Water use efficiency and yield of winter wheat under different irrigation regimes in a semi-arid region

In irrigation schemes under rotational water supply in semi-arid region, the water allocation and irrigation scheduling are often based on a fixed-area proportionate water depth with every irrigation cycle irrespective of crops and their growth stages, for an equitable water supply. An experiment was conducted during the 2004- 2005 season in Haouz irrigated area in Morocco, which objective was 1) to evaluate the effects of the surface irrigation scheduling method (ex-isting rule) adopted by the irrigation agency on winter wheat production compared to a full ir-rigation method and 2) to evaluate drip irrigation versus surface irrigation impacts on water sav-ing and yield of winter wheat. The methodology was based on the FAO-56 dual approach for the surface irrigation scheduling. Ground measure- ments of the Normalized Difference Vegetation Index (NDVI) were used to derive the basal crop coefficient and the vegetation fraction cover. The simple FAO-56 approach was used for drip irrigation scheduling. For surface irrigation, the existing rule approach resulted in yield and WUE reductions of 22% and 15%, respectively, compared with the optimized irrigation sched-uling proposed by the FAO-56 for full irrigation treatment. This revealed the negative effects of the irrigation schedules adopted in irrigation schemes under rotational water supply on crops productivity. It was also demonstrated that drip irrigation applied to wheat was more efficient with 20% of water saving in comparison with surface irrigation (full irrigation treatment). Drip irrigation gives also higher wheat yield com-pared to surface irrigation (+28% and +52% for full irrigation and existing rule treatments re-spectively). The same improvement was ob-served for water use efficiency (+24% and +59% respectively)

Potential for early forecast of Moroccan wheat yields based on climatic drivers

Wheat production plays an important role in Morocco. Current wheat forecast systems use weather and vegetation data during the crop growing phase, thus limiting the earliest possible release date to early spring. However, Morocco' s wheat production is mostly rainfed and thus strongly tied to fluctuations in rainfall, which in turn depend on slowly evolving climate dynamics. This offers a source of predictability at longer time scales. Using physically guided causal discovery algorithms, we extract climate precursors for wheat yield variability from gridded fields of geopotential height and sea surface temperatures which show potential for accurate yield forecasts already in December, with around 50% explained variance in an out‐of‐sample cross validation. The detected interactions are physically meaningful and consistent with documented ocean‐atmosphere feedbacks. Reliable yield forecasts at such long lead times could provide farmers and policy makers with necessary information for early action and strategic adaptation measurements to support food security

Modelling LAI at a regional scale with ISBA-A-gs: comparison with satellite-derived LAI over southwestern France

International audienceA CO2-responsive land surface model (the ISBAA- gs model of M´et´eo-France) is used to simulate photosynthesis and Leaf Area Index (LAI) in southwestern France for a 3-year period (2001–2003). A domain of about 170 000 km2 is covered at a spatial resolution of 8 km. The capability of ISBA-A-gs to reproduce the seasonal and the interannual variability of LAI at a regional scale, is assessed with satellite-derived LAI products. One originates from the CYCLOPES programme using SPOT/VEGETATION data, and two products are based on MODIS data. The comparison reveals discrepancies between the satellite LAI estimates and between satellite and simulated LAI values, both in their intensity and in the timing of the leaf onset. The model simulates higher LAI values for the C3 crops than the satellite observations, which may be due to a saturation effect within the satellite signal or to uncertainties in model parameters. The simulated leaf onset presents a significant delay for C3 crops and mountainous grasslands. In-situ observations at a mid-altitude grassland site show that the generic temperature response of photosynthesis used in the model is not appropriate for plants adapted to the cold climatic conditions of the mountainous areas. This study demonstrates the potential of LAI remote sensing products for identifying and locating models' shortcomings at a regional scale

Remote sensing imagery

This chapter presents the basis of data assimilation by limiting the mathematical developments as much as possible. It emphasizes the data assimilation of remote sensing observations in land surface models, although the assimilation techniques presented can be applied to different kinds of dynamical models. The chapter considers three documents to be benchmarks in their respective fields for anyone who wishes to study them in greater depth. They are as follows: dynamic modeling of natural land surfaces, methodological aspects of data assimilation, and data assimilation applications to hydrology. The chapter introduces different elements of an assimilation system, including a few insights into the dynamic modeling of natural surfaces. Data assimilation and parameter identification, as well as their foundations, are presented from a theoretical point of view. Finally, the chapter describes more practical aspects of data assimilation

Evaluation of Nonparametric Machine-Learning Algorithms for an Optimal Crop Classification Using Big Data Reduction Strategy

International audienceAccurate crop classification can support analyses of food security, environmental, and climate changes. Most of the current research studies have focused on applying available algorithms to classify dominant crops on the landscape using one source of remotely sensed data due to geoprocessing constraints (e.g., big data access, availability, and processing power). In this research, we compared four classification algorithms, including the support vector machine (SVM), random forest (RF), regression tree (CART), and backpropagation network (BPN), to select a robust and efficient classification algorithm able to classify accurately many crop types. We used multiple sources of satellite images such as Sentinel-1 (S1) and Sentinel-2 (S2) and developed a new cropping classification method for a study site in the Bekaa valley, Lebanon, fully implemented on Google Earth Engine Platform, which minimized those geoprocessing constraints. The algorithm selection was based on their popularity, availability, simplicity, similarity, and diversity. In addition, we adopted different strategies that included changing the number of crops. The first strategy is to reduce the number of collected S2 images thereafter S1; the second strategy is to use S2 images separately and then combining S2 and S1. This study results proved that the RF is the most robust algorithm for crop classification, showing the highest overall accuracy (OA) (95.4%) and a kappa index of 0.94, followed by BPN, SVM, and CART, respectively. The performance of these algorithms based on major crop types such as wheat or potato showed that CART is the highest with OA (98%) followed by RF, SVM, and BPN, respectively. Nevertheless, CART fails to classify other minor crop types. We concluded that RF is the best algorithm for classifying different crop types in the study area, using multiple remote sensing data sources

Projected changes on the surface water resources of the Rherhaya basin (High Atlas, Morocco) by a set of Med-CORDEX models.

International audienceTo anticipate the potential changes in water quantity available within the Rherhaya mountainous watershed (near to Marrakech), it’s important to know the evolution of this resource in relation with climate changes. In this study we use the GR4J model with a snow module with time series of precipitations and discharge (1989 - 2009). The model was calibrated and validated successfully over various periods. Then we used an ensemble of 5 regional climate models (RCM) provided by the Med-CORDEX program with a method of perturbation by quantiles to simulate future scenarios of flow predictions.The evaluation of the precipitations simulated by the RCMs models (RCM) shows a strong underestimation of ~50% but a good reproduction of the cycle for the temperatures. The future changes according to two scenarios RCP4.5 and RCP8.5 show a rise of the temperatures (+1.4°, +2.6° respectively) in conjunction with a decrease in total precipitation (-19%,- 31%). Concerning the hydrological modeling with GR4J, stable results are obtained for calibration and validation whatever the chosen period, with maximum bias of 15% in validation on the monthly flows. Flow forecasts (2049-2065) present a strong projected decrease in surface runoff (-30%, -60%) and significant drops of the snow-covered reservoir levels, related to the precipitation decrease and the temperature increase