Abstract The aim of on-farm research is to identify and test a new technology, product or management practice (e.g. more efficient seeding rate, enhanced row spacing, better disease management treatment, etc.) suited to local conditions by comparing it to a standard farmer practice across several farmers’ fields. Typically, each trial includes two treatments (new practice vs. standard control practice) replicated at least three times in each field. The statistical analysis of yield data collected in such trials provides growers with useful information about the effectiveness of the tested farming practice on crop productivity and its uncertainty. We used a random-effects model to i) estimate the performance of a treatment compared to a control in individual trials, ii) estimate the overall mean yield response across all trials, iii) compute prediction intervals describing a range of plausible yield response for a new (out-of-sample) field at the trial level, and iv) compute the probability that the tested management practice will be ineffective in a new field. We used frequentist (classical) and Bayesian approaches for data collected in 26 on-farm trial categories managed by the Iowa Soybean Association. Depending on the level of between-trial variability, we found that prediction intervals were 2.2–12.1 times larger than confidence intervals for the estimated mean yield responses for all tested management practices. We conclude that prediction intervals should be systematically reported to provide additional information about future trials or experiments with associated uncertainties. Nevertheless, prediction intervals should be interpreted with caution when the between-trial variance is small. Using prediction intervals and, when appropriate, the probability of ineffective treatment will prevent farmers from overoptimistic expectations that a significant effect at the overall population level will lead with high certainty to a yield gain on their own farms.

中文翻译：

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超越平均效应大小：呈现农场网络试验分析的预测区间

摘要 农场研究的目的是通过与标准进行比较，确定和测试适合当地条件的新技术、产品或管理实践（例如，更有效的播种率、增加行距、更好的疾病管理治疗等）。农民在几个农田的实践。通常，每个试验包括在每个领域至少重复 3 次的两个处理（新实践与标准对照实践）。在此类试验中收集的产量数据的统计分析为种植者提供了有用的信息，说明所测试的农业实践对作物生产力的有效性及其不确定性。我们使用随机效应模型来 i) 估计与单个试验中的对照相比处理的性能，ii) 估计所有试验的总体平均产量响应，iii) 计算描述试验水平的新（样本外）油田的一系列合理产量响应的预测区间，以及 iv) 计算经测试的管理实践在新油田中无效的概率。我们对爱荷华州大豆协会管理的 26 个农场试验类别中收集的数据使用频率论（经典）和贝叶斯方法。根据试验间变异性的水平，我们发现预测区间比所有测试管理实践的估计平均产量响应的置信区间大 2.2-12.1 倍。我们得出结论，应该系统地报告预测区间，以提供有关未来试验或具有相关不确定性的实验的额外信息。尽管如此，当试验间方差较小时，应谨慎解释预测区间。使用预测区间以及在适当情况下无效处理的可能性将防止农民过度乐观地预期总体人口水平的显着影响将导致他们自己农场的产量增加具有高度确定性。