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Emerging Technologies to Benefit Farmers in Sub-Saharan Africa and South Asia (2008)
Board on Agriculture and Natural Resources (BANR)

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. "Executive Summary." Emerging Technologies to Benefit Farmers in Sub-Saharan Africa and South Asia. Washington, DC: The National Academies Press, 2008.

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Emerging Technologies to Benefit Farmers in Sub-Saharan Africa and South Asia

TABLE ES-1 Priority Technologies and Applications for Improving Agriculture

Focus of Technology

Tier I High Priority for Development

Tier II High Priority for Additional Exploration

Natural Resources Management

  • Soil management techniques

  • Integrated water management

  • Climate and weather prediction

  • Soil-related nanomaterials

  • Manipulation of the rhizosphere

  • Remote sensing of plant physiology

Improving Genetics of Crops and Animals

  • Annotated crop genomes

  • Genome-based animal breeding

  • Site-specific gene integration

  • Spermatogonial stem cell transplantation

  • Microbial genomics of the rumen

Overcoming Biotic Constraints

  • Plant-mediated gene silencing

  • Biocontrol and biopesticides

  • Disease-suppressive soils

  • Animal vaccines

 

Energy Production

 

  • Solar energy technologies

  • Photosynthetic microbe-based biofuels

  • Energy storage technology

Although these technologies offer many opportunities to address the challenges to agricultural production in sub-Saharan Africa and South Asia, a broader set of factors will influence the ability of a technology to have a positive impact on productivity:

  • A system-wide approach: Agricultural production is a complex system; consequently, agricultural technologies are interdependent. For example, it is difficult to improve livestock or increase meat or milk production if the animals are chronically infected with pathogens and are fed low-quality, poorly digestible forages. Solving the problem of poor agricultural productivity requires a multifaceted approach.

  • Local expertise and participation: Agricultural technologies developed in industrialized countries may not always work in sub-Saharan Africa and South Asia. Crop breeding requires the evaluation of traits under local environmental conditions; weather prediction algorithms need data collected at the ground level; farmers need an opportunity to provide input and acquire information. These tasks require a committed, trained, local workforce—a

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