Despite huge gains in productivity, environmental impacts of industrial agriculture based on a few high-yielding crop cultivars and the massive use of chemical fertilisers and pesticides have led to a search for new pathways leading to more sustainable agriculture in both temperate and tropical regions. New strategies incorporating ecological knowledge gained from the observation of natural ecosystems is an alternative to design “ecologically intensive” agroecosystems. Such systems are indeed both ecological and productive. Designing ecologically intensive agroecosystems calls for in-depth knowledge of biological regulations in ecosystems, and for the integration of traditional agricultural knowledge held by local farmers. This article reviews the main initiatives underlying ecologically intensive agroecosystems, analyses basic concepts, and proposes a framework for action. The rainforest model, the dry forest model, and the American Prairie are exemplified as three main natural systems at the basis of the mimicry concept. The link between biodiversity and the mimicry hypotheses, and the use of the concepts of productivity, efficiency, stability, and resilience for agroecosystems are discussed. Six main principles for cropping system design based on natural ecosystem mimicry are identified. A three-step framework for action is proposed, including nature observation, experimental design, and participatory design. Although far from being a panacea, the mimicry approach can provide new ways for agroecosystem design both in temperate and tropical countries.

In the value chain, yields are key information for both growers and other stakeholders in market supply and exports. However, orchard yields are often still based on an extrapolation of tree production which is visually assessed on a limited number of trees; a tedious and inaccurate task that gives no yield information at a finer scale than the orchard plot. In this work, we propose a method to accurately map individual tree production at the orchard scale by developing a trade-off methodology between mechanistic yield modelling and extensive fruit counting using machine vision systems. A methodological toolbox was developed and tested to estimate and map tree species, structure, and yields in mango orchards of various cropping systems (from monocultivar to plurispecific orchards) in the Niayes region, West Senegal. Tree structure parameters (height, crown area and volume), species, and mango cultivars were measured using unmanned aerial vehicle (UAV) photogrammetry and geographic, object-based image analysis. This procedure reached an average overall accuracy of 0.89 for classifying tree species and mango cultivars. Tree structure parameters combined with a fruit load index, which takes into account year and management effects, were implemented in predictive production models of three mango cultivars. Models reached satisfying accuracies with R² greater than 0.77 and RMSE% ranging from 20% to 29% when evaluated with the measured production of 60 validation trees. In 2017, this methodology was applied to 15 orchards overflown by UAV, and estimated yields were compared to those measured by the growers for six of them, showing the proper efficiency of our technology. The proposed method achieved the breakthrough of rapidly and precisely mapping mango yields without detecting fruits from ground imagery, but rather, by linking yields with tree structural parameters. Such a tool will provide growers with accurate yield estimations at the orchard scale, and will permit them to study the parameters that drive yield heterogeneity within and between orchards.