{"id":{"repo_id":"liege","oai_identifier":"oai:orbi.ulg.ac.be:2268/79500"},"canonical_url":"https://search.dev.ndltd.org/etd/liege/oai:orbi.ulg.ac.be:2268/79500","repository":{"repo_id":"liege","name":"Université de Liège","base_url":"https://orbi.uliege.be/oai/request"},"display":{"title":"Prévision de la production nationale d’arachide au Sénégal à partir du modèle agrométéorologique AMS et du NDVI","abstract":"As many subsaharian countries, the agriculture of Senegal is widely dependent on the climate. This agriculture takes up 60% of active population and contributes at 20% of the GDP. It’s dominated by many crops industries, whose groundnut industry. The aim of this study is to find a forecasting model of the national production of groundnut at the third decade of September and October. This model is based on the yield forecasting at departmental scale with the outputs of the agrometeorological model AgroMetShell, NDVI data and other meteorological data. This study which is one first approach in groundnut’s yield forecasting, shows the relation between yield and the explanatory variables at the third decade of October provide a best forecast of the yield of groundnut at national scale (R² = 0.55 and RMSE = 28 kg/ha).","abstract_html":"As many subsaharian countries, the agriculture of Senegal is widely dependent on the climate. This agriculture takes up 60% of active population and contributes at 20% of the GDP. It’s dominated by many crops industries, whose groundnut industry. The aim of this study is to find a forecasting model of the national production of groundnut at the third decade of September and October. This model is based on the yield forecasting at departmental scale with the outputs of the agrometeorological model AgroMetShell, NDVI data and other meteorological data. This study which is one first approach in groundnut’s yield forecasting, shows the relation between yield and the explanatory variables at the third decade of October provide a best forecast of the yield of groundnut at national scale (R² = 0.55 and RMSE = 28 kg/ha).","abstract_has_math":false,"creators":["Kouadio, Amani Louis"],"institution":"ULiège - Université de Liège","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Tychon, Bernard"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2007,"date_issued":"2007-09","date_published":"2007-09","updated_at":"2026-07-24T02:49:10Z","subjects":["Yield forecasting, AMS, NDVI, groundnut, Senegal","Prévision de rendement, AMS, NDVI, arachide, Sénégal","Life sciences","Environmental sciences & ecology","Sciences du vivant","Sciences de l’environnement & écologie"],"languages":["fr"],"rights":["open access","info:eu-repo/semantics/openAccess"],"rights_urls":["http://purl.org/coar/access_right/c_abf2"],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["info:hdl:2268/79500"],"render_values":[{"text":"info:hdl:2268/79500","href":null,"code":true}]}]},"links":{"outbound_url":"https://orbi.uliege.be/handle/2268/79500","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Tychon, Bernard"]},{"key":"dc:creator","label":"Author","values":["Kouadio, Amani Louis"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2007-09"]},{"key":"dc:publisher","label":"Institution","values":["ULiège - Université de Liège"]},{"key":"dc:type","label":"Dc Type","values":["master thesis","http://purl.org/coar/resource_type/c_bdcc","info:eu-repo/semantics/masterThesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Yield forecasting, AMS, NDVI, groundnut, Senegal","Prévision de rendement, AMS, NDVI, arachide, Sénégal","Life sciences","Environmental sciences & ecology","Sciences du vivant","Sciences de l’environnement & écologie"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["fr"]},{"key":"dc:rights","label":"Dc Rights","values":["open access","http://purl.org/coar/access_right/c_abf2","info:eu-repo/semantics/openAccess"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://orbi.uliege.be/handle/2268/79500","info:hdl:2268/79500","https://orbi.uliege.be/bitstream/2268/79500/1/TFE_Louis-Kouadio.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["As many subsaharian countries, the agriculture of Senegal is widely dependent on the climate. This agriculture takes up 60% of active population and contributes at 20% of the GDP. It’s dominated by many crops industries, whose groundnut industry. The aim of this study is to find a forecasting model of the national production of groundnut at the third decade of September and October. This model is based on the yield forecasting at departmental scale with the outputs of the agrometeorological model AgroMetShell, NDVI data and other meteorological data. This study which is one first approach in groundnut’s yield forecasting, shows the relation between yield and the explanatory variables at the third decade of October provide a best forecast of the yield of groundnut at national scale (R² = 0.55 and RMSE = 28 kg/ha).","Au Sénégal, à l’instar de la plupart des pays subsahariens, l’agriculture est largement tributaire des conditions climatiques. L’agriculture paysanne occupe 60% de la population active et contribue pour 20% au PIB. Elle est dominée par plusieurs filières dont la filière arachide. L’objectif de cette étude est de trouver un modèle de prévision de la production nationale d’arachide à la troisième décade des mois de septembre et d’octobre. Ce modèle est basé sur la prévision du rendement de la culture au niveau départemental à partir des sorties du modèle agrométéorologique AgroMetShell, des données NDVI et de données météorologiques. Cette étude qui constitue une première approche dans la prévision du rendement de l’arachide, montre que la relation trouvée entre le rendement à l’échelle départementale à la troisième décade d’octobre et les variables explicatives fournit une bonne prévision du rendement de l’arachide à l’échelle nationale, avec un R² = 0.55 et une erreur de prédiction faible (RMSE = 28 kg/ha)."]},{"key":"dc:format","label":"Dc Format","values":["54"]},{"key":"dc:title","label":"Title","values":["Prévision de la production nationale d’arachide au Sénégal à partir du modèle agrométéorologique AMS et du NDVI"]}]}],"canonical_facts":{"dc:contributor":["Tychon, Bernard"],"dc:creator":["Kouadio, Amani Louis"],"dc:date":["2007-09"],"dc:description":["As many subsaharian countries, the agriculture of Senegal is widely dependent on the climate. This agriculture takes up 60% of active population and contributes at 20% of the GDP. It’s dominated by many crops industries, whose groundnut industry. The aim of this study is to find a forecasting model of the national production of groundnut at the third decade of September and October. This model is based on the yield forecasting at departmental scale with the outputs of the agrometeorological model AgroMetShell, NDVI data and other meteorological data. This study which is one first approach in groundnut’s yield forecasting, shows the relation between yield and the explanatory variables at the third decade of October provide a best forecast of the yield of groundnut at national scale (R² = 0.55 and RMSE = 28 kg/ha).","Au Sénégal, à l’instar de la plupart des pays subsahariens, l’agriculture est largement tributaire des conditions climatiques. L’agriculture paysanne occupe 60% de la population active et contribue pour 20% au PIB. Elle est dominée par plusieurs filières dont la filière arachide. L’objectif de cette étude est de trouver un modèle de prévision de la production nationale d’arachide à la troisième décade des mois de septembre et d’octobre. Ce modèle est basé sur la prévision du rendement de la culture au niveau départemental à partir des sorties du modèle agrométéorologique AgroMetShell, des données NDVI et de données météorologiques. Cette étude qui constitue une première approche dans la prévision du rendement de l’arachide, montre que la relation trouvée entre le rendement à l’échelle départementale à la troisième décade d’octobre et les variables explicatives fournit une bonne prévision du rendement de l’arachide à l’échelle nationale, avec un R² = 0.55 et une erreur de prédiction faible (RMSE = 28 kg/ha)."],"dc:format":["54"],"dc:identifier":["https://orbi.uliege.be/handle/2268/79500","info:hdl:2268/79500","https://orbi.uliege.be/bitstream/2268/79500/1/TFE_Louis-Kouadio.pdf"],"dc:language":["fr"],"dc:publisher":["ULiège - Université de Liège"],"dc:rights":["open access","http://purl.org/coar/access_right/c_abf2","info:eu-repo/semantics/openAccess"],"dc:subject":["Yield forecasting, AMS, NDVI, groundnut, Senegal","Prévision de rendement, AMS, NDVI, arachide, Sénégal","Life sciences","Environmental sciences & ecology","Sciences du vivant","Sciences de l’environnement & écologie"],"dc:title":["Prévision de la production nationale d’arachide au Sénégal à partir du modèle agrométéorologique AMS et du NDVI"],"dc:type":["master thesis","http://purl.org/coar/resource_type/c_bdcc","info:eu-repo/semantics/masterThesis"]},"updated_at":"2026-07-24T02:49:10Z"}