Parameter Estimation for Predicting Flowering Date of Soybean Cultivars

Abstract

Soybean [Glycine max (L.) Merr.] growth and yield models depend on good predictions of phenological events such as flowering. Parameters for predicting flowering date of 12 cultivars were estimated for various development‐rate models. Date of flowering is predicted by accumulating a daily rate of development, which depends on night length and temperature, until a threshold is reached. Daily development rate is computed by a multiplicative relationship containing two functions: one for describing the variation in development rate with night length under optimal temperature and the other describing variation with temperature under optimal night length. There were 39 to 115 year‐location‐sowing date combinations for each cultivar, covering latitudes from 18°03′ to 45°25′ N lat. The downhill simplex method was used to estimate phenological parameters for each cultivar by minimizing the error sum of squares between observed and simulated flowering dates. Many formulations of the development‐rate model were compared. Linear‐plateau functions for both night length and temperature effects provided the best fit and yielded the most consistent results. The root mean squarerror between observed and simulated dates of flowering ranged from 3.45 to 5.28 d. Correlation coefficients between observed and simulated days from sowing to flowering varied from 0.987 to 0.841, with a decreasing trend from late toward early‐maturity cultivars. There was a clear difference among cultivars with respect to night‐length sensitivity, but a similaresponse to temperature.

Comparison of two soya bean simulation models under climate change. I. Model calibration and sensitivity analyses

“…After emergence, the period up to the end of leaf growth and the start of seed filling (i.e. vegetative period) is calculated on the basis of thermal time and photoperiod, as based on the approach and data given by Grimm et al (1993), , and Hesketh et al (1973). This relationship between thermal time and photoperiod, and the phenological development is cultivarspecific.…”

Understanding and modeling the effect of temperature and daylength on soybean phenology under high-yield conditions

“…When only temperature and photoperiod are considered for simulating phenology, options for an overall algorithm include multiplicative (Grimm et al, 1993, Hodges and French, 1985, Jones et al, 2000, Major et al, 1975, Streck et al, 2003, and Wang and Engel, 1998, additive (Sinclair et al, 1991, Steward et al, 2003, and Summerfield et al, 1991, subtractive (Steward et al, 2003), or a mixture of additive and multiplicative approaches (Cober et al, 2001). The multiplicative approach is widely used in crop models such as WO-FOST, STICS, or CROPGRO (Boogard et al, 1998, Brisson et al, 1998, and Jones et al, 2000.…”

“…Considering the well-known photoperiod flower induction response in soybean, the use of a photoperiod function in soybean phenology modeling is reasonable. Most soybean phenology research on photoperiod response has focused on developing a photoperiod function for flowering (Grimm et al, 1993, Grimm et al, 1994, Major et al, 1975, and Steward et al, 2003. However, the empirical evidence of responsiveness of soybean post-flowering development to daylength (Johnson et al, 1960, Shibles, 1978, and Summerfield et al, 1998, the proposed involvement phytochromes as receptors for photoperiod signal during post-flowering phase (Han et al, 2006), and the existent of genes controlling time of maturity (independent of flowering time) in soybean suggest that photoperiod response function should also be considered in modeling postflowering soybean development.…”

Improving Soybean Cultivars for Adaptation to Climate Change and Climate Variability

“…The model considers N mobilization and canopy selfsenescence as features during seed filling. It has a well-tested phenology subroutine that is sensitive to daylength and temperature, with genetic coefficients for MG 00 to 9 solved from extensive data on soybean (Grimm et al 1993(Grimm et al , 1994Mavromatis et al 2001Mavromatis et al , 2002. The model has been tested intensively with time-series growth analyses as well as extensive final yield data sets (Boote et al 1997;Piper et al 1998).…”