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作 者:李娟[1] 章明清[1] 孔庆波[1] 姚宝全 沈金泉
机构地区:[1]福建省农业科学院土壤肥料研究所,福建福州350013 [2]福建省农田建设与土壤肥料技术总站,福建福州350003
出 处:《土壤通报》2012年第5期1156-1161,共6页Chinese Journal of Soil Science
基 金:国际植物营养研究所(IPNI)合作项目(Fujian-13);国家测土配方施肥项目(2005-2010);福建省科技厅重点项目(2008Y0023)
摘 要:应用"3414"设计的田间肥料试验结果,探讨蔬菜氮磷钾三元肥效模型的Monte Carlo建模法。莴苣试验结果表明,采用最小二乘法回归建模,NP、NK二元肥效模型属于非典型式,PK二元和NPK三元肥效模型属于典型式;而改用MonteCarlo建模法,NP、NK和PK二元肥效模型以及NPK三元肥效模型均属于典型式。采用最小二乘法回归建模,不同蔬菜作物建立的34个三元肥效模型的典型式出现机率为41.2%,而Monte Carlo建模法的典型式出现机率为91.2%,提高了2.2倍。Monte Carlo建模法是适当放弃数学上偏差平方和最小的最优性,使待估参数达到专业上最优而数学上较优,从而提高典型肥效模型的出现机率。对莴苣氮钾非典型肥效模型的推荐施肥表明,Monte Carlo法的结果明显优于产量频率分析法。因此,Monte Carlo建模法为建立蔬菜多元肥效模型和推荐施肥提供了一种有效方法。This paper applied field NPK experimental results of vegetables using "3414" design to study Monte Carlo modeling method of NPK fertilizers response model. Experimental result of lettuce showed that NP and NK duality fertilizer response models belonged to non-representative model, PK and NPK multivariate fertilizer response models belonged to representative model, using least square method to regression modeling. But NP and NK and PK and NPK multivariate fertilizer response models belonged to representative models using Monte Carlo modeling. About 34 three-nutrient second degree polynomial models are developed using the least square method. Among these models, the representative fertilizer response models only account 41.2%. While the rates increased to 91.2% by using the Monte Carlo method to estimate the model parameters. Compared to the least square method, the Monte Carlo method may obtain the best parameters in plant nutrition and fitting sum of square in error mathematically by abandoning properly best character in sum of square in error in the least square method. Therefore, the rates wase increased. As a case in this study, there was a non-representative NK duality model which adopts the least squares method to estimate their parameters. Fertilization recommendation results were obvious better than those of yield frequency analysis method. Therefore, the Monte Carlo method could provide a new technique to estimate parameters and fertilization recommendation for multivariate fertilizer response models.
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