Agriculture is the backbone of Indian economy and farmers play significant role in sustaining the sector. However, farmers face several challenges such as poor market access and limited access to market information which adversely affect their marketing performance. Therefore, this study aimed to examine the marketing performance and factors influencing farmers choice for agricultural output marketing channels in garden pea in the Indian state of Himachal Pradesh. The study utilized primary data collected through structured questionnaires administered via the interview method to a sample size of 400 farmers selected through a multistage stratified random sampling technique. The data collected were analyzed using descriptive statistics and acharya approach to study the marketing performance. Further, a multinomial logistic regression model was used to determine the factors that influence farmers marketing choice of marketing channels. Additionally, the constraints experienced by farmers during marketing were investigated using garret ranking. The study revealed that farmers who sold their pea directly to consumer had higher marketing performance than those who sold through market intermediaries such as local traders, commission agents and wholesalers. Further, the results indicated that farm income, farm experience, distance to the market and market information were significant determinants of farmers choice for marketing channels. Whereas, lack of market consultancy, high commission chargers and poor transportation were the major constraints faced by farmers during marketing of pea. The findings of this study highlight the need for policymakers to develop and implement interventions that enhance farmers marketing skill, facilitate access to markets, improve negotiation power which can lead to higher incomes for farmers, improve their livelihoods, better quality produce for consumers and a more efficient and sustainable food system.

Estimators proposed under classical statistics fail if data are vague or indeterminate. Neutrosophic Statistics are the only alternative because its deal with indeterminacy. Extensive reserch has been conducted in this field because of its wide applicability. This study aimed to further develop the theory of neutosophic simple random sampling without replacement. In this study, a generalized neutrosophic exponential robust ratio-type estimator was proposed, and five of its member neutrosophic estimators were developed. Derivations of the bias and Mean Square Error were provided up to the first-order approximation. To demonstrate the high efficiency of the proposed neutrosophic estimators an empirical study on the stock price of Moderna and four simulation studies have been conducted, and the results show that the proposed neutrosophic estimators are more efficient than similar existing ratio type estimators discussed in this paper in neutrosophic as well as classical forms.

This study investigates the compromise allocation of multivariate stratified sampling with complete response and nonresponse. We have formulated a multivariate stratified sampling problem as a mathematical programming problem to estimate p-population means with complete response and nonresponse for a fixed cost. Then, the compromise allocations for sample designs are determined by implementing intuitionistic fuzzy programming using optimistic and pessimistic solution strategies. A simulation study is carried out using the Stratify R software program to demonstrate the complete solution process. In wildlife, agricultural and marketing-related surveys, the study could be helpful. Also, the national planning policies related to surveys in such cases this study could be helpful. This study is an attempt to solve the sampling optimization problem using the Lingo-18 optimization program.

The novel coronavirus disease (COVID‐19) pandemic has had devastating effects on healthcare systems and the global economy. Moreover, coronavirus has been found in human feces, sewage, and in wastewater treatment plants. In this paper, we highlight the transmission behavior, occurrence, and persistence of the virus in sewage and wastewater treatment plants. Our approach follows the process of identifying a coronavirus hotspot through existing wastewater plants in major cities of Saudi Arabia. The mathematical distributions, including the log‐normal distribution, Gaussian model, and susceptible‐exposed‐infected‐recovered (SEIR) model, are adopted to predict the coronavirus load in wastewater plants. We highlight not only the potential virus removal techniques from wastewater treatment plants but also methods of tracing SARS‐CoV‐2 in humans through wastewater treatment plants. The results indicate that our modeling approach may facilitate the analysis of SARS‐CoV‐2 loads in wastewater for early prediction of the epidemic outbreak and provide significant implications to the public health system.

This study aims to determine an optimal policy to deal with a situation where a retailer should have enough stock to meet customer demands and prevent supply loss and deterioration. In this study, we demonstrate the impact of partial backlogging, shortages, and inflation on decaying items to provide the most relevant solutions to this problem. The model uses a two-parameter Weibull deterioration distribution with an exponential demand rate. Numerical results and sensitivity analysis are combined with a graphical demonstration, and the proposed approach is reliable and accurate, providing a new explanation for a different type of system. This paper determines the total cost and optimal production run time by developing an inventory model. Sensitivity analysis is carried out to demonstrate the proposed model.