ANALYSIS OF SPATIAL YIELD VARIABILITY USING A COMBINED CROP MODEL-EMPIRICAL APPROACH

摘要

The introduction of yield monitors, global positioning systems, and geographic information systems have provided new means to measure crop yield within a field, allowing very fine description of the spatial variability. However, only limited progress has been made on diagnosing reasons for yield variability and on identifying and managing areas of the field to maximize profit or reduce environmental impacts. Methods are needed to determine causes of yield variability and simulate spatial variability of yield across fields. The objective of this article was to quantify the effects of three yield-limiting factors (water stress, soybean cyst nematodes [SCN], and weeds) on soybean yield in a spatially variable field using a combined crop model-regression approach. The procedure was tested on 77 grid cells (0.2 ha) in the McGarvey field in Perry, Iowa, for 1995, 1997, and 1999. After estimating soil water and site parameters, predicted soybean yields were in good agreement with measured yield (r{sup}2 = 0.88). The root mean square of error (RMSE) was 243 kg ha{sup}(-1), within 11% of mean actual yield for each year. The combined model was then used to simulate the effect of each factor separately on yield loss for each grid cell. Water stress had the biggest impact on soybean yield among the yield-limiting factors for all years. Soybean yields were reduced by an average of 1391 kg ha{sup}(-1) as a result of water stress in 1997, while average yield reductions due to weeds and SCN were 167 and 109 kg ha{sup}(-1), respectively. Average estimated yield loss in 1997 due to the combined effects of water stress, SCN, and weeds in each grid cell was 1667 kg ha{sup}(-1). The regression coefficients for attributing yield losses to site factors were field-specific and may not be transferable to other fields and years, but they can be computed for fields where spatial layers include data on these factors.