Combining ecophysiological models and genomics to decipher the GEM-to-P problem

Figures & data

Table 1 The six levels of genetic detail in crop simulation models [3].

1. Generic: no consideration of difference among species.

2. Species-specific: species described but no cultivar differences recognized.

3. Cultivar-specific parameters used to describe cultivar differences.

4. Cultivar differences represented by genotypes with linear model parameters.

5. Cultivar differences expressed through processes described using knowledge of gene expression and gene products.

6. Full representation of gene regulators, gene products, etc., in networks.

Fig. 1 Comparison of observed vs. simulated days to flowering using gene-based model parameters. Both datasets were independent of calibration data. (A) For 10 sorghum recombinant inbred lines varying for three maturity (Ma) loci and grown at two locations [8]. (B) For 29 wheat cultivars of the International Winter Wheat Production Nurseries grown at 34 locations [15].

Table 2 Comparisons of results for conventional and gene-based simulation models of time to flower for 10 sorghum lines [8] and 29 wheat cultivars [15]. Evaluation datasets are independent from calibration sets, predominantly involving different locations. The variation that was explained by assuming no differences among cultivars was tested by evaluating simulations based on parameters for a single generic cultivar.

Crop	Dataset	N	Model type	Mean	r^2	Slope	RMSE
Sorghum	Calibration	108	Generic	64	0.23^**	0.76	12.5
			Conventional	67	0.61^**	0.76	8.8
			Gene-based	67	0.55^**	0.73	9.5
	Evaluation	74	Generic	58	0.31^**	1.41	8.8
			Conventional	61	0.60^**	0.91	6.7
			Gene-based	61	0.61^**	0.97	6.6
Wheat	Calibration	540	Generic	208	0.95^**	0.95	9.7
			Conventional	209	0.98^**	0.95	6.6
			Gene-based	208	0.96^**	0.92	8.6
	Evaluation	1499	Generic	214	0.89^**	1.02	10.4
			Conventional	213	0.92^**	1.00	9.0
			Gene-based	214	0.90^**	0.99	9.9

** Statistically significant at the p < 0.01 level.

Fig. 2 Hypothetical plot of progress in predicting phenotypes for an arbitrary trait (e.g., flowering date) from genetic, environmental and management data. Note interrupted axis between 1700 and 1970. (A) First conceptualization of accumulated temperature affecting phenology by Reaumur [50]. (B) Inclusion of photoperiod in simple developmental models. (C) First attempts to introduce genetic information into models. The trajectories I, II and III correspond to differing expectations concerning the complexity of genetic control of quantitative traits (see text).