Abstract

The health and wealth of a nation and its potential to develop and grow depend on its ability to feed its people. To help ensure that food will remain available to those who need it, there is nothing more important to give priority to than agriculture. Accurate and timely statistics about the basic produce and supplies of agriculture are essential to assess the agricultural situation. To help policy maker's deal with the fundamental challenge they are faced within the agricultural sector of the economy and develop measures and policies to maintain food security, there should be a continuous provision of statistics. The collection of reliable, comprehensive and timely data on agriculture is thus required for the above purposes. In this perspective, the Central Statistical Agency (CSA) has endeavored to generate agricultural data for policy makers and other users. The general objective of CSA's annual Agricultural Sample Survey (AgSS) is to collect basic quantitative information on the country's agriculture that is considered essential for development planning, socio-economic policy formulation, food security, etc. The AgSS is composed of four components: Crop production forecast survey, Main (“Meher”) season survey, Livestock survey, and survey of the “Belg” season crop area and production.

The specific objectives of the Main (“Meher”) season area and production survey are: - To estimate the total cultivated land area, production and yield per hectare of major crops (temporary). - To estimate the total farm inputs applied area and quantity of inputs applied by type for major temporary and permanent crops.

Geographic coverage

The survey covered all sedentary rural agricultural population in all regions of the country except urban and nomadic areas which were not included in the survey.

Analysis unit

Agricultural household/ Holder/ Crop

Universe

Agricultural households

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2000/2001 (1993 E.C) Meher season agricultural sample survey covered the rural part of the country except three zones in Afar regional state and six zones in Somalie regional state that are predominantly nomadic. A two-stage stratified sample design was used to select the sample. Each zones/special wereda was adopted as stratum for which major findings of the survey are reported except the four regions; namely, Gambella, Harari, Addis Ababa and Dire Dawa which were considered as strata/reporting levels. The primary sampling units (PSUs) were enumeration areas (EAs) and agricultural households were the secondary sampling units. The survey questionnaires were administered to all agricultural holders within the sample households. A fixed number of sample EAs were determined for each stratum/reporting level based on precision of major estimates and cost considerations. Within each stratum EAs were selected using probability proportional to size systematic sampling; size being total number of agricultural households in the EAs as obtained from the 1994 population and housing census. From each sample EA, 40 agricultural households were systematically selected for the annual agricultural sample survey from a fresh list of households prepared at the beginning of the field work of the annual agricultural survey. Of the forty agricultural households, the first twenty-five were used for obtaining information on area under crops, Meher and Beleg season production of crops, land use, agricultural practices, crop damage, and quantity of agricultural households sampled in each of the selected EAs, data on crop cutting were collected for only the fifteen households (11th - 25th households selected). A total of 1,430 EAs were selected for the survey. However, 8 EAs were closed for various reasons beyond the control of the Authority and the survey succeeded in covering 1422 (99.44%) EAs. Within respect to ultimate sampling units, for the Meher season agricultural sample survey, it was planned to cover 35,750 agricultural households.

Note: Distribution of the number of sampling units sampled and covered by strata is given in Appendix I of the 2000-2001 annual Agricultural Sample Survey report which is provided as external resource.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2000-2001 annual Agricultural Sample Survey used structured questionnaires to collect agricultural information from selected sample households. Lists of forms in the questionnaires: - AgSS Form 93/0: Used to list all households and agricultural holders in the sample enumeration areas. - AgSS Form 93/1: Used to list selected households and agricultural holders in the sample enumeration areas. - AgSS Form 93/3A: Used to list fields and agricultural practices only pure stand temporary and permanent crops, list of fields and agricultural practices for mixed crops, other land use, quantity of improved and local seeds by type of crop and type and quantity of crop protection chemicals. - AgSS Form 93/4A: Used to collect results of area measurement. - AgSS Form 93/5: Used to list fields for selecting fields for crop cuttings and collect information about details of crop cutting.

Note: The questionnaires are presented in the Appendix IV of the 2000-2001 Agricultural Sample Survey Volume I report which is provided as external resource.

Cleaning operations

Editing, Coding and Verification: In order to insure the quality of the collected survey data an editing, coding and verification instruction manual was prepared and printed. Then 23 editors-coders and 22 verifiers were trained for two days in the editing, coding and verification operation using the aforementioned manual as a reference and teaching aid. The completed questionnaires were edited, coded and later verified on a 100% basis before the questionnaires were passed over to the data entry unit. The editing, coding and verification exercise of all questionnaires was completed in about 30 days.

Data Entry, Cleaning and Tabulation: Before starting data entry, professional staff of Agricultural Statistics Department prepared edit specifications to use on personal computers utilizing the Integrated Microcomputer Processing System (IMPS) software for data consistency checking purposes. The data on the coded questionnaires were then entered into personal computers using IMPS software. The data were then checked and cleaned using the edit specification prepared earlier for this purpose. The data entry operation involved about 31 data encoders and it took 28 days to complete the job. Finally, tabulation was done on personal computers to produce results as indicated in the tabulation plan.

Response rate

A total of 1,430 EAs were selected for the survey. However, 8 EAs were closed for various reasons beyond the control of the Authority and the survey succeeded in covering 1422 (99.44%) EAs. Within respect to ultimate sampling units, for the Meher season agricultural sample survey, it was planned to cover 35,750 agricultural households. The response rate was found to be 99.14%.

Sampling error estimates

Estimation procedures of parameters of interest (total and ratio) and their sampling error is presented in Appendix II of the 2000-2001 annual Agricultural Sample Survey report which is provided as external resource.

The Agricultural Statistics of the People's Republic of China, 1949-1990 is an historical collection of agricultural statistical data compiled by China's State Statistical Bureau (SSB). The collection contains 297 variables covering social and economic indicators, commodities, price index, production, trade, and consumption. The data are provided at the national level (1949-1990) and the provincial level (1979-1990). This data set is produced in collaboration with the United States Department of Agriculture (USDA), SSB, and the Center for International Earth Science Information Network (CIESIN).

ABSTRACT Accurate crop data are essential for reliable irrigation water requirements (IWR) calculations, which can be acquired during the crop growth season for a given region using earth observation (EO) satellite time series. In addition, a relationship between crop coefficients and the NDVI can be established to allow for crop evapotranspiration estimation. The main objective of the present work was to develop a methodology, and its implementation in an application software, to extract crop parameters from EO image time series for a set of parcels of different types of crops, to be used as input data for a soil water balance model to compute IWR. The methodology was tested at two distinct test sites, the Vila Franca de Xira (site I) and Vila Velha de Ródão (site II) municipalities, Portugal. Landsat-7 and −8 images acquired from April to October 2013 were used for site I, while SPOT-5 Take-5 images from April to September 2015 were considered for site II. EO data were used to estimate the basal crop coefficients, planting dates, and crops growth stage lengths. Based on crop, soil and meteorological data, the IWR for the main crops of both test regions were estimated using the IrrigRotation model. The crop coefficient curves obtained from the EO data proved to be reliable for IWR estimation.

This data comprises processed weather, soil, yield, and cultivation area for corn yield prediction in Sub-Sahara Africa, with emphasis on Nigeria. The data was collected to design a corn yield prediction model to help smallholder farmers make smart farming decisions. However, the data can serve several other purposes through analysis and interpretation.

The reference study region in Africa is Nigeria. The focuses on corn crop because there are over 211.4 million people, of which a large percentage of the population are smallholder farmers. Nigeria [9.0820° N, 8.6753° E] is within an arable land area of 34 million hectares located on the west coast of Africa. The region comprises of 36 states with the most and least number of districts being 214 and 10, respectively. For each state, the environment data are collected as follows.

Grid map climate data – This data spans spatial resolutions between ~1 km2 to ~340 km2 from the high spatial resolution WorldClim global climate database22. Each grid point on the map is monthly data from January to December between 1970 and 2000 years and records 8 climate variables. The variables are average temperature C0, minimum temperature C0, maximum temperature C0, precipitation (mm), solar radiation (kJ m^(-2) day(-1), wind speed (m s(-1)), and water vapor (kPa) taken at 30 seconds (s), 2.5 minutes m, 5 m, and 10 m.

Grid map soil data – This data is obtained from 250 minutes of spatial resolution AfSIS soil data23 from year 1960 to 2012. The variables are wet soil bulk density, dry bulk density (kg dm-3), clay percentage of plant available water content, hydraulic conductivity, the upper limit of plant available water content, the lower limit of, organic matter percentage, pH, sand percentage (g 100 g-1), silt percentage (g 100 g-1) and, clay percentage (g 100 g-1), and saturated volumetric water content variables measured at depths 0–5, 5–10, 10–15, 15–30, 30–45, 45–60, 60–80, 80–100, and 100–120 measured in centimeters (cm).

Corn yield data – This data is available on Kneoma Corporation website24. It ranged from years 1995 to 2006 and consisted of a corn yield of 1000 metric tonnes and a cultivation area of 1000 hectares.

Geolocation coordinates (latitude and longitude) – The geolocation of each of the 36 states with their districts is sampled from Google Maps. The output feds into the Esri-ArcGIS 2.5, a professional geographical software, for extracting the point-cloud values of each environmental variable (weather and soil) at specific geolocation of the 36 states of Nigeria.

Other Descriptions: Data type - Continous and Categorical Dataset Characteristics - Tabular Associated Tasks - Regression Feature Type - Real Number of Instances - 1828 Number of Features: 12

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 Philippines were selected based on the following criterion: (a) smallholder rice growers Location: Luzon - Mindoro (Southern Luzon) mid-tier (sub-optimal CP/SE use): mid-tier growers use generic CP, cheaper CP, non hybrid (conventional) seeds
Smallholder farms with average to high levels of mechanization
Should be Integrated Pest Management advocates
less accessible to technology: poor farmers, don't have the money to buy quality seeds, fertilizers,... Don't use machinery yet
simple knowledge on agronomy and pests
influenced by fellow farmers and retailers
not strong financial status: don't have extra money on bank account and so need longer credit to pay (as a consequence: interest increases) may need longer credit

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.

United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals compare how much data is in institutional vs. domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered. Search methods We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review: Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection. Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation. See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

This data collection contains time series data on a selected set of agricultural variables for 105 Kansas counties over the period 1874-1936. The study, part of a larger research project on American agriculture funded by the National Science Foundation, was prepared at Yale University as Version 2 of KANATICS (a Kansas Agricultural Time Series/Cross Section), which extends and corrects the first version, a 1977 working paper. The series data in Part 1 are an exact representation of the data found in various quarterly, annual, and biennial reports of the Kansas State Board of Agriculture. Data include information on acreage sown, number of bushels and value of different field crops harvested, and number and value of livestock. Part 2 contains consolidated and manipulated series data, including manipulations of the series in Part 1, e.g., the value per bushel of various field crops, adjusted total cultivated acres, adjusted field crop income, and prices per animal.

Abstract

During 2018, the National Statistical Agency (NSA) of Namibia received technical assistance from the Global Strategy to Improve Agricultural and Rural Statistics hosted by FAO on the measurement of Post-Harvest Losses (PHL). In this regard, a pilot survey was conducted by NSA in the Kavango West region to compare estimations using subjective and objective methods. The main crops analyzed are millet and maize. Subjective measurement methods included farmer recall, while the objective methods chosen were implemented through crop cutting, and samples of harvested crop analyzed in a lab. Unfortunately, the project ended before the samples were received from the lab, so these data are not available.

The pilot survey was conducted in Kavango West region only and the Primary Sampling Units (PSUs) were derived from the 2013/14 Agricultural Census frame. Staff from the Ministry of Agriculture, Water and Forestry (MAWF), (agricultural technicians as enumerators and agricultural technician as team supervisors) carried out field activities. In total, a sample of 350 farms were enumerated. The data collection took place from May 2018 to August 2018 (30 working days) and included both the subjective and objective measure of the PHL.

Geographic coverage

Regional coverage

Analysis unit

Households

Universe

Agricultural households in the Kavango West region

Kind of data

Sample survey data [ssd]

Sampling procedure

The PHL pilot study mainly followed the National Census of Agriculture (NCA) 2013/14 methodology. The NCA 2013/14 used a stratified two stage cluster sample design. At the first stage, primary sampling units (PSUs) were selected with Probability Proportional to Size (PPS) from the sampling frame based on the Enumeration Areas of 2011 Population and Housing Census. The size measure of a PSU in the sampling frame was the number of agricultural households which was derived from the questions included in 2011 Population and Housing Census as per the FAO recommendations.

The list of agricultural households was prepared through the listing process within a selected PSU to compile the sampling frame for agricultural households which was selected systematically.

A third stage of sampling was also conducted to select plots which contained the two main crops, maize, and millet for objective measurement as described below.

A list of plots planted with maize or millet in each sampled PSU was created. Then, one plot was randomly selected from the two main crops of the holder. An area was then marked within the selected plot according to the FAO guidelines and the matured crop inside this marked area was cut and weighed when the crop was wet and dry.

Crop cutting enable estimation of the yield of a crop and the losses during harvesting, threshing/shelling, and cleaning/winnowing. This was done through processing the produce of sub-plots in selected fields. Interviewers did the crop cutting manually according to the techniques used by the farmer. After the manual harvesting was done, the second team of supervisors entered the field and collected all fallen ears/cobs, grains and weighed them after which the information was recorded. These figures are used to estimate the average yields of each of the crops.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

The dataset received by the Office of Chief Statistician (OCS) team was already cleaned by Aliou Mballo directly with NSA. During the cleaning process, all direct identifiers were removed. Furthermore, the declaration, phyiscal measurement, and storage data for the second crops, were transposed from wide to long. So instead of the farmer declaration variables of the second crop captured by the variables titled from “D6” to “D10-6” in the questionnaire being in their own columns, there is a second row in the dataset containing data from sections C, D, E and G containing data for the second crop, spread across columns “crop_code” to “D5-6”. The same logic applies to the physical measurements and storage data.

The sections CDEG dataset contains data for some crops which do not correspond to records in the Section C dataset on agricultural practices. This is due to a mistake amongst some enumerators which filled in directly Section D for some crops and skipped agricultural practices. This is especially prevelant for measurement data for maize. The data from the lab was not received in time for the project deadline. Accordingly, section “H_Storage_Lab” from the questionnaire was not available to be included in the dataset.

Abstract

Agriculture is the major contributor to the Ethiopian economy. A majority of the Ethiopian populations are engaged in agriculture to earn their livelihood and most of the nation's exports are made up of agriculture produces. The collection of reliable, comprehensive and timely data on agriculture is, thus, essential for policy formulation, decision making and other uses. In this regard the Central Statistical Agency(CSA) has exerted effort to provide users and policy makers with reliable and timely agriculture data.

The general objectives of CSA's annual Agricultural Sample Survey (AgSS) is to collect basic quantitative information on the country's agriculture that is considered essential for development planning, socio-economic policy formulation, food assistance, etc.

The specific objectives of the Main (“Meher”) season area and production survey are: - To estimate the total cultivated land area, production and yield per hectare of major crops (temporary). - To estimate the total farm inputs applied area and quantity of inputs applied by type for major temporary and permanent crops.

Geographic coverage

The survey covered all sedentary rural agricultural population in all regions of the country except urban and nomadic areas which were not included in the survey.

Analysis unit

Agricultural household/ Holder/ Crop

Universe

Agricultural households

Kind of data

Sample survey data [ssd]

Sampling procedure

The 1998-1999 (1991 E.C) Main ("Mehere") season agricultural survey covered the rural part of the country except two zones in Afar region and six zones in Somalie region that are predominantly nomadic. A two-stage stratified sample design was used to select the sample EAs and the agricultural households. Each zone/ special wereda in the sampled population of Tigray, Afar, Amhara, Oromiya, Somalie, Benishangul_Gumuz, SNNP regions was adopted as stratum for which major finings of the survey are reported. But each of the four regions, namely; Gambela, Harari, Addis Ababa and Dire Dawa were considered as reporting levels. The primary sampling units (PSUs) were enumeration areas (EAs) and agricultural households were the secondary sampling units. The survey questionnaires were administered to all agricultural holders within the sample households. A fixed number of sample EAs was determined fro each stratum/reporting level based on precision of major estimates and cost considerations. Within each stratum EAs were selected using probability proportional to size; size being total number of households in the EAs as obtained form the 1994 population and housing census. From each sample EA, 25 agricultural households were systematically selected for the 'Meher" season survey from a fresh list of households prepared at the beginning of the fieldwork of the survey.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 1998-1999 annual Agricultural Sample Survey used structured questionnaires to collect agricultural information from selected sample households. List of forms in the questionnaire: - AgSS Form 91/0: Used to list all agricultural households and holders in the sample enumeration areas. - AgSS Form 91/1: Used to list selected households and agricultural holders in the sample enumeration areas. - AgSS Form 91/2: Used to collect information about crop condition. - AgSS Form 91/3A: Used to list fields and agricultural practices only pure stand temporary and permanent crops, list of fields and agricultural practices for mixed crops, other land use, quantity of improved and local seeds by type of crop and type and quantity of crop protection chemicals. - AgSS Form 91/3B: Used to collect information about quantity of production of crops. - AgSS Form 91/4A: Used to collect information about results of area measurement and field area measurement. - AgSS Form 91/4B: Used to collect information about results of area measurement and field area measurement. - AgSS Form 91/5: Used to list fields for selecting fields for crop cuttings and collect information about details of crop cutting. - AgSS Form 91/6: Used to collect information about cattle by sex, age and purpose.

Note: The questionnaires are provided as external resource.

Cleaning operations

Editing, Coding and Verification: In order to insure the quality of collected survey data an editing, coding and verification instruction manual was prepared and fifty editors/coders and ten verifiers were trained for two days to edit, code and verify the data using the aforementioned manual as a reference and teaching aid. The filled-in questionnaires were edited, coded and later verified by supervisors on a 100% basis before the questionnaires were sent to the data processing unit for data entry. The editing, coding and verification of all questionnaires was completed in fourty days.

Data Entry, Cleaning and Tabulation: Before starting data entry professional staffs of Agricultural Statistics Department of Central Statistical Authority prepared edit specification that used to developed data entry and cleaning computer programs by data processing staffs using Integrated Microcomputer Processing System (IMPS). The edited and coded questionnaires were captured into computers and later cleaned using cleaning program that was developed for this purpose earlier. Fifty data encoders were involved in this process and it took thirty-five days to complete the job. Finally, using tabulations format provided by the subject matter specialist computer program was developed and survey results were produced accordingly.

Response rate

A total of 1,450 EAs (2.9 % of total EAs in the rural areas of the country) were selected for the survey. However, 22 EAs were not covered by the survey due to various reasons that are beyond the control of the Agency. Thus, the survey succeeded in covering 1428 (98.48%) EAs. With respect to ultimate sampling units, it was planned to cover a total of 36,250 agricultural households for area measurement and 21,750 agricultural households for crop cutting (see Appendix III in the report which is provided as external resource). The response rate was found to be 98.94 % for area measurement and 95.50 % for crop cutting.

Sampling error estimates

Estimation procedures of parameters of interest (total and ratio) and their sampling error is presented in Appendix II of the 1998-1999 annual Agricultural Sample Survey, Volume I report which is provided in this documentation.

This table contains data on land use, arable farming, horticulture, grassland, grazing livestock and housed animals, at regional level, by general farm type.

The figures in this table are derived from the agricultural census. Data collection for the agricultural census is part of a combined data collection for a.o. agricultural policy use and enforcement of the manure law.

Regional breakdown is based on the main location of the holding. Due to this the region where activities (crops, animals) are allocated may differ from the location where these activities actually occur.

The agricultural census is also used as the basis for the European Farm Structure Survey (FSS). Data from the agricultural census do not fully coincide with the FSS. In the FSS years (2000, 2003, 2005, 2007 and 2010) additional information was collected to meet the requirements of the FSS.

Reference date for livestock is 1 April and for crops 15 May.

Data available from: 2000

Status of the figures: The figures are final.

Changes as of March 28, 2025: the final figures for 2024 have been added.

When will new figures be published? According to regular planning provisional figures are published in November and the definite figures will follow in March of the following year.

Abstract

The Annual Agricultural Sample Survey (AASS) for the year 2022/23 aimed to enhance the understanding of agricultural activities across the United Republic of 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 aggregated 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 access conditions are provided in the data processing and data access 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

The Agricultural Management Dataset contains two variables which help describe management regimes within agricultural lands of China. The first variable, Irrigation Index, reports the fraction of cropland in a county which is under irrigation, excluding rice paddies. The Second variable, Nitrogen Fertilizer, reports the tonnes of nitrogen fertilizer applied in the county per year.

See the references for the sources of these data.

China County Data collection contains seven datasets which were compiled in the early 1990s for use as inputs to the DNDC (Denitrification-Decomposition) model at UNH. DNDC is a computer simulation model for predicting carbon (C) and nitrogen (N) biogeochemistry in agricultural ecosystems. The datasets were compiled from multiple Chinese sources and all are at the county scale for 1990. The datasets which comprise this collection are listed below.

1) Agricultural Management 2) Crops 3) N-Deposition 4) Geography and Population 5) Land Use 6) Livestock 7) Soil Properties

This data set collection contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs. Ortho imagery provides an effective, intuitive means of communication about farm program administration between FSA and stakeholders. New technology and innovation is identified by fostering and maintaining a relationship with vendors and government partners, and by keeping pace with the broader geospatial community. As a result of these efforts the NAIP program provides three main products: DOQQ tiles, Compressed County Mosaics (CCM), and Seamline shape files. The Contract specifications for NAIP imagery have changed over time reflecting agency requirements and improving technologies. These changes include image resolution, horizontal accuracy, coverage area, and number of bands. In general, flying seasons are established by FSA and are targeted for peak crop growing conditions. The NAIP acquisition cycle is based on a minimum 3 year update of base ortho imagery. The tiling format of the NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. NAIP quarter quads are formatted to the UTM coordinate system using the North American Datum of 1983. NAIP imagery may contain as much as 10% cloud cover per tile.

This data set is provided by EOS-EARTHDATA (formerly EOS-Webster). It provides acreage, production and yield statistics for U.S. field crops from the National Agricultural Statistical Service (NASS) for the years 1970 through 2003. Data can be subset by irrigated and non-irrigated areas. Sucrose content, where applicable, is also included. Data are at the county scale and include all counties in the conterminous USA. No spatial subsets are available. For more information, see the Data Guide. Data after 2003 may be obtained from NASS.

Data required to replicate the results of Bell et al. "Calibrating primary crop parameters to capture undersown species impacts". Instructions and code to do so can be found at https://github.com/qdbell/TWINWIN-Intercrop. "Kumpula"-prefixed files are retrieved from FMI open data, TWINWIN_workspace and barley_sole_default.csv are modified from STICS to create suitable model set ups. The remaining files contain measurements from the TWINWIN experiment in Viikki.

For a detailed description of this dataset, based on the Datasheets for Datasets (Gebru, Timnit, et al. "Datasheets for datasets." Communications of the ACM 64.12 (2021): 86-92.), check the ACRE_datasheet.md file.

For what purpose was the dataset created? The ACRE dataset was created within the scope of the METRICS project to serve as a benchmark for weed detection models in various tasks, including object detection, semantic segmentation, and instance segmentation. The Agri-Food Competition for Robot Evaluation (ACRE) is a benchmarking competition specifically designed for autonomous robots and smart implements, with a primary focus on agricultural activities like weed removal and field navigation. These capabilities play a vital role in facilitating the transition to Digital Agriculture. The ACRE competition, which can be found at https://metricsproject.eu/agri-food, is part of the METRICS project, an EU-funded initiative dedicated to the metrological evaluation and testing of autonomous robots.

What do the instances that comprise the dataset represent? The instances consist of RGB images depicting both crop and weed plants. The crop category encompasses two species: maize (Zea mays) and beans (Phaseolus vulgaris). On the other hand, the weed category encompasses four species: ryegrass (Lolium perenne), mustard (Sinapis arvensis), matricaria (Matricaria chamomilla), and lamb's quarter (Chenopodium album).

Is there a label or target associated with each instance? Every image in the dataset is accompanied by an XML file that contains instance segmentation annotations.

What mechanisms or procedures were used to collect the data? The data collection process involved the use of a four-wheel skid-steering robot that was equipped with a Basler acA2000-50gc RGB camera. The camera was mounted on the robot in such a way that its principal axis was directed perpendicular to the ground. It had a resolution of 2046 x 1080 pixels. The robot was teleoperated and operated at an average speed of 0.2 m/s. To capture the data, the camera's stream was recorded in rosbag format. For this purpose, the camera was connected to a PC running Ubuntu 18.04 and ROS Melodic via an Ethernet interface.

Abstract

The programme for the World Census of Agriculture 2000 is the eighth in the series for promoting a global approach to agricultural census taking. The first and second programmes were sponsored by the International Institute for Agriculture (IITA) in 1930 and 1940. Subsequent ones up to 1990 were promoted by the Food and Agriculture Organization of the United Nations(FAO). FAO recommends that each country should conduct at least one agricultural census in each census programme decade and its programme for the World Census of Agriculture 2000 for instance corresponds to agricultural census to be undertaken during the decade 1996 to 2005. Many countries do not have sufficient resources for conducting an agricultural census. It therefore became an acceptable practice since 1960 to conduct agricultural census on sample basis for those countries lacking the resources required for a complete enumeration.

In Nigeria's case, a combination of complete enumeration and sample enumeration is adopted whereby the rural (peasant) holdings are covered on sample basis while the modern holdings are covered on complete enumeration. The project named “National Agricultural Sample Census” derives from this practice. Nigeria through the National Agricultural Sample Census (NASC) participated in the 1970's, 1980's, 1990's programmes of the World Census of Agriculture. Nigeria failed to conduct the Agricultural Census in 2003/2004 because of lack of funding. The NBS regular annual agriculture surveys since 1996 had been epileptic and many years of backlog of data set are still unprocessed. The baseline agricultural data is yet to be updated while the annual regular surveys suffered set back. There is an urgent need by the governments (Federal, State, LGA), sector agencies, FAO and other International Organizations to come together to undertake the agricultural census exercise which is long overdue. The conduct of 2006/2008 National Agricultural Sample Census Survey is now on course with the pilot exercise carried out in the third quarter of 2007.

The National Agricultural Sample Census (NASC) 2006/08 is imperative to the strengthening of the weak agricultural data in Nigeria. The project is phased into three sub-projects for ease of implementation; the Pilot Survey, Modern Agricultural Holding and the Main Census. It commenced in the third quarter of 2006 and to terminate in the first quarter of 2008. The pilot survey was implemented collaboratively by National Bureau of Statistics.

The main objective of the pilot survey was to test the adequacy of the survey instruments, equipments and administration of questionnaires, data processing arrangement and report writing. The pilot survey conducted in July 2007 covered the two NBS survey system-the National Integrated Survey of Households (NISH) and National Integrated Survey of Establishment (NISE). The survey instruments were designed to be applied using the two survey systems while the use of Geographic Positioning System (GPS) was introduced as additional new tool for implementing the project.

The Stakeholders workshop held at Kaduna on 21st-23rd May 2007 was one of the initial bench marks for the take off of the pilot survey. The pilot survey implementation started with the first level training (training of trainers) at the NBS headquarters between 13th - 15th June 2007. The second level training for all levels of field personnels was implemented at headquarters of the twelve (12) concerned states between 2nd - 6th July 2007. The field work of the pilot survey commenced on the 9th July and ended on the 13th of July 07. The IMPS and SPSS were the statistical packages used to develop the data entry programme.

Geographic coverage

State

Analysis unit

Household crop farmers

Universe

Crop farming household

Kind of data

Census/enumeration data [cen]

Sampling procedure

The survey was carried out in 12 states falling under 6 geo-political zones. 2 states were covered in each geo-political zone. 2 local government areas per selected state were studied. 2 Rural enumeration areas per local government area were covered and
4 Crop farming housing units were systematically selected and canvassed .

Sampling deviation

No deviation

Mode of data collection

Face-to-face [f2f]

Research instrument

The NASC crop questionnaire was divided into the following sections: - Holding identification - Holding characteristics - Access to land - Access to credit and funds used - Production input utilization, quantity and cost - Sources of inputs/equipment - Area harvested - Agric machinery - Production - Farm expenditure - Processing facilities - Storage facilities - Employment in agric. - Farm expenditure - Sales - Consumption - Market channels - Livestock farming - Fish farming

Cleaning operations

The data processing and analysis plan involved five main stages: training of data processing staff; manual editing and coding; development of data entry programme; data entry and editing and tabulation. Census and Surveys Processing System (CSPro) software were used for data entry, Statistical Package for Social Sciences (SPSS) and CSPro for editing and a combination of SPSS, Statistical Analysis Software (SAS) and EXCEL for table generation. The subject-matter specialists and computer personnel from the NBS and CBN implemented the data processing work. Tabulation Plans were equally developed by these officers for their areas and topics covered in the three-survey system used for the exercise. The data editing is in 2 phases namely manual editing before the data entry were done. This involved using editors at the various zones to manually edit and ensure consistency in the information on the questionnaire. The second editing is the computer editing, this is the cleaning of the already entered data. The completed questionnaires were collected and edited manually (a) Office editing and coding were done by the editor using visual control of the questionnaire before data entry (b) Cspro was used to design the data entry template provided as external resource (c) Ten operator plus two suppervissor and two progammer were used (d) Ten machines were used for data entry (e) After data entry data entry supervisor runs fequency on each section to see that all the questionnaire were enterd

Response rate

The response rate at EA level was 100 percent, while 98.44 percent was achieved at crop farming housing units level

Sampling error estimates

No computation of sampling error

Data appraisal

The Quality Control measures were carried out during the survey, essentially to ensure quality of data. There were two levels of supervision involving the supervisors at the first level, NBS State Officers and Zonal Controllers at second level and finally the NBS Headquarters staff constituting the second level supervision.

Snapshot img

Crop Production Software Market Size 2024-2028

The crop production software market size is forecast to increase by USD 2.22 billion at a CAGR of 17.59% between 2023 and 2028.

The agricultural market is experiencing substantial growth due to several notable trends and challenges. One notable trend is the increasing use of precision farming, which employs advanced technologies to optimize crop yields and reduce waste. Another significant development is the integration of artificial intelligence (AI) and machine learning (ML) into crop production software. This innovation enables predictive analytics and the automation of farming processes, leading to improved efficiency and productivity. However, the substantial upfront capital investments required by farmers pose a significant barrier to market expansion. Despite this obstacle, the potential benefits of these technologies are compelling, making the agricultural sector an intriguing and dynamic area to monitor.

What will the size of the market be during the forecast period?

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The agribusiness sector is witnessing significant advancements in crop production, driven by the global population's increasing demand for food and the challenges of urbanization, climate change, and the depletion of arable land. Sustainable agriculture solutions, such as precision farming, real-time data collection and analysis, predictive modeling, monitoring, and control, are becoming essential for optimizing food production.



Companies are pioneering the use of Satellite IoT (SatIoT) and sensors, actuators, and devices to create greenhouses and monitor microclimates. Government investments in satellite imaging, in-field sensors, artificial intelligence, and machine learning are also playing a crucial role in developing regions. The integration of drones and Internet of Things (IoT) devices into crop production software is revolutionizing planting schedules and enhancing overall productivity in the agricultural sector.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  On-premises
  Cloud


Type

  Small
  Medium
  Large


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China


  South America



  Middle East and Africa

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

Agribusinesses, farmers, ranchers, and growers worldwide are increasingly adopting crop production software to optimize food production in the face of global population growth, urbanization, climate change, and the need for sustainable agriculture. On-premises deployment of these solutions requires farmers to invest in hardware (servers, network equipment, security devices) and software, making it a significant upfront cost. However, the benefits include enhanced data security, real-time data collection and analysis, predictive modeling, monitoring, and control. Smart greenhouses utilize sensors, actuators, and devices to optimize microclimates, while Satellite IoT (SatIoT) and drones provide valuable data for precision farming.

Furthermore, in-field sensors, satellite imaging, and artificial intelligence enable advanced analytics and automation capabilities. Government investments in agriculture technology and cloud services facilitate the integration of mobile applications and data analysis tools. Despite the advantages, the high deployment costs may limit the adoption of on-premises crop production software, particularly in developing regions. However, the potential for increased efficiency, productivity, and profitability makes it an attractive option for agribusinesses and farmers alike.

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The on-premises segment was valued at USD 465.49 million in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 43% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The market is experiencing significant growth due to the integration of advanced technologies in agriculture. High-speed imagery services are becoming increasingly crucial for farmers to monitor crop quality and resource use, leading to improved precision in agriculture. This, in turn, helps in reducing input costs and enhancing food security. Sustainability is a key focus area, with weather con

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

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 Paraguay were selected based on the following criterion: (a) smallholder soybean growers Medium to high technology farms Regions: - Hohenau (Itapúa) - Edelira (Itapúa) - Pirapó (Itapúa) - La Paz (Itapúa) - Naranjal (Alto Paraná) - San Cristóbal (Alto Paraná)
corn and soybean in rotation
first grow corn and soybean secondly

(b) smallholder maize growers
Medium to high technology farms Regions: - Hohenau (Itapúa) - Edelira (Itapúa) - Pirapó (Itapúa) - La Paz (Itapúa) - Naranjal (Alto Paraná) - San Cristóbal (Alto Paraná)
corn and soybean in rotation
first grow corn and soybean secondly

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.