Conservation agriculture has been developed as a means to improve the sustainability of agricultural systems and reduce drawbacks of conventional agricultural practices. Cropping practices can have a large influence on soil properties such as water retention. Proper tools are needed to assess and model effects of conservation agriculture on soil properties. As measuring soil water retention is expensive and time consuming, pedotransfer functions (PTFs) are now commonly used to predict them. The objectives of this study were to (i) present a new dataset of conservation agriculture data, (ii) assess performances of existing PTFs in predicting soil water retention of soils under conservation agriculture and (iii) develop new specific PTFs to predict water retention in conservation agriculture more accurately. We used data collected only in fields under conservation agriculture in France to evaluate several published PTFs with three evaluation criteria (RMSE, prediction bias (ME) and Nash-Sutcliffe Efficiency (EF)). We then developed new PTFs using three methods ― multiple linear regression, regression tree and random forest ― to predict soil water content at matric heads of -100 (θ₁₀₀, field capacity for sandy soils), -330 (θ₃₃₀, field capacity for other soils) and -15 000 cm (θ_{15 000}, wilting point). Soil tillage, presence of a cover crop, rotation length and previous reduced/no tillage were used as predictors in addition to basic soil properties for regression trees and random forests. The quality of prediction (RMSE, ME and EF) was calculated for each new PTF using a cross-validation procedure. Generally, predictions of wilting point had lower absolute error than those of sandy-soil field capacity (RMSE = 0.044 and 0.066 cm³/cm³, respectively). EF was usually negative for all water contents. The cross-validation performance of the new PTFs was similar for multiple linear regression (RMSE: 0.028, ME: 0.000, EF: 0.34 for θ₁₀₀) and random forest (RMSE: 0.027, ME: 0.000, EF: 0.36 for θ₁₀₀), and generally worse for regression tree (especially EF). Multiple linear regression that did not consider cropping practices performed as well as random forest and thus did not identify any major influence of agricultural management on predicted water content. Future research on developing PTFs should focus on identifying more relevant predictors.

发展保护性农业是改善农业系统可持续性并减少传统农业做法的弊端的一种手段。种植实践会对土壤特性（例如保水率）产生很大影响。需要适当的工具来评估和模拟保护性农业对土壤特性的影响。由于测量土壤保水量既昂贵又费时，因此现在通常使用pedotransfer函数（PTF）进行预测。这项研究的目的是（i）提出一个新的保护性农业数据集，（ii）评估现有PTF在预测保护性农业下土壤水保持率方面的性能，以及（iii）开发新的特定PTF来预测保护性农业中的水保持能力。保护农业更加准确。我们仅使用法国保护性农业领域收集的数据，通过三个评估标准（RMSE，预测偏差（ME）和纳什-苏特克利夫效率（EF））评估了几个已发布的PTF。然后，我们使用三种方法（多元线性回归，回归树和随机森林）开发了新的PTF，以预测-100（θ₁₀₀，对砂质土壤田间持水量）-330（θ ₃₃₀，字段容量用于其它土壤）和-15 000厘米（θ _{15 000}，萎蔫点）。除回归树和随机森林的基本土壤特性外，土壤耕作，有盖作物的存在，轮作长度和先前的减耕/免耕还用作预测指标。使用交叉验证程序为每个新的PTF计算预测质量（RMSE，ME和EF）。通常，相对于沙质土壤田间持水量（RMSE = 0.044和0.066 cm ³ / cm ³， 分别）。EF通常对所有水分都是阴性的。新的PTF的交叉验证性能是为多元线性回归（RMSE：0.028，ME：0.000，EF：0.34θ类似₁₀₀）和随机森林（RMSE：0.027，ME：0.000，EF：0.36θ ₁₀₀） ，对于回归树（尤其是EF）而言通常更糟。多元线性回归没有考虑种植方式的效果，也没有考虑随机森林的情况，因此没有发现农业管理对预测含水量的任何重大影响。未来开发PTF的研究应集中于确定更相​​关的预测因素。