Search Results (13)

Developing a more productive, resource-efficient, and climate-smart rubber agroforestry model is essential for the sustainable growth of natural rubber cultivation. In this study, we evaluated whether a double-row rubber plantation intercropped with the medicinal crop Ficus hirta Vahl. (DR-F) could achieve this goal, using a single-row rubber plantation (SR) as the control. We assessed the feasibility of the DR-F system based on productivity, solar utilization efficiency (SUE), partial factor productivity of applied nitrogen (PFPN), carbon efficiency (CE), net ecosystem carbon balance (NECB), and carbon footprint (CF). No significant difference was observed in rubber tree biomass between the DR-F (10.49 t·ha⁻¹) and SR (8.49 t·ha⁻¹) systems. However, the DR-F system exhibited significantly higher total biomass productivity (23.34 t·ha⁻¹) than the SR systems due to the substantial contribution from intercropped Ficus hirta Vahl., which yielded 12.84 t·ha⁻¹(p < 0.05). The root fresh weight yield of Ficus hirta Vahl. reached 17.55 t·ha⁻¹, generating an additional profit of 20,417 CNY ha⁻¹. The DR-F system also exhibited higher solar radiation interception and greater availability of soil nutrients. Notably, the roots of rubber trees and Ficus hirta Vahl. did not overlap at a 4 m distance from the rubber trees. The DR-F system achieved higher SUE (0.64%), PFPN (51.40 kg·kg⁻¹ N), and CE (6.93 kg·kg⁻¹ C) than the SR system, with the SUE and PFPN differences being statistically significant (p < 0.05). Although the NECB remained unaffected, the DR-F system demonstrated significantly higher productivity and a substantially lower CF (0.33 kg CO₂·kg⁻¹, a 56% reduction; p < 0.05). In conclusion, the DR-F system represents a more sustainable and beneficial agroforestry approach, offering improved productivity, greater resource use efficiency, and reduced environmental impact. Full article

The rubber industry is facing major socio-economic and environmental constraints. Rubber-based agroforestry systems represent a more sustainable solution through the diversification of income and the provision of greater ecosystem services than monoculture plantations. Participative approaches are known for their ability to co-construct solutions with stakeholders and to promote a positive impact on smallholders. This study therefore implemented a participatory research process with stakeholders in the natural rubber sector for the purpose of improving inclusion, relevance and impact. Facilitation training sessions were first organised with academic actors to prepare participatory workshops. A working group of stakeholder representatives was set up and participated in these workshops to share a common representation of the value chain and to identify problems and solutions for the sector in Indonesia. By fostering collective intelligence and systems thinking, the process is aimed at enabling the development of adaptive technical solutions and building capacity across the sector for future government replanting programmes. The resulting adaptive technical packages were then detailed and objectified by the academic consortium and are part of a participatory plant breeding approach adapted to the natural rubber industry. On-station and on-farm experimental plans have been set up to facilitate the drafting of projects for setting up field trials based on these outcomes. Research played a dual role as both knowledge provider and facilitator, guiding a co-learning process rooted in social inclusion, equity and ecological resilience. The initiative highlighted the potential of rubber cultivation to contribute to climate change mitigation and food sovereignty, provided that it can adapt through sustainable practices like agroforestry. Continued political and financial support is essential to sustain and scale these innovations. Full article

Agroforestry ecosystems are an efficient strategy for enhancing soil nutrient conditions and sustainable agricultural development. Soil extracellular enzymes (EEAs) are important drivers of biogeochemical processes. However, changes in EEAs and chemometrics in rubber-based agroforestry systems and their mechanisms of action are still not fully understood. Distribution of EEAs, enzymatic stoichiometry, and microbial nutrient limitation characteristics of rubber plantations under seven planting patterns (RM, rubber monoculture system; AOM, Hevea brasiliensis-Alpinia oxyphylla Miq; PAR, Hevea brasiliensis-Pandanus amaryllifolius Roxb; AKH, Hevea brasiliensis-Alpinia katsumadai Hayata; CAA, Hevea brasiliensis-Coffea Arabica; CCA, Hevea brasiliensis-Cinnamomum cassia (L.) D. Don, and TCA, Hevea brasiliensis-Theobroma Cacao) were analyzed to investigate the metabolic limitations of microorganisms and to identify the primary determinants that restrict nutrient limitation. Compared with rubber monoculture systems, agroforestry ecosystems show increased carbon (C)-acquiring enzyme (EEA_C), nitrogen (N)-acquiring enzyme (EEA_N), and phosphorus (P)-acquiring enzyme (EEA_P) activities. The ecoenzymatic stoichiometry model demonstrated that all seven plantation patterns experienced C and N limitation. Compared to the rubber monoculture system, all agroforestry systems exacerbated the microbial limitations of C and N by reducing the vector angle and increasing vector length. P limitation was not detected in any plantation pattern. In agroforestry systems, progression from herbs to shrubs to trees through intercropping results in a reduction in soil microbial nutrient constraints. This is primarily because of the accumulation of litter and root biomass in tree-based systems, which enhances the soil nutrient content (e.g., soil organic carbon, total nitrogen, total phosphorus, and ammonium nitrogen) and accessibility. Conversely, as soil depth increased, microbial nutrient limitations tended to become more pronounced. Partial least squares path modelling (PLS-PM) indicated that nutrient ratios and soil total nutrient content were the most important factors influencing microbial C limitation (−0.46 and 0.40) and N limitation (−0.30 and −0.42). This study presented novel evidence regarding the constraints and drivers of soil microbial metabolism in rubber agroforestry systems. Considering the constraints of soil nutrients and microbial metabolism, intercropping of rubber trees with arboreal species is recommended over that of herbaceous species to better suit the soil environment of rubber plantation areas on Hainan Island. Full article

Agroforestry is often seen as a sustainable land-use system for agricultural production providing ecosystem services. Intercropping with food crops leads to equal or higher productivity than monoculture and results in food production for industry and subsistence. Low rubber price and low labor productivity in smallholdings have led to a dramatic conversion of rubber plantations to more profitable crops. The literature analysis performed in this paper aimed at better understanding the ins and outs that could make rubber-based agroforestry more attractive for farmers. A comprehensive search of references was conducted in March 2023 using several international databases and search engines. A Zotero library was set up consisting of 415 scientific references. Each reference was carefully read and tagged in several categories: cropping system, country, main tree species, intercrop type, intercrop product, level of product use, discipline of the study, research topic, and intercrop species. Of the 232 journal articles, 141 studies were carried out on rubber agroforestry. Since 2011, the number of studies per year has increased. Studies on rubber-based agroforestry systems are performed in most rubber-producing countries, in particular in Indonesia, Thailand, China, and Brazil. These studies focus more or less equally on perennials (forest species and fruit trees), annual intercrops, and mixed plantations. Of the 47 annual crops associated with rubber in the literature, 20 studies dealt with rice, maize, banana, and cassava. Agronomy is the main discipline in the literature followed by socio-economy and then ecology. Only four papers are devoted to plant physiology and breeding. The Discussion Section has attempted to analyze the evolution of rubber agroforestry research, progress in the selection of food crop varieties adapted to agroforestry systems, and to draw some recommendations for rubber-based agroforestry systems associated with food crops. Full article

Agroforestry is known to significantly improve long-term land productivity, potentially enhancing the ability to cope with climate stress. However, there is limited information regarding the accurate monitoring of greenhouse gases (GHGs) in rubber-based agroforestry systems. Before GHGs can be accurately estimated, the diurnal variations and suitable sampling times must be studied to reduce the uncertainty of the manual static chamber method. In this study, the soil GHGs emitted from conventional single-row (SR) and improved double-row (DR) rubber plantations were compared across the dry and wet seasons in Hainan, China. A total of 1728 GHG samples from a field trial were collected, analyzed, and related to environmental factors. The results demonstrated that the diurnal fluxes of CO₂ in rubber plantations were likely to remain fluctuating, with the maximum typically occurring during the night-time and daytime hours of the dry and wet seasons, respectively. A clearer double-peak (around 2:00 and 14:00) during the dry season and a daytime peak (14:00) during the wet season of the N₂O were recorded. In addition to the commonalities, different seasons and different types of GHGs and rubber plantations also differed in their detailed fluctuation times and ranges; therefore, the determination of suitable sampling times should not ignore these factors in certain cases. Based on this study, it was determined that the late afternoon (16:00–18:00) was the suitable sampling time of soil GHGs in rubber plantations, instead of the most common morning times (with an underestimation of 25% on average). In addition, the air humidity during the dry season and the soil temperature during the wet season were both positively correlated with GHGs (p < 0.05). This study highlights the significance of accurately monitoring soil GHGs in rubber-based agroforestry systems, providing a basic reference for the development and management of climate-smart land use practices in rubber plantations. Full article

Rubber tree is one of the essential tropical economic crops, and rubber tree powdery mildew (PM) is the most damaging disease to the growth of rubber trees. Accurate and timely detection of PM is the key to preventing the large-scale spread of PM. Recently, unmanned aerial vehicle (UAV) remote sensing technology has been widely used in the field of agroforestry. The objective of this study was to establish a method for identifying rubber trees infected or uninfected by PM using UAV-based multispectral images. We resampled the original multispectral image with 3.4 cm spatial resolution to multispectral images with different spatial resolutions (7 cm, 14 cm, and 30 cm) using the nearest neighbor method, extracted 22 vegetation index features and 40 texture features to construct the initial feature space, and then used the SPA, ReliefF, and Boruta–SHAP algorithms to optimize the feature space. Finally, a rubber tree PM monitoring model was constructed based on the optimized features as input combined with KNN, RF, and SVM algorithms. The results show that the simulation of images with different spatial resolutions indicates that, with resolutions higher than 7 cm, a promising classification result (>90%) is achieved in all feature sets and three optimized feature subsets, in which the 3.4 cm resolution is the highest and better than 7 cm, 14 cm, and 30 cm. Meanwhile, the best classification accuracy was achieved by combining the Boruta–SHAP optimized feature subset and SVM model, which were 98.16%, 96.32%, 95.71%, and 88.34% at 3.4 cm, 7 cm, 14 cm, and 30 cm resolutions, respectively. Compared with SPA–SVM and ReliefF–SVM, the classification accuracy was improved by 6.14%, 5.52%, 12.89%, and 9.2% and 1.84%, 0.61%, 1.23%, and 6.13%, respectively. This study’s results will guide rubber tree plantation management and PM monitoring. Full article

Natural rubber is an important and strategic raw material, used in tires, gloves, and insulating products, that is mainly obtained by cutting the bark of rubber trees. However, the complex contour curve of the rubber tree trunk is hard to fit using a tapping machine. Thus, a trunk contour curve collection would be useful for the development of tapping machines. In this study, an acquisition system based on laser-ranging technology was proposed to collect the point cloud data of rubber tree trunks, and a B-spline fitting program was compiled in Matrix Laboratory (MATLAB) to extract the trunks’ contour curves. The acquisition system is composed of power, a controller, a driver, a laser range finder, and data transmission modules. An automatic extraction experiment on the contour curves of rubber tree trunks was carried out to verify the feasibility and accuracy of using the acquisition system. The results showed that the degree of rubber tree trunk characteristic recognition reached 94.67%, which means that the successful extraction of the rubber tree trunk contour curves and the B-spline fitting program are suitable for the extraction of irregular curves of rubber tree trunks. The coefficient of variation of repeated collection was 0.04%, which indicates that changes in relative positions and acquisition directions have little influence on the extraction and the accuracy of the acquisition system, which are high and stable. Therefore, it was unnecessary to adjust the position of the acquisition device before the collecting process, which helped to improve the efficiency of acquisition considerably. The acquisition system proposed in this study is meaningful to the practical production and application of agroforestry and can not only improve the precision of the rubber tapping process by combining with an automatic rubber tapping machine but can also provide technical support for the prediction of rubber wood volume and the development of ring-cutting equipment for other fruit trees. Full article

Agroforestry systems (AFS) offer viable solutions for climate change because of the aboveground biomass (AGB) that is maintained by the tree component. Therefore, spatially explicit estimation of their AGB is crucial for reporting emission reduction efforts, which can be enabled using remote sensing (RS) data and methods. However, multiple factors including the spatial distributions within the AFS, their structure, their composition, and their variable extents hinder an accurate RS-assisted estimation of the AGB across AFS. The aim of this study is to (i) evaluate the potential of spaceborne optical, SAR and LiDAR data for AGB estimations in AFS and (ii) estimate the AGB of different AFS in various climatic regions. The study was carried out in three climatic regions covering Côte d’Ivoire and Burkina Faso. Two AGB reference data sources were assessed: (i) AGB estimations derived from field measurements using allometric equations and (ii) AGB predictions from the GEDI level 4A (L4A) product. Vegetation indices and texture parameters were generated from optical (Sentinel-2) and SAR data (Sentinel-1 and ALOS-2) respectively and were used as predictors. Machine learning regression models were trained and evaluated by means of the coefficient of determination (R²) and the RMSE. It was found that the prediction error was reduced by 31.2% after the stratification based on the climatic conditions. For the AGB prediction, the combination of random forest algorithm and Sentinel-1 and -2 data returned the best score. The GEDI L4A product was applicable only in the Guineo-Congolian region, but the prediction error was approx. nine times higher than the ground truth. Moreover, the AGB level varied across AFS including cocoa (7.51 ± 0.6 Mg ha⁻¹) and rubber (7.33 ± 0.33 Mg ha⁻¹) in the Guineo-Congolian region, cashew (13.78 ± 0.98 Mg ha⁻¹) and mango (12.82 ± 0.65 Mg ha⁻¹) in the Guinean region. The AFS farms in the Sudanian region showed the highest AGB level (6.59 to 82.11 Mg ha⁻¹). AGB in an AFS was mainly determined by the diameter (R² = 0.45), the height (R² = 0.13) and the tree density (R² = 0.10). Nevertheless, RS-based estimation of AGB remain challenging because of the spectral similarities between AFS. Therefore, spatial assessment of the prediction uncertainties should complement AGB maps in AFS. Full article

In recent years, agroforestry has gained increasing attention as an option to simultaneously alleviate poverty, provide ecological benefits, and mitigate climate change. The present study simulates small-scale farmers’ agroforestry adoption decisions to investigate the consequences for livelihoods and the environment over time. To explore the interdependencies between agroforestry adoption, livelihoods, and the environment, an agent-based model adjusted to a case study area in rural Indonesia was implemented. Thereby, the model compares different scenarios, including a climate change scenario. The agroforestry system under investigation consists of an illipe (Shorea stenoptera) rubber (Hevea brasiliensis) mix, which are both locally valued tree species. The simulations reveal that farmers who adopt agroforestry diversify their livelihood portfolio while increasing income. Additionally, the model predicts environmental benefits: enhanced biodiversity and higher carbon sequestration in the landscape. The benefits of agroforestry for livelihoods and nature gain particular importance in the climate change scenario. The results therefore provide policy-makers and practitioners with insights into the dynamic economic and environmental advantages of promoting agroforestry. Full article

Rainforest conversion to woody croplands impacts the carbon cycle via ecophysiological processes such as photosynthesis and autotrophic respiration. Changes in the carbon cycle associated with land-use change can be estimated through Land Surface Models (LSMs). The accuracy of carbon flux estimation in carbon fluxes associated with land-use change has been attributed to uncertainties in the model parameters affecting photosynthetic activity, which is a function of both carboxylation capacity (V_cmax) and electron transport capacity (J_max). In order to reduce such uncertainties for common tropical woody crops and trees, in this study we measured V_cmax25 (V_cmax standardized to 25 °C), J_max25 (J_max standardized to 25 °C) and light-saturated photosynthetic capacity (A_max) of Elaeis guineensis Jacq. (oil palm), Hevea brasiliensis (rubber tree), and two native tree species, Eusideroxylon zwageri and Alstonia scholaris, in a converted landscape in Jambi province (Sumatra, Indonesia) at smallholder plantations. We considered three plantations; a monoculture rubber, a monoculture oil palm, and an agroforestry system (jungle rubber plantation), where rubber trees coexist with some native trees. We performed measurements on leaves at the lower part of the canopy, and used a scaling method based on exponential function to scale up photosynthetic capacity related traits to the top of the canopy. At the lower part of the canopy, we found (i) high V_cmax25 values for H. brasiliensis from monoculture rubber plantation and jungle rubber plantation that was linked to a high area-based leaf nitrogen content, and (ii) low value of A_max for E. guineensis from oil palm plantation that was due to a low value of V_cmax25 and a high value of dark respiration. At the top of the canopy, A_max varied much more than V_cmax25 among different land-use types. We found that photosynthetic capacity declined fastest from the top to the lower part of the canopy in oil palm plantations. We demonstrate that photosynthetic capacity related traits measured at the lower part of the canopy can be successfully scaled up to the top of the canopy. We thus provide helpful new data that can be used to constrain LSMs that simulate land-use change related to rubber and oil palm expansion. Full article

Intercropping the sharp-leaf galangal with the rubber tree could help to improve the sustainability of the rubber tree planting industry. However, our understanding of belowground competition in such agroforestry systems is still limited. Therefore, we used stable isotope methods (i.e., water δ²H and δ¹⁸O and leaf δ¹³C) to investigate plant water-absorbing patterns and water use efficiency (WUE) in a monocultural rubber plantation and in an agroforestry system of rubber trees and sharp-leaf galangal. We also measured leaf carbon (C), nitrogen (N), and phosphorus (P) to evaluate the belowground competition effects on plant nutrient absorption status. Through a Bayesian mixing model, we found that the monocultural rubber trees and the intercropped sharp-leaf galangal absorbed much more surface soil water at a depth of 0–5 cm, while the rubber trees in the agroforestry system absorbed more water from the shallow and middle soil layers at a depth of 5–30 cm. This phenomenon verified the occurrence of plant hydrologic niche segregation, whereas the WUE of rubber trees in this agroforestry system suggested that the competition for water was weak. In addition, the negative correlation between the leaf P concentration of the rubber trees and that of the sharp-leaf galangal demonstrated their competition for soil P resources, but this competition had no obvious effects on the leaf nutrient status of the rubber trees. Therefore, this study verified that the belowground competition between rubber trees and sharp-leaf galangal is weak, and this weak competition may benefit their long-term intercropping. Full article

Increasing agroforestry areas with improper management has produced serious environmental problems, such as soil erosion. It is necessary to rapidly predict the spatial distribution of such erosion risks in a large area, but there is a lack of approaches that are suitable for mountainous regions. The objective of this research was to develop an approach that can effectively employ remotely-sensed and ancillary data, to map soil erosion risks in an agroforestry ecosystem in a mountainous region. This research employed field survey data, soil-type maps, digital elevation model data, weather station data, and Landsat imagery, for extraction of potential variables. It used the random forest approach to identify eight key variables—slope, slope of slope, normalized difference greenness index at leaf-on season, soil organic matter, fractional vegetation at leaf-on season, fractional soil at leaf-off season, precipitation in June, and percent of soil clay—for mapping soil erosion risk distribution in hickory plantations in Western Zhejiang Province, China. The results showed that an overall accuracy of 89.8% was obtained for three levels of soil erosion risk. Approximately one-fourth of hickory plantations were at high-risk, requiring the owners or decision makers to take proper measures to reduce the soil erosion problem. This research provides a new approach to predict soil erosion risk, based on the primary variables that can be extracted directly from remotely-sensed data and ancillary data. This proposed approach will be valuable for other agroforestry and plantations, such as Torreya grandis, eucalyptus, and the rubber tree, that are playing important roles in improving economic conditions for the local farmers but face soil erosion problems. Full article

Many tropical forest landscapes are now complex mosaics of intact forests, recovering forests, tree crops, agroforestry, pasture, and crops. The small patch size of each land cover type contributes to making them difficult to separate using satellite remote sensing data. We used Sentinel-2 data to conduct supervised classifications covering seven classes, including oil palm, rubber, and betel nut plantations in Southern Myanmar, based on an extensive training dataset derived from expert interpretation of WorldView-3 and UAV data. We used a Random Forest classifier with all 13 Sentinel-2 bands, as well as vegetation and texture indices, over an area of 13,330 ha. The median overall accuracy of 1000 iterations was >95% (95.5%–96.0%) against independent test data, even though the tree crop classes appear visually very similar at a 20 m resolution. We conclude that the Sentinel-2 data, which are freely available with very frequent (five day) revisits, are able to differentiate these similar tree crop types. We suspect that this is due to the large number of spectral bands in Sentinel-2 data, indicating great potential for the wider application of Sentinel-2 data for the classification of small land parcels without needing to resort to object-based classification of higher resolution data. Full article