Modeling the 3D structure and rhythmic growth responses to environment in dioecious yerba-mate

Fabio Takeshi Matsunaga, Miroslava Rakocevic and Jacques Duílio Brancher

Ecological Modelling, 2014, vol. 290, issue C, 34-44

Abstract: Environmental factors, as incident light, temperature and night/day length mainly determine the dynamics of growth and development of dioecious yerba-mate. The complex interactions among these factors and growth responses highlight the need for growth model, which describes plant modifications under natural and stress conditions, accounting for the growth unit formations in male and female individuals. The rhythmic growth of yerba-mate considers the existence of two annual growth flushes, (spring and autumn) and two annual growth pauses (summer and winter). We developed an individual-based ecological model (InterpolMateS1) that incorporates some aspects of growth and development of yerba-mate referent to two cultivation environments – monoculture and forest understory. The environmental time series, together with plant morphological time series and information about periods of rhythmic growth and respective growth pauses, were used for artificial neural network (ANN) training. The back-propagation algorithm was implemented to refine the weights generated in ANN from the monthly organized input and to adjust using expected output morphological data sets. The probability of meristem ability to continue the growth in the next growth unit and to preserve the leaf in each internode along the axes of 1st–3rd branching order was calculated and implemented into the model. The cubic splines interpolation was more accurate to define the growth parameters curves of yerba-mate. The InterpolMateS1 was daily-step programmed to calculate the growth of yerba-mate for biennial period between two subsequent prunings. The software was tested to simulate the reduction in growth and biomass production when long-term stress conditions were applied. Virtual females were found to be more sensitive to changes of environmental conditions than males, when low water availability and low temperatures occurred during spring and autumn growth flushes.

Keywords: Artificial neural network; Back-propagation algorithm; Cubic splines; Growth unit; VegeSTAR; VPlants (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:290:y:2014:i:c:p:34-44

DOI: 10.1016/j.ecolmodel.2013.10.035

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