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The aim of this work is to use the AquaCrop model for irrigation and water productivity assessment of Table grapes in arid region of Mexico during 2005 and 2006 cropping seasons. The irrigation efficiency was investigated by comparing the irrigation scheduling design used by the farmer to the AquaCrop model recommendations. Data analysis showed that the farmer irrigates almost every day, which results in the water content in the root zone always exceeding the soil moisture at field capacity (FC). This generates substantial losses of water through deep percolation. By using the AquaCrop model, the optimization of irrigation water scheduling in order to avoid both water stress and deep percolation was about 547 mm and 510 mm, which it is about half of what was applied by the farmer (1006 mm and 930 mm) during 2005 and 2006, respectively. This large difference, lost through deep percolation, reduces the water productivity (WP) by about 45%.

The Plio-Quaternary aquifer in the EL-Haouz-Mejjate region of Morocco is critical for water supply, necessitating accurate characterization for sustainable management. This study pioneers machine learning (ML) and deep learning (DL) techniques to elucidate the aquifer’s properties. Supervised algorithms, including random forest, regression, support vector machines, Gaussian process regression and neural networks, are trained on available hydrogeological data. Diverse features capture complex input-output relationships to predict key hydrodynamic factors like hydraulic conductivity and transmissivity fields. Aquifer architecture attributes, including substratum depth, thickness, and height, are also estimated. Model outputs are validated with field measurements, demonstrating promising accuracy. Enhanced hydrodynamic insights improve the conceptual model and groundwater flow modeling confidence. Uncertainties are reduced through this data-driven approach, enabling optimized aquifer management. Overall, this work shows how useful it is to combine ML and DL with traditional hydrogeology in order to get a better understanding of complicated aquifer systems. The techniques pioneered provide a pathway for sustainable management of this vital water resource.

Accurately estimating latent heat flux (LE) is crucial for achieving efficiency in irrigation. It is a fundamental component in determining the actual evapotranspiration (ETa), which in turn, quantifies the amount of water lost that needs to be adequately compensated through irrigation. Empirical and physics-based models have extensive input data and site-specific limitations when estimating the LE. In contrast, the emergence of data-driven techniques combined with remote sensing has shown promising results for LE estimation with minimal and easy-to-obtain input data. This paper evaluates two machine learning-based approaches for estimating the LE. The first uses climate data, the Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (LST), while the second uses climate data combined with raw satellite bands. In-situ data were sourced from a flux station installed in our study area. The data include air temperatures (Ta), global solar radiation (Rg), and measured LE for the period 2015-2018. The study uses Landsat 8 as a remote sensing data source. At first, 12 raw available bands were downloaded. The LST is then derived from thermal bands using the Split Window algorithm (SW) and the NDVI from optical bands. During machine learning modeling, the CatBoost model is fed, trained, and evaluated using the two data combination approaches. Cross-validation of 3-folds gave an average RMSE of 27.54 W.nr2 using the first approach and 27.05 W.nr2 using the second approach. Results raise the question: Do we need additional computational layers when working with remote sensing products combined with machine learning? Future work is to generalize the approach and test it for other applications such as soil moisture retrieval, and yield prediction.

Africa is facing an urgent need to increase food production to meet increasing demands. Targeted investments in integrated agriculture and, water management systems are required to meet this challenge. However, there is a lack of comprehensive information on the potential applications of climate-smart agriculture (CSA). This paper reviews current crop modeling technologies and their applications within the scope of climate change and the CSA framework in Africa. It evaluates current research trends in various crop simulation models and suggest advanced approaches to improve crop and environmental assessment, crop management, and decision-making. A total of 140 relevant papers were considered. Results showed that 84% of studies used process-based models, with Maize being the most studied crop. Additionally, DSSAT crop models and analysis of variance models have the highest contribution of physical and empirical crop modeling studies respectively. Over 72% of studies have contributed to adaptation strategies and reducing yield gaps, while only 8% of studies have been conducted on climate change mitigation and their trade-offs with adaptation using crop models under CSA. To ensure food security through sustainable agricultural practices in Africa, there is crucial to implement CSA models with a focus on the climate change mitigation component.

Rainfed agriculture is becoming increasingly vulnerable to climate change. This situation is expected to worsen under most future climate projections, which might increase the risks linked to food security and economies which depend on it. Providing insights about the potential responses of rainfed crops to climate change will helps on designing future adaptation strategies. In this study, large amount of data and the agro-hydrological model SWAT have been used to investigate future climate change impacts on rainfed wheat and sunflower crops in a semiarid watershed in Morocco (R’dom watershed). Downscaled CORDEX climate projections were used in generating future plants growth simulation for R’dom watershed in the 2031 to 2050 horizon under two Representative Concentration Pathways (RCPs): 4.5 and 8.5. The main results of climate change scenarios highlighted that R’dom watershed will undergo significant decrease in water resources availability with more impact under the scenario RCP 8.5. Water productivities of both studied crops could be lower by up to -21% in comparison with baseline situation. Different sustainable management strategies have been simulated using SWAT model under climate change context. The adopted approach succeeded in building up sustainable management strategies toward secured food security in the future.

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Copyright of Revue Francaise de Photogrammetrie et de Teledetection is the property of Societe Francaise de Photogrammetrie et de Teledetection and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)