The effect of sowing time on the growth of chia (Salvia hispanica L.): What do nonlinear mixed models tell us about it?

Abstract

Chia (Salvia hispanica L.) is an annual short-day plant whose growth has not been studied extensively in low-altitudes and at temperatures outside of its optimal range. The objective of this study was to describe the growth dynamics of a chia crop from an experimental plantation in south-east Mexico, on three different sowing dates. The chia grew at temperatures (18-37°C) and an altitude (9 m a.s.l.) outside of the recommended conditions (20-30°C, 500-1000 m a.s.l.). Three individual-plant responses were measured weekly, before seed harvest: height, number of leaves and number of inflorescences. Three theoretical nonlinear growth models were fitted to the data, a different model for each response. Mixed-effect model parameters were estimated by maximum likelihood, and the goodness of fit for each model was evaluated using two criteria: Modeling Efficiency and Root Mean Square Error. Chia seed yield was also measured in each treatment. Estimated parameters for plant height confirmed that medium sowing time (MST) and late sowing time (LST) plants had smaller heights than the early sowing time (EST) plants. Moreover, at the end of their life cycle, EST plants had a greater number of leaves and inflorescences, and higher seed yield. All of these differences were associated to the extended time of vegetative growth of EST plants favored by optimal photoperiod and temperature. Growth dynamics of chia during its ontogenic phases was explored, in more detail, with relative growth parameters derived from fitted models: a decrease in photoperiod influences the beginning of the reproductive phase, with the consequent reduction in speed of vegetative growth. In addition, nonlinear mixed-effects models can be useful in understanding the relation between growth parameters, plant maturity, and the suitable time for chia seed harvest. Our results suggest chia crops are adaptable to non-conventional environmental conditions.