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Improving Crop Yield Predictions in Morocco Using Machine Learning Algorithms

Ed-Daoudi Rachid, Alaoui Altaf, Ettaki Badia, Zerouaoui Jamal

Journal of Ecological Engineering

2023

Vol. 24, nr 6

392--400

In Morocco, agriculture is an important sector that contributes to the country’s economy and food security. Accurately predicting crop yields is crucial for farmers, policy makers, and other stakeholders to make informed decisions regarding resource allocation and food security. This paper investigates the potential of Machine Learning algorithms for improving the accuracy of crop yield predictions in Morocco. The study examines various factors that affect crop yields, including weather patterns, soil moisture levels, and rainfall, and how these factors can be incorporated into Machine Learning models. The performance of different algorithms, including Decision Trees, Random Forests, and Neural Networks, is evaluated and compared to traditional statistical models used for crop prediction. The study demonstrated that the Machine Learning algorithms outperformed the Statistical models in predicting crop yields. Specifically, the Machine Learning algorithms achieved mean squared error values between 0.10 and 0.23 and coefficient of determination values ranging from 0.78 to 0.90, while the Statistical models had mean squared error values ranging from 0.16 to 0.24 and coefficient of determination values ranging from 0.76 to 0.84. The Feed Forward Artificial Neural Network algorithm had the lowest mean squared error value (0.10) and the highest R² value (0.90), indicating that it performed the best among the three Machine Learning algorithms. These results suggest that Machine Learning algorithms can significantly improve the accuracy of crop yield predictions in Morocco, potentially leading to improved food security and optimized resource allocation for farmers.

Application of satellite observations for the evaluation of the land surface temperature from GEM model forecast

Gawuc L., Struzewska J., Kaminski J. W., Durka P.

Challenges of Modern Technology

2014

Vol. 5, no. 2

25--32

Application of satellite observations for the evaluation of the land surface temperature from GEM model forecastAbstract: The Global Environmental Multiscale model (GEM) was evaluated against satellite observations and measurements from synoptic stations. The computational grid was set up in the global variable mode with the resolution of ~25 km over Central Europe. Model evaluation was performed over Central Europe within a window of 43-56°N latitude and 10-25°E longitude. Surface temperature forecasts were compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature product. Air temperature measured at the height of 2 metres was obtained from about 480 synoptic stations from 13 Central Europe countries. Air temperature measurements collected at 9 UTC and 12 UTC during five days (31 January, 2 February, 3 March, 27 April and 18 June 2012) was compared with the GEM model results. Evaluation showed good agreement between modelled and observed data. In case of air temperature, the averaged value of the Mean Bias Error (MBE) was -0.42, the averaged Root Mean Square Error (RMSE) and the Mean Absolute Gross Errors (MAGE) were 3.21 and 2.32, respectively. Land surface temperature comparisons gave results of -2.01; 3.91 and 3.24 of the (MBE), (RMSE), and (MAGE), respectively. Also, correlation of derived modelling errors between surface temperature and air temperature are discussed. In each case the correlation coefficient was positive. The highest value (0.70) was obtained for periods when surface – atmosphere radiative exchange processes were dominant.