The relationship between canopy microclimate, fruit and seed yield, and quality in Xanthoceras sorbifolium

Published: 25 July 2022| Version 1 | DOI: 10.17632/r7ncdy9yfk.1
Lijin Ou,
Yi Zhang,
Zishuo Zhang,
Yuxin Chen,
Kexin Wang,
Yue Wen,
Yan Ao


The comprehensive utilization value of Xanthoceras sorbifolium is high, but its development is limited by the problem of low yield. This study investigated the relationship between the canopy microclimate, fruit yield, and fruit quality of Xanthoceras sorbifolium. Difference between the distributions of canopy microclimate factors as well as fruit and seed parameters in the inner and outer canopies of the lower layer, as well as between the inner and outer canopies of the upper layer, were investigated for a period of one year. Canopy structure induced significant differences between canopy microclimate factors during various periods of the year. Light intensity and temperature of the outer and upper canopies were higher than those of inner and lower canopies. However, relative humidity showed an opposing trend. Light intensity was significantly and positively correlated with fruit set percentage, fruit yield, and seed yield. Temperature was significantly and positively correlated with fruit yield and seed yield, but significantly negatively correlated with the oil concentration of seed kernels. Fruit and seed yields significantly decreased from the outer to the inner canopy and from the upper to the lower canopy. Fruit set percentage in the outer canopy was also significantly higher than that in the inner canopy. However, oil concentrations in the seed kernels of the lower layer were significantly higher than those of the upper layer. Additionally, regression analysis was used to construct evaluation models for microclimate, fruit, and seed parameters. Regression equations corresponding to the association between single microclimatic factors during different periods and the fruit and seed parameters may provide a reference for canopy pruning and help develop an optimal regression model that may be used to predict and estimate fruit and seed parameters.



Forestry, Horticulture, Oilseed Crops