Objective 1: Use a GxExM research approach to develop decision support tools for on-farm implementation of sustainable and resilient cropping systems. 1a: Develop knowledge to aid planting-time decision support for optimizing corn emergence on variable soils and landscapes. 1b: Improve decision support for variable-rate grain crop nitrogen management. 1c: Develop and evaluate new and improved soil health assessments. 1d: Develop and evaluate proximal sensing approaches to provide spatially-dense information important in soil management and soil health applications. Objective 2: Develop and evaluate sustainable and resilient cropping systems using a site-specific GxExM framework. 2a: Evaluate production and soil health of grain and perennial grass cropping systems on degraded claypan soil landscapes. 2b: Evaluate effects of cover crops and reduced tillage on soil health and crop productivity. 2c: Evaluate spatial aspects of sustainability in site-specific management systems.
In this project, our interdisciplinary team will address key knowledge and technology gaps limiting the development of site-specific management systems using a genetics by environment by management (GxExM) research approach. In the first objective we focus on developing new decision support tools and the underlying knowledge needed to facilitate improved, targeted crop management systems. Here we will conduct field studies to understand how to vary planting depth to optimize corn emergence and yield and investigate the effect of emergence date on crop modeling (1a). We will conduct multiple analyses of a previously collected dataset to develop decision support guidelines for in-season variable-rate nitrogen management in corn (1b) We will collaborate with ARS colleagues in Oregon in developing decision support technology for variable-rate nitrogen management in wheat (1b). We will develop new laboratory-based soil health assessments and evaluate them in field experiments (1c). We will develop and evaluate in the field new proximal soil sensing techniques to support soil health and other management decisions (1d). In the second objective we develop, apply, and evaluate innovative management systems that incorporate information about spatially variable soil resources. Many of the studies incorporate application and evaluation of the decision tools described above. In long-term field experiments, we will investigate the effect of cropping systems and landscape variability on soil health and crop production and profitability (2a). We will quantify differences in energy yield of bioenergy crops grown across variable landscapes (2a). Also in field experiments, we will investigate the effects of cover crops and reduced tillage on soil health and crop productivity (2b). We will use a model-based approach to spatially compare production between site-specific and whole field management and validate model results with measured field data (1c). We will conduct field research that uses crop sensor technology to evaluate soybean drought and flood tolerance (1c). Much of the research in the second objective supports, and is coordinated with the Central Mississippi River Basin Long-Term Agroecosystem Research (CMRB LTAR) project, which is part of another research project within this ARS unit. Specifically, decision tools and knowledge from this project will inform possible future changes to the aspirational cropping system design for the CMRB LTAR common experiment.
Objective 1: (1) Following completion of the original objectives in the area of corn emergence uniformity and yield, additional research has been implemented that expands upon the original objective, with two manuscripts under development. (2) Work to improve corn nitrogen fertilization tools has been completed and data from this project have been published. Two additional publications, beyond those listed as milestones, are under development with an anticipated journal submission date of September 2022. (3) The user-friendly public-domain software for nitrogen (N) fertilizer application was not released due to the resignation of the developer prior to final software packaging, although the functional components of the software are substantially complete. Alternatives to complete the project are being considered. (4) Five soil health indicators were selected for a national soil health assessment tool and version 1.0 of the Soil Health Assessment Protocol and Evaluation (SHAPE) tool was completed. A GitHub Repository and Shiny App were made publicly available for scoring soil health data. The primary stakeholder, the Natural Resources Conservation Service (NRCS), is operationalizing the tool into conservation platforms. The first SHAPE manuscript has been published for soil organic carbon. The second SHAPE manuscript for active carbon, total protein, and soil respiration is nearly complete. (5) Detailed laboratory protocols for a suite of soil enzymes and microbial phospholipid fatty acid profiles were published as chapters in the Soil Science Society of America’s two volume set "Approaches to Soil Health Analysis" and "Laboratory Methods for Soil Health Analysis" to provide standardized protocols for public and private laboratories offering soil health testing. (6) Data collection and soil sample analysis for a three-dimensional soil carbon mapping project has been completed, along with a preliminary analysis. Next, more complex machine learning modeling techniques will be applied and evaluated. The first phase of a project evaluating commercial sensors for estimating soil organic matter and other soil properties has been completed and a journal article summarizing results has been submitted. Estimates of soil organic matter by these sensors are considerably less accurate than those from more complex and expensive full-spectrum instruments but may provide useful qualitative estimates for soil carbon monitoring applications. Objective 2: (1) In research on yield stability in perennial versus grain systems, a decade-long dataset was assembled and certified. Data analysis and publication is expected within the next 12 months. (2) Efforts to analyze shifts in the soil microbial community under variable management have been renewed following the addition of a bioinformatician to the cooperator’s team at Brigham Young University. The bioinformatician completed the DNA sequencing and compiled and reanalyzed the previous genomic data. A manuscript is near completion. (3) The comparison of energy yield between switchgrass and corn has been expanded to include miscanthus and additional years of data. The new plan includes analysis of water use efficiency, nitrogen use efficiency, and associated energy yield calculations. (4) Following an initial delay due to emergence issues, the study of conservation management and soil health in cotton systems is moving forward as planned and in-season growth data has been collected annually. Preliminary findings from the study were presented at the 15th International Conference on Precision Agriculture. (5) Following a delay in soil analysis due to maximized telework, laboratory work on the soil health samples collected in 2020 from the rotation and cover crop study is nearing completion. Soil health properties include soil texture, soil organic carbon, total nitrogen, total protein, a suite of four soil enzymes, active carbon, and soil respiration. (6) The crop modelling work has been delayed due to a vacancy in the cooperator’s crop modeler position. We provided the requisite data, including Digital Elevation Model (DEM) data, soil depth, management, precipitation, and crop yield data, to the cooperator. The cooperator is in the process of hiring a crop modeler with the plan to continue the work. (7) The crop canopy sensor study is no longer possible due to the prevalence of ongoing dicamba herbicide crop foliage damage that renders canopy sensor data unreliable.
1. Developed SHAPE, a national soil health interpretation and recommendation tool for landowners. Adoption of soil health practices has been hampered in part by the lack of a scientifically robust and user-friendly interpretation tool. An ARS researcher in Columbia, Missouri, led a team of scientists from multiple other institutions in development of a framework called the Soil Health Assessment Protocol and Evaluation (SHAPE). This tool accounts for inherent site conditions, such as soil type and climate, and provides a soil health score for up to four soil health indicators at any location across the continental United States. Version 1.0 of SHAPE is complete and is publicly available online via GitHub and as a Shiny App. This research benefits producers and scientists by providing an improved soil health interpretation tool to monitor changes in soil health, provide management recommendations to landowners, and inform soil health programming efforts.
2. Improved field-scale soil carbon monitoring to better quantify effects of management systems. Estimating soil carbon through reflectance spectroscopy is well-established, but practical implementation is impeded by the need to collect and analyze calibration samples. This limitation can be overcome with calibrations derived from spectral libraries, which contain information on many samples collected over regional or larger areas. However, library-based local (i.e., field or farm) calibrations have generally been inaccurate. With international collaborators, an ARS researcher in Columbia, Missouri, applied a machine learning approach called “deep transfer learning” to a global spectral library and established local soil carbon estimates that were more accurate than when local calibration samples were used. This new approach may benefit farmers and the environment by providing a more accurate and efficient way to monitor soil carbon at the field scale. This can help farmers document the effects of changing management practices on soil carbon as an indicator of soil health.
3. Determined variable seeding depth resulted in more uniform corn stand for optimal yields. For corn farmers to get optimal yield from their planting operations, seeds need to germinate and emerge at the same time, giving a uniform stand across the field. However, when soil conditions vary greatly within fields, planting in the same way everywhere may not result in a uniform plant stand. ARS researchers in Columbia, Missouri, in collaboration with University of Missouri scientists, quantified corn emergence with daily hand-counts of newly emerged plants. For loamy river-bottom soils under warm and dry conditions, planting at least two inches deep improved stand uniformity and yield. In contrast, stand uniformity was not affected by planting depth in sandy soils. On upland soils, the optimal planting depth depended on the weather during the emergence period: warm (plant deeper) versus cool (plant shallower). This research helps farmers adjust planting depth for optimal corn stands and yield based on soil and weather characteristics.
4. Improved corn nitrogen fertilizer management to improve farmer profitability and reduce negative environmental impact. Applying nitrogen (N) fertilizer at a rate that meets but does not exceed crop N needs can improve farmers’ profits and help reduce N loss from agricultural fields. However, it is difficult to know the right N rate for any year or field because of weather and soil variation. Research conducted in eight states during three growing seasons by ARS scientists in Columbia, Missouri, in collaboration with university scientists from seven other U.S. Midwest states, examined different ways to improve tools for making N fertilizer recommendations, evaluating numerous soil and weather properties within the framework of the USDA soil hydrologic classifications. Generally, soil organic matter, clay content, and growing-season rainfall evenness were most important for better predicting economically optimal N rates for corn in the U.S. Midwest—especially for fields that needed less than 100 pounds of N fertilizer per acre. This research contributes to the development of tools that use site-specific plant, soil, and weather information for improved N fertilization decisions for corn. In turn, farmer profitability can be improved and environmental impacts from excessive N fertilization can be reduced.
5. Established standardized soil health assessment protocols for public and private laboratories. The demand for soil health assessment and interpretation has grown, yet laboratory methods for soil health indicators vary widely across laboratories. The lack of standardized protocols for soil health indictors has hampered the ability to make comparisons using data from different laboratories. ARS researchers in Columbia, Missouri, Lubbock, Texas, and Brookings, South Dakota, published standardized soil health protocols for multiple soil biological properties. These chapters, published by the Soil Science Society of America, included protocols for a suite of soil enzymes and phospholipid fatty acid profiles, and outlined the future of in-situ soil sensing methods for soil health assessment. These published protocols benefit private and public laboratories offering soil health testing, and support agency specialists and programming efforts related to soil health testing and sustainable agriculture.
Franzen, D.W., Miao, Y., Kitchen, N.R., Schepers, J.S., Scharf, P.C. 2021. Sensing for health, vigour and disease detection in row and grain crops. In: Kerry, R., Escolà, A. Sensing Approaches for Precision Agriculture. Cham, Switzerland: Springer. p. 159-193. https://doi.org/10.1007/978-3-030-78431-7_6.
Zhou, P., Sudduth, K.A., Veum, K.S., Li, M. 2022. Extraction of reflectance spectra features for estimation of surface, subsurface, and profile soil properties. Computers and Electronics in Agriculture. 196. Article 106845. https://doi.org/10.1016/j.compag.2022.106845.
Bean, G.M., Ransom, C.J., Kitchen, N.R., Scharf, P.C., Veum, K.S., Camberato, J.J., Ferguson, R.B., Fernandez, F.G., Franzen, D.W., Laboski, C.A., Nafziger, E.D., Sawyer, J.E., Nielsen, R.L. 2021. Soil hydrologic grouping guide which soil and weather properties best estimate corn nitrogen need. Agronomy Journal. 113(6):5541-5555. https://doi.org/10.1002/agj2.20888.
Shen, Z., Ramirez-Lopez, L., Behrens, T., Cui, L., Zhang, M., Walden, L., Wetterlind, J., Shi, Z., Sudduth, K.A., Baumann, P., Song, Y., Catambay, K., Viscarra Rossel, R.A. 2022. Deep transfer learning of global spectra for local soil carbon monitoring. Journal of Photogrammetry and Remote Sensing. 188:190-200. https://doi.org/10.1016/j.isprsjprs.2022.04.009.
Li, D., Miao, Y., Ransom, C.J., Bean, G.M., Kitchen, N.R., Fernandez, F.G., Sawyer, J.E., Camberato, J.J., Carter, P., Ferguson, R.B., Franzen, D.W., Laboski, C.A., Nafziger, E.D., Shanahan, J. 2022. Corn nitrogen nutrition index prediction improved by integrating genetic, environmental, and management factors with active canopy sensing using machine learning. Remote Sensing. 14(2). Article 394. https://doi.org/10.3390/rs14020394.
Dos Santos, C.L., Abendroth, L.J., Coulter, J.A., Nafziger, E.D., Suyker, A.E., Yu, J., Schnable, P.S., Archontoulis, S.V. 2022. Maize leaf appearance rates: a synthesis from the United States corn belt. Frontiers in Plant Science. 13. Article 872738. https://doi.org/10.3389/fpls.2022.872738.
Vong, C., Conway, L.S., Feng, A., Zhou, J., Kitchen, N.R., Sudduth, K.A. 2022. Corn emergence uniformity estimation and mapping using UAV imagery and deep learning. Computers and Electronics in Agriculture. 198. Article 107008. https://doi.org/10.1016/j.compag.2022.107008.
Unger, I.M., Kremer, R.J., Veum, K.S., Goyne, K.W. 2022. Immediate and long-term effects of invasive plant species on soil characteristics. Soil Ecology Letters. 4(3):276–288. https://doi.org/10.1007/s42832-021-0104-4.
Nunes, M.R., Veum, K.S., Parker, P.A., Holan, S.H., Karlen, D.L., Amsili, J.P., Van Es, H.M., Wills, S.A., Seybold, C.A., Moorman, T.B. 2021. The soil health assessment protocol and evaluation applied to soil organic carbon. Soil Science Society of America Journal. 85(4):1196-1213. https://doi.org/10.1002/saj2.20244.
Veum, K.S., Nunes, M., Sudduth, K.A. 2021. The future of soil health assessments: tools and strategies. In: Karlen, D.L., Stott, D.E., Mikha, M.M., editors. Soil Health Series: Volume 1 Approaches to Soil Health Analysis. John Wiley and Sons, Inc. p. 169-198. https://doi.org/10.1002/9780891189817.ch8.
Acosta Martinez, V., Perez-Guzman, L., Veum, K.S., Nunes, M.R., Dick, R. 2021. Metabolic Activity - Enzymes. In: Karlen, D.L., Stott, D.E., Mikha, M.M. Soil Health Series. Volume 2 Laboratory Methods for Soil Health Analysis. Hoboken, NJ: John Wiley & Sons. 194-250. https://doi.org/10.1002/9780891189831.
Kitchen, N.R., Ransom, C.J., Schepers, J.S., Hatfield, J.L., Massey, R., Drummond, S.T. 2022. A new perspective when examining maize fertilizer nitrogen use efficiency, incrementally. PLoS ONE. 17(5). Article e0267215. https://doi.org/10.1371/journal.pone.0267215.
Stewart, S.A., Kitchen, N.R., Yost, M.A., Conway, L.S., Carter, P.R. 2021. Planting depth and within-field soil variability impacts on corn stand establishment and yield. Agrosystems, Geosciences & Environment. 4(3). Article e20186. https://doi.org/10.1002/agg2.20186.
Bagnall, D.K., Morgan, C.L., Cope, M., Bean, G.M., Cappellazzi, S.B., Greub, K.L., Liptzin, D., Baumhardt, R.L., Dell, C.J., Derner, J.D., Ducey, T.F., Dungan, R.S., Fortuna, A., Kautz, M.A., Kitchen, N.R., Leytem, A.B., Liebig, M.A., Moore Jr, P.A., Osborne, S.L., Sainju, U.M., Sherrod, L.A., Watts, D.B., Ashworth, A.J., Owens, P.R., et al. 2022. Carbon-sensitive pedotransfer functions for plant-available water. Soil Science Society of America Journal. 86(3):612-629. https://doi.org/10.1002/saj2.20395.
Reike, E., Cappellazzi, S.B., Cope, M., Liptzin, D., Bean, G.M., Greub, K.L., Norris, C.E., Tracy, P.W., Aberle, E., Ashworth, A.J., Baumhardt, R.L., Dell, C.J., Derner, J.D., Ducey, T.F., Fortuna, A., Kautz, M.A., Kitchen, N.R., Moore Jr., P.A., Osborne, S.L., Owens, P.R., Sainju, U.M., Sherrod, L.A., Watts, D.B., et al. 2022. Linking soil microbial community structure to potential carbon mineralization: A continental scale assessment of reduced tillage. Soil Biology and Biochemistry. 168. Article 108618. https://doi.org/10.1016/j.soilbio.2022.108618.
Veum, K.S., Acosta Martinez, V., Lehman, R.M., Li, C., Cano, A., Nunes, M.R. 2021. PLFA and EL-FAME indicators of microbial community composition. In: Karlen, D.L., Stott, D.E., Mikha, M.M., editors. Laboratory Methods for Soil Health Analysis, Volume 2. John Wiley and Sons, Inc. p. 251-288. https://doi.org/10.1002/9780891189831.ch12.
Ransom, C.J., Clark, J., Bean, G.M., Bandura, C., Schafer, M., Kitchen, N.R., Camberato, J.J., Carter, P.R., Ferguson, R.B., Fernandez, F.G., Franzen, D.W., Laboski, C.A., Myers, B.D., Nafziger, E.D., Sawyer, J.E., Shanahan, J. 2021. Data from a public–industry partnership for enhancing corn nitrogen research. Agronomy Journal. 113(5):4429-4436. https://doi.org/10.1002/agj2.20812.