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. Resources in this dataset:Resource Title: GeoData catalog record. File Name: Web Page, url: https://geodata.nal.usda.gov/geonetwork/srv/eng/catalog.search#/metadata/459d2dba-a346-4e54-9750-ef3178c18f38

The global crop yield forecasting market is experiencing robust growth, driven by increasing demand for efficient agricultural practices and the need to mitigate risks associated with climate change and resource scarcity. The market, valued at approximately $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This substantial growth is fueled by several key factors. Technological advancements in remote sensing, artificial intelligence (AI), and machine learning (ML) are enabling the development of increasingly accurate and sophisticated forecasting models. The integration of these technologies into both software and service offerings is revolutionizing agricultural planning, enabling farmers to optimize resource allocation, improve yields, and reduce waste. Furthermore, government initiatives promoting sustainable agriculture and precision farming are providing further impetus to market expansion. The rising adoption of precision agriculture techniques, coupled with the growing awareness among farmers about the benefits of data-driven decision-making, is further contributing to the market's upward trajectory. The market segmentation reveals a strong preference for crop yield forecasting software, owing to its scalability and potential for integration with existing farm management systems. The commercial sector dominates the application segment, reflecting the higher adoption rate among large-scale agricultural businesses seeking to enhance operational efficiency and profitability. Geographically, North America and Europe currently hold significant market share, driven by early adoption of advanced technologies and well-established agricultural infrastructure. However, rapidly developing economies in Asia-Pacific, particularly India and China, represent lucrative growth opportunities, with increasing investment in agricultural modernization and technological advancements. The market's growth, while promising, faces certain restraints including the high initial investment costs associated with advanced technologies and the need for reliable internet connectivity and digital literacy among farmers, particularly in developing regions. Overcoming these barriers will be crucial to unlocking the full potential of the crop yield forecasting market in the coming years.

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.

The global crop yield forecasting market is experiencing robust growth, driven by the increasing need for precision agriculture and improved food security. The market's expansion is fueled by several key factors, including the rising adoption of advanced technologies like AI, machine learning, and remote sensing, which enable more accurate and timely yield predictions. Furthermore, climate change and its unpredictable impact on agricultural production are pushing farmers and stakeholders to leverage predictive analytics to mitigate risks and optimize resource allocation. The market is segmented based on various factors, including technology (satellite imagery, weather data, soil sensors), application (field-level forecasting, regional forecasting), and crop type. While the precise market size is not provided, considering a typical CAGR (Compound Annual Growth Rate) of 15-20% for such a technology-driven market, a reasonable estimate for the 2025 market size could be in the range of $500 million to $750 million, given the involvement of established players like Pessl Instruments and emerging companies like AgroMetShell and CropProphet. This growth trajectory is projected to continue throughout the forecast period (2025-2033), driven by continuous technological advancements and increasing adoption rates across different regions. Constraints such as high initial investment costs for technology and data accessibility limitations, especially in developing countries, may pose some challenges, but are likely to be offset by the significant economic and environmental benefits offered by accurate crop yield forecasting. The competitive landscape comprises a mix of established players and innovative startups. Companies like EOSDA and Agronomy Insights are leveraging their expertise in data analytics and remote sensing to offer comprehensive solutions, while others focus on niche applications or specific crop types. The market is also witnessing increasing collaboration between agricultural research institutions (like CCAFS and WUR) and technology companies, further accelerating innovation. Geographical expansion, particularly in developing economies with significant agricultural sectors, presents significant growth opportunities. The integration of crop yield forecasting with other precision agriculture technologies, such as variable-rate fertilization and irrigation, promises to further enhance efficiency and sustainability in agricultural practices, strengthening the market's long-term prospects. Overall, the crop yield forecasting market is poised for substantial growth, contributing significantly to enhancing agricultural productivity and addressing global food security concerns.

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 ---

Chile's wheat crop yield was forecast to reach 6.29 metric tons per hectare by 2033, the highest figure among the indicated Latin American countries. Mexico ranked second with 6.13 metric tons per hectare, followed by Argentina, with a forecast yield of 3.28 metric tons per hectare.

This dataset simulates real-world smart farming operations powered by IoT sensors and satellite data. It captures environmental and operational variables that affect crop yield across 500 farms located in regions like India, the USA, and Africa.

Designed to reflect modern agritech systems, the data is ideal for: - Predictive modeling using ML/AI - Time-series analysis - Sensor-based optimization - Environmental data visualizations - Crop health analytics

🧠 Ideal For

• Supervised ML models (regression, classification)
• Yield prediction and optimization
• Agricultural decision support systems
• Smart irrigation strategy analysis
• Data visualization of regional farm efficiency

📌 Columns Description

📫 Let's Collaborate!

If you build a notebook, model, or dashboard using this dataset — feel free to tag me or leave a comment. Happy growing! 🌱🚜

Global Machine Learning For Crop Yield Prediction market size is expected to reach $2.58 billion by 2029 at 26.6%, rising demand for sustainable agriculture driving the growth of the market due to environmental and food security concerns

CY-Bench: A comprehensive benchmark dataset for sub-national crop yield forecasting

Overview

CY-Bench is a dataset and benchmark for subnational crop yield forecasting, with coverage of major crop growing countries of the world for maize and wheat. By subnational, we mean the administrative level where yield statistics are published. When statistics are available for multiple levels, we pick the highest resolution. The dataset combines sub-national yield statistics with relevant predictors, such as growing-season weather indicators, remote sensing indicators, evapotranspiration, soil moisture indicators, and static soil properties. CY-Bench has been designed and curated by agricultural experts, climate scientists, and machine learning researchers from the AgML Community, with the aim of facilitating model intercomparison across the diverse agricultural systems around the globe in conditions as close as possible to real-world operationalization. Ultimately, by lowering the barrier to entry for ML researchers in this crucial application area, CY-Bench will facilitate the development of improved crop forecasting tools that can be used to support decision-makers in food security planning worldwide.

* Crops : Wheat & Maize
* Spatial Coverage : Wheat (29 countries), Maize (38).
See CY-Bench paper appendix for the list of countries.
* Temporal Coverage : Varies. See country-specific data

Data

Data format

The benchmark data is organized as a collection of CSV files (with the exception of location information, see below), with each file representing a specific category of variable for a particular country. Each CSV file is named according to the category and the country it pertains to, facilitating easy identification and retrieval. The data within each CSV file is structured in tabular format, where rows represent observations and columns represent different predictors related to a category of variable.

Data content

All data files are provided as .csv.

Folder structure

1. cybench-data: The CY-Bench dataset has been structure at first level by crop type and subsequently by country. For each country, the folder name follows the ISO 3166-1 alpha-2 two-character code. A separate .csv is available for each predictor data and crop calendar as shown below. The csv files are named to reflect the corresponding country and crop type e.g. **variable_croptype_country.csv**.
   ```
   CY-Bench
   │
   └─── maize
   │ │
   │ └─── AO
   │ │ -- crop_calendar_maize_AO.csv
   │ │ -- fpar_maize_AO.csv
   │ │ -- meteo_maize_AO.csv
   │ │ -- ndvi_maize_AO.csv
   │ │ -- soil_maize_AO.csv
   │ │ -- soil_moisture_maize_AO.csv
   │ │ -- yield_maize_AO.csv
   │ │
   │ └─── AR
   │ -- crop_calendar_maize_AR.csv
   │ -- fpar_maize_AR.csv
   │ -- ...
   │
   └─── wheat
   │ │
   │ └─── AR
   │ │ -- crop_calendar_wheat_AR.csv
   │ │ -- fpar_wheat_AR.csv
   │ │ ...
   ```

   Example : CSV data content for maize in country X

   ```
   X
   └─── crop_calendar_maize_X.csv
   │ -- crop_name (name of the crop)
   │ -- adm_id (unique identifier for a subnational unit)
   │ -- sos (start of crop season)
   │ -- eos (end of crop season)
   │
   └─── fpar_maize_X.csv
   │ -- crop_name
   │ -- adm_id
   │ -- date (in the format YYYYMMdd)
   │ -- fpar
   │
   └─── meteo_maize_X.csv
   │ -- crop_name
   │ -- adm_id
   │ -- date (in the format YYYYMMdd)

   │ -- tmin (minimum temperature)
   │ -- tmax (maximum temperature)
   │ -- prec (precipitation)
   │ -- rad (radiation)
   │ -- tavg (average temperature)
   │ -- et0 (evapotranspiration)
   │ -- cwb (crop water balance)
   │
   └─── ndvi_maize_X.csv
   │ -- crop_name
   │ -- adm_id
   │ -- date (in the format YYYYMMdd)
   │ -- ndvi
   │
   └─── soil_maize_X.csv
   │ -- crop_name
   │ -- adm_id
   │ -- awc (available water capacity)
   │ -- bulk_density
   │ -- drainage_class
   │
   └─── soil_moisture_maize_X.csv
   │ -- crop_name
   │ -- adm_id
   │ -- date (in the format YYYYMMdd)
   │ -- ssm (surface soil moisture)
   │ -- rsm ()
   │
   └─── yield_maize_X.csv
   │ -- crop_name
   │ -- country_code
   │ -- adm_id
   │ -- harvest_year
   │ -- yield
   │ -- harvest_area
   │ -- production

2. centroids.zip and polygons.zip include shapes or geometries as centroids ( x and y coordinates) and polygons (multipolygons) of administrative regions respectively. They are organized as follows:

   centroids

   │ └─── AO
   │ │ -- AO.cpg
   │ │ -- AO.dbf
   │ │ -- AO.prj
   │ │ -- AO.shp
   │ │ -- AO.shx
   │ └─── AR
   │ │ -- AR.cpg
   │ │ -- AR.dbf
   │ │ -- AR.prj
   │ │ -- AR.shp
   │ │ -- AR.shx

   ...

   polygons

   │ └─── AO
   │ │ -- AO.cpg
   │ │ -- AO.dbf
   │ │ -- AO.prj
   │ │ -- AO.shp
   │ │ -- AO.shx
   │ └─── AR
   │ │ -- AR.cpg
   │ │ -- AR.dbf
   │ │ -- AR.prj
   │ │ -- AR.shp
   │ │ -- AR.shx

   ...

Data access

The full dataset can be downloaded directly from Zenodo or using the ```zenodo_get``` library

License and citation

We kindly ask all users of CY-Bench to properly respect licensing and citation conditions of the datasets included.

BDM Forecast: CETES Yield: 28 Days: Median: Plus 3 Years data was reported at 6.950 % pa in Dec 2018. This records an increase from the previous number of 5.760 % pa for Dec 2017. BDM Forecast: CETES Yield: 28 Days: Median: Plus 3 Years data is updated monthly, averaging 5.525 % pa from Dec 2013 (Median) to Dec 2018, with 6 observations. The data reached an all-time high of 7.000 % pa in Dec 2016 and a record low of 4.670 % pa in Dec 2013. BDM Forecast: CETES Yield: 28 Days: Median: Plus 3 Years data remains active status in CEIC and is reported by Bank of Mexico. The data is categorized under Global Database’s Mexico – Table MX.M004: Securities Yield: Forecast.

Brazil's yield for soybeans was forecast to reach 3.71 metric tons per hectare by 2033. Ranking second was Colombia, with a soybean yield of 3.3 metric tons per hectare.

Idaho and Lebanon rely on potatoes as an economically important crop. NDVI (Normalized Difference Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and MSAVI2 (Modified Soil Adjusted Vegetation Index 2) indices were calculated from PlanetScope satellite imagery for the 2017 growing season cloud free days. Variations in vegetation health were tracked over time and correlated to yield data provided by growers in Idaho. Based on ordinary least squares regression an Idaho yield forecast model was developed. Vegetation response during the growth stage at which potato tubers were filling out was significant in predicting yield for both the Norkotah and Russet potato variety. This corresponded to a week with high recorded temperatures that impacted the health status of the crops. The yield forecasting model was validated with a cross validation approach and then applied to potato fields in Lebanon. The Idaho model successfully displayed yield variation in crops for Lebanon. Spectral indices along with field topography allow the prediction of yield based on the crop type and variety.

In December 2024, the yield on a 10-year U.S. Treasury note was **** percent, forecasted to decrease to reach **** percent by August 2025. Treasury securities are debt instruments used by the government to finance the national debt. Who owns treasury notes? Because the U.S. treasury notes are generally assumed to be a risk-free investment, they are often used by large financial institutions as collateral. Because of this, billions of dollars in treasury securities are traded daily. Other countries also hold U.S. treasury securities, as do U.S. households. Investors and institutions accept the relatively low interest rate because the U.S. Treasury guarantees the investment. Looking into the future Because these notes are so commonly traded, their interest rate also serves as a signal about the market’s expectations of future growth. When markets expect the economy to grow, forecasts for treasury notes will reflect that in a higher interest rate. In fact, one harbinger of recession is an inverted yield curve, when the return on 3-month treasury bills is higher than the ten-year rate. While this does not always lead to a recession, it certainly signals pessimism from financial markets.

Orchard Yield Forecast Cloud Market Outlook

According to our latest research, the global Orchard Yield Forecast Cloud market size is estimated at USD 1.34 billion in 2024, with a robust growth trajectory expected throughout the forecast period. The market is projected to reach USD 5.21 billion by 2033, reflecting a remarkable CAGR of 16.2% from 2025 to 2033. This impressive growth is primarily driven by increasing adoption of precision agriculture technologies, growing demand for data-driven decision-making in orchard management, and the need to optimize yield and resource utilization in response to climate variability and labor shortages.

The rapid expansion of the Orchard Yield Forecast Cloud market is fundamentally fueled by the agriculture sector's digital transformation. Orchards worldwide are embracing cloud-based solutions to address the pressing need for accurate yield forecasts, which are critical for supply chain planning, resource allocation, and revenue optimization. The proliferation of Internet of Things (IoT) sensors, advanced weather forecasting models, and satellite imagery has enabled these platforms to deliver actionable insights with unprecedented accuracy. Furthermore, the integration of artificial intelligence and machine learning algorithms is enhancing predictive capabilities, allowing orchard managers to anticipate yield fluctuations, identify disease outbreaks early, and optimize harvest timing. This technological convergence is creating a strong value proposition for orchard owners, driving widespread adoption across commercial and research-oriented settings.

Another significant growth factor is the rising global demand for high-quality fruit and nut produce, which is placing immense pressure on orchard operators to maximize output while maintaining sustainability. The Orchard Yield Forecast Cloud market is responding to this challenge by offering solutions that not only predict yields but also monitor crop health, soil conditions, and pest risks in real time. These platforms enable data-driven interventions that reduce input costs, minimize waste, and improve overall orchard profitability. Additionally, the increasing prevalence of climate-related uncertainties, such as droughts and unseasonal frosts, is compelling growers to invest in predictive analytics to safeguard their crops and ensure consistent supply to the market. As governments and industry bodies promote the adoption of smart agriculture, the market is poised for sustained expansion.

The evolution of regulatory frameworks and sustainability mandates is further accelerating market growth. Many countries are introducing policies that incentivize the use of precision agriculture tools to enhance food security and environmental stewardship. Cloud-based yield forecasting platforms are well-positioned to help orchard operators comply with these regulations by providing traceability, documentation, and reporting capabilities. Moreover, the scalability and flexibility of cloud solutions make them accessible to orchards of all sizes, including smallholders and cooperatives, thus democratizing access to advanced agricultural technology. As digital literacy among farmers improves and connectivity infrastructure expands, especially in emerging economies, the addressable market for Orchard Yield Forecast Cloud solutions is set to broaden significantly.

From a regional perspective, North America currently leads the global market, driven by early adoption of agri-tech innovations and the presence of large-scale commercial orchards. Europe follows closely, propelled by stringent sustainability standards and strong government support for digital agriculture. Asia Pacific is emerging as the fastest-growing region, with countries like China, India, and Australia investing heavily in smart farming to meet rising food demand and counteract climatic challenges. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as awareness of the benefits of yield forecasting and farm management platforms spreads. The regional dynamics underscore the global relevance and growth potential of the Orchard Yield Forecast Cloud market.

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.

United States CBO Projection: Treasury Notes Yield: 10 Years data was reported at 3.753 % in Dec 2028. This records an increase from the previous number of 3.745 % for Sep 2028. United States CBO Projection: Treasury Notes Yield: 10 Years data is updated quarterly, averaging 3.677 % from Mar 2013 (Median) to Dec 2028, with 64 observations. The data reached an all-time high of 3.958 % in Sep 2021 and a record low of 1.563 % in Sep 2016. United States CBO Projection: Treasury Notes Yield: 10 Years data remains active status in CEIC and is reported by Congressional Budget Office. The data is categorized under Global Database’s United States – Table US.M009: Treasury Securities Yields: Projection: Congressional Budget Office.

Context

A number of research is underway in the agricultural sector to better predict crop yield using machine learning algorithms. Many machine learning algorithms require large amounts of data in order to give useful results. One of the major challenges in training and experimenting with machine learning algorithms is the availability of training data in sufficient quality and quantity remains a limiting factor. In the paper, “Wild blueberry yield prediction using a combination of computer simulation and machine learning algorithms”, we used dataset generated by the Wild Blueberry Pollination Model, a spatially explicit simulation model validated by field observation and experimental data collected in Maine USA during the last 30 years. The blueberry yields predictive models require data that sufficiently characterize the influence of plant spatial traits, bee species composition, and weather conditions on production. In a multi-step process, we designed simulation experiments and conducted the runs on the calibrated version of the blueberry simulation model. The simulated dataset was then examined, and important features were selected to build four machine-learning-based predictive models. This simulated data provides researchers who have actual data collected from field observation and those who wants to experiment the potential of machine learning algorithms response to real data and computer simulation modelling generated data as input for crop yield prediction models.

Acknowledgements

Qu, Hongchun; Obsie, Efrem; Drummond, Frank (2020), “Data for: Wild blueberry yield prediction using a combination of computer simulation and machine learning algorithms”, Mendeley Data, V1, doi: 10.17632/p5hvjzsvn8.1

Climate, nitrogen (N) and leaf area index (LAI) are key determinants of crop yield. N additions can enhance yield but must be managed efficiently to reduce pollution. Complex process models estimate N status by simulating soil-crop N interactions, but such models require extensive inputs that are seldom available. Through model-data fusion (MDF), we combine climate and LAI time-series with an intermediate-complexity model to infer leaf N and yield. The DALEC-Crop model was calibrated for wheat leaf N and yields across field experiments covering N applications ranging from 0 to 200 kg N ha-1 in Scotland, UK. Requiring daily meteorological inputs, this model simulates crop C cycle responses to LAI, N and climate. The model, which includes a leaf N-dilution function, was calibrated across N treatments based on LAI observations, and tested at validation plots. We showed that a single parameterization varying only in leaf N could simulate LAI development and yield across all treatments--the mean normalized root-mean-square-error (NRMSE) for yield was 10%. Leaf N was accurately retrieved by the model (NRMSE = 6%). Yield could also be reasonably estimated (NRMSE = 14%) if LAI data are available for assimilation during periods of typical N application (April and May). Our MDF approach generated robust leaf N content estimates and timely yield predictions that could complement existing agricultural technologies. Moreover, EO-derived LAI products at high spatial and temporal resolutions provides a means to apply our approach regionally. Testing yield predictions from this approach over agricultural fields is a critical next step to determine broader utility.

Forecast: Soybean Yield in the US 2022 - 2026 Discover more data with ReportLinker!

Mexico BDM Forecast: Fixed Rate Bond Yield: 10 Years: Median: Plus 3 Years data was reported at 8.500 % pa in Dec 2018. This records an increase from the previous number of 7.300 % pa for Dec 2017. Mexico BDM Forecast: Fixed Rate Bond Yield: 10 Years: Median: Plus 3 Years data is updated monthly, averaging 7.300 % pa from Dec 2014 (Median) to Dec 2018, with 5 observations. The data reached an all-time high of 8.500 % pa in Dec 2018 and a record low of 7.200 % pa in Dec 2015. Mexico BDM Forecast: Fixed Rate Bond Yield: 10 Years: Median: Plus 3 Years data remains active status in CEIC and is reported by Bank of Mexico. The data is categorized under Global Database’s Mexico – Table MX.M004: Securities Yield: Forecast.