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Accurate quantification of evapotranspiration (ET) at the watershed scale remains an important research challenge for managing water resources in arid and semiarid areas. In this study, daily latent heat flux (LE) maps at the kilometer scale were derived from the two-source energy budget (TSEB) model fed by the MODIS leaf area index (LAI), land surface temperature (LST) products, and meteorological data from ERA-Interim reanalysis from 2001 to 2015 on the Tensift catchment (center of Morocco). As a preliminary step, both ERA-Interim and predicted LE at the time of the satellite overpass are evaluated in comparison to a large database of in situ meteorological measurements and eddy covariance (EC) observations, respectively. ERA-Interim compared reasonably well to in situ measurements, but a positive bias on air temperature was highlighted because meteorological stations used for the evaluation were mainly installed on irrigated fields while the grid point of ERA-Interim is representative of larger areas including bare (and hot) soil. Likewise, the predicted LE was in good agreement with the EC measurements gathered on the main crops of the region during 15 agricultural seasons with a correlation coefficient r = 0.70 and a reasonable bias of 30 W/m2. After extrapolating the instantaneous LE estimates to ET daily values, monthly ET was then assessed in comparison to monthly irrigation water amounts provided by the local agricultural office added to CRU precipitation dataset with a reasonable agreement; the relative error was more than 89% but the correlation coefficient r reached 0.80. Seasonal and interannual evapotranspiration was analyzed in relation to local climate and land use. Lastly, the potential use for improving the early prediction of grain yield, as well as detecting newly irrigated areas for arboriculture, is also discussed. The proposed method provides a relatively simple way for obtaining spatially distributed daily estimates of ET at the watershed scale, especially for not ungauged catchments. [ABSTRACT FROM AUTHOR]

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Wheat production in Morocco is crucial for economy and food security. However, wheat production is difficult because the semi-arid climate causes very variable wheat yields. To solve this issue, we need better prediction of the impact of drought on wheat yields to adapt cropping management to the semi-arid climate. Here, we adapted the models WOFOST and CropSyst to agro-climatic conditions in Morocco. Six soft and durum wheat varieties were grown during the 2011-2012 and 2012-2013 growing seasons in the experimental sites of Sidi El Aydi, Khemis Zemamra and Marchouch. Drip irrigation and rainfed treatments were arranged in a randomised-block design with three replicates. We determined the phenological stages of emergence, tillering, stem elongation, flowering and maturity. We measured aboveground biomass six times along the season. These data were used to adapt WOFOST and CropSyst to local conditions. Our results show that both models achieved good estimations, with R always higher than 0.91, and positive values for Nash and Sutcliffe modelling efficiencies. Results of spatially distributed simulations were then analysed for the whole country in terms of different response to drought. [ABSTRACT FROM AUTHOR]

Abstract: In Morocco, no operational system actually exists for the early prediction of the grain yields of wheat (Triticum aestivum L.). This study proposes empirical ordinary least squares regression models to forecast the yields at provincial and national levels. The predictions were based on dekadal (10-daily) NDVI/AVHRR, dekadal rainfall sums and average monthly air temperatures. The Global Land Cover raster map (GLC2000) was used to select only the NDVI pixels that are related to agricultural land. Provincial wheat yields were assessed with errors varying from 80 to 762kgha−1, depending on the province. At national level, wheat yield was predicted at the third dekad of April with 73kgha−1 error, using NDVI and rainfall. However, earlier forecasts are possible, starting from the second dekad of March with 84kgha−1 error, at least 1 month before harvest. At the provincial and national levels, most of the yield variation was accounted for by NDVI. The proposed models can be used in an operational context to early forecast wheat yields in Morocco. [Copyright &y& Elsevier]

Accurate seasonal forecasting of cereal yields is an important decision support tool for countries, such as Morocco, that are not self-sufficient in order to predict, as early as possible, importation needs. This study aims to develop an early forecasting model of cereal yields (soft wheat, barley and durum wheat) at the scale of the agricultural province considering the 15 most productive over 2000–2017 (i.e., 15 × 18 = 270 yields values). To this objective, we built on previous works that showed a tight linkage between cereal yields and various datasets including weather data (rainfall and air temperature), regional climate indices (North Atlantic Oscillation in particular), and drought indices derived from satellite observations in different wavelengths. The combination of the latter three data sets is assessed to predict cereal yields using linear (Multiple Linear Regression, MLR) and non-linear (Support Vector Machine, SVM; Random Forest, RF, and eXtreme Gradient Boost, XGBoost) machine learning algorithms. The calibration of the algorithmic parameters of the different approaches are carried out using a 5-fold cross validation technique and a leave-one-out method is implemented for model validation. The statistical metrics of the models are first analyzed as a function of the input datasets that are used, and as a function of the lead times, from 4 months to 2 months before harvest. The results show that combining data from multiple sources outperformed models based on one dataset only. In addition, the satellite drought indices are a major source of information for cereal prediction when the forecasting is carried out close to harvest (2 months before), while weather data and, to a lesser extent, climate indices, are key variables for earlier predictions. The best models can accurately predict yield in January (4 months before harvest) with an R2 = 0.88 and RMSE around 0.22 t. ha−1. The XGBoost method exhibited the best metrics. Finally, training a specific model separately for each group of provinces, instead of one global model, improved the prediction performance by reducing the RMSE by 10% to 35% depending on the provinces. In conclusion, the results of this study pointed out that combining remote sensing drought indices with climate and weather variables using a machine learning technique is a promising approach for cereal yield forecasting. [ABSTRACT FROM AUTHOR]

In Morocco, cereal production shows high interannual variability due to uncertain rainfall and recurrent drought periods. Considering the socioeconomic importance of cereal for the country, there is a serious need to characterize the impact of drought on cereal yields. In this study, drought is assessed through (1) indices derived from remote sensing data (the vegetation condition index (VCI), temperature condition index (TCI), vegetation health ind ex (VHI), soil moisture condition index (SMCI) and soil water index for different soil layers (SWI)) and (2) key land surface variables (Land Area Index (LAI), soil moisture (SM) at different depths, soil evaporation and plant transpiration) from a Land Data Assimilation System (LDAS) over 2000–2017. A lagged correlation analysis was conducted to assess the relationships between the drought indices and cereal yield at monthly time scales. The VCI and LAI around the heading stage (March-April) are highly linked to yield for all provinces (R = 0.94 for the Khemisset province), while a high link for TCI occurs during the development stage in January-February (R = 0.83 for the Beni Mellal province). Interestingly, indices related to soil moisture in the superficial soil layer are correlated with yield earlier in the season around the emergence stage (December). The results demonstrate the clear added value of using an LDAS compared with using a remote sensing product alone, particularly concerning the soil moisture in the root-zone, considered a key variable for yield production, that is not directly observable from space. The time scale of integration is also discussed. By integrating the indices on the main phenological stages of wheat using a dynamic threshold approach instead of the monthly time scale, the correlation between indices and yield increased by up to 14%. In addition, the contributions of VCI and TCI to VHI were optimized by using yield anomalies as proxies for drought. This study opens perspectives for the development of drought early warning systems in Morocco and over North Africa, as well as for seasonal crop yield forecasting. [ABSTRACT FROM AUTHOR]

Climate change and rising atmospheric CO 2 levels are critical factors influencing agricultural productivity, particularly in Morocco, where cereal crops are essential for food security. The primary objective of this study is to evaluate the combined effects of atmospheric CO 2 variations and climatic changes on cereal yields up to 2099 using the CARAIB dynamic vegetation model. This evaluation is driven by four future scenarios based on the Euro-CORDEX initiative's regional climate models under the Representative Concentration Pathway 8.5. As part of this evaluation, the study also validates the CARAIB model for Morocco's major cereal crops: soft wheat, durum wheat, and barley, in order to ensure the model's accuracy in simulating crop responses under projected environmental conditions. The CARAIB model effectively simulated historical cereal yield trajectories across major farming regions in Morocco from 2000 to 2016, demonstrating its robust predictive capability. Our future projections suggest that elevated CO 2 levels might initially sustain cereal yields at approximately 90 % of current levels until 2050. This trend indicates that the increase in atmospheric CO 2 may exert a moderating influence on the negative impacts of other environmental stressors on crop yields. However, despite this initial buffering effect, the overall yield trend from the present until 2099 indicates a decrease for most combinations of crop, zone, and climate model, even with the CO 2 fertilization effect, except in some cases, the model exhibits slight increases or stabilization in yields. Additionally, the CARAIB model predicts potential yield shocks of 10–35 % below current levels from the 2080 s onwards, primarily due to periodic droughts. This variation underscores the complexity of the interplay between CO 2 fertilization and climatic changes, emphasizing the urgent need for Morocco to develop adaptive agricultural strategies for long-term sustainability in the face of climatic challenges. • Evaluated CO 2 /climate change impacts on Morocco's cereal yields with CARAIB. • Validated CARAIB for Morocco's cereals: soft wheat, durum wheat, and barley. • Forecasted cereal yields stay near 90 % of current levels by 2050 due to CO 2 rise. • Long-term yield decrease expected, despite CO 2 buffer, significant after 2080. • Urged adaptive strategies for Moroccan agriculture against climate change. [ABSTRACT FROM AUTHOR]

In Morocco, cereal production shows a high interannual variability due to uncertain rainfall and recurrent drought periods that may occur at key phenological stages. In view of the importance of this resource to the country's economy, it is important to better characterize the impact of drought strength and duration on yields in order to better anticipate production at the seasonal scale. While meteorological drought is usually derived from precipitation anomalies, the monitoring of agronomical drought may be more complex because of data availability issues. In this study, drought is assessed through, on one hand, classical indices derived from remote sensing data (Vegetation Condition Index -VCI-, Temperature Condition Index -TCI- and Vegetation Health Index -VHI-) and on the other hand, the outputs of a land data assimilation system (LDAS). NDVI and land surface temperature MODIS products are used to compute VCI, TCI and VHI. The LDAS has been developed by the CNRM/Meteo-France and is composed of the ISBA-A-gs land surface model forced by ERA-5 re-analysis constrained by LAI product and a surface soil moisture derived from scatterometer data. LAI, Transpiration and soil moisture at 20 and 40 cm depth are used. The study period is 2001-2017. The first results obtained on the Settat agricultural province which is the most productive province of cereal in Morocco exhibit a strong correlation between cereal production obtained from agricultural statistics and VCI and VHI during February to March (R=0.8-0.9) corresponding to the critical flowering and grain-filling stages. By contrast, the correlation with TCI was found to be significant during the tillering stage (December-January). TCI is a normalized temperature index computed from the observed extreme values of the time series. It thus represents the anomaly due to air temperature condition. A modified temperature index hereafter called Temperature Hydric Conditions Index (THCI) for which extreme temperature values are computed spatially over each agricultural province at the daily time step is proposed. In order to discard temperature change due to elevation conditions, pixel are filtered using the SRTM MNT. A higher correlation (R=0.87) was observed between THCI and cereal production during the grain maturity stage in April. Finally, outputs from the LDAS appeared very promising as strong correlation were obtained with LAI assimilated in March (R=0.92), with transpiration from February until March (R=0.85-0.9) and for soil moisture at 20 and 40 cm deep at the beginning of crop season (December R=0.86) during emergence. Our current work is focused on the development of empirical models for early forecasting cereal production at the provincial and national scales based on linear and non linear (support vector machine regression) approaches. [ABSTRACT FROM AUTHOR]