A New Approach for Analyzing and Interpreting Data on Fruit Drop in Mango

Abstract

Mango yields are frequently reduced by premature fruit drop, induced by plant stresses during the fruit set period in response to unsuitable climatic or crop management conditions. There are varying strategies for assessing premature fruit drop, which render the comparison and interpretation of published data difficult to draw general conclusions. Therefore, the objective was to provide a mathematical model that is generally valid for describing fruit losses of mango. The model was tested and validated by monitoring the fruit drop for the two local North Vietnamese cultivars, Hôi and Tròn, in different management systems over six consecutive growing seasons: 1) mango–maize intercropping and mango monocropping; 2) irrigation; and 3) plant growth regulator applications with 10 ppm N-(2-chloro-4-pyridyl)-N′-phenylurea (CPPU), 40 ppm 1-naphthaleneacetic acid (NAA), and 40 ppm gibberellins (GA3 and GA4+7). The timely pattern of fruit drop was best described with a sigmoid function (r2 = 0.85) and formed the basis for defining three distinct drop stages. The post-bloom drop, from full bloom to the maximum daily rate of fruit drop [FD(x)], had the highest fruit losses. The following midseason drop stage ends at 1% FD(x), a threshold that is suggested after a comprehensive literature review. Thereafter, during the preharvest drop stage, treatment and cultivar differences appear to remain constant despite continued fruit drop. In contrast to other mango intercropping studies, fruit loss was not greater in the mango–maize intercropping than in the mango monocropping. Irrigation resulted in approximately three times higher fruit retention compared with the non-irrigated control. A single application of NAA at marble fruit stage (BBCH-scale 701) resulted consistently in the highest fruit retention for both cultivars in midseason and at harvest. The model permits the separation between the drop stages, thus allowing the evaluation of 1) natural variation before treatment effects during post-bloom drop; 2) treatment efficacies during midseason drop; and 3) yield forecasting at the beginning of the preharvest stage.