Modeling of Grapefruit External Color as Affected by Drought Stress

Grapefruit are well-adapted to arid and warm climatic conditions, but well-irrigated trees usually produce better-quality fruits. Because water is a major component of the fruits, there is a strong relationship between drought stress and fruits quality traits such as fruits size, external fruits color, and juice quality. The object of this study was to develop a computer model to predict postharvest external grapefruit color as a function of drought stress. During model development, drought stress was quantified using a concise water balance model based on crop evapotranspiration, precipitation, and irrigation. Data collected from Murcia, Spain, during the 2007 and 2008 growing seasons were used for model development, and the model was optimized by comparing model predictions and observations for each growing season. The root mean square error and Nash and Sutcliffe coefficient of efficiency ( NSE ) were used to evaluate model performance. Then, the model was evaluated with independent data collected from Florida during the 2005–06 growing season. A second-order polynomial relationship was found between external fruits color and drought stress, with less drought stress resulting in better external fruits color. Model optimization revealed good model performance for predicting external fruits color in Murcia, with NSE values of 0.975 and 0.979 for the 2007 and 2008 growing seasons, respectively. Model evaluation with the data from Florida showed that model predictions were reliable, with a NSE value of 0.984. A robust model to predict external grapefruit color as affected by drought stress was developed during the present study and could be potentially applied to supply information for suitable irrigation management of various grapefruit cultivars grown under different climatic conditions. Model performance could be confirmed by future research with data collection during further multiple seasons for different cultivars and a range of climatic conditions.