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.

Structure of the farming industry including crop areas, yields and production, livestock populations, detailed labour force figures and diversification activity for England and the United Kingdom. Selected results for England are produced at a regional and county level as well as other local geographies. This data is available from the June Survey of Agriculture page at: https://data.gov.uk/dataset/june_survey_of_agriculture_and_horticulture_england

Source agency: Environment, Food and Rural Affairs Designation: National Statistics Language: English

If you require the datasets in a more accessible format, please contact farming-statistics@defra.gsi.gov.uk

This dataset consists of ecology data from 16 paired field sites; each pair consisting of an organic and conventional farm. A multiscale sampling design was employed to assess the impact of (i) location-within-field (field margin vs. edge vs. centre), (ii) crop type (arable cereal vs. permanent pasture), (iii) farm management (organic vs. conventional) and (iv) landscape-scale management (landscapes that contained low or high fractions of organic land) on a wide range of taxa. Studied taxa include birds, insect pollinators (hoverflies, bumblebees and solitary bees), epigeal arthropods, aphids and their natural enemies, earthworms and plants. The study is part of the NERC Rural Economy and Land Use (RELU) programme. A move to organic farming can have significant effects on wildlife, soil and water quality, as well as changing the ways in which food is supplied, the economics of farm business and indeed the attitudes of farmers themselves. Two key questions were addressed in the SCALE project: what causes organic farms to be arranged in clusters at local, regional and national scales, rather than be spread more evenly throughout the landscape; and how do the ecological, hydrological, socio-economic and cultural impacts of organic farming vary due to neighbourhood effects at a variety of scales. The research was undertaken in 2006-2007 in two study sites: one in the English Midlands, and one in southern England. Both are sites in which organic farming has a 'strong' local presence, which we defined as 10 per cent or more organically managed land within a 10 km radius. Potential organic farms were identified through membership lists of organic farmers provided by two certification bodies (the Soil Association and the Organic Farmers and Growers). Most who were currently farming (i.e. their listing was not out of date) agreed to participate. Conventional farms were identified through telephone listings. Respondents' farms ranged in size from 40 to 3000 acres, with the majority farming between 100 and 1000 acres. Most were mixed crop-livestock farmers, with dairy most common in the southern site, and beef and/or sheep mixed with arable in the Midlands. In total, 48 farms were studied, of which 21 were organic farmers. No respondent had converted from organic to conventional production, whereas 17 had converted from conventional to organic farming. Twelve of the conventional farmers defined themselves as practicing low input agriculture. Farmer interview data from this study are available at the UK Data Archive under study number 6761. Soil data from agricultural land under differing crop and management regimes,are also available. Further documentation for this study may be found through the RELU Knowledge Portal and the project's ESRC funding award web page (see online resources).

Abstract copyright UK Data Service and data collection copyright owner.

The overall aim of the research was to assess the influence of changes in agricultural policy and regulation on those farms which, during the productivist era, their operators had successfully navigated to a position of dominance in the industry. Four specific objectives arose from this broad aim, which sought to examine large-scale commercial farming overall and to track development and succession on individual matched farms:
to investigate how farms, which were relatively large in 1977, subsequently developed during the period of agricultural policy adjustment in the 1980s/1990s;
to examine the dynamics of agricultural production systems in relation to the development of large-scale farm businesses between 1941 and the late 1990s;
to investigate the opinions and responses of these large-scale farmers to agricultural policy adjustment with the intention of establishing their level of engagement with post-productivity policy measures;
to contribute to the development of large-scale farm businesses in the light of the post-productivist transition model of farm business adjustment and agri-environmental change.
The data file for 1978 and 1981 for this study contains a subset of the variables from part of the dataset 'Labour and Machinery on Large Arable Farms, 1978-1980', held at the UK Data Archive under SN:1872.
Main Topics:

The dataset comprises a series of files relating to large-scale farms in the South East of England, which are defined by reference to their total area as either 182 hectares or more in 1941 and/or 300 hectares or more in 1978. One data file comprises a transcription of mainly quantitative elements of the National Farm Survey records of 1941-1943, and includes information relating to farm area, crop and livestock production, labour and machinery usage and an assessment of the physical and managerial condition of the farm. The data file for the 1978 and 1981 surveys cover a similar range of information relating to farm area, crop and livestock production, and labour and machinery usage, though the 1981 survey is more limited. The data file for the most recent survey, carried out between November 1998 and April 1999, also includes a similar range of agricultural variables together with information about farmer involvement with a range of non-agricultural activities and other contemporary issues affecting agriculture. These cover government conservation schemes, diversification, contracting, succession and use of information technology. The 1998-1999 survey includes both factual and attitudinal responses.

Provides information on how farm practices are affected by environmental issues and to assess the impact of agriculture on the environment. Source agency: Environment, Food and Rural Affairs Designation: National Statistics Language: English Alternative title: Farm Practices

Farm Practices Survey October 2014

Survey methodology
The results provided here are based on responses from approximately 2 500 holdings. Holdings were targeted by farm type and size to ensure a representative sample.

To be included in the sample, holdings had to be considered commercial as detailed below:

Thresholds for the EU Farm Structure Survey
Characteristics Threshold
Utilised agricultural area Arable land, kitchen gardens, permanent grassland, permanent crops >5 ha
Permanent outdoor crops Fruit, berry, citrus and olive plantations, vineyards and nurseries >1 ha
Outdoor intensive production Hops >0.5 ha
Tobacco >0.5 ha
Cotton >0.5 ha
Fresh vegetables, melons and strawberries, which are outdoors or under low (not accessible) protective cover >0.5 ha
Crops under glass or other (accessible) protective cover Fresh vegetables, melons and strawberries >0.1 ha
Flowers and ornamental plants (excluding nurseries) >0.1 ha
Bovine animals All >10 Head
Pigs All >50 Head
Breeding sows >10 Head
Sheep All >20 Head
Goats All >20 Head
Poultry All >1,000 Head
Hardy nursery stock >1 ha
Mushrooms All mushroom holdings to be included >0

Farm size
The farm size bands used within these detailed results tables are shown below. Standard Labour Requirement (SLR) is defined as the theoretical numbers of workers required each year to run a holding, based on its cropping and livestock activities.

Farm size Definition
Small <3 SLR Medium >=3 and <5 SLR Large >= 5 SLR

For more details of survey methodology and analysis please refer to the Survey details section within the Statistical Release on the Farm Practices Survey landing page:

https://www.gov.uk/government/collections/farm-practices-survey

Farm Practices Survey February 2015 - greenhouse gas mitigation practices There are historical datasets not included.

Survey methodology
The latest results provided here are based on approximately 2 600 responses to the 2015 survey. Holdings were targeted by farm type and size to ensure a representative sample.

Thresholds were applied to ensure that very small holdings with little agricultural activity were not included in the survey. To be included in the sample, holdings had to have at least 50 cattle, 100 sheep, 100 pigs, 1,000 poultry or 20 hectares of arable crops or orchards. Therefore results are national estimates that reflect only on the 60 thousand holdings that exceed these thresholds.

For more details of survey methodology and analysis please refer to the Survey details section within the Statistical Release on the Farm Practices Survey webpage:

https://www.gov.uk/government/collections/farm-practices-survey

Farm size
The farm size bands used within these detailed results tables are shown below. Standard Labour Requirement (SLR) is defined as the theoretical numbers of workers required each year to run a holding, based on its cropping and livestock activities.

    Farm size  Definition     
    Small  <2 SLR     
    Medium >=2 and <3 SLR     
    Large  >= 3 SLR      

Farmed area
The farmed area here is defined as the total area on agricultural holdings on 1 June. This includes all arable and horticultural crops, temporary and permanent grassland, sole right rough grazing, woodland, land used for outdoor pigs and uncropped arable land. The farmed area used in the latest nutrient management section is taken from the 2014 June Survey of Agriculture and Horticuture.

Data revisions
In 2013 the 2012 results by farm size were reproduced for some sections - marked with a footnote. This was due to some holdings previously being classified into the incorrect size band. Although the number of responses used in each size band has changed the changes seen in the survey results have not been statistically significant unless indicated in each table. No revisions have occurred since.

If you require the datasets in a more accessible format, please contact farming-statistics@defra.gsi.gov.uk

The June survey of Agriculture and Horticulture is an annual survey which collects detailed information on arable and horticultural cropping activities, land usage, livestock populations and labour force figures. Source agency: Environment, Food and Rural Affairs Designation: National Statistics Language: English Alternative title: June Survey of Agriculture and Horticulture, UK If you require the datasets in a more accessible format, please contact farming-statistics@defra.gsi.gov.uk

Provides estimates of pig numbers in the UK from the Agricultural and Horticultural Surveys.

Source agency: Environment, Food and Rural Affairs Designation: National Statistics Language: English Alternative title: June Survey of Agriculture and Horticulture, UK: Results for Pigs

This release now forms part of the June Survey of Agriculture and Horticulture, UK which is available: https://data.gov.uk/dataset/june_survey_of_agriculture_and_horticulture_uk

Survey name: Herne Bay, Frogs Island Farm Post 1988 Agricultural Land Classification (ALC) site survey data – scanned original paper maps and survey reports for individual sites surveyed in detail between 1989 and 1999 by the Ministry of Agriculture Fisheries and Food. Where Grade 3 is mapped this includes the subdivision of Grade 3 into subgrades 3a and 3b. Surveys use the current grading methodology as described in "Agricultural Land Classification of England and Wales," a link for which is provided with the data. Individual sites have been mapped at varying scales and level of detail from 1:5,000 to 1:50,000 (typically 1:10,000). Unedited sample point soils data and soil pit descriptions are also available for some surveys. Attribution statement: © Natural England copyright. Contains Ordnance Survey data © Crown copyright and database right [year].

Abstract copyright UK Data Service and data collection copyright owner.The aims of this study were to: identify the range of factors operating in the tied cottage system and the way these interact; to obtain first-hand information from farmers and farm workers about the satisfactory and unsatisfactory features of the system; to assess the value of the tied cottage in both socio-economic and narrowly economic terms.

Survey name: Edmondsley, Broomy Holm Farm

Post 1988 Agricultural Land Classification (ALC) site survey data - scanned original paper maps and survey reports for individual sites surveyed in detail between 1989 and 1999 by the Ministry of Agriculture Fisheries and Food. Where Grade 3 is mapped this includes the subdivision of Grade 3 into subgrades 3a and 3b. Surveys use the current grading methodology as described in "Agricultural Land Classification of England and Wales," a link for which is provided with the data. Individual sites have been mapped at varying scales and level of detail from 1:5,000 to 1:50,000 (typically 1:10,000). Unedited sample point soils data and soil pit descriptions are also available for some surveys. Attribution statement: Natural England copyright. Contains Ordnance Survey data. Crown copyright and database right [year].

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms. The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 4,000 farms and covers more than 20 different crops in 46 countries. The data (except USA data and for Barley in UK, Germany, Poland, Czech Republic, France and Spain) was collected, consolidated and reported by Kynetec (previously Market Probe), an independent market research agency. It can be used as benchmarks for crop yield and input efficiency.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms. The reference farms were selected by Syngenta and the benchmark farms were randomly selected by Kynetec within the same cluster.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done by Kynetec based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

C. Selection procedure The respondents were picked randomly using a “quota based random sampling” procedure. Growers were first randomly selected and then checked if they complied with the quotas for crops, region, farm size etc. To avoid clustering high number of interviews at one sampling point, interviewers were instructed to do a maximum of 5 interviews in one village.

BF Screened from Japan were selected based on the following criterion: Location: Hokkaido Tokachi (JA Memuro, JA Otofuke, JA Tokachi Shimizu, JA Obihiro Taisho) --> initially focus on Memuro, Otofuke, Tokachi Shimizu, Obihiro Taisho // Added locations in GGP 2015 due to change of RF: Obhiro, Kamikawa, Abashiri
BF: no use of in furrow application (Amigo) - no use of Amistar

Contract farmers of snacks and other food companies --> screening question: 'Do you have quality contracts in place with snack and food companies for your potato production? Y/N --> if no, screen out

Increase of marketable yield --> screening question: 'Are you interested in growing branded potatoes (premium potatoes for processing industry)? Y/N --> if no, screen out

Potato growers for process use
Background info: No mention of Syngenta Background info: - Labor cost is very serious issue: In general, labor cost in Japan is very high. Growers try to reduce labor cost by mechanization. Percentage of labor cost in production cost. They would like to manage cost of labor - Quality and yield driven

Mode of data collection

Face-to-face [f2f]

Research instrument

Data collection tool for 2019 covered the following information:

(A) PRE- HARVEST INFORMATION

PART I: Screening PART II: Contact Information PART III: Farm Characteristics a. Biodiversity conservation b. Soil conservation c. Soil erosion d. Description of growing area e. Training on crop cultivation and safety measures PART IV: Farming Practices - Before Harvest a. Planting and fruit development - Field crops b. Planting and fruit development - Tree crops c. Planting and fruit development - Sugarcane d. Planting and fruit development - Cauliflower e. Seed treatment

(B) HARVEST INFORMATION

PART V: Farming Practices - After Harvest a. Fertilizer usage b. Crop protection products c. Harvest timing & quality per crop - Field crops d. Harvest timing & quality per crop - Tree crops e. Harvest timing & quality per crop - Sugarcane f. Harvest timing & quality per crop - Banana g. After harvest PART VI - Other inputs - After Harvest a. Input costs b. Abiotic stress c. Irrigation

See all questionnaires in external materials tab

Cleaning operations

Data processing:

Kynetec uses SPSS (Statistical Package for the Social Sciences) for data entry, cleaning, analysis, and reporting. After collection, the farm data is entered into a local database, reviewed, and quality-checked by the local Kynetec agency. In the case of missing values or inconsistencies, farmers are re-contacted. In some cases, grower data is verified with local experts (e.g. retailers) to ensure data accuracy and validity. After country-level cleaning, the farm-level data is submitted to the global Kynetec headquarters for processing. In the case of missing values or inconsistences, the local Kynetec office was re-contacted to clarify and solve issues.

Quality assurance Various consistency checks and internal controls are implemented throughout the entire data collection and reporting process in order to ensure unbiased, high quality data.

• Screening: Each grower is screened and selected by Kynetec based on cluster-specific criteria to ensure a comparable group of growers within each cluster. This helps keeping variability low.

• Evaluation of the questionnaire: The questionnaire aligns with the global objective of the project and is adapted to the local context (e.g. interviewers and growers should understand what is asked). Each year the questionnaire is evaluated based on several criteria, and updated where needed.

• Briefing of interviewers: Each year, local interviewers - familiar with the local context of farming -are thoroughly briefed to fully comprehend the questionnaire to obtain unbiased, accurate answers from respondents.

• Cross-validation of the answers: o Kynetec captures all growers' responses through a digital data-entry tool. Various logical and consistency checks are automated in this tool (e.g. total crop size in hectares cannot be larger than farm size) o Kynetec cross validates the answers of the growers in three different ways: 1. Within the grower (check if growers respond consistently during the interview) 2. Across years (check if growers respond consistently throughout the years) 3. Within cluster (compare a grower's responses with those of others in the group) o All the above mentioned inconsistencies are followed up by contacting the growers and asking them to verify their answers. The data is updated after verification. All updates are tracked.

• Check and discuss evolutions and patterns: Global evolutions are calculated, discussed and reviewed on a monthly basis jointly by Kynetec and Syngenta.

• Sensitivity analysis: sensitivity analysis is conducted to evaluate the global results in terms of outliers, retention rates and overall statistical robustness. The results of the sensitivity analysis are discussed jointly by Kynetec and Syngenta.

• It is recommended that users interested in using the administrative level 1 variable in the location dataset use this variable with care and crosscheck it with the postal code variable.

Data appraisal

Due to the above mentioned checks, irregularities in fertilizer usage data were discovered which had to be corrected:

For data collection wave 2014, respondents were asked to give a total estimate of the fertilizer NPK-rates that were applied in the fields. From 2015 onwards, the questionnaire was redesigned to be more precise and obtain data by individual fertilizer products. The new method of measuring fertilizer inputs leads to more accurate results, but also makes a year-on-year comparison difficult. After evaluating several solutions to this problems, 2014 fertilizer usage (NPK input) was re-estimated by calculating a weighted average of fertilizer usage in the following years.

The dataset should be cited as: Soil Survey of Scotland Staff (1981). Land Capability for Agriculture maps of Scotland at a scale of 1:250 000. Macaulay Institute for Soil Research, Aberdeen. 10.5281/zenodo.6322683Sourced from the James Hutton Institute : linkThematic Maps and Capability maps: Thematic maps show the distribution of a specific soil property or theme, such as topsoil organic carbon content or soil texture.Capability maps use soil data along with information on other properties, such as climate or landscape, to classify the land for a specific purpose such as the Land Capability for Agriculture Classification (LCA) – an assessment of the capacity of land to produce food. We strongly recommend that the 1:50,000 Land Capability for Agriculture is used in preference to 1:250,000. Where it exists (predominantly in the agricultural lowlands of the south and east of the country) the 1:50,000 scale map information is seen as the definitive mapped assessment. For a more site-specific query or to resolve a planning issue, we recommend that a further and very detailed site inspection is carried out. A scan of the printed handbook “Land Capability Classification for Agriculture” (Bibby, J. S., Douglas, H. A., Thomasson, A. J., Robertson, J. S. (1991). Land Capability for Agriculture, Aberdeen: Macaulay Land Use Research Institute) may be downloaded by clicking here.Land Capability for Agriculture national cover (1:250,000)

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

For the 70 percent of the world's poor who live in rural areas, agriculture is the main source of income and employment. But depletion and degradation of land and water pose serious challenges to producing enough food and other agricultural products to sustain livelihoods here and meet the needs of urban populations. Data presented here include measures of agricultural inputs, outputs, and productivity compiled by the UN's Food and Agriculture Organization.

Survey name: Watnall, Opencast Coal Proposal, Crowhill Farm Post 1988 Agricultural Land Classification (ALC) site survey data – scanned original paper maps and survey reports for individual sites surveyed in detail between 1989 and 1999 by the Ministry of Agriculture Fisheries and Food. Where Grade 3 is mapped this includes the subdivision of Grade 3 into subgrades 3a and 3b. Surveys use the current grading methodology as described in "Agricultural Land Classification of England and Wales," a link for which is provided with the data. Individual sites have been mapped at varying scales and level of detail from 1:5,000 to 1:50,000 (typically 1:10,000). Unedited sample point soils data and soil pit descriptions are also available for some surveys. Attribution statement: © Natural England copyright. Contains Ordnance Survey data © Crown copyright and database right [year]. Attribution statement: Attribution statement: © Natural England copyright. Contains Ordnance Survey data © Crown copyright and database right [year].

Abstract copyright UK Data Service and data collection copyright owner.The Farm Business Survey (FBS) is conducted annually to collect business information from c.2,400 farms in England and Wales. The FBS provides information on the financial position and physical and economic performance of farm businesses, to inform policy decisions on matters affecting farm businesses and to enable analysis of impacts of policy options. It is intended to serve the needs of farmers, farming and land management interest groups, government (both national and European), government partners, and researchers. The primary objective of survey results is to contrast the performance or other business characteristics of different groupings of farm, such as between regions or other geographical or environmental designations, farm types, farm size, age or education of farmer etc. Up to and including the 2001/02 survey, FBS estimates were based on matching of the sample between two adjacent years. Farm weights were still calculated to present a matched sample however. From the 2002/03 survey onwards, matching between adjacent years was dropped altogether, and weights are now calculated for the full sample. The typology used to determine the FBS farm type classification was revised for 2009 onwards. The FBS typology is now based on standard outputs expressed in euros, with a minimum threshold of 25,000 euro (irrespective of the SLR) for FBS eligibility. Between 2009 and 2011, FBS farm type classification has been based on 2007 standard output (SO) coefficients. From 2012 to 2016, FBS farm type classification was based on 2010 SO coefficients, and from 2017 the FBS farm type classifications are based on 2013 SO coefficients. The change in typology has had an effect on the distribution of farms by farm type and income averages. Further information regarding the change in typology is available from the 'FBS Documents' section on the gov.uk Farm Business Survey – technical notes and guidance webpage. The Farm Business Survey is available under Special Licence access conditions. For further details on how to apply for access to the data, see the Access section below. New edition informationFor the second edition (September 2019), a new version of the FBS 2016-2017 dataset was deposited, derived from version 3 of the FBS2016 database; the database now contains details on tenanted land data (Section R), English data for fertiliser usage (additional data within Section F), and Fully Independent Activities of farmers and spouses (Section K) and Household income (Section N) for Wales. In addition, some corrections have been made to Section I data where some rows had been duplicated on a small number of farms. Main Topics: Variables cover general and physical farm characteristics, labour, crops (previous and current harvest year, set-aside, by-products, forage and cultivations); miscellaneous receipts, livestock (dairy and beef cattle, sheep, pigs, poultry, miscellaneous livestock), variable and fixed costs, assets, investment, liabilities, income from diversified activities (integrated and semi-integrated into the farm business), farmer and spouse off-farm hours and incomes, subsidies. One-stage stratified or systematic random sample Telephone interview Face-to-face interview

Survey name: Wing, West Park Farm Post 1988 Agricultural Land Classification (ALC) site survey data – scanned original paper maps and survey reports for individual sites surveyed in detail between 1989 and 1999 by the Ministry of Agriculture Fisheries and Food. Where Grade 3 is mapped this includes the subdivision of Grade 3 into subgrades 3a and 3b. Surveys use the current grading methodology as described in "Agricultural Land Classification of England and Wales," a link for which is provided with the data. Individual sites have been mapped at varying scales and level of detail from 1:5,000 to 1:50,000 (typically 1:10,000). Unedited sample point soils data and soil pit descriptions are also available for some surveys. Attribution statement: © Natural England copyright. Contains Ordnance Survey data © Crown copyright and database right [year]. Attribution statement: Attribution statement: © Natural England copyright. Contains Ordnance Survey data © Crown copyright and database right [year].