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Liu B.,Chinese Academy of Sciences | Chen B.,Hainan Agricultural Reclamation Academy of science | Zhang J.,Chinese Academy of Sciences | Wang P.,Wenzhou University | Feng G.,Chinese Academy of Sciences
Toxicological and Environmental Chemistry | Year: 2016

In this study, the environmental fate of thymol, including hydrolysis, aqueous photolysis, soil sorption and soil degradation, was studied under conditions that simulated the tropical agricultural environment. This study was undertaken to supply basic information for evaluating the environmental risks of applying this new botanical pesticide to tropical crop production. The results showed that the hydrolysis of thymol was pH-dependent and accelerated by acidic conditions and high temperatures. However, the hydrolysis rate was far lower than the aqueous photolysis rate, indicating that direct photolysis is an important dissipation pathway for thymol in water. The sorption of thymol by three tropical soils was consistently well described by the Freundlich model, and the sorption coefficients increased in the order sandy soil < loamy soil < clay soil, a characterization that depended on the organic carbon contents of the soil. The soil degradation rate of thymol decreased in the order sandy soil > loamy soil > clay soil, which has a negative correlation with the sorption of thymol in soils. We concluded that the degradation rates of thymol in tropical soil and water are fast: thymol in water is photodegraded (50%) by sunlight within 28 h, and the thymol in soils is degraded (50%) within 8.4 d. Therefore, the environmental risk to the surrounding soils and water of thymol application for tropical crop production is low. © 2016 Informa UK Limited, trading as Taylor & Francis Group


Kong H.Z.,Hainan University | Jiang J.S.,Hainan Agricultural Reclamation Academy of science | Peng Z.B.,Hainan Agricultural Reclamation Academy of science | Zhou Y.J.,Hainan Agricultural Reclamation Academy of science
Applied Mechanics and Materials | Year: 2014

In this paper, we take secondary forest, orchard, and woodland soils of rubber in different planting years as a research subject and analyze the influence of different land use on soil organic matter. The results show that land use has significant influence on soil organic matter components (p <0.01). We conducted a survey and sampling on 10 age classes of Hainan Dongfang Daguangba (3, 8, 13, 18,23,29,33,35,38,42 years old) rubber plantation plots' soil layer (0 cm-20 cm, 20 cm-40 cm), and conducted in-house testing analysis of its organic matter content, and achieved preliminary exploration that soil organic matter content of different land use patterns in Dongfang City in Hainan: secondary forest> orchard> rubber plantation. These differences are mainly due to the litter under different tillage quantity, quality and variety of management measures. While orchards and rubber plantation have used different tillage method, as a plantation by human, it was greatly influenced by human. © (2014) Trans Tech Publications, Switzerland.


Guo P.-T.,Chinese Academy of Sciences | Li M.-F.,Southwest University | Luo W.,Chinese Academy of Sciences | Tang Q.-F.,Hainan Agricultural Reclamation Academy of science | And 2 more authors.
Geoderma | Year: 2015

Soil organic matter (SOM) plays an important role in soil fertility and C cycle. Detailed information about the spatial distribution of SOM is vital to effective management of soil fertility and better understanding of the process of C cycle. To date, however, few studies have been carried out to digitally map the spatial variation of SOM for rubber (Hevea brasiliensis) plantation at the regional scale in Hainan Island, China. In this study, a hybrid approach, random forest plus residuals kriging (RFRK), was proposed to predict and map the spatial pattern of SOM for the rubber plantation. A total of 2511 topsoil (0-20cm) samples were extracted from a soil fertility survey data set of the Danzhou County. These soil samples were randomly divided into calibration dataset (1757 soil samples) and validation dataset (754 soil samples). In this study, stepwise linear regression (SLR), random forest (RF), and random forest plus residuals kriging (RFRK) were used to predict and map the spatial distribution of SOM for the rubber plantation, while generalized additive mixed model (GAMM) and classification and regression tree (CART) were employed to uncover relationships between SOM and environmental variables and further to identify the main factors influencing SOM variation. The RFRK model was developed to predict spatial variability of SOM on the basis of terrain attributes, geological units, climate factors, and vegetation index. Performance of RFRK was compared with SLR. Mean error (ME), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) were selected as comparison criteria. Results showed that RFRK performed much better than SLR in predicting and mapping the spatial distribution of SOM for the rubber plantation. The RFRK model had much lower prediction errors (ME, MAE, and RMSE) and higher R2 than SLR. Values of ME, MAE, RMSE, and R2 were 0.26g/kg, 1.35g/kg, 2.19g/kg, and 0.86 for RFRK model, and were 0.65g/kg, 2.99g/kg, 4.37g/kg, and 0.43 for SLR equation, respectively. Moreover, RFRK model yielded a more realistic spatial distribution of SOM than SLR equation. The good performance of RFRK model could be ascribed to its capabilities of dealing with non-linear and hierarchical relationships between SOM and environmental variables and of accounting for unexplained information in the random forest (RF) model residuals. These results suggested that RFRK was a promising approach in predicting spatial distribution of SOM for rubber plantation at regional scale. © 2014 Elsevier B.V.


Guo P.,Chinese Academy of Sciences | Li M.,Chinese Academy of Sciences | Luo W.,Chinese Academy of Sciences | Lin Q.,Chinese Academy of Sciences | And 2 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2015

Soil total nitrogen (STN) plays an important role in soil fertility and N cycle. Detailed information about the spatial distribution of STN is vital to effective management of soil fertility and better understanding of the process of N cycle. To date, however, few studies have been conducted to digitally map the spatial variation of STN for rubber (Hevea brasiliensis) plantation at the regional scale in Hainan Island, China. In this study, a relatively new method, random forest (RF) was proposed to predict and map the spatial pattern of STN for the rubber plantation. A total of 2511 topsoil (0-20 cm) samples were collected, and their STN contents were measured. Then these soil samples were randomly divided into calibration dataset (1757 soil samples) and validation dataset (754 soil samples). Fourteen environmental variables were also collected. They are parent materials, mean precipitation, mean temperature, mean normalized difference vegetation index, elevation, slope, aspect, horizontal curvature, profile curvature, relief, convergence index, relative position index, stream power index, and topographic wetness index. In this study, stepwise linear regression (SLR), generalized additive mixed model (GAMM), classification and regression tree (CART), and random forest (RF) were used to predict and map the spatial distribution of STN for the rubber plantation. In addition, GAMM and CART were also employed to uncover relationships between STN and environmental variables and further to identify the main factors influencing STN variation. The RF model was developed to predict spatial variability of STN on the basis of parent materials, mean precipitation, mean temperature, and mean normalized difference vegetation index. Performance of RF was compared with SLR, GAMM, and CART. Mean error (ME), mean absolute error (MAE), root mean squared error (RMSE), and correlation coefficient between measured STN and predicted STN were selected as comparison criteria. Results showed that RF performed much better than SLR, GAMM, and CART in predicting and mapping the spatial distribution of STN for the rubber plantation at regional scale in this study. The RF model had much higher correlation coefficient value and lower prediction errors (ME, MAE, and RMSE) than SLR, GAMM, and CART. Values of correlation coefficient, ME, MAE, and RMSE were 0.82,-0.003 g/kg, 0.088 g/kg, and 0.131 g/kg, 0.69, 0.003 g/kg, 0.121 g/kg, and 0.162 g/kg, 0.70,-0.004 g/kg, 0.120 g/kg, and 0.160 g/kg, and 0.68,-0.008 g/kg, 0.121 g/kg, 0.163 g/kg for RF, CART, GAMM, and SLR equation, respectively. Moreover, RF model yielded a more realistic spatial distribution of STN than SLR, GAMM, and CART equations. Finally, results of CART and GAMM showed that the relationships between STN and selected environmental variables (parent materials, mean precipitation, mean temperature, and mean normalized difference vegetation index) were hierarchical and non-linear in this study area. Analysis of variable importance indicated that parent materials and mean precipitation were the most important factors influencing spatial distribution of STN for rubber plantation at regional scale in this study. Overall, the good performance of RF model could be ascribed to its good capabilities of dealing with non-linear and hierarchical relationships between STN and environmental variables. These results suggested that RF is a promising approach in predicting spatial distribution of STN for rubber plantation at regional scale, and can be applied to predict other soil properties in regions with complex soil-environmental relationships. ©, 2015, Chinese Society of Agricultural Engineering. All right reserved.


Guo P.,Chinese Academy of Sciences | Li M.,Chinese Academy of Sciences | Lin Z.,Chinese Academy of Sciences | Luo W.,Chinese Academy of Sciences | And 2 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2014

A rubber planation of the state farm in Hainan Island, China is traditionally managed with unified soil practices (e.g. fertilization, cultivation). This inevitably results in an inefficient use of resources since it ignores soil heterogeneity (e.g. variability in soil fertility and environmental conditions) of the rubber plantations. Soil management zones can be used to overcome the limitations above of the uniform soil management practices. However, studies on soil management zones are mainly carried out at the field scale. Additionally, data of some soil properties used to delineate soil management zones are difficult or expensive to acquire. This study selected easily available environmental variables and aimed to evaluate their validity in delineating soil management zones of rubber plantation at regional scale. Four types of environmental variables, including terrain attributes (elevation, slope, and aspect), parent materials, climate factors (precipitation and temperature) and vegetation index (normalized difference vegetation index), were selected as data source, and principal component analysis as well as fuzzy-C means clustering algorithm were applied to delineate soil management zones for a rubber plantation with an area of approximately 26000 ha. Two indices, fuzzy performance index (FPI), and normalized classification entropy (NCE) were used as criterion to determine the optimal number of soil management zones. Results showed that the optimal number of soil management zones for the rubber plantation was three. To test the validity of the soil management zones, 486 soil samples were collected and analyzed for 12 soil properties including pH, soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP), available potassium (AK), exchangeable calcium (Ca), exchangeable magnesium (Mg), available sulfur (S), available copper (Cu), available ferrum (Fe), available manganese (Mn), and available zinc (Zn). One-way analysis of variance was employed to test the difference in the soil properties and environmental variables among the three soil management zones. Statistically significant differences in selected soil properties (except Zn) and environmental variables were found among the three management zones. In addition, the mean coefficients of variation (C.V.) of the soil properties and the environmental variables in the three management zones were much lower than that obtained before the management zones were applied to the rubber plantation. The results above verified that easily available environmental variables could be used to delineate soil management zones for rubber plantation at the regional scale. Further, the management practices corresponding to the characteristics of each zone should be adopted to improve the soil management efficiency of the rubber plantation.

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