Monthly report on crop acreage, yield and production in major countries worldwide. Sources include reporting from FAS’s worldwide offices, official statistics of foreign governments, and analysis of economic data and satellite imagery.

This dataset contains detailed annual data on crop and livestock production compiled by the Food and Agriculture Organization (FAO) of the United Nations. It spans 1961 to 2023, covering more than 200 countries and territories, and includes:

🌾 Crops & Livestock: From staple grains like wheat and rice to niche items like anise or caraway.

🌍 Geographic Coverage: Global, with specific data for each country or region.

📆 Time Period: 1961–2023

📐 Metrics Provided: Area harvested (hectares) Production (tonnes) Yield (kg/ha)

This cleaned version of the dataset (NOFLAG) removes flags and notes, making it ideal for data analysis and machine learning projects.

Columns Column Name : Description Area Code : Numeric code for the country or region Area : Country or region name Item Code : Numeric code for the crop/livestock item Item : Name of the crop or livestock item Element Code : Numeric code indicating the metric type Element : Type of measurement (Area, Yield, Production) Unit : Unit of measurement (ha, t, kg/ha) Y1961 to Y2023 : Annual values for the metric in that year

Each row represents one (Country, Crop, Metric) combination across years.

Snapshot img

Autonomous Crop Management Market Size 2024-2028

The autonomous crop management market size is forecast to increase by USD 5.76 billion at a CAGR of 10.45% between 2023 and 2028.

The market is experiencing significant growth due to the increasing focus on productivity and efficiency in the agriculture sector. The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies into autonomous crop management systems is driving this trend, enabling farmers to optimize crop yields and reduce operational costs. However, the high initial investment required for implementing these advanced technologies poses a significant challenge for many farmers and agricultural businesses. Despite this hurdle, the market's potential for innovation and improved agricultural outcomes is substantial. Companies seeking to capitalize on this opportunity should focus on developing cost-effective solutions that cater to the unique needs of various farming sectors and geographies.
Additionally, collaborations and partnerships with technology providers, agricultural institutions, and government organizations can help facilitate the adoption of autonomous crop management systems and mitigate the initial investment barrier. Overall, the market represents an exciting and dynamic landscape for businesses and investors alike, offering significant opportunities for innovation and growth in the agriculture sector.

What will be the Size of the Autonomous Crop Management Market during the forecast period?

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The market continues to evolve, driven by advancements in technology and the growing demand for sustainable agriculture. Farmers are increasingly adopting solutions that leverage artificial intelligence, machine learning, and computer vision to optimize crop yield, improve harvest efficiency, and enhance farm management. Precision spraying and fertilizer management systems enable farmers to apply inputs more effectively, reducing waste and increasing profitability. autonomous vehicles and automated irrigation systems streamline farm operations, while soil health monitoring and variable rate application help improve crop production and reduce environmental impact. Farm management software and digital farming solutions offer real-time data integration, data visualization, and data-driven decision making, allowing farmers to optimize their operations and respond to changing conditions.
Drones and satellite imagery provide valuable insights into crop health and growth patterns, enabling farmers to make informed decisions and improve overall farm efficiency. The market for agricultural innovation is diverse, with a range of entities focusing on yield optimization, water conservation, and labor reduction. Smart sensors and GPS guidance systems enable farmers to monitor and manage their fields more effectively, while weather forecasting and disease management solutions help mitigate risks and protect crops. As the market for autonomous crop management continues to unfold, new applications and integrations are emerging. data security and data integration are becoming increasingly important, as farmers seek to protect their valuable agricultural data and leverage it to improve their operations.
The integration of carbon sequestration and sustainable agriculture solutions is also gaining momentum, as farmers seek to reduce their environmental footprint and enhance the long-term sustainability of their operations.

How is this Autonomous Crop Management Industry segmented?

The autonomous crop management industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Component

  Hardware
  Software
  Services


Deployment

  On-premises
  Cloud-based


Technology

  IoT-Based Systems
  AI and Machine Learning
  Robotics


Application

  Precision Irrigation
  Weed Control
  Harvesting


Crop Type

  Cereals
  Fruits and Vegetables
  Oilseeds


Farm Size

  Large Farms
  Small and Medium Farms


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa

    Egypt
    KSA
    Oman
    UAE


  APAC

    China
    India
    Japan


  South America

    Argentina
    Brazil


  Rest of World (ROW)

By Component Insights

The hardware segment is estimated to witness significant growth during the forecast period.

Autonomous crop management is revolutionizing agriculture through advanced technologies such as yield forecasting, carbon sequestration, and precision farming solutions. Agtech startups leverage satellite imagery and agricultural data to develop crop modeling and Farm Equipment automation, enhancing crop production and optimizing farm profitability. Farmers utilize mac

This dataset encompasses extensive information on crop production in India, spanning multiple years and offering insights into agricultural trends and patterns. The dataset consists of over 246,000 records, capturing a wide array of variables related to crop production, and is intended to facilitate advanced analyses such as predictive modeling and the extraction of key insights for stakeholders in the agri-food sector.

Temporal Coverage: - The dataset covers multiple years, providing a longitudinal view of crop production trends in India. This temporal dimension is crucial for analyzing changes over time and understanding long-term patterns.

Geographical Scope: - Data is collected across various states and regions of India, reflecting the diverse agricultural landscape of the country. Regional variations in crop production can be analyzed to identify local factors affecting yields.

Crop Types: - The dataset includes information on various crop types grown across different regions. This classification allows for detailed analysis of specific crops, their production levels, and their sensitivity to various factors.

Production Metrics: - Metrics related to crop production such as yield (e.g., tons per hectare), total production volume, and harvested area are included. These metrics are essential for evaluating productivity and efficiency.

Data Quality and Completeness: - The dataset is likely to include a mix of structured and unstructured data. Data quality may vary, and preprocessing steps such as cleaning and normalization may be necessary to ensure accurate analyses.

Applications and Objectives:

Predictive Modeling: - The primary goal of analyzing this dataset is to develop predictive models for crop production. By leveraging historical data, machine learning algorithms can forecast future production levels and identify potential risks.

Insight Extraction: - The dataset provides an opportunity to uncover key indicators and metrics that significantly influence crop production. Insights can help stakeholders make informed decisions regarding crop management, resource allocation, and policy formulation.

Trend Analysis: - Longitudinal analysis of the data can reveal trends and patterns in crop production, helping to understand how factors such as technological advancements, policy changes, and environmental conditions affect agriculture.

Stakeholder Collaboration: - The dataset supports the development of collaboration platforms that connect various stakeholders in the agri-food sector. By integrating data from multiple sources, stakeholders can collaborate more effectively to address challenges and optimize production.

Key Features: 1. State_Name: Represents the name of the state in India where the crop data was recorded. 2. District_Name: Specifies the district within the state where the crop data was collected. 3. Crop_Year: Indicates the year in which the crop was harvested. 4. Season: Denotes the agricultural season (e.g., Kharif, Rabi) during which the crop was grown. 5. Crop: Identifies the type of crop that was cultivated. 6. Area: Represents the total land area used for cultivating the crop. 7. Production: Indicates the total quantity of the crop produced from the specified area.

The Twentieth Century Crop Statistics, 1900-2017 data set consists of national or subnational maize and wheat production, yield, and harvested area statistics for all available years for the period 1900-2017. It combines a new digitization of crop statistics from Italy, Spain, Indonesia, China, Mexico, Uruguay, Chile, Sweden, and Morocco with existing, publicly available, digitized data sets from India, Australia, the United States, Canada, Southern Brazil, Argentina, England, Austria, Belgium, Croatia, Czech Republic, Finland, Germany, Spain, Portugal, France, the Netherlands, and South Africa. All Units are converted to hectares (ha) for Units of harvested areas, tonnes for Units of production, and tonnes/ha for Units of yield. A ratio of 1/36.744 is used to convert wheat bushels to tonnes, and a value of 1/39.368 is used to convert maize bushels to tonnes. In all cases, the Harvest_year reported in the data set is the harvest year for the crop.

This data set provides median cover management factors (C-Factor) for agriculture, pasture, and barren land cover classes for each USDA Crop Management Zone. The C-Factors were calculated based on a Normalized Difference Vegetation Index. MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI values were obtained at 250 m resolution for 16-day intervals between 2000-2014 to calculate a mean annual NDVI. The data in this file correspond To Table 2 in the associated journal article. This dataset is associated with the following publication: Woznicki, S., P. Cada, J. Wickham, M. Schmidt, J. Baynes, M. Mehaffey, and A. Neale. Sediment retention by natural landscapes in the conterminous United States. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 745: 140972, (2020).

In the realm of global agriculture

The Relationship between Crop Production and Climate Change

Explore the Relationship between Crop Production and Climate Change over time

About this dataset

This dataset provides data on crop yields, harvested areas, and production quantities for wheat, maize, rice, and soybeans. Crop yields are the harvested production per unit of harvested area for crop products. In most cases yield data are not recorded but are obtained by dividing the production data by the data on the area harvested. The actual yield that is captured on a farm depends on several factors such as the crop's genetic potential, the amount of sunlight, water, and nutrients absorbed by the crop, the presence of weeds and pests. This indicator is presented for wheat, maize, rice, and soybean. Crop production is measured in tonnes per hectare.

This dataset includes information on crop production from 2010-2016

How to use the dataset

https://www.kaggle.com/usda/crop-production

Crop production is an important economic activity that affects commodity prices and macroeconomic uncertainty. This dataset provides data on crop yields, harvested areas, and production quantities for wheat, maize, rice, and soybeans. The data are presented in tonnes per hectare, in thousand hectares, and in thousand tonnes.

This dataset can be used to examine the effect of different crops on climate change and to compare yields between different climates

Research Ideas

• Determining how various factors affect crop production and yields
• Comparing crop yields between different types of crops
• Examining the impact of climate change on crop production

Acknowledgements

This dataset provides data on crop yields, harvested areas, and production quantities for wheat, maize, rice, and soybeans. The data are presented in tonnes per hectare

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: crop_production.csv | Column name | Description | |:---------------|:------------------------------------------------------------| | LOCATION | The country or region where the crop is grown. (String) | | INDICATOR | The indicator used to measure the crop production. (String) | | SUBJECT | The subject of the indicator. (String) | | MEASURE | The measure of the indicator. (String) | | FREQUENCY | The frequency of the data. (String) | | TIME | The time period of the data. (String) | | Value | The value of the indicator. (Float) | | Flag Codes | The flag codes of the data. (String) |

Agricultural Data Management Market Outlook

According to our latest research, the global agricultural data management market size reached USD 3.21 billion in 2024, reflecting a strong surge in adoption across diverse agricultural sectors. The market is projected to grow at a CAGR of 13.6% from 2025 to 2033, reaching a forecasted value of USD 9.06 billion by 2033. This robust growth is primarily driven by the increasing integration of advanced digital technologies in agriculture, rising demand for food security, and the need to optimize resource use for sustainability.

The growth trajectory of the agricultural data management market is underpinned by the rapid digitization of farming practices worldwide. Farmers and agribusinesses are increasingly leveraging data-driven solutions to enhance productivity, monitor crop health, and manage resources more efficiently. The proliferation of Internet of Things (IoT) devices, sensors, and drones has enabled real-time data collection, which, when integrated with sophisticated software platforms, allows for actionable insights and informed decision-making. This digital transformation is further catalyzed by supportive government policies promoting smart agriculture, as well as the growing need to address the challenges posed by climate change and limited arable land.

Another significant growth factor in the agricultural data management market is the escalating demand for precision farming solutions. Precision agriculture relies heavily on accurate and timely data to optimize planting, irrigation, fertilization, and pest management. The adoption of big data analytics, artificial intelligence, and machine learning in agriculture has empowered stakeholders to analyze large volumes of data from various sources—such as satellite imagery, weather stations, and field sensors—to maximize yields and minimize losses. This technological evolution has not only improved farm profitability but also contributed to environmental sustainability by reducing waste and resource overuse.

Moreover, the increasing emphasis on supply chain transparency and traceability is fueling the expansion of the agricultural data management market. Consumers, regulators, and supply chain partners are demanding greater visibility into the origins and handling of agricultural products. Advanced data management systems enable seamless tracking of produce from farm to fork, ensuring compliance with food safety standards and boosting consumer confidence. As global supply chains become more complex, the role of data management in ensuring quality, reducing spoilage, and optimizing logistics has become indispensable, driving further investments in this sector.

Regionally, North America currently dominates the agricultural data management market, accounting for the largest share in 2024, followed by Europe and the Asia Pacific. The United States and Canada are at the forefront of adopting digital agriculture solutions, driven by large-scale commercial farming and strong technological infrastructure. Europe is witnessing significant growth due to stringent regulatory frameworks and sustainability initiatives. Meanwhile, the Asia Pacific region is poised for the fastest growth, propelled by increasing government investments in agri-tech, a rapidly expanding population, and a burgeoning need to enhance agricultural productivity to ensure food security.

Component Analysis

The component segment of the agricultural data management market is broadly categorized into software, hardware, and services. Software solutions form the backbone of data management by providing platforms for data collection, analytics, visualization, and reporting. These platforms are increasingly being designed with user-friendly interfaces and advanced analytics capabilities, enabling farmers to make data-driven decisions with ease. The integration of artificial intelligence and machine learning algorithms within these software tools further enhances predictive analytics, helping stakeholders ant

Using a variety of inputs, IFPRI's Spatial Production Allocation Model (SPAM) uses a cross-entropy approach to make plausible estimates of crop distribution within disaggregated units. Moving the data from coarser units such as countries and sub-national provinces, to finer units such as grid cells, reveals spatial patterns of crop performance, creating a global gridscape at the confluence between geography and agricultural production systems. Improving spatial understanding of crop production systems allows policymakers and donors to better target agricultural and rural development policies and investments, increasing food security and growth with minimal environmental impacts.

In South Asia, wheat is typically grown in favorable environments, although policies promoting intensification in Bangladesh's stress-prone coastal zone have resulted in expanded cultivation in this non-traditional area.

Relatively little is known about howto best manage wheat in these unique environments. Research is thus needed to identify ‘best-bet’ entry points to optimize productivity, but classical parametric analyses offer limited applicability to elucidate the relative importa nce of the multiple factors and interactions that influence yield under such conditions. This problem is most evident in datasets derived from farmer-participatory research, where missing values and skewed data are common.

This paper examines the predictive power of three nonparametric approaches, including linear mixed effects models (LMMs), and two binary recursive partitioning methods: classification and regression trees (CARTs)and Random Forests

We collected yield, crop management, and environmental observations from 422 wheat fields in the 2012–13 season, across six production environments spanning southern Bangladesh, where nutrient rates and genotypes were imposed, but management of other p roduction factors varied from farmer to farmer. Fields were grouped into categories including early and late-sowing, depending on crop establishment before or after December 15, respectively, and in combination, across both early- and late-sowing groups.

For each of these groups, we investigated how each non-parametric analysis predicted the factors influencing yield. All three approaches identified nitrogen rate and environment as the most important factors, regardless of sowing category. CART also identified assemblages of high- and low-yielding environments, although those located in saline and warmer thermal zones were not necessarily the lowest yielding, indicating that farmers can optimize crop management to overcome these constra ints.

The number of days farmers sowed wheat before or after December 15, days to maturity, and the number of irrigations and weedings also influenced yield, though each method weighted these factors differently.

LMMs also indicated a slight yield advantage when farmers used stress-tolerant genotypes, though CART and Random Forests did not. One-to-one plots for observed vs. predicted yields from LMMs and Random Forests showed better performance by the former than the latter, wit h smaller root mean square and mean absolute error for the combined, early- and late-sowing groups, respectively.

While the LMMs were superior in this case, Random Forests may still prove useful in the classification and interpretation of farm survey data in which no treatment interventions have been administered.

Sweden Agricultural Production Yield: Crop: Winter Wheat data was reported at 7,360.000 kg/ha in 2017. This records an increase from the previous number of 6,680.000 kg/ha for 2016. Sweden Agricultural Production Yield: Crop: Winter Wheat data is updated yearly, averaging 5,660.000 kg/ha from Dec 1965 (Median) to 2017, with 53 observations. The data reached an all-time high of 7,570.000 kg/ha in 2015 and a record low of 3,120.000 kg/ha in 1966. Sweden Agricultural Production Yield: Crop: Winter Wheat data remains active status in CEIC and is reported by Statistics Sweden. The data is categorized under Global Database’s Sweden – Table SE.B026: Agriculture Production Yield.

The Agri-Food Statistics Update series is designed to provide users with commentary on current issues, trends and new developments related to agriculture and the food and beverage processing industries. Issues related to crop production estimates are based on the latest crop production data released by Statistics Canada. Up-to-date statistics are supplemented with informative charts and diagrams. To gauge Alberta's performance, comparative data and information are often available for Canada and the provinces.

Success.ai’s Agricultural Data provides unparalleled access to verified profiles of agriculture and farming leaders worldwide. Sourced from over 700 million LinkedIn profiles, this dataset includes actionable insights and contact details for professionals shaping the global agricultural landscape. Whether your objective is to market agricultural products, establish partnerships, or analyze industry trends, Success.ai ensures your outreach is powered by accurate, enriched, and continuously updated data.

Why Choose Success.ai’s Agricultural Data? Comprehensive Professional Profiles

Access verified LinkedIn profiles of farm owners, agricultural consultants, supply chain managers, agribusiness executives, and industry leaders. AI-validated data ensures 99% accuracy, minimizing wasted outreach and improving communication efficiency. Global Coverage Across Agricultural Sectors

Includes professionals from crop farming, livestock production, agricultural technology, and sustainable farming practices. Covers key regions such as North America, Europe, APAC, South America, and Africa. Continuously Updated Dataset

Real-time updates reflect role changes, organizational shifts, and emerging trends in agriculture and farming. Tailored for Agricultural Insights

Enriched profiles include professional histories, areas of specialization, and industry affiliations for deeper audience understanding. Data Highlights: 700M+ Verified LinkedIn Profiles: Gain access to a global network of agricultural and farming professionals. 100M+ Work Emails: Communicate directly with decision-makers in agribusiness and farming. Enriched Professional Histories: Understand career trajectories, expertise, and organizational affiliations. Industry-Specific Segmentation: Target professionals in crop farming, agtech, and sustainable agriculture with precision filters. Key Features of the Dataset: Agriculture and Farming Professional Profiles

Identify and connect with farm operators, agricultural consultants, supply chain managers, and agribusiness leaders. Engage with professionals responsible for farm management, equipment procurement, and sustainable farming initiatives. Detailed Firmographic Data

Leverage insights into farm sizes, crop or livestock focus, geographic distribution, and operational scales. Customize outreach to align with specific farming practices or market needs. Advanced Filters for Precision Targeting

Refine searches by region, type of agriculture (crop farming, livestock, horticulture), or years of experience. Customize campaigns to address unique challenges such as climate adaptation or supply chain optimization. AI-Driven Enrichment

Enhanced datasets deliver actionable data for personalized campaigns, highlighting certifications, achievements, and key projects. Strategic Use Cases: Marketing Agricultural Products and Services

Promote farm equipment, crop protection solutions, or livestock management tools to decision-makers in agriculture. Engage with professionals seeking innovative solutions to enhance productivity and sustainability. Collaboration and Partnerships

Identify agricultural leaders for collaborations on sustainability programs, research projects, or community initiatives. Build partnerships with agribusinesses, cooperatives, or government bodies driving agricultural development. Market Research and Industry Analysis

Analyze trends in crop yields, livestock production, and agricultural technology adoption. Use insights to refine product development and marketing strategies tailored to evolving industry needs. Recruitment and Talent Acquisition

Target HR professionals and agricultural firms seeking skilled farm managers, agronomists, or agtech specialists. Support hiring for roles requiring agricultural expertise and leadership. Why Choose Success.ai? Best Price Guarantee

Access industry-leading Agricultural Data at the most competitive pricing, ensuring cost-effective campaigns and strategies. Seamless Integration

Easily integrate verified agricultural data into CRMs, recruitment platforms, or marketing systems using APIs or downloadable formats. AI-Validated Accuracy

Depend on 99% accurate data to minimize wasted outreach and maximize engagement outcomes. Customizable Solutions

Tailor datasets to specific agricultural segments, regions, or areas of focus to meet your strategic objectives. Strategic APIs for Enhanced Campaigns: Data Enrichment API

Enhance existing records with verified agricultural profiles to refine targeting and engagement. Lead Generation API

Automate lead generation for a consistent pipeline of qualified professionals in the agriculture sector, scaling your outreach efficiently. Success.ai’s Agricultural Data empowers you to connect with the leaders and innovators transforming global agriculture. With verified contact details, enriched professional profiles, and global reach, your marketing, partn...

In 2023, agricultural crop production in Brazil amounted to approximately *** billion Brazilian reals, a decrease of nearly *** percent in comparison to the previous year. That same year, the planted agricultural area in the country amounted to **** million hectares.

The California Department of Water Resources (DWR) has been collecting land use data throughout the state and using it to develop agricultural water use estimates for statewide and regional planning purposes, including water use projections, water use efficiency evaluations, groundwater model developments, climate change mitigation and adaptations, and water transfers. These data are essential for regional analysis and decision making, which has become increasingly important as DWR and other state agencies seek to address resource management issues, regulatory compliances, environmental impacts, ecosystem services, urban and economic development, and other issues. Increased availability of digital satellite imagery, aerial photography, and new analytical tools make remote sensing-based land use surveys possible at a field scale that is comparable to that of DWR’s historical on the ground field surveys. Current technologies allow accurate large-scale crop and land use identifications to be performed at desired time increments and make possible more frequent and comprehensive statewide land use information. Responding to this need, DWR sought expertise and support for identifying crop types and other land uses and quantifying crop acreages statewide using remotely sensed imagery and associated analytical techniques. Currently, Statewide Crop Maps are available for the Water Years 2014, 2016, 2018- 2022 and PROVISIONALLY for 2023.

For the latest Land Use Legend, 2022-DWR-Standard-Land-Use-Legend-Remote-Sensing-Version.pdf, please see the Data and Resources section below.

Historic County Land Use Surveys spanning 1986 - 2015 may also be accessed using the CADWR Land Use Data Viewer: https://gis.water.ca.gov/app/CADWRLandUseViewer.

For Regional Land Use Surveys follow: https://data.cnra.ca.gov/dataset/region-land-use-surveys.

For County Land Use Surveys follow: https://data.cnra.ca.gov/dataset/county-land-use-surveys.

For a collection of ArcGIS Web Applications that provide information on the DWR Land Use Program and our data products in various formats, visit the DWR Land Use Gallery: https://storymaps.arcgis.com/collections/dd14ceff7d754e85ab9c7ec84fb8790a.

Recommended citation for DWR land use data: California Department of Water Resources. (Water Year for the data). Statewide Crop Mapping—California Natural Resources Agency Open Data. Retrieved “Month Day, YEAR,” from https://data.cnra.ca.gov/dataset/statewide-crop-mapping.

Quick Stats is the National Agricultural Statistics Service's (NASS) online, self-service tool to access complete results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. The census collects data on all commodities produced on U.S. farms and ranches, as well as detailed information on expenses, income, and operator characteristics. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production.

Armenia Agricultural Production: Crops: Yield Capacity: Vegetables data was reported at 287.000 ha in 2023. This records an increase from the previous number of 282.200 ha for 2022. Armenia Agricultural Production: Crops: Yield Capacity: Vegetables data is updated yearly, averaging 282.200 ha from Dec 1995 (Median) to 2023, with 29 observations. The data reached an all-time high of 350.500 ha in 2014 and a record low of 181.500 ha in 1997. Armenia Agricultural Production: Crops: Yield Capacity: Vegetables data remains active status in CEIC and is reported by Statistical Committee of the Republic of Armenia. The data is categorized under Global Database’s Armenia – Table AM.B011: Agriculture Production: Crops: Yield Capacity.

Get statistical data on the estimated harvested area, yield, production, price and farm value of field crops in Ontario.

India Agricultural Production: Major Crops: Achievements: Pulses data was reported at 27.504 Ton mn in 2023. This records an increase from the previous number of 27.302 Ton mn for 2022. India Agricultural Production: Major Crops: Achievements: Pulses data is updated yearly, averaging 12.840 Ton mn from Mar 1956 (Median) to 2023, with 68 observations. The data reached an all-time high of 27.504 Ton mn in 2023 and a record low of 8.350 Ton mn in 1967. India Agricultural Production: Major Crops: Achievements: Pulses data remains active status in CEIC and is reported by Directorate of Economics and Statistics, Department of Agriculture and Farmers Welfare. The data is categorized under India Premium Database’s Agriculture Sector – Table IN.RIB002: Agricultural Production: Targets & Achievement of Major Crops.