ABSys

This study aims reporting on 23 gridded precipitation datasets (P-datasets) reliability across West Africa through direct comparisons with rain gauges measurement at the daily and monthly time scales over a 4 years period (2000)(2001)(2002)(2003). All P-datasets reliability vary in space and time. The most efficient P-dataset in term of Kling-Gupta Efficiency (KGE) changes at the local scale and the Pdataset performance is sensitive to seasonal effects. Satellite-based P-datasets performed better during the wet than the dry season whereas the opposite is observed for reanalysis P-datasets. The best overall performance was obtained for MSWEP v.2.2 and CHIRPS v.2 for daily and monthly timestep, respectively. Part of the differences in P-dataset performance at daily and monthly time step comes from the time step used to proceed the gauges adjustment (i.e day or month) and from a mismatch between gauge and satellite reporting times. In comparison to the others P-datasets, TMPA-Adj v.7 reliability is stable and reach the second highest KGE value at both daily and monthly time step. Reanalysis P-datasets (WFDEI, MERRA-2, JRA-55, ERA-Interim) present among the lowest statistical scores at the daily time step, which drastically increased at the monthly time step for WFDEI and MERRA-2. The non-adjusted P-datasets were the less efficient, but, their near-real time availability should be helpful for risk forecast studies (i.e. GSMaP-RT v.6). The results of this study give important elements to select the most adapted P-dataset for specific application across West Africa.

In many areas of the world, maintaining grapevine production will require adaptation to climate change. While rigorous evaluations of adaptation strategies provide decision makers with valuable insights, those that are published often overlook major constraints, ignore local adaptive capacity, and suffer from a compartmentalization of disciplines and scales. The objective of our study was to identify current knowledge of evaluation methods and their limitations, reported in the literature. We reviewed 111 papers that evaluate adaptation strategies in the main vineyards worldwide. Evaluation approaches are analyzed through key features (e.g., climate data sources, methodology, evaluation criteria) to discuss their ability to address climate change issues, and to identify promising outcomes for climate change adaptations. We highlight the fact that combining adaptation levers in the short and long term (location, vine training, irrigation, soil, and canopy management, etc.) enables local compromises to be reached between future water availability and grapevine productivity. The main findings of the paper are three-fold: (1) the evaluation of a combination of adaptation strategies provides better solutions for adapting to climate change; (2) multi-scale studies allow local constraints and opportunities to be considered; and (3) only a small number of studies have developed multi-scale and multi-lever approaches to quantify feasibility and effectiveness of adaptation. In addition, we found that climate data sources were not systematically clearly presented, and that climate uncertainty was hardly accounted for. Moreover, only a small number of studies have assessed the economic impacts of adaptation, especially at farm scale. We conclude that the development of methodologies to evaluate adaptation strategies, considering both complementary adaptations and scales, is essential if relevant information is to be provided to the decision-makers of the wine industry.

Abstract Aim Macroinvertebrates comprise a highly diverse set of taxa with great potential as indicators of soil quality. Communities were sampled at 3,694 sites distributed world‐wide. We aimed to analyse the patterns of abundance, composition and network characteristics and their relationships to latitude, mean annual temperature and rainfall, land cover, soil texture and agricultural practices. Location Sites are distributed in 41 countries, ranging from 55° S to 57° N latitude, from 0 to 4,000 m in elevation, with annual rainfall ranging from 500 to >3,000 mm and mean temperatures of 5–32°C. Time period 1980–2018. Major taxa studied All soil macroinvertebrates: Haplotaxida; Coleoptera; Formicidae; Arachnida; Chilopoda; Diplopoda; Diptera; Isoptera; Isopoda; Homoptera; Hemiptera; Gastropoda; Blattaria; Orthoptera; Lepidoptera; Dermaptera; and “others”. Methods Standard ISO 23611‐5 sampling protocol was applied at all sites. Data treatment used a set of multivariate analyses, principal components analysis (PCA) on macrofauna data transformed by Hellinger’s method, multiple correspondence analysis for environmental data (latitude, elevation, temperature and average annual rainfall, type of vegetation cover) transformed into discrete classes, coinertia analysis to compare these two data sets, and bias‐corrected and accelerated bootstrap tests to evaluate the part of the variance of the macrofauna data attributable to each of the environmental factors. Network analysis was performed. Each pairwise association of taxonomic units was tested against a null model considering local and regional scales, in order to avoid spurious correlations. Results Communities were separated into five clusters reflecting their densities and taxonomic richness. They were significantly influenced by climatic conditions, soil texture and vegetation cover. Abundance and diversity, highest in tropical forests (1,895 ± 234 individuals/m 2 ) and savannahs (1,796 ± 72 individuals/m 2 ), progressively decreased in tropical cropping systems (tree‐associated crops, 1,358 ± 120 individuals/m 2 ; pastures, 1,178 ± 154 individuals/m 2 ; and annual crops, 867 ± 62 individuals/m 2 ), temperate grasslands (529 ± 60 individuals/m 2 ), forests (232 ± 20 individuals/m 2 ) and annual crops (231 ± 24 individuals/m 2 ) and temperate dry forests and shrubs (195 ± 11 individuals/m 2 ). Agricultural management decreased overall abundance by ≤54% in tropical areas and 64% in temperate areas. Connectivity varied with taxa, with dominant positive connections in litter transformers and negative connections with ecosystem engineers and Arachnida. Connectivity and modularity were higher in communities with low abundance and taxonomic richness. Main conclusions Soil macroinvertebrate communities respond to climatic, soil and land‐cover conditions. All taxa, except termites, are found everywhere, and communities from the five clusters cover a wide range of geographical and environmental conditions. Agricultural practices significantly decrease abundance, although the presence of tree components alleviates this effect.

Coffee is deemed to be a high-risk crop in light of upcoming climate changes. Agroforestry practices have been proposed as a nature-based strategy for coffee farmers to mitigate and adapt to future climates. However, with agroforestry systems comes shade, a highly contentious factor for coffee production in terms of potential yield reduction, as well as additional management needs and interactions between shade trees and pest and disease. In this review, we summarize recent research relating to the effects of shade on (i) farmers' use and perceptions, (ii) the coffee microenvironment, (iii) pest and disease incidence, (iv) carbon assimilation and phenology of coffee plants, (v) coffee quality attributes (evaluated by coffee bean size, biochemical compounds, and cup quality tests), (vi) breeding of new Arabica coffee F1 hybrids and Robusta clones for future agroforestry systems, and (vii) coffee production under climate change. Through this work, we begin to decipher whether shaded systems are a feasible strategy to improve the coffee crop sustainability in anticipation of challenging climate conditions. Further research is proposed for developing new coffee varieties adapted to agroforestry systems (exhibiting traits suitable for climate stressors), refining extension tools by selecting locally-adapted shade trees species and developing policy and economic incentives enabling the adoption of sustainable agroforestry practices.

In the face of increasing agricultural demands and environmental concerns, the effective management of weeds presents a pressing challenge in modern agriculture. Weeds not only compete with crops for resources but also pose threats to food safety and agricultural sustainability through the indiscriminate use of herbicides, which can lead to environmental contamination and herbicide-resistant weed populations. Artificial Intelligence (AI) has ushered in a paradigm shift in agriculture, particularly in the domain of weed management. AI's utilization in this domain extends beyond mere innovation, offering precise and eco-friendly solutions for the identification and control of weeds, thereby addressing critical agricultural challenges. This article aims to examine the application of AI in weed management in the context of weed detection and the increasing impact of deep learning techniques in the agricultural sector. Through an assessment of research articles, this study identifies critical factors influencing the adoption and implementation of AI in weed management. These criteria encompass factors of AI adoption (food safety, increased effectiveness, and eco-friendliness through herbicides reduction), AI implementation factors (capture technology, training datasets, AI models, and outcomes and accuracy), ancillary technologies (IoT, UAV, field robots, and herbicides), and the related impact of AI methods adoption (economic, social, technological, and environmental). Of the 5821 documents found, 99 full-text articles were assessed, and 68 were included in this study. The review highlights AI's role in enhancing food safety by reducing herbicide residues, increasing effectiveness in weed control strategies, and promoting eco-friendliness through judicious herbicide use. It underscores the importance of capture technology, training datasets, AI models, and accuracy metrics in AI implementation, emphasizing their synergy in revolutionizing weed management practices. Ancillary technologies, such as IoT, UAVs, field robots, and AI-enhanced herbicides, complement AI's capabilities, offering holistic and data-driven approaches to weed control. Additionally, the adoption of AI methods influences economic, social, technological, and environmental dimensions of agriculture. Last but not least, digital literacy emerges as a crucial enabler, empowering stakeholders to navigate AI technologies effectively and contribute to the sustainable transformation of weed management practices in agriculture.

The evolution of cocoa farming was quickly confronted with the development of pests and diseases. These sanitary constraints have shaped the geographical distribution of production over the centuries. Current climate change adds an additional constraint to the plant health constraints, making the future of cocoa farming more uncertain. Climate change is not only affecting the areas where cocoa is grown for physiological reasons, particularly in relation to changes in water regimes, but also affects the distribution of pests and diseases affecting this crop. These different points are discussed in the light of the trajectories observed in the different cocoa-growing areas. The breeding programs of cocoa trees for sustainable resistance to plant health constraints and climate change are therefore particularly important challenges for cocoa farming, with the other management practices of plantations.

Abstract Climate change associated with a greater variability of inter‐ and intra‐annual droughts and the occurrence of extreme events act in combination to challenge semi‐natural and sown productive grasslands in Europe. Successful plant strategies under drought strongly depend on stress intensity. Drought resistance to maintain leaf growth under moderate stress trades off with drought survival after growth cessation under life‐threatening drought. Substantial intra‐specific variability exists in key forage grasses originating from the Mediterranean to the cool‐temperate climates and represents a great potential for adaptation of future ecotypes and cultivars to a larger range of drought intensities. Plant species diversity offers an opportunity to stabilize forage production in two ways. First, growth reduction under stress is significantly smaller for diverse compared to simple plant communities because the former offers the opportunity to include drought‐resistant (or drought‐surviving) species. Second, positive interactions among species increase ecosystem functioning of more diverse plant communities under moderate drought, allowing them to compensate for drought‐induced yield reductions. Currently, available cultivars of perennial forage species adapted to dry climate are still rare and only a few forage species are used in productive systems. Thus, both intra‐ and inter‐specific plant diversity should be better valued to reduce vulnerability and increase resilience of productive grasslands.

Abstract In response to the sustainability issues that agriculture faces in advanced economies, agroecology has gained increasing relevance in scientific, political, and social debates. This has promoted discussion about transitions to agroecology, which represents a significant advancement. Accordingly, it has become a growing field of research. We reviewed the literature on and in support of farm transitions to agroecology in advanced economies in order to identify key research challenges and suggest innovative research paths. Our findings can be summarized as follows: (1) Research that supports exploration and definition of desired futures, whether based on future-oriented modeling or expert-based foresight approaches, should more explicitly include the farm level. It should stimulate the creativity and design ability of farmers and other stakeholders, and also address issues of representation and power among them. (2) Research that creates awareness and assesses farms before, during or after transition requires more holistic and dynamic assessment frameworks. These frameworks need to be more flexible to adapt to the diversity of global and local challenges. Their assessment should explicitly include uncertainty due to the feedback loops and emergent properties of transitions. (3) Research that analyzes and supports farms during transition should focus more on the dynamics of change processes by valuing what happens on the farms. Research should especially give more credence to on-farm experiments conducted by farmers and develop new tools and methods (e.g., for strategic monitoring) to support these transitions. This is the first review of scientific studies of farm transitions to agroecology. Overall, the review indicates that these transitions challenge the system boundaries, temporal horizons, and sustainability dimensions that agricultural researchers usually consider. In this context, farm transitions to agroecology require changes in the current organization and funding of research in order to encourage longer term and more adaptive configurations.

Earthworms are an important soil taxon as ecosystem engineers, providing a variety of crucial ecosystem functions and services. Little is known about their diversity and distribution at large spatial scales, despite the availability of considerable amounts of local-scale data. Earthworm diversity data, obtained from the primary literature or provided directly by authors, were collated with information on site locations, including coordinates, habitat cover, and soil properties. Datasets were required, at a minimum, to include abundance or biomass of earthworms at a site. Where possible, site-level species lists were included, as well as the abundance and biomass of individual species and ecological groups. This global dataset contains 10,840 sites, with 184 species, from 60 countries and all continents except Antarctica. The data were obtained from 182 published articles, published between 1973 and 2017, and 17 unpublished datasets. Amalgamating data into a single global database will assist researchers in investigating and answering a wide variety of pressing questions, for example, jointly assessing aboveground and belowground biodiversity distributions and drivers of biodiversity change.

Abstract The yield of crops in both agrivoltaic (AV) and agroforestry (AF) systems is difficult to predict. The shade pattern of an AV system is not typical and is quite different from the one of AF systems. Most countries allow AV systems on croplands only if the crop productivity is maintained (e.g., in France) or slightly reduced, as in Japan and Germany, with 80% and 66% minimum relative yield (RY) required, respectively. I suggest using the Ground Coverage Ratio (GCR: ratio of area of photovoltaic panels to area of land) as an indicator of the crop potential productivity in AV systems. The GCR can easily be computed and controlled for all kinds of AV systems with panels that are either fixed (horizontal, tilted, or vertical) or mobile (on 1- or 2-axis trackers). Here, I provide a synthesis of published data for crop productivity under AV systems. Only publications that provided both the GCR of the system and the crop RYs were included. Measuring RYs requires a reliable non-AV control plot. Several publications were excluded because of doubts regarding the measurements’ validity (e.g., systems that are too small, resulting in strong edge effects, or unreliable control plots). Despite the scattering of results, a clear pattern is evidenced: RYs decrease rapidly when GCRs increase. It appears that a GCR < 25% is required to ensure that most crop RYs stay > 80%. These results are consistent with a recent meta-analysis examining the impact of shade on crops. The use of the GCR criterion to validate AV projects is a simple and cost-effective alternative to the tricky control of crop yields in the fields.

BACKGROUND AND AIMS: Mango (Mangifera indica L.) is the fifth most widely produced fruit in the world. Its cultivation, mainly in tropical and sub-tropical regions, raises a number of issues such as the irregular fruit production across years, phenological asynchronisms that lead to long periods of pest and disease susceptibility, and the heterogeneity of fruit quality and maturity at harvest. To address these issues, we developed an integrative functional-structural plant model that synthesizes knowledge about the vegetative and reproductive development of the mango tree and opens up the possible simulation of cultivation practices. METHODS: We designed a model of architectural development in order to precisely characterize the intricate developmental processes of the mango tree. The appearance of botanical entities was decomposed into elementary stochastic events describing occurrence, intensity and timing of development. These events were determined by structural (position and fate of botanical entities) and temporal (appearance dates) factors. Daily growth and development of growth units and inflorescences were modelled using empirical distributions and thermal time. Fruit growth was determined using an ecophysiological model that simulated carbon- and water-related processes at the fruiting branch scale. KEY RESULTS: The model simulates the dynamics of the population of growth units, inflorescences and fruits at the tree scale during a growing cycle. Modelling the effects of structural and temporal factors makes it possible to simulate satisfactorily the complex interplays between vegetative and reproductive development. The model allowed the characterization of the susceptibility of mango tree to pests and the investigatation of the influence of tree architecture on fruit growth. CONCLUSIONS: This integrative functional-structural model simulates mango tree vegetative and reproductive development over successive growing cycles, allowing a precise characterization of tree phenology and fruit growth and production. The next step is to integrate the effects of cultivation practices, such as pruning, into the model.

A bstract Cocoa farmers must decide on whether to rehabilitate (Rh) or to renovate (Re) a cocoa orchard when its productivity declines due to ageing, disease outbreaks or other causes. Deciding on Rh/Re is often a complex, expensive and conflictive process. In this review, we (1) explore the diversity of contexts, driving forces, stakeholders and recommended management practices involved in Rh/Re initiatives in key cocoa-producing countries; (2) summarise the often conflicting views of farmers and extension agents on Rh/Re programmes; (3) review the evidence of age-related changes in planting density and yield of cocoa, given the weight of these variables in Rh/Re decision processes; (4) describe the best known Rh/Re systems and their most common management practices; (5) propose an agroforestry Re approach that overcomes the limitation of current Rh/Re diagnosis protocols, which do not consider the regular flow of food crop and tree products , and the need to restore site soil quality to sustain another cycle of cultivation of cocoa at the same site; and (6) explore the effects of climate change considerations on Rh/Re decision-making and implementation processes. Each Rh/Re decision-making process is unique and highly context-dependent (household and farm, soil, climate, culture). Tailored solutions are needed for each farmer and context. The analysis, concepts and models presented for cocoa in this paper may also apply to coffee orchards.

Water scarcity is already set to be one of the main issues of the 21st century, because of competing needs between civil, industrial, and agricultural use. Agriculture is currently the largest user of water, but its share is bound to decrease as societies develop and clearly it needs to become more water efficient. Improving water use efficiency (WUE) at the plant level is important, but translating this at the farm/landscape level presents considerable challenges. As we move up from the scale of cells, organs, and plants to more integrated scales such as plots, fields, farm systems, and landscapes, other factors such as trade-offs need to be considered to try to improve WUE. These include choices of crop variety/species, farm management practices, landscape design, infrastructure development, and ecosystem functions, where human decisions matter. This review is a cross-disciplinary attempt to analyse approaches to addressing WUE at these different scales, including definitions of the metrics of analysis and consideration of trade-offs. The equations we present in this perspectives paper use similar metrics across scales to make them easier to connect and are developed to highlight which levers, at different scales, can improve WUE. We also refer to models operating at these different scales to assess WUE. While our entry point is plants and crops, we scale up the analysis of WUE to farm systems and landscapes.

Abstract To promote greater sustainability in agriculture, development of agroecology is increasingly being invoked. What are the conditions for establishing agroecological production in tropical regions? Based upon case studies in several tropical areas, we provide here some answers to this question. We review the “pillars” (i.e. principles) and the “implementation levers” (i.e., tools) for the development of agroecology. We identify three main pillars: (1) the mobilization and management of ecological processes for the sustainable production and the resilience of agroecosystems; (2) the development of interactions between technical, social, environmental, and institutional components of agroecosystems for a holistic approach to agroecology; and (3) the scaling up of agroecology that takes place with a plurality of actions and pathways at different organization levels rather than an increase in resources and a replication of standardized technical processes. To implement these three pillars, we identify 11 main bio-technical, cognitive, socio-political, and organizational levers. Bio-technical levers include those for (1) mobilizing complementarity between crop species to optimize natural resources use, (2) mobilizing functional biodiversity at the plot scale to optimize natural regulation of pests and diseases, (3) managing biodiversity at landscape and territorial scales, (4) increasing the efficiency of biogeochemical cycles, and (5) renewing targets for genetic improvement. Cognitive, socio-political, and organizational levers include those for (6) political and institutional action at the national and global level, (7) action at the local level to support producers, (8) political and organizational action at the territorial level, (9) the marketing and the development of new agri-chains, (10) the development of new methods for evaluating production systems, and (11) the recognition of the values of gender and generation within families and other organisational levels. This paper provides an overall orientation for the agroecological transition in tropical agriculture and also considers the socio-political context that underlies this transition.

Annual global coffee consumption growth (1–2%) has been largely met (>50%) mainly by Brazil and Vietnam through high-input monocrop system adoption. Smallholders account for >80% of global producers and >60% of global supply despite limited farm sizes (<2 ha), yields, and input usage. Production concentration in areas with high-yielding systems has fulfilled global demand growth while keeping coffee prices low. However, climate shocks demonstrate the vulnerability of all supply models, strengthening the voice of those advocating more resilient and diversified systems. We review current agroforestry knowledge to identify key trade-offs and synergies between sustainability/performance indicators (i.e. economic, environmental, and social) and explore pathways for a more sustainable coffee future with three examples representative of global coffee production system diversity.

In this study, we present an operational methodology for mapping irrigated areas at plot scale, which overcomes the limitation of terrain data availability, using Sentinel-1 (S1) C-band SAR (synthetic-aperture radar) and Sentinel-2 (S2) optical time series. The method was performed over a study site located near Orléans city of north-central France for four years (2017 until 2020). First, training data of irrigated and non-irrigated plots were selected using predefined selection criteria to obtain sufficient samples of irrigated and non-irrigated plots each year. The training data selection criteria is based on two irrigation metrics; the first one is a SAR-based metric derived from the S1 time series and the second is an optical-based metric derived from the NDVI (normalized difference vegetation index) time series of the S2 data. Using the newly developed irrigation event detection model (IEDM) applied for all S1 time series in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations, an irrigation weight metric was calculated for each plot. Using the NDVI time series, the maximum NDVI value achieved in the crop cycle was considered as a second selection metric. By fixing threshold values for both metrics, a dataset of irrigated and non-irrigated samples was constructed each year. Later, a random forest classifier (RF) was built for each year in order to map the summer agricultural plots into irrigated/non-irrigated. The irrigation classification model uses the S1 and NDVI time series calculated over the selected training plots. Finally, the proposed irrigation classifier was validated using real in situ data collected each year. The results show that, using the proposed classification procedure, the overall accuracy for the irrigation classification reaches 84.3%, 93.0%, 81.8%, and 72.8% for the years 2020, 2019, 2018, and 2017, respectively. The comparison between our proposed classification approach and the RF classifier built directly from in situ data showed that our approach reaches an accuracy nearly similar to that obtained using in situ RF classifiers with a difference in overall accuracy not exceeding 6.2%. The analysis of the obtained classification accuracies of the proposed method with precipitation data revealed that years with higher rainfall amounts during the summer crop-growing season (irrigation period) had lower overall accuracy (72.8% for 2017) whereas years encountering a drier summer had very good accuracy (93.0% for 2019).

Realizing more sustainable food, feed, and bioenergy systems will require interventions such as increased recycling of nutrients and coordination of biomass flows among farms. Innovative tools to explore the co-benefits and trade-offs of improving flow circularity in agro-food systems at different scales are needed to better understand the efficacy of these sustainability solutions. Here, we applied the FAN (‘Flows in Agro-food Networks’) agent-based model to simulate contrasting scenarios of material flows locally in a small farming region of France. These scenarios aim to enhance: (1) best management practices at the farm scale; (2) organic material recycling and biogas production collectively across the agricultural landscape; and (3) system redesign towards complete local circularity through crop and livestock symbiosis, fewer livestock, and elimination of external inputs. Scenario simulation outcomes are assessed in terms of their degree of circularity and food production. We find that best management practices at the farm scale and collective solutions for recycling (organic fertilization and anaerobic digestion) substantially improved the degree of circularity by tightening the local nitrogen (N) cycle without affecting food production. Among other co-benefits, changes in farm rotations to feed livestock locally increased the degree of circularity without appreciably impacting food production. The maximum circularity scenario showed considerable potential to mitigate greenhouse gas (GHG) emissions, however, they involved large trade-offs with food production that were even more pronounced with fewer livestock animals. Although regulating livestock numbers combined with eliminating chemical fertilizers was the most effective at mitigating GHG emissions, when applied simultaneously it substantially impacted food and bioenergy production. Such trade-offs for soil fertility demonstrate the importance of coupling crops and livestock for reaching self-sufficient circular systems. Our study illustrates how the FAN agent-based model can be applied to account for multiple types of interactions involved in transitions toward circularity in local agro-food systems, including the potential for co-benefits and unintended consequences of interventions.

Sustainability and functioning of silvopastoral ecosystems are being threatened by the forecasted warmer and drier environments in the Mediterranean region. Scattered trees of these ecosystems could potentially mitigate the impact of climate change on herbaceous plant community but this issue has not yet tested experimentally. We carried out a field manipulative experiment of increased temperature (+2–3 °C) using Open Top Chambers and rainfall reduction (30%) through rain-exclusion shelters to evaluate how net primary productivity and digestibility respond to climate change over three consecutive years, and to test whether scattered trees could buffer the effects of higher aridity in Mediterranean dehesas. First, we observed that herbaceous communities located beneath tree canopy were less productive (351 g/m2) than in open grassland (493 g/m2) but had a higher digestibility (44% and 41%, respectively), likely promoted by tree shade and the higher soil fertility of this habitat. Second, both habitats responded similarly to climate change in terms of net primary productivity, with a 33% increase under warming and a 13% decrease under reduced rainfall. In contrast, biomass digestibility decreased under increased temperatures (−7.5%), since warming enhanced the fiber and lignin content and decreased the crude protein content of aerial biomass. This warming-induced effect on biomass digestibility only occurred in open grasslands, suggesting a buffering role of trees in mitigating the impact of climate change. Third, warming did not only affect these ecosystem processes in a direct way but also indirectly via changes in plant functional composition. Our findings suggest that climate change will alter both the quantity and quality of pasture production, with expected warmer conditions increasing net primary productivity but at the expense of reducing digestibility. This negative effect of warming on digestibility might be mitigated by scattered trees, highlighting the importance of implementing strategies and suitable management to control tree density in these ecosystems.

Assessing benefits and limits of agroecological transitions in different contexts is of foremost importance to steer and manage agroecological transitions and to feed evidence-based advocacy. However, assessing agroecological transitions remains a methodological challenge. The objective of this research was to investigate to what extent existing multiscale and multidimensional assessment methods were suitable to assess agroecological transitions. We used a literature review to identify and select 14 existing multiscale and multidimensional assessment methods related to sustainable or resilient agriculture. We then analyzed these 14 methods according to five evaluation criteria that reflected key requirements for assessing agroecological transitions: 1) be adaptable to local conditions, 2) consider social interactions among stakeholders involved in the transitions, 3) clarify the concept of agroecology, 4) consider the temporal dynamics of the transitions to better understand barriers and levers in their development and 5) use a participatory bottom-up approach. The methods adopted different approaches to consider each evaluation criterion, but none of them covered all five. The two evaluation criteria most often employed were the adaptability to local conditions (used by 13 of the methods) and the consideration of social interactions (used by all 14 of the analyzed methods). To be adaptable, methods mobilized generic guidelines with flexible content and/or included a contextualization phase. For social interactions, most methods mobilized social-related indicators, and two included stakeholder mapping. Two methods clarified the agroecological concept by mobilizing different sets of principles. Two other methods considered temporal dynamics of the transitions, mobilizing a trajectory of change to understand barriers and levers in their development. Finally, seven methods adopted a bottom-up participatory approach, involving stakeholders in both their development and use. To balance the existing trade-offs between the evaluation purpose, the time requirement and the level of participation in the different approaches adopted by the 14 methods studied, we suggest combining some of the approaches in a complementary mode to cover all 5 criteria and therefore improve the assessment of agroecological transitions.

International audience