AGroécologie, Innovations, teRritoires

Crop pathogens and pests reduce the yield and quality of agricultural production. They cause substantial economic losses and reduce food security at household, national and global levels. Quantitative, standardized information on crop losses is difficult to compile and compare across crops, agroecosystems and regions. Here, we report on an expert-based assessment of crop health, and provide numerical estimates of yield losses on an individual pathogen and pest basis for five major crops globally and in food security hotspots. Our results document losses associated with 137 pathogens and pests associated with wheat, rice, maize, potato and soybean worldwide. Our yield loss (range) estimates at a global level and per hotspot for wheat (21.5% (10.1-28.1%)), rice (30.0% (24.6-40.9%)), maize (22.5% (19.5-41.1%)), potato (17.2% (8.1-21.0%)) and soybean (21.4% (11.0-32.4%)) suggest that the highest losses are associated with food-deficit regions with fast-growing populations, and frequently with emerging or re-emerging pests and diseases. Our assessment highlights differences in impacts among crop pathogens and pests and among food security hotspots. This analysis contributes critical information to prioritize crop health management to improve the sustainability of agroecosystems in delivering services to societies.

Crop models are essential tools for assessing the threat of climate change to local and global food production. Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature. Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 °C to 32 °C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each °C of further temperature increase and become more variable over space and time.

While there is a lot of literature from a natural or technical sciences perspective on different forms of digitalization in agriculture (big data, internet of things, augmented reality, robotics, sensors, 3D printing, system integration, ubiquitous connectivity, artificial intelligence, digital twins, and blockchain among others), social science researchers have recently started investigating different aspects of digital agriculture in relation to farm production systems, value chains and food systems. This has led to a burgeoning but scattered social science body of literature. There is hence lack of overview of how this field of study is developing, and what are established, emerging, and new themes and topics. This is where this article aims to make a contribution, beyond introducing this special issue which presents seventeen articles dealing with social, economic and institutional dynamics of precision farming, digital agriculture, smart farming or agriculture 4.0. An exploratory literature review shows that five thematic clusters of extant social science literature on digitalization in agriculture can be identified: 1) Adoption, uses and adaptation of digital technologies on farm; 2) Effects of digitalization on farmer identity, farmer skills, and farm work; 3) Power, ownership, privacy and ethics in digitalizing agricultural production systems and value chains; 4) Digitalization and agricultural knowledge and innovation systems (AKIS); and 5) Economics and management of digitalized agricultural production systems and value chains. The main contributions of the special issue articles are mapped against these thematic clusters, revealing new insights on the link between digital agriculture and farm diversity, new economic, business and institutional arrangements both on-farm, in the value chain and food system, and in the innovation system, and emerging ways to ethically govern digital agriculture. Emerging lines of social science enquiry within these thematic clusters are identified and new lines are suggested to create a future research agenda on digital agriculture, smart farming and agriculture 4.0. Also, four potential new thematic social science clusters are also identified, which so far seem weakly developed: 1) Digital agriculture socio-cyber-physical-ecological systems conceptualizations; 2) Digital agriculture policy processes; 3) Digitally enabled agricultural transition pathways; and 4) Global geography of digital agriculture development. This future research agenda provides ample scope for future interdisciplinary and transdisciplinary science on precision farming, digital agriculture, smart farming and agriculture 4.0.

Abstract Integrated Pest Management (IPM) provides an illustration of how crop protection has (or has not) evolved over the past six decades. Throughout this period, IPM has endeavored to promote sustainable forms of agriculture, pursued sharp reductions in synthetic pesticide use, and thereby resolved myriad socio-economic, environmental, and human health challenges. Global pesticide use has, however, largely continued unabated, with negative implications for farmer livelihoods, biodiversity conservation, and the human right to food. In this review, we examine how IPM has developed over time and assess whether this concept remains suited to present-day challenges. We believe that despite many good intentions, hard realities need to be faced. 1) We identify the following major weaknesses: i) a multitude of IPM definitions that generate unnecessary confusion; ii) inconsistencies between IPM concepts, practice, and policies; iii) insufficient engagement of farmers in IPM technology development and frequent lack of basic understanding of its underlying ecological concepts. 2) By diverting from the fundamental IPM principles, integration of practices has proceeded along serendipitous routes, proven ineffective, and yielded unacceptable outcomes. 3) We show that in the majority of cases, chemical control still remains the basis of plant health programs. 4) Furthermore, IPM research is often lagging, tends to be misguided, and pays insufficient attention to ecology and to the ecological functioning of agroecosystems. 5) Since the 1960s, IPM rules have been twisted, its foundational concepts have degraded and its serious (farm-level) implementation has not advanced. To remedy this, we are proposing Agroecological Crop Protection as a concept that captures how agroecology can be optimally put to the service of crop protection. Agroecological Crop Protection constitutes an interdisciplinary scientific field that comprises an orderly strategy (and clear prioritization) of practices at the field, farm, and agricultural landscape level and a dimension of social and organizational ecology.

The idea that noncrop habitat enhances pest control and represents a win-win opportunity to conserve biodiversity and bolster yields has emerged as an agroecological paradigm. However, while noncrop habitat in landscapes surrounding farms sometimes benefits pest predators, natural enemy responses remain heterogeneous across studies and effects on pests are inconclusive. The observed heterogeneity in species responses to noncrop habitat may be biological in origin or could result from variation in how habitat and biocontrol are measured. Here, we use a pest-control database encompassing 132 studies and 6,759 sites worldwide to model natural enemy and pest abundances, predation rates, and crop damage as a function of landscape composition. Our results showed that although landscape composition explained significant variation within studies, pest and enemy abundances, predation rates, crop damage, and yields each exhibited different responses across studies, sometimes increasing and sometimes decreasing in landscapes with more noncrop habitat but overall showing no consistent trend. Thus, models that used landscape-composition variables to predict pest-control dynamics demonstrated little potential to explain variation across studies, though prediction did improve when comparing studies with similar crop and landscape features. Overall, our work shows that surrounding noncrop habitat does not consistently improve pest management, meaning habitat conservation may bolster production in some systems and depress yields in others. Future efforts to develop tools that inform farmers when habitat conservation truly represents a win-win would benefit from increased understanding of how landscape effects are modulated by local farm management and the biology of pests and their enemies.

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.

The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1◦C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify ‘method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security.

Abstract Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32‐multi‐model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO 2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low‐rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO 2 . Introducing genotypes adapted to warmer temperatures (and also considering changes in CO 2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by −1.1 percentage points, representing a relative change of −8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production.

Concerns about the negative impacts of productivist agriculture have led to the emergence of two forms of ecological modernisation of agriculture. The first, efficiency-substitution agriculture, aims to improve input use efficiency and to minimise environmental impacts of modern farming systems. It is currently the dominant modernisation pathway. The second, biodiversity-based agriculture, aims to develop ecosystem services provided by biological diversity. It currently exists only as a niche. Here we review challenges of implementing biodiversity-based agriculture: managing, at the local level, a consistent transition within and among farming systems, supply chains and natural resource management. We discuss the strengths and weaknesses of existing conceptual frameworks developed to analyse farming, social-ecological and socio-technical systems. Then we present an integrative framework tailored for structuring analysis of agriculture from the perspective of developing a territorial biodiversity-based agriculture. In addition, we propose a participatory methodology to design this agroecological transition at the local level. This design methodology was developed to support a multi-stakeholder arena in analysing the current situation, identifying future exogenous changes and designing (1) targeted territorial biodiversity-based agriculture, (2) the pathway of the transition and (3) the required adaptive governance structures and management strategies. We conclude by analysing key challenges of designing such a complex transition, developing multi-actor and multi-domain approaches based on a combination of scientific and experiential knowledge and on building suitable boundary objects (computer-based and conceptual models, indicators, etc.) to assess innovative systems designed by stakeholders.

The improvement and application of pest and disease models to analyse and predict yield losses including those due to climate change is still a challenge for the scientific community. Applied modelling of crop diseases and pests has mostly targeted the development of support capabilities to schedule scouting or pesticide applications. There is a need for research to both broaden the scope and evaluate the capabilities of pest and disease models. Key research questions not only involve the assessment of the potential effects of climate change on known pathosystems, but also on new pathogens which could alter the (still incompletely documented) impacts of pests and diseases on agricultural systems. Yield loss data collected in various current environments may no longer represent a adequate reference to develop tactical, decision-oriented, models for plant diseases and pests and their impacts, because of the ongoing changes in climate patterns. Process-based agricultural simulation modelling, on the other hand, appears to represent a viable methodology to estimate the impacts of these potential effects. A new generation of tools based on state-of-the-art knowledge and technologies is needed to allow systems analysis including key processes and their dynamics over appropriate suitable range of environmental variables. This paper offers a brief overview of the current state of development in coupling pest and disease models to crop models, and discusses technical and scientific challenges. We propose a five-stage roadmap to improve the simulation of the impacts caused by plant diseases and pests; i) improve the quality and availability of data for model inputs; ii) improve the quality and availability of data for model evaluation; iii) improve the integration with crop models; iv) improve the processes for model evaluation; and v) develop a community of plant pest and disease modelers.

This paper outlines how light Unmanned Aerial Vehicles (UAV) can be used in remote sensing for precision farming. It focuses on the combination of simple digital photographic cameras with spectral filters, designed to provide multispectral images in the visible and near-infrared domains. In 2005, these instruments were fitted to powered glider and parachute, and flown at six dates staggered over the crop season. We monitored ten varieties of wheat, grown in trial micro-plots in the South-West of France. For each date, we acquired multiple views in four spectral bands corresponding to blue, green, red, and near-infrared. We then performed accurate corrections of image vignetting, geometric distortions, and radiometric bidirectional effects. Afterwards, we derived for each experimental micro-plot several vegetation indexes relevant for vegetation analyses. Finally, we sought relationships between these indexes and field-measured biophysical parameters, both generic and date-specific. Therefore, we established a robust and stable generic relationship between, in one hand, leaf area index and NDVI and, in the other hand, nitrogen uptake and GNDVI. Due to a high amount of noise in the data, it was not possible to obtain a more accurate model for each date independently. A validation protocol showed that we could expect a precision level of 15% in the biophysical parameters estimation while using these relationships.

Abstract Understanding the drivers of yield levels under climate change is required to support adaptation planning and respond to changing production risks. This study uses an ensemble of crop models applied on a spatial grid to quantify the contributions of various climatic drivers to past yield variability in grain maize and winter wheat of European cropping systems (1984–2009) and drivers of climate change impacts to 2050. Results reveal that for the current genotypes and mix of irrigated and rainfed production, climate change would lead to yield losses for grain maize and gains for winter wheat. Across Europe, on average heat stress does not increase for either crop in rainfed systems, while drought stress intensifies for maize only. In low-yielding years, drought stress persists as the main driver of losses for both crops, with elevated CO 2 offering no yield benefit in these years.

Summary A coordinated continental‐scale field experiment across 31 sites was used to compare the biomass yield of monocultures and four species mixtures associated with intensively managed agricultural grassland systems. To increase complementarity in resource use, each of the four species in the experimental design represented a distinct functional type derived from two levels of each of two functional traits, nitrogen acquisition ( N 2 ‐fixing legume or nonfixing grass) crossed with temporal development (fast‐establishing or temporally persistent). Relative abundances of the four functional types in mixtures were systematically varied at sowing to vary the evenness of the same four species in mixture communities at each site and sown at two levels of seed density. Across multiple years, the total yield (including weed biomass) of the mixtures exceeded that of the average monoculture in >97% of comparisons. It also exceeded that of the best monoculture (transgressive overyielding) in about 60% of sites, with a mean yield ratio of mixture to best‐performing monoculture of 1·07 across all sites. Analyses based on yield of sown species only (excluding weed biomass) demonstrated considerably greater transgressive overyielding (significant at about 70% of sites, ratio of mixture to best‐performing monoculture = 1·18). Mixtures maintained a resistance to weed invasion over at least 3 years. In mixtures, median values indicate <4% of weed biomass in total yield, whereas the median percentage of weeds in monocultures increased from 15% in year 1 to 32% in year 3. Within each year, there was a highly significant relationship ( P < 0·0001) between sward evenness and the diversity effect (excess of mixture performance over that predicted from the monoculture performances of component species). At lower evenness values, increases in community evenness resulted in an increased diversity effect, but the diversity effect was not significantly different from the maximum diversity effect across a wide range of higher evenness values. The latter indicates the robustness of the diversity effect to changes in species' relative abundances. Across sites with three complete years of data (24 of the 31 sites), the effect of interactions between the fast‐establishing and temporal persistent trait levels of temporal development was highly significant and comparable in magnitude to effects of interactions between N 2 ‐fixing and nonfixing trait levels of nitrogen acquisition. Synthesis and applications . The design of grassland mixtures is relevant to farm‐level strategies to achieve sustainable intensification. Experimental evidence indicated significant yield benefits of four species agronomic mixtures which yielded more than the highest‐yielding monoculture at most sites. The results are relevant for agricultural practice and show how grassland mixtures can be designed to improve resource complementarity, increase yields and reduce weed invasion. The yield benefits were robust to considerable changes in the relative proportions of the four species, which is extremely useful for practical management of grassland swards.

Summary Ecosystem functioning relies heavily on below‐ground processes, which are largely regulated by plant fine‐roots and their functional traits. However, our knowledge of fine‐root trait distribution relies to date on local‐ and regional‐scale studies with limited numbers of species, growth forms and environmental variation. We compiled a world‐wide fine‐root trait dataset, featuring 1115 species from contrasting climatic areas, phylogeny and growth forms to test a series of hypotheses pertaining to the influence of plant functional types, soil and climate variables, and the degree of manipulation of plant growing conditions on species fine‐root trait variation. Most particularly, we tested the competing hypotheses that fine‐root traits typical of faster return on investment would be most strongly associated with conditions of limiting versus favourable soil resource availability. We accounted for both data source and species phylogenetic relatedness. We demonstrate that: (i) Climate conditions promoting soil fertility relate negatively to fine‐root traits favouring fast soil resource acquisition, with a particularly strong positive effect of temperature on fine‐root diameter and negative effect on specific root length ( SRL ), and a negative effect of rainfall on root nitrogen concentration; (ii) Soil bulk density strongly influences species fine‐root morphology, by favouring thicker, denser fine‐roots; (iii) Fine‐roots from herbaceous species are on average finer and have higher SRL than those of woody species, and N 2 ‐fixing capacity positively relates to root nitrogen; and (iv) Plants growing in pots have higher SRL than those grown in the field. Synthesis . This study reveals both the large variation in fine‐root traits encountered globally and the relevance of several key plant functional types and soil and climate variables for explaining a substantial part of this variation. Climate, particularly temperature, and plant functional types were the two strongest predictors of fine‐root trait variation. High trait variation occurred at local scales, suggesting that wide‐ranging below‐ground resource economics strategies are viable within most climatic areas and soil conditions.

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.

Microsatellites (or SSRs: simple sequence repeats) are among the most frequently used DNA markers in many areas of research. The use of microsatellite markers is limited by the difficulties involved in their de novo isolation from species for which no genomic resources are available. We describe here a high-throughput method for isolating microsatellite markers based on coupling multiplex microsatellite enrichment and next-generation sequencing on 454 GS-FLX Titanium platforms. The procedure was calibrated on a model species (Apis mellifera) and validated on 13 other species from various taxonomic groups (animals, plants and fungi), including taxa for which severe difficulties were previously encountered using traditional methods. We obtained from 11,497 to 34,483 sequences depending on the species and the number of detected microsatellite loci ranged from 199 to 5791. We thus demonstrated that this procedure can be readily and successfully applied to a large variety of taxonomic groups, at much lower cost than would have been possible with traditional protocols. This method is expected to speed up the acquisition of high-quality genetic markers for nonmodel organisms.

), daytime hypercapnia (arterial carbon dioxide tension ≥45 mmHg) and sleep disordered breathing, after ruling out other disorders that may cause alveolar hypoventilation. OHS prevalence has been estimated to be ∼0.4% of the adult population. OHS is typically diagnosed during an episode of acute-on-chronic hypercapnic respiratory failure or when symptoms lead to pulmonary or sleep consultation in stable conditions. The diagnosis is firmly established after arterial blood gases and a sleep study. The presence of daytime hypercapnia is explained by several co-existing mechanisms such as obesity-related changes in the respiratory system, alterations in respiratory drive and breathing abnormalities during sleep. The most frequent comorbidities are metabolic and cardiovascular, mainly heart failure, coronary disease and pulmonary hypertension. Both continuous positive airway pressure (CPAP) and noninvasive ventilation (NIV) improve clinical symptoms, quality of life, gas exchange, and sleep disordered breathing. CPAP is considered the first-line treatment modality for OHS phenotype with concomitant severe obstructive sleep apnoea, whereas NIV is preferred in the minority of OHS patients with hypoventilation during sleep with no or milder forms of obstructive sleep apnoea (approximately <30% of OHS patients). Acute-on-chronic hypercapnic respiratory failure is habitually treated with NIV. Appropriate management of comorbidities including medications and rehabilitation programmes are key issues for improving prognosis.

1 Alpine plant species are particularly vulnerable to climate change. Therefore, estimating the adaptive potential of alpine species is of vital importance for determining their future viability. In alpine plants, adaptive potential depends on (i) altitudinal genetic differentiation among populations, combined with gene flow along an altitudinal gradient; (ii) phenotypic plasticity for the traits under selection and (iii) co-gradient variation between genetic and environmental influences on these traits. 2 The adaptive potential of Festuca eskia Ramond (Poaceae), a perennial alpine grass common in the Pyrenean Mountains, was examined in this study. A reciprocal transplant experiment involving 180 individuals along three altitudinal gradients (from 1500 to 2500 m) was established, and survival, functional and reproductive traits were recorded. In addition, four neutral sequence-tagged site and simple sequence repeat molecular markers were chosen to estimate gene flow among populations. 3 Genetic differentiation attributable to selection was detected in all traits between populations along the altitudinal gradient despite the existence of restricted gene flow. For traits directly related to fitness, local altitudinal adaptation was clearly evident. The patterns of local adaptation suggested that selection patterns differed along an altitudinal gradient. Selection for reproductive output was predominant at low altitudes, whereas differential survivorship was observed at higher altitudes. 4 Genetic differentiation with increasing altitude resulted in reduced plant stature and reproductive output but increased specific leaf area (SLA). This increased SLA at higher altitude is interpreted as a resource acquisition strategy. 5 Phenotypic plasticity was seen in all traits at the population level. Evidence of co-gradient variation between genetic differentiation and plastic response was found for all traits except SLA, suggesting that adaptive phenotypic plasticity operates in F. eskia. 6 Synthesis. Local adaptation occurs in F. eskia. It involves different adaptive traits according to the altitude. Such differentiation occurs at a small scale along altitudinal gradients despite the existence of gene flow and phenotypic plasticity. The coexistence of genetic differentiation, gene flow and phenotypic plasticity along altitudinal gradients provides an adaptive potential for F. eskia to successfully adapt to climate change.

In most current farming system classifications (e.g. “conventional” versus “organic”), each type of farming system encompasses a wide variety of farming practices and performances. Classifying farming systems using concepts such as “ecological”, “sustainable intensification” or “agro-ecology” is not satisfactory because the concepts “overlap in…definitions, principles and practices, thus creating…confusion in their meanings, interpretations and implications”. Existing classifications most often focus either on biotechnical functioning or on socio-economic contexts of farming systems. We reviewed the literature to develop an original analytical framework of the diversity of farming systems and agriculture models that deal with these limits. To describe this framework, we first present the main differences between three biotechnical types of farming systems differing in the role of ecosystem services and external inputs: chemical input-, biological input- and biodiversity-based farming systems. Second, we describe four key socio-economic contexts which determine development and functioning of these farming systems: globalised commodity-based food systems, circular economies, alternative food systems and integrated landscape approaches. Third, we present our original analytical framework of agriculture models, defined as biotechnical types of farming systems associated with one or a combination of socio-economic contexts differing in the role of relationships based on global market prices and “territorial embeddedness”. We demonstrate the potential of this framework by describing six key agriculture models and reviewing key scientific issues in agronomy associated with each one. We then analyse the added value of our analytical framework and its generic character. Lastly, we discuss transversal research issues of the agriculture models, concerning the technologies required, their function in the bioeconomy, their multi-criteria and multi-level assessments, their co-existence and the transitions between them.

Legume growing has many benefits. Indeed legumes provide plant proteins for animal feed and human food. Legumes fix atmospheric N2 and, in turn, provide cheap and green N fertilisers. Additionally, legumes are used as diversification crops in rotations based on oilseed rape and cereals. Despite those benefits, legume crops in Europe represent less than 4 % of arable lands, and European legume seeds are underused for animal and human nutrition. Nonetheless, European authorities are now fostering the development of legume crops for sustainable agriculture. Here, we analyse forage and grain legume-producing systems since 1950 in order to identify the actual constraints of legume development. We show that legumes can contribute to the agroecological transition for sustainable agriculture, food and energy and for sustainable agri-food systems. Then, we point out that high added-value niche markets are required for supporting legume production. The major research needs identified are (1) analysing the constraints of the current systems and identifying ways of moving towards systems that include more legumes, (2) identifying new and diversified uses for legumes in a sustainable food chain, (3) assessing and improving the ecosystem services provided by legumes at cropping system and territory scales and (4) promoting agroecology through and for legume crop management.