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The purpose of this paper is to verify if, in the dairy business ecosystem (dairy farmers, dairy cooperatives, government, research agencies, consultants and financial institutions) perspective, the dairy production barriers presented by Bonamigo et al. (2016b) can be mitigated through the dairy production key factors indicated in the literature.

To achieve the proposed goal of this study, the methodology used for the study comprises three stages: exploratory search in the literature to identify barriers and drivers of dairy production, data collection and statistical model. For the first stage, the authors conducted exploratory research in the literature, to better understand the dairy production ecosystem and to find arguments that characterize the barriers and drivers of this activity. In the second stage, a structured questionnaire with 13 closed-ended questions in a single block, with ordinal responses, following the suggestion of Likert (1932), was developed and applied to the dairy production ecosystem players of Santa Catarina, Brazil. A total of 305 responses suitable for analysis based, that were analyzed through an exploratory factorial analysis and modeling of structural equations via partial least squares, resulting in the Statistical model.

Based on the analyses results, the authors verified that there is an inverse association between the characteristics of the barriers and the dairy production key factors. In this sense, the authors can confirm that the increase of the loads in the dairy production key factors, reduce loads of the milk production barriers. Based on the results of this test, new theoretical and practical insights can be identified to develop new studies with the aim of boosting the dairy sector development as well as providing support in the decision making of the dairy system players.

This study may not have enabled a complete coverage of all existing peer-reviewed articles in the field of dairy production. It seems reasonable to assume that the review process covered a significant proportion of studies available.

It is the first study that compares identifies barriers and key factors of dairy production in Santa Catarina using as an analytical lens the business ecosystem.

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 purpose of this paper is to develop a model for the production planning decision of a dairy plant in a multi-product setting under supply disruption risk and demand uncertainty while determining the optimal product-mix and material planning requirement.

A mixed-integer nonlinear programming model is proposed to determine the optimal product-mix that maximizes the expected profit of a dairy. The data are collected through visits to the dairy site, conducting brainstorming sessions with the plant manager and marketing head at the corporate office. Disruption data are collected from the India Meteorological Department, Odisha.

From the analysis, it is recommended that the dairy should not produce curd during the planning period. Moreover, turnover from toned, double toned and baby food is maximum than that of the curd and these products are produced in the planning period. The expected profit increases from its present value when an optimal product-mix is followed. Sensitivity analysis is performed to analyze the effect of demand uncertainty, supply disruption and production quota. The expected profit decreases as the supply failure probability increases.

The model is implemented in a dairy plant under Orissa State Cooperative Milk Producers Federation, Odisha, India. The proposed methodology has not been validated, theoretically. The concerned dairy is based on the Indian context, but the authors believe that the study is highly relevant to other dairies as well.

This study provides a methodology for dairy plant managers to plan production effectively under supply disruption risk with demand uncertainty. It also suggests material requirement planning at different factories of the dairy plant.

This paper develops a mathematical model for the production planning decision of a dairy plant that determines the optimal product-mix, which maximizes the expected profit of a dairy under disruption risk and demand uncertainty (in the Indian context).

The purpose of this paper is to empirically test the dairy production barriers: lack of cooperation between the chain actors, milk quality deficiencies, rural exodus and, productivity limitations in southern Brazil, based on Santa Catarina’s dairy production ecosystem players perceptions.

From inquiry result analysis with 305 dairy sector experts, the authors performed statistical tests using the technique of factorial analysis and confirmatory factorial analysis to confirm and/or refute the dairy production barriers presented by Bonamigo et al. (2016b).

The results confirmed the presence of the barriers presented by Bonamigo et al. (2016b). The barrier lack of cooperation between the chain actors is presented as the biggest obstacle in the sector, according to the interviewees. The authors also found that rural exodus is the barrier with the smallest impact on the dairy sector development in relation to other barriers presented by Bonamigo et al. (2016b). The confirmation of these barriers in practice allows developing new studies to eliminate and/or mitigate these barriers of the dairy sector. Furthermore, the study might shed some light on dairy production activities and help improve management skills.

This study does not cover all the players that make up the dairy production ecosystem. It is limited only to the main players that are inserted in this sector. The authors observed the lack of data characterizing the dairy production system in Santa Catarina.

This study presents theoretical and practical contribution. By the confirmation that these barriers are present in the field, it is possible to direct new studies that seek to mitigate them and results in dairy production improvements. As for the practical contribution, the confirmation of those barriers can serve as a basis for the dairy sector decision-making actors, such as government, research institutions, and extension, producers, cooperatives, among others, and assist them in developing strategic actions that concern a cooperative way to develop the whole sector.

This study aims to examine the impact of Lean 4.0 practices on value co-creation in the dairy ecosystem.

Data collection were carried out through a questionary application with 126 professionals linked to the dairy ecosystem, including milk producers, milk cooperatives and milk transporters. The data were analyzed using Cluster Analysis, Mann-Whitney test and Chi-Square test.

A strong relation was found between the use of Lean 4.0 tools and the increase in operational performance, in addition to milk quality. Moreover, it can be noted that the use of digital technologies from Industry 4.0 has a strong relation with dairy production optimization, in other words, it is possible to be more efficient in the dairy process via Lean 4.0 adoption.

The study is limited to analyzing the Brazilian dairy ecosystem. The results presented may not reflect the characteristics of the other countries.

Once the potential empirical impacts of the relation between Lean 4.0 and value co-creation are elucidated, it is possible to direct strategies for decision-making and guide efforts by researchers and professionals to deal with the waste mitigation present in the dairy sector.

Lean 4.0 proves to be a potential solution to improve the operational performance of the dairy production system. Lean 4.0, linked to value co-creation, allows the integration of the production sector with consumers, through smart technologies, so new services and experiences can be provided to the consumer market. Additionally, the consumer experience can be stimulated based on Lean 4.0, once the quality specification is highlighted based on data science and smart management control.

To the best of the authors’ knowledge, this is the first study that analyzes the interrelationship between the Lean 4.0 philosophy and the value co-creation in the dairy ecosystem. In this sense, the study reveals the main contributions of this interrelation to the dairy sector via value co-creation, which demonstrates a new perspective on the complementarity of resources, elimination of process losses and new experiences for the user through digital technologies integrated with the Lean Thinking approach.

The dairy industry needs to make important decisions regarding its supply chain. In a context with many available suppliers, deciding which of them will be part of the supply chain and deciding when to buy raw milk is key to the supply chain performance. This study aims to propose a mathematical model to support milk supply decisions. In addition to determining which producers should be chosen as suppliers, the model decides on a milk pickup schedule over a planning horizon. The model addresses production decisions, inventory, setup and the use of by-products generated in the raw milk processing.

The model was formulated using mixed integer linear programming, tested with randomly generated instances of various sizes and solved using the Gurobi Solver. Instances were generated using parameters obtained from a company that manufactures dairy products to test the model in a more realistic scenario.

The results show that the proposed model can be solved with real-world sized instances in short computational times and yielding high quality results. Hence, companies can adopt this model to reduce transportation, production and inventory costs by supporting decision making throughout their supply chains.

The novelty of the proposed model stems from the ability to integrate milk pickup and production planning of dairy products, thus being more comprehensive than the models currently available in the literature. Additionally, the model also considers by-products, which can be used as inputs for other products.

The study measures the technology gap and performance of the Norwegian dairy farms accounting for farm heterogeneity.

The analysis was based on a meta-frontier and unbalanced farm-level panel data for 1991–2014 from 417 Norwegian farms specialized in dairy production in five regions of Norway.

The result of the analysis provides empirical evidence of regional differences in technical efficiencies, technological gap ratios (TGRs) and input use. Consequently, the paper provides some insights into policies to increase the efficiency of dairy production in the country across all regions.

The author used a meta-frontier approach for modeling regional differences based on a single-output production function specification. This approach has commonly been used in the economics literature since Battese et al. (2004). To get more informative and useful results, it would be necessary to repeat the analysis within terms of multiple input-output frameworks using, for instance, the input distance function approach. Moreover, the author estimated the meta-frontier using the non-parametric approach, thus it is also a need for further analysis if the values are different by estimating using a parametric approach.

One implication for farmers (and their advisers) is that dairy farms in all regions used available technology in the area sub-optimally. Thus, those lagging the best-performing farms need to look at the way the best-performing farmers are operating. Policymakers might reduce the gap is through training, including sharing information about relevant technologies from one area to another, provided that the technologies being shared fit the working environment of the lagging area. Moreover, some of the dairy technologies they use may not fit other regions, suggesting that agricultural policies that aim to encourage efficient dairy production, such as innovation of improved technology (like breeding, bull selection and improved feed varieties) through research and development, need to account the environmental differences between regions.

For both taxpayers and consumers, one implication is that the contributions they pay that go to subsidize dairy farmers appear to bring some benefits in terms of more efficient milk production that, in turn, increases the supply of some foods so possibly making food prices more affordable.

The paper contributes to the literature in several ways. In contrast to Battese et al. (2004), the author accounts for farm-level performance differences by applying the model devised by Greene (2005), thus may serve as a model for future studies at more local levels or of other industries. Moreover, the author is fortunate to able to use a large level farm-level panel data from 1991 to 2014.

Purpose – Dairy has been the backbone of agriculture in regional Norway, and the processing of milk has been dominated by co-operatives owned by milk farmers. During the social democratic order (1945–1979), productivist agriculture thrived, while a more multifunctional agriculture was developed after 1980. As a measure against overproduction, a quota system was introduced in 1983. The purpose of this study is to see if there are signs of a neo-productivism revival after climate change and other global shocks, like the food crisis, featured prominently on the political agenda.

Design/methodology/approach – The chapter reviews the radical structural changes in Norwegian dairy production since the early 1960s, which reduced the number of milk farms radically from 148,000 in 1959 to almost 16,000 in 2009. According to the Agricultural Agreement between the Norwegian government and the farmers' organisations, the co-operatives are given an important semi-public role as market-price regulators and stock keepers. This Norwegian system may be described as a classical regulated dairy regime. The Norwegian dairy regime has been through several deregulations and re-regulations over the last 20 years, partly forced by internal pressures and partly inspired by liberalisation tendencies abroad.

Findings – After mid-1990s, there has been an increase in the number of joint dairy farms, where individual ownership of land is maintained while herds, buildings and machinery are merged. Three thousand six hundred thirty dairy farmers are now participating in 1,510 joint farming firms, producing 29 per cent of the milk in Norway. This rapid growth of joint farming is transforming the dairy sector in Norway. Analysis has shown that its evolution is closely tied to farmer socio-economic demands, including social benefits, such as increased leisure time, and security during illness. While there has been pressure to increase productivity, the food crisis changed attitudes, making the current policy of import tariffs and subsidies easier to defend.

Originality/value – This chapter shows that neo-liberalism in Norway was not pursued as far as in most other OECD countries, although some deregulation was taking place. Norwegian agricultural policies are still regulating the sector to a substantial degree, with the annual Agricultural Agreement negotiations serving as a centrepiece. Norway has ambitious climate goals, and by 2020 greenhouse gases emissions should be reduced to 30 per cent of the 1990 rate. A further goal is that Norway will be carbon neutral by 2030. As part of the implementation of its climate policy, a White Paper on agriculture and climate change was put forward in May 2009. For Norwegian food production as a whole, a change towards more grazing at the expense of crops would improve carbon storage and reduce the overall use of fertiliser. Such a shift in land use would benefit the dairy sector, in part because of easier access to domestically grown cow feed.

Changing conditions for farming force farmers to search for new ways to organize agricultural production. In dairy farming, households experience long working hours, inconvenient working conditions, and low incomes. Dairy markets are beleaguered by overproduction, low prices on staple dairy products, and low return to labor and capital. This structural squeeze, which is aggravated by quick technological changes and the globalization of markets, is negotiated in various ways by dairy farmers in different agricultural regimes. A recent coping strategy for dairy farmers in Norway has been joint farming, a process whereby two, three, or even more farmers establish a joint company to merge their resources and work together. These joint farmers enjoy more leisure time, greater security in case of illness, and improved work environments. Why is joint farming so successful in Norway? One main explanation is the difference between agricultural regimes, which places the Norwegian dairy farmer in a privileged position when it comes to building coping capacity.

The purpose of this paper is to analyse the determinants of technical efficiency (TE) in dairy farms located in the South of Brazil, aiming for a better understanding of the topic for academics, dairy farmers and policymakers to improve the productivity and competitiveness of dairy farms.

This study was developed using a two-stage approach. Data envelopment analysis was used to estimate the TE level and regression models to understand the factors affecting TE in dairy farms. The sample size is 253 dairy farms in the South of Brazil.

The variation in the mean TE indexes reported in the literature can be explained by the attributes of the analysed studies, including the education of the farm operator, farm size (number of cows and milk), feed and labour costs, and use of services. Additionally, the results suggest that dairy farmers in the sample could increase milk output by 50.1 per cent (level of inefficiency) on average if they improve their TE.

This study makes three important contributions: first, it formulates hypotheses from the previous literature’s propositions on the estimation of TE in dairy farms; second, it tests the hypotheses in an empirical study to understand the main factors affecting the TE in dairy farms of the selected municipalities in the South of Brazil; and third, it compares previous findings on the determinants of TE in dairy farms serving different stakeholders, such as researchers, farmers and government representatives, to improve the productivity and competitiveness of dairy farms.