Defining agricultural management zones using gis techniques: Case study of drip-irrigated cotton fields

Abstract

Fuzzy c-means clustering was used to define soil-nutrient management zones, soil sampling data was tested to identify which data source was the best for partitioning optimum zones, using a geographical information system and various statistical techniques. The study area was a region of large-scale drip-irrigated cotton cultivation in China. For soil data sources, the area was portioned into three zones. To confirm the resulting zones, the coefficient of variation of the nutrient index was calculated for the soil data. The least spatial variation in soil nutrient content was found within the same management zones, with larger variation between zones. The degree of conformity (84.40%) with zones derived using actual cotton production data was found for the management zones defined using the combination of soil data. The method proposed here, using fuzzy c-means clustering and soil sampling data, can be useful in determining zones for optimal fertilizer application and resource management in cotton systems. © 2013 Asian Network for Scientific Information.