Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco
International audience 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 yield...
Published in: | Remote Sensing |
---|---|
Main Authors: | , , , , , , |
Other Authors: | , , , , , , , , , , , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
HAL CCSD
2021
|
Subjects: | |
Online Access: | https://hal.inrae.fr/hal-03326563 https://hal.inrae.fr/hal-03326563/document https://hal.inrae.fr/hal-03326563/file/Bouras_2021_RS.pdf https://doi.org/10.3390/rs13163101 |
id |
ftsupagro:oai:HAL:hal-03326563v1 |
---|---|
record_format |
openpolar |
institution |
Open Polar |
collection |
Portail HAL Institut Agro Montpellier |
op_collection_id |
ftsupagro |
language |
English |
topic |
Climate indices Crop yield forecasting Remote sensing drought indices Semiarid region [SDE]Environmental Sciences [SDU.STU]Sciences of the Universe [physics]/Earth Sciences [INFO]Computer Science [cs] |
spellingShingle |
Climate indices Crop yield forecasting Remote sensing drought indices Semiarid region [SDE]Environmental Sciences [SDU.STU]Sciences of the Universe [physics]/Earth Sciences [INFO]Computer Science [cs] Bouras, El Houssaine Jarlan, Lionel Er-Raki, Salah Balaghi, Riad Amazirh, Abdelhakim Richard, Bastien Khabba, Saïd Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco |
topic_facet |
Climate indices Crop yield forecasting Remote sensing drought indices Semiarid region [SDE]Environmental Sciences [SDU.STU]Sciences of the Universe [physics]/Earth Sciences [INFO]Computer Science [cs] |
description |
International audience 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 ... |
author2 |
Université Cadi Ayyad Marrakech (UCA) Centre d'études spatiales de la biosphère (CESBIO) Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) Institut national de la recherche agronomique Maroc (INRA Maroc) Université Mohammed VI Polytechnique Ben Guerir (UM6P) Gestion de l'Eau, Acteurs, Usages (UMR G-EAU) Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-AgroParisTech-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro) ARTS program from IRD, France H2020 PRIMA ALTOS project MISTRALS/SICMED2 PHC Toubkal - 39064WG/2018 PRIMA-IDEWA project CHAAMS project - ERANETMED03-62 ACCWA project - 823965 SAGESSE - PPR/2015/48 |
format |
Article in Journal/Newspaper |
author |
Bouras, El Houssaine Jarlan, Lionel Er-Raki, Salah Balaghi, Riad Amazirh, Abdelhakim Richard, Bastien Khabba, Saïd |
author_facet |
Bouras, El Houssaine Jarlan, Lionel Er-Raki, Salah Balaghi, Riad Amazirh, Abdelhakim Richard, Bastien Khabba, Saïd |
author_sort |
Bouras, El Houssaine |
title |
Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco |
title_short |
Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco |
title_full |
Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco |
title_fullStr |
Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco |
title_full_unstemmed |
Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco |
title_sort |
cereal yield forecasting with satellite drought-based indices, weather data and regional climate indices using machine learning in morocco |
publisher |
HAL CCSD |
publishDate |
2021 |
url |
https://hal.inrae.fr/hal-03326563 https://hal.inrae.fr/hal-03326563/document https://hal.inrae.fr/hal-03326563/file/Bouras_2021_RS.pdf https://doi.org/10.3390/rs13163101 |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
ISSN: 2072-4292 Remote Sensing https://hal.inrae.fr/hal-03326563 Remote Sensing, 2021, 13 (16), pp.3101. ⟨10.3390/rs13163101⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.3390/rs13163101 hal-03326563 https://hal.inrae.fr/hal-03326563 https://hal.inrae.fr/hal-03326563/document https://hal.inrae.fr/hal-03326563/file/Bouras_2021_RS.pdf doi:10.3390/rs13163101 IRD: fdi:010082762 WOS: 000690023600001 |
op_rights |
http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.3390/rs13163101 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
16 |
container_start_page |
3101 |
_version_ |
1786197276032499712 |
spelling |
ftsupagro:oai:HAL:hal-03326563v1 2023-12-24T10:23:21+01:00 Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco Bouras, El Houssaine Jarlan, Lionel Er-Raki, Salah Balaghi, Riad Amazirh, Abdelhakim Richard, Bastien Khabba, Saïd Université Cadi Ayyad Marrakech (UCA) Centre d'études spatiales de la biosphère (CESBIO) Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) Institut national de la recherche agronomique Maroc (INRA Maroc) Université Mohammed VI Polytechnique Ben Guerir (UM6P) Gestion de l'Eau, Acteurs, Usages (UMR G-EAU) Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-AgroParisTech-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro) ARTS program from IRD, France H2020 PRIMA ALTOS project MISTRALS/SICMED2 PHC Toubkal - 39064WG/2018 PRIMA-IDEWA project CHAAMS project - ERANETMED03-62 ACCWA project - 823965 SAGESSE - PPR/2015/48 2021-08-02 https://hal.inrae.fr/hal-03326563 https://hal.inrae.fr/hal-03326563/document https://hal.inrae.fr/hal-03326563/file/Bouras_2021_RS.pdf https://doi.org/10.3390/rs13163101 en eng HAL CCSD MDPI info:eu-repo/semantics/altIdentifier/doi/10.3390/rs13163101 hal-03326563 https://hal.inrae.fr/hal-03326563 https://hal.inrae.fr/hal-03326563/document https://hal.inrae.fr/hal-03326563/file/Bouras_2021_RS.pdf doi:10.3390/rs13163101 IRD: fdi:010082762 WOS: 000690023600001 http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess ISSN: 2072-4292 Remote Sensing https://hal.inrae.fr/hal-03326563 Remote Sensing, 2021, 13 (16), pp.3101. ⟨10.3390/rs13163101⟩ Climate indices Crop yield forecasting Remote sensing drought indices Semiarid region [SDE]Environmental Sciences [SDU.STU]Sciences of the Universe [physics]/Earth Sciences [INFO]Computer Science [cs] info:eu-repo/semantics/article Journal articles 2021 ftsupagro https://doi.org/10.3390/rs13163101 2023-11-25T22:29:39Z International audience 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 ... Article in Journal/Newspaper North Atlantic North Atlantic oscillation Portail HAL Institut Agro Montpellier Remote Sensing 13 16 3101 |