Data description and availability

This page describes the data sources used in the RTBMaps Atlas, including references, published articles and information on how to acquire each one of the data sets.



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RTB Crop Layers
Cassava, Potato, Sweet Potato and Banana Harvested Area

Global crop area distribution as the proportion of each grid cell. National and subnational agricultural census records were combined with satellite imagery to map the harvested area for 175 crops CIRCA-2000. The crop area harvested data represent conditions around the year 2000.

The dataset can be download from: Earth Stat/ Land Use and the Global Environment Lab (McGill University).

Monfreda, C., N. Ramankutty, and J.A. Foley (2008). Farming the planet. Part 2: Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Global Biogeochemical Cycles 22, GB1022, doi: 10.1029/2007GB002947. [Published article]


Cassava and Potato Yield Gap

Cassava and Potato yield gap in tons per hectare at five minutes grid cell resolution. Yield gaps were estimated by comparing observed crop yield and the potential yields, determined by identifying high-yielding areas with zones of similar climate. The authors divided the world into areas of similar growing characteristics on the basis of climate, and then calculate potential yield and the factors contributing to yield gaps for 16 major crops.

The dataset can be download from: Earth Stat/ Land Use and the Global Environment Lab (McGill University).

Foley JA, Ramankutty N, Brauman KA,Cassidy ES, Gerber JS, Johnston M, Mueller ND, O’Connell C, Ray DK, West PC, Balzer C, Bennett EM, Carpenter SR, Hill J, Monfreda C, Polasky S, Rockström J, Sheehan J, Siebert S, Tilman D, Zaks DP: Solutions for a cultivated planet. Nature; 2011 Oct 20; 478(7369):337-42. [Published article]


Cassava and Potato Potential Yield

Cassava and Potato potential yield in tons per hectare at five minutes grid cell resolution. Global yield variability is heavily controlled by fertilizer use, irrigation and Climate. It is estimated that bringing the world's yields to withing 95% of their potential for 16 important food and feed crops could increase current production by 58%.

The dataset can be download from: Earth Stat/ Land Use and the Global Environment Lab (McGill University).

Foley JA, Ramankutty N, Brauman KA,Cassidy ES, Gerber JS, Johnston M, Mueller ND, O’Connell C, Ray DK, West PC, Balzer C, Bennett EM, Carpenter SR, Hill J, Monfreda C, Polasky S, Rockström J, Sheehan J, Siebert S, Tilman D, Zaks DP: Solutions for a cultivated planet. Nature; 2011 Oct 20; 478(7369):337-42. [Published article]


Cassava, Potato, Sweet Potato, Yam and Banana/Plantain Suitability index

Crop suitability index (value) estimated for high input level rain-fed cassava, white potato, sweet potato and banana/plantain. Crop suitability index (SI) reflects suitability and distribution within grid cells by values based on SI values between 0 and 10000 (SI index times 100). This dataset is the result of the calculation procedures of GAEZ Module V (Integration of climatic and edaphic evaluation) which executes the final step in the GAEZ crop suitability and land productivity assessment.

The dataset can be download from: GAEZ Global Agro-Ecological Zones.

Note: After clicking on this link, do a search for Suitability under "Theme Menu" to find instructions for downloading

FAO/IIASA, 2010. Global Agro-ecological Zones (GAEZ v3.0). FAO, Rome, Italy and IIASA, Laxenburg, Austria. [GAEZ methodology]



Biotic Constraints
Cassava Mealybug

Potential distribution of Cassava Mealybug (Phenacoccus maniholi). Pest risk map, based on model predicting climatic suitability for Phenococcus maniholi. Climatic suitability is estimated by the Eco climatic index (EI), values greater than 20 indicate high risk of infestations.

The dataset can be download from: CIAT-RTBMaps.

Parsa S, Kondo T, Winotai A. (2012). The Cassava Mealybug (Phenacoccus manihoti) in Asia: First Records, Potential Distribution, and an Identification Key. PLos ONE 7(10): ed47675. Doi:10.1371/journal.pone.0047675. [PLoS One article]


Cassava Greenmite

The potential distribution map of the Cassava Greenmite is the result of weighted overlay combination of several ecological models and a set of environmental variables. The map provides a global potential distribution in order to know the places where this species has habitat suitability.

The dataset can be download from: CIAT - RTBMaps.

Herrera Campo, BV, Hyman G, and Bellotti A. (2011). Threats to cassava production: known and potential geographic distribution of four key biotic constraints. Food Security: 3 (3) 329-345. [Published article]


Cassava Whitefly

The potential distribution map of whitefly (Bemisa tabacci) provides a global potential distribution in order to know the places where this species has habitat suitability. The potential distribution map is the result of weighted overlay combination of several ecological models and a set of environmental variables.

The dataset can be download from: CIAT - RTBMaps.

Herrera Campo, BV, Hyman G, and Bellotti A. (2011). Threats to cassava production: known and potential geographic distribution of four key biotic constraints. Food Security: 3 (3) 329-345. [Published article]


Potato Resistant and Susceptible Late Blight Risk

Global blight risk were created by a three month moving window to provide the average daily blight unit accumulation for 12 time periods representing three-month pototo growing season. Average daily blight unit accumulation per three-month growing season for resistant or susceptible potato varieties, predicted by mmMonthly metamodel and planting dates predicted by ECOCROP model.

The dataset can be download from: CIAT - RTBMaps.

Sparks A.H., Forbes G.A., Hijmans R.J., Garrett K.A. Climate change effect on potato late blight. Crop and Environmental Sciences Division, International Rice Research Institute (IRRI), Los Banos, Laguna Philippines. [Documentation]


Potato Tuber Moth Generation Index 2000 and 2050

Global Potato Tuber Moth 2000 and 2050: Change in the abundance (damage potential) of the potato tuber moth (Phthorimaea Operculella - Zeller) in potato production systems worldwide according to model predictions, using the generation index for the year 2000 and 2050.

The dataset can be download from: CIAT - RTBMaps.

J.Kroschel, M. Sporleder, H.E.Z. Tonnang, H. Juarez, P.Carhuapoma, J.C. Gonzalez, R. Simon. 2013. Predicting climate change caused changes in global temperature on potato tuber moth Phthorimaea operculella (Zeller) distribution and abundance using phenology modeling and GIS mapping. Agricultural and Forest Meteorology-ELSEVIER 170.228-241. [Published article]



Abiotic Constraints
Cassava Edapho-Climatic

Cassava climate regions is based on means growing season temperature, number of dry season months, daily temperature range and seasonality.

The dataset can be download from: CIAT - RTBMaps.

CIAT 2002. Climate Regions of Cassava in Africa. Cali, Colombia. International Center for Tropical Agriculture. [Documentation]


Failed Season drought probability

This map estimates the drought probability based on the water balance and simulated daily rainfall. A failed season occurs when there is insufficient water to meet crop requirements. The failed season algorithm (FSA) has been used in a number of application including Hyman et al. 2008

The dataset can be download from: CIAT - RTBMaps

Key references related to this data set are:

Jones, P.G., Thornton, P.K., 2000. MarkSim: software to generate daily weather data for Latin America and Africa. Agron. J. 93, 445–453.

Keig, G., McAlpine, J.R., 1974. A computer system for analysis of soil moisture regimes from simple climatic data. Tech. Memo 74/4. Division of Land Research, Commonwealth Scientific and Industrial Research Organisation, Canberra,Australia.

Hyman, G., S. Fujisaka, P. Jones, S. Wood, C. de Vicente and J. Dixon. 2008. Strategic approaches to targeting technology generation: Assessing the coincidence of poverty and drought-prone crop production. Agricultural Systems. 98:50-61. [Published article]


Soil constraints

This map is based on the Fertility Capability Classification described by Pedro Sanchez and colleagues. 21 different soil constraints were mapped. Several of these were of particular interest to the RTBMaps. Cracking clays (vertisols), High erosion risk and Low nutrient capital reserves are the focus of research efforts by the program. The base map upon which these constraints maps are developed is the one to 5 million digital soil map of the world.

These soil constraints maps can be download from: CIAT - RTBMaps.

Sanchez, P.A., C.A. Palm, and S.W. Buol. (2003). Fertility Capability Soil Classification: a tool to help assess soil quality in the tropics. Geoderma 114:157-185. [Published article]


Length of Growing Period

Length of growing period is defined as the period during the year when average temperatures are greater than or equal to 5 (Celsius degrees) (Tmean >= 5oC) and precipitation plus moisture store in the soil exceed half the potential evapotranspiration (P > 0.5PET). Coarse resolution LGP data is part of IIASA/FAO's initial GAEZ project (IIASA/FAO 2011).

Fischer, G. 2009. Length of growing period data. Data not available for distribution.


Total Annual Precipitation/ Mean Annual Temperature

This map was derived from the Worldclim bioclimatic variables dataset: BIO12 and BI01. Data from WorldClim is at 1 km-squared spatial resolution with a temporal range of approximately 1950-200. It is based on an interpolated climate data from thousands of weather stations across the world.

This dataset can be download from: WorldClim.

Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones, and A. Jarvis. (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25:1965-1978. [Published article]


Climate change: Temperature and Precipitation by 2050

The mean annual temperature and average precipitation change. At the regional scale, both increases and decreases in precipitation are projected. These layers depicts on the A1B scenario of the predicted change in median annual temperature (Celsius degrees) and annual precipitation (in mm) by the end of the year 2050. They have been downscaled from very large pixel sizes to 1 km grid cells.

More information on these maps can be found at: CCAFS - Climate.

Ramirez, J., Jarvis, A. 2008. High resolution statistically downscaled future climate surfaces. International Center for Tropical Agriculture (CIAT); CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Cali-Colombia. [Documentation]



Biodiversity
Banana and Plantain Gap richness

Gap richness in high priority species for the banana and plantain genepool. The genebank collections of the crop wild relative (CWR) are threatened in the wild by habitat modification, the modernization of agricultural areas, and invasive species, among other factors, and climate change is likely to exacerbate their vulnerability. The CWR species are distributed in Southeast Asia, particularly in the Philippines and in Southern China, and the gaps in collections for the six high priority species are concentrated in these same areas.

This dataset can be download from: CIAT-RTBMaps.

Ramirez-Villegas J, Khoury C, Jarvis A, Debouck DG, Guarino L, (2010). A Gap Analysis Methodology for Collecting Crop Genepools: A Case Study with Phaseolus Beans. PLos ONE 5(10): e13497, doi:10.1371/journal.pone.0013497. [PLoS One article]


Cassava Gap richness

Gap richness in high priority species for the Cassava genepool. The 135 species related to Cassava are found in highest concentration in Central and South America region. Significant gaps in collection of this species are designated as high priority for collecting to conservation.

This dataset can be download from: CIAT-RTBMaps.

Ramirez-Villegas J, Khoury C, Jarvis A, Debouck DG, Guarino L, (2010). A Gap Analysis Methodology for Collecting Crop Genepools: A Case Study with Phaseolus Beans. PLos ONE 5(10): e13497, doi:10.1371/journal.pone.0013497. [PLoS One article]


Potato Gap richness

Gap richness in high priority species for the Potato genepool. The 79 species related to potato are found in highest concentration in the central and northern Andes and in central Mexico. Collecting gaps persist throughout the geographic ranges of the genepool, but the 29 high priority species for collecting are concentrated in the north central Andes, in particular in Peru.

This dataset can be download from: CIAT-RTBMaps.

Ramirez-Villegas J, Khoury C, Jarvis A, Debouck DG, Guarino L, (2010). A Gap Analysis Methodology for Collecting Crop Genepools: A Case Study with Phaseolus Beans. PLos ONE 5(10): e13497, doi:10.1371/journal.pone.0013497. [PLoS One article]


Sweet Potato Gap richness

Gap richness in high priority species for the Sweet Potato genepool. The 14 wild relatives of Sweet Potato are concentrated in South America and in Central Mexico up into the south-eastern United States. Areas identified for collecting the 12 high priority CWR species are distributed throughout this range.

This dataset can be download from: CIAT-RTBMaps.

Ramirez-Villegas J, Khoury C, Jarvis A, Debouck DG, Guarino L, (2010). A Gap Analysis Methodology for Collecting Crop Genepools: A Case Study with Phaseolus Beans. PLos ONE 5(10): e13497, doi:10.1371/journal.pone.0013497. [PLoS One article]


Yam Lagos Gap richness

Gap richness in high priority species for the Yam Lagos (Dioscorea cayennensis) genepool. The gap analysis methodology relies on taxonomic, geographic and environmental occurrence information, which is used to model the potential distribution of each CWR species of interest.

This dataset can be download from: CIAT-RTBMaps.

Ramirez-Villegas J, Khoury C, Jarvis A, Debouck DG, Guarino L, (2010). A Gap Analysis Methodology for Collecting Crop Genepools: A Case Study with Phaseolus Beans. PLos ONE 5(10): e13497, doi:10.1371/journal.pone.0013497. [PLoS One article]


Yam Water Gap richness

Gap richness in high priority species for the Yam Water (Dioscorea alata) genepool. The gap analysis methodology also includes an expert evaluation, in which researcher with knowledge of the conservation status and distribution of CWR in specific genepools are asked to analyse the gap analysis results.

This dataset can be download from: CIAT-RTBMaps.

Ramirez-Villegas J, Khoury C, Jarvis A, Debouck DG, Guarino L, (2010). A Gap Analysis Methodology for Collecting Crop Genepools: A Case Study with Phaseolus Beans. PLos ONE 5(10): e13497, doi:10.1371/journal.pone.0013497. [PLoS One article]


Yam White Guinea Gap richness

Gap richness in high priority species for the Yam White Guinea (Dioscorea rotundata) genepool. The gap analysis methodology relies on taxonomic, geographic and environmental occurrence information, which is used to model the potential distribution of each CWR species of interest.

This dataset can be download from: CIAT-RTBMaps.

Ramirez-Villegas J, Khoury C, Jarvis A, Debouck DG, Guarino L, (2010). A Gap Analysis Methodology for Collecting Crop Genepools: A Case Study with Phaseolus Beans. PLos ONE 5(10): e13497, doi:10.1371/journal.pone.0013497. [PLoS One article]


High priority species from all RTB crops genepools combined

The number of high priority of the crop wild relative (CWR) species that are in need of collecting in any particular area. CWR species richness for all RTB crop genepools combined; illustrate the geographic regions around the world where the CWR of the assessed genepools are in greatest need of collecting.

This dataset can be download from: CIAT-RTBMaps.

Ramirez-Villegas J, Khoury C, Jarvis A, Debouck DG, Guarino L, (2010). A Gap Analysis Methodology for Collecting Crop Genepools: A Case Study with Phaseolus Beans. PLos ONE 5(10): e13497, doi:10.1371/journal.pone.0013497. [PLoS One article]


High priority species per crop genepool from all RTB crops

The number of high priority of the crop wild relative (CWR) species that are in need of collecting in any particular area. These collecting priorities hotspots maps display the concentration of areas considered gaps in genebank collections for high priority species.

This dataset can be download from: CIAT-RTBMaps.

Ramirez-Villegas J, Khoury C, Jarvis A, Debouck DG, Guarino L, (2010). A Gap Analysis Methodology for Collecting Crop Genepools: A Case Study with Phaseolus Beans. PLos ONE 5(10): e13497, doi:10.1371/journal.pone.0013497. [PLoS One article]


Species richness for all RTB crops combined

The global distribution of the crop wild relative (CWR) of the 38 assessed genepools for all RTB crop combined. CWR species richness for all RTB crop genepools combined. The species richness map illustrates the concentration of all assessed CWR species, regardless of final priority category, which indicate geographic area where very large number of CWR species exist.

This dataset can be download from: CIAT-RTBMaps.

Ramirez-Villegas J, Khoury C, Jarvis A, Debouck DG, Guarino L, (2010). A Gap Analysis Methodology for Collecting Crop Genepools: A Case Study with Phaseolus Beans. PLos ONE 5(10): e13497, doi:10.1371/journal.pone.0013497. [PLoS One article]



Socioeconomic
Total Population

Global total population, version 3 (GPWv3) - Estimates of human population for the year 2005 by 2.5 arc-minute grid cells and associated dataset from CIRCA-2000. The population count grid are derived from extrapolated data (based on combination of subnational growth rates) from census dates and national growth rates from United Nations statistics. GPWv3 is produced by CIESIN in collaboration with CIAT.

This dataset can be download from: SEDAC, CIESIN at Columbia University.

CIESIN, FAO and CIAT, 2005. Gridded Population of the World, version 3 (GPWv3): Population count grid, future estimates. Palisades, NY: NASA Socioeconomic Data and Application Center (SEDAC).


Total Population Rural

Global rural population, version 1 (GRUMPv1) - Estimates of human population for the year 1990, 1995 and 2000 by 30 arc-second (1 km) grid cells and associated dataset from CIRCA-2000. The urban extent grid distinguish urban and rural areas based on a combination of population counts (persons), settlement points and the presence of nighttime lights. This data set is produced by CIESIN, IFPRI, the World Bank and CIAT.

This dataset can be download from: SEDAC, CIESIN at Columbia University.

CIESIN, IFPRI, The World Bank, and CIAT 2011. Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Population count grid. Palisades, NY: NASA Socioeconomic Data and Application Center (SEDAC).


Global Human Footprint

Global human footprint, version 2 (LWPv2), provide an updated map of anthropogenic impacts on the environment. The Human Footprint Index (HF), expresses as a percentage the relative human influence in each terrestrial biome. The HF values from 0 to 100: 0 represents the least influence the "most wild" part of the biome. 100 repreenting the most influenced "least wild" part of the biome.

This dataset can be download from: SEDAC, CIESIN at Columbia University.

WCS, CIESIN, 2005. Last of the Wild Project, version 2, 2005 (LPW-2): Global Human Footprint Dataset (Geographic). Palisades, NY: NASA Socioeconomic Data and Application Center (SEDAC).


Percent of children underweight

The Global sub-national prevalence of child malnutrition data set consists of estimates of the percentage of children with weight-for-age z-scores that are more than two standard deviations below the median of the NCHS/CDC/WHO International Reference Population. These global maps show the distribution of chronic undernutrition.

This dataset can be download from: SEDAC, CIESIN at Columbia University.

Center for International Earth Science Information Network (CIESIN) Columbia University, 2005. Poverty Mapping Project: Global Subnational Prevalence of Child Malnutrition, Palisades NY, NASA Socioeconomic Data and Application Center (SEDAC).


Stunting among children five

Prevalence of stunting among children under five. These global map shows the distribution of chronic undernutrition at national and subnational levels using stunting in growth among children under five years of ages as an indicator. This indicator reflects long-term cumulative effects of inadequate food intake and poor health conditions as a result of lack of hygiene and recurrent illness in poor and unhealthy environments.

This dataset can be download from: FAO - Geonetwork.

Note: After clicking on this link, do a search for the stunting map to find instructions for downloading

FAO, 2007. Prevalence of stunting among children under five, by lowest available subnational Administrative unit, varying years (FGGD- Digital Atlas).


Global map of Accessibility

Travel time of major cities: A global map of accessibility - developed by the European Commission and the World Bank - captures the connectivity and the concentration of economic activity. The map shows how accessible some parts of the world have become whilst other regions have remained isolated. The data are in geographic projection with a resolution of 30 arc seconds. The pixel values representing minutes of land based travel time to the nearest city of 50,000 people (year 2000).

This dataset can be download from: Joint Research Centre - Land Resource Management Unit.

Nelson, A., 2008. Travel time to major cities: A global map of Accessibility. Global Environment Monitoring Unit - Joint Research Centre of the European Commission, Ispra Italy.



Management
Nitrogen Fertilizer Application

The Nitrogen fertilizer application data set of the Global Fertilizer and Manure, version 1. Data collection represents the amount of nitrogen fertilizer nutrients applied to croplands, in kilograms applied per hectare. Data at 0.5 degree resolution.

This dataset can be download from: Earth Stat/ Land Use and the Global Environment Lab (McGill University).

Potter, P., N. Ramankutty, E. Bennett and S. Donner (2010). Characterizing the Spatial Patterns of Global Fertilizer Application and Manure Production, Earth Interactions, 14, 2010. [Published article]

Phosphorus Fertilizer Application

The Phosphorus fertilizer application data set of the Global Fertilizer and Manure, version 1. Data collection represents the amount of nitrogen fertilizer nutrients applied to croplands, in kilograms applied per hectare. Data at 0.5 degree resolution.

This dataset can be download from: Earth Stat/ Land Use and the Global Environment Lab (McGill University).

Potter, P., N. Ramankutty, E. Bennett and S. Donner (2010). Characterizing the Spatial Patterns of Global Fertilizer Application and Manure Production, Earth Interactions, 14, 2010. [Published article]