Abstract

The 2003 Agriculture Sample Census was designed to meet the data needs of a wide range of users down to district level including policy makers at local, regional and national levels, rural development agencies, funding institutions, researchers, NGOs, farmer organisations, etc. As a result the dataset is both more numerous in its sample and detailed in its scope compared to previous censuses and surveys. To date this is the most detailed Agricultural Census carried out in Africa.

The census was carried out in order to: · Identify structural changes if any, in the size of farm household holdings, crop and livestock production, farm input and implement use. It also seeks to determine if there are any improvements in rural infrastructure and in the level of agriculture household living conditions; · Provide benchmark data on productivity, production and agricultural practices in relation to policies and interventions promoted by the Ministry of Agriculture and Food Security and other stake holders. · Establish baseline data for the measurement of the impact of high level objectives of the Agriculture Sector Development Programme (ASDP), National Strategy for Growth and Reduction of Poverty (NSGRP) and other rural development programs and projects. · Obtain benchmark data that will be used to address specific issues such as: food security, rural poverty, gender, agro-processing, marketing, service delivery, etc.

Geographic coverage

Tanzania Mainland and Zanzibar

Analysis unit

• Households
• Individuals

Universe

Large scale, small scale and community farms.

Kind of data

Census/enumeration data [cen]

Sampling procedure

The Mainland sample consisted of 3,221 villages. These villages were drawn from the National Master Sample (NMS) developed by the National Bureau of Statistics (NBS) to serve as a national framework for the conduct of household based surveys in the country. The National Master Sample was developed from the 2002 Population and Housing Census. The total Mainland sample was 48,315 agricultural households. In Zanzibar a total of 317 enumeration areas (EAs) were selected and 4,755 agriculture households were covered. Nationwide, all regions and districts were sampled with the exception of three urban districts (two from Mainland and one from Zanzibar).

In both Mainland and Zanzibar, a stratified two stage sample was used. The number of villages/EAs selected for the first stage was based on a probability proportional to the number of villages in each district. In the second stage, 15 households were selected from a list of farming households in each selected Village/EA, using systematic random sampling, with the village chairpersons assisting to locate the selected households.

Mode of data collection

Face-to-face [f2f]

Research instrument

The census covered agriculture in detail as well as many other aspects of rural development and was conducted using three different questionnaires: • Small scale questionnaire • Community level questionnaire • Large scale farm questionnaire

The small scale farm questionnaire was the main census instrument and it includes questions related to crop and livestock production and practices; population demographics; access to services, resources and infrastructure; and issues on poverty, gender and subsistence versus profit making production unit.

The community level questionnaire was designed to collect village level data such as access and use of common resources, community tree plantation and seasonal farm gate prices.

The large scale farm questionnaire was administered to large farms either privately or corporately managed.

Questionnaire Design The questionnaires were designed following user meetings to ensure that the questions asked were in line with users data needs. Several features were incorporated into the design of the questionnaires to increase the accuracy of the data: • Where feasible all variables were extensively coded to reduce post enumeration coding error. • The definitions for each section were printed on the opposite page so that the enumerator could easily refer to the instructions whilst interviewing the farmer. • The responses to all questions were placed in boxes printed on the questionnaire, with one box per character. This feature made it possible to use scanning and Intelligent Character Recognition (ICR) technologies for data entry. • Skip patterns were used to reduce unnecessary and incorrect coding of sections which do not apply to the respondent. • Each section was clearly numbered, which facilitated the use of skip patterns and provided a reference for data type coding for the programming of CSPro, SPSS and the dissemination applications.

Cleaning operations

Data processing consisted of the following processes: · Data entry · Data structure formatting · Batch validation · Tabulation

Data Entry Scanning and ICR data capture technology for the small holder questionnaire were used on the Mainland. This not only increased the speed of data entry, it also increased the accuracy due to the reduction of keystroke errors. Interactive validation routines were incorporated into the ICR software to track errors during the verification process. The scanning operation was so successful that it is highly recommended for adoption in future censuses/surveys. In Zanzibar all data was entered manually using CSPro.

Prior to scanning, all questionnaires underwent a manual cleaning exercise. This involved checking that the questionnaire had a full set of pages, correct identification and good handwriting. A score was given to each questionnaire based on the legibility and the completeness of enumeration. This score will be used to assess the quality of enumeration and supervision in order to select the best field staff for future censuses/surveys.

CSPro was used for data entry of all Large Scale Farm and community based questionnaires due to the relatively small number of questionnaires. It was also used to enter data from the 2,880 small holder questionnaires that were rejected by the ICR extraction application.

Data Structure Formatting A program was developed in visual basic to automatically alter the structure of the output from the scanning/extraction process in order to harmonise it with the manually entered data. The program automatically checked and changed the number of digits for each variable, the record type code, the number of questionnaires in the village, the consistency of the Village ID Code and saved the data of one village in a file named after the village code.

Batch Validation A batch validation program was developed in order to identify inconsistencies within a questionnaire. This is in addition to the interactive validation during the ICR extraction process. The procedures varied from simple range checking within each variable to the more complex checking between variables. It took six months to screen, edit and validate the data from the smallholder questionnaires. After the long process of data cleaning, tabulations were prepared based on a pre-designed tabulation plan.

Tabulations Statistical Package for Social Sciences (SPSS) was used to produce the Census tabulations and Microsoft Excel was used to organize the tables and compute additional indicators. Excel was also used to produce charts while ArcView and Freehand were used for the maps.

Analysis and Report Preparation The analysis in this report focuses on regional comparisons, time series and national production estimates. Microsoft Excel was used to produce charts; ArcView and Freehand were used for maps, whereas Microsoft Word was used to compile the report.

Data Quality A great deal of emphasis was placed on data quality throughout the whole exercise from planning, questionnaire design, training, supervision, data entry, validation and cleaning/editing. As a result of this, it is believed that the census is highly accurate and representative of what was experienced at field level during the Census year. With very few exceptions, the variables in the questionnaire are within the norms for Tanzania and they follow expected time series trends when compared to historical data. Standard Errors and Coefficients of Variation for the main variables are presented in the Technical Report (Volume I).

Sampling error estimates

The Sampling Error found on page (21) up to page (22) in the Technical Report for Agriculture Sample Census Survey 2002-2003

The Clean Energy Fund (CEF) Agriculture Audit program identifies energy efficiency measures for eligible farms and on-farm producers, including but not limited to: dairies, orchards, greenhouses, vegetables, vineyards, grain dryers, and poultry/egg. NYSERDA assigns Flexible Technical Assistance (FlexTech) Program Consultants to perform energy audits for eligible farms. Participating farms receive a customized plan with recommended energy efficiency upgrades. The Clean Energy Fund (CEF) Agriculture Audits dataset contains information collected from the audits such as location, electric and natural gas utility provider, and amount of CEF funding awarded to each audit. The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, accelerate economic growth, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

Brazil Sales: Agriculture: Excl Irrigation: Cleaning & Select Eggs, Fruit or Other Agricultural Products data was reported at 92,699.720 BRL th in 2017. This records an increase from the previous number of 75,324.697 BRL th for 2016. Brazil Sales: Agriculture: Excl Irrigation: Cleaning & Select Eggs, Fruit or Other Agricultural Products data is updated yearly, averaging 67,878.000 BRL th from Dec 2005 (Median) to 2017, with 13 observations. The data reached an all-time high of 106,037.000 BRL th in 2015 and a record low of 4,926.000 BRL th in 2006. Brazil Sales: Agriculture: Excl Irrigation: Cleaning & Select Eggs, Fruit or Other Agricultural Products data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Machinery and Equipment Sector – Table BR.RMB003: Machinery and Equipment Sales: Agriculture.

The global grain and seed cleaning equipment market, valued at $453.9 million in 2025, is projected to experience steady growth, driven by several key factors. Increasing demand for high-quality grains and seeds for food processing and agricultural applications is a primary driver. The rising global population and the consequent need for enhanced food security are significantly boosting the market. Furthermore, advancements in cleaning technology, leading to more efficient and precise separation of impurities, are fueling adoption. The shift towards mechanized farming practices, particularly in developing economies, also contributes to market expansion. Specific application segments, such as pre-cleaning and fine cleaning equipment for grains and seeds, demonstrate strong growth potential, reflecting the increasing sophistication of agricultural operations. Competition among established players like Buhler AG, AGCO Corporation, and PETKUS Technologie GmbH, alongside smaller, specialized companies, ensures market dynamism and innovation. However, factors such as the high initial investment costs for advanced equipment and regional variations in agricultural practices could pose some restraints on market growth. The market is geographically diverse, with North America and Europe currently holding significant market shares, while Asia-Pacific is expected to show considerable growth potential in the coming years, driven by expanding agricultural sectors in countries like India and China. The forecast period (2025-2033) anticipates a continuation of this positive trajectory, with the CAGR of 4.6% indicating consistent expansion. This growth will be influenced by technological improvements, including the integration of automation and data analytics to optimize cleaning processes and reduce waste. Government initiatives promoting sustainable agricultural practices and investments in agricultural infrastructure in several regions are also expected to support the market's upward trend. While the high cost of equipment might remain a barrier for smaller farms, the long-term benefits in terms of improved yield and quality are expected to outweigh the initial investment, driving continued market growth. The market segmentation by application (grain vs. seed) and type of cleaning equipment will likely remain relevant, reflecting distinct needs and preferences across different agricultural sectors.

This is USDA-ARS data from the publication: "Evaluation of Alternative-Design Cotton Gin Lint Cleaning Machines on Fiber Length Uniformity Index". The study was conducted in 2018 & 2019 with continued sample and data analysis through August 2023. Developing cotton ginning methods that improve fiber length uniformity index to levels that are compatible with the newer and more efficient spinning technologies would expand market share and increase the demand for cotton products and give U.S. cotton a competitive edge to synthetic fibers. Older studies on lint cleaning machines showed that the most widely used feed mechanism that places fiber on the cleaning cylinder damages fiber and reduces uniformity. The present study evaluates how conventional and experimental feed mechanisms affect uniformity. The lint cleaners were used with both saw and roller gin stands. Four diverse cotton cultivars from the Far West, Southwest, and Mid-South were used in the test. The data included gining process variables, raw seed cotton characteristics, raw lint High Volume Instrument (HVI), Advanced Fiber Information System (AFIS), and micro-dust and trash analyzer (MDTA3) measurements.

Brazil Production: Agriculture: excluding Irrigation: Machines Parts for Cleaning, Sorting data was reported at 71,631.438 BRL th in 2017. This records an increase from the previous number of 68,515.028 BRL th for 2016. Brazil Production: Agriculture: excluding Irrigation: Machines Parts for Cleaning, Sorting data is updated yearly, averaging 43,096.269 BRL th from Dec 2005 (Median) to 2017, with 13 observations. The data reached an all-time high of 73,621.000 BRL th in 2015 and a record low of 20,769.000 BRL th in 2005. Brazil Production: Agriculture: excluding Irrigation: Machines Parts for Cleaning, Sorting data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Machinery and Equipment Sector – Table BR.RMA006: Machinery and Equipment Production: Agriculture.

Abstract

The ECVM/A is an integrated multi-topic household survey done for the purpose of evaluating poverty and living conditions in Niger.

The main objectives of the ECVMA are to: - Gauge the progress made with achievement of the Millennium Development Goals (MDGs); - Facilitate the updating of the social indicators used in formulating the policies aimed at improving the living conditions of the population; - Provide data related to several areas that are important to Niger without conducting specific surveys on individual topics ; - Provide data on several important areas for Niger that are not necessarily collected in other more specific surveys.

The ECVM/A involves two visits, which means that each household is visited twice. The first visit takes place during the planting season. The second visit takes place during the harvest season. The household and agriculture/livestock, as well as the community/price questionnaire are administered during the first visit. During the second visit, only the household and agriculture/livestock questionnaires are administered.

Geographic coverage

National

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The ECVM/A 2011 has been designed to have national coverage, including both urban and rural areas in all the regions of the country. The domains are defined as the entire country, the city of Niamey; and other urban areas, rural areas, and in the rural areas, agricultural zones, agro-pastoral zones and pastoral zones. Taking this into account, 26 explicit sampling strata were selected: Niamey, and urban, agriculture, agro-pastoral and pastoral zones of the seven regions other than Niamey.

The target population is drawn from households in all 8 regions of the country with the exception of certain strata found in Arlit (Agadez Region) because of difficulties in going there, the very low population density, and collective housing. The portion of the population excluded from the sample represents less than 0.4% of the total population of Niger. Of a total of 36,000 people not included in the sample design, about 29,000 live in Arlit and 7,000 in collective housing.

The sample was chosen through a random two stage process:

• In the first stage a certain number of Enumeration Areas (known as Zones de Dénombrement or ZDs) will be selected with Probability Proportional to Size (PPS) using the 2001 General Census of Population and Housing as the base for the sample, and the number of households as a measure of size.
• In the second stage, 12 or 18 households will be selected with equal probability in each urban or rural ZD respectively. The base for the sample will be an exhaustive listing of households that will be done before the start of the survey.

The total estimated size of the sample is 4,074 households. The fact that this is the first survey with panel households to be revisited in the future was taken into account in the design and therefore it is possible to lose households between the two surveys with minimal adverse effects on the analyses.

Mode of data collection

Face-to-face [f2f]

Research instrument

The household questionnaire comprises 13 sections, not including the cover page which covers information of a general nature (identity, name of household head) and Section 0 which covers detailed information on household identification and the results of the survey. The second visit household questionnaire is a reduced version of the version used in the first round. It includes information to determine if members who were in the household in the first visit are still in the household and if there are any new members. When there are new members, the questionnaire is used to collect basic information on their socio-demographic.

Like the household questionnaire, the agriculture/livestock questionnaire is divided into sections and sub-sections. The different sections, numbering 8 in all, address the issues of access to land, rainy season agriculture, "contre-saison" agriculture (dry season), livestock, forestry, agricultural equipment, access to agricultural extension services, and climate change. The agriculture and livestock questionnaire, second visit, collects information on harvests from the recently completed season and information on livestock rearing and production. In addition, information was collected on tree crops, agricultural extension, and climate change.

The community questionnaire has 7 sections. In addition, the cover pages collects general information (identification information, etc.) and section 0 provides the names of the respondents. In the second visit, the community questionnaire was used only to collect local prices.

Cleaning operations

The data entry was done in the field simultaneously with the data collection. Each data collection team included a data entry operator who key entered the data soon after it was collected. The data entry program was designed in CSPro, a data entry package developed by the US Census Bureau. This program allows three types of data checks: (1) range checks; (2) intra-record checks to verify inconsistencies pertinent to the particular module of the questionnaire; and (3) inter-record checks to determine inconsistencies between the different modules of the questionnaire.

The data entry from the first passage was completed in September 2011 and data cleaning was completed in December. The data cleaning process took longer than expected because it was done simultaneously with preparing for the second visit. Data entry from the second visit was completed in January 2012 and the data cleaning for both rounds was completed in August 2012.

Abstract

The Census of Agriculture & Fisheries (AGC 2011) is a national government operation geared towards the collection and compilation of statistics in the agriculture sector of the country. The collected data will constitute the bases from which policymakers and planners will formulate plans for the country's development.

The first Census of Agriculture (CoA) in the Cook Islands was conducted in 1988 and the second in 2000. Both censuses were supported technically by FAO. The Cook Islands also has a long history of population census taking at 5-yearly intervals in years ending in 1 and 6. Traditionally the Census of Population and Dwellings (CoPD) has included questions on agricultural activity at the household level, types of crops grown, livestock numbers, farm machinery and involvement in fishing and pearl farming activities. Section 3 of this report looks at data collected in the CoPD 2011 related to agricultural, fishing and pearl farming activities

Geographic coverage

National coverage.

Analysis unit

Household; Holding; Parcel; Individual.

Universe

The census covered all households, agricultural operators, agricultural establishments, fishing operators and pearl farmers.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

The census of population and dwellings had 4 categories of agricultural activity, namely: subsistence only, commercial only, subsistence and commercial and no agriculture. For those engaged in agricultural activity a further breakdown was collected, namely: vegetables, fruit, flowers and other. The census of agriculture also had 4 categories but for crop growing only, namely, non-agricultural, minor agricultural, subsistence and commercial. The differences in these classifications and the types of agriculture included make comparisons difficult, however, it is useful to evaluate the two sets of data and draw conclusions as to the extent of agricultural activity in the cook islands from these two sources.

The questionnaires used for the census of agriculture 2000 and the census of population and dwellings 2006, related to agriculture, were reviewed and efforts made to avoid duplication. In particular, the question on the numbers of livestock kept by the household was dropped from the census of population and dwellings as this data was being collected in the census of agriculture. Likewise, information on machinery and equipment was dropped from the census of agriculture as this was being collected in the census of population and dwelling. Questions on the extent of involvement in agricultural activity at the household level were maintained in both censuses as was the extent of involvement in fishing and pearl farming. This provided a useful coverage check for the census of agriculture, in particular, although it was noted that there were definitional differences between the two censuses especially related to flower cultivation which was considered an agricultural activity in the census of population and dwellings but not in the census of agriculture. At the individual level, data on labour inputs was recorded in the census of agriculture by age and sex but other data at the individual level has then to be obtained through linkages to the census of population and dwellings through the person and household number.

The household questionnaire was administered in each household, which collected various information on levels of agricultural activity, holdings detail (including name of operator, total area, number of separate parcels, location), crops currently growing and/or harvested (including crops currently growing, total area, number of plants,crops planted and/or harvested, total area, number of plants), proportion of income from agriculture, loans for agriculture purposes, fertilizers, agricultural chemicals, improved varieties, other selected activities during the last 12 months (including bee keeping, hydroponic, floriculture, handicrafts), traditional methods on food storage and planting, travelling with locally grown food, water usage

In addition to a household questionnaire, questions were administered in each household for holding which collected various information on holding iidentification, parcel details during the lasts 12 months (including location, area, land tenure, land use, months used), scattered plants/trees (including number of plants), labour input for persons 15 years and over working during the last month (including sex, age, status, type, average hours worked per week, wages per month, benefits and other paid job)

In addition to a holding questionnaire, questions were administered for parcels which collected various information (during the last 12 months) on plot details (including proportion to parcel area, crops grown, method of planting, number of plants and proportion for sale), crops planted and harvested (including area harvested, number of plants and proportion for sale)

In addition to a household questionnaire, questions were administered in each household for livestock which collected various information on type and number of livestock, type of operation, nature of disposal during the last 12 months (including kind of livestock, number disposed (including home use, feast/gifts, sold, slaughtered, live)

In addition to a household questionnaire, questions were administered in each household for fishing which collected various information on household members engaged, main purpose of fishing activity, household members (including average hours spent per week), details of fishing activities (including forms of fishing, number of people fishing, location, average number of fishing trips, average hours per fishing trip), boat details (including type of boat, length, engine), proportion of fish caught/collected and sold, proportion consumed

In addition to a household questionnaire, questions were administered in each household for pearl farming which collected various information (during the last 12 months) on farming details (including farm lines, spat collector lines, spat details, number of farm shells, labour input (including person number, sex, age, status, type, average hours worked per week, wages per month, benefits received, other paid job) , boat operation (including times used per week), type of equipment and facility, number of times per week, number owned, hired, borrowed), shelling details, proportion of income, loan details

The questionnnaires, that were developed in English, contain was divided into 5 forms: -Household Form: Levels of agricultural activity, List of agricultural holdings, Crops, Income from agricultural activities, Loans, Fertilizers, Other relevant questions. -Holding Form: Parcel details, Scattered plants/trees, Labour inputs. -Parcel Form: Number of sepearate plots, Plot details, Crops. -Livestock Form: Livestock details, Type of operation, Nature of disposal. -Fishing & Pearl Farming Form: Fisheries activities details, Pearl farm information, Labour inputs, Boats and other equipment used, Other relevant information.

Cleaning operations

The length and complexity of the census of agriculture forms made the exercise much more time consuming and virtually all records had to be edited. The data capture and data cleaning exercise for the census of agriculture took the best part of 12 months, including the adjustments following the re-enumeration of Aitutaki. Tabulation also proved to be challenging because of the need for considerable internal computation of areas and numbers of plants. The final database was then split up into a number of smaller databases designed for each set of tables. The tabulation was done using Microsoft EXCEL and ACCESS

In interpreting the results of the census of agriculture, account needs to be taken of the fact that households classified as having no agricultural or fishing activities in the census of population and dwellings were excluded from the census of agriculture, especially on Rarotonga. Other definitional differences between the two censuses should also be noted. The census of population and dwellings defined agricultural activity as crops, livestock and floriculture whereas the ensus of agriculture definition was primarily crops. Livestock and poultry raising was treated separately in the census of agriculture and flower growing was only included in the census of agriculture if it was a commercial activity or was carried out in conjunction with food crop activities.

The global grain and seed cleaning equipment market is experiencing steady growth, projected to reach a value of $924 million in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 4.0% from 2025 to 2033. This growth is fueled by several key factors. Increasing global demand for food, driven by a burgeoning population and rising disposable incomes, necessitates efficient and high-quality grain and seed processing. Technological advancements in cleaning equipment, leading to improved efficiency, reduced waste, and enhanced product quality, are further contributing to market expansion. Furthermore, the growing adoption of precision agriculture techniques, requiring better seed quality control, is boosting the demand for sophisticated cleaning solutions. The market is segmented by equipment type (fixed and mobile) and application (grain and seed processing plants, grain depots, and others), with the grain and seed processing plant segment currently dominating due to higher volumes of processing. Competitive dynamics involve a mix of established players like Bühler AG and AGCO Corporation (Cimbria), alongside smaller, specialized companies focusing on niche applications. Geographic growth is expected to be spread across regions, with North America and Europe maintaining significant market shares, while Asia-Pacific is poised for substantial growth given the region's expanding agricultural sector. Market restraints include fluctuating raw material prices, potential economic downturns impacting investment in agricultural infrastructure, and the need for skilled labor to operate and maintain advanced equipment. However, the long-term outlook remains positive, driven by the increasing focus on food security, improved agricultural practices, and the continued development of innovative cleaning technologies. The market is expected to witness increased adoption of automation and data analytics to optimize cleaning processes and enhance overall efficiency, further driving growth in the coming years. The consistent demand for higher-quality grains and seeds for food, feed, and biofuel production will continue to be a primary catalyst for market expansion throughout the forecast period.

The global ditch cleaner market is experiencing robust growth, driven by the increasing demand for efficient irrigation and drainage solutions in agriculture. The market, valued at approximately $500 million in 2025, is projected to expand at a compound annual growth rate (CAGR) of 6% from 2025 to 2033, reaching an estimated $800 million by 2033. This growth is fueled by several key factors. Firstly, the rising adoption of precision agriculture techniques necessitates improved water management, increasing the demand for efficient ditch cleaning equipment. Secondly, the expansion of arable land, particularly in developing economies, contributes to the growing need for reliable drainage solutions. Furthermore, government initiatives promoting sustainable agricultural practices and water conservation further stimulate market demand. The market is segmented by application (vegetables, row crops, tobacco, fruit) and type (single-wheel and double-wheel ditch cleaners). Double-wheel ditch cleaners are gaining traction due to their enhanced cleaning capabilities and increased efficiency. While North America and Europe currently hold significant market shares, Asia-Pacific is projected to witness substantial growth over the forecast period, driven by rapid agricultural expansion and modernization in countries like India and China. However, the market faces some restraints, including high initial investment costs for advanced ditch cleaning equipment and the availability of alternative, less expensive drainage methods in certain regions. The competitive landscape is characterized by both established players and emerging regional manufacturers. Key players like AP Machinebouw, COSMECO, DONDI, PEECON, Quivogne, ROLMEX, SOVEMA, and Spearhead are continuously innovating to enhance product features and expand their market reach. Strategies such as mergers and acquisitions, strategic partnerships, and technological advancements are expected to shape the market dynamics in the coming years. The focus is shifting towards developing environmentally friendly and energy-efficient ditch cleaners, responding to the increasing concerns about sustainability in agriculture. This trend will influence product design, manufacturing, and marketing strategies of major players. Overall, the global ditch cleaner market presents a significant opportunity for growth and innovation, driven by the need for improved agricultural efficiency and sustainable water management.

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This statistic denotes the global market size across several regions including APAC, Europe, North America, MEA, and South America. The household cleaning products market size was estimated to be at $ 20.13 bn in 2020-2024.

The size of the global household cleaning products market has been derived by triangulating data from multiple sources and approaches. While arriving at the market size, we have considered data points, such as the size of the parent market and the revenues of key market participants, such as Church & Dwight Co. Inc., Colgate-Palmolive Co., Godrej Consumer Products Ltd., Henkel AG & Co. KGaA, Kao Corp., Reckitt Benckiser Group Plc, S. C. Johnson & Son Inc., The Clorox Co., The Procter & Gamble Co., and Unilever Group

Comments for our proposed process for developing voluntary clean water guidance for agriculture

The construction of the dataset involved meticulous processes, including converting production into standardized unit, yield calculation for each dataset, standardization of column names, assembly of data, extensive data cleaning, and making it a hopefully robust and reliable resource for understanding spatial yield distribution in the region.

Data Sources: The dataset comprises seven spatialized yield data sources, six of which are from the LSMS-ISA program (Mali 2014, Mali 2017, Mali 2018, Benin 2018, Burkina Faso 2018, Niger 2018) and one from the RHoMIS study (only Mali 2017 and Burkina Faso 2018 data selected).

Dataset Preparation Methods: The preparation involved integration of machine-readable files, data cleaning and finalization using Python/Jupyter Notebook. This process should ensure the accuracy and consistency of the dataset. Yield have been calculated with declared production quantities and GPS-measured plot areas. Each yield value corresponds to a single plot.

Discussion: This dataset, with its extensive data compilation, presents an invaluable resource for agricultural productivity-related studies in West Africa. However, users must navigate its complexities, including potential biases due to survey and due to UML units, and data inconsistencies. The dataset's comprehensive nature requires careful handling and validation in research applications.

Authors Contributions:

• Data treatment: Eliott Baboz, Jérémy Lavarenne.
• Documentation: Jérémy Lavarenne.

Funding: This project was funded by the INTEN-SAHEL TOSCA project (Centre national d’études spatiales). "123456789" was chosen randomly and is not the actual award number because there is none, but it was mandatory to put one here on Zenodo.

Changelog:

• v1.0.0 : initial submission

Abstract

The general objective of CSA's Agricultural Sample Survey (AgSS) is to collect basic quantitative information on the country's agriculture that is essential for planning, policy formulation, monitoring and evaluation of mainly food security and other agricultural activities. The AgSS is composed of four components: Crop Production Forecast Survey, Meher Season Post Harvest Survey (Area and production, land use, farm management and crop utilization), Livestock Survey and Belg Season Survey.

The specific objectives of Meher Season Post Harvest Survey are to estimate the total crop area, volume of crop production and yield of crops for Meher Season agriculture in Ethiopia.

Geographic coverage

The annual Agricultural Sample Survey (Meher season) covered the entire rural parts of the country except the non-sedentary population of three zones of Afar and six zones of Somali regions

Analysis unit

Agricultural household/ Holder/ Crop

Universe

The survey covered agricultural households in the sample selected regions.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame The list containing EAs of all regions and their respective households obtained from the 2007 (1999 E.C) cartographic census frame was used as the sampling frame in order to select the primary sampling units (EAs). Consequently, all sample EAs were selected from this frame based on the design proposed for the survey. The second stage sampling units, households, were selected from a fresh list of households that were prepared for each EA at the beginning of the survey.

Sample Design In order to select the sample a stratified two-stage cluster sample design was implemented. Enumeration areas (EAs) were taken to be the primary sampling units (PSUs) and the secondary sampling units (SSUs) were agricultural households. The sample size for the 2010/11 agricultural sample survey was determined by taking into account of both the required level of precision for the most important estimates within each domain and the amount of resources allocated to the survey. In order to reduce non-sampling errors, manageability of the survey in terms of quality and operational control was also considered.

All regions were taken to be the domain of estimation for which major findings of the survey are reported.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2011-2012 annual Agricultural Sample Survey used structured questionnaires to collect agricultural information from selected sample households. List of forms in the questionnaires: - AgSS Form 2004/0: It contains forms that used to list all households in the sample areas. - AgSS Form 2004/1: It contains forms that used to list selected agricultural households and holders in the sample areas. - AgSS Form 2004/2A: It contains forms that used to collect information about crops, results of area measurements covered by crops and other land uses. - AgSS Form 2004/2B: It contains forms that used to collect information about miscellaneous questions for the holders. - AgSS Form 2004/4: It contains forms that used to collect information about list of temporary crop fields for selecting crop cutting plots. - AgSS Form 2004/5: It contains forms that used to collect information about list of temporary crop cutting results.

Cleaning operations

Editing, Coding and Verification Statistical data editing plays an important role in ensuring the quality of the collected survey data. It minimizes the effects of errors introduced while collecting data in the field, hence the need for data editing, coding and verification. Although coding and editing are done by the enumerators and supervisors in the field, respectively, verification of this task is done at the Head Office.

An editing, coding and verification instruction manual was prepared and reproduced for this purpose. Then 66 editors-coders and verifiers were trained for two days in editing, coding and verification 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 took 18 days.

Data Entry, Cleaning and Tabulation Before data entry, the Agriculture, Natural Resources and Environment Statistics Directorate of the CSA prepared edit specification for the survey for use on personal computers for data consistency checking purposes. The data on the edited and coded questionnaires were then entered into personal computers. The data were then checked and cleaned using the edit specifications prepared earlier for this purpose. The data entry operation involved about 70 data encoders, 10 data encoder supervisors, 12 data cleaning operators and 55 personal computers. The data entered into the computers using the entry module of the CSPRO (Census and Survey Processing System) software, which is a software package developed by the United States Bureau of the Census. Following the data entry operations, the data was further reviewed for data inconsistencies, missing data … etc. by the regular professional staff from Agriculture, Natural Resources and Environment Statistics Directorate. The final stage of the data processing was to summarizing the cleaned data and produce statistical tables that present the results of the survey using the tabulation component of the PC based CSPRO software produced by professional staff from Agriculture, Natural Resources and Environment Statistics Directorate.

Response rate

A total of 2,290 Enumeration Areas (EAs) were selected. However, due to various reasons that are beyond control, in 17 EAs the survey could not be successful and hence interrupted. Thus, all in all the survey succeeded to cover 2,273 EAs (99.25 %) throughout the regions. The Annual Agricultural Sample survey (Meher season) was conducted on the basis of 20 agricultural households selected from each EA. Regarding the ultimate sampling units, it was intended to cover aa total of 47,080 gricultural households, however, 45,575 (98.9 %) were actually covered by the survey.

Sampling error estimates

Estimation procedure of totals, ratios, sampling error and the measurement of precision of estimates (CV) are given in Appendix-I and II of the final report. Distribution of sampling units (sampled and covered EAs and households) by stratum is also presented in Appendix-III of the final report.

Analysis of ‘Clean Energy Fund Agriculture Audits: Beginning 2016’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/c2c40cbb-4d7a-4e9f-8b39-8c359927e24c on 27 January 2022.

--- Dataset description provided by original source is as follows ---

The Clean Energy Fund (CEF) Agriculture Audit program identifies energy efficiency measures for eligible farms and on-farm producers, including but not limited to: dairies, orchards, greenhouses, vegetables, vineyards, grain dryers, and poultry/egg. NYSERDA assigns Flexible Technical Assistance (FlexTech) Program Consultants to perform energy audits for eligible farms. Participating farms receive a customized plan with recommended energy efficiency upgrades. The Clean Energy Fund (CEF) Agriculture Audits dataset contains information collected from the audits such as location, electric and natural gas utility provider, and amount of CEF funding awarded to each audit.

The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, accelerate economic growth, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on Twitter, Facebook, YouTube, or Instagram.

--- Original source retains full ownership of the source dataset ---

Abstract

The availability of statistical data on agriculture is necessary to draw up policies and plans for the future development of this sector. Agriculture plays a vital role and represents a significant share of the Palestinian Gross Domestic Product (GDP), and also of the Palestinian labour force. There is a pressing need for specialized surveys to be conducted that will complement the first Agricultural Census in the Palestinian Territory that was conducted in 2010.

Geographic coverage

Palestinian Territory

Analysis unit

Agricultural Holding

Universe

All agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample is a one-stage stratified simple random sample due to: 1 - An updated framework. 2 - Strata depend on the type of holdings. 3 - Strata depend on the size of holdings. 4 - Deal directly with the holdings.

Mode of data collection

Face-to-face [f2f]

Research instrument

The agricultural statistics questionnaire was designed based on the recommendations of the Food and Agriculture Organization of the United Nations (FAO) and the questionnaire used for the Agricultural Census of 2010. The special situation of the Palestinian Territory was taken into account, in addition to the specific requirements of the technical phase of field work and of data processing and analysis.

Cleaning operations

This phase included the following operations: - Preparation of Data Entry Program. The data entry program was prepared using ACCESS software and data entry screens were designed. Rules of data entry were established to guarantee successful entry of questionnaires and queries checked data after each entry. These queries examined variables on the questionnaire. - Data Entry Having designed the data entry program and tested it to verify readiness, and after training staff on data entry programs, data entry began on 12 February 2012 and finished on 20 May 2012 with 12 staff engaged in the data entry process. - Editing of Entered Data Special rules were formulated for editing the stored data to guarantee reliability and ensure accurate and clean data.

Response rate

93.3%

Sampling error estimates

Survey data may be affected by statistical errors due to the use of the sample. Therefore, certain differences may emerge from the true values anticipated through censuses. The variation of the most important indicators was calculated and dissemination levels of the data were particularized at governorate level in the West Bank and Gaza Strip, according to the sample design and the variance calculations for the different indicators.

Abstract

Recognizing the national titling agenda and the importance of strengthening land rights to promote long-term agricultural investments, the National Cooperative Business Association CLUSA (NCBA CLUSA) incorporated a gender-targeted land-use title component into their Promotion of Climate Smart Agriculture (PROMAC) II Program. The program used the Cadasta Foundation’s spatial data collection system to demarcate and process land-use titles. NCBA CLUSA also developed a long-term investment bundle to assist household investment in their newly secure land, providing fruit trees and other inputs to program households.

The World Bank Africa Gender Innovation Lab is conducting a rigorous impact evaluation of the titling and long-term investment bundle components implemented under PROMAC II to measure their respective impacts on land tenure security, women’s economic empowerment, agricultural productivity, and food security. This study will inform the design of future land regularization efforts and examine whether additional constraints are limiting household investments.

These data represent the third round of data collection (endline) for the impact evaluation. The sample comprises 970 smallholder farmers in Molumbo district in Zambézia Province

Geographic coverage

Mozambique, Province of Zambezia, District of Molumbo

Analysis unit

The unit of analysis is farmer or plot or individual depending on the survey section.

Kind of data

Sample survey data [ssd]

Sampling procedure

The endline sample comprises 970 smallholder farmers living in Molumbo district in the Zambezia Province in Mozambique. To define the sample, we partnered with two NGOs, NCBA-CLUSA and the Cadasta Fondation. We conducted a baseline survey on the total sample of 1,063 farmers in 2020. We selected the sample among the PROMAC (Promotion of Climate Smart Agriculture) II Program’s existing participants and newly recruited households eligible to participate in the program. To be included in the sample, beneficiaries had to meet three eligibility criteria: (i) female-headed or married/cohabitating; (ii) own their land; and (iii) do not already have land-use titles.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The data consists of responses from households to questions pertaining to (i) agricultural production and sales, crop choices, input usage, and farming practices; (ii) trees; (iii) social network in the community; (iv) women empowerment and intra-household bargaining; (v) household and farm assets; (vi) employment including off-farm; (vii) consumption; and (viii) perceived land tenure security.

Whenever relevant, the agricultural module of the household questionnaire was collected at the season level rather than at the yearly level.

Cleaning operations

Data was anonymized through decoding and local suppression.

Response rate

The response rate for this midline survey is 91% (970 farmers responded out of 1,063 total sample).

The global grain and seed fine cleaning equipment market is experiencing robust growth, driven by increasing demand for high-quality agricultural products and stringent food safety regulations. The market, estimated at $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 5.5% from 2025 to 2033, reaching an estimated value of $4.0 billion by 2033. This growth is fueled by several key factors, including the rising global population requiring increased food production, advancements in cleaning technology leading to improved efficiency and yield, and a growing focus on minimizing post-harvest losses. The adoption of precision agriculture techniques and the increasing mechanization of farming practices further contribute to market expansion. Significant growth is anticipated in regions like Asia-Pacific, driven by expanding agricultural sectors and rising disposable incomes. However, factors such as high initial investment costs for advanced equipment and fluctuating raw material prices pose challenges to market growth. The market is segmented by equipment type (horizontal and vertical), application (grain and seed cleaning), and geography, with North America and Europe currently holding the largest market shares due to established agricultural infrastructure and technological advancements. The competitive landscape is characterized by a mix of established global players and regional manufacturers. Key players such as Bühler AG, AGCO Corporation (Cimbria), and PETKUS Technologie GmbH are focusing on technological innovation and strategic partnerships to maintain their market leadership. Smaller companies are gaining traction through niche product offerings and regional focus. Future market growth will likely be driven by innovations in automated cleaning systems, improved sensor technology for precise grain sorting, and the integration of data analytics for optimized cleaning processes. The increasing adoption of sustainable farming practices will further influence market trends, with a demand for energy-efficient and environmentally friendly equipment expected to grow. Market participants are likely to see success by focusing on providing tailored solutions that address specific customer needs in different geographical regions.

Ethylene (C2H4), orb 1 ionization (Ag), wavefn run, 1.0:2.5:100.0 - photoionization calculations with ePolyScat (ePS) + ePSproc.*Web version*: https://phockett.github.io/ePSdata/C2H4_1.0-100.0eV/C2H4_1.0-100.0eV_orb1_Ag.htmlFor more details of the calculations, see readme.txt, or: About ePSdataAbout ePSprocAbout ePS

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