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：农业工作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。尽管其重要性不言而喻，但关于全球农业劳动力的详细空间数据仍然匮乏。本文提出了一种新的网格化数据集，以0.083°×0.083°的分辨率（赤道处约10km×10km）绘制了2000-2100年间每十年全球农业劳动力的分布情况。该数据集采用基于广义加性混合模型（GAMMs）的经验建模框架开发，整合了人均国内生产总值、总人口、农村人口规模和农业用地等社会经济变量。预测结果与共享社会经济路径（SSPs）保持一致，我们提供了SSP 1至SSP 5的完整时间序列数据。该数据集为未来劳动力健康、生产力和风险研究开辟了新途径，并对制定具有前瞻性的知情战略以应对农业气候韧性挑战具有重要价值。数据集及复现代码已向用户社区公开[发布后可通过DOI: 10.5281/zenodo.14443333获取]。