基于空间模拟退火算法的橡胶园土壤取样布局优化

实施合理的养分综合管理是提高橡胶树生产力的重要手段，正确科学的土壤取样方法是橡胶园养分精准管理 成功的关键。应用地统计学方法可以指导土壤属性的取样布局，但其无法解决区域取样约束问题。本研究将空间模拟 退火算法引入到橡胶土壤取样布局中，探讨了无约束条件和约束条件下田块尺度橡胶园土壤取样方案。研究结果表明： 在橡胶园土壤取样过程中，如果研究区域既无先验方差也无早期观测点情况下，在给定一定取样数量情况下可以基于 最小均值距离准则进行优化布局；如果具有早期观测样点或者具有类似区域的先验方差，则可结合先验方差知识和最 小克里格方差准则进行指导取样布局。空间模拟退火算法在处理橡胶园土壤取样区域障碍以及充分利用先验知识方面 有现实指导意义。

Optimization of Spatial Soil Sampling Using Continuous Simulated Annealing in Rubber Plantation

The best nutrient management practice for natural rubber is a major pathway to improve its yield and quality. Prior to best management practice, putting forward feasible soil-sampling schemes to obtain a reliable representative sample is the critical step of successful fertilizer recommendation for rubber trees. Geostatistical methods could improve the quality of sampling schemes, but is unsolvable for the constraints information. In the study, we introduced spatial simulation annealing to optimize soil sampling design with no-constrained and constrained condition in the field scale. If there were no priori variance and no priori sampling, soil sampling design should be optimized based on minimizations of the mean of shouted distances criterion with given a certain number of samples. If there had priori variance or priori sampling points in the study area, soil sampling design should be optimized based on minimization of the mean of kriging estimation variance criterion. Spatial stimulated annealing could be well applied to optimize soil sampling strategy in those study area, where had constrained area or priori knowledge.