Predicting yield is an important objective in agricultural research. We developed a linear regression model to predict the olive fruit yield (FY) for four olive cultivars (Sivillano, Conservolia, Zard and Clonavis) by monitoring soil moisture, response to root growth and its characteristics including root weight density (RWD), root length (RL) and root biomass (RB). Our results show the model predicts fruit yield based on a simple linear function of root characteristics (R² = 0.85). A principal component analysis provided a meaningful combined factor (the first principal component) that showed a clear discrimination in olive fruit yield among four cultivars. The model could be applied to rapidly evaluate olive fruit yield using the measured values of root characteristics and to support decision making for orchard management.

预测产量是农业研究的一个重要目标。我们开发了一个线性回归模型，通过监测土壤湿度、对根系生长的响应及其特征（包括根重密度 (RWD)、根长）来预测四个橄榄品种（Sivillano、Conservolia、Zard 和 Clonavis）的橄榄果实产量 (FY) (RL) 和根生物量 (RB)。我们的结果表明，该模型根据根特征的简单线性函数（R ²  = 0.85）来预测果实产量。主成分分析提供了一个有意义的组合因素（第一个主成分），显示四个品种之间橄榄果实产量的明显区别。该模型可用于利用根部特征的测量值快速评估橄榄果实产量，并支持果园管理决策。