A cultivated planet in 2010: 2. the global gridded agricultural production maps

Abstract

Abstract. Data on global agricultural production are usually available as statistics at administrative units, which does not give any diversity and spatial patterns thus is less informative for subsequent spatially explicit agricultural and environmental analyses. In the second part of the two-paper series, we introduce SPAM2010 – the latest global spatially explicit datasets on agricultural production circa year 2010 – and elaborate on the improvement of the SPAM (Spatial Production Allocation Model) dataset family since year 2000. SPAM2010 adds further methodological and data enhancements to the available crop downscaling modeling: it not only applies the latest global synergy cropland layer (see Lu et al., submitted to the current journal) and other relevant data, but also expands the estimates of crop area, yield and production from 20 to 42 major crops under four farming systems across a global 5 arc-minute grid. All the SPAM maps are freely available at the MapSPAM website (http://mapspam.info/), which not only acts as a tool for validating and improving the performance of the SPAM maps by collecting feedbacks from users, but also dedicates as platform providing archived global agricultural production maps for better targeting the Sustainable Development Goals by making proper agricultural and rural development policies and investments. In particular, SPAM2010 can be downloaded via an open-data repository (DOI: https://doi.org/10.7910/DVN/PRFF8V, IFPRI, 2019).

Climate change risks to push one-third of global food production outside Safe Climatic Space

“…For assessing the potential impacts of climate change on food production, we used openly available global spatial datasets. For crop production, we used the total crop production data SPAM 31 that include altogether 27 major food crops (we intentionally left out 15 non-food crops labelled as nonfood crops in the SPAM data 31 , including for example sugarcane and sugar beet) for year 2010 with resolution of 5 arc-min.…”

Precolonial States and Development: Evidence from Agriculture in Africa

“…Our measure of agricultural output comes from the widely used IFPRI Spatial Allocation Model (SPAM 2010 v1.0) dataset, which uses a combination of satellite imagery and official statistics on country-level and province-level administrative data on total crop production to assign estimates of agricultural output measured in international dollars to pixels at a 5 arcminute resolution (where each pixel represents approximately 56km 2 ) for the year 2010. A documentation can be found in (Yu et al 2020). In brief, the SPAM database obtains satellite imagery-based estimates of land cover across pixels, exploiting the fact that different types of land cover (e.g.…”

A cultivated planet in 2010 – Part 1: The global synergy cropland map

“…Cropland areas estimated from satellite-based datasets are often inconsistent with statistics, which limits their applications in agricultural economics and food policy. First, the existing datasets usually focus on the land cover rather than land use because of the direct nature of remote-sensing observation (Kerr and Cihlar, 2003;Zeng et al, 2018). Cropland, as an integration of land cover and land use, is not only defined as the crops covering the land surface but also is influenced by human activities for food production.…”

“…Cropland, as an integration of land cover and land use, is not only defined as the crops covering the land surface but also is influenced by human activities for food production. However, satellite-based cropland maps may fail to detect cropland features of land use (Zeng et al, 2018). For example, according to estimates using GlobeLand30, the cropland area in Europe increased by 22 090 km 2 from 2000 to 2010 (Xiang et al, 2018).…”

“…For example, GlobCover 2005/2009, Climate Change Initiative Land Cover (CCI-LC), and MODIS Collection 5 (MODIS C5) include mosaic classes that mix cropland with other land cover types. Therefore, these products often under-or overestimate cropland areas, depending on how these mosaic classes are counted (Zeng et al, 2018). Agricultural statistics are usually collected by interviews and sample surveys and then computed by aggregating them with administrative data (Gallego et al, 2010).…”