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Following the trend in the world over and on the African continent, the Parliament of Ghana passed a new law in 2020, the Narcotics Control Commission Act (Act 1019) that eased the legal restrictions on cannabis cultivation and use, subject to obtaining license from the relevant authority/authorities. This paper aims to examine the implications of Ghana’s decision to legalise the cultivation of cannabis for industrial purposes for the production of fibre and for medicinal purposes.

The author adopted the qualitative analysis critical review methodology in sourcing information from peer-reviewed articles, coupled with the author’s own professional knowledge, background and training, to critically analyse and review the reasons behind Ghana’s decision to legalise cannabis cultivation and the implications such a major policy change/shift.

It was found that, while the law mentions industrial and medicinal purposes for the legalisation of cannabis, the real reason, at least in the immediate term, is mainly economic. This agrees with those of other African countries that have legalised cannabis cultivation but appears to contrast with those of Europe and America.

This research is limited to Ghana and the few African countries that have legalised cannabis cultivation so far. The main reason for the legalisation (economic benefits) may also change in the future when the economics of scale turn in the favour of the country. Therefore, Ghana’s case may not represent the case of all countries legalising cannabis cultivation.

The research implication of this paper is that it brings to the world the law on cannabis cultivation in Ghana. It provides a professional and scientific position paper on Ghana’s drug law regime regarding cannabis, thus, serving as a useful reference literature to the academic and research community on the topic. More importantly, it discusses the implications of such major policy change from an objective perspective, unravelling the real reason behind the State’s decision to legalise the cultivation of cannabis in Ghana.

This paper will bring to the fore the other consequences of Ghana’s cannabis cultivation legalisation policy change, which little attention has been devoted to so far.

This paper, apart from reference to other published texts on the topic, which have been duly acknowledged, represents the sole work of the author. It has not been produced anywhere or by anyone else either in Ghana or elsewhere.

The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India using effective predictive models. Thus, this study aims to investigate how internal and external predictors impact pearl millet yield and Stover yield.

Descriptive analytics and artificial neural network are used to investigate the impact of predictors on pearl millet yield and Stover yield. From descriptive analytics, 473 valid responses were collected from semi-arid zone, and the predictors were categorized into internal and external factors. Multi-layer perceptron-neural network (MLP-NN) model was used in Statistical Package for the Social Sciences version 25 to model them.

The MLP-NN model reveals that rainfall has the highest normalized importance, followed by irrigation frequency, crop rotation frequency, fertilizers type and temperature. The model has an acceptable goodness of fit because the training and testing methods have average root mean square errors of 0.25 and 0.28, respectively. Also, the model has R² values of 0.863 and 0.704, respectively, for both pearl millet and Stover yield.

To the best of the authors’ knowledge, the current study is first of its kind related to impact of predictors of both internal and external factors on pearl millet yield and Stover yield.

The literature reveals that most studies have estimated crop yield using limited parameters and forecasting approaches. However, this research will examine the impact of various predictors such as internal and external of both yields. The outcomes of the study will help policymakers in developing strategies for stakeholders. The current work will improve pearl millet yield literature.

This study aims to investigate the impact of employees’ engagement in government social media (GSM) on their cybersecurity compliance attitude, protection motivation and protective behavior, thereby contributing to effective cybersecurity practices at organizations.

A quantitative cross-sectional field survey was conducted to collect primary data in big cities and large provinces in Vietnam. The final data set of 323 responses was analyzed using the partial least squares-structural equation modeling approach to interpret the results and test research hypotheses.

Engagement in GSM positively influences employees’ cybersecurity compliance attitude (ATT). Perceived threat vulnerability and response efficacy also contribute to a positive compliance attitude, although self-efficacy has a negative impact. Moreover, the cybersecurity compliance ATT significantly explains the information protection motivation, which in turn influences employee protective behaviors. However, the relationship between compliance attitude and protective behaviors is weak, unlike previous studies that found a strong correlation.

Although recent studies have explored specific information security practices in corporate and home contexts, the influence of GSM on individuals’ cybersecurity behaviors has received limited attention because of its novelty. This study contributes to the existing body of knowledge by investigating the impact of GSM on cybersecurity behaviors. This study provides significant contributions to understanding social media’s effects of social media on individuals’ cultivation processes, by expanding upon the protective motivation theory and cultivation theory. The results lead to practical suggestions for organizational managers and policymakers so that they can enhance their understanding of the importance of cybersecurity, encourage the implementation of self-defense strategies and highlight the significance of threat and coping evaluations in influencing attitudes and motivations.

Because of the importance of the wheat industry in the economy, a real-featured performance measurement approach is essential for the wheat production process. The purpose of this paper is to develop a data envelopment analysis (DEA) model that is fully compatible with the wheat production process so that managers and farmers can use it to evaluate the efficiency of wheat farms for strategic decisions.

A dynamic multi-stage network DEA model is developed to evaluate the efficiency of wheat production farms in short-term (two-year) and long-term (eight-year) periods.

The results of this study show that because of the lack of long-term planning and excessive reliance on rain, most of the investigated regions have no stability in efficiency, and the efficiency of the regions changes in a zigzag manner over time. Among studied regions, only the Hashtrood region has high and stable efficiency, and other regions can follow the example of this region's cultivation method.

To the best of the authors’ knowledge, this study is the first one that uses the dynamic multi-stage network DEA considering every other year cultivation method and direct–indirect inputs in the agricultural section.

This study aims to construct a mental health service system for middle school students in the post-COVID-19 era with the framework of Six Sigma DMAIC (define, measure, analyze, improve and control) and analyze the influencing factors of the mental health service system to study the implementation strategies of quality-oriented mental health services in middle schools.

This study was conducted in Tianjin, China, from September to November 2022, and 350 middle school students from Tianjin Public Middle School were selected as subjects. A questionnaire survey was used to collect data. In this study, the Six Sigma DMAIC method, sensitivity analysis method, exploratory factor analysis and principal component analysis were used to analyze the mental health services provided to middle school students.

Based on the Six Sigma DMAIC framework, this study indicates that the contribution rate of the mental health service process factor is the largest in the post-COVID-19 era. The mental health cultivation factor ranks second in terms of its contribution. Mental health quality and policy factors are also important in the construction of middle school students’ mental health service system. In addition, the study highlights the importance of parental involvement and social support in student mental health services during the post-COVID-19 era.

To the best of the authors’ knowledge, a study on middle school students’ mental health in the post-Covid-19 era has not yet been conducted. This study developed a quality-oriented mental health system and analyzed the influencing factors of mental health for middle school students based on data analysis and the Six Sigma DMAIC method.

This research aims to investigate the role of digitalization in facilitating the integration of circular economy (CE) principles within a firm operating in the Italian agrifood sector. The study seeks to explore the evidence and effects emerging from the adoption of digital technologies in a small and medium enterprise (SME) operational setting.

An interpretative case study was conducted on an SME operating in the Italian agrifood sector. The selected firm is known to adopt a business model oriented towards circularity by using entirely digitized closed-loop hydroponic cultivation.

The findings reveal that the digitalization of the production process, supported by an integrated information system, enables optimizing the use and consumption of natural resources and minimizes waste during the production stage. Additionally, the authors observed that digitalization triggers a complex mechanism of interaction between various firm factors, market dynamics and forms of institutionalization, which are intrinsically intertwined with the concepts of sustainability and resilience in the agrifood sector.

From a theoretical point of view, the interpretive reading key – historically appropriate to embrace the complexity of the phenomena under study – can foster a deeper understanding of the dynamics underlying digitalization as an enabling factor to facilitating the adoption of CE principles in the agrifood sector. Regarding managerial implications, the study contributes to the debate on the importance of digital transition in the agrifood industry, which in the Italian context shows considerable resistance due, especially, to the size of the firms (mainly SMEs and micro) and managerial conservatism tradition.

This study aims to examine the technical efficiency of the chemical-free farming system in India using a hybrid combination of data envelopment analysis (DEA) and machine learning (ML) approaches.

The study used a two-stage approach. In the first stage, the efficiency scores of decision-making units’ efficiency (DMUs) are obtained using an input-oriented DEA model under the assumption of a variable return to scale. Based on these scores, the DMUs are classified into efficient and inefficient categories. The 2nd stage of analysis involves the identification of the most important predictors of efficiency using a random forest model and a generalized logistic regression model.

The results show that by using their resources efficiently, growers can reduce their inputs by 34 percent without affecting the output. Orchard's size, the proportion of land, grower's age, orchard's age and family labor are the most important determinants of efficiency. Besides, growers' main occupation and footfall of intermediaries at the farm gate also demonstrate significant influence on efficiency.

The study used only one output and a limited set of input variables. Incorporating additional variables or dimensions like fertility of the land, climatic conditions, altitude of the land, output quality (size/taste/appearance) and per acre profitability could yield more robust results. Although pineapple is cultivated in all eight northeastern states, the data for the study has been collected from only two states. The production and marketing practices followed by the growers in the remaining six northeastern states and other parts of the country might be different. As the growers do not maintain farm records, their data might suffer from selective retrieval bias.

Given the rising demand for organic food, improving the efficiency of chemical-free growers will be a win-win situation for both growers and consumers. The results will aid policymakers in bringing necessary interventions to make chemical-free farming more remunerative for the growers. The business managers can act as a bridge to connect these remote growers with the market by sharing customer feedback and global best practices.

Although many developments have happened to the DEA technique, the present study used a traditional form of DEA. Therefore, future research should combine ML techniques with more advanced versions like bootstrap and fuzzy DEA. Upcoming research should include more input and output variables to predict the efficiency of the chemical-free farming system. For instance, environmental variables, like climatic conditions, degree of competition, government support and consumers' attitude towards chemical-free food, can be examined along with farm and grower-specific variables. Future studies should also incorporate chemical-free growers from a wider geographic area. Lastly, future studies can also undertake a longitudinal estimation of efficiency and its determinants for the chemical-free farming system.

No prior study has used a hybrid framework to examine the performance of a chemical-free farming system.

This study aims to analyse how the adoption of Industry 4.0 technologies can help different types of agri-food supply chains introduce and manage innovations in response to the challenges and opportunities that emerged following the COVID-19 pandemic.

A systematic literature review methodology was used to bring together the most relevant contributions from different disciplines and provide comprehensive results on the use of I4.0 technologies in the agri-food industry.

Four technological clusters are identified, which group together the I4.0 technologies based on the applications in the agri-food industry, the objectives and the advantages provided. In addition, three types of agri-food supply chains have been identified and their configuration and dynamics have been studied. Finally, the I4.0 technologies most suited for each type of supply chain have been identified, and suggestions on how to effectively introduce and manage innovations at different levels of the supply chain are provided.

The study highlights how the effective adoption of I4.0 technologies in the agri-food industry depends on the characteristics of the supply chains. Technologies can be used for different purposes and managers should carefully consider the objectives to be achieved and the synergies between technologies and supply chain dynamics.

This study aims to examine the role of edible mushrooms in the Brazilian diet, considering their strategic significance in meeting nutritional goals within sustainable development. Despite their potential in the nutrition of the Brazilian population, significant knowledge gaps still exist. To address this, the authors formulated this study into five main sections: the consumption of edible mushrooms in Brazil, the factors influencing the consumption, the occurrence of edible mushrooms in Brazil, the nutritional contribution of mushrooms consumed in Brazil and sustainable mushroom production in Brazil.

The authors compiled current literature to develop this viewpoint paper using systematic review, systematic search and narrative review search methods.

Mushrooms are sporadically consumed in Brazil, primarily by the urban population, with challenges in estimating the most used species. Social, economic and cultural factors, health considerations and reduced meat consumption influence mushroom consumption behavior. While Pleurotus ostreatus, Lentinula edodes and Agaricus bisporus are primary species, ethnomycological studies highlight a more diverse consumption among traditional indigenous communities. Brazil hosts approximately 133 wild mushroom species safe for human consumption. Some can be sustainably cultivated using substrates derived from agricultural and urban waste, offering high-protein, high-fiber, low-fat foods with bioactive compounds holding antioxidant and prebiotic potential.

To the best of the authors’ knowledge, no previous study has investigated how edible mushrooms contribute to the food and nutrition of the Brazilian population. This study emphasizes the crucial role of edible mushrooms in preserving Brazil’s cultural heritage, contributing to food and nutritional security and enhancing the overall diet quality.

The choice of crops to produce at a location depends to a large degree on the climate. As the climate changes and food demand evolves, farmers may need to produce a different mix of crops. This study assesses how much cropland may be subject to such upheavals at the global scale, and then focuses on China as a case study to examine how spatial heterogeneity informs different contexts for adaptation within a country.

A global agricultural economic model is linked to a cropland allocation algorithm to generate maps of cropland distribution under historical and future conditions. The mix of crops at each location is examined to determine whether it is likely to experience a major shift.

Two-thirds of rainfed cropland and half of irrigated cropland are likely to experience substantial upheaval of some kind.

This analysis helps establish a global context for the local changes that producers might face under future climate and socioeconomic changes. The scale of the challenge means that the agricultural sector needs to prepare for these widespread and diverse upheavals.