Combining breeding traits and agronomic indicators to characterize the impact of cultivar on the nitrogen use efficiency of bread wheat

Field Crops Research 242 (2019) 107588

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Combining breeding traits and agronomic indicators to characterize the impact of cultivar on the nitrogen use efficiency of bread wheat

T

Jean-Pierre Cohana, , Christine Le Souderb, Coline Guicherda, Josiane Lorgeoub, Philippe Du Cheyronc, Michel Bonnefoyd, Alexis Decarriere, François Pirauxb, François Laurentb ⁎

a

ARVALIS-Institut du végétal, station expérimentale de La Jaillière, La Chapelle Saint-Sauveur, 44370 Loireauxence, France ARVALIS-Institut du végétal, station expérimentale, 91720 Boigneville, France c ARVALIS-Institut du végétal, Route de Chateaufort – ZA des Graviers, 91190, Villiers le Bâcle, France d ARVALIS-Institut du végétal, 45 voie Romaine - BP 23 41240 Ouzouer-Le-marché, France e ARVALIS-Institut du végétal, Complexe Agricole du Mont Bernard, Route de Suippes, 51035, Chalons en Champagne, France b

ARTICLE INFO

ABSTRACT

Keywords: Nitrogen NUE Cultivar Apparent fertilizer recovery Bread wheat

Nitrogen (N) is an essential compound for the production and grain quality of bread wheat, which must respect market requirements. Improving nitrogen use efficiency (NUE) is a priority to better manage nitrogen for the benefit of farm economy and the environment. Among all the available levers to do this, the choice of the right cultivar is crucial. To this end, an accurate NUE cultivar characterization is needed both for breeding programmes and for agronomic extension services dedicated to advise farmers. These two fields of expertise (genetic and agronomy) do not share exactly the same protocols and indicators to assess the impact of cultivar on wheat NUE. Moreover, although some recent NUE genetic studies have been conducted on cultivars currently available in France, no recent NUE agronomic studies have been made on current French wheat cultivars. Moreover, the latest agronomic studies to date on cultivar effect on wheat N requirement (coefficient “b” used in France) were conducted without considering the need to reach a grain protein concentration of 11.5% (French bread wheat market requirement) in addition to the objective of achieving the yield potential. By using a classical agronomic protocol (network of 12 field trials over 3 years testing bread-wheat cultivars along a complete nitrogen response curve), the presented study aimed to address three specific NUE related topics by characterizing recent French cultivars with a large range of NUE indicators used by geneticists and/or agronomists. Firstly, we demonstrated that N rate and recent French cultivars had a significant effect on NUE and its components (nitrogen uptake efficiency – NupE and nitrogen utilization efficiency – NutE), as well as on apparent fertilizer recovery (AFR). Secondly, we showed that NupE and AFR were strongly correlated, allowing us to compare genetic studies (mainly using NupE) and agronomic studies (mainly using AFR) without any difficulty. Thirdly, we designed a set of new N requirement indicators (coefficients “bc” and “bq”) helping the farmer to grow each bread wheat cultivar with a dual objective of optimum yield and grain protein content in line with the market requirements. This could be achieved without significant impact on nitrogen post-harvest losses. The presented study provides a large set of results, usable for breeding and agronomic research programs.

1. Introduction Nitrogen (N) is an essential element in the production and quality of harvested organs for most crops (Jensen et al. 2011). Specifically for bread wheat, nitrogen is one of the main production factors and the level of grain protein concentration is a market requirement of which the value could vary depending on the final use of the grain (ARVALIS 2013). For example, the target value for the French internal breadmaking market is 11.5 expressed as a percentage of dry matter of grain

⁎

(% DM), and this threshold is also relevant for most of the exportation markets addressed by this country (Méléard 2007, ARVALIS 2013). Before the 1980s, nitrogen nutrition was managed through the nonoptimized application of mineral fertilizer and various types of manures and slurries. This situation was favoured by low fertilizer prices and non-binding public regulation of crop nitrogen management (Laurent and Leveau 2014). As a consequence, the misuse of reactive nitrogen and leaks from the nitrogen cascade are now assumed to be involved in strong environmental impacts all over the world, including ammonia

Corresponding author. E-mail address: [email protected] (J.-P. Cohan).

https://doi.org/10.1016/j.fcr.2019.107588 Received 8 March 2019; Received in revised form 16 July 2019; Accepted 8 August 2019 Available online 05 September 2019 0378-4290/ © 2019 Elsevier B.V. All rights reserved.

Field Crops Research 242 (2019) 107588

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and nitrogen oxide emissions in the atmosphere and the eutrophication of coastal water (Galloway et al. 2003, Sutton et al. 2011). Along with this environmental issue, the instability and the uptrend of mineral fertilizers prices in Western Europe contributed to weaken the financial conditions of most conventional farms (Laurent et Leveau 2014). To face these challenges, farmers urgently need to increase the economic and environmental sustainability of crop nutrition. One of the main levers to do this is the improvement of nutrition use efficiency (NUE) (Hawkesford 2014). Concerning bread wheat, researchers and farmers have developed several agronomic tools to improve NUE during the last twenty years in France, and more generally in most Western European countries. These include 1) the nitrogen balance sheet method which aims to forecast the total N rate (Rémy and Hébert, 1977; Rémy and Viaux, 1983); 2) N status diagnosis tools whether based on the analysis of nitrate concentration in stem juice (Justes et al., 1997), the measurement of light transmittance through the leaves (based on the use of a leaf chlorophyll meter analogous to the Minolta SPAD-502™), or more recently on the measurement of light reflectance of the green cover by satellite (Soenen et al., 2017); and 3) the optimization of N fertilizer application periods (Bouthier 1997; Limaux et al., 1999; Cohan and Bouthier 2010) and the adoption of more efficient fertilizer types (Lloyd et al., 1997, Chambers and Dampney 2009, Sylvester-Bradley et al. 2014). Along with these agronomic levers, choosing bread-wheat cultivars with improved NUE could also provide a powerful opportunity, if the appropriate traits are identified and the genetic progress is significant enough (Good et al., 2004; Hirel et al. 2007; Foulkes et al., 2009; Cormier et al., 2016). Although NUE is a quantitative trait subject to large ‘genotype x environment x agricultural management’ interactions, substantial genetic progress has been reported for it (Cormier et al., 2013; Guttieri et al., 2017). Nevertheless, the challenges that modern agriculture must face imply that a serious boost of NUE genetic progress is needed. Given that this objective cannot be supported just by geneticists or agronomists, we must adopt an approach that combines both fields of expertise. For a given harvested product (in practice, wheat NUE is almost always calculated for grain yield, but it could also be calculated for other crop outputs such as total aboveground biomass), NUE is generally defined as the yield achieved per unit of nitrogen available to the crop (from soil plus applied fertilizer) (Moll et al., 1982). It is traditionally considered to be the product of two components: N uptake efficiency (NupE: ratio of N taken up by the crop on N soil + fertiliser supply), and N utilisation efficiency (NutE: the amount of harvested product produced per unit of N taken up by the crop) (Moll et al., 1982; Hirel et al., 2007; Sylvester-Bradley and Kindred 2009). These definitions have traditionally been used by geneticists and breeders studying long list of genotypes (25 to 300 for the biggest panels) evaluated under two or three levels of N factor (Cormier et al. 2013, Ortiz-Monasterio et al., 1997; Brancourt-Hulmel et al., 2003). NupE and NutE could thus be considered as NUE breeding traits. Since the beginning of the 1980s, French agronomists have tended to carry out trials within a N response curve design, which allows them to calculate indicators corresponding to the a posteriori optimum N rate for a given harvested product, and then extrapolate these indicators for under- or over-fertilized situations. In most cases, agronomic indicators are therefore ‘standardized’ by clearly referring to this optimum (Makowski et al., 1999, Jensen et al. 2011, Comifer, 2013). In France, the N uptake efficiency agronomic indicator is usually the apparent (nitrogen) fertilizer recovery (AFR, also called apparent nitrogen recovery ANR) (Foulkes et al., 1998; Chambers and Dampney 2009) which could be likened to NupE although no complete study is available to support this comparison. In the nitrogen management tools currently used in France, no cultivar effect on AFR is considered. In parallel, the N utilization efficiency is often represented by the ‘b’ coefficient which is the N requirement per tonne of harvested yield to achieve the optimum grain yield (Le Souder and Bernicot, 1993, Le Souder and Gate 1999). It is the reverse of NutE calculated at the optimum N rate for yield, a posteriori obtained using a

complete N response curve trial design. Coefficient ‘b’ is assumed to be strongly impacted by cultivars and an updated list of cultivar ‘b’ coefficients is published each year for the purpose of extension services (www.comifer.asso.fr, www.arvalis.fr). AFR and coefficient ‘b’ could be considered as NUE agronomic indicators. Although recent genetic studies have been conducted to assess the impact of French bread-wheat cultivars on NUE and its components (Brancourt-Hulmel et al., 2003, Cormier et al. 2013), no agronomic NUE screening of recent cultivars is available, given that the most recently published works of this type are from the 1990s (Le Souder and Bernicot 1993, Le Souder and Gate 1999). These somewhat dated works are the only scientific basis for practical cultivar NUE classification used in France and were made without considering the need to reach a grain protein concentration of 11.5% in addition to the objective of achieving the yield potential. By conducting a field trial network testing the response of cultivars to nitrogen fertilizer rate for three years in the main bread-wheat production regions in France, our study aimed to meet three objectives: 1) determining if the cultivars recently registered in France had an effect on NUE components studied with breeding traits and agronomic indicators, 2) assessing the consistency between the indicators used in the genetic area and the agronomic area, and 3), exploring the relevance of a nitrogen uptake requirement indicator designed to allow each cultivar to achieve the bread market requirements (regarding production and grain quality) without causing significant nitrogen losses in the environment. 2. Materials and method 2.1. Experimental sites and general trial management Twelve trials were carried out during three production campaigns: five in 2013-2014, five in 2014-2015, and two in 2015-2016 in four of the main French bread-wheat production regions: Beauce, Picardie, Champagne and South-West. S-Tables 1 and 2 (supplementary material) give some general details on experimental sites and soil analysis. Soil types varied from deep loamy to chalky soils, and displayed a range of water-holding capacity going from 101 to 200 mm. Preceding crops were various (mainly oilseed rape but also onion, potato, sunflower and sugar beet), and soil tillage management practices were minimum tillage or deep tillage without inversion of the upper soil layer. All experiments were sown in October using an experimental sowing machine and harvested between the end of June and the beginning of August of the next year, using an experimental harvester. Two experiments out of 12 were slightly irrigated, mainly to allow an optimal uptake of the nitrogen from fertilizer applications. If needed, phosphorus and potassium fertilizers were applied during autumn in order to avoid any shortage of these nutrients affecting nitrogen uptake, yield and grain quality. The crop protection programs were typical of the production areas, including fungicides, herbicides and insecticides to prevent any pest damage which could impact the crop. The soil mineral nitrogen stock at the end of winter (60 or 90 cm deep depending on the trial site) was assessed in each trial (KCl extraction through colorimetric reactions in a continuous flow auto-analyser). It showed a wide diversity which is the consequence of different agricultural histories during winter (previous crop, weather impact on mineralization processes, soil characteristics e.g. soil organic matter, etc.). 2.2. Trial protocol The principle of the protocol was to test bread-wheat cultivars against a range of nitrogen fertilizer rates. To do so, the experiments were implemented with two factors, either in split-plot or in criss-cross layout with at least three replicates (some trials had four, due to experimental platform constraints). The single experimental micro-plot was typically 2 m wide and 11 m long (11 sown rows for 7 harvested rows). The first factor was the bread-wheat cultivar (factor in large 2

Trial*

3 1 1 1 5

1 1 1

6

6

1 1 1

1

1

1

1 1

1

6

6

7

1 1 1 1 8

1

1

1 1

1 1

1 1 1

1 1 1 1 9

1 1

1 1

1

6

1 1 1

1

1

1

ARV.BUN91.16

ARV.MOU51.14

ARV.VRA51.15

1

1

ARV.THI36.15

1 1 1

1 1 1

ARV.MON32.15

1

1

1

ARV.MAL63.14

1

1

1

ARV.BIN41.14

Trial*

1

1

ARV.FOR02.15

LD LG RAG LG AO SEC SEC SU SU LG FD RAG RAG FD UNI

Breeder

5

1 1 1 1

1

5

1 1 1

1 1

ARV.THI36.16

ARV.SAI32.14

3 8 2 7 1 1 3 6 4 2 1 12 12 12 2 76

Total number of cultivar occurrence

7

1 1 1

1

1

1 1

ARV.SAI36.14

(h): hybrid cultivar. SP: cultivar registered in Spain. UK: cultivar registered in United-Kingdom. Breeders: AO = Agri-Obtentions; FD = Florimon-Desprez; LD = Lemaire-Deffontaines; LG = Limagrain; RAG = RAGT Semences; SEC = Secobra; SU = Saaten-Union; UNI = Unisigma. * The number at the end of the trial code is the harvest year: 14 = 2013-2014 campaign; 15 = 2014-2015 campaign; 16 = 2015-2016 campaign.

Aerobic Apache Arezzo Arlequin Barok Descartes Garcia Hyfi (h) Hyxpress (h) Lear Nogal Pakito Rubisko Soissons Trapez Total number of cultivar

ARV.BIN41.15

2009 1998 2008 2007 2009 2014 2006 2013 2012 2007UK 2006SP 2011 2012 1988 2009

Aerobic Apache Arezzo Arlequin Barok Descartes Garcia Hyfi (h) Hyxpress (h) Lear Nogal Pakito Rubisko Soissons Trapez Total number of cultivar

Cultivar

Date of registration in France

Cultivar

Table 1 Tested cultivars in each trial.

J.-P. Cohan, et al.

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plots for split-plot designs): six to nine cultivars in each trial. Three were common to all trials (Pakito, Rubisko and Soissons), 3 were common to most of the trials (Apache, Arlequin and Hyfi), and some were specific to the trial cultivation region. Table 1 gives a detailed list of cultivars tested in the trial network. Cultivars were chosen to represent a range of the NUE-genetic diversity of the past 15 years in French breeding programs and cultivar registrations. One cultivar (Soissons) is older (date of registration 1988). It was chosen in order to have a common control with previous analogous trial networks carried out in France. Among the tested cultivars, we included two CHA hybrids (Hyfi and Hyxpress) to study the impact of this technology on NUE. The second factor was the total nitrogen applied along a six rates complete nitrogen response curve. The N rates ranged from a zero-N control to 300 or 350 kg N.ha-1, centred by the forecasted N rate (computed with the N balance sheet method adapted for each trial sites) with a step of 40 or 50 kg N.ha-1. The nitrogen was spread in three applications (Zadoks growth stages 25, 30 and 39; Zadoks et al. 1974) using ammonium nitrate fertilizer. At harvest, grain yield (GY) was measured with an experimental harvester and the harvest index (HI) was assessed by manual samples (3 samples of 50 stems in each microplots) for all micro-plots. Straw biomass was then calculated. N concentration in grain and straw were measured using the Dumas method after oven drying at 80 °C for 48 h. Nitrogen Uptake in Grain (GNU) and straw and the nitrogen harvest index (NHI) were then calculated. The total N uptake (TNU) including roots was calculated by multiplying the N uptake of grain plus straw by 1.25 (Meynard 1985). The grain protein concentration (GPC) was calculated by multiplying the grain N concentration by 5.7. The soil mineral nitrogen stock (SMN) was measured at harvest at 60 cm deep for some selected cultivars in each trial (KCl extraction through colorimetric reactions in a continuous flow autoanalyser).

required to reach a grain protein concentration of 11.5%. Along NRopt, NRgpc was used in the following steps of the procedure. In the third step, the response of total nitrogen uptake at harvest to nitrogen rate was fitted with a linear model. Using NRopt and NRgpc, we defined TNUopt as the total N uptake required to obtain the optimum grain yield and, if necessary, TNUgpc as the total N uptake required to reach the optimum grain yield and a grain protein concentration of 11.5%. In the fourth step, the soil mineral N stock response to nitrogen fertilizer at harvest was fitted using an exponential model described in Equation 2. Using NRopt and NRgpc, we defined SMNopt as the soil mineral nitrogen stock corresponding to the application of NRopt and, if necessary, SMNgpc as the soil mineral nitrogen stock corresponding to the application of NRgpc.

SMN =

if N

x 0)2

(2)

×N

Where:N = nitrogen rate applied (kg N.ha ) SMN = soil mineral N stock at harvest (kg N.ha-1) = ‘floor’ parameter (kg N. ha-1) = curve parameter (kg N. ha-1) = curve parameter ((kg N. ha-1)-1) At the network scale, the relationship between fertilizer N rate (centred by NRopt) and the soil mineral nitrogen stock at harvest (centred by SMNopt) was also fitted using an exponential model that uses the same pattern as described in Equation 2. 2.4. Traits and indicators calculation 2.4.1. Calculation of NUE and NUE components Following the definitions given in the literature (Moll et al. 1982; Hirel et al., 2007; Sylvester-Bradley and Kindred 2009), we calculated the nitrogen use efficiency for grain yield (NUE, Equation 3) and its two components, nitrogen uptake efficiency (NupE, Equation 4) and nitrogen utilization efficiency (NutE, Equation 5) for each cultivar in each trial. This calculation was firstly made for each nitrogen rate, giving as many values of the parameter by cultivar as there are rates of nitrogen in the trial excluding the zero-N control. Then, the calculation was made at the optimum nitrogen rate for yield, giving one value of the parameter by cultivar in the trial (NUEopt, NupEopt and NutEopt). For the calculation of NupE, we needed to estimate the nitrogen amount supplied by the soil. Given that we needed to take into account the spatial variability of nitrogen supplied by the soil and that we also needed to assume that the amount of nitrogen provided by the soil was the same for all cultivars in each trial, we used the total nitrogen uptake of the zero-N control specific to each cultivar in each trial. By doing so, we made the hypothesis that there was no difference between cultivars on this Variable. This hypothesis was then tested in the ANOVA procedure (see 2.5)

The response of cultivar to nitrogen was assessed by using a fitting procedure in four steps. Fig. 1 gives an overview of this procedure. In the first step, the grain yield response to nitrogen fertilizer was fitted with a statistical model adjusted for each cultivar in each trial. The statistical equation chosen displays a ‘quadratic-plateau’ pattern and is described in Equation 1 (Makowski et al. 1999). The optimum grain yield (GYopt) was defined as 97% of the ‘plateau’. We made this choice because the confidence interval of the model was increasing in the vicinity of the ‘plateau’. For the two trials conducted in 2016, the ‘quadratic-plateau’ pattern was not suitable for modelling the response of grain yield to the nitrogen fertilizer rate. Instead, we then used a quadratic pattern and defined GYopt as 97% of the maximum modelled grain yield. The optimum nitrogen rate (NRopt) was the modelled fertilizer nitrogen rate required to reach the optimum yield, which was used in the following steps of the procedure.

× (N

×e

-1

2.3. Nitrogen response curve fitting

if N < x 0 : GY = p

+

NUE =

(1)

GY Available N

(3) -1

Where:NUE = Nitrogen Use Efficiency (t 85% DM.kgN ) NupE × NutE GY = Grain Yield (t 85% DM.ha-1) Available N = N from fertilizer + N from soil (kg N.ha-1)

x 0 : GY = p

Where:N = nitrogen rate applied (kg N.ha-1) GY = grain yield (t 85% DM.ha-1) p = grain yield value at the ‘plateau’ (t 85% DM.ha-1) x0 = minimum N rate to reach p (kg N. ha-1) = curve parameter (t 85% DM.(kg N². ha-1)-1) In the second step, the grain protein concentration response to nitrogen fertilizer was fitted with a linear model excluding the zero-N control (the zero-N control point is plotted in Fig. 1 step 2, but it is not used in the fitting procedure). Using NRopt, we defined the grain protein concentration corresponding to optimum grain yield (GPCopt). We also defined NRgpc as the nitrogen rate needed to reach a grain protein concentration of 11.5 %, if this objective was not achieved with NRopt. In this case, the difference between NRgpc and NRopt is the extra-N rate

NupE =

TNU Available N

=

(4)

Where:NupE = N uptake efficiency (unit less) TNU = Total N Uptake at harvest (kg N.ha-1) Available N = N from fertilizer + N from soil (kg N.ha-1)

NutE =

GY TNU

Where:NutE = N utilization efficiency (t 85% DM.kgN-1) GY = Grain Yield (t 85% DM.ha-1) TNU = Total N Uptake at harvest (kg N.ha-1) 4

(5)

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J.-P. Cohan, et al.

Fig. 1. The four steps of nitrogen response curve fitting. Data from ARV.BIN41.15 trial, cultivar Pakito.

2.4.2. N agronomic-indicators calculation Alongside the nitrogen harvest index (NHI) for each N rate and cultivar in each trial which was a direct output of the measurements made in the field, we calculated NHIopt as the nitrogen harvest index at the optimum nitrogen rate for yield for each cultivar in each trial. For each nitrogen rate and cultivar, we calculated the apparent (nitrogen) fertilizer recovery (AFR) (Chambers and Dampney 2009) using the Equation 6. Like all the other indicators, we also calculated AFRopt as the apparent fertilizer recovery at the optimum nitrogen fertilizer rate for grain yield. To do that, we used two methods: the classical one described in Equation 6 (AFRopt) and the method using the slope of the linear part of the modelled response of TNU to N rate (AFRopt-Reg) (Fig. 1 -Step 3) (Cohan et al. 2018).

AFR =

TNU

TNU 0 NR

fertilizer rate for yield and calculated as the reverse of NutEopt (Equation 7). The extra-N requirement per yield unit to reach a grain protein concentration of 11.5 % at the optimum yield is defined as ‘bc’ coefficient and calculated following the equation 8. The total N requirement per yield unit to reach a grain protein concentration of 11.5 % at the optimum yield is defined as ‘bq’ coefficient and is the sum of ‘b’ and ‘bc’ coefficients (bq = b + bc).

b coefficient =

TNUopt GYopt

(7)

Where:b coefficient = nitrogen requirement per yield unit at the optimum N rate for yield (kgN.(t 85% DM)-1) TNUopt = Total N Uptake at the optimum N rate for yield (kgN.ha1 ) GYopt = Grain Yield at the optimum N rate (t 85% DM.ha-1)

(6)

bc coefficient =

Where:AFR = Apparent (nitrogen) Fertilizer Recovery (unit less) TNU = Total N Uptake at harvest (kg N.ha-1) TNU0 = Total N Uptake at harvest in the zero-N control (kg N. ha-1) NR = Nitrogen fertilizer Rate applied (kg N.ha-1) As described in Le Souder and Bernicot (1993), ‘b’ coefficient is defined as the nitrogen requirement per yield unit at the optimum N

Extra TNUgpc GYopt

(8)

Where:bc coefficient = extra-N requirement per yield unit to reach a grain protein concentration of 11.5 % at the optimum yield (kg N.(t 85% DM)-1) Extra TNUgpc = Extra TNU needed to reach a GPC of 11.5% (kg N.ha-1) 5

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Fig. 2. Weather conditions during May and June and pattern of the response of grain yield to the nitrogen fertilizer rate according to the year of harvest: a-Cumulative rain and global radiation during May and June of the year of harvest for each trial. The labels represent the year of harvest. Meteorological data sources: ARVALIS-Institut du vegetal, METEO-France and INRA; b, c and d-Response of grain yield to the nitrogen fertilizer rate for the harvest years 2014, 2015 and 2016 respectively (cultivar Rubisko, trial sites on the same agro-climatic area in Central France: ARV.SAI36.14, ARV.THI36.15 and ARV.THI36.16). Points, squares and triangles are measurements, plain lines are the fitted curves (quadratic-plateau pattern in 2014 and 2015, quadratic pattern in 2016), and dotted lines are the confidence intervals (95%) of the fitted curve.

GYopt = Grain Yield at the optimum N rate (t 85% DM.ha-1)

Where:Yijk = Variable Y measured in trial i, for cultivar j and nitrogen rate k μ = General mean of Variable Y cultj = effect of cultivar j NRk = effect of nitrogen rate k considered as a discrete Variable Triali = effect of trial i ijk = prediction error of Yijk measured in trial I, for cultivar j and nitrogen rate k × = interaction of 2 terms Inside | | = random effect The model of variance analyses made at the optimum nitrogen rate is described in Equation 10.

2.5. Statistical methods 2.5.1. Software All statistical analyses were performed with R software version 3.4.1 (R Core Team, 2017). The packages are mentioned in the specific procedures described in paragraphs 2.5.2, 2.5.3 and 2.5.4. 2.5.2. Models for response to N rate Nonlinear model fittings were performed with a Gauss-Newton algorithm minimizing the root mean square error (R package nls2), following the general guidelines published by Archontoulis and Miguez (2015). Fitted curve confidence intervals were calculated with a bootstrap procedure (R packages proto and boot). Linear model fittings were performed using the R package lm.

Yoptij = µ+ cultj + |triali | +

2.5.4. Statistical relationship between indicators In some cases, the comparisons between two indicators were performed using a linear regression model (R² from R package lm) and a spearman correlation to compare the ranking of cultivars according to the two indicators (R package Hmisc).

Yijk = µ+ cultj + NRk + cultj × NRk + |triali | + |triali × cultj | + |triali ijk

(10)

Where:Yoptij = Variable Y at the optimum N rate calculated in trial i for cultivar j μ = General mean of Variable Y at the optimum N rate cultj = effect of cultivar j Triali = effect of trial i ij = prediction error of Yoptij calculated on cultivar j in trial i Inside | | = random effect

2.5.3. Variance analysis Variance analyses (ANOVA type III) were carried out with a mixed model using the Restricted Maximum Likelihood (REML) procedure (R packages car, lme4, lmerTest, emmeans) on the mean data par treatments, differentiating fixed and random effects with some interactions. The comparisons between the adjusted means of cultivars were assessed using the Tuckey method. The model of variance analyses including the effect on N rate is described in Equation 9.

× NRk | +

ij

(9)

6

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Fig. 3. Total Nitrogen uptake at harvest (grain, straws and roots) on the zero-N control treatment for the cultivar Rubisko in each trial. The vertical bars are the standard deviations between the replicates.

3. Results

contribution to total variance. Among the random effects (which were all significant), the trial had the stronger impact on these Variables. NUE, NupE and NutE all followed the same pattern. NUE showed a clear decreasing relationship against N rate, as well as NutE (S-Fig 3 in the supplementary material). The same figure also shows the impact of nitrogen rate on NupE. NupE was fairly constant before the optimum nitrogen rate and started to decrease when nitrogen rate reach a limit slightly above the optimum. Concerning the harvest ratios, cultivar had a significant effect on HI and NHI, N rate had an effect on NHI only, and the [cultivar × N rate] interaction had no effect on either ratio. Concerning the agronomic indicator AFR, cultivar and N rate showed significant effects but not their interaction. Like most Variables, it is the trial that had the stronger impact among the random effect, followed by the [trial × N rate] and the [trial × cultivar] interactions. It is interesting to note that the contribution of the residual errors to total variance was quite low for all Variables, indicating the accuracy of our variance model.

3.1. Impact of trial conditions on the response to fertilizer nitrogen rate Agro-climatic contexts have varied according to the years and the experimental sites (S-Fig. 1 in the supplementary material gives the ombrothermic diagrams for each trial). The main climatic issue was the extreme weather event during the grain filling period of 2016. As shown in Fig. 2a, significantly higher rain and smaller global radiation were received by the crop in 2016, compared to 2014 and 2015. In parallel, trials harvested in 2016 showed atypical behaviour of the grain yield response to the nitrogen fertilizer rate compared to 2014 and 2015. In 2014 and 2015, the best fitted model followed a quadraticplateau pattern while, in 2016, the best fitted model followed a quadratic pattern (relevant to fit the decreasing of grain yield from a median nitrogen fertilizer rate without lodging phenomenon) (Fig. 2b, 2c and 2d). Fig. 3 shows the TNU measured on the zero-N control for the cultivar Rubisko in each trial. This illustrates well the fact that the nitrogen amount supplied by the soil was very variable from one trial to another. A varying nitrogen amount supplied by the soil also impacted the parameters of the N response curve and therefore the absolute value of each calculated optima. All these sources of variability could also explain the range of residual standard errors of the nitrogen response curve fittings for all Variables (S-Fig. 2).

3.3. Impact of the cultivar and the trial at the optimum N rate The results of the variance analysis on Variables calculated at the optimum nitrogen rate are given in Table 3. The trial random effect was significant in all cases and was in most cases the main contributor to total variance. The cultivar had a significant effect on GYopt and on GPCopt, but not on NRopt. The cultivar also had a significant effect on TNUopt (however less strong than the cultivar effect on other Variables) but not on SMNopt. The cultivar had a significant effect on the nitrogen rate needed to reach a grain protein concentration of 11.5% (NRgpc) but not on the corresponding total N uptake (TNUgpc) and SMNgpc. Cultivar had a significant effect on harvest ratios (HIopt and NHIopt). It is interesting to note that the cultivar showed no effect at all on the total nitrogen uptake of the zero-N-control treatment.

3.2. Combined impact of the cultivar, the N rate and the trial on measured and calculated Variables The results of the variance analysis on Variables measured or calculated for each nitrogen fertilizer rate are given in Table 2. Cultivar, N rate and their interaction all had a significant effect on GY, GPC, TNU and SMN, with N rate supporting the main part of the fixed effects 7

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Table 2 Variance analysis on production Variables, harvest ratios, NUE components and N agronomic indicators considering all the N rates.

Production variables GY (t 85%DM. ha-1) GPC (% DM) TNU (kgN. ha-1) SMN (kgN. ha-1) Harvest ratios HI (-) NHI (-) NUE components NupE (-) NutE (t grain 85%DM.kgN-1) NUE (t grain 85%DM.kgN-1) N agronomic indicators AFR (-)

Contribution to total variance (%) (and significance of each tested effect) Fixed effects Cultivar

N rate

Cultivar × N rate

Random effects Trial × cultivar

Trial × N rate

Trial

Residual

4.6*** 14.9*** 1.1*** 2.6***

34.1*** 54.8*** 63.5*** 7.6***

0.6*** 1.4*** 0.7*** 7.5**

0.9*** 1.8*** 0.7*** 16.3***

5.9*** 7.4*** 6.1*** 0.7***

53.2*** 18.5*** 26.7*** 62.6***

0.7 1.2 1.2 2.7

11.8*** 4.3***

0.6ns 5.7***

1.2ns 2.2ns

2.1*** 1.6***

10.0*** 14.9***

70.0*** 65.1***

4.3 6.2

4.8*** 8.5*** 7.1***

16.7*** 48.7*** 56.6***

5.2** 1.5*** 1.3***

4.0*** 1.4*** 1.7***

26.1*** 6.9*** 4.3***

33.4*** 30.8*** 27.5***

9.8 2.2 1.5

5.7*

4.5*

5.0ns

9.2***

20.5***

42.7***

12.4

Abbreviations: GY=Grain Yield; GPC=Grain protein Concentration; TNU=Total N Uptake at harvest (grain + straw + roots); SMN=Soil Mineral Nitrogen at harvest (60 cm depth); HI=Harvest Index; NHI=Nitrogen Harvest Index; NupeE=Nitrogen uptake Efficiency; NutE=Nitrogen utilization Efficiency for grain yield; NUE=Nitrogen Use Efficiency for grain yield; AFR=Apparent (Nitrogen) Fertilizer Recovery. (-): unit less. P-value scale 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘(*)’ 0.1 ‘ns’ 1.

Table 3 Variance analysis on production Variables, harvest ratios, NUE components and N agronomic indicators calculated at the optimum N rate for grain yield. Contribution to total variance (%) (and significance of each tested effect)

Production variables GYopt (t 85%DM. ha-1) NRopt (kgN. ha-1) GPCopt (% DM) TNUopt (kgN. ha-1) SMNopt (kgN. ha-1) NRgpc (kgN. ha-1) TNUgpc (kgN. ha-1) SMNgpc (kgN. ha-1) TNU-0 N (kgN.ha-1) Harvest ratios HIopt (-) NHIopt (-) NUE components NupEopt (-) NutEopt (t grain 85%DM.kgN-1) NUEopt (t grain 85%DM.kgN-1) N agronomic indicators AFRopt (-) AFRopt-reg (-) b coefficient (kg N.tgrain-1) bc coefficient (kg N.tgrain-1) bq coefficient (kg N.tgrain-1)

Cultivar fixed effect

Trial random effect

Residual

5.0*** 8.3ns 52.4*** 8.2* 7.9ns 18.9** 6.4ns 15.9ns 1.4ns

91.1*** 59.8*** 25.2*** 72.8*** 52.2*** 46.8*** 72.8*** 30.5(*) 90.9***

3.9 31.9 22.4 19.0 39.9 34.3 20.8 53.6 7.7

11.5*** 8.2***

82.1*** 80.2***

6.4 11.6

10.0* 30.7*** 24.4***

68.0*** 52.7*** 47.8***

22.0 16.6 27.8

8.8(*) 7.8*** 25.7*** 63.4*** 18.9**

65.7*** 83.8*** 56.0*** 14.9** 50.3***

25.5 8.4 18.3 21.7 30.8

Abbreviations: GY=Grain Yield; NR=Nitrogen fertilizer Rate=GPC: Grain protein Concentration; TNU=Total N Uptake at harvest (grain + straw + roots); SMN=Soil Mineral Nitrogen stock at harvest (60 cm depth); NHI=Nitrogen Harvest Index; NupeE=Nitrogen uptake Efficiency; NutE=Nitrogen utilization Efficiency for grain yield; NUE=Nitrogen Use Efficiency for grain yield; AFR=Apparent (Nitrogen) Fertilizer Recovery. (-): unit less. Suffix ‘opt’: indicator calculated at the optimum N rate for grain yield; suffix ‘gpc’: indicator calculated at the optimum N rate for grain yield and a grain protein concentration of 11.5%; suffix ‘-reg’ for AFR refers to the calculation method using the linear regression between TNU and NR. P-value scale: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘(*)’ 0.1 ‘ns’ 1.

Concerning NUE, the cultivar had a significant effect on it and on its components, however less strong for the nitrogen uptake efficiency (NupE) than for the nitrogen utilization efficiency (NutE). The cultivar effect was significant for apparent fertilizer recovery, but less significant for the calculation method leading to AFRopt than for the method leading to AFRopt-Reg. Finally, all the ‘b’ coefficients (‘b’, ‘bc’ and ‘bq’) were significantly impacted by the cultivar, with a high contribution of cultivar to total variance (even higher than the trial effect concerning bc coefficient). The few Variables which were not

significantly impacted by cultivar often displayed a high contribution of ANOVA residuals to total variance. Fig. 4 shows the comparative distributions of NRopt and GYopt. It shows a tendency of co-increasing of NRopt and GYopt, but with a strong variability linked to the cultivars and the trials. In the same figure, the trials conducted in 2016 showed a clearly different behaviour than those conducted in 2014 and 2015 with significantly lower NRopt and GYopt. The supplementary material gives more detail on the different behaviour of the trials regarding their harvest year. S-Fig.4 shows the comparative distribution of GYopt and 8

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Fig. 4. Relationship between the optimum nitrogen fertilizer rate (NRopt) and the optimum grain yield (GYopt). Boxplots on the right and at the top of the plot represent the distribution of GYopt and NRopt respectively, for each harvest year.

Fig. 5. Relationship between the optimum nitrogen fertilizer rate (NRopt) and the optimum grain yield (GYopt) for each cultivar (adjusted means from the ANOVA procedure). The statistical groups of the Tukey comparisons are given on the right for GYopt and at the top for NRopt.

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Fig. 6. Relationship between GYopt and GPCopt for each cultivar (adjusted means from the ANOVA procedure). The dotted lines are the iso-lines for grain nitrogen uptake (GNU) ranging from 125 to 200 kg N.ha-1. The statistical groups of the Tukey comparisons are given on the right for GPCopt and at the top for GYopt. Fig. 7. Relationship between TNUopt and GYopt for each cultivar (adjusted means from the ANOVA procedure). The dotted lines are the iso-lines for nitrogen utilization efficiency (NutE) ranging from 0.030 to 0.045 t 85% DM.kgN-1. The statistical groups of the Tukey comparisons are given on the right for GYopt and at the top for TNUopt.

GPCopt along iso-GNU lines. No clear relationship could be seen between GYopt and GPCopt. Nevertheless, it is interesting to note than more than half of the situation (cultivar × trial) showed a GPCopt below the market objective of 11.5 % DM. Once again, the trials

harvested in 2016 showed a particular pattern, grouped around the isoGNU line equal to 100 kg N.ha-1. On the other hand, 2014 and 2015 trials displayed situations (trial × cultivar) which covered almost the entire spectrum of GNU (below the iso-GNU line 125 kg N.ha-1 to above 10

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Fig. 8. Relationship between NupEopt and NutEopt for each cultivar (adjusted means from the ANOVA procedure). The dotted lines are the iso-lines for NUE ranging from 0.025 to 0.040 t 85%DM.kgN-1. The statistical groups of the Tukey comparisons are given on the right for NutEopt, at the top for NupEopt, and under each point for NUEopt.

the iso-GNU 200 kg N.ha-1) line. S-Fig. 5 shows the comparative distribution of GYopt and TNUopt along iso-NutE lines. It is interesting to note that, for the same GYopt, the dynamic of nitrogen uptake has led to various NutE, depending on cultivars and trials. In this regard, the trials harvested in 2016 covered a wide range of NutE at the optimum, notably showing the lowest situations (around the iso-NutE line equal to 0.025 t 85% DM.kgN-1). 2014 and 2015 trials displayed similar NutE distribution from iso-NutE line 0.025 t 85% DM.kgN-1 to iso-NutE line 0.045 t 85% DM.kgN-1). Fig. 5 displays the relationship between GYopt and NRopt for each cultivar. Unlike Fig. 4, there was no clear relationship between NRopt and GYopt at the cultivar scale, although NRopt and GYopt both displayed a large range of variation. For example, the cultivar Soissons displayed a significantly different GYopt compared to the cultivar Hyfi while the cultivars Rubisko and Pakito showed an intermediate pattern. As a reminder, the ANOVA procedure did not show any significant effect of cultivar on NRopt (Table 3). Fig. 6 shows a negative relationship between GYopt and GPCopt framed by the GNU-isolines 150 and 175 kg N.ha-1, although two cultivars seemed not to follow this trend (Arlequin and Lear). In Fig. 7, most of the cultivars have a GYopt-TNUopt relationship bordered by the NuteE-isolines 0.035 and 0.040 t 85% DM.kgN-1, giving a positive relationship pattern between GYopt and TNUopt. Fig. 8 shows the adjusted means of NupEopt and NutEopt for each cultivar, positioned along the iso-NUE lines. As already shown by the results of the variance analysis, the cultivar had a significant impact on NUEopt and its components. For a given value of NUEopt, cultivars displayed various combinations of NupEopt and NutEopt to achieve it. Some particular behaviours worth mentioning: Arlequin showed one of the highest NUE (above 0.035 t 85% DM.kgN-1) combining the lowest NupE and the highest NutE, Hyfi displayed a good combination with the highest and fairly satisfactory NutE, and a series of cultivars (Apache, Trapez, Garcia, Pakito, Rubisko and Hyxpress) formed a “medium” group with a compromise between NupE and NutE. Finally, it is interesting to note that the NUE cultivar ranking is mainly supported by the NutE ranking. This is due to the small variation in NupE, even if this

component could serve as an adjustment Variable. The two CHA hybrids (cultivar Hyfi and Hyxpress) did not show a particular pattern due to their breeding origins, as they are grouped with some non-hybrid cultivars showing the same NUE pattern. 3.4. Relationship between NupE and AFR Fig. 9a shows the comparison between NupE and AFR calculated for each N rate. The statistical link between the two indicators was high (R² = 0.94). Fig. 9b shows the same comparison but at the optimum nitrogen fertilizer rate. The R² coefficient was also high (R² = 0.91). In both comparisons, the spearman coefficient was also high, meaning that the ranking of cultivar is not significantly impacted by the choice of the compared indicators. S.Fig.6 (in the supplementary material) shows the comparison between the two methods of calculation for AFR. The statistical link between them was quite low (R² = 0.62) and the ‘slope’ method often gave a lower value of AFR than the ‘optimum’ method. Nevertheless, the comparison showed a spearman coefficient of 0.84, indicating that the ranking between cultivars was rather stable when we switched from one indicator to the other. 3.5. Analysis of coefficients b, bc and bq Fig. 10 shows the adjusted means of ‘b’, ‘bc’ and ‘bq’ coefficients for each cultivar. Differences appeared between cultivar for each coefficient, with some extreme patterns represented by the cultivar Aerobic (high ‘b’ coefficient with ‘bc’ coefficient equal to 0) and the cultivar Lear (low ‘b’ coefficient with high ‘bc’ coefficient). Between these two extremes, a range of behaviour exists with no systematic relationship between the two indicators. It is interesting to note that eleven out of fifteen cultivars displayed a’ b’ coefficient below 30 kg N.t grain 85% DM-1. Fig. 11a shows the impact of the nitrogen fertilizer rate on the SMN. SMN was quite stable or very slightly increasing up to a nitrogen rate equal to 35 kgN.ha-1 above the optimum rate (the so-called ‘Extra N rate limit’, also corresponding to the difference between cultivar 11

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Fig. 9. Comparisons between N uptake Efficiency (NupE) and Apparent Fertilizer Recovery (AFR). The sub-plot -a- shows both indicators calculated for each single N rate. The sub-plot -b- shows both indicators calculated at the optimum N rate. The dotted lines are the bisectrix. The plain lines are the linear regression lines. R² refer to linear regression. Spearman = Spearman’s correlation coefficient.

Fig. 10. ‘b’, ‘bc’ and ‘bq’ coefficients calculated for each cultivar (adjusted means from the ANOVA procedure). The vertical bar is the standard deviation of the residual error of the ANOVA procedure for ‘bq’ (=’b’+’bc’). The letters correspond to the statistical group of the Tukey comparisons for ‘b’ (bottom line), ‘bc’ (middle line) and ‘bq’ (top line). The number labels correspond to the number of occurrence of each cultivar in the field-trial network.

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Fig. 11. (a) Relationship between the N rate applied and the Soil Mineral Nitrogen (SMN) measured at harvest at 60 cm depth. Both Variables are centred by their values at the N optimum rate for grain yield. The plain line corresponds to a fitted exponential model. (b) Distribution of the extra N rates needed to reach a grain protein concentration (GPC) of 11.5% DM at the optimum grain yield . In both sub-plots, the vertical dotted line corresponds to the limit beyond which the application of extra N implies a too high increase of SMN at harvest (defined as 35 kg applied N.ha-1).

adjusted means of NRgpc and NRopt). Subsequently, the SMN increased rapidly. Represented on the same X-scale than Fig. 11a, Fig. 11b shows the distribution of the extra-nitrogen rate needed to reach a GPC equal to 11.5% (i.e. the impact of the use of ‘bq’ coefficient for all cultivars in all trials). The majority of the situations were under the Extra N rate limit.

strongly between experimental sites. Given that our statistical analysis properly took into account the trial site as a random effect, all these variations linked to the trial sites could be seen as a good way to test the robustness of the cultivar effect on NUE that we want to address.

4. Discussion

The significant effect of N rate on NUE and its components is well known among geneticists (Le Gouis et al. 2000, Cormier et al. 2013) and agronomists (Diekmann and Fischbeck 2005, Sylvester-Bradley and Kindred 2009). It is directly linked to the different response of grain yield and nitrogen uptake to nitrogen supplying (Sylvester-Bradley and Kindred 2009). Given that the effect of [cultivar X N rate] interaction was significant in most cases, it is important to be able to compare cultivar at their respective nitrogen optimum rate to clearly address the effect of cultivar on NUE and its components. Although not totally original, this part of our results is important to briefly highlight how our results can be linked with the basic knowledge in wheat N nutrition.

4.2. Impact of N rate of NUE and its components

4.1. Trial site impact on response to N rate The atypical weather conditions during the grain filling period of 2016 trials are not a specificity of those two trial sites in that year. Indeed, a great part of France was affected by this phenomenon (Deudon 2016, Ben-Ari 2018), leading to a general collapse of wheat yield in most of the traditional production regions. Consequently, farm economic results in general were significantly impaired (Leveau et al. 2016). Some regions of other European countries like southern Germany or north-western Austria were also affected (Deudon 2016). According to French agronomists, this situation was caused by some serious spike fertility problems and bad grain filling conditions triggered by the combination of very bad weather conditions (high rain amount and low radiation) and disease development on leaves and spikes (Streiff et al. 2017; Deswarte 2016, Ben-Ari et al. 2018). The rapidly negative yield response to nitrogen rate was seen in several situations across the country (Deswarte 2016). The weather particularities in 2016 led to atypical behaviours in the trials conducted during that year. The great diversity of TNU-0 N could easily be explained by the differences in soil characteristics in the different trials. First of all, the amount of soil mineral nitrogen at the end of winter varied significantly between trial sites and we know that this is a key aspect of the management of nitrogen fertilization on a given soil (Comifer 2013). Secondly, the tryptic soil concentration of organic matter, clay, and CaCO3 which are known to be the key soil characteristics explaining the mineralization dynamic of organic nitrogen (Clivot et al. 2017), varied

4.3. Impact of cultivar on NUE and its components As cultivar had an effect on GYopt and NUE, the lack of cultivar effect on NRopt could be surprising. We assume that this apparent contradiction is partially linked to the imprecision of the statistical method to calculate NRopt (the confidence interval of an X axis value of a quadratic-plateau model for a given Y value is quite high, Fig. 1 a). The negative relationship between GYopt and GPCopt at the cultivar scale is highly consistent with what we already know about the dilution phenomenon of the protein in the grain, and how we can use the deviation from this trend to characterize the cultivar regarding their fitness to accumulate protein in the grain (Monaghan et al. 2001). Nevertheless, the co-called Grain Protein Deviation (GPD) indicator could not be used in our study because it required a larger dataset to be robust enough (Oury and Godin, 2007). The significant effect of cultivar on harvest ratios (HIopt and NHIopt) was also already shown in other 13

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studies (Ortiz-Monasterio et al. 1997, Le Gouis et al., 2000, Cormier et al., 2013). We demonstrated that bread-wheat cultivars recently registered in France had a significant effect on NUE and NutE, in agreement with numerous studies around the world (Ortiz-Monasterio et al. 1997, Foulkes et al. 1998, Le Gouis et al. 2000, Brancourt-Hulmel et al., 2003, Gooding et al. 2005, Cormier et al. 2013, Sadras and Lawson 2013) which assessed a significant effect of cultivar from various countries on NUE and NutE. More specifically for France, Cormier et al. (2013) showed a significant effect of the date of cultivar registration in France on the NUE genetic progress slope over time, and that this progress was mainly supported by NutE. Our cultivar panel was not wide enough to test this temporal hypothesis but we can note that the oldest tested cultivar Soissons was also one of the least efficient cultivars. Differences in NutE between cultivars could be related to their respective capacity of remobilization of the nitrogen uptake. Gooding et al. 2005 also linked cultivar-dependent difference on NutE with the duration of growth period. We further demonstrated that the tested cultivars had a significant effect on N uptake efficiency, whether it was calculated with the classical definition of NupE or with the proxy AFR, and whether it was calculated for each N rate or at the optimum N rate for grain yield. Le Gouis et al. 2000 indeed demonstrated a significant effect of cultivar on NupE, and Ortiz-Monasterio et al. (1997) and Foulkes et al. (1998) both demonstrated too that NUE genetic progress could partly be supported by an improvement of NupE or AFR. Nevertheless, not all studies agree on this point (Cormier et al. 2013). The effect of cultivar on N uptake efficiency could be related (1) to contrasted above-ground growth kinetic, which is known to influence winter wheat AFR (Limaux et al. 1999), and/or (2) to roots traits impacting both the root architecture (roots density and depth) and functioning (Hawkesford 2014, Thorup-Kristensen et al. 2009). Nevertheless, in Allard et al. (2013), significant differences between breadwheat cultivars were observed for root N amount, but they had little impact on genotype ranking for NUE. To calculate NupE, we used TNU0 N of each cultivar in each trial to estimate the nitrogen amount supplied by the soil. By doing so, we assumed that there was no cultivar effect on this last Variable. This assumption was validated by the variance analysis, although we must be cautious as the uncertainty of N uptake measurements could prevent the demonstration of small differences between treatments. It is interesting to note that this question is still a point of debate in the geneticist community with various possible solutions: Cormier et al. (2013) calculated the N supply denominator of NUE with the 95th percentile of total N uptake at maturity of all varieties in a panel trial (more than 200 genotypes), OrtizMonasterio et al. (1997) did not estimate it at all (the variation of N supply was only supported by the variation of N rate), and Le Gouis et al. (2000) simply estimated it with the soil mineral N at the beginning of the growth period. The statistical link (by linear regression and also with the spearman coefficient) that we found between NupE and AFR is crucial. Firstly, by simply referring to N supplied by fertilizer, AFR avoids the already mentioned debate on the best way to calculate N supplied by soil. Secondly, it allows us to compare the choice of cultivar with all available means known to impact the AFR. Indeed, AFR has been used by agronomists for 40 years to study, for example, the N effect of fertilizer type (Chambers and Dampney 2009, SylvesterBradley et al. 2014). The adjusted means of AFRopt in our trial network was pretty high, leading us to think that the agronomic and weather conditions were globally favourable to a good N uptake by winter wheat. We showed that the correlation between the two methods of calculation of AFR was less satisfactory (R2 = 0.62). Both methods are found in the literature, either in agronomic studies (Chambers and Dampney 2009) or in genetic studies (Foulkes et al. 1998, Good et al. 2004). AFR calculated as the slope of the linear part of the regression between TNU and N rate is generally smaller than AFR simply calculated at the optimum rate. This is due to the fact that the slope could be strongly influenced by the behaviour of individual points, mainly in the

vicinity of the optimum N rate for grain yield. It is interesting to note that the cultivar effect on AFR is more significant when calculated with the slope than at the optimum rate and that the ranking of cultivar was rather stable between the two methods of calculation. These facts lead us to think that the slope method deserves to be used, despite its sensitivity to the accuracy of the linear model fitted between TNU and N rate. Some values of NupE and AFR exceeded 1. This artefact could sometimes be seen in field trials displaying high value of N fertilizer uptake efficiency and a rather low precision of TNU measurements. 4.4. N requirement to reach both optimum yield and GPC = 11.5% Compared to the previous similar studies in France (Le Souder and Bernicot 1993, Le Souder and Gate 1999), we confirmed that there is a strong cultivar effect on the N uptake requirement per unit of yield at the optimum N rate for yield (so-called’ b’ coefficient). Compared to the standardized value of 30 kg N.t grain 85% DM-1 largely used by French extension services (Comifer 2013), almost all the cultivars we tested displayed a lower value. Our panel was too small to determine whether we assumed for certain that this was another proof of genetic NUE progress in France (as already shown in Cormier et al. 2013) and/or if it was due to a majority of trials in 2014 and 2015 with a favourable grain filling period leading to a rather low’ b’ coefficient (e.g. high NutE). We demonstrated that there was also a cultivar effect on the N requirement to reach both optimum yield and a grain protein concentration of 11.5% (significant effect of cultivar on ‘bq’ coefficient). The differential capacities of cultivars to reach a given grain protein concentration while maintaining yield is now known to be linked to post-anthesis N uptake (Bogard et al. 2010, Taulemesse et al. 2016). Our dataset did not include the measurements needed to study this aspect properly but new work could be undertaken in this direction with the help of crop models. By characterizing the cultivar N need for both yield and grain protein concentration, we provided the basic elements to build some efficient rules for choosing the right cultivar for each market purposes, depending on the demand (or not) of a minimum of grain protein concentration (ARVALIS-Institut du végétal, 2013). We built a statistical model linking applied fertilizer N rate to the increase of SMN which is consistent with previous studies and practical knowledge (Comifer 1997, Comifer 2013). It appeared clearly that, if we wish to add an Extra-N for some cultivars to reach GPC = 11.5%, this addition must not exceed 35 kgN.ha-1 to prevent a significant increase of SMN. For a yield of around 9 t 85%DM.ha-1 (the general cultivar adjusted mean of our study is 9.12 t 85%DM.ha-1), this means that a bc must not exceed an approximate value of 4 kg N. t 85%DM-1. 5. Conclusions Our study was conducted to achieve three objectives. The first one was to determine if the bread-wheat cultivars recently registered in France had an effect on NUE components and on agronomic indicators for nitrogen management. The results clearly confirmed this hypothesis for NUE, but also for each of its components NupE and NutE, and also on AFR. By acting on both NUE components, the choice of the right cultivar proved to be a relevant lever to enhance the uptake of nitrogen supplied to the crop (mainly from fertilizer, given that we did not demonstrate any significant effect of the cultivar on N uptake from the soil) and the conversion of nitrogen into grain yield and grain protein concentration. The second objective was to assess the relationships between breeding NUE traits and agronomic NUE indicators. The question basically concerned the link between NupE and AFR (respectively N uptake trait and indicator), because, concerning N utilization trait and indicator, the ‘b’ coefficient was “simply” the reverse of NutE calculated at the N rate for optimum grain yield. We demonstrated that NupE and AFR were highly correlated and were almost equivalent for ranking the cultivars. As a result, all the genetic studies based on NupE 14

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could now be compared to agronomic studies based on AFR and so, allowing researchers to bridge the gap between the two fields of expertise for the benefit of a global NUE enhancement. The third objective was to explore the relevance of a nitrogen uptake requirement indicator to allow each cultivar to achieve the bread market requirements (regarding production and grain quality) without causing significant nitrogen losses in the environment. When fertilized at the optimum fertilizer N rate for optimum yield (e.g. using only the NutE ‘b’ coefficient), not all the tested bread-wheat cultivars were able to reach a grain protein concentration of 11.5% DM. We designed a complementary N requirement ‘bc’ coefficient as a relevant tool to allow those cultivars to reach the market demand. Furthermore, we showed that this complement must be capped to avoid significant losses of nitrogen by nitrate leaching during the bare soil period following the harvest. In these cases, other levers than a complement of N requirement must be used to satisfy the market demand. All these results now need to be handled by breeders to focus on the right traits in their breeding programmes and by field agronomists to include the choice of cultivar in all N management tools to help farmers and their extension services.

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