Climate change, competition for land, limited natural resources, and the co-occurrence of abiotic and biotic stresses all threaten global wheat production. One option to address this is increasing productivity through the adoption of cultivars with improved genetic potential (Reynolds et al., 2012). Over the last 50 years, breeding and agronomic efforts have led to a reported tripling of cereal yields (Pingali, 2012). However, wheat productivity is increasing at a global rate of 1.1% per year (Dixon et al., 2009) against a predicted demand requirement of 1.7% year^–1 until 2050 (Rosegrant and Agcaoili, 2010).

Current wheat production demands a range of agrochemical inputs, including nitrogen (N) fertilizer. Between 85 and 90 million tons of N fertilizers are added to agricultural soils worldwide each year, and current predictions based on projected food demand show that this could increase to 240 million tons by 2050 (Good et al., 2004). N fertilizer represents the single most expensive input into wheat production and accounts for more than 70% of its associated greenhouse gas emissions (Mortimer et al., 2004). Excessive usage has a range of negative impacts on the environment, and it is estimated that 50−70% of applied N is lost from the plant–soil system through a combination of leaching, surface run-off, denitrification, volatilization, and microbial consumption (Peoples et al., 1995).

Plants use N as part of many biological processes (Masclaux-Daubresse et al., 2008), and it is taken up primarily as nitrate and ammonium, with nitrate being the predominant form in most agricultural soils (Crawford and Forde, 2002). Several physiological and biochemical changes occur in plants as adaptive responses to N limitation, including an increase in N uptake by high-affinity transporters, remobilization of N from older to younger leaves and reproductive parts, retardation of growth and photosynthesis, and increased anthocyanin accumulation (Ono et al., 1996; Chalker-Scott, 1999; Ding et al., 2005; Diaz et al., 2006). The effect of low N availability on plant biomass, nitrate uptake, and root architecture has already been widely studied, and it is known that plants modify their root architecture, changing their lateral/primary root ratio and simultaneously decreasing shoot/root ratio in order to forage soil nutrients (Brouwer, 1962; Drew and Saker, 1975; Van der Werf and Nagel, 1996; Lea and Azevedo, 2007; Lemaître et al., 2008).

N use efficiency (NUE) is generally defined as the yield of grain achieved per unit of N available to the crop from soil and applied fertilizer, and it can be divided into two biological components, N uptake efficiency (NUpE) and N utilization efficiency (NUtE) (Moll et al., 1982). Wheat in Northern Europe is typically grown at high N input levels, and N recovery and NUE have been estimated to be between 30 and 65% and 25 kg of DM kg-1 N, respectively (Raun et al., 2002; Sylvester-Bradley and Kindred, 2009; Gaju et al., 2011). A major challenge is optimizing N use (e.g., selecting cultivars that make the best use of applied N; termed here as N-efficient germplasm) while maintaining yield in order to minimize negative environmental impacts and production input costs (Kichey et al., 2007; Kant et al., 2011). A recent study showed that modern cultivars have improved yield performance along with enhanced nutrient use efficiency (Voss-Fels et al., 2019), demonstrating that high NUE is likely to be indirectly increased through selection for high yield.

A prerequisite for breeding and selection of N-efficient germplasm is access to genetic variation. Previous work in wheat field trials conducted under a range of N levels has shown that significant genetic variability exists for NUE along with the component traits (NUpE and NUtE) (Cox et al., 1985; Van Sanford and MacKown, 1986; Dhugga and Waines, 1989; Ortiz-Monasterio et al., 1997; Foulkes et al., 1998; Le Gouis et al., 2000; Brancourt-Hulmel et al., 2003; Barraclough et al., 2010; Gaju et al., 2011, 2014; Cormier et al., 2013; Guttieri et al., 2017; Nehe et al., 2018).

However, the magnitude and relative contribution of the components to overall genetic variability in NUE varies between experiments. For example, NUpE has been shown to account for a greater proportion of variation in NUE at low compared to high N levels (Ortiz-Monasterio et al., 1997; Le Gouis et al., 2000) as has NUtE (Gaju et al., 2011), although Dhugga and Waines (1989) found NUpE to be equally important at both levels.

Breeding and cultivar registration trials are typically conducted at high N levels to ensure maximum expression of genetic potential (Hitz et al., 2016). Voss-Fels et al. (2019) reported that this has also resulted in indirect selection for optimal performance under reduced input scenarios. However, trait heritability within production systems and the magnitude of genotype × production system interaction are key factors when comparing breeding strategies, i.e., direct or indirect selection between conventional (high-input) and organic (or low-input) farming systems (Annicchiarico et al., 2010).

Estimates of heritability at high- and low-input levels together with genetic correlation between input levels have been used for the prediction of relative efficiency of direct vs. indirect selection in wheat to give recommendations for selection programs, aimed at producing cultivars for low-input or organic agriculture (Brancourt-Hulmel et al., 2005; Przystalski et al., 2008; Annicchiarico et al., 2010; Cormier et al., 2013; Šarčević et al., 2014).

Brancourt-Hulmel et al. (2005) and Cormier et al. (2013) concluded that breeding programs aiming to produce N-efficient cultivars for low-input environments should include testing and selection at low input to maximize selection gains for grain yield (GY). Przystalski et al. (2008) suggested combining information from both organic (low input) and non-organic (high input) experiments to optimize the selection of wheat cultivars for organic farming systems, and Hitz et al. (2016) indicated that selection at low N is necessary to identify high NUE genotypes. However, Annicchiarico et al. (2010) found no advantage when targeting organic production of direct selection for GY in organic systems relative to indirect selection in conventional systems. Similarly, Šarčević et al. (2014) reported high efficiency of indirect selection under high N for performance under low N, which was close to 1.0 for GY and for most studied bread-making quality traits.

N use efficiency and its components have been widely studied including in released CIMMYT wheat cultivars (Ortiz-Monasterio et al., 1997), Northwestern European (Le Gouis et al., 2000; Barraclough et al., 2010; Gaju et al., 2011, 2014; Cormier et al., 2013), North American (Guttieri et al., 2017; Russell et al., 2017), and Indian wheats (Nehe et al., 2018). However, the information on variation for these traits is lacking for wheat germplasm adapted to and selected within South-eastern European production conditions.

The objectives of this study were to (1) evaluate the effect of N treatment (low vs. high) on the agronomic performance of a set of wheat cultivars predominantly originating from South-eastern Europe; (2) to estimate variance components and heritability for GY, NUE and its components under low and high N, and related changes caused by breeding and selection; and (3) to identify the relationship between NUE and its components in the cultivar panel to inform future selection and breeding for N efficiency.

There are two routes to improving NUE in wheat: improving fertilizer management and/or improving cultivars. The first option is directed at optimizing the application of fertilizer N to crop requirements and weather and soil conditions. The second is to breed cultivars with improved N efficiency in terms of enhanced N uptake and utilization (Barraclough et al., 2010). The importance of these components in relation to NUE will depend on the way of measuring and deriving these traits, the level of N stress, and germplasm diversity used in the evaluation (Han et al., 2015).

In this study, we evaluated the agronomic response of a panel of 48 winter wheat cultivars in six South-eastern European environments at two N levels [representing optimal/standard field N availability (high, HN) and sub-optimal (low, LN)]. Yield and protein significantly decreased by 10% and 14%, respectively, at LN compared to HN. Reductions in grain N yield and aboveground N per unit area were also reduced by approximately 20% at LN. In contrast, the majority of the derived NUE traits had significantly higher mean values at LN compared to HN, ranging from 7% for NUE_PROT to 19% for BPE.

A previous report in Croatian growing conditions showed similar reductions at LN for GY (10%), GPC (13%), and GNY (21%) in a set of 19 European wheat cultivars (Šarčević et al., 2014). Trait responses to reduced N were more pronounced in a study of 225 European wheat cultivars, which showed ∼20% reduction in GY and GPC and a corresponding 30% increase in NUE and NUtE (Cormier et al., 2013). Although the difference in applied N between HN and LN treatments were consistent across studies, the magnitude of trait mean differences suggests the presence of stronger N stress in experiments conducted by Cormier et al. (2013) compared to that of Šarčević et al. (2014) and the present study. Establishment of N stress earlier in the season in the study of Cormier et al. (2013) could be an explanation for the observed differences. These differences could also reflect environmental difference between north-western Europe (Cormier et al., 2013) and assessment in South-eastern European conditions where the wheat growing season is shorter and the more extreme weather conditions lead to lower average yields and a wider gap in yield potential (Schils et al., 2018). In addition to the environmental differences, there is a relationship between yield and N uptake, reflected in the inverse relationship between yield per unit of N uptake and grain protein content (Sadras and Lemaire, 2014), which could explain the differences between studies.

In this study, genotypic (G) variance was significant for all traits, except for NRE, genotype × environment (G × E) variance for all traits, except for NHI, NUtE_PROT, BPE, and PANU, whereas genotype × nitrogen (G × N) interaction was not significant for any trait. Previously, Cormier et al. (2013) reported significant G × N for GY, GPC, and several NUE traits, and significant G × N interactions for GY and other agronomic traits have also been reported in several other wheat studies (Ortiz-Monasterio et al., 1997; Le Gouis et al., 2000; Guarda et al., 2004; Laperche et al., 2006; Barraclough et al., 2010; Gaju et al., 2011). Hitz et al. (2016) suggested that the existence of G × N interaction could be useful for breeders, allowing differentiation of performance between two N environments. In the previous Croatian study by Šarčević et al. (2014), G × N interaction was not significant for GY, but it was significant for all grain quality traits and for most rheological parameters, for which the negative effect of reduced N fertilization was much stronger as compared to its effect on GY. Other recent studies by Guttieri et al. (2017) and Russell et al. (2017) found no significant G × N interactions for GY or any NUE traits for wheat grown in field experiments in the United States. A recent study showed that G × N interactions for GY in wheat were more frequently observed when three or more N rates were used in the experimental design (Brasier et al., 2020), indicating that further experimentation is likely required to robustly dissect G × N interactions.

In the present study, genetic correlations between HN and LN were high for GY, GPC, and for derived NUE traits, reflecting the absence of significant G × N interaction for these traits (Table 4). Heritability estimates for analyzed traits were either similar for the two N fertilization levels or higher at HN than LN. Reflecting this, the efficiency of indirect selection at HN for performance at LN relative to direct selection at LN (indirect selection efficiency, ISE) was ≥ 1 for most traits. Therefore, we conclude that indirect selection for GY, GPC, and most other studied traits under optimal fertilization will be at least as efficient as direct selection under reduced fertilization. This is in agreement with previous assessments of Croatian (Šarčević et al., 2014) and northern European (Voss-Fels et al., 2019) wheat and has major implications for the selection of wheat cultivars for N efficiency. Previous studies have reported lower ISE estimates, although these have been in high N stress comparisons, which exhibit much larger reductions between LN and HN for GY and/or GPC (Brancourt-Hulmel et al., 2005; Cormier et al., 2013). This is in line with the conclusion of Cormier et al. (2016), who found that indirect selection is efficient in moderate N stresses, but it does not surpass direct selection in extreme low N conditions. Similarly, Hitz et al. (2016), studying NUE and related traits in US winter wheats under contrasting N fertilization rates, which resulted in severe N stress, concluded that without screening breeding lines in low N environments concurrently, it will not be possible to identify high NUE genotypes. However, in a European context, it is unlikely that the target environment will be characterized as low N outside of specialist organic production. However, the occurrence of moderate N stress is much more likely within the current production and breeding framework in areas with generally low soil fertility, decreased water availability due to drought or adverse weather conditions or in zones, which have restrictions on N fertilizer use due to run-off concerns. This is also confirmed in the present study, where the effect of location (Poreč vs. two other locations) was more pronounced than the effect of LN treatment in reducing GY (Supplementary Table 6). A similar pattern was observed in the study of Gaju et al. (2011), who reported that in four out of seven environments, mean GY of 16 European wheat cultivars grown at LN was significantly higher than the corresponding GY means at both HN and LN in the lowest yielding environment, although the difference in the amount of N fertilizer applied between HN and LN treatments was as high as 200 kg N ha^–1. In a later study, Gaju et al. (2014) compared the N accumulation in crop components at anthesis between the highest and lowest yielding location from the same experiment and found significantly higher content of N in leaf lamina, stem-and-leaf-sheath, and ear at the highest yielding location, regardless of the level of N fertilization. Similarly, in the study of Brasier et al. (2020), including 12 US soft red winter wheat lines and cultivars, the effect of year on GY was more pronounced than the effect of N fertilization applied in the range from 45 to 134 kg N ha^–1.

In practical terms, promising breeding material is trialed across multiple environments, and it is likely this exposes lines to a mix of HN and moderate LN environments (Cormier et al., 2013). This is likely to explain the similarity in genetic progress at HN and LN seen in this, and previous studies (Voss-Fels et al., 2019). Further optimization of the multi-environment framework could incorporate a reduced N fertilizer rate at a limited number of locations in order to increase the frequency of stress environments.

This has been previously explored as a component of a NUE selection framework by Cormier et al. (2013) and would give better insight into the production yield stability of a genotype. This would be particularly useful for evaluating bread-making quality properties, which have been shown to be more sensitive to reduced N fertilization than GY (Šarčević et al., 2014).

Our analysis shows that significant genetic (breeding) progress has been made for yield and the majority of analyzed traits at both LN and HN in the 48 cultivars assessed. Genetic improvement in GY was estimated at 0.31 and 0.34% year^–1, with concomitant decreases in PH (− 0.38 and−0. 42% year^–1) and BPE (−0.13 and −0.11% year^–1) at LN and HN, respectively. As a consequence of reducing PH and BPE while increasing GY, HI was increased by 0.24 and 0.28% year^–1 at LN and HN, respectively.

From the perspective of genetic improvement for NUE, Cormier et al. (2013) estimated NUE progress in European wheat between 1985 and 2010 to be 0.37 and 0.30% year^–1, respectively, at LN and HN. Ortiz-Monasterio et al. (1997) reported genetic progress for NUE at 0.4–1.1% year^–1 depending on the N levels in CIMMYT’s spring wheat released between 1962 and 1985.

In the present study, NUE progress between 1936 and 2016 was 0.33 and 0.30% year^–1, which translates to genetic improvement in NUE of 8.26 and 7.94 kg of DM kg^–1 N or in total 26.7 and 24.3% at LN and HN, respectively.

Similar levels of genetic gain have been recorded in Canadian spring wheat (Kubota et al., 2018; 0.34% year^–1 in NUE under HN). As NUE is a complex trait and is defined as the product of NUpE and NUtE (Moll et al., 1982), its improvement should be realized through selection on one of its components. In this study, we found that NUtE had a stronger effect on NUE than NUpE based on estimated correlation coefficients (0.80 vs. 0.70 at LN, and 0.76 vs. 0.49 at HN). This is in agreement with other authors who found genetic variation in NUE more related to NUtE (Brancourt-Hulmel et al., 2003; Gaju et al., 2011; Uzik et al., 2012; Cormier et al., 2013). However, several other studies have reported similar contributions (Van Sanford and MacKown, 1986) or dominance of NUpE (Dhugga and Waines, 1989), particularly at LN (Ortiz-Monasterio et al., 1997; Le Gouis et al., 2000). We found both positive impacts of breeding on both NUpE (an annual increase of 0.15 and 0.12%), and for NUtE (0.15 and 0.20% at LN and HN, respectively).

As in other reports (e.g., Voss-Fels et al., 2019), NUE measured under LN was higher than that measured under HN in our dataset (Table 1). This suggests that the cultivars selected thus far have an inherent capacity for greater NUE, which declines under HN conditions. Identifying cultivars that can maintain high NUE in HN conditions should enable yield improvement with a lower environmental impact. An alternative target to NUE has been proposed by Sylvester-Bradley and Kindred (2009), who defined the economic N optima as the N level necessary to achieve a high yield with the lowest input cost to maximize profits. Although the economic N optima cannot be used as a breeding target per se, it highlights the relevance of N responsiveness to the improvement of NUE (Swarbreck et al., 2019). Cultivars showing high N responsiveness that is maintained under high N show lower economic N optima. Here, selection under varying N levels evaluates a genotype’s N responsiveness (Sylvester-Bradley et al., 2015), potentially allowing for the selection of cultivars that are highly responsive to both low N and high N conditions. The ideal genotype should possess high genetic N efficiency and high N responsiveness, so traits for N efficiency and responsiveness should not be genetically linked in order to select genotype that performs better under both conditions (Han et al., 2015). For producers, on the other side, De Oliveira Silva et al. (2020) found that genotypes with high mean response and high variability in their response to higher N levels across years could offer greater opportunities to maximize yield.

In summary, we report that while agronomic and N-related traits vary significantly between N levels and environments, traits are consistently correlated irrespective of N level. All of the cultivars tested showed a common directional response at LN and HN, and performance at HN generally predicted performance at LN although direct selection offers opportunities to optimize some N-related traits. Analysis of breeding progress revealed improvements in NUE over time linked to yield improvement. Our results are significant for informing future selection and breeding for N efficiency in South-eastern European wheat and add further evidence confirming that selection at optimal N is most relevant for delivering enhanced N efficiency in wheat.

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.

DN, HŠ, KD, BP, ZL, and AB contributed to the conceptualization of the study. DN, HŠ, SG, KD, MI, BP, ZL, and IP contributed to the methodology. MI, DN, IP, and SS conducted the formal analysis. MI, BR, MM, AL, and MČ performed the investigations. MI and IP performed data curation. MI, IP, SG, KD, DN, HŠ, AL, and MČ involved in writing the original draft preparation. AB, SS, BP, ZL, and KD contributed to writing, reviewing, and editing. DN, HŠ, AB, and SS supervised the work. All authors contributed to manuscript revision, read, and approved the submitted version.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.