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

Location: Cropping Systems and Water Quality Research

2019 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) The third and final field season of the corn emergence study is underway. Data collection with in-furrow soil sensors mounted on the planter was added as an enhancement to the study. Work on related modeling activities has begun by compiling historical datasets. Delineation of a study field into zones of homogeneous crop yields that coincide with zones based on soil properties is in progress. (2) Analysis continues on the 49 site-year dataset collected to evaluate in-season corn nitrogen management, resulting in five journal submissions this fiscal year (FY). (3) Initial plans were formulated to modify the existing Yield Editor software to accommodate wheat protein mapping and software coding will begin later this FY. (4) Soil health sampling was completed at the Central Mississippi River Basin Long-Term Agroecosystem Research (CMRB LTAR) site and a full suite of soil health analyses were completed including Cornell soil respiration and total protein, the four enzyme panel, and phospholipid fatty analysis (PLFA) profiles. We are waiting on results for ester-linked fatty acid (EL-FAME) analysis and the combined enzyme assay from collaborators in Texas for comparison, but have submitted a preliminary paper comparing PLFA with EL-FAME on an alternative set of samples. (5) An analysis of soil electrical conductivity sensor data from diverse Missouri field sites showed promise for being able to generate maps of depth-specific estimates of soil texture, as might be useful for model inputs, in many soil types. Ongoing work is focused on improving results on the remainder of the study fields. 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 grain and perennial crop plots. Grain and biomass samples have been processed and achieved in preparation for lab energy analyses. (2) Soil sampling at the CMRB LTAR site and analysis of a suite of soil health indicators have been completed, including microbial community structure and function using enzymology and PLFA analysis. The genomic DNA samples have been collected and stored in the cryogenic freezer, but not analyzed due to budget limitations. The PLFA and associated data are ready for statistical analysis. (3) Initiation of cover crop research in Southeast Missouri has been delayed for a year because weather conditions made timely planting of cover crops impossible this FY. Soil sampling in the Central Missouri cover crop study has been postponed from year 3 to year 5 after project initiation to allow more time for the conservation management practices to impact soil health measurements. (4) Work on the model comparison project has begun by compiling historical datasets. Delineation of a study field into zones of homogeneous crop yields that coincide with zones based on soil properties is in progress. (5) A high-clearance sprayer tractor was obtained to serve as a sensor platform to allow data collection later in the growing season with minimal impact on the crop for multiple projects, including the soybean sensing research. The soybean experiment was planted and canopy sensing has been initiated.

Accomplishments
1. Evaluated and improved canopy sensing for corn nitrogen fertilization. Knowing the amount of nitrogen fertilizer to apply to match the need of a corn crop is challenging due to year-to-year changes in crop nitrogen need and variation in soil nitrogen. ARS scientists at Columbia, Missouri, in collaboration with scientists from eight U.S. Midwest universities, evaluated the performance of canopy reflectance algorithms used for making corn nitrogen fertilizer recommendations across the U.S. Corn Belt. Averaged over sites, all three tested algorithms recommended less than the optimum amount of nitrogen. However, when algorithms were modified using weather (e.g., evenness of rainfall from planting to nitrogen application) and soil (e.g., clay content in the top foot of soil) information, nitrogen recommendations were greatly improved. Using measured soil information improved recommendations more than using the information found in USDA soil maps. This research demonstrated that farmers using canopy sensing approaches can improve corn nitrogen management decisions by incorporating site-specific soil and weather information.

2. Demonstrated utility of soil health indicators. Conservation management practices have been shown to improve soil health and provide many environmental benefits, yet the sensitivity and utility of soil health measurements have not been adequately determined. ARS researchers at Columbia, Missouri, in collaboration with scientists from the University of Missouri, evaluated multiple biological and physical soil properties in Midwestern perennial and row cropped systems. Microbial enzyme activity, microbial biomass via phospholipid fatty acid analysis, and soil thermal properties were shown to be sensitive to tillage and cover cropping practices in corn-soybean rotations and showed improved soil health in perennial systems over annual cropping systems. Phospholipid fatty acid analysis was shown to be highly sensitive to sample handling conditions. These results illustrate the need for standardized sampling and handling protocols and highlight the potential utility of multiple soil health indicators. This information benefits producers by providing a better understanding of indicators for soil health assessment and aids in making more informed management decisions.

3. Evaluated a soil health tool for improving corn nitrogen recommendations. Many new soil health measurements are available to producers, yet little information is available to help producers use these tests for making nutrient management decisions. ARS scientists at Columbia, Missouri, and scientists at eight U.S. Midwest universities evaluated the Haney Soil Health Nutrient Tool across the U.S. Corn Belt, finding that corn nitrogen recommendations from this tool were poorly related to the economic optimum nitrogen rate. The results were similar when the majority of nitrogen fertilization was applied at planting and when nitrogen fertilization was delayed until corn was about waist high. However, one component of the tool, a measure of the 24-hour soil microbe carbon dioxide respiration, was highly correlated to the economic optimum nitrogen rate. Additional studies are investigating whether this result holds true over different cropping seasons. These findings will help advance the development and use of soil biological tests for making fertilizer recommendations, thus helping farmers be more profitable and guarding against over-application of nitrogen fertilizer.

4. Improved sensor-based estimates of soil properties. Soil property estimates from in-field reflectance spectroscopy soil sensors are useful for precision agriculture, soil health assessment, and other applications. Estimates of some soil properties may be improved by simultaneous data collection with other complementary sensors, and commercial instruments facilitating this are available. ARS scientists at Columbia, Missouri, and university colleagues investigated optimum data collection and analysis methods for these multi-sensor instruments, identifying a best-performing combination of processing and modeling approaches. In addition, methods were developed and implemented to allow calibrations generated on laboratory data to be successfully applied to field-collected data, improving efficiency of the calibration and estimation process. These results provide information that scientists and practitioners can use to improve in-field sensor based data collection for more informed agroecosystem management.

5. Developed a low-cost imaging sensor for crop temperature measurement. Quantifying spatial and temporal variability in plant stress is important for precision agriculture applications, including variable rate irrigation. A common approach is measuring crop canopy temperature measurement using infrared sensors, but current methods have limitations, including cost and reduced accuracy due to an inability to discriminate canopy from soil. ARS scientists at Columbia, Missouri, and colleagues from the University of Missouri developed a low-cost infrared imaging system that could discriminate sunlit crop areas from shadows and background. The system was successfully calibrated to measure temperature, and was able to discriminate plant from non-plant areas in field-collected images. Results of this research will be of use to researchers and instrumentation developers interested in inexpensive ways to improve crop canopy temperature measurements.

Review Publications
Pei, X., Sudduth, K.A., Veum, K.S., Li, M. 2019. Improving in-situ estimation of soil profile properties using a multi-sensor probe. Sensors. 19(5):1011. https://doi.org/10.3390/s19051011.
Bean, G.M., Kitchen, N.R., Camberato, J.J., Ferguson, R.B., Fernandez, F.G., Franzen, D.W., Laboski, C., Nafziger, E.D., Sawyer, J.E., Scharf, P.C., Schepers, J.S., Shanahan, J.F. 2018. Improving an active-optical reflectance sensor algorithm using soil and weather information. Agronomy Journal. 110(6):2541-2551. https://doi.org/10.2134/agronj2017.12.0733.
Bean, G.M., Kitchen, N.R., Camberato, J.J., Ferguson, R.B., Fernandez, F.G., Franzen, D.W., Laboski, C., Nafziger, E.J., Sawyer, J.E., Scharf, P.C., Schepers, J.S., Shanahan, J.F. 2018. Active-optical reflectance sensing corn algorithms evaluated over the United States Midwest corn belt. Agronomy Journal. 110(6):2552-2565. https://doi.org/10.2134/agronj2018.03.0217.
Boardman, D.L., Clark, K.M., Kitchen, N.R., Easterby, S.O., Staples, J.S., Reinbott, T.M., Kremer, R.J. 2018. Do tillage, cover crops, and compost management within organic grain cropping affect greenhouse gas emissions? Agronomy Journal. 110:1893-1904. https://doi.org/10.2134/agronj2018.01.0023.
Qin, Z., Myers, D.B., Ransom, C.J., Kitchen, N.R., Liang, S., Camberato, J.J., Carter, P.R., Ferguson, R.B., Fernandez, F.G., Franzen, D.W., Laboski, C., Malone, B., Nafziger, E.D., Sawyer, J.E., Shanahan, J.F. 2018. Application of machine learning methodologies for predicting corn economic optimal nitrogen rate. Agronomy Journal. 110(6):2596-2607. https://doi.org/10.2134/agronj2018.03.0222.
Veum, K.S., Parker, P., Sudduth, K.A., Holan, S.H. 2018. Predicting profile soil properties with reflectance spectra via Bayesian covariate-assisted external parameter orthogonalization. Sensors. 18(11):3869. https://doi.org/10.3390/s18113869.
Yost, M.A., Veum, K.S., Kitchen, N.R., Sawyer, J.E., Camberato, J.J., Carter, P.R., Ferguson, R.B., Fernandez, F.G., Franzen, D.W., Laboski, C.A., Nafziger, E.D. 2018. Evaluation of the Haney Soil Health Nutrient Tool for corn nitrogen recommendations across eight Midwest states. Journal of Soil and Water Conservation. 73(5):587-592. https://doi.org/10.2489/jswc.73.5.587.
Feng, A., Zhang, M., Sudduth, K.A., Vories, E.D., Zhou, J. 2019. Cotton yield estimation from UAV-based plant height. Transactions of the ASABE. 62(2):393-403.
Drew, P.L., Sudduth, K.A., Sadler, E.J., Thompson, A.T. 2019. Development of a multi-band sensor for crop temperature measurement. Computers and Electronics in Agriculture. 162:269-280. https://doi.org/10.1016/j.compag.2019.04.007.
Sudduth, K.A., Franzen, A.J., Zhu, H., Drummond, S.T. 2018. Variable-rate application technologies in precision agriculture. In: Stafford, J.V., editor. Precision Agriculture for Sustainability. Cambridge, United Kingdom: Burleigh Dodds Science Publishing Limited. p. 171-194.
Veum, K.S., Lorenz, T.E., Kremer, R.J. 2019. Phospholipid fatty acid profiles of soils under variable handling and storage conditions. Agronomy Journal. 111(3):1090-1096. https://doi.org/10.2134/agronj2018.09.0628.
Yost, M.A., Sudduth, K.A., Walthall, C.L., Kitchen, N.R. 2019. Public–private collaboration toward research, education and innovation opportunities in precision agriculture. Precision Agriculture. 20:4-18. https://doi.org/10.1007/s11119-018-9583-4.
Zaibon, S.B., Anderson, S.H., Veum, K.S., Haruna, S.L. 2019. Soil thermal properties affected by topsoil thickness in switchgrass and row crop management systems. Geoderma. 350:93-100. https://doi.org/10.1016/j.geoderma.2019.05.005.
Vories, E.D., Jones, A., Meeks, C., Stevens, G. 2019. Variety effects on cotton yield monitor calibration. Applied Engineering in Agriculture. 35(3):345-354.
Harmel, R.D., Baffaut, C., Douglas-Mankin, K. 2018. Review and development of ASABE Engineering Practice 621: Guidelines for calibrating, validating, and evaluating hydrologic and water quality models. Transactions of the ASABE. 61(4):1393-1401. https://doi.org/10.13031/trans.12806.
Pan, X., Richardson, M.D., Deng, S., Kremer, R.J., English, J.T., Mihail, J.T., Sams, C.E., Scharf, P.C., Veum, K.S., Xiong, X. 2017. Effect of organic amendment and cultural practice on large patch occurrence and soil microbial community. Crop Science. 57(4):2263-2272. https//doi.org/10.2135/cropsci2016.09.0809.
Rankoth, L.M., Udawatta, R.P., Veum, K.S., Jose, S., Alagele, S.M. 2019. Cover crop influence on soil enzymes and selected chemical parameters for a claypan corn–soybean rotation. Journal of Agriculture. 9(6):125. https://doi.org/10.3390/agriculture9060125.
Rankoth, L.M., Udawatta, R.P., Gantzer, C.J., Jose, S., Veum, K.S., Dewanto, H.A. 2019. Cover crops on temporal and spatial variations in soil microbial communities by phospholipid fatty acid profiling. Agronomy Journal. 111(4):1693-1703. https://doi.org/10.2134/agronj2018.12.0789.