Calibration and validation of the STICS crop model for managing wheat irrigation in the semi-arid Marrakech/Al Haouz Plain

In the first part of this work, the shoot growth module and grain yield of the STICS crop model were calibrated and validated by using field data which was collected from irrigated winter wheat fields in the Haouz plain near Marrakech. The calibration was performed on the thermal units between the four phenological stages that control the dynamics of leaf area index and the thermal unit between emergence and the beginning of grain filling. The plant phenology was calibrated for three fields monitored during the 2002/03 season. Evaluation of the grain yields and the temporal evolution of leaf area index were done for six validation fields during 2003/04. The results showed the significant accuracy of the model in simulating these variables, and also indicated that the plants mainly suffered from lack of nitrogen. The results in the second part show the potential of crop modeling to schedule irrigation water, on the assumption that the plants were growing under optimal conditions of fertilization. In this case, the model was used to manage the time of irrigation according to a threshold for water deficit. Various simulations displayed logical trends in the relationship between the grain yield and both the amount and timing of irrigation water. These results were finally compared with those obtained from real irrigation practices. For the particular climate of 2003/04, the comparison showed that 70 mm and 40 mm of water could be saved in case of early and late sowing, respectively

Modelling agricultural drought: a review of latest advances in big data technologies

Open Access Journal; Published online: 12 Oct 2022This article reviews the main recent applications of multi-sensor remote sensing and Artificial Intelligence techniques in multivariate modelling of agricultural drought. The study focused mainly on three fundamental aspects, namely descriptive modelling, predictive modelling, and spatial modelling of expected risks and vulnerability to drought. Thus, out of 417 articles across all studies on drought, 226 articles published from 2010 to 2022 were analyzed to provide a global overview of the current state of knowledge on multivariate drought modelling using the inclusion criteria. The main objective is to review the recent available scientific evidence regarding multivariate drought modelling based on the joint use of geospatial technologies and artificial intelligence. The analysis focused on the different methods used, the choice of algorithms and the most relevant variables depending on whether they are descriptive or predictive models. Criteria such as the skill score, the given game complexity used, and the nature of validation data were considered to draw the main conclusions. The results highlight the very heterogeneous nature of studies on multivariate modelling of agricultural drought, and the very original nature of studies on multivariate modelling of agricultural drought in the recent literature. For future studies, in addition to scientific advances in prospects, case studies and comparative studies appear necessary for an in-depth analysis of the reproducibility and operational applicability of the different approaches proposed for spatial and temporal modelling of agricultural drought

Agrometerological study of semi-arid areas : an experiment for analysing the potential of time series of FORMOSAT-2 images (Tensift-Marrakech plain)

Earth Observing Systems designed to provide both high spatial resolution (10m) and high capacity of time revisit (a few days) offer strong opportunities for the management of agricultural water resources. The FORMOSAT-2 satellite is the first and only satellite with the ability to provide daily high-resolution images over a particular area with constant viewing angles. As part of the SudMed project, one of the first time series of FORMOSAT-2 images has been acquired over the semi-arid Tensift-Marrakech plain. Along with these acquisitions, an experimental data set has been collected to monitor land-cover/land-use, soil characteristics, vegetation dynamics and surface fluxes. This paper presents a first analysis of the potential of these data for agrometerological study of semi-arid areas

Système d'irrigation intelligent à faible coût utilisant la technologie RF sans fil LoRa

Water is essential for ensuring food security in the world, and agriculture is by far the largest consumer of the earth's available freshwater. Food demand and world’s population are expected to increase over the next years. However, climate change will continue to affect water resources and make the problem of food security more complex. Therefore, agriculture must be smarter and agricultural water management must be more efficient and sustainable. This paper presents a low-cost and smart irrigation system based on LoRa technology to better manage irrigation water at field scale. The system essentially consists of a set of nodes distributed on the field for irrigation control and monitoring of soil parameters, a complete weather station for measuring climate data and a remote server for remote decision-making support. The weather station is connected to the internet via a gateway and the measured climatic data are collected and stored regularly on a server, where access to data is provided by a confidential service. The remote server allows the user to select the irrigation mode and method and to store the soil condition variables collected by the wireless sensor network in order to process them for a more optimal management of irrigation. The irrigation system also allows to compute the Penman–Monteith reference evapotranspiration (ET0) on the daily scale, turn on or off the pump remotely, and automatically fill the water tank. Based on our experimental tests, the low investment cost and the high energy autonomy make the proposed irrigation system more suitable for off grid areas with limited water resources.L'eau est essentielle pour assurer la sécurité alimentaire d'une population mondiale croissante, et l'agriculture est de loin le plus grand consommateur d'eau douce disponible sur terre. La demande en produits alimentaire et la population mondiale augmenteront au cours des années à venir, et le changement climatique continuera à affecter les ressources en eau, déjà limitées, et rendra le problème de la sécurité alimentaire plus complexe. Par conséquent, l’agriculture doit être intelligente et la gestion de l’eau d’irrigation doit être plus raisonnée et plus durable. Cet article présente un système d’irrigation intelligent à faible coût, basé sur la technologie de communication sans fil LoRa, pour la gestion des eaux d'irrigation de manière plus efficace et précise à l’échelle de la parcelle. Le système comprend essentiellement un ensemble de noeuds distribués au niveau de la parcelle pour le pilotage d’irrigation et la surveillance des paramètres du sol, une station météorologique complète pour la mesure des données climatiques en temps réel et un serveur distant pour l’aide à la prise de décisions à distance. La station météorologique est connectée à internet via une passerelle et les données climatiques mesurées sont collectées et stockées régulièrement sur un serveur, dont l’accès aux données est assuré par un service confidentiel. Le serveur distant permet à l’utilisateur de sélectionner le mode et la méthode d’irrigation et de stocker les variables d’état du sol collectées par le réseau de capteurs sans fil afin de les traiter pour une gestion plus optimale de l’irrigation. Le système d’irrigation permet également le calcul de l’évapotranspiration de référence ET0 journalière selon le modèle de Penman-Monteith, la commande à distance pour la mise en marche ou l’arrêt de la pompe et le control automatique du niveau d’eau dans le réservoir. Le faible coût d'investissement et la grande autonomie énergétique du système d'irrigation proposé le rendent plus approprié pour les zones isolées et limitées en ressources d'eau

Combining FAO-56 model and ground-based remote sensing to estimate water consumptions of wheat crops in a semi-arid region

This study was performed to test three methods based on the FAO-56 ‘‘dual'' crop coefficient approach to estimate actual evapotranspiration (AET) for winter wheat under different irrigation treatments in the semi-arid region of Tensift Al Haouz, Marrakech (center of Morocco). The three methods differ in the calculation of the basal crop coefficient (Kcb) and the fraction of soil surface covered by vegetation ( fc). The first approach strictly follows the FAO-56 procedure, with Kcb given in the FAO-56 tables and fc calculated from Kcb (No- Calibration method). The second method uses local Kcb and fc values estimated from field measurements (Local-Calibration method) and the last approach uses a remotely-sensed vegetation index to estimate Kcb and fc (NDVI-Calibration method). The analysis was performed on three fields using actual (AET) measured by Eddy Correlation systems. It was shown that the Local-Calibration approach gave best results. Accurate estimates of Kcb and fc were necessary for FAO-56 ‘‘dual'' crop coefficient application. The locally derived Kcb for winter wheat taken at initial, mid-season, and maturity crop growth were 0.15, 0.90 and 0.23, respectively. The Kcb value at the mid-season stage was found to be considerably less than that suggested by the FAO-56. Similarity between the seasonal pattern of normalized difference vegetation index (NDVI) and Kcb showed potential for modelling NDVI into a Kcb. The obtained relationships between Kcb and NDVI, and between fc and NDVI could be easily incorporated within the FAO-56 ‘‘dual'' crop coefficient model and, thereby, provide a means to apply remotely sensed observation for real-time wheat irrigation scheduling. The results obtained were very acceptable especially when the soil evaporation is negligible. Therefore, the Kcb–NDVI relationship employed in the FAO-56 ‘‘dual'' crop coefficient model holds great potential for estimating crop water requirements on an operational basis and consumption at a regional scale

Monitoring water stress using time series of observed to unstressed surface temperature difference

Remote sensing data in the thermal infra red (TIR) part of the spectrum provides indirect estimates of water stress – defined as a function of the ratio between actual and potential evaporation rates – at the earth surface. During the first stage of evaporation (‘‘energy limited'' evaporation), this ratio is close to one. During the second stage of evaporation (‘‘soil controlled'' evaporation) water stress occurs and as a result this ratio drops below one. Recently, methods using TIR data to monitor stress have shifted from establishing empirical relationships between combined vegetation cover/temperature indices and soil moisture status to data assimilation of surface temperature into complex soil–vegetation–atmosphere transfer models. However, data and expertise are often lacking to widely apply those methods. In this paper we investigate the proof-of-concept of using solely the difference between actual and unstressed surface temperature as a baseline to monitor water stress. The unstressed temperature is the equilibrium temperature of a given surface expressed in potential conditions, computed with an energy balance model. Theoretical, modelling, and experimental documentation of the proof-of-concept are shown for datasets acquired within the frame of two international experiments in semi-arid region. We show that the difference between the observed and the unstressed surface temperatures is almost linearly related to water stress. A sensitivity study is carried out to test the impact of modelling errors on the evaluation of the unstressed temperature. We found that even with inaccurate but realistic values of the surface parameters used to solve the energy balance and compute the unstressed temperature, the observed to unstressed surface temperature difference is still more relevant to detect second-stage processes than the difference between the observed surface temperature and the air temperature. The perspective of using an empirical index based on this difference is also investigated. These results are especially attractive for application based on TIR satellite imagery at a regional scale

Effect of water stress on Berkane clementine yield and fruits quality: Towards a precision citrus growing

Cette étude consiste à identifier la quantité d’eau d’irrigation optimale pour assurer une meilleure productivité du clémentinier de Berkane en utilisant la méthode d’irrigation déficitaire continue. Trois doses d’irrigation réduites par rapport à un témoin ont été étudiées. Dans le cas du témoin, la quantité d'eau d’irrigation utilisée est estimée à partir de l’évapotranspiration de référence moyenne historique de la région et mise à jour à partir des données climatiques de la saison en cours (stratégie de l’agriculteur). Les trois doses étudiées correspondent à 80%, 60% et 50% de celle utilisée pour le témoin. Les résultats présentés dans cet article sont obtenus à partir d’expérimentations réalisées en plein champs au niveau d’un jeune verger d’agrumes dans la plaine de Triffa (province de Berkane) pendant deux campagnes contrastées en termes de la pluviométrie : une campagne pluvieuse (2017-2018) et une autre au-dessous de la moyenne (2018-2019). Les résultats obtenus montrent que la réduction de la dose de l’irrigation a un effet significatif sur les différentes variables étudiées (rendement par arbre, poids et calibre du fuit, jus produit et taux de sucre). La réduction de 20% de la dose d’irrigation par rapport au témoin n’a pas d’effet significatif sur les variables précitées. Cette étude montre aussi que la sécheresse de la campagne agricole affecte le calibre du fruit malgré la stratégie de l’agrumiculteur qui consiste à augmenter la dose d’irrigation pendant les mois d’avril, mai, juin et juillet. Cette stratégie doit donc être revue pour éviter les pertes dues aux écarts de triage au niveau des stations de conditionnement. Le control continu de l’humidité du sol au niveau de la zone racinaire des arbres et l’exploitation des nouvelles solutions offertes par l’agriculture de précision devraient aider les agrumiculteurs à améliorer leurs stratégies de prise de décision.This study consists on identifying the optimal amount of irrigation water to ensure better productivity of Berkane clementine by using deficit irrigation method. Three degrees of irrigation restriction, compared to a control one, were studied. The amount of irrigation water used in the control treatment is estimated from the historical average reference evapotranspiration of the region, updated from the climate data of the current season (farmer's strategy). The amounts of irrigation water used for the three other treatments correspond to 80%, 60% and 50% of that used for the control. Presented results were obtained from an experimental study carried out on a young citrus orchard in Triffa plain (province of Berkane, Morocco) during two contrasting campaigns in term of annual rainfall: Rainy season (2017-2018) and dry season (2018 -2019). Obtained results showed that the amount of irrigation water has a significant effect on the different studied variables (yield, weight of the fruit, produced juice and total soluble solids). Also, reducing the amount of irrigation water by 20% compared to the control had no significant effect on the above-mentioned variables. Finally, this study showed that drought affects the size of the fruit despite the citrus grower's strategy contesting to increase the amount of irrigation water during the period of April-July. This strategy should therefore be reviewed to avoid losses due to fruits size at the packing stations. Continuous monitoring of soil moisture in the trees root zone and the exploitation of the new solutions offered by smart agriculture should help citrus growers to improve their decision-making strategies