A comparison of direct and indirect methods for estimating leaf area in peach (<i>Prunus persica</i>) and plum (<i>Prunus salicina</i>) cultivars

Authors

DOI:

https://doi.org/10.17584/rcch.2017v11i1.6143

Keywords:

deciduous crops, regression analysis, leaf length, leaf width

Abstract

Leaf area is a widely used parameter in the study of crop ecophysiology because of its direct implications on fruit production. Different destructive, non-destructive or indirect methods are used for its determination. The estimation of the leaf area is an important biometric observation that must be made when comparing the behavior of plants in response to different agricultural treatments. The objective of the study was to determine and validate the most accurate regression functions for non-destructive estimation of leaf area in some cultivars of peach and plum. In this work, mathematical functions were applied to estimate leaf area in the plum cultivars Gold Fruly, Ecuatoriano, Methley and Horvin, and in the peach cultivars Dorado, Rubidoux, Diamante and Rey Negro. The leaves were collected from trees grown in Paipa, Colombia. A regression function was selected for each cultivar and the estimated leaf area data was compared with the functions and data measured with a leaf area integrated analyzer. The product for the length by the width of the leaf was used as independent variable (X) of the function. In all cases, the Pearson correlation coefficient presented values higher than 0.9, indicating the close relationship between the estimated and observed data. With the use of these functions, it is possible to non-destructively estimate the leaf area in the peach and plum cultivars involved in the study.

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Published

2017-06-01

How to Cite

Casierra-Posada, F., Zapata-Casierra, V., & Cutler, J. (2017). A comparison of direct and indirect methods for estimating leaf area in peach (<i>Prunus persica</i>) and plum (<i>Prunus salicina</i>) cultivars. Revista Colombiana De Ciencias Hortícolas, 11(1), 30–38. https://doi.org/10.17584/rcch.2017v11i1.6143

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