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.

The Farmland Protection Policy Act, part of the 1981 Farm Bill, is intended to limit federal activities that contribute to the unnecessary conversion of farmland to other uses. The law applies to construction projects funded by the federal government such as highways, airports, and dams, and to the management of federal lands. As part of the implementation of this law, the Natural Resources Conservation Service identifies high quality agricultural soils as prime farmland, unique farmland, and land of statewide or local importance. Each category may contain one or more limitations such as Prime Farmland if Irrigated. For a complete list of categories and definitions, see the National Soil Survey Handbook.All areas are prime farmlandFarmland of local importanceFarmland of statewide importanceFarmland of statewide importance, if drainedFarmland of statewide importance, if drained and either protected from flooding or not frequently flooded during the growing seasonFarmland of statewide importance, if irrigatedFarmland of statewide importance, if irrigated and drainedFarmland of statewide importance, if irrigated and either protected from flooding or not frequently flooded during the growing seasonFarmland of statewide importance, if irrigated and reclaimed of excess salts and sodiumFarmland of statewide importance, if irrigated and the product of I (soil erodibility) x C (climate factor) does not exceed 60Farmland of statewide importance, if protected from flooding or not frequently flooded during the growing seasonFarmland of statewide importance, if warm enoughFarmland of statewide importance, if warm enough, and either drained or either protected from flooding or not frequently flooded during the growing seasonFarmland of unique importanceNot prime farmlandPrime farmland if drainedPrime farmland if drained and either protected from flooding or not frequently flooded during the growing seasonPrime farmland if irrigatedPrime farmland if irrigated and drainedPrime farmland if irrigated and either protected from flooding or not frequently flooded during the growing seasonPrime farmland if irrigated and reclaimed of excess salts and sodiumPrime farmland if irrigated and the product of I (soil erodibility) x C (climate factor) does not exceed 60Prime farmland if protected from flooding or not frequently flooded during the growing seasonPrime farmland if subsoiled, completely removing the root inhibiting soil layerDataset SummaryPhenomenon Mapped: FarmlandUnits: ClassesCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerData Coordinate System: USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WGS 1984 Albers (Alaska), Hawaii Albers Equal Area Conic (Hawaii), Western Pacific Albers Equal Area Conic (Guam, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa)Mosaic Projection: Web Mercator Auxiliary SphereExtent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaSource: Natural Resources Conservation ServicePublication Date: July 2020ArcGIS Server URL: https://landscape11.arcgis.com/arcgis/Data from the gNATSGO database was used to create the layer for the contiguous United States, Alaska, Puerto Rico, and the U.S. Virgin Islands. The remaining areas were created with the gSSURGO database (Hawaii, Guam, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa).This layer is derived from the 30m (contiguous U.S.) and 10m rasters (all other regions) produced by the Natural Resources Conservation Service (NRCS). The value for farmland class is derived from the gSSURGO map unit table field Farm Class (farmlndcl).What can you do with this Layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "farmland" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "farmland" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

The Census of Agriculture is a complete count of U.S. farms and ranches and the people who operate them. Even small plots of land - whether rural or urban - growing fruit, vegetables or some food animals count if $1,000 or more of such products were raised and sold, or normally would have been sold, during the Census year. The Census of Agriculture, taken only once every five years, looks at land use and ownership, operator characteristics, production practices, income and expenditures. For America's farmers and ranchers, the Census of Agriculture is their voice, their future, and their opportunity. The Census Data Query Tool (CDQT) is a web-based tool that is available to access and download table level data from the Census of Agriculture Volume 1 publication. The data found via the CDQT may also be accessed in the NASS Quick Stats database. The CDQT is unique in that it automatically displays data from the past five Census of Agriculture publications. The CDQT is presented as a "2017 centric" view of the Census of Agriculture data. All data series that are present in the 2017 dataset are available within the CDQT, and any matching data series from prior Census years will also display (back to 1997). If a data series is not included in the 2017 dataset, then data cells will remain blank in the tool. For example, one of the data series had a label change from "Operator" to "Producer." This means that data from prior Census years labelled "Operator" will not show up where the label has changed to “Producer” for 2017. The new Census Data Query Tool application can be used to query Census data from 1997 through 2017. Data are searchable by Census table and are downloadable as CSV or PDF files. 2017 Census Ag Atlas Maps are also available for download. Resources in this dataset:Resource Title: 2017 Census of Agriculture - Census Data Query Tool (CDQT). File Name: Web Page, url: https://www.nass.usda.gov/Quick_Stats/CDQT/chapter/1/table/1 The Census Data Query Tool (CDQT) is a web based tool that is available to access and download table level data from the Census of Agriculture Volume 1 publication. The data found via the CDQT may also be accessed in the NASS Quick Stats database. The CDQT is unique in that it automatically displays data from the past five Census of Agriculture publications. The CDQT is presented as a "2017 centric" view of the Census of Agriculture data. All data series that are present in the 2017 dataset are available within the CDQT, and any matching data series from prior Census years will also display (back to 1997). If a data series is not included in the 2017 dataset, then data cells will remain blank in the tool. For example, one of the data series had a label change from "Operator" to "Producer." This means that data from prior Census years labelled "Operator" will not show up where the label has changed to "Producer" for 2017. Using CDQT:

Upon entering the CDQT, a data table is present. Changing the parameters at the top of the data table will retrieve different combinations of Census Chapter, Table, State, or County (when selecting Chapter 2). For the U.S., Volume 1, US/State Chapter 1 will include only U.S. data; Chapter 2 will include U.S. and State level data. For a State, Volume 1 US/State Level Data Chapter 1 will include only the State level data; Chapter 2 will include the State and county level data. Once a selection is made, press the “Update Grid” button to retrieve the new data table. Comma-separated values (CSV) download, compatible with most spreadsheet and database applications: to download a CSV file of the data as it is currently presented in the data grid, press the "CSV" button in the "Export Data" section of the toolbar. When CSV is chosen, data will be downloaded as numeric. To view the source PDF file for the data table, press the "View PDF" button in the toolbar.

Abstract (en): This collection includes county-level data from the United States Censuses of Agriculture for the years 1840 to 2012. The files provide data about the number, types, output, and prices of various agricultural products, as well as information on the amount, expenses, sales, values, and production of machinery. Most of the basic crop output data apply to the previous harvest year. Data collected also included the population and value of livestock, the number of animals slaughtered, and the size, type, and value of farms. Part 46 of this collection contains data from 1980 through 2010. Variables in part 46 include information such as the average value of farmland, number and value of buildings per acre, food services, resident population, composition of households, and unemployment rates. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Response Rates: Not applicable. Datasets:DS0: Study-Level FilesDS1: Farm Land Value Data Set (County and State) 1850-1959DS2: 1840 County and StateDS3: 1850 County and StateDS4: 1860 County and StateDS5: 1870 County and StateDS6: 1880 County and StateDS7: 1890 County and StateDS8: 1900 County and StateDS9: 1910 County and StateDS10: 1920 County and State, Dataset 1DS11: 1920 County and State, Dataset 2DS12: 1925 County and StateDS13: 1930 County and State, Dataset 1DS14: 1930 County and State, Dataset 2DS15: 1935 County and StateDS16: 1940 County and State, Dataset 1DS17: 1940 County and State, Dataset 2DS18: 1940 County and State, Dataset 3DS19: 1940 County and State, Dataset 4 (Water)DS20: 1945 County and StateDS21: 1950 County and State, Dataset 1DS22: 1950 Crops, County and State, Dataset 2DS23: 1950 County, Dataset 3DS24: 1950 County and State, Dataset 4DS25: 1954 County and State, Dataset 1DS26: 1954 Crops, County and State, Dataset 2DS27: 1959 County and State, Dataset 1DS28: 1959 Crops, County and State, Dataset 2DS29: 1959 County, Dataset 3DS30: 1964 Dataset 1DS31: 1964 Crops, County and State, Dataset 2DS32: 1964 County, Dataset 3DS33: 1969 All Farms, County and State, Dataset 1DS34: 1969 Farms 2500, County and State, Dataset 2DS35: 1969 Crops, County and State, Dataset 3DS36: 1974 All Farms, County and State, Dataset 1DS37: 1974 Farms 2500, County and State, Dataset 2DS38: 1974 Crops, County and State, Dataset 3DS39: 1978 County and StateDS40: 1982 County and StateDS41: 1987 County and StateDS42: 1992 County and StateDS43: 1997 County and StateDS44: 2002 County and StateDS45: 2007 County and StateDS46: State and County Data, United States, 1980-2010DS47: 2012 County and State Farms within United States counties and states. Smallest Geographic Unit: FIPS code The sample was the universe of agricultural operating units. For 1969-2007, data were taken from computer files from the Census Bureau and the United States Department of Agriculture. 2018-08-20 The P.I. resupplied data and documentation for 1935 County and State (dataset 15) and 1997 County and State (dataset 43). Additionally, documentation updates and variable label revisions have been incorporated in datasets 22, 26, 28, 31, 35, and 38 at the request of the P.I.2016-06-29 The data and documentation for 2012 County and State (data set 47) have been added to this collection. The collection and documentation titles have been updated to reflect the new year.2015-08-05 The data, setup files, and documentation for 1964 Dataset 1 have been updated to reflect changes from the producer. Funding insitution(s): National Science Foundation (NSF-SES-0921732; 0648045). United States Department of Health and Human Services. National Institutes of Health (R01 HD057929).

This metadata report documents tabular data sets consisting of items from the Census of Agriculture. These data are a subset of items from county-level data (including state totals) for the conterminous United States covering the census reporting years (every five years, with adjustments for 1978 and 1982) beginning with the 1950 Census of Agriculture and ending with the 2012 Census of Agriculture. Historical (1950-1997) data were extracted from digital files obtained through the Intra-university Consortium on Political and Social Research (ICPSR). More current (1997-2012) data were extracted from the National Agriculture Statistical Service (NASS) Census Query Tool for the census years of 1997, 2002, 2007, and 2012. Most census reports contain item values from the prior census for comparison. At times these values are updated or reweighted by the reporting agency; the Census Bureau prior to 1997 or NASS from 1997 on. Where available, the updated or reweighted data were used; otherwise, the original reported values were used. Changes in census item definitions and reporting as well as changes to county areas and names over the time span required a degree of manipulation on the data and county codes to make the data as comparable as possible over time. Not all of the census items are present for the entire 1950-2012 time span as certain items have been added since 1950 and when possible the items were derived from other items by subtracting or combining sub items. Specific changes and calculations are documented in the processing steps sections of this report. Other missing data occurs at the state and (or) county level due to census non-disclosure rules where small numbers of farms reporting an item have acres and (or) production values withheld to prevent identification of individual farms. In general, caution should be exercised when comparing current (2012) data with values reported in earlier censuses. While the 1974-2012 data are comparable, data prior to 1974 will have inflated farm counts and slightly inflated production amounts due to the differences in collection methods, primarily, the definition of a farm. Further discussion on comparability can be found the comparability section of the Supplemental Information element of this metadata report. Excluded from the tabular data are the District of Columbia, Menominee County, Wisconsin, and the independent cities of Virginia with the exception of the three county-equivalent cities of Chesapeake City, Suffolk, and Virginia Beach. Data for independent cities of Virginia prior to 1959 have been included with their surrounding or adjacent county. Please refer to the Supplemental Information element for information on terminology, the Census of Agriculture, the Inter-university Consortium for Political and Social Research (ICPSR), table and variable structure, data comparability, all farms and economic class 1-5 farms, item calculations, increase of farms from 1974 to 1978, missing data and exclusion explanations, 1978 crop irregularities, pastureland irregularities, county alignment, definitions, and references. In addition to the metadata is an excel workbook (VariableKey.xlsx) with spreadsheets containing key spreadsheets for items and variables by category and a spreadsheet noting the presence or absence of entire variable data by year. Note: this dataset was updated on 2016-02-10 to populate omitted irrigation values for Miami-Dade County, Florida in 1997.

This EnviroAtlas data set depicts estimates for mean cash rent paid for land by farmers, sorted by county for irrigated cropland, non-irrigated cropland, and pasture by for most of the conterminous US. This data comes from national surveys which includes approximately 240,000 farms and applies to all crops. According to the USDA (U.S. Department of Agriculture) National Agricultural Statistics Service (NASS), these surveys do not include land rented for a share of the crop, on a fee per head, per pound of gain, by animal unit month (AUM), rented free of charge, or land that includes buildings such as barns. For each land use category with positive acres, respondents are given the option of reporting rent per acre or total dollars paid. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

The Census of Agriculture provides a detailed picture every five years of U.S. farms and ranches and the people who operate them. Conducted by USDA's National Agricultural Statistics Service, the 2012 Census of Agriculture collected more than six million data items directly from farmers. The Ag Census Web Maps application makes this information available at the county level through a few clicks. The maps and accompanying data help users visualize, download, and analyze Census of Agriculture data in a geospatial context. Resources in this dataset:Resource Title: Ag Census Web Maps. File Name: Web Page, url: https://www.nass.usda.gov/Publications/AgCensus/2012/Online_Resources/Ag_Census_Web_Maps/Overview/index.php/ The interactive map application assembles maps and statistics from the 2012 Census of Agriculture in five broad categories:

Crops and Plants – Data on harvested acreage for major field crops, hay, and other forage crops, as well as acreage data for vegetables, fruits, tree nuts, and berries. Economics – Data on agriculture sales, farm income, government payments from conservation and farm programs, amounts received from loans, a broad range of production expenses, and value of buildings and equipment. Farms – Information on farm size, ownership, and Internet access, as well as data on total land in farms, land use, irrigation, fertilized cropland, and enrollment in crop insurance programs. Livestock and Animals – Statistics on cattle and calves, cows and heifers, milk cows, and other cattle, as well as hogs, sheep, goats, horses, and broilers. Operators – Statistics on hired farm labor, tenure, land rented or leased, primary occupation of farm operator, and demographic characteristics such as age, sex, race/ethnicity, and residence location.

The Ag Census Web Maps application allows you to:

Select a map to display from a the above five general categories and associated subcategories. Zoom and pan to a specific area; use the inset buttons to center the map on the continental United States; zoom to a specific state; and show the state mask to fade areas surrounding the state. Create and print maps showing the variation in a single data item across the United States (for example, average value of agricultural products sold per farm). Select a county and view and download the county’s data for a general category. Download the U.S. county-level dataset of mapped values for all categories in Microsoft ® Excel format.

This data set represents the estimated percentage of the 1-km grid cell that is covered by or subject to the agricultural conservation practice (CPIS05), Combination of Irrigation Sources (CIS) on agricultural land by county. A combination of irrigation sources means one or more sources of irrigation, such as wells, ponds, or streams are used on agricultural land. (U.S. Department of Agriculture, 1995) This data set was created with geographic information systems (GIS) and database management tools. The acres on which CIS's are applied were totaled at the county level in the tabular NRI database and then apportioned to a raster coverage of agricultural land within the county based on the Enhanced National Land Cover Dataset (NLCDe) 1-kilometer resolution land cover grids (Nakagaki, 2003). Federal land is not considered in this analysis because NRI does not record information on those lands.

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms. The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 4,000 farms and covers more than 20 different crops in 46 countries. The data (except USA data and for Barley in UK, Germany, Poland, Czech Republic, France and Spain) was collected, consolidated and reported by Kynetec (previously Market Probe), an independent market research agency. It can be used as benchmarks for crop yield and input efficiency.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms. The reference farms were selected by Syngenta and the benchmark farms were randomly selected by Kynetec within the same cluster.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done by Kynetec based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

C. Selection procedure The respondents were picked randomly using a “quota based random sampling” procedure. Growers were first randomly selected and then checked if they complied with the quotas for crops, region, farm size etc. To avoid clustering high number of interviews at one sampling point, interviewers were instructed to do a maximum of 5 interviews in one village.

BF Screened from Philippines were selected based on the following criterion: (a) smallholder rice growers Location: Luzon - Mindoro (Southern Luzon) mid-tier (sub-optimal CP/SE use): mid-tier growers use generic CP, cheaper CP, non hybrid (conventional) seeds
Smallholder farms with average to high levels of mechanization
Should be Integrated Pest Management advocates
less accessible to technology: poor farmers, don't have the money to buy quality seeds, fertilizers,... Don't use machinery yet
simple knowledge on agronomy and pests
influenced by fellow farmers and retailers
not strong financial status: don't have extra money on bank account and so need longer credit to pay (as a consequence: interest increases) may need longer credit

Mode of data collection

Face-to-face [f2f]

Research instrument

Data collection tool for 2019 covered the following information:

(A) PRE- HARVEST INFORMATION

PART I: Screening PART II: Contact Information PART III: Farm Characteristics a. Biodiversity conservation b. Soil conservation c. Soil erosion d. Description of growing area e. Training on crop cultivation and safety measures PART IV: Farming Practices - Before Harvest a. Planting and fruit development - Field crops b. Planting and fruit development - Tree crops c. Planting and fruit development - Sugarcane d. Planting and fruit development - Cauliflower e. Seed treatment

(B) HARVEST INFORMATION

PART V: Farming Practices - After Harvest a. Fertilizer usage b. Crop protection products c. Harvest timing & quality per crop - Field crops d. Harvest timing & quality per crop - Tree crops e. Harvest timing & quality per crop - Sugarcane f. Harvest timing & quality per crop - Banana g. After harvest PART VI - Other inputs - After Harvest a. Input costs b. Abiotic stress c. Irrigation

See all questionnaires in external materials tab.

Cleaning operations

Data processing:

Kynetec uses SPSS (Statistical Package for the Social Sciences) for data entry, cleaning, analysis, and reporting. After collection, the farm data is entered into a local database, reviewed, and quality-checked by the local Kynetec agency. In the case of missing values or inconsistencies, farmers are re-contacted. In some cases, grower data is verified with local experts (e.g. retailers) to ensure data accuracy and validity. After country-level cleaning, the farm-level data is submitted to the global Kynetec headquarters for processing. In the case of missing values or inconsistences, the local Kynetec office was re-contacted to clarify and solve issues.

Quality assurance Various consistency checks and internal controls are implemented throughout the entire data collection and reporting process in order to ensure unbiased, high quality data.

• Screening: Each grower is screened and selected by Kynetec based on cluster-specific criteria to ensure a comparable group of growers within each cluster. This helps keeping variability low.

• Evaluation of the questionnaire: The questionnaire aligns with the global objective of the project and is adapted to the local context (e.g. interviewers and growers should understand what is asked). Each year the questionnaire is evaluated based on several criteria, and updated where needed.

• Briefing of interviewers: Each year, local interviewers - familiar with the local context of farming -are thoroughly briefed to fully comprehend the questionnaire to obtain unbiased, accurate answers from respondents.

• Cross-validation of the answers: o Kynetec captures all growers' responses through a digital data-entry tool. Various logical and consistency checks are automated in this tool (e.g. total crop size in hectares cannot be larger than farm size) o Kynetec cross validates the answers of the growers in three different ways: 1. Within the grower (check if growers respond consistently during the interview) 2. Across years (check if growers respond consistently throughout the years) 3. Within cluster (compare a grower's responses with those of others in the group) o All the above mentioned inconsistencies are followed up by contacting the growers and asking them to verify their answers. The data is updated after verification. All updates are tracked.

• Check and discuss evolutions and patterns: Global evolutions are calculated, discussed and reviewed on a monthly basis jointly by Kynetec and Syngenta.

• Sensitivity analysis: sensitivity analysis is conducted to evaluate the global results in terms of outliers, retention rates and overall statistical robustness. The results of the sensitivity analysis are discussed jointly by Kynetec and Syngenta.

• It is recommended that users interested in using the administrative level 1 variable in the location dataset use this variable with care and crosscheck it with the postal code variable.

Data appraisal

Due to the above mentioned checks, irregularities in fertilizer usage data were discovered which had to be corrected:

For data collection wave 2014, respondents were asked to give a total estimate of the fertilizer NPK-rates that were applied in the fields. From 2015 onwards, the questionnaire was redesigned to be more precise and obtain data by individual fertilizer products. The new method of measuring fertilizer inputs leads to more accurate results, but also makes a year-on-year comparison difficult. After evaluating several solutions to this problems, 2014 fertilizer usage (NPK input) was re-estimated by calculating a weighted average of fertilizer usage in the following years.

The STRIVE project, funded by USAID's Displaced Children and Orphans Fund (DCOF) and managed by FHI 360, used market-led economic strengthening initiatives to improve the well-being of vulnerable children. Through STRIVE, ACDI/VOCA implemented the Agriculture for Children’s Empowerment (ACE) Project in Liberia, which is founded on the premise that increased household economic security will stimulate more consistent investments in children’s well being via longer term social investments in education and nutrition. ACE’s primary focus was on the horticulture value chain (VC) — the production and marketing of vegetables by smallholder farmers in Montserrado, Bong, and Nimba counties of Liberia. ACE also strengthened smallholder rice farming to increase household food security using a market-sensitive approach to rice seed lending and cultivation. This dataset contains information about each household surveyed.

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms. The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 4,000 farms and covers more than 20 different crops in 46 countries. The data (except USA data and for Barley in UK, Germany, Poland, Czech Republic, France and Spain) was collected, consolidated and reported by Kynetec (previously Market Probe), an independent market research agency. It can be used as benchmarks for crop yield and input efficiency.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms. The reference farms were selected by Syngenta and the benchmark farms were randomly selected by Kynetec within the same cluster.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done by Kynetec based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

C. Selection procedure The respondents were picked randomly using a “quota based random sampling” procedure. Growers were first randomly selected and then checked if they complied with the quotas for crops, region, farm size etc. To avoid clustering high number of interviews at one sampling point, interviewers were instructed to do a maximum of 5 interviews in one village.

Screening of Costa Rica BF: Cariari (Pococí cantón), Guápiles cantón, Matina cantón, Siquirres cantón and Sarapiquí cantón

Low: - Productivity: between 1800 and 3200 boxes/ha/year (approved by Syngenta, original cut-off: < 2350 boxes/ha/year) - Generic CP use: use mostly generic products (use mix of generic products and branded products)

Background info: - reduced access to market - disease affection: Sigatoka: Sigatoka disease is already present in all banana farms in Costa Rica; it is an endemic disease in Latin America region

Banana growers in the province of Limon

Mode of data collection

Face-to-face [f2f]

Research instrument

Data collection tool for 2019 covered the following information:

(A) PRE- HARVEST INFORMATION

PART I: Screening PART II: Contact Information PART III: Farm Characteristics a. Biodiversity conservation b. Soil conservation c. Soil erosion d. Description of growing area e. Training on crop cultivation and safety measures PART IV: Farming Practices - Before Harvest a. Planting and fruit development - Field crops b. Planting and fruit development - Tree crops c. Planting and fruit development - Sugarcane d. Planting and fruit development - Cauliflower e. Seed treatment

(B) HARVEST INFORMATION

PART V: Farming Practices - After Harvest a. Fertilizer usage b. Crop protection products c. Harvest timing & quality per crop - Field crops d. Harvest timing & quality per crop - Tree crops e. Harvest timing & quality per crop - Sugarcane f. Harvest timing & quality per crop - Banana g. After harvest PART VI - Other inputs - After Harvest a. Input costs b. Abiotic stress c. Irrigation

See all questionnaires in external materials tab

Cleaning operations

Data processing:

Kynetec uses SPSS (Statistical Package for the Social Sciences) for data entry, cleaning, analysis, and reporting. After collection, the farm data is entered into a local database, reviewed, and quality-checked by the local Kynetec agency. In the case of missing values or inconsistencies, farmers are re-contacted. In some cases, grower data is verified with local experts (e.g. retailers) to ensure data accuracy and validity. After country-level cleaning, the farm-level data is submitted to the global Kynetec headquarters for processing. In the case of missing values or inconsistences, the local Kynetec office was re-contacted to clarify and solve issues.

Quality assurance Various consistency checks and internal controls are implemented throughout the entire data collection and reporting process in order to ensure unbiased, high quality data.

• Screening: Each grower is screened and selected by Kynetec based on cluster-specific criteria to ensure a comparable group of growers within each cluster. This helps keeping variability low.

• Evaluation of the questionnaire: The questionnaire aligns with the global objective of the project and is adapted to the local context (e.g. interviewers and growers should understand what is asked). Each year the questionnaire is evaluated based on several criteria, and updated where needed.

• Briefing of interviewers: Each year, local interviewers - familiar with the local context of farming -are thoroughly briefed to fully comprehend the questionnaire to obtain unbiased, accurate answers from respondents.

• Cross-validation of the answers: o Kynetec captures all growers' responses through a digital data-entry tool. Various logical and consistency checks are automated in this tool (e.g. total crop size in hectares cannot be larger than farm size) o Kynetec cross validates the answers of the growers in three different ways: 1. Within the grower (check if growers respond consistently during the interview) 2. Across years (check if growers respond consistently throughout the years) 3. Within cluster (compare a grower's responses with those of others in the group) o All the above mentioned inconsistencies are followed up by contacting the growers and asking them to verify their answers. The data is updated after verification. All updates are tracked.

• Check and discuss evolutions and patterns: Global evolutions are calculated, discussed and reviewed on a monthly basis jointly by Kynetec and Syngenta.

• Sensitivity analysis: sensitivity analysis is conducted to evaluate the global results in terms of outliers, retention rates and overall statistical robustness. The results of the sensitivity analysis are discussed jointly by Kynetec and Syngenta.

• It is recommended that users interested in using the administrative level 1 variable in the location dataset use this variable with care and crosscheck it with the postal code variable.

Data appraisal

Due to the above mentioned checks, irregularities in fertilizer usage data were discovered which had to be corrected:

For data collection wave 2014, respondents were asked to give a total estimate of the fertilizer NPK-rates that were applied in the fields. From 2015 onwards, the questionnaire was redesigned to be more precise and obtain data by individual fertilizer products. The new method of measuring fertilizer inputs leads to more accurate results, but also makes a year-on-year comparison difficult. After evaluating several solutions to this problems, 2014 fertilizer usage (NPK input) was re-estimated by calculating a weighted average of fertilizer usage in the following years.

For lands used to produce crops, CEC developed a suitability model to simultaneously evaluate several factors that impact an area’s relative implication for croplands. In the CEC land use screens, implication is defined as a possible significance or a likely consequence of an action. For example, planning for energy infrastructure development in areas with more factors that support high-value croplands has implications for opportunities to preserve agricultural land. The variables used in the CEC Cropland Index Model contain information on soil quality (CA Revised Storie Index, Electrical Conductivity, and Sodium Adsorption Ratio), farmland designations (Prime Farmland, Unique Farmland and Farmland of Statewide Importance), and current existence of crops (as indicated by the California Statewide Crop Mapping). The CEC Cropland Index Model does not include statewide information for grazing lands or rangelands, and it is only applied to solar technology. Each input data layer is transformed onto a common scale and weighted according to each dataset’s relative importance. The result is a summation of the input data layers into a single-gridded map. This final model output provides a numerically weighted index of importance for croplands at a given _location. The classified version of the model output, given in this dataset, partitions the CEC Cropland Index Model at the mean into areas of high and low implication. The high implication area is used as an exclusion in the CEC Land Use Screens for solar technology. These regions have a relatively higher implication for cropland than the lower implication region. The table below provides data sources that the CEC Cropland Index Model relies on. For a complete description of the model and its use in the 2023 CEC Land-Use Screens, please refer to the Land Use Screens Staff Report in the CEC Energy Planning Library. Dataset Name Source Usage Gridded Soil Survey Geographic (gSSURGO) Database Soil Survey Staff. 2020. "The Gridded Soil Survey Geographic (gSSURGO) Database for California." United States Department of Agriculture, Natural Resources Conservation Service. https://gdg.sc.egov.usda.gov/ Provides CA Revised Storie Index, Electrical Conductivity, and Sodium Adsorption Ratio for the CEC Cropland Index Model for the Core and SB 100 Terrestrial Climate Resilience Screens for solar resource potential California Important Farmland "2018 California Important Farmland.” Farmland Mapping and Monitoring Program." California Department of Conservation. https://www.conservation.ca.gov/dlrp/fmmp Prime Farmland, Unique Farmland, and Farmland of Statewide Importance is used in the CEC Cropland Index Model for the Core and SB 100 Terrestrial Climate Resilience Screens for solar resource potential California Statewide Crop Mapping (2019) "2019 California Statewide Crop Mapping." California Department of Water Resources. https://data.cnra.ca.gov/dataset/statewide-crop-mapping The footprint is used as part of the mask for the CEC Cropland Index Model’s _domain of analysis for the Core and SB 100 Terrestrial Climate Resilience Screens for solar resource potential

This data set consists of general soil association units. It was develped by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) data set published in 1994. It consists of a broad based inventory of soils and nonsoil areas that occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. The data set was created by generalizing more detailed soil survey maps. Where more detailed soil survey maps were not available, data on geology, topography, vegetation, and climate were assembled, together with Land Remote Sensing Satellite (LANDSAT) images. Soils of like areas were studied, and the probable classification and extent of the soils were determined. Map unit composition was determined by transecting or sampling areas on the more detailed maps and expanding the data statistically to characterize the whole map unit. This data set consists of georeferenced vector digital data and tabular digital data. The map data were collected in 1-by 2-degree topographic quadrangle units and merged into a seamless national data set. It is distributed in state/territory and national extents. The soil map units are linked to attributes in the National Soil Information System data base which gives the proportionate extent of the component soils and their properties.

Summary:

There are over 608 million farms around the world but they are not the same. We developed high spatial resolution maps telling where small and large farms were located and which crops were planted for 56 countries. We checked the reliability and have the confidence to use them for the country-level and global studies. Our maps will help more studies to easily measure how agriculture policies, water availabilities, and climate change affect small and large farms respectively.

The code, source data, and the simultaneously farm-size- and crop-specific harvested area datasets, including the GAEZv4 crop map based dataset and SPAM2010 crop map based dataset, are open-access, free, and available, which can be found below. The resulting dataset is available in *.csv and *.nc (netCDF) for each crop and farming system. For each crop, farming system, and farm size, we provide the gridded harvested area in the coordinate Systems of EPSG:4326 - WGS 84. Gridded summaries over crops and farming systems are also available.

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How to cite this dataset:

Su, H., Willaarts, B., Luna-Gonzalez, D., Krol, M.S. and Hogeboom, R.J., 2022. Gridded 5 arcmin datasets for simultaneously farm-size-specific and crop-specific harvested areas in 56 countries. Earth System Science Data, 14(9), pp.4397-4418.

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Update history:

I am happy to receive any questions, comments, or potential collaboration on further dataset development. Please drop your email to Han Su (h.su@utwente.nl, han_su20@163.com)

Version 1.03.1: Fix bugs in data format; Netcdf didn't show properly before in QGIS. Data underlying the three versions are the same.

Version 1.02: New data summary, add Netcdf data format

Version 1: Initial dataset for peer-review, CSV format only

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Note: please cite the original publications/sources if any data source based on which this dataset was developed is reused for your own study.

SPAM2010:

Yu, Q., You, L., Wood-Sichra, U., Ru, Y., Joglekar, A. K. B., Fritz, S., Xiong, W., Lu, M., Wu, W., and Yang, P.: A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps, Earth System Science Data, 12, 3545-3572, 10.5194/essd-12-3545-2020, 2020.

GAEZv4:

FAO and IIASA: Global Agro Ecological Zones version 4 (GAEZ v4), FAO UN, Rome, Italy, 2021

The dataset of Ricciardi et al.'s:

Ricciardi, V., Ramankutty, N., Mehrabi, Z., Jarvis, L., and Chookolingo, B.: How much of the world's food do smallholders produce?, Global Food Security, 17, 64-72, 2018.

The global dominant field size dataset:

Lesiv, M., Laso Bayas, J. C., See, L., Duerauer, M., Dahlia, D., Durando, N., Hazarika, R., Kumar Sahariah, P., Vakolyuk, M., Blyshchyk, V., Bilous, A., Perez-Hoyos, A., Gengler, S., Prestele, R., Bilous, S., Akhtar, I. U. H., Singha, K., Choudhury, S. B., Chetri, T., Malek, Z., Bungnamei, K., Saikia, A., Sahariah, D., Narzary, W., Danylo, O., Sturn, T., Karner, M., McCallum, I., Schepaschenko, D., Moltchanova, E., Fraisl, D., Moorthy, I., and Fritz, S.: Estimating the global distribution of field size using crowdsourcing, Glob Chang Biol, 25, 174-186, 10.1111/gcb.14492, 2019.

GLC-Share:

Latham, J., Cumani, R., Rosati, I., and Bloise, M.: Global land cover share (GLC-SHARE) database beta-release version 1.0-2014, FAO, Rome, Italy, 2014.

CAAS-IFPRI cropland extent map:

Lu, M., Wu, W., You, L., See, L., Fritz, S., Yu, Q., Wei, Y., Chen, D., Yang, P., and Xue, B.: A cultivated planet in 2010 – Part 1: The global synergy cropland map, Earth System Science Data, 12, 1913-1928, 10.5194/essd-12-1913-2020, 2020.

This data set represents the estimated percentage of the 1-km grid cell that is covered by or subject to the agricultural conservation practice (CPIS05), Combination of Irrigation Sources (CIS) on agricultural land by county. A combination of irrigation sources means one or more sources of irrigation, such as wells, ponds, or streams are used on agricultural land. (U.S. Department of Agriculture, 1995) This data set was created with geographic information systems (GIS) and database management tools. The acres on which CIS's are applied were totaled at the county level in the tabular NRI database and then apportioned to a raster coverage of agricultural land within the county based on the Enhanced National Land Cover Dataset (NLCDe) 1-kilometer resolution land cover grids (Nakagaki, 2003). Federal land is not considered in this analysis because NRI does not record information on those lands.

This data set represents the estimated percentage of the 1-km grid cell that is covered by or subject to the agricultural conservation practice (CP320), Irrigation Canals or Laterals on agricultural land by county. An Irrigation Canals or Laterals (ICL) is a "permanent channel constructed to convey irrigation water from the source of supply to one or more irrigated areas." (U.S. Department of Agriculture, 1995) This data set was created with geographic information systems (GIS) and database management tools. The acres on which ICL's are applied were totaled at the county level in the tabular NRI database and then apportioned to a raster coverage of agricultural land within the county based on the Enhanced National Land Cover Dataset (NLCDe) 1-kilometer resolution land cover grids (Nakagaki, 2003). Federal land is not considered in this analysis because NRI does not record information on those lands.

The Census of Agriculture, produced by the United States Department of Agriculture (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2022, and provides an in-depth look at the agricultural industry.This layer was produced from data obtained from the USDA National Agriculture Statistics Service (NASS) Large Datasets download page. The data were transformed and prepared for publishing using the Pivot Table geoprocessing tool in ArcGIS Pro and joined to county boundaries. The county boundaries are 2022 vintage and come from Living Atlas ACS 2022 feature layers.Dataset SummaryPhenomenon Mapped: 2022 Turkey ProductionCoordinate System: Web Mercator Auxiliary SphereExtent: 48 Contiguous United States, Alaska, and HawaiiSource: USDA National Agricultural Statistics ServicePublication Date: 2022AttributesNote that some values are suppressed as "Withheld to avoid disclosing data for individual operations", "Not applicable", or "Less than half the rounding unit". These have been coded in the data as -999, -888, and -777 respectively.Turkeys - InventoryTurkeys - Operations With InventoryTurkeys - Operations With SalesTurkeys - Sales, Measured In HeadTurkeys, Production Contract - Operations With ProductionTurkeys, Production Contract - Production, Measured In Head In Alaska, one or more county-equivalent entities (borough, census area, city, municipality) are included in an agriculture census area.

Amazonia holds the largest continuous area of tropical forests with intense land use change dynamics inducing water, carbon, and energy feedbacks with regional and global impacts. Much of our knowledge of land use change in Amazonia comes from studies of the Brazilian Amazon, which accounts for two thirds of the region. Amazonia outside of Brazil has received less attention because of the difficulty of acquiring consistent data across countries. We present here an agricultural statistics database of the entire Amazonia region, with a harmonized description of crops and pastures in geospatial format, based on administrative boundary data at the municipality level. The spatial coverage includes countries within Amazonia and spans censuses and surveys from 1950 to 2012. Harmonized crop and pasture types are explored by grouping annual and perennial cropping systems, C3 and C4 photosynthetic pathways, planted and natural pastures, and main crops. Our analysis examined the spatial pattern of ratios between classes of the groups and their correlation with the agricultural extent of crops and pastures within administrative units of the Amazon, by country, and census/survey dates. Significant correlations were found between all ratios and the fraction of agricultural lands of each administrative unit, with the exception of planted to natural pastures ratio and pasture lands extent. Brazil and Peru in most cases have significant correlations for all ratios analyzed even for specific census and survey dates. Results suggested improvements, and potential applications of the database for carbon, water, climate, and land use change studies are discussed. The database presented here provides an Amazon-wide improved data set on agricultural dynamics with expanded temporal and spatial coverage