Abstract As the development of predictive tools to aid decision making in agriculture increases, it must be acknowledged that the ability to incorporate management decisions as input data is limited. The data is either not recorded, or highly inadequate in terms of the volume, variety or relevance. Some circumstances are further hampered by the lack of benchmark data for comparison, and soil compaction is an example of this. The premise of this work was to take a probability based approach to decision making, utilising both qualitative and quantitative data to provide a probability distribution of risk against decisions made, in the context of grains production systems as an example of an agricultural enterprise. A Bayesian Belief Network (BBN) was constructed for soil compaction risk, as an exemplar. The BBN conditional probability tables for nodes were populated via a combination of biophysical model output (namely SoilFlex for soil stress distribution, and the APSIM package for soil-water and crop parameters) and expert opinion. Input nodes were parameterised with measured soil data, and the risk of soil compaction, given the soil stress at the wheel of a particular vehicle, was provided as the output. Potential effect on yield was subsequently calculated on the basis of percent change in soil bulk density, which was determined using literature based information (expert opinion). The tool broadly estimated yield impacts due to various agricultural traffic scenarios, providing means to highlight the financial consequences of failing to adopt controlled traffic farming management for a particular agricultural enterprise. Of significance, the BBN approach was determined useful for data limiting environments where empirical models struggle, thus providing a pragmatic and novel approach to on farm decision making incorporating management nuance.

中文翻译：

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使用定性信息为农场决策提供信息的贝叶斯方法：土壤压实的例子

摘要 随着帮助农业决策制定的预测工具的发展增加，必须承认将管理决策作为输入数据的能力是有限的。数据要么没有记录，要么在数量、种类或相关性方面非常不足。由于缺乏用于比较的基准数据，某些情况进一步受到阻碍，土壤压实就是一个例子。这项工作的前提是采用基于概率的决策方法，利用定性和定量数据提供决策风险的概率分布，以粮食生产系统作为农业企业的例子。作为示例，贝叶斯信念网络 (BBN) 被构建用于土壤压实风险。节点的 BBN 条件概率表是通过生物物理模型输出（即用于土壤应力分布的 SoilFlex，以及用于土壤-水和作物参数的 APSIM 包）和专家意见的组合填充的。输入节点使用测量的土壤数据进行参数化，并提供给定特定车辆车轮上的​​土壤应力的土壤压实风险作为输出。随后根据土壤容重变化百分比计算对产量的潜在影响，这是使用基于文献的信息（专家意见）确定的。该工具广泛地估计了由于各种农业交通情景对产量的影响，提供了强调未能对特定农业企业采用受控交通农业管理的财务后果的方法。意义重大，