Due to the continuous rise of global temperatures and heatwaves worldwide as a result of climate change, concerns for the health and safety of working populations have increased. Workers in the food production chain, particularly farmworkers, are especially vulnerable to heat stress due to the strenuous nature of their work, which is performed primarily outdoors under poor working conditions. At the cross-section of climate change and farmworkers' health, a scoping review was undertaken to summarize the existing knowledge regarding the health impacts associated with climate change and heat stress, guide future research toward better understanding current and future climate change risks, and inform policies to protect the health and safety of agricultural workers. A systematic search of 5 electronic databases and gray literature websites was conducted to identify relevant literature published up until December 2021. A total of 9045 records were retrieved from the searches, of which 92 articles were included in the final review. The majority of the reviewed articles focused on heat-related illnesses (n = 57) and kidney diseases (n = 28). The risk factors identified in the reviewed studies included gender, dehydration, heat strain, wearing inappropriate clothing, workload, piece-rate payment, job decision latitude, and hot environmental conditions. On the other hand, various protective and preventive factors were identified including drinking water, changing work hours and schedule of activities, wearing appropriate clothing, reducing soda consumption, taking breaks in shaded or air-conditioned areas, and increasing electrolyte consumption in addition to improving access to medical care. This review also identified various factors that are unique to vulnerable agricultural populations, including migrant and child farmworkers. Our findings call for an urgent need to expand future research on vulnerable agricultural communities including migrant workers so as to develop effective policies and interventions that can protect these communities from the effects of heat stress.

https://www.frontiersin.org/articles/10.3389/fpubh.2022.782811/pdf

Impacts of Climate Change and Heat Stress on Farmworkers' Health: A Scoping Review

Data Mining – that intersection of statistics, computer science, and machine learning – is increasingly recognized as a discipline in its own right. Still, its statistical linear is undeniable. Pave l Cichosz’s new book helps to keep data mining and statistics close, in part, by describing some of the important pieces of data mining using code in R, a tool with which many statisticians will be very familiar. The book, he says, is not intended for statisticians as such but “for a mixed audience consisting of students of computer science and related fields, researchers. . .in any area where data analysis capabilities are used [and] analysts. . .working with data and creating or using predictive models.”

https://www.jstatsoft.org/article/view/v066b02/v66b02.pdf

Data Mining Algorithms Explained Using R

Evaluation of mist-spraying environment on thermal sensations, thermal environment, and skin temperature under different operation modes

Outdoor thermal comfort and somatic symptoms among students in a tropical city

Improving the suitability of selected thermal indices for predicting outdoor thermal sensation in Tehran

Human susceptibility to outdoor hot environment

Human heat stress and strain indices have been developed to ascertain the thresholds of human health and wellbeing. This study illustrates the matrices of environmental and behavioural indicators of warmth assessment (human thermal indices), referring to seasonal meteorological trend across the varied climatic regions of India. Effectiveness of the indices were explored using spatial and temporal analysis of regions. Human thermal indices are broadly grouped as direct (climatic, e.g., WBGT), rational (energy balance, e.g., SET*), perception (sensation voting, e.g., PMV) indices and CFD simulations. The warmth classification as regard to the temporal trend evidently differs to a great extent between the indices. Further to the spatial patterns in the studied regions also demonstrate inconsistent gradation. Elaborate derivation of the indices shows large deviations of the relative weightages in different indices, which may partly be attributed to the inconsistencies observed. For example, In case of WBGT, higher weightage is given to relative humidity, resulting in flattening in the climate trend, whereas, the energy balance models cover heat exchange between human body and environment. The rational indices, e.g., SET* and Esk (evaporation through skin) that take into account both meteorological and behavioural dimensions respond more effectively to both temporal and spatial trend analysis.

Effectiveness of human-thermal indices: Spatio–temporal trend of human warmth in tropical India

Objectives. Given the burgeoning impacts of climatic variability on human health, suitable computational paradigms are used to explore the subsequent ergonomic repercussions. The artificial neural network (ANN), in particular, exhibits near-accurate input-output mapping. However, employment of the ANN to trace the inter-dependencies between the climatic and human thermoregulatory parameters in real-world fuzzy problem landscapes is relatively inadequate. In the present study, the ANN models examined the relationships between climatic, behavioral and intrinsic input factors and the thermoregulatory outputs, namely, sweating and the evaporative heat transfer at the skin surface (Esk). Methods. The data were obtained from nearly 1800 subjects who were exposed to a hot and humid climate outdoors. The ANN models were trained using the Levenberg-Marquardt algorithm combined with Bayesian regularization. Results. The predictability of the ANN models was statistically substantiated. The clothing insulation factor was not included as an input parameter, given its similar values. Intriguingly, the ANN results indicated that fabrics with similar thermal resistances could still affect Esk, plausibly owing to the temporal variation in the evaporative resistance of fabrics among individuals. Conclusion. The reasonably accurate results affirmed the suitability of ANN as a pragmatic technique that could elucidate heat-induced ergonomic challenges.

Climate-induced thermoregulatory responses in a non-linear thermal environment: investigating the inter-dependencies using a facile artificial neural network-based predictive strategy.

In India, small and marginal farmers have emerged as a distinct and dominant category. While farm mechanization represents a rapid transformation from traditional to modern methods of farming, it is not uniform across the crops and regions. The level of mechanization, however, remains scattered due to the compulsiveness to the situation dominated by the economic layout of farm holdings, land size, and large-scale deprivation of access to the technology suitable to small holdings. This present contribution elucidates the extent of use tools and machinery among the rice farmers of the state of Wes Bengal, India. Analysis revealed that the total number of man-days involved in paddy cultivation was 120-140 per ha, i.e., 900-1000 man-hours depending upon the availability of labour, tools, and machinery used for the individual operation. Analysis of farm work in small and marginal holdings evolved that over 90% of the total number of farmers use either tractor or power tiller for land preparation. Use of the animal-drawn country plough is gradually phased out in the study regions. For sowing and transplanting operations are primarily manual methods using hand tools. The study provided an insight of the issues of work methods and practices of the farmworkers in small and marginal farm holdings.

/pdf/human-energy-utilization-and-effectiveness-in-small-and-1s6kfks5a8.pdf

Human energy utilization and effectiveness in small and marginal farm holdings

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Human susceptibility to outdoor hot environment

Effectiveness of human-thermal indices: Spatio–temporal trend of human warmth in tropical India

Climate-induced thermoregulatory responses in a non-linear thermal environment: investigating the inter-dependencies using a facile artificial neural network-based predictive strategy.

Human energy utilization and effectiveness in small and marginal farm holdings