Abstract
The quality of yield prediction is linked to that of leaf area. We first analysed the consequences of flowering time and environmental conditions on the area of individual leaves in 127 genotypes presenting contrasting flowering times in fields of Europe, Mexico, and Kenya. Flowering time was the strongest determinant of leaf area. Combined with a detailed field experiment, this experiment showed a large effect of flowering time on the final leaf number and on the distribution of leaf growth rate and growth duration along leaf ranks, in terms of both length and width. Equations with a limited number of genetic parameters predicted the beginning, end, and maximum growth rate (length and width) for each leaf rank. The genotype-specific environmental effects were analysed with datasets in phenotyping platforms that assessed the effects (i) of the amount of intercepted light on leaf width, and (ii) of temperature, evaporative demand, and soil water potential on leaf elongation rate. The resulting model was successfully tested for 31 hybrids in 15 European and Mexican fields. It potentially allows prediction of the vertical distribution of leaf area of a large number of genotypes in contrasting field conditions, based on phenomics and on sensor networks.
Highlights
Optimal use of genetic resources is required for food security in a changing climate (Tester and Langridge, 2010; IPCC, 2014)
We argued that the modelling of the growth dynamics of individual leaves is necessary if one wants to take into account the genetic variability of individual processes, which is difficult to do in a ‘big leaf ’ model (Hammer et al, 2010; Parent and Tardieu, 2014)
The analysis of 119 maize lines (Dataset B) presenting the Nfinal from 12 to 25 was performed in two temperate field sites. This revealed that the effect of the genetic variability of time to flowering was adequately represented by that of Nfinal (Fig. 1, R2=0.79), whereas the time elapsed between the appearance of successive leaves was independent of flowering time in the same diversity panel (Supplementary Fig. S2)
Summary
Optimal use of genetic resources is required for food security in a changing climate (Tester and Langridge, 2010; IPCC, 2014). The rapid development of sensor networks and of environmental grids makes it possible to characterize environmental conditions in any field (Chenu et al, 2013; Harrison et al, 2014) This information can be combined with the genomic prediction of the sensitivity of individual genotypes to environmental conditions, thereby making possible the prediction of. Poor prediction of leaf area is often a cause of inaccurate simulations, as shown by comparison of 27 crop models (Martre et al, 2015) This is true if the aim of modelling is to predict the genotype×environment interaction in a range of climatic conditions, rather than to predict the yield of a reference genotype in this range (Parent and Tardieu, 2014). We argued that the modelling of the growth dynamics of individual leaves is necessary if one wants to take into account the genetic variability of individual processes, which is difficult to do in a ‘big leaf ’ model (Hammer et al, 2010; Parent and Tardieu, 2014)
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