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Improving Physiological Assumptions Of Simulation Models By Using Gene-Based Approaches

Hoogenboom, Gerrit, White, Jeffrey W.
Agronomy journal 2003 v.95 no.1 pp. 82-89
Phaseolus vulgaris, beans, canopy, crop models, cultivars, data collection, flowering, genes, harvest index, photoperiod, plant breeding, quantitative traits, seed yield, simulation models, temperature
Application of crop models to plant breeding has been limited, in part due to the restricted capabilities of models to accurately represent genetic differences and genotype-induced crop responses. A gene-based model, GeneGro, was developed to simulate the effects of seven genes on growth and developmental processes in common bean (Phaseolus vulgaris L.) and was published in 1996. The objective of this paper is to describe the improvements that were made in GeneGro to incorporate the effect of the Tip gene. Presence of the Tip gene in photoperiod-sensitive cultivars reduces the inhibitory effect of low temperature on photoperiod sensitivity of flowering. A mechanistic approach, further guided by information on two other genes affecting photoperiod response, i.e., Ppd and Hr, was used to incorporate the effect of the Tip gene. In the modified GeneGro, this inhibitory effect is reduced under cooler temperatures ranging from 15 to 20°C for the mean daily temperature. However, in the presence of Tip, no such reduction occurs. For the calibration data, GeneGro explained 75% of the variation in days to flower vs. 61% for the original model. For an extensive evaluation data set, the modification explained 72% of the variation in days to flower vs. 70% for the original version while days to maturity, seed yield, canopy dry mass, and harvest index showed no improvement. These results reflect two major constraints to effective use of gene-based approaches in crop modeling. The first is the lack of reliable characterizations of cultivars for genes used in the model. The second is the scarcity of data from conditions where phenotypic differences between the Tip and tip gene would be expected. It can be concluded that understanding the genetic control of quantitative traits can guide improvements to simulation models.