Gridded 5 arcmin datasets for simultaneously farm-size-specific and crop-specific harvested areas in 56 countries

Abstract

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).

Water footprints and crop water use of 175 individual crops for 1990–2019 simulated with a global crop model

“…For instance, there was no differentiation between production systems in FAOSTAT and, hence, both rainfed and irrigated crop yields were scaled with the same scaling factors. In future updates, these factors could be adjusted according to farm sizes 76 and farming intensity 19 .…”

Global modeling of SDG indicators related to small-scale farmers: testing in a changing climate

“…However, few studies shed light on modeling of small-scale farmers at the global scale, although many have presented modeling of hunger risk, another target in Goal 2 (Hasegawa et al 2018, Fujimori et al 2019, Fuso Nerini et al 2019, Soergel et al 2021. Household surveys have played a role as a source of local data, but reliable data on small-scale farmers across the globe are still lacking despite efforts to identify their number, production share, and geographic distribution in global agricultural systems (Samberg et al 2016, Ricciardi et al 2018, Lowder et al 2021, Su et al 2022. This makes global modeling of SDG indicators related to small-scale farmers challenging.…”

The hidden role of small-scale farmers in global food security

“…The roles of different farmers in agricultural production have been elucidated in recent years owing to the use of more granular data, from agricultural census surveys [7,5], geospatial analysis of farms [6,8,9], and field-level assessment [10]. However, such datasets are typically used to compare the role of farmers in domestic food production and not national food security.…”