Theoretical approach for how species abundance distributions change across scales

Peer reviewe

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

Irrigation optimization with a deep reinforcement learning model: Case study on a site in Portugal

In the field of agriculture, the water used for irrigation should be given special treatment, as it is responsible for a large proportion of total water consumption. Irrigation scheduling is critical to food production because it guarantees producers a consistent harvest and minimizes the risk of losses due to water shortages. Therefore, the creation of an automatic irrigation method using new technologies is essential. New methods such as deep learning algorithms have attracted a lot of attention in agriculture and are already being used successfully. In this work, a Deep Q-Network was trained for irrigation scheduling. The agent was trained to schedule irrigation for a tomato field in Portugal. Two Long Short Term Memory models were used as the agent environment. One predicts the total water in the soil profile on the next day. The other one was employed to estimate the yield based on the environmental condition during a season and then measure the net return. The agent uses this information to decide the following irrigation amount. An Artificial Neural Network, a Long Short Term Memory, and a Convolutional Neural Network were used to estimating the Q-table during training. Unlike the Long-Short Terms Memory model, the Artificial Neural Network and the Convolutional Neural Network could not estimate the Q-table, and the agent’s reward decreased during training. The comparison of the performance of the model was done with fixed base irrigation and threshold based irrigation. The trained model increased productivity by 11% and decreased water consumption by 20–30% compared to the fixed method.This work is supported by the project Centro-01-0145- FEDER000017-EMaDeS-Energy, Materials, and Sustainable Development, co-funded by the Portugal 2020 Program (PT 2020), within the Regional Operational Program of the Center (CENTRO 2020) and the EU through the European Regional Development Fund (ERDF). Fundaç˜ ao para a Ciˆencia e a Tecnologia (FCT-MCTES) also provided financial support via project UIDB/00151/2020 (C-MAST). Saeid Alirezazadeh was supported by operation Centro-01-0145-FEDER-000019 - C4 - Centro de Competˆencias em Cloud Computing, co-financed by the European Regional Development Fund (ERDF) through the Programa Operacional Regional do Centro (Centro 2020), in the scope of the Sistema de Apoio a ` Investigaçao ˜ Científica e Tecnologica - Programas Integrados de IC&DT. We would like to express our sincere gratitude for the support provided by AppiZˆezere and DRAP-Centro with the data from the meteorological stations near Fadagosa.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