation management. The soil health concept has evolved over the past several decades, recognizing that the response of dynamic soil properties to management is dependent on site-specific factors. The Soil Health Assessment Protocol and Evaluation (SHAPE) tool was developed by an ARS scientist at Columbia, Missouri, in collaboration with scientists from the Natural Resources Conservation Service (NRCS) Soil and Plant Science Division, NRCS Soil Health Division, Cornell University, the University of Missouri, the University of California–Santa Cruz, and the Oak Ridge Institute for Science and Education. SHAPE provides benchmarks and scoring curves for six common soil health indicators offered to producers by service laboratories: soil organic carbon, active carbon, soil respiration, total protein, and two aggregate stability methods. The publicly available SHAPE tool accounts for site-specific soil and climate conditions to provide a soil health score, site-specific benchmark values that can be used to set goals for soil health improvement, and calculate a “soil health opportunity gap” for landowners. This regionally relevant soil health tool is useful for assessing and monitoring changes in soil health based on land use and for setting realistic conservation goals for producers.

2. Improved sensor estimates of key soil properties using machine learning techniques. Various modeling approaches have been used to relate visible and near infrared soil spectral reflectance to soil properties, including models based on soil spectral libraries. These spectral libraries generally consist of data from samples collected over national, continental, or global scales. Because of scale differences, it is often difficult to obtain good local (farm or field) estimates of soil properties. An international research team, including an ARS researcher at Columbia, Missouri, applied machine learning methods called "transfer learning" (TL) to a global soil spectral library and developed local soil property estimates for 12 individual farms or fields from 10 countries in the seven continents. The TL methods successfully extracted information that was most directly applicable for estimating soil properties in the test fields and provided improved results compared to other methods in 10 of the 12 cases. With this new approach, calibrations developed from existing soil spectral libraries will provide more accurate estimates of soil properties at a within-field scale. These estimates of how soil varies with a field can then be used by farmers and their advisors for making crop management decisions at a lower cost than traditional methods that require soil sample collection and laboratory analysis.

3. Validated tools for optimized corn fertilizer recommendations. To achieve economic and environmental sustainability goals, increased attention has focused on the potential reduction of fertilizer inputs, especially for corn, and existing methods for determining phosphorus and nitrogen recommendations have been questioned. ARS scientists at Columbia, Missouri, and university collaborators evaluated current phosphorus recommendations for South Dakota using field and fertility data from 117 sites with variable management, landform, and soil type over four years. The current critical phosphorus value correctly predicted corn phosphorus response 70% of the time, and when using random forest models, results were not substantially improved by the addition of other soil measurements. Therefore, Olsen soil test phosphorus, with a critical value of 16 mg/kg, remains the most important measurement for phosphorus recommendations in South Dakota. Further, critical nitrogen dilution curves (CNDC) developed in France and China for in-season nitrogen fertilization requirements were tested across eight Midwestern states over three years. Results showed that the French CNDC provided 91% nitrogen status classification accuracy versus 62% accuracy using the Chinese CNDC. However, threshold values varied significantly across the region, indicating the CNDC tool is better suited for assessing corn nitrogen status at the regional scale than for farm-specific nitrogen management. This information helps producers understand available fertility recommendation tools for more sustainable corn production in the U.S. Midwest.

4. Quantified reference conditions and the legacy effects of agricultural practices on biodiversity, productivity, and soil health. Agricultural production has intensified to meet rising demands on productivity, yet the long-term effects of reduced biodiversity on productivity and soil health are unclear. ARS scientists at Columbia, Missouri, in collaboration with ARS scientists at five other locations and university researchers, assessed the relationship between plant diversity and grassland productivity across nine ecoclimatic domains of the U.S. using gross primary productivity data from 1986–2018 and plant diversity data measured at different spatial scales. Across all sites and spatial scales, positive relationships were observed among species richness, productivity, and the temporal stability of mean annual biomass production. Further assessment of soil health across 35 reconstructed, restored, and remnant prairie systems in Missouri illustrated the long-term legacy of intensive agricultural production and reduced plant biodiversity on soil microbial biomass and community structure, even after several decades. This work highlights the need for improved biodiversity indices to monitor the effects of management intensification and restoration efforts in prairies and grasslands.

Review Publications
Lord, S., Veum, K.S., Sullivan, L., Anderson, S., Acosta Martinez, V., Clark, K. 2024. Ancient prairies as a reference for soil organic carbon content and microbial community structure. Applied Soil Ecology. 198. Article 105355. https://doi.org/10.1016/j.apsoil.2024.105355.
Nunes, M.R., Veum, K.S., Parker, P.A., Holan, S.H., Amsili, J.P., van Es, H.M., Willis, S.A., Seybold, C.A., Karlen, D.L. 2024. SHAPEv1.0 scoring curves and peer group benchmarks for dynamic soil health indicators. Soil Science Society of America Journal. 88(3):858–875. https://doi.org/10.1002/saj2.20668.
Crookston, B.S., Yost, M.A., Bowman, M., Veum, K.S., Stevens, J.R. 2023. Microbial respiration gives early indication of soil health improvement following cover crops. Journal of Soil and Water Conservation. 78(3):272-281. https://doi.org/10.2489/jswc.2023.00015
Sonnier, G., Augustine, D.J., Paudel, S., Porensky, L.M., Silveira, M., Toledo, D.N., Azad, S., Boughton, R., Browning, D.M., Clark, P., Fay, P.A., Kaplan, N.E., Thibault, K., Swain, H.M., Veum, K.S., Boughton, E. 2024. Impact of plant diversity and management intensity on magnitude and stability of productivity in North American grazing lands. Applied Vegetation Science. 27(2). Article e12776. https://doi.org/10.1111/avsc.12776.
Johnson, F.E., Lerch, R.N., Motavalli, P.P., Veum, K.S., Scharf, P.C. 2024. Comparative analysis of three next-generation sequencing techniques to measure nosZ gene abundance in Missouri claypan soils. Environmental Research. 249. Article 118346. https://doi.org/10.1016/j.envres.2024.118346.
Schreiner-McGraw, A.P., Ransom, C.J., Veum, K.S., Wood, J.D., Sudduth, K.A., Abendroth, L.J. 2023. Quantifying the impact of climate smart agricultural practices on soil carbon storage relative to conventional management. Agricultural and Forest Meteorology. 344. Article 109812. https://doi.org/10.1016/j.agrformet.2023.109812.
Schreiner-McGraw, A.P., Baffaut, C. 2023. Quantifying links between topsoil depth, plant water use, and yield in a rainfed maize field in the U. S. Midwest. Agricultural Water Management. 290. Article 108569. https://doi.org/10.1016/j.agwat.2023.108569
Groebner, B., Clark, J.D., Svedin, J., Ransom, C.J., Clay, D.E. 2024. Soil biological and physical measurements did not improve the predictability of corn response to phosphorus fertilization. Agronomy Journal. 116:2048-2059. https://doi.org/10.1002/agj2.21612.
Tian, F., Ransom, C.J., Zhou, J., Wilson, B., Sudduth, K.A. 2024. Assessing the impact of soil and field conditions on cotton crop emergence using UAV-based imagery. Computers and Electronics in Agriculture. 218. Article 108738. https://doi.org/10.1016/j.compag.2024.108738
Shao, H., Miao, Y., Fernandez, F.G., Kitchen, N., Ransom, C.J., Camberato, J.J., Carter, P.R., Ferguson, R.B., Franzen, D.W., Laboski, C.A., Nafziger, E.D., Sawyer, J.E., Shanahan, J.F. 2023. Evaluating critical nitrogen dilution curves for assessing maize nitrogen status across the US Midwest. Agronomy. 13(7):1948. https://doi.org/10.3390/agronomy13071948.
Viscarra Rossel, R.A., Shen, Z., Ramirez-Lopez, L., Behrens, T., Shi, Z., Wetterlind, J., Sudduth, K.A., Stenberg, B., Guerrero, C., Gholizadeh, A., Ben-Dor, E., St. Luce, M., Orellano, C. 2024. An imperative for soil spectroscopic modelling is to think global but fit local with transfer learning. Earth-Science Reviews. 254. Article 104797. https://doi.org/10.1016/j.earscirev.2024.104797
Taysom, T.W., LeMonte, J.J., Ransom, C.J., Stark, J.C., Hopkins, A.P., Hopkins, B.G. 2023. Polymer coated urea in 'Russet Burbank' potato: yield and tuber quality. American Journal of Potato Research. 100:451-463. https://doi.org/10.1007/s12230-023-09931-5.