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Crop Science 43:1259-1265 (2003)
© 2003 Crop Science Society of America

CROP BREEDING, GENETICS & CYTOLOGY

Improving Nitrogen-Use Efficiency in European Maize

Estimation of Quantitative Genetic Parameters

T. Presterl*,a, G. Seitzb, M. Landbeckc, E. M. Thiemta, W. Schmidtc and H. H. Geigera

a State Plant Breeding Institute, Univ. of Hohenheim, D-70593 Stuttgart, Germany
b AgReliant Genetics, Westfield, IN 46074, USA
c KWS SAAT AG, 37555 Einbeck, Germany

* Corresponding author (presterl{at}uni-hohenheim.de)


   ABSTRACT
TOP
 NOTES
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Maize (Zea mays L.) cultivars with improved N-use efficiency would be beneficial for low-input production systems. Our objective was to estimate quantitative genetic parameters to optimize breeding programs for improving productivity under low N levels. Results of 21 field experiments with European breeding materials belonging to the flint and dent gene pool are presented. The study was performed during 1989 and 1999 at several locations in typical maize growing regions of Germany and France. All experiments were conducted at high (HN) and low (LN, no N fertilizer applied) N levels. Average grain yield was reduced by 37% at LN compared with HN. Coefficients of genotypic correlation between HN and LN were variable with an average of rG = 0.74 for grain yield and generally high for grain dry matter content. For grain yield, analyses of variance were computed from relative data, where plot values were expressed as percentage of the trial mean. Variances caused by genotype (G), G x location (L) interaction, and error effects were higher at LN compared with HN, with similar heritabilities at both N levels. For the untransformed data, components of variance were higher at HN than at LN. Genotype x N as well as G x L x N level interaction variances were significant in most experiments. Efficiency of improvement of grain yield at LN through indirect selection at HN was 70% compared with direct selection at LN. A trend toward increased efficiency of direct selection under LN conditions was evident with decreasing grain yield at LN.

Abbreviations: HN, High N level • LN, Low N level


   INTRODUCTION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
FROM 1960 to 1990, the use of N fertilizer in modern agricultural systems has increased in parts of Asia, North America, and West Europe (Food and Agricultural Organization of the United Nations, 2000). This was accompanied by a steady growth in average maize yield during the same period. On the other hand, negative impacts of N-compounds on the atmosphere, the ground water, and other components of the ecosystems have also been reported (Socolow, 1999). Because of these negative effects, the European Community developed specific directives (Nitrates directive 91/676/EEC) to prevent nitrate contamination of water from agricultural sources, such as by restricting the use of N fertilizers, enlarging the water protected areas, as well as increasing the acreage of organic farming (Organic Centre Wales, 2001). In many tropical and subtropical regions, and in countries of the former Soviet Union, farmers cannot increase yield, as the availability of N fertilizers in crop production is often limited (Food and Agricultural Organization of the United Nations, 2000). Apart from factors such as crop rotation and type of N fertilizer and its application, crop cultivars with improved N-use efficiency have much to contribute to production systems where input of N fertilizers is restricted due to environmental and/or economical reasons.

N-use efficiency is defined as the ability of a genotype to produce superior grain yields under low soil N conditions in comparison with other genotypes (Graham, 1984; Sattelmacher et al., 1994). Experiments with the U.S. Corn-Belt (Balko and Russell, 1980), tropical (Muruli and Paulsen, 1981; Lafitte and Edmeades, 1994; Bänziger et al., 1997), and European maize (Bertin and Gallais, 2000) indicated that genotypes can differ considerably in their N-use efficiency. Hence, breeding for adaptation to low soil N seems feasible.

It would be desirable to combine the breeding goals of yield improvement for conditions with high input of N fertilizers and yield improvement for low N input conditions. In principle, the following two breeding strategies are possible (Atlin et al., 2000; Falconer, 1952): (i) Indirect improvement: selection at only one N level, whereby performance at the other N level is improved by correlated response; (ii) Combined improvement: selection is based on an index of the weighted performance means at high and low input of N. If combination of the two breeding goals proves to be ineffective, it would be necessary to breed for the HN and LN environments separately. To decide which of the strategies would be the most appropriate, knowledge of quantitative genetic parameters such as genotypic variance components, heritabilities, coefficients of genotypic correlation, as well as economic weights for yield under high and low N conditions is necessary. So far, quantitative genetic parameters for the adaptation of European maize to low soil N conditions were only provided by Bertin and Gallais (2000). The authors studied the testcross performance of 99 recombinant inbred lines at two N levels. However, comprehensive studies on the N-use efficiency of maize using different sets of materials across a wide range of environments are only available for tropical maize (Bänziger et al., 1997).

Our study summarizes results of 21 experiments with European maize breeding materials. The objectives were (i) to evaluate differences in N-use efficiency of the genotypes and (ii) to estimate quantitative genetic parameters to design and optimize breeding programs for cultivars with improved N-use efficiency.


   MATERIALS AND METHODS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Genetic Materials
The genetic materials consisted of 21 sets of Northern European maize breeding lines developed by the seed company KWS SAAT AG (Table 1). Those inbred lines belong to the flint and dent gene pool and were tested for their combining ability with tester lines or in factorial crosses. Flint lines in Exp. 13, 14, and 19 were derived from European landraces, which were selected for good combining ability with elite dent tester lines at low N conditions. In all other experiments, current elite breeding materials were evaluated. Lines used in Exp. 1 to 4, 11, 12, 15, 17, and 20 were developed at HN. All other genetic materials were derived from selection under either LN or HN conditions. The number of entries included in the single experiments varied from 48 to 144. Each experiment included a limited number of check varieties, which were in most cases related to the entries and had similar maturity and yield potential. Those check varieties were included in the analysis, except for the experiments with landrace-derived materials.


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  		Table 1. Genetic materials, selection history, number of entries, and locations in 21 maize experiments conducted between 1989 and 1999.

 
Field Experiments
The 21 field experiments were performed between 1989 and 1999 in typical maize growing regions of Germany and France. All experiments were 1-yr experiments except Exp. 18, which was conducted for 2 yr. Most of the experiments were located in the Upper Rhine Valley in southwest Germany: Emmendingen, Eckartsweier, Forchheim, Walldorf. Other locations were Stuttgart-Hohenheim, Grucking, Bernburg, and Chartres in France. A detailed description of the location characteristics is given in Table 2.


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  		Table 2. Characteristics of the eight locations where the 21 maize experiments were conducted between 1989 and 1999.

 
The LN and HN experiments were grown side-by-side on the same field and each trial (L x N level combination) was laid out as a lattice design with two replications. No N fertilizer was applied at LN. The HN fields received fertilizer N at the time of planting. The amount of fertilizer N was calculated on the basis of an estimated N requirement of 200 kg N ha-1 minus the soil mineral N measured 4 wk before planting. At all locations, except Forchheim and Hohenheim, the soils had been depleted of N by growing the low N trials every year on the same field. This was necessary to achieve a significant level of N-deficiency stress on the fertile soils at these locations. Forchheim has a sandy soil where N-deficiency stress occurred already in the same year when no N fertilizer was applied. At Hohenheim, the same field for the low N trials was used until 1994, and from 1996 to 1998 experiments were planted on a different field.

Herbicide treatment and application of other fertilizers were the same for both N levels. Plots consisted of two rows and plot size varied from 6 to 9 m2. Plant densities were between 9 and 11 plants m-2 in accordance with the expected water availability at the different locations. Plots were harvested with a combine after the genotypes reached the stage of physiological maturity. Grain samples of {approx}500 g were oven-dried to a constant weight at 110°C to determine grain dry matter content. Grain yield was computed on a 100% dry matter basis.

Statistical Analyses
Lattice analyses of variance were performed for each trial (Cochran and Cox, 1957). Adjusted means and effective error mean squares from these calculations were used in the combined analyses across locations and N levels. Estimates of error variances were calculated on an entry mean basis. The effects of N levels were regarded as fixed, and all other effects were assumed to be random variables. Variance components were estimated according to Snedecor and Cochran (1980) and their standard errors following Searle (1971). For grain yield, analyses of variance were computed from relative data, where plot values were standardized within each experiment by expressing them as percentage of the trial mean. This procedure eliminated the scaling effect on variances of large mean differences between N levels or locations and allowed the direct comparison of variance components estimated at the two N level. All other parameters (heritabilities, coefficients of correlation, efficiencies of indirect selection) were computed from untransformed grain yield data. Heritabilities were calculated with their 95% confidence intervals (Knapp and Bridges, 1987). Coefficients of genotypic correlation and their standard errors are based on the procedures described by Mode and Robinson (1959). All statistical computations were performed with the PLABSTAT computer program (Utz, 1993).

The predicted efficiency of indirect selection under high N conditions compared with direct selection at low N (target environment) was calculated according to Falconer and Mackay (1996):

where CRLN is the correlated or indirect selection response for LN when selection is performed at HN and RLN is the direct response of selection at LN. The selection intensities are denoted i; h is the square root of the heritability, rHN/LN is the coefficient of genotypic correlation between the performance at the two N levels, and {sigma} the genotypic standard deviation of the considered trait. Assuming equal selection intensities at both N levels, the formula reduces to:

For CRLN/RLN = 1, direct and indirect selection are predicted to be equally efficient. Values < 1 indicate that indirect selection at HN would be less efficient than direct selection at LN.


   RESULTS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Across all experiments, average grain yield at LN was significantly (P = 0.05) reduced by 37% compared with HN. Yield reduction ranged from 14.5% in Exp. 1 to 55.4% in Exp. 14 (Table 3). The trials in 1989 showed the lowest yield reduction. Grain dry matter content at LN was similar to that measured at HN in all experiments and coefficients of genotypic and phenotypic correlation between the performance at HN and LN were generally high for this trait, except for Exp. 20. For grain yield, coefficients of genotypic correlation were on average lower (rG = 0.74) and ranged between 0.04 and 1.16. Coefficients of phenotypic correlation were in most experiments lower than the corresponding genotypic correlation, resulting in a mean rP = 0.53. For relative grain yield, genotypic variance and variance of G x L effects were significant (P = 0.05) within all sets of material (Table 4). At LN, the estimates of genotypic variance components differed among the 21 experiments. Experiments 13 and 14, including lines derived from landraces, showed the highest genotypic variance at LN. On average, components of genotypic variance were 2.3 times higher at LN than at HN. In seven out of 18 experiments conducted at more than one location, standard errors of genotypic variance components at LN did not overlap those at HN, indicating significantly larger variation at LN. Only two experiments (12, 17) showed a significantly higher genotypic variance at HN.


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  		Table 3. Means of genotypes at high (HN) and low (LN) N levels, relative difference between grain yield at HN and LN ({Delta}GY), coefficients of genotypic (rG) and phenotypic (rP) correlation between performance of genotypes at HN and LN, and efficiency of indirect selection at HN compared to direct selection at LN (CR/R) for grain yield and grain dry matter content in 21 maize experiments (Exp.) conducted between 1989 and 1999.

 

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  		Table 4. Variance components estimates and their standard errors (± SE) for genotypes (2G), genotype (G) x location (L) interaction (2GL), and error (2E) for relative grain yield in 21 maize experiments at high and low N levels conducted between 1989 and 1999.

 
This higher genotypic variation at LN did not result in a higher average estimate for heritability (Table 5) because components of G x L variance and error variance were also increased at the LN (Table 4). Heritability estimates for grain yield at HN ranged from 35.9 (Exp. 2 and 15) to 94.1% (Exp. 12) and at LN between 40.7 (Exp. 5) and 88.0% (Exp. 12). Confidence intervals of heritabilities at HN and LN overlapped in all experiments. In 11 experiments heritabilities at HN were higher than at LN, whereas in 10 experiments the estimates at LN were higher.


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  		Table 5. Estimated heritabilities (h2) and corresponding 95% confidence intervals (CI) for grain yield in 21 maize experiments at high and low N levels conducted between 1989 and 1999.

 
For grain dry matter content, genotypic variance and G x L interaction variance were highly significant at both N levels in all experiments, except for Exp. 10 and 14 where interaction variances were not significant at LN (data not shown). Genotypic variance components were on the average 43% higher at LN compared with HN. However, due to higher G x L interaction variance, this did not result in higher heritability estimates at LN. Average heritabilities for grain dry matter were 0.88 at HN and 0.89 at LN.

Coefficients of genotypic correlation between grain yield at HN and LN decreased significantly with increasing levels of N-deficiency stress (Fig. 1). The coefficient of correlation between the two parameters (rG, N-deficiency stress, calculated from 18 experiments, conducted at more than one location) was r = -0.49 (P = 0.05). Components of genotypic variance at LN, calculated from relative as well as from untransformed grain yield data, were positively associated with N-deficiency stress (r = 0.59 and r = 0.53, P = 0.05, respectively), while estimates for heritability and G x L interaction variance were not correlated with the level of N-deficiency stress.



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  		Fig. 1. Relationship between N-deficiency stress [(grain yield at HN - grain yield at LN)/grain yield at HN] and the coefficient of genotypic correlation (for Exp. 5, 6, and 20, coefficients of phenotypic correlation) between grain yield at high and low N levels. The 21 experiments are numbered as in Table 1. Regression line is shown with its 95% confidence interval (R2 = coefficient of determination of regression).

 
For relative grain yield, analysis of variance across locations and N levels revealed highly significant genotypic and G x L interaction variance in all 18 multisite experiments (data not shown). Interaction variance between genotypes and N levels (G x N) were significant in 11 out of 18 experiments. This interaction variance was not significant in all four experiments of the year 1989. The highest estimate for G x N variance was calculated in Exp. 13, which included a set of lines derived from flint landraces. In Exp. 10 and 21, the G x N variance was higher than the genotypic variance. Highly significant G x L x N interaction was observed in all experiments, except for Exp. 13. Genotypic variance was on average the most important source of variation, followed by error variance and the G x L and G x L x N interaction components which were of similar magnitude but 27% smaller than the genotypic variance. The G x N interaction variance component was on average half the size of the genotypic variance component. The relative size of G x N interaction variance ({sigma}2GN/{sigma}2G) showed a close association with the coefficient of genotypic correlation between grain yield at HN and LN (r = 0.94, P = 0.01). This relationship was influenced by Exp. 10 and 21. Both sets included hybrids developed at either HN or LN. Excluding those two experiments, the correlation coefficient was 0.85.

The average efficiency of indirect selection at HN for grain yield at LN was 70% compared with direct selection at LN (Table 3). The average difference between hHN x rHN/LN and hLN was significant (P = 0.05). Yet, considerable variation for efficiency estimates occurred between the various experiments. The lowest efficiency (0.04) was obtained for Exp. 10, which included hybrids developed at either HN or LN. The highest efficiency occurred in Exp. 17, where a set of dent lines was evaluated for their performance in testcrosses. Two experiments revealed efficiencies greater than one, and in five trials this parameter exceeded 0.9. In contrast, efficiencies of indirect selection for grain dry matter content were >0.88 in all experiments, except in Exp. 20 (data not shown). This particular experiment was conducted at one location only. Efficiency of indirect selection for grain yield showed a significant relationship to yield reduction (r = -0.58; P = 0.05), where efficiency estimates decreased with increasing levels of N-deficiency stress.


   DISCUSSION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
Genotypic variability is an indispensable prerequisite for breeding progress. For the two economically important traits, grain yield and grain dry matter content, genotypic variance was significant in all experiments at both N levels. For relative grain yield, genotypic variability was larger at LN, while the same analysis with the original data pointed out a greater genotypic variance at HN (data not shown). This indicated that the greater genotypic variance observed at HN was caused by the scaling effect of the higher mean at HN and the analysis of the relative grain yield data was, therefore, justified. Estimates of heritability, coefficient of genotypic correlation, and efficiency of indirect selection were based on the original data. A comparison with the results obtained from relative grain yield showed only negligible differences between the parameters obtained by the two methods. Correlation between the parameters based on relative and untransformed grain yield data were 0.98 for the coefficients of genotypic correlation, 0.93 for heritabilities at LN, 0.88 for heritabilities at HN, and 0.99 for the efficiencies of indirect selection.

There is a parametric relationship between G x N interaction variance and the coefficient of genotypic correlation between the two environments (Falconer, 1952; Itoh and Yamada, 1990; Lynch and Walsh, 1998; Yamada et al., 1988). Therefore, the problem of selection in two different environments can be investigated by using rG instead of G x N variance. For grain yield, the differential reaction of genotypes to the change of N supply is indicated by an average coefficient of rG < 1 between performance at HN and LN. Experiments with low rG also showed significant G x N interaction, which corroborates the theoretical expectations that G x environment interaction reduces rG below one (Lynch and Walsh, 1998). The relationship between the G x N variance and rG was r = 0.45. Significant G x N interaction was often associated with changes in the ranking of the genotypes at LN compared with HN (data not shown).

The trend toward decreasing coefficients of rG between grain yield at HN and LN with increasing N-deficiency stress (Fig. 1) is similar to the experimental results from 14 experiments with tropical maize evaluated at one location with two N levels in Mexico (Bänziger et al., 1997). The authors observed that 27% of the variation in rG could be explained through relative yield reduction at low N, which is similar to the value of R2 = 31% estimated in the present study.

Bertin and Gallais (2000) also found significant G x N variance in experiments with European maize. The yield difference between LN and HN was 38% and the authors observed rG = 0.75 between grain yield at LN and HN. This is in agreement with the results of the present study with yield differences of 38% (Fig. 1). Bänziger et al. (1997) reported rG = 0.38 at a level of N-deficiency stress of 54%, which is also consistent with the results of the present study. Furthermore, significant G x N interactions were reported by Balko and Russell (1980) in the U.S. Corn-Belt and by Agrama et al. (1999) in Egypt.

For grain dry matter content, rG values between LN and HN were high and G x N variance was low compared with genotypic variance (data not shown). This is in agreement with the results of Bertin and Gallais (2000). In the present study, nine out of 21 experiments were conducted with materials selected at HN and LN. Variability for N-use efficiency is expected to be larger in such materials representing a wide range of adaptation to N availability. The comparison of the experiments including genotypes not preselected for N-use efficiency (Exp. 1–4, 11, 12, 15, 17) with those including materials preselected on two N levels (Exp. 7–10, 16, 18, 21) confirmed this expectation (only multisite experiments were included in this comparison). The average rG was lower in the preselected materials (0.56) compared with the nonpreselected ones (0.91). This was also the case when Exp. 1 to 4 of 1989, in which only small yield reduction at LN occurred, were left out in this comparison.

Our results suggest that direct selection under low N conditions is more efficient to improve N-use efficiency for grain yield than indirect selection at HN, which is in agreement with results of tropical germplasm (Bänziger et al., 1997). Relative efficiency of indirect selection in our study was determined by the magnitude of the coefficient of correlation between the performance at the two N levels. Average heritabilities were similar at LN and HN. This differs from other studies that reported lower heritabilities under stress than under favorable conditions (Brun and Dudley, 1989; Bänziger et al., 1997; Bertin and Gallais, 2000). However, results of equal or even higher heritability estimates at low-input conditions can also be found in the literature (Ceccarelli, 1994; Lafitte and Edmeades, 1994; Agrama et al., 1999). Calculating the relative efficiency of indirect selection takes only the quantitative genetic parameters into account. The next step would be to run model calculations including restrictions in budget for the breeding program as well as economic weights of grain yield at HN and LN. Under the assumption of equal economic weights for both goals, equal heritabilities, and equal genotypic variation at LN and HN, Harrer and Utz (1990) concluded that combined improvement will be the most effective strategy as long as rHN/LN ranged between 0.65 and 1. For rG below 0.65, a subdivision of the program leading to a development of specific low and high-input cultivars will be the most suitable strategy. In the present study, heritabilities at LN and HN were similar and the rG for grain yield was 0.74, indicating that a combined breeding program would be the most promising strategy for improving N-use efficiency of European maize. Selection under divergent N levels should result in genotypes with adaptation to either low or high inputs of N fertilizer (Muruli and Paulsen, 1981). The authors developed high and low-input synthetics from a tropical population at levels of 0 and 200 kg N ha-1. The N-use efficient low-input synthetic outyielded the high-input synthetic and the original population at low N rates, while at HN the high-input synthetic yielded higher.

The high G x L and G x L x N variances emphasize the need for multienvironment testing to identify N-use efficient cultivars with broad adaptation to different levels of N availability. The locations used in our study range from the dry and warm locations in Upper Rhine Valley, often with sandy soils (e.g., Forchheim), to the locations with cooler temperatures but more rainfall and fertile soils (e.g., Bernburg, Grucking, and Hohenheim) (Table 2). Diversity in soil type and climatic conditions resulted in large productivity differences at the various locations and may have contributed to the strong three-way interaction for grain yield. As most of the published studies have been conducted at only one location, hardly any estimate of G x L x N interaction variance is in the literature.

A trend toward increased efficiency of direct selection under N-stress conditions was evident with decreasing productivity under low-N conditions. Following the prediction given by the regression equation of efficiency of direct selection on N-deficiency stress (data not shown), indirect selection at HN will be less efficient than direct selection when relative grain yield reduction at LN is >21%. Therefore, breeding for N-use efficiency appears to hold the most promise for the harsh marginal environments of tropical and subtropical regions, as well as for countries where N input is restricted through high fertilizer prices. However, our results show clearly that sufficient variability for N-use efficiency exists also in European breeding materials and that development of cultivars adapted to LN conditions is feasible. Furthermore, cultivars with improved N-use efficiency possessed a higher level of yield stability across a wide range of stress and nonstress environments in Germany (Thiemt, 2002). Economic importance of low-input agriculture in industrialized countries depends primarily on political decisions. Ongoing efforts of supporting sustainable agriculture methods in Europe, mainly through increasing the acreage of organic farming and increasing the water protected areas, is opening up new perspectives for the breeding of low-input cultivars.


   ACKNOWLEDGMENTS
 
The study would not have been possible without the efforts of M. Erhardt, K. Hamann, H. Hilscher, H. Jahnke, F. Kafka, G. Kraft, D. Klein, S. Koch, E. Langmann, Mrs. and Mr. Lanzinger, A. Lehmann, B. Lieberherr, K. Mastel, R. Metzger, F. Möllenbrock, T. Schmidt, H. Streif, P. Walch, D. Wiebe, J. Wortmann, and G. Zieger, along with the staff at the Hohenheim, Gondelsheim, Bernburg, Emmendingen, and Eckartsweier experimental stations who carefully managed the field experiments and assisted with data collection. Thanks to the former diploma students S. Groh, N. Heinrich, E. Holzhausen, C. Hauser, and K. Uhrig for their valuable contributions to the study. Many thanks to K. vom Brocke, A. Hartmann, H.K. Parzies, and H.F. Utz for discussion of the data and helpful comments and suggestions on the manuscript.


   NOTES
TOP
 NOTES
ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Funding for this study was provided by the Ministry of Agriculture of Baden-Württemberg (Grant No. 89.23-20, No. 23-92.9, and No. 23-95.8) and the KWS SAAT AG, Einbeck.

Received for publication November 8, 2001.


   REFERENCES
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 




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