Predicting the optimal harvest date after crop physiological maturity is highly relevant for maize (Zea mays L.). While harvesting before achieving the commercial kernel moisture implies additional costs of grain drying, a delayed harvest of maize crops is linked to grain yield and quality losses. The main objective of this work was to identify weather variables affecting the post-maturity grain dry-down coefficient (k) in order to develop models to predict kernel moisture loss and time to harvest (harvest readiness) under a wide range of sowing date environments. Kernel moisture datasets from field experiments in Pergamino (Argentina) and Kansas (US) were used for training and testing post-maturity grain dry-down models. Two k coefficients were defined based on the solar radiation and the VPD explored during the pre- and post-maturity period (k_pre and k_post). Models including k_pre and k_post were tested under a wide range of sowing date environments, presenting high accuracy in predicting kernel moisture (R² ∼ 0.80; RRMSE ∼ 0.15) and harvest readiness (R² = 0.99; RRMSE ∼ 0.05). This study provides the foundation for developing an interactive digital platform to estimate harvest time to assist farmers and agronomists with this critical decision.

预测作物生理成熟后的最佳收获日期与玉米 ( Zea mays L.)高度相关。虽然在达到商业籽粒水分之前收获意味着谷物干燥的额外成本，但玉米作物的延迟收获与谷物产量和质量损失有关。这项工作的主要目的是确定影响成熟后谷物干燥系数 ( k ) 的天气变量，以便开发模型来预测各种播种日期环境下的籽粒水分损失和收获时间（收获准备） . 来自佩加米诺（阿根廷）和堪萨斯州（美国）田间试验的籽粒水分数据集被用于训练和测试成熟后谷物干燥模型。两千_系数是根据太阳辐射和成熟前和成熟后期间探索的 VPD 定义的 ( k _pre和k _post )。包括k _pre和k _post在内的模型在广泛的播种日期环境下进行了测试，在预测籽粒水分（R ² ∼ 0.80；RRMSE ∼ 0.15）和收获准备（R ²  = 0.99；RRMSE ∼ 0.05）方面表现出高精度。这项研究为开发一个交互式数字平台来估计收获时间提供了基础，以帮助农民和农艺师做出这一关键决定。