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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Research Project #430063

Research Project: Sustainable Intensification of Grain and Biomass Cropping Systems using a Landscape-Based GxExM Approach

Location: Cropping Systems and Water Quality Research

2018 Annual Report

Objective 1: Develop decision support tools to support a GxExM approach in research and for on-farm implementation of sustainable and resilient cropping systems. 1a. Integrate improved soil-landscape information into decision support. 1b. Improve decision support for nitrogen management in corn and cotton. 1c. Develop and evaluate new soil information sources. Objective 2: Develop and evaluate sustainable cropping systems resilient to increasing climatic variability through the application of site-specific soil and crop management using a GxExM framework. 2a. Evaluate production and soil health of innovative cropping systems designed to reduce vulnerability across landscapes. 2b. Develop and implement an innovative site-specific management system and evaluate compared to conventional practice. (CMRB LTAR).

Our interdisciplinary team will employ a GxExM approach to address key knowledge and technology gaps limiting the development of landscape-based site-specific management systems. We will develop methods that use the spatial soil, crop, and yield data collected with precision farming technologies to assess production and production risk of current and alternative management systems. To better understand soil health impacts of different management systems, we will develop and evaluate laboratory methods for important indicators as well as electronic sensor technology that can be deployed for field measurements. We will evaluate management systems that increase nitrogen use efficiency and that incorporate landscape targeting of conservation measures for improved resilience to climatic variability. We will also conduct field research to evaluate the production, profitability, and environmental ramifications of bioenergy crops. We will participate in the LTAR comparison of conventional and “aspirational” production systems through measurement and analysis of production, soil health, and mass and energy fluxes. Products of this research will include soil health indicators, sensors for measurement of multiple soil properties, contributions to long-term datasets, and agricultural and conservation practices specifically designed to deal with landscape variability.

Progress Report
This is the final report for project 5070-12160-004-00D, scheduled to terminate in October 2018. Substantial results were realized over the three years of the project under Objective 1, “Develop decision support tools to support a “genetics by environment by management" (GxExM) approach in research and for on-farm implementation of sustainable and resilient cropping systems.” (1) Geospatial analysis methods were developed and applied to identify fields where soils were variable and environmentally vulnerable. These results can be used to locate fields that may benefit most from application of precision agriculture methods and technologies. (2) Data from an 8-state, 3-year project to investigate the performance of in-season corn nitrogen management tools have been compiled and summarized. To date, the project has resulted in a published overview journal article, two more accepted, and one in revision. An additional three to five manuscripts will likely be submitted for journal publication in the next year. (3) A three-year study investigating varying water stress levels in cotton due to different furrow irrigation patterns was completed. Canopy sensor data were collected and analyzed to identify water and nitrogen stress. A journal article was prepared and is currently in revision. (4) Data collection and initial analysis for a regional, in-situ sensor-fusion project has been completed. Datasets obtained at multiple field sites in Missouri and other Midwest states include soil electrical conductivity, soil strength, visible and near-infrared soil spectroscopy data, passive gamma emissions from the soil and, in the laboratory, mid-infrared spectroscopy. Preliminary results show that the fusion approach, where data from multiple sensors are combined to estimate soil properties, generally provides better results than a single sensor alone. (5) Laboratory analyses for active carbon, potentially mineralizable nitrogen, and total protein have been completed on multiple sample sets from Missouri sites and manuscripts are in preparation. New soil respiration and rapid particle size methods were implemented in the laboratory to expand soil health analytical capabilities, and a combined soil enzyme assay is being evaluated. Multiple soil health indicators were evaluated for sensitivity to management in Missouri forest and prairie soils to provide an expanded dataset for algorithm development. Significant results have also been realized for Objective 2, “Develop and evaluate sustainable cropping systems resilient to increasing climatic variability through the application of site-specific soil and crop management using a GxExM framework.” (1) A six-year comparison of production and profitability of grain crops (corn and soybean) to a switchgrass bioenergy crop on claypan soil landscapes was completed and published. Another publication identified what soil and landscape properties were most closely related to Miscanthus productivity. Soil health was compared under grain and switchgrass production systems with no significant difference found within five years of implementation. Soil health analyses on other innovative systems, including agroforestry and organic systems, are in process. (2) Collection of the initial three years of production and associated data for the common experiment at the Central Mississippi River Basin (CMRB) Long-Term Agroecosystem Research (LTAR) site has been completed. Previous long-term research evaluating production of a precision agriculture system has been published and a companion profitability paper is nearing submission. (3) Baseline soil health at the initiation of the LTAR project has been documented through soil sampling, laboratory analysis, and calculation of soil health scores. Data analysis relating soil health scores to productivity has been completed and a manuscript is in preparation. (4) Eddy-covariance flux instrumentation has been established at both CMRB LTAR fields, and data have been obtained over multiple years. Post-processing of these very large datasets has been more difficult than initially anticipated, but is now nearing completion. Screened, gap-filled, and integrated daily flux data is expected to be available in FY18.

1. Optimum soil fertility management varies across claypan soil landscapes. In the claypan soil region of the Midwest, significant soil erosion during decades of row crop production has led to varying topsoil thickness, which has resulted in variable yields. ARS scientists at Columbia, Missouri, in collaboration with scientists at the University of Missouri, studied the impact of this erosion on soil fertility to help understand how growers might better optimize fertilizer inputs. Topsoil thickness affected soil fertility and fertilizer needs, but not in the same way for all nutrients. A three-year grain crop rotation that included no-tillage and cover crops had lower soil test phosphorus and potassium, greater soil organic matter, and was less susceptible to change in soil test phosphorus than two-year grain crop rotations. These findings demonstrate that adjusting fertilizer rates based on cropping system and topsoil depth could improve crop nutrient efficiency. This information can help farmers improve fertility management in conventional and conservation-based cropping systems.

2. Impact of a precision agriculture system on grain production. Targeting crop management practices and inputs using precision agriculture can help meet sustainability goals, including simultaneously improving crop yields and reducing environmental impacts. ARS scientists at Columbia, Missouri, assessed the impact of ten years of a field-scale precision agriculture system (PAS) on grain production. The PAS system included no-tillage, cover crops, winter wheat instead of corn on areas with shallow topsoil and low corn profitability, and variable-rate fertilizer application. This research showed that the PAS maintained, but did not increase, grain yield. However, the PAS did result in less variation in yield from year to year in many areas of the field, despite more extreme weather conditions than in the previous ten year period under conventional management. Thus, yield may be more stable with precision agriculture and conservation management. This information will help increase the use of these practices by farmers and farm advisors on these and similar soils.

3. Public-sector and industry partnership for corn nitrogen research. Inefficient use of nitrogen fertilizer for corn production has profound economic and environmental consequences. While a wide variety of tools are available to assist farmers in making nitrogen decisions, many of them have not been compared in side-by-side evaluations. ARS scientists from Columbia, Missouri, in collaboration with scientists from seven U.S. Midwest land-grant universities and a major seed company conducted field studies to evaluate the performance of nitrogen decision tools across diverse soil and weather environments. Additionally, the project has generated a valuable dataset that will allow investigators to evaluate future nitrogen decision tools across a wide array of weather and soils. If fertilizer recommendations can be better matched to actual crop need, farm profits would increase. Additionally, fertilizer loss to the environment would be reduced, thus helping to protect soil, water, and air resources.

4. Variability and vulnerability index identifies precision agriculture opportunity. Prior to investing in precision agriculture tools and technologies, it would be advantageous for producers and their service providers to have a quick way to screen fields for variability. ARS scientists at Columbia, Missouri, in cooperation with scientists at the University of Missouri, developed maps of Missouri that identified where fields had the most variable soils and were most vulnerable to erosion. Although the benefits of precision agriculture are heavily situational and dependent on the specific variation present within a field, this research will help producers and their providers get a general idea of which fields and regions have the greatest potential for optimizing agricultural inputs using precision agriculture technologies.

5. On-farm assessment of Miscanthus growth. Miscanthus is a proposed bioenergy crop for which growth has not been well characterized across a range of conditions. Of particular interest is its productivity in eroded areas where grain crops perform poorly. ARS scientists at Columbia, Missouri, in cooperation with scientists at the University of Missouri, quantified Miscanthus growth within 22 commercial fields in Missouri and Arkansas. The research identified what field and soil characteristics gave the highest miscanthus yields. Weather conditions and degraded soils did not strongly influence Miscanthus production, but excessive soil moisture often limited its growth because stand was reduced. Miscanthus may be especially well-suited for sloped field areas prone to or previously eroded as a result of annual row crop production. These results will help farmers to understand the optimal placement and cultivation of Miscanthus on Midwest landscapes.

6. Cover crops impact soil properties. Cover crops play an important role in soil nutrient management and have been shown to provide many environmental benefits, including improved soil quality, yet the short-term impacts of cover crops on soil properties are not well understood. In this study, ARS scientists at Columbia, Missouri, in cooperation with scientists from the University of Missouri, evaluated nutrient cycling and various soil properties under hairy vetch and cereal rye on two Missouri soils. Microbial carbon cycling activity increased while soil carbon, total nitrogen, and phosphorus declined over the growing season. These results highlight the potential impact of cover crops on soil nutrients and microbial community activity during the growth cycle. The results of this study will benefit producers by providing a better understanding of the short-term effects of cover crops and aid in making management decisions related to cover cropping practices.

Review Publications
Cho, Y., Sheridan, A.H., Sudduth, K.A., Veum, K.S. 2017. Comparison of field and laboratory VNIR spectroscopy for profile soil property estimation. Transactions of the ASABE. 60(5):1503-1510.
Thompson, A.L., Sudduth, K.A. 2018. Terracing and contour farming. In: Delgado, J., Sassenrath, G., and Mueller, T. editors. Precision Conservation: Geospatial Techniques for Agricultural and Natural Resources Conservation. Agronomy Monograph 59, ASA and CSSA, Madison, WI. doi:10.2134/agronmonogr59.2016.0010.
Shannon, D.K., Clay, D., Sudduth, K.A. 2018. An introduction to precision agriculture. In: Shannon, D.K., Clay, D.E., and Kitchen, N.R. editors. Precision Agriculture Basics. Madison, WI: ASA, CSSA, and SSSA. p. 1-12. doi:10.2134/precisionagbasics.2016.0084.
Conway, L., Yost, M.A., Kitchen, N.R., Sudduth, K.A. 2017. Using topsoil thickness to improve site-specific phosphorus and potassium management on claypan soil. Agronomy Journal. 109(5):2291-2301. doi:10.2134/agronj2017.01.0038.
Yost, M.A., Kitchen, N.R., Sudduth, K.A., Sadler, E.J., Drummond, S.T., Volkmann, M.R. 2017. Long-term impact of a precision agriculture system on grain crop production. Precision Agriculture. 18(5):823-842. doi:10.1007/s11119-016-9490-5.
Conway, L.S., Yost, M.A., Kitchen, N.R., Sudduth, K.A., Veum, K.S. 2018. Cropping system, landscape position, and topsoil depth affect soil fertility and nutrient buffering. Soil Science Society of America Journal. 82(2):382-391. doi:10.2136/sssaj2017.08.0288.
Shannon, D.K., Clay, D.A., Kitchen, N.R. editors. 2018. Precision Agriculture Basics. Madison, WI: ASA, CSSA, and SSSA. doi:10.2134/precisionagbasics.2018.summary. 265 p.
Clay, D.E., Kitchen, N.R., Byamukama, E., Bruggeman, S. 2017. Calculations supporting management zones. In: Clay, D.E., Clay, S.A., and Bruggeman, S.A. editors. Practical Mathematics for Precision Farming. Madison, WI: ASA, CSSA, and SSSA. p. 122-135. doi:10.2134/practicalmath2017.0024.
Kitchen, N.R., Clay, S. 2018. Understanding and identifying variability. In: Shannon, D.K., Clay, D.A., Kitchen, N.R. editors. Precision Agriculture Basics. Madison, WI: ASA, CSSA, and SSSA. p. 13-24. doi:10.2134/precisionagbasics.2016.0033.
Bobryk, C.W., Yost, M.A., Kitchen, N.R. 2018. Field variability and vulnerability index to identify regional precision agriculture opportunity. Precision Agriculture. 19:589-605. doi:10.1007/s11119-017-9541-6.
Kitchen, N.R., Shanahan, J.F., Ransom, C.J., Bandura, C.J., Bean, G.M., Camberato, J.J., Carter, P.R., Clark, J.D., Ferguson, R.B., Fernandez, F.G. 2017. A public-industry partnership for enhancing corn nitrogen research and datasets: project description, methodology, and outcomes. Agronomy Journal. 109(5):2371-2388. doi:10.2134/agronj2017.04.0207.
Weerasekara, C.S., Kitchen, N.R., Jose, S., Motavalli, P.P., Bardhan, S., Mitchell, R. 2018. Biomass yield of warm-season grasses affected by nitrogen and harvest management. Agronomy Journal. 110(3):890-899. doi:10.2134/agronj2017.04.0196.
Weerasekara, C.S., Udawatta, R.P., Gantzer, C.J., Kremer, R.J., Jose, S., Veum, K.S. 2017. Effects of cover crops on soil quality: selected chemical and biological parameters. Communications in Soil Science and Plant Analysis. 48(17):2074-2082. doi:10.1080/00103624.2017.1406103.
Yost, M.A., Kitchen, N.R., Sudduth, K.A., Allphin, E. 2018. Miscanthus × giganteus growth and nutrient export on 22 producer fields. BioEnergy Research. 11(2):426-439. doi:10.1007/s12155-018-9907-2.
Lee, S., Sadeghi, A.M., McCarty, G.W., Baffaut, C., Lohani, S., Thomson, A., Yeo, I., Wallace, C. 2018. Evaluating the suitability of the Soil Vulnerability Index (SVI) classification scheme using the SWAT model. Catena. 167:1-12.