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; R² = 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; R² = 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; R² = 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 dominated by herbaceous vegetation, it was the PEAK metric. The proposed approach outperformed the established biomass NDVI-based product (MAE = 818 kg·DM/ha and R² = 0.51) and should improve the operational monitoring of forage resources in Sahelian rangelands.

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.

A method was developed for crop area mapping inspired by spectral matching techniques (SMTs) and based on phenological characteristics of different crop types applied using 100-m Proba-V NDVI data for the season 2014–2015. Ten-daily maximum value NDVI composites were created and smoothed in SPIRITS (spirits.jrc.ec.europa.eu). The study sites were globally spread agricultural areas located in Flanders (Belgium), Sria (Russia), Kyiv (Ukraine) and Sao Paulo (Brazil). For each pure pixel within the field, the NDVI profile of the crop type for its growing season was matched with the reference NDVI profile based on the training set extracted from the study site where the crop type originated. Three temporal windows were tested within the growing season: green-up to senescence, green-up to dormancy and minimum NDVI at the beginning of the growing season to minimum NDVI at the end of the growing season. Post classification rules were applied to the results to aggregate the crop type at the plot level. The overall accuracy (%) ranged between 65 and 86, and the kappa coefficient changed from 0.43–0.84 according to the site and the temporal window. In order of importance, the crop phenological development period, parcel size, shorter time window, number of ground-truth parcels and crop calendar similarity were the main reasons behind the differences between the results. The methodology described in this study demonstrated that 100-m Proba-V has the potential to be used in crop area mapping across different regions in the world.

According to Monteith’s theory, crop biomass is linearly correlated with the amount of absorbed photosynthetically active radiation (APAR) and a constant radiation use efficiency (RUE) down-regulated by stress factors such as CO₂ fertilisation, temperature and water stress. The objective was to investigate the relative importance of these stress factors in relation to regional biomass production and yield. The production efficiency model Copernicus Global Land Service-Dry Matter Productivity (CGLS-DMP), which follows Monteith’s theory, was modified and evaluated for common wheat and silage maize in France, Belgium and Morocco using SPOT VEGETATION for the period 1999–2012. For each study site the stress factor that has the highest correlation with crop yield was retained. The correlation between crop yield data and cumulative modified DMP, CGLS-DMP, fAPAR, and NDVI values were analysed for different crop growth stages. A leave-one-year-out cross validation was used to test the robustness of the model. On average, R² values increased from 0.49 for CGLS-DMP to 0.68 for modified DMP, RMSE (t/ha) decreased from 0.84–0.61, RRMSE (%) reduced from 13.1–8.9, MBE (t/ha) decreased from 0.05–0.03 and the index of model performance (E₁) increased from 0.08–0.28 for the selected sites and crops. The best results were obtained by including combinations of the most appropriate stress factors for each selected region and cumulating the modified DMP during part of the growing season that includes the reproductive stage. Though no single solution to the improvement of a global product could be demonstrated, our findings encourage an extension of the methodology to other regions of the world.

Depuis la fin des années 1960, la région sahélienne est touchée par une terrible sécheresse. Cet article analyse les variations climatiques enregistrées dans la région de Gouré, Niger oriental, entre 1936 et 2003. Il apparaît que la zone étudiée est sortie de la « Grande Sécheresse » à la fin des années 1980. Cependant, les conséquences de ce retour à des conditions plus humides ne se serait pas accompagné d’une amélioration significative de la longueur de la saison des pluies. En ce qui concerne l’analyse des vents, il appert que la station synoptique de Gouré n’a reporté des données cohérentes que durant les deux premières années complètes de sa mise en fonction (1984 et 1985). Par la suite, la vitesse du vent a été biaisée suite à l’absence d’entretien de l’anémomètre et les mesures se sont définitivement arrêtées dès 1997. Le pire est que les stations voisines connaissent le même destin depuis plusieurs années et qu’il n’y a donc plus de données de vent disponibles pour le Niger oriental. Sur base des vents mesurés et des cas de déflation observés simultanément durant 1984, nous avons pu déterminer que la vitesse limite du vent efficace est 7 m/s. Finalement, à partir des mesures de visibilité horizontale réduite par des poussières minérales (lithométéores), nos résultats suggèrent que la qualité de l’air est fortement dégradée suite aux processus d’érosion éolienne. La confrontation des nos résultats avec de récentes études environnementales dans la région suggère que le retour des pluies ne s’est pas accompagné par une amélioration paysagère. La pression anthropique croissante paraît être actuellement le véritable moteur de la dégradation des ressources naturelles.

The first step in the formulation of disease management strategy for any cropping system is to identify the most important risk factors. This is facilitated by basic epidemiological studies of pathogen life cycles, and an understanding of the way in which weather and cropping factors affect the quantity of initial inoculum and the rate at which the epidemic develops. Weather conditions are important factors in the development of fungal diseases in winter wheat, and constitute the main inputs of the decision support systems used to forecast disease and thus determine the timing for efficacious fungicide application. Crop protection often relies on preventive fungicide applications. Considering the slim cost−revenue ratio for winter wheat and the negative environmental impacts of fungicide overuse, necessity for applying only sprays that are critical for disease control becomes paramount for a sustainable and environmentally friendly crop production. Thus, fungicides should only be applied at critical stages for disease development, and only after the pathogen has been correctly identified. This chapter provides an overview of different weather-based disease models developed for assessing the real-time risk of epidemic development of the major fungal diseases (Septoria leaf blotch, leaf rusts and Fusarium head blight) of winter wheat in Luxembourg.