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 (firstname.lastname@example.org) or to Julia Berazneva (email@example.com).
Gridded temperature and precipitation datasets:
- Re-analysis datasets from the European Centre for Medium-Range Weather Forecasts (ECMWF). While the ECMWF primarily forecasts weather, they additionally provide re-analysis (modeled) datasets of recent weather patterns, such as the ERA-interim dataset, with atmospheric variables at varying intervals (e.g., hourly, daily, monthly) at a 79-km grid between 1979 and today.
- Africa Rainfall Climatology version 2 (ARC2) from the National Oceanic and Atmospheric Administration (NOAA). This project provides historical re-analysis rainfall data for Africa between 1983 and 2012, gridded at ~0.1° spatial resolution (~10km), and provided in 10-day intervals.
- Willmott and Matsuura’s Gridded Monthly Time Series V 4.01. These datasets provide monthly, interpolated temperature averages and monthly precipitation totals for the entire world, from 1900 to 2014, in grids size 0.5° latitude by 0.5°
- NASA’s Modern-Era Retrospective Analysis for Research and Applications (MERRA). This project provides re-analysis data on a range of weather and climate variables, at a range of time-scales, gridded at 2/3° longitude and 0.5° latitude.
- CHIRPS data from the Climate Hazards Group (CHG). The quasi-global rainfall data span 50° S to 50° N, gridded at 0.05° resolution. Data includes totals by day, pentad (6 pentads = 1 month) or dekad (3 dekads = 1 month), from 1981 until near-present.
- TRMM data from NASA’s Tropical Rainfall Measuring Mission. This is another quasi-global rainfall data spaning 50° S to 50° N, and gridded at 0.25° resolution. The data begin in 1997 and end in 2015, and are gridded at a three-hour temporal resolution.
- The Africa & Latin America Flood and Drought Monitors, run out of Princeton University. This effort contains a range of historical/monitored and forecasted data (hydrologic, soil moisture, precipitation, etc.) at daily, weekly or monthly time scales, gridded at 0.25° resolution. Info on data construction here.
- Global Land Surface Temperature (LST) from the Terrestrial Hydrology Research Group at Princeton. The hourly, global dataset covers 1971-2009, gridded at 0.5°. In GrADS format.
- Global Land Data Assimilation Systems (GLADIS) from NASA. This is a collection of global datasets at various spatial (0.25° – 1° resolution) and temporal (3 hour or monthly) resolutions, covering 1948/1979/2000 until 2012/the present. They are each made via similar (not identical) land surface models , and each dataset contains predicted soil moisture, soil temperature, and many other variables.
- Climate Research Unit (CRU) data from the University of East Anglia. Contains precipitation data as well as other variables, a global dataset excluding Antarctica, covering 1901-2014, gridded at 0.5°, and available monthly. Data here, after applying for (instantaneous) access. More info here.
- 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:
- IFPRI’s Spatial Production Allocation Model (SPAM) database compiles crop production data gridded at 10×10 km resolution for several countries.
- FAO’s Global Agro-Ecological Zones (GAEZ) data, which provides spatially referenced time series data as well as time-averaged data on climactic variables, crop suitability/productivity variables, and yields and production gaps.