Data and code for the paper: "Soil Aptitude for Livestock (SAFL): A Dataset for Brazilian Municipalities"

1. Step 1 (step1.py): Generates an image illustrating how to identify and classify soil composition on a municipality-by-municipality basis. 2. Step 2 (step2.py): Calculates the SAFL index for each municipality and stores the resulting data frame as a `.shp` file in the `output` folder. 3. Step 3 (step3.py): Uses the previously created database to produce a map of Brazil, highlighting the municipalities most suitable for livestock production.1. 步骤1（step1.py）：生成可视化图像，用以演示如何逐市镇（municipality）识别并分类土壤组分。 2. 步骤2（step2.py）：计算各市镇的SAFL指数，并将生成的数据框以`.shp`格式文件存储至`output`文件夹内。 3. 步骤3（step3.py）：基于此前构建的数据库，生成巴西全境地图，并高亮标注出最适宜开展畜牧生产的市镇。