Sugarcane Yield Forecast in Ivory Coast (West Africa) Based on Weather and Vegetation Index Data

One way to use climate services in the case of sugarcane is to develop models that forecast yields to help the sector to be better prepared against climate risks. In this study, several models for forecasting sugarcane yields were developed and compared in the north of Ivory Coast (West Africa). The...

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Autores principales:	Edouard Pignède, Philippe Roudier, Arona Diedhiou, Vami Hermann N’Guessan Bi, Arsène T. Kobea, Daouda Konaté, Crépin Bi Péné
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	crop modeling sugarcane Ivory Coast machine learning vegetation index yield forecast Meteorology. Climatology QC851-999
Acceso en línea:	https://doaj.org/article/179ef84e0dd8437aa89783827331db57
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spelling	oai:doaj.org-article:179ef84e0dd8437aa89783827331db572021-11-25T16:45:03ZSugarcane Yield Forecast in Ivory Coast (West Africa) Based on Weather and Vegetation Index Data10.3390/atmos121114592073-4433https://doaj.org/article/179ef84e0dd8437aa89783827331db572021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4433/12/11/1459https://doaj.org/toc/2073-4433One way to use climate services in the case of sugarcane is to develop models that forecast yields to help the sector to be better prepared against climate risks. In this study, several models for forecasting sugarcane yields were developed and compared in the north of Ivory Coast (West Africa). These models were based on statistical methods, ranging from linear regression to machine learning algorithms such as the random forest method, fed by climate data (rainfall, temperature); satellite products (NDVI, EVI from MODIS Vegetation Index product) and information on cropping practices. The results show that the forecasting of sugarcane yield depended on the area considered. At the plot level, the noise due to cultivation practices can hide the effects of climate on yields and leads to poor forecasting performance. However, models using satellite variables are more efficient and those with EVI alone may explain 43% of yield variations. Moreover, taking into account cultural practices in the model improves the score and enables one to forecast 3 months before harvest in 50% and 69% of cases whether yields will be high or low, respectively, with errors of only 10% and 2%, respectively. These results on the predictive potential of sugarcane yields are useful for planning and climate risk management in this sector.Edouard PignèdePhilippe RoudierArona DiedhiouVami Hermann N’Guessan BiArsène T. KobeaDaouda KonatéCrépin Bi PénéMDPI AGarticlecrop modelingsugarcaneIvory Coastmachine learningvegetation indexyield forecastMeteorology. ClimatologyQC851-999ENAtmosphere, Vol 12, Iss 1459, p 1459 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	crop modeling sugarcane Ivory Coast machine learning vegetation index yield forecast Meteorology. Climatology QC851-999
spellingShingle	crop modeling sugarcane Ivory Coast machine learning vegetation index yield forecast Meteorology. Climatology QC851-999 Edouard Pignède Philippe Roudier Arona Diedhiou Vami Hermann N’Guessan Bi Arsène T. Kobea Daouda Konaté Crépin Bi Péné Sugarcane Yield Forecast in Ivory Coast (West Africa) Based on Weather and Vegetation Index Data
description	One way to use climate services in the case of sugarcane is to develop models that forecast yields to help the sector to be better prepared against climate risks. In this study, several models for forecasting sugarcane yields were developed and compared in the north of Ivory Coast (West Africa). These models were based on statistical methods, ranging from linear regression to machine learning algorithms such as the random forest method, fed by climate data (rainfall, temperature); satellite products (NDVI, EVI from MODIS Vegetation Index product) and information on cropping practices. The results show that the forecasting of sugarcane yield depended on the area considered. At the plot level, the noise due to cultivation practices can hide the effects of climate on yields and leads to poor forecasting performance. However, models using satellite variables are more efficient and those with EVI alone may explain 43% of yield variations. Moreover, taking into account cultural practices in the model improves the score and enables one to forecast 3 months before harvest in 50% and 69% of cases whether yields will be high or low, respectively, with errors of only 10% and 2%, respectively. These results on the predictive potential of sugarcane yields are useful for planning and climate risk management in this sector.
format	article
author	Edouard Pignède Philippe Roudier Arona Diedhiou Vami Hermann N’Guessan Bi Arsène T. Kobea Daouda Konaté Crépin Bi Péné
author_facet	Edouard Pignède Philippe Roudier Arona Diedhiou Vami Hermann N’Guessan Bi Arsène T. Kobea Daouda Konaté Crépin Bi Péné
author_sort	Edouard Pignède
title	Sugarcane Yield Forecast in Ivory Coast (West Africa) Based on Weather and Vegetation Index Data
title_short	Sugarcane Yield Forecast in Ivory Coast (West Africa) Based on Weather and Vegetation Index Data
title_full	Sugarcane Yield Forecast in Ivory Coast (West Africa) Based on Weather and Vegetation Index Data
title_fullStr	Sugarcane Yield Forecast in Ivory Coast (West Africa) Based on Weather and Vegetation Index Data
title_full_unstemmed	Sugarcane Yield Forecast in Ivory Coast (West Africa) Based on Weather and Vegetation Index Data
title_sort	sugarcane yield forecast in ivory coast (west africa) based on weather and vegetation index data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/179ef84e0dd8437aa89783827331db57
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Sugarcane Yield Forecast in Ivory Coast (West Africa) Based on Weather and Vegetation Index Data

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