Record Details

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

CGSpace

View Archive Info
 
 
Field Value
 
Title Predicting Wheat Yield Gap and its Determinants in Rainfed Mediterranean Climate of Morocco: Using Ground Information, Satellite Images and Machine Learning
 
Creator Devkota, Krishna Prasad
Devkota Wasti, Mina
Bouasria, Abdelkrim
 
Subject wheat
morocco
machine learning
yield prediction
random forest
yield gaps
vegetation indices
 
Description 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.
 
Date 2022-12-30
2023-01-24T17:29:42Z
2023-01-24T17:29:42Z
 
Type Report
 
Identifier Krishna Prasad 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).
https://hdl.handle.net/10568/128099
 
Language en
 
Rights Limited Access
 
Format application/pdf
 
Publisher International Center for Agricultural Research in the Dry Areas