Survival Analysis of Plants Grown in Long‐Term Field Experiments. (1st September 2018)

• Record Type: Journal Article
• Authors: Załuski, Dariusz; Mielniczuk, Jacek; Bronowicka‐Mielniczuk, Urszula; Stolarski, Mariusz J.; Krzyżaniak, Michał; Szczukowski, Stefan; Tworkowski, Józef
• Is Part Of: Agronomy Journal. Volume 110:Number 5(2018)
• Page Start: 1791
• Page End: 1798
• Publication Date: 2018-09-01
• Subjects: Agronomy -- Periodicals; 630
• DOI: 10.2134/agronj2018.01.0062
• Languages: English
• ISSNs: 0002-1962

Abstract

Abstract : Core Ideas: The probability distribution of plant survival was presented. A new HS‐II Mielniczuk lifetime distribution model was presented for the first time. Smith–Bain distribution was the optimal model for willow density of 24, 000 plants ha −1 . The HS‐II Mielniczuk was the optimal model for willow densities of 48, 000 and 96, 000 ha −1 . Plant survival was evaluated in a statistical analysis. The analysis was conducted based on data from a long‐term field experiment where five willow ( Salix L.) genotypes were grown at four densities (12, 000, 24, 000, 48, 000, and 96, 000 ha −1 ) in a plantation with an annual harvesting cycle. Four exponential power distributions that best fit plant survival data were selected, including an alternative parametric distribution, which is presented for the first time in this study. The developed models supported predictions of the future distribution of willow plants in groups characterized by different planting density, willow cultivars, and clones. At the recommended density of 24, 000 plants ha −1, the exponential power distribution proposed by Smith and Bain best fit willow survival time and the plantation's productive lifespan. The new hyperbolic secant distribution was characterized by the highest explanatory power measured by Akaike information criterion, Schwarz's Bayesian information criterion, and a heavy‐tailed distribution. This model was optimal for higher planting densities (48, 000 and 96, 000 ha −1 ) with significantly lower plant survival rates.