Data from: Observed changes in agroclimate metrics relevant for specialty crop production in California

This Data folder contains the MATLAB code, final product, tables used in Parker, L.E.; Zhang, N.; Abatzoglou, J.T.; Ostoja, S.M.; Pathak, T.B. Observed Changes in Agroclimate Metrics Relevant for Specialty Crop Production in California. Agronomy 2022, 12, 205. https://doi.org/10.3390/agronomy12010205.Data Source: The primary data source for this study was the GridMET dataset, which provides high-resolution meteorological data across the contiguous United States.Analytical Tools: We employed MATLAB for all data processing and analysis, ensuring rigorous computational accuracy. The specific scripts and methodologies used are included within the dataset to facilitate replication.Spatial Analysis: Geographic Information Systems (GIS) were utilized to overlay crop cover data with climate metrics, allowing for a nuanced analysis of regional impacts. This includes shapefiles for California’s state and county boundaries, as well as specific agricultural regions.Agroclimatic Metric Calculation: Metrics such as growing degree days, frost days, and reference evapotranspiration were computed to assess climate trends and their agricultural impacts.Trend Analysis: We applied statistical techniques to identify significant changes and trends in climate metrics over the 40-year study period.The collection and use of climatological data from the GridMET dataset comply with public data use agreements. No personally identifiable information (PII) or restricted environmental data were used in this study.This dataset encompasses a detailed analysis of agroclimate metrics relevant to specialty crop production in California over the period from 1981 to 2020. Using the GridMET meteorological dataset, we calculated 12 distinct agroclimatic metrics that are crucial for understanding the impact of climate variability on agricultural outputs.This dataset is intended for researchers and policymakers interested in agricultural planning and climate adaptation strategies. It provides a valuable resource for developing sustainable agricultural practices in response to changing climatic conditions in California.

本数据文件夹包含了 MATLAB 代码、最终产品以及用于 Parker, L.E.; Zhang, N.; Abatzoglou, J.T.; Ostoja, S.M.; Pathak, T.B. 发表的《加利福尼亚州特色作物生产相关农业气候指标观测变化》一文中使用的表格。数据来源：本研究的主要数据来源为 GridMET 数据集，该数据集提供了美国大陆范围内的高分辨率气象数据。分析工具：我们采用了 MATLAB 进行所有数据处理和分析，确保了严格的计算精确度。数据集中包含了具体的脚本和方法，以促进复现。空间分析：地理信息系统（GIS）被用于将作物覆盖数据与气候指标叠加，从而实现对区域影响的细致分析。这包括加利福尼亚州州和县边界以及特定农业区域的 shapefile。农业气候指标计算：通过计算生长度日、霜冻日和参考蒸散量等指标，评估气候趋势及其对农业的影响。趋势分析：我们应用了统计技术，以识别在 40 年研究期间气候指标中的显著变化和趋势。GridMET 数据集中的气候数据的收集和使用符合公共数据使用协议。本研究未使用任何个人身份信息（PII）或受限环境数据。本数据集涵盖了从 1981 年至 2020 年期间加利福尼亚州特色作物生产相关的农业气候指标的详细分析。利用 GridMET 气象数据集，我们计算了 12 个关键的农业气候指标，这些指标对于理解气候变化对农业产出影响至关重要。本数据集旨在为关注农业规划和气候适应策略的研究人员和政策制定者提供参考。它为应对加利福尼亚州不断变化的气候条件，发展可持续的农业实践提供了宝贵的资源。