Farms are not homogeneous. Smaller farms generally have different planted crops, yields, agricultural inputs, and irrigation applications compared to larger farms. However, gridded farm-size-specific data that are moreover crop specific, are currently lacking. This obscures our understanding of differences between small-scale and large-scale farms, e.g., with respect to climate change adaptation and mitigation strategies, contribution to (local) food security, and water consumption patterns. This study fills a significant part of the current data gap, by developing high-resolution gridded, simultaneously farm-size-specific and crop-specific datasets of harvested areas for 56 countries (i.e., covering about half the global cropland). Hereto, we downscaled the most complete global direct measurements of farm size and crop type by compiling state of the art datasets, including crop maps, cropland extent maps, and dominant field size distribution, representative for the year 2010. Using two different crop map sources, we were able to produce two new 5 arcmin gridded datasets on simultaneously derived farm-size-specific and crop-specific harvested areas: one for 11 farm sizes, 27 crops, and 2 farming systems, and one for 11 farm sizes, 42 crops, and 4 farming systems. In line with previous findings, our resulting datasets show major differences in planted crops and irrigated area (%) between farm sizes. Consistency between our resulting datasets and (i) observations from satellite images on farm-size-specific oil palm, (ii) household surveys on the farm-size-specific irrigated area (%), and (iii) previous studies that mapped noncrop-specific farm sizes and support the validity of our datasets. Although at grid level some uncertainties remain to be overcome, particularly those stemming from uncertainties in crop maps, results at country level seem robust. Source data, code, and resulting datasets are open access and freely available at https://doi.org/10.5281/zenodo.6976249 (Su et al., 2022).

中文翻译：

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网格化 5 个 arcmin 数据集，同时用于 56 个国家的特定农场规模和特定作物收获区域

农场不是同质的。与较大的农场相比，较小的农场通常具有不同的种植作物、产量、农业投入和灌溉应用。然而，目前还缺乏特定于作物的特定农场规模的网格化数据。这模糊了我们对小型和大型农场之间差异的理解，例如，在气候变化适应和减缓战略、对（当地）粮食安全的贡献以及用水模式方面。本研究通过为 56 个国家（即覆盖全球约一半农田）的收割区域开发高分辨率网格化、同时针对特定农场规模和特定作物的数据集，填补了当前数据空白的重要部分。至此，我们通过编译最先进的数据集（包括代表 2010 年的作物地图、农田范围地图和主要农田大小分布），缩小了对农场规模和作物类型的最完整的全球直接测量。使用两种不同的作物地图来源，我们能够在同时派生的特定农场规模和特定作物收获区域上生成两个新的 5 arcmin 网格数据集：一个用于 11 个农场规模、27 个作物和 2 个耕作系统，一个用于 11 个农场规模、42 个作物和4种耕作系统。与之前的研究结果一致，我们得到的数据集显示了农场规模之间种植作物和灌溉面积 (%) 的主要差异。我们得到的数据集与 (i) 对特定农场规模油棕的卫星图像观察结果，(ii) 对特定农场规模灌溉面积 (%) 的家庭调查之间的一致性，(iii) 以前的研究绘制了非作物特定农场规模并支持我们数据集的有效性。尽管在网格层面仍有一些不确定性有待克服，尤其是那些源于作物地图不确定性的不确定性，但国家层面的结果似乎是稳健的。源数据、代码和生成的数据集是开放访问的，可在 https://doi.org/10.5281/zenodo.6976249 (Su et al., 2022) 免费获得。