CEC Cropland Index Model (Classified)

For lands used to produce crops, CEC developed a suitability model to simultaneously evaluate several factors that impact an area’s relative implication for croplands. In the CEC land use screens, implication is defined as a possible significance or a likely consequence of an action. For example, planning for energy infrastructure development in areas with more factors that support high-value croplands has implications for opportunities to preserve agricultural land. The variables used in the CEC Cropland Index Model contain information on soil quality (CA Revised Storie Index, Electrical Conductivity, and Sodium Adsorption Ratio), farmland designations (Prime Farmland, Unique Farmland and Farmland of Statewide Importance), and current existence of crops (as indicated by the California Statewide Crop Mapping). The CEC Cropland Index Model does not include statewide information for grazing lands or rangelands, and it is only applied to solar technology. Each input data layer is transformed onto a common scale and weighted according to each dataset’s relative importance. The result is a summation of the input data layers into a single-gridded map. This final model output provides a numerically weighted index of importance for croplands at a given location. The classified version of the model output, given in this dataset, partitions the CEC Cropland Index Model at the mean into areas of high and low implication. The high implication area is used as an exclusion in the CEC Land Use Screens for solar technology. These regions have a relatively higher implication for cropland than the lower implication region. The table below provides data sources that the CEC Cropland Index Model relies on. For a complete description of the model and its use in the 2023 CEC Land-Use Screens, please refer to the Land Use Screens Staff Report in the CEC Energy Planning Library. Dataset Name Source Usage Gridded Soil Survey Geographic (gSSURGO) Database Soil Survey Staff. 2020. "The Gridded Soil Survey Geographic (gSSURGO) Database for California." United States Department of Agriculture, Natural Resources Conservation Service. https://gdg.sc.egov.usda.gov/ Provides CA Revised Storie Index, Electrical Conductivity, and Sodium Adsorption Ratio for the CEC Cropland Index Model for the Core and SB 100 Terrestrial Climate Resilience Screens for solar resource potential California Important Farmland 2022. "2018 California Important Farmland.” Farmland Mapping and Monitoring Program." California Department of Conservation. https://www.conservation.ca.gov/dlrp/fmmp Prime Farmland, Unique Farmland, and Farmland of Statewide Importance is used in the CEC Cropland Index Model for the Core and SB 100 Terrestrial Climate Resilience Screens for solar resource potential California Statewide Crop Mapping (2019) 2022. "2019 California Statewide Crop Mapping." California Department of Water Resources. https://data.cnra.ca.gov/dataset/statewide-crop-mapping The footprint is used as part of the mask for the CEC Cropland Index Model’s domain of analysis for the Core and SB 100 Terrestrial Climate Resilience Screens for solar resource potential针对用于作物生产的土地，加州能源委员会（California Energy Commission, CEC）开发了一套适宜性模型，可同步评估影响某区域作为农田的相对适宜程度的多项因素。在CEC土地筛选框架中，"适宜性（implication）"被定义为某项行动可能产生的重要性或潜在后果。例如，在更有利于高价值农田的区域规划能源基础设施开发，会对保护农业用地的机遇产生相应影响。 CEC农田指数模型所使用的变量涵盖土壤质量相关信息（加州修订斯托里指数（CA Revised Storie Index）、电导率（Electrical Conductivity）与钠吸附比（Sodium Adsorption Ratio））、农田认定类别（优质农田（Prime Farmland）、独特农田（Unique Farmland）及全州重要农田（Farmland of Statewide Importance）），以及当前作物种植情况（以加州全州作物制图（California Statewide Crop Mapping）为依据）。 CEC农田指数模型未涵盖放牧地或牧场的全州级信息，且仅适用于太阳能技术场景。每个输入数据图层均被统一至同一量纲，并根据各数据集的相对重要性赋予权重。最终结果为将各输入数据图层叠加整合为一幅单网格地图。该模型的最终输出结果为给定位置的农田重要性数值化加权指数。 本数据集提供的模型输出分类版本，以平均值为分界点，将CEC农田指数模型的结果划分为高适宜性区域与低适宜性区域。在太阳能技术的CEC土地筛选中，高适宜性区域被作为避让区域。相较于低适宜性区域，此类区域的农田适宜性相对更高。 下表列出了CEC农田指数模型所依赖的数据源。如需了解该模型及其在2023年CEC土地筛选框架中的完整应用说明，请参阅CEC能源规划库中的《土地筛选工作人员报告》（Land Use Screens Staff Report）。 | 数据集名称 | 数据来源 | 用途 | | --- | --- | --- | | 网格土壤调查地理数据库（Gridded Soil Survey Geographic, gSSURGO） | 土壤调查团队（Soil Survey Staff）. 2020. "加州网格土壤调查地理数据库". 美国农业部自然资源保护局. https://gdg.sc.egov.usda.gov/ | 为CEC农田指数模型、核心场景与SB 100陆地气候韧性筛选场景提供加州修订斯托里指数、电导率与钠吸附比数据，用于太阳能资源潜力评估 | | 2022年加州重要农田 | 2018年加州重要农田. 农田制图与监测计划（Farmland Mapping and Monitoring Program）. 加州自然资源保护部. https://www.conservation.ca.gov/dlrp/fmmp | 将优质农田、独特农田及全州重要农田纳入CEC农田指数模型，用于核心场景与SB 100陆地气候韧性筛选场景的太阳能资源潜力评估 | | 2019年加州全州作物制图（California Statewide Crop Mapping, 2019） | 2022年. "2019年加州全州作物制图". 加州水资源部. https://data.cnra.ca.gov/dataset/statewide-crop-mapping | 将作物覆盖范围作为CEC农田指数模型分析域的掩码数据，用于核心场景与SB 100陆地气候韧性筛选场景的太阳能资源潜力评估