Temperature thresholds for Eucalyptus genotypes growth across tropical and subtropical ranges in South America

• Autor(a) principal: Queiroz, Túlio Barroso [UNESP]
• Data de Publicação: 2020
• Outros Autores: Campoe, Otávio Camargo [UNESP], Montes, Cristian Rodrigo, Alvares, Clayton Alcarde [UNESP], Cuartas, Mauricio Zapata, Guerrini, Iraê Amaral [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1016/j.foreco.2020.118248; http://hdl.handle.net/11449/201846
• topic: Climate change; Dendrometer; Meteorological seasonality; Process-based models

Temperature is a crucial factor influencing the growth of forest plantations, and a key variable used in process-based models of forest productivity. In the present study, we evaluated the relationship between temperature and growth of seven Eucalyptus genotypes in eight sites across a 3500 km latitude gradient in South America. From 18 months after planting until 72 months of age, climatic data and tree diameter increments at breast height (1.3 m above ground level) were monitored in each site intensively (every 15-30 days). The optimum temperature to growth varied among genotypes, ranging from 18 to 22 °C. The minimum annual temperature required for growth was 6 °C, while the maximum was 31 °C. The results at a monthly scale indicate that the minimum temperature to growth is between 7 °C and 23 °C, while the monthly maximum temperature is between 15 °C and 31 °C. When we used annual temperature or large temporal scales, we potentially minimize the effects of climate on plant metabolism; this should be taken into account in process based-models. The study results enhance our understanding of the influence of air temperature on tree growth dynamics. The Eucalyptus genotypes most planted in Brazil and Uruguay have specific thermal demands for growth maintenance and different optimal temperatures for maximum growth. Understanding variation in growth under different climatic conditions would facilitate accurate prediction of forest productivity based on thermal requirements. In addition, the data presented here could facilitate tree breeding by enhancing phenotypic analyses based on ecophysiological factors during the selection process.