Predicting Wheat Yield Gap and its Determinants in Rainfed  Mediterranean Climate of Morocco: Using Ground Information, Satellite Images and Machine Learning

Devkota, Krishna; Devkota Wasti, Mina; Bouasria, Abdelkrim

Predicting Wheat Yield Gap and its Determinants in Rainfed Mediterranean Climate of Morocco: Using Ground Information, Satellite Images and Machine Learning

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Date

2022-12-30

Date Issued

2022-12-30

Contributes to SDGs

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Krishna Devkota, Mina Devkota Wasti, Abdelkrim Bouasria. (30/12/2022). Predicting Wheat Yield Gap and its Determinants in Rainfed Mediterranean Climate of Morocco: Using Ground Information, Satellite Images and Machine Learning. Lebanon: International Center for Agricultural Research in the Dry Areas (ICARDA).

Abstract

Wheat is the main food crop grown in more than 2.8 million ha in Morocco and almost 16.8 million ha in 21 Middle East and North Africa (MENA) region countries. It is primarily grown in rainfed conditions in the country and in MENA region, with diverse soil and climatic conditions and a varying range of rainfall patterns, mainly characterized by drought due to poor rainfall distribution within the season. Large disparities in attainable yield and profit gaps have been reported, and closing these gaps is important for meeting domestic demand and reducing imports. The main aim of this study was to determine field- and landscape-level yield and yield gaps for wheat and its drivers in the Central region of Morocco using ground information, remote sensing and machine learning approaches. To this end, we prepared a time series map of six vegetation indices (EVI2, CGVI, MSR, NDVI, OSAVI, and RVI) derived from Sentinel-2 images (10 m) over three consecutive crop seasons (2018-2019, 2019-2020, and 2020-2021). Vegetation indices datasets were combined with the climate, soil, and crop management, and the random forest model was calibrated and validated for each cropping season. The models that gave good performance were applied to predict actual yield, potential yield, and the yield gaps at the plot level. The models were used for mapping yield at the regional scale, Rabat-Sale-Kenitra region of Morocco. Based on those datasets, the main drivers of this gap were determined. The findings reveal that RVI, EVI2, and GCVI vegetation indices well predicted wheat yield for the 2018-2019, 2019-2020, and 2020-2021 seasons with R2 of 0.869, 0.863, and 0.844, respectively. The predicted rainfed potential wheat yields were 5.99, 1.53, and 4.66 t per ha, respectively for three crop seasons. Combined over all three seasons, the most important yield determinants are soil moisture, cumulative rainfall during the crop growing period, followed by actual evapotranspiration, and silt content of the soil. When combining soil, climate and management practices in 2019-2020, the major determinants are still soil moisture and the variables of climate followed by the management practices and soil texture. The results and maps produced are of great importance for predicting wheat yield in advance using in-season vegetation indices which is important for the farmers and policymakers for planning at regional and national scales.