Development Of A Statistical Model Predicting Rice Production By Rain Precipitation Intensity And Water Harvesting

Research Article
Dago Dougba Noel, Silué Pebanagnanan David, Fofana Inza Jesus, Diarrassouba Nafan, Lallié Hermann Désiré N. M and Coulibaly Adama
Multiple Linear Regression (MLR), Rice Yield, Rain Precipitation, Water Harvesting.

Global climate change combined with high rain intensity variation can have detrimental effects on the yield of crop plants such as rice especially in north of Côte d’Ivoire where rice production meanly depend on the wetland cultivation system. Here we developed a multiple linear regression (MLR) statistical model to appreciate the mathematical relationship between rain precipitation intensity (rainfall intensity), water harvesting (rainfall water management) and rice production evaluating the impact of global climate change on the rice yield in north of Côte d’Ivoire. The present analysis showed that the production of rice in this area of the world relatively depend on both rainfall and rainfall water management. However, the developed multiple linear regression (MLR) model predicted that a decent management of the rainfall water (water harvesting) can improve the production of rice.