H Legendre

J.-P Goby

A Duprat

T Gidenne

G Martin

Herbage intake and growth of rabbits under different pasture type, herbage allowance and quality conditions in organic production

Keywords: rabbit, organic breeding, grazing, weight gain, herbage allowance Implications

A lack of knowledge about rabbit herbage intake during grazing limits the development of organic rabbit production. This study describes rabbit herbage intake under a wide range of grazing conditions and characterises the factors that decrease rabbit herbage intake and daily weight gain. It was conducted with growing rabbits reared in moving cages with 0.4 m 2 of grazing area per rabbit. Rabbits grazed on pastures dominated by legumes (LEG) or grass and forbs (GRF) and received 60 g/day per rabbit of a complete pelleted feed. Three trials were performed in winter, summer and spring. Mean herbage allowance was 27% higher in LEG (62.3 g dry matter (DM)/kg metabolic weight (MW), equal to kg 0.75 ) than in GRF (49.2 g DM/kg MW). Herbage intake varied greatly (36.3 ± 18.0 g DM/kg MW) among trials and was higher in LEG than in GRF (39.5v.34.1 g DM/kg MW). For both pasture types, herbage intake was logarithmically related to herbage allowance and plateaued around 75 g DM/kg MW. Crude protein and digestible energy (DE) intake differed by pasture type and season. Mean CP intake was 40% higher in LEG (15.0 g/kg MW) than in GRF (10.7 g/kg MW). In summer, mean DE intake was 27% higher in LEG than in GRF but no significant differences in DE intake were found between LEG and GRF in winter and spring. Maximum DE intake plateaued near 1000 kJ/kg MW. Daily weight gain was always higher for rabbits grazing LEG (mean = 22.6 g) than GRF (mean = 16.0 g). Weight gain was significantly related to CP intake, whereas DE intake had no significant effect. Meeting the objective of mean daily weight gain of 20 g requires herbage intake of 32 and 50 g DM/kg MW in LEG and GRF, respectively. Therefore, according to the herbage use efficiency observed in our experiments, herbage allowance must reach 42 and 78 g DM/kg MW in LEG and GRF, respectively. When herbage allowance is lower, rabbits cannot meet the CP intake (13 g/kg MW) required for this weight gain objective.

Introduction

For organic rabbit producers, herbage is the least expensive, but also least known, the source of feed, particularly in terms of intake and nutritive value. Therefore, a better understanding of herbage intake is needed to appropriately adjust the size of the grazing area and the amount of feed supplementation with cereal/legume mixtures or commercial pelleted feeds [START_REF] Itab | Lapin bio une filère attentive et volontaire[END_REF][START_REF] Martin | Herbage intake regulation and growth of rabbits raised on grasslands: back to basics and looking forward[END_REF]. In an initial study, [START_REF] Martin | Herbage intake regulation and growth of rabbits raised on grasslands: back to basics and looking forward[END_REF] estimated that, in the conditions of their study, herbage represented 43% (30.6 g dry matter (DM)/kg metabolic weight (MW), equal to kg 0.75 ) of daily total intake (71.4 DM/kg MW) for grazing rabbits in organic production. Rabbits use herbage efficiently [START_REF] Martin | Herbage intake regulation and growth of rabbits raised on grasslands: back to basics and looking forward[END_REF], meaning that they graze a large part of the biomass offered to them. However, rabbits tend to be selective and prefer young and protein-rich vegetation [START_REF] Cooke | Daily food intake of free-ranging wild rabbits in semiarid South Australia[END_REF][START_REF] Gidenne | Le lapin[END_REF]. † E-mail: guillaume.martin@inra.fr Animal (2019), 13:3, pp 495-501 © The Animal Consortium 2018 doi:10.1017/S1751731118001775 animal To improve grazing management in organic rabbit systems, it is particularly important to identify factors that influence rabbit herbage intake and their consequences for rabbit growth and health. [START_REF] Xiccato | Energy and protein metabolism and requirements[END_REF] indicated that rabbits have a highly effective appetite control that is regulated by a chemostatic mechanism when digestible energy (DE) in the diet exceeds 9.0 to 9.5 MJ/kg DM. Below 9.0 MJ DE/kg DM, stomach capacity may limit intake [START_REF] Gidenne | Le lapin[END_REF]. Currently, few data are available to assess herbage intake according to the management of rabbits fattened on pastures: area per rabbit, herbage allowance, etc. [START_REF] Martin | Herbage intake regulation and growth of rabbits raised on grasslands: back to basics and looking forward[END_REF] indicated that herbage allowance was the main determinant of herbage intake in the conditions of their study. The aim of this study was to characterise rabbit herbage intake and weight gain, and the factors that restrict them under different pasture type, herbage allowance and quality conditions (owing to the season, climate, pasture plant species and grazing management).

Material and methods

Experimental design

The study was performed at the experimental unit of Perpignan University in France, under a Mediterranean climate, and according to French organic standards for rabbit farming. Three trials were conducted in winter 2014/15, summer 2015 and spring 2016 (one per season) to obtain a wide range of herbage allowance and quality conditions. Table 1 lists the main characteristics of each trial. Each trial began at weaning (around 45 days old), when rabbits were transferred to moving cages and ended at slaughter (100 days old). Within each trial, 30 rabbits were allocated to 10 moving cages (three rabbits per cage) with 0.4 m 2 of grazing area per rabbit (the minimum density specified by the standards). Cages were moved each morning. Rabbits that died (two in trial 1, one in trial 2 and two in trial 3) were not replaced. Rabbits were hybrids of traditional breeds (Fauve de Bourgogne and Papillon) and were born at the experimental unit.

During each trial, one group (five moving cages) of rabbits grazed pastures dominated by legumes (LEG, mainly sainfoin -Onobrychis viciifolia) and one group (five moving cages) grazed pastures dominated by grasses and forbs (GRF). Groups were balanced according to weight at weaning and litter of origin. Five pastures were grazed: In addition to grazing, rabbits received a complete organic pelleted feed. According to the supplier (EVIALIS, Saint Nolff, France), it contained 86.5% DM, 16.5% CP (N × 6.25), 3.5% crude fat, 37.5% NDF, 21.2% ADF and 9.60 MJ DE/kg DM, and was made up of wheat bran (38.1%), lucerne (35%), sunflower meal (18.6%), maize (5%), bentonite (2%), soya bean meal (1%), calcium carbonate (0.2%) and sodium chloride (0.1%). The pelleted feed was limited to 60 g fresh matter (FM)/rabbit per day, and was always completely consumed by the rabbits.

Grazing measurements Herbage samples were collected weekly at two locations (0.25 m 2 each) for each cage: on the side of the cage, to measure herbage allowance, and in the former cage location immediately after moving the cage, to measure grazing refusals. Electric grass shears were used to cut herbage at the height of 3 cm. Samples were weighed (corresponding to FM), dried at 60°C for 48 h and weighed again to estimate DM concentrations. Herbage intake was estimated as the difference between herbage allowance and refusals, and expressed per kilogram of MW. Fibre concentrations (NDF and ADF) of herbage allowance and refusals were measured according to the sequential procedure of van Soest et al. (1991) every 3 weeks. Crude protein concentrations of herbage allowance and refusals were measured weekly according to the Dumas method (NF ISO 16634-1, 2008), including full combustion of the samples and analysis of the resulting nitrogen gas using a vario EL cube instrument (Elementar Analysensysteme GmbH, Hanau, Germany). Fibre and CP concentrations of herbage intake were calculated as the weighted mean difference between concentrations of herbage allowance and refusals. Digestible energy of herbage intake was then calculated with the equation of Fernández-Carmona et al. (1996) as a function of ADF concentration: DE = 15:9À0:219 ADF Individual rabbits were weighed weekly. Mean daily weight gain was calculated for each week and the fattening period. A subset of the rabbits were slaughtered following the French regulations on animal welfare, that is, with electric stunning before slaughter, on day 50 for trial 1 (one rabbit per cage), and at the end of the fattening for trial 2 (two rabbits per cage) and trial 3 (three rabbits per cage). Their gastrointestinal tracts were extracted and weighed.

Data analysis

Data considered unrealistic for herbage intake were removed. Based on the frequency analysis of rabbit DM intake, we removed outliers that lay outside 0 to 90 g DM/kg MW. Outliers may be related to measurement errors because of variability in herbage biomass, sampling difficulties when herbage is the dense or little difference between allowance and refusals when allowance is high. In total, 216 observations, from 300 possible observations, were retained. Logarithmic regressions Legendre, Goby, Duprat, Gidenne and Martin of herbage intake as a function of herbage allowance were calculated. Analysis of variance was performed to determine the effect of pasture type (two levels, 216 observations) on herbage allowance, intake and refusals, CP and DE concentrations, DE and CP intake. Type III ANOVA with Helmert contrast was also performed for herbage allowance, intake and refusals, CP and DE concentrations, DE and CP intake to determine the effect of conditions (pasture type × season, seven levels, 216 observations) using R software ('car' package). The Tukey's posthoc test was used to test differences across all pasture type × season combinations using P < 0.05 as the significance threshold. Digestible energy intake and CP intake (mean per cage from weaning to the end of the experiment) were the most relevant indicators of the nutrition level from quantitative variables measured or calculated in our study. Regression analysis was performed to determine the effect of DE intake (after log-transforming) and CP intake (after log-transforming) on daily weight gain (35 observations). Afterwards, logarithmic regressions of daily weight gain as a function of CP intake were calculated.

Results

Herbage allowance and quality

As herbage allowance was not controlled, it varied widely (54.7 ± 48.6 g DM/kg MW). Over the three seasons, mean herbage allowance was 27% higher (P < 0.01) in LEG (62.3 g DM/kg MW) than in GRF (49.2 g DM/kg MW) (Table 2). Crude protein concentration was 141.6 g/kg DM on average, but 56% higher (P < 0.01) in LEG than in GRF (179 and 115 g/kg DM, respectively). Digestible energy concentration of herbage allowance was 9.1 MJ/kg DM on average, and lower in LEG than in GRF (9.0 and 9.2 MJ/kg DM, respectively, P < 0.05).

Herbage intake

As expected, herbage intake varied greatly (36.3 ± 18.0 g DM/kg MW) among trials and pasture types (Table 2). Mean herbage intake was higher in LEG than in GRF (39.5 and 34.1 g DM/kg MW, respectively, P < 0.05, Table 2) and increased (28 to 60 g DM/kg MW) with rabbit mean live weight (1.24 to 2.37 kg). Herbage intake (HI) was logarithmically related to herbage allowance (HA) and increased to a plateau (Figure 1):

HI = À73:5 + 65:2 log HA ð Þ R 2 = 0:64; P < 0:001 À Á
Only 30 observations of herbage allowance (out of 216) were considered unrestricting, as they exceeded 85.0 g DM/kg MW. The maximum of mean herbage intake within a cage measured in these conditions was 88.7 g DM/kg MW in LEG and 81.0 g DM/kg MW in GRF. Mean CP concentration in herbage intake was 164 g/kg DM, but was 58% higher in LEG than in GRF (208 and 132 g/kg DM, respectively, P < 0.01). The calculated DE concentration (9.8 MJ/kg DM) did not differ according to pasture type.

Total intake Pellet intake averaged 34 g DM/kg MW (it was limited to 54 g DM/day per rabbit). This corresponds to about 51% of total DM intake. The highest total intake expressed in DM was observed during summer on NMP with 103.7 ± 9.0 g DM/kg MW, corresponding to 468 g FM/kg MW. Total FM intake equalled 17% of BW on average, but the maximum intake as a percentage of rabbit BW, 42% (840 g FM/rabbit of 2 kg or 499 g FM/kg MW), occurred during spring on sainfoin when DM concentration in herbage intake fell to 13% (mean = 25%).

Mean CP intake was 12.9 ± 3.5 g/kg MW. It was higher in LEG (15.0 ± 4.3 g/kg MW) than in GRF (10.7 ± 2.0 g/kg MW, P < 0.01) (Table 2). Mean DE intake was 709 ± 176 kJ/kg MW and it did not differ significantly (P = 0.08) between LEG and GRF; however, DE intake was 27% higher in LEG than in GRF in summer (Table 2). The highest DE intakes were observed on sainfoin in summer (1035 kJ/kg MW) and in spring (1019 kJ/kg MW).

Daily weight gain

Daily weight gain ranged from 3.7 to 38.2 g and varied greatly from week to week (Figure 2). Within each season, mean daily weight gain from weaning to slaughter was always higher for Organic rabbit herbage intake and growth rabbits grazing LEG (22.6 ± 10.6 g) than GRF (16.0 ± 9.4 g, P < 0.05). During spring, however, mean (of LEG and GRF) daily weight gain was 37% lower than that in summer (16.4 and 22.4 g, respectively, P < 0.01). When all trials were considered together, contrary to DE intake, CP intake had a significant effect on daily weight gain (Supplementary Table S1):

daily weight gain = À20:0 + 35:9 log CP intake ð Þ R 2 = 0:40; P < 0:001 À Á

The relative weight of the gastrointestinal tract accounted on average for 21% of the live weight of the rabbits slaughtered.

Discussion

Feed intake of grazing rabbits Herbage intake increased with increasing herbage allowance to a plateau. Studies and meta-analysis describe a non-linear or exponential relationship between herbage allowance and herbage intake for dairy cows and ruminants [START_REF] Delagarde | Ingestion de l'herbe par les ruminants au pâturage[END_REF][START_REF] Peyraud | Managing variations in dairy cow nutrient supply under grazing[END_REF][START_REF] Delagarde | Meta-analysis of the effect of pasture allowance on pasture intake, milk production, and grazing behavior of dairy cows grazing temperate grasslands[END_REF], with maximum intake regulated by nutritional characteristics. In our study, only 30 observations of herbage allowance (out of 216) were considered unrestricting as they exceeded 85.0 g DM/kg MW. No solid conclusion can be drawn on the nutritional characteristics that regulated intake, but the influence of ADF and DE concentrations are to be explored in future studies.

Maximum daily total intake was 104 g DM/kg MW. Similarly, the intake of rabbits fed pelleted feed ad libitum in conventional systems ranged from 89 to 101 g DM/kg MW [START_REF] Gidenne | Feeding behaviour in rabbits[END_REF]. Fresh matter intake of grazing rabbits was much higher (+293%) than rabbits fed pelleted feed ad libitum. For highly palatable feedstuff (whole fresh carrots; [START_REF] Goby | Intake growth and digestion of the growing rabbit fed alfalfa hay or green whole carrot: first results[END_REF], growing rabbits were able to ingest up to 642 g FM/ rabbit or 443 g FM/kg MW, corresponding to 40% of their live weight (1.64 kg). Feeders in that study were empty each morning, which indicates that rabbits did not reach maximum intake. Results from [START_REF] Goby | Intake growth and digestion of the growing rabbit fed alfalfa hay or green whole carrot: first results[END_REF] and our study indicate that when fed fresh forage (alone or with pelleted feed), rabbits have higher intake than when fed pelleted feed alone. [START_REF] Gidenne | Feeding behaviour in rabbits[END_REF] claimed that this increase is linked to decreased residence time in the digestive tract because of higher fibre concentration in the feed. [START_REF] Mugnai | Effect of pasture availability and genotype on welfare, immune function, performance and meat characteristics of growing rabbits[END_REF] observed that New Zealand and Leprino breeds grazing rabbits Values with different superscripts differ significantly at P < 0.05 (Tukey's posthoc test). *Rabbits on Sainfoin were moved to Clover on day 49 for 12 days. + Live weights were measured for all rabbits on day 50 before one rabbit from each cage was slaughtered. ***P < 0.01. had a higher ratio of the full gastrointestinal tract to live weight (18.6% and 19.0%, respectively) than those reared in cages with no access to grazing (18.0% and 18.1%, respectively). We also observed a high relative weight of the gastrointestinal tract (21% of live weight), which could indicate that grazing increased its development.

Crude protein intake and other factors that influence daily weight gain From our observations, herbage CP concentration has a direct impact on growth as already observed by [START_REF] Martin | Herbage intake regulation and growth of rabbits raised on grasslands: back to basics and looking forward[END_REF]. Yet, along with the difference in CP concentration between GRF and LEG, differences in their protein digestibility and contents of essential amino acids, such as methionine, cysteine, lysine and threonine, must also be considered [START_REF] Xiccato | Energy and protein metabolism and requirements[END_REF]. The concentration of condensed tannins in sainfoin may limit protein digestibility, as [START_REF] Legendre | Nutritive value and anthelmintic effect of sainfoin pellets fed to experimentally infected growing rabbits[END_REF] observed recently for conventionally reared rabbits.

Other factors may decrease rabbit growth, such as stressful events (e.g. temperature, predator visits). [START_REF] Seltmann | Effects of body mass and huddling on resting metabolic rates of post-weaned European rabbits under different simulated weather conditions[END_REF] determined that post-weaning growth decreased when rabbits were exposed to cold and wet conditions before weaning, especially for the lightest kits. Consequently, environmental conditions during pre-weaning could influence results of fattening experiments. Differences in weight gain among seasons may be related to this phenomenon, since the pre-weaning periods in winter 2014 and summer 2015 were, respectively, 5.9°C and 7.3°C warmer than that in spring 2016. Parasitism, which was not explicitly considered in this study, could also explain part of the differences in weight gain among trials. For instance, the lower daily weight gain during spring 2016 might have been related to the presence of nematode eggs found in faeces, which will be the subject of future studies.

Potential to decrease concentrate supplementation With an objective for daily weight gain of 20 g (from 1.4 kg live weight at weaning (45 days old) to 2.5 kg live weight at slaughter (100 days old)), and under our experimental conditions, the pelleted feed supplied half of the protein intake, and herbage needed to supply the remaining half (Table 3). To do so, the grazing area (0.4 m 2 /rabbit) is sufficient when LEG are grazed but it should be increased to 0.65 m 2 /rabbit when GRF are grazed. This could be done by decreasing the number of rabbits per cage, by increasing the area of the cage or by moving the cage twice a day. For rabbits fed only herbage, the grazing area of LEG must increase to 1 m 2 /rabbit, that is, by a factor of 2.5. For GRF, even if rabbits ate 100 g DM/kg MW, the grazing area must increase by a factor of 9.3 owing to the lower CP concentration on GRF and accordingly to a different log relationship between herbage intake and herbage allowance on this type of pasture. This suggests the possibility of feeding growing rabbits without supplementation, but more easily on legume pastures and overall with more grazing area per rabbit.

Herbage allowance seemed to be a major factor regulating energy and protein intake, and consequently growth. These initial results for intake and growth of rabbits raised on pastures under organic production standards indicate the need for further studies and modelling to better understand the relation between herbage intake and growth. The results can provide farmers with recommendations on feed supplementation and grazing area per rabbit, depending on their objectives.

Table 3 Determination of the grazing area required to fatten rabbits from 1.4 kg live weight to 2.5 kg within 55 days (daily weight gain (DWG) equal to 20 g/day) under our experimental conditions CPI required for a DWG of 20 g/day based on DWG = -20.0 + 35.9 × log(CPI) 13.0 g/kg MW CPI supplied by 60 g/day per rabbit pelleted feed 6.5 g/kg MW Pasture dominated by GRF LEG HI required to supply 6.5 g/kg MW based on mean CP concentration 50 32 HA required to supply 6.5 g/kg MW based on HI = -73.5 + 65.2 × log(HA) 78 42 Grazing area required to supply 6.5 g/kg MW based on mean HA 0.65 m 2 <0.40 m 2 Grazing area required to supply 13.0 g/kg MW based on mean HA 3.72 m 2 1.00 m 2 CPI = CP intake; MW = metabolic weight; GRF = GRass and Forbs; LEG = LEGumes; HI = herbage intake; HA = herbage allowance.

Legendre, Goby, Duprat, Gidenne and Martin

GRF=

  GRass and Forbs; LEG = LEGumes; RMSE = root mean square error; NMP = natural Mediterranean pasture; DM = dry matter; MW = metabolic weight. a,b,c,d,e

Figure 1 (

 1 Figure 1 (Colour online) Logarithmic relationship (solid line) between herbage allowance and herbage intake (g dry matter (DM)/kg metabolic weight (MW)) by rabbits. The dashed line corresponds to situations where herbage intake is equal to herbage allowance. Pastures dominated by legumes are clover and sainfoin, the others are dominated by grass and forbs. NMP = natural Mediterranean pasture.

Figure 2 (

 2 Figure 2 (Colour online) Rabbit mean daily weight gain over time according to pasture type (grass and forbs: left; legumes: right) and season (winter 2014/15, summer 2015 and spring 2016). NMP = natural Mediterranean pasture.

Table 1

 1 Main characteristics (mean ± 1 SD) of the trials on organic rabbit production GRass and Forbs; LEG = LEGumes; NMP = natural Mediterranean pasture; DM = dry matter. *Rabbits on Sainfoin were moved to Clover on day 49 for 12 days.

			1		2		3
	Trials
	Seasons		Winter 2014/15		Summer 2015	Spring 2016
	Age at weaning (days)		48		41		43
	Live weight at weaning (g)		1364 ± 238		1079 ± 113	1367 ± 228
	Grazing duration (days)		62		50		57
	Daily temperature (°C)		9.3 ± 2.9		24.5 ± 3.3	14.4 ± 1.9
	Daily rainfall (mm)		0.8 ± 2.9		0.7 ± 2.7	1.0 ± 2.15
	Pasture type	GRF	LEG	LEG	GRF	LEG	GRF	LEG
	Pasture dominated by	Fescue	Sainfoin	Clover*	NMP	Sainfoin	Fescue	Sainfoin
	% of Sainfoin in aboveground DM		70			56		70
	GRF =

Table 2

 2 Analysis of variance analysis of the effect of pasture type × season on herbage allowance and refusals, digestible energy (DE) and CP intake, rabbit weight gain and live weight at slaughtering

	Trials		1		2		3
	Seasons		Winter 2014/15		Summer 2015	Spring 2016
	Pasture type	GRF	LEG	LEG	GRF	LEG	GRF	LEG
	Pasture dominated by	Fescue	Sainfoin	Clover	NMP	Sainfoin	Fescue	Sainfoin	RMSE	ANOVA P-value
	Herbage allowance above 3 cm
	(t DM/ha)	1.6 de	1.9 cd	1.1 e	1.6 de	2.9 b	2.2 c	4.5 a	0.8	***
	(g DM/kg MW)	43.1 d	49.6 cd	23.2 e	47.2 cd	76.1 b	58.4 c	107.8 a	19.4	***
	Herbage refusals above 3 cm
	(t DM/ha)	0.5 cd	0.7 bc	0.2 d	0.4 d	0.9 b	0.7 bc	2.1 a	0.5	***
	(g DM/kg MW)	14.4 cde	19.7 bcd	5.5 e	10.5 de	23.4 b	20.0 bc	52.5 a	12.5	***
	Herbage intake (g DM/kg MW)	28.6 cd	29.9 bcd	17.7 d	36.7 bc	52.7 a	38.4 b	55.3 a	14.8	***
	DE intake (kJ/kg MW)	644 cd	652 cd	521 d	702 cd	890 a	736 bc	880 ab	140	***
	CP intake (g/kg MW)	10.9 c	12.9 b	8.7 c	10.5 c	17.4 a	10.7 c	16.8 a	2.3	***
	Daily weight gain (g)	17.5 bc	25.7 a *	14.6 cd *	18.6 bc	26.0 a	12.3 d	21.1 ab	4.4	***
	Live weight at slaughtering (g)	2222 +	2569*, +	2569*, +	2032	2389	2067	2576

Acknowledgements

This study was supported by a grant from the INRA division PHASE (MarkPast project), the INRA committee for organic farming AgriBio4 (CuniPat project) and the INRA metaprogram Integrated Management of Animal Health (GISA, PROF project). The authors thank the AVEM association for providing organic sainfoin seeds, and all the colleagues involved in data collection and analysis, especially J. Le Stum (IUT Perpignan), A. Meynard and C. Bannelier (INRA UMR GenPhySE).

Declaration of interest

The authors declare that they have no conflicts of interest.

Ethics statement

The trials were conducted following the French regulations on organic animal breeding and animal welfare.

Software and data repository resources

The data are deposited in an official repository.

Supplementary material

To view supplementary material for this article, please visit https://doi.org/10.1017/S1751731118001775


Sebastien Lacube

Christian Fournier

Carine Palaffre

Emilie J Millet

François Tardieu

Boris Parent
email: boris.parent@inra.fr

Distinct controls of leaf widening and elongation by light and evaporative demand in maize

Keywords: Evaporative demand, VPD, light, leaf length, leaf width, leaf elongation, leaf widening, GWAS, QTL

Whilst an appreciable part of the variance of leaf area in the field is explained by leaf width, only few papers analysed separately the effects of environmental conditions on leaf widening and elongation.Here we show that light and evaporative demand have distinct controls on leaf elongation and widening, with very low genetic correlations. The resulting equations allowed better predictions of leaf length, width and final leaf area in contrasted environments over Europe.

Introduction

Leaf area affects the amount of light intercepted by plants, but also their transpiration. In case of drought or heat constraints, reducing leaf area reduces photosynthesis but allows plants to save water for later stages of the crop cycle without risking the over-heating that often accompanies stomatal closure [START_REF] Guilioni | On the relationships between stomatal resistance and leaf temperatures in thermography[END_REF]. The understanding of genetic and environmental controls of leaf expansion is therefore central for assessing the trade-off between transpiration and carbon assimilation. For example, a comparison of crop models has shown that the quality of yield prediction by crop models is to a large extent linked to the quality of the modelling of plant leaf area [START_REF] Martre | Multimodel ensembles of wheat growth: many models are better than one[END_REF].

Light and water affect leaf expansion in both monocotyledonous and dicotyledonous species. In the latter, in which leaves expand in two dimensions, light and water affect leaf expansion with markedly different periods of sensitivity within a leaf (Granier & Tardieu, 1999a, b). Young expanding leaves are affected by light through carbon starvation, whilst older expanding leaves are more affected by water via hydraulic limitations [START_REF] Pantin | Coming of leaf age: control of growth by hydraulics and metabolics during leaf ontogeny[END_REF][START_REF] Pantin | Control of Leaf Expansion: A Developmental Switch from Metabolics to Hydraulics[END_REF]. In monocotyledonous leaves, which essentially grow in one dimension, leaf elongation responds to soil water deficit, evaporative demand and temperature in maize, wheat and rice [START_REF] Haj | Control of leaf expansion rate of droughted maize plants under fluctuating evaporative demand -A superposition of hydraulic and chemical messages[END_REF][START_REF] Mahdid | Rapid changes in leaf elongation, ABA and water status during the recovery phase following application of water stress in two durum wheat varieties differing in drought tolerance[END_REF][START_REF] Parent | Rice leaf growth and water potential are resilient to evaporative demand and soil water deficit once the effects of root system are neutralized[END_REF]. Light has no direct effect on leaf length [START_REF] Haj | Quantitative analysis of the combined effects of temperature, evaporative demand and light on leaf elongation rate in well-watered field and laboratorygrown maize plants[END_REF][START_REF] Bos | Morphological analysis of leaf growth of maize: responses to temperature and light intensity[END_REF], whereas it affects leaf width [START_REF] Bos | Morphological analysis of leaf growth of maize: responses to temperature and light intensity[END_REF][START_REF] Sonohat | Variations de structure aérienne de peuplements de maïs en fonction de la densité de semis. Implications pour la modélisation[END_REF]. However, the effect of light on leaf growth of monocotyledons via changes in leaf width has been the object of few studies. For example, entering "leaf elongation" and "leaf widening" in The Web of Science ® (https://webofknowledge.com/) results in 3203 and 63 hits, respectively.

The hypothesis of separate controls for leaf elongation and widening of monocotyledons leaves is supported by several observations. First, final leaf width and length are largely uncorrelated in biparental populations [START_REF] Wei | Epistatic and QTLxenvironment interaction effects on leaf area-associated traits in maize[END_REF][START_REF] Yang | Genetic mapping of QTL for the sizes of eight consecutive leaves below the tassel in maize (Zea mays L.)[END_REF] resulting in few overlapping QTLs in maize [START_REF] Tian | Genome-wide association study of leaf architecture in the maize nested association mapping population[END_REF] and wheat [START_REF] Yang | Genetic dissection of flag leaf morphology in wheat (Triticum aestivum L.) under diverse water regimes[END_REF]. Second, leaf elongation and widening occur at different places in the leaf meristematic zone (Maurice, [START_REF] Gastal | Generation of form and associated mass deposition during leaf development in grasses: a kinematic approach for non-steady growth[END_REF][START_REF] Muller | Association of specific expansins with growth in Maize leaves is maintained under environmental, genetic, and developmental sources of variation[END_REF]. Third, environmental variables that control length and width may differ. Differential effects on leaf length and leaf width have been observed in response to plant density in maize [START_REF] Sonohat | Variations de structure aérienne de peuplements de maïs en fonction de la densité de semis. Implications pour la modélisation[END_REF]. Furthermore, leaf width variations have been shown to closely match the decrease in light interception per plant with plant density [START_REF] Fournier | Virtual plant models for studying interactions between crops and environment[END_REF], whereas leaf length may respond positively or negatively to increased density depending on This article is protected by copyright. All rights reserved.

leaf rank [START_REF] Song | Morphological Characteristics of Maize Canopy Development as Affected by Increased Plant Density[END_REF][START_REF] Sonohat | Variations de structure aérienne de peuplements de maïs en fonction de la densité de semis. Implications pour la modélisation[END_REF]. This suggests independent effects of light and water limitations.

The above paragraphs suggest a need for the reassessment of the respective roles of carbon and water supplies on leaf expansion in monocotyledons, by distinguishing their respective effects on leaf elongation and widening. This may have a large impact on (i) the modelling of environmental effects, because the response of leaf expansion would not be the consequence of a unique sensitivity to environmental conditions but the combination of the sensitivities of leaf widening and elongation; (ii) in the simulation of the genetic variability of plant leaf area, because leaf length and width may have distinct genetic controls and their sensitivities to light or water may largely differ independently in panels of genotypes.

In this study, we have first identified, via a specific experiment, the phenological phases during which leaf widening and elongation occur. We have then analysed the dynamic responses of leaf This article is protected by copyright. All rights reserved.

Material and methods

Calculation of Intercepted radiation in the platform and in the field

In all field experiments, the amount of light intercepted by canopies was simulated by using the crop model APSIM-maize [START_REF] Hammer | Adapting APSIM to model the physiology and genetics of complex adaptive traits in field crops[END_REF] previously parameterised for the genotype B73 [START_REF] Harrison | Characterizing drought stress and trait influence on maize yield under current and future conditions[END_REF]. Briefly, this model uses the Beer-Lambert approach, calculating intercepted light from the leaf area index and an extinction coefficient. We used for that daily meteorological data, sowing date and sowing density, maintaining optimal water and nitrogen conditions during simulations. The amount of light intercepted by individual plants was then calculated by using plant density.

In platform experiments, the amount of light intercepted by each plant was estimated as in [START_REF] Cabrera-Bosquet | High-throughput estimation of incident light, light interception and radiation-use efficiency of thousands of plants in a phenotyping platform[END_REF]. Briefly, 3D plant architectures (point cloud with resolution = 0.512 cm 3 ) were obtained from binarized RBG images with a space carving algorithm (Kutulakos & Seitz, 2000). These 3D plants were used to reconstruct the 3D maize canopy in the platform during the whole experiment. Intercepted light was calculated by using the RATP model [START_REF] Sinoquet | RATP: a model for simulating the spatial distribution of radiation absorption, transpiration and photosynthesis within canopies: application to an isolated tree crown[END_REF].

Timing of leaf elongation and widening

An experiment was performed in Mauguio (France, near Montpellier, SI Table S1) with the hybrid Déa at a density of 10 plants m -² irrigated twice a week. Incident radiation was measured every 15 minutes (LI-190SB, Li-Cor, Lincoln, USA), together with air humidity and temperature (HMP35A Vaisala Oy, Helsinki, Finlande; SI Table S1 and SI Table S3). Leaf appearance rate was measured in a set of 10 plants by scoring phenological stages every second day over the whole experiment.

Destructive plant measurements were carried out on 10 plants every second day. Sampled plants were chosen to represent the median of phenological stage over the whole field. For each plant, width and length of each leaf were measured after plant dissection, thereby allowing measurement of leaves enclosed in the whorl. This was performed with a ruler when leaves were more than 4-mm long, and with image analysis connected to a binocular lens (Bioscan-Optimas V 4.10, Edmonds, WA) otherwise.

Daily mean values of leaf length and maximum width were used to estimate the increase in width and length over time. To compare chronological patterns of leaf width and length on the same scale, data were first normalized by the maximum width or length. A curve was then fitted on time courses with a local regression model ('loess' function in R, -span = 0.6). Thermal time was calculated as in the APSIM model [START_REF] Hammer | Adapting APSIM to model the physiology and genetics of complex adaptive traits in field crops[END_REF]. This article is protected by copyright. All rights reserved.

Dynamic response of leaf elongation rate to environmental conditions

Three plants of the hybrid Déa were grown under well-watered conditions in a soil 40/60% (v/v) of clay and organic compost (SI. Table S3), in the greenhouse of the phenotyping platform PhenoDyn (Montpellier, France -https://www6.montpellier.inra.fr/lepse/M3P -SI.Table S2). with contrasted leaf-to-air vapour pressure deficit (VPD la ) defined as the difference between saturation at leaf temperature (estimated as in [START_REF] Millet | Genome-Wide Analysis of Yield in Europe: Allelic Effects Vary with Drought and Heat Scenarios[END_REF] and actual vapour pressure in the air. LER was expressed per unit of thermal time at each 15 minute time step, with thermal time calculated as in the APSIM model [START_REF] Hammer | Adapting APSIM to model the physiology and genetics of complex adaptive traits in field crops[END_REF]. The response of LER of leaf 6 to VPD la was estimated by using LER and VPD la data at the time step of 15 min for the 3 plants from 14h to 17h, i.e. during daily peaks of vapour pressure deficit. Because the relationship was tight (see results), this resulted in an equation relating LER to VPD la .

(Eq. 1)

With a i (mm °Cd -1 ) the potential leaf elongation rate for the considered leaf rank i in well-watered conditions and low evaporative demand, and b (mm °Cd -1 kPa -1 ) the sensitivity of LER to evaporative demand (leaf-to-air vapour pressure deficit VPD la ), considered common for all leaf ranks. This equation was fitted on leaf 6 results, thereby allowing estimation of a 6 and b. The potential LER for each leaf (a i ) was inferred from a 6 for each leaf rank i by using relationship of [START_REF] Chenu | Short-term responses of leaf growth rate to water deficit scale up to whole-plant and crop levels: an integrated modelling approach in maize[END_REF]. At each time t between beginning and end of leaf elongation, the lamina length of leaf rank i (L i ) was simulated as the cumulated leaf elongation rate at each time step:

(Eq. 2)

The final leaf length was calculated at the end of leaf elongation (t = t final ). This article is protected by copyright. All rights reserved.

Dynamic responses of leaf length and width to light

An experiment was performed in Grignon (France, near Paris, SI Table S1) with the hybrid Déa at a density of 10 plants m -². The field (deep clay loam) was fully irrigated for the whole experiment.

Minimum, maximum temperatures, incident light and VPD were recorded every day. Temperature varied naturally from 0.0°C to 25.2°C with mean value of 8.8°C and VPD reached 2.6 kPa (mean value, 0.5 kPa).Three plots of 10 plants were marked at emergence for identification of phenological stages (leaf tip and ligule appearances) every second day on ten plants per plot before the onset of experimental treatments.

One plot stayed under normal light conditions during the whole experiment ("Light", daily mean value of 16.6 MJ m -2 ). The second plot was shaded from leaf-6 emergence to the end of the experiment with a horticultural net transmitting 20% of light for all wavelengths in the visible spectrum, placed at 30 cm above plant tops ("Shaded"). The same shading nets were used for the last treatment, alternating light and shade conditions ("Alternated") by placing and removing the nets : shade from leaf 6 to leaf 10 emergence, normal light conditions from leaf 10 to leaf 14 emergence, and shade conditions afterwards. Final individual leaf area and shape were measured for all leaves of all plants at the end of stem growth by image analysis [START_REF] Sinoquet | Estimating the 3-dimensional geometry of a maize crop as an input of radiation models -comparison between 3-dimensional digitizing and plant profiles[END_REF].

A position-time model was established to relate each longitudinal position on the leaf to the time when the considered leaf element left the leaf growing zone because it was pushed by younger elements [START_REF] Haj | Temperature affects expansion rate of maize leaves without change in spatial-distribution of cell length -analysis of the coordination between celldivision and cell expansion[END_REF][START_REF] Silk | Steady form from changing cells[END_REF]. Thegrowing zone is located in the first 8 cm beyond the leaf insertion point in maize [START_REF] Tardieu | Spatial distributions of expansion rate, cell division rate and cell size in maize leaves: a synthesis of the effects of soil water status, evaporative demand and temperature[END_REF]. Leaf elongation rate was simulated by using Eq. 1, for which leaf length was predicted every hour from the beginning to the end of linear leaf elongation (Eq. 2). The beginning of leaf linear elongation was calculated as the time when the leaf reached 8 cm, estimated for each leaf as in [START_REF] Chenu | Short-term responses of leaf growth rate to water deficit scale up to whole-plant and crop levels: an integrated modelling approach in maize[END_REF]. A relationship could therefore be established, for each time step, between leaf length and time after beginning of leaf linear elongation. Each longitudinal position along the leaf could therefore be attributed a time after the corresponding leaf element left the leaf growing zone (see [START_REF] Haj | Temperature affects expansion rate of maize leaves without change in spatial-distribution of cell length -analysis of the coordination between celldivision and cell expansion[END_REF]. We could therefore predict at which time the width at any leaf position was produced and identify the environmental conditions at that time, and finally relate these conditions to the width at the corresponding position. This has been performed for leaves 6 to 9 in the three treatments.

At each time after beginning of linear leaf elongation, a ratio was defined for comparing leaf widths produced in the alternated treatment to those in the shade and light treatments. This article is protected by copyright. All rights reserved.

Width ratio = (W alternated -W shade ) / (W light -W shade ) (Eq.3)

This ratio is equal to 0 when leaf width of plants in the alternated treatment matches that of shaded plants and 1 when it matches that of plants under full-light.

Because we observed a dynamic relationship between leaf widening and whole-plant intercepted light during the period of widening, this resulted in the equation:

(Eq. 4)

With Wmin i the x-intercept (minimum leaf width under low light) or the considered leaf i, and k the sensitivity to intercepted radiation, considered common for all leaves.

External dataset in the platform and the field

Fifteen field experiments were carried out in a West-East transect across Europe and one site in Chile in 2011 to 2013 (DROPS network -Millet et al., 2016, SI.Table S1, SI.Table S3) with the maize hybrid B73 x UH007 (named 'B73' afterwards). Soil water status was measured every second day and maintained higher than -0.1 MPa by irrigation (SI. Table S3). Nitrogen and pesticides application were performed following local practices. Air temperature and humidity, wind speed and incident light were recorded every hour in each experiment. Raw measurements were first converted to common units and compared to local meteorological data to assess the dataset [START_REF] Millet | Genome-Wide Analysis of Yield in Europe: Allelic Effects Vary with Drought and Heat Scenarios[END_REF].

Vapour pressure deficit was calculated hourly using temperature and humidity, corrected by radiation in low light conditions as in [START_REF] Chenu | Short-term responses of leaf growth rate to water deficit scale up to whole-plant and crop levels: an integrated modelling approach in maize[END_REF]. Environmental conditions are summarised for each experiment in SI.Table S3. This article is protected by copyright. All rights reserved.

pixels) were taken every night (one from top and 12 side images with a 30° horizontal rotation).

Temperature and VPD la were recorded every 15 min in 8 sites of the greenhouse (SI. Table S3). The B73 hybrid was grown in all experiments (from 2 to 3 replicates, SI. Table S2), together with other 251 hybrids [START_REF] Millet | Genome-Wide Analysis of Yield in Europe: Allelic Effects Vary with Drought and Heat Scenarios[END_REF]) (SI.Table S2). In each experiment, leaf width was measured on hybrid B73 by image analysis on ligulated leaves from rank 7 to 12 for 2 to 5 plants per experiment (SI. Method S1). It was compared to planimeter measurements (LI-3100C Area Meter) in plants of the experiment Spr16 (R² = 0.93, not shown).

The dynamic relationships determined previously (Eq.1 and Eq.4) were tested in this external dataset. We have first tested over the whole dataset the relationships between final dimensions and environmental variables, considering a unique sensitivity to environmental conditions for all leaves.

Environmental variables were calculated as the conditions sensed by plants during the growth of each individual leaf. The effect of leaf rank was calculated as the intercept of the relationship for each leaf. The test of the model itself consisted in simulating final leaf dimensions with the equations determined in dynamic experiments (Eq. 1 and Eq. 4) and comparing with observed values.

Genetic analyses

Experiments were performed in the field and in the platform with a panel of 251 maize hybrids (DROPS panel -Millet et al. 2016) obtained by crossing 251 dent lines with a common flint parent (UH007). The panel structure is further described in [START_REF] Millet | Genome-Wide Analysis of Yield in Europe: Allelic Effects Vary with Drought and Heat Scenarios[END_REF]. In the field (Saint-Martin de Hinx (France, SI.Table S1), plants were sown on 12 th May at a density of 8.5 plants m -2 with 10 plants of each genotype sown in a row and soil water status maintained at high values by irrigation.

Environmental conditions and crop management were collected as above and are summarised in SI.Table S3. The leaf length and width of leaves 7 to 10 were measured within 3 days before This article is protected by copyright. All rights reserved.

where is the vector of phenotypic observations in a given experiment, is the vector of fixed genotypic effect, and and are vectors of random column and row effects, which are used to capture linear trends that might exist across rows and columns. The residual error was assumed to follow a zero mean multivariate normal distribution, with covariance determined by autoregressive processes of order one in the row and column direction. Calculations were performed with ASReml-R [START_REF] Butler | Mixed models for S language environments[END_REF]. Narrow-sense heritability was estimated with a model assuming additive SNP effects using the R-package Heritability [START_REF] Kruijer | Marker-Based Estimation of Heritability in Immortal Populations[END_REF].

Lines were genotyped using a 50K Infinium HD Illumina array [START_REF] Ganal | A Large Maize (Zea mays L.) SNP Genotyping Array: Development and Germplasm Genotyping, and Genetic Mapping to Compare with the B73 Reference Genome[END_REF], and a 600K Axiom Affymetrix array [START_REF] Unterseer | A powerful tool for genome analysis in maize: development and evaluation of the high density 600 k SNP genotyping array[END_REF]. The genetic analysis was performed with the singleenvironment method (GWAS, Genome Wide Association Study) presented in [START_REF] Millet | Genome-Wide Analysis of Yield in Europe: Allelic Effects Vary with Drought and Heat Scenarios[END_REF] with

FaST-LMM v2.07 [START_REF] Lippert | FaST linear mixed models for genome-wide association studies[END_REF] on individual traits for each experiment using the single locus mixed model:

(Eq. 6)
where is the vector of phenotypic values, the overall mean, is the vector of SNP scores, is the additive effect, and and represent random polygenic and residual effects. The variancecovariance matrix of was determined by a genetic relatedness (or kinship) matrix, derived from all SNPs except those on the chromosome containing the SNP being tested as described in Millet et al. This article is protected by copyright. All rights reserved.

Results

Different time courses for leaf elongation and widening.

The time courses of elongation and widening were analysed in a field experiment in which leaves 8 to 11 of ten plants were sampled every second day from leaf initiation to end of elongation, after dissection of the plant whorl. The increase in leaf length, defined as the distance from the leaf insertion point to the tip, was delayed compared to the increase in leaf width (Fig. 1). The beginning of rapid elongation (5% of final leaf length) occurred 35°Cd later than the beginning of rapid leaf widening (5% of final leaf width). Cessation of elongation (95% of final length) occurred 39°Cd after cessation of widening. This temporal pattern, presented in Fig. 1 for leaf 8, was also observed in leaves 6, 7, 9, 10 and 11 (SI Fig. S1). In analyses presented below, the effects of environmental conditions were therefore tested during the respective periods corresponding to 5-95% of either elongation or widening.

Leaf width increased with intercepted light with no effect of evaporative demand

Shading largely affected leaf width in a field experiment where plants were grown either under natural light conditions ("Light"), shaded (80% light attenuation from the appearance of leaf-6 tip onwards, "Shaded") or undergoing alternating periods of normal light and shade ("Alternated"), with shading from leaf-6 appearance to leaf-10 appearance and from leaf-14 appearance onwards (Fig. 2, 3). Plants with maximum incident light had wider leaves than those permanently shaded (Fig. 2c; SI.Fig. 2). Shading during part of the period of leaf widening (Fig. 3a) resulted in intermediate values (Fig. 2c) and suggested a dynamic effect because differences between treatments changed with time (Fig. 2a). This effect was analysed via a position-time model that allowed associating each longitudinal position on the leaf to a time after beginning of linear elongation. New cells are continuously produced in the 3-cm-long meristem close to the leaf insertion point and flow through the 8-cm-long growing zone to mature zones [START_REF] Tardieu | Spatial distributions of expansion rate, cell division rate and cell size in maize leaves: a synthesis of the effects of soil water status, evaporative demand and temperature[END_REF]. One can therefore assign, to each longitudinal position on the leaf (Fig. 2a), a time after the considered cells have left the elongation zone (Eq. 1 and 2, SI.Fig. 3). A width ratio (Eq.3) was defined for comparing leaf widths in the three treatments at any longitudinal position of the leaf. It equalled 100% if width were equal in 'Alternated' and 'Light' treatments and 0% if width were equal in 'Alternated' and 'Shaded' treatments (Fig. 3a). This was performed for leaves 7 to 9 whose development phases were shifted by 35 °Cd per leaf rank.

The width ratio of leaf 7 rapidly decreased when plants were shaded and increased after full light was restored (Fig. 3a). Because the beginning of leaf expansion differed for each leaf rank, leaves of The validity of the relationship between intercepted light per plant and leaf width (Fig. 3b; Eq.4) was tested in an external dataset of 15 experiments in the field and six experiments in the phenotyping platform. A large range of environmental conditions was observed (SI. Table 3), with lower values of intercepted light per plant and evaporative demand in the greenhouse than in the field. Leaf width was significantly higher in the field than in the greenhouse for leaves 8 to 11 (50% of mean values for each leaf rank, SI. Fig. 4). For a given leaf rank, it was closely related to the amount of light intercepted by plants for the period elapsing from 5% to 95% final width. A common relationship between leaf width and intercepted light per plant accounted for variations in width between field experiments and for the difference between field and greenhouse (Fig. 4a). Leaf width was also negatively related to minimum and mean temperatures (Table 1), probably via the effect of temperature on the amount of light intercepted over the considered period of development. Indeed, because the latter has a constant duration in thermal time, its duration in calendar time decreased with temperature. Leaf width showed no relationship with vapour pressure deficit (Table 1).

Leaf length decreased with evaporative demand and light

Leaf elongation was analysed in the shading experiment presented above and in one experiment in controlled environment. In the latter, the elongation rate of leaf 6 was maximum during the night, with no light and low VPD la , and minimum during periods with high light and VPD la . Plants reacted within minutes after changes in environmental conditions with a strong negative effect of VPD la on leaf elongation rate (Fig. 5a,b). Leaf elongation rate measured every 15 min was closely related with VPD la over the same periods (Fig. 5c), probably via a negative hydraulic effect decreasing leaf water potential in the leaf growing zone [START_REF] Tardieu | Modelling the coordination of the controls of stomatal aperture, transpiration, leaf growth, and abscisic acid: update and extension of the Tardieu-Davies model[END_REF].

Shading had opposite effects on leaf length and width in the shading experiment (Fig. 2b,c, Fig. S2).

Final leaf length of plants growing under shading was longer by 6 to 14 cm compared to those grown This article is protected by copyright. All rights reserved.

under full light, with intermediate values for alternated shading (Fig. 2b). In the platform experiment, light had a negative effect on leaf elongation rate, probably via its contribution to VPD la due to leaf heating and, eventually, its effect on leaf water potential [START_REF] Haj | Quantitative analysis of the combined effects of temperature, evaporative demand and light on leaf elongation rate in well-watered field and laboratorygrown maize plants[END_REF].

We have tested if the relationship between leaf elongation rate (LER) and VPD la in the greenhouse (Eq. 1) may account for variations of final leaf length in 15 experiments in the field and one experiment in the platform. Final leaf length was closely related to the mean value of afternoon VPD la over the period of maximum leaf elongation. The latter was calculated in each field based on the thermal times at which leaf length reached 5 and 95% of their final values according to Fig. 1. A clear relationship was observed between fields, which also accounted for the difference between field and greenhouse (Table 1 and Fig. 4b). Final leaf length had no relationship with intercepted light per plant or with mean temperature over the same period.

Taking into account environmental effects on leaf length and width largely improved the prediction of individual leaf area.

The results presented above resulted in Eq. 1 to 4, with parameters established in short term experiments. These equations adequately predicted leaf length and width observed in the independent dataset of field and platform experiments carried out over the whole vegetative period (Fig. 4) with CV of 7% and 11% for leaf length and width, respectively. The model of leaf width based on intercepted light captured a large part of the observed variance, with predictions highly improved (R 2 =0.71, CV of error = 11%) compared to the algorithm in which leaf width would be considered as proportional to leaf length (R 2 = 0.01, CV of errors = 26%). This resulted in an adequate prediction of individual leaf area in the whole field dataset (Fig. 6c, R 2 =0.62, CV = 16%), compared to the model in which leaf width was calculated as a proportion of leaf length (R 2 =0.04, CV = 34% ).

The genetic controls of leaf length and width were largely independent, with consistent allelic effects in field and platform experiments.

Genetic analyses were performed on the mean length and width of leaves 7 and 8 in the platform and of leaves 8 to 10 in the field, in a panel of 251 maize hybrids. Heritabilities were medium to high, except for width measured in the field (Table 2b). The width of leaf 8 largely varied from 5.1 to 9.9 cm in the field and from 5.6 to 8.3 cm in the platform. Correlations between leaf width and leaf length were low in both platform and field experiments (R 2 = 0.001 and 0.26, Table 2). Measured leaves were longer and narrower in the platform than in the field (respectively +30% and -26% for This article is protected by copyright. All rights reserved.

and evaporative demand (Fig. 7). Hence, the effects studied in detail in one hybrid applied to the whole panel.

GWAS analysis led to the identification of twenty nine and fourteen significant QTLs in our dataset for leaf width and leaf length, respectively, either in the field or in the platform (Table 3).

We have first tested if QTLs that affected leaf length or leaf width in the field also affected the same trait in the platform and vice versa. For that, we have considered allelic effects of all QTLs in field and platform, regardless of their significance in the considered condition (by allelic effect, we mean the effect of the allele of the reference hybrid B73 vs the other allele at each QTL, expressed as a percentage of the general mean). This can be visualized in Table 3 (upper part) in which the allelic effects of the 15 QTLs of leaf width identified in the field are also presented for leaf width in the platform and the 15 QTLs identified for leaf width in the platform are also presented for the field. A visual inspection shows that QTLs that positively affected leaf width in one environment also positively affected it in the other one (consistent colours in Table 3). Allelic effects had contrasting amplitudes in the field and in the platform, so most significant QTLs in the field did not reach the threshold of significance (only one colocation of significant QTLs). However, allelic effects were clearly correlated between experiments, for instance allelic effects of QTLs of width identified in the platform were highly correlated with allelic effects of the same QTLs in the field (R 2 =0.88, and R 2 =0.68 for the reciprocal analysis). The same conclusion applied to QTLs of leaf length, in which the nine QTLs of leaf length identified in the field also affected leaf length in the platform, with consistent signs of allelic effects and a high correlation (R 2 =0.91, and R 2 =0.91 for the reciprocal analysis). Hence, we can conclude in a largely common genetic control in the field and in the platform for both leaf width and leaf length, in spite of a high QTL x experiment interaction.

We have then tested if QTLs of leaf width also affected leaf length and vice versa. Overall, this was not the case because QTLs which had high allelic effects on leaf width had small effects on leaf length and vice versa (R 2 =0.18). This can be visualized in Table 3 via the difference in colour and colour intensity between the left and right parts of the table. Hence, we can conclude that the genetic controls of leaf width and leaf length were largely independent. This article is protected by copyright. All rights reserved.

Discussion

This study reconciles the results on the effects of light interception on leaf growth in dicotyledonous and monocotyledonous species. Whereas a clear effect was shown in dicotyledonous species (Granier & Tardieu, 1999a[START_REF] Pantin | Coming of leaf age: control of growth by hydraulics and metabolics during leaf ontogeny[END_REF][START_REF] Pantin | Control of Leaf Expansion: A Developmental Switch from Metabolics to Hydraulics[END_REF], most physiological studies on monocotyledons concluded in a strong effect of hydraulics and an absence of effect of intercepted light [START_REF] Haj | Control of leaf expansion rate of droughted maize plants under fluctuating evaporative demand -A superposition of hydraulic and chemical messages[END_REF][START_REF] Mahdid | Rapid changes in leaf elongation, ABA and water status during the recovery phase following application of water stress in two durum wheat varieties differing in drought tolerance[END_REF][START_REF] Parent | Rice leaf growth and water potential are resilient to evaporative demand and soil water deficit once the effects of root system are neutralized[END_REF]. This was because leaf width was not taken into account in these studies. Here, both shortterm dynamic experiments and statistical analyses of a large dataset in the field and in phenotyping platform conclude in a clear effect of light interception on the growth of individual leaf area.

Leaf width and length are under different genetic and physiological controls. The lack of genetic correlation between length and width is consistent with studies on biparental populations [START_REF] Wei | Epistatic and QTLxenvironment interaction effects on leaf area-associated traits in maize[END_REF][START_REF] Yang | Genetic mapping of QTL for the sizes of eight consecutive leaves below the tassel in maize (Zea mays L.)[END_REF], but this study extends them to the dent maize populations originating from breeding programmes in the USA and Europe and, potentially, to the whole maize species. We also confirm the large variability of leaf width from both genetic and environmental points of view.

Within the few studies considering leaf width, most of them have shown that a larger part of variance of individual leaf area in biparental population was explained by leaf width compared to that by leaf length [START_REF] Wei | Epistatic and QTLxenvironment interaction effects on leaf area-associated traits in maize[END_REF][START_REF] Yang | Genetic mapping of QTL for the sizes of eight consecutive leaves below the tassel in maize (Zea mays L.)[END_REF]. This contrasts to a study in wheat [START_REF] Yang | Genetic dissection of flag leaf morphology in wheat (Triticum aestivum L.) under diverse water regimes[END_REF], working with biparental population found that leaf width was less variable compared to leaf length. In our diversity panel, we found an even larger importance of leaf width on individual leaf area compared to that of leaf length (85% vs 15 % of explained variance). Several underlying mechanisms could explain the differences in responses of leaf elongation and widening. It has been shown that lower water status results in shorter cells in maize [START_REF] Haj | Control of leaf expansion rate of droughted maize plants under fluctuating evaporative demand -A superposition of hydraulic and chemical messages[END_REF] but at the present time and in our knowledge, there is no evidence in favour of the hypothesis of larger cells of more cell rows due to higher intercepted radiation.

The impact of variation of leaf width in multi-environment field trials was also larger than expected.

It contrasted with the results of [START_REF] Chenu | Short-term responses of leaf growth rate to water deficit scale up to whole-plant and crop levels: an integrated modelling approach in maize[END_REF] that did not take into account the variations of leaf width for predicting plant leaf area in Australian maize experiments, but still obtained an adequate prediction of leaf area. We can hypothesise that fields in Australia received high and relatively similar light, resulting in similar leaf widths. Conversely, the experiments presented here, which covered a large part of the European maize growing area presented large differences in light, resulting in large impacts on leaf width and area. Overall, this study to some extent reconciles the results of short-term experiments, which have focused on leaf length with essentially hydraulic effects, with those of a family of crop models that involve carbon supply in the control of leaf area. This article is protected by copyright. All rights reserved.

Indeed, short-term experiments found an effect of intercepted light per plant on leaf area, provided that leaf width is taken into account.

Extrapolating relationships observed at different levels of integration (temporal, spatial) was successful here although they are largely debated [START_REF] Passioura | Simulation models: Science; snake oil, education, or engineering[END_REF][START_REF] Passioura | The drought environment: physical, biological and agricultural perspectives[END_REF][START_REF] Poorter | Pampered inside, pestered outside? Differences and similarities between plants growing in controlled conditions and in the field[END_REF].

Indeed, many processes show trades-off between rates and duration. For instance, temperature has a large effect on leaf elongation rate of several species, but a very low effect on final length because of a total compensation of rate by duration [START_REF] Parent | Temperature responses of developmental processes have not been affected by breeding in different ecological areas for 17 crop species[END_REF]. The success in the upscaling performed here probably involves that such compensations had a minor role in the effects of environmental conditions on leaf width and length. This opens the way to a dialogue between platform and field experiments for the prediction of the genetic variability of the control of leaf growth, in spite of the fact that traits largely differ between field and controlled environment [START_REF] Poorter | Pampered inside, pestered outside? Differences and similarities between plants growing in controlled conditions and in the field[END_REF].

A crucial question in crop modelling is whether adding complexity to the system could significantly improve simulations [START_REF] Parent | Towards parsimonious ecophysiological models that bridge ecology and agronomy[END_REF]. It is therefore a relevant modelling question if both leaf widening and elongation have to be considered compared to simpler formalisms. At present, in most crop models, two categories of algorithms have been proposed, giving contrasting roles for water vs carbon availabilities in the control of leaf growth. The first category considers that the sensitivity of leaf expansion to environmental conditions is indirect and acts through photosynthesis and carbon allocation (e.g. GECROS; [START_REF] Yin | Crop Systems Dynamics. An ecophysiological simulation model of genotype-by-environment interactions[END_REF]. The second category considers direct empirical responses to environmental conditions, regardless of carbon availability, either at the whole-plant level (big leaf models, e.g. APSIM-maize, [START_REF] Hammer | Adapting APSIM to model the physiology and genetics of complex adaptive traits in field crops[END_REF] or at individual leaf level. (e.g. [START_REF] Chenu | Short-term responses of leaf growth rate to water deficit scale up to whole-plant and crop levels: an integrated modelling approach in maize[END_REF][START_REF] Lawless | A wheat canopy model linking leaf area and phenology[END_REF][START_REF] Lizaso | Enhancing the ability of CERES-Maize to compute light capture[END_REF], 2011). All these models consider the expansion of individual leaf area as a whole, thereby implicitly considering that, in monocotyledons, differential responses of leaf widening and elongation to environmental variables do not need to be distinguished. The same implicit assumption can be found in [START_REF] Chenu | Short-term responses of leaf growth rate to water deficit scale up to whole-plant and crop levels: an integrated modelling approach in maize[END_REF][START_REF] Chenu | Simulating the Yield Impacts of Organ-Level Quantitative Trait Loci Associated with Drought Response in Maize -A 'Gene-to-Phenotype' Modeling Approach[END_REF]) that simulated the impact of QTLs of maize leaf elongation on yield, without considering that leaf width may respond differentially to environment. By contrast, this study shows that it can be a relevant choice to consider separately leaf widening and elongation in the range of studied This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  Comment citer ce document : Lacube, S., Fournier, C., Palaffre, C., Millet, E. J., Tardieu, F., Parent, B. (2017). Distinct controls of leaf widening and elongation by light and evaporative demand in maize. Plant, Cell and Environment, 40 (9), 2017-2028. DOI : 10.1111/pce.13005

  widening and elongation to light and evaporative demand in two experiments. We have finally tested to what extent the equations derived from dynamic analyses allow simulation of leaf length and width in 15 field experiments in Europe and 6 experiments in controlled environment. A genetic analysis performed in 251 maize hybrids in the field and in controlled environment is also presented to support the hypothesis of separate genetic controls for leaf length and width in response to light and evaporative demand. Comment citer ce document : Lacube, S., Fournier, C., Palaffre, C., Millet, E. J., Tardieu, F., Parent, B. (2017). Distinct controls of leaf widening and elongation by light and evaporative demand in maize. Plant, Cell and Environment, 40 (9), 2017-2028. DOI : 10.1111/pce.13005

  Comment citer ce document : Lacube, S., Fournier, C., Palaffre, C., Millet, E. J., Tardieu, F., Parent, B. (2017). Distinct controls of leaf widening and elongation by light and evaporative demand in maize. Plant, Cell and Environment, 40 (9), 2017-2028. DOI : 10.1111/pce.13005

  Comment citer ce document : Lacube, S., Fournier, C., Palaffre, C., Millet, E. J., Tardieu, F., Parent, B. (2017). Distinct controls of leaf widening and elongation by light and evaporative demand in maize. Plant, Cell and Environment, 40 (9), 2017-2028. DOI : 10.1111/pce.13005

  Comment citer ce document : Lacube, S., Fournier, C., Palaffre, C., Millet, E. J., Tardieu, F., Parent, B. (2017). Distinct controls of leaf widening and elongation by light and evaporative demand in maize. Plant, Cell and Environment, 40 (9), 2017-2028. DOI : 10.1111/pce.13005

  The network of field experiments showed a large variety of scenarios of temperature, vapour pressure deficit and incident light through the growing season as presented inMillet et al. (2016) (detail in SI Table. S3). In each of the 15 experiments, the final length (from ligule to leaf tip) and final width (maximum over the leaf) of leaves 8 to 11 were measured in 10 plants per experiment. Six experiments were carried out in the phenotyping platform PhenoArch (Montpellier, France, Cabrera-Bosquet et al., 2016; https://www6.montpellier.inra.fr/lepse/M3P) from 2011 to 2016, during different seasons of the year (SI. Table S2). Three seeds per pot of the hybrid B73 were sown at 0.025m depth, and thinned to one plant per pot at three-leaf stage. Plants were grown in 9L PVC pots in a substrate composed of a mixture of clay and organic compost (30/70 volume). Soil water content was maintained at retention capacity in each pot by daily watering (soil water potential of -0.05MPa). Briefly, the PhenoArch platform is equipped with automated weighting and watering stations, imaging stations and environmental sensors. For each plant, 13 RGB images (2056×2454 Comment citer ce document : Lacube, S., Fournier, C., Palaffre, C., Millet, E. J., Tardieu, F., Parent, B. (2017). Distinct controls of leaf widening and elongation by light and evaporative demand in maize. Plant, Cell and Environment, 40 (9), 2017-2028. DOI : 10.1111/pce.13005

  Incident light was estimated at each individual plant position as in[START_REF] Cabrera-Bosquet | High-throughput estimation of incident light, light interception and radiation-use efficiency of thousands of plants in a phenotyping platform[END_REF] from incident light outside the greenhouse and local extinction coefficients. Daily mean temperature in the greenhouse was 18 ± 2 °C (night) and 25 ± 3°C (day), with small differences between experiments. Incident light varied between experiments, with maximum values (7.7 MJ.m -².day -1 ) was measured in Spring 2016 and lowest in Autumn 2011 (2.8 MJ.m -² day -1 ).

  flowering for three plants per hybrid. Leaf width and length were also measured in the platform experiment on the 251 genotypes (Exp. Spr16) for leaves 7 and 8. Genotypic means were calculated for each hybrid using a mixed model based on spatial design: (Eq. 5) Comment citer ce document : Lacube, S., Fournier, C., Palaffre, C., Millet, E. J., Tardieu, F., Parent, B. (2017). Distinct controls of leaf widening and elongation by light and evaporative demand in maize. Plant, Cell and Environment, 40 (9), 2017-2028. DOI : 10.1111/pce.13005

(

  2016) following the method of[START_REF] Rincent | Recovering Power in Association Mapping Panels with Variable Levels of Linkage Disequilibrium[END_REF]. The SNP effects were estimated by generalized least squares, and their significance (H 0 : = 0) tested with an F-statistic. Candidate SNPs distant less than 0.1 cМ were considered as belonging to a common QTL, described via the most significant SNP in the QTL and the interval between all SNPs belonging to the QTL. Co-localisations between QTLs were checked by comparing QTLs intervals and checking for overlap (defined by all SNPs contained in the QTL). Physical positions of significant SNPs were projected on the consensus genetic map for Dent genetic material[START_REF] Lehermeier | Linkage Disequilibrium with Linkage Analysis of Multiline Crosses Reveals Different Multiallelic QTL for Hybrid Performance in the Flint and Dent Heterotic Groups of Maize[END_REF]. Comment citer ce document : Lacube, S., Fournier, C., Palaffre, C., Millet, E. J., Tardieu, F., Parent, B. (2017). Distinct controls of leaf widening and elongation by light and evaporative demand in maize. Plant, Cell and Environment, 40 (9), 2017-2028. DOI : 10.1111/pce.13005

  Comment citer ce document : Lacube, S., Fournier, C., Palaffre, C., Millet, E. J., Tardieu, F., Parent, B. (2017). Distinct controls of leaf widening and elongation by light and evaporative demand in maize. Plant, Cell and Environment, 40 (9), 2017-2028. DOI : 10.1111/pce.13005This article is protected by copyright. All rights reserved.different ranks underwent different timing of full-light and shading in the 'Alternated' treatment.The width of leaf 8 decreased when plants were shaded and increased under full light after a lag time. Leaf 9 started growth just before full-light, with rapid increase of the width ratio, followed by a stabilization of this ratio at the beginning of the second period of shading. Overall, the final leaf width corrected for the effect of leaf rank was closely related to the cumulated light intercepted per plant for the period with maximum widening rate (Fig 3b;calculated with the crop model APSIMmaize). It is noteworthy that a common calculation was adopted for intercepted light per plant in the three treatments without considering the effect of light on leaf width, to avoid circular reasoning (i.e. effect on light interception on leaf width and effect of leaf width on light interception).

  Comment citer ce document : Lacube, S., Fournier, C., Palaffre, C., Millet, E. J., Tardieu, F., Parent, B. (2017). Distinct controls of leaf widening and elongation by light and evaporative demand in maize. Plant, Cell and Environment, 40 (9), 2017-2028. DOI : 10.1111/pce.13005

  leaf 8; mean value for all genotypes), consistent with the difference in intercepted light per plant Comment citer ce document : Lacube, S., Fournier, C., Palaffre, C., Millet, E. J., Tardieu, F., Parent, B. (2017). Distinct controls of leaf widening and elongation by light and evaporative demand in maize. Plant, Cell and Environment, 40 (9), 2017-2028. DOI : 10.1111/pce.13005

  Comment citer ce document : Lacube, S., Fournier, C., Palaffre, C., Millet, E. J., Tardieu, F., Parent, B. (2017). Distinct controls of leaf widening and elongation by light and evaporative demand in maize. Plant, Cell and Environment, 40 (9), 2017-2028. DOI : 10.1111/pce.13005

  Comment citer ce document : Lacube, S., Fournier, C., Palaffre, C., Millet, E. J., Tardieu, F., Parent, B. (2017). Distinct controls of leaf widening and elongation by light and evaporative demand in maize. Plant, Cell and Environment, 40 (9), 2017-2028. DOI : 10.1111/pce.13005

  situations and genotypes, which caused large variations of leaf width. Because the quality of simulations of crop models are linked to the goodness of leaf area predictions[START_REF] Martre | Multimodel ensembles of wheat growth: many models are better than one[END_REF], we propose that identifying leaf widening and elongation in a plant model and therefore improving leaf area predictions could significantly improve predictions This conclusion is consistent with physiological considerations, namely that the controls of carbon-driven and hydraulic-driven Comment citer ce document : Lacube, S., Fournier, C., Palaffre, C., Millet, E. J., Tardieu, F., Parent, B. (2017). Distinct controls of leaf widening and elongation by light and evaporative demand in maize. Plant, Cell and Environment, 40 (9), 2017-2028. DOI : 10.1111/pce.13005 This article is protected by copyright. All rights reserved. mechanisms at the cellular scale, are under the control of very different genes, and translating into contrasted sensitivities of final leaf length and width to environmental conditions in different genotypes. Comment citer ce document : Lacube, S., Fournier, C., Palaffre, C., Millet, E. J., Tardieu, F., Parent, B. (2017). Distinct controls of leaf widening and elongation by light and evaporative demand in maize. Plant, Cell and Environment, 40 (9), 2017-2028. DOI : 10.1111/pce.13005 This article is protected by copyright. All rights reserved. Comment citer ce document : Lacube, S., Fournier, C., Palaffre, C., Millet, E. J., Tardieu, F., Parent, B. (2017). Distinct controls of leaf widening and elongation by light and evaporative demand in maize. Plant, Cell and Environment, 40 (9), 2017-2028. DOI : 10.1111/pce.13005 Comment citer ce document : Lacube, S., Fournier, C., Palaffre, C., Millet, E. J., Tardieu, F., Parent, B. (2017). Distinct controls of leaf widening and elongation by light and evaporative demand in maize. Plant, Cell and Environment, 40 (9), 2017-2028. DOI : 10.1111/pce.13005

  Comment citer ce document : Lacube, S., Fournier, C., Palaffre, C., Millet, E. J., Tardieu, F., Parent, B. (2017). Distinct controls of leaf widening and elongation by light and evaporative demand in maize. Plant, Cell and Environment, 40 (9), 2017-2028. DOI : 10.1111/pce.13005

  Fournier, C., Palaffre, C., Millet, E. J., Tardieu, F., Parent, B. (2017). Distinct controls of leaf widening and elongation by light and evaporative demand in maize. Plant, Cell and Environment, 40 (9), 2017-2028. DOI : 10.1111/pce.13005

  Comment citer ce document : Lacube, S., Fournier, C., Palaffre, C., Millet, E. J., Tardieu, F., Parent, B. (2017). Distinct controls of leaf widening and elongation by light and evaporative demand in maize. Plant, Cell and Environment, 40 (9), 2017-2028. DOI : 10.1111/pce.13005 This article is protected by copyright. All rights reserved. experiment for which the QTL has been detected significantly, together with its associated LOD score. The allelic effects of the same QTLs are presented for length and width in field and platform, regardless of the significance of the QTL in the considered trait and environment. Red and blue, positive and negative allelic effects (respectively) of the allele carried by genotype B73. * indicates significant effect (LOD >5). R int is the cumulated intercepted radiation during leaf widening and VPD is the mean value of leaf to air vapour pressure deficit during the period of leaf elongation. Comment citer ce document : Lacube, S., Fournier, C., Palaffre, C., Millet, E. J., Tardieu, F., Parent, B. (2017). Distinct controls of leaf widening and elongation by light and evaporative demand in maize. Plant, Cell and Environment, 40 (9), 2017-2028. DOI : 10.1111/pce.13005

Figure 1 :

 1 Figure 1 : Time courses of widening and elongation of leaf 8. Circle and square dots: mean of leaf width and length for 10 plants per date (data are normalized by maximum value). Dashed and solid lines are local regressions ("loess" function) for width and length, respectively. Grey vertical dotted lines show 5% and 95% thermal time thresholds for each variable.

Figure 2 :

 2 Figure 2 : Leaf-8 shape in three light treatments. Leaf width plotted against the distance from leaf ligule (a), mean values for maximum leaf length (b) and width (c) for 10 plants. Blue : control treatment without shade; Red: plants continuously shaded since leaf-6 appearance; Green: alternated treatment between shade and light. In (a) Points: experimental data. Lines: local regression (fonction "loess", span = 0.3). (b, c) Mean values and standard deviation of maximum leaf length and width.

Figure 3 :

 3 Figure 3 : Response of leaf widening to intercepted light. (a) Leaf widening in the alternated treatment, relative to the full-light and shaded treatments in leaves 7 to 9. On the y-axis, width values in the alternated treatment are normalized by the difference between control and shade treatments, and showed as a percentage. 0% stands for leaf width in the shaded treatment (80% light reduction) and 100% for the full-light treatment. See text for position-time correspondence (b) Maximum leaf width plotted against plant intercepted light (R int ) in the three treatments and three leaf ranks. ). Leaf width are corrected for leaf rank; (W max,8 ; see M&M). Colours displays the different leaf numbers as in (a).

Figure 4 :

 4 Figure 4 : Response of (a) leaf width to light intercepted by plants during leaf widening (R int ) and (b) leaf length to leaf to air vapor pressure deficit during leaf elongation (VPD la , mean of maximum daily values). Blue dots : Field data. Red dots: Platform data. Symbols represent leaf ranks from 8 to 11, circles, squares, diamonds and triangles, respectively. Leaf width and length values of the four leaf ranks are corrected for leaf rank so equivalent values for leaf 8 are presented (see M&M). Black lines, linear regressions.

Figure 5 :

 5 Figure 5: Time courses of vapour pressure deficit (VPD la ) (a, solid black line), light intensity (Rn) (adashed grey line) and leaf elongation rate (b) during a 4-d experiment in the platform PhenoDyn in well-watered conditions. Data are smoothed with local regression (function loess, span = 0.02). Grey rectangles periods from 14 and 17h, during which VPD la . has been taken into account (c) Response of leaf elongation rate (raw data, non-smoothed) to VPD la during highlighted periods.

Figure 6 :

 6 Figure 6 : Observed vs simulated final leaf length, width and area. Circles : leaf 8 ; Squares : leaf 9 ; Diamonds : leaf 10 ; Triangles : leaf 11.

Table 1 :

 1 Correlations of environmental variables with leaf width and length.The coefficient of correlation (r) was calculated with the R function "corr", and corresponding pvalues were calculated with the "cor.test" function. Data is summarized from leaf beginning to end of expansion: mean of daily minimum temperature, mean of maximum temperature and mean temperature (T min , T max and T mean ); mean of daily minimum, mean of maximum and mean leaf-to-air vapour pressure deficit (VPD max , VPD min and VPD mean ); mean of daily incident light (Rn). Significant

	threshold was fixed at a p-value of 0.01 (p-value > 0.01 : non-significant ; p-value ≤ 0.01 : significant ;
	p-value ≤ 0.001 : highly significant ).

Table 2 :

 2 Coefficient of correlation between leaf length and width (a), narrow sense heritability, number of QTLs identified by the GWAS analysis and prediction capacity of each set of QTLs on the measured variable (b). Data come from 251 genotypes for one platform experiment and one field experiment. (b) For each set of QTLs on the measured variable in the field or in the platform.

	(a)		Platform	Field
			Length	Width	Length	Width
	Platform	Length Width	1	0.03 1	0.24 0.06	-0.14 0.42
	Field	Length Width			1	0.51 1
	(b)	Trait	Heritability	QTLs	Prediction
	Platform	Length Width	0.80 0.73		5 16	0.35 0.64
	Field	Length Width	0.48 0.10		10 15	0.19 0.39

Comment citer ce document : Lacube, S.,

Table 3 :

 3 Comparison of allelic effects of QTLs identified for leaf width or length in the platform and in the field.

				Field	Platform	Field	Platform
				R int = 1.4	R int = 0.4	VPD = 2.4	VPD = 0.8
				MJ/m²	MJ/m²	kPa	kPa
	S1_14810807 1.02	35.2	2.2*	0.1	0.7	-0.5
	AX-90656316 1.02	53.4	2.4*	0.4	0.3	-0.3
	S1_204867908 1.07 155.9	-2.3*	-1.6	-1.3	0.0
	S1_205242298 1.07 156.2	2.5*	1.9	0.9	0.2
	AX-91404589 2.06 128.3	2.2*	1.3	0.6	0.1
	AX-91846273 2.06 129.5	2.4*	1.5	0.8	0.3
	AX-90618756 2.07 159.5	2.4*	1.0	0.0	0.1
	AX-90782812 2.07 160.1	2.3*	1.2	0.0	-0.2
	AX-90841086 3.06 111.4	2.3*	1.0	1.2	0.5
	AX-90841149 3.06 111.6	2.3*	1.0	1.2	0.5
	AX-91623171 4.05	55.7	2.4*	1.5	0.0	-0.4
	AX-90977036 5.08 187.1	-2.3*	-0.5	0.0	1.1
	SYN19549	10.1	87.9	2.3*	1.0	0.6	0.0
	S10_141613269 10.1 109.6	-2.3*	-1.0	-1.2	-0.4
	AX-91194831 10.1 116.8	2.3*	-0.2	0.0	-0.5
	AX-90700654 1.06 147.8	-1.4	-2.8*	0.0	-0.9
	AX-90700772 1.06 148.3	-1.0	-2.5*	0.3	-1.2
	AX-91402250 1.07 156.5	-2.1*	-2.7*	0.1	0.1
	AX-90610562 1.08 189.7	-1.5	-3.1*	-0.9	-0.5
	AX-90733206 2.02	39.1	-1.1	-2.7*	0.2	0.3
	S2_11564804 2.02	39.7	1.4	2.6*	-0.3	-0.6
	AX-91388874 2.02	42.8	-1.4	-2.6*	0.0	0.3
	AX-90598044 2.03	59.1	0.7	2.7*	0.3	-0.1
	AX-90775864 2.06 110.6	0.6	2.9*	-0.2	-0.1
	AX-91541880 2.06 111.0	-0.7	-2.7*	-0.3	-0.2
	PZE-102124387 2.06 111.2	-0.8	-2.7*	-0.4	-0.1
	AX-90776150 2.06 111.9	-1.1	-2.7*	0.1	-0.1
	AX-91542155 2.06 112.8	-0.7	-2.7*	-0.3	-0.2
	AX-91412182 6.01	12.5	0.8	2.8*	0.4	0.1
	S8_172254661 8.08 170.6	0.9	2.7*	-0.2	0.0
	AX-91418229 1.08 200.5	1.9	0.8	1.8*	0.7
	AX-90716443 1.09 207.6	-1.2	-0.3	-1.8*	-1.0
	AX-90602493 4.03	41.2	-1.1	-0.6	-1.9*	-0.9
	AX-90896424 4.06	63.2	-0.4	0.5	-2.0*	-0.8
	AX-90547782 5.07 149.7	0.9	0.9	2.1*	1.0
	S6_103026220 6.03	42.2	1.1	0.4	1.9*	0.5
	S6_113779929 6.04	56.1	-0.6	-1.2	-2.0*	-1.3
	PZE-108093081 8.06 129.4	0.5	-0.5	1.8*	1.0
	AX-90624676 8.06 145.4	-0.7	-1.2	-1.9*	-0.4
	AX-91488109 1.06 141.9	-0.7	0.7	-0.1	2.0*
	AX-91525911 2.05	95.0	1.5	2.9	-0.2*	0.5*
	AX-90575329 5.05 108.3	-0.1	-0.5	-0.9	-2.0*
	S9_90366933 9.03	59.2	-0.1	1.1	0.2	2.0*
	AX-91445646 9.05	81.6	0.3	-0.6	-0.9	-1.8*

Data come from leaf width and length measurements on 251 genotypes for one platform experiment and one field experiment. The first column ("Variable") indicates the trait and

Acknowledgements

This work was supported by the EU FP 7 project FP7-KBBE-244374 and the the Agence Nationale de la Recherche project ANR-10-BTBR-01 (Amaizing). Authors are grateful to Llorenç Cabrera and Antonin Grau for help in platform experiments, and to Aude Coupel-Ledru for genetic analyses. The matrices of genotypic data were built by Sandra Negro, Stephane Nicolas and Alain Charcosset, INRA Le Moulon. The field experiment in Grignon was supervised by Bruno Andrieu, and supported by Agence de l'Environnement et de la Maîtrise de l'Energie (ADEME), with experimental help of Pierre Bonchrétien and Michel Lauransot.

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