Predicting Rice Yield Under Salinity Stress Using K/Na Ratio Variable in Plant Tissue |
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| Authors: | Valère Cesse Mel Vincent Boubié Bado Saliou Ndiaye Koffi Djaman Delphine Aissata Bama Nati Baboucarr Manneh |
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| Institution: | 1. Sustainable Productivity Enhancement Program, Africa Rice Center (AfricaRice), Saint-Louis, Senegal;2. Ecole Doctorale Développement Durable et Société, Université de Thiès (UT), Thiès, Sénégal;3. Sahelian Center, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Niamey, Niger;4. Ecole Doctorale Développement Durable et Société, Université de Thiès (UT), Thiès, Sénégal;5. Department of Plant and Environmental Sciences, New Mexico State University, Agricultural Science Center, Farmington, NM, USA;6. Production végétale, Institut de l’environnement et de la recherché agricole (INERA), Ouagadougou, Burkina Faso;7. Genetic Diversity and Improvement Program, Africa Rice Center (AfricaRice), Saint-Louis-Senegal |
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| Abstract: | Estimation of yield reduction in crop caused by the salinity stress is mostly based on variations of soil electrical conductivity and the severity of water stress. Crop response curves to salinity were developed without considering ion toxicity and nutritional imbalance in the plant. The objective of this study was to explore the possibility of using the ratio of the concentration of potassium by sodium in rice leaf (leaf-K/Na) to predict yield under the salinity stress. The rice (Oryza sativa L.) yield under fresh and saline condition and the leaf-K/Na related database was created. Data were collected from consecutive three seasons of a field experiment in the Africa Rice Center experimental farm in Senegal (16° 11? N, 16° 15?W). We studied the relationship between the relative yield (Yr), a ratio of yield under the salinity stress to the potential yield and the leaf-K/Na (x). Furthermore, we did regression analyses and F-test to determine the best fitting function. Results indicate that the exponential function i.e. Yr = 100 exp (-b x)] was the best fitting model with the lowest root mean square error (9.683) and the highest R2 value (0.90). Example applications on independent data from published papers showed relatively good predictions, suggesting that the model can be used to predict rice yield in saline soils. |
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| Keywords: | Model nutritional imbalance Oryza sativa yield reduction |
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