Bernard Tychon

Assessing ecological sustainability in agricultural landscapes requires approaches that integrate land-cover change, its ecological effects, and their spatial determinants. This study analysed changes between 2023 and 2024 across six agricultural landscapes in northern Benin using the Landscape Ecological Sustainability Index (LESI), calculated for 1 km² landscape cells from Human Disturbance Coefficients (HDCs) assigned to satellite-derived land-cover classes. Interannual changes were assessed using ΔLESI, the Wilcoxon signed-rank test, Global Moran’s I, and the Local Indicators of Spatial Association (LISA). The contribution of land-cover transitions to HDC change was quantified and a Monte Carlo sensitivity analysis based on classification accuracy was additionally used to assess the robustness of the observed interannual changes to classification uncertainty, and complementary univariate and bivariate regression models examined the relationships between LESI variations and 11 biophysical and geographical variables, including their pairwise interactions. The interannual comparison between 2023 and 2024 showed a decrease in ecological sustainability in four sites, a slight increase in one, and relative stability in another. Significant spatial autocorrelation was detected in all sites (Moran’s I = 0.44–0.65; p < 0.001). Some spatially limited transitions exerted important effects on HDC change. Sensitivity analysis confirmed that the direction of ΔHDC was robust to classification uncertainty in five of the six landscapes. The low explanatory power of the univariate models (R² ≤ 0.081) indicates that no single variable independently explains the observed changes. The bivariate interaction analyses further showed that some associations were context-dependent, although their explanatory power remained limited, with the best-performing model accounting for only 10.9% of the variation in ΔLESI. This integrated framework provides a reproducible approach for assessing, mapping, and prioritising interannual change of ecological sustainability from remote sensing data to support evidence-based agricultural landscape planning and sustainable land management.

Assessing ecological sustainability in agricultural landscapes requires approaches that integrate land-cover change, its ecological effects, and their spatial determinants. This study analysed changes between 2023 and 2024 across 6 agricultural landscapes in northern Benin using the Landscape Ecological Sustainability Index (LESI), calculated for 1-km² landscape cells from Human Disturbance Coefficients (HDCs) assigned to satellite-derived land-cover classes. Spatio-temporal dynamics were assessed using ΔLESI, the Wilcoxon signed-rank test, Global Moran's I, and Local Indicators of Spatial Association (LISA). The contribution of land-cover transitions to HDC change was quantified, and univariate regression models examined the relationships between LESI variations and 11 biophysical and geographical variables. Ecological sustainability declined in 4 sites, improved slightly in one, and remained relatively stable in another. Significant spatial autocorrelation was detected in all sites (Moran's I = 0.44–0.65; p < 0.001). Some spatially limited transitions exerted important effects on HDC change. The low explanatory power of the univariate models (R² ≤ 0.081) indicates that no single variable independently explains the observed changes. This integrated framework provides a reproducible approach for assessing, mapping, and prioritising ecological sustainability trajectories from remote sensing data to support evidence-based agricultural landscape planning and sustainable land management.

Digital twin (DT) technology is attracting increasing interest as a potentially valuable tool for the future of agriculture. By offering a dynamic virtual representation of real agricultural systems, it opens up new possibilities for real-time monitoring, simulation, and decision support. In principle, such approaches could improve predictive capacity, optimize resource use, and support more responsive management strategies. However, agriculture cannot be treated as an engineered system, and this is where important challenges emerge. Agroecosystems are living, context-dependent, and inherently variable, shaped by diverse processes that remain only partly observable and often difficult to model. This makes their representation and prediction considerably more complex than in many industrial applications. In this review, we critically examine the conceptual foundations, architectural frameworks, and current applications of agricultural digital twins (ADTs), while also identifying key scientific and practical constraints that continue to limit their development. Particular attention is given to two recurring issues: the assumption that increasing data availability necessarily improves prediction, and the persistent gap between observable variables and the underlying biological and ecological processes that govern system behaviour. Drawing on conceptual figures and comparative analyses, we highlight important research gaps and argue for a shift in perspective. Rather than pursuing increasingly precise predictions, there is a need to develop digital twins that explicitly account for uncertainty and support more resilient forms of decision-making. In this context, the value of ADTs may lie less in predictive accuracy alone, and more in their ability to help decision-makers navigate complexity, variability, and change.

The dual benefit of wastewater and microalgal biomass is a major advantage of high-rate algal ponds, enabling the environmental valorization of these byproducts. This research explored the effect of treated wastewater on the agri-food species Hordeum vulgare (L.) and its associated weed, Emex spinosa (L.) Campd., along with the effects of algal biomass (primarily composed of Closterium, Chlorella, and Scenedesmus spp.) and Diplotaxis harra leaf powder. Initial pot trials applied microalgae and D. harra at 2, 4, and 6 g·kg⁻¹ soil, also confirming that the treated wastewater met reuse standards and did not affect plant growth. The combined treatment at 4 g·kg⁻¹ led to the highest H. vulgare increases in fresh weight (162.71%), root length (73.75%), and shoot length (72.87%), while reducing E. spinosa shoot and root lengths by 30.79% and 52.18%, and fresh weight by 68.24%. Subsequent field experiments using 1.26 t ha⁻¹ of 0.5-cm-applied D. harra and microalgae powders enhanced H. vulgare growth, while reducing the growth of E. spinosa. The reduction in E. spinosa growth was associated with increased electrolyte leakage and malondialdehyde content. These results support the integration of high-rate algal ponds into agriculture, promoting water reuse and reducing reliance on synthetic fertilizers and herbicides in barley production.

The intensification of anthropogenic pressures, particularly those related to agriculture driven by increasing demands for food and cash crops, generates negative environmental externalities. Assessing these externalities is essential to better identify and implement measures that promote the environmental sustainability of rural landscapes. This study aims to develop a multi-criteria assessment method of the negative environmental externalities of rural landscapes in the northern Benin agricultural basin, based on satellite-derived data. Starting from a 12-class land cover map produced through satellite image classification, the evaluation was conducted in three steps. First, the 12 land cover classes were reclassified into Human Disturbance Coefficients (HDCs) via a weighted sum model multi-criteria analysis based on nine criteria related to the negative environmental externalities of anthropogenic activities. Second, the HDC classes were spatially aggregated using a regular grid of 1 km² landscape cells to produce the Landscape Environmental Sustainability Index (LESI). Finally, various discretization methods were applied to the LESI for cartographic representation, enhancing spatial interpretation. Results indicate that most areas exhibit moderate environmental externalities (HDC and LESI values between 2.5 and 3.5), covering 63–75% (HDC) and 83–94% (LESI) of the respective sites. Areas of low environmental externalities (values between 1.5 and 2.5) account for 20–24% (HDC) and 5–13% (LESI). The LESI, derived from accessible and cost-effective satellite data, offers a scalable, reproducible, and spatially explicit tool for monitoring landscape sustainability. It holds potential for guiding territorial governance and supporting transitions towards more sustainable land management practices. Future improvements may include, among others, refining the evaluation criteria and introducing variable criteria weighting schemes depending on land cover or region.

The increasing anthropogenic pressures on rural landscapes, particularly those arising from agricultural intensification, necessitate robust methods to assess environmental sustainability. This study proposes a multi-criteria approach for evaluating the environmental sustainability of rural landscape in northern Benin, using satellite-derived land cover data. The methodology was applied to 3 sites representative of rural landscapes in northern Benin. A 12-class land cover map, produced via the Moringa processing chain, was reclassified into Human Disturbance Coefficients (HDC) based on nine weighted environmental impact criteria. These were then spatially aggregated into 1 km² grid cells to produce the Landscape Environmental Sustainability Index (LESI). Results indicate that most areas exhibit moderate anthropogenic impact (HDC and LESI values between 2.5 and 3.5), covering 63–75% (HDC) and 83–94% (LESI) of the respective sites. Areas of low impact (values between 1.5 and 2.5) account for 20–24% (HDC) and 5–13% (LESI). The LESI, derived from accessible and cost-effective satellite data, offers a scalable, reproducible, and spatially explicit tool for monitoring landscape sustainability. It holds potential for guiding territorial governance and supporting transitions towards more sustainable land management practices. Future improvements may include refining evaluation criteria and introducing variable weighting schemes by land cover or region.

Universities and other institutes of higher education could be considered as key actors in the implementation of sustainability pillars, such as the adoption of sustainable practices in wastewater management. However, the adoption of such practices is still an emerging issue. This paper discusses the design and operation of the first combined Oxylag and high rate algal pond (COHRAP) constructed at the university campus in Tunisia for irrigation. Performance was evaluated based on the removal efficiencies of nutrients, chemical oxygen demand (COD), biochemical oxygen demand (BOD), heavy metals, coliforms, and biomass productivity. The potential reuse of sludge and algal biomass is discussed based on the Tunisian national standard regulation for sludge reuse in agriculture (NT 106.20) and the European regulation (EC, 2019/1009) for fertilizer products. Effluent phytotoxicity is tested on the germination and growth on Zea mays L. The results indicate that the COHRAP performance was globally satisfactory; however, biomass productivity (1.4 g m⁻²d⁻¹) was low, indicating the need for adjustments in the operational parameters. Despite the effluent limitations for TSS and Hg, no phytotoxic effect was observed. Regarding the heavy metal content in sludge and algal biomass, the results obtained were in compliance with NT 106.20 and EC, 2019/1009), respectively. The energy consumption of COHRAP is 1.05 kWh/m³ resulting in operational costs of 0.29 euros/m³. This study revealed that COHRAP could be a sustainable option to treat wastewater from university campuses with resource recovery. Such a choice can be improved by the implementation of an algae recovery step.

In Burkina Faso, small-scale, community-managed irrigation systems play a crucial role in stabilizing agricultural production and improving food security. Over the past three decades, the state has transferred the management of these irrigation systems to local farmer organizations in the hope of improving efficiency and sustainability. This study assesses the long-term performance of six irrigation perimeters Dakiri, Gorgo, Itenga, Mogtedo, Savili, and Wedbila through an in-depth analysis of governance models, infrastructure conditions, and financial sustainability. Performance indicators such as relative water supply (RWS), gross production per unit of irrigation water (PbIr), and water charge recovery rates were used to assess the effectiveness of farmer-led irrigation management. The results reveal persistent governance and financial challenges as well as issues such as water wastage and low yield persisting, despite decades of implementation of farmer-led management. The degradation of irrigation infrastructure, coupled with declining water fee collection rates, threatens the sustainability of these systems. A comparative analysis of international cases suggests that a hybrid governance model, in which the state provides technical and financial support while strengthening accountability mechanisms, could improve the performance of these irrigation systems. This study recommends a shift towards greater state intervention, improved financial mechanisms, and the adoption of digital monitoring tools to ensure a more efficient and sustainable management framework.

Contexte: L’évaluation des ressources fourragères est un élément clé de la gouvernance des crises alimentaires du bétail au Burkina Faso. Objectif-Méthodes: Cette étude visait l’évaluation, pour la première fois, de la possibilité d’estimer les rendements fourragers des pâturages dans les espaces climatiques du Burkina Faso via l’utilisation de modèles statistiques linéaires uni et multivariés construits à partir de données de biomasse végétale fourragère collectées sur le terrain en 2017, 2018 et 2019, de variables satellitaires phénologiques (indice de végétation de la différence normalisée [NDVI] et fraction de rayonnement photosynthétiquement actif absorbé [FAPAR]) et agroclimatiques (précipitations, humidité du sol, évapotranspiration, température de surface). Résultats: Une recherche exhaustive des meilleurs modèles statistiques linéaires comportant une à quatre variables a été réalisée et les meilleurs modèles selon le critère d’information bayésien (BIC) identifiés. La performance des modèles uni à quadrivariés obtenus s’est avérée assez faible avec, pour l’ensemble des espaces climatiques excepté l’espace sahélien, des RRMSE press variant de 55 % à 61 % (R² press de 0,07 à 0,36), et pour l’espace climatique sahélien des RRMSE press variant de 42 % à 49 % (R² press de 0,59 à 0,69). La baisse de corrélation de la majorité des variables avec la biomasse végétale fourragère selon le gradient nord-sud résulte en une baisse de performance des modèles selon ce gradient. Les variables agroclimatiques se sont révélées inutiles, et celles issues du FAPAR sont globalement plus performantes que celles issues du NDVI. Une très faible plus-value des modèles multivariés comparés aux modèles univariés a été observée, excepté pour l’espace sahélien. Les modèles développés sur des espaces climatiques plus homogènes se sont montrés plus performants. Conclusions: Une série de recommandations a été identifiée pour améliorer le couplage entre données de biomasse végétale fourragère collectées sur le terrain et variables extraites des images satellitaires, et ainsi améliorer la performance des modèles.