Transactions of the ASAE Vol. 44(6): 1409–1414 E 2001 American Society of Agricultural Engineers ISSN 0001–2351 1409 USING YIELD MONITOR DATA TO DETERMINE SPATIAL CROP PRODUCTION POTENTIAL R. K. Taylor, G. J. Kluitenberg, M. D. Schrock, N. Zhang, J. P. Schmidt, J. L. Havlin ABSTRACT. Consistent spatial–temporal yield patterns should help determine spatial production potential. Our objective was to evaluate methods for using yield monitor data to develop spatial yield goal maps. Three to seven years of yield monitor data were analyzed for five sprinkler–irrigated cornfields in central and western Kansas. Yield data were block–averaged to 55 m square cells, normalized based on the mean yield, and then used to develop spatial yield goals for subsequent years using six different methods. One method used a uniform yield goal, two methods combined normalized yield monitor data with a uniform yield goal (transitional), and three methods used only normalized yield monitor data from previous years. Methods were evaluated based on their ability to predict the spatial yield pattern of the subsequent year better than the uniform method. Yield monitor data were also segregated based on the temporal CV of each field during the time of the study, and the six methods were evaluated only on the data that were deemed temporally stable. The result of incorporating yield monitor data into yield goals was inconsistent across sites and years. For one site, the two transitional and three yield monitor methods were significantly better predictors of normalized yield. On another field, the uniform method was a better predictor of normalized yield than the yield monitor methods in three of six years, while the yield monitor methods were better than the uniform method in another year. On a third field, the yield monitor method predicted normalized yield better than the uniform method in one of four years with no difference in the other three years. In general, when the correlation coefficient between two years of yield monitor data exceeded 0.70, the methods that incorporated yield monitor data into the yield goal were better predictors of normalized yield than the uniform method. Evaluating these methods using only data from cells where the temporal CV was less than the average temporal CV for the field did not improve the results sufficiently to warrant widespread use of this practice. Keywords. Precision agriculture, Site–specific crop management, Yield maps, Corn, Irrigation. otal nutrient use is greater on corn than any other major field crop grown in the United States, and most Kansas corn acreage requires the addition of at least some nitrogen (N) fertilizer to achieve full production potential (Lamond, 1994). As agricultural producers strive to become more efficient with inputs and as environmental concerns related to soil nitrate leaching increase, N fertilizer applications are being examined more closely for better management opportunities. With the availability of precision agriculture technologies, variable– rate management of N is currently a possible means of improving N use efficiency (Snyder et al., 1997). Article was submitted for review in December 2000; approved for publication by the Power & Machinery Division of ASAE in July 2001. Presented at the 1998 ASAE Annual Meeting as Paper No. 98–1048. Contribution No. 01–217–J Kansas Agricultural Experiment Station, Kansas State University. The authors are Randal K. Taylor, ASAE Member Engineer,Associate Professor, Biological and Agricultural Engineering, Gerard J. Kluitenberg, Professor, Agronomy, Mark D. Schrock, ASAE Member Engineer , Professor, Biological and Agricultural Engineering, Naiqian Zhang, ASAE Member Engineer, Professor, Biological and Agricultural Engineering, John P. Schmidt, Assistant Professor, Agronomy, Kansas State University, Manhattan, Kansas; and John L. Havlin, Professor and Head, Soil Science, North Carolina State University, Raleigh, North Carolina. Corresponding author: Randal K. Taylor, 237 Seaton Hall, Kansas State University, Manhattan, KS 66506; phone: 785–532–2931; fax: 785–532–6944; e–mail: rktaylor@ksu.edu. In Kansas, the N recommendation model for corn incorporates use of a yield goal (Lamond, 1994). Nitrogen recommendation models for corn in several other states also make use of a yield goal or are based on yield potential. Schrock (1994) documented spatial corn yield variations in several Kansas fields. This spatial yield variation certainly could imply spatially variable yield potential and, thus, an opportunity for the development of spatial yield goals. Traditionally, yield goals have been calculated on a whole–field basis in which a single value for yield goal is established for a field. With the availability of grain yield monitors, the development of spatially variable yield goals, or yield goal maps, can be considered. Inasmuch as yield monitors have been available for a relatively short time and their use in production agriculture will increase, farmers and researchers are faced with the problem of making the transition from the use of whole–field yield goals to yield goal maps. Traditionally, multiple years of production history are used to establish the yield goal for a particular field. Observing the actual yield for a field for multiple years and tempering this information with knowledge of seasonal growing conditions and yields from similar fields is a typical approach to determining whole–field yield goals. Determining yield goals for areas within a field is simply a change in scale. Yield monitors have given us the ability to measure the output of areas within the field. We can now use this yield information together with the knowledge of growing conditions and yield from similar areas to determine an appropriate yield goal for localized T