Agroforestry Systems最新文献

Agroforestry is widely recognized as a promising strategy for mitigating climate change impacts and restoring degraded lands. Nonetheless, adoption rates remain low in low- and middle-income countries. This limited uptake is largely attributed to the insufficient design of context-specific initiatives, stemming from a lack of understanding of farmers’ knowledge, perceptions, and attitudes toward integrating tree planting into agricultural systems. This paper seeks to predict farmers’ intentions to adopt agroforestry practices in Rwanda’s Eastern Province and to elucidate the underlying decision-making processes shaping these intentions. Grounded in the Theory of Planned Behaviour as primary analytical framework, the study drew on data from 940 households, which were analysed using structural equation modelling. Results indicate that farmers’ intentions toward agroforestry are predicted by attitudes and perceived behavioural control. Various background factors also play a significant role. Overall, only the number of agricultural plots managed by farmers demonstrates significant total effects on their intentions. Awareness of agroforestry technology positively influences intention through attitude and perceived behavioural control. Age and food insecurity negatively influence intention through attitude, while membership in saving groups has a positive influence. Additionally, gender and family size negatively affect intention through perceived behavioural control. This study suggests that policy efforts should consider programs that foster positive attitudes and enhance perceived control regarding agroforestry adoption among farmers. It further recommends promoting community awareness of agroforestry technology, focusing on farmer demographics, including women, the elderly, impoverished households, and those experiencing food insecurity.

The study analyzed labile and stable SOC fractions in different land use systems in the Colombian Amazon. The research included cacao-based Agroforestry Systems (cAFS), full-sun cocoa plantations, forests, and pastures, using a factorial design with land use and soil depth as factors. Land use significantly affected carbon fractions, explaining 51% of variance (P < 0.01). In the surface layer (0–15 cm), fulvic acids were highest in Huito (7.32 g C kg⁻¹) and lowest in Pasture (2.76 g C kg⁻¹). For humic acids, full-sun cacao showed the highest values (9.55 g C kg⁻¹) followed by Capiron (7.75 g C kg⁻¹). Total Humic Extract was maximum in full-sun cacao (27.68 g C kg⁻¹) and minimum in Pasture (18.99 g C kg⁻¹). The polymerization index was higher in Pasture (1.87) and full-sun cacao (1.46). The degree of humification was highest in full-sun cacao (64.78%), while the humification ratio showed maximum values in full-sun cacao (84.9%) and minimum in Forest (36.86%). These patterns generally persisted at deeper soil layers, demonstrating the potential of cAFS as a sustainable alternative for land use and carbon sequestration.

This study reviews agroforestry research in the European Union (EU) by analyzing 902 articles published between 1984 and 2025. We apply bibliometric and content analysis following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, complemented by network analysis using VOSviewer to provide a comprehensive synthesis of the literature. Particularly, our study focuses on three key research questions: (1) How has agroforestry research in the EU evolved over time and space? (2) What are the most common types of agroforestry systems and structures studied? (3) What are the key research areas covered in the literature? Our results reveal that (i) research activity has increased substantially, with 42% of articles published between 2021 and 2025, identifying a geographical focus in Spain, Italy, Portugal, Germany, and France; (ii) silvopastoral systems dominate the literature, whereas agrosilvopastoral practices receive less attention, with studies focusing on dehesa landscapes, alley cropping, and orchards as the most frequently investigated agroforestry structures; and (iii) research areas are strongly oriented toward ecosystem services, especially regulating and provisioning services, while cultural services, economic dimensions, and stakeholder perspectives are still underrepresented, despite their recognized importance for the wider adoption of agroforestry systems. In conclusion, future research should extend beyond the currently dominant regions to better represent underexplored countries. Specifically, policy incentives should provide targeted funding and promote holistic, practice-oriented approaches. In order to increase adoption and close key gaps, agroforestry research requires greater attention to the economic dimensions and stakeholder perceptions.

Perennial in nature, coffee is an integral part of climate-smart farming practice that sequesters carbon in both below- (BGB) and above-ground biomass (ABG). The contribution of BGB to carbon sequestration in standing coffee biomass is poorly understood due to methodological challenges. Allometry has been used to estimate BGB for Arabica coffee plants, but applicability of the functions to Robusta coffee in Uganda is uncertain. Robusta coffee plants (n = 60) were randomly selected from contrasting pedoclimatic districts of southwestern Uganda across a time gradient of organic certification. From each plant, girth at base (G_base, cm), girth at 15 cm above ground (G₁₅, cm) girth at nodal separation (G_t, cm) and plant height (cm) were taken before excavating and collecting all roots > 2 mm. The total fresh weight of each biomass part was measured on a digital scale. A fresh sample of each biomass part was measured before oven drying to constant weight to determine BGB and ABG in kg dry weight (DW) per plant. The data set was randomly split into a calibration sub-dataset (n = 40) and an evaluation sub-dataset (n = 20). The existing and developed functions were evaluated qualitatively using unit plots and quantitatively using the root mean square error (RMSE), %bias and squared correlation coefficient (R²). The best biomass model was BGB = 0.2006G_base−2.757 (RMSE = 0.0930, R² = 0.978, %bias = 1.03), and AGB = 0.5799G_base−4.71 (RMSE = 0.140. R² = 0.772, %bias = 0.117). These functions are more reliable for estimation of the BGB and AGB of Robusta coffee bushes across pedoclimatic zones and coffee management regimes in Uganda than existing ones.

There is an urgent need to diversify global food systems to improve food security and increase resilience to climate change. Indigenous nut trees, such as Canarium indicum, have the potential to enhance food security and resilience, but these species are often understudied. C. indicum is native to the South Pacific and is becoming the basis of a growing nut industry. This species holds cultural significance in many Pacific islands. We conducted a study on the fruit development and abscission patterns of C. indicum trees. Our findings show that the fruit reaches full size 3–4 months after flowering and matures 7–8 months later, with a complete development cycle of 10–12 months. Peak flowering occurs in July–August, earlier than previously reported in Papua New Guinea. The fruit transitions from green to purple/black between April and May, indicating maturity. Major fruit abscission peaks were observed between August and October, with secondary peaks between April and July. Farmers and processors can use this information to plan for fruit harvest 12 months after peak flowering and for peak processing operations from July to December. Understanding fruit production patterns can lead to improved financial outcomes by optimizing harvest timing, enhancing processing efficiency, and guiding tree selection for higher yields. This knowledge can also benefit food security and income generation at the village level.

The significant role of tropical agroforestry (AF) systems in the global carbon budget has increased the need for accurate biomass estimates. The absence of biomass allometric equations limits our understanding of tree biomass and carbon stocks. Most existing allometric equations are developed for natural forest, which restricts their application to the fruit tree species commonly found in homegardens AF systems. This limitation hampers precise biomass estimation, which is essential for effective climate change mitigation. This study aimed to develop species-specific and mixed-species aboveground biomass (AGB) models for the dominant fruit trees in the Agroforestry Systems of the Upper Gibe Region of Ethiopia. We destructively harvested 96 sample trees representing four dominant species, with diameters at breast height (DBH) ranging from 2.5 to 62.8 cm. The models were formulated using DBH, tree height (Ht), and wood basic density (WBD) as predictor variables. We evaluated model performance based on parameter significance, the Akaike Information Criterion (AIC), pseudo-R², Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). Results indicated that DBH alone was the most effective predictor for Persea americana (AGB = 0.365 × (DBH)^2.029). In contrast, other species exhibited better prediction performance when additional variables were considered. For Mangifera indica (AGB = 0.315 × (DBH)^2.0922 × (WBD)^0.192) and the mixed-species dataset (AGB = 0.839 × (DBH)^1.835 × (WBD)^0.331), models that combined DBH and wood density (WBD) yielded the highest predictive accuracy. Conversely, Prunus persica (AGB = 0.792 × (DBH)^1.907 × (Ht)^0.022 × (WBD)^0.305) and Psidium guajava (AGB = 0.439 × (DBH)^1.604 × (Ht)^0.646 × (WBD)^0.455) were best predicted using models that incorporated DBH, tree height (Ht), and wood density. Overall, species-specific and mixed-species models outperformed regional and pantropical equations, underscoring the limited effectiveness of generalized models for agroforestry trees. Additionally, multivariable models yielded greater predictive accuracy than single-variable approaches. These results enhance the estimation of biomass and carbon stocks in agroforestry systems. In conclusion, this study presents robust, locally calibrated allometric equations that enhance biomass and carbon assessments. We recommend applying these models for regional carbon accounting, land management, and climate initiatives like REDD + , rather than relying on pantropical equations.

Many indigenous communities in Northeastern India have a long history of utilizing traditional edible flowers as food, which is an essential component of their culture. Most ethnic communities rely on these edible blooms for daily sustenance, making them one of nature's most valuable gifts. Edible flowers constitute an integral component of the traditional diet of the Northeastern population. These resources should be utilized in a way that ensures indigenous people receive their fair share, as they have preserved them throughout the ages using their traditional knowledge. The North-eastern people's traditional cuisines and ceremonies make extensive use of edible flowers. Sustainable utilisation and management of these flowers are essential with the goal of closing the knowledge gap in the traditional field and utilizing the potential hidden resources. This review explores the nutritional composition, dietetic uses, traditional medicinal uses, phytochemical constituents, and economic potential of four key ethnobotanical edible flowers—Tupistra clarkei Hook.f., Oroxylum indicum (L.) Kurz, Rhododendron arboreum Sm, and Phlogacanthus thyrsiflorus Nees from Northeastern India. The review highlights how crucial it is to integrate traditional knowledge with contemporary scientific findings in order to improve rural lives, food security, and sustainable resource use.

Semi-natural areas support a variety of pollinators, which can enhance pollination and increase the yields of many crops. This study evaluates the influence of both pollination and forest-stand configuration on the seed dry mass and fatty acid composition of soybean. At each site, plots with pollinator-excluded and pollinator-exposed plants were established at different distances from the forest. We collected flowers (to quantify pollen grains on stigma) and seeds from these treatments across ten soybean stands. The amount of potentially suitable habitat for pollinators (forests and semi-natural areas) was quantified around each of the sampling sites. Pollination had a positive effect on all variables. Specifically, pollinator-exposed plants had 93% higher pollen deposition, 31% heavier dry seed mass, 6% more seed oil content and a 6.8% greater oleic acid concentration. Furthermore, most response variables in pollinator-exposed plants increased as the proportion of forest surrounding the crop stands increased. Conversely, only seed mass decreased with increasing distance from the forest edge. Despite soybeans being autogamous, our results highlight that native forests act as a source of pollinators and pollination services for soybean, thereby improving yields. The larger the forested area surrounding soybean stands, the greater the benefits of pollination on yield and seed-oil profiles; however, these positive effects at the forest-stand level can diminish with increasing distance of soybean plants from the forest edge. Overall, these results underscore the critical importance of conservation policies for the remaining forests in agro-ecosystems to enhance pollination and to increase yields in this and other pollinator-dependent crops.

Pollination is a key ecosystem service for cacao (Theobroma cacao L.) production, yet natural pollination rates are critically low, partly due to agricultural intensification and the widespread use of agrochemicals. We investigated how conventional crop management practices—specifically the application of an insecticide (cypermethrin), a fungicide (copper oxychloride), and a pollinator breeding substrate (oil palm fibre) affect pollination dynamics in cacao agroforests in Arauca, Colombia. We assessed their individual and combined effects on natural and hand pollination success, alongside environmental factors such as shade, ground cover, and precipitation. We also quantified the pollination gap, defined as the difference between natural and hand pollination, to evaluate pollination efficiency. Using a factorial design and generalized linear mixed models, we found that natural pollination was significantly reduced by pesticide applications, particularly when insecticides and fungicides were combined. In contrast, hand pollination remained largely unaffected by environmental variables. Surprisingly, insecticide application (alone or combined with fungicide) increased hand pollination success, possibly due to reduced flower damage (florivory). As a result, pesticide treatments substantially widened the pollination gap, ranging from nearly eightfold to more than tenfold compared to the control. Notably, increasing shade cover consistently improved natural pollination and narrowed the pollination gap; under high-shade conditions, natural pollination occasionally even outperformed hand pollination. These findings underscore the need for a shift from input-intensive approaches toward integrated management strategies that reconcile productivity with ecological sustainability. Based on our results, we recommend restricting pesticide use to periods of low pollinator activity and maintaining moderate shade levels (~ 50%).

Agroforestry systems help address global challenges by supporting biodiversity, rural livelihoods, and climate change adaptation. While Arabica coffee (Coffea arabica L.) is traditionally grown in agroforestry systems, full-sun plantations have been promoted too, fueling the debate over optimal shade tree integration for coffee production. The present study assessed coffee yields in relation to canopy cover and carbon stocks at the plot level in the mid-hills of Nepal. We established monitoring plots and quantified coffee yields through cherry counting. The canopy cover was estimated from canopy photos. Shade trees were identified and their above-ground carbon stocks were measured using tree height and diameter at breast height. We found coffee yields ranging from 36 to 1081 kg ha⁻¹, with an overall mean of 364 kg ha⁻¹. Canopy cover varied from 0 to 90% across all plots. Shade trees stored an average of 43.5 Mg ha⁻¹ of carbon in their above-ground biomass. The total mean carbon stock—including above-ground, below-ground, and soil organic carbon to 30 cm depth—was 138.8 Mg ha⁻¹. Medium canopy cover levels supported the highest yields. In contrast, both low (< 25%) and high (> 70%) canopy covers resulted in significant yield reductions. The agroforestry systems with the highest above-ground carbon stocks were associated with dense canopy covers (> 70%) and reduced coffee yields, highlighting a trade-off between maximizing carbon sequestration and maximizing coffee yields.