![]() La modélisation de la dynamique épidémiologique des ascomycètes parasites des arbres fruitiers présente deux aspects très importants : l’évaluation de la quantité d'inoculum et de son évolution, et l’identification des évènements climatiques donnant lieu à une contamination. Finally, an inversion technique is applied to estimate the overall yield of the entire studied site. The validation and calibration of this methodology is presented, on the basis of two subsets of observations derived from the experimental database. This study is based on the analysis of a rich database, which was acquired over a period of two years (2010–2011, 2012–2013) at the Merguellil site in central Tunisia (North Africa) from more than 60 test fields and 20 optical satellite SPOT/HRV images. The LAI is retrieved from the SAFY model, and calibrated using SPOT/HRV data. Grain yields are then statistically estimated as a function of Leaf Area Index (LAI) during the maximum growth period between 25 March and 5 April. In this paper, a new approach to yield estimation by combining data from the Simple Algorithm for Yield estimation (SAFY) agro-meteorological model with optical SPOT/ High Visible Resolution (HRV) satellite data is proposed. However, monitoring the crop canopy and production capacity of plants, especially for cereals, can be challenging. In semi-arid areas characterized by frequent drought events, there is often a strong need for an operational grain yield forecasting system, to help decision-makers with the planning of annual imports. Thus, the proposed simple light-use-efficiency model can be considered as a useful tool to correctly reproduce DM and GY values. For GY, R2, and RMSE values were respectively 0.71 and 0.44 t/ha for the developed approach and 0.88 and 0.37 t/ha for AquaCrop. Likewise, although of its simplicity, the accuracy of the proposed model seems to be comparable to that of the AquaCrop model. These correspond to a relative RMSE of about 19% for DM and 20% for GY. For the model validation, the obtained results showed a good agreement between the estimated and observed values with a Root Mean Square Error (RMSE) of about 1.07 and 0.57 t/ha for DM and GY, respectively. By contrast, a linear evolution was sufficient to represent the relationship between HI and CGDD. The model calibration allowed the parameterization of εmax in four periods according to the wheat phenological stages. Further, the outputs of the simple model were also evaluated against the AquaCrop model estimates. The calibration and validation of the proposed model were performed based on the observations of wheat dry matter (DM) and grain yield (GY) which were collected on the R3 irrigated district of the Haouz plain (center of Morocco), during three agricultural seasons. The originality of the proposed method consists in (1) the modifying of the expression of the conversion coefficient (εconv) by integrating an appropriate stress threshold (ksconv) for triggering irrigation, (2) the substitution of the product of the two maximum coefficients of interception (εimax) and conversion (εconv_max) by a single parameter εmax, (3) the modeling of εmax as a function of the Cumulative Growing Degree Days (CGDD) since sowing date, and (4) the dynamic expression of the harvest index (HI) as a function of the CGDD and the final harvest index (HI0) depending on the maximum value of the Normalized Difference Vegetation Index (NDVI). In this study, a simple model, based on a light-use-efficiency model, was developed in order to estimate growth and yield of the irrigated winter wheat under semi-arid conditions. ![]()
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