Intensive agriculture has led to several drawbacks such as biodiversity loss, climate change, erosion, and pollution of air and water. A potential solution is to implement management practices that increase the level of provision of ecosystem services such as soil fertility and biological regulation. There is a lot of literature on the principles of agroecology. However, there is a gap of knowledge between agroecological principles and practical applications. Therefore, we review here agroecological and management sciences to identify two facts that explain the lack of practical applications: (1) the occurrence of high uncertainties about relations between agricultural practices, ecological processes, and ecosystem services, and (2) the site-specific character of agroecological practices required to deliver expected ecosystem services. We also show that an adaptive-management approach, focusing on planning and monitoring, can serve as a framework for developing and implementing learning tools tailored for biodiversity-based agriculture. Among the current learning tools developed by researchers, we identify two main types of emergent support tools likely to help design diversified farming systems and landscapes: (1) knowledge bases containing scientific supports and experiential knowledge and (2) model-based games. These tools have to be coupled with well-tailored field or management indicators that allow monitoring effects of practices on biodiversity and ecosystem services. Finally, we propose a research agenda that requires bringing together contributions from agricultural, ecological, management, and knowledge management sciences, and asserts that researchers have to take the position of "integration and implementation sciences."

Paradoxically, the number of crop-livestock farms is declining across Europe, despite the fact that crop-livestock farms are theoretically optimal to improve the sustainability of agriculture. To solve this issue, crop-livestock integration may be organized beyond the farm level. For instance, local groups of farmers can negotiate land-use allocation patterns and exchange materials such as manure, grain, and straw. Development of such a collective agricultural system raises questions, rarely documented in the literature, about how to integrate crops and livestock among farms, and the consequences, impacts, and conditions of integrating them. Here, we review the different forms of crop-livestock integration beyond the farm level, their potential benefits, and the features of decision support systems (DSS) needed for the integration process. We identify three forms of crop-livestock integration beyond the farm level: local coexistence, complementarity, and synergy, each with increasingly stronger temporal, spatial, and organizational coordination among farms. We claim that the forms of integration implemented define the nature, area, and spatial configuration of crops, grasslands, and animals in farms and landscapes. In turn, these configurations influence the provision of ecosystem services. For instance, we show that the synergy form of integration promotes soil fertility,

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The need to adapt to decrease farm vulnerability to adverse contextual events has been extensively discussed on a theoretical basis. We developed an integrated and operational method to assess farm vulnerability to multiple and interacting contextual changes and explain how this vulnerability can best be reduced according to farm configurations and farmers’ technical adaptations over time. Our method considers farm vulnerability as a function of the raw measurements of vulnerability variables (e.g., economic efficiency of production), the slope of the linear regression of these measurements over time, and the residuals of this linear regression. The last two are extracted from linear mixed models considering a random regression coefficient (an intercept common to all farms), a global trend (a slope common to all farms), a random deviation from the general mean for each farm, and a random deviation from the general trend for each farm. Among all possible combinations, the lowest farm vulnerability is obtained through a combination of high values of measurements, a stable or increasing trend and low variability for all vulnerability variables considered. Our method enables relating the measurements, trends and residuals of vulnerability variables to explanatory variables that illustrate farm exposure to climatic and economic variability, initial farm configurations and farmers’ technical adaptations over time. We applied our method to 19 cattle (beef, dairy, and mixed) farms over the period 2008–2013. Selected vulnerability variables, i.e., farm productivity and economic efficiency, varied greatly among cattle farms and across years, with means ranging from 43.0 to 270.0 kg protein/ha and 29.4–66.0% efficiency, respectively. No farm had a high level, stable or increasing trend and low residuals for both farm productivity and economic efficiency of production. Thus, the least vulnerable farms represented a compromise among measurement value, trend, and variability of both performances. No specific combination of farmers’ practices emerged for reducing cattle farm vulnerability to climatic and economic variability. In the least vulnerable farms, the practices implemented (stocking rate, input use…) were more consistent with the objective of developing the properties targeted (efficiency, robustness…). Our method can be used to support farmers with sector-specific and local insights about most promising farm adaptations.

Agroecological transition corresponds to a systemic transformation consisting in the ecologisation of agriculture and food. It concerns multiple stakeholders (farmers, supply chains, natural resource managers, etc.) and is characterised by a deliberate political intention to bring about change. This chapter highlights a set of determinants of agroecological transition at play in transforming the techniques and the values underpinning both agricultural production and food consumption choices -both of which can lead to various new agri-food systems. Based on the literature on transition studies, we focus on several considerations that could help stakeholders to better engage in such a process: (i) transition takes place over time intervals that vary, depending on the analysis scale (the farm or the agri-food system as a whole); (ii) transition is complex, systemic and requires changes of the whole sociotechnical regime; (iii) transition implies strong connections between nicheinnovations and the dominant sociotechnical regime; and (iv) changes in values and individuals' abilities are fundamental drivers. Hence, by focusing on the plurality of factors and stakeholders at work, we unpack the complexity of this transition, and in this way help the stakeholders to design and execute it. To conclude, we examine specific issues around the governance of agroecological transition.

Stakeholders from academic, political, and social spheres encourage the development of more sustainable forms of agriculture. Given its scale and scope, the sustainability transition is a challenge to the entire agricultural sector. The main question is, how to support the transition process? In this article, we explore how agricultural science can address the sustainability transition of farming systems to understand and support transition processes. We discuss the potential for articulating three research approaches: comprehensive analysis, co-design, and simulation modeling. Comprehensive analysis of the sustainability transition provides perspectives on the interplay between resources, resource management, and related performances of farming systems on the one hand and technical, economic, and sociocultural dimensions of change on the other. Co-design of the sustainability transition stimulates local-scale transition experiments in the real world and identification of alternatives for change. Simulation modeling explores future-oriented scenarios of management at multiple levels and assesses their impacts. We illustrate the articulation of research approaches with two examples of research applied to agricultural water management and autonomy in crop-livestock systems. The resulting conceptual framework is the first one developed to organize research to understand and support the sustainability transition of farming systems.

Finding ways of increasing animal production with low external inputs and without compromising reproductive performances is a key issue of livestock systems sustainability. One way is to take advantage of the diversity and interactions among components within livestock systems. Among studies that investigate the influence of differences in animals' individual abilities in a herd, few focus on combinations of cow breeds with contrasting features in dairy cattle herds. This study aimed to analyse the performances and management of such multi-breed dairy cattle herds. These herds were composed of two types of dairy breeds: 'specialist' (Holstein) and 'generalist' (e.g. Montbeliarde, Simmental, etc.). Based on recorded milk data in southern French region, we performed ANOVA: (i) to compare the performances of dairy herds according to breed-type composition: multi-breed, single specialist breed or single generalist breed and (ii) to test the difference of milk performances of specialist and generalist breed cows (n = 10 682) per multi-breed dairy herd within a sample of 22 farms. The sampled farmers were also interviewed to characterise herd management through multivariate analysis. Multi-breed dairy herds had a better trade-off among milk yield, milk fat and protein contents, herd reproduction and concentrate-conversion efficiency than single-breed herds. Conversely, they did not offer advantages in terms of milk prices and udder health. Compared to specialist dairy herds, they produce less milk with the same concentrate-conversion efficiency but have better reproductive performances. Compared to generalist dairy herds, they produce more milk with better concentrate-conversion efficiency but have worse reproductive performances. Within herds, specialist and generalist breed cows significantly differed in milk performances, showing their complementarity. The former produced more milk for a longer lactation length while the latter produced milk with higher protein and fat contents and had a slightly longer lactation rank. Our results also focus on the farmers' management of multi-breed dairy herds underlying herd performances. Three strategies of management were identified and structured along two main axes. The first differentiates farmers according to their animal-selection practices in relation with their objectives of production: adapting animal to produce milk with low-feeding inputs v. focussing on milk yield trait to intensify milk production. The second refers to the purpose farmers give to multi-breed dairy herds: milk v. milk/meat production. These initial insights on the performances and management of multi-breed dairy herds contribute to better understanding the functioning of ruminant livestock systems based on individual variability.