Data Download - EarthStat

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Data may be freely downloaded for research, study, or teaching, but must be cited appropriately. Re-release of the data, or incorporation of the data into a commercial product, is allowed only with explicit permission. If you would like to request permission to use EarthStat data for another purpose, please contact us at earthstat.data@gmail.com.

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[title type=”h2″ color=””]Data Description:[/title]

Agricultural activities have dramatically altered our planet’s land surface. To understand the extent and spatial distribution of these changes, we combine a global data set of croplands and pastures circa 2000 by combining agricultural inventory data and satellite-derived land cover data. The agricultural inventory data is used to train a land cover classification data set obtained by merging two different satellite-derived products (Boston University’s MODIS-derived land cover product and the GLC2000 data set). The data are presented at 5 min (~10 km) spatial resolution in latitude by longitude. According to the data, there were 15 million km2 of cropland (12% of the Earth’s ice-free land surface) and 28 million km2 of pasture (22%) in the year 2000.

[title type=”h2″ color=””]Detailed methods and the citation for this data set:[/title]

Ramankutty, N., A.T. Evan, C. Monfreda, and J.A. Foley (2008), Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Global Biogeochemical Cycles 22, GB1003, doi:10.1029/2007GB002952.

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Applications include further work in global agricultural sustainability, water use and quality, and modeling the impact of climate change on agricultural systems. As an example, Smith et al. 2012 used the EarthStat cropland and pasture area data in combination with satellite observations to evaluate global bioenergy capacity. While most forecasts of global energy needs include bioenergy as a source, the potential scale for global bioenergy is still very uncertain. In their analysis, Smith et al. used climate constrained, satellite derived net primary productivity (NPP) data to estimate global bioenergy potential and investigate the question—Is there room to increase productivity in the future? After using MODIS NPP data to constrain global bioenergy potential, the authors accounted for global agricultural land use with the EarthStat cropland and pasture area data set. The researchers concluded that the maximum bioenergy potential is signficantly constrained, compared to previous estimates, ranging from 41-108% depending on the inclusion of pasturelands and remote regions.

Read Global Bioenergy Capacity as Constrained by Observed Biospheric Productivity Rates

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[title type=”h3″ color=””]175 Crops [/title]

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[title type=”h3″ color=””]Major CropsMajor Crops: Maize, Rice, Wheat, Soybean, Barley, Rye, Millet, Sorghum, Sunflower, Potato, Cassava, Sugarcane, Sugarbeet, Oil Palm, Rapeseed, Groundnut, Cotton[/title]

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[title type=”h2″]Data Description:[/title]

Croplands cover ~15 million km² of the planet and provide the bulk of the food and fiber essential to human well-being. Most global land cover data sets from satellites group croplands into just a few categories, thereby excluding information that is critical for answering key questions ranging from biodiversity conservation to food security to biogeochemical cycling. Information about agricultural land use practices like crop selection, yield, and fertilizer use is even more limited. Here we present land use data sets created by combining national, state, and county level census statistics with a recently updated global data set of croplands on a 5 minute by 5 minute (~10 km by 10 km) latitude/longitude grid. The resulting land use data sets depict circa the year 2000 the area (harvested) and yield for 175 crops.

[title type=”h2″]Detailed methods and the citation for this data set:[/title]

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.

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Lobell and Gourdji (2013) used harvested area data for wheat, maize, rice, and soybean to estimate decadal warming trends in growing season since 1980 for major global cereal cropping regions. The researchers first aggregated the five-minute resolution EarthStat harvested area maps to half-degree resolution, then randomly selected samples from cells containing more than 10% harvested area for any of the four crops in the study. The selected sample cells were then combined with historical gridded temperature and crop calendar data to summarize warming trends during the growing season since 1980.

Read The Influence of Climate Change on Crop Productivity

View Figure

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[title type=”h2″ color=””]Data Description:[/title]

Stabilizing greenhouse gas (GHG) emissions from croplands as agricultural demand grows is a critical component of climate change mitigation and these datasets provide spatial and crop specific estimates. Greenhouse gas emissions are calculated from global croplands circa year 2000 for 172 crops. The Monfreda, et al. crop dataset together with various datasets on manure (Gerber, et al.), irrigation (Portmann, et al.) and others provide insights into emissions related to agricultural practices. This download provides three datasets aggregated from the original output of the 172 crops; total emissions from croplands, per kilocalorie emissions from croplands and per food kilocalorie emissions from cropland.

[title type=”h2″ color=””]Detailed methods and the citation for this data set:[/title]

Carlson KM, JS Gerber, ND Mueller, M Herrero, GK MacDonald, KA Brauman, P Havlik, CS O’Connell, JA Johnson, S Saatchi, and PC West. 2016 Greenhouse gas emissions intensity of global croplands. Nature Climate Change: Early Online.

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Emissions from top emitting food crops:

Rice accounts for 51% of total crop emissions (a) because of high CH₄ emissions associated with rice paddy flooding (e). Coconut has high overall production intensity (b) due to 2.2% its of harvested area located on peatlands (c). Fertilizer production intensity is elevated for rapeseed and potato (d). Oil palm’s mean global annual caloric yields are 14-587% greater than other top crops, leading to low intensity across peat and fertilizer intensity metrics; substantial peat development (5.2% of harvested area) generates higher overall production intensity. Total country emissions (f) are dominated by China, with extensive paddy rice and high fertilizer application rates. Vietnam’s triple-cropped rice and Indonesia’s peatland development generate high overall GHG production intensity (g). Peat production intensity (h) exceeds fertilizer (i) and rice (j) intensities. Production intensity includes all crop calories. Food intensity excludes industrial and non-food calories, and assumes that 12% of calories used as livestock feed are available in foods for human consumption.

Read greenhouse gas emissions intensity of global croplands.

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[accordion-item id=”ClimateVariation” title=”Climate Variation Effects on Crop Yields for Maize, Soybean, Rice and Wheat”]

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[title type=”h2″ color=””]Data Description:[/title]

Our geospatial crop data covers the period between 1961 and 2008 annually, and tracks maize, rice, wheat and soybean performance across ~13,500 political units derived from agricultural censuses. This allows for an examination of how recent climate variability led to variations in maize, rice, wheat and soybean crop yields worldwide. This study uniquely illustrates spatial patterns in the relationship between climate variability and crop yield variability, highlighting where variations in temperature, precipitation or their interaction explain yield variability. This download provides three datasets, two are categorical while the other is the coefficient of variation. The first of the two categorical datasets shows the total crop yield variability explained by climate and the second explains crop yield variability as classified into different categories of temperature and precipitation variations. The third dataset provides the coefficient of variation for crop yields over the study period (1961-2008).

[title type=”h2″ color=””]Detailed methods and the citation for this data set:[/title]

Ray DK, JS Gerber, GK MacDonald, PC West. 2015. Climate variation explains a third of global crop yield variability. Nature Communications. doi:10.1038/ncomms6989

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Regional Variations in Wheat:

Approximately 75% of global wheat production came from ~66% of the harvested lands in the United States, Canada, Argentina, Europe, North Africa, India, China and Australia. In these highly productive wheat belts, ~36% of the year-to-year yield variability was explained by climate variability . Approximately 34–45% of the wheat yield variability in the United States, Canada, United Kingdom, Turkey, Australia and Argentina was explained by climate variability. To give an indication of the magnitude of this effect, the climate driven variability in the United States wheat yields equates to, on average, more than half the entire annual production of wheat in Mexico.

Read Climate variation explains a third of global crop yield variability.

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[accordion-item id=”YieldTrends” title=”Yield Trends and Changes for Maize, Soybean, Rice and Wheat”]

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[title type=”h2″ color=””]Data Description:[/title]

Our geospatial crop data covers the period between 1961 and 2008 annually, and tracks maize, rice, wheat and soybean performance across ~13,500 political units derived from agricultural censuses. The information is available at three variable spatial levels based on data availability: national, state and county/district/municípios/departments, geographic units. Data availability varies among regions. This download comes with two datasets. The first is a categorical analysis of yield trends over time while the second provides the rate of yield change in 2050 based on a regression analysis of data from 1989 – 2008.

[title type=”h2″ color=””]Detailed methods and the citation for this data set:[/title]

Ray DK, N Ramankutty, ND Mueller, PC West, JA Foley. 2012. Recent patterns of crop yield growth, stagnation, and collapse. Nature Communications. 3:1293 doi: 10.1038/ncomms2296

Ray DK, ND Mueller, PC West, JA Foley. 2013. Yield trends are insufficient to double global crop production by 2050.Public Library of Science – ONE. doi: 10.1371/journal.pone.006642

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The yield trends data that the world’s maize, rice, wheat and soybean crops are continuing to experience yield increases in over 70, 63, 61 and 76% of their harvested areas, respectively—corresponding to 103, 96, 130 and 63 million hectares (m. ha). Globally, however, rice (35%) and wheat (37%) have substantial areas that are now witnessing yield stagnation. Maize (26%) and soybean (23%) have less area in yield stagnation. Furthermore, we find that 3% of maize, 1% rice and 1% of wheat areas have experienced yield collapse.

Areas where yields are still increasing currently contribute roughly 79%, 57%, 56% and 82% of the total global production in maize, rice, wheat and soybean, respectively. The remainder comes primarily from regions witnessing yield stagnation. This then means that for wheat and rice, at least, yield stagnation may have profound implications on the ability of agriculture to meet the growing global demands for these commodities.

Read Yield Trends Are Insufficient to Double Global Crop Production by 2050.

Read Recent Patters of Crop Yield Growth and Stagnation.

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[title type=”h2″ color=””]Data Description:[/title]

Water depletion is a measure of the fraction of available renewable water consumptively used by human activities within a watershed. Our characterization of water depletion uses calculations from WaterGAP3 to assess long-term average annual consumed fraction of renewably available water, then integrates seasonal depletion and dry-year depletion, also based on WaterGAP3 calculations, with average annual depletion into a unified scale. There are 8 water depletion categories: <5% depleted, 5-25% depleted, 25-50% depleted, 50-75% depleted, dry-year depleted, seasonally depleted, 75-100% depleted, and >100% depleted. For data reliability reasons, we include only the 15,091 watersheds larger than 1,000 km2, which constitute 90% of total land area. A large number of small coastal watersheds are excluded.

[title type=”h2″ color=””]Detailed methods and the citation for this data set:[/title]

Brauman, KA, BD Richter, S Postel, M Malby, M Flörke. (2016) “Water Depletion: An improved metric for incorporating seasonal and dry-year water scarcity into water risk assessments.” Elementa: Science of the Anthropocene.

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The global fraction of food and people potentially affected by water shortage is far greater than the fractional areal extent of water depletion. For example, only 2.2% of watershed land area is more than 75% depleted on an annual average basis, but this area includes 15% of global irrigated land. When seasonal and periodic water depletion are incorporated, this figure rises substantially: 71% of irrigated area occurs in watershed areas that are depleted either seasonally or in dry years. This suggest that the vast majority of irrigated agriculture is at least periodically vulnerable to water shortages

Read Water Depletion: An improved metric for incorporating seasonal and dry-year water scarcity into water risk assessments
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[title type=”h2″]Data Description:[/title]

Large yield variations exist across the world, even among regions with similar growing conditions, suggesting the existence of ‘yield gaps’. Here we define a yield gap as the difference between observed crop yields and the potential yield at the same location. It is estimated that bringing the world’s yields to within 95% of their potential for 16 important food and feed crops could increase current production by 58%. We estimated yield gaps by comparing observed yields to potential yields determined by identifying high-yielding areas within zones, or bins, of similar climate.

Included in each download are yield gaps (tons/ha), potential yield (tons/ha) and climate bins (based on growing degree days and precipitation).

[title type=”h2″]Detailed methods and the citation for this data set:[/title]

1. 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

2.Mueller, ND, JS Gerber, M Johnston, DK Ray, N Ramankutty, and JA Foley. 2012. Closing yield gaps through nutrient and water management. Nature doi:10.1038/nature11420. 490:254-257. also:Licker et al. 2010, Johnston et al. 2011

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Mueller et al. (2012) used yield gap and climate zone data to assess where yields may be limited by management factors. This study used input-yield crop models to analyze where yields may be limited by irrigation, nutrients, or technology (reached current yield ceiling). The yield gap information in this data set provides a comparison of yields across different climates, which allows for comparison of where yields show strong responses to management factors like nutrient or water application.

Read Closing Yield Gaps Through Nutrient and Water Management (subscription required)

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[title type=”h2″]Data Description:[/title]

Fertilizer application rate and consumption data were compiled for nations and subnational units across the globe. Application rates for missing crop–country combinations were estimated as described in the methods portion of the metadata document. Crop- and crop-group-specific application rates were then distributed across detailed maps of crop and pasture areas, and rates were harmonized with subnational and national nutrient consumption data.

[info_message style=”info”]These data include mineral fertilizers, manure, and atmospheric deposition. If you are interested in just one of these individual parts, please contact us.[/info_message]

[title type=”h2″]Detailed methods and the citation for this data set:[/title]

Mueller, ND, JS Gerber, M Johnston, DK Ray, N Ramankutty, and JA Foley. 2012. Closing yield gaps through nutrient and water management. Nature doi:10.1038/nature11420. 490:254-257.

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[title type=”h2″]Data Description:[/title]

Nutrient consumption data were compiled for nations and subnational units across the globe. Application rates for crop–country combinations missing data were estimated as described in the methods portion of the metadata document. Crop- and crop-group-specific application rates were then distributed across detailed maps of crop and pasture areas, and rates were harmonized with subnational and national nutrient consumption data.

[title type=”h2″]Detailed methods and the citation for this data set:[/title]

Mueller, ND, JS Gerber, M Johnston, DK Ray, N Ramankutty, and JA Foley. 2012. Closing yield gaps through nutrient and water management. Nature doi:10.1038/nature11420. 490:254-257.

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[title type=”h2″]Data Description:[/title]

Chemical fertilizers, manure and leguminous crops have been key to agricultural intensification. However, they have also led to widespread nutrient pollution and the degradation of lakes, rivers and coastal oceans. Excess nutrients also incur energy costs associated with converting atmospheric nitrogen and mining phosphorus.

Excess nutrients are calculated as a simple mass balance, which is similar to recent efforts to estimate nutrient balances (Liu et al., MacDonald et al.). Chemical fertilizer and manure data sets are inputs for both nitrogen and phosphorous models. The nitrogen has additional inputs from nitrogen deposition (Dentener et al.) and nitrogen fixation by legumes. Nitrogen fixation is scaled as a function of yields using a range of Nfix values from the literature (Smil 1999) and yields (Monfreda et al. 2008). Nutrient removal from harvest is estimated as the product of yield (Monfreda et al. 2008), dry fraction (Monfreda et al. 2008), and nutrient density (USDA).

[title type=”h2″]Detailed methods and the citation for this data set:[/title]

West, PC, JS Gerber, ND Mueller, KA Brauman, KM Carlson, ES Cassidy, PM Engstrom, M Johnston, GK MacDonald, DK Ray, and S Siebert. 2014. Leverage points for improving food security and the environment. Science. 345:325-328.

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[title type=”h2″]Data Description:[/title]

We derive a global map of natural vegetation at a 5 min resolution classified into 15 vegetation types. This data set is derived mainly from the DISCover land cover data set, with the regions dominated by land use filled using the vegetation data set of Haxeltine and Prentice. Thus our natural vegetation data set is consistently derived from the same source-the DISCover data-as our croplands data set. This data set does not necessarily represent the world’s natural pre-agricultural vegetation. Rather, it is representative of the world’s “potential” vegetation (i.e., vegetation that would most likely exist now in the absence of human activities). In regions not dominated by human land use, our vegetation types are those currently observed from a satellite. This differs from pre-settlement natural vegetation to the extent that vegetation types have changed because of changing environmental conditions such as climate and CO2 concentrations

[title type=”h2″]Detailed methods and the citation for this data set:[/title]

Ramankutty, N., and J.A. Foley (1999). Estimating historical changes in global land cover: croplands from 1700 to 1992. Global Biogeochemical Cycles 13(4), 997-1027

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Potential natural vegetation data was initially used by Ramankutty and Foley (1999) to understand the environmental consequences of global land use for agriculture. By combining the potential vegetation data with historical maps of croplands from 1700-1992, the researchers summarized the extent to which natural vegetation has been cleared for croplands historically, including more than 11 million km2 of forests and woodlands and 7 million km2 of savannas, grasslands, and steppes cleared for cropland, globally.

Read Estimating historical changes in global land cover: Croplands from 1700 to 1992

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[accordion-item id=”CarbonStocks” title=”Carbon Stocks in Potential Natural Vegetation”]

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[title type=”h2″]Data Description:[/title]

Land used for agricultural production presents a tradeoff to society. On one hand, agricultural lands provide essential food, feed, fiber, and increasingly, biofuels. On the other hand, in their natural state, these lands could provide additional important ecosystem services. Agricultural practices affect carbon storage, with consequences for greenhouse gasses and climate change. How do we balance the need to expand agricultural production with the need to maintain or even expand ecosystem carbon stocks? To help answer this question, in this data set we use International Panel on Climate Change’s Tier 1 methodology to combine above-and below-ground biomass observations with a data set of potential natural vegetation.

[title type=”h2″]Detailed methods and the citation for this data set:[/title]

West, P.C., H.K. Gibbs, C. Monfreda, J. Wagner, C.C. Barford, S.R. Carpenter, and J.A. Foley (2010). Trading carbon for food: Global comparison of carbon stocks vs. crop yields on agricultural land. Proceedings of the National Academy of Sciences (PNAS) 107(46), 19645–19648.

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West et al. (2010) used carbon stocks maps to estimate carbon trade-offs from converting natural vegetation to cropland. This study combined the carbon stocks in potential vegetation maps with calculated standing carbon stocks for current crops in production, globally. Results of this analysis showed that clearing temperate land for agriculture emits far less carbon and yields more production than clearing tropical lands.

Read Trading carbon for food: Global comparison of carbon stocks vs. crop yields on agricultural land

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