Improving a weed population model using a sequential Monte Carlo method
Improving a weed population model using a sequential Monte Carlo method
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DOI：
10.1111/j.1365-3180.2010.00786.x
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发表时间：
2010-08-01
期刊：
影响因子：
1.7
通讯作者：
Munier-Jolain, N.
中科院分区：
文献类型：
--
作者：
Makowski, D.;Chauvel, B.;Munier-Jolain, N.
野外测量和杂草种群模型预测已被提出作为建议是否需要化学或机械处理的基础，但这两种方法都有一定的局限性。本研究展示了序贯蒙特卡罗（SMC）方法如何将杂草数量测量和模型预测相结合，从而更好地估计杂草种群特征。应用SMC建立了一个模拟黑草（Alopecurus myosuroides）杂草密度、种子产量和种子库密度的动态模型。在6年的时间里，通过在7个地块进行的试验，对几种类型的杂草计数数据进行了量化。与初始模型预测相比，SMC将均方根误差（RMSE）降低了33.5-81.5%。与仅由杂草计数得出的杂草密度相比，SMC使RMSE降低了1.2 ~ 10%。SMC优于单一使用模型或杂草计数数据，因为它可以提高杂草密度的预测，因为SMC计算的概率分布可以用来分析系统状态的不确定性。
P>Field measurements and weed population model predictions have been proposed as the basis for recommendations on the need for chemical or mechanical treatment, but both approaches have some limitations. This study shows how a Sequential Monte Carlo (SMC) method can be used to combine weed count measurements and model predictions, to derive a better estimate of weed population characteristics. SMC was applied to a dynamic model simulating weed densities, seed production and seedbank densities for Alopecurus myosuroides (blackgrass). The benefit resulting from SMC was quantified for several types of weed count data, using experiments carried out in seven plots during 6 years. Compared with the initial model predictions, SMC reduced the root mean squared error (RMSE) by 33.5-81.5%. Compared with the weed densities derived from the weed counts alone, SMC reduced the RMSE by 1.2-10%. SMC should be preferred to the single use of model or of weed count data, because it can improve weed density predictions and because the probability distributions computed by SMC can be used to analyse the uncertainty about the state of the system.