Identifying management zones using soil, crop, and remote sensing information

Management zones based on field history, yield maps, topography, remote sensing, and producer preference has the potential to reduce sampling costs and improve fertilizer recommendations. The objectives of this study were: (i) to determine the influence of different approaches to define nutrient management zones based on soil nutrient and crop yield variability; (ii) evaluate fertilizer recommendation errors; and (iii) determine if remote sensing combined with readily available soil attribute information can be used to predict crop yield.; This research was conducted in two 65 and a 40-ha fields located in east South Dakota. Soil samples were analyzed for Olsen P and NO₃-N. Crop yield data were collected using AgLeader 2000 yield monitor for six years in fields with corn (Zea mays L.)/soybean (Glycine max L.) rotation. The management zone boundaries were based on block sampling, equal interval, cluster analysis, soil types, and old homestead location. Different types of information (remote sensing and soil attributes) were used for generating models to estimate corn yield.; Soil nutrient concentrations (Olsen P and NO₃-N) had spatial variability. Old homestead locations had higher soil nutrient contents than the rest of fields. The yields in summit/shoulder areas were limited by too little water, while in wet-spring years yields in footslope areas were limited by too much water. This result suggests that if water ponds in the footslope areas, then wet fertilizer recommendation should be used, and if water pond does not occur, then dry fertilizer recommendation should be used.; For all the methods tested to identify management zones, splitting the fields into 4-ha blocks had the lowest nutrient, yield, and fertilizer recommendations pooled variances. The impact of block sampling on fertilizer recommendations was attributed to field management and soil forming processes. This result suggests that if areas are not physically connected, they should not be composited into a single sample, and that both intrinsic and prior management must be considered in developing nutrient management zones.; When models from principal component analysis were based on one, two, or three dates remote sensing dates, the amount of ability to explain corn yield variability was highest when the models included the fall remote sensing data. Adding soil attribute and plant information to the models had a relatively small impact of explaining yield variability. In model validation, the models developed from two years data sets estimated corn yield better than models developed from one year data set.