Sugar cane was the most produced crop or livestock product worldwide in 2021, at **** billion metric tons. This was followed by maize, of which **** billion metric tons worth was produced. Sugar cane is grown for both sugar production and ethanol for biofuel production. Grain production Aside from sugar cane in first place, the next the top three most produced crops in the world are all classified as grains. Grains include cereals and legumes and are such a widespread crop because they can grow in almost any climate. Grains can also be used in a variety of ways; eaten whole, ground up for flour, or used for livestock feed. Due to all of these factors, it is no surprise that grains are a staple food in so many areas of the world. Potatoes Potatoes are also among the top produced crops worldwide. Unlike grains, potatoes are more likely to grow in an environment with a lot of sun and little chance of frost. Even so, over *** million metric tons of potatoes were produced in 2021. That year, China was the leading potato producer worldwide, accounting for about a quarter of total production, followed by India and Ukraine.

Sugar cane was the most produced crop worldwide in 2023, at approximately two billion metric tons. The quantity of sugar cane produced annually has increased massively since 1961, when roughly 450 million metric tons was produced.

Cropland Index The Cropland Index evaluates lands used to produce crops based on the following input datasets: Revised Storie Index, California Important Farmland data, Electrical Conductivity (EC), and Sodium Adsorption Ratio (SAR). Together, these input layers were used in a suitability model to generate this raster. High values are associated with better CroplandsCalifornia Important Farmland data – statistical data used for analyzing impacts on California’s agricultural resources from the Farmland Mapping and Monitoring Program. Agricultural land is rated according to soil quality and irrigation status. The maps are updated every two years (on even numbered years) with the use of a computer mapping system, aerial imagery, public review, and field reconnaissance. Cropland Index Mask - This is a constructed data set used to define the model domain. Its footprint is defined by combining the extent of the California Important Farmland data (2018) classifications listed above and the area defined by California Statewide Crop Mapping for the state of California.Prime Farmland – farmland with the best combination of physical and chemical features able to sustain long term agricultural production. This land has the soil quality, growing season, and moisture supply needed to produce sustained high yields. Land must have been used for irrigated agricultural production at some time during the four years prior to the mapping date.Farmland of Statewide Importance – farmland similar to Prime Farmland but with minor shortcomings, such as greater slopes or less ability to store soil moisture. Land must have been used for irrigated agricultural production at some time during the four years prior to the mapping date. Unique Farmland – farmland of lesser quality soils used for the production of the state’s leading agricultural crops. This land is usually irrigated but may include Non irrigated orchards or vineyards as found in some climatic zones in California. Land must have been cropped at some time during the four years prior to the mapping date. Gridded Soil Survey Geographic Database (gSSURGO) – a database containing information about soil as collected by the National Cooperative Soil Survey over the course of a century. The information can be displayed in tables or as maps and is available for most areas in the United States and the Territories, Commonwealths, and Island Nations served by the USDA-NRCS. The information was gathered by walking over the land and observing the soil. Many soil samples were analyzed in laboratories. California Revised Storie Index - is a soil rating based on soil properties that govern a soil’s potential for cultivated agriculture in California. The Revised Storie Index assesses the productivity of a soil from the following four characteristics: Factor A, degree of soil profile development; factor B, texture of the surface layer; factor C, slope; and factor X, manageable features, including drainage, microrelief, fertility, acidity, erosion, and salt content. A score ranging from 0 to 100 percent is determined for each factor, and the scores are then multiplied together to derive an index rating.Electrical Conductivity - is the electrolytic conductivity of an extract from saturated soil paste, expressed as Deci siemens per meter at 25 degrees C. Electrical conductivity is a measure of the concentration of water-soluble salts in soils. It is used to indicate saline soils. High concentrations of neutral salts, such as sodium chloride and sodium sulfate, may interfere with the adsorption of water by plants because the osmotic pressure in the soil solution is nearly as high as or higher than that in the plant cells. Sodium Adsorption Ratio - is a measure of the amount of sodium (Na) relative to calcium (Ca) and magnesium (Mg) in the water extract from saturated soil paste. It is the ratio of the Na concentration divided by the square root of one-half of the Ca + Mg concentration. Soils that have SAR values of 13 or more may be characterized by an increased dispersion of organic matter and clay particles, reduced saturated hydraulic conductivity (Ksat) and aeration, and a general degradation of soil structure.

In 2022, sugar cane was the most highly produced agricultural crop in South Africa, with about 18 million metric tons harvested. Maize followed closely with the production amounting to 16.14 million metric tons. Potatoes and wheat ranked next with around 2.5 million and 2.1 million metric tons, respectively.

USA Crop Frequency is a thematic imagery service which serves the USDA National Agricultural Statistics Service Crop Frequency Data Layers. The service displays how many years corn, cotton, soybeans, or wheat were grown on a pixel since 2008. First, connect to the USA Crop Frequency service, then choose the processing template for the commodity you would like to view/analyze, whether corn, soybeans, wheat, or cotton.The default view of the USA Crop Frequency service shows how many years since 2008 that a pixel grows any of these four commodity crops. (Note: If two ore more commodity crops are both grown on the same pixel during a year, this counts as only one year in which any of the commodity crops was grown.)Variable mapped: Number of years corn, cotton, soybeans, and wheat were grown from 2008 to 2018.Data Projection: AlbersMosaic Projection: AlbersExtent: Conterminous USACell Size: 30mSource Type: ThematicVisible Scale: All scalesSource: USDA NASSPublication Date: 2019This service and the data making up the service are all in Albers Projection. Albers is an equal area projection, and this allows users of this service to accurately calculate acreage without additional data preparation steps. This also means it takes a tiny bit longer to project on the fly into web mercator, if that is the destination projection of the service.Use processing templates to display frequency of corn, soybeans, wheat, or cottonCorn, soybeans, wheat, and cotton are the chief produce crops by value in the United States, excepting alfalfa and hay. To see how many years just corn, soybeans, wheat, or cotton are grown, choose the processing template that is appropriate for that commodity. Two templates exist for each commodity, one built by USDA with the default USDA color scheme, and one built by Esri.In ArcGIS Online, choose a processing template by clicking ... under crop frequency in the Table of Contents, then choose Image Display.Next, choose a renderer in the dialogue to see just corn, soybeans, wheat, or cotton in either an Esri or USDA color scheme.Value in Billions of US Dollars, 2014:Corn $52.4Soybeans $40.3Wheat $11.9Cotton $5.1Corn (Zea mays) is the most widely produced feed grain in the United States. The largest share of the corn produced in the USA (33%) is used to feed livestock, followed by 27% used to make ethanol for fuel. 11% of it is used to create food for humans, including high fructose corn syrup, sweeteners, starch, beverage alcohol, and cereals.Soybeans (Glycine max) are a widely grown crop in the United States. The beans are edible and have many uses. The beans are 38-45% protein and constitute the most important protein source for feed farm animals in the United States. They are also widely used to extract soybean oil, and in processed foods.Wheat (Triticum spp.) is a grass grown for seed and is used to make pasta (durum wheat), bread, baked goods, and other foods. For this service, "wheat" is a combination of durum, spring, and winter wheat, spelt, and triticale. These subclasses of wheat are identified by pixel in the USA Cropland thematic imagery service for years 2008-2019.Cotton (Gossypium spp.) is a flowering plant grown for its balls of soft, fluffy fibers that grow in a boll. Almost all of the boll is used as fiber in textiles, but the seeds may also be used to make oils, and the seed hulls used to feed livestock.

This statistic shows the worldwide production of grain in 2024/25, sorted by type. In that year, worldwide wheat production came to about 793.24 million metric tons. The most important grain was corn, based on a production amount of over 1.2 billion metric tons. Grain Humans have been harvesting the small, dry seeds known as grain for thousands of years. The two main categories of grains are cereals, such as wheat, rye, and corn, and legumes, such as beans, lentils, peanuts and soybeans. Many grains are capable of being stored for long periods of time, easily transported over long distances, processed into flour, oil, and gas, and consumed by animals and humans. Most grain in the U.S. is used as animal feed, while slightly less is converted into ethanol. The smallest portion is consumed by humans. There has been recent debate about the health and ethics of grain feeding animals such as cows, goats, and sheep, animals biologically better suited to consuming grass. Though more cost effective than grass feeding, some argue this practice has an adverse effect on the quality of the meat as well as on the health of the animal and the consumer. The use of grains in producing ethanol has increased significantly in recent years. Global ethanol production has tripled since the year 2000. Ethanol is a semi-renewable energy formed by the fermentation of a feedstock, often sugar cane or corn cobs. It can be mixed with gasoline and used as motor vehicle fuel. This hybrid motor fuel emits fewer pollutants than standard gasoline.

The Crops in Cameroon Market Report is Segmented by Crop Type (Cereals, Cash Crops, Fruits, and Vegetables), and by Region (Northern Sahelian Zone, Western Highlands, and More). The Report Includes the Production Analysis, Consumption Analysis, Export Analysis, Import Analysis, and Price Trend Analysis. The Market Forecasts are Provided in Terms of Value (USD) and Volume (Metric Tons).

USA Crop Frequency is a thematic imagery service which serves the USDA National Agricultural Statistics Service Crop Frequency Data Layers. The service displays how many years corn, cotton, soybeans, or wheat were grown on a pixel since 2008. First, connect to the USA Crop Frequency service, then choose the processing template for the commodity you would like to view/analyze, whether corn, soybeans, wheat, or cotton.The default view of the USA Crop Frequency service shows how many years since 2008 that a pixel grows any of these four commodity crops. (Note: If two ore more commodity crops are both grown on the same pixel during a year, this counts as only one year in which any of the commodity crops was grown.) Variable mapped: Number of years corn, cotton, soybeans, and wheat were grown from 2008 to 2018.Data Projection: AlbersMosaic Projection: AlbersExtent: Conterminous USACell Size: 30mSource Type: ThematicVisible Scale: All scalesSource: USDA NASSPublication Date: 2019This service and the data making up the service are all in Albers Projection. Albers is an equal area projection, and this allows users of this service to accurately calculate acreage without additional data preparation steps. This also means it takes a tiny bit longer to project on the fly into web mercator, if that is the destination projection of the service.Use processing templates to display frequency of corn, soybeans, wheat, or cottonCorn, soybeans, wheat, and cotton are the chief produce crops by value in the United States, excepting alfalfa and hay. To see how many years just corn, soybeans, wheat, or cotton are grown, choose the processing template that is appropriate for that commodity. Two templates exist for each commodity, one built by USDA with the default USDA color scheme, and one built by Esri.In ArcGIS Online, choose a processing template by clicking ... under crop frequency in the Table of Contents, then choose Image Display. Next, choose a renderer in the dialogue to see just corn, soybeans, wheat, or cotton in either an Esri or USDA color scheme.Value in Billions of US Dollars, 2014:Corn $52.4Soybeans $40.3Wheat $11.9Cotton $5.1Corn (Zea mays) is the most widely produced feed grain in the United States. The largest share of the corn produced in the USA (33%) is used to feed livestock, followed by 27% used to make ethanol for fuel. 11% of it is used to create food for humans, including high fructose corn syrup, sweeteners, starch, beverage alcohol, and cereals. Soybeans (Glycine max) are a widely grown crop in the United States. The beans are edible and have many uses. The beans are 38-45% protein and constitute the most important protein source for feed farm animals in the United States. They are also widely used to extract soybean oil, and in processed foods. Wheat (Triticum spp.) is a grass grown for seed and is used to make pasta (durum wheat), bread, baked goods, and other foods. For this service, "wheat" is a combination of durum, spring, and winter wheat, spelt, and triticale. These subclasses of wheat are identified by pixel in the USA Cropland thematic imagery service for years 2008-2019.Cotton (Gossypium spp.) is a flowering plant grown for its balls of soft, fluffy fibers that grow in a boll. Almost all of the boll is used as fiber in textiles, but the seeds may also be used to make oils, and the seed hulls used to feed livestock.

This table provides information per crop about the cultivated and harvested area, yield per hectare and the total yield in a crop year. The data are available for the Netherlands as a whole and by province.

Applying crop rotation helps a farmer to avoid deterioration in soil fertility. A cultivation plan is prepared annually, to make sure that the same crop is not cultivated in the same place year after year. Usually, one third of the arable land is covered with cereals (mainly winter wheat and spring barley), a quarter is covered with potatoes, one eighth is covered with sugar beet, and one tenth is used for vegetables(mainly onions) as well as a green fodder crop (mainly green maize).

To obtain the figure for the yield, first a preliminary harvest estimate is made. This takes place from August to October.

The estimate is made definite from December to March.

The yields per hectare are rounded off to the nearest 100 kilograms. The total yields are rounded off to the nearest 1000 kilograms.

Data available from 1994 to 2023

Status of the figures: The data about 2023 are provisional. Since this table has been discontinued, the data is no longer finalized.

Changes as of 3 oktober 2023: None, this table has been discontinued.

When will new figures be published? Not applicable anymore. This table is followed by Arable crops; production, regio. See paragraph 3.

Important Note: This item is in mature support as of April 2025 and will be retired in December 2026. New data is available for your use directly from the Authoritative Provider. Esri recommends accessing the data from the source provider as soon as possible as our service will not longer be available after December 2026. Rice (Oryza sativaandO. glaberrima) is one of the world"s most important staple food crops. Over half of the world"s population relies on rice. The people in some parts of Africa have been cultivating rice for over 3,500 years. Dataset Summary This layer provides access to a5 arc-minute(approximately 10 km at the equator)cell-sized raster of the 1999-2001 annual average area ofrice harvested in Africa. The data are in units of hectares/grid cell. TheSPAM 2000 v3.0.6 data used to create this layerwere produced by theInternational Food Policy Research Institutein 2012.This dataset was created by spatially disaggregating national and sub-national harvest datausing theSpatial Production Allocation Model. Link to source metadata For more information about this dataset and the importance of rice as a staple food see theHarvest Choice webpage. For data on other agricultural species in Africa see these layers:Cassava Groundnut (Peanut) Maize (Corn) Millet PotatoSorghum Sweet Potato and Yam Wheat Data for important agricultural crops in South America are availablehere. What can you do with this layer? This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer hasquery,identify, andexportimage services available. This layer is restricted to a maximum area of 24,000 x 24,000 pixelswhich allows access to the full dataset. The source data for this layer are availablehere. This layer is part of a larger collection oflandscape layersthat you can use to perform a wide variety of mapping and analysis tasks. TheLiving Atlas of the Worldprovides an easy way to explore the landscape layers and many otherbeautiful and authoritative maps on hundreds of topics. Geonetis a good resource for learning more aboutlandscape layers and the Living Atlas of the World. To get started follow these links: Landscape Layers - a reintroductionLiving Atlas Discussion Group

In 2022, around 7.27 million tons of rice were produced in Japan, making rice the most commonly cultivated crop within the Japanese farming industry. The agricultural product with the second highest output was paddy field rice, followed by sugar beats.

AbstractResearch into the origins of food plants has led to the recognition that specific geographical regions around the world have been of particular importance to the development of agricultural crops. Yet the relative contributions of these different regions in the context of current food systems have not been quantified. Here we determine the origins (‘primary regions of diversity’) of the crops comprising the food supplies and agricultural production of countries worldwide. We estimate the degree to which countries use crops from regions of diversity other than their own (‘foreign crops’), and quantify changes in this usage over the past 50 years. Countries are highly interconnected with regard to primary regions of diversity of the crops they cultivate and/or consume. Foreign crops are extensively used in food supplies (68.7% of national food supplies as a global mean are derived from foreign crops) and production systems (69.3% of crops grown are foreign). Foreign crop usage has increased significantly over the past 50 years, including in countries with high indigenous crop diversity. The results provide a novel perspective on the ongoing globalization of food systems worldwide, and bolster evidence for the importance of international collaboration on genetic resource conservation and exchange. Usage notesTableS1_crops_regions_tableTable S1. Crop commodities assessed in food supplies and agricultural production systems analyses and their primary regions of diversity. Taxonomy follows GRIN (2015) [25].TableS2_countries_regions_tableTable S2. Countries assessed in food supplies and agricultural production systems analyses and their associated regions.TableS3_regionalcomposition_tocountriesTable S3. Importance of primary regions of diversity of agricultural crops in contribution to national food supplies [as measured in contribution of crops to calories (kcal/capita/day), protein (g/capita/day), fat (g/capita/day), and food weight (g/capita/day)] and national agricultural production [production quantity (tonnes), harvested area (ha), and production value (million US$)], averaged over years 2009-2011. Importance was estimated by grouping the contribution of consumed/produced crops by their primary regions of diversity. As some crops pertain to more than one primary region of diversity, total values across all primary regions per country is not equivalent to total per capita food supply/ total agricultural production values per country. Percentages provide a comparison of the relative importance of primary regions in contribution to the food supply/national production of each country.TableS4_regionalcomposition_toregions_2009-2011Table S4. Importance of primary regions of diversity of agricultural crops in contribution to regional food supplies [as measured in contribution of crops to calories (kcal/capita/day), protein (g/capita/day), fat (g/capita/day), and food weight (g/capita/day),] and total regional agricultural production [production quantity (tonnes), harvested area (ha), and production value (million US$)], averaged over years 2009-2011. Regional food supplies values (kcal or g, /capita/day) were formed by deriving a population-weighted average of national food supplies values across countries comprising each region. Regional production values were formed by summing national production values across countries comprising each region. Importance was estimated by grouping the contribution of consumed/produced crops by their primary regions of diversity. As some crops pertain to more than one primary region of diversity, total values across all primary regions per consuming/producing region is not equivalent to total per capita food supply/ total agricultural production values per consuming/producing region. Percentages provide a comparison of the relative importance of primary regions in contribution to the food supply/total production of each region.TableS5_cropcomposition_ofregionsTable S5. Crop commodity composition of regional food supplies [as measured in contribution of crops to calories (kcal/capita/day), protein (g/capita/day), fat (g/capita/day), and food weight (g/capita/day),] and total regional agricultural production [production quantity (tonnes), harvested area (ha), and production value (million US$)], averaged over years 2009-2011. Regional food supplies values (kcal or g, /capita/day) were formed by deriving a population-weighted average of national food supplies values across countries comprising each region. Regional production values were formed by summing national production values across countries comprising each region.TableS6_util_foreign_2009-2011Table S6. Estimated percent use of foreign crops in current national food supplies and agricultural production systems. Data includes the raw mean minimum and maximum use values across years 2009-2011 per country, and the mean value between minimum and maximum per country across these years, as well as modeled mean values and...

US: Cereal Yield: per Hectare data was reported at 8,142.900 kg/ha in 2016. This records an increase from the previous number of 7,430.600 kg/ha for 2015. US: Cereal Yield: per Hectare data is updated yearly, averaging 4,576.200 kg/ha from Dec 1961 (Median) to 2016, with 56 observations. The data reached an all-time high of 8,142.900 kg/ha in 2016 and a record low of 2,522.300 kg/ha in 1961. US: Cereal Yield: per Hectare data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Agricultural Production and Consumption. Cereal yield, measured as kilograms per hectare of harvested land, includes wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed grains. Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded. The FAO allocates production data to the calendar year in which the bulk of the harvest took place. Most of a crop harvested near the end of a year will be used in the following year.; ; Food and Agriculture Organization, electronic files and web site.; Weighted average;

Slovakia Agricultural Production: Vol: Crops: More Yearly Fodder of Arable Land data was reported at 651,909.000 Ton in 2016. This records an increase from the previous number of 573,247.000 Ton for 2015. Slovakia Agricultural Production: Vol: Crops: More Yearly Fodder of Arable Land data is updated yearly, averaging 1,160,727.000 Ton from Dec 1970 (Median) to 2016, with 47 observations. The data reached an all-time high of 1,800,759.000 Ton in 1985 and a record low of 530,779.000 Ton in 2003. Slovakia Agricultural Production: Vol: Crops: More Yearly Fodder of Arable Land data remains active status in CEIC and is reported by Statistical Office of the Slovak Republic. The data is categorized under Global Database’s Slovakia – Table SK.B007: Agricultural Production: Crops.

Slovakia Agricultural Production Yield: Crops: More Yearly Fodder of Arable Land data was reported at 4.740 Ton/ha in 2016. This records an increase from the previous number of 4.070 Ton/ha for 2015. Slovakia Agricultural Production Yield: Crops: More Yearly Fodder of Arable Land data is updated yearly, averaging 6.000 Ton/ha from Dec 1970 (Median) to 2016, with 47 observations. The data reached an all-time high of 8.980 Ton/ha in 1989 and a record low of 3.750 Ton/ha in 2012. Slovakia Agricultural Production Yield: Crops: More Yearly Fodder of Arable Land data remains active status in CEIC and is reported by Statistical Office of the Slovak Republic. The data is categorized under Global Database’s Slovakia – Table SK.B009: Agricultural Production Yield.

USA Cropland is a time-enabled imagery layer of the USDA Cropland Data Layer dataset from the National Agricultural Statistics Service (NASS). The time series shows the crop grown during every growing season in the conterminous US since 2008. Use the time slider to select only one year to view, or press play to see every growing season displayed sequentially in an animated map.The USDA is now serving the Cropland Data Layer in their own application called CropCros which allows selection and display of a single product or growing season. This application will eventually replace their popular CropScape application.This dataset is GDA compliant. Compliancy information can be found here.Why USA Cropland masks out NLCD land cover in its default templateUSDA Cropland Data Layer, by default as downloaded from USDA, fills in the non-cultivated areas of the conterminous USA with land cover classes from the MRLC National Land Cover Dataset (NLCD). The default behavior for Esri's USA Cropland layer is a little bit different. By default the Esri USA Cropland layer uses the analytic renderer, which masks out this NLCD data. Why did we choose to mask out the NLCD land cover classes by default?While crops are updated every year from USDA NASS, the NLCD data changes every several years, and it can be quite a bit older than the crop data beside it. If analysis is conducted to quantify landscape change, the NLCD-derived pixels will skew the results of the analysis because NLCD land cover in a yearly time series may appear to remain the same class for several years in a row. This can be problematic because conclusions drawn from this dataset may underrepresent the amount of change happening to the landscape.Since the 2018 Cropland Data Layer was posted (early 2019), MRLC issued an update to the NLCD Land Cover dataset. The 2019 and 2020 cropland frames have this more current NLCD data, but the years before that contain NLCD land cover data from 2011 or older.To display the most current land cover available from both sources, add both the USA NLCD Land Cover service and USA Cropland time series to your map. Use the analytical template with the USA Cropland service, and draw it on top of the USA NLCD Land Cover service. When a time slider is used with these datasets together, the map user will see the most current land cover from both services in any given year.Variable mapped: Crop grown in each pixel since 2008.Data Projection: AlbersMosaic Projection: AlbersExtent: Conterminous USACell Size: 30mSource Type: ThematicVisible Scale: All scales are visibleSource: USDA NASSPublication Date: 2/2/2022This layer and the data making up the layer are in the Albers map projection. Albers is an equal area projection, and this allows users of this layer to accurately calculate acreage without additional data preparation steps. This also means it takes a tiny bit longer to project on the fly into web Mercator, if that is the destination projection of the layer.Processing templates available with this layerTo help filter out and display just the crops and land use categories you are interested in showing, choose one of the thirteen processing templates that will help you tailor the symbols in the time series to suit your map application. The following are the processing templates that are available with this layer:Analytic RendererUSDA Analytic RendererThe analytic renderer is the default template. NLCD codes are masked when using analytic renderer processing templates. There is a default esri analytic renderer, but also an analytic renderer that uses the original USDA color scheme that was developed for the CropScape layers. This is useful if you have already built maps with the USDA color scheme or otherwise prefer the USDA color scheme.Cartographic RendererUSDA Cartographic RendererThese templates fill in with NLCD land cover types where crops are not cultivated, thereby filling the map with color from coast to coast. There is also a template using the USDA color scheme, which is identical to the datasets as downloaded from USDA NASS.In addition to different ways to display the whole dataset, some processing templates are included which help display the top 10 agricultural products in the United States. If these templates seem to overinclude crops in their category (for example, tomatoes are included in both the fruit and vegetables templates), this is because it's easier for a map user to remove a symbol from a template than it is to add one.Corn - Corn, sweet corn, popcorn or ornamental corn, plus double crops with corn and another crop.Cotton - Cotton and double crops, includes double crops with cotton and another crop.Fruit - Symbolized fruit crops include not only things like melons, apricots, and strawberries, but also olives, avocados, and tomatoes. Nuts - Peanuts, tree nuts, sunflower, etc.Oil Crops - Oil crops include rapeseed and canola, soybeans, avocado, peanut, corn, safflower, sunflower, also cotton and grapes.Rice - Rice crops.Sugar - Crops grown to make sugars. Sugar beets and cane are displayed of course, but so are corn and grapes.Soybeans - Soybean crops. Includes double crops where soybeans are grown at some time during the growing season.Vegetables - Vegetable crops, and yes this includes tomatoes. Wheat - Winter and spring wheat, durum wheat, triticale, spelt, and wheat double crops.In many places, two crops were grown in one growing season. Keep in mind that a double crop of corn and soybeans will display in both the corn and soybeans processing templates.Index to raster values in USA Cropland:0,Background (not a cultivated crop or no data)1,Corn2,Cotton3,Rice4,Sorghum5,Soybeans6,Sunflower10,Peanuts11,Tobacco12,Sweet Corn13,Popcorn or Ornamental Corn14,Mint21,Barley22,Durum Wheat23,Spring Wheat24,Winter Wheat25,Other Small Grains26,Double Crop Winter Wheat/Soybeans27,Rye28,Oats29,Millet30,Speltz31,Canola32,Flaxseed33,Safflower34,Rape Seed35,Mustard36,Alfalfa37,Other Hay/Non Alfalfa38,Camelina39,Buckwheat41,Sugarbeets42,Dry Beans43,Potatoes44,Other Crops45,Sugarcane46,Sweet Potatoes47,Miscellaneous Vegetables and Fruits48,Watermelons49,Onions50,Cucumbers51,Chick Peas52,Lentils53,Peas54,Tomatoes55,Caneberries56,Hops57,Herbs58,Clover/Wildflowers59,Sod/Grass Seed60,Switchgrass61,Fallow/Idle Cropland62,Pasture/Grass63,Forest64,Shrubland65,Barren66,Cherries67,Peaches68,Apples69,Grapes70,Christmas Trees71,Other Tree Crops72,Citrus74,Pecans75,Almonds76,Walnuts77,Pears81,Clouds/No Data82,Developed83,Water87,Wetlands88,Nonagricultural/Undefined92,Aquaculture111,Open Water112,Perennial Ice/Snow121,Developed/Open Space122,Developed/Low Intensity123,Developed/Med Intensity124,Developed/High Intensity131,Barren141,Deciduous Forest142,Evergreen Forest143,Mixed Forest152,Shrubland176,Grassland/Pasture190,Woody Wetlands195,Herbaceous Wetlands204,Pistachios205,Triticale206,Carrots207,Asparagus208,Garlic209,Cantaloupes210,Prunes211,Olives212,Oranges213,Honeydew Melons214,Broccoli215,Avocados216,Peppers217,Pomegranates218,Nectarines219,Greens220,Plums221,Strawberries222,Squash223,Apricots224,Vetch225,Double Crop Winter Wheat/Corn226,Double Crop Oats/Corn227,Lettuce228,Double Crop Triticale/Corn229,Pumpkins230,Double Crop Lettuce/Durum Wheat231,Double Crop Lettuce/Cantaloupe232,Double Crop Lettuce/Cotton233,Double Crop Lettuce/Barley234,Double Crop Durum Wheat/Sorghum235,Double Crop Barley/Sorghum236,Double Crop Winter Wheat/Sorghum237,Double Crop Barley/Corn238,Double Crop Winter Wheat/Cotton239,Double Crop Soybeans/Cotton240,Double Crop Soybeans/Oats241,Double Crop Corn/Soybeans242,Blueberries243,Cabbage244,Cauliflower245,Celery246,Radishes247,Turnips248,Eggplants249,Gourds250,Cranberries254,Double Crop Barley/Soybeans

Crop Production Market Outlook

The global crop production market size was valued at approximately USD 2.3 trillion in 2023, and it is projected to reach around USD 3.5 trillion by 2032, growing at a compound annual growth rate (CAGR) of 4.8% during the forecast period. The rising global population and the consequent increase in food demand are the primary growth factors driving this market. This surge requires substantial enhancements in crop yield and quality, which are facilitated by advancements in agricultural technologies and practices. The growing awareness regarding food security and the need for sustainable agricultural practices further propel this market's expansion.

One of the key growth drivers of the crop production market is the rapid technological advancements in precision agriculture and smart farming techniques. These technologies enable farmers to optimize crop production by using data analytics and satellite imagery, thereby increasing yield while minimizing resource usage. The integration of IoT devices and AI in agriculture has transformed the way farming is done, allowing for real-time monitoring and automation. These innovations not only increase productivity but also help in reducing environmental impact, aligning with global sustainability goals. As a result, these technology-driven solutions are seeing significant adoption and are expected to continue propelling market growth.

Another significant factor contributing to the market's growth is the increasing preference for organic farming. Consumers worldwide are becoming more health-conscious and are demanding food products that are free from synthetic chemicals. This shift has encouraged many farmers to adopt organic farming practices, which not only fetch higher prices but also ensure environmental conservation. The organic segment, although currently smaller than conventional farming, is witnessing rapid growth due to these consumer preferences. Government initiatives and subsidies supporting organic farming also play a crucial role in this segment's expansion.

Climate change and the need for climate-resilient farming practices are also critical growth factors for the crop production market. Farmers are increasingly adopting innovative practices to cope with the challenges posed by changing weather patterns, which directly affect crop yield. The development of drought-resistant and heat-tolerant crop varieties is becoming a priority, and the adoption of such technologies is supported by both government and private sector investments. These factors not only help in stabilizing crop production but also ensure long-term sustainability, which is vital for meeting global food demands.

Regionally, the Asia Pacific holds a significant share of the crop production market due to its vast agricultural land and favorable climatic conditions for diverse crop cultivation. The region is expected to continue its dominance, driven by countries like China and India, which are major agricultural producers. North America and Europe are also significant markets, where technological adoption is more advanced, contributing to higher productivity and efficiency in crop production. Latin America, with its large arable land, is emerging as a key player, while the Middle East & Africa are focusing on increasing productivity through technology adoption and investment in agriculture infrastructure.

Crop Type Analysis

The crop production market is extensively categorized by crop types, including cereals & grains, oilseeds & pulses, fruits & vegetables, and others. Cereals and grains hold a dominant share in the market, owing primarily to their staple nature and fundamental role in human nutrition worldwide. Wheat, rice, and maize are the most cultivated crops, with rice and wheat being crucial for meeting the dietary needs of a large portion of the global population. The demand for these grains is projected to rise steadily due to population growth and dietary shifts in developing countries. Governments and private players are investing significantly in research to improve yield and pest resistance for these crops.

Oilseeds and pulses represent another vital segment within the crop production market. These crops are essential for providing plant-based proteins, oils, and other nutritional benefits. Soybeans, sunflowers, and peanuts are some of the prominent oilseeds, while lentils, chickpeas, and various beans represent the pulses segment. The growing vegan population and the rising demand for plant-based products are driving the increase in cultivation and consumption of these crops.

The question whether there is a correlation between fluctuations of crop yields and economic cycles has been answered very differently from the very start. Answers range from the thesis of a passive dependency of the industrial cycle on the state of the agriculture to the thesis of a passive dependency of the agricultural cycle on industrial cycles.

As the author explains in a short overview, Pigou and Robertson assume that the increase in purchasing power, which, they say, the agriculture experienced in the case of a rich harvest, triggers a commercial upswing. According to Timoshenko, however, the low prices of agricultural products in the years of rich harvests have caused a stimulation of the whole economy. Following Arthur Spiethoff, the author suggests an analysis of the influence of fluctuating crops on the economic cycles in certain periods. For this purpose, he examines closely the harvest yields of the most important crops (and for some colonial countries the external sales volumes respectively), the prices of agricultural products, and of industrial raw materials for six European and six non-European countries.

Datatables in the search and downloadsystem HISTAT (Historical Statistics, www.histat.gesis.org):

1. Crop yields, prices of agricultural products, indeces of agricultural production, prices, purchasing power and monetary value of agricultural products in Germany (1878-1914)

2. Crop yields, prices of agricultural products, indeces in England (1844-1914)

3. Crop yields, prices of agricultural products, indeces of agricultural production, prices, purchasing power and monetary value in France (1871-1914)

4. Crop yields, prices of agricultural products, indeces of agricultural production, prices, purchasing power and monetary value in Austria and Hungary (1881-1914)

5. Crop yields and index of agricultural production in Romania (1886-1914)

6. Crop yields, average price of corn, index of agricultural production, index of prices of agricultural products, index of price, purchasing power and monetary value of agricultural products in Russia (1871-1914)

7. Crop yields, average corn prices, index of agricultural production, index of prices of agricultural products, index of price, purchasing power and monetary value of agricultural production in the United States (1866-1914)

8. Total exports and prices of agricultural products, index of agricultural production, index of prices of agricultural products, index of price, purchasing power and monetary value of agricultural products in Canada (1868-1914)

9. Total exports, prices in gold pesos, agricultural indeces in Argentina (1874-1913)

10. Total exports and export prices for coffee in Brazil (1871-1912)

11. Total exports and export prices for coffee, cotton, rice, corn, jute, seeds, tea in India, indeces of production, prices, purchasing power, monetary value (1873-1914)

12. Crop yields, prices of agricultural products, agricultural indeces in Australia (1860-1914)

13. Index of world crops and world prices (1873-1910).

Timeseries are downloadable via the online system HISTAT (www.histat.gesis.org).

Sugarcane was the leading crop produced in the Philippines, with a total volume of production at 21.65 million metric tons in 2023. Palay, coconut, and banana were also among the crops with the highest production volume in that year.

Production systems that feature temporal and spatial integration of crop and livestock enterprises, also known as integrated crop-livestock systems (ICLS), have the potential to intensify production on cultivated lands and foster resilience to the effects of climate change without proportional increases in environmental impacts. Yet, crop production outcomes following livestock grazing across environments and management scenarios remain uncertain and a potential barrier to adoption, as producers worry about the effects of livestock activity on the agronomic quality of their land. To determine likely production outcomes across ICLS and to identify the most important moderating variables governing those outcomes, we performed a meta-analysis of 66 studies comparing crop yields in ICLS to yields in unintegrated controls across 3 continents, 12 crops, and 4 livestock species. We found that annual cash crops in ICLS averaged similar yields (-7% to +2%) to crops in comparable unintegrated systems. The exception was dual-purpose crops (crops managed simultaneously for grazing and grain production), which yielded 20% less on average than single-purpose crops in the studies examined. When dual-purpose cropping systems were excluded from the analysis, crops in ICLS yielded more than in unintegrated systems in loamy soils and achieved equal yields in most other settings, suggesting that areas of intermediate soil texture may represent a “sweet-spot” for ICLS implementation. This meta-analysis represents the first quantitative synthesis of the crop production outcomes of ICLS and demonstrates the need for further investigation into the conditions and management scenarios under which ICLS can be successfully implemented.

Methods We conducted a comprehensive literature search using three academic databases (Web of Science, CAB Abstracts, and Agricola) and the Google Scholar internet search engine in English, French, Spanish, and Portuguese. The most recent database search was conducted in September 2018. We gleaned further records from the reference lists of review articles and research articles meeting the initial eligibility criteria. Targeted searches of governmental and independent agricultural research organizations were also performed in countries where medium-to-large scale, commercially oriented ICLS are known to occur. Finally, we performed a manual search of the grey literature including theses and dissertations and data from long-term experiments, both published and unpublished, in consultation with prominent integrated crop-livestock system researchers.

No prior review protocol existed for this study. The following search terms were employed for abstracts, titles, and keywords: (crop-livestock AND yield) NOT mixed); (“crop-livestock” AND integ*) OR “integração lavoura-pecuária" OR "integración agropecuaria”; "crop-livestock" AND yield; (crop*livestock OR crop OR livestock) AND (French OR France) AND yield AND graz*; (crop*livestock OR crop OR livestock) AND (Spain OR Spanish OR “Latin America” OR “South America”) AND yield AND graz*; intégration ("polyculture-élevage" OR polyculture OR élevage OR agriculture) rendement pâturage expérimental -arbres. Search results were deduplicated and restricted to full-text journal articles. Google Scholar results were additionally restricted to the years 2008-2018 due to the volume of results; other databases were searched for the full range of available years.

A total of 2,702 studies were identified from the database searches, unpublished dissertations, reference lists of eligible studies and literature reviews, and long-term datasets provided by ICLS researchers (Fig 1). The initial screening process involved manual scanning of titles and abstracts for clear instances of ineligibility, e.g. wrong field of study, wrong scope, wrong subject, or wrong language. A total of 2,569 records were excluded in the initial screening process, leaving the full text of 133 articles to be assessed in greater detail based on the following eligibility criteria:

Study scope was restricted to agropastoral systems with annual crops. Duck-rice-azolla, agro-silvo-pastoral systems, and systems integrating livestock with perennial crops were excluded;
Study involved a replicated field trial with both an integrated system (grazed treatment) and an unintegrated control (ungrazed treatment) and included at least one season each of the cropping component and the grazing component;
Crops and livestock were co-located, i.e. spatially integrated at the field level. Cut-and-carry, manure amendments, or farm-level mixed systems were excluded due to disparities in system objectives and constraints as well as difficulties in determining adequate experimental controls for farm-level integration;
Study was original research, dataset, or dissertation, i.e. not a review, book chapter, or conference proceeding.

Data on categorical environmental moderating variables were also collected for each study. Studies were grouped into climate and soil classes according to the Köppen climate classifications, which were extracted from the updated world map of the Köppen-Geiger climate , and soil texture characteristics extracted from the Harmonized World Soil Database v1.2. Additional moderating variables included crop species, livestock species, and the occurrence of dry weather anomalies. The latter was defined as a season during which precipitation accumulation was abnormally low according to specifications set by the authors of the relevant study. Crop species were grouped according to broad agronomic similarities: cereals (corn and sorghum), small grains (wheat, oat, barley, triticale, and rye), fiber (cotton harvested for lint), soybean, other legumes (peanuts and common bean), and oilseeds (canola). For animals, goats and sheep were grouped into small ruminants and beef and dairy cattle were grouped under cattle.