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This study analyzes the impact of COVID-19 on agricultural production in South India by evaluating the influence of market channels and socioeconomic conditions on the production decisions of farmers during two key cropping seasons. We base our analysis on primary data from 200 marginal, small and medium farmers, primarily focusing on the key seasonal crops, namely paddy and black gram.

We studied the downstream supply chains of paddy and black gram crops in the district of Villupuram, situated in the South Indian state of Tamil Nadu. Using a Bi-Probit model, we analyzed the production decisions of marginal, small and medium farmers engaged in paddy and black gram cultivation. Various factors are considered, including farmers’ socioeconomic characteristics, gender, market channels accessed and the coping strategies employed.

After the easing of lockdown measures in June 2020, our research revealed substantial disruptions in agricultural production during the critical Kharif and Rabi seasons. Most farmers refrained from returning to their fields during the Kharif season; those who did produced millet as the main crop. Factors such as choice of market channels in previous seasons, economic status, access to all-weather roads, labor availability, gender and coping strategies played an important role in the return to production in the subsequent Kharif and Rabi seasons.

Our data revealed several interesting threads related to price volatility, irrigation and access to markets and their impact on food security. The role of intermediaries and market channels in providing liquidity emerges as an important aspect of farmers' choice of markets. The pandemic impacted all these factors, but a detailed analysis was beyond the scope of this study.

We also find that resilience to economic shocks varies not only by economic status but also by gender and social groups. Farmers with female members are more likely to be resilient, and marginal and small farmers primarily belong to social groups that are economically less developed.

This study contributes to the literature on factors influencing farmer choice and decision-making and provides nuances to discussions by analyzing crop-specific supply chains, highlighting the critical role of socioeconomic factors. It also highlights the role of demographics and infrastructural factors like access to all-weather roads and access to markets that influence farmers’ production decisions.

This paper aims to investigate the current state of biocomposites used in fused deposition modelling (FDM) with a focus on their mechanical characteristics.

The study presents a variety of biocomposite materials that have been used in filaments for 3D printing by different researchers. The process of making filaments is then described, followed by a discussion of the process parameters associated with the FDM.

To achieve better mechanical properties of 3D-printed parts, it is essential to optimize the process parameters of FDM while considering the characteristics of the biocomposite material. Polylactic acid is considered the most promising matrix material due to its biodegradability and lower cost. Moreover, the use of natural fibres like hemp, flax and sugarcane bagasse as reinforcement to the polymer in FDM filaments improves the mechanical performance of printed parts.

The paper discusses the influence of critical process parameters of FDM like raster angle, layer thickness, infill density, infill pattern and extruder temperature on the mechanical properties of 3D-printed biocomposite.

To meet energy demand and tackle the challenges posed by global warming, Bagasse-based Cogeneration Power Generation (BCPG) plant in sugar mills have tremendous potential due to large-scale supply of renewable fuel called bagasse. To meet this goal, an integrated framework has been proposed for analyzing performance issues of BCPG.

Intuitionistic Fuzzy Lambda-Tau (IFLT) approach was implemented to compute various reliability parameters. Intuitionistic Fuzzy Failure Mode and Effect Analysis (IF-FMEA) approach has been implemented for studying risk issues results in decrease in plant's availability. Moreover, IF- Technique for Order Performance by Similarity to Ideal Solution (IF-TOPSIS) is implemented to verify accuracy of IF-FMEA approach.

For membership and non-membership functions, availability decreases to 0.0006% and 0.0020% respectively for spread ±15% to ±30%, and further decreases to 0.0127% and 0.0221% for spread ±30% to ±45%. Under risk assessment failure causes namely Storage tank (ST3), Valve (VL6), Transfer pump (TF8), Deaerator tank (DT11), High pressure heater and economiser (HP15), Boiler drum and super heater (BS22), Forced draft and Secondary air fan (FS25), Air preheater (AH29) and Furnace (FR31) with Intuitionistic Fuzzy Hybrid Weighted Euclidean Distance (IFHWED) based output scores – 0.8988, 0.9752, 0.9400, 0.8988, 0.9267, 1.1131, 1.0039, 0.8185, 1.0604 were identified as the most critical failure causes.

Reliability and risk analysis results derived from IFLT and IF-FMEA approaches respectively, to address the performance issues of BCPG is based on the quantitative and qualitative data collected from the industrial experts and maintenance log book. Moreover, to take care of hesitation in expert's knowledge, IF theory-based concept is incorporated so as to achieve more accuracy in analysis results. Reliability and risk analysis results together will be helpful in analyzing the performance characteristics and diagnosis of critical failure causes, which will minimize frequent failure in BCPG.

The framework will help plant managers to frame optimal maintenance policy in order to enhance the operational aspects of the considered unit. Moreover, the accurate and early detection of failure causes will also help managers to take prudent decision for smooth operation of plant.

The results obtained ensure continuous operation of plant by utilizing the bagasse as fuel in boiler and also mitigate the wastages of fuel. If this bagasse (green fuel) is not properly utilized, there remains a dependency on coal-based power plants to meet the power demand. The results obtained are useful for decreasing dependency on coal, and promoting bagasse as the green, and alternative fuel, the emission by burning of these fuels are not harmful for environment and thereby contribute in preventing the environment from harmful effect of GHGs gases.

IFLT approach has been implemented to develop reliability modeling equations of the BCPG unit, and furthermore to compute various reliability parameters for both membership and non-membership function. The ranking results of IF-FMEA are compared to IF-TOPSIS approach. Sensitivity analysis is done to check stability of proposed framework.

The Internet of Things (IoT) is a cloud system that saves energy by being involved in the decision-making process of machines. By this means, machines create an environment of direct interaction without the need for explicit instructions. In this chapter, answers have been sought to the questions of what kind of research has been done on IoT-based technological devices, and how IoT-based technologies are effective in sustainable food production. The systematic literature review examined the scientific research on IoT and Food in the range of 2010–2022 in Scopus. The general framework of the research has been carried out in the context of 6Ws (who, when, where, what, why, and how), which is one of the systematic literature reviews. The results obtained have been analyzed and interpreted in the MAXQDA 2020 qualitative data analysis program. In the findings obtained, it has been determined that IoT and food have gained importance worldwide, especially in England, India, and China. Furthermore, it has been determined that most of the studies on IoT are based on case studies, and all the articles examined are collected in three main focus points. Subcodes have been created under the main codes ‘Food Supply Chain, Smart and Sustainable Agriculture ve Waste Management’, and problem points have been tried to be customized.

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 examine the effects of climate change and agricultural technologies on crop production in Vietnam for the period 1990–2018.

Several econometric techniques – such as the augmented Dickey–Fuller, Phillips–Perron, the autoregressive distributed lag (ARDL) bounds test, variance decomposition method (VDM) and impulse response function (IRF) are used for the empirical analysis.

The results of the ARDL bounds test confirm the significant dynamic relationship among the variables under consideration, with a significance level of 1%. The primary findings indicate that the average annual temperature exerts a negative influence on crop yield, both in the short term and in the long term. The utilization of fertilizer has been found to augment crop productivity, whereas the application of pesticides has demonstrated the potential to raise crop production in the short term. Moreover, both the expansion of cultivated land and the utilization of energy resources have played significant roles in enhancing agricultural output across both in the short term and in the long term. Furthermore, the robustness outcomes also validate the statistical importance of the factors examined in the context of Vietnam.

This study provides persuasive evidence for policymakers to emphasize advancements in intensive agriculture as a means to mitigate the impacts of climate change. In the research, the authors use average annual temperature as a surrogate measure for climate change, while using fertilizer and pesticide usage as surrogate indicators for agricultural technologies. Future research can concentrate on the impact of ICT, climate change (specifically pertaining to maximum temperature, minimum temperature and precipitation), and agricultural technological improvements that have an impact on cereal production.

To the best of the authors’ knowledge, this study is the first to examine how climate change and technology effect crop output in Vietnam from 1990 to 2018. Various econometrics tools, such as ARDL modeling, VDM and IRF, are used for estimation.

This study aims to draw observations on the current status and potentials of the Philippines as a farm tourism destination and identify the underlying factors that inhibit farm tourism development. It intends to gauge the challenges that Filipino farmers face in diversifying farms and operating farm sites and uses these challenges in crafting strategies and policies for relevant stakeholders. It also provides Philippine farm tourism literature to address the limitations of references in the topic.

The study adopts an exploratory type of inquiry method and secondary data collection from various sources, such as published journal articles, news articles and reports, to gain insights and relevant information on farm tourism. The study also uses a threats, opportunities, weaknesses and strengths analysis approach to develop competitive farm tourism strategies.

The Philippines, with vast agricultural land, has the necessary base for farm tourism, and the enactment of the Farm Tourism Development Act of 2016 bridges this potential. With low agricultural outputs, the country draws relevance for farm tourism as a farm diversification strategy to supplement income in rural communities. While having these potentials, crucial initiatives in physical characteristics, product development, education and training, management and entrepreneurship, marketing and customer relations and government support must be implemented. Farmers' lack of skills, training and capital investment potential to convert their farms into farm tourism sites serves as the major drawback. Thus, developing entrepreneurial and hospitality skills is crucial.

This work presents a historical narrative of initiatives and measures of the Philippine farm tourism sector. It also provides a holistic discussion and in-depth analysis of the current state, potentials, strategies and forward insights for farm tourism development.

The purpose of this paper is to investigate the efficacy of bacterial exopolysaccharides (Eps) in reactive black 5 (RB5) textile dye wastewater bioremediation.

The Eps were produced by bacteria isolated from cotton gin trash soils collected from different cotton-growing regions in Kenya for comparison purposes. A broth medium reconstituted using molasses was assessed for its capacity to produce the Eps. RB5 textile dye wastewater was optimized for dye removal under different temperatures, times and molasses concentrations. Dye removal was studied by Lovibond-Day Light Comparator, UV–Vis spectrophotometer and FTIR.

It was found that cotton gin trash soils contained Eps-producing bacteria. Three of the Eps studied were found to have the capacity to remove at least 80% of the dye from the wastewater.

This research did not assess the efficacy of the RB5 dye removal from the wastewater by mixtures of the Eps.

Bioremediation of textile dye wastewater with Eps produced by bacteria cultured from cotton gin trash soil is significant because it will offer an effective and cleaner alternative to the chemical coagulants.

Alternative treatment of textile wastewater with the Eps would result in safer water being released into the water bodies as opposed to the chemically treated wastewater that contains remnant chemicals.

Research on the use of Eps produced by bacteria isolated from cotton gin trash soils for removal of RB5 dye from textile wastewater has not been done before.

This research assesses the economic impact of biomass plant installations on Brazilian municipalities, focusing on (1) labor income, (2) sectoral labor income and (3) income inequality.

Municipal data from the Annual Social Information Report, the National Electric Energy Agency and the National Institute of Meteorology spanning 2002 to 2020 are utilized. The Synthetic Difference-in-Differences methodology is employed for empirical analysis, and robustness checks are conducted using the Doubly Robust Difference in Differences and the Double/Debiased Machine Learning methods.

The findings reveal that biomass plant installations lead to an average annual increase of approximately R$688.00 in formal workers' wages and reduce formal income inequality, with notable benefits observed for workers in the industry and agriculture sectors. The robustness tests support and validate the primary results, highlighting the positive implications of renewable energy integration on economic development in the studied municipalities.

This article represents a groundbreaking contribution to the existing literature as it pioneers the identification of the impact of biomass plant installation on formal employment income and local economic development in Brazil. To the best of our knowledge, this study is the first to uncover such effects. Moreover, the authors comprehensively examine sectoral implications and formal income inequality.