Optimization of sampling and monitoring of vegetative flushing in citrus orchards
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2020-05-20
Autores
Carvalho, Everton Vieira de
Cifuentes-Arenas, Juan Camilo [UNESP]
Santos de Jesus, Carlos Augusto
Stuchi, Eduardo Sanches
Lopes, Silvio Aparecido
Girardi, Eduardo Augusto
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Public Library Science
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Citrus trees produce flushes throughout the year, but there are no criteria established for a precise shoot monitoring in orchards under tropical climate. Methods for quantification of flush dynamics would be useful for horticultural and pest management studies because different insect vectors feed and reproduce on flushes. We estimated the minimum number and distribution of trees for sampling and determined the flushing pattern over time in 'Valencia Late' orange trees grafted onto 'Swingle' citrumelo rootstock. Shoots within a square frame (0.25 m(2)) on two sides of the canopy were counted and classified by their phenological stage. The minimum number of samples was estimated using the mean number of shoots and area under the flush shoot dynamics (AUFSD). The temporal and spatial distribution analysis was performed by Taylor's power law and by multiple correspondence analysis (MCA). Additionally, a shoot maturity index (SMI) based on visual qualitative assessment of flushes is proposed. Considering the mean number of shoots, it was necessary to sample two sides of 16 trees to reach a relative sampling error (E-r) of 25%, whereas by the AUFSD, only five trees were necessary to reach an E-r of 10%. Flushes were predominantly randomly distributed over time and space. Testing eight transects, sampled trees should be distributed throughout the block, avoiding sampling concentration in a certain area. MCA showed that the west side and the upper sampling positions of trees were more likely to be associated with younger shoots. AUFSD and the evaluation of both sides of the canopy yielded a smaller number of trees to be assessed. The SMI was a reliable metric to estimate the shoot phenology of orange trees, and correlated well (R-2 > 70%) with the mean number of shoots within the square frame. Therefore, SMI has the potential to make shoot monitoring in the field more practical.
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Plos One. San Francisco: Public Library Science, v. 15, n. 5, 19 p., 2020.