Bakary Djaby

Estimating regional wheat yield from the shape of decreasing curves of green area index temporal profiles retrieved from MODIS data

Earth observation data, owing to their synoptic, timely and repetitive coverage, have been recognized as a valuable tool for crop monitoring at different levels. At the field level, the close correlation between green leaf area (GLA) during maturation and grain yield in wheat revealed that the onset and rate of senescence appeared to be important factors for determining wheat grain yield. Our study sought to explore a simple approach for wheat yield forecasting at the regional level, based on metrics derived from the senescence phase of the green area index (GAI) retrieved from remote sensing data. This study took advantage of recent methodological improvements in which imagery with high revisit frequency but coarse spatial resolution can be exploited to derive crop-specific GAI time series by selecting pixels whose ground-projected instantaneous field of view is dominated by the target crop: winter wheat. A logistic function was used to characterize the GAI senescence phase and derive the metrics of this phase. Four regression-based models involving these metrics (i.e., the maximum GAI value, the senescence rate and the thermal time taken to reach 50% of the green surface in the senescent phase) were related to official wheat yield data. The performances of such models at this regional scale showed that final yield could be estimated with an RMSE of 0.57 ton ha−1, representing about 7% as relative RMSE. Such an approach may be considered as a first yield estimate that could be performed in order to provide better integrated yield assessments in operational systems.

Fodder Biomass Monitoring in Sahelian Rangelands Using Phenological Metrics from FAPAR Time Series

Timely monitoring of plant biomass is critical for the management of forage resources in Sahelian rangelands. The estimation of annual biomass production in the Sahel is based on a simple relationship between satellite annual Normalized Difference Vegetation Index (NDVI) and in situ biomass data. This study proposes a new methodology using multi-linear models between phenological metrics from the SPOT-VEGETATION time series of Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and in situ biomass. A model with three variables—large seasonal integral (LINTG), length of growing season, and end of season decreasing rate—performed best (MAE = 605 kg· DM/ha; R 2 = 0.68) across Sahelian ecosystems in Senegal (data for the period 1999-2013). A model with annual maximum (PEAK) and start date of season showed similar performances (MAE = 625 kg· DM/ha; R 2 = 0.64), allowing a timely estimation of forage availability. The subdivision of the study area in ecoregions increased overall accuracy (MAE = 489.21 kg· DM/ha; R 2 = 0.77), indicating that a relation between metrics and ecosystem properties exists. LINTG was the main explanatory variable for woody rangelands with high leaf biomass, whereas for areas

Do Agrometeorological Data Improve Optical Satellite-Based Estimations of the Herbaceous Yield in Sahelian Semi-Arid Ecosystems?

Quantitative estimates of forage availability at the end of the growing season in rangelands are helpful for pastoral livestock managers and for local, national and regional stakeholders in natural resource management. For this reason, remote sensing data such as the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) have been widely used to assess Sahelian plant productivity for about 40 years. This study combines traditional FAPAR-based assessments with agrometeorological variables computed by the geospatial water balance program, GeoWRSI, using rainfall and potential evapotranspiration satellite gridded data to estimate the annual herbaceous yield in the semi-arid areas of Senegal. It showed that a machine-learning model combining FAPAR seasonal metrics with various agrometeorological data provided better estimations of the in situ annual herbaceous yield (R2 = 0.69; RMSE = 483 kg·DM/ha) than models based exclusively on FAPAR metrics (R2 = 0.63; RMSE = 550 kg·DM/ha) or agrometeorological variables (R2 = 0.55; RMSE = 585 kg·DM/ha). All the models provided reasonable outputs and showed a decrease in the mean annual yield with increasing latitude, together with an increase in relative inter-annual variation. In particular, the additional use of agrometeorological information mitigated the saturation effects that characterize the plant indices of areas with high plant productivity. In addition, the date of the onset of the growing season derived from smoothed FAPAR seasonal dynamics showed no significant relationship (0.05 p-level) with the annual herbaceous yield across the whole studied area. The date of the onset of rainfall however, was significantly related to the herbaceous yield and its inclusion in fodder biomass models could constitute a significant improvement in forecasting risks of a mass herbaceous deficit at an early stage of the year.

Estimating crop-specific evapotranspiration using remote-sensing imagery at various spatial resolutions for improving crop growth modelling

By governing water transfer between vegetation and atmosphere, evapotranspiration (ET) can have a strong influence on crop yields. An estimation of ET from remote sensing is proposed by the EUMETSAT ‘Satellite Application Facility’ (SAF) on Land Surface Analysis (LSA). This ET product is obtained operationally every 30 min using a simplified SVAT scheme that uses, as input, a combination of remotely sensed data and atmospheric model outputs. The standard operational mode uses other LSA-SAF products coming from SEVIRI imagery (the albedo, the downwelling surface shortwave flux, and the downwelling surface longwave flux), meteorological data, and the ECOCLIMAP database to identify and characterize the land cover. With the overall objective of adapting this ET product to crop growth monitoring necessities, this study focused first on improving the ET product by integrating crop-specific information from high and medium spatial resolution remote-sensing data. A Landsat (30 m)-based crop type classification is used to identify areas where the target crop, winter wheat, is located and where crop-specific Moderate Resolution Imaging Spectroradiometer (MODIS) (250 m) time series of green area index (GAI) can be extracted. The SVAT model was run for 1 year (2007) over a study area covering Belgium and part of France using this supplementary information. Results were compared to those obtained using the standard operational mode. ET results were also compared with ground truth data measured in an eddy covariance station. Furthermore, transpiration and potential transpiration maps were retrieved and compared with those produced using the Crop Growth Monitoring System (CGMS), which is run operationally by the European Commissions Joint Research Centre to produce in-season forecast of major European crops. The potential of using ET obtained from remote sensing to improve crop growth modelling in such a framework is studied and discussed. Finally, the use of the ET product is also explored by integrating it in a simpler modelling approach based on light-use efficiency. The Carnegie–Ames–Stanford Approach (CASA) agroecosystem model was therefore applied to obtain net primary production, dry matter productivity, and crop yield using only LSA-SAF products. The values of yield were compared with those obtained using CGMS, and the dry matter productivity values with those produced at the Flemish Institute for Technological Research (VITO). Results showed the potential of using this simplified remote-sensing method for crop monitoring.

Vulnerability Assessment for Food Crisis Management in the Sahel Region

In the Sahel region the unfavourable climatic conditions and the natural resources degradation are recognised to be the main constraints affecting the agricultural productivity; nevertheless more complex and less evident causes of food insecurity of population groups need to be investigated.

Mapping of zones at risk (ZAR) in West Africaby using NGI, VCI and SNDVI from the e-station.

Issa Garba 1 , Illa Salifou 4 , Abdoul Hamid Sallah 2 , Abdallah Samba 1 , Ibra Toure 3 , Yapi Yapo 1 , Alio Agoumo 1 , Salamatou Soumana 1 , Amina Oumarou 1 , Bernard Tychon 2 And Bakary Djaby 2 . 1. AGRHYMET Regional Centre, CILSS. 2. University of Liège (ULg). 3. Center for Agricultural Research for Development (CIRAD). 4. University of Niamey (UAM). ...................................................................................................................... Manuscript Info Abstract ......................... ........................................................................ Manuscript History