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Mapping Water Stress Incidence and Intensity, Optimal Plant Populations, and Cultivar Duration for African Groundnut Productivity Enhancement

Groundnut production is limited in Sub-Saharan Africa and water deficit or “drought,” is often considered as the main yield-limiting factor. However, no comprehensive study has assessed the extent and intensity of “drought”-related yield decreases, nor has it explored avenues to enhance productivity...

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Autores principales: Vadez, Vincent, Halilou, Oumarou, Hissene, Halime M., Sibiry-Traore, Pierre, Sinclair, Thomas R., Soltani, Afshin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5370244/
https://www.ncbi.nlm.nih.gov/pubmed/28405198
http://dx.doi.org/10.3389/fpls.2017.00432
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author Vadez, Vincent
Halilou, Oumarou
Hissene, Halime M.
Sibiry-Traore, Pierre
Sinclair, Thomas R.
Soltani, Afshin
author_facet Vadez, Vincent
Halilou, Oumarou
Hissene, Halime M.
Sibiry-Traore, Pierre
Sinclair, Thomas R.
Soltani, Afshin
author_sort Vadez, Vincent
collection PubMed
description Groundnut production is limited in Sub-Saharan Africa and water deficit or “drought,” is often considered as the main yield-limiting factor. However, no comprehensive study has assessed the extent and intensity of “drought”-related yield decreases, nor has it explored avenues to enhance productivity. Hence, crop simulation modeling with SSM (Simple Simulation Modeling) was used to address these issues. To palliate the lack of reliable weather data as input to the model, the validity of weather data generated by Marksim, a weather generator, was tested. Marksim provided good weather representation across a large gradient of rainfall, representative of the region, and although rainfall generated by Marksim was above observations, run-off from Marksim data was also higher, and consequently simulations using observed or Marksim weather agreed closely across this gradient of weather conditions (root mean square of error = 99 g m(-2); R(2) = 0.81 for pod yield). More importantly, simulation of yield changes upon agronomic or genetic alterations in the model were equally predicted with Marksim weather. A 1° × 1° grid of weather data was generated. “Drought”-related yield reduction were limited to latitudes above 12–13° North in West Central Africa (WCA) and to the Eastern fringes of Tanzania and Mozambique in East South Africa (ESA). Simulation and experimental trials also showed that doubling the sowing density of Spanish cultivars from 20 to 40 plants m(-2) would increase yield dramatically in both WCA and ESA. However, increasing density would require growers to invest in more seeds and likely additional labor. If these trade-offs cannot be alleviated, genetic improvement would then need to re-focus on a plant type that is adapted to the current low sowing density, like a runner rather than a bush plant type, which currently receives most of the genetic attention. Genetic improvement targeting “drought” adaptation should also be restricted to areas where water is indeed an issue, i.e., above 12–13°N latitude in WCA and the Eastern fringes of Tanzania and Mozambique.
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spelling pubmed-53702442017-04-12 Mapping Water Stress Incidence and Intensity, Optimal Plant Populations, and Cultivar Duration for African Groundnut Productivity Enhancement Vadez, Vincent Halilou, Oumarou Hissene, Halime M. Sibiry-Traore, Pierre Sinclair, Thomas R. Soltani, Afshin Front Plant Sci Plant Science Groundnut production is limited in Sub-Saharan Africa and water deficit or “drought,” is often considered as the main yield-limiting factor. However, no comprehensive study has assessed the extent and intensity of “drought”-related yield decreases, nor has it explored avenues to enhance productivity. Hence, crop simulation modeling with SSM (Simple Simulation Modeling) was used to address these issues. To palliate the lack of reliable weather data as input to the model, the validity of weather data generated by Marksim, a weather generator, was tested. Marksim provided good weather representation across a large gradient of rainfall, representative of the region, and although rainfall generated by Marksim was above observations, run-off from Marksim data was also higher, and consequently simulations using observed or Marksim weather agreed closely across this gradient of weather conditions (root mean square of error = 99 g m(-2); R(2) = 0.81 for pod yield). More importantly, simulation of yield changes upon agronomic or genetic alterations in the model were equally predicted with Marksim weather. A 1° × 1° grid of weather data was generated. “Drought”-related yield reduction were limited to latitudes above 12–13° North in West Central Africa (WCA) and to the Eastern fringes of Tanzania and Mozambique in East South Africa (ESA). Simulation and experimental trials also showed that doubling the sowing density of Spanish cultivars from 20 to 40 plants m(-2) would increase yield dramatically in both WCA and ESA. However, increasing density would require growers to invest in more seeds and likely additional labor. If these trade-offs cannot be alleviated, genetic improvement would then need to re-focus on a plant type that is adapted to the current low sowing density, like a runner rather than a bush plant type, which currently receives most of the genetic attention. Genetic improvement targeting “drought” adaptation should also be restricted to areas where water is indeed an issue, i.e., above 12–13°N latitude in WCA and the Eastern fringes of Tanzania and Mozambique. Frontiers Media S.A. 2017-03-29 /pmc/articles/PMC5370244/ /pubmed/28405198 http://dx.doi.org/10.3389/fpls.2017.00432 Text en Copyright © 2017 Vadez, Halilou, Hissene, Sibiry-Traore, Sinclair and Soltani. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Vadez, Vincent
Halilou, Oumarou
Hissene, Halime M.
Sibiry-Traore, Pierre
Sinclair, Thomas R.
Soltani, Afshin
Mapping Water Stress Incidence and Intensity, Optimal Plant Populations, and Cultivar Duration for African Groundnut Productivity Enhancement
title Mapping Water Stress Incidence and Intensity, Optimal Plant Populations, and Cultivar Duration for African Groundnut Productivity Enhancement
title_full Mapping Water Stress Incidence and Intensity, Optimal Plant Populations, and Cultivar Duration for African Groundnut Productivity Enhancement
title_fullStr Mapping Water Stress Incidence and Intensity, Optimal Plant Populations, and Cultivar Duration for African Groundnut Productivity Enhancement
title_full_unstemmed Mapping Water Stress Incidence and Intensity, Optimal Plant Populations, and Cultivar Duration for African Groundnut Productivity Enhancement
title_short Mapping Water Stress Incidence and Intensity, Optimal Plant Populations, and Cultivar Duration for African Groundnut Productivity Enhancement
title_sort mapping water stress incidence and intensity, optimal plant populations, and cultivar duration for african groundnut productivity enhancement
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5370244/
https://www.ncbi.nlm.nih.gov/pubmed/28405198
http://dx.doi.org/10.3389/fpls.2017.00432
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