Contamination of durum wheat grain by cadmium (Cd) threatens food safety and is of increasing concern because regulations concerning Cd are becoming stricter due to its toxicity. This work aimed at using soil variables and cultivar types to build models to predict whether durum wheat grain Cd will conform with current and possibly lower regulatory thresholds. We combined multiple Gaussian and logistic regressions and the random forest algorithm to take advantage of their strength. Models tested using cross-validation produced excellent performances including for the lowest regulatory threshold of 0.1 mg Cd/kg, half of the current one: 79–85% of the non-conformity cases were detected and the reliability of predictions was 69–82%. The models enabled identification of a x1.4 variability in grain Cd content between cultivars that do not have the low Cd accumulation allele of the Cdu1 gene. The models confirmed that for the grain Cd content, the between-cultivar variability had much less influence than the phytoavailability of Cd in soil, the critical contexts of which were characterized by the models. For farmers, these models are valuable tools to predict whether durum wheat production will conform with existing and future Cd regulation in foodstuffs.

镉对硬质小麦籽粒的污染威胁到食品安全，并日益受到关注，因为有关镉的法规由于其毒性而变得越来越严格。这项工作旨在利用土壤变量和品种类型来建立模型，以预测硬粒小麦Cd是否将符合当前的以及可能更低的监管阈值。我们结合了多个高斯和逻辑回归以及随机森林算法，以利用它们的优势。使用交叉验证测试的模型具有出色的性能，包括最低监管阈值为0.1 mg Cd / kg，只有一半：检测到79–85％的不合格案例，预测的可靠性为69–82％ 。这些模型可以识别x1。Cdu1基因。该模型证实，对于谷物中的Cd含量，品种间变异性的影响要比土壤中Cd的植物有效性低得多，该模型的临界环境已得到表征。对于农民而言，这些模型是预测硬粒小麦产量是否符合食品中现有和未来Cd法规的有价值的工具。