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.

-Introduction. Mango-based orchards in Senegal occur in a large diversity of cropping systems, but few typologies of these systems exist and none are associated with their comprehensive and quantitative analysis. In this study we defined and characterized the typology of these systems based on a quantitative assessment of their planting design, management, vegetative state, hedgerow structure and infestation by a major pest of mango, the Bactrocera invadens fly. Materials and methods. Multivariate analysis and clustering methods were applied to data from 64 mango-based orchards and their surrounding hedgerows sampled in the Dakar and Thiès regions, in Senegal. Results and discussion. Four types of cropping systems were identified according to orchard design and management patterns: (1) 'No-input mango diversified orchards', (2) 'Low-input mango orchards', (3) 'Medium-input citrus-predominant orchards' and (4) 'Medium-input large mango-or citrus-predominant orchards'. Orchard characteristics varied among these patterns. For instance, vegetation was dense and homogeneous in system 1, and the mortality rate of trees was high in system 2 but low in system 3. Orchards of systems 3 and 4 were mostly associated with hedgerows with, respectively, boundary-marking and defensive species. Lastly, the number of B. invadens flies was high in orchards of system 4, whereas it was low in those of system 2. Conclusion. The diversity of mango-based cropping systems in Senegal is now well described and quantified. This characterization is a preliminary step that is essential for further studies aiming to improve these systems.Senegal / Mangifera indica / fruit trees / orchards / typology / design / crop management / Bactrocera invadens / hedges / multivariate analysis Vergers à base de manguiers au Sénégal : diversité des modèles de conception et de gestion.

Disease-driven selection favours evasive, tolerant, and resistant cultivars, changing cultivar diversity significantly. Since its outbreak in Burundi in the late 1980s, Banana Bunchy Top Disease (BBTD) has now spread to 5 out of 18 provinces across the country, principally through informal seed exchanges. Control approaches have focused on using tissue culture clean planting material and eradicating infected mats. This study investigated the impact of BBTD and its control measures on seed selection practices and banana cultivars diversity in Burundi, by comparing two BBTD endemic sites and one where the disease wasn’t reported. Results have shown that in addition to agronomic traits used in all sites, some BBTD-typical symptoms were used in seed selection in the endemic areas. Own seed provisioning and formal seed sources networks were more likely to be observed in BBTD-endemic areas, compared with the non-endemic area. Disease control using certified tissue culture planting materials reduced the varietal diversity of local cultivars but enabled the introduction of new cultivars. A general reduction in the diversity of local cultivars grown by farmers in the BBTD endemic zones was observed, with about half of the diversity per farmer compared to the non-endemic zone. Farmer demand for varieties (local and improved) was not different between the two areas. Sustainable conservation of crop genetic diversity in the presence of disease invasions remains a problem to be addressed. Thus, implementing seed system-linked intervention with an explicit and monitored diversity conservation objective would increase the sustainability of agricultural production in such situations.

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