Interpolation techniques for climate variables
This paper examines statistical approaches for interpolating climatic data over large regions, providing a brief introduction to interpolation techniques for climate variables of use in agricultural research, as well as general recommendations for future research to assess interpolation techniques. Three approaches—1) inverse distance weighted averaging (IDWA), 2) thin plate smoothing splines and 3) co-kriging—were evaluated for a 20,000 km2 square area covering the state of Jalisco, Mexico. Taking into account valued error prediction, data assumptions, and computational simplicity, we recommend use of thin-plate smoothing splines for interpolating climate variables.