Rubber Plantations In Xishuangbanna Research Articles

Construction of the Ecological Security Pattern in Xishuangbanna Tropical Rainforest Based on Circuit Theory

Urban modernization and economic globalization have led to significant changes in traditional natural landscapes. The unregulated and large-scale expansion of rubber plantations in Xishuangbanna has resulted in water and scenic forests being replaced by rubber forests and the complex rainforest ecosystem being replaced by simple artificial forests. This has resulted in increasingly prominent ecological problems such as soil erosion, regional microclimate changes, and sharp declines in biodiversity. The ecological security pattern is an important way to protect regional ecological sustainability. Taking the tropical rainforest in Xishuangbanna as an example, this study identified ecological sources through the evaluation of the importance of ecosystem services, constructed resistance surfaces through ecological sensitivity evaluation, and used circuit theory to simulate ecological processes in heterogeneous landscapes by calculating “electricity” or “resistance”, thereby identifying ecological corridors and key ecological nodes. The results identified 31 ecological source areas, 65 key ecological corridors, 7 potential ecological corridors, 37 ecological pinch points, and 99 ecological barriers. The overall distribution of ecological sources was scattered, with higher density in the northwest and southeast regions. Ecological corridors were distributed along high mountains, and both ecological sources and corridors were mainly composed of forest land. Based on circuit theory, this study filled the gap in the MCR model’s inability to identify the true width of corridors due to ignoring the randomness of biological migration. It determined the spatial range of ecological corridors and the specific locations of ecological nodes and barriers, providing a reference for solving ecological problems in Xishuangbanna, such as “rainforest fragmentation”.

Analysis of 10-m Sentinel-2 imagery and a re-normalization approach reveals a declining trend in the latest rubber plantations in Xishuangbanna

Natural rubber is one of the four main raw materials of industry and a globally important strategic material. Rubber plantations with agroforestry attributes have undergone rapid growth in the northern part of mainland Southeast Asia (including Xishuangbanna) since the 1990s, leading to a series of eco-environmental effects on biological diversity, climate change, carbon stocks, and hydrological processes. Accurate, detailed, and updated spatial information on rubber plantations, therefore, is fundamental for developing efficient management strategies. Currently, the booming rubber plantations are being experienced new changes in Xishuangbanna. The commonly used coarser spatial resolution satellite data (e.g., 250-m MODIS and/or 30-m Landsat) have limited applications because most plantations are often small and scattered, especially in the mountainous areas. Here, we developed a straightforward and effective re-normalization of red green normalized difference vegetation index (ReNDI) approach based on finer resolution (10-m) Sentinel-2 imagery, and then mapped the first annual 10-m rubber plantations in the entire Xishuangbanna during 2018–2021. Interestingly, rubber plots no longer expanded as rapidly as in past decades and even decreased slightly. The latest area of rubber plantations was 2514.7 km2, a decrease of 31.9 km2 comparing since 2018, with an average overall accuracy and kappa coefficient of four-year reached up to 95.37 % and 0.90, respectively. Among them, Jinghong city and Menghai county increased by 52.4 km2 and 14.8 km2 respectively, and Mengla county decreased by 99.1 km2. The main area of rubber expansion was around the Jinghong city, where rubber was initially planted. More importantly, owing to pursuing increased economic benefits and requirements of environmental protection, a considerable portion of the rubber encroachment has disappeared, particularly near roads and rivers. Our phenology-based ReNDI algorithm not only enriches the remotely-sensed methods for other industrial plantations mapping, but also provides a new chance to understanding the current patterns of rubber plantations (i.e., increase and decrease), which will contributed to rational planting planning and agroforestry management in the future, especially in the tropics.

Determinants of Smallholder Farmers’ Income-Generating Activities in Rubber Monoculture Dominated Region Based on Sustainable Livelihood Framework

The rapid expansion of rubber plantations in Xishuangbanna, China, has severely decreased rubber prices in the last several years. The income loss and other adverse environmental impacts have brought livelihood challenges and opportunities to develop and adopt more diversified and environmental-friendly income-generating activities (IGAs). To better understand the constraints and opportunities for smallholder rubber farmers to sustain or improve their livelihoods, this study investigates which IGAs are applied by local farmers and the factors that determine their adoption, using the sustainable livelihood framework as the theoretical guideline. The qualitative approach is applied using semi-structured interviews and thematic analysis. Five directions of changes in IGAs (i.e., intercropping, other cash crops, livestock raising for marketing, part-time job, and land renting) are identified, and local farmers are categorized into three groups (i.e., rejective, affirmative, and hesitate groups) based on their attitudes towards these IGAs. The results reveal that the main adoption motivations of certain IGAs are closely related to price shock, economic return, information, and extension service. Based on the results, suggestions are provided to enhance farmers’ motivation to make changes and improve the adoption of emerging IGAs with attention given to vulnerability contexts, livelihood assets and institutional assistance.

Estimation of Applicability of Soil Model for Rubber (Hevea brasiliensis) Plantations in Xishuangbanna, Southwest China

Soil water content (SWC) plays a vital role in the process of evapotranspiration (ET) in rubber plantations. To investigate the influence of the ET process on soil water balance in rubber plantations, we measured SWC at depths of 10, 20, 30, 40, 70, 100, 130 cm, measured the characteristics of root density distribution, and estimated the ET and deep percolation at a rubber plantation in Xishuangbanna using the Hydrus-1D model. Our results indicate the Hydrus-1D model can accurate simulate soil water dynamics in the 0–130 cm of rubber plantations with Nash-Sutcliffe Efficiency Coefficient (NSE) was 0.80–0.96, the Root Mean Squared Error (RMSE) was 0.05–0.02, and the Pearson Correlation Coefficient (R) was 0.82–0.97. Obviously, there were seasonal variation characteristics in soil moisture changes in the rubber plantations of Xishuangbanna. The soil water storage (SWS) dropped to its lowest value of 194 mm and reached its maximum value of 504 mm from the dry season to the rainy season. The simulated ET of the rubber plantation was 1166.1 mm. The large uptake and utilization of soil water by rubber plantations in the dry season affects or exacerbates seasonal drought in Xishuangbanna and leads to a shortage of regional water resources.

Integrating Phenological and Geographical Information with Artificial Intelligence Algorithm to Map Rubber Plantations in Xishuangbanna

Most natural rubber trees (Hevea brasiliensis) are grown on plantations, making rubber an important industrial crop. Rubber plantations are also an important source of household income for over 20 million people. The accurate mapping of rubber plantations is important for both local governments and the global market. Remote sensing has been a widely used approach for mapping rubber plantations, typically using optical remote sensing data obtained at the regional scale. Improving the efficiency and accuracy of rubber plantation maps has become a research hotspot in rubber-related literature. To improve the classification efficiency, researchers have combined the phenology, geography, and texture of rubber trees with spectral information. Among these, there are three main classifiers: maximum likelihood, QUEST decision tree, and random forest methods. However, until now, no comparative studies have been conducted for the above three classifiers. Therefore, in this study, we evaluated the mapping accuracy based on these three classifiers, using four kinds of data input: Landsat spectral information, phenology–Landsat spectral information, topography–Landsat spectral information, and phenology–topography–Landsat spectral information. We found that the random forest method had the highest mapping accuracy when compared with the maximum likelihood and QUEST decision tree methods. We also found that adding either phenology or topography could improve the mapping accuracy for rubber plantations. When either phenology or topography were added as parameters within the random forest method, the kappa coefficient increased by 5.5% and 6.2%, respectively, compared to the kappa coefficient for the baseline Landsat spectral band data input. The highest accuracy was obtained from the addition of both phenology–topography–Landsat spectral bands to the random forest method, achieving a kappa coefficient of 97%. We therefore mapped rubber plantations in Xishuangbanna using the random forest method, with the addition of phenology and topography information from 1990–2020. Our results demonstrated the usefulness of integrating phenology and topography for mapping rubber plantations. The machine learning approach showed great potential for accurate regional mapping, particularly by incorporating plant habitat and ecological information. We found that during 1990–2020, the total area of rubber plantations had expanded to over three times their former area, while natural forests had lost 17.2% of their former area.

Monitoring Spatial and Temporal Patterns of Rubber Plantation Dynamics Using Time-Series Landsat Images and Google Earth Engine

Rubber plantation is an important strategic material related to the national economy and people's livelihoods. Up-to-date and accurate rubber plantation maps are critical for monitoring the area and spatial distribution of rubber plantations and assessing their impacts on society, the economy, and the environment. However, existing optical images are greatly limited by frequent cloud cover, which seriously affects the accuracy of rubber plantation area extraction. To overcome this issue, we used dense Landsat time series stacks based on Google Earth Engine, combined phenological features, and applied random forest algorithms to monitor rubber plantations in Xishuangbanna from 1987 to 2020. The results showed that 1 ) the phenological characteristics of rubber plantations in Xishuangbanna indicated that the leaf-off period lasts from late December to mid-February of the following year, while the leaf-on of rubber plantations occurred in other months; 2 ) the overall accuracy and kappa coefficient values ranged from 0.82 to 0.96 and 0.76 to 0.95, respectively, showing that the extraction accuracy of rubber plantation information can meet the accuracy requirement; 3 ) the rubber plantation area in Xishuangbanna increased between 1987 and 2020 from 7.05×104 to 47.78×104 hm2. The peak rubber plantation area occurred in 2015 (49.60×104 hm2) followed by a downward trend; 4 ) spatially, the rubber plantation is mainly distributed in Jinghong City and Mengla County, while less abundant in Menghai County. Overall, this article is expected to contribute to the rapid and accurate mapping of rubber plantations in large-scale applications and analysis.

The potential for biochar application in rubber plantations in Xishuangbanna, Southwest China: a pot trial

Biochar can enhance crop production and sequester carbon, but there have been few studies with tree crops. Rubber plantations cover more than 8 million hectares in Southeast Asia, so we assessed the feasibility of biochar application in these plantations with a pot trial. Rubber seedlings were planted in soil with four concentrations (0, 1.25%, 2.5% and 5%, w/w) of biochar combined with two concentrations of compound fertilizer (0 kg/ha and 300 kg/ha). Soil properties and seedling growth were measured, and a leaching experiment was conducted in the rainy season. Our results show that biochar increased pH, water content (27.4–65.1%), total carbon (25.4–53.6%), nitrate nitrogen, and available phosphorus in the soil, and decreased bulk density (3.2–23.9%). Biochar treatment reduced leaching of ammonium nitrogen and ortho-P. Biochar increased seedling nutrient uptake (C, N, P and K), with 2.5% and 5% biochar showing the largest effects, but seedling biomass was the highest with 1.25%, and declined in 2.5% and 5%. Our results suggest that biochar addition is an effective way to improve rubber plantation soils, sequester more carbon and decrease nutrient leaching, but the optimum application rate under field conditions needs further research.

Mapping rubber plantations in Xishuangbanna, southwest China based on the re-normalization of two Landsat-based vegetation–moisture indices and meteorological data

Information on where rubber plantations are located and when they were established is essential for understanding changes in the regional carbon cycle, biodiversity, hydrology and ecosystem services. Here, we proposed a simple and modified phenology-based method to map rubber plantations and evaluate the effectiveness of this method in Xishuangbanna in southwest China, the second largest area of natural rubber cultivation. Our phenological algorithm is supported by local meteorological data and involves the re-normalization of two Landsat-8 Operational Land Imager-derived vegetation moisture indices (i.e. the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI)). We then applied air temperature data (daily records from 1981 to 2015) and periodic in situ observations of rubber plantations (weekly records from 2017 to 2018) to determine the phenological stages of rubber tree growth with the goal of selecting the most effective Landsat images. The re-normalization algorithm was able to highlight the temporal differences in the vegetation canopy and moisture content of rubber plantations, because rubber trees in Xishuangbanna display unique defoliation-foliation signals during the dry season. The resultant map of rubber plantations showed a high overall accuracy of 92.3% and a Kappa coefficient of 0.846. The developed phenological re-normalization method with meteorological data greatly enriches remote sensing-based approaches for mapping rubber plantations.

A SD-MaxEnt-CA model for simulating the landscape dynamic of natural ecosystem by considering socio-economic and natural impacts

Background climate and socio-economy significantly affect the landscape dynamics of natural ecosystem. And the simulated method to understand the landscape dynamics of natural ecosystems in responding to human and natural effects is increasingly important for ecosystem management. In this study, we integrated system dynamics, MaxEnt and cellular automata models (called as SD-MaxEnt-CA model) to consider climatic effects and human activities during the simulation of natural ecosystems landscape dynamics. In the integrated model, the MaxEnt process was used for the interactive coupling of the top-down SD and bottom-up CA, which improve the model’s ability to accurately acquire the weights of each layer and transition rules during the simulation with the effects of climate changes. This proposed model was applied to simulate the landscape degradation of natural forest caused by the expanding of farmland, tea gardens and rubber plantations in Xishuangbanna from 2000 to 2010, one of the world’s 34 biodiversity hotspots. By the comparison with other algorithms, the MaxEnt achieve a good performance in acquiring the weights of each layer. The spatial consistent between simulated map and actual result reached to 84.2%, indicating the promising grid-to-grid agreement. Proposed model considered the driving of climate changes and socio-economy, which improved the accuracy of simulating the landscape dynamics of natural ecosystems. The method was further used in simulating the landscape dynamics of natural forest in Xishuangbanna from 2010 to 2030 under four different climate scenarios. The variation of conversion area among different climate scenarios is consistent with their response to climate changes. The simulation results demonstrate that the proposed model is effective for the landscape simulation of natural ecosystems.

Monitoring annual dynamics of mature rubber plantations in Xishuangbanna during 1987-2018 using Landsat time series data: A multiple normalization approach

Accurately monitoring rubber plantations dynamics is essential for assessing eco-environmental effects in soil, hydrology and biodiversity especially in the northern edge of the Asian tropics (e.g. Xishuangbanna, China). In this study, a novel phenology-based multiple normalization approach was firstly proposed to annually map rubber plantations between two critical phenological phase (defoliation and foliation) in Xishuangbanna during 1987–2018. It included three key steps, namely (1) Landsat non-visible bands normalization for calculating the Normalized Difference Moisture Index (NDMI) and Normalized Burn Ratio (NBR), (2) normalization of NDMI and NBR for the Normalized Vegetation Index (NVI), and (3) re-normalization of NVIs for the Re-Normalized Vegetation Index (RNVI). The NVI highlighted the temporal differences of NDMI in land surface moisture content and NBR in soil moisture as well as vegetation survival of rubber plantations during the shifting period of defoliation and foliation. The RNVI fully considered the inverse patterns of the NVIs between defoliation and foliation phase. Rubber plantations were featured by negative NVI and RNVI values within the two temporal windows, while the positive NVI and RNVI values or zero stood for other non-rubber land cover types. The average overall accuracy of five-year mature rubber plantations maps (2010, 2013, 2015, 2016, and 2018) was up to 94.7% with the average kappa coefficient of 0.88, showing the great potential of multiple normalization approach. The total area of rubber plantations increased about 5.9 times from 1987 to 2018, showing clear expansion trends from centralization to scattering in Xishuangbanna as well as continuous spread in Sino-Lao (near Luang Namtha) and Sino-Myanmar (near Mongphak of Shan) border regions on the Chinese side. In addition, annual average analysis showed that about 91.4% of rubber plantations were invariably distributed around Jinghong City and Mengla County in the past decades.

Expansion dynamics of deciduous rubber plantations in Xishuangbanna, China during 2000–2010

Monoculture rubber plantations have been replacing tropical rain forests substantially in Southern China and Southeast Asia over the past several decades, which have affected human wellbeing and ecosystem services. However, to the best of our knowledge on the extent of rubber plantation expansion and their stand ages is limited. We tracked the spatiotemporal dynamics of deciduous rubber plantations in Xishuangbanna, the second largest natural rubber production region in China, from 2000 to 2010 using time-series data from the Phased Array type L-band Synthetic Aperture Radar (PALSAR), Landsat, and Moderate Resolution Imaging Spectroradiometer (MODIS). We found that rubber plantations have been expanding across a gradient from the low-elevation plains to the high elevation mountains. The areas of deciduous rubber plantations with stand ages ≤5, 6–10, and ≥11-year old were ~1.2 × 105 ha, ~0.8 × 105 ha, and ~2.9 × 105 ha, respectively. Older rubber plantations were mainly located in low-elevation and species-rich regions (500–900 m) and younger rubber trees were distributed in areas of relative high-elevation with fragile ecosystems. Economic and market factors have driven the expansion of rubber plantations, which is not only a threat to biodiversity and environmental sustainability, but also a trigger for climatic disasters. This study illustrates that the integration of microwave, optical, and thermal data is an effective method for mapping deciduous rubber plantations in tropical mountainous regions and determining their stand ages. Our results demonstrate the spatiotemporal pattern of rubber expansions over the first decade of this century.

Impact of rubber plantation age on erosive potential studied with USLE model

Rubber (Hevea brasiliensis) plantations have a lifespan in the range of 25–40 years. We aimed at assessing soil losses in rubber plantations of different ages (4, 12, 18, 25 and 36 years old) and relating erosion risk to surface cover and fine root density by applying the Universal Soil Loss Equation (USLE) model. We measured runoff and sediment yields in rubber plantations in Xishuangbanna, Southwest China during 1 year. Fine root density, surface cover and understory plant cover were measured monthly. Annual soil loss in different plantations varied from 0.33 to 2.80 t ha−1. Combined annual cover, management and support practice factor CP from the USLE model varied with the growth phase of rubber in the range of 0.006–0.03. Monthly CP factor was related exponentially with the surface cover. Inhibited undergrowth and high decomposition both contributed to decreasing surface cover in mid-age rubber resulting in highest erosion risk phase.

Comparison of Pixel- and Object-Based Approaches in Phenology-Based Rubber Plantation Mapping in Fragmented Landscapes

The increasing expansion of rubber plantations throughout East and Southeast Asia urgently requires improved methods for effective mapping and monitoring. The phenological information from rubber plantations was found effective in rubber mapping. Previous studies have mostly applied rule-pixel-based phenology approaches for rubber plantations mapping, which might result in broken patches in fragmented landscapes. This study introduces a new paradigm by combining phenology information with object-based classification to map fragmented patches of rubber plantations in Xishuangbanna. This research first delineated the time windows of the defoliation and foliation phases of rubber plantations by acquiring the temporal profile and phenological features of rubber plantations and natural forests through the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data. To investigate the ability of finer resolution images at capturing the temporal profile or phenological information, 30 m resolution Landsat image data were used to capture the temporal profile, and a phenology algorithm to separate rubber plantations and natural forests was then defined. The derived phenology algorithm was used by both the object-based and pixel-based classification to investigate whether the object-based approach could improve the mapping accuracy. Whether adding the phenology information to the object-based classification could improve rubber plantation mapping accuracy in mountainous Xishuangbanna was also investigated. This resulted in three approaches: rule-pixel-based phenology, rule-object-based phenology, and nearest-neighbor-object-based phenology. The results showed that the rule-object-based phenology approaches (with overall accuracy 77.5% and Kappa Coefficients of 0.66) and nearest-neighbor-object-based phenology approach (91.0% and 0.86) achieved a higher accuracy than that of the rule-pixel-based phenology approach (72.7% and 0.59). The results proved that (1) object-based approaches could improve the accuracy of rubber plantation mapping compared to the pixel-based approach and (2) incorporating the phenological information from vegetation improved the overall accuracy of the thematic map.

Temporal Changes of Ecosystem Carbon Stocks in Rubber Plantations in Xishuangbanna, Southwest China

Rubber (Hevea brasiliensis) plantations have been expanded rapidly in tropical regions of Southwest China and cover more than 20% of land area in Xishuangbanna. Several studies have addressed changes of soil C stocks in rubber plantations; however, estimates of total ecosystem C stocks and their temporal changes in rubber plantations have been inadequately studied. This study calculated C stocks in soil, biomass, litter, and dry rubber (latex), and estimated the total ecosystem C stocks of rubber plantations in an age sequence of 3–, 7–, 9–, 21–, 27–, and 34-year-old in Xishuangbanna, Southwest China. The total C stocks in rubber plantations ranged from 150.3 to 283.7 Mg C ha−1, with a mean of 213.4 Mg C ha−1. The plantations ≤ 9-year-old had significantly lower (P < 0.05) total C stocks than those older than 9 years. Carbon stocks in soil (mass equivalent) were between 107.1–170.5 Mg C ha−1 and in living biomass were between 2.8–95.7 Mg C ha−1, representing the largest and the second largest C components in a rubber plantation ecosystem. Neglect of dry rubber C stock resulted in an underestimate of 7.8%–14.2% of the total C stock in the rubber plantations older than 9 years, which was much larger than the contribution of litter C stock (less than 2%).

A Mixed Application of Geographically Weighted Regression and Unsupervised Classification for Analyzing Latex Yield Variability in Yunnan, China.

This paper introduces a mixed method approach for analyzing the determinants of natural latex yields and the associated spatial variations and identifying the most suitable regions for producing latex. Geographically Weighted Regressions (GWR) and Iterative Self-Organizing Data Analysis Technique (ISODATA) are jointly applied to the georeferenced data points collected from the rubber plantations in Xishuangbanna (in Yunnan province, south China) and other remotely-sensed spatial data. According to the GWR models, Age of rubber tree, Percent of clay in soil, Elevation, Solar radiation, Population, Distance from road, Distance from stream, Precipitation, and Mean temperature turn out statistically significant, indicating that these are the major determinants shaping latex yields at the prefecture level. However, the signs and magnitudes of the parameter estimates at the aggregate level are different from those at the lower spatial level, and the differences are due to diverse reasons. The ISODATA classifies the landscape into three categories: high, medium, and low potential yields. The map reveals that Mengla County has the majority of land with high potential yield, while Jinghong City and Menghai County show lower potential yield. In short, the mixed method can offer a means of providing greater insights in the prediction of agricultural production.

基于Biome-BGC模型的西双版纳橡胶林碳收支模拟

PDF HTML阅读 XML下载 导出引用 引用提醒 基于Biome-BGC模型的西双版纳橡胶林碳收支模拟 DOI: 10.5846/stxb201606081104 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 中国科学院战略性先导科技专项X（DA05050206） Simulation of carbon budget in rubber plantations in Xishuangbanna based on the Biome-BGC model Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:以西双版纳橡胶适宜种植区（海拔550-600m）的橡胶林（Hevea brasiliensis）为研究对象，应用参数同化后的Biome-BGC模型模拟了1959-2012年橡胶林的碳循环。结果表明，（1）与涡度相关监测结果相比，橡胶林年总初级生产力（Gross Primary Productivity，GPP）、年总呼吸（Total Respiration，Rt）的模拟精度分别为98.37%和90%。由于对年GPP的过低估计和对年Rt的过高估计，年净生态系统交换量（Net Ecosystem Exchange，NEE）的模拟值比实测值低157.35 g C m-2 a-1。但若考虑干胶碳（139 g C m-2 a-1），模拟值与实测值十分接近；（2）橡胶林在模拟进行的前8年里因异养呼吸较高，以碳排放为主，NEE平均约357 g C m-2a-1；之后转为以碳固定为主，NEE平均约-146 g C m-2 a-1；（3）橡胶林在40年的更新周期中可固定碳1835 g C m-2，是一个弱的碳汇。但与热带雨林相同周期固碳6720 g C m-2相比，仍为碳源。以上结果为深入了解橡胶种植对区域碳循环的影响提供了科学依据，建议当地政府一方面要有计划的对老胶林进行更新，以维持当前橡胶林生态系统中的碳平衡；另一方面要注重对热带雨林的保护，从而实现区域经济和生态环境保护的协调发展。 Abstract:The carbon (C) cycle in rubber plantations, which are located in a suitable planting region (altitude 550-600 m) in Xishuangbanna, was simulated using the Biome-BGC model for the period 1959-2012. We obtained the following results. (1) Compared with the actual measurements, the accuracy was 98.37% and 90% for the simulated annual gross primary productivity (GPP) and total respiration (Rt), respectively. Because of an underestimate of GPP and an overestimate of Rt, the simulated annual net ecosystem exchange (NEE) was 157.35 g C m-2 a-1 lower than the measured value. However, the accuracy was greatly enhanced when dry rubber C stock (139 g C m-2 a-1) was taken into account. (2) During the initial 8 years, rubber plantation acted as a C source because of the higher heterotrophic respiration rate, with an average simulated NEE of 357 g C m-2 a-1. Subsequently, however, the balance changed to C sequestration, with an average simulated NEE of -146 g C m-2 a-1. (3) Rubber plantation sequestrated 1 835 g C m-2 during a 40-year rotation length, which is lower than that in tropical rain forests, which fixed 6 720 g C m-2 a-1 during the same period, indicating that rubber plantations established in areas of former tropical rain forest might lead to C release, even during a complete life cycle. These results enabled us to gain a better understanding of the impact of rubber plantation on the local carbon cycle. In order to reach a sustainable balance between the regional economy and environmental protection, it is suggested that the local government should not only regenerate older rubber plantations in a planned way to maintain C balance in the rubber plantation ecosystem, but also pay considerable attention to the protection of tropical rain forests. 参考文献 相似文献 引证文献

How do plants share water sources in a rubber-tea agroforestry system during the pronounced dry season?

Extensive cultivation of rubber plantations in Xishuangbanna in southwest (SW) China has resulted in negative hydrological consequences, particularly drought, during the pronounced dry season. Although rubber-tea agroforestry is regarded as the most successful agroforestry system for improving the sustainability of rubber agriculture and environmental conservation, plant water use patterns and their related interactions have rarely been examined in such systems. How do coexisting plants compete and share water under water deficit remains to be explored. Therefore, we used stable isotope (δD and δ18O) methods to determine the spatial water use patterns of both rubber trees and tea trees in a rubber-tea agroforestry system during the pronounced dry season and explored the movement of soil water in this system. The results of the MixSIAR model (a Bayesian mixing model) indicated that tea trees primarily uptake water from the 5–30cm soil layer (40.3%, on average), and rubber trees primarily uptake water from the 30–80cm soil layer (35.3%, on average) and absorb soil water evenly along slopes during the dry season. These results suggest that rubber trees and tea trees have different but complementary water use patterns. We also observed that the soil of the uphill and downhill tea rows contained much more water; however, the collaborative hydraulic redistribution in the studied agroforestry system could redistribute the soil water along the slope and below the ground well. Therefore, soil drought on terraces can be alleviated during the dry season. Our results confirmed that the tea tree is an appropriate crop for intercropping with rubber trees when considering water sharing and water management and provided a practical analysis of water use benefits from a rubber agroforestry system during drought stress.

Pushing the Limits: The Pattern and Dynamics of Rubber Monoculture Expansion in Xishuangbanna, SW China.

The rapidly growing car industry in China has led to an equally rapid expansion of monoculture rubber in many regions of South East Asia. Xishuangbanna, the second largest rubber planting area in China, located in the Indo-Burma biodiversity hotspot, supplies about 37% of the domestic natural rubber production. There, high income possibilities from rubber drive a dramatic expansion of monoculture plantations which poses a threat to natural forests. For the first time we mapped rubber plantations in and outside protected areas and their net present value for the years 1988, 2002 (Landsat, 30 m resolution) and 2010 (RapidEye, 5 m resolution). The purpose of our study was to better understand the pattern and dynamics of the expansion of rubber plantations in Xishuangbanna, as well as its economic prospects and conservation impacts. We found that 1) the area of rubber plantations was 4.5% of the total area of Xishuangbanna in 1988, 9.9% in 2002, and 22.2% in 2010; 2) rubber monoculture expanded to higher elevations and onto steeper slopes between 1988 and 2010; 3) the proportion of rubber plantations with medium economic potential dropped from 57% between 1988 and 2002 to 47% in 2010, while the proportion of plantations with lower economic potential had increased from 30% to 40%; and 4) nearly 10% of the total area of nature reserves within Xishuangbanna has been converted to rubber monoculture by 2010. On the basis of our findings, we conclude that the rapid expansion of rubber plantations into higher elevations, steeper terrain, and into nature reserves (where most of the remaining forests of Xishuangbanna are located) poses a serious threat to biodiversity and environmental services while not producing the expected economic returns. Therefore, it is essential that local governments develop long-term land use strategies for balancing economic benefits with environmental sustainability, as well as for assisting farmers with the selection of land suitable for rubber production.

Developing indicators of economic value and biodiversity loss for rubber plantations in Xishuangbanna, southwest China: A case study from Menglun township

In Xishuangbanna, southwest China, rubber plantations are lucrative and have expanded dramatically in the past two decades, leaving little natural forest. The local government hopes to use eco-compensation mechanisms to promote forest restoration without forcing smallholders to forego profits. Based on a landscape productivity model for two management systems and given a range of prices, we assessed the feasibility of this concept by constructing a spatially explicit map of net present value (NPV) of rubber plantations. We found that roughly 7% of existing plantations generally had negative NPV, therefore opportunity costs would be negligible. But to restore forest in buffer zones along roads and rivers as recommended by the state government, more substantial opportunity costs were observed because these areas have high NPV values. Additionally, plantations situated above 900m elevation or on slopes>24° were not profitable and a conversion ban should be enforced. Opportunity costs and plant species biodiversity are positively correlated, so any policy to protect species-rich areas must take this relationship into consideration. Eco-compensation schemes can feasibly promote forest restoration on marginal lands and spatially-explicit mapping of NPV can provide monetary targets to adequately recover opportunity costs for smallholders.