Research Project: Sustainable Intensification of Cropping Systems on Spatially Variable Landscapes and Soils

Location: Cropping Systems and Water Quality Research

2020 Annual Report

Objectives
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.

Approach
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.

Progress Report
Progress under Objective 1, “Use a genetics by environment by management (GxExM) research approach to develop decision support tools for on-farm implementation of sustainable and resilient cropping systems”: (1) Completed the third and final year of field studies examining the effects of corn seeding depth and within-field soil variability of claypan and alluvial soil fields on emergence and yield. Findings were reported at international meeting, and a journal manuscript is being developed. Related research was initiated to evaluate commercial planter sensor systems for accuracy, precision, and repeatability in estimating seedzone soil properties for planting and other agronomic decisions. (2) Determined that the APEX0806 version of the Agricultural Policy/Environmental eXtender (APEX) an ARS model, is not sensitive to seeding depth. Modeled crop yields are not sensitive to seeding density within a reasonable range of seeding density. We will continue to investigate this issue with the newer APEX1501 version. (3) Analysis continues on a 49 site-year dataset collected to evaluate in-season corn nitrogen (N) management. During the past year, five additional journal manuscripts were submitted. A related study examined crop modeling for N fertilizer management decisions, with results presented at an international meeting. (4) The successful “Yield Editor” software (over 11,000 downloads to date) has been updated and enhanced to accommodate wheat protein mapping and development of variable-rate fertilizer prescription maps. Testing and revision of the software is planned in the next three months. (5) A manuscript describing soil health assessment at the Central Mississippi River Basin Long-Term Agroecosystem Research (CMRB LTAR) site is nearing completion. Additional manuscripts describing development and evaluation of soil health assessment methods have also been completed using data from other experimental sites. (6) Field studies have demonstrated the feasibility of mapping within-season soil water content dynamics using repeated soil electrical conductivity sensing campaigns, for both alluvial and claypan soils. (7) Completed one year of on-farm research and initiated a second year in 50 fields in three Midwestern states to investigate corn tissue and yield response to phosphorus, potassium, and sulfur fertilization as impacted by soil fertility and soil health metrics. Research will also relate soil health indicators to soil and crop management and pedogenic soil characteristics. Progress under Objective 2, “Develop and evaluate sustainable and resilient cropping systems using a site-specific GxExM framework”: (1) Annual operations, including field work, data collection and validation have been completed for year seven of a planned 10-year analysis comparing grain and perennial grass cropping systems. (2) Three manuscripts were published assessing the effects of management on the soil microbial community. (3) A field study assessing the effects of cover crops and tillage on soil health in cotton production systems was initiated after a one-year delay due to excessively wet weather. Replicated cotton emergence data were obtained in the three seedbed treatments. (4) Sampling of the extended rotation field experiment has been delayed to allow more time for management practices to affect soil health measurements. A manuscript on the effect of extended rotations on soil health has been published using data from across the United States. (5) In the crop model comparison project, data exchange has been initiated with the developers of the ARS mechanistic crop models for soybean (GLYCIM) and corn (MAIZSIM). Initial results from this collaboration are expected in FY21. Research has begun to evaluate the sensitivity of the newest version of APEX (APEX1501) on crop yields as a function of topsoil depth, which drives soil water availability. Results are promising as they match observed trends. Parameterization of APEX1501 is challenging and guidance has been sought from the APEX developers. (6) Drought tolerant soybean genotypes and high-yielding commercial varieties will be planted this year under a variable-rate irrigation center pivot system to evaluate crop growth and yield under varying levels of irrigation. Mobile sensors will be used monitor crop development.

Accomplishments
1. A long-term precision agriculture system sustains grain profitability. Targeting management practices and inputs with precision agriculture has high potential to meet some of the grand challenges of sustainability in the coming century, including simultaneously improving crop profitability and reducing environmental impacts. To better understand long-term effects of precision agriculture on crop profitability, ARS scientists at Columbia, Missouri, monitored a 90-acre field in central Missouri for over a decade under conventional management (1993-2003) and then for another decade under a precision agriculture system (PAS) (2004-2014). Conventional management was a corn-soybean rotation, annual tillage, and uniform fertilizer and herbicide inputs. Key aspects of the PAS were no-tillage, cover crops, winter wheat instead of corn on areas with shallow topsoil and low corn profitability, and variable-rate fertilizer (nitrogen, phosphorus, potassium, and lime) applications. Results indicated that PAS sustained profits in the majority (97%) of the field without subsidies for cover crops or payments for enhanced environmental protection. Profit was only lower with PAS in a drainage channel where no-till sometimes hindered soybean stands and wet soils caused wheat disease. These results should help growers gain confidence in the success of precision agriculture management and conservation practices.

2. Continental scale soil health assessment. There is enormous demand from producers for science-based interpretation of soil health indicators, but knowledge and tools at the continental scale are lacking. It is widely recognized that climate and soil characteristics play a key role in soil health indicator measurements, yet a framework to account for these large-scale factors has yet to be developed. ARS scientists at Columbia, Missouri, and Ames, Iowa, outlined the state of soil health assessment tools and evaluated the sensitivity of biological soil health indicators in a meta-analysis of data that spanned the continental United States, representing a wide range of climate and soil types. Results confirm the sensitivity of biological soil health indicators across the United States and provide guidance to scientists for future research to improve soil health assessment tools for more informed agroecosystem management at the continental scale. In addition, producers and agency specialists will benefit from a better understanding of the science behind current soil health assessment tools.

3. Comparing tools for corn nitrogen fertilizer recommendations. Limiting nitrogen (N) fertilizer application to the rate sufficient for crop N needs can improve farmer profits and help reduce loss of N from agricultural fields. ARS scientists at Columbia, Missouri, and scientists at eight different U.S. Midwest universities evaluated 31 different publicly-available tools used for determining N fertilizer rates across a wide range of U.S. Corn Belt growing conditions. They found no one tool performed well for predicting the economic optimal N rate across all 49 fields, but the better performing tools included those that utilized a soil nitrate test or measurements of the canopy color. Tools that on average came close to the economic optimal N rate included soil nitrate tests and a tool commonly used in the Midwest called "maximum return to N", or MRTN. Because they recommended more N than was needed, yield-goal based tools would have resulted in the greatest negative environmental impact. This research demonstrated that better tools are needed for N rate recommendations in US Midwest corn production, particularly tools that are adaptive to varying soil and weather conditions. This research will help producers and their consultants recognize the need for recommendation tools that are more adaptive to reactive fertilizer N in an era of changing climate.

4. Laboratory methods for soil health assessment. The demand for soil health assessment and interpretation is growing, yet laboratory methods for measurement of soil health indicators often vary across laboratories, providing results that may not be comparable. One popular soil health indicator, known as soil respiration, is a measurement that reflects the microbial decomposition and mineralization of soil organic matter. Despite the popularity of this soil health indicator, several laboratory methods for soil respiration exist and comparisons across methods are lacking. ARS scientists at Columbia, Missouri, and Raleigh, North Carolina, compared two popular soil respiration methods using a range of soil samples. Results from this comparison illustrate the differences between the two methods and highlight the need for standardized protocols to improve the utility of soil health indicators. In addition, these results provide the framework for development of a conversion factor to accommodate the use of both methods in nationwide soil health assessments. This information benefits producers and agency specialists by providing a better understanding of indicators for soil health assessment and providing information to public and private laboratories for selection and standardization of protocols.

5. Soil health and agronomic sustainability. Improved soil health provides many environmental benefits, yet the utility of soil health indicators for agronomic decisions has not been adequately determined. In particular, producers need soil health tools that can be used to improve nitrogen fertilizer decisions for economic and environmental sustainability. ARS researchers at Columbia, Missouri, along with collaborators from multiple institutions, evaluated mineralizable nitrogen as a tool for improved prediction of nitrogen fertilizer needs and agronomic outcomes such as corn yield and nitrogen use efficiency. Soil samples and agronomic data were collected from across five states in the Midwestern U.S. Corn Belt. Results determined that several factors affected the utility of mineralizable nitrogen, including the choice of laboratory method, timing of soil sampling, timing and rate of nitrogen fertilization, and weather and soil characteristics. These results provide information that can improve soil testing tools for producers for enhanced agronomic and environmental outcomes.

6. Field scale soil health assessment. Soil health indicators are dynamic and sensitive soil properties that reflect important environmental and agronomic outcomes, yet a better understanding of the spatial and temporal variation is needed to improve interpretation at the field scale. ARS scientists in Columbia, Missouri, in collaboration with University of Missouri scientists, evaluated field-scale and short-term (seasonal and annual) variability of multiple biological soil health indicators at an on-farm study located on claypan soils in Missouri. Results demonstrated that soil microbial community composition and activity varied across the landscape due to management and topography, and also varied seasonally and annually, likely due to differences in weather patterns. These results highlight the need to account for the interaction of management practices with landscape-level site characteristics as well as short-term changes in weather and growing conditions for more informed interpretation of soil biological measurements. This study provides producers and land managers with a better understanding of biological soil health indicators for more informed management and enhanced sustainability.

Review Publications
Ransom, C.J., Kitchen, N.R., Camberato, J.J., Carter, P.R., Ferguson, R.B., Fernandez, F.G., Franzen, D.W., Laboski, C., Nafziger, E.D., Sawyer, J.E., Scharf, P.C., Shanahan, J.F. 2020. Corn nitrogen rate recommendation tools’ performance across eight US midwest corn belt states. Agronomy Journal. 112(1):470-492. https://doi.org/10.1002/agj2.20035.
Clark, J.D., Veum, K.S., Fernandez, F.G., Kitchen, N.R., Camberato, J.J., Carter, P.R., Ferguson, R.B., Franzen, D.W., Kaiser, D.E., Laboski, C.A., Nafziger, E.D., Rosen, C.J., Sawyer, J.E., Shanahan, J.F. 2020. Soil sample timing, nitrogen fertilization, and incubation length influence anaerobic potentially mineralizable nitrogen. Soil Science Society of America Journal. 84(2):627-637. https://doi.org/10.1002/saj2.20050.
Alagele, S.M., Anderson, S.H., Udawatta, R.P., Veum, K.S., Rankoth, L.M. 2019. Effects of conservation practices on soil quality compared with a corn-soybean rotation on a claypan soil. Journal of Environmental Quality. 48(6):1694-1702. https://doi.org/10.2134/jeq2019.03.0121.
Alagele, S.M., Anderson, S.H., Udawatta, R.P., Veum, K.S., Rankoth, L.M. 2020. Long-term perennial management and cropping effects on soil microbial biomass for claypan watersheds. Agronomy Journal. 112(2):815-827. https://doi.org/10.1002/agj2.20116.
Yost, M.A., Kitchen, N.R., Sudduth, K.A., Massey, R.E., Sadler, E.J., Drummond, S.T., Volkmann, M.R. 2019. A long-term precision agriculture system sustains grain profitability. Precision Agriculture. 20:1177-1198. https://doi.org/10.1007/s11119-019-09649-7.
Clark, J.D., Veum, K.S., Fernandez, F.G., Camberato, J.J., Carter, P.R., Ferguson, R.B., Franzen, D.W., Kaiser, D.E., Kitchen, N.R., Loboski, C.A., Nafziger, E.D., Rosen, C.J., Sawyer, J.E., Shanahan, J.F. 2019. United States Midwest soil and weather conditions influence anaerobic potentially mineralizable nitrogen. Soil Science Society of America Journal. 83(4):1137-1147. https://doi.org/10.2136/sssaj2019.02.0047.
Clark, J.D., Fernandez, F.G., Veum, K.S., Camberato, J.J., Carter, P.R., Ferguson, R.B., Franzen, D.W., Kaiser, D.E., Kitchen, N.R., Loboski, C.A., Nafziger, E.D., Rosen, C.J., Sawyer, J.E., Shanahan, J.F. 2019. Predicting economic optimal nitrogen rate with the anaerobic potentially mineralizable nitrogen test. Agronomy Journal. 111(6):3329-3338. https://doi.org/10.2134/agronj2019.03.0224.
Mouazen, A., Alexandridis, T., Buddenbaum, H., Cohen, Y., Moshou, D., Mulla, D., Nawar, S., Sudduth, K.A. 2020. Monitoring. In: Castrignano, A., Buttafuoco, G., Khosla, R., Mouazen, A.M., Moshou, D., Naud, O. Agricultural Internet of Things and Decision Support for Smart Farming. Cambridge, MA: Academic Press. p. 35-138. https://doi.org/10.1016/B978-0-12-818373-1.00002-0.
Karlen, D.L., Veum, K.S., Sudduth, K.A., Obrycki, J.F., Nunes, M.R. 2019. Soil health assessment: Past accomplishments, current activities, and future opportunities. Soil & Tillage Research. 195(104356). https://doi.org/10.1016/j.still.2019.104365.
Zhang, M., Zhou, J., Sudduth, K.A., Kitchen, N.R. 2019. Estimation of maize yield and effects of variable-rate nitrogen application using UAV-based RGB imagery. Biosystems Engineering. 189:24-35. https://doi.org/10.1016/j.biosystemseng.2019.11.001.
Ransom, C.J., Kitchen, N.R., Camberato, J.J., Carter, P.R., Ferguson, R.B., Fernandez, F.G., Franzen, D.W., Laboski, A.M., Myers, D.B., Nafziger, E.D., Sawyer, J.E., Shanahan, J.F. 2019. Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations. Computers and Electronics in Agriculture. 164:104872. https://doi.org/10.1016/j.compag.2019.104872.
Sudduth, K.A., Woodward Greene, M.J., Penning, B., Locke, M.A., Rivers, A.R., Veum, K.S. 2020. AI down on the farm. IEEE IT Professional. 22(3):22-26. https://doi.org/10.1109/MITP.2020.2986104.
Baffaut, C., Ghidey, F., Lerch, R.N., Veum, K.S., Sadler, E.J., Sudduth, K.A., Kitchen, N.R. 2020. Effects of combined conservation practices on soil and water quality in the Central Mississippi River Basin. Journal of Soil and Water Conservation. 75(3):340-351. https://doi.org/10.2489/jswc.75.3.340.
Feng, A., Zhou, J., Vories, E.D., Sudduth, K.A. 2020. Evaluation of cotton emergence using UAV-based narrow-band spectral imagery with customized image alignment and stitching algorithms. Remote Sensing. 12(11):1764. https://doi.org/10.3390/rs12111764.
Feng, A., Zhou, J., Vories, E.D., Sudduth, K.A., Zhang, M. 2020. Yield estimation in cotton using UAV-based multi-sensor imagery. Biosystems Engineering. 193:101-114. https://doi.org/10.1016/j.biosystemseng.2020.02.014.
Nunes, M.R., Karlen, D.L., Veum, K.S., Moorman, T.B., Cambardella, C. 2020. Biological soil health indicators respond to tillage intensity: A US meta-analysis. Geoderma. 369:114335. https://doi.org/10.1016/j.geoderma.2020.114335.
Franzluebbers, A.J., Veum, K.S. 2020. Comparison of two alkali trap methods for measuring the flush of CO2. Agronomy Journal. 112:1279-1286. https://doi.org/10.1002/agj2.20141.