Harvested crop varieties, livestock breeds, fish species and non domesticated wild resources within field, forest, rangeland including tree products, wild animals hunted for food and in aquatic ecosystems e. Non-harvested species in production ecosystems that support food provision, including soil micro-biota, pollinators and other insects such as bees, butterflies, earthworms, greenflies; and.
Non-harvested species in the wider environment that support food production ecosystems agricultural, pastoral, forest and aquatic ecosystems. Agrobiodiversity is the result of the interaction between the environment, genetic resources and management systems and practices used by culturally diverse peoples, and therefore land and water resources are used for production in different ways. Thus, agrobiodiversity encompasses the variety and variability of animals, plants and micro-organisms that are necessary for sustaining key functions of the agro-ecosystem, including its structure and processes for, and in support of, food production and food security FAO, a.
Local knowledge and culture can therefore be considered as integral parts of agrobiodiversity, because it is the human activity of agriculture that shapes and conserves this biodiversity. The variety and variability of animals, plants and micro-organisms that are used directly or indirectly for food and agriculture, including crops, livestock, forestry and fisheries.
It comprises the diversity of genetic resources varieties, breeds and species used for food, fodder, fibre, fuel and pharmaceuticals. It also includes the diversity of non-harvested species that support production soil micro-organisms, predators, pollinators , and those in the wider environment that support agro-ecosystems agricultural, pastoral, forest and aquatic as well as the diversity of the agro-ecosystems.
A trait-based approach was recently applied to multitrophic systems composed of plants and pollinators Lavorel et al. However, studies linking environmental perturbations or stresses, ecological groups, and ecosystem services are based on descriptive statistics, and cropping system models that simulate such interactions are still in their infancy. At the landscape level, efforts have been made to characterize relations between seminatural habitats e. It also allows rules for their design and management to be formalized. With a similar objective, Herzog et al. This indicator set has been used to link farmland habitats seminatural and cultivated to functional biodiversity, e.
In a similar approach, including a more precise description of seminatural habitats at the farm level and a classic coarser description at the landscape level, Sarthou et al. Such findings indicate powerful mechanisms available to farmers to favor beneficial insects by managing seminatural habitats at the farm level and, in contrast, less influential landscape features that farmers have less control over. Complementarily, Thies et al. In brief, recent advances in functional ecology and landscape ecology make it possible to better characterize functional diversity for sets of organisms and to better model interactions between environmental factors and ecosystem services.
The main objective of this transition is to replace anthropogenic inputs by input ecosystem services to deliver agricultural services provisioning services.
The variability of input ecosystem services is expected to increase during the transition, before recovering to a similar or even lower level, since the new regime is expected to be more resilient than the initial one. In addition to uncertainties about biophysical entities and processes, there are social-based uncertainties due to different or contradictory representations of ecosystem services among stakeholders, their respective importance and priority, and the adapted-management mechanisms to use to promote such services Barnaud et al.
The more numerous and diverse are the farming systems and landscape-matrix management of stakeholders, the greater the difficulties in developing shared objectives and thus achieving consistency among stakeholder practices. Regardless of the domain considered, biophysical or social, management complexity and issues increase with the number of organization levels considered. This increase in management complexity and issues is intrinsically bound to the complex hierarchical nested system considered: the more hierarchically nested levels and domains, the more interactions between components within and between levels and domains Ewert et al.
Finally, more than anything, agroecological practices have to be adapted to the unique characteristics of each production site, regardless of the ecosystem processes and services considered Caporalli In this way, plant—soil interactions Eviner , especially in conservation agriculture Koohafkan et al. When implementing biodiversity-based agriculture, while agroecological mechanisms are numerous, the challenge for farmers lies in designing, implementing, and managing consistent cropping and farming systems, and possibly, in interaction with others stakeholders, landscape structures that promote a high level of input services, and consequently of agricultural services in their production situations.
In other words, farmers have to identify, in a large space of possible options, the adapted spatiotemporal distribution of planned biodiversities and agroecological practices that allow them to reach their objectives while respecting their constraints. During this transition, while faced with numerous uncertainties and ambiguities, farmers have to identify and implement ill-known complex practices, the effectiveness of which depends greatly on their production situations.
To face the uncertainties described above, and ill-known and site-based practices, farmers use a variety of networking devices to support learning, especially sharing experiences with other farmers Ingram , for instance in farmer field schools. Demonstration, training programs, and brainstorming sessions are also important for designing and implementing agroecological management practices that are necessarily knowledge intensive Coquil et al.
In such innovation processes, one main role of researchers is to structure and steer the design process Martin Adaptive management is a scientific approach particularly adapted to situations with high uncertainty and multiple possible controls via management options Allen et al. Developed in the late s in ecology for the management of complex adaptive systems, adaptive management is based on incremental, experiential learning, and decision making, supported by active monitoring of, and feedback from, the effects and outcomes of decisions.
A key aspect of adaptive management is the acknowledgement of uncertainty. It is thus built on devising experiments to reduce that uncertainty and collect information about the system. Stakeholders then learn from the outcomes of their experiments and redesign their management practices based on the knowledge gained. In this way, stakeholders continuously reconsider the effectiveness of the management practices implemented, the accuracy of predicted consequences of actions, the relation between actions and indicators, and learn about trade-offs.
Through adaptive management, stakeholders gradually and implicitly acquire a wide range of perceptual and cognitive skills. Step 1 aims to define a set of actions, i. When implementing biodiversity-based agriculture for farming systems, objectives are to design a spatiotemporal distribution of planned biodiversity e.
For this task, there is great need for designing and developing tools that can stimulate knowledge exchanges. User-friendliness is also an important key point, as is the accuracy of predicted effects of management practices, because the main objective is to design a coherent foundation of the complex agroecosystem to implement and manage. Step 2 aims to monitor changes in agroecosystem structure and ecosystem service levels during the transition.
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Field indicators usable by farmers are essential for monitoring. Feedback can be used to plan management in subsequent years in the same situation or for other farmers in similar contexts. Studies about adaptive management in IPM Shea et al. In summary, we highlight two main difficulties in implementing biodiversity-based agriculture from current knowledge in ecology and agronomy.
The first challenge is that strong uncertainties exist about relations between agricultural practices, ecological processes, and ecosystem services. The second challenge is that agroecological practices required to deliver ecosystem services are site specific. The review shows 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 and for overcoming the above-mentioned difficulties.
One great challenge for researchers seeking to provide useful knowledge to farmers implementing biodiversity-based agriculture is to develop learning tools that ease understanding and transfer of this knowledge. They are designed to be used in a farmer-centered participatory setting Klerkx et al.
Developing learning tools to support biodiversity-based agriculture is a particular challenge since: i variability and ambiguity in the results of an experiment increase the risk of erroneous learning, in which the learner draws incorrect conclusions, while stochasticity in results can also forestall investigation, when an unlucky first experience discourages further experimentation; ii delays between actions and effects due to slow ecological processes can complicate implementation; and iii it is difficult to accumulate and organize information produced by experimental and monitoring activities that can be stimulated by learning tools.
Three main features of learning tools are required to insure their effectiveness in supporting participatory learning and change in practices: saliency, legitimacy, and credibility Cash et al. In the case of biodiversity-based agriculture, we identify key criteria that these tools should satisfy to have these features. For saliency, which is the relevance to the intended users, tools classically must purposely consider characteristics of the context in which users manage and act Bergen They must provide farmers with information allowing them to put knowledge into practice.
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When built for designing management practices, the scale at which the tools are to be applied should be clearly defined Martin In addition, the tools must incorporate uncertainty due to relations between management, biodiversity and ecosystem services, in addition to the uncertainty caused by contextual factors such as climatic conditions.
Finally, learning tools must be flexible and robust, i. It implies building and using relatively simple tools, flexible enough to allow interactive integration of new information and immediately see the results Eikelboom and Janssen It also implies that the support tool can represent the system and its environment with the type of information usually used by farmers to make management decisions, e. Credibility concerns the scientific trustworthiness of the technical evidence and scientific documentation.
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This feature is provided by the use of up-to-date scientific knowledge and by well-founded design and evaluation methods Giller et al. Scientific knowledge is particularly needed to represent relations between management practices, biodiversity, and ecosystem services, and develop methods to assess model uncertainties. Considering the biodiversity-based agriculture management issues and expected features of learning tools, we identify in this section the main limits of existing tools based on scientific knowledge and examples of promising ones. Researchers, farmers, and agricultural advisors are not well-equipped to deal with complex adaptive system dynamics.
Few mechanistic models dealing with agroecosystems address relations among management, biodiversities, and input and agricultural services. Most existing models focus on representations of the plant—soil—atmosphere system with mechanistic modeling of abiotic resources flows water, N, C, and energy.
Recently, some modeling approaches have been developed to represent the impact of cropping practices and agricultural mosaics at the landscape level on pest dynamics e. However, these approaches usually require input variables that are difficult to estimate at the landscape level and address only a small part of the biological community, all of which should be considered for biodiversity-based agriculture.
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Furthermore, these spatially explicit models usually require intensive calculations, which can prevent the use of optimization techniques for the design of innovative agroecological strategies that enhance the pest regulation service. Mathematical networks are promising methods to address management of food webs or the collective management of slightly endocyclic pests Tixier et al. Mathematical complexity and inflexibility Jones et al. Unlike mechanistic models, statistical models based on ecological groups have been applied in several fields of ecology.
However, they have two limitations: results i usually cannot be transferred to sites other than those used to develop the model i. New research projects have been launched to bridge this gap through a simplified plant functional-group method Duru et al. For other, more complex ecological groups soil biota and viruses , research results have at least allowed construction of conceptual models of agroecosystems or definition of proxies of traits Barrios ; Cortois and Deyn ; Friesen that are essential for learning about the groups, but not sufficiently adapted to put knowledge into practice.
For cropping systems based on a variety of mixtures, intercrops, cover crops, and complex rotations, we lack simple operational models and, to our knowledge, the ecological-group approach has not yet produced the successful results it promised. Accordingly, farmers and their advisors lack tools to put biodiversity-based practices into action while coping with uncertainties.
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They are i knowledge bases that contain structured scientific facts and empirical information compiled from cumulative experiences and demonstrated skills and that enable biodiversity management to be inferred in specific situations and ii model-based games to stimulate knowledge exchange and learning about the effects of planned and associated biodiversity on ecosystem services. We illustrate each with examples of promising tools, and we examine the extent to which the three necessary criteria saliency, legitimacy, and credibility are fulfilled.
Knowledge bases have been developed recently to help choose cover-crop species by providing information about suitable production situations main cropping system, climate, and soil and expected ecosystem services. Some are built from plant-trait-based functional profiles Damour et al.
These kinds of supports are considered salient and legitimate by farmers involved in a biodiversity-based agriculture process since they provide key information about potential planned biodiversity that they can implement. However, we think that this information can be reinforced with deeply rooted knowledge from ecological science about interactions between biotic and abiotic factors and between organisms e. This may allow plant sequences and species mixtures to be designed, as well as enlarge the scope towards more numerous trophic levels to account for the soil food web.
Such interactive approaches are already used for agrobiodiversity conservation via seed exchanges among farmers Pautasso et al. A second type of knowledge-based approach for dealing with complexity consists of using an inferential method for qualitative hierarchical multiattribute decision modeling, to cope with complexity while searching for operational outputs. Based on a two-level categorization of the degree of endocyclism of harmful organisms, Aubertot and Robin built an innovative modeling framework IPSIM, Injury Profile SIMulator that combines vertical control methods and horizontal different pests: weeds, plant pathogens, and animal pests dimensions of IPM.
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