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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #257166
Title: Analyzing Landscape Effects on Corn and Soybean Yield and Yield Risk from a Large Yield Monitor Dataset

Author
item MYERS, DAVID - University Of Florida
item Kitchen, Newell
item Sudduth, Kenneth - Ken
item TRACY, PAUL - Missouri Farmers Association (MFA INCORPORATED)
item Sadler, Edward
item YOUNG, FRED - Natural Resources Conservation Service (NRCS, USDA)

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 8/3/2010
Publication Date: 11/1/2011
Citation: Myers, D.B., Kitchen, N.R., Sudduth, K.A., Tracy, P., Sadler, E.J., Young, F. 2011. Analyzing Landscape Effects on Corn and Soybean Yield and Yield Risk from a Large Yield Monitor Dataset [abstract]. ASA-CSSA-SSSA 2010 International Meeting, October 31-November 4, 2010. ASA-CSSA-SSSA Annual Meeting Abstracts. Paper No. 177-8.

Interpretive Summary:

Technical Abstract: Crop yield variability is due to a variety of factors including many manageable variables such as genetics, weeds and pests, drainage, irrigation, and nutrient supply, but many factors cannot be managed and/or they have un-manageable interactions with climate. Therefore climate and it’s interactions with plant, soil and landscape properties are the primary unknowns that producers face, and are significant causes of yield risk. Until the advent of precision agriculture, most field or plot experiments designed to understand these spatial-temporal interactions had taken a plot, or single field measurement approach. Even after yield monitors have become common, many studies rely on the yield data from one or a few fields. The collection of yield-monitor data from farmers over large geographical regions into large data warehouses offers a new avenue to explore these relationships. Our strategy is to use the multi-temporal and spatial replication of crop yield monitor data to empirically quantify production risks due to soil and landscape factors. The general approach we follow is to collect yield data, collect soil and landscape data (continuous and full coverage), merge these two, then model yield and yield variance with data mining techniques. Using the full coverage soil landscape data layers we apply the model throughout the study area. We have collected a large database of corn and soybean yield monitor data (greater than 50,000 acre years and 900 field years) and a large set of soil-landscape data both at high resolution (10 m). Our aim is to produce regional coverage maps of mean yield and yield variance for Northeast Missouri.