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 under project objective 1, “Develop decision support tools to support a GxExM approach in research and for on-farm implementation of sustainable and resilient cropping systems.”: (1) An eight state effort led by a project scientist, in collaboration with researchers at 8 Land Grant universities, is in its third year. Two growing seasons of data (32 site-years) have been compiled and analyzed to investigate the performance of in-season corn nitrogen management tools. Additional research is being conducted on 17 research sites during the 2016 growing season, and will be added to this robust regional dataset for analysis and subsequent journal publications. (2) Near-infrared spectroscopy data were combined with in-field soil apparent electrical conductivity and penetrometer data to improve estimation of soil health indicators, showing that sensor fusion has the potential to improve soil health assessment. (3) Multiple soil health assessment indicators are currently under evaluation for their utility and efficacy on Missouri claypan soils. Three methods for measuring potentially mineralizable nitrogen were evaluated and a 7-day anaerobic method was selected for future assessments. A method for measuring total soil protein has also been selected and is being implemented to expand the suite of biological soil health indicators. In addition, active carbon is being measured on hundreds of soil samples from across Missouri to develop a regional algorithm for scoring active carbon in the Soil Management Assessment Framework. Progress under project 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 journal article comparing six-year (2009-2014) production and profitability of grain crops (corn and soybean) with a switchgrass bioenergy crop on claypan soil landscapes has been submitted. Baseline soil samples and recently collected soil samples are currently undergoing soil health analysis to evaluate and compare short-term (4-5 year) changes under the same cropping systems. (2) A journal article has been submitted comparing long-term yield and yield stability of a precision agriculture grain cropping system (PAS) to conventional management. Additional profitability analysis comparing these two systems is nearly complete, with a journal manuscript expected to be submitted within six months. (3) Soil samples were collected at the plot and field scale to compare soil health under conventional and PAS management. Biological assessments are underway, including extraction of soil microbial DNA and phospholipid fatty acids for microbial community structure, as well as enzyme assays to assess microbial function. (4) Eddy-covariance flux instrumentation has been established and data are being collected both at the PAS research field, which represents the aspirational (ASP) system of the Central Mississippi River Basin Long-Term Agroecosystem Research site, and at a nearby farmer-operated field that serves as the comparison business-as-usual system.
1. Sustainability of long-term conservation cropping systems. Claypan soils of the midwestern United States are highly prone to erosion and associated environmental concerns, and likely require special soil and crop management practices for long term sustainability. Over two decades, ARS researchers at Columbia, Missouri, and cooperators investigated the impact of conservation practices and different soil-landscape conditions on claypan-soil corn, soybean, and wheat grain yield. Grain yields of conservation cropping systems with no-till and reduced chemical inputs, diversified rotations, cover crops, and adaptive chemical inputs were equal to those from a conventional grain cropping system. Also, conservation systems increased yield and yield stability, and reduced crop yield variability over a conventional system on the most eroded and vulnerable part of the landscape. Results of this study will aid in the further acceptance and use of conservation practices by farmers and farm advisors on claypan landscapes. Increased use of conservation practices should allow farmers to maintain long-term crop yields, reduce variability in crop yield, and increase the stability of yields across a range of growing conditions, including extremes associated with increased climate variability.
2. Improved classification of soils for precision farming. Traditional crop management methods account for little of the within-field variation affecting crop production -- a basic need for the successful implementation of precision farming approaches. ARS researchers at Columbia, Missouri, and collaborators developed and tested the performance of a new soil classification system called “Environmental Response Units” (ERU) by examining how well it accounted for yield variation within farmers’ fields compared to publically-available USDA soil maps (i.e. Soil Survey Geographic Database (SSURGO)). When compared on over 400 farmers’ fields in four US Midwest states, the ERU classification accounted for more yield variation on average and gave better results in 86% of the fields. These findings show that soil classification with ERU soil maps better delineates soil and landscape characteristics within fields and can improve precision agriculture management applications. Farmers will benefit from this research because it can help them optimize their seed and fertilizer inputs to match production potential within fields. Matching input applications to a better-characterized soil resource will also help minimize field losses of agrichemicals, and thereby benefit the general public with cleaner lakes and streams.
3. Evaluation of nitrogen decision tools for corn. Many decision tools are now available to help manage nitrogen (N) fertilizer applications, but corn farmers are often uncertain what tools work best in their specific conditions. ARS researchers at Columbia, Missouri, cooperators at numerous Midwest land-grant Universities, and industry partners worked together to review canopy reflectance sensor decision rules for N management. They found that the sensors could provide an in-season prediction of yield potential and crop N response but additional information on growing conditions was also needed to optimize N fertilizer recommendations. In field investigations, corn N fertilization based on canopy sensing was directly compared to N fertilization based on crop growth modeling. Although the N rate prescribed by model-based approaches was closer to the optimal N rate, recent versions of sensor decision rules may make the results with the two approaches more alike. Farmers benefit from this research because they can reduce excess N applications and costs. If fertilizer can be better matched with crop need, N fertilizer loss to the environment will be reduced, thus helping to protect soil, water, and air resources.
4. Sensor fusion estimates available soil phosphorous (P) and potassium (K). Measuring the variation in soil fertility within fields is an important component of precision agriculture and sensors that could estimate P and K in soil without sampling and laboratory analysis would make the process of variable-rate fertilization more feasible. Using a database of over 1500 soil samples from the state of Missouri, ARS researchers at Columbia, Missouri and international collaborators showed that P and K estimates from visible and near-infrared (VNIR) reflectance spectroscopy were not sufficiently accurate for control of variable-rate fertilization. In another approach, they combined VNIR spectroscopy with rapid electrochemical analysis by ion-selective electrodes. Data obtained from both types of sensors improved results compared to either sensor alone, and provided P and K accuracies sufficient for use in variable-rate fertilization. This study showed that combining the outputs of multiple sensors, sometimes called “sensor fusion”, has potential for improving soil fertility estimates, and should be investigated further. If proven, this combination approach has the potential to benefit producers by providing them with a rapid, accurate method of quantifying variation in soil properties, as needed for fertility management in precision agriculture.
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