A comparison of interpolation methods to predict chill accumulation in a Mediterranean stone fruit production area ( Region de Murcia, SE Spain)

One of the most important agroclimatic variables for stone fruit production is winter chill accumulation. To estimate chill accumulation in locations where climatic data is not recorded, spatial interpolation is necessary. In this study, we compare different interpolation methods for mean and Safe Winter Chill (SWC) in a Mediterranean stone fruit production area (Region of Murcia, SE Spain) using data from 49 climatic stations. To choose the most accurate interpolation method, as its choice may substantially influence the prediction accuracy, the predictive capability of several interpolation methods with different parameterizations (for a total number of 32 instances) was compared through out-of-bag bootstrap cross-validation, concluding that the best ones were Radial Basis Functions applied on the altitude-dependent regression residuals for mean winter chill and the altitude+latitude linear regression for SWC. The incorporation of altitude in the interpolation increased greatly the accuracy of the estimation. In fact, most of the chill accumulation spatial dependency was explained through altitude. The accuracy of the interpolation was not homogeneous across the study area. Chill accumulation in warmer coastal localities was overestimated by all the methods, possibly due to the proximity to the sea, highlighting the importance of microclimatic variables at higher-resolution spatial interpolations. Differences between methods were more notable in higher locations, where distance-only based methods underestimated chill accumulation and methods that consider altitude slightly overestimated it. This study demonstrates the importance of comparing the performance of multiple spatial interpolation methods before applying any for chill accumulation data.

Ministerio de Ciencia e Innovación de España TED2021-131953B-C21 y PID2020-118008RB-C21

PRIMA del Programa Marco de la Unión Europea para la Investigación y la Innovación H2020 y Ministerio de Ciencia e Innovación de España «AdaMedOr» PCI2020-112113