No abstract

Agricultural diversification could diversify diets, increase nutrient availability and improve child anthropometry. However, these interventions need to be accompanied by sanitation improvements, family planning, nutritional education and women's empowerment to strengthen their positive effect on diet and nutrition.

Abstract. 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).

Abstract. Farms are not homogeneous. Smaller farms generally have different planted crops, yields, agricultural input, and irrigations compared to larger farms. Mapping farm size could facilitate studies to quantify how water availability and climate change affect small and large farms respectively. Given the lack of gridded farm-size specific data, this study aims to develop a global gridded simultaneously farm-size- and crop-specific dataset of harvested area. We achieved it by downscaling a best-available dataset, which collected direct measurements on crop and farm size, using crop maps, cropland extent, and dominant field size distributions for 2010. Uncertainties in crop maps were explicitly considered by using two crop maps separately during downscaling. Due to data availability, our downscaled maps cover 56 countries accounting for half of the global cropland. Based on the two different crop maps, we have one 5-arcmin gridded, simultaneously farm-size- and crop-specific dataset of harvested area for 11 farm sizes, 27 crops, and 2 farming systems and the other one for 11 farm sizes, 42 crops, and 4 farming systems. The downscaled maps show major planted crops and irrigation change along with farm sizes which support previous findings. Validations show well consistencies with observations on farm-size specific oil palm from satellite images, farm-size specific irrigation from household surveys, and previous studies that map farm size but are not crop-specific. We observed some uncertainties at the grid cell level and found conclusions at the country-level are robust to these uncertainties including the uncertainties from the crop maps. Our downscaled maps will help to explicitly include farm size into global agriculture modeling. The source data, code, and downscaled maps are open-access and free available at https://doi.org/10.5281/zenodo.5747616 (Su et al., 2022).