Assessing the plant nitrogen (N) status is one of the major challenges for improving N fertilization management and reducing its environmental footprint on forage-based agricultural systems. We re-analyzed a dataset of biomass (W) and plant N concentration (%N) of tall fescue (Festuca arundinacea Schreb.) using a Bayesian statistical model for estimating the coefficients of the critical N (%Nc) dilution curve (Nc = A₁ W ^−A2) with two objectives in mind: i) to revise the reference %Nc dilution curve established for tall fescue by Lemaire and Salette (1984), and ii) to analyze how the determination of %Nc curves is affected by the structure of the dataset (number of sampling dates and N rates) along with the range of shoot %N and W achieved. Our analysis suggests that the original tall fescue %Nc curve of Lemaire and Salette (1984) was overestimated. A critical %N curve for tall fescue was obtained using the Bayesian method across 14 unique genotype × environment × management (G × E × M) conditions (Nc = 3.93 W ^-0.42). We show that the high uncertainty associated with the %Nc curve could be reduced by increasing the number of experiments. When a single dataset was used, 95 % credibility intervals (95 % CI) were [2.52, 5.66] for A₁ and [0.06, 0.63] for A₂. However, 95 % CI were reduced up to a 73 % when the critical N dilution curve was based on 14 studies. We also show that a minimum of five studies (+100 data points for our study) are needed to avoid large biases and uncertainty levels in coefficient estimations with the Bayesian method. However, these studies must be carefully designed. The reliability of critical %N curves is greatly reduced when they are estimated with datasets comprising only a few data under non-limiting N conditions or data covering only very low or high W values. Our results suggest that more reliable critical N dilution curves for species can be developed by grouping numerous datasets covering a broad range of G × E × M conditions.

评估植物氮 (N) 状态是改善氮肥管理和减少其对基于草料的农业系统的环境足迹的主要挑战之一。我们使用贝叶斯统计模型重新分析了高羊茅 ( Festuca arundinacea Schreb.)的生物量 (W) 和植物 N 浓度 (%N) 的数据集，以估计临界 N (%Nc) 稀释曲线的系数 (Nc = A ₁ W ^-A2) 考虑两个目标：i) 修订 Lemaire 和 Salette (1984) 为高羊茅建立的参考 %Nc 稀释曲线，以及 ii) 分析数据集结构如何影响 %Nc 曲线的确定（采样日期和 N 率的数量）以及达到的芽 %N 和 W 范围。我们的分析表明，Lemaire 和 Salette (1984) 的原始高羊茅 %Nc 曲线被高估了。使用贝叶斯方法在 14 个独特的基因型 × 环境 × 管理 (G × E × M) 条件 (Nc = 3.93 W ^-0.42 ) 中获得了高羊茅的临界 %N 曲线。我们表明，可以通过增加实验次数来降低与 %Nc 曲线相关的高不确定性。当使用单个数据集时，A 的 95% 可信区间 (95% CI) 为 [2.52, 5.66]₁和 [0.06, 0.63] 对于 A ₂。然而，当临界氮稀释曲线基于 14 项研究时，95% CI 降低至 73%。我们还表明，至少需要五项研究（我们的研究为 +100 个数据点）以避免使用贝叶斯方法进行系数估计时出现大的偏差和不确定性水平。然而，这些研究必须仔细设计。当使用仅包含非限制 N 条件下的少数数据或仅覆盖非常低或高 W 值的数据的数据集来估计临界 %N 曲线时，它们的可靠性会大大降低。我们的结果表明，通过对涵盖广泛 G × E × M 条件的众多数据集进行分组，可以开发出更可靠的物种临界氮稀释曲线。