To create crop distribution maps with mapspamc, various
types of data are required, including national and subnational
agricultural statistics, cropland and irrigated area extent, and
spatially explicit information on economic and bio-physical suitability.
Users of mapspamc can use country-specific sources for each
of these categories, such as national irrigation, cropland and crop
suitability maps. However, apart from the subnational statistics, most
of the data can be taken from global data products. To support easy
implementation of the package, mapspamc is accompanied by a
public data
repository (referred to as the mapspamc_db) that contains a large
number of global maps that are publicly available. The table below shows
the global data products that are included in the database. They are
briefly discussed below.
| Description | Included | Scale | Source |
|---|---|---|---|
| Shapefile with location of administrative units | - | National | National statistics and Yu et al. (2020) |
| Subnational crop statistics | - | National | National statistics and Yu et al. (2020) |
| National crop and price statistics | 1961-2020 | Global | fao.org/faostat/en |
| Land cover map | 2015, 2019 | Global | land.copernicus.eu/global/products/lc |
| Land cover map | 2000, 2005, 2010, 2015, 2020 | Global | esa-landcover-cci.org |
| Land cover map | 2020 | Global | livingatlas.arcgis.com/landcover |
| Land cover map | 2003, 2007, 2011, 2015, 2019 | Global | glad.umd.edu/dataset/croplands |
| Land cover map | 2015 | Global | lpdaac.usgs.gov/products/mcd12q1v006/ |
| Synergy cropland map | 2010 | Global | Lu et al. (2020) |
| Table to rank cropland products | 2015 | Global | authors |
| Irrigated area map | 2010 | Global | Meier et al. (2018) |
| Irrigated area map | 2005 | Global | fao.org/aquastat/en/geospatial-information/global-maps-irrigated-areas |
| Biophysical suitability and potential yield maps | 2000 | Global | IIASA and FAO (2012) |
| Travel time map | 2015 | Global | malariaatlas.org/research-project/accessibility-to-cities |
| Population maps | 2010, 2015, 2020 | Global | worldpop.org |
| Urban area map | 2010, 2015, 2020 | Global | ghsl.jrc.ec.europa.eu |
The aim of mapspamc is to spatially allocate national
and subnational information on harvested and physical crop area for four
different production systems (subsistence, low-input rainfed, high-input
rainfed and high-input irrigated). The use of subnational statistics is
of key importance and greatly improves the quality of the gridded maps
(Joglekar, Wood-Sichra, and Pardey 2019).
Four pieces of information need to be collected by the user:
Harvested area statistics. Data on crop level
harvested area at administrative unit level 1 and, if possible, level 2.
mapspamc is designed to handle missing information at the
subnational level so any data is useful.
Subnational administrative unit map.. It is essential to have a map (e.g. shapefile or any other polygon format) with the location of the subnational administrative units that corresponds with the subnational statistics. These two sources of information need to be perfectly consistent or made consistent.
Cropping intensity statistics. Information on the cropping intensity (e.g. the number of crop rotations in case of multicropping) per crop at the national level and, if available, at the subnational 1 level. This information will be combined with the harvested area statistics to estimate the physical crop area at the national and subnational level.
production system area shares. Data on the area share for each crop and all four production systems, preferably at the national and, if available, subnational 1 level. This information will be combined with physical area estimation to calculate the physical crop area for each of the four production systems times crop combinations at the national and subnational level.
Finding subnational information on harvested area is not easy as they are not always collected and/or published by national statistical agencies and if they are available they often cover a selection of crops and might have many missing values. Cropping intensity and production system shares are probably even much more difficult to find and often requires making a lot of assumptions to fill data gaps (e.g. assuming the same cropping intensity for similar crops).
Some places where you might look for subnational statistics:
National statistical agencies and agricultural research institutes, are the best places to find subnational agricultural statistics.
CountryStat is database with Food and agriculture statistics at the subnational level. Its predecessor Agro-Maps with older data can also still be accessed. Coverage of both databases is, however, limited.
Knowing in which regions crops are not produced is also very useful. By setting the harvested area to 0 for some regions the model will be forced to allocate the (national) statistics in other subnational units.
Agricultural trade statistics in FAOSTAT might give you an idea about the production system shares. If most crop production is exported, a large share of the farmers can probably be categorized as high-input (or irrigated) farming.
As mentioned above, the AQUASTAT database provides information on irrigated area shares.
Information on harvested area at the national level can be obtained from FAOSTAT, which is also included in mapspamc_db. Data on irrigated crop area can be taken from AQUASTAT.
To allocate the physical area statistics mapspamc
requires a cropland extent, which shows the location of cropland in a
country for the target year. There are several global cropland products
and often countries produce a national land cover map, which shows the
location of cropland. To account for the uncertainty in the location of
cropland, Fritz et al. (2011) combined
several different products into one so-called synergy cropland map.
The synergy cropland approach combines all available (global)
cropland maps and creates a ranking for each grid cell that measures the
level of agreement between the various input maps. In the
mapspamc the grid cells with the least uncertainty are
selected first when allocating physical area of individual crops. Apart
from the ranking, the synergy cropland approach also prepares maps with
the mean and maximum cropland area per grid cell. The mean area product
is used as the base layer by mapspamc but in case this is
not sufficient to allocate all the physical area, grid cells from the
maximum cropland map can be used (see Model preparation for more information
on how this is done).
There are two options to obtain a synergy cropland map. The first is
to take an existing product. If mapspamc is used to produce
crop distribution maps for around 2010, the user can use an existing
global product (Lu et al. 2020) that was
also used as input for SPAM2010. A second option is to construct a
country specific synergy cropland extent. mapspamc_db includes several
recent cropland products that can be combined using scripts in
mapspamc to create a country-specific synergy cropland
map.
To allocate the irrigated crops, mapspamc needs an
irrigated area extent. Similar to the synergy cropland map, we create a
synergy irrigated area map that takes into account the uncertainties
related to the location of irrigated areas. At present, there are only
two global products that provide this information. the Global Map of
Irrigated Areas (GMIA) (Siebert et al.
2013), shows the areas that are equipped for irrigation based on
national survey data and maps for 2005 at 5 arc minutes. The Global
Irrigated Areas (GIA) map (Meier, Zabel, and
Mauser 2018) depicts actual irrigated areas around the period
2005. It combines normalized difference vegetation index (NDVI) maps,
crop suitability data and information on the location of areas equipped
for irrigation to create an irrigated area map at a resolution of 30 arc
seconds. In comparison to the GMIA, it shows 18% more irrigated area
globally.
Spatially explicit information on biophysical suitability and
potential yield is taken from IIASA and FAO
(2012). The International Institute for Applied Systems Analysis
(IIASA) in collaboration with FAO, developed the global agro-ecological
zones (GAEZ) methodology that assesses the biophysical potential for a
large number of crops across three production systems: low-input
rainfed, high-input rainfed and high-input irrigated systems. The first
class is used for both the subsistence and low-input system in
mapspamc. GAEZ presents spatially explicit information on
the biophysical suitability (on a scale from 1 to 100) and potential
yield (in t/ha) separate for each production system. mapspamc_db
includes data for GAEZv3.1.
As a proxy for access to markets and quality of road infrastructure we used a global map with travel time to high-density urban centers. Weiss et al. (2018) depicts the global road infrastructure in 2015 by presenting travel time to urban areas at a resolution of 1×1 kilometer.
We used the WorldPop (Tatem 2017) database as a our primary source of information for national population density. WorldPop combines a random forest model with census data to generate a gridded prediction of population density at ~100 meter spatial resolution (Stevens et al. 2015). Data is available for at a number of spatial resolutions and various years. We used the global maps at 1x1 kilometer as input for our analysis. To identify the rural population, we used the urban extent from Schiavina et al. (2022). Rural population was selected by removing grid cells that are located within the urban extent.
To calculate the potential revenue of a crop at the grid cell level, we followed the SPAM approach (Wood-Sichra, Joglekar, and You 2016) and multiplied the potential yield from IIASA and FAO (2012) with crop prices from FAO (2019).
Recently GAEZv4 was released, which we aim to add in an update of mapspamc_db↩︎