This data series was compiled by AAFC and Statistics Canada using a combination of agroclimate data and satellite-derived Normalized Difference Vegetation Index (NDVI) data for the current growing season. The forecast is made based on a statistical model using historical yield, climate and NDVI data.

The Global Dataset of Historical Yield (GDHYv1.2+v1.3) offers annual time series data of 0.5-degree grid-cell yield estimates of major crops worldwide for the period 1981-2016. The crops considered in this dataset are maize, rice, wheat and soybean. The unit of yield data is t/ha. The grd-cell yield data were estimated using the satellite-derived crop-specific vegetation index and FAO-reported country yield statistics. Maize and rice have the data for each of two growing seasons (major/secondary). "Winter" and "spring" are used as the growing season categories for wheat. Only "major" growing season is available for soybean. These growing season categories are based on Sacks et al. (2010, doi:10.1111/j.1466-8238.2010.00551.x). The geographic distribution of harvested area changes with time in reality, but we used the time-constant data in 2000 (Monfreda et al., 2008, doi:10.1029/2007GB002947). Many missing values are found in the first (1981) and last (2016) years because grid-cell yields are not estimated for these years when growing season spans two calendar years. The data for the period 1981-2010 are the same with the version 1.2 (doi:10.20783/DIAS.528). For the period 2011-2016, a newly created version 1.3 using the satellite products that are different with earlier versions was alighned to ensure the continuity of yield time series. This version is therefore called "the alighned version v1.2+v1.3".

The average for 2022 based on 176 countries was 3866 kg per hectar. The highest value was in Saint Vincent and the Grenadines: 31621 kg per hectar and the lowest value was in Cape Verde: 12 kg per hectar. The indicator is available from 1961 to 2022. Below is a chart for all countries where data are available.

Average estimated yields and associated CV values for current (2018) model runs. Based on work done by Harsimran Kaur et al in 2017. The following is from her thesis:

Agro-ecological classes (AECs) of dryland cropping systems in the inland Pacific Northwest have been predicted to become more dynamic with greater use of annual fallow under projected climate change. At the same time, initiatives are being taken by growers either to intensify or diversify their cropping systems using oilseed and grain legume crops. The main objective of this study was to use a mechanistic model (CropSyst) to provide yield and soil water forecasts at regional scales which could compare fallow versus spring crop choices (flex/opportunity crop). Model simulations were based on historic weather data (1981-2010) as well as combined with actual year weather data for simulations at pre-planting dates starting in Dec. for representative years. Yield forecasts of spring pea, canola and wheat were compared to yield simulations using only weather of the representative year via linear regression analysis to assess pre-plant forecasts. Crop yield projections on pre-plant forecast date of Feb 1st had higher R2 with yield simulated using actual years weather data and lower CVs across the region as compared to forecasts based on historic weather data and other pre-season forecast dates (Dec. 1st and Jan. 1st). Therefore, Feb. 1st was considered the most reliable time to predict yield and other relevant outputs such as available water forecasts on a regional scale. Regional forecast maps of predicted spring crop yields and CVs showed ranges of 1 to 4367 kg/ha and 11 to 293% for spring canola, 72 to 2646 kg/ha and 11 to 143% for spring pea and 39 to 5330 kg/ha and 11 to 158% for spring wheat across study region for a representative year. These data combined with predicted available water after fallow and following spring crop yield as well as estimates of winter wheat yield reduction would collectively serve as information contributing to decisions related to crop intensification and diversification.

This dataset contains detailed information on crop yields across various states in India for the year 1997. It includes data on different crops, their production, area under cultivation, season of cultivation, and state-specific information. Additionally, the dataset provides supplementary details such as annual rainfall, fertilizer use, pesticide use, and yield for each crop. This comprehensive dataset can be used for agricultural analysis, trend prediction, and studying the impact of various factors on crop yields in India.

In crop year 2023/2024, the yield of grain crops in Brazil was forecasted to amount to approximately **** metric tons per hectare of area planted, down from **** tons per hectare in the preceding crop year. Figures include crops such as rice, wheat, corn, barley, soy, rye, cotton, peanut, oat, canola, sunflower, castor oil seed, sorghum, triticale and beans. Rice was the grain crop with the highest yield in Brazil.

Detailed and accurate statistics on crop productivity are key to inform decision-making related to sustainable food production and supply ensuring global food security. However, annual and high-resolution crop yield data provided by official agricultural statistics are generally lacking. Earth observation (EO) imagery, geodata on meteorological and soil conditions, as well as advances in machine learning (ML) provide huge opportunities for model-based crop yield estimation in terms of covering large spatial scales with unprecedented granularity. This study proposes a novel yield estimation approach that is bottom-up scalable from parcel to administrative levels by leveraging ML-ensembles, comprising of six regression estimators (base estimators), and multi-source geodata, including EO imagery. To ensure the approach’s robustness, two ensemble learning techniques are investigated, namely meta-learning through model stacking and majority voting. ML-ensembles were evaluated multi-annually and crop-specifically for three major winter crops, namely winter wheat (WW), winter barley (WB), and winter rapeseed (WR) in two German federal states, covering 140,000 to 155,000 parcels per year. ML-ensembles were evaluated at the parcel and district level for two German federal states against official yield reports, ranging from 2019 to 2022, based on metrics such as coefficient of determination (RSQ) and normalized root mean square error (nRMSE). Overall, the most robustly performing ensemble learning technique was majority voting yielding RSQ and nRMSE values of 0.74, 13.4% for WW, 0.68, 16.9% for WB, and 0.66, 14.1% for WR, respectively, through cross-validation at parcel level. At the district level, majority voting reached RSQ and nRMSE ranges of 0.79–0.89, 7.2–8.1% for WW, 0.80–0.84, 6.0–9.9% for WB, and 0.60–0.78, 6.1–10.4% for WR, respectively. Capitalizing on ensemble learning-based majority voting, examples of unprecedented high-resolution crop yield maps at 1×1km spatial resolution are presented. Implementing a scalable yield estimation approach, as proposed in this study, into crop yield reporting frameworks of public authorities mandated to provide official agricultural statistics would increase the spatial resolution of annually reported yields, eventually covering the entire cropland available. Such unprecedented data products delivered through map services may improve decision-making support for a variety of stakeholders across different spatial scales, ranging from parcel to higher administrative levels.

I) SUMMARY

This database contains harmonized time series for the study of crop yields using remote sensing data and meteorological data. We collected information on soybean, corn, and wheat yields (t/ha) over the CONUS (continuous US) from USDA-NASS for years 2015–2018 at a county level, and collocated time series for the following variables:

Enhanced Vegetation Index (EVI) from MODIS satellite (MOD13C1 v6 product)

Soil Moisture (SM) from SMAP satellite through MT-DCA algorithm

Vegetation Optical Depth (VOD) from SMAP satellite through MT-DCA algorithm

Maximum temperature (TMAX) from Daymet v3

Precipitation (PRCP) from Daymet v3

II) CONTACT

For questions, please email Laura Martínez-Ferrer at laura.martinez-ferrer@uv.es

III) DATABASE

For each crop type, we provided CSV files containing the time series of the variables and yield described above. Furthermore, additional information for spatial and temporal identification such as a county identifier and a year are included. Lastly, country-shapefiles (.shp) are added for geospatial representation. Further details in readme.txt file.

IV) CITE

We kindly encourage to cite the following works if this database is used

L. Martínez-Ferrer, M. Piles, G. Camps-Valls, Crop Yield Estimation and Interpretability With Gaussian Processes, IEEE Geoscience and Remote Sensing Letters, 2020, vol. 18, no 12, p. 2043-2047, DOI: 10.1109/LGRS.2020.3016140

A. Mateo-Sanchis, J. E. Adsuara, M. Piles, J. Muñoz-Marí, A. Pérez-Suay and G. Camps-Valls, "Interpretable Long-Short Term Memory Networks for Crop Yield Estimation," in IEEE Geoscience and Remote Sensing Letters, DOI: 10.1109/LGRS.2023.3244064

Analysis of ‘Crop Yield Prediction Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/patelris/crop-yield-prediction-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

The science of training machines to learn and produce models for future predictions is widely used, and not for nothing. Agriculture plays a critical role in the global economy. With the continuing expansion of the human population understanding worldwide crop yield is central to addressing food security challenges and reducing the impacts of climate change.

Crop yield prediction is an important agricultural problem. The Agricultural yield primarily depends on weather conditions (rain, temperature, etc), pesticides and accurate information about history of crop yield is an important thing for making decisions related to agricultural risk management and future predictions.

Acknowledgements

All dataset(publicly available dataset) here are taken form FAO (Food and Agriculture Organization) and World Data Bank. http://www.fao.org/home/en/ https://data.worldbank.org/

--- Original source retains full ownership of the source dataset ---

Maize and soybean yield data set for Precision Zonal Management (PZM) project from 2012-2015. Project compared chisel plow tillage against ridge tillage (PZM) systems, with and without winter cereal rye cover crops. Experimental sites in four US states: IL, MI, MN and PA. Data set provides plot-level yield data (kg/ha) for each site-year and for both crops. File also contains data set of maize and soybean yield stability, with soil properties measured in 2015 (end of experimental period) and delta values (values in 2015 minus values prior to experiment establishment in 2011). Resources in this dataset:Resource Title: Data for: A regionally-adapted implementation of conservation agriculture delivers rapid improvements to soil properties associated with crop yield stability. File Name: PZM_yields_stability_soil.xlsxResource Description: Data files combined into a single excel document.Resource Title: Data Dictionary. File Name: PZM_data_dictionary.csvResource Title: Yield data for: A regionally-adapted implementation of conservation agriculture delivers rapid improvements to soil properties associated with crop yield stability. File Name: PZM_Yields.csvResource Title: Stable Soil data for: A regionally-adapted implementation of conservation agriculture delivers rapid improvements to soil properties associated with crop yield stability. File Name: PZM_stable_soil.csv

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

Explore the detailed Indian Crop Yield Data from 1997, including production, area under cultivation, rainfall, fertilizer use.

The global crop yield forecasting market is experiencing robust growth, driven by the increasing need for efficient agricultural practices and enhanced food security in a changing climate. The market, estimated at $2.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated value of $8 billion by 2033. This expansion is fueled by several key factors. Technological advancements in remote sensing, data analytics, and artificial intelligence are enabling the development of sophisticated forecasting models, providing farmers with more accurate and timely predictions of crop yields. Furthermore, the rising adoption of precision agriculture techniques, coupled with the growing awareness of climate change's impact on crop production, is further propelling market growth. The increasing demand for higher crop yields to meet the rising global food demand also contributes significantly to this expansion. The market segmentation reveals strong growth across both software and service offerings, with the commercial application segment dominating due to its larger scale operations and greater investment capacity in advanced technologies. Geographic growth is anticipated to be particularly strong in regions like Asia-Pacific and North America, driven by higher technology adoption rates and significant agricultural sectors. However, challenges remain, including the high initial investment costs associated with implementing these technologies, a lack of digital literacy among some farming communities, and potential data security concerns. Despite these restraints, the long-term outlook for the crop yield forecasting market remains positive, with continued innovation and technological advancements expected to drive further market expansion in the coming years.

We used historical data for crop yield in 27 Indian states and 3 Union Territories of India, covering the years 1997 to 2020. The dataset consists of 19,689 data points, each with ten features including Crop, Season, Crop_Year, State, Annual_Rainfall, Area, Production, Pesticide, Fertilizer, and Yield. The dataset encompasses 55 different types of crops cultivated across India. The crop yield dataset was used to prediction of crop yield using regression with stacking ensemble model. The dataset is split into training 80% and testing 20%.

Graph and download economic data for Index of Crop Yield Per Acre Harvested, Twelve Crops for United States (A01297USA343NNBR) from 1866 to 1940 about crop, yield, interest rate, interest, rate, indexes, and USA.

In 2023, rice production accounted for the highest yield volume among main crops in Vietnam, amounting to over **** million metric tons, followed by the yield volume of cassava amounting to around **** million metric tons. In that year, the yield for rice increased by approximately *** percent compared to the previous year. Rice production and exports in Vietnam Vietnam is among the leading rice producing countries worldwide. Although rice is grown across the country, the Mekong Delta accounts for the majority of rice production volume due to the region's favorable condition for agriculture. Rice is an important crop for both domestic consumption and for exports in Vietnam. In 2023/2024, Vietnam ranked third among the rice exporting countries globally. Crop production in Vietnam The main annual crops in Vietnam include vegetables, sugar cane, peanut, soy, and sesame. Annual crops complete a life cycle within one season from germination to the production of seeds to when it dies. Vietnam’s main perennial crops are coffee, coconut, rubber, tea, cashew nut, and pepper. Perennial crops do not need to be replanted each year. They can automatically grow back after harvest which differentiates them from annual crops.

Crop Production, Yield, Harvested Area (Global - National - Annual - FAOSTAT)

• Area harvested (ha)
• Production (tonnes)
• Yield (hg/ha)

Crop statistics are recorded for 173 products, covering the following categories: Crops Primary, Fibre Crops Primary, Cereals, Coarse Grain, Citrus Fruit, Fruit, Jute Jute-like Fibres, Oilcakes Equivalent, Oil crops Primary, Pulses, Roots and Tubers, Treenuts and Vegetables and Melons. Data are expressed in terms of area harvested, production quantity and yield. The objective is to comprehensively cover production of all primary crops for all countries and regions in the world.

Cereals: Area and production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed or silage or used for grazing are therefore excluded.

Area data relate to harvested area. Some countries report sown or cultivated area only

agronomists

As farmers adapt to changing climate, they modify practices and technologies to manage evolving...

USA County Level Crop Yield Dataset

  Dataset Summary

This dataset contains county level crop yield across 763 counties from 1984 till 2018 in the US Corn Belt. The data was originally collected in Khaki et al. 2020, then further processed, augmented dedup-ed in Hasan et al. 2024. Each row of the CSV includes:

Weather: 6 weekly mean weather variables over 52 weeks for each of the past n_past_years + 1 years. The 6 weather variables (W_1-W_6) are precipitation, solar… See the full description on the dataset page: https://huggingface.co/datasets/notadib/usa-corn-belt-crop-yield.