Priority selection of agro-meteorological parameters for integrated plant diseases management through analytical hierarchy process

To understand the influence of agro-meteorological parameters to take decisions related to various factors in an integrated plant disease management, it becomes vital to carry out scientific studies on the factors affecting it. The different agro-meteorological parameters namely temperature, humidity, moisture, rain, phenological week, cropping season, soil type, location, precipitation, heat index, and cloud coverage have been considered for this study. Each parameter has been allocated the ranking by using a technique called analytical hierarchical process (AHP). The parameter priorities are determined by calculating the Eigenvalues. This helps to make decisions related to integrated plant disease management where the prediction of plant disease occurrence, yield prediction, irrigation requirements, and fertilization recommendations can be taken. To take these decisions which parameters are good indicators can be identified using this method. The parameters majorly contribute to plant diseases and pest management decision making while delivers minor contribution in irrigation and fertilizer management related decision making. The manual results are compared with software generated results which indicates that both the results correlate with each other. Therefore, AHP technique can be successfully implemented for prioritizing agro-meteorological parameters for integrated plant diseases management as the results for both levels are consistent (consistency ratio < 0.1).

The heat is on: Observations and trends of heat stress metrics during Florida summers

Extreme heat is annually the deadliest weather hazard in the U.S. and is strongly amplified by climate change. In Florida, summer heat waves have increased in frequency and duration, exacerbating negative human health impacts on a state with a substantial older population and industries (e.g., agriculture) that require frequent outdoor work. However, the combined impacts of temperature and humidity (heat stress) have not been previously investigated. For eight Florida cities, this study constructs summer climatologies and trend analyses (1950–2020) of two heat stress metrics: heat index (HI) and wet bulb globe temperature (WBGT). While both incorporate temperature and humidity, WBGT also includes wind and solar radiation, and is a more comprehensive measure of heat stress on the human body. With minor exceptions, results show increases in average summer daily maximum, mean, and minimum HI and WBGT throughout Florida. Daily minimum HI and WBGT exhibit statistically significant increases at all eight stations, emphasizing a hazardous rise in nighttime heat stress. Corresponding to other recent studies, HI and WBGT increases are largest in coastal subtropical locations in Central and South Florida (i.e., Daytona Beach, Tampa, Miami, Key West), but exhibit no conclusive relationship with urbanization changes. Finally, danger (103–124°F) HI and high (> 88°F) WBGT summer days exhibit significant frequency increases across the state. Especially at coastal locations in the Florida Peninsula and Keys, danger HI and high WBGT days now account for > 20% of total summer days, emphasizing a substantial escalation in heat stress, particularly since 2000.

Seasonal and microclimatic effects on leaf beetles (Coleoptera, Chrysomelidae) in a tropical forest fragment in northeastern Mexico

Leaf beetles (Coleoptera: Chrysomelidae) constitute a family of abundant, diverse, and ecologically important herbivorous insects, due to their high specificity with host plants, a close association with vegetation and a great sensitivity to microclimatic variation (factors that are modified gradually during the rainy and dry seasons). Therefore, the effects of seasonality (rainy and dry seasons) and microclimate on the community attributes of chrysomelids were evaluated in a semideciduous tropical forest fragment of northeastern Mexico. Monthly sampling was conducted, between March 2016 and February 2017, with an entomological sweep net in 18 plots of 20 × 20 m, randomly distributed from 320 to 480 m a.s.l. Seven microclimatic variables were simultaneously recorded during each of the samplings, using a portable weather station. In total, 216 samples were collected at the end of the study, of which 2,103 specimens, six subfamilies, 46 genera, and 71 species were obtained. The subfamily Galerucinae had the highest number of specimens and species in the study area, followed by Cassidinae. Seasonality caused significant changes in the abundance and number of leaf beetle species: highest richness was recorded in the rainy season, with 60 species, while the highest diversity (lowest dominance and highest H’ index) was obtained in the dry season. Seasonal inventory completeness of leaf beetles approached (rainy season) or was higher (dry season) than 70%, while the faunistic similarity between seasons was 0.63%. The outlying mean index was significant in both seasons; of the seven microclimatic variables analyzed, only temperature, heat index, evapotranspiration and wind speed were significantly related to changes in abundance of Chrysomelidae. Association between microclimate and leaf beetles was higher in the dry season, with a difference in the value of importance of the abiotic variables. The results indicated that each species exhibited a different response pattern to the microclimate, depending on the season, which suggests that the species may exhibit modifications in their niche requirements according to abiotic conditions. However, the investigations must be replicated in other regions, in order to obtain a better characterization of the seasonal and microclimatic influence on the family Chrysomelidae.

Comparative study of multiple heat indices in revisiting summer heat across China based on meteorological observations

Multiple indices have been created to measure hot conditions that may cause discomfort, stress even death to humans. However, distinctions among these indices and their performance in measuring heat remain undisclosed. We conduct a comparative study of multiple heat indices and revisit the spatiotemporal changes in summer heat across China. The maximum temperature-based index, more sensitive to average and maximum temperatures, suggests a larger increasing trend (0.42°C/10a) in heat intensity than those average temperature-based ones which are more sensitive to minimum temperature. The absolute threshold-based heat-day indices are not so applicable as the relative ones in measuring the increasing heat days over the Tibetan Plateau. During 1960–2018, significant ( p < 0.05) increasing trends in heat intensity (0.11–0.42°C/10a) and heat day (0.63–2.67 days/10a) are revealed with a jump-like increase after the mid-1990s at the country level. Stronger heat intensity occurs over the southeast and north China with larger increasing trends over the Tibetan Plateau and northwest China. Northern China with larger increasing heat intensity and days should take effective measures of adaptation to reduce suffering from the summer heat. Given differences among multiple indices and the performance over different regions, a rational selection of heat index considering the research subject of interest and regional climatology is highly recommended.

Thermal Comfort of the Environment with Internet of Things, Big Data and Machine Learning

This research proposes to evaluate the level of thermal comfort of the environment in real time using Internet of Things (IoT), Big Data and Machine Learning (ML) techniques for collecting, storage, processing and analysis of the concerned information. The search for thermal comfort provides the best living and health conditions for human beings. The environment, as one of its functions, must present the climatic conditions necessary for human thermal comfort. In the research, wireless sensors are used to monitor the Heat Index, the Thermal Discomfort Index and the Temperature and Humidity Index of remote indoor environments to intelligently monitor the level of comfort and alert possible hazards to the people present. Machine learning algorithms are also used to analyse the history of stored data and formulate models capable of making predictions of the parameters of the environment to determine preventive actions or optimize the environment control for reducing energy consumption.

Retrospection of heatwave and heat index

The frequency and intensity of extreme events especially heat waves (HW) are growing all around the world which ultimately poses a serious threat to the health of individuals. To quantify the effects of extreme temperature, appropriate information, and the importance of HW and heat index (HI) are carefully discussed for different parts of the world. Varied definitions of the HW and HI formula proposed and used by different countries are carried out systematically continent-wise. Different studies highlighted the number of definitions of HW; however, mostly used Steadman’s formulae, which was developed in the late 1970s, for the calculation of HI that uses surface air temperature and relative humidity as climatic fields. Since then, dramatic changes in climatic conditions have been observed as evident from the ERA5 datasets which need to be addressed; likewise, the definition of HW, which is modified by the researchers as per the geographic conditions. It is evident from the ERA5 data that the temperature has increased by 1–2 °C as compared to the 1980s. There is a threefold increase in the number of heatwave days over most of the continents in the last 40 years. This study will help the researcher community to understand the importance of HW and HI. Furthermore, it opens the scope to develop an equation based on the present scenario keeping in mind the basics of an index as considered by Steadman.