Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Estimating Freezing Injury on Olive Trees: A Comparative Study of Computing Models Based on Electrolyte Leakage and Tetrazolium Tests

Version 1 : Received: 28 April 2023 / Approved: 5 May 2023 / Online: 5 May 2023 (11:34:27 CEST)

A peer-reviewed article of this Preprint also exists.

Rezaei, M.; Rohani, A. Estimating Freezing Injury on Olive Trees: A Comparative Study of Computing Models Based on Electrolyte Leakage and Tetrazolium Tests. Agriculture 2023, 13, 1137. Rezaei, M.; Rohani, A. Estimating Freezing Injury on Olive Trees: A Comparative Study of Computing Models Based on Electrolyte Leakage and Tetrazolium Tests. Agriculture 2023, 13, 1137.

Abstract

Winter frost injury is a major limiting factor for olive cultivation in temperate regions. The response of olive shoots to freezing stress can be used for selecting resistant genotypes to freezing. The electrolyte leakage (EL) and tetrazolium tests (TZ) are commonly used to evaluate dead tissues in cold stress studies. The temperature-response curve of dead tissues to lethal temperature (LT) is measured with models to calculate LT50 and LT90. In this study, we evaluated the accuracy and efficiency of eighteen non-linear regression models (NLRs) in calculating LT50 and LT90 of freezing stress in different olive cultivars at various stages of dormancy. After evaluating the prediction performance of NLR models, it was found that only eight models were suitable for the purpose of this research out of the 18 models examined. The 2p-logistic and Gompertz models were selected for modeling EL and TZ, respectively. Our research findings indicate that the Roughani, Kawi, and Zard varieties of olive trees exhibit the best performance in cold weather conditions. Our findings provide valuable insights into selecting frost-resistant cultivars and designing effective strategies for cold acclimation in olive cultivation.

Keywords

olive trees; freezing injury; electrolyte leakage; tetrazolium tests; nonlinear regression models

Subject

Biology and Life Sciences, Agricultural Science and Agronomy

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