Data

This is a geo-spatial data repository for agricultural economists. Everything listed here is openly accessible. For development economists with interest in other open source data sets, we refer you to a major and comprehensive data set collection effort on DEVECONDATA.

We add datasets to this list as we find them, so if you’re aware of a dataset not listed, please send it along! You can write to Leah Bevis (leah.bevis@gmail.com) or to Julia Berazneva (jb793@cornell.edu).

Gridded temperature and precipitation datasets:

Drought Indices:

  • Palmer Drought Severity Index (PDSI) from Aiguo Dai and co-authors. Modeled using NCEP climate prediction precipitation data and surface temperature data from CRU as inputs, PDSI captures atmospheric moisture (i.e. meteorological drought) through a standardized index ranging from -10 (dry) to 10 (wet). The effect of temperature on atmospheric moisture, or potential evapotranspiration, is calculated through Thornthwaite’s (1948) formula. Four years of lagged temperature and precipitation data contributed to the PDSI index of each grid-month, capturing the “build up” of drought. While atmospheric moisture is correlated with soil moisture, or agricultural drought, it is not identical; more details can be found here. The PDSI data is global, at a 2.5° spatial resolution, in monthly time-steps, and the most recent scPDSIpm data covers 1950 to 2014. Interpretation of a PDSI value depends on local mean climate conditions; each grid-month value essentially compares moisture over the last 4 years to the historical grid mean. Thus, a value of +4 might imply floods in the central US but only moderate rainfall in northern Africa.
  • Standardized Precipitation Index (SPI) from NCAR/UCAR. The SPI is the number of standard deviations by which precipitation (they use CRU) lies above or below a long-term mean. Temperature data is not incorporated. Data is global, at 1° spatial resolution, in monthly time steps, and available with “long-term mean” defined around 3-month, 6-month, and 12-month intervals. Interpretation of index values, as with PDSI, changes with mean rainfall… For example, the 6-month SPI value for each grid-month compares a moving 6-month precipitation record against the long-term (since 1948) distribution for the same 6-month period. More info here.
  • Standardized Precipitation Evapotranspiration Index (SPEI). This index is available in multiple datasets, each with “long-term mean” defined by different month-intervals (1 mo, 6 mo, etc.), like SPI. Unlike SPI, but like PDSI, SPEI also allows for temperature to effect drought conditions through potential evapotranspiration (PET). (SPEI version 1 used the Thornthwaite equation of PDSI to calculate PET; the current version uses the supposedly superior Penman-Montheith equation.) Datasets should be chosen according to analysis intent: shorter month-intervals will predict soil water content and river discharge, medium time scales relate to reservoir storage/discharge, and long time scales should predict groundwater storage. Data is global, with 0.5° spatial resolution, covering 1901-2014, and long-term means defined as anything between 1 and 38 months in the various datasets. More info here.

Gridded soil/land datasets and databases:

  • Harmonized World Soil Database from FAO, IIASA, ISRIC, ISSCAS, and JRC. This massive database provides interpolated soil quality estimates for the entire world, including nutrient availability and a number of other variables, in grids spaced at 30 arc sections (approximately 1 km).
  • African SoilGrids from AfSIS/ISRIC. This source provides data on a number of interpolated soil quality indicators (soil pH, sand, soil organic matter, cation exchange capacity, etc.) in 250-meter grids, for the African continent.
  • Soil Map of the World from FAO/UNESCO. The link is down as of March 2016, but generally if “Digital Soil Map of the World (Geonetwork)” will lead to this ESRI shapefile of soil types across the world, as well as Erdas and IDRISI files.
  • Global Land Surface Model from the Terrestrial Hydrology Research Group at Princeton. A global dataset of land surface hydrology, created via multiple land surface simulations.

Gridded crop production/suitability datasets:

 

Note: For more digitized — but not georeferenced — soil maps, see other maps under this FAO portal. For digitized soil maps in Africa specifically, see The Soil Maps of Africa by EuDASM.