Classification of Crop Yield Variability in Irrigated Production Fields

A. Dobermann, J. L. Ping, V. I. Adamchuk, G. C. Simbahan, R. B. Ferguson
2003 Agronomy Journal  
yield map represent grain mixed from a certain area, and some uncertainty is associated with the exact size Crop yield maps reflect stable yield patterns and annual random and geographical location of this area as well as meayield variation. Procedures for classifying a sequence of yield maps surement error. For the same location, this uncertainty to delineate yield zones were evaluated in two irrigated maize (Zea mays L.) fields. Yield classes were created using empirically defined is likely
more » ... vary from year to year because of different yield categories or through hierarchical or nonhierarchical cluster combine travel paths. Therefore, a single-year yield map analysis techniques. Cluster analysis was conducted using average is useful for interpretation of possible causes of yield yield (MY), average yield and its standard deviation (MS), or all variation but may be of limited value for more strategic individual years (AY) as input variables. All methods were compared SSCM decisions over medium-to long-term periods. based on the average yield variability accounted for (RVc). Methods Procedures must be developed to correct or eliminate in which yield was empirically classified into three or four classes recognizable errors of yield monitor measurement and accounted for less than 54% of the yield variability observed and integrate multiyear sequences of yield maps. Here, we failed to delineate high-yielding areas. Six to seven yield classes established by cluster analysis of MY accounted for 60 to 66% of the yield assume that a sequence of corrected and interpolated variability. Differences among cluster analysis methods were small yield maps, which need to be classified to delineate areas for MY as data source. However, fuzzy-k-means clustering had lower with different yield expectation within a field, has been RVc than other methods if used with the MS or AY data. The spatial obtained. Such classification will result in a map of past fragmentation of yield class maps increased in the order MY Ͻ MS Ͻ yield performance. With multiple years of georefer-AY. Univariate cluster analysis of mean relative yield measured for enced yield data, repeating patterns and their more staat least 5 yr should be used for yield classification in irrigated fields ble natural causes may be separated from random variawhere six to seven classes appear to provide sufficient resolution of the yield variability observed. More research should be conducted to
doi:10.2134/agronj2003.1105 fatcat:l4jf4fsninervfcxa5dc3etjpm