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...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-11-01
|
| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/12/11/1459 |
| _version_ | 1797511189878538240 |
|---|---|
| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:41:54Z |
| format | Article |
| id | doaj.art-179ef84e0dd8437aa89783827331db57 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-10T05:41:54Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-179ef84e0dd8437aa89783827331db572023-11-22T22:24:43ZengMDPI AGAtmosphere2073-44332021-11-011211145910.3390/atmos12111459Sugarcane Yield Forecast in Ivory Coast (West Africa) Based on Weather and Vegetation Index DataEdouard Pignède0Philippe Roudier1Arona Diedhiou2Vami Hermann N’Guessan Bi3Arsène T. Kobea4Daouda Konaté5Crépin Bi Péné6IRD—Institut de Recherche pour le Développement, Université Paris-Dauphine, CNRS, LEDa, DIAL, 75010 Paris, FranceAFD—Agence Française de Développement, 75012 Paris, FranceLASMES—African Centre of Excellence on Climate Change, Biodiversity and Sustainable Development, Université Félix Houphouët Boigny, 22 BP 801 Abidjan, Côte d’IvoireCURAT—Centre Universitaire de Recherche Appliquées en Télédétection, Université Félix Houphouët Boigny, 22 BP 801 Abidjan, Côte d’IvoireLASMES—African Centre of Excellence on Climate Change, Biodiversity and Sustainable Development, Université Félix Houphouët Boigny, 22 BP 801 Abidjan, Côte d’IvoireSODEXAM—Société d’Exploitation et de Développement Aéroportuaire, Aéronautique et Météorologique, 15 BP 990 Abidjan, Côte d’IvoireSucrerie Africaine, BP 150 Ferkéssedougou, Côte d’IvoireOne 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.https://www.mdpi.com/2073-4433/12/11/1459crop modelingsugarcaneIvory Coastmachine learningvegetation indexyield forecast |
| spellingShingle | 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 Atmosphere crop modeling sugarcane Ivory Coast machine learning vegetation index yield forecast |
| title | 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_short | 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 |
| topic | crop modeling sugarcane Ivory Coast machine learning vegetation index yield forecast |
| url | https://www.mdpi.com/2073-4433/12/11/1459 |
| work_keys_str_mv | AT edouardpignede sugarcaneyieldforecastinivorycoastwestafricabasedonweatherandvegetationindexdata AT philipperoudier sugarcaneyieldforecastinivorycoastwestafricabasedonweatherandvegetationindexdata AT aronadiedhiou sugarcaneyieldforecastinivorycoastwestafricabasedonweatherandvegetationindexdata AT vamihermannnguessanbi sugarcaneyieldforecastinivorycoastwestafricabasedonweatherandvegetationindexdata AT arsenetkobea sugarcaneyieldforecastinivorycoastwestafricabasedonweatherandvegetationindexdata AT daoudakonate sugarcaneyieldforecastinivorycoastwestafricabasedonweatherandvegetationindexdata AT crepinbipene sugarcaneyieldforecastinivorycoastwestafricabasedonweatherandvegetationindexdata |