The Current Population Survey Food Security Supplement (CPS-FSS) is the source of national and State-level statistics on food insecurity used in USDA's annual reports on household food security. The CPS is a monthly labor force survey of about 50,000 households conducted by the Census Bureau for the Bureau of Labor Statistics. Once each year, after answering the labor force questions, the same households are asked a series of questions (the Food Security Supplement) about food security, food expenditures, and use of food and nutrition assistance programs. Food security data have been collected by the CPS-FSS each year since 1995. Four data sets that complement those available from the Census Bureau are available for download on the ERS website. These are available as ASCII uncompressed or zipped files. The purpose and appropriate use of these additional data files are described below: 1) CPS 1995 Revised Food Security Status data--This file provides household food security scores and food security status categories that are consistent with procedures and variable naming conventions introduced in 1996. This includes the "common screen" variables to facilitate comparisons of prevalence rates across years. This file must be matched to the 1995 CPS Food Security Supplement public-use data file. 2) CPS 1998 Children's and 30-day Food Security data--Subsequent to the release of the April 1999 CPS-FSS public-use data file, USDA developed two additional food security scales to describe aspects of food security conditions in interviewed households not captured by the 12-month household food security scale. This file provides three food security variables (categorical, raw score, and scale score) for each of these scales along with household identification variables to allow the user to match this supplementary data file to the CPS-FSS April 1998 data file. 3) CPS 1999 Children's and 30-day Food Security data--Subsequent to the release of the April 1999 CPS-FSS public-use data file, USDA developed two additional food security scales to describe aspects of food security conditions in interviewed households not captured by the 12-month household food security scale. This file provides three food security variables (categorical, raw score, and scale score) for each of these scales along with household identification variables to allow the user to match this supplementary data file to the CPS-FSS April 1999 data file. 4) CPS 2000 30-day Food Security data--Subsequent to the release of the September 2000 CPS-FSS public-use data file, USDA developed a revised 30-day CPS Food Security Scale. This file provides three food security variables (categorical, raw score, and scale score) for the 30-day scale along with household identification variables to allow the user to match this supplementary data file to the CPS-FSS September 2000 data file. Food security is measured at the household level in three categories: food secure, low food security and very low food security. Each category is measured by a total count and as a percent of the total population. Categories and measurements are broken down further based on the following demographic characteristics: household composition, race/ethnicity, metro/nonmetro area of residence, and geographic region. The food security scale includes questions about households and their ability to purchase enough food and balanced meals, questions about adult meals and their size, frequency skipped, weight lost, days gone without eating, questions about children meals, including diversity, balanced meals, size of meals, skipped meals and hunger. Questions are also asked about the use of public assistance and supplemental food assistance. The food security scale is 18 items that measure insecurity. A score of 0-2 means a house is food secure, from 3-7 indicates low food security, and 8-18 means very low food security. The scale and the data also report the frequency with which each item is experienced. Data are available as .dat files which may be processed in statistical software or through the United State Census Bureau's DataFerret http://dataferrett.census.gov/. Data from 2010 onwards is available below and online. Data from 1995-2009 must be accessed through DataFerrett. DataFerrett is a data analysis and extraction tool to customize federal, state, and local data to suit your requirements. Through DataFerrett, the user can develop an unlimited array of customized spreadsheets that are as versatile and complex as your usage demands then turn those spreadsheets into graphs and maps without any additional software. Resources in this dataset:Resource Title: December 2014 Food Security CPS Supplement. File Name: dec14pub.zipResource Title: December 2013 Food Security CPS Supplement. File Name: dec13pub.zipResource Title: December 2012 Food Security CPS Supplement. File Name: dec12pub.zipResource Title: December 2011 Food Security CPS Supplement. File Name: dec11pub.zipResource Title: December 2010 Food Security CPS Supplement. File Name: dec10pub.zip

This dataset measures food availability and access for 76 low- and middle-income countries. The dataset includes annual country-level data on area, yield, production, nonfood use, trade, and consumption for grains and root and tuber crops (combined as R&T in the documentation tables), food aid, total value of imports and exports, gross domestic product, and population compiled from a variety of sources. This dataset is the basis for the International Food Security Assessment 2015-2025 released in June 2015. This annual ERS report projects food availability and access for 76 low- and middle-income countries over a 10-year period. Countries (Spatial Description, continued): Democratic Republic of the Congo, Ecuador, Egypt, El Salvador, Eritrea, Ethiopia, Gambia, Georgia, Ghana, Guatemala, Guinea, Guinea-Bissau, Haiti, Honduras, India, Indonesia, Jamaica, Kenya, Kyrgyzstan, Laos, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Moldova, Mongolia, Morocco, Mozambique, Namibia, Nepal, Nicaragua, Niger, Nigeria, North Korea, Pakistan, Peru, Philippines, Rwanda, Senegal, Sierra Leone, Somalia, Sri Lanka, Sudan, Swaziland, Tajikistan, Tanzania, Togo, Tunisia, Turkmenistan, Uganda, Uzbekistan, Vietnam, Yemen, Zambia, and Zimbabwe. Resources in this dataset:Resource Title: CSV File for all years and all countries. File Name: gfa25.csvResource Title: International Food Security country data. File Name: GrainDemandProduction.xlsxResource Description: Excel files of individual country data. Please note that these files provide the data in a different layout from the CSV file. This version of the data files was updated 9-2-2021

More up-to-date files may be found at: https://www.ers.usda.gov/data-products/international-food-security.aspx

This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information. “Food security means access by all people at all times to enough food for an active, healthy life. USDA’s Economic Research Service (ERS) plays a leading role in research on food security and food security measurement in U.S. households and communities. USDA, ERS provides data access and technical support to social science scholars to facilitate their research on food security. USDA, ERS research focuses on: - Food security in U.S. households (see annual report below) - Food security's effect on the well-being of children, adults, families, and communities - Food security's relationship to public policies, public assistance programs, and the economy” [Quote from https://www.ers.usda.gov/topics/food-nutrition-assistance/food-security-in-the-us] “Food Security Data Access and Documentation Downloads This data section provides information about publicly available national surveys that include questions from the U.S. Food Security Survey Module. Information on each survey and directions for accessing data files are available in the Documentation. [Quote from https://www.ers.usda.gov/data-products/food-security-in-the-united-states]

{"definition": "Prevalence of household-level food insecurity by State. Food-insecure households were unable, at times during the year, to provide adequate food for one or more household members because the household lacked money and other resources for food. For most food-insecure households, inadequacy was in quality and variety of foods; for about a third\u2014those with very low food security\u2014amounts were also inadequate.", "availableYears": "2007-09 (aggregate data)", "name": "Household food insecurity (%, three-year average), 2007-09*", "units": "Percent", "shortName": "FOODINSEC_07_09", "geographicLevel": "State", "dataSources": "ERS estimates using 3 years of data from the Current Population Survey Food Security Supplement, as reported in Table 5 in Coleman-Jensen, Alisha, Mark Nord, and Anita Singh, Household Food Security in the United States in 2012, ERR-155, USDA/ERS, September 2013 (http://www.ers.usda.gov/publications/err-economic-research-report/err155.aspx). The food security survey asks one adult respondent in each household a series of questions about experiences and behaviors that indicate food insecurity. The food security status of the household was assessed based on the number of food-insecure conditions reported (such as being unable to afford balanced meals, cutting the size of meals because of too little money for food, or being hungry because of too little money for food). Note: margins of error are substantial for some States; comparisons between States should take into consideration margins of error published in the source report."}

© FOODINSEC_07_09 This layer is sourced from gis.ers.usda.gov.

https://map.feedingamerica.org/Every community in the country is home to people who struggle with hunger. Since federal nutrition programs don’t reach everyone in need, food banks help fill the gap. Learn more about local food insecurity by exploring data from Feeding America’s annual Map the Meal Gap study. When we better understand hunger, we can help end hunger.What is food insecurity and what does it look like in America?Food insecurity refers to USDA’s measure of lack of access, at times, to enough food for an active, healthy life for all household members and limited or uncertain availability of nutritionally adequate foods. Food-insecure households are not necessarily food insecure all the time. Food insecurity may reflect a household’s need to make trade-offs between important basic needs, such as housing or medical bills, and purchasing nutritionally adequate foods.Notes from Feeding America regarding dIfferences from previous studies:1. Beginning in 2020, we enhanced our food insecurity model through the inclusion of a disability rate variable and refining our poverty measure to reflect non-undergraduate student poverty. The details surrounding this changed are discussed in our technical brief. Because of this methodology changes, the estimates from Map the Meal Gap 2020 are not comparable to estimates from previous years.2. In response to COVID-19, we expanded on Map the Meal Gap to include a companion study and interactive map that discuss our projections in food insecurity as a result of the pandemic. They may also be of interest to check out.

Gundersen, C., A. Dewey, E. Engelhard, M. Strayer & L. Lapinski. Map the Meal Gap 2020: A Report on County and Congressional District Food Insecurity and County Food Cost in the United States in 2018. Feeding America, 2020.

Food Security in US Households Report 2018

  Full report of Food Security in US Households in 2018 (USDA, September 2019) About USDA Food Security and Allocations Data: Links to several different USDA food security and allocations datasets, including a Census-level Food Access Research Atlas, county-level SNAP participation data through FY2020, and state-level total participant counts from FY2015 through FY2019 for the Commodity Supplemental Food Program, Emergency Food Assistance Program, and Food Distribution Program.
  Geography Level: NationalItem Vintage: 2018
  Update Frequency: YearlyAgency: USDAAvailable File Type: PDF 

  Return to Other Federal Agency Datasets Page

https://map.feedingamerica.org/Every community in the country is home to people who struggle with hunger. Since federal nutrition programs don’t reach everyone in need, food banks help fill the gap. Learn more about local food insecurity by exploring data from Feeding America’s annual Map the Meal Gap study. When we better understand hunger, we can help end hunger.What is food insecurity and what does it look like in America?Food insecurity refers to USDA’s measure of lack of access, at times, to enough food for an active, healthy life for all household members and limited or uncertain availability of nutritionally adequate foods. Food-insecure households are not necessarily food insecure all the time. Food insecurity may reflect a household’s need to make trade-offs between important basic needs, such as housing or medical bills, and purchasing nutritionally adequate foods.Gundersen, C., A. Dewey, M. Kato, A. Crumbaugh & M. Strayer. Map the Meal Gap 2019: A Report on County and Congressional District Food Insecurity and County Food Cost in the United States in 2017. Feeding America, 2019.

This public dataset published by USDA summarizes the total number of enrollees in the Supplemental Nutrition Assistance Program (SNAP) by region. SNAP provides nutrition benefits to supplement the food budget of families and persons meeting eligibility criteria related to monthly income. Program enrollment data offers a direct look into some of the most important underlying social determinants of health (SDoH) by county, including financial insecurity and food insecurity. Analysis of this data can also provide information about the characteristics of the subsidy program’s reach and market penetration over time. As an objective marker of the welfare benefit program’s utilization, these data also offer a complementary view of these SDoH alongside the survey-based questions about SNAP that are included in the ACS dataset. States report these administrative data to the USDA twice a year. The dataset includes total count of people, households and issuance of SNAP benefits by county or county/program. For more information, please refer to the USDA’s SNAP website (link )

Precision Farming Agriculture Market Outlook

The global precision farming agriculture market size was estimated at USD 7.0 billion in 2023 and is projected to reach USD 15.6 billion by 2032, growing at a compound annual growth rate (CAGR) of 9.2% from 2024 to 2032. The growth of this market is primarily driven by advancements in technology and increasing demand for food production efficiency.

One of the most significant growth factors in the precision farming agriculture market is the rapid advancement in agricultural technologies. Innovations such as Internet of Things (IoT), big data analytics, and artificial intelligence have enabled farmers to collect and analyze data from their fields, leading to more informed decision-making processes. This shift towards data-driven farming helps optimize resources such as water, fertilizers, and pesticides, thereby increasing yield and reducing costs. Additionally, the integration of Geographic Information Systems (GIS) and GPS technology in farming practices allows for more precise planting, cultivating, and harvesting, which further boosts productivity and efficiency.

Another critical factor contributing to the market's growth is the increasing global population, which drives the demand for more efficient food production systems. With the world's population projected to reach 9.7 billion by 2050, there is an urgent need to produce more food while minimizing environmental impact. Precision farming offers a viable solution by enabling farmers to manage their resources more effectively, reducing waste, and maximizing crop yields. The rising awareness about sustainable farming practices and the need to address climate change concerns have also led to the adoption of precision farming techniques.

Government initiatives and subsidies aimed at promoting precision farming are further propelling the market's growth. Various governments across the globe are recognizing the potential benefits of precision farming in enhancing food security and agricultural sustainability. As a result, they are investing in research and development, providing financial assistance, and implementing favorable policies to encourage the adoption of precision farming technologies. For instance, the European Union's Common Agricultural Policy (CAP) includes measures to support precision farming practices, while the United States Department of Agriculture (USDA) offers grants and loans to farmers for implementing advanced agricultural technologies.

Agriculture Variable Rate Technology (VRT) is revolutionizing the way farmers manage their fields by allowing precise application of inputs like fertilizers, pesticides, and water. This technology leverages data from various sources, such as soil sensors and yield monitors, to customize the application rates for different areas within a field. By doing so, VRT not only enhances crop productivity but also minimizes the environmental impact of farming practices. Farmers can achieve significant cost savings by reducing the overuse of chemicals and water, which is increasingly important in the context of sustainable agriculture. As awareness about the benefits of VRT grows, more farmers are expected to adopt this technology, further driving the precision farming market.

From a regional perspective, North America holds a significant share of the precision farming agriculture market, driven by the early adoption of advanced technologies and the presence of a large number of key market players. The Asia Pacific region is expected to witness substantial growth during the forecast period, primarily due to the increasing demand for food production and the adoption of modern farming practices in countries such as China and India. Europe is also a noteworthy market, with countries like Germany and France focusing on sustainable agriculture and smart farming initiatives.

Technology Analysis

The precision farming agriculture market can be segmented by technology into guidance systems, remote sensing, variable rate technology, and others. Guidance systems, such as GPS-based auto-steering, play a crucial role in precision farming by enabling farmers to automate their machinery, ensuring precise and efficient field operations. These systems help in reducing overlaps and gaps in field activities, thereby saving time, fuel, and labor costs. The increasing adoption of autonomous tractors and drones equipped with guidance systems is further driving the growth of this segme

{"definition": "Prevalence of household-level very low food security by State. In households with very low food security, food intake of one or more members was reduced and eating patterns were disrupted at times during the year because of insufficient money and other resources for food.", "availableYears": "2007-09 (aggregate data)", "name": "Household very low food security (%, three-year average), 2007-09*", "units": "Percent", "shortName": "VLFOODSEC_07_09", "geographicLevel": "State", "dataSources": "ERS estimates using 3 years of data from the Current Population Survey Food Security Supplement, as reported in Table 5 in Coleman-Jensen, Alisha, Mark Nord, and Anita Singh, Household Food Security in the United States in 2012, ERR-155, USDA/ERS, September 2013 (http://www.ers.usda.gov/publications/err-economic-research-report/err155.aspx). The food security survey asks one adult respondent in each household a series of questions about experiences and behaviors that indicate food insecurity. The food security status of the household was assessed based on the number of food-insecure conditions reported (such as being unable to afford balanced meals, cutting the size of meals because of too little money for food, or being hungry because of too little money for food). Note: margins of error are substantial for some States; comparisons between States should take into consideration margins of error published in the source report."}

© VLFOODSEC_07_09 This layer is sourced from gis.ers.usda.gov.

Description of the experiment setting Data collection for the Study of Nutrition and Activity in Childcare Settings (SNACS) started in January 2017 and continued through September 2017. The complex study included web-based surveys, pre-interview surveys, on-site interviews, environmental observations, and telephone interviews of childcare sponsors and providers, as well as interviews of parents of some of the children from the sampled providers. The data were collected from a nationally representative sample of programs, children, and meals. The data cover a range of subjects including the provider’s characteristics, the nutritional quality of meals and snacks served, the dietary intake of children in childcare, the activities of children over the course of the childcare day, and the financial conditions of the childcare operations. Processing methods and equipment used SNACS data were collected via web-based surveys, pre-interview surveys, on-site interviews, environmental observations, and telephone interviews of childcare sponsors and providers, as well as interviews of parents of some of the children from the sampled providers. The study team cleaned the raw data to ensure the data were as correct, complete, and consistent as possible. They used many different methods to check the data depending on the data type. The details are described in the study document called “Appendix A: Methods” (https://fns-prod.azureedge.us/sites/default/files/resource-files/SNACS-AppendixA.pdf) available at the study website. Study date(s) and duration Data collection for the Study of Nutrition and Activity in Childcare Settings (SNACS) started in January 2017 and continued through September 2017. The final public data set was produced in 2021. Study spatial scale (size of replicates and spatial scale of study area) The study is nationally representative and the sample design reflects the complexity of the sample needed to answer the research questions. The primary sampling units were 20 states randomly selected with six states selected with certainty due to their size. Secondary sampling units were selected from a random sample of metropolitan areas and clusters of non-metropolitan counties from the 20 States. Further details about the sample design are described in the “Appendix A: Methods” document available at the study website. Level of true replication See the document, “Appendix A: Methods,” available at the study website. Sampling precision (within-replicate sampling or pseudoreplication) See the document, “Appendix A: Methods,” available at the study website. Level of subsampling (number and repeat or within-replicate sampling) See the document, “Appendix A: Methods,” available at the study website. Study design (before–after, control–impacts, time series, before–after-control–impacts) Non-experimental Description of any data manipulation, modeling, or statistical analysis undertaken The public use data files contain constructed variables used for analytic purposes. The files do include weights created to produce national estimates for the Study of Nutrition and Activity in Childcare Settings final reports available at the study website. The data files do not include any identifying information about childcare sponsors, providers, or individuals who completed the questionnaires or participated in the study in other ways. Description of any gaps in the data or other limiting factors See the document, “Appendix A: Methods,” available at the study website for a detailed explanation of the study’s limitations. Outcome measurement methods and equipment used The height and weight of sampled children were measured with scales provided by data collectors. See the document, “Appendix A: Methods,” available at the study website for details on other outcomes measured through statistical analysis of the survey responses about outcomes such as food insecurity. Resources in this dataset:

Resource Title: Study of Nutrition and Activity in Childcare Settings (SNACS) - SAS Data Sets, Data Codebooks and Documentation Guides File Name: SNACS-I Public Use Files.zip Description: The zip file contains 19 Data Codebooks, 7 Data Documentation Guides, 19 SAS Datasets and one SAS Formats File.

The "State Fact Sheets" provide the most recently available farm and rural data compiled by the Economic Research Service, USDA. It provide 2 pages of facts for each state and also a summary fact sheet for the United States. Included are data on population, jobs, income and poverty, and farms. The data comes from a variety of sources including the Bureau of Labor Statistics, Bureau of Economic Analysis, the Bureau of the Census, and ERS.

Collection Organization: Economic Research Service.

Collection Methodology: The data come from a variety of sources including the Bureau of Labor Statistics, Bureau of Economic Analysis, the Bureau of the Census, and ERS.

Collection Frequency: Varies by data source.

Update Characteristics: Selective updates 2 times a year.

STATISTICAL INFORMATION:

The data reside in 52 ASCII text files. LANGUAGE:

English ACCESS/AVAILABILITY:

Data Center: USDA Economic Research Service Dissemination Media: Diskette, Internet gopher, Internet home page File Format: ASCII, Lotus/dBase 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.

URL: 'http://usda.mannlib.cornell.edu/usda'

Access to the data or reports may be achieved through the ERS-NASS information system:

WWW: 'http://usda.mannlib.cornell.edu/usda' Gopher client: 'gopher://gopher.mannlib.cornell.edu:70/'

For subscription direct to an e-mail address, send an e-mail message to:

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Type the word "lists" (without quotes) in the body of the message.

Agricultural Support Tower Market Outlook

The global agricultural support tower market size was valued at $1.2 billion in 2023 and is projected to reach $2.5 billion by 2032, exhibiting a CAGR of 8.5% during the forecast period. The market growth is driven by increasing demand for enhanced agricultural productivity and sustainability. Rising awareness about the benefits of agricultural support towers in optimizing crop yield and resource management is further propelling market expansion.

One of the significant growth factors in the agricultural support tower market is the increasing need for efficient farming techniques due to the growing global population. With the world's population expected to reach 9.7 billion by 2050, there is an urgent necessity to enhance agricultural productivity. Agricultural support towers play a crucial role in providing structural support to various farming activities, thereby improving the overall efficiency and output. Additionally, these towers aid in effective pest control and irrigation management, which are vital for ensuring healthy crop production.

Technological advancements in the design and materials used for agricultural support towers are also contributing to market growth. Modern towers are being manufactured using high-strength, lightweight materials such as composite and aluminum, which offer durability and ease of installation. Innovations such as smart towers equipped with sensors and IoT capabilities are enabling farmers to monitor and manage their fields remotely, further enhancing productivity. These advancements are attracting investments from both public and private sectors, fostering market expansion.

Government initiatives and subsidies aimed at promoting sustainable agriculture practices are another significant driver for the agricultural support tower market. Many countries are offering financial incentives and technical support to farmers for adopting advanced farming infrastructure, including support towers. These initiatives are not only facilitating the adoption of modern agricultural practices but also ensuring food security. For instance, the USDA (United States Department of Agriculture) provides various grants and loans to farmers for implementing sustainable farming solutions, which is expected to boost market growth in the region.

Regionally, the Asia Pacific is expected to witness significant growth in the agricultural support tower market owing to the large agricultural sector and increasing adoption of modern farming techniques. Countries like China and India, which are major agricultural producers, are investing heavily in agricultural infrastructure to meet the rising food demand. North America and Europe are also anticipated to witness substantial growth due to the presence of advanced farming technologies and supportive government policies. In contrast, the market in Latin America and the Middle East & Africa is projected to grow at a moderate pace, driven by improving agricultural practices and increasing awareness about the benefits of support towers.

Product Type Analysis

The agricultural support tower market is segmented based on product types into steel towers, aluminum towers, and composite towers. Steel towers have been the traditional choice for agricultural support due to their robustness and high load-bearing capacity. These towers are widely used in large-scale farming operations where durability and long-term performance are critical. The demand for steel towers is expected to remain steady, driven by ongoing investments in large agricultural projects and infrastructural developments in emerging economies.

Aluminum towers, on the other hand, are gaining popularity due to their lightweight nature and resistance to corrosion. These towers are easier to install and maintain, making them ideal for small to medium-sized farms. Aluminum towers offer a balance between strength and flexibility, which is essential for various agricultural applications such as supporting trellises and irrigation systems. The market for aluminum towers is expected to grow at a significant rate, driven by increasing demand from small-scale farmers and horticulturists looking for cost-effective and durable solutions.

Composite towers represent the latest innovation in the agricultural support tower market. Made from a combination of materials such as fiberglass, carbon fiber, and resins, composite towers offer superior strength-to-weight ratios and excellent resistance to environmental factors. These features make composite towers highly suitable for high-tech

Increasing the efficiency of agricultural production—getting more output from the same amount of resources—is critical for improving food security. To measure the efficiency of agricultural systems, we use total factor productivity (TFP). TFP is an indicator of how efficiently agricultural land, labor, capital, and materials (agricultural inputs) are used to produce a country’s crops and livestock (agricultural output)—it is calculated as the ratio of total agricultural output to total production inputs. When more output is produced from a constant amount of resources, meaning that resources are being used more efficiently, TFP increases. Measures of land and labor productivity—partial factor productivity (PFP) measures—are calculated as the ratio of total output to total agricultural area (land productivity) and to the number of economically active persons in agriculture (labor productivity). Because PFP measures are easy to estimate, they are often used to measure agricultural production performance. These measures normally show higher rates of growth than TFP, because growth in land and labor productivity can result not only from increases in TFP but also from a more intensive use of other inputs (such as fertilizer or machinery). Indicators of both TFP and PFP contribute to the understanding of agricultural systems needed for policy and investment decisions by allowing for comparisons across time and across countries and regions. The data file provides estimates of IFPRI's TFP and PFP measures for developing countries for three-sub-periods between 1991 and 2014(1991-2000,2001-2010 and 2010-2014). These TFP and PFP estimates were generated using the most recent data on outputs and inputs from the Economic Research Service of the United States Department of Agriculture (ERS-USDA), an internationally consistent and comparable dataset on production and input quantities built using data from the FAOSTAT database of the Food and Agriculture Organization of the United Nations (FAO), supplemented with data from national statistical sources.

The "State Farm Income Data" dataset contains information on farm income indicators, cash receipts by commodity groups and selected commodities, and ranking tables for the 25 leading commodities and for major commodity groups for states and the U.S. Also includes ranking tables for the top 25 states for 25 leading commodities, 50-state summary of cash receipts, and farm income and leading commodities by state. Data are updated anually.

LANGUAGE:

English ACCESS/AVAILABILITY:

Data Center: USDA Economic Research Service Dissemination Media: Diskette, Internet gopher, Internet home page File Format: ASCII, Lotus/dBase 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.

    URL: 'http://usda.mannlib.cornell.edu/usda'

Access to the data or reports may be achieved through the ERS-NASS information system:

    WWW:

'http://usda.mannlib.cornell.edu/usda' Gopher client: 'gopher://gopher.mannlib..cornell.edu:70/'

For subscription direct to an e-mail address, send an e-mail message to:

     usda-reports@usda.mannlib.cornell.edu

Type the word "lists" (without quotes) in the body of the message. CONTENTS:

Cash Receipts, by Commodity Groups and Selected Commodities, 1990-1994 Farm Income Indicators for the U.S. and 50 States, 1990-94 Ranking Tables for the 25 Leading Commodities and Major Groups, 1994

Genetically Modified Food Market Outlook

The global genetically modified (GM) food market size was valued at approximately USD 25 billion in 2023 and is projected to reach USD 50 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.5% during the forecast period. The growth of the GM food market can be attributed to the increasing demand for high-yield crops, advancements in biotechnology, and supportive government policies in various developing and developed nations.

The rising global population is a significant driving factor for the GM food market. With the world's population expected to reach 9.7 billion by 2050, there is a pressing need to enhance agricultural productivity to ensure food security. Genetically modified foods, which have been engineered to improve yield, resistance to pests, and adaptability to various environmental conditions, offer a viable solution to meet this demand. Additionally, these crops often require fewer inputs such as water and pesticides, making them more sustainable and cost-effective for farmers.

Advancements in genetic engineering and biotechnology have also been pivotal in the expansion of the GM food market. Innovations such as CRISPR-Cas9 and other gene-editing technologies have made it easier and more cost-effective to develop GM crops with desired traits. These advancements have not only improved the efficiency of production but also expanded the range of traits that can be engineered. For instance, researchers are now able to develop crops that are not only resistant to pests and herbicides but also fortified with essential nutrients, thereby addressing issues of malnutrition in various parts of the world.

Another significant growth factor for the GM food market is the regulatory environment. Various countries have established frameworks to ensure the safety and efficacy of GM foods, thus building public trust and acceptance. For example, the United States Department of Agriculture (USDA) and the Food and Drug Administration (FDA) have stringent regulations that GM foods must meet before they can be commercialized. These regulations have helped to alleviate consumer concerns regarding the safety of GM foods and have paved the way for wider adoption.

In recent years, there has been a growing interest in Non Gmo Corn Seed as an alternative to genetically modified varieties. Non-GMO corn seeds are cultivated using traditional breeding methods, which do not involve genetic engineering. This approach appeals to consumers and farmers who are concerned about the potential environmental and health impacts of GMOs. Non-GMO corn is often perceived as a more natural option, aligning with the preferences of those seeking organic or minimally processed foods. The demand for Non-GMO corn seed is also driven by the increasing consumer awareness and desire for transparency in food production. As a result, some farmers are exploring the cultivation of Non-GMO corn to cater to niche markets and meet the diverse needs of consumers.

From a regional perspective, North America and Asia-Pacific are expected to be the leading markets for genetically modified foods. North America, particularly the United States, has been at the forefront of adopting GM crops due to favorable government policies and high investment in research and development. In contrast, Asia-Pacific is witnessing rapid adoption due to the need to enhance food security and agricultural productivity in densely populated countries such as China and India. Europe, however, has shown resistance due to stringent regulations and public skepticism, but there is a slow shift towards acceptance driven by ongoing research and positive outcomes from other regions.

Crop Type Analysis

The genetically modified food market is segmented by crop type into corn, soybean, cotton, canola, and others. Among these, corn holds a significant share of the market due to its widespread use in food products, animal feed, and biofuels. Genetically modified corn is engineered to resist pests and tolerate herbicides, which increases its yield and reduces the need for chemical interventions. This has made it highly popular among farmers, particularly in the United States and Brazil, which are leading producers of GM corn.

Soybean is another dominant crop in the GM food market. Ge

Pest Resistant Crops Market Outlook

The pest resistant crops market size is projected to grow from USD 26.5 billion in 2023 to USD 45.8 billion by 2032, at a CAGR of 6.2% during the forecast period. This growth is driven by increasing global food demand and the need for sustainable agricultural practices. The adoption of biotechnology and genetically modified organisms (GMOs) in agriculture has played a pivotal role in enhancing crop yields and reducing the reliance on chemical pesticides. Furthermore, the growing awareness about the adverse effects of chemical pesticides on health and the environment has accelerated the shift towards pest resistant crops.

A significant growth factor for the pest resistant crops market is the increasing global population, which necessitates a higher food production rate. The United Nations projects that the world population will reach 9.7 billion by 2050, exerting immense pressure on agriculture to produce more food sustainably. Pest resistant crops, with their ability to withstand pest attacks, ensure higher yields and reduce crop losses, making them a crucial component of modern agriculture. Additionally, climate change has been causing unpredictable weather patterns and increased pest infestations, further driving the need for resilient crop varieties.

Technological advancements in biotechnology and genetic engineering have significantly contributed to the growth of the pest resistant crops market. Innovations such as CRISPR gene editing and transgenic technology have made it possible to develop crops with enhanced resistance to pests and diseases. These technologies have enabled scientists to introduce specific genes into crops, giving them the ability to withstand pest attacks without the need for chemical pesticides. This not only improves crop yields but also reduces the environmental footprint of farming practices.

Government initiatives and policies supporting the adoption of pest resistant crops are also propelling market growth. Many countries have implemented favorable regulations and provided subsidies to encourage farmers to adopt genetically modified crops. For instance, the United States Department of Agriculture (USDA) and the European Food Safety Authority (EFSA) have established guidelines to ensure the safe use of GMOs, thereby promoting their acceptance among farmers. Additionally, public-private partnerships and collaborations between research institutions and agricultural companies are fostering innovation in crop protection technologies.

From a regional perspective, the Asia Pacific region is expected to witness significant growth in the pest resistant crops market. Countries like China, India, and Japan are investing heavily in agricultural biotechnology to enhance food security and reduce the dependence on chemical pesticides. North America, with its advanced agricultural infrastructure and favorable regulatory environment, is also a major market for pest resistant crops. Europe, despite its stringent regulations on GMOs, is gradually embracing biotechnology to address food security challenges. Latin America and the Middle East & Africa are also exhibiting growing interest in pest resistant crops due to their potential to boost agricultural productivity and mitigate pest-related losses.

Crop Type Analysis

The pest resistant crops market is segmented by crop type into corn, soybean, cotton, rice, and others. Corn holds a dominant position in this segment due to its extensive cultivation and high susceptibility to pests. The adoption of genetically modified corn varieties with pest resistance traits has significantly improved yields and reduced crop losses. The United States, being one of the largest producers of corn, has extensively adopted these genetically modified varieties, leading to enhanced productivity and profitability for farmers. Moreover, developing countries in Asia and Latin America are increasingly adopting pest resistant corn to address food security challenges.

Soybean is another major crop type in the pest resistant crops market. The increasing demand for soy-based products, such as oil and animal feed, has driven the adoption of pest resistant soybean varieties. Genetically modified soybeans with herbicide tolerance and insect resistance traits have gained popularity among farmers due to their ability to withstand pest attacks and reduce the need for chemical herbicides. The United States, Brazil, and Argentina are the leading producers of genetically modified soybeans, contributing significantly to the growth of this segment.

Cotton, which

The Grapes Value Chain Analysis Market size was valued at USD 207.31 Million in 2023 and is projected to reach USD 357.60 Million by 2032, exhibiting a CAGR of 8.10 % during the forecast periods. This growth is attributed to the increasing demand for hybrid seeds, government initiatives to promote grape cultivation, rising food security concerns, and technological advancements in the industry. Hybrid seeds offer numerous benefits, including improved yield, disease resistance, and adaptability to different climatic conditions, making them a popular choice among farmers. Government initiatives in countries like India and the United States provide farmers financial assistance and technical support, encouraging them to adopt modern farming practices and cultivate high-quality grapes. Moreover, rising food security concerns and population growth are driving the demand for grapes as a nutritious and affordable food source. Technological advancements in irrigation systems, pest management, and harvesting techniques are further enhancing productivity and reducing production costs, contributing to the growth of the market. Recent developments include: October 2022: A University of Minnesota-led team of researchers received the first round of funding from a $10 million grant awarded by the U.S. Department of Agriculture (USDA) to follow up on their work with VitisGen2, a multi-disciplinary, collaborative project focused on cultivating disease-resistant grapes that can be grown sustainably with reduced pesticide and fossil fuel use., March 2022: Two newly released grape varieties developed collaboratively between Cornell AgriTech and Sun World International, a global fruit genetics and licensing company, offer new consumer flavors and better-growing characteristics for farmers., February 2021: Agricultural Products Export Development Authority (APEDA) adopted the next-generation Blockchain and Cloud migration-enabled GrapeNet System to ensure a secure, scalable, and cost-effective interface for all the stakeholders in the export value chain. GrapeNet is a web-based certification and traceability software for monitoring fresh grapes exported from India to the European Union. APEDA can trace details of the consignment right up to the farm plot level. After the integration of Blockchain, GrapeNet will be more secure.. Key drivers for this market are: Rising Consumption of Cashew Nuts in the Country, Favorable Government Initiatives. Potential restraints include: Hazardous Climatic Condition Hinders Cashew Production, Stringent Regulations Related to Food Quality Standards. Notable trends are: Increased Production Due to Rise in Consumer Demand and Awareness.

Aquaponic and Hydroponic System Market Outlook

The global aquaponic and hydroponic system market size is projected to grow from USD 1.2 billion in 2023 to an impressive USD 3.5 billion by 2032, registering a robust CAGR of 12.7% during the forecast period. This significant growth is driven by an increasing demand for sustainable agricultural practices, urban farming initiatives, and advancements in technology that make these systems more accessible and efficient.

One of the primary growth factors for the aquaponic and hydroponic system market is the increasing awareness of sustainable and eco-friendly farming methods. As traditional agricultural practices come under scrutiny for their environmental impact, aquaponic and hydroponic systems offer a viable alternative. They use significantly less water, reduce the need for chemical fertilizers, and can be implemented in urban environments, making them an attractive option for both commercial and residential growers. The rising global population and the need for food security further amplify the demand for these innovative farming solutions.

Technological advancements play a crucial role in the growth of this market. Innovations in grow lights, nutrient solutions, and automated systems have made aquaponic and hydroponic farming more efficient and scalable. For instance, the development of LED grow lights that can mimic natural sunlight has significantly improved crop yields. Additionally, automated systems that monitor and adjust pH levels, nutrient concentrations, and water quality are making it easier for farmers to manage these systems, even with minimal technical know-how. The integration of the Internet of Things (IoT) and data analytics in these systems is also expected to drive market growth by providing real-time insights and optimizing resource utilization.

The market is also benefiting from government initiatives and funding aimed at promoting sustainable agriculture. Various countries are providing subsidies and grants to encourage the adoption of aquaponic and hydroponic systems. For example, the European Union has been actively supporting urban farming projects through its Horizon 2020 program, which aims to promote research and innovation in sustainable agriculture. Similarly, the United States Department of Agriculture (USDA) offers various grants and funding opportunities aimed at supporting innovative farming practices. These initiatives are expected to further boost market growth during the forecast period.

Hydroponic Living Walls are becoming an increasingly popular feature in urban environments, offering both aesthetic and environmental benefits. These vertical gardens utilize hydroponic systems to grow plants on walls, providing a unique solution for space-constrained areas. By incorporating Hydroponic Living Walls, buildings can improve air quality, reduce noise pollution, and enhance the overall aesthetic appeal. This innovative approach not only contributes to urban greening but also supports biodiversity by creating habitats for various plant species. As cities continue to grow, the integration of Hydroponic Living Walls is expected to play a crucial role in sustainable urban development, aligning with the broader trends in the aquaponic and hydroponic system market.

Regionally, the market exhibits varying growth patterns. North America leads the global market, driven by technological advancements and high adoption rates in the United States and Canada. The Asia Pacific region is expected to witness the fastest growth, fueled by rapid urbanization, increasing disposable incomes, and a growing awareness of sustainable farming practices. Europe also shows significant potential, particularly in countries like the Netherlands, which are pioneers in innovative agricultural techniques. On the other hand, regions like Latin America and the Middle East & Africa are gradually catching up, thanks to increasing investments and growing awareness of the benefits of these systems.

System Type Analysis

The aquaponic and hydroponic system market is segmented by system type into Nutrient Film Technique (NFT), Deep Water Culture (DWC), Media Filled Grow Beds, Ebb and Flow, and Others. Each system type offers unique advantages and is suited to different applications, making the choice of system an important consideration for growers.

The Nutrient Film Technique (NFT) is one of the most popular hydroponic systems due to its simplicity and e

{"definition": "Percentage of households with children in which children were food insecure, by State. Households with food-insecure children were unable, at times during the year, to provide adequate food for one or more child because the household lacked money and other resources for food. For most of these households, inadequacy was in quality and variety of foods; for about one in ten, amounts of food provided were also inadequate.", "availableYears": "2003-2011 (aggregate data)", "name": "Child food insecurity (% households, multiple-year average), 2003-11*", "units": "Percent", "shortName": "FOODINSEC_CHILD_03_11", "geographicLevel": "State", "dataSources": "Data are from an annual survey conducted by the U.S. Census Bureau as a supplement to the monthly Current Population Survey. USDA sponsors the annual survey, and USDA's Economic Research Service (ERS) compiles and analyzes the responses. The surveys were of representative samples of the U.S. civilian population and included between 15,000 and 18,000 households with children each year. (However, about a fourth of the sample in the 2007 survey was not used for food security estimates because a proposed wording change tested in those households did not perform adequately.) The survey is conducted both by telephone and in person so that households with no telephone are not underrepresented. The food security survey asked one adult respondent in each household a series of questions about experiences and behaviors that indicate food insecurity. The food security status of children in the household was assessed by responses to a subset of questions about the conditions and experiences of children. For more information on the methodology, see Coleman-Jensen, Alisha, William McFall and Mark Nord. Food Insecurity in Households With Children: Prevalence, Severity, and Household Characteristics, 2010-11, EIB 113, USDA/ERS, May 2013 (Table 3), (http://www.ers.usda.gov/publications/eib-economic-information-bulletin/eib113.aspx). Note: margins of error are substantial for some States; comparisons between States should take into consideration margins of error published in the source report."}

© FOODINSEC_CHILD_03_11 This layer is sourced from gis.ers.usda.gov.