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This study explores value-added food products in smallholder dairy farming in developing countries by analyzing external pressures, supply chain learning, farmer innovation, education level, and food safety compliance.

We employed a quantitative approach by surveying 418 smallholder dairy farmers in three districts of Pakistan using interviewer-administered questionnaires. Data analysis involved confirmatory factor analysis and structural equation modeling.

The results indicate that external pressure significantly affects value-added smallholder dairy farms. This relationship is mediated by supply chain learning and farmers' innovative behavior, and moderated by farmers' education level and compliance with food safety standards.

Further research is required to explore the drivers of value addition at the supply chain level.

This study contributes to the understanding of smallholder dairy farming dynamics and provides practical implications for improving value addition by managing the interplay between antecedents and promoting best practices in the industry.

This study examined the effects of adopting dairy feed technology bundles on the milk production of smallholder dairy farmers.

The study was based on Multinomial Endogenous Switching Regression (MESR) to estimate the effects of the adoption of three feed technology bundles on milk production using data collected from 518 dairy farm households in West Java, Indonesia.

The findings indicated that adopting technology bundles had positive and robust effects on milk production, with gradual positive effects between non-adoption and the adoption of different bundles of technologies.

This study focused on the association between the adoption of feed technology bundles and milk production. However, further analysis of the causal links between the adoption of feed technologies and milk production as well as the inclusion of other outcomes in the analysis, such as production costs and risk mitigation, are required.

Most of the literature on agricultural technology adoption focuses on the adoption of individual technologies, crop farming and conservation practices. Therefore, this study examined the effects of the adoption of dairy feed technology bundles.

The global research evidences indicate that the technology adoption in case of agribusiness has a potential to enhance the performance and bring operational efficiency. India is the world’s largest producer as well as consumer of milk but struggles with yield per cattle, overall productivity, low rate of technology acceptance and adoption, health detection of milching units, animal data recording and presence of dairy products in the global market. The purpose of this study is to focus on identifying the challenges of technology adoption in dairy farms and constructing a hierarchical model using soft systems methodology.

This study uses nominal group technique-based discussion with domain experts and personal interviews with dairy farm owners/managers for the identification of challenges, fuzzy interpretative structural modeling as well as FMICMAC to develop a hierarchical model of challenging elements and to divide the identified elements into four categories based on the dominance of driving-dependence power.

This research has developed a list of 12 challenges affecting the technology adoption in a dairy farm business unit, identified through the personal interviews with 60 dairy farms across three highest milk-producing states of India in terms of annual milk output – Haryana, Punjab and Uttar Pradesh. Lack of government support followed by lack of educational opportunities in dairy-based education were found as the most crucial and high driving challenges, whereas high cost, huge investment and low acceptance of decision-maker were found as the most dependent challenges of technology adoption.

This research is one step ahead of interpretive structural modeling that considers the fuzzy-based dominance in the model to showcase the degree of relationship along with its existence, but it lacks to statistically validate the findings using techniques like SEM.

This paper has developed a list of challenges in adoption of technology along with their inter-relationships to highlight the required focus challenge that drives or is dependent on the other challenges. The goal is to bring performance improvement and assist Indian dairy farm business stakeholders or decision-makers in formulating strategic and action plans and help policy planners to make favorable policies based on the understanding of contextual relationship between challenges.

In Indian context, dairy is an important part of agriculture sector, and milk is an essential item that facilitates income generation to small and rural households and a source item for several other businesses and activities. The results of this research suggested the policy planners and government to ensure subsidized and insured technologies, training support and facilities, educational opportunities and efforts for promotion of technology adoption among dairy farmers. The suggestions are purely on the basis of the relevance of challenges in the hierarchy and can play a significant role in improving the level of technology adoption and can ultimately uplift the social and economic well-being from micro-level of farmers to macro-stage concerning economic development of India.

To the best of the authors’ knowledge, this study is purely original and outcome of the research conducted by authors.

The global outbreak of COVID-19 and its subsequent varied impacts on different economic activities necessitate to examine its disruptions and impacts on livestock sector in India due to its recently surging potential as an unrivaled alternative to boost farmer’s income.

The studies for review were identified through search in different databases using relevant keywords. Only full text papers written in English language were reviewed. The review was organized and streamlined using Covidence software.

Analysis of the literature reveals adverse effects of COVID-19 on functioning of input and output stages of livestock supply chains. This has resulted in upstream and downstream economic losses that affect livelihoods of the producers.

Scale of unprecedented crisis due to COVID-19 pandemic requires creative policy decisions to make livestock production systems robust, resilient and sustainable. Organized production systems are required to integrate with livestock-tech startups to modernize their supply chains, whereas local supply chains are required to reorient with government’s intervention in terms of developing on-farm production and postproduction processing facilities.

Although there exist some evidence on COVID-19-related impacts on livestock sector of India, but an integrated review of evidence on COVID-19 related disruptions at all the stages (from input supply to marketing) of livestock supply chains was missing.

This study aims to identify the psychological factors predicting sociopreneurial intention (SEI) and gain insight into the conversion of SEI to sociopreneurial behaviour (SEB) in the presence of facilitating events (FE) as a moderating factor.

Hypotheses are statistically tested using a partial least square structural equation modelling (PLS-SEM) based on purposive survey data (n = 110) from the leaders of dairy cooperatives in Indonesia. The measurement items are specifically developed for this research after thoroughly analysing the questionnaire items provided by prior studies.

The findings support the hypotheses that empathy (EM), perceived social responsibility (PSR) and self-efficacy (SEFF) are strong predictors of SEI. Moreover, although SEI can be directly converted to SEB, FE significantly moderated that conversion.

Future research should also involve the impact of SEB on the community and the sociopreneurs.

This research empirically examines the influence of external factors in converting SEI into SEB, which prior studies overlooked. Moreover, it involves the leaders of dairy cooperatives in Indonesia who perform SEB to help smallholder farmers’ communities facing dairy farming issues such as capital, milk productivity, land size and others as research participants.

This article examines the dairy value chain in Southern Peru and identifies four critical success factors that can enhance the local situation.

The study employed descriptive research using semi-structured interviews with entrepreneurs from 17 cheese factories across eight districts, namely Azángaro, Ayaviri, Pucara, Lampa, Cabana, Acora, Pomata and Puno. Quantitative market data were also gathered and analyzed alongside qualitative views.

The study identified four critical issues: quality concerns in milk production, suboptimal managerial practices of cheese-processing plants, lack of compliance to regulations, particularly hygiene and environmental ones, and inadequate access to finance. The findings reveal a gap between the practices of the Puno region's dairy industry and world-class standards for cheese production. Urgent actions are required to improve product quality, increase access to finance, enhance managerial education and ensure compliance with regulations.

Results suggest critical issues to be prioritized, but the article does not propose how to solve the problems identified. External factors, such as economic changes, were also not considered. Interviews were conducted exclusively with cheese processing entrepreneurs, not milk producers.

This case study provides an insight into the interior of Peru, an under-researched region facing several development challenges. The findings have significant implications for dairy value chain stakeholders in Peru and other similar contexts.

With the current wave of modernization in the dairy industry, the global dairy market has seen significant shifts. Making the most of inventory planning, machine learning (ML) maximizes the movement of commodities from one site to another. By facilitating waste reduction and quality improvement across numerous components, it reduces operational expenses. The focus of this study was to analyze existing dairy supply chain (DSC) optimization strategies and to look for ways in which DSC could be further improved. This study tends to enhance the operational excellence and continuous improvements of optimization strategies for DSC management

Preferred reporting items for systematic reviews and meta-analyses (PRISMA) standards for systematic reviews are served as inspiration for the study's methodology. The accepted protocol for reporting evidence in systematic reviews and meta-analyses is PRISMA. Health sciences associations and publications support the standards. For this study, the authors relied on descriptive statistics.

As a result of this modernization initiative, dairy sector has been able to boost operational efficiency by using cutting-edge optimization strategies. Historically, DSC researchers have relied on mathematical modeling tools, but recently authors have started using artificial intelligence (AI) and ML-based approaches. While mathematical modeling-based methods are still most often used, AI/ML-based methods are quickly becoming the preferred method. During the transit phase, cloud computing, shared databases and software actually transmit data to distributors, logistics companies and retailers. The company has developed comprehensive deployment, distribution and storage space selection methods as well as a supply chain road map.

Many sorts of environmental degradation, including large emissions of greenhouse gases that fuel climate change, are caused by the dairy industry. The industry not only harms the environment, but it also causes a great deal of animal suffering. Smaller farms struggle to make milk at the low prices that large farms, which are frequently supported by subsidies and other financial incentives, set.

This paper addresses a need in the dairy business by giving a primer on optimization methods and outlining how farmers and distributors may increase the efficiency of dairy processing facilities. The majority of the studies just briefly mentioned supply chain optimization.

Family farms, in which business and family life are intricately interwoven, offer an interesting context for better understanding the interdependence between the family and business system. Many family farms struggle to survive, and the succession process is a key period in which the low returns on investment become evident but also the emotional attachment of the family to the farm and the willingness to transfer the business to the next generation. We take the perspective of non-succeeding siblings since they are crucial for a successful succession but their role and position in this process is far from clear. This study will help to increase our knowledge of how fairness is perceived by non-successors and of the impact of perceived (in)justice on the family business system.

To analyze the effect on sibling relationships of an unequal outcome of the succession process, we choose the family farm context. We used interview data from multiple family members from several family farms in the Netherlands in different stages of succession. We utilized a framework based on justice theory to analyze perceptions of fairness among non-succeeding siblings. The central research question for this study is as follows: How do non-succeeding siblings perceive justice with regard to family firm succession?

The acceptance of the outcomes of the succession process by non-succeeding siblings is influenced by their perception of the fairness of the process itself and decisions made by the incumbent and successor with regard to these outcomes. It seems that stakeholders who occupy multiple roles with conflicting justice perspectives handle these contradictions with the help of an overarching goal—in this study, preserving the continuity of the family farm—and by prioritizing and adjusting the justice perspectives accordingly. The findings further show that both distributive justice and procedural justice are important and interact with each other.

Our study contributes to the literature by applying the theoretical framework of distributive and procedural justice to the context of family farm succession. This helps us to understand the position of non-succeeding siblings and their role and position in the succession process, which is important because sibling relationships have a significant impact on family harmony, with potential consequences for the business as well.

Milk is a perishable food product, one of the primary sources of nutrition. Reports worldwide indicate numerous food frauds and foodborne diseases associated with adulterated milk products. These safety concerns highlight the importance of a visible milk supply chain, which can be achieved by cutting-edge technologies. However, these technologies come with high costs. So, this study aims to propose a framework that integrates blockchain, Internet of Things (IoT) and cloud to enhance visibility with reduced cost in an Australian milk supply chain (AMSC).

A design science research methodology is used, where a proof of concept is also developed at the retailer end to show how blockchain, IoT and cloud can improve visibility with reduced cost in an AMSC.

According to cost and visibility analysis, blockchain implementation in AMSC would generate a high return on investment (ROI). For the given case, ROI becomes positive for all stakeholders after 750 cycles. Integrating IoT, cloud and blockchain is more profitable than just using blockchain. Additionally, technology implementation may not benefit all stakeholders equally. For example, the retailer needs 10 cycles to benefit, but the transporter needs 50 in the given case.

The findings of this study assist milk industries in decision-making regarding technology implementation in their supply chain and motivate them to implement these technologies, resulting in improved trust and coordination among entities and consumers.

A cost and visibility analysis are performed to evaluate the impact of technology implementation on cost and visibility in an AMSC. A SOAR (Strength Opportunities Aspiration Results) analysis is also performed for the strategic planning framework.

This study aims to implement Lean and Green principles within the agribusiness sector, focusing specifically on employing Environmentally Sustainable Value Stream Mapping (Sus-VSM) to assess critical indicators for both present and future states of an orange farm. The objective is to enhance value while simultaneously mitigating environmental impacts.

Employing a mixed-method research design, this study integrates both qualitative and quantitative methodologies. It adapts Sus-VSM and introduces inventory analysis frameworks for application within the agricultural domain, conducting a case study on an orange farm located in Sergipe, Brazil. This research seeks to provide actionable insights into the application of Lean and Green principles in agribusiness and introduces the Overall Lean-Green Effectiveness (OLGE) as an innovative decision-making tool for managers.

The study underscores the efficacy of Sus-VSM in the agricultural sector, albeit noting the necessity for certain process modifications to ensure successful implementation. Upon comparison of the two states, it is observed that the current state incurs 70.55% higher impacts on climate change (0.47 tCO₂eq./ha), whereas the future state could yield a 4.08% increase in aggregated value. Improvements from the current to the future scenario can primarily be achieved through enhanced management of in-field inventory of inputs, given that OLGE in this case study is significantly influenced by efficient inventory management.

The adaptation of VSM for agricultural operations, coupled with the integration of environmental sustainability indicators, represents an innovative strategy for enhancing agricultural processes while minimizing environmental impacts. The proposition of a new Lean and Green indicator, the OLGE, aims to facilitate the interpretation of results and guide improvements.