Abstract Phosphorus (P) is a nutrient essential to living organisms and ecosystems. Accurate information regarding extractable soil P is necessary for agricultural management and environmental quality. Direct measurements of extractable soil P at large scales are usually impeded by considerable time, labour, and economic resources required for implementation. To meet agronomic and environmental monitoring needs, multiple extraction methods have been developed worldwide to estimate the different components of soil P. In France, three extraction methods are used, namely the Dyer method for acidic soils, Joret-Hebert for calcareous soils, and Olsen for all soils. Therefore, it is difficult to compare data obtained nationwide for monitoring purposes. Consequently, it is of significant importance to develop pedotransfer functions (PTFs) to harmonise extractable soil P data obtained from different extraction methods with the assistance of other easily available predictors from soil information systems. In this study, we used an extensive dataset from the French soil-monitoring programme for the calibration and evaluation of PTFs. We implemented the partial least squares regression to relate extractable P measured by the Dyer or Joret-Hebert method to extractable P determined by the Olsen method considering 14 soil properties (total P2O5, pH, cation exchange capacity (CEC), CaCO3, soil texture (clay, silt and sand contents), total organic carbon, and exchangeable Fe, Al, CaO, Mn, MgO, and K2O). We constructed patrimonial models by selecting the most important predictors. According to the results of 10 iterations cross-validation, the average R2, root mean-square error (RMSE), and mean error (ME) of the PTF of calcareous soils were 0.66, 25.81, and −0.11 mg kg−1, whereas those of acidic soils were 0.70, 24.02, and −0.87 mg kg1, respectively. The Joret-Hebert P2O5, silt, pH, total P2O5, CEC, and K were the most important predictors for estimating Olsen P2O5 in calcareous soils, whereas Dyer P2O5, exchangeable Al, K, and pH were the most important predictors for estimating Olsen P2O5 in acidic soils. We observed that the explanatory power of the soil properties was more important in calcareous than in acidic soils. As expected, the proxies of Olsen P2O5, namely, Dyer P2O5 and Joret-Hebert P2O5, were the most important variables in modelling Olsen P2O5 variations. In addition, the relationship between Olsen P2O5 and Dyer P2O5 was much stronger than that between Olsen P2O5 and Joret-Hebert P2O5. The results confirmed the feasibility of estimating extractable P in soil by PTFs that were constructed using statistical methods, such as partial least squares regression. The addition of more predictors that are related to agricultural practices and topography attributes may improve the prediction accuracy.

中文翻译：

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开发土壤转移函数以协调用不同方法测量的可提取土壤磷含量：法国大陆的案例研究

摘要 磷（P）是生物体和生态系统所必需的营养素。关于可提取土壤磷的准确信息对于农业管理和环境质量是必要的。大规模直接测量可提取土壤磷通常受到实施所需的大量时间、劳动力和经济资源的阻碍。为了满足农艺和环境监测的需要，世界范围内开发了多种提取方法来估算土壤磷的不同成分。法国采用三种提取方法，即酸性土壤的Dyer法、钙质土壤的Joret-Hebert法和Olsen适用于所有土壤。因此，很难将全国范围内获得的数据进行比较以进行监测。最后，开发土壤传递函数 (PTF) 以协调从不同提取方法获得的可提取土壤 P 数据，并借助来自土壤信息系统的其他易于获得的预测器，这一点非常重要。在这项研究中，我们使用了法国土壤监测计划的大量数据集来校准和评估 PTF。我们实施了偏最小二乘回归，以将 Dyer 或 Joret-Hebert 方法测量的可提取 P 与 Olsen 方法确定的可提取 P 相关联，考虑 14 种土壤特性（总 P2O5、pH、阳离子交换容量 (CEC)、CaCO3、土壤质地（粘土、淤泥和沙子含量）、总有机碳和可交换的 Fe、Al、CaO、Mn、MgO 和 K2O）。我们通过选择最重要的预测变量来构建世袭模型。根据10次迭代交叉验证的结果，钙质土壤PTF的平均R2、均方根误差（RMSE）和平均误差（ME）分别为0.66、25.81和-0.11 mg kg-1，而酸性土壤分别为 0.70、24.02 和 -0.87 mg kg1。Joret-Hebert P2O5、淤泥、pH、总 P2O5、CEC 和 K 是估算钙质土壤中 Olsen P2O5 的最重要预测因子，而 Dyer P2O5、可交换 Al、K 和 pH 是估算 Olsen P2O5 的最重要预测因子在酸性土壤中。我们观察到，土壤性质的解释力在石灰质土壤中比在酸性土壤中更重要。正如预期的那样，Olsen P2O5 的代理，即 Dyer P2O5 和 Joret-Hebert P2O5，是建模 Olsen P2O5 变化的最重要变量。此外，Olsen P2O5 与 Dyer P2O5 之间的关系远强于 Olsen P2O5 与 Joret-Hebert P2O5 之间的关系。结果证实了通过使用统计方法（如偏最小二乘回归）构建的 PTF 估计土壤中可提取 P 的可行性。添加更多与农业实践和地形属性相关的预测因子可能会提高预测精度。