Cereal yield forecasting with satellite drought-based indices, weather data and regional climate indices using machine learning in morocco


Authors / Editors


Research Areas

No matching items found.


Publication Details

Output type: Journal article

UM6P affiliated Publication?: Yes

Author list: Bouras E.H., Jarlan L., Er-Raki S., Balaghi R., Amazirh A., Richard B., Khabba S.

Publisher: MDPI

Publication year: 2021

Journal: Remote Sensing (2072-4292)

Volume number: 13

Issue number: 16

ISSN: 2072-4292

eISSN: 2072-4292

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112308659&doi=10.3390%2frs13163101&partnerID=40&md5=4eafc53c384c057551fe73e701b2ce51

Languages: English (EN-GB)


View in Web of Science | View on publisher site | View citing articles in Web of Science


Abstract

Accurate seasonal forecasting of cereal yields is an important decision support tool for countries, such as Morocco, that are not self-sufficient in order to predict, as early as possible, importation needs. This study aims to develop an early forecasting model of cereal yields (soft wheat, barley and durum wheat) at the scale of the agricultural province considering the 15 most productive over 2000–2017 (i.e., 15 × 18 = 270 yields values). To this objective, we built on previous works that showed a tight linkage between cereal yields and various datasets including weather data (rainfall and air temperature), regional climate indices (North Atlantic Oscillation in particular), and drought indices derived from satellite observations in different wavelengths. The combination of the latter three data sets is assessed to predict cereal yields using linear (Multiple Linear Regression, MLR) and non-linear (Support Vector Machine, SVM; Random Forest, RF, and eXtreme Gradient Boost, XGBoost) machine learning algorithms. The calibration of the algorithmic parameters of the different approaches are carried out using a 5-fold cross validation technique and a leave-one-out method is implemented for model validation. The statistical metrics of the models are first analyzed as a function of the input datasets that are used, and as a function of the lead times, from 4 months to 2 months before harvest. The results show that combining data from multiple sources outperformed models based on one dataset only. In addition, the satellite drought indices are a major source of information for cereal prediction when the forecasting is carried out close to harvest (2 months before), while weather data and, to a lesser extent, climate indices, are key variables for earlier predictions. The best models can accurately predict yield in January (4 months before harvest) with an R2 = 0.88 and RMSE around 0.22 t. ha−1 . The XGBoost method exhibited the best metrics. Finally, training a specific model separately for each group of provinces, instead of one global model, improved the prediction performance by reducing the RMSE by 10% to 35% depending on the provinces. In conclusion, the results of this study pointed out that combining remote sensing drought indices with climate and weather variables using a machine learning technique is a promising approach for cereal yield forecasting. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.


Keywords

No matching items found.


Documents

No matching items found.


Last updated on 2021-20-10 at 23:20