Pesticide Exposure or Pesticide Poisoning and the Risk of Depression in Agricultural Populations: A Systematic Review and Meta-Analysis.

Evidence suggests that farm workers represent a high-risk population for the development of depression. The aim of the study was to clarify the association between pesticide exposure/poisoning and depression. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis were conducted in this systematic review and meta-analysis. Relevant studies were included through searching in PubMed, Scopus, Web of Science, ProQuest, CINAHL and pre-print services databases. A total of eight studies were included. A positive but non-significant association was observed between pesticide use and depression (OR = 1.123; 95% CI, 0.932-1.354, p-value = .221) while a significant positive association was observed between pesticide poisoning and depression (OR = 2.942; 95% CI, 1.791-4.831, p-value < .001). The present meta-analysis suggested clearly a significant positive association between pesticide poisoning and depression, confirming the hypothesis that pesticide poisoning is a risk factor of depression. It also revealed positive, yet non-significant association, between pesticide exposure and depression, a finding that enhances recent research but requires to be further supported by future cohort studies, including socioeconomic factors and biomarkers of depression.

A Stochastically Optimized Two-Echelon Supply Chain Model: An Entropy Approach for Operational Risk Assessment.

Minimizing a company's operational risk by optimizing the performance of the manufacturing and distribution supply chain is a complex task that involves multiple elements, each with their own supply line constraints. Traditional approaches to optimization often assume determinism as the underlying principle. However, this paper, adopting an entropy approach, emphasizes the significance of subjective and objective uncertainty in achieving optimized decisions by incorporating stochastic fluctuations into the supply chain structure. Stochasticity, representing randomness, quantifies the level of uncertainty or risk involved. In this study, we focus on a processing production plant as a model for a chain of operations and supply chain actions. We consider the stochastically varying production and transportation costs from the site to the plant, as well as from the plant to the customer base. Through stochastic optimization, we demonstrate that the plant producer can benefit from improved financial outcomes by setting higher sale prices while simultaneously lowering optimized production costs. This can be accomplished by selectively choosing producers whose production cost probability density function follows a Pareto distribution. Notably, a lower Pareto exponent yields better supply chain cost optimization predictions. Alternatively, a Gaussian stochastic fluctuation may be proposed as a more suitable choice when trading off optimization and simplicity. Although this may result in slightly less optimal performance, it offers advantages in terms of ease of implementation and computational efficiency.

ESGs and Customer Choice: Some Empirical Evidence.

The goal of this paper is to determine whether a company's performance on environmental, social, and governance (ESG) indicators influences customer choice, and if so, which ones are the most important, as well as whether the COVID-19 pandemic had an effect on changing this hierarchy. Additionally, it intends to investigate the influence of regional and demographic factors on its formation. To achieve this goal, primary data were gathered in Greece via a questionnaire survey. According to the findings, a company's performance on ESGs influences consumer choice, with an emphasis on environmental and social indicators. It was also demonstrated that a company's social indicator performance is relevant to both urban and suburban customers. Customers in urban areas place a higher value on a company's performance in governance indicators than those in suburban areas, who place a higher value on a company's performance in environmental indicators. Finally, no significant COVID-19 effect was evidenced on the findings, although the emphasis on "social indicators" was further reinforced, probably due to the increase in social awareness of citizens during the pandemic.

COVID-19 and protected areas: Impacts, conflicts, and possible management solutions.

During the first wave of the COVID-19 pandemic, management authorities of numerous Protected Areas (PAs) had to discourage visitors from accessing them in order to reduce the virus transmission rate and protect local communities. This resulted in social-ecological impacts and added another layer of complexity to managing PAs. This paper presents the results of a survey in Snowdonia National Park capturing the views of over 700 local residents on the impacts of COVID-19 restrictions and possible scenarios and tools for managing tourist numbers. Lower visitor numbers were seen in a broadly positive way by a significant number of respondents while benefit sharing issues from tourism also emerged. Most preferred options to manage overcrowding were restricting access to certain paths, the development of mobile applications to alert people to overcrowding and reporting irresponsible behavior. Our findings are useful for PA managers and local communities currently developing post-COVID-19 recovery strategies.

(Quasi)-Binomial vs. Gaussian Models to Evaluate Thiamethoxam, Pirimiphos-Methyl, Alpha-Cypermethrin and Deltamethrin on Different Types of Storage Bag Materials Against Ephestia kuehniella Zeller (Lepidoptera: Pyralidae) and Tribolium confusum Jacquelin du Val (Coleoptera: Tenebrionidae).

The Mediterranean flour moth, Ephestia kuehniella Zeller (Lepidoptera: Pyralidae) and the confused flour beetle, Tribolium confusum Jacquelin du Val (Coleoptera: Tenebrionidae) are worldwide spread and notorious organisms of numerous stored-products. Both species are dangerous for bagged commodities as penetrators and invaders. The aim of the current study was to examine the efficacy of thiamethoxam, pirimiphos-methyl, alpha-cypermethrin, and deltamethrin, against E. kuehniella and T. confusum larvae, on different types of storage bag materials, i.e., woven propylene, biaxially oriented polypropylene and kraft paper through a (quasi)-binomial modeling approach. The type of the tested storage bag material did not affect the mortality rates of both species when treated with the tested insecticides. Thiamethoxam and pirimiphos-methyl showed statistically significant higher mortality rates on E. kuehniella and T. confusum (beta coefficient = 0.141; p-value < 0.05) compared to alpha-cypermethrin and deltamethrin. In addition, T. confusum exhibited significantly higher mortality rate in comparison to E. kuehniella. Our results also showed that the tested doses and surface treatments had a significant effect on the mortality E. kuehniella and T. confusum larvae. Significantly higher mortality rates were recorded when larvae were exposed on bag materials having both surfaces treated or on the single treated surface than when they were exposed on the untreated surface. Our findings can be useful towards an effective management strategy against stored-product insect pests.

Towards a Semi-Automatic Early Warning System for Vector-Borne Diseases.

The emergence and spread of vector-borne diseases (VBDs) is a function of biotic, abiotic and socio-economic drivers of disease while their economic and societal burden depends upon a number of time-varying factors. This work is concerned with the development of an early warning system that can act as a predictive tool for public health preparedness and response. We employ a host-vector model that combines entomological (mosquito data), social (immigration rate, demographic data), environmental (temperature) and geographical data (risk areas). The output consists of appropriate maps depicting suitable risk measures such as the basic reproduction number, R0, and the probability of getting infected by the disease. These tools consist of the backbone of a semi-automatic early warning system tool which can potentially aid the monitoring and control of VBDs in different settings. In addition, it can be used for optimizing the cost-effectiveness of distinct control measures and the integration of open geospatial and climatological data. The R code used to generate the risk indicators and the corresponding spatial maps along with the data is made available.

Using multilevel models to explore the impact of abiotic and biotic conditions on the efficacy of pirimiphos-methyl against Tenebrio molitor L.

In this study, we utilized a hierarchical multilevel modeling approach to test the hypothesis that the activity of the organophosphate insecticide pirimiphos-methyl against the cosmopolitan serious secondary pest of stored products, the yellow mealworm, Tenebrio molitor (Coleoptera: Tenebrionidae), is affected by temperature, relative humidity (RH), and developmental stage (adults, small larvae, large larvae). Our results showed that as temperature increased from 20 to 25 °C, the observed mortality of T. molitor was significantly higher. Furthermore, mortality at 25 °C did not significantly differ from that of 30 °C. An ultimate increase at 35 °C resulted in the highest mortality rate of T. molitor. However, an increase of RH from 55 to 75% adversely affected the efficacy of pirimiphos-methyl. In our study, it is also shown that the insect developmental stage is a critical feature of pirimiphos-methyl efficacy. Tenebrio molitor adults exhibited significantly higher mortality than larvae. In addition, small larvae showed significantly higher mortality than large larvae. Thus, adult is the most susceptible developmental stage of T. molitor to pirimiphos-methyl treatment. Our results could be useful tools for the management of T. molitor by indicating the optimum combination of temperature and RH that favors the insecticidal treatment against this species. In addition, we expect that the percentage of developmental stages in a whole population of T. molitor affects the insecticidal efficacy of pirimiphos-methyl.

A quantitative analysis of the spatial and temporal evolution patterns of the bluetongue virus outbreak in the island of Lesvos, Greece, in 2014.

Bluetongue virus (BTV) causes an infectious disease called bluetongue, a vector-borne viral disease of ruminants, which has major implications and causes severe economic damage due to its effect on livestock. These economic costs are mostly ascribed to the trade restrictions imposed during the epidemic period. In August 2014, an epidemic of bluetongue occurred in the island of Lesvos, Greece. The epidemic was severe and evolved over time, lasting until December 2014. The total cases of infected farms were 490, including a total number of 136,368 small ruminants. In this paper, we describe a bluetongue virus serotype 4 (BTV-4) epidemic and utilize Bayesian epidemic models to capture the spatio-temporal spread of the disease. Our study provides important insights into the drivers of BTV transmission and has implications for designing control strategies. The results showed strong spatial autocorrelations, with BTV being more likely to spread between farms located nearby. The spatial modelling results proposed a certain spatial radius (~12 km) around the onset of a similar epidemic for imposing restrictions on animal movement, which can be sufficient for the control of the disease and limit economic damage.

Do temperature, relative humidity and interspecific competition alter the population size and the damage potential of stored-product insect pests? A hierarchical multilevel modeling approach.

The premises of stored agricultural products and food consists of a complex ecosystem in which several pests can seriously affect the quality and quantity of the products. In this study we utilize a 4-level hierarchical linear multilevel model in order to assess the effect of temperature, relative humidity (RH) and interspecific competition on the population size and damage potential of the larger grain borer, Prostephanus truncatus (Horn) (Coleoptera: Bostrychidae) and the lesser grain borer, Rhyzopertha dominica (F.) (Coleoptera: Bostrychidae). As RH was increased, we observed higher percentage of live insects, while increased levels of temperature significantly decreased the percentage of live insects. The combination of R. dominica and P. truncatus lead to reduction of the percentages of live insects in comparison to single species treatments. However, P. truncatus is more damaging than R. dominica in maize, based on the proportion of damaged kernels which were infested by each insect species. We expect our results to have bearing in the management of these species.

Adaptive Diversity of Beech Seedlings Under Climate Change Scenarios.

The ability of beech (Fagus sylvatica L.) populations to adapt to the ongoing climate change is especially important in the southern part of Europe, where environmental change is expected to be more intense. In this study, we tested the existing adaptive potential of eight beech populations from two provenances in N.E. Greece (Evros and Drama) that show differences in their environmental conditions and biogeographical background. Seedling survival, growth and leaf phenological traits were selected as adaptive traits and were measured under simulated controlled climate change conditions in a growth chamber. Seedling survival was also tested under current conditions in the field. In the growth chamber, simulated conditions of temperature and precipitation for the year 2050 were applied for 3 years, under two different irrigation schemes, where the same amount of water was distributed either frequently (once every week) or non-frequently (once in 20 days). The results showed that beech seedlings were generally able to survive under climate change conditions and showed adaptive differences among provenances and populations. Furthermore, changes in the duration of the growing season of seedlings were recorded in the growth chamber, allowing them to avoid environmental stress and high selection pressure. Differences were observed between populations and provenances in terms of temporal distribution patterns of precipitation and temperature, rather than the average annual or monthly values of these measures. Additionally, different adaptive strategies appeared among beech seedlings when the same amount of water was distributed differently within each month. This indicates that the physiological response mechanisms of beech individuals are very complex and depend on several interacting parameters. For this reason, the choice of beech provenances for translocation and use in afforestation or reforestation projects should consider the small scale ecotypic diversity of the species and view multiple environmental and climatic parameters in connection to each other.

Bayesian epidemic models for spatially aggregated count data.

Epidemic data often possess certain characteristics, such as the presence of many zeros, the spatial nature of the disease spread mechanism, environmental noise, serial correlation and dependence on time-varying factors. This paper addresses these issues via suitable Bayesian modelling. In doing so, we utilize a general class of stochastic regression models appropriate for spatio-temporal count data with an excess number of zeros. The developed regression framework does incorporate serial correlation and time-varying covariates through an Ornstein-Uhlenbeck process formulation. In addition, we explore the effect of different priors, including default options and variations of mixtures of g-priors. The effect of different distance kernels for the epidemic model component is investigated. We proceed by developing branching process-based methods for testing scenarios for disease control, thus linking traditional epidemiological models with stochastic epidemic processes, useful in policy-focused decision making. The approach is illustrated with an application to a sheep pox dataset from the Evros region, Greece. Copyright © 2017 John Wiley & Sons, Ltd.

A spatial predictive model for malaria resurgence in central Greece integrating entomological, environmental and social data.

Malaria constitutes an important cause of human mortality. After 2009 Greece experienced a resurgence of malaria. Here, we develop a model-based framework that integrates entomological, geographical, social and environmental evidence in order to guide the mosquito control efforts and apply this framework to data from an entomological survey study conducted in Central Greece. Our results indicate that malaria transmission risk in Greece is potentially substantial. In addition, specific districts such as seaside, lakeside and rice field regions appear to represent potential malaria hotspots in Central Greece. We found that appropriate maps depicting the basic reproduction number, R0, are useful tools for informing policy makers on the risk of malaria resurgence and can serve as a guide to inform recommendations regarding control measures.