Investigation of seedling emergence of bean and maize affected by sowing depth with using probit models

Subject Areas : Journal of Plant Ecophysiology

Behnam  Behtari ¹ (Ph.D. student of Crop Ecology, Faculty of Agriculture, University of Tabriz , East Azerbijan, Iran.)
Adel  Dabbagh mohammadi nasab ² (Dept of Ecophysiology, Faculty of Agriculture, University of Tabriz, Tabriz, Iran)
Kazem  Ghassemi Golezani ³ (Dept of Ecophysiology, Faculty of Agriculture, University of Tabriz, Tabriz, Iran.)
Mohammad reza  Shakiba ⁴ (Dept of Ecophysiology, Faculty of Agriculture, University of Tabriz, Tabriz, Iran.)

Received: 2016-08-11 Accepted : 2018-04-15 Published : 2019-01-21

Keywords: Modeling, Emergence indices, Probit curves, Seedling mortality,

Abstract :

Seedling emergence probably is the single most important phenological event that influences the success of an annual plant. The main objective of this study was to develop a seedling emergence model for green bean and maize and select a best-fitted model associated with sowing depth. A factorial experiment based on completely randomized design was conducted in 2015 at Research Farm of Mohaghegh Ardabili University, to quantify the response of seedling emergence to sowing depth. Treatments were four sowing depths (2, 4, 6 and 8 cm) in three replications. The results indicated that the percentage emergences of both species in the first two levels of sowing depth (2 and 4 cm) were high, but at deeper levels, seedling emergence were suffering a severe loss. Emergence indicators (MED, ERI, D50%) showed that seedling emergence of bean was greater than maize. For two species, an increase in pre-emergence mortality with increasing depth was observed. So that the highest germinated seeds mortality occurred at 8 cm depth. However, in probit fitted curves for each dataset, the rates of increasing between plants varied. The rate of emergence varied between plants and based on values of statistical criteria, because of less parameters number in linear probit model, it was showed suitable to fit model. Therefore, these models may provide a better basis for broad practical application in crop management.

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