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The dairy sector has been poised for fierce competitiveness due to the commercialization of the dairy sector and the presence of a global market size of 893 billion dollars. It has received a new dimension with the challenge of environmental sustainability. Considering all these challenges, the present research paper studies the major growth factors and the sustainable approach to dairy development in an emerging economy like India. Fifteen major dairy-developed states of India have contributed 90% of the milk production of the country examined from 2001 to 2019. The significance of key growth factors for dairy farming and the dairy value chain is assessed. The opportunity for sustainable dairying is examined. The authors have found that the growth factors of dairy farming and dairy value chain are significant by 1% through the fixed effect (FE) model and random effect (RE) model respectively. Indian dairy farming practices and their approach create optimism about environmental sustainability. The present study has observed a series of operational activities that are conducive to the triple bottom approach. With that, this study has mentioned strategies that can steer growth momentum and support sustainable Indian dairy development in the coming times. [Display omitted] • Three key areas, namely, dairy farming, dairy value chain and environmentally friendly practices for sustainable dairy development. • Two main types of variables (internal and external growth factors) are involved in the development of the dairy farming. • Strategies are formulated to improve milk productivity. • In line with the triple bottom line approach, a series of operational activities are suggested. [ABSTRACT FROM AUTHOR]

As elsewhere in Western Europe, large scale intensive agriculture dominates the landscape of the Netherlands. Grassland for dairy production occupies more than a quarter of its land surface. The high production intensity on conventionally farmed grassland leads to poor habitat quality, resulting in sharp declines in bird and insect numbers. Nature-inclusive agriculture (NIA) comprises innovations in farm management, technology and resource use that have the potential to address farmland biodiversity decline, but few Dutch farmers implement these. We aim to analyze the adoption of NIA practices in the Dutch dairy sector. Specifically, we study the influence of the dominant agri-food regime on the innovation system for NIA. Innovation Systems Analysis was performed to identify the various structural barriers which hinder adoption. Our study used a multi-method design in which data on NIA in the Dutch dairy sector was collected via a literature study, four workshops and a focus group discussion. We identified five key blocking mechanisms that hinder adoption of nature-inclusive agriculture in the Dutch dairy sector: (1) insufficient economic incentives for farmers, (2) limited action perspective of many dairy farmers in the Netherlands, (3) lack of a concrete and shared vision for NIA, (4) lack of NIA-specific and integral knowledge and (5) regime resistance, which moreover is connected to each of the previous blocking mechanisms. Our analysis shows that one of the empirical novelties of this paper is that these blocking mechanisms are strongly interlinked in the Dutch dairy sector, thereby perpetuating a situation of lock-in. We conclude that in order to accelerate adoption of nature-inclusive farming practices, problems need to be addressed in conjunction with one another, and therefore holistic approaches are key. A second important conclusion is that in order to foster growth of the innovation system around NIA, the focus should not only be on innovation, but also on transforming current regimes, in particular the currently dominant economic paradigms of growth and yield maximization. By unraveling strongly interlinked blocking mechanisms, this paper provides intervention points to accelerate the transition towards NIA in the Netherlands. These intervention points are not only located within the innovation system, but should preferable be sought for in the broader structures and institutions of the dominant agri-food regime. [Display omitted] • Uptake of nature-inclusive practices in the Dutch dairy sector is currently low. • Through Innovation Systems Analysis we identified five blocking mechanisms hindering uptake. • These include lack of financial incentives and shared visions, limited action perspectives and knowledge transfer problems • The productivist agriculture paradigm is a recurring blocking mechanism requiring institutional change • Change needs to come from incumbents, requiring policies aimed at regime transformation [ABSTRACT FROM AUTHOR]

Digitalization and the use of Smart Farming Technologies are considered a major opportunity for the future of agriculture. However, realisation of full benefits is constrained by: (1) farmers' interest in and use of big data to improve farm decision making; (2) issues of data sovereignty and trust between providers and users of data and technology; (3) institutional arrangements associated with the governance of data platforms. This paper examines the case of Australia's dairy herd milk recording system, arguably one of agriculture's first cases of 'big data' use, which collects, analyses and uses farm-level data (milk production, lactation and breeding records) to provide individual cow and herd performance information, used by individual farmers for farm management decisions. The aim of this study was to 1) examine the use of big data to add value to farm decision making; and 2) explore factors and processes, including institutional arrangements, which influence farmer engagement with and use of big data. This paper traces the Australian history of the organisation of dairy herd recording (established in 1912 and digitalized in late 1970s) and then uses findings from a longitudinal study of 7 case study dairy farms, which were incentivised to become involved in herd recording in 2015. Applying a conceptual framework linking path dependency in farm decision making and collaborative governance capacity, we find three new important dimensions of the farm user context influencing farmer demand for big data applications: 1) the transition to a new business stage; 2) the additionality farmers seek from data generated in one component of the farm system to other subsystems, and 3) the use of data in long term or strategic decision making. Further, we identified critical attributes of support services in addressing digital literacy, capacity and capability issues at farm level, including diversity in data presentation formats and facilitation of the on-farm transition process through intermediary herd test organisations. The role of farmers as governance actors, or citizens in the decisions of the trajectory of big data applications, adds to understanding of the nature of collaborative governance arrangements that support farm engagement. • Farmer interest in data relates to business stage and the decision's importance. • Cooperative governance structures facilitate farmer trust and use of big data. • Support services reduce the impact of low digital literacy, capacity and capability. • Inter-dependence occurs between existing data processes and optimising data use. [ABSTRACT FROM AUTHOR]

Regenerative agriculture is a farming approach that uses soil health as the entry point to contribute to multiple objectives, such as improved nutrient cycling and climate regulation. To reach these objectives farmers can apply different practices. The objectives and practices, however, are not equally relevant or applicable for every farming system and local context. The main objective of this paper, therefore, was to find out how tailor-made solutions towards regenerative agriculture can be identified and evaluated as such that they result in meaning-full advice for farmers. In this study a well-established modelling framework to redesign farming systems was applied to three typical but diverse Dutch farming systems. The modelling framework combined the models Soil Navigator and FarmDESIGN to simultaneously assess five soil functions at field-level and general sustainability indicators (e.g. greenhouse gas emissions) at farm-level. We applied the modelling framework to an arable farm on clay soil, a dairy farm on peat soil, and a mixed farm on sand soil. We subsequently explored a multitude of tailor-made solutions composed of combinations of practices for these farming systems, each showing solutions that contributed in varying degrees towards the objectives of regenerative agriculture. In total, we created 4000 alternative solutions per case-study farm. For all farming systems, environmental performance was improved in the solutions dominated by the use of regenerative practices. For example, for the arable, the dairy, and the mixed case-study farm, greenhouse gas emissions were reduced by 50% (from 4 to 2 Mg CO 2 eq. ha−1), 6% (from 30 to 28 Mg CO 2 eq. ha−1), and 23% (from 21 to 16 Mg CO 2 eq. ha−1), respectively, while maintaining soil functionality at high capacity for four out of the five soil functions. This overall improvement in environmental performance due to the application of regenerative practices, also resulted in reduced farm profitability for all case-study farms by on average 50%. We discuss that a mechanism to incentivize farmers for their tailor-made contribution to regenerative agriculture is for stakeholders to shift focus from solely primary productivity to also other ecosystem services. This study contributes to the wider implementation of regenerative agriculture, by showing which regenerative objectives and farming practices can contribute to the transition towards regenerative agriculture in contrasting contexts. The modelling framework that is used, can underpin regenerative management for farmers and other stakeholders to help, for example, the valorization of multiple regenerative objectives in business models. [Display omitted] • This study explored a novel modelling framework to redesign farms towards regenerative agriculture. • This framework can be used to analyze a multitude of tailor-made solutions for diverse farming systems. • From this framework farmers can select the solutions that fit their local context and intrinsic motivations best. • For our case-study farms, environmental performance improved when regenerative management practices increased. • This improvement in environmental performance, however, came at the expense of farm profitability. [ABSTRACT FROM AUTHOR]