Agricultural workers play a vital role in the global economy and food security by cultivating, transporting, and processing food for populations worldwide. Despite their importance, detailed spatial data on the global agricultural workforce have remained scarce. Here, we present a new gridded dataset that maps the global distribution of agricultural workers for every decade over the years 2000-2100, distributed at 0.083$\times$0.083 degrees resolution, roughly $\sim$10km$\times$10km at the Equator. The dataset is developed using an empirical modeling framework relying on generalized additive mixed models (GAMMs) that integrate socioeconomic variables, including gross domestic product per capita, total population, rural population size, and agricultural land use. The predictions are consistent with Shared Socio-economic Pathways and we distribute full time series data for all SSPs 1 to 5. This dataset opens new avenues for future research on labour force health, productivity and risk, and could be very useful for developing informed, forward-looking strategies that address the challenges of climate resilience in agriculture. The dataset and code for reproducing it are available for the user community [publicly available on publication at DOI: 10.5281/zenodo.14443333].

翻译：农业劳动者通过为全球人口种植、运输和加工粮食，在全球经济和粮食安全中发挥着至关重要的作用。尽管他们至关重要，但关于全球农业劳动力的详细空间数据仍然匮乏。在此，我们提出了一个新的格网化数据集，以0.083°×0.083°的分辨率（在赤道处约为~10km×10km）绘制了2000-2100年间每十年全球农业劳动力的分布图。该数据集是使用一个经验建模框架开发的，该框架依赖于广义可加混合模型（GAMMs），该模型整合了社会经济变量，包括人均国内生产总值、总人口、农村人口规模和农业土地利用。预测结果与共享社会经济路径（SSPs）保持一致，我们发布了所有SSP 1至5的完整时间序列数据。该数据集为未来关于劳动力健康、生产力和风险的研究开辟了新途径，并且对于制定明智的、前瞻性的战略以应对农业气候适应力挑战可能非常有用。用于复现该数据集的数据和代码可供用户社区使用[公开发布于DOI: 10.5281/zenodo.14443333]。