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 Corn 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.Corn - Operations With SalesCorn - Sales, Measured In US Dollars ($)Corn, Grain - Acres HarvestedCorn, Grain - Operations With Area Harvested - Area Harvested: (1.0 To 24.9 Acres)Corn, Grain - Operations With Area Harvested - Area Harvested: (25.0 To 99.9 Acres)Corn, Grain - Operations With Area Harvested - Area Harvested: (100 To 249 Acres)Corn, Grain - Operations With Area Harvested - Area Harvested: (250 To 499 Acres)Corn, Grain - Operations With Area Harvested - Area Harvested: (500 To 999 Acres)Corn, Grain - Operations With Area Harvested - Area Harvested: (1,000 Or More Acres)Corn, Grain - Operations With Area HarvestedCorn, Grain - Production, Measured In BushelsCorn, Grain, Irrigated - Acres HarvestedCorn, Grain, Irrigated - Operations With Area HarvestedCorn, Silage - Acres HarvestedCorn, Silage - Operations With Area Harvested - Area Harvested: (1.0 To 24.9 Acres)Corn, Silage - Operations With Area Harvested - Area Harvested: (25.0 To 99.9 Acres)Corn, Silage - Operations With Area Harvested - Area Harvested: (100 To 249 Acres)Corn, Silage - Operations With Area Harvested - Area Harvested: (250 To 499 Acres)Corn, Silage - Operations With Area Harvested - Area Harvested: (500 To 999 Acres)Corn, Silage - Operations With Area Harvested - Area Harvested: (1,000 Or More Acres)Corn, Silage - Operations With Area HarvestedCorn, Silage - Production, Measured In TonsCorn, Silage, Irrigated - Acres HarvestedCorn, Silage, Irrigated - Operations With Area HarvestedCorn, Traditional Or Indian - Acres HarvestedCorn, Traditional Or Indian - Operations With Area HarvestedCorn, Traditional Or Indian - Production, Measured In LbsCorn, Traditional Or Indian, Irrigated - Acres HarvestedCorn, Traditional Or Indian, Irrigated - Operations With Area Harvested In Alaska, one or more county-equivalent entities (borough, census area, city, municipality) are included in an agriculture census area.

Maize (Zea mays), also known as corn, is a crop of world wide importance. Originally domesticated in what is now Mexico, its tolerance of diverse climates has lead to its widespread cultivation. Globally, it is tied with rice as the second most widely grown crop. Only wheat is more widely grown. In Africa it is grown throughout the agricultural regions of the continent from the Nile Delta in the north to the country of South Africa in the south. In sub-Saharan Africa it is relied on as a staple crop for 50% of the population.Dataset SummaryThis layer provides access to a 5 arc-minute (approximately 10 km at the equator) cell-sized raster of the 1999-2001 annual average area of maize harvested in Africa. The data are in units of hectares/grid cell.The SPAM 2000 v3.0.6 data used to create this layer were produced by the International Food Policy Research Institute in 2012. This dataset was created by spatially disaggregating national and sub-national harvest data using the Spatial Production Allocation Model. Link to source metadataFor more information about this dataset and the importance of maize as a staple food see the Harvest Choice webpage.For data on other agricultural species in Africa see these layers:CassavaGroundnut (Peanut)MilletPotatoRiceSorghumSweet Potato and YamWheatData for important agricultural crops in South America are available here.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 has query, identify, and export image services available. This layer is restricted to a maximum area of 24,000 x 24,000 pixels which allows access to the full dataset.The source data for this layer are available here.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started follow these links:Landscape Layers - a reintroductionLiving Atlas Discussion Group

Global Maize Yield by Country, 2023 Discover more data with ReportLinker!

The Census of Agriculture, produced by the USDA National Agricultural Statistics Service (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 2017, and provides an in-depth look at the agricultural industry.This layer summarizes corn production from the 2017 Census of Agriculture at the county level.This layer was produced from data downloaded using the USDA's QuickStats Application. The data was transformed using the Pivot Table tool in ArcGIS Pro and joined to the county boundary file provided by the USDA. The layer was published as feature layer in ArcGIS Online. Dataset SummaryPhenomenon Mapped: 2017 Corn ProductionCoordinate System: Web Mercator Auxiliary SphereExtent: 48 Contiguous United States and HawaiiVisible Scale: All ScalesSource: USDA National Agricultural Statistics Service QuickStats ApplicationPublication Date: 2017AttributesThis layer provides values for the following attributes. Note that some values are not disclosed (coded as -1 in the layer) to protect the privacy of producers in areas with limited production.Operations with SalesSales in US DollarsGrain - Area Harvested in AcresGrain - Operations with Area HarvestedGrain - Production in BushelsGrain - Irrigated Area Harvested in AcresGrain - Operations with Irrigated Area HarvestedSilage - Area Harvested in AcresSilage - Operations with Area HarvestedSilage - Production in TonsSilage - Irrigated Area Harvested in AcresSilage - Operations with Area HarvestedTraditional or Indian - Area Harvested in AcresTraditional or Indian - Operations with Area HarvestedTraditional or Indian - Production in PoundsTraditional or Indian - Irrigated Area Harvested in AcresTraditional or Indian - Operations with Area HarvestedAdditionally attributes of State Name, State Code, County Name and County Code are included to facilitate cartography and use with other layers.What can you do with this layer?This layer can be used throughout the ArcGIS system. Feature layers can be used just like any other vector layer. You can use feature layers as an input to geoprocessing tools in ArcGIS Pro or in Analysis in ArcGIS Online. Combine the layer with others in a map and set custom symbology or create a pop-up tailored for your users.For the details of working with feature layers the help documentation for ArcGIS Pro or the help documentation for ArcGIS Online are great places to start. The ArcGIS Blog is a great source of ideas for things you can do with feature layers.This layer is part of ArcGIS Living Atlas of the World that provides an easy way to find and explore many other beautiful and authoritative layers, maps, and applications on hundreds of topics.

Global Maize Production by Country, 2024 Discover more data with ReportLinker!

Corn decreased 3.39 USd/BU or 0.74% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Corn - values, historical data, forecasts and news - updated on March of 2025.

Forecast: Corn Production in the US 2023 - 2027 Discover more data with ReportLinker!

This dataset includes daily two-band Enhanced Vegetation Index (EVI2) at 30-m resolution over a Landsat scene (path 26 and row 31) in central Iowa. Fourteen years of daily EVI2 from 2001 to 2015 (except 2012) were generated through fusing and interpolating Landsat-MODIS data.Landsat surface reflectances were order and used in this study. Mostly clear Landsat images from each year were chosen to pair with MODIS images acquired from the same day to generate daily Landsat-MODIS surface reflectance using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). Partially clear Landsat images were also used in generating the smoothed and gap-filled daily VI time-series. All available Landsat data including Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) were used in this study.The MODIS data products were downloaded and processed. These include the daily surface reflectance at both 250m (MOD09GQ) and 500m (MOD09GA) resolution, the MODIS Bidirectional Reflectance Distribution Function (BRDF) parameters at 500m resolution, and the MODIS land cover types at 500m resolution (MCD12Q1). They were used to generated daily nadir BRDF-adjusted reflectance (NBAR) at 250m resolution for fusing with Landsat.The Landsat-MODIS data fusion results for 2001-2014 were generated from a previous study (Gao et al, 2017; doi: 10.1016/j.rse.2016.11.004). Data fusion results for 2015 were generated using Landsat 8 OLI images from day 194, 226, 258 and 338 in this study. Cloud masks were extracted from Landsat and MODIS QA layers and were used to exclude cloud, cloud shadow and snow pixels. Since Landsat 5 TM operational imaging ended in November 2011 and Landsat 8 OLI has not been launched until February 2013, Landsat 7 ETM+ Scan Line Corrector (SLC)-off images are the only available Landsat data. For this reason, 2012 was not included.Due to the cloud contamination in the Landsat and MODIS images, the fused Landsat-MODIS results still have invalid values or gaps. To fill these gaps, a modified Savitzky-Golay (SG) filter approach was built and applied to smooth and gap-fill EVI2. The SG filter is a moving fitting approach. Each point is smoothed using the value computed from the polynomial function fit to the observations within the moving window. The program removes spike points if the fitting errors are larger than the predefined threshold (default 3 standard deviation). The modified SG filter allows us to retain small variations but also fill large gaps in an unevenly distributed time-series EVI2.Daily EVI2 files are saved in one tar file per year. Each tar file contains a binary image file and a text header file that can be displayed in the ENVI software. The binary image file has the dimension of 7201 lines by 8061 samples by 365 days and is saved in BIP (band interleaved by pixel) format. EVI2 data are saved in 4-byte float number. The text header file contains necessary information including projection and geolocation. Daily EVI2 file is named as "flexfit_evi2.026031.yyyy.bin", where "026031" refers to the Landsat path and row, and yyyy represents year and ranges from 2001-2015.Resources in this dataset:Resource Title: Daily EVI2 Data Packages .File Name: Web Page, url: https://app.globus.org/file-manager?origin_id=904c2108-90cf-11e8-9672-0a6d4e044368&origin_path=/LTS/ADCdatastorage/NAL/published/node22870/These Daily EVI2 data packages are grouped by year. Each package includes a plain binary file that saves daily EVI2, and a ENVI header file (in text) that contains metadata and geolocation information. Contents are as follows: dailyVI.026031.2000.tar.gz dailyVI.026031.2001.tar.gz dailyVI.026031.2002.tar.gz dailyVI.026031.2003.tar.gz dailyVI.026031.2004.tar.gz dailyVI.026031.2005.tar.gz dailyVI.026031.2006.tar.gz dailyVI.026031.2007.tar.gz dailyVI.026031.2008.tar.gz dailyVI.026031.2009.tar.gz dailyVI.026031.2010.tar.gz dailyVI.026031.2011.tar.gz dailyVI.026031.2013.tar.gz dailyVI.026031.2014.tar.gz dailyVI.026031.2015.tar.gzSCINet users: The .tar.gz files can be accessed/retrieved with valid SCINet account at this _location: /LTS/ADCdatastorage/NAL/published/node22870/See the SCINet File Transfer guide for more information on moving large files: https://scinet.usda.gov/guides/data/datatransferGlobus users: The files can also be accessed through Globus by following this data link. The user will need to log in to Globus in order to retrieve this data. User accounts are free of charge with several options for signing on. Instructions for creating an account are on the login page.

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 Viet Nam were selected based on the following criterion:

(a) smallholder maize growers Corn growers in Dong Nai & Son La province Second season
Low investment
Use of little or no seed treatment or crop protection (--> all use CPP but BF should use generics)
Average cultivation skills: mid-tier (sub-optimal CP/SE use) (mid-tier growers use generic CP, cheaper CP, non-Syngenta hybrid seeds)
Not progressive: simple knowledge on agronomy and pests; less accessible to technology
influenced by fellow farmers and retailers
not strong financial status, 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.

Groundnut (Arachis hypogaea), also known as peanut, is grown around the world in a broad region between 40 degrees north and south latitude. Originally from South America, major producers of groundnut include China, India and the United States. Producing 30% of Africa's total, Nigeria leads the continent's production followed by Senegal, Sudan, Ghana, and Chad. Groundnut is a valuable source of protein and oil. It has the additional benefit of enriching depleted soils by converting nitrogen from the air into a form that is required by most plants.Dataset SummaryThis layer provides access to a 5 arc-minute (approximately 10 km at the equator) cell-sized raster of the 1999-2001 annual average area of groundnut harvested in Africa. The data are in units of hectares/grid cell.The SPAM 2000 v3.0.6 data used to create this layer were produced by the International Food Policy Research Institute in 2012. This dataset was created by spatially disaggregating national and sub-national harvest data using the Spatial Production Allocation Model. Link to source metadataFor more information about this dataset and the importance of casava as a staple food see the Harvest Choice webpage.For data on other agricultural species in Africa see these layers:Groundnut (Peanut)Maize (Corn)MilletPotatoRiceSorghumSweet Potato and YamWheatData for important agricultural crops in South America are available here.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 has query, identify, and export image services available. This layer is restricted to a maximum area of 24,000 x 24,000 pixels which allows access to the full dataset.The source data for this layer are available here.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started follow these links:Landscape Layers - a reintroductionLiving Atlas Discussion Group

Understanding the cycle of carbon emissions resulting from agricultural practices is critical for evaluating their effect on environmental quality. This study investigates the influence of corn production on environmental quality across six major corn producing provinces in China: Hebei, Heilongjiang, Henan, Hubei, Shandong, and Sichuan, using panel datasets spanning from 1990 to 2022. Utilizing a robust methodological framework and advanced econometric techniques such as the Panel Mean Group Autoregressive Distributed Lag model (PMG-ARDL), Panel Quantile Regressions (PQR), Panel Least Square regression (PLSR), this study offers a comprehensive analysis of both short-term and long-term impacts of several agricultural inputs, agricultural GDP, and temperature on environmental quality. The findings reveal significant long-term contributions to carbon emissions from the use of agricultural water, agricultural credit, and fertilizers use, indicating the environmental costs associated with intensive agricultural practices. The study shows carbon emissions have a long-term negative relationship with corn production. The results from the PMG-ARDL model are consistent with those obtained from the PQR, and PLSQR analyses, demonstrating strong positive correlations between agricultural loans, fertilizer use, agricultural water usage, and carbon emissions. The Dumitrescu and Hurlin results show unidirectional causation of carbon emissions from pesticide use, temperature, and agricultural GDP, and bidirectional causal relationship between carbon emissions, corn production, fertilizer use, agricultural water usage, and agricultural credit. The study underscores the critical need for policies that balance agricultural productivity with environmental quality, suggesting directions for future research to explore diverse agricultural systems and incorporate more dynamic modeling approaches to better understand and mitigate the environmental impacts of agriculture.

This is digital research metadata corresponding to a published manuscript in Energies (MDPI) entitled "Biochar stability in a highly weathered sandy soil under four years of continuous corn production", Volume 14, Issue 19, 6157. Dataset may be accessed via the included link at the Dryad data repository. Biochar is being considered a climate change mitigation tool by increasing soil organic carbon contents (SOC), however, questions remain concerning its longevity in soil. We applied 30,000 kg ha−1 of biochars to plots containing a Goldsboro sandy loam (Fine-loamy, siliceous, sub-active, thermic Aquic Paleudults) and then physically disked all plots. Thereafter, the plots were agronomically managed under 4 years (Y) of continuous corn (Zea mays, L.) planting. Annually, incremental soil along with corresponding bulk density samples were collected and SOC concentrations were measured in topsoil (down to 23-cm). The biochars were produced from Lodgepole pine (Pinus contorta) chip (PC) and Poultry litter (PL) feedstocks. An untreated Goldsboro soil (0 biochar) served as a control. After four years, SOC contents in the biochar treated plots were highest in the top 0–5 and 5–10 cm depth suggesting minimal deeper movement. Declines in SOC contents varied with depth and biochar type. After correction for SOC declines in controls, PL biochar treated soil had a similar decline in SOC (7.9 to 10.3%) contents. In contrast, the largest % SOC content decline (20.2%) occurred in 0–5 cm deep topsoil treated with PC biochar. Our results suggest that PC biochar had less stability in the Goldsboro soil than PL biochar after 4 years of corn grain production. Methods are described in the manuscript: https://doi.org/10.3390/en14196157. Descriptions corresponding to each figure and table in the manuscript are placed on separate tabs in the Excel file to clarify abbreviations and summarize the data headings and units. Resources in this dataset:Resource Title: Digital research data for Biochar stability in a highly weathered sandy soil under four years of continuous corn production. File Name: Web Page, url: https://doi.org/10.5061/dryad.xpnvx0kh2 Novak, Jeffrey et al. (2021), Digital research data from: Biochar stability in a highly weathered sandy soil under four years of continuous corn production, Dryad, Dataset, https://doi.org/10.5061/dryad.xpnvx0kh2

Estimated areas, yield and production. Type of crop (Total corn for grain; Genetically modified corn for grain; Total soybeans; Genetically modified soybeans).

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 Indonesia were selected based on the following criterion: (a) Corn growers in East Java - Location: East Java (Kediri and Probolinggo) and Aceh
- Innovative (early adopter); Progressive (keen to learn about agronomy and pests; willing to try new technology); Loyal (loyal to technology that can help them)
- making of technical drain (having irrigation system)
- marketing network for corn: post-harvest access to market (generally they sell 80% of their harvest)
- mid-tier (sub-optimal CP/SE use)
- influenced by fellow farmers and retailers
- may need longer credit

(b) Rice growers in West and East Java - Location: West Java (Tasikmalaya), East Java (Kediri), Central Java (Blora, Cilacap, Kebumen), South Lampung
- The growers are progressive (keen to learn about agronomy and pests; willing to try new technology)
- Accustomed in using farming equipment and pesticide. (keen to learn about agronomy and pests; willing to try new technology) - A long rice cultivating experience in his area (lots of experience in cultivating rice)
- willing to move forward in order to increase his productivity (same as progressive)
- have a soil that broad enough for the upcoming project
- have influence in his group (ability to influence others) - mid-tier (sub-optimal CP/SE use)
- 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.

Global Maize Oil Production by Country, 2023 Discover more data with ReportLinker!

This land suitability for Maize/corn raster data (in GeoTIFF format) represents areas of potential suitability for this crop and its specific irrigation management systems in the Flinders and Gilbert catchments of North Queensland. The data is coded 1-5: 1 - Suitable with no limitations; 2 - Suitable with minor limitations; 3 - Suitable with moderate limitations; 4 - Marginal; 5 - Unsuitable. The land suitability evaluation methods used to produce this data are a modification of methods of the Food and Agriculture Organisation of the UN (FAO). This data is part of the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project and is designed to support sustainable regional development in Australia being of importance to Australian Governments and agricultural industries. The project identifies new opportunities for irrigation development in these remote areas by providing improved soil and land evaluation data to identify opportunities and promote detailed investigation. A companion dataset exists, “Confidence of suitability data for the FGARA project”. A link to this dataset can be found in the “related materials” section of this metadata record. Lineage: These suitability raster data for Maize/corn and its individual irrigation management systems have been created from a range of inputs and processing steps. Below is an overview. For more information refer to the CSIRO FGARA published reports and in particular: Bartley R, Thomas MF, Clifford D, Phillip S, Brough D, Harms D, Willis R, Gregory L, Glover M, Moodie K, Sugars M, Eyre L, Smith DJ, Hicks W and Petheram C (2013) Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project, CSIRO. Broadly, the steps were to: 1. Collate existing data (data related to: climate, topography, soils, natural resources, remotely sensed etc of various formats; reports, spatial vector, spatial raster etc). 2. Select additional soil and attribute site data by Latin hypercube statistical sampling method applied across the covariate space. 3. Carry out fieldwork to collect additional soil and attribute data and understand geomorphology and landscapes. 4. Build models from selected input data and covariate data using predictive learning via rule ensembles in the RuleFit3 software. 5. Create Digital Soil Mapping (DSM) key attributes output data. DSM is the creation and population of a geo-referenced database, generated using field and laboratory observations, coupled with environmental data through quantitative relationships. It applies pedometrics - the use of mathematical and statistical models that combine information from soil observations with information contained in correlated environmental variables, remote sensing images and some geophysical measurements. 6. Choose land management options and create suitability rules for DSM attributes. 7. Run suitability rules to produce limitation datasets using a modification on the FAO methods. 8. Create final suitability data for all land management options. Two companion datasets exist for this dataset. The first is linked to in the “related materials” section of this metadata record, entitled “Confidence of suitability data for the FGARA project”. The second (held by CSIRO Land and Water) includes expert opinion and knowledge about landscape processes or conditions that will influence agricultural development potential in these catchments, but were not captured sufficiently in the modelling process (and areas of expert opinion where the Mahanabolis method underestimates confidence). The two landscape features that require special attention are the basalt rock outcrops in the Upper Flinders catchment that were not well captured by the covariate data, and the secondary salinisation hazard in the central Flinders catchment. For more information refer to the report “Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project”.

This dataset contains simulations - performed as part of the project "Predicting agricultural impacts of large-scale drought:2012 and the case for better modeling" -- for county and state-level maize yields and production totals in the conterminous US for the year 2012 as well as hindcasts for maize yields and production totals from 1979-2011 for use in validation and analysis. The zero lead time forecasts for 2012 are estimated from simulations of pDSSAT, a gridded high-resolution version of the detailed biophysical crop growth model CERES-Maize (as part of DSSAT). The simulations were conducted in December 2012 and published to figshare in January 2013 in anticipation of the release of the official statistics on county-level US maize yields from USDA NASS in February 2013. Paper detailing this work is Joshua Elliott, Michael Glotter, Neil Best, Ken Boote, Jim Jones, Jerry Hatfield, Cynthia Rosenzweig, Leonard A. Smith, and Ian Foster, (2013). Predicting agricultural impacts of large-scale drought: 2012 and the case for better modeling. Mathematics and Computer Science Division Preprint ANL/MCS-P4034-0213 Argonne National Laboratory, 2013. RDCEP Working Paper No. 13-01. Available at http://www.rdcep.org/predicting-agricultural-impacts-large-scale-drought-2012-and-case-better-modeling and http://www.agmip.org/blog/2013/02/21/predicting-agricultural-impacts-of-large-scale-drought/

Wheat (Triticum spp.) is one of the world's most important crops. In the developing world only rice is more important as a food source. In Africa, Ethiopia and South Africa are important producers.Dataset SummaryThis layer provides access to a 5 arc-minute (approximately 10 km at the equator) cell-sized raster of the 1999-2001 annual average area of wheat harvested in Africa. The data are in units of hectares/grid cell.The SPAM 2000 v3.0.6 data used to create this layer were produced by the International Food Policy Research Institute in 2012. This dataset was created by spatially disaggregating national and sub-national harvest data using the Spatial Production Allocation Model. Link to source metadataFor more information about this dataset and the importance of wheat as a staple food see the Harvest Choice webpage.For data on other agricultural species in Africa see these layers:CassavaGroundnut (Peanut)Maize (Corn)MilletPotatoRiceSorghumSweet Potato and YamData for important agricultural crops in South America are available here.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 has query, identify, and export image services available. This layer is restricted to a maximum area of 24,000 x 24,000 pixels which allows access to the full dataset.The source data for this layer are available here.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started follow these links:Landscape Layers - a reintroductionLiving Atlas Discussion Group

Potato (Solanum tuberosum) a native of South America was first domesticated between 8000 and 5000 BC. In the middle of the 16th century it was introduced to Europe, Asia and Africa. Africa produces about 5% of the world's potato crop.Dataset SummaryThis layer provides access to a 5 arc-minute (approximately 10 km at the equator) cell-sized raster of the 1999-2001 annual average area of potato harvested in Africa. The data are in units of hectares/grid cell.The SPAM 2000 v3.0.6 data used to create this layer were produced by the International Food Policy Research Institute in 2012. This dataset was created by spatially disaggregating national and sub-national harvest data using the Spatial Production Allocation Model. Link to source metadataFor more information about this dataset and the importance of potato as a staple food see the Harvest Choice webpage.For data on other agricultural species in Africa see these layers:CassavaGroundnut (Peanut)Maize (Corn)MilletRiceSorghumSweet Potato and YamWheatData for important agricultural crops in South America are available here.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 has query, identify, and export image services available. This layer is restricted to a maximum area of 24,000 x 24,000 pixels which allows access to the full dataset.The source data for this layer are available here.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started follow these links:Landscape Layers - a reintroductionLiving Atlas Discussion Group

Millet is a group of grass species that produce small-seeded grains used for food and animal feed around the world. Common species include finger millet (Eleusine coracana), proso millet (Panicum miliaceum), pearl millet (Pennisetum glaucum), and foxtail millet (Setaria italica). Africa is a major producer growing 56% of the worlds millet. Pearl millet is an important food source in Africa and is known for its tolerance of hot and dry climates.Dataset SummaryThis layer provides access to a 5 arc-minute (approximately 10 km at the equator) cell-sized raster of the 1999-2001 annual average area of millet harvested in Africa. The data are in units of hectares/grid cell.The SPAM 2000 v3.0.6 data used to create this layer were produced by the International Food Policy Research Institute in 2012. This dataset was created by spatially disaggregating national and sub-national harvest data using the Spatial Production Allocation Model. Link to source metadataFor more information about this dataset and the importance of millet as a staple food see the Harvest Choice webpage.For data on other agricultural species in Africa see these layers:CassavaGroundnut (Peanut)Maize (Corn)PotatoRiceSorghumSweet Potato and YamWheatData for important agricultural crops in South America are available here.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 has query, identify, and export image services available. This layer is restricted to a maximum area of 24,000 x 24,000 pixels which allows access to the full dataset.The source data for this layer are available here.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started follow these links:Landscape Layers - a reintroductionLiving Atlas Discussion Group