Fond bitcoin pour l'amélioration du site: 1memzGeKS7CB3ECNkzSn2qHwxU6NZoJ8o
  Dogecoin (tips/pourboires): DCLoo9Dd4qECqpMLurdgGnaoqbftj16Nvp

Home | Publier un mémoire | Une page au hasard


L'alerte précoce et la prévision des rendements agricoles au Burkina Faso: cas de trois provinces Passoré, Yatenga et Soum

( Télécharger le fichier original )
par Paul RAMDE O. Paul Sylvestre
Université de Liège - Master gestion des risques naturels 2009

précédent sommaire suivant

Bitcoin is a swarm of cyber hornets serving the goddess of wisdom, feeding on the fire of truth, exponentially growing ever smarter, faster, and stronger behind a wall of encrypted energy


Burkina Faso lies around the Niger River. In the Northern part of country, food insecurity is a worry for inhabitants, authorities, and rural development partners. Therefore, knowledge of food production level by local yield forecasting before harvest is important to the local Early Warning Systems (EWS) in areas where there are significant climate change as shown by famers and National Direction of meteorology. It's also a way to know food deficient localities and to define in time the way and opportunity for intervention. In this Complementary Master thesis work, we intend to forecast yield of two basis food cereals using meteorological, soil and crops information of three Burkina Faso's province: Passoré, Yatenga and Soum. The different data enumerated above permitted to propose crop yield forecasts. The evaluation of the models given by the generalization error shows that, in the province of Passore, none forecasting model is reliable to predict yield of millet or sorghum. It's the same for the sorghum yield prediction in the province of Yatenga. The main model performance parameters are: R2=19.63 %, R2p=18.62%, RMSE=221.97 Kg/ha and RRMSE=35.35%. The models to forecast yield of millet and sorghum in the province of Soum are much more reliable. Similar good results are found for the millet yield forecasting in the province of Yatenga. In this last case performance parameters show that: R2=65.89%, R2p=62.43%, RMSE=132.70% and RRMSE=22.53%. These reliable models use two to three explicative's variables. These variables have an agronomic meaning and they are early enough in several cases to serve for actual prediction. So, they may be of potential usefulness in local Early Warning System. The comparison of yield forecasting results based on one hand on weather station data and on the other hand on data taken from the Tutiempo internet web site shows that meteo station data is still better for yield forecasting process.

Key words: Burkina, forecast, yield, local EWS, R2p, RRMSE, variable, Early.


CRC : Croix Rouge Canadienne

DGPER1 : Direction Générale de la Promotion de l'Economie Rurale

DRMAHRH/N: Direction Régionale du Ministère de l'Agriculture, de l'Hydraulique et des Ressources

Halieutiques du Nord

FAO: Food and Agricultural Organization

FEWSNET: Famine Early Warning Systems Network

INERA : Institut de l'Environnement et de la Recherche Agricole

INSD: Institut National des Statistiques et de la Démographie

MAHRH : Ministère de l'Agriculture de l'Hydraulique et des Ressources Halieutiques

MCTC : Ministère de la Culture du Tourisme et de la Communication

MRA : Ministère des Ressources Animales

MT : Ministère des Transports

MED : Ministère de l'Economie et du Développement

NDVI: Normalized Difference Vegetative Index

R2 : Coefficient de détermination entre les rendements historiques et les variables explicatives

R2p : Coefficient de détermination en phase de validation

RMSE: Root Mean Square Error

RRMSE: Relative Root Mean Square Error

SAP : Système d'Alerte Précoce

SISA : Système d'Information sur la Sécurité Alimentaire

TFE : Travail de Fin d'Etudes.

1 Ce service portait le nom de Direction Générale de la Prévision et des Statistiques Agricoles (DGPSA).

précédent sommaire suivant

La Quadrature du Net