Mapping cultivated area in West Africa using modis imagery and agroecological stratification

To predict and respond to famine and other forms of food insecurity, different early warning systems are using remote analyses of crop condition and agricultural production, using satellite-based information. To improve these predictions, a reliable estimation of the cultivated area at national scale must be carried out. In this study, we develop a methodology for extracting cultivated domain based on their temporal behaviour as captured in time-series of moderate resolution remote sensing MODIS images. We also used higher resolution SPOT and LANDSAT images for identifying cultivated areas used in training. We tested this methodology in Senegal and Mali at national scale. Both studied areas were stratified in homogeneous areas from an ecological and a remote sensing point of view, to reduce the land surface reflectance variability in the dataset in order to improve the classification efficiency. A spatiotemporal (K-means) classification was finally made on the MODIS NDVI time series, inside each of the agro-ecological regions For Senegal, we obtained an updated map of crop area with a better resolution than the USAID map (which is 1 km resolution) and with a nomenclature more specific of the Senegal region than suggested in the POSTEL map. For Mali, the results showed that MODIS data set can provide a completely satisfactory representation of the cultivated domain in one FEWS zone, in combination with external data. Results at national scale are being processed and will be presented at the conference. (Résumé d'auteur

Analysis of the relationship between start of the rainy season and farmer's soving date in the Niamey area and its impacts on the pearl millet yield

Relationship between start of the rainy season and farmer's planting date have been analysed at local scale through (i) in situ daily rainfall records and (ii) agronomical measurement provided by on-farm survey at plot scale for 10 villages network located on the mesoscale AMMA-CATCH Niger site during the 2004-2007 rainy seasons. Many classical empirical methods of monsoon onset dectection (e.g. Benoit, Kowal, Sivakumar, Ati, Marteau, Balme, Sultan) and numerical methods based on crop model SARRAH have been compared to the observed farmer's sowing date. Results show that SARRAH and Balme mean onset date are close to sowing date. But spatial and temporal variability of the sowing date is not correctly reproduced by any start of the rainy season. Nevertheless, almost 50% of the successful observed sowing date are in phase with the start of the rainy season defined by Sivakumar, Balme and SARRAH whereas about 40% of sowing are in advance comparing to Sivakumar or SARRAH onset date. Farmers' management of the sowing date is systematically based on risk strategy in order to ensure a maximum yield for most of the year. Thus, 23% of first sowing, usually earlier than the start of the rainy season, have failed and require re-sowing due to the intermittent dry spells and low rainfall intensity after sowing. Indeed, it causes a strong crop water stress during germination and panicle initiation. Otherwise, the start of the rainy season and sowing date does not seem to be the main factor of the yield variability, because these parameters explain only 10% and 16% respectively of the yield variation. However, mean yield appears to be affected by the delay of the start of the rainy season and/or latter sowing date which reduces grain yield of about 30% than ealier start of the rainy season or sowing