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Combined agronomic and climatic approaches for sorghum adaptation in Mali

Sissoko S., Tekete M., Kouressy M., Thera K., Dembélé Y., Doumbia M., Sissoko A., Sanogo S., Diarra Y., Samaké M., Rami J.F., Diallo A.G., Vaksmann M., Niangado O., Teme N.. 2018. African Journal of Agricultural Research, 13 (35) : p. 1816-1827.

In Sub-Saharan Africa, Genotype-Environment interaction plays a key role in formulating strategies for crop improvement. Multi-location trials have created enabling structure to determine varieties yield performance and stability. Crop modeling led to prediction of long-term and spatial effects of climate variability. Three improved varieties were compared to three landraces. Optimum cultivation areas minimizing the risk of crop failure were delineated by comparing predicted flowering dates and end of rainy seasons. Agronomic values were determined in trials from three climatically different zones in 27 farms. Yield stability was determined using linear regression depending on each environmental mean and the AMMI model. Photoperiod sensitive varieties have wider optimal cultivation areas whereas early-maturing varieties (photoperiod insensitive) are subjected to strong constraints on sowing date. In low productivity conditions, landraces and improved varieties are not distinct. As the environmental cropping conditions increase, improved lines become significantly superior to landraces. Photoperiod insensitive landrace is subservient to climate conditions of its area of origin and its productivity drops sharply when moved to a wetter area. Varieties studied combined productivity and stability traits. These findings are important steps toward breeding climate resilient varieties for meeting the challenges of climate smart agriculture and sustainable intensification.

Mots-clés : photopériodicité; modélisation des cultures; rendement des cultures; changement climatique; intéraction génotype environnement; amélioration des plantes; sorgho; sorghum; mali

Thématique : Génétique et amélioration des plantes; Météorologie et climatologie; Méthodes mathématiques et statistiques

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