For many decades, researchers have relied on small-scale agronomic experiments to provide input management recommendations to farmers. However, such experiments have most often provided production functions with large standard errors in addition to uncertainty about how well the estimates apply to different fields. To avoid such limitations, there has been a movement toward on-farm field experiments where experiments are conducted on the whole field. But questions remain as how best to conduct these on-farm field experiments and when it is most profitable to quit them. This study addressed these questions using a fully Bayesian decision-theoretic approach. Data were from Monte Carlo simulations assuming a linear response stochastic plateau production function from one field. Only uniform rate application was considered. The base model had corn and N prices of $0.116 kg⁻¹ and $0.993 kg⁻¹ and used 100 plots with an experimental design that allocated 10 plots to 0 kg of N, half of current optimal N (N*), and 150% of N*, with 70 using N*. With the base model, it was most profitable to quit such trials in year two. Sensitivity analysis suggested that the optimal strategy was to experiment on fewer plots, use levels of N closer to the optimum, and continue the experiment for longer than was done with the base model. These changes reduced losses from using nonoptimal levels of nitrogen. The assumptions used in the base model, however, might be more economical if plot sizes were small and so a smaller percentage of plots were used for experimentation.

几十年来，研究人员一直依靠小规模农艺实验向农民提供投入管理建议。然而，这些实验通常会提供具有较大标准误差的生产函数，此外还不确定估计值在不同领域的适用程度。为了避免这种限制，已经出现了向在整个田地进行实验的农场田间试验的趋势。但问题仍然是如何最好地进行这些农场现场实验以及何时退出它们最有利可图。本研究使用完全贝叶斯决策理论方法解决了这些问题。数据来自蒙特卡洛模拟，假设一个领域的线性响应随机高原生产函数。仅考虑统一速率应用。⁻¹和 $0.993 kg ⁻¹并使用 100 个样地进行实验设计，将 10 个样地分配给 0 kg N、当前最优 N (N*) 的一半和 N* 的 150%，其中 70 个使用 N*。使用基本模型，在第二年退出此类试验是最有利可图的。敏感性分析表明，最佳策略是在较少的地块上进行试验，使用更接近最佳值的 N 水平，并且比使用基本模型进行的试验持续时间更长。这些变化减少了使用非最佳氮水平造成的损失。然而，如果地块面积较小，则基本模型中使用的假设可能更经济，因此用于实验的地块比例较小。