2018
DOI: 10.12688/aasopenres.12822.1
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Using climate analogue tools to explore and build smallholder farmer capacity for climate smart agriculture

Abstract: Background: The phenomenon of climate change (CC) and its attendant challenges in agriculture have been widely document. Climate Smart Agriculture (CSA) focuses on sustainable agriculture intensification for food sovereignty through the adoption of mitigation and adaptation practices. Agriculture provides the livelihood for 70% of rural poor in the developing world, so building farmer capacity in CSA is imperative for food security. Studies show that transformative change must be bottom-up – integrating scient… Show more

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Cited by 4 publications
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“…The elucidation of stress profiles along with collateral constraints can be addressed by trialling representative heat- and drought-adapted genotypes under different abiotic stress profiles at a range of international target sites, as well as locations that represent a range of predicted environments (hereafter referred to as ‘future climate analogue sites’) ( Ramírez-Villegas et al , 2011 ; Opare et al , 2018 ). Agronomic, phenological, and physiological data, plus metadata (weather, soils, and crop management), can be used to show which traits are most sensitive to GEI and which environmental factors are driving the interactions ( Reynolds et al , 2004 ).…”
Section: Improving Crop Design Targets For Key Production Regions By Modelling Historic and De Novo Big Data Setsmentioning
confidence: 99%
“…The elucidation of stress profiles along with collateral constraints can be addressed by trialling representative heat- and drought-adapted genotypes under different abiotic stress profiles at a range of international target sites, as well as locations that represent a range of predicted environments (hereafter referred to as ‘future climate analogue sites’) ( Ramírez-Villegas et al , 2011 ; Opare et al , 2018 ). Agronomic, phenological, and physiological data, plus metadata (weather, soils, and crop management), can be used to show which traits are most sensitive to GEI and which environmental factors are driving the interactions ( Reynolds et al , 2004 ).…”
Section: Improving Crop Design Targets For Key Production Regions By Modelling Historic and De Novo Big Data Setsmentioning
confidence: 99%