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<p>The expansion of irrigated agriculture and recurrent drought periods poses a serious threat to the renewability and sustainability of common water resources in arid and semi-arid regions. These shared resources can take the form of dam water which is shared between farmers according to a predefined schedule or groundwater which the farmers independently extract. The dam water is less expensive to use but this source can be limited in drought years risking crop productivity. Groundwater is a more reliable resource but is more expensive to extract and can cause soil salinity. Simulating agricultural management systems requires understanding and quantifying how biophysical and socio-economical constraints influence the decisions of farmers. Therefore, this research aimed to develop an agent-based modelling (ABM) approach to simulate farmer behaviour in irrigation management. The Theory of Planned Behaviour was used as a theoretical framework to simulate decision models that were integrated with a biophysical model describing the interaction of farmers with water resources and how limitations of water resources and salinity impact crop yield. Through modelling, we explore various strategies to improve sustainable water use. The methodology is applied to an irrigated perimeter of Al Haouz Basin, Morocco, as a case study, where there are different stakeholders and water user associations with conflicting objectives. The ABMs were parameterised using data collected by surveying 70 farmers. The findings indicate that the existing irrigation scheduling was usually satisfactory. However, with the exacerbation of drought periods, the use of dam water resources is unreliable. Farmers responded by seeking alternative water resources and changing their irrigation systems and cropping patterns which led to the potential of overexploitation of groundwater and increased accumulated salt content.</p>

<p>Strong interannual variability in precipitation amounts and distribution, as well as recurrent droughts, are cornerstones of African countries. These phenomena primarily impact rainfed crops, of which wheat is the most important and accounting for more than 80% of cultivated areas in Morocco. An early and consistent projection of pre-harvest grain production would help decision-makers anticipate management demands, detect yield gaps, and better understand wheat response to local climatic circumstances. How early a prediction is needed and the required depend on the nature of the stakeholder. In other words, early in-season forecasts are useful for producers so that they can adjust their inputs accordingly, whereas late-season forecasts are acceptable for other stakeholders, for example those interested in production monitoring.</p> <p>In this work, we used satellite-derived phenology measures, climate, and soil data to generate in-season yield prediction models for rainfed and irrigated wheat in Morocco. The primary aims were to evaluate the predictive capabilities of the models as time progresses and the transferability of the models outside the area of their implementation. The findings demonstrated that the generated models' accuracy increases over time (i.e., when additional phenological measures are integrated into the models) and that Ensemble models and Random Forest models outperformed the conventional MLR models, including the regularised regression models (Lasso, Ridge, ElasticNet).</p> <p> </p>

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.