Leveraging Google Earth Engine for a More Effective Grassland Management: A Decision Support Application Perspective.

Grasslands cover a substantial portion of the earth's surface and agricultural land and is crucial for human well-being and livestock farming. Ranchers and grassland management authorities face challenges in effectively controlling herders' grazing behavior and grassland utilization due to underdeveloped infrastructure and poor communication in pastoral areas. Cloud-based grazing management and decision support systems (DSS) are needed to address this issue, promote sustainable grassland use, and preserve their ecosystem services. These systems should enable rapid and large-scale grassland growth and utilization monitoring, providing a basis for decision-making in managing grazing and grassland areas. In this context, this study contributes to the objectives of the EU LIFE IMAGINE project, aiming to develop a Web-GIS app for conserving and monitoring Umbria's grasslands and promoting more informed decisions for more sustainable livestock management. The app, called "Praterie" and developed in Google Earth Engine, utilizes historical Sentinel-2 satellite data and harmonic modeling of the EVI (Enhanced Vegetation Index) to estimate vegetation growth curves and maturity periods for the forthcoming vegetation cycle. The app is updated in quasi-real time and enables users to visualize estimates for the upcoming vegetation cycle, including the maximum greenness, the days remaining to the subsequent maturity period, the accuracy of the harmonic models, and the grassland greenness status in the previous 10 days. Even though future additional developments can improve the informative value of the Praterie app, this platform can contribute to optimizing livestock management and biodiversity conservation by providing timely and accurate data about grassland status and growth curves.

Environmental effectiveness of swine sewage management: a multicriteria AHP-based model for a reliable quick assessment.

Environmental issues related to swine production are still a major concern for the general public and represent a key challenge for the swine industry. The environmental impact of higher livestock concentration is particularly significant where it coincides with weaker policy standards and poor manure management. Effective tools for environmental monitoring of the swine sewage management process become essential for verifying the environmental compatibility of farming facilities and for defining suitable policies aimed at increasing swine production sustainability. This research aims at the development and application of a model for a quick assessment of the environmental effectiveness of the pig farming sewage management process. In order to define the model, multicriteria techniques, and in particular, Saaty's analytic hierarchy process, were used to develop an iterative process in which the various key factors influencing the process under investigation were analyzed. The model, named EASE (Environmental Assessment of Sewages management Effectiveness), was optimized and applied to the Lake Trasimeno basin (Umbria, Italy), an area of high natural, environmental and aesthetic value. In this context, inadequate disposal of pig sewage represents a potential source of very considerable pollution. The results have demonstrated how the multicriteria model can represent a very effective and adaptable tool also in those decision-making processes aimed at the sustainable management of livestock production.