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A correlation has been shown in the literature between credit constraints and the adoption of agricultural technologies, technical efficiencies and measures for adapting to climate change. The relationship between credit constraints, risk management strategy adoption and income, however, is not well understood. Consequently, the purpose of this study was to investigate how credit constraints affect the income and risk management practices adopted by Northern Nigerian maize farmers.

Cross-sectional data were collected from 300 maize farmers in Northern Nigeria using a multi-stage sampling technique. Descriptive statistics, seemingly unrelated regression and double hurdle regression models were the analysis methods.

The results showed that friends and relatives, banks, “Adashe”, cooperatives and farmer groups were the main sources of credit in the study area. The findings also revealed that the sources of risk in the study area included production risk, economic risk, financial risk, institutional risk, technological risk and human risk. In addition, the risk management strategies used to mitigate observed risks were fertilizer application, insecticides, planting of disease-resistant varieties, use of herbicides, practising mixed cropping, modern planning, use of management tools as well as making bunds and channels. Furthermore, we found that interest rate, farm size, level of education, gender and marital status were significant determinants of statuses of credit constraints while the age of the farmer, gender, household size, primary occupation, access to extension services and income from maize production affected the choice and intensity of adoption of risk management strategies among the farmers.

The study concluded that credit constrained status condition of farmers negatively affected the adoption of some risk management strategies and maize farmers’ income.

The study concluded that credit constrained status condition of farmers negatively affected the adoption of some risk management strategies and maize farmers’ income. It therefore recommends that financial service providers should be engaged to design financial products that are tailored to the needs of smallholder farmers in the study area.

This paper incorporates the role of constraints in influencing farmers’ decisions to uptake credits and subsequently their adoption behaviours on risk management strategies. The researcher approached the topic with a state-of-the-art method which allows for obtaining more reliable results and hence more specific contributions to research and practice.

This study aims to understand how climatic drivers of change will affect rural communities living in the hot semiarid region of Bhokardan Taluka of Jalna district in the Indian state of Maharashtra. In the context of the economic and social change they are experiencing, the concern is to evolve ways that enable them to cope with, adapt to and benefit from these challenges.

The focus of most of the climate change studies is on the short- to long-term trends of weather parameters such as rainfall, temperature and extreme weather events. The impact of climate variability and changing patterns on the local communities, the local economy, livelihoods and social life in specific geographies is less explored.

As the impacts of climatic and nonclimatic drivers of change are cross-sectoral, diverse, multidimensional, interlinked and dynamic, this study has adopted a transdisciplinary “research-in-use” approach involving multidisciplinary teams covering the aspects such as changes in land use and land cover, surface and groundwater status, edaphic conditions, crops and livestock, climate analysis including projected changes, socioeconomic analysis, people’s experience of climate variability and their current coping strategies and resilience (vulnerability) analysis of communities and various livelihood groups.

The study was based on the peoples’ perspective and recommendation based on the local communities ability to cope up with climate change. However, a statistical analysis perspective is missing in the present study.

Based on these findings, a set of implementation-focused recommendations are made that are aimed at conserving and enhancing the resilience of the foundations that uphold and sustain the social and economic well-being of the rural communities in Bhokardan taluka, namely, land, water, agriculture, livestock, food and nutrition security, livelihoods, market access and social capital.

This study looked at the potential applications of geographic information systems (GIS) and remote sensing (RS) for inclusive community development and participation, sustainable tourism, and rural community-based natural resource management (CBNRM) in sub-Saharan Africa and other rural areas worldwide.

To evaluate resource management systems for rural tourism and the environment in Africa and abroad. The study makes use of reviews of relevant literature and documents, and while linking applications for sustainable tourism and local community empowerment with CBNRM and GIS, vital content was manually analyzed.

The study shows a potential affinity between agricultural and tourism businesses that GIS in line with the CBNRM conception can strengthen. In many rural and underdeveloped regions of the continent, this highlights the need for a credible and varied tourism strategy to develop and empower the relevant communities.

Most agricultural communities in Africa are located in low-income regions. Such areas are rich in natural wildlife and have popular tourist destinations. A mix of regional community development initiatives can be built using GIS, sustainable tourism, CBNRM, and community-based tourism (CBT).

The new technology aspects of Industry 4.0 (I4.0), such as digital technologies including artificial intelligence (AI), block chain, big data analysis and the internet of things (IoT) as a digital cosmos, have the potential to fundamentally transform the future of business and supply chain management. By augmenting the functional components of the food supply chain (FSC), these technologies can transform it into an intelligent food supply chain (iFSC). The purpose of this study is to identify the I4.0 utilization for FSC to become an iFSC. Additionally, it suggests future research agendas to bridge the academic knowledge gaps.

This study utilizes the bibliometric analysis methodology to investigate the techno-functional components of iFSC in the context of I4.0. The study followed steps of bibliometric analysis to assess existing components’ knowledge in the area of intelligent food supply chain management. It further reviews the selected articles to explore the need for I4.0 technologies’ adoption as well as its barriers and challenges for iFSC.

This study examines the integration of emerging technologies in FSC and concludes that the main emphasis is on the adoption of blockchain and internet of things technology. To convert it into iFSC, it should be integrated with I4.0 and AI-driven FSC systems. In addition to traditional responsibilities, emerging technologies are acknowledged that are relatively uncommon but possess significant potential for implementation in FSC. This study further outlines the challenges and barriers to the adoption of new technologies and presents a comprehensive research plan or collection of topics for future investigations on the transition from FSC to iFSC. Utilizing artificial intelligence techniques to enhance performance, decision-making, risk evaluation, real-time safety, and quality analysis, and prioritizing the elimination of barriers for new technologies.

The uniqueness of this study lies in the provision of an up-to-date review of the food supply chain. In doing so, the authors have expanded the current knowledge base on the utilization of all I4.0 technologies in FSC. The review of designated publications yield a distinctive contribution by highlighting hurdles and challenges for iFSC. This information is valuable for operations managers and policymakers to consider.

The purpose of this study is to explore the potential of trainable activation functions to enhance the performance of deep neural networks, specifically ResNet architectures, in the task of image classification. By introducing activation functions that adapt during training, the authors aim to determine whether such flexibility can lead to improved learning outcomes and generalization capabilities compared to static activation functions like ReLU. This research seeks to provide insights into how dynamic nonlinearities might influence deep learning models' efficiency and accuracy in handling complex image data sets.

This research integrates three novel trainable activation functions – CosLU, DELU and ReLUN – into various ResNet-n architectures, where “n” denotes the number of convolutional layers. Using CIFAR-10 and CIFAR-100 data sets, the authors conducted a comparative study to assess the impact of these functions on image classification accuracy. The approach included modifying the traditional ResNet models by replacing their static activation functions with the trainable variants, allowing for dynamic adaptation during training. The performance was evaluated based on accuracy metrics and loss profiles across different network depths.

The findings indicate that trainable activation functions, particularly CosLU, can significantly enhance the performance of deep learning models, outperforming the traditional ReLU in deeper network configurations on the CIFAR-10 data set. CosLU showed the highest improvement in accuracy, whereas DELU and ReLUN offered varying levels of performance enhancements. These functions also demonstrated potential in reducing overfitting and improving model generalization across more complex data sets like CIFAR-100, suggesting that the adaptability of activation functions plays a crucial role in the training dynamics of deep neural networks.

This study contributes to the field of deep learning by introducing and evaluating the impact of three novel trainable activation functions within widely used ResNet architectures. Unlike previous works that primarily focused on static activation functions, this research demonstrates that incorporating trainable nonlinearities can lead to significant improvements in model performance and adaptability. The introduction of CosLU, DELU and ReLUN provides a new pathway for enhancing the flexibility and efficiency of neural networks, potentially setting a new standard for future deep learning applications in image classification and beyond.

This study aims to identify seasonal drought using standardized precipitation index (SPI). The following specific objectives are to generate result and identify seasonal drought and determine different scale of seasonal drought and its impacts on cropping season.

Seasonal SPI was calculated using long-term rainfall data for three seasons. The SPI was calculated using the formula and it is effective for the determinants. This study showed the functional relationship between drought duration, frequency and drought time scale using the SPI. SPI=X−X¯σ.

Seasonal drought occurs more frequently in Bangladesh that affects crops and the agricultural economy every year. More severe drought was recorded during the Kharif-1 and Kharif-2 seasons and most crops were affected in these two seasons. No severe or moderate drought was recorded during the Rabi season. The results showed that monsoon crops were severely affected severely by extreme and severe droughts during the Kharif-2 season. Eventually, the people remain jobless during the monsoon, and they experience food shortages like monga. Several obstacles were recorded during the season, including delayed preparation of land, sowing, transplanting and other farming activities because of monsoon droughts. This study revealed that very frequently, mild dryness occurs in winter, but crop loss is minimal. The scale and occurrence of extreme droughts are more frequent during monsoons and reduce crop yields, affecting livelihoods in the study area. Seasonal drought affects cropping patterns as well as reduce crop yields.

The outcome of this study derived from the secondary data and field data.

Millets are underused crops that have the potential to withstand harsh environmental conditions. Recent research has proved immense nutritional benefits associated with millets which have increased their utilization to some extent but yet their sole potential is left to be exploited. Different millet varieties have exceptional nutritional and nutraceutical properties which can ameliorate even the deadly conditions of cancers. They have significant protein composition ranging from 10% to 12% which possess effective bioactive potential. Protein hydrolysates containing bioactive peptides have been evaluated for their therapeutic effects against a variety of diseases. This review aims to discuss the bioactive potential of different millet protein hydrolysates to encourage research for development of effective natural therapeutics.

The present article elaborates on effective studies on the therapeutic effects of millet protein hydrolysates.

Several effective millet peptides have been reported for their therapeutic effect against different diseases and their antioxidant, anti-inflammatory, anticancer, antimicrobial and antidiabetic effects have been investigated.

This review focuses on millet bioactive peptides and their significance in treating variety of diseases. Thus, will further encourage research to explore the novel therapeutic effects of millet proteins hydrolysates which can eventually result in the development of natural and safe therapeutics.

Small-scale farmers are highly heterogeneous with regard to their types of farming, levels of technology adoption, degree of commercialization and many other factors. Such heterogeneous types, respectively groups of small-scale farming systems require different forms of government interventions. This paper applies a machine learning approach to analyze the typologies of small-scale farmers in South Africa based on a wide range of objective variables regarding their personal, farm and context characteristics, which support an effective, target-group-specific design and communication of policies.

A cluster analysis is performed based on a comprehensive quantitative and qualitative survey among 212 small-scale farmers, which was conducted in 2019 in the Limpopo Province of South Africa. An unsupervised machine learning approach, namely Partitioning Around Medoids (PAM), is applied to the survey data. Subsequently, the farmers' risk perceptions between the different clusters are analyzed and compared.

According to the results of the cluster analysis, the small-scale farmers of the investigated sample can be grouped into four types: subsistence-oriented farmers, semi-subsistence livestock-oriented farmers, semi-subsistence crop-oriented farmers and market-oriented farmers. The subsequently analyzed risk perceptions and attitudes differ considerably between these types.

This is the first typologisation of small-scale farmers based on a comprehensive collection of quantitative and qualitative variables, which can all be considered in the analysis through the application of an unsupervised machine learning approach, namely PAM. Such typologisation is a pre-requisite for the design of more target-group-specific and suitable policy interventions.

This study has empirically investigated the impacts of climate change on agricultural productivity worldwide, considering the ranking of agriculture productivity. Additionally, the study has estimated the extent to which climate change favoured agriculture productivity from a global perspective.

The study prepared a suitable econometric model and employed the quantile panel Autoregressive Distributed Lag technique with a two-step Error Correction Mechanism to assess the influence of global warming on worldwide agrarian productivity.

The estimated results provide evidence for the nonlinear impacts of climate change on agriculture productivity across all quantiles. Moreover, threshold levels of average annual temperature rise with the improvement of agricultural productivity, depicting that low-productive areas are highly vulnerable to global warming. Additionally, agricultural inputs like labour, capital and irrigated land are positively related to agricultural productivity, with relatively substantial marginal productivity in highly productive regions. Nevertheless, technological innovations are found to be more productive in low-productive areas.

Policymakers should prioritize region-specific climate-smart agriculture by targeting policies to increase agricultural productivity and minimize the effects of climate change on food security and nutrition.

Despite significant research in this area, there remains a knowledge gap on the nature of this relationship, especially regarding productivity thresholds under warming. The study aims to fill this gap, offering valuable insights to guide policy actions and adaptation strategies to mitigate the adverse impacts of climate change on agriculture productivity.

Sustainable development is inseparable from rational and responsible use of resources and promotion of green entrepreneurship. The contemporary green development agenda encompasses climate, economic, technical, social, cultural, and political dimensions. International efforts to greening the global development are conducted by the major economies, including China as the world’s largest consumer of energy and the biggest emitter of greenhouse gases. China is aware of its environmental problems, as well as of its part of the overall responsibility for the accomplishment of the sustainable development goals. By means of the decarbonization efforts, the latter are integrated both into the national development agenda (the concept of ecological civilization) and China’s international initiatives (the greening narrative within the Belt and Road Initiative). Over the past decade, China has made a breakthrough on the way to promoting green entrepreneurship and greening of its development (better quality of air and water, renewable energy, electric vehicles, and organic farming). On the other hand, emissions remain high, agricultural land loses productivity, and freshwater resources degrade due to climate change. In conventional industries (oil, coal mining, and electric and thermal energy), decarbonization faces an array of impediments. In this chapter, the authors summarize fundamental provisions of China’s approach to building an ecological civilization and measures to reduce emissions and achieve the carbon neutrality status within the nearest decades. The analysis of obstacles to the decarbonization of the economy and possible prospects for the development of green entrepreneurship summarizes China’s practices for possible use in other countries.