The United States was responsible for almost one third of the world's corn production in 2023/24. Most of the U.S. production is attributable to the Corn Belt, which is in the Midwest of the United States. Since the 1850s, corn has been the predominant crop in this area.

U.S. corn production

Most of the corn grow in in the U.S. is field corn. Field corn is not primarily for human consumption but is used to produce hundreds of other products such as ethanol, livestock feed, and other manufactured goods. In 2023, the U.S. harvested over 86 million acres of corn for grain. Wisconsin, California, and South Dakota were the top states producing corn for silage in the U.S. that year.

Global corn consumption: It’s Corn!

In 2022/23, just under 46 billion bushels of corn were consumed worldwide. The United States and China were the top consumers of corn in the world, each consuming over 11 billion bushels that year. Even though the U.S. leads in consumption globally, the consumption of sweet corn has declined drastically since 2015. Mexico, was the top importer of U.S. corn in 2022, followed by China.

Corn (Zea mays) is one of the most widely grown crops throughout the world. However, many corn fields develop pest problems such as corn borers every year that seriously affect its yield and quality. Corn's response to initial insect damage involves a variety of changes to the levels of defensive enzymes, toxins, and communicative volatiles. Such a dramatic change secondary metabolism necessitates the regulation of gene expression at the transcript level. This Data In Brief paper summarizes the datasets of the transcriptome of corn plants in response to corn stalk borers (Ostrinia furnacalis) and/or methyl jasmonate (MeJA). Altogether, 39, 636 genes were found to be differentially expressed. The sequencing data are available in the NCBI SRA database under accession number SRS965087. This dataset will provide more scientific and valuable information for future work such as the study of the functions of important genes or proteins and develop new insect-resistant maize varieties. Includes supplementary tables and data in fasta and GTF format. Resources in this dataset:Resource Title: Datasets for transcriptomic analyses of maize leaves in response to Asian corn borer feeding and/or jasmonic acid. File Name: Web Page, url: https://www.sciencedirect.com/science/article/pii/S2352340916301792 Data in Brief Article including supplemental data in fasta and GTF format.

The USDA-Agricultural Research Service carried out a water productivity field trial for irrigated maize (Zea mays L.) at the Limited Irrigation Research Farm (LIRF) facility in northeastern Colorado in 2008 through 2011. The dataset includes daily measurements of irrigation, precipitation, soil water storage, and plant growth; daily estimates of crop evapotranspiration; and seasonal measurement of crop water use and crop yield. Soil parameters and hourly and daily weather data are also provided. The dataset can be useful to validate and refine maize crop models. The data are presented in spreadsheet format. The primary data files are the four annual LIRF Maize 20xx.xlsx files that include the daily water balance and phenology, final yield and biomass data, and crop management logs. Annual LIRF Weather 20xx.xlsx files provide hourly and daily weather parameters including reference evapotranspiration. The LIRF Soils.xlsx file gives soil parameters. Each spreadsheet contains a Data Descriptions worksheet that provides worksheet or column specific information. Comments are embedded in cells with specific information. A LIRF photos.pdf file provides images of the experimental area, measurement processes and crop conditions. Photo credit Peggy Greb, ARS; copyright-free, public domain copyright policy. Resources in this dataset:Resource Title: LIRF Weather 2008. File Name: LIRF Weather 2008.xlsxResource Description: LIRF hourly and daily weather data for 2008Resource Title: LIRF Weather 2009. File Name: LIRF Weather 2009.xlsxResource Description: LIRF hourly and daily weather data for 2009Resource Title: LIRF Weather 2010. File Name: LIRF Weather 2010.xlsxResource Description: LIRF hourly and daily weather data for 2010Resource Title: LIRF Weather 2011. File Name: LIRF Weather 2011.xlsxResource Description: LIRF hourly and daily weather data for 2011Resource Title: LIRF Soils. File Name: LIRF Soils.xlsxResource Description: LIRF soil maps, soil texture, moisture retention, and chemical constituentsResource Title: LIRF Photo Log. File Name: LIRF Photo Log.pdfResource Description: Photos of the LIRF Water Productivity field trials and instrumentation.Resource Title: Data Dictionaries. File Name: DataDictionary r1.xlsxResource Description: Data descriptions of all the data resources (also included in their respective data files).Resource Title: LIRF Methodology. File Name: LIRF Methodology r1.pdfResource Description: Description of data files, data, and data collection methodology for the LIRF 2008-2011 Water Productivity field trials.Resource Title: LIRF Maize 2008. File Name: LIRF Maize 2008 r1.xlsxResource Description: Water balance and yield data for 2008 LIRF field trialResource Title: LIRF Maize 2009. File Name: LIRF Maize 2009 r1.xlsxResource Description: Water balance and yield data for 2009 LIRF field trialResource Title: LIRF Maize 2010. File Name: LIRF Maize 2010 r1.xlsxResource Description: Water balance and yield data for 2010 LIRF field trialResource Title: LIRF Maize 2011. File Name: LIRF Maize 2011 r1.xlsxResource Description: Water balance and yield data for 2011 LIRF field trial

Two datasets in the EOS-WEBSTER US County Data Collection provide county-level data for crop acreage, production and yield statistics. Crop data for 22 different field crops were acquired from the National Agricultural Statistical Service (NASS) for 1972 through 1998. One dataset provides data for individual varieties/types of each crop while the second dataset provides summary data by crop only. Data can be subset by irrigated and non-irrigated areas. Sucrose content, where applicable, is also included.

EOS-WEBSTER provides seven datasets which provide county-level data on agricultural management, crop production, livestock, soil properties, geography and population. These datasets were assembled during the mid-1990's to provide driving variables for an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data (except nitrogen fertilizer use) were all derived from publicly available, national databases. Each dataset has a separate DIF.

The US County data has been divided into seven datasets.

 US County Data Datasets:

 1) Agricultural Management
 2) Crop Data (NASS Crop data)
 3) Crop Summary (NASS Crop data)
 4) Geography and Population
 5) Land Use
 6) Livestock Populations
 7) Soil Properties

Corn Combine Harvesters Market Outlook

The global corn combine harvesters market size was valued at approximately USD 2.3 billion in 2023 and is projected to reach around USD 4.1 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 6.3% from 2024 to 2032. Major growth factors include technological advancements in agricultural machinery, increasing global food demand, and the need for improved crop yield efficiency. The rising awareness about the benefits of mechanized farming and government incentives to adopt advanced agricultural tools are also contributing to market growth.

One of the primary growth drivers for the corn combine harvesters market is the technological advancements in agricultural machinery. Innovations such as GPS-enabled systems, automated steering, and real-time data analytics are enhancing the efficiency and productivity of these machines. These advancements are allowing farmers to optimize their operations, reduce labor costs, and significantly improve crop yields. As a result, the adoption of technologically advanced corn combine harvesters is expected to escalate, driving market growth.

The increasing global food demand is another crucial factor propelling the corn combine harvesters market. With the world population projected to reach 9.7 billion by 2050, the demand for food is expected to rise significantly. Corn, being a staple food in many countries, plays a vital role in meeting this demand. Consequently, there is a growing need for efficient harvesting solutions to ensure high crop productivity and minimize post-harvest losses. Corn combine harvesters, with their ability to efficiently harvest large areas of corn fields, are becoming indispensable in meeting the increasing food demand.

Additionally, the need for improved crop yield efficiency is driving the growth of the corn combine harvesters market. Traditional methods of corn harvesting are labor-intensive and time-consuming, often resulting in considerable post-harvest losses. Corn combine harvesters, on the other hand, offer a more efficient and faster harvesting process, significantly reducing crop losses and ensuring higher yield efficiency. This efficiency is particularly crucial for large-scale commercial farming operations, where maximizing yield and minimizing losses are essential for profitability.

The role of a Forage Harvester in modern agriculture cannot be understated, especially when it comes to enhancing the efficiency of harvesting operations. These machines are designed to cut and process crops like corn, grass, and other forage plants into silage, which is crucial for livestock feed. Forage harvesters are equipped with advanced cutting and processing mechanisms that ensure the crops are chopped into uniform pieces, facilitating better fermentation and storage. This not only helps in preserving the nutritional value of the feed but also reduces wastage. As the demand for high-quality livestock feed continues to grow, the adoption of forage harvesters is expected to rise, complementing the use of corn combine harvesters in large-scale farming operations.

Regionally, the corn combine harvesters market is witnessing significant growth across various geographies. North America, for instance, has a mature market with high adoption of advanced agricultural machinery. The presence of key market players and favorable government policies promoting mechanized farming are contributing to market growth in this region. Asia Pacific, on the other hand, is expected to witness the highest growth rate due to the increasing adoption of modern farming techniques and the rising demand for food. Countries such as China and India are experiencing rapid agricultural mechanization, further driving market growth in this region.

Product Type Analysis

In the corn combine harvesters market, product type plays a pivotal role in determining the market dynamics. The segment is primarily divided into self-propelled, tractor-mounted, and others. Self-propelled corn combine harvesters are highly preferred due to their advanced features and greater operational efficiency. These machines are equipped with powerful engines, advanced control systems, and large storage capacities, making them ideal for large-scale farming operations. The rising demand for high-capacity machines that can efficiently handle large volumes of crops is driving the growth of this segment.

Tractor-mounted corn combine harvesters, on the other hand,

IntroductionWhile globally appreciated for reliable, intensification-friendly phenotypes, modern corn (Zea mays L.) genotypes retain crop plasticity potential. For example, weather and heterogeneous field conditions can overcome phenotype uniformity and facilitate tiller expression. Such plasticity may be of interest in restrictive or otherwise variable environments around the world, where corn production is steadily expanding. No substantial effort has been made in available literature to predict tiller development in field scenarios, which could provide insight on corn plasticity capabilities and drivers. Therefore, the objectives of this investigation are as follows: 1) identify environment, management, or combinations of these factors key to accurately predict tiller density dynamics in corn; and 2) test outof-season prediction accuracy for identified factors.MethodsReplicated field trials were conducted in 17 diverse site-years in Kansas (United States) during the 2019, 2020, and 2021 seasons. Two modern corn genotypes were evaluated with target plant densities of 25000, 42000, and 60000 plants ha -1. Environmental, phenological, and morphological data were recorded and evaluated with generalized additive models.ResultsPlant density interactions with cumulative growing degree days, photothermal quotient, mean minimum and maximum daily temperatures, cumulative vapor pressure deficit, soil nitrate, and soil phosphorus were identified as important predictive factors of tiller density. Many of these factors had stark non-limiting thresholds. Factors impacting growth rates and photosynthesis (specifically vapor pressure deficit and maximum temperatures) were most sensitive to changes in plant density. Out-of-season prediction errors were seasonally variable, highlighting model limitations due to training datasets.DiscussionThis study demonstrates that tillering is a predictable plasticity mechanism in corn, and therefore could be incorporated into decision tools for restrictive growing regions. While useful for diagnostics, these models are limited in forecast utility and should be coupled with appropriate decision theory and risk assessments for producers in climatically and socioeconomically vulnerable environments.

Details of markers used in Sh2 gene diversity analysis.

Interactive chart of historical daily corn prices back to 1959. The price shown is in U.S. Dollars per bushel.

North America Maize Market size was valued at USD 143.18 Billion in 2023 and is projected to reach USD 190.29 Billion by 2031, growing at a CAGR of 3.6% from 2024 to 2031.

North America Maize Market: Definition/ Overview

Maize, usually known as corn, is a very versatile crop with numerous culinary and non-food applications. Cornmeal, tortillas, and popcorn are common staple foods consumed around the world. Maize is also crushed into flour for baking and found in processed goods such as maize syrup, oils, and snacks. It also serves as a key source of animal feed for livestock, poultry, and fish due to its high energy content.

Maize has a variety of industrial applications in addition to food. Maize starch is a vital element in paper, textiles, and adhesives. Maize is a major source of ethanol in the biofuel sector, which is used to produce renewable energy and is becoming increasingly essential as an alternative for fossil fuels.

The Global Hyperspectral Imaging Spectral-library of Agricultural crops (GHISA) is a comprehensive compilation, collation, harmonization, and standardization of hyperspectral signatures of agricultural crops of the world. This hyperspectral library of agricultural crops is developed for all major world crops and was collected by United States Geological Survey (USGS) and partnering volunteer agencies from around the world. Crops include wheat, rice, barley, corn, soybeans, cotton, sugarcane, potatoes, chickpeas, lentils, and pigeon peas, which together occupy about 65% of all global cropland areas. The GHISA spectral libraries were collected and collated using spaceborne, airborne (e.g., aircrafts and drones), and ground based hyperspectral imaging spectroscopy.The GHISA for Central Asia (GHISACASIA) Version 1 product provides dominant crop data (wheat, rice, corn, alfalfa, and cotton) in different growth stages across the Galaba and Kuva farm fields in the Syr Darya river basin in Central Asia. The GHISA hyperspectral library for the two irrigated areas was developed using Earth Observing-1 (EO-1) Hyperion hyperspectral data acquired in 2007 and ASD (Analytical Spectral Devices, Inc.) Spectroradiometer data acquired in 2006 and 2007. GHISACASIA is extracted from three Hyperion hyperspectral images and several thousands of field ASD Spectroradiometer data. Measurements were taken from 1,232 randomly chosen points scattered across the two farm sites throughout the growing season. All the processing algorithms are coded in Statistical Analysis System (SAS) format and available for download.Provided in the .xlsx files are the spectral library including image information, plot IDs, study area, instrument, Julian or acquisition date, and crop type labels for Central Asia sample fields.

Corn Peeling Threshers Market Outlook

The global market size for corn peeling threshers was estimated to be USD 1.2 billion in 2023, and it is projected to reach USD 2.1 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 6.2% during the forecast period. The growth of the corn peeling threshers market is driven by several factors, including the increasing demand for efficient and high-capacity agricultural machinery, the rising awareness regarding the benefits of mechanization in farming, and the growing adoption of advanced technologies in agricultural practices.

A significant growth factor in the corn peeling threshers market is the increasing global population, which underscores the need for higher agricultural productivity. As the world population grows, the demand for food, particularly staple crops like corn, escalates. This has led to an increased adoption of mechanized agricultural equipment that enhances productivity and efficiency. Corn peeling threshers reduce the time and labor required for corn processing, thereby enabling farmers to meet the higher demand more effectively. The adoption of these machines is particularly critical in developing regions where traditional farming practices are still prevalent.

Another factor contributing to the market's growth is the technological advancements in agricultural machinery. Modern corn peeling threshers are equipped with features that improve their efficiency and versatility. These advancements include automation, improved threshing techniques, and better energy efficiency. Such technological improvements make these machines more attractive to farmers looking to increase their yield and streamline their operations. Furthermore, the integration of IoT and AI in agricultural equipment allows for better monitoring and maintenance, ensuring optimal performance and reducing downtime.

Government initiatives and subsidies aimed at promoting agricultural mechanization also play a crucial role in driving the market. Various governments around the world are offering financial assistance and subsidies to farmers for purchasing modern agricultural equipment. This support is intended to boost agricultural productivity and enhance food security. Additionally, training programs and awareness campaigns are being conducted to educate farmers about the benefits of using advanced machinery, further driving the adoption of corn peeling threshers.

From a regional perspective, Asia Pacific is expected to be a significant market for corn peeling threshers. The region's large agricultural base and the presence of major corn-producing countries such as China and India contribute to the high demand for efficient agricultural machinery. North America and Europe are also key markets, driven by high technological adoption and the presence of established agricultural machinery manufacturers. In contrast, regions like Latin America and the Middle East & Africa are gradually catching up due to increasing agricultural activities and supportive government policies.

The role of a Corn Harvester in modern agriculture cannot be overstated. These machines are pivotal in ensuring the efficient harvesting of corn, which is a critical step before the processing stage involving corn peeling threshers. Unlike threshers, which focus on separating the corn kernels from the cob, corn harvesters are designed to cut and gather the corn stalks from the field. The integration of corn harvesters in the agricultural process significantly reduces the time and labor required for harvesting, allowing farmers to manage larger fields with ease. This efficiency is particularly beneficial in regions with vast agricultural lands, where timely harvesting is crucial to prevent crop loss due to weather conditions. The use of corn harvesters complements the function of corn peeling threshers, creating a seamless workflow from field to processing.

Product Type Analysis

The corn peeling threshers market is segmented by product type into manual corn peeling threshers, electric corn peeling threshers, and tractor-mounted corn peeling threshers. Manual corn peeling threshers are traditionally used in regions with small-scale farming practices. These threshers are cost-effective and suitable for farmers with limited resources. However, their efficiency and capacity are limited compared to electric an

Summary statistics of genotyping assay of 48 inbred lines.

Non-GMO Corn Seed Market Outlook

The global non-GMO corn seed market size was valued at approximately USD 3.2 billion in 2023 and is projected to grow to USD 5.6 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 6.4% over the forecast period. This impressive growth is driven primarily by increasing consumer demand for clean-label and organic food products, as well as a heightened awareness of the environmental and health impacts of genetically modified organisms (GMOs).

One of the major growth factors for the non-GMO corn seed market is the rising consumer preference for non-GMO and organic products. Consumers around the world are becoming more health-conscious and are actively seeking food products that are free from synthetic chemicals and genetically modified organisms. This shift in consumer behavior is compelling food manufacturers to source non-GMO ingredients, thereby driving the demand for non-GMO corn seeds. Additionally, the regulatory landscape is becoming increasingly favorable for non-GMO and organic products, further fueling market growth.

Another key growth driver is the increasing adoption of non-GMO corn seeds in the animal feed industry. With the rising demand for organic meat and dairy products, farmers are looking for non-GMO feed options to maintain the organic status of their livestock. Non-GMO corn seeds are particularly popular in the poultry and cattle farming sectors, where they are used as a primary source of nutrition. The growing organic livestock farming industry, particularly in regions like North America and Europe, is expected to significantly boost the demand for non-GMO corn seeds.

The biofuel industry is also contributing to the growth of the non-GMO corn seed market. As the world grapples with the effects of climate change, there is an increasing push towards sustainable and renewable energy sources. Non-GMO corn seeds are being used to produce bioethanol, a type of biofuel that is considered to be more environmentally friendly compared to its conventional counterparts. The growing demand for bioethanol, particularly in countries like the United States and Brazil, is expected to further drive the market for non-GMO corn seeds.

Regionally, North America is expected to dominate the non-GMO corn seed market over the forecast period. The region's strong agricultural infrastructure, coupled with a high level of consumer awareness about GMOs, is driving the demand for non-GMO corn seeds. Europe is another key market, with stringent regulations against GMOs and a strong consumer preference for organic and non-GMO products. The Asia Pacific region is also expected to witness significant growth, driven by increasing agricultural activities and rising consumer awareness about the benefits of non-GMO products.

Product Type Analysis

Field corn, sweet corn, and popcorn are the primary product types in the non-GMO corn seed market. Field corn dominates the market due to its extensive use in animal feed and industrial applications. Field corn possesses high starch content, making it ideal for bioethanol production and various industrial uses. The demand for non-GMO field corn is particularly high in regions like North America and Europe, where it is used extensively in both the food and biofuel industries. Moreover, the high yield and adaptability of field corn make it a preferred choice among farmers.

Sweet corn, on the other hand, is primarily grown for direct human consumption. It is widely used in a variety of food products, including canned and frozen foods, salads, and snacks. The growing demand for organic and non-GMO food products is driving the market for non-GMO sweet corn. This segment is particularly popular in regions like North America and Europe, where consumer awareness about GMOs is high. Sweet corn is also gaining traction in the Asia Pacific region, where it is increasingly being used in traditional dishes and modern cuisine.

Popcorn is another important segment in the non-GMO corn seed market. Popcorn is a popular snack food that is enjoyed by consumers of all ages. The growing trend of healthy snacking is driving the demand for non-GMO popcorn. Consumers are increasingly looking for snacks that are free from artificial ingredients and GMOs. The United States is the largest market for non-GMO popcorn, followed by Europe and the Asia Pacific region. The increasing popularity of home entertainment and snacking is expected to further boost the demand for non-GMO popcorn.

Report Scope</str

The statistic illustrates the global grain harvested area in million hectares in 2023, broken down by type. The harvested area for corn amounted to over *** million hectares worldwide in 2023.

Snapshot img

Maize Market Size 2024-2028

The maize market size is forecast to increase by USD 34.7 billion at a CAGR of 2.21% between 2023 and 2028. The market is driven by the rising demand for maize as a primary ingredient in various industries, including food processing and animal feed production. In the food sector, maize is extensively used in the production of corn syrup, corn flour, tortillas, and thickening agents such as cornstarch and corn oil. These products are essential in cooking and food manufacturing. However, the market faces several challenges, including logistics and distribution issues. Maize is a bulky and perishable crop, making efficient storage and warehouse management essential to maintain its quality and prevent losses. The complex supply chain, from farmers to consumers, requires effective coordination and management to ensure a steady flow of maize to meet the increasing demand. In the industrial sector, maize serves as a feedstock for the production of alcohol, with whiskey and vodka being notable examples. Furthermore, maize is also used as a raw material in the pharmaceutical industry for medication tablets. However, the market faces challenges in logistics and distribution due to the bulky nature and perishable characteristics of maize. Additionally, the increasing preference for organically cultivated maize is another trend shaping the market landscape.

What will be the Size of the Market During the Forecast Period?

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Maize, also known as corn, is a significant cereal crop worldwide, with various applications in food, livestock feed, and industrial purposes. This comprehensive analysis provides an insightful exploration of The market, focusing on its role as a staple food, feed grain, and raw material for diverse industries. Maize is a vital food source for numerous populations, particularly in North America. It is consumed in various forms, including field corn, sweet corn, popcorn, baby corn, and cornmeal. Cornmeal is further processed into various food products, such as tortillas, bread, and snacks.

Furthermore, the nutritional value of maize is attributed to its rich antioxidant content, including phenols and phytosterols. Maize serves as a primary feed grain for livestock production, particularly for poultry and swine. Its high energy content, primarily derived from its oil, makes it an essential component of livestock diets. Maize is extensively used in industrial processes, including the production of corn-based biofuels, plastics, and adhesives. In the food industry, it is used as a sweetener and thickening agent. Maize and soybean are two essential crops in the agriculture industry, with maize contributing significantly to the global protein supply.

Also, while maize is a primary source of feed protein, soybean is a major source of protein for human consumption. The synergy between these crops enhances their overall value in the agriculture sector. The Green Revolution marked a significant turning point in maize cultivation, leading to increased productivity and yield through the introduction of high-yielding varieties and modern agricultural practices. Biotechnological development has further revolutionized maize production, with genetically modified varieties offering enhanced resistance to pests and improved nutritional value. Urea cultivation is a crucial aspect of maize production, as it is used as a fertilizer to enhance crop growth and yield.

Market Segmentation

The market 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.

End-user

  Industrial
  Retail
  Food service


Geography

  North America

    US


  APAC

    China


  South America

    Brazil
    Argentina


  Europe



  Middle East and Africa

By End-user Insights

The industrial segment is estimated to witness significant growth during the forecast period. Maize and its derivatives, including corn syrup, corn flour, and cornstarch, hold significant value in various industries. In food processing, maize is utilized in the production of tortillas, thickening agents, and alcoholic beverages such as whiskey and vodka. Beyond food applications, maize is employed as a raw material and feedstock in the manufacturing of pharmaceuticals, particularly in the production of medication tablets. Maize starch, a powdered byproduct, is utilized as a filler in the fabrication of plastics, adhesives, glues, resins, and artificial leather.

Additionally, maize oil, a refined vegetable oil extracted from corn germ, is employed in cooking and industrial processes, serving as a diluent and carrier in the production of insecticides and pesticides. Furthermore, it is incorporated into soaps, inks, and paints. Maize's versatility and wide-ranging app

"Agricultural Chemical Usage, Field Crops Summary" contains state and U.S. fertilizer and pesticide use data for corn, cotton, peanuts, rice, sorghum, soybeans, wheat, fall potatoes. Includes pesticide use by active ingredient, application rates, and acres treated.

  Collection Organization: ERS and NASS

  Collection Methodology: The information presented is the result
  of a sample survey conducted for the crop year (end of harvest
  for previous crop through harvest of current crop). A random
  sample of fields was selected with probability proportional to
  size, using information, obtained earlier in the year, from two
  surveys of farm operators. Personal interviews were used to
  obtain the information. Chemical data were collected at the
  product level and converted to active ingredient for
  summarization.

  Collection Frequency: Annual survey.

  Update Characteristics: Dataset not updated.

  STATISTICAL INFORMATION:

  The data reside in one ASCII text file.
  LANGUAGE:

  English
  ACCESS/AVAILABILITY:

  Data Center: National Agricultural Statistics Service
  Dissemination Media: Diskette, Internet home page
  File Format: ASCII delimited
  Access Instructions: Call NASS at 1-800-999-6779 for historical
  series data available on diskette. For historical series data
  available online, connect to the Internet home page at Cornell
  University.

  Or connect at the NASS Internet home page.

        URL: 'http://www.nass.usda.gov/index.asp'

Estimates of crop nitrogen (N) uptake and offtake are critical in estimating N balances, N use efficiencies and potential losses to the environment. Calculation of crop N uptake and offtake requires estimates of crop product yield (e.g. grain or beans) and crop residue yield (e.g. straw or stover) and the N concentration of both components. Yields of crop products are often reasonably well known, but those of crop residues are not. While the harvest index (HI) can be used to interpolate the quantity of crop residue from available data on crop product yields, harvest indices are known to vary across locations, as do N concentrations of residues and crop products. The increasing availability of crop data and advanced statistical and machine learning methods present us with an opportunity to move towards more locally relevant estimates of crop harvest index and N concentrations using more readily available data. This dataset includes maize field experiment data. It is a culmination of summary statistic data collected from the literature as well as raw data requested from various researchers and organisations from around the world. These data will enable more locally relevant estimates of crop nutrient offtake, nutrient balances and nutrient use efficiency at national, regional or global levels, as part of strategies towards more sustainable nutrient management. Methods Maize field experiment data were collected from a search across the world using two main sources. One source was from summary statistics gleaned from the peer reviewed literature and the other source was for raw datasets obtained through requests of various researchers and organisations. Further details of how these two sources of data were collected and processed are described by Ludemann et al. (2022). Reference Ludemann CI, Hijbeek R, van Loon MP, Murrell ST, Dobermann A, van Ittersum MK, 2022 Estimating maize harvest index and nitrogen concentrations in grain and residue using globally available data Field Crops Research 284:108578 https://doi.org/10.1016/j.fcr.2022.108578

This dataset provides estimates of topsoil loss and economic loss associated with decreased crop productivity resulting from topsoil loss at county- and state-levels across the Corn Belt region of the Midwestern USA. Intermediate products used to derive topsoil loss are provided and include 4 m gridded estimates of study sites elevation, curvature, slope, soil organic carbon index (SOCI), and the probability of exposed B-horizon soil. Topsoil loss at the county- and state-levels was derived from analyses of agricultural land at selected sites across the study area. From WorldView imagery, 759 fields were identified that had exposed bare soil (210 km2) and were grouped into 28 sites. Gridded estimates of the SOCI and of the probability of exposed B-horizon soil were determined for each field within the sites. Topography measures, including elevation (m), curvature (m-1), and slope (deg), were extracted over the entire study area from LiDAR-derived digital elevation models at a 4 m resolution acquired from 2003-2018. Within each of the 28 study sites, the SOCI and topographic curvature values were extracted from co-located pixels. Topsoil loss was estimated from the relationship between subsoil exposure and topography and averaged across each site.The relationship between topsoil loss and topographic curvature was used to up-scale and predict topsoil and economic losses at the county and state-levels across the entire 375,000 km2 study area. The data have been used to demonstrate a robust and scalable method for estimating the magnitude of erosion in agricultural landscapes.

The corn silage market size expects a significant bump in its value, from US$ 348.5 million in 2024 to US$ 688.4 million in 2034. The updated report points to a CAGR of 7.0% CAGR from 2024 to 2034. That is a slight improvement from the previous CAGR of 6.60% observed between 2019 and 2023.

Category-wise Insights

Country-wise Insights

Herein lies 2355 images of maize leaves. These images were taken over a variety of times and locations in South Africa.

The diseases labelled herein are: - Grey Leaf Spot (GLS) - Northern Corn Leaf Blight (NCLB) - Common Rust (CR) - Southern Rust (SR) - Phaeosphaeria Leaf Spot (PLS).

See the Readme.txt contained within or the file description of the parent folder for more details as to how the images are stored and annotated.

This dataset contains a realistic representation of field conditions. There are images of leaves destroyed by bugs, protein deficiencies, leaves with hands occluding them, different lighting conditions, some leaves are wet, backgrounds vary wildly, anthers, bird droppings, several simultaneous and sometimes even overlapping diseases...you name it.

This is a challenging dataset. Good luck!