The average per capita food consumption in the Gulf Cooperation Council in 2027 is expected to be about *** kilograms. This is a roughly 32-kilogram increase per person from 2022. Overall, food consumption per capital in the GCC is expected to rise steadily each year during this period. GCC food consumption  Historically, the per capita volume of food consumed among GCC member countries (******) has varied. Saudi Arabia, having the largest population in the GCC, makes up the greatest share of total food consumption in the council. Still, it was on the lower end of per capita consumption. Food consumption growth projections in the region differ noticeably from country to country. With growing populations and developing, and diversifying economies, food consumption is only expected to rise in the coming years. GCC food market The GCC food market revenue is worth billions each year. Although local food production is growing, most GCC member states rely on food imports to fulfill consumer demand. The distribution of food imports covers every category of food products. Additionally, multinational food conglomerates and a wide variety of Western restaurant chains have increased their footprint in the GCC. Nestled between Europe, Asia, and Africa, the GCC is well-connected and has prime access to most of the world’s fresh food supply. Nevertheless, there is also a push to be more self-sufficient. Countries like Oman, which has a strong agricultural and fishing industry, and Saudi Arabia, which has been scaling its indigenous agriculture industry, have done well in this regard. Further initiatives, such as the United Arab Emirates' cooperation with research centers in the Netherlands, are also producing promising results in innovative farming.

The FAO indices of agricultural production show the relative level of the aggregate volume of agricultural production for each year in comparison with the base period 1999-2001. They are based on the sum of price-weighted quantities of different agricultural commodities produced after deductions of quantities used as seed and feed weighted in a similar manner. The resulting aggregate represents, therefore, disposable production for any use except as seed and feed. The commodities covered in the computation of indices of agricultural production are all crops and livestock products originating in each country. Practically all products are covered, with the main exception of fodder crops.

Net Production Index Number (PIN) base 1999-2001

Presents Net Production (Production - Feed - Seed) indices. All indices are calculated by the Laspeyres formula. Net production quantities of each commodity are weighted by 1999-2001average international commodity prices and summed for each year. To obtain the index, the aggregate for a given year is divided by the average aggregate for the base period 1999-2001. Indices are calculated from net production data presented on a calendar year basis.

This dataset is the basis for the International Food Security Assessment, 2016-2026 released in June 2016. This annual ERS report projects food availability and access for 76 low- and middle-income countries over a 10-year period. 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 record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Excel file listing For complete information, please visit https://data.gov.

US: Cereal Yield: per Hectare data was reported at 8,142.900 kg/ha in 2016. This records an increase from the previous number of 7,430.600 kg/ha for 2015. US: Cereal Yield: per Hectare data is updated yearly, averaging 4,576.200 kg/ha from Dec 1961 (Median) to 2016, with 56 observations. The data reached an all-time high of 8,142.900 kg/ha in 2016 and a record low of 2,522.300 kg/ha in 1961. US: Cereal Yield: per Hectare data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Agricultural Production and Consumption. Cereal yield, measured as kilograms per hectare of harvested land, includes wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed grains. Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded. The FAO allocates production data to the calendar year in which the bulk of the harvest took place. Most of a crop harvested near the end of a year will be used in the following year.; ; Food and Agriculture Organization, electronic files and web site.; Weighted average;

Kazakhstan Agricultural Production: Crops: All Enterprises: Food Melons data was reported at 2,070.900 Ton th in 2016. This records a decrease from the previous number of 2,087.600 Ton th for 2015. Kazakhstan Agricultural Production: Crops: All Enterprises: Food Melons data is updated yearly, averaging 675.400 Ton th from Dec 1995 (Median) to 2016, with 22 observations. The data reached an all-time high of 2,087.600 Ton th in 2015 and a record low of 162.000 Ton th in 1995. Kazakhstan Agricultural Production: Crops: All Enterprises: Food Melons data remains active status in CEIC and is reported by The Agency of Statistics of the Republic of Kazakhstan. The data is categorized under Global Database’s Kazakhstan – Table KZ.B013: Agriculture Production (Annual).

In 2023, the percentage change in average daily production of the food products manufacturing industry in the Netherlands was modeled to be -0.7 percent. Between 2005 and 2023, the figure dropped by 5.1 percentage points, though the decline followed an uneven course rather than a steady trajectory.

AbstractResearch into the origins of food plants has led to the recognition that specific geographical regions around the world have been of particular importance to the development of agricultural crops. Yet the relative contributions of these different regions in the context of current food systems have not been quantified. Here we determine the origins (‘primary regions of diversity’) of the crops comprising the food supplies and agricultural production of countries worldwide. We estimate the degree to which countries use crops from regions of diversity other than their own (‘foreign crops’), and quantify changes in this usage over the past 50 years. Countries are highly interconnected with regard to primary regions of diversity of the crops they cultivate and/or consume. Foreign crops are extensively used in food supplies (68.7% of national food supplies as a global mean are derived from foreign crops) and production systems (69.3% of crops grown are foreign). Foreign crop usage has increased significantly over the past 50 years, including in countries with high indigenous crop diversity. The results provide a novel perspective on the ongoing globalization of food systems worldwide, and bolster evidence for the importance of international collaboration on genetic resource conservation and exchange. Usage notesTableS1_crops_regions_tableTable S1. Crop commodities assessed in food supplies and agricultural production systems analyses and their primary regions of diversity. Taxonomy follows GRIN (2015) [25].TableS2_countries_regions_tableTable S2. Countries assessed in food supplies and agricultural production systems analyses and their associated regions.TableS3_regionalcomposition_tocountriesTable S3. Importance of primary regions of diversity of agricultural crops in contribution to national food supplies [as measured in contribution of crops to calories (kcal/capita/day), protein (g/capita/day), fat (g/capita/day), and food weight (g/capita/day)] and national agricultural production [production quantity (tonnes), harvested area (ha), and production value (million US$)], averaged over years 2009-2011. Importance was estimated by grouping the contribution of consumed/produced crops by their primary regions of diversity. As some crops pertain to more than one primary region of diversity, total values across all primary regions per country is not equivalent to total per capita food supply/ total agricultural production values per country. Percentages provide a comparison of the relative importance of primary regions in contribution to the food supply/national production of each country.TableS4_regionalcomposition_toregions_2009-2011Table S4. Importance of primary regions of diversity of agricultural crops in contribution to regional food supplies [as measured in contribution of crops to calories (kcal/capita/day), protein (g/capita/day), fat (g/capita/day), and food weight (g/capita/day),] and total regional agricultural production [production quantity (tonnes), harvested area (ha), and production value (million US$)], averaged over years 2009-2011. Regional food supplies values (kcal or g, /capita/day) were formed by deriving a population-weighted average of national food supplies values across countries comprising each region. Regional production values were formed by summing national production values across countries comprising each region. Importance was estimated by grouping the contribution of consumed/produced crops by their primary regions of diversity. As some crops pertain to more than one primary region of diversity, total values across all primary regions per consuming/producing region is not equivalent to total per capita food supply/ total agricultural production values per consuming/producing region. Percentages provide a comparison of the relative importance of primary regions in contribution to the food supply/total production of each region.TableS5_cropcomposition_ofregionsTable S5. Crop commodity composition of regional food supplies [as measured in contribution of crops to calories (kcal/capita/day), protein (g/capita/day), fat (g/capita/day), and food weight (g/capita/day),] and total regional agricultural production [production quantity (tonnes), harvested area (ha), and production value (million US$)], averaged over years 2009-2011. Regional food supplies values (kcal or g, /capita/day) were formed by deriving a population-weighted average of national food supplies values across countries comprising each region. Regional production values were formed by summing national production values across countries comprising each region.TableS6_util_foreign_2009-2011Table S6. Estimated percent use of foreign crops in current national food supplies and agricultural production systems. Data includes the raw mean minimum and maximum use values across years 2009-2011 per country, and the mean value between minimum and maximum per country across these years, as well as modeled mean values and...

Number of Businesses statistics on the Baking Mix & Prepared Food Production industry in the US

The ERS Food Expenditure Series annually measures total U.S. food expenditures, including purchases by consumers, governments, businesses, and nonprofit organizations. The ERS Food Expenditure Series contributes to the analysis of U.S. food production and consumption by constructing a comprehensive measure of the total value of all food expenditures by final purchasers. This series annually measures total U.S. food expenditures, including purchases by consumers, governments, businesses, and nonprofit organizations. Because the term expenditure is often associated with household decisionmaking, it is important to recognize that ERS's series also includes nonhousehold purchases. For example, the series includes the dollar value of domestic food purchases by military personnel and their dependents at military commissary stores and exchanges, the value of commodities and food dollars donated by the Federal government to schools, and the value of food purchased by airlines for serving during flights.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Web page with links to Excel files For complete information, please visit https://data.gov.

Agricultural production in the Great Plains provides a significant amount of food for the United States while contributing greatly to farm income in the region. However, recurrent droughts and expansion of crop production are increasing irrigation demand, leading to extensive pumping and attendant depletion of the Ogallala aquifer. In order to optimize water use, increase the sustainability of agricultural production, and identify best management practices, identification of food–water conflict hotspots in the Ogallala Aquifer Region (OAR) is necessary. We used satellite remote sensing time series of agricultural production (net primary production, NPP) and total water storage (TWS) to identify hotspots of food–water conflicts within the OAR and possible reasons behind these conflicts. Mean annual NPP (2001–2018) maps clearly showed intrusion of high NPP, aided by irrigation, into regions of historically low NPP (due to precipitation and temperature). Intrusion is particularly acute in the northern portion of OAR, where mean annual TWS (2002–2020) is high. The Oklahoma panhandle and Texas showed large decreasing TWS trends, which indicate the negative effects of current water demand for crop production on TWS. Nebraska demonstrated an increasing TWS trend even with a significant increase of NPP. A regional analysis of NPP and TWS can convey important information on current and potential conflicts in the food–water nexus and facilitate sustainable solutions. Methods developed in this study are relevant to other water-constrained agricultural production regions

Abstract

The Annual Agricultural Sample Survey (AASS) for the year 2022/23 aimed to enhance the understanding of agricultural activities across Tanzania by collecting comprehensive data on various aspects of the agricultural sector. This survey is crucial for policy formulation, development planning, and service delivery, providing reliable data to monitor and evaluate national and international development frameworks.

The 2022/23 survey is particularly significant as it informs the monitoring and evaluation of key agricultural development strategies and frameworks. The collected data will contribute to the Tanzania Development Vision 2025, Zanzibar Development Vision 2020, the Five-Year Development Plan 2021/22–2025/26, the National Strategy for Growth and Reduction of Poverty (NSGRP) known as MKUKUTA, and the Zanzibar Strategy for Growth and Reduction of Poverty (ZSGRP) known as MKUZA. The survey data also supports the evaluation of Sustainable Development Goals (SDGs) and Comprehensive Africa Agriculture Development Programme (CAADP). Key indicators for agricultural performance and poverty monitoring are directly measured from the survey data.

The 2022/23 AASS provides a detailed descriptive analysis and related tables on the main thematic areas. These areas include household members and holder identification, field roster, seasonal plot and crop rosters (Vuli, Masika, and Dry Season), permanent crop production, crop harvest use, seed and seedling acquisition, input use and acquisition (fertilizers and pesticides), livestock inventory and changes, livestock production costs, milk and eggs production, other livestock products, aquaculture production, and labor dynamics. The 2022/23 AASS offers an extensive dataset essential for understanding the current state of agriculture in Tanzania. The insights gained will support the development of policies and interventions aimed at enhancing agricultural productivity, sustainability, and the livelihoods of farming communities. This data is indispensable for stakeholders addressing challenges in the agricultural sector and promoting sustainable agricultural development.

STATISTICAL DISCLOSURE CONTROL (SDC) METHODS HAVE BEEN APPLIED TO THE MICRODATA, TO PROTECT THE CONFIDENTIALITY OF THE INDIVIDUAL DATA COLLECTED. USERS MUST BE AWARE THAT THESE ANONYMIZATION OR SDC METHODS MODIFY THE DATA, INCLUDING SUPPRESSION OF SOME DATA POINTS. THIS AFFECTS THE AGREGATED VALUES DERIVED FROM THE ANONYMIZED MICRODATA, AND MAY HAVE OTHER UNWANTED CONSEQUENCES, SUCH AS SAMPLING ERROR AND BIAS. ADDITIONAL DETAILS ABOUT THE SDC METHODS AND DATA ACESS CONDITIONS ARE PROVIDED IN THE DATA PROCESSING AND DATA ACESS CONDITIONS BELOW.

Geographic coverage

National, Mainland Tanzania and Zanzibar, Regions

Analysis unit

Households for Smallholder Farmers and Farm for Large Scale Farms

Universe

The survey covered agricultural households and large-scale farms.

Agricultural households are those that meet one or more of the following two conditions: a) Have or operate at least 25 square meters of arable land, b) Own or keep at least one head of cattle or five goats/sheep/pigs or fifty chicken/ducks/turkeys during the agriculture year.

Large-scale farms are those farms with at least 20 hectares of cultivated land, or 50 herds of cattle, or 100 goats/sheep/pigs, or 1,000 chickens. In addition to this, they should fulfill all of the following four conditions: i) The greater part of the produce should go to the market, ii) Operation of farm should be continuous, iii) There should be application of machinery / implements on the farm, and iv) There should be at least one permanent employee.

Kind of data

Sample survey data [ssd]

Sampling procedure

The frame used to extract the sample for the Annual Agricultural Sample Survey (AASS-2022/23) in Tanzania was derived from the 2022 Population and Housing Census (PHC-2022) Frame that lists all the Enumeration Areas (EAs/Hamlets) of the country. The AASS 2022/23 used a stratified two-stage sampling design which allows to produce reliable estimates at regional level for both Mainland Tanzania and Zanzibar.

In the first stage, the EAs (primary sampling units) were stratified into 2-3 strata within each region and then selected by using a systematic sampling procedure with probability proportional to size (PPS), where the measure of size is the number of agricultural households in the EA. Before the selection, within each stratum and domain (region), the Enumeration Areas (EAs) were ordered according to the codes of District and Council which reflect the geographical proximity, and then ordered according to the codes of Constituency, Division, Wards, and Village. An implicit stratification was also performed, ordering by Urban/Rural type at Ward level.

In the second stage, a simple random sampling selection was conducted . In hamlets with more than 200 households, twelve (12) agricultural households were drawn from the PHC 2022 list with a simple random sampling without replacement procedure in each sampled hamlet. In hamlets with 200 households or less, a listing exercise was carried out in each sampled hamlet, and twelve (12) agricultural households were selected with a simple random sampling without replacement procedure. A total of 1,352 PSUs were selected from the 2022 Population and Housing Census frame, of which 1,234 PSUs were from Mainland Tanzania and 118 from Zanzibar. A total number of 16,224 agricultural households were sampled (14,808 households from Mainland Tanzania and 1,416 from Zanzibar).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The 2022/23 Annual Agricultural Survey used two main questionnaires consolidated into a single questionnaire within the CAPIthe CAPI System, Smallholder Farmers and Large-Scale Farms Questionnaire. Smallholder Farmers questionnaire captured information at household level while Large Scale Farms questionnaire captured information at establishment/holding level. These questionnaires were used for data collection that covered core agricultural activities (crops, livestock, and fish farming) in both short and long rainy seasons. The 2022/23 AASS questionnaire covered 23 sections which are:

1. COVER; The cover page included the title of the survey, survey year (2022/23), general instructions for both the interviewers and respondents. It sets the context for the survey and also it shows the survey covers the United Republic of Tanzania.

2. SCREENING: Included preliminary questions designed to determine if the respondent or household is eligible to participate in the survey. It checks for core criteria such as involvement in agricultural activities.

3. START INTERVIEW: The introductory section where basic details about the interview are recorded, such as the date, location, and interviewer’s information. This helped in the identification and tracking of the interview process.

4. HOUSEHOLD MEMBERS AND HOLDER IDENTIFICATION: Collected information about all household members, including age, gender, relationship to the household head, and the identification of the main agricultural holder. This section helped in understanding the demographic composition of the agriculture household.

5. FIELD ROSTER: Provided the details of the various agricultural fields operated by the agriculture household. Information includes the size, location, and identification of each field. This section provided a comprehensive overview of the land resources available to the household.

6. VULI PLOT ROSTER: Focused on plots used during the Vuli season (short rainy season). It includes details on the crops planted, plot sizes, and any specific characteristics of these plots. This helps in assessing seasonal agricultural activities.

7. VULI CROP ROSTER: Provided detailed information on the types of crops grown during the Vuli season, including quantities produced and intended use (e.g., consumption, sale, storage). This section captures the output of short rainy season farming.

8. MASIKA PLOT ROSTER: Similar to Section 4 but focuses on the Masika season (long rainy season). It collects data on plot usage, crop types, and sizes. This helps in understanding the agricultural practices during the primary growing season.

9. MASIKA CROP ROSTER: Provided detailed information on crops grown during the Masika season, including production quantities and uses. This section captures the output from the main agricultural season.

10. PERMANENT CROP PRODUCTION: Focuses on perennial or permanent crops (e.g., fruit trees, tea, coffee). It includes data on the types of permanent crops, area under cultivation, production volumes, and uses. This section tracks long-term agricultural investments.

11. CROP HARVEST USE: In this, provided the details how harvested crops are utilized within the household. Categories included consumption, sale, storage, and other uses. This section helps in understanding food security and market engagement.

12. SEED AND SEEDLINGS ACQUISITION: Collected information on how the agriculture household acquires seeds and seedlings, including sources (e.g., purchased, saved, gifted) and types (local, improved, etc). This section provided insights into input supply chains and planting decisions based on the households, or head.

13. INPUT USE AND ACQUISITION (FERTILIZERS AND PESTICIDES): It provided the details of the use and acquisition of agricultural inputs such as fertilizers and pesticides. It included information on quantities used, sources, and types of inputs. This section assessed the input dependency and agricultural practices.

14. LIVESTOCK IN STOCK AND CHANGE IN STOCK: The questionnaire recorded the

Abstract

Ethiopian farming largely produces only enough food for the peasant holder and his family for consumption, leaving little to sell. This inadequate volume of production is ascribed to the tardy progress in the farming methods and scattered pieces of land holdings. Under this traditional sector, agriculture is practiced on public land and most of the produce is mainly for own consumption. The diverse climate of the country and the multiple utilizations of crops have prompted the vast majority of agricultural holders to grow various temporary and permanent crops. Despite the variation in the volume of production, the relative importance and pattern of growth of these crops are largely similar across many of the regions.

There is a general agreement that the performance of an agricultural system should achieve a steady supply of food to the people of a country. But, unless special attention is focused on agriculture, its performance can be impeded by vagaries of nature, population growth and scarcity and fragmentation of land, thus, affecting food supply and posing a challenge to the federal and regional governments. This situation calls for an overhaul of the agricultural system in the country or the regions.

In order to have a flourishing agriculture, which sustains reliable food supply, the federal and regional governments have to formulate and implement farm programs that ensure food security. The preparation, execution, monitoring and assessment of these programs entail statistics on agriculture particularly crop production since it is the prime target that national or regional agricultural policies aim at.

The collection of data on crop production should encompass all crop seasons in the agricultural calendar and farming activities in both rural and urban areas. It should also include the wide range of crops that are grown and embodied in the food security system, which are indispensable for a sustained provision of staple diet and other cash crops like coffee and Chat.

In view of this, crop production data for private peasant holdings for both “Meher” and “Belg” seasons in both rural and urban areas were collected in the census to provide the basis for decision making in the process of implementing timely food security measures and to make policy makers aware of the food situation in the country.

Geographic coverage

The 2001-2002 (1994 E.C) Agricultural Sample Enumeration was designed to cover the rural and urban parts of all districts (weredas) in the country on a large-scale sample basis excluding the pastoralist areas of the Afar and Somali regional states.

Analysis unit

Household/ Holder/ Crop

Universe

Agricultural households

Kind of data

Census/enumeration data [cen]

Sampling procedure

Sampling Frame The list of enumeration areas for each wereda was compiled from the 1994 Ethiopian Population and Housing Census cartographic work and was used a frame for the selection of the Primary Sampling Units (PSU). The 1994 Population and Housing Census enumeration area maps of the region for the selected sample EA's were updated, and the EA boundaries and descriptions were further clarified to reflect the current physical situation. The sampling frame used for the selection of ultimate sampling units (agricultural households) was a fresh list of households, which was prepared by the enumerator assigned in the sampled EA's using a prescribed listing instruction at the beginning of the launching of the census enumeration.

Sample Design In order to meet the objectives and requirements of the EASE, a stratified two-stage cluster sample design was used for the selection of ultimate sampling units. Thus, in the regions each wereda was treated as stratum for which major findings of the sample census are reported. The primary sampling units are the enumeration areas and the agricultural households are secondary (ultimate) sampling units. Finally, after the selection of the sample agricultural households, the various census forms were administered to all agricultural holders within the sampled agricultural households.

For the private peasant holdings in the rural areas a fixed number (25) of sample EA's in each wereda and 30 agricultural households in each EA were randomly selected (determined). In urban areas, weredas with urban EA's of less than or equal to 25, all the EA's were covered. However, for weredas with greater than 25 urban EA's, sample size of 25 EA's was selected. In each sampled urban EA, 30 agricultural households were randomly selected for the census. The sampled size determination in each wereda and thereby in each EA was based upon the required precision level of the major estimates and the cost consideration. The pilot survey and the previous year annual agricultural sample survey results were used to determine the required sample sizes per wereda.

Sample Selection of Primary Sampling Units Within each wereda (stratum) in the region, the selection of EAs was carried out using probability proportional to size systematic sampling. In this case, size being total number of agricultural households in each EA obtained from the listing exercise undertaken in the 1994 Ethiopian Population and Housing Census of the region.

Listing of Households and Selection of Agricultural Households In each sampled enumeration area of the region, a complete and fresh listing of households was carried out by canvassing the households in the EA. After a complete listing of the households and screening of the agricultural households during the listing operation in the selected EA, the agricultural households were serially numbered. From this list, a total of 30 agricultural households were selected systematically using a random start from the pre-assigned column table of random numbers. The sampling interval for each EA was determined by dividing the total number of agricultural households by 30. For crop cutting exercise purposes (rural domain) a total of 20 agricultural households were randomly selected from the 30 sampled agricultural households. The systematical random sampling technique was employed in this case, because its application is simple and flexible, and it can easily yield a proportionate sample.

Mode of data collection

Face-to-face [f2f]

Research instrument

Forms and equipment are instrumental in gathering information from various sources. The census forms are the vehicle and basic document for collecting the desired data. These include general-purpose forms covering farm management practices, demographic and economic characteristics, area, and production of both temporary and permanent crops; livestock, poultry and beehives ... etc. These forms are formulated for recording data generated through interview as well as objective measurements. Although the planning, organization and execution of the census were the responsibilities that rested within the CSA, development of the census forms was a tedious task that involved the formation of a working group composed of members of government and non-governmental organizations who are major users of agricultural data. Members of the working group were given the opportunity to identify their data requirements, define the needs of others and determine the specific questions that the forms should contain. The working group included the staff of the organizations that are involved in agricultural planning, collection of agricultural statistics and the use of data within the agricultural sector. The working group designed different forms for the various data items on crop area, production, and other variables of interest to meet the needs of current data users and also considered the future expectations. Attempt was made to make the content of the forms of acceptable length by distributing the variables to be collected in the different census forms. The rural census questionnaires/forms included: - Forms 94/0 and 94/1 that are used to record all households in the enumeration area, identify the agricultural households and select the units to be covered by the census. - Form 94/2 is developed to list all the members of the sampled agricultural households and record the demographic and economic characteristics of each of the members. - Forms 94/3A, 94/3B, 94/3C and 94/3D are prepared to enumerate crop data through interview and objective measurement. - Form 94/5 is designed to record crop area data via the physical or objective measurement of crop fields. - Form 94/6 is used to list all the fields under crop and select a crop field for each type of crop randomly for crop cutting exercise. - Forms 94/7A, 94/7B, and 94/7C are developed for recording yield data on cereals, oil seeds, pulses, vegetables root crops and permanent crops by weighing their yields obtained from sub-plots and/or trees selected for crop-cuttings. - Form 94/8 is prepared to enumerate livestock, poultry and beehives data by type, age, sex and purpose including products through interview (subjective approach). - Forms 94/9, 94/10 and 94/11 are used to collect data on crop and livestock product usage; miscellaneous items and farm tools, implements, draught animals and storage facilities, in that order, by interviewing the sample holders.

“Belg” season questionnaires identified as: - Form 94/12A and 94/12B that are used to record data on farm management practices of the “Belg” season. - Form 94/4 was the questionnaire used for collecting data on crop production forecast for 2001-2002 and the data collected using this form was published in December 2001 subjectively, while 94/12C is for recording “Belg” season crop area through objective measurement and volume of production through

The MENA Agriculture Market report segments the industry into Food Crops/Cereals, Fruits, Vegetables, and Oilseeds/Non-Food Crops. The report includes Production Analysis, Consumption Analysis by Value and Volume, Import Analysis by Value and Volume, Export Analysis by Value and Volume, and Price Trend Analysis. Five years of historical data and forecasts are covered.

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

BM: Production Index: 2014-2016: Crop data was reported at 101.650 2014-2016=100 in 2018. This records an increase from the previous number of 100.680 2014-2016=100 for 2017. BM: Production Index: 2014-2016: Crop data is updated yearly, averaging 77.080 2014-2016=100 from Dec 1961 (Median) to 2018, with 58 observations. The data reached an all-time high of 103.470 2014-2016=100 in 2012 and a record low of 42.550 2014-2016=100 in 1972. BM: Production Index: 2014-2016: Crop data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bermuda – Table BM.World Bank.WDI: Agricultural Production Index. Crop production index shows agricultural production for each year relative to the base period 2014-2016. It includes all crops except fodder crops. Regional and income group aggregates for the FAO's production indexes are calculated from the underlying values in international dollars, normalized to the base period 2014-2016.;Food and Agriculture Organization, electronic files and web site.;Weighted average;

Goal 2End hunger, achieve food security and improved nutrition and promote sustainable agricultureTarget 2.1: By 2030, end hunger and ensure access by all people, in particular the poor and people in vulnerable situations, including infants, to safe, nutritious and sufficient food all year roundIndicator 2.1.1: Prevalence of undernourishmentSN_ITK_DEFC: Prevalence of undernourishment (%)SN_ITK_DEFCN: Number of undernourish people (millions)Indicator 2.1.2: Prevalence of moderate or severe food insecurity in the population, based on the Food Insecurity Experience Scale (FIES)AG_PRD_FIESMS: Prevalence of moderate or severe food insecurity in the adult population (%)AG_PRD_FIESMSN: Total population in moderate or severe food insecurity (thousands of people)AG_PRD_FIESS: Prevalence of severe food insecurity in the adult population (%)AG_PRD_FIESSN: Total population in severe food insecurity (thousands of people)Target 2.2: By 2030, end all forms of malnutrition, including achieving, by 2025, the internationally agreed targets on stunting and wasting in children under 5 years of age, and address the nutritional needs of adolescent girls, pregnant and lactating women and older personsIndicator 2.2.1: Prevalence of stunting (height for age SH_STA_STNT: Proportion of children moderately or severely stunted (%)SH_STA_STNTN: Children moderately or severely stunted (thousands)+2 or SH_STA_WAST: Proportion of children moderately or severely wasted (%)SH_STA_WASTN: Children moderately or severely wasted (thousands)SN_STA_OVWGT: Proportion of children moderately or severely overweight (%)SN_STA_OVWGTN: Children moderately or severely overweight (thousands)Indicator 2.2.3: Prevalence of anaemia in women aged 15 to 49 years, by pregnancy status (percentage)SH_STA_ANEM: Proportion of women aged 15-49 years with anaemia (%)SH_STA_ANEM_PREG: Proportion of women aged 15-49 years with anaemia, pregnant (%)SH_STA_ANEM_NPRG: Proportion of women aged 15-49 years with anaemia, non-pregnant (%)Target 2.3: By 2030, double the agricultural productivity and incomes of small-scale food producers, in particular women, indigenous peoples, family farmers, pastoralists and fishers, including through secure and equal access to land, other productive resources and inputs, knowledge, financial services, markets and opportunities for value addition and non-farm employmentIndicator 2.3.1: Volume of production per labour unit by classes of farming/pastoral/forestry enterprise sizePD_AGR_SSFP: Productivity of small-scale food producers (agricultural output per labour day, PPP) (constant 2011 international $)PD_AGR_LSFP: Productivity of large-scale food producers (agricultural output per labour day, PPP) (constant 2011 international $)Indicator 2.3.2: Average income of small-scale food producers, by sex and indigenous statusSI_AGR_SSFP: Average income of small-scale food producers, PPP (constant 2011 international $)SI_AGR_LSFP: Average income of large-scale food producers, PPP (constant 2011 international $)Target 2.4: By 2030, ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production, that help maintain ecosystems, that strengthen capacity for adaptation to climate change, extreme weather, drought, flooding and other disasters and that progressively improve land and soil qualityIndicator 2.4.1: Proportion of agricultural area under productive and sustainable agricultureTarget 2.5: By 2020, maintain the genetic diversity of seeds, cultivated plants and farmed and domesticated animals and their related wild species, including through soundly managed and diversified seed and plant banks at the national, regional and international levels, and promote access to and fair and equitable sharing of benefits arising from the utilization of genetic resources and associated traditional knowledge, as internationally agreedIndicator 2.5.1: Number of plant and animal genetic resources for food and agriculture secured in either medium- or long-term conservation facilitiesER_GRF_ANIMRCNTN: Number of local breeds for which sufficient genetic resources are stored for reconstitutionER_GRF_PLNTSTOR: Plant breeds for which sufficient genetic resources are stored (number)Indicator 2.5.2: Proportion of local breeds classified as being at risk of extinctionER_RSK_LBREDS: Proportion of local breeds classified as being at risk as a share of local breeds with known level of extinction risk (%)Target 2.a: Increase investment, including through enhanced international cooperation, in rural infrastructure, agricultural research and extension services, technology development and plant and livestock gene banks in order to enhance agricultural productive capacity in developing countries, in particular least developed countriesIndicator 2.a.1: The agriculture orientation index for government expendituresAG_PRD_ORTIND: Agriculture orientation index for government expendituresAG_PRD_AGVAS: Agriculture value added share of GDP (%)AG_XPD_AGSGB: Agriculture share of Government Expenditure (%)Indicator 2.a.2: Total official flows (official development assistance plus other official flows) to the agriculture sectorDC_TOF_AGRL: Total official flows (disbursements) for agriculture, by recipient countries (millions of constant 2018 United States dollars)Target 2.b: Correct and prevent trade restrictions and distortions in world agricultural markets, including through the parallel elimination of all forms of agricultural export subsidies and all export measures with equivalent effect, in accordance with the mandate of the Doha Development RoundIndicator 2.b.1: Agricultural export subsidiesAG_PRD_XSUBDY: Agricultural export subsidies (millions of current United States dollars)Target 2.c: Adopt measures to ensure the proper functioning of food commodity markets and their derivatives and facilitate timely access to market information, including on food reserves, in order to help limit extreme food price volatilityIndicator 2.c.1: Indicator of food price anomaliesAG_FPA_COMM: Indicator of Food Price Anomalies (IFPA), by type of productAG_FPA_CFPI: Consumer Food Price IndexAG_FPA_HMFP: Proportion of countries recording abnormally high or moderately high food prices, according to the Indicator of Food Price Anomalies (%)

This dataset comprises spatial and temporal data related to our analysis on blue and green water consumption (WC) of global crop production in high spatial resolution (5 arc-minutes – approximately 10 km at the equator) for the years 2020, 2010 and 2000.

Modelling water consumption of SPAM data

We use SPAM (Spatial Production Allocation Model) data, released by the International Food Policy research Institute (IFPRI). We use SPAM2020 data for the year 2020 (46 crops), SPAM2010 data for the year 2010 (42 crops) and SPAM2000 data for the year 2000 (20 crops).

We develop a Python-based global gridded crop green and blue WC assessment tool, entitled CropGBWater. Operating on a daily time scale, CropGBWater dynamically simulates rootzone water balance and related fluxes. We provide this model open access in the related Nature Food paper.

SPAM2020 crop data are modelled for the years 2018-2022, SPAM2010 crop data for the years 2008-2012 and SPAM2000 crop data for the years 1998-2002. We compute WCbl (blue WC) and WCgn (green WC), with components WCgn,irr (green WC of irrigated area) and WCgn,rf (green WC of rainfed area)

File description:

The data-set consists of the following files:

• Data_S4: Data_S4_Y2020_WC_m3_gridded.zip
  Folder with 46 individual crop grid files (5arc min resolution, with x & y coordinates), monthly and annual WCbl, WCgn,irr and WCgn,rf values in m3 in csv format, year 2020. Individual crop GIS-Rasters for annual m3 amounts are provided as Data_S17
• Data_S5: Data_S5_YR2020_WC_mm_gridded_csv
  Folder with 46 individual crop grid files (5arc min resolution, with x & y coordinates), monthly and annual WCbl, WCgn,irr and WCgn, rf in mm as well as SPAM harvested area values in csv format, year 2020. Individual crop GIS-Rasters for annual mm amounts are provided as Data_S18
• Data_S6: Data_S6_YR2020_WC_gridded_individual-crops-m3_annual.xlsx
  One grid file (5arc min resolution, with x & y coordinates) with annual WCbl, WCgn,irr and WCgn, rf values in m3, differentiating between individual crops, year 2020.
• Data_S7: Data_S7_YR2020_WC_gridded_sum-of-crops-m3_monthly-annual.csv
  One grid file (5arc min resolution, with x & y coordinates) with monthly and annual WCbl, WCgn,irr and WCgn, rf values in m3, for the sum of all crops, year 2020
• Data_S8: Data_S8_YR2000_WC_mm_m3_gridded.zip
  Grid (5arc min resolution, with x & y coordinates) with annual WCbl, WCgn,irr and WCgn, rf values in mm and m3, as well as SPAM harvested area amounts, for each crop, year 2000
• Data_S9: Data_S9_YR2010_WC_mm_m3_gridded.zip
  Grid (5arc min resolution, with x & y coordinates) with annual WCbl, WCgn,irr and WCgn, rf values in mm and m3, as well as SPAM harvested area amounts, for each crop, year 2010
• Data_S11: Data_S11_YR2020_WC-mm_Rice_RiceAtlas.xls
  Grid (5arc min resolution, with x & y coordinates) with monthly and annual WCbl, WCgn,irr and WCgn, rf values in mm, for rice, year 2020, RiceAtlas calendar
• Data_S12: Data_S12_INPUT_YR2020_cropcalendars.zip
  Modelling INPUT data for year 2020: Crop calendars
• Data_S13: Data_S13_INPUT_YR2020_ET0.zip
  Modelling INPUT data for year 2020: daily ET0
• Data_S14: Data_S14_INPUT_YR2020_Precip.zip
  Modelling INPUT data for year 2020: daily Precipitation
• Data_S15: Data_S15_INPUT_YR2020_Soil.zip
  Modelling INPUT data for year 2020: Soil
• Data_S16: Data_S16_INPUT_YR2020_Climate-SPAM-grid_&_Scripts.zip
  Modelling INPUT for year 2020: Climate-SPAM-grid and different processing scripts
• Data_S17: Data_S17_Y2020_WC_m3_GisRasters
  GIS-Rasters of individual crops for year 2020 (as well as the sum of all crops), values in m3 per year, differentiation between WCbl, WCgn,irr and WCgn
• Data_S18: Data_S18_Y2020_WC_mm_GisRasters
  GIS-Rasters of individual crops for year 2020, values in mm per year, differentiation between WCbl, WCgn,irr and WCgn

Publication:

For all details, please refer to the related open access paper Chukalla et al (2025) in Nature Food

Funding:

This research, led by IWMI, a CGIAR centre, was carried out under the CGIAR Initiative on Foresight (www.cgiar.org/initiative/foresight/) as well as the CGIAR “Policy innovations” Science Program (www.cgiar.org/cgiar-research-porfolio-2025-2030/policy-innovations). The authors would like to thank all funders who supported this research through their contributions to the CGIAR Trust Fund (www.cgiar.org/funders).

Cellular Agriculture Market Outlook

The global cellular agriculture market size was valued at approximately USD 0.6 billion in 2023 and is projected to reach around USD 2.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 17.5% from 2024 to 2032. This robust growth is driven by advancements in biotechnology and increasing consumer demand for sustainable and ethical food production methods. The market's expansion is underpinned by a confluence of factors, including technological innovations, rising environmental concerns, and a shift towards more sustainable agricultural practices.

One of the primary growth factors for the cellular agriculture market is the increasing awareness and concern about the environmental impact of traditional livestock farming. Conventional meat production is a significant contributor to greenhouse gas emissions, deforestation, and water pollution. Cellular agriculture, which involves producing animal products such as meat and dairy in a lab setting, offers a more sustainable alternative. This method significantly reduces the ecological footprint of food production, addressing major environmental issues and driving market growth.

Another major growth factor is the ethical considerations surrounding animal welfare. Traditional livestock farming often involves practices that are harmful to animals, leading to a growing number of consumers seeking cruelty-free alternatives. Cellular agriculture provides a solution by enabling the production of animal products without harming animals. This shift in consumer preferences is fueling demand for lab-grown meat, dairy, and other products, thus propelling the market forward.

Technological advancements in cellular agriculture are also playing a crucial role in market growth. Innovations in biotechnology, such as improved cell culturing techniques, bioreactor designs, and plant-based scaffolding systems, are enhancing the efficiency and scalability of lab-grown food production. These advancements are making cellular agriculture more economically viable and enabling the production of a wider variety of products, further boosting market growth. Additionally, significant investments from both private and public sectors are accelerating research and development in this field.

Regionally, North America is expected to be a major player in the cellular agriculture market, driven by a combination of technological innovation, favorable regulatory environments, and strong consumer demand for sustainable food products. Europe is also anticipated to show significant growth due to stringent environmental regulations and a growing focus on sustainability. The Asia Pacific region, with its large population and increasing disposable incomes, presents substantial growth opportunities, particularly in countries like China and Japan. Latin America and the Middle East & Africa, although currently smaller markets, are also expected to experience growth as awareness and technological adoption increase.

Product Type Analysis

The cellular agriculture market is segmented into various product types, including cultured meat, dairy, eggs, gelatin, and others. Cultured meat, also known as lab-grown or cell-based meat, is one of the most prominent segments. This segment is expected to witness significant growth due to increasing consumer demand for sustainable and cruelty-free meat alternatives. Advances in tissue engineering and bioreactor technologies are making it possible to produce cultured meat at a lower cost, thereby making it more accessible to a broader audience. Moreover, collaborations between food tech startups and traditional meat companies are accelerating the commercialization of cultured meat products.

Dairy products produced through cellular agriculture are another important segment. This includes lab-grown milk, cheese, and yogurt. The demand for these products is driven by a growing number of consumers who are lactose intolerant, vegan, or concerned about the environmental impact of dairy farming. Cellular agriculture offers a way to produce dairy products without the need for cows, thus eliminating issues related to animal welfare and reducing greenhouse gas emissions. Technological advancements in microbial fermentation and precision fermentation are key drivers for this segment, enabling the production of high-quality dairy alternatives.

The eggs segment in cellular agriculture is also gaining traction. Lab-grown eggs can be produced without using chickens, addressing concerns related to animal welfare and reducing the environmental impact