基于空间模拟退火算法的橡胶园土壤取样布局优化
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摘要
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。
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.
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.
引文
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[24]王劲峰.空间分析[M].北京:科学出版社,2006.
[25]杨建宇,岳彦利,宋海荣,等.基于空间模拟退火算法的耕地质量布样及优化方法[J].农业工程学报,2015,31(20):253-261.
[26]Lin Y P,Chang T.Simulated annealing and kriging method for identifying the spatial patterns and variability of soil heavy metal[J].Journal of Environmental Science and Health,2000,A35(7):1 089-1 115.
[27]Dong J,Xiao X,Sheldon S,et al.Mapping tropical forests and rubber plantations in complex landscapes by integrating PALSAR and MODIS imagery[J].ISPRS Journal of Photogrammetry and Remote Sensing,2012,74(11):20-33.
[28]Domenech M B,Castro-Franco M,Costa J L,et al.Sampling scheme optimization to map soil depth to petrocalcic horizon at field scale[J].Geoderma,2017,290(75):75-82.
[2]Oliver M A.An overview of geostatistics and precision agriculture[J].Geostatistical Applications for Precision Agriculture,2010,1-34.
[3]Oliver M A,Webster R.A tutorial guide to geostatistics:computing and modelling variograms and kriging[J].Catena,2014,113(2):56-69.
[4]Mcbratney A B,Webster R.How many observations are needed for regional estimation of soil properties?[J].Soil Science,1983,135(3):177-183.
[5]李子忠,龚元石.农田土壤水分和电导率空间变异性及确定其采样数的方法[J].中国农业大学学报,2000,5(5):59-66.
[6]Gupta R K.Spatial variability and sampling strategies for NO3-N,P,and K determinations for site-specific farming[J].Transactions of the Asae,1997,40(2):337-343.
[7]Shi Z,Wang K,Bailey J S,et al.Sampling strategies for mapping soil phosphorus and soil potassium distributions in cool temperate grassland[J].Precision Agriculture,2000,2(4):347-357.
[8]Gao L,Shao M.The interpolation accuracy for seven soil properties at various sampling scales on the Loess Plateau,China[J].Journal of Soils and Sediments,2012,12(2):128-142.
[9]Lin Q H,Li H,Li B G,et al.Assessment of spatial uncertainty for delineating optimal soil sampling sites in rubber tree management using sequential indicator simulation[J].Industrial Crops and Products,2016,91:231-237.
[10]Lin Q,Li H,Luo W,et al.Optimal soil-sampling design for rubber tree management based on fuzzy clustering[J].Forest Ecology and Management,2013,308:214-222.
[11]Kirkpatrick S,Gelatt C D,Vecchi M P.Optimization by simulated annealing[J].Science,1983,220(4 598):671-680.
[12]Deutsch C V.GSLIB geostatistical software library and user's guide[M].New York:Oxford University Press,1992.
[13]Sacks J,Schiller S.Spatial designs[M]//Guptaa S S,Berger JO.Statistical decision theory and related topics IV.New York:Springer,1988:385-399.
[14]van Groenigen J W,Stein A.Constrained optimization of spatial sampling using continuous simulated annealing[J].Journal of Environment Quality,1998,27(5):1 078-1 086.
[15]Barca E,Passarella G,Uricchio V.Optimal extension of the rain gauge monitoring network of the Apulian Regional Consortium for Crop Protection[J].Environmental Monitoring and Assessment,2008,145(1-3):375-386.
[16]Ferreyra R A,Apeztegu??a H P,Sereno R,et al.Reduction of soil water spatial sampling density using scaled semivariograms and simulated annealing[J].Geoderma,2002,110(3-4):265-289.
[17]Scudiero E,Deiana R,Teatini P,et al.Constrained optimization of spatial sampling in salt contaminated coastal farmland using EMI and continuous simulated annealing[J].Procedia Environmental Sciences,2011,7(8):234-239.
[18]Chen B,Pan Y,Wang J,et al.Even sampling designs generation by efficient spatial simulated annealing[J].Mathematical and Computer Modelling,2013,58(3):670-676.
[19]胡山鹰,陈丙珍,何小荣.连续变量问题全局优化的模拟退火法[J].系统工程理论与实践,1995,15(9):73-80.
[20]Christakos G,Olea R A.Sampling design for spatially distributed hydrogeologic and environmental processes[J].Advances in Water Resources,1992,15(4):219-237.
[21]van Groenigen J W,Siderius W,Stein A.Constrained optimisation of soil sampling for minimisation of the kriging variance[J].Geoderma,1999,87(3):239-259.
[22]van Groenigen J W,Stein A.Constrained optimization of spatial sampling in a model-based setting using SANOSsoftware[M]//Heuvelink G B M,Lemmens M J P.Accuracy2000:Proceedings of the 4th International Symposium on Spatial Accuracy Assesment in Nature Resources and Environmental Sciences,Amsterdam,2000.Delft:Delft University Press,2000:679-685.
[23]林清火.海南丘陵区橡胶园土壤养分精准管理取样方法研究[D].北京:中国农业大学,2013.
[24]王劲峰.空间分析[M].北京:科学出版社,2006.
[25]杨建宇,岳彦利,宋海荣,等.基于空间模拟退火算法的耕地质量布样及优化方法[J].农业工程学报,2015,31(20):253-261.
[26]Lin Y P,Chang T.Simulated annealing and kriging method for identifying the spatial patterns and variability of soil heavy metal[J].Journal of Environmental Science and Health,2000,A35(7):1 089-1 115.
[27]Dong J,Xiao X,Sheldon S,et al.Mapping tropical forests and rubber plantations in complex landscapes by integrating PALSAR and MODIS imagery[J].ISPRS Journal of Photogrammetry and Remote Sensing,2012,74(11):20-33.
[28]Domenech M B,Castro-Franco M,Costa J L,et al.Sampling scheme optimization to map soil depth to petrocalcic horizon at field scale[J].Geoderma,2017,290(75):75-82.