Cultivar recommendation is crucial for achieving high and stable yields, and it can be successfully supported by crop models because of their capability of exploring genotype × environment × management interactions. Different modelling approaches have been developed to this end, mostly relying on dedicated field trials to characterize the germplasm of interest. Here, we show how even data routinely collected in operational contexts can be used for model-based cultivar recommendation, with a case study on phenological traits and field pea (Pisum sativum L.). Eight hundred and four datasets including days from sowing to plant emergence, first flower, and maturity were collected in Northern Italy from 2017 to 2020 and they were used to optimize six parameters (base, optimum, and maximum temperature for development, growing degree days to reach emergence, flowering and maturity) of the crop model WOFOST-GT2 for 13 cultivars. This allowed obtaining the phenotypic profiles for these cultivars at functional traits level, without the need of carrying out dedicated phenotypizations. Sensitivity analysis (SA) techniques (E-FAST) and the statistical distributions of the optimized parameters were used to design pea ideotypes able to maximize yields and yield stability in 24 agro-climatic contexts (three soil conditions × two sowing times × four agro-climatic classes). For each of these contexts, the 13 cultivars were ranked according to their similarity to the ideotype based on the weighted Euclidean distance. Results of SA identified growing degree days to reach flowering as the trait mainly affecting crop productivity, although cardinal temperatures also played a role, especially in case of early sowings. This reflected in the ideotypes and, therefore, in cultivar ranking, leading to recommend a panel of cultivars characterized by low base temperature and high thermal requirements to reach flowering. Despite the limits of the study, which is focused only on phenological traits, it represents an extension of available approaches for model-aided cultivar recommendation, given the methodology we propose is able to take full advantage of the potentialities of crop models without requiring dedicated experiments aimed at profiling the germplasm of interest at the level of functional traits.

中文翻译：

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通过作物建模分解复杂性状以支持品种推荐。以物候和豌豆为重点的概念验证

品种推荐对于实现高产和稳定产量至关重要，并且可以成功地得到作物模型的支持，因为它们具有探索基因型×环境×管理相互作用的能力。为此开发了不同的建模方法，主要依靠专门的田间试验来表征感兴趣的种质。在这里，我们通过物候性状和豌豆（Pisum sativum L.）的案例研究，展示了在操作环境中常规收集的数据如何用于基于模型的品种推荐。从 2017 年到 2020 年在意大利北部收集了 804 个数据集，包括从播种到植物出苗、第一朵花和成熟的天数，用于优化六个参数（基础温度、最佳发育温度和最高温度，作物模型WOFOST-GT2 13个品种的出苗、开花和成熟的生长期天数。这允许在功能性状水平上获得这些品种的表型谱，而无需进行专门的表型分析。敏感性分析 (SA) 技术 (E-FAST) 和优化参数的统计分布用于设计能够在 24 种农业气候环境（三种土壤条件 × 2 播种时间 × 4气候等级）。对于这些背景中的每一个，根据加权欧几里得距离与理想型的相似性，对 13 个品种进行排名。SA的结果确定了达到开花的生长期天数是主要影响作物生产力的性状，尽管基本温度也发挥了作用，尤其是在早期播种的情况下。这反映在理想型上，因此也反映在品种排名中，导致推荐一组以低基础温度和高热量要求为特征的品种才能开花。尽管该研究存在局限性，仅关注物候性状，但它代表了模型辅助品种推荐可用方法的扩展，因为我们提出的方法能够充分利用作物模型的潜力而无需专门的实验旨在在功能性状水平上分析感兴趣的种质。导致推荐一组以低基础温度和高热量要求为特征的品种，以达到开花。尽管该研究存在局限性，仅关注物候性状，但它代表了模型辅助品种推荐可用方法的扩展，因为我们提出的方法能够充分利用作物模型的潜力而无需专门的实验旨在在功能性状水平上分析感兴趣的种质。导致推荐一组以低基础温度和高热量要求为特征的品种，以达到开花。尽管该研究存在局限性，仅关注物候性状，但它代表了模型辅助品种推荐可用方法的扩展，因为我们提出的方法能够充分利用作物模型的潜力而无需专门的实验旨在在功能性状水平上分析感兴趣的种质。