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How scientists might better contribute to SDGs in the land sector? At the invitation of INRAE, a group of high-level international experts delivers a comprehensive and systemic approach.

Agroforestry systems can be an effective means of maintaining or even enhancing crop yields under climate change. In agroforestry, trees compete with crops for soil resources, but they can also improve the growing conditions of crops under drought by providing shade. They can promote higher crop yields and higher harvest quality. However, the beneficial effect of tree shade may depend on the seasonal pattern of rainfall which determines the compensation between yield components. In this study, we evaluated the production of two annual crops (durum wheat and faba bean) in olive agroforestry in northern Morocco. We manipulated water supply in a field experiment to span the high inter-annual rainfall variability at the site and tested whether olive trees reduce or improve crop yields. We assessed the effect of water addition on crop growth, yield components and final yields and estimated the land equivalent ratio of olive agroforestry. Agroforestry penalized crop growth and yield whatever the water regime. The number of grains per unit area was the most impacted yield component. However, agroforestry improved individual grain weight and the protein content of grains but not sufficiently to compensate for yield loss. Overall, we show that agroforestry systems are generally more land productive than sole crops and trees and we present examples of how changing water supply may impact the performance of olive agroforestry in a drier future.

In Northern Laos, many farmers have switched within 15 years from a manual slash and burn system of long fallow and upland rice to a hybrid maize mono-cropping system with moto-mechanized plowing and chemical input use. This transition brought higher incomes to a majority of farmers, but sustainability of this system is henceforth challenged. This study identifies sustainability criteria that have to be considered to assess current cropping systems and alternatives. Our method considers 2 scales of analysis: 1. FARM level analysis identifies the diversity of farming systems and sustainability criteria from the farmer's perspective. It is based on 120 surveys in 6 villages (factorial analysis for mixed data and hierarchical clustering), focus groups discussions and card games (20 farmers in 4 villages). 2. FIELD level analysis identifies the factors explaining the variability of maize performances and impacts. It is based on surveys, 2-year monitoring of 38 plots, and a participatory game on tactical choices at cropping system scale (3 villages, groups of 20 farmers). At the farm level, we identified four farm types: the first type is composed of poor farmers, the second type is also poor farmers but with more land; farmers from the third type focus on rice production, whereas farmers from the fourth type focus on upland crops (maize, livestock, with higher total income). Sustainability criteria identified by farmers include: self-sufficiency in rice, farm transmissibility, farm income, diversity of activities to reduce risks, workload, and cash requirement for activities. Field level monitoring showed that poor performances of maize are explained by the difficulties to manage the first stages of the cropping cycle: poor soil tillage quality due to inadequate machinery leading to a heterogeneous stand with low density, erosion risks due to bare soil, strong weed pressure and consequently increasing use of herbicide. This diagnosis stage proved to be very useful as the main findings are different from the common view about the cause of maize declining yield i.e. soil nutrient depletion under continuous mono-cropping. At the cropping system level, sustainability criteria (both determined by farmers and field monitoring) are: land and labor productivity, gross margin, resources use efficiency (nitrogen, water, light), erosion and herbicide risks, technical complexity, storage duration of harvest, and soil fertility. This field level analysis led to add two criteria at farm level: fertility transfer and farmers' health risk in relation with exposure to herbicide. We derived indicators from this set of criteria to identify potential responses to sustainability issues that arose from the rapid evolution of cropping systems. Our study also prompted us to assume that maize is not a crop to be absolutely eliminated and could probably remain a sustainable option under more effective management.

Farm modelling has been widely used over the past ten years for the assessment of agricultural systems in face of policy changes, technological innovation, economic and climate changes. Beyond the various modelling methods (optimisation, rule-based, agent-based...), these models are framed by their objectives -defining input data and users- which can be: policy assessment, technological innovation assessment, farming systems resilience studies, optimisation of water management at regional level, agricultural landscape design, a " boundary object " for strategic thinking in a stakeholder arena. Indeed these farm modelling tools are still poorly used for the design of innovative cropping systems which remains mostly based on field level experiments and modelling. Nevertheless driving forces and assessment criteria for the design of sustainable cropping systems are increasingly expressed at larger scales than field or even farm. For exemple most environmental services are provisionned at landscape (e.g. biodiversity) or whatershed levels (e.g. water quality), at regional level (e.g. climate mitigation) and in more global socio-systems (e.g. food security, rural development). On the other hand, a large part of the functions supporting these services are operating at field level where the interactions between farmer's decisions (crop selection, combination and management) and biophysical processes (soil, plants, pests, weeds and diseases) are occuring. A large part of these biophysical processes also occur at landscape level (biodiversity, water flows…) but they require information at field level to be upscaled in a landscape mosaïc. In-between these two levels (field and region) the farm level plays a key role in agricultural systems simulation: (i) it is the decision level of farm activities (crops, livestock, trees…) further applied to field level ; (ii) it is the first level of expression of socio-economical services (labour, income, food production…) and (iii) farm diversity is a major driver of assessment indicators value and evolution at regional scale. In order to combine these up and downscalling processes between farm, field and regional levels for a multi-criteria design of agricultural systems, we propose a "farm-centered" integrated modelling of agricultural systems framework. In this approach farm models and typologies are the central node of scenario based analysis of technological innovation in a biophysical and socio-economic context. We will illustrate how it has been used in a wide range of assessment studies and how we plan to further develop it for the design of a " plant-diversity based " agroecological transition of agricultural systems.

Rainfed cropping systems are critically affected by climate change in the South Mediterranean, especially by increasing rainfall scarcity and irregularity. Agroforestry which consists of the combination of trees and understory crops, can be a key agroecological adaptive innovation that may both mitigate the negative effects of climate change and promote more productive and efficient cropping systems. Inspired by ecological theory, the stress gradient hypothesis (SGH) suggests that positive interactions become more important than negative interactions between trees and crops with increasing environmental stress (e.g. drought intensity). To test the SGH in the context of Mediterranean agroforestry, a field survey was conducted along a rainfall gradient in Northern Morocco to quantify interactions between olive (Olea europaea) and barley (Hordeum vulgare). Three main sites with contrasting mean annual rainfall (534 mm, 560 mm and 655 mm) were selected. The responses of barley in terms of biomass production and grain yield were assessed along the rainfall gradient and compared between agroforestry and barley in sole cropping systems. Using 42 field surveys across the three sites, we determined how farmers manage barley in sole cropping systems, how they manage the interactions between olive and barley in agroforestry systems, and we estimate the final yield of barley at harvest. In agroforestry systems, barley biomass production and grain yield at harvest was significantly higher (4.04 t/ ha and 1.11 t/ha respectively) under the intermediate rainfall level compared to the wettest site (1.85 t/ha and 0.58 t/ha respectively) and (2.37 t/ha et 0.71 t/ha respectively) in the driest one. While barley grain yield at harvest in the sole cropping systems was significantly higher under the driest site (2.37 t/ha) compared to the wettest site (1.31 t/ ha). In all cases, barley biomass production was significantly reduced by its intercropping in olive trees, the gap between barley biomass production in agroforestry and sole cropping systems ranged from 3.36 t/ha under the intermediate rainfall level, 3.52 t/ha under the wettest site to 4.76 t/ha under the driest one. Barley grain yield in agroforestry was significantly reduced under the driest and the intermediate rainfall level compared to barley grain yield in sole cropping systems, where the gap between barley grain yields in agroforestry and sole cropping systems was largest in the driest site 1.65 t/ha and 0.73 t/ha in the intermediate rainfall level. These results suggest that the SGH do not apply in agroforestry systems where other limiting factor and farmers practices may interact with water. For better understanding of tree-crop interactions, the study of different mechanisms by which trees influence crop biomass and yield should be more studied.