630 Landwirtschaft und verwandte Bereiche
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With the growing urbanized population, vertical farming becomes indispensable in eradicating hunger and ensuring food security for the future. Food security has become a major challenge for the future owing to the scarcity of land, rising global population, urbanization, and polluted soil and water resources. In this scenario, vertical farming as an innovative food production technology caters increased productivity with limited resources and improved carbon footprint. The review begins with a brief introduction to the concept of vertical farming, its opportunities and implications on the economy, environment, and society as a whole, following with an in-depth qualitative analysis of the cutting-edge innovations/technologies are presented in detail. Contrary to the numerous research focusing on mere analysis of economic viability and statistical analysis, the aim is to introduce real cutting-edge technologies that address the challenges of vertical farms such as lack of technical skills or energy requirements. The recent advances of vertical farms includes construction of outer vertical structure and growing shelves using light weight and transparent polymer composites, engineered and tunable LEDs lightings, 3-D printed and recyclable growing substrates, AI-integrated IoTs for environment control, harvesting materials for renewable energy storage etc. Such material innovations with inclusion of AI and robotics transform automated vertical farming into ‘state of the art’ agricultural technology. Production shelf fabricated using UV cured GBOV-glass fiber composite with 60% transparency can overcome the material weight limitation, corrosion, light obstructions etc. associated with the conventional steel shelves. Replacing non-renewable fossil fuels using renewable energy harvested using carbon dots, florescent materials, and perovskite solar cells is recommended to minimize the high capital investments associated with energy as well as to reduce the environmental impact. Smart materials such as phase change materials and thermoresponsive windows control the amount of energy expended whereas smart substrate promotes resource management. Application of the newest technology AI in vertical farming facilitates precise control of environment, early pest detection, automation of farming processes, data-driven decision making and precision agriculture. In short, vertical farms utilizing modern innovations of science and technology caters enhanced productivity, improved quality, reduced cost, resource management and sustainability. Hence, automated and sustainable vertical farms promoting global food security and circularity have the potential to transform into burgeoning technology of future.
Extensive pastoral livestock systems in Central Europe provide multiple ecosystem services and support biodiversity in agricultural landscapes but their viability is challenged by livestock depredation (LD) associated with the recovery of wolf populations. Variation in the spatial distribution of LD depends on a suite of factors, most of which are unavailable at the appropriate scales. To assess if LD patterns can be predicted sufficiently with land use data alone at the scale of one federal state in Germany, we employed a machine-learning-supported resource selection approach. The model used LD monitoring data, and publicly available land use data to describe the landscape configuration at LD and control sites (resolution 4 km * 4 km). We used SHapley Additive exPlanations to assess the importance and effects of landscape configuration and cross-validation to evaluate the model performance. Our model predicted the spatial distribution of LD events with a mean accuracy of 74%. The most influential land use features included grassland, farmland and forest. The risk of livestock depredation was high if these three landscape features co-occurred with a specific proportion. A high share of grassland, combined with a moderate proportion of forest and farmland, increased LD risk. We then used the model to predict the LD risk in five regions; the resulting risk maps showed high congruence with observed LD events. While of correlative nature and lacking specific information on wolf and livestock distribution and husbandry practices, our pragmatic modelling approach can guide spatial prioritisation of damage prevention or mitigation practices to improve livestock-wolf coexistence in agricultural landscapes.