Table Of ContentsNext Page

Simulating yield impact of QTL controlling leaf and silk expansion under drought in maize

Karine Chenu1,2, Franois Tardieu1, Scott Chapman3, Greg McLean2, Claude Welcker1 and Graeme Hammer4

1 INRA, UMR 759 LEPSE, 2 place Viala, 34060 Montpellier cedex 01, France
2
APSRU, Department of Primary Industries and Fisheries, Toowoomba, Qld 4350, Australia. Email: karine.chenu@dpi.qld.gov.au
3
CSIRO Plant Industry, St Lucia, Qld 4072, Australia
4
APSRU, School of Land, Crop and Food Sciences, University of Queensland, Brisbane, Qld 4072, Australia

Abstract

Part of the impact of drought on corn yield arises from reduction in leaf expansion that affects light interception and from reduction in silk expansion that affects grain set. Recently, quantitative trait loci (QTL - genome regions) have been shown to be associated with responses of leaf and silk expansion to soil water status and evaporative demand. In this study, we combined (i) a short-term model (hourly) of maize leaf expansion that captures the effects of genetic and environmental variations, with (ii) a new model co-ordinating the development of all leaves of a plant, and (iii) the APSIM crop model which takes account of the complex plant-crop-environment interactions at a daily time scale. The integrated model adequately predicted the profile of leaf area in 12 field situations with contrasting evaporative demand and soil water conditions, and it accurately simulated biomass accumulation and yield in 3 field situations. The model was used to quantify the impact at the crop level of combinations of QTL involved in leaf and silk expansion for a range of drought and climate scenarios. Results showed a large effect of the QTL for leaf expansion on yield. The impact of these QTL was further amplified when the simulation was performed by taking into account the effects of some of these QTL on both leaf and silk expansion. This study exemplifies the potential for functional whole-plant modelling in bridging the gene-to-phenotype gap.

Key Words

Crop model, genotype-environment interaction, gene-to-phenotype, leaf expansion, yield, drought.

Introduction

Organ growth is one of the first processes affected by drought (e.g. Boyer 1970; Saab and Sharp 1989) and reductions in leaf and silk elongation lead respectively to reduced light interception and increased anthesis-silking interval (ASI), both affecting corn productivity. To quantify the yield impact of QTL controlling leaf and silk elongation, we outline here a model that bridges the gap between physiological and genetic short-term organ responses, and whole-plant models that predict biomass accumulation, transpiration and yield at crop level. The method consisted, firstly, of identifying the major environmental conditions involved in leaf development: temperature, evaporative demand and soil water deficit had an overriding effect on leaf growth rate, whereas light and plant carbon balance had minor effects (Ben Haj Salah and Tardieu 1996 and 1997; Sadok et al. 2007). Secondly, response curves of leaf elongation rate to these three major environmental variables were established and the parameters of these responses were analysed genetically (Reymond et al. 2003; Welcker et al. 2007). As this method has been only applied to one leaf position (leaf six) and to simple environment scenarios, the third step consisted of integrating the results to the whole-plant level and capturing the complex interactions of plants with their environment (e.g. feedback of leaf growth on soil water depletion) using the APSIM crop model (Wang et al. 2002; Keating et al. 2003).

The aim of this study was to scale up the existing single-leaf model to the whole-plant and crop levels, and to predict crop level consequences of the genetic variability in leaf growth response to environment (Chenu et al. 2008). In addition, QTL of leaf elongation rate responses to drought that co-localised with QTL of ASI (an indicator of silk expansion response) (Welcker et al. 2007) were further used to affect the reproductive development. The vegetative and reproductive impacts of these QTL on yield were explored by simulation in various drought scenarios.

Methods

Maize (Zea mays L., hybrid Dea) was sown in 12 field experiments between 1992 and 1998 in North France (Grignon) and South France (Montpellier) under contrasting temperature, evaporative demand and soil water conditions. Visible and ligulated leaves were counted every second or third day on ten plants and final length and width were measured for each leaf. Five to eight plants with similar development stage were sampled every second or third day in order to record the number of initiated leaves and the lamina length of all leaves of a plant. These experimental data were used to estimate in the model the developmental stage of each leaf (initiation, appearance, ligulation and the beginning and end of the linear expansion phase) and the variation in leaf elongation rate among leaves of a plant. Responses of leaf elongation rate to meristem temperature (Tm), meristem-to-air vapour pressure deficit (VPDm-a) and soil water deficit (predawn leaf water potential, Ψ) were determined from growth chamber and greenhouse experiments (Reymond et al. 2003). Maximum leaf width of each leaf was estimated as a function of leaf position and the width of the widest leaf on the plant. Lamina area was calculated as the product of lamina length by maximal width, corrected by a shape factor of 0.75. The resulting leaf model was incorporated as a replacement module for canopy leaf development in the APSIM-Maize model of the APSIM platform (Wang et al. 2002; Keating et al. 2003). A new micrometeorological module was added to APSIM to calculate weather data at an hourly time step and estimate environmental conditions as sensed by leaves (Tm, VPDm-a, Ψ). The drought effect on silk elongation rate was simulated by varying parameters of the relationship between plant growth rate and grain number set (Andrade et al. 1999).

The ability of the combined model to predict final length of all leaves of the plant was tested in the 12 French experiments, while its ability to predict leaf area index (LAI), biomass accumulation and yield was tested in three field experiments carried out in Gatton (Australia) with the hybrid Hycorn 53 (Pacific Seeds, Toowoomba, Australia) (Lemaire et al., 2007). In these latter experiments, leaf, grain and whole-plant biomass were sampled on 10 plants every 3 to 4 weeks. LAI was estimated from measurements of leaf weight and specific leaf area on these plants. The QTL impact on leaf area index and yield was simulated for genotypes carrying different allelic combinations and grown under contrasting temperature, evaporative demand and soil water conditions.

Results

Experimental basis of the model

Results showed stable patterns of leaf development over a large range of field conditions, when expressed in thermal-time units (for details, see Chenu et al. 2008). Stable responses of leaf elongation rate were observed for leaf 6 in growth chamber and greenhouse experiments. Leaf elongation rate increased linearly with meristem temperature. When expressed per thermal-time unit, the leaf elongation rate decreased linearly with meristem-to-air VPD in well-watered conditions and also decreased linearly with predawn leaf water potential in the absence of evaporative demand during the night, as previously observed in maize (Ben Haj Salah and Tardieu 1996 and 1997; Reymond et al. 2003). Large effects of evaporative demand and water deficit were also observed on final leaf area in field experiments, consistent with the effect on elongation rate in controlled conditions (Fig. 1).

Figure 1. Observed and simulated final lamina lengths in plants grown with contrasting evaporative demand (A) and soil water (B) conditions. A: meristem-to-air VPD of 1.1 and 2.6 kPa (averaged during the vegetative period). B: well-watered and water deficit conditions (with a lowest predawn leaf water potential of -0.3 MPa). Error bars, standard errors.

Based on these results, the leaf model combined a model that predicts leaf 6 elongation rate as affected by environmental conditions (4 parameters estimated from growth chamber and greenhouse experiments) and a model that coordinates the development of all leaves of the plant (13 parameters estimated from a single field experiment). The latter estimated the beginning and end of linear elongation for all leaves and predicted the variation in leaf elongation rate among leaves of a plant. It thus fixed the time frame of expansion for all leaves, while the leaf growth model simulated the leaf elongation rate as affected by the leaf environmental conditions (Tm, VPDm-a, Ψ) that were estimated within the APSIM model (Fig. 2).

Figure 2. Simplified schematic view of the interactions between the leaf model and the APSIM crop model.

Model evaluation at leaf and crop levels

The model was used to predict (with a single set of parameters) the final length of all leaves in the 12 field experiments carried out in France (Fig. 1). Effects of changes with time in soil water potential were accurately simulated for reduction in length of individual leaves. Only leaves exposed to water deficit during their development (either hidden in the whorl or partly emerged) had a reduced final area in the model as in the field experiments (for illustration, see Chenu et al. 2008). Overall the model adequately simulated the final lamina length for effects of both the environment and leaf position on the stem (for all the tested environments: y = 1.018 x, r2 = 0.922, Cve= 0.147).

Tests over the crop cycle were performed in the three Australian experiments using a local hybrid with slightly longer lamina for the first leaves. Interfacing the leaf model with the crop model APSIM allowed estimation of integrated phenotypes at canopy level. The model adequately predicted leaf area index (y=0.919 x, r2 = 0.619, CVe = 0.347), biomass (y = 0.839 x, r2 = 0.955, CVe = 0.177) and grain yield (y=0.986 x, r2 = 0.849, CVe = 0.432) under well-watered conditions that varied in seasonal temperature and VPD across contrasting sowing dates.

Yield impact of QTL controlling leaf and silk expansion

The model was further used to quantify the impact of QTL identified for responses of leaf elongation rate to temperature, VPD and water deficit (Reymond et al., 2003; Welcker et al., 2007). Half of these QTL co-localised with QTL for ASI (related to silk expansion). The impact of QTL controlling both leaf and/or silk expansion was explored by using the model for a range of drought and climatic scenarios (Fig. 3). Results showed substantial effects of leaf expansion QTL on LAI (up to 2 units) with consequent effects on yield. The impact of these QTL was further amplified when the simulation was performed by taking into account the effects of QTL on both leaf and silk expansion as the grain set was then reduced by both low crop growth rate and high ASI.

Figure 3. Simulation of maximum LAI and yield for virtual genotypes characterised by different drought-sensitivity in leaf elongation rate (A) and leaf and silk elongation rate (B), under different evaporative demand and soil water conditions. Genotypes were simulated with extremes responses to VPD and water-deficit, based on the QTL analysis performed in the P1xP2 population (Welcker et al., 2007). Impact of the QTL on silk elongation rate was added for the second set of genotypes (B).

Conclusion

The study presented here shows that it is possible to integrate a leaf growth model with a time scale of hours into a canopy model with a time scale of months. Furthermore the parameters used in the leaf model are related to QTL which are independent of environment, so that we could model the genetic variability at the whole-plant level under fluctuating conditions and evaluate the contribution to yield of QTL for individual leaf traits. This study exemplifies the potential for functional whole-plant modelling in bridging the gene-to-phenotype gap.

References

Andrade FH, Vega C, Uhart S, Cirilo A, Cantarero M and Valentinuz O (1999) Kernel number determination in maize. Crop Science 39, 453-459.

Ben Haj Salah H and Tardieu F (1996). Quantitative analysis of the combined effects of temperature, evaporative demand and light on leaf elongation rate in well-watered field and laboratory-grown maize plants. Journal of Experimental Botany 47, 1689-1698.

Ben Haj Salah H and Tardieu F (1997). Control of leaf expansion rate of droughted maize plants under fluctuating evaporative demand. A superposition of hydraulic and chemical messages ? Plant Physiology 114, 893-900.

Boyer JS (1970). Leaf enlargement and metabolic rates in corn, bean and sunflower at various leaf water potential. Plant Physiology 46, 233-235.

Chenu K, Chapman SC, Hammer GL, McLean, G, Ben Haj Salah H and Tardieu F (2008). Short-term responses of leaf growth rate to water deficit scale up to whole-plant and crop levels: an integrated modelling approach in maize. Plant, Cell & Environment 31, 378-391.

Keating BA, Carberry PS, Hammer GL, Probert ME, Robertson MJ, Holzworth D, Huth NI, Hargreaves JNG, Meinke H, Hochman Z, McLean G, Verburg K, Snow V, Dimes JP, Silburn M, Wang E, Brown S, Bristow KL, Asseng S, Chapman S, McCown RL, Freebairn DM and Smith CJ (2003) An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy 18, 267-288.

Lemaire G, van Oosterom E, Sheehy J, Jeuffroy MH, Massignam A and Rossato L (2007). Is crop N demand more closely related to dry matter accumulation or leaf area expansion during vegetative growth? Field Crops Research 100, 91–106.

Reymond M, Muller B, Leonardi A, Charcosset A and Tardieu F (2003). Combining quantitative trait loci analysis and an ecophysiological model to analyze the genetic variability of the responses of maize leaf growth to temperature and water deficit. Plant Physiology 131, 664-675.

Saab IN and Sharp RE (1989). Non-hydraulic signals from maize roots in drying soil : inhibition of leaf elongation but not stomatal conductance. Planta 179, 466-474.

Sadok W, Naudin P, Boussuge B, Muller B, Welcker C and Tardieu F (2007). Leaf growth rate per unit thermal time follows QTL-dependent daily patterns in hundreds of maize lines under naturally fluctuating conditions. Plant Cell and Environment 30, 135-146.

Wang E, Robertson M, Hammer GL, Carberry P, Holzworth D, Meinke H, Chapman SC, Hargreaves J, Huth N and Campbell GS (2002). Development of a generic crop model template in the cropping system model APSIM. Euopean Journal of Agronomy 18, 121-140.

Welcker C, Boussuge B, Benciveni C, Ribaut JM and Tardieu F (2007). Are source and sinks strengths genetically linked in maize plants subjected to water deficit ? A QTL study of the responses of leaf growth and Anthesis-Silking Interval to water deficit. Journal of Experimental Botany 58, 339-349.

Top Of PageNext Page