Informations et ressources scientifiques
sur le développement des zones arides et semi-arides

Accueil du site → Master → Pays Bas → 2020 → A diagnostic framework for water productivity variations in irrigated agriculture, setting targets and actions for improvement [A case study on the Bekka Valley, Lebanon]

UNESCO-IHE Institute for Water Education, Delft (2020)

A diagnostic framework for water productivity variations in irrigated agriculture, setting targets and actions for improvement [A case study on the Bekka Valley, Lebanon]

Safi, Abdur Rahim

Titre : A diagnostic framework for water productivity variations in irrigated agriculture, setting targets and actions for improvement [A case study on the Bekka Valley, Lebanon]

Auteur : Safi, Abdur Rahim

Université de soutenance : UNESCO-IHE Institute for Water Education, Delft

Grade : Master of Science (MS) 2020

Résumé
The current generation of the world is challenged by simultaneous pressures of food insecurity and water scarcity. In future, the situation will get even worst with the onset of the frequent droughts, variability in agricultural water supply, increased water demand by other sectors, amplified population, rise in meat and dairy consumption and an upsurge in biofuel use. Therefore, the widely promoted approach to cope with the food and water challenges is enhancing water productivity (WP). Thus, multiple key international organizations have featured WP as their major policy goal, and substantial public and private investments have been made in this domain. As remote sensing allows accurate, rapid and cost-effective WP analysis ; therefore, the development community has focused on the availability of open-source remote sensing data for agricultural monitoring. However, translating the data to actionable information seems fraught with the difficulties. This study is aimed to provide a standard procedure (framework) that can be used by practitioners to translate open-source remote sensing data to actionable information. The framework was developed based on the available high resolution open-source remote sensing data including WaPOR, Landsat-8, Sentinel-2 and SoilGrids. Then the framework is applied to the case study on Bekaa Valley in Lebanon. Firstly, the well-performing and poor-performing crop fields (clusters) were identified in the study area. Secondly, the reasons behind low performances were diagnosed by comparing the practices of both clusters with each other. Thereafter, a proper method was selected to set improvement targets and quantify the scope of improvement in terms of an increase in yield and WP whereas reduction in water consumption. Finally, actions for improvement were recommended based on the diagnostic analysis. It was found that the current available open-source remote sensing data has enough higher spatial and temporal resolution to detect field-level variability and capture physiological changes during the crop growth cycle. The framework identified seven WP factors including water stress, irrigation practices, soil salinity, nitrogen application, crop rotation, sowing date and soil type that have affected yield and WP performance in the Bekaa Valley. The spatial distribution of land and WP score also identified locations on the map that needs urgent attention and offers larger improvements than other parts of the Valley. The research results reveal that wheat, potatoes and table-grapes yield can be improved by 14.73, 17.80 and 8.24 % with a simultaneous reduction in water-use of 7.35, 12.69 and 10.14%, respectively. It was recognized that besides the tremendous opportunities, errors and uncertainties are associated with the remote sensing data, which should be cautiously dealt. An important implication of the study is that the available open-source remote sensing can greatly contribute to improving yield and WP on field-level. However, the error and uncertainties associated with the data should be dealt cautiously. Also, field data with regard to factors that cannot be detected remotely is essential to back remote sensing information in the formulation of improvement actions. The results show that optimizing yield and ET in the Bekaa Valley can have larger implications on the total agricultural production and local water budget of the Valley

Sujets  : water productivity Lebanon agriculture irrigation remote sensing

Présentation et version intégrale

Page publiée le 22 avril 2021