Semios Incorporated, Great Northern Way Vancouver, Vancouver, BC, Canada.
PLoS One. 2022 May 19;17(5):e0267607. doi: 10.1371/journal.pone.0267607. eCollection 2022.
Weather is the most important driver of crop development. However, spatial variability in weather makes it hard to obtain reliable high resolution datasets across large areas. Most growers rely on data from a single station that can be up to 50km away to make decisions about irrigation, pest management and penology-associated cultural practices at the block level. In this regard, we hypothesize that kriging a large network of weather stations can improve thermal time data quality compared to using the closest station. This study aims to explore the spatial variability in California's Central Valley and what is the relationship between the density of weather stations used and the error in the measurement of temperature related metrics and derived models. For this purpose, we used temperature records from January 1st 2020 to March 1st 2021 collected by the California Irrigation Management Information System (CIMIS) and a system of 731 weather stations placed above the canopy of trees in commercial orchards (in-orchard). We observed large discrepancies (>300 GDDTb0) in thermal time accumulation between using an interpolation of all stations available and just using the closest CIMIS station. Our data suggests these differences are not systematic bias but true differences in mesoclimate. Similar results were observed for chill accumulation in areas especially prone to not meeting pistachio chill requirements where the discrepancies between using the site-specific in-orchard weather station network and not using them were up to 10 CP. The use of this high resolution network of weather stations revealed spatial patterns in grape, almond, pistachio and pests phenology not reported before. Whereas previous studies have been focused on predictions at the county or state or regional level, our data suggests that a finer resolution can result in major improvements in the quality of data crucial for crop decision making.
天气是作物生长的最重要驱动因素。然而，天气的空间变异性使得很难在大面积范围内获得可靠的高分辨率数据集。大多数种植者依赖于距离可达 50 公里的单个气象站的数据来做出灌溉、害虫管理和与耕作相关的文化实践的决策。在这方面，我们假设对大量气象站进行克里金插值可以提高热时间数据的质量，而不是使用最近的气象站。本研究旨在探索加利福尼亚中央山谷的空间变异性，以及使用的气象站密度与温度相关指标和衍生模型测量误差之间的关系。为此，我们使用了加利福尼亚灌溉管理信息系统（CIMIS）收集的 2020 年 1 月 1 日至 2021 年 3 月 1 日的温度记录和一个由 731 个气象站组成的系统，这些气象站位于商业果园树冠上方（果园内）。我们观察到，在使用所有可用气象站的插值和仅使用最近的 CIMIS 气象站之间，热时间积累存在很大差异（>300 GDDTb0）。我们的数据表明，这些差异不是系统偏差，而是中尺度气候的真实差异。在特别容易达不到开心果需冷量的地区，冷量积累也观察到了类似的结果，使用特定地点的果园内气象站网络和不使用它们之间的差异高达 10 CP。使用这个高分辨率气象站网络揭示了葡萄、杏仁、开心果和害虫物候的空间模式，这些模式以前没有报道过。虽然以前的研究集中在县、州或地区一级的预测，但我们的数据表明，更精细的分辨率可以大大提高对作物决策至关重要的数据质量。
