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.

中文翻译：

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预测地中海核果产区（西班牙东南部穆尔西亚地区）寒冷积累的插值方法比较


核果生产最重要的农业气候变量之一是冬季寒冷的积累。为了估计未记录气候数据的地点的寒冷积累，需要进行空间插值。在这项研究中，我们使用 49 个气候站的数据，比较了地中海核果产区（西班牙东南部穆尔西亚地区）的平均冬季寒冷 (SWC) 和安全冬季寒冷 (SWC) 的不同插值方法。为了选择最准确的插值方法，因为其选择可能会极大地影响预测精度，通过袋外引导交叉验证比较了具有不同参数化的几种插值方法（总共 32 个实例）的预测能力，结论是，最好的方法是针对平均冬季寒冷的海拔相关回归残差应用径向基函数，以及针对 SWC 的海拔+纬度线性回归。插值中纳入高度大大提高了估计的准确性。事实上，大部分寒气积累的空间依赖性是通过海拔高度来解释的。整个研究区域内插值的准确性并不均匀。所有方法都高估了较温暖沿海地区的寒冷积累，这可能是由于靠近海洋，这凸显了小气候变量在更高分辨率空间插值中的重要性。在较高位置，方法之间的差异更为显着，仅基于距离的方法低估了寒冷的积累，而考虑海拔的方法则略微高估了寒冷的积累。这项研究证明了在应用任何寒冷累积数据之前比较多种空间插值方法的性能的重要性。