| Production of natural rubber is a typical resource-constrained industry. Natural rubber is of strategic importance in national economy and defense in China. The best management practice (BMP) for natural rubber’s nutrient management is a major pathway to improve its yield and quality. Prior to BMP in nutrient management, the spatial distribution of key soil physical and chemical properties in rubber plantation have to be identified and mapped out. In-field soil sampling is a widely-accepted method to acquire key soil chemical properties and their spatial variations. Hence, a sampling method based on a sound-developed spatial sampling theory is crucial for precision nutrient management. Therefore, we must study the sampling design in the rubber plantation to obtain essential, prior knowledge on BMP for rubber tree management. At present, the conventional soil sampling sites in rubber plantation are in the shrub and ruderal zones, which are usually located in a specific free land between the adjacent rubber planting strips with vegetative growth of controlling nature. Whether it could reliable represent the nutrient level in the rubber plantation is unknown. In this dissertation, we first studied the sampling locations of soil properties in an individual rubber scale. Second, we investigated the optimal soil sampling numbers in the rubber field scale based on the geostatistics methods. Third, we introduced the spatial simulation annealing to optimize the soil sampling design in the rubber field scale. The main results were as follows:(1) The study was conducted around nine selected rubber trees in a typical hilling area of84m2at Yangjiang State-owned Farm, Hainan Province, China. The experimental plot was divided into168equivalent rectangles. The dimension of each rectangle grid for sampling was1m x0.5m. Ordinary Kriging (OK) was employed to interpolate five soil variables (i.e. Total Nitrogen or TN, Organic Matter or OM, Available Phosphorous of AP, Available Potassium or AK and pH) into a0.5m grid cell in the non-sampling locations and delineated spatial distribution for the five soil chemical properties. Results showed that the distributions of soil chemical properties were obviously different. Contour maps of TN, AP and OM variables showed as an island of fertility. Area of the island of TN variable was the biggest, OM was second, and AP came the last and revealed a sharp decrease in the nearby locations. The range for the AK variable was3.36m and its contour map was relatively uniform. Management practices, such as digging fertilization caves and building contour ledges, resulted in high spatial variability of soil chemical properties in rubber tree plantation. The coefficients of variation for the soil chemical properties revealed considerable spatial variability and soil nutrients could be classified into several zones for management purposes.(2) Principal Component Analysis (PCA) was applied to transform original five soil variables into new, uncorrelated variables (axes) called the principal components (PCs), which retain as much as possible information present in the original data. The PCs with eigenvalues larger than1.0were selected for the development of management zone classes. Results showed that fuzzy cluster algorithms could classify the chemical properties in the soil into three zones such as rubber rhizome neck areas, shrub and ruderal zone and the areas around fertilization caves. The conventional soil-sampling sites were in the shrub and ruderal zones, where the soil TN and OM variables were approximate equal to the mean values and AP and AK concentrations were slightly lower than the mean values of interpolation estimations. Soil samples in the shrub and ruderal zones were not disturbed by human activity, therefore the contents of TN and OM variables in this zone could be more reliable for corresponding levels.(3) Based on descriptive statistics, we divided the five soil properties in the rubber plantation into two groups:normality group and abnormality group. We used the OK method to analyze the spatial distribution of TN and AK variables and the sampling sites of the combined nutrients was determined. Results indicated that the optimum sampling site of combined soil TN and AK was determined by distribution of the AK variable. The sampling site of combined soil TN and AK variables were located between the rubber rhizome neck and in the shrub and ruderal zone which was far away the fertilization cave. As the location between the rubber rhizome neck is often affected by some uncertain factors, so the best sampling sites are in the shrub and ruderal zone which was far away the fertilization cave.(4) As soil organic matter content in the rubber plantation was showed as abnormality,we employed nonparametric indicator kriging to analyze the spatial probability distribution of soil TN and OM between the mean±10%relative standard deviation (RSD) respectively and the probability maps of the combined index was presented. The results indicated that the high probability location of the soils,where total nitrogen concentration and organic matter content were between the individual mean±10%RSD, were between the rubber rhizome neck and in the shrub and ruderal zone in the high terrain. As the location between the rubber rhizome neck is often influenced by some uncertain factors, so the best sampling sites should be in the shrub and ruderal zone with the high terrain. The highest probability of combined index of TN and OM variables are located in the middle of the line spacing of rubber tree. The results present a new method to evaluate the reasonable soil sampling site of Latosol for multi-years of growth period of crops planted in hilly region of South China.(5) In the field scale, the study was conducted around231rubber trees covering an area of4200m2. The plot was divided into100equivalent rectangles. The dimension of each rectangle grid for sampling was16m×7m. Results indicated that all the soil properties were with medium spatial variability in the field scale. The coefficient of variability of AP and AK concentrations were double that of TN and OM. Spatial distribution of the soil properties revealed that concentrations of TN, OM and AP were higher in the west than in the eastern areas. Concentrations of AK were displayed as a fertilizer island, which was low in the center and high in the periphery. The optimal sampling numbers of TN, OM, AP and AK variables were9,9,29and32, respectively. Sampling efficiency based on geostatistics was2to5times than the estimated by classical statistics in this study.(6) In this study, we also introduced spatial stimulation annealing to optimize soil sampling design with different constrained conditions in the field scale. If there were no priori variance, no priori sampling points and sample numbers was available, soil sampling design should be optimized based on minimizations of the mean of shortest distrances (MMSD) criterion in the study area. If there had priori variance or priori sampling points in the study area, soil sampling design should be optimized based on minimizations of the mean of kriging estimation variance (MMKEV) criterion or MMSD plus MMKEV criterion. Spatial stimulated annealing could be well applied to optimize soil sampling strategy in those study area, where had constrained area or priori knowledge. |
PDF Full Text Request