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This literature review examines the environmental constraints to, and impacts of, wheat production systems in South Asia (SA) and Sub-Saharan Africa (SSA). The review highlights crop-environment interactions at three stages of the wheat value chain: pre-production (e.g., land availability), production (e.g., heat, water, and soil), and post-production (e.g. storage, crop residues, and transport). At each stage we emphasize environmental constraints on production (e.g., poor soil quality, water scarcity, crop pests, etc.) and also environmental impacts of crop production (e.g., soil degradation, water depletion and pollution, greenhouse gas emissions, etc.). We then highlight published best practices for overcoming environmental constraints and minimizing environmental impacts in wheat production systems. We find that wheat is a significant crop that will need to increase production to meet increasing demand. Most land suitable for wheat production is already under cultivation. Improved production methods are needed to address the demand and avert environmental impacts of producing wheat. It should not be assumed that improved varieties alone will be able to realistically address growing demands for wheat. Improved variety seeds should be combined with best practices of improved crop management techniques: optimal planting time, zero tillage, fertilizer management, intercropping, crop residue incorporation, and improved storage techniques.
This brief present our analysis of sorghum and millet cultivation in Tanzania using data from the 2008/2009 wave of the Tanzania National Panel Survey (TZNPS), part of the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA). In the 2007-2008 long and short rainy seasons, 13% of Tanzanian farming households cultivated sorghum and 6% cultivated millet, making these crops some of the least frequently cultivated priority crops in Tanzania. As a result, detailed analysis and determining statistical significance was limited by the low number of observations, particularly of millet. While sorghum and millet are often grouped together, our results suggest that in Tanzania there were differences among the households that cultivated these distinct crops. A separate appendix includes additional detail on our analyses.
This is "Section B" of a report that presents estimates and summary statistics from the 2008/2009 wave of the Tanzania National Panel Survey (TZNPS), part of the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA). We present our analyses of household characteristics by gender and by administrative zone, considering landholding size, number of crops grown, yields, livestock, input use, and food consumption.
This is "Section E" of a report that presents estimates and summary statistics from the 2008/2009 wave of the Tanzania National Panel Survey (TZNPS), part of the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA). We present our analyses of livestock and livestock by-product characteristics by gender of household head and by zones, as well as our analyses of livestock disease, vaccines, and theft.
This is "Section D" of a report that presents estimates and summary statistics from the 2008/2009 wave of the Tanzania National Panel Survey (TZNPS), part of the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA). We present our analyses of basic farm characteristics, land and labor productivity, crop sales, yield measures, intercropping, and pre- and post-harvest losses, including comparisons by gender of household head and by zone.
This is the introductory section of a report that presents estimates and summary statistics from the 2008/2009 wave of the Tanzania National Panel Survey (TZNPS), part of the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA). We present an overview of report sections, as well as an executive summary of findings on crops and livestock, constraints to productivity, and productivity and nutrition outcomes.
This is "Section C" of a report that presents estimates and summary statistics from the 2008/2009 wave of the Tanzania National Panel Survey (TZNPS), part of the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA). We present our analyses of the basic characteristics of household heads and other household members, as well as our analyses of education for adults, children, and household heads by gender and zone.
This presentation summarizes the biotic (insects, viruses, fungi, bacteria, weeds, and post-harvest pests) and abiotic (drought and soil nutrients) stresses that may be addressed or countered in order to improve crop yield in Sub-Saharan Africa and South Asia. Data is sourced from FAOSTAT, GAEZ, a series of academic papers by Waddington & Dixon, and IMPACT model estimates. Slides compare area harvested, yield, and yield gap percentage with total calories per year, the 2005 value of production, and projected growth between 2005-2030.
This brief presents selected material from the Fourth African Agricultural Markets Program (AAMP) policy symposium, Agricultural Risks Management in Africa: Taking Stock of What Has and Hasn’t Worked, organized by the Alliance for Commodity Trade in Eastern and Southern Africa and the Common Market for Eastern and Southern Africa that took place in Lilongwe, Malawi, September 6-10, 2010. We draw almost exclusively from Rashid and Jayne’s summary, “Risk Management in African Agriculture: A review of experiences.” This article summarizes across the background papers, with major findings grouped into three broad categories: cross cutting, government-led policies, and modern instruments.
This brief explores agricultural data for Tanzania from the LSMS-ISA and Farmer First household surveys. We first present the differences in the LSMS and Farmer First survey design and in basic descriptives from the two data sources. We then present the results of our initial LSMS data analysis using the 2008/2009 wave of the Tanzania National Panel Survey (TZNPS), focusing on the agricultural data, before presenting our analysis of farmer aspirations and of gender differences using the Farmer First data.