Abstract
This study assesses and predicts the impact of climate change on the harvest of agricultural crops in Morocco using the data mining approach. Several econometric models have been tested based on primary data. These models made it possible to establish part of the relationship between agricultural income and climatic variables (temperature and precipitation) and, on the other hand, to analyze the sensitivity of agricultural incomes to these climatic variables. The field of agriculture is extremely sensitive to the change of the climate, the variations intra and inter-seasonal cause the increase in the temperatures and the variations on the modes of precipitation which decreases the seasonal crop yields and increases the probability of bad short-term harvests and a reduction of the long-term production. However, this relation between climate change and agriculture are not yet foreseeable for the future, it will be thus interesting to make a predictive study which will allow the climatic analysis of data followed by an Agro climatic study of data to establish the connection between climate change and agricultural production and suggested afterward plans of adaptation to this change. In this study, we will carry out a comparative study, between the various methodology and tools of analysis of data of data mining to choose the algorithms that will adapt the best for our predictive analysis which will allow us to determine the threat of the impact of the climate change on the production of certain agricultural crops in morocco.
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Tsouli Fathi, M., Ezziyyani, M., El Mamoune, S. (2019). Data Mining for Predicting the Quality of Crops Yield Based on Climate Data Analytics. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2018). AI2SD 2018. Advances in Intelligent Systems and Computing, vol 911. Springer, Cham. https://doi.org/10.1007/978-3-030-11878-5_8
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