Modelling hill country pasture production: a decision tree approach

Abstract

Decision tree models were applied to predict annual and seasonal pasture production and investigate the interactions between pasture production and environmental and management factors in the North Island hill country. The results showed that spring rainfall was the most important factor influencing annual pasture production, while hill slope was the most important factor influencing spring and winter production. Summer and autumn rainfall were the most important factors influencing summer and autumn production respectively. The decision tree models for annual, spring, summer, autumn and winter pasture production correctly predicted 82%, 71%, 90%, 88% and 90 % of cases in the model validation. By integrating with a geographic information system (GIS), the outputs of these decision tree models can be used as a tool for pasture management in assessing the impacts of alternative phosphorus fertiliser application strategies, or potential climate change, such as summer drought on hill pasture production. This can assist farmers in making decisions such as setting stocking rate and assessing feed supply. Keywords: data mining, decision tree, GIS, hill slope, rainfall