Year Published
- 2008 (3) Apply 2008 filter
- 2009 (10) Apply 2009 filter
- 2010 (10) Apply 2010 filter
- 2011 (6) Apply 2011 filter
- (-) Remove 2012 filter 2012
- (-) Remove 2013 filter 2013
- 2014 (0)
- 2015 (5) Apply 2015 filter
- 2016 (8) Apply 2016 filter
- 2017 (6) Apply 2017 filter
- 2018 (1) Apply 2018 filter
- 2019 (2) Apply 2019 filter
- 2020 (0)
- 2021 (0)
Research Topics
Populations
- Countries/Governments (0)
- Rural Populations (0)
- Smallholder Farmers (1) Apply Smallholder Farmers filter
- Women (0)
Types of Research
Geography
- East Africa Region and Selected Countries (2) Apply East Africa Region and Selected Countries filter
- Global (0)
- South Asia Region and Selected Countries (0)
- Southern Africa Region and Selected Countries (0)
- Sub-Saharan Africa (1) Apply Sub-Saharan Africa filter
- West Africa Region and Selected Countries (0)
Dataset
- ASTI (0)
- FAOSTAT (0)
- Farmer First (2) Apply Farmer First filter
- LSMS & LSMS-ISA (2) Apply LSMS & LSMS-ISA filter
- Other Datasets (0)
Current search
- (-) Remove Food Security & Nutrition filter Food Security & Nutrition
- (-) Remove 2012 filter 2012
- (-) Remove Technology filter Technology
- (-) Remove 2013 filter 2013
Consumer attitudes are a key component in private sector market segmentation. Knowledge about consumers’ tastes can lead to better product design and more effective communication with target markets. Similarly, evidence suggests that farmers’ attitudes influence whether they adopt productivity-increasing technologies. Using consumer insights from the private sector, agricultural intervention programs can use market research, product development, and communication strategies to better understand farmers as consumers and best target interventions. This brief provides an overview of how farmers' attitudes affect their willingness to adopt new technology, and how knowledge of farmer attitudes can improve program design and implementation.
This desk study reports on the small-scale machinery sector in China and a selection of SSA countries: Ethiopia, Tanzania, Nigeria, Burkina Faso, and Uganda. The report is organized into three sections. Section 1 discusses the current state of small-scale agricultural machinery in SSA for crop and livestock production in each of the SSA countries identified. It also seeks to identify major areas of need in terms of agricultural mechanization and major constraints to agricultural machinery adoption, dissemination and maintenance. Section 2 focuses on the agricultural machinery sector in China and Chinese Africa relationships in agricultural development. It also identifies the major government players in the Chinese agricultural machinery sector. Section 3 is a “directory” of small-scale agricultural machinery manufactured in China with potential relevance for SSA smallholder farmers. We divide machines by function (e.g. threshing) although many Chinese machines are multi-function and can serve multiple purposes. We also note applicable crops, if listed by the manufacturers, and technical specifications as available.
This brief explores how two datasets – The Tanzania National Panel Survey (TZNPS) and the TNS-Research International Farmer Focus (FF) – predict the determinants of inorganic fertilizer use among smallholder farmers in Tanzania by using regression analysis. The (TZNPS) was implemented by the Tanzania National Bureau of Statistics, with support from the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) team and includes extensive information on crop productivity and input use. The FF survey was funded by the Bill and Melinda Gates Foundation and implemented by TNS Research International and focuses on the on the behaviors and attitudes of smallholder farmers in Tanzania. The two datasets produce relatively comparable results for the primary predictors of inorganic fertilizer use: agricultural extension and whether or not a household grows cash crops. However, other factors influencing input use produce results that vary in magnitude and direction of the effect across the two datasets. Distinct survey instrument designs make it difficult to test the robustness of the models on input use other than inorganic fertilizer. This brief uses data inorganic fertilizer use, rather than adoption per se. The TZNPS did not ask households how recently they began using a certain product and although the FF survey asked respondents how many new inputs were tried in the past four planting seasons, they did not ask specifically about inorganic fertilizer.
This report investigates the potential environmental and socio-economic benefits and costs of glyphosate resistant cassava. Glyphosate resistant crops (also referred to as glyphosate tolerant) have been rapidly adopted by a number of crop producers because they simplify and/or reduce the cost of weed management. Glyphosate resistant crops also provide external environmental benefits by promoting reduced tillage agriculture, decreasing erosion and increasing soil health. However, glyphosate resistant crops also have some environmental costs, potentially leading to increased use of herbicides and environmental contamination. Because transgenic glyphosate resistant cassava is not currently in use, literature on its potential environmental and socioeconomic costs and benefits is limited. Therefore, this report draws on the literature for glyphosate resistant crops that are in current use, including maize, soybeans, sugar beets and canola (rapeseed). We find that socioeconomic and environmental impacts of glyphosate resistant crops differ by crop-type, agroecological conditions, production systems and local regulatory structure. Therefore, some benefits and costs associated with other glyphosate resistant crops may not be applicable to glyphosate resistant cassava.
This brief provides an overview of the national and zonal characteristics of agricultural production in Tanzania using the 2008/2009 wave of the Tanzania National Panel Survey (TZNPS), part of the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA). More detailed information and analysis is available in the separate EPAR Tanzania LSMS-ISA Reference Report, Sections A-G.