Yield increases mediated by pollination and carbon payments can offset restoration costs in coffee landscapes

This dataset and code can be used to reproduce results from the article with the same title The scripts to generate the farm-level scenarios and the regional-level scenarios are as following: farm-level = optimization_scenarios.R regional-level = optimization_scenarios_rl.R The properties boundaries and APP limits for the municipalities considered in this study can be obtained from the adress: https://www.car.gov.br/publico/imoveis/index. The list of municipalities is located at the file "list_mun.csv" within the tables folder. The raster with land-use and land-cover classes is classified as follows: class 0: all other uses; class 1: natural vegetation; class 2: coffee; The estimated yield per property is at the yield_estimation.csv file. Each property (n=507) is identified with an unique ID. The dataframe contains: the predicted yield in coffee bags/ha (predictec_coffee_bags); yield in USD/ha (yield_USD_future); the difference in yield bewteen the future scenarios and the baseline scenario (diff_USD); the net present value of the difference between the future scenarios and the baseline (npv_yield_USD_future) The script "merging_scen_land_use.R" was used to combine the restoration scenarios generated by the optimization model with the baseline land-use and land-cover;

本数据集及配套代码可用于复现同名学术文章的研究结果。用于生成农场级与区域级情景的脚本如下：农场级情景对应脚本为`optimization_scenarios.R`，区域级情景对应脚本为`optimization_scenarios_rl.R`。本研究涉及的各市政辖区的地块边界及APP限制可通过以下地址获取：https://www.car.gov.br/publico/imoveis/index。市政辖区列表存放在`tables`文件夹下的`list_mun.csv`文件中。用于土地利用与土地覆被分类的栅格数据分类标准如下：类别0：其他所有用地类型；类别1：自然植被；类别2：咖啡种植用地。各地块的估算单产存放在`yield_estimation.csv`文件中。本研究共包含507个地块（n=507），每个地块均配有唯一标识符。该数据框包含以下字段：以咖啡袋/公顷为单位的预测单产（`predictec_coffee_bags`）、以美元/公顷为单位的未来情景单产（`yield_USD_future`）、未来情景与基准情景的单产差值（`diff_USD`），以及未来情景与基准情景的差值的净现值（`npv_yield_USD_future`）。脚本`merging_scen_land_use.R`用于将优化模型生成的修复情景与基准土地利用/土地覆被数据进行合并。