Quick Stats is the National Agricultural Statistics Service's (NASS) online, self-service tool to access complete results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. The census collects data on all commodities produced on U.S. farms and ranches, as well as detailed information on expenses, income, and operator characteristics. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production.

Abstract

NASC is an exercise designed to fill the existing data gap in the agricultural landscape in Nigeria. It is a comprehensive enumeration of all agricultural activities in the country, including crop production, fisheries, forestry, and livestock activities. The implementation of NASC was done in two phases, the first being the Listing Phase, and the second is the Sample Survey Phase. Under the first phase, enumerators visited all the selected Enumeration Areas (EAs) across the Local Government Areas (LGAs) and listed all the farming households in the selected enumeration areas and collected the required information. The scope of information collected under this phase includes demographic details of the holders, type of agricultural activity (crop production, fishery, poultry, or livestock), the type of produce or product (for example: rice, maize, sorghum, chicken, or cow), and the details of the contact persons. The listing exercise was conducted concurrently with the administration of a Community Questionnaire, to gather information about the general views of the communities on the agricultural and non-agricultural activities through focus group discussions.

The main objective of the listing exercise is to collect information on agricultural activities at household level in order to provide a comprehensive frame for agricultural surveys. The main objective of the community questionnaire is to obtain information about the perceptions of the community members on the agricultural and non-agricultural activities in the community.

Additional objectives of the overall NASC program include the following: · To provide data to help the government at different levels in formulating policies on agriculture aimed at attaining food security and poverty alleviation · To provide data for the proposed Gross Domestic Product (GDP) rebasing

Geographic coverage

Estimation domains are administrative areas from which reliable estimates are expected. The sample size planned for the extended listing operation allowed reporting key structural agricultural statistics at Local Government Area (LGA) level.

Analysis unit

Agricultural Households.

Universe

Population units of this operation are households with members practicing agricultural activities on their own account (farming households). However, all households in selected EAs were observed as much as possible to ensure a complete coverage of farming households.

Kind of data

Census/enumeration data [cen]

Sampling procedure

An advanced methodology was adopted in the conduct of the listing exercise. For the first time in Nigeria, the entire listing was conducted digitally. NBS secured newly demarcated digitized enumeration area (EA) maps from the National Population Commission (NPC) and utilized them for the listing exercise. This newly carved out maps served as a basis for the segmentation of the areas visited for listing exercise. With these maps, the process for identifying the boundaries of the enumeration areas by the enumerators was seamless.

The census was carried out in all the 36 States of the Federation and FCT. Forty (40) enumeration Areas (EAs) were selected to be canvassed in each LGA, the number of EAs covered varied by state, which is a function of the number of LGAs in the state. Both urban and rural EAs were canvassed. Out of 774 LGAs in the country, 767 LGAs were covered and the remaining 7 LGAs (4 in Imo and 3 in Borno States) were not covered due to insecurity (99% coverage). In all, thirty thousand, nine hundred and sixty (30,960) EAs were expected to be covered nationwide but 30,546 EAs were canvassed.

The Sampling method adopted involved three levels of stratification. The objective of this was to provide representative data on every Local Government Area (LGA) in Nigeria. Thus, the LGA became the primary reporting domain for the NASC and the first level of stratification. Within each LGA, eighty (80) EAs were systematically selected and stratified into urban and rural EAs, which then formed the second level of stratification, with the 80 EAs proportionally allocated to urban and rural according to the total share of urban/rural EAs within the LGA. These 80 EAs formed the master sample from which the main NASC sample was selected. From the 80 EAs selected across all the LGAs, 40 EAs were systematically selected per LGA to be canvassed. This additional level selection of EAs was again stratified across urban and rural areas with a target allocation of 30 rural and 10 urban EAs in each LGA. The remaining 40 EAs in each LGA from the master sample were set aside for replacement purposes in case there would be need for any inaccessible EA to be replaced.

Details of sampling procedure implemented in the NASC (LISTING COMPONENT). A stratified two-phase cluster sampling method was used. The sampling frame was stratified by urban/rural criteria in each LGA (estimation domain/analytical stratum).

First phase: in each LGA, a total sample of 80 EAs were allocated in each strata (urban/rural) proportionally to their number of EAs with reallocations as need be. In each stratum, the sample was selected with a Pareto probability proportional to size considering the number of households as measure of size.

Second phase: systematic subsampling of 40 EAs was done (10 in Urban and 30 in Rural with reallocations as needed, if there were fewer than 10 Urban or 30 Rural EAs in an LGA). This phase was implicitly stratified through sorting the first phase sample by geography.

With a total of 773 LGAs covered in the frame, the total planned sample size was 30920 EAs. However, during fieldwork 2 LGAs were unable to be covered due to insecurity and additional 4 LGAs were suspended early due to insecurity. For the same reason, replacements of some sampled EAs were needed in many LGAs. The teams were advised to select replacement units where possible considering appurtenance to the same stratum and similarity including in terms of population size. However about 609 EAs replacement units were selected from a different stratum and were discarded from data processing and reporting.

Sampling deviation

Out of 774 LGAs in the country, 767 LGAs were covered and the remaining 7 LGAs (4 in Imo and 3 in Borno states) were not covered due to insecurity (99% coverage).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The NASC household listing questionnaire served as a meticulously designed instrument administered within every household to gather comprehensive data. It encompassed various aspects such as household demographics, agricultural activities including crops, livestock (including poultry), fisheries, and ownership of agricultural/non-agricultural enterprises.

The questionnaire was structured into the following sections:

Section 0: ADMINISTRATIVE IDENTIFICATION Section 1: BUILDING LISTING Section 2: HOUSEHOLD LISTING (Administered to the Head of Household or any knowledgeable adult member aged 15 years and above).

Cleaning operations

Data processing of the NASC household listing survey included checking for inconsistencies, incompleteness, and outliers. Data editing and cleaning was carried out electronically using the Stata software package. In some cases where data inconsistencies were found a call back to the household was carried out. A pre-analysis tabulation plan was developed and the final tables for publication were created using the Stata software package.

Sampling error estimates

Given the complexity of the sample design, sampling errors were estimated through re-sampling approaches (Bootstrap/Jackknife)

The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by the USDA National Agricultural Statistics Service (NASS). It allows you to customize your query by commodity, location, or time period. You can then visualize the data on a map, manipulate and export the results as an output file compatible for updating databases and spreadsheets, or save a link for future use. Quick Stats contains official published aggregate estimates related to U.S. agricultural production. County level data are also available via Quick Stats. The data include the total crops and cropping practices for each county, and breakouts for irrigated and non-irrigated practices for many crops, for selected States. The database allows custom extracts based on commodity, year, and selected counties within a State, or all counties in one or more States. The county data includes totals for the Agricultural Statistics Districts (county groupings) and the State. The download data files contain planted and harvested area, yield per acre and production. NASS develops these estimates from data collected through:

hundreds of sample surveys conducted each year covering virtually every aspect of U.S. agriculture

the Census of Agriculture conducted every five years providing state- and county-level aggregates Resources in this dataset:Resource Title: Quick Stats database. File Name: Web Page, url: https://quickstats.nass.usda.gov/ Dynamic drill-down filtered search by Commodity, Location, and Date range, beginning with Census or Survey data. Filter lists are refreshed based upon user choice allowing the user to fine-tune the search.

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

Quick Stats API is the programmatic interface to the National Agricultural Statistics Service's (NASS) online database containing results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. The census collects data on all commodities produced on U.S. farms and ranches, as well as detailed information on expenses, income, and operator characteristics. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production.

Farm Crops: Yield: Grain Crops: Beans: Red Bean: Zhejiang data was reported at 8.300 Ton th in 2006. This records a decrease from the previous number of 8.900 Ton th for 2005. Farm Crops: Yield: Grain Crops: Beans: Red Bean: Zhejiang data is updated yearly, averaging 7.400 Ton th from Dec 2002 (Median) to 2006, with 5 observations. The data reached an all-time high of 8.900 Ton th in 2005 and a record low of 5.800 Ton th in 2003. Farm Crops: Yield: Grain Crops: Beans: Red Bean: Zhejiang data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Agriculture Sector – Table CN.RIB: Yield of Farm Crops: Grain: By Region and Crop Variety.

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

Abstract

For more than 150 years, the U.S. Department of Commerce, Bureau of the Census, conducted the census of agriculture. However, the 2002 Appropriations Act transferred the responsibility from the Bureau of the Census to the U.S. Department of Agriculture (USDA), National Agricultural Statistics Service (NASS). The 2007 Census of Agriculture for the U.S. Virgin Islands is the second census in the U.S. Virgin Islands conducted by NASS. The census of agriculture is taken to obtain agricultural statistics for each county, State (including territories and protectorates), and the Nation. The first U.S. agricultural census data were collected in 1840 as a part of the sixth decennial census. From 1840 to 1920, an agricultural census was taken as a part of each decennial census. Since 1920, a separate national agricultural census has been taken every 5 years. The 2007 census is the 14th census of agriculture of the U.S. Virgin Islands. The first, taken in 1920, was a special census authorized by the Secretary of Commerce. The next agriculture census was taken in 1930 in conjunction with the decennial census, a practice that continued every 10 years through 1960. The 1964 Census of Agriculture was the first quinquennial (5-year) census to be taken in the U.S. Virgin Islands. In 1976, Congress authorized the census of agriculture to be taken for 1978 and 1982 to adjust the data-reference year to coincide with the 1982 Economic Censuses covering manufacturing, mining, construction, retail trade, wholesale trade, service industries, and selected transportation activities. After 1982, the agriculture census reverted to a 5-year cycle. Data in this publication are for the calendar year 2007, and inventory data reflect what was on hand on December 31, 2007. This is the same reference period used in the 2002 census. Prior to the 2002 census, data was collected in the summer for the previous 12 months, with inventory items counted as what was on hand as of July 1 of the year the data collection was done.

Objectives: The census of agriculture is the leading source of statistics about the U.S. Virgin Islands’s agricultural production and the only source of consistent, comparable data at the island level. Census statistics are used to measure agricultural production and to identify trends in an ever changing agricultural sector. Many local programs use census data as a benchmark for designing and evaluating surveys. Private industry uses census statistics to provide a more effective production and distribution system for the agricultural community.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit was a farm, defined as "any place from which USD 500 or more of agricultural products were produced and sold, or normally would had been sold, during the calendar year 2007". According to the census definition, a farm is essentially an operating unit, not an ownership tract. All land operated or managed by one person or partnership represents one farm. In the case of tenants, the land assigned to each tenant is considered a separate farm, even though the landlord may consider the entire landholding to be one unit rather than several separate units.

Kind of data

Census/enumeration data [cen]

Sampling procedure

(a) Method of Enumeration As in the previous censuses of the U.S. Virgin Islands, a direct enumeration procedure was used in the 2007 Census of Agriculture. Enumeration was based on a list of farm operators compiled by the U.S. Virgin Islands Department of Agriculture. This list was compiled with the help of the USDA Farm Services Agency located in St. Croix. The statistics in this report were collected from farm operators beginning in January of 2003. Each enumerator was assigned a list of individuals or farm operations from a master enumeration list. The enumerators contacted persons or operations on their list and completed a census report form for all farm operations. If the person on the list was not operating a farm, the enumerator recorded whether the land had been sold or rented to someone else and was still being used for agriculture. If land was sold or rented out, the enumerator got the name of the new operator and contacted that person to ensure that he or she was included in the census.

(b) Frame The census frame consisted of a list of farm operators compiled by the U.S. Virgin Islands DA. This list was compiled with the help of the USDA Farm Services Agency, located in St. Croix.

(c) Complete and/or sample enumeration methods The census was a complete enumeration of all farm operators registered in the list compiled by the United States of America in the CA 2007.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire (report form) for the CA 2007 was prepared by NASS, in cooperation with the DA of the U.S. Virgin Islands. Only one questionnaire was used for data collection covering topics on:

• Land owned
• Land use
• Irrigation
• Conservation programs and crop insurance
• Field crops
• Bananas, coffee, pineapples and plantain crops
• Hay and forage crops
• Nursery, Greenhouse, Floriculture, Sod and tree seedlings
• Vegetables and melons
• Hydroponic crops
• Fruit
• Root crops
• Cattle and calves
• Poultry
• Hogs and pigs
• Aquaculture
• Other animals and livestock products
• Value of sales
• Organic agriculture
• Federal and commonwealth agricultural program payments
• Income from farm-related sources
• Production expenses
• Farm labour
• Fertilizer and chemicals applied
• Market value of land and buildings
• Machinery, equipment and buildings
• Practices
• Type of organization
• Operator characteristics

The questionnaire of the 2007 CA covered 12 of the 16 core items' recommended for the WCA 2010 round.

Cleaning operations

DATA PROCESSING The processing of the 2007 Census of Agriculture for the U.S. Virgin Islands was done in St. Croix. Each report form was reviewed and coded prior to data keying. Report forms not meeting the census farm definition were voided. The remaining report forms were examined for clarity and completeness. Reporting errors in units of measures, illegible entries, and misplaced entries were corrected. After all the report forms had been reviewed and coded, the data were keyed and subjected to a thorough computer edit. The edit performed comprehensive checks for consistency and reasonableness, corrected erroneous or inconsistent data, supplied missing data based on similar farms, and assigned farm classification codes necessary for tabulating the data. All substantial changes to the data generated by the computer edits were reviewed and verified by analysts. Inconsistencies identified, but not corrected by the computer, were reviewed, corrected, and keyed to a correction file. The corrected data were then tabulated by the computer and reviewed by analysts. Prior to publication, tabulated totals were reviewed by analysts to identify inconsistencies and potential coverage problems. Comparisons were made with previous census data, as well as other available data. The computer system provided the capability to review up-to-date tallies of all selected data items for various sets of criteria which included, but were not limited to, geographic levels, farm types, and sales levels. Data were examined for each set of criteria and any inconsistencies or potential problems were then researched by examining individual data records contributing to the tabulated total. W hen necessary, data inconsistencies were resolved by making corrections to individual data records.

Sampling error estimates

The accuracy of these tabulated data is determined by the joint effects of the various nonsampling errors. No direct measures of these effects have been obtained; however, precautionary steps were taken in all phases of data collection, processing, and tabulation of the data in an effort to minimize the effects of nonsampling errors.

Farm Crops: Grain: Corn: Yield: Heilongjiang data was reported at 43,789.561 Ton th in 2023. This records an increase from the previous number of 40,384.243 Ton th for 2022. Farm Crops: Grain: Corn: Yield: Heilongjiang data is updated yearly, averaging 6,220.000 Ton th from Dec 1949 (Median) to 2023, with 75 observations. The data reached an all-time high of 43,789.561 Ton th in 2023 and a record low of 1,285.000 Ton th in 1959. Farm Crops: Grain: Corn: Yield: Heilongjiang data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Agriculture Sector – Table CN.RIB: Yield of Farm Crops: Grain: By Region and Crop Variety.

Farm Crops: Fruits: Yield: Beijing data was reported at 417.656 Ton th in 2023. This records an increase from the previous number of 383.336 Ton th for 2022. Farm Crops: Fruits: Yield: Beijing data is updated yearly, averaging 618.000 Ton th from Dec 1989 (Median) to 2023, with 33 observations. The data reached an all-time high of 1,248.907 Ton th in 2007 and a record low of 256.589 Ton th in 1989. Farm Crops: Fruits: Yield: Beijing data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Agriculture Sector – Table CN.RIB: Yield of Farm Crops: Fruits: By Region.

Farm Crops: Fruits: Yield: Xinjiang data was reported at 16,726.000 Ton th in 2022. This records an increase from the previous number of 16,595.121 Ton th for 2021. Farm Crops: Fruits: Yield: Xinjiang data is updated yearly, averaging 5,299.269 Ton th from Dec 1989 to 2022, with 34 observations. The data reached an all-time high of 16,726.000 Ton th in 2022 and a record low of 732.751 Ton th in 1989. Farm Crops: Fruits: Yield: Xinjiang data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Agriculture Sector – Table CN.RIB: Yield of Farm Crops: Fruits.

Graph and download economic data for Employment for Agriculture, Forestry, Fishing and Hunting: Crop Production (NAICS 111) in the United States (IPUAN111W201000000) from 1988 to 2023 about crop, hunting, forestry, fishing, agriculture, NAICS, IP, production, employment, and USA.

Farm Crops: Yield: Grain Crops: Beans: Mung: Fujian data was reported at 2.000 Ton th in 2023. This records a decrease from the previous number of 2.300 Ton th for 2022. Farm Crops: Yield: Grain Crops: Beans: Mung: Fujian data is updated yearly, averaging 2.400 Ton th from Dec 2002 (Median) to 2023, with 22 observations. The data reached an all-time high of 64.500 Ton th in 2005 and a record low of 1.200 Ton th in 2016. Farm Crops: Yield: Grain Crops: Beans: Mung: Fujian data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Agriculture Sector – Table CN.RIB: Yield of Farm Crops: Grain: By Region and Crop Variety.

Graph and download economic data for Wheat Crop for United States (A01009USA391NNBR) from 1866 to 1952 about crop, wheat, production, and USA.

Farm Crops: Yield: Sugar Crops: Henan data was reported at 79.628 Ton th in 2023. This records a decrease from the previous number of 84.400 Ton th for 2022. Farm Crops: Yield: Sugar Crops: Henan data is updated yearly, averaging 103.600 Ton th from Dec 1950 (Median) to 2023, with 73 observations. The data reached an all-time high of 325.700 Ton th in 2000 and a record low of 5.900 Ton th in 1961. Farm Crops: Yield: Sugar Crops: Henan data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Agriculture Sector – Table CN.RIB: Yield of Farm Crops: Sugar Crops: By Region.

Farm Crops: Yield: Oil Bearing Crops: Benne: Jiangsu data was reported at 0.200 Ton th in 2001. This records a decrease from the previous number of 0.700 Ton th for 2000. Farm Crops: Yield: Oil Bearing Crops: Benne: Jiangsu data is updated yearly, averaging 0.450 Ton th from Dec 2000 (Median) to 2001, with 2 observations. The data reached an all-time high of 0.700 Ton th in 2000 and a record low of 0.200 Ton th in 2001. Farm Crops: Yield: Oil Bearing Crops: Benne: Jiangsu data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Agriculture Sector – Table CN.RIB: Yield of Farm Crops: Oil Bearing: By Region.

Farm Crops: Fiber Crops: Yield: Hubei data was reported at 7.697 Ton th in 2023. This records a decrease from the previous number of 8.652 Ton th for 2022. Farm Crops: Fiber Crops: Yield: Hubei data is updated yearly, averaging 51.000 Ton th from Dec 1971 (Median) to 2023, with 53 observations. The data reached an all-time high of 603.060 Ton th in 1985 and a record low of 2.446 Ton th in 2016. Farm Crops: Fiber Crops: Yield: Hubei data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Agriculture Sector – Table CN.RIB: Yield of Farm Crops: Fiber: By Region.

Farm Crops: Beans: Yield: Jiangsu data was reported at 797.906 Ton th in 2023. This records an increase from the previous number of 732.660 Ton th for 2022. Farm Crops: Beans: Yield: Jiangsu data is updated yearly, averaging 816.300 Ton th from Dec 1989 (Median) to 2023, with 35 observations. The data reached an all-time high of 1,059.000 Ton th in 2002 and a record low of 269.000 Ton th in 1991. Farm Crops: Beans: Yield: Jiangsu data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Agriculture Sector – Table CN.RIB: Yield of Farm Crops: Grain: By Region and Crop Variety.

Farm Crops: Yield: Sugar Crops: Yunnan data was reported at 15,868.934 Ton th in 2023. This records an increase from the previous number of 15,537.000 Ton th for 2022. Farm Crops: Yield: Sugar Crops: Yunnan data is updated yearly, averaging 5,298.500 Ton th from Dec 1949 (Median) to 2023, with 75 observations. The data reached an all-time high of 19,506.700 Ton th in 2013 and a record low of 245.400 Ton th in 1950. Farm Crops: Yield: Sugar Crops: Yunnan data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Agriculture Sector – Table CN.RIB: Yield of Farm Crops: Sugar Crops: By Region.

Farm Crops: Yield: Grain Crops: Cereal: Millet: Henan data was reported at 92.000 Ton th in 2023. This records a decrease from the previous number of 97.400 Ton th for 2022. Farm Crops: Yield: Grain Crops: Cereal: Millet: Henan data is updated yearly, averaging 146.850 Ton th from Dec 1978 (Median) to 2023, with 46 observations. The data reached an all-time high of 520.000 Ton th in 1983 and a record low of 41.000 Ton th in 2014. Farm Crops: Yield: Grain Crops: Cereal: Millet: Henan data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Agriculture Sector – Table CN.RIB: Yield of Farm Crops: Grain: By Region and Crop Variety.