Comparison of On-Policy Deep Reinforcement Learning A2C with Off-Policy DQN in Irrigation Optimization: A Case Study at a Site in Portugal

Precision irrigation and optimization of water use have become essential factors in agricul- ture because water is critical for crop growth. The proper management of an irrigation system should enable the farmer to use water efficiently to increase productivity, reduce production costs, and maxi- mize the return on investment. Efficient water application techniques are essential prerequisites for sustainable agricultural development based on the conservation of water resources and preservation of the environment. In a previous work, an off-policy deep reinforcement learning model, Deep Q-Network, was implemented to optimize irrigation. The performance of the model was tested for tomato crop at a site in Portugal. In this paper, an on-policy model, Advantage Actor–Critic, is implemented to compare irrigation scheduling with Deep Q-Network for the same tomato crop. The results show that the on-policy model Advantage Actor–Critic reduced water consumption by 20% compared to Deep Q-Network with a slight change in the net reward. These models can be developed to be applied to other cultures with high production in Portugal, such as fruit, cereals, and wine, which also have large water requirements.info:eu-repo/semantics/publishedVersio

Microplastics captured by snowfall: A study in Northern Iran.

Samples of fresh snow (n = 34) have been collected from 29 locations in various urban and remote regions of northern Iran following a period of sustained snowfall and the thawed contents examined for microplastics (MPs) according to established techniques. MP concentrations ranged from undetected to 86 MP L-1 (mean and median concentrations ~20 MP and 12 MP L-1, respectively) and there was no significant difference in MP concentration between sample location type or between different depths of snow (or time of deposition) sampled at selected sites. Fibres were the dominant shape of MP and μ-Raman spectroscopy of selected samples revealed a variety of polymer types, with nylon most abundant. Scanning electron microscopy coupled with energy-dispersive X-ray analysis showed that some MPs were smooth and unweathered while others were more irregular and exhibited significant photo-oxidative and mechanical weathering as well as contamination by extraneous geogenic particles. These characteristics reflect the importance of both local and distal sources to the heterogeneous pool of MPs in precipitated snow. The mean and median concentrations of MPs in the snow samples were not dissimilar to the published mean and median concentrations for MPs in rainfall collected from an elevated location in southwest Iran. However, compared with rainfall, MPs in snow appear to be larger and more diverse in their shape and composition (and include rubber particulates), possibly because of the greater size but lower terminal velocities of snowflakes relative to raindrops. Snowfall represents a significant means by which MPs are scavenged from the atmosphere and transferred to soil and surface waters that warrants further attention

Species accumulation curves and extreme value theory

The species–area relationship (SAR) has been described as one of the few general patterns in ecology. Although there are many types of SAR, here we are concerned solely with the so-called species accumulation curve (SAC). The theoretical basis of this relationship is not well established. Here, we suggest that extreme value theory, also known as the statistics of extremes, provides a theoretical foundation for, as well as functions to fit, empirical species accumulation curves. Among the several procedures in extreme value theory, the appropriate way to deal with the species accumulation curve is the so-called block minima procedure. We first provide a brief description of this approach and the relevant formulas. We then illustrate the application of the block minima approach using data on tree species from a 50 ha plot in Barro Colorado Island, Panama. We conclude by discussing the extent to which the assumptions under which the extreme types theorem occurs are confirmed by the data. Although we recognize limitations to the present application of extreme value theory, we predict that it will provide fertile ground for future work on the theory of SARs and its application in the fields of ecology, biogeography and conservation.info:eu-repo/semantics/publishedVersio