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The present study provides evidence on export advantages of horticultural commodities based on competitiveness, trade balance and seasonality dimensions.

The study delineated horticultural commodities in terms of comparative advantage, examined temporal shifts in export advantages (mapping) and estimated seasonality. Product mapping was carried out using the Revealed Symmetric Comparative Advantage (RSCA) and Trade Balance Index (TBI). Seasonal advantages were examined through a graphical approach along with the objective tests, namely, modified QS-test (QS), Friedman-test (FT) and using a seasonal dummy.

Cucumbers/gherkins, onions, preserved vegetables, fresh grapes, shelled cashew nuts, guavas, mangoes, and spices emerged as the most favorable horticultural products. India has a strong seasonal advantage in dried onions, cucumber/gherkins, shelled cashew nut, dried capsicum, coriander, cumin, and turmeric. The untapped potential in horticulture can be addressed by handling the trade barriers effectively, particularly the sanitary and phytosanitary issues, affecting the exports. Proper policies must be enacted to facilitate the investment in advanced agricultural technologies and logistics to ensure the desired quality and cost effectiveness.

Commodity-specific studies on value chain analysis would provide valuable insights into the issues hindering exports and realizing the untapped export potential.

There is no holistic and recent study illustrating the horticulture export advantages covering a large number of commodities in the Indian context. The study would be helpful to the stakeholders for drawing useful policy implications.

In this study, workplace spirituality is discussed in the context of remote work and the COVID-19 pandemic. First, the focus is on the changes in the meaning and function of employees in the organization during the evolutionary process of industrialization and management paradigms. Afterward, conceptual frameworks for spirituality and workplace spirituality are presented, and in the last section, academic studies that deal with workplace spirituality with distance or hybrid work arrangements are included. Early studies offer insights and recommendations on conceptualizing, developing, and managing workplace spirituality. They all emphasize that spirituality is necessary for remote work as a tool to overcome stress and mental health problems and increase employee well-being. Unfortunately, the remote work and workplace spirituality literature is relatively narrow and needs to be expanded.

In a volatile agricultural postharvest market, producers require more personalized information about market dynamics for informed decisions on the marketed surplus. However, this adaptive strategy fails to benefit them if the selection of a computational price predictive model to disseminate information on the market outlook is not efficient, and the associated risk of perishability, and storage cost factor are not assumed against the seemingly favourable market behaviour. Consequently, the decision of whether to store or sell at the time of crop harvest is a perennial dilemma to solve. With the intent of addressing this challenge for agricultural producers, the study is focused on designing an agricultural decision support system (ADSS) to suggest a favourable marketing strategy to crop producers.

The present study is guided by an eclectic theoretical perspective from supply chain literature that included agency theory, transaction cost theory, organizational information processing theory and opportunity cost theory in revenue risk management. The paper models a structured iterative algorithmic framework that leverages the forecasting capacity of different time series and machine learning models, considering the effect of influencing factors on agricultural price movement for better forecasting predictability against market variability or dynamics. It also attempts to formulate an integrated risk management framework for effective sales planning decisions that factors in the associated costs of storage, rental and physical loss until the surplus is held for expected returns.

Empirical demonstration of the model was simulated on the dynamic markets of tomatoes, onions and potatoes in a north Indian region. The study results endorse that farmer-centric post-harvest information intelligence assists crop producers in the strategic sales planning of their produce, and also vigorously promotes that the effectiveness of decision making is contingent upon the selection of the best predictive model for every future market event.

As a policy implication, the proposed ADSS addresses the pressing need for a robust marketing support system for the socio-economic welfare of farming communities grappling with distress sales, and low remunerative returns.

Based on the extant literature studied, there is no such study that pays personalized attention to agricultural producers, enabling them to make a profitable sales decision against the volatile post-harvest market scenario. The present research is an attempt to fill that gap with the scope of addressing crop producer's ubiquitous dilemma of whether to sell or store at the time of harvesting. Besides, an eclectic and iterative style of predictive modelling has also a limited implication in the agricultural supply chain based on the literature; however, it is found to be a more efficient practice to function in a dynamic market outlook.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

In this introduction to the special issue, ‘Marxist Thought in South Asia’, we detail the long history of Marxist politics and theorizing in South Asia and highlight the unique contributions and perspectives of South Asian Marxists to global Marxism. Three contributions we find particularly significant are (1) South Asian Marxists' approach to thinking about questions of capitalism, colonialism and imperialism, (2) the treatment of agrarian and feudal continuities in Marxist theories from South Asia and (3) unique South Asian contributions to theorizing caste from a Marxist perspective.

This study aims to use a multi-stage scale development process to identify the dimensions of philanthropic corporate social responsibility (PCSR) in India’s fast-moving consumer goods (FMCG) sector.

The authors conducted a study to develop a comprehensive, reliable and valid scale for measuring PCSR based on the customer perception of FMCG product manufacturers. This research adopted a comprehensive and detailed scale development process using multi-stage sampling for scale development. This final study was conducted on a sample of 402 respondents from the city of Jaipur, India.

The results have underlined the multi-dimensional aspect of PCSR; these dimensions are: altruism towards society, volunteering for local community development, generosity towards ecology, benevolent spirit and problem-solving charity.

This study gives valuable insights into philanthropic scale development in the FMCG sector that can immensely help domestic and international marketers to formulate CSR as a strategy. This research provides insights into a wide range of scales which can be base for future research studies that aim to explore different organizational settings.

PCSR and CSR are important for developing strategies for sustainable businesses across the globe. Dimensions of PCSR will be useful for practitioners and researchers in developing second-order constructs for future studies.

A pandemic causes abrupt and unanticipated disruptions in many facets of society. A lot of authorities have quickly turned to online teaching methods. The best methods for online teaching have become a hot topic of discussion due to this urgent fast transmission. It was difficult to teach physiology to medical and paramedical students online because of concerns about how to give the students an effective interactive online teaching practice and how to guarantee successful outcomes. Therefore, three approaches have been individually applied to medical and nursing students for the first time in the physiology department of the Faculty of Medicine in Rabigh, King Abdulaziz University. Through online lectures and assignments, the strategies attempted to capture the students’ interest and interaction. The second-year nursing students were given a mind map project to complete after the lecture. The third-year medical students used a crossword puzzle game to test the students’ understanding. The third-year medical students were presented with short stories to better comprehend the physiological processes covered in the lectures. Overall, the three instructional strategies received positive feedback from the students. Incorporating such cutting-edge and imaginative educational approaches, in conclusion, could significantly aid in managing the pressures that arise during pandemics.