Guillaume Martin

How to implement biodiversity-based agriculture to enhance ecosystem services: a review

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.”

Crop–livestock integration beyond the farm level: a review

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, erosion control, and field-level biological regulation services through organizational coordination among farmers and spatiotemporal integration between crops, grasslands, and animals. We found that social benefits of the synergy form of integration include collective empowerment of farmers. We claim that design of the complementarity and synergy forms of crop–livestock integration can best be supported by collective participatory workshops involving farmers, agricultural consultants, and researchers. In these workshops, spatialized simulation modeling of crop–livestock integration among farms is the basis for achieving the upscaling process involved in integrating beyond the farm level. Facilitators of these workshops have to pay attention to the consequences on governance and equity issues within farmers groups.

Designing crop–livestock integration at different levels: Toward new agroecological models?

Integrated crop–livestock systems have been shown to improve nutrient cycling, particularly by re-coupling nitrogen and carbon cycles. Yet the number of mixed crop–livestock farms has been falling steadily in Europe. Integration between crops and livestock at the local level, through exchanges between already specialised farms, is rarely implemented. Given the lack of knowledge on new ways to maintain or to reintegrate crops and livestock from the farm up to the local level, concrete guidelines are needed. In this paper, we developed a transversal analysis of three complementary case studies regarding development of crop–livestock integration at the farm and beyond farm level. To this aim, we reviewed three French case studies in which participatory approaches were used to design scenarios of crop–livestock integration. When crop–livestock integration disappears from the farm level due to labour organisation, exchanges between specialised crop farmers and livestock farmers is a way to redevelop such integration at the local level. Transversal analysis of case-studies allowed us to suggest guidelines for further research regarding the design of agroecological crop–livestock integration. Articulating options of change at farm level and collective level allows to consider the appropriate level of design and trade-offs between (1) farm and beyond farm level, and (2) social, environmental and economic dimensions. Considering these different levels of organisation is needed to identify possible pathways to and policy incentives for integrated crop–livestock systems. Developing specific Decision Support Systems and participative research is needed to conceive locally adapted scenarios of crop-livestock integration.

Mutual learning between researchers and farmers during implementation of scientific principles for sustainable development: the case of biodiversity-based agriculture

A part of scientific knowledge that aims to promote sustainable development consists in management principles of complex systems. Its implementation requires a precise understanding of the situation of action and of the actors’ involvement in the situation. It can no longer be thought of in terms of transfer. Successful implementation relies on changing the ways of understanding and valuing the local context, as well as the actors’ practices. Transdisciplinary approaches are proposed to facilitate mutual learning between researchers and local actors that lead to a better understanding of the action situation. We explore the benefits of such approaches and their implications for those involved in the field of agroecology. Agroecology is based on the implementation of scientific principles that aim to make agriculture more sustainable. These include the creation of agricultural production based on biodiversity. Analysis of three case studies concerning the biodiversification of forage production shows that implementation is not getting farmers involved in the researcher’s project, but rather that researcher’s intentions find a place in the farmer’s projects. Researchers adapt their scientific production to the farmer’s needs while farmers review their goals and means as a result of these interactions. The result is a better understanding of the situation to be transformed by both researchers and farmers. This new insight justifies making implementation an integral part of the scientific approach. However, both researchers and farmers committed to the situation need to be ready to leave their comfort zone.

An Integrated Method to Analyze Farm Vulnerability to Climatic and Economic Variability According to Farm Configurations and Farmers’ Adaptations

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.