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Abstract

The program of land tenure regularization (LTR) aims to clarify rights on all of Rwanda estimated 10 million land parcels as a precondition for their formalization and full legal recognition, manifested in the award of title certificates to land holders.

For this study, researchers from the World Bank assessed the impact of the rural pilots that preceded the national roll-out of Rwanda's LTR program using a geographic discontinuity design with spatial fixed effects. The study focused on the following questions: - the extent of perceived land tenure security; - the level of land transactions; - land-related investment undertaken; - the treatment of boys and girls in terms of inheritance; - perception about the fairness of the process and access to information.

In the absence of a usable baseline survey, researchers relied on cross-sectional data, sampled from a narrow band on both sides of the pilot cell borders to assess program impacts. A survey administered in April–May 2010, about two and a half years after the start of LTR, was used to obtain information for 3,554 households with some 6,330 parcels.

Geographic coverage

Biguhu, Kabushinge, Nyamugali and Mwoga

Analysis unit

Household and parcel (land) level

Kind of data

Sample survey data [ssd]

Sampling procedure

The LTR pilots applied a participatory and low-cost process to systematically cover a total of 3,513 households with some 15,000 plots in four areas (one of them urban) that were chosen to reflect the country's heterogeneity.

The challenge of this study was lack of baseline data to make a credible assessment of the pilot program. This challenge was addressed by sampling on both sides of the borders of the pilot areas-using high precision satellite images and the cadastral survey-that allows the comparison of outcome variables between households inside (treated) and outside (non-treated) of the borders of the pilot cells. The discontinuity created by administrative boundaries in the introduction of the pilot program is, therefore, exploited as an identification strategy on the assumption that households close to a cell boundary, before the start of the program, were similar in unobservable factors affecting relevant outcomes. The sample was designed to yield numbers of households in each pilot cell equivalent to their share in the total, with a size of 3,554 households with some 6,330 land parcels, intended to be split equally across pilot and their neighboring cells.

The sample was to be distributed equally on both sides of the pilot cell boundary to create a treatment group (within the titled cell) and a control group (those just across the border in nonprogram cells). Parcel index maps created by the program were used to sample within pilot cells. For adjacent (control) cells, researchers used high resolution satellite imagery to visually identify dwellings that could then serve as a sample frame.

Mode of data collection

Computer Assisted Personal Interview [capi]

Understanding smallholder decision-making to increase farm tree diversity: Enablers and barriers for forest landscape restoration in Western Kenya https://doi.org/10.5061/dryad.mw6m90666 ## Description of the data and file structure The survey_data file contains anonymised survey responses to the TPB questions. An explanation of all variables in the data file can be found in the metadata or README files. To ensure that no more than three indirect identifiers for human subjects were included (here: household status, education, and relative wealth), the following variables were excluded from the open-access dataset: age, sex, household size, and land tenure status. ### README | variable | explanation | | :--------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------- | | ID | assigned ID for each farmer | | hh_head | household head status (yes =1/no =0) | | education | level of completed education (0 = no formal education, 1 = primary education, 2 = secondary education, 3 = post school education) | | relative_wealth | relative wealth calculated based on the KenyaEquity Tool | | farm_size | size of the farm in points (1 point = 1/8 acre) | | distance | distance to the forest (near/far) | | member | whether farmer is a member in a farmer group | | other_income | whether farmer has other income apart from farming | | importance_pollinators | perceived importance of pollinators | | supported_pollinators | has farmer supported pollinators in the last year | | supported_pollinators_byplanting | has farmer supported pollinators in the last year by planting trees/shrubs | | planted_exotic | planted any exotic trees in the last year | | planted_indigenous | planted any indigenous trees in the last year | | planted_indigenous_exotic | planted any trees (exotic or indigenous) in the last year | | consequences_eucalyptus | experience negative consequences from eucalyptus | | intention | likelihood to diversify trees and shrubs on the farm in the next year | | attitude1 | perceived importance of diversifying | | attitude2 | level of contendedness with diversifying | | attitude3 | perceived level of benefit from diverisfying | | e_soil | expected likelihood of diversification to increase soil fertility | | e_pollinators | expected likelihood of diversification to increase pollinators | | e_yield | expected likelihood of diversification to increase yield | | e_livelihoods | expected likelihood of diversification to increase livelihood benefits | | e_harmful_animals | expected likelihood of diversification to increase visitation of harmful wildlife | | e_climatechange | expected likelihood of diversification to contribute towards mitigating climate change | | b_soil | perceived importance of increasing soil fertility | | b_pollinators | perceived importance of increasing pollinator abundance | | b_yield | perceived importance of increasing crop yield | | b_livelihoods | perceived importance of increasing livelihood benefits | | b_harmful_animals | perceived importance of avoiding visitation of harmful wildlife | | b_climatechange | perceived importance of mtigating climate change | | subnorm1 | opinion of other people about me diversifying | | subnorm2 | likelihood of others to diversify trees on their own farms | | n_family | opinion of family members toward diversification | | n_leaders | opinion of community elders and leaders toward diversification | | n_otherfarmers | opinion of other farmers toward diversification | | n_NGOs | opinion of NGOs toward diversification | | n_farmersgroups | opinion of farmers groups toward diversification | | n_ministry | opinion of Mnistry of Agriculture toward diversification | | n_media | opinion of media programmes toward diversification | | s_family | importance of opinion of family members | | s_leaders | importance of opinion of community elders and leaders | | s_otherfarmers | importance of opinion of other farmers | | s_NGOs | importance of opinion of NGOs | | s_farmergroups | importance of opinion of farmers groups | | s_ministry | importance of opinion of Ministry of Agriculture | | s_media | importance of opinion of media programmes | | pbcontrol1 | Perceived control over diversifying trees | | pbcontrol2 | Perceived difficulty to diversify trees | | c_knowledge | Level of available knowledge to diversify | | c_farmsize | Level of available farm size to diversify | | c_material | Level of available planting material to diversify | | c_finance | Level of available financial resources to diversify | | c_time | Level of available time to diversify | | p_knowledge | Perceived difficulty of not having sufficient knowledge to diversify | | p_farmsize | Perceived difficulty of not having enough land to diversify | | p_time | Perceived difficulty of not having sufficient time to diversi...

The Laguna Loop survey is a farm household level data set collected by the IRRI-Agricultural Economics Department prior to the introduction of the high yielding varieties of rice. This initial survey refers to 1965 dry season - data that provided excellent benchmark information from rice farming respondents. The study was based upon a survey in which information was obtained regarding farm practices and costs and returns and other associated data related to rice production. The subsequent surveys have been taken every two to four years to note the pattern of adoption of the high yielding varieties, paddy yield, prices, various farming inputs, labor use, land tenure arrangement and the changes that have occurred in farming practices and costs and returns that have occurred overtime. These data are extremely useful historical records of the changes that have occurred during the four decades, the dynamic period of Philippine rice production. The sample farms are selected from the six municipalities in Laguna province - namely: Calauan, Bay, Calamba, Cabuyao, Sta. Rosa and Binan . In the later years - some of the farmers discontinue farming because their rice farms were converted into industrial and residential purpose. Hence, there was a reduction in the number of respondents per year or per season in the later years. Generally, the focal objective of this study is to track the changes in the farmer's rice technology, cultural practices, land tenure, mechanization, and labor practices that occurred during the survey period from 1965 to 2008. A number of publications, research papers, reports, and vast collections of Masters and Dissertations theses have already been published that utilized this very exhaustive panel data sets.

To document historical changes that occurred in the village in the past two-three decades. It also aims to trace the major forces that promote the changes in rice farming in the rural village. To outline the process of Modern Variety (MV) diffusion and its impacts in rice yield, inputs and productivity.

"The Myanmar Aquaculture Agriculture Survey (MAAS) was implemented in April-May 2016 in four townships of Ayeyarwady and Yangon regions, close to the city of Yangon. Respondents from 1,102 households were interviewed, representing a total population of 37,390 households. MAAS was designed with the following objectives (1) Generate baseline information on fish and paddy farm yields, size, tenure status, management practices, and profitability. (2) Quantify the relative advantages of, and tradeoffs between, aquaculture and agriculture by estimating the size of growth linkages and employment multipliers in the local rural economy. (3) Compare the relative performance of large- and small-scale commercial aquaculture in terms of spillover effects, demand for labor, productivity, and returns. (4) Evaluate patterns of rural-rural and rural-urban migration, agricultural mechanization, and the extent and terms of access to credit in aquaculture and agriculture. In order to address these questions, MAAS adopted a two stage sampling strategy to facilitate comparison of the rural economy and livelihoods in groups of village tracts with high concentrations of fish farms (referred to as the aquaculture cluster), and in areas where paddy cultivation was the main farming activity (the agriculture cluster). The MAAS survey instrument was comprised of three elements: (1) A household section, containing modules on household composition, migration, employment, land and asset ownership, production of non-field crops, and consumption expenditures. (2) An aquaculture section, comprised of modules on: pond acquisition and tenure status; input utilization and costs (including labor); harvesting and marketing; trends in production over the preceding 10 years; and credit utilization. (3) An agriculture section, incorporating modules on: land ownership and tenure; irrigation; agricultural machinery and draft animal use; input application; marketing practices and costs; changes in production practices over the last decade; and access and utilization of agricultural credit. A short community survey was conducted with small focus groups of knowledgeable long-term residents in 73 of the 78 selected enumerations areas covered by the household survey. The questionnaire was designed to generate additional data on the physical accessibility of the communities, changing village populations, historical wage data, landownership structures and historical inventories of non-farm enterprises over the period 2011-2016, including those playing a role in aquaculture and agriculture value chains.

Snapshot img

Agribusiness Market Size 2025-2029

The agribusiness market size is forecast to increase by USD 843.4 million, at a CAGR of 4.6% between 2024 and 2029.

The market is experiencing significant shifts driven by population growth, which is leading to an increased demand for food production. This trend is placing immense pressure on the industry to find innovative solutions to meet the rising demand, particularly in the context of a shrinking amount of arable land. One response to this challenge is the adoption of automation in agriculture, with technologies such as precision farming, drones, and robotics gaining traction. These solutions aim to optimize resource usage and increase efficiency, enabling farmers to produce more with less land. However, the implementation of automation also presents challenges, including high upfront costs and the need for significant investment in technology and infrastructure.
Additionally, regulatory compliance and data security concerns add complexity to the adoption process. To capitalize on the market opportunities presented by population growth and the need for more efficient agriculture, companies must navigate these challenges and invest in technologies that can help them stay competitive while addressing the sustainability and productivity demands of the industry.

What will be the Size of the Agribusiness Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, shaped by various dynamics that impact different sectors. Land use patterns are shifting, with an increasing focus on carbon sequestration and sustainable farming practices. Precision agriculture is revolutionizing yield optimization, while food processing and packaging technologies advance to ensure food safety and traceability. Seed production and agricultural biotechnology are driving innovation in crop production, and harvesting equipment is becoming more efficient and eco-friendly. Food security remains a critical concern, leading to the adoption of irrigation systems, biodiversity conservation, and sustainable farming practices. Livestock feed and breeding are undergoing transformations, with a focus on disease prevention and environmental sustainability.

Pest control methods are evolving, with a shift towards more natural and less harmful alternatives. Supply chain management is becoming more complex, with the integration of farm management software and crop rotation techniques. Water conservation is a priority, with new technologies and practices emerging to address this challenge. Agribusiness investment is on the rise, driven by market volatility and price fluctuations due to climate change adaptation and commodity trading. Direct marketing and consumer dietary trends are influencing food distribution, with a growing demand for locally sourced and organic produce. Vertical farming and agricultural finance are gaining traction, offering solutions to land tenure issues and providing access to capital for small-scale farmers.

The ongoing unfolding of these market activities and evolving patterns underscores the continuous nature of the agribusiness landscape.

How is this Agribusiness Industry segmented?

The agribusiness industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Grains and cereals
  Dairy
  Oilseeds
  Livestock
  Others


Application

  Agrichemicals
  Seed business
  Breeding
  Machinery and equipment


Technology

  Traditional agriculture
  Mechanized farming
  Precision agriculture and smart farming
  Organic and sustainable Farming


Distribution Channel

  Retail chains and supermarkets
  Wholesale distribution
  Direct-to-consumer
  Food processing and manufacturing companies


Farm Size

  Small-Scale Farms
  Medium-Scale Farms
  Large-Scale Farms


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan
    South Korea


  South America

    Argentina
    Brazil


  Rest of World (ROW)

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By Product Insights

The grains and cereals segment is estimated to witness significant growth during the forecast period.

The market encompasses various sectors, including grains and cereals, dairy production, land use, carbon sequestration, agricultural machinery, organic farming, yield optimization, food security, irrigation systems, biodiversity conservation, supply chain management, livestock feed, livestock breeding, pest control, poultry farming, government subsidies, farm management software, crop rotation, water conservati

Abstract

In the past, the census of agriculture has aimed to provide data on the structure of agricultural holdings, with attention given to providing data for small administrative units and other detailed cross-tabulations of structural characteristics. Agricultural censuses have also been used to provide benchmarks to improve current crop and livestock statistics and to provide sampling frames for agricultural sample surveys. Previous agricultural censuses have focused on the activities of agricultural production units; that is, households or other units operating land or keeping livestock. They have not been seen as censuses of rural households.

This census is conducted to collect data needed by the government to make sound planning concerning the production of the agricultural produce.

Geographic coverage

Regional coverage

Analysis unit

Households

Universe

The agricultural holding is the enumeration unit, "comprising all land used and all livestock kept wholly or partly for agricultural production purposes. An individual or household may exercise management, jointly by two or more individuals or households, or by a juridical person such as a corporation, cooperative or government agency". The CA 2012/2013 covered the holdings in the household and the non-household sectors.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Enumeration area (EA): The 2012/13 Agricultural Census uses the maps of the previous population census enumeration areas (EAs). These maps divide the rural areas into E.A's. Identifying boundary features on land are roads, mountain ranges, and railway lines, etc. This was used to form EA boundaries. Each EA consists of a number of homesteads. For the purpose of agricultural census, these E.A's are verified. Normally in the agricultural census, an enumerator is assigned, at most, two E.A's or more. The EA is used as Primary Sampling Unit (PSU). The CA was a combination of: (i) a complete enumeration for certain items (in the first phase) and (ii) a sample enumeration of holdings for the items related to area and yield measurements and "general agricultural enquiries" (in the second phase). The census frame was built based on the EAs established for the Population and Housing Census (PHC) 2007.

Mode of data collection

Face-to-face [f2f]

Research instrument

One questionnaire with different modules was used for the holdings. Of the 16 core items required by the WCA 2010, the CA questionnaire covered 13 items.

(a) Holding location. (b) Legal status of holder. (c) Purpose of production. (d) Integration of holding with enterprises engaged in other economic activity (ies). (e) Basic demographic characteristics of holder and household. (f) Inventory of production factors: - Source of manpower used on the holding (family workers, hired agricultural workers); - Number and area of land parcels; - Area by land use; - Area harvested, by crop; - Number of cultivated trees by crop; - Number of livestock by type; - Type of machinery and equipment used; - Number of forest trees on the holding; - Agricultural buildings; (g) Tenure arrangements for production factors: - Land tenure; - Source of machinery and equipment used; (h) Other features: - Shifting cultivation; - Use of irrigation; - Drainage; - Fertilizers; - Pesticides; - High variety seeds; (i) Fishery or forestry activities if carried out on a holding; (j) Livestock system.

Cleaning operations

a. DATA PROCESSING AND ARCHIVING Manual data entry and editing (such as range and consistency edits, and imputations) were done using CSPro software. Tables were generated and edited using, respectively, CSPro and Microsoft Excel.

b. CENSUS DATA QUALITY The fieldwork was organized in several phases, to ensure the quality of the data collected. An enumerator was responsible for data collection in three EAs established for the PHC 2007, according to an adjusted workload. S/he was part of a team of ten enumerators, supervised by a field supervisor, who checked the coherence and quality of each filled questionnaire.

Data appraisal

Dissemination workshops were organized to release the census results. At the time of publication, the CA report with the final results had not yet been released.