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United States Department of Agriculture

Agricultural Research Service

Research Project: Landscape-Based Crop Management for Food, Feed, and Bioenergy

Location: Cropping Systems and Water Quality Research

2012 Annual Report


1a.Objectives (from AD-416):
Develop and evaluate food, feed, and bioenergy cropping systems resilient to increasing climatic variability through the application of site-specific soil and crop management. Develop biological assays and soil sensors that are capable of describing soil quality variations between diverse management systems and across landscapes.


1b.Approach (from AD-416):
In this project, our interdisciplinary team will 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 and yield data collected with precision farming technologies to determine where on the landscape to best place alternative crops, such as perennial bioenergy crops. We will also conduct field research to evaluate the production, profitability, and environmental ramifications of bioenergy crops. To better understand soil quality impacts of different management systems, we will develop systems incorporating biological assays and electronic sensor technology that can be deployed for field measurements. We will evaluate site-specific management systems that increase nitrogen use efficiency and that incorporate landscape targeting of conservation measures for improved resilience to climatic variability. Management system evaluations will include on-farm research with active participation by crop producers and crop advisors. Products of this research will include soil quality indicators, sensors for measurement of multiple soil properties, and agricultural and conservation practices specifically designed to deal with landscape variability.


3.Progress Report:
Progress under project objective 1, “Develop and evaluate food, feed, and bioenergy cropping systems”: (1) Yield maps from an 11- county area in Missouri were analyzed using various statistical and mathematical tools to evaluate the impact of soil and landscape features on yield. The investigation revealed that the effect of different management factors (such as planting date) and year-to-year weather differences dominate over soil and landscape effects. Other analytical techniques are being explored. (2) Bioenergy crops were harvested and data analyzed to understand the impact of topsoil depth on grain and switchgrass yields. Miscanthus field research was initiated and baseline soil sampling was conducted on producers’ fields prior to spring 2012 miscanthus planting. (3) A new study was initiated to determine the interaction of soil type, hybrid, population, and side-dress nitrogen fertilizer management strategy on yield performance of sensor-based nitrogen application systems. This study is in cooperation with the University of Nebraska and South Dakota State University, for a total of six field sites. Other crop canopy sensor studies were postponed due to logistics, and corn crop failure at non-irrigated sites. (4) Precision agriculture system field operations were conducted according to protocol. The 2012 soybean crop showed less drought stress than many other nearby fields, presumably because of no-till cropping and aggressive use of cover crops to build a substantial residue base. Progress under project objective 2, “Develop biological assays and soil sensors for describing soil quality”: (1) Baseline analyses of soil quality indicators (SQI) on cropping systems plots are nearly complete and provide insights on effects of management on soil properties after 20 years. Biological activity, aggregate stability, and active carbon content appear to be good indicators for detecting soil quality improvement caused by consistent inputs of organic matter in systems with long crop rotations that include cover crops and with perennial vegetation in forage plots. These findings will be critical in validating a soil quality index model for crop-landscape systems and serve as basis for studies evaluating soil protein characterization (proteomics) as a sensitive SQI. (2) Initial studies relating sensor data to SQI were completed and documented. Based on initial results, refinements in procedures for future data collection are under consideration.


4.Accomplishments
1. Soil quality in diverse agricultural systems. Buffer strips consisting of perennial, deep-rooted grasses or grasses plus trees (agroforestry practice) and cattle pastures integrated with row crop fields across the landscape provide a management strategy for maintaining or improving soil quality while increasing overall productivity with reduced adverse effects on the environment. However, soil quality benefits of such diversified systems are not well known. In cooperation with colleagues at the University of Missouri, ARS researchers at Columbia, Missouri, assessed soil quality attributes in a diversified farming system in silt loam soil on the hilly landscape bordering the Missouri River floodplain in central Missouri. Results demonstrate that conservation practices established with grass and agroforestry buffers greatly improved biological properties of soil quality; inclusion of cattle for grazing in pasture areas was not detrimental to soil quality and offers additional farm income. This diversified system can be adapted to other agricultural landscapes with soils vulnerable to erosion and other degradation factors.

2. Automated methods for removing yield map errors. Yield maps are a key component of precision agriculture, used both in developing and evaluating precision management strategies. All yield maps require correction of errors associated with machine and operating characteristics before the data are appropriate for decision-making. In cooperation with a visiting scientist from Korea, ARS researchers in Columbia, Missouri, developed a new method that allows automatic correction of errors due to the variable time lag between the cutting of the crop from the field and measurement of the grain flow by the sensor in the combine. They incorporated this new approach along with other automation features into a new version of their “Yield Editor” software which is freely available by web download. The automation provided by this new version makes the process much more efficient for users, increasing the likelihood that yield data cleaning will be applied to improve data quality for researchers and producers involved in precision agriculture. Improved yield map data will allow more accurate development and assessment of precision management strategies for improved farm profitability and environmental protection.

3. Improved crop sensing for in-season corn nitrogen fertilization. In recent years crop reflectance sensors have been commercialized to help farmers diagnose crop nitrogen (N) need and determine the amount of N fertilizer to apply. While canopy reflectance sensing has been successful in many farmers’ fields, it has failed in others. To provide information needed for more universal success with canopy sensing, ARS researchers in Columbia, Missouri, evaluated reflectance differences among similar maturing hybrids and identified typical upper and lower values of reflectance as a function of corn height, which improved the reliability of N recommendations based on reflectance data. Also, ARS investigators at Lincoln, Nebraska, and Columbia, Missouri, also found that creating management zones based on soil electrical conductivity and soil spectral reflectance improved the accuracy of N recommendations. These studies provide critical information for altering the decision rules used with canopy sensing technology, making them applicable for a wider range of field situations. Farmers will benefit from this research because they can reduce excess N applications (which should save them money) and reduce N fertilizer loss to the environment.


Review Publications
Sheridan, A.H., Kitchen, N.R., Sudduth, K.A., Drummond, S.T. 2011. Corn hybrid growth stage influence on crop reflectance sensing. Agronomy Journal. 104:158-164.

Myers, D.B., Kitchen, N.R., Sudduth, K.A., Miles, R.J., Sadler, E.J., Grunwald, S. 2011. Peak functions for modeling high resolution soil profile data. Geoderma. 166(1):74-83. DOI:10.1016/j.geoderma.2011.07.014.

Viscarra Rossel, R.A., Adamchuk, V.I., Sudduth, K.A., Mckenzie, N.J., Lobsey, C. 2011. Proximal soil sensing: an effective approach for soil measurements in space and time. Advances in Agronomy. 113:237-282.

Chaudhary, V.P., Sudduth, K.A., Kitchen, N.R., Kremer, R.J. 2012. Reflectance spectroscopy detects management and landscape differences in soil carbon and nitrogen. Soil Science Society of America Journal. 76(2):597-606.

Lee, D., Sudduth, K.A., Drummond, S.T., Chung, S., Myers, D.B. 2012. Automated yield map delay identification using phase correlation methodology. Transactions of the ASABE. 55(3):743-752.

Paudel, B.R., Udawatta, R., Kremer, R.J., Anderson, S.H. 2012. Soil quality indicator responses to row crop, grazed pasture, and agroforestry buffer management. Agroforestry Systems. 84(2):311-323.

Zobiole, L.H., Kremer, R.J., Oliveira, R.S., Constantin, J. 2012. Glyphosate effects on photosynthesis, nutrient accumulation, and nodulation in glyphosate-resistant soybean. Journal of Plant Nutrition and Soil Science. 175(2):319-330.

Roberts, D.F., Ferguson, R.B., Kitchen, N.R., Adamchuk, V.I., Shanahan, J.F. 2011. Relationships between soil-based management zones and canopy sensing for corn nitrogen management. Agronomy Journal. 104(1):119-129.

Landers, G.W., Thompson, A.L., Kitchen, N.R., Massey, R.E. 2012. Comparative breakeven analysis of annual grain and perennial switchgrass cropping systems. Agronomy Journal. 104: 639–648. DOI: 10.2134/agronj2011.0229.

Last Modified: 8/31/2014
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