Integrating climate into the Digital Soil Assessment framework to assess land suitability

Assessment of climatic temperature variability as part of a Digital Soil Assessment (DSA) is important to quantify and can determine areas where certain plants or crops are able to successfully propagate. This paper investigates how specific climatic variables including frost risk, chilling hours and Growing Degree Day (GDD) potential can be quantified, using Digital Soil Mapping (DSM) methods, and assimilated into a DSA framework to assess overall land suitability. The climate assessment involved deploying 271 temperature loggers and 7 weather stations which were programmed to record temperature for twelve months. The loggers were correlated to long term climate stations. Multivariate interpolation techniques such as regression kriging, regression trees and random forests were used to quantify climatic temperature variables (between logger/weather station sites). The surfaces were then integrated with digital soil attribute surfaces through a suitability model to produce Enterprise Suitability Surfaces for 20 different enterprise crops.