Predicting the habitat distribution of rubber plantations with topography, soil, land use, and climatic factors

Comparative evaluation of physicochemical and antimicrobial properties of rubber seed oil from different regions of Bangladesh

In Bangladesh, the annual production of rubber seeds is typically left untapped although the seeds contained a high percentage of oil but underutilized without any value-added utilization. This study aims to evaluate the geographical effect on physicochemical properties, fatty acid composition and the antimicrobial activity of oil extracted from rubber seeds. Seeds were collected from three different regions of Bangladesh and the oil was extracted by the soxhlet method using n-hexane as a solvent. Results demonstrated that the geographical regions have some significant effect on the properties of rubber seed oil (RSO). The physicochemical properties of RSO varied from region to region. For example, the percent of yield, higher heating value, and flash point varied from 50.0 to 50.8 %, 31.8-33.3 kJ/g, and 237-245 °C, respectively. The chemical parameters, such as acid value, iodine value, and hydroxyl value varied from 13.3 to 18.2 mg KOH/g, 132-137 g I2/100g, and 47.7-55.8 mg KOH/g, respectively. Chromatographic analysis showed that RSO mainly contains palmitic, linoleic, linolenic, and stearic acid. Regional variations were also seen in the composition of these fatty acids. Most notably, regardless of the rubber seeds collected from various locations, RSO exhibited inhibitory activity against only gram positive bacteria. The zone of inhibition range for different tested gram positive bacteria was 2.33-11.17 mm irrespective of different RSO samples.

Multi-Decadal Mapping and Climate Modelling Indicates Eastward Rubber Plantation Expansion in India

Automated long-term mapping and climate niche modeling are important for developing adaptation and management strategies for rubber plantations (RP). Landsat imageries at the defoliation and refoliation stages were employed for RP mapping in the Indian state of Tripura. A decision tree classifier was applied to Landsat image-derived vegetation indices (Normalized Difference Vegetation Index and Difference Vegetation Index) for mapping RPs at two-three years intervals from 1990 to 2017. A comparison with actual plantation data indicated more than 91% mapping accuracy, with most RPs able to be identified within six years of plantation, while several patches were detected after six years of plantations. The RP patches identified in 1990 and before 2000 were used for training the Maxent species distribution model, wherein bioclimatic variables for 1960–1990 and 1970–2000 were used as predictor variables, respectively. The model-estimated suitability maps were validated using the successive plantation sites. Moreover, the RPs identified before 2017 and the Shared Socioeconomic Pathways (SSP) climate projections (SSP126 and SSP245) were used to predict the habitat suitability for 2041–2060. The past climatic changes (decrease in temperature and a minor reduction in precipitation) and identified RP patches indicated an eastward expansion in the Indian state of Tripura. The projected increase in temperature and a minor reduction in the driest quarter precipitation will contribute to more energy and sufficient water availability, which may facilitate the further eastward expansion of RPs. Systematic multi-temporal stand age mapping would help to identify less productive RP patches, and accurate monitoring could help to develop improved management practices. In addition, the existing RP patches, their expansion, and the projected habitat suitability maps could benefit resource managers in adapting climate change measures and better landscape management.

Effects of Climate Change on the Carbon Sequestration Potential of Forest Vegetation in Yunnan Province, Southwest China

Ongoing climate changes reportedly affect the potential distribution and carbon sequestration potential (CSP) of forest vegetation. The combined effects of increasing temperature and decreasing precipitation on these features of forest vegetation are poorly understood. In this study, classification and regression tree (CART) models were used to predict the potential distribution and estimate the CSP of forest vegetation in Yunnan Province, Southwest China, under different simulation scenarios. The minimum temperature of the coldest month (TMW) was the main factor limiting the suitable habitat of all forest vegetation types except for warm–temperate coniferous (WTC) forests. When the temperature increased by 1 °C and the precipitation decreased by 20%, the potential distribution area of the 7 forest vegetation types decreased by 12.41% overall. The potential distribution of WTC forests was the least sensitive to temperature increases and precipitation decreases. The CSP of vegetation was higher (1187.69 TgC) under the constant temperature and 10% precipitation decrease scenario than the CSP of vegetation under the 2 °C temperature increase and constant precipitation scenario (647.24 TgC). Specifically, the highest CSP (1337.88 TgC) was observed under the 1 °C temperature increase and 10% precipitation decrease scenario, and the lowest (617.91 TgC) occurred under the constant temperature and 20% precipitation decrease scenario. In summary, the forest vegetation in Yunnan Province has a high CSP under climate change, and the combined effect of increased temperature and decreased precipitation can increase the CSP of forest vegetation in Yunnan Province. This finding is important for improving scientific decision making and policy planning.

The effects of terrain factors on the drainage area threshold: comparison of principal component analysis and correlation analysis

How and to which extent terrain factors affecting the drainage area threshold (DAT) are disputable. This paper uses principal component analysis (PCA) and correlation analysis to study the influence degree of terrain factors on DAT. Firstly, 22 watersheds, locating in the severe soil erosion region (SSER) of Loess Plateau of China, are picked out as the example areas. The purpose of the mean change point method (MCP) to detect the relationship between DAT and gully density (GD) is to get a reasonable DAT. Secondly, nine terrain factors are calculated, and their statistical values are compared and put in the matrix to clear the different effects on DAT. Finally, the effects of statistical eigenvalues of terrain factors on DAT are compared with PCA and the correlation analysis. According to the PCA, the nine terrain factors are summarized into three principal components, which are slope, height variation, and relief factor. By calculating the score weighted by each factor coefficient matrix and eigenvalue, the result states that slope (S), terrain curvatures (K), and surface roughness (SR) are the factors that have great influence on DAT. Meanwhile, the results of correlation analysis indicate that S, SR, and K have exerted a great influence on the DAT, and the significance level was above 0.05. Both the results of PCA and correlation analysis make clear that the slope is the most direct and influential factor affecting DAT, while other factors are more or less related to slope directly and indirectly. The result implies that the vertical variation of terrain has a strong correlation with the slope, and also has a great influence on DAT. This research not only would be of great significance to recognize the mechanism of gully development, but also able to provide a scientific reference for soil and water conservation in the Loess Plateau.