Precision agriculture requires many local measurements. Sometimes two measurements are available: a low-cost noisy measurement and an accurate expensive one. For example, soil testing in a laboratory is expensive and accurate. On-the-go pH meters are available, but they are not as accurate. The question addressed here is what is the best way to combine these measures to guide precision applications? The first step is to estimate the joint spatial distribution of the two measures. The joint distribution is estimated using Bayesian Kriging since it can consider the information when the measures are spatially autocorrelated. The second step is to determine the economic optimum of how many of each measure to use. This study obtained the ratio of expensive and accurate measurements by maximizing the expected net present value using Bayesian Decision Theory and a grid search procedure. To demonstrate the method, a harmonization process that uses no spatial information was compared with Bayesian Kriging using Monte Carlo data. A wheat production example was used to parameterize the Monte Carlo simulation. Soil pH lab sampling and on-the-go soil pH sensors were simulated as the two different measurements for soil mapping in wheat fields. Bayesian Kriging led to more accurate soil mapping and a higher expected net present value.

精准农业需要许多地方测量。有时有两种测量方法可用：一种是低成本的噪声测量方法，另一种是准确的昂贵测量方法。例如，在实验室进行土壤测试既昂贵又准确。可以使用移动式 pH 计，但它们不够准确。这里要解决的问题是，结合这些措施来指导精确应用的最佳方式是什么？第一步是估计两个措施的联合空间分布。联合分布是使用贝叶斯克里金法估计的，因为它可以考虑度量空间自相关时的信息。第二步是确定每种措施使用多少的经济最优值。本研究通过使用贝叶斯决策理论和网格搜索程序最大化预期净现值，获得了昂贵和准确测量的比率。为了演示该方法，将不使用空间信息的协调过程与使用蒙特卡罗数据的贝叶斯克里金法进行了比较。小麦生产示例用于参数化蒙特卡罗模拟。土壤 pH 实验室采样和移动土壤 pH 传感器被模拟为麦田土壤测绘的两种不同测量方法。贝叶斯克里金法导致更准确的土壤测绘和更高的预期净现值。小麦生产示例用于参数化蒙特卡罗模拟。土壤 pH 实验室采样和移动土壤 pH 传感器被模拟为麦田土壤测绘的两种不同测量方法。贝叶斯克里金法导致更准确的土壤测绘和更高的预期净现值。小麦生产示例用于参数化蒙特卡罗模拟。土壤 pH 实验室采样和移动土壤 pH 传感器被模拟为麦田土壤测绘的两种不同测量方法。贝叶斯克里金法导致更准确的土壤测绘和更高的预期净现值。