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Inst. of Crop Sci. and Plant Breeding, Dep. of Grass and Forage Sci./Organic Agric., Christian-Albrechts-Univ. Kiel, Olshausenstr. 40, D-24098 Kiel, Germany
* Corresponding author (aherrmann{at}email.uni-kiel.de).
Received for publication November 7, 2003.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Abbreviations: DM, dry matter • Ncrit, critical nitrogen concentration
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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The third kind of indicators relies on total N concentration of specific plant organs or the whole plant, as for example, in the concept of Ncrit. At any growth stage of a crop, Ncrit is defined as the minimum N concentration required for maximum crop growth rate (Ulrich, 1952). It was demonstrated that the Ncrit can be assumed as a mononomial function of aboveground biomass, called the critical N dilution curve. Based on the Ncrit, a N nutrition index (NNI) can be defined as the ratio of actual N concentration to Ncrit (Lemaire et al., 1989). An NNI value of 1.0 or larger indicates non-N-limiting growth, whereas NNI values below 1.0 correspond to N deficiency situations. The concept of Ncrit was successfully applied to various crops, e.g., grasses (Lemaire and Salette, 1984), wheat (Triticum aestivum L.) (Justes et al., 1994), rapeseed (Brassica napus L.) (Colnenne et al., 1998), rice (Oryza sativa L.) (Sheehy et al., 1998), and grain sorghum (Sorghum bicolor L.) (van Oosterom et al., 2001).
With respect to maize, Cerrato and Blackmer (1991) concluded that for grain yield, the Ncrit of the leaf opposite or below the ear is not a sensitive indicator of the N status. Similarly, studies of Binford et al. (1992) indicated that for early growth stages, the total N concentration of the whole crop does not provide a reliable tool for assessing the N availability. A similar point was made by Plénet and Lemaire (1999) by assuming Ncrit of the whole crop to remain constant in early growth stages (biomass < 1 t DM ha–1), which they explained by limited competition for light of isolated plants and only a small decline of Ncrit with increasing biomass. Above the 1-t threshold, however, they verified the existence and the mononomiality of the Ncrit-to-biomass relationship up to silking plus 25 d. Beyond that developmental stage, they assessed the validity to be restricted due to the cessation of N uptake and subsequent N remobilization from leaf and stalk into the cob, which is paralleled by an increase of starch accumulation. Our own data and findings by Reed et al. (1988) and Coors et al. (1997) indicate that the impact of N remobilization and starch accumulation on the relationship between Ncrit and biomass is not very pronounced, particularly in the case of forage maize.
Most German dairy farmers make use of the services of the existing network of private and official consultancy agencies to analyze forage quality of their roughages and therefore often possess information about the N concentration of their silages. Provided that these farmers would record their forage maize yield and that the mononomial model extends until silage maturity, the Ncrit at silage maturity could serve as a simple and effective tool for the assessment of the N status of their forage maize. Such knowledge could be exploited to adjust the N management of the following vegetation period by means of a learning by doing strategy, i.e., by an adaptive management approach where the N management is regarded as a continuously ongoing process using observations and feedback, raising awareness, and encouraging farmers to develop N fertilization strategies that decrease the pollution risk. This approach assumes that only negligible N losses occur during the ensiling process. It is therefore of major interest to investigate whether the critical N dilution curve can be extended until silage maturity. And since so far only mineral N fertilization has been taken into consideration for the derivation of critical N dilution curves, it would be also of interest for the application in practical agriculture to test whether its validity can be extended to organic fertilization and therefore serve as an indicator of N status.
Accordingly, the main objective of the present study was to verify by field experiments the extendibility of the mononomial critical N dilution curve until the stage of silage maturity. For that purpose, we aimed to quantify the relationship between DM yield and Ncrit and to validate the obtained model function with an independent data set. Furthermore, we intended to clarify whether the model applies to mineral as well as organic N fertilization.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Growth and quality change of the maize crop was recorded fortnightly throughout the whole vegetation period from beginning of June until late September/early October, which resulted in 8, 11, and 10 sampling dates for 1997, 1998, and 1999, respectively. On each date, 10 adjacent plants were sampled by hand-clipping. Half of the sample, i.e., five plants, were fractionated into leaf, stalk, and ear and chopped while the remaining five plants of the sample were not fractionated but chopped as a whole. Subsequently, representative subsamples were dried to constant weight at 65°C. All samples were fine-grounded using a Cyclotec mill (Foss Tecator AB, Höganäs, Sweden), which was fitted with a 1-mm screen. Nitrogen content of the samples was estimated by near-infrared reflection spectroscopy (NIRS). All samples were scanned on a NIRSystems Model 5000 Monochromator (NIRSystems, Silver Spring, MD, USA). Calibration and validation were developed using software from Infrasoft International (ISI, Port Matilda, PA, USA). Calibration and validation subsets were analyzed with the Kjeldahl method according to the procedure of Naumann and Bassler (1976). The standard error of NIRS technique prediction for the estimation of N concentration was about 0.13 g N kg–1 DM.
The Ostenfeld Experiment
The validity of the critical N dilution curve was tested by using an independent data set collected in a field experiment during 1996 and 1997 where the performance of seven cultivars was analyzed with respect to productivity and N response. The study site was located in Ostenfeld, northern Germany (54°18' N, 9°46' E; alt. 14 m.), where the soil is a loamy sand. Average daily temperature is 8.2°C, and mean annual rainfall is 764.0 mm. For climatic conditions during the experimental period, see Table 1. For both years, the experiment was conducted on the same plots (6 by 9 m), which had been planted with maize for at least 3 yr before this study. The trial was laid out in a complete block design with four replicates. The three N treatments (0, 50, and 150 kg N ha–1) and the seven hybrids (the mid-early cultivar Helmi and the six early cultivars Alarik, Kid, Naxos, Jericho, Helix, and Pirat) were randomly allocated to the plots. Phosphorus and K fertilization were applied according to soil test recommendations. The plots were sown at the end of April with a plant population of 90000 plants ha–1. Nitrogen fertilizer was applied shortly after planting. Plots were sampled in 3-wk intervals starting 4 wk after emergence. Whole plants were weighed and shredded and a subsample dried to constant weight at 65°C. The N content was determined by the micro-Kjeldahl procedure.
Data Processing
Commonly, Ncrit values are determined by a method described in Justes et al. (1994), which as a first step, partitions the data of each sampling date into two groups, namely into (i) the group of N-limiting treatments where increasing N supply results in a significant response in yield and N concentration and (ii) the group of non-N-limiting treatments where additional N supply does not lead to further increase of yield but only of N concentration. This partitioning is achieved by a sequel of t-test comparisons. In a second step, regression lines are calculated separately for each group of data points. The N concentration at the intersection point of these two regression lines constitutes the desired Ncrit value. This method unfortunately fails on the one hand when the N application rates of adjacent treatment levels are too close to discriminate between corresponding yields and on the other hand if the number of data points is too small for a meaningful regression line calculation.
With respect to the Karkendamm data set, the method failed because adjacent N treatment levels were too close to each other. A combination of four levels of mineral N fertilizer and three levels of slurry application resulted in a total of 12 treatments with relatively small differences with respect to applied N amounts. For this reason, the following alternative method was developed to determine the Ncrit value of each sampling date. Instead of partitioning the corresponding data points into N-limiting and non-N-limiting treatments, the entire data set of each sampling date was used to fit a quadratic-plateau–shaped function describing DM yield as response of N concentration, i.e.
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After establishing the extendibility of the mononomial model until silage maturity, the critical N dilution curve was derived by fitting to the untransformed data the function
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RESULTS AND DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Our data (Karkendamm experiment) did not confirm this departure from mononomiality, but showed a different pattern. In contrast to Plénet and Lemaire (1999), but in agreement with Ta and Weiland (1992), we observed that the N uptake in optimal and supra-optimal N treatments increased almost until the stage of silage maturity. Figure 2 exemplifies this by displaying for the year 1999 the N yield of the four pure mineral N treatments (0, 50, 100, and 150 kg N ha–1) and the corresponding (combined) treatments where each mineral N treatment was supplemented by a 40-m3 slurry application. Figure 2 shows that the N uptake for all treatments above 50 kg mineral N ha–1 (with or without slurry supplement) increased over the entire vegetation period. A cessation of N uptake is only observed in the N-deficient treatments, for instance 0 M/0 S, 50 M/0 S, and 0 M/136 S (where M = mineral N and S= slurry, see Fig. 2). All studies cited above use day units to measure developmental events, such as maximum N uptake. This kind of scale limits the comparability between studies. Due to large environmental impacts, particularly of temperature on plant development, day units are not suitable for comparisons of different field experiments or genotypes. For this reason, we included in Fig. 2 a second x axis representing growing degree units based on 6°C base temperature.
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The analysis of covariance of the log-transformed data, as described in the Materials and Methods section, with developmental phase serving as classifying factor, supports the validity of our extension hypothesis (see Fig. 3) . The interaction biomass x developmental phase proved to be statistically nonsignificant and allowed, therefore, the assumed relationship between biomass and the Ncrit to be regarded as valid for the pre- and postsilking stages. For reasons of sample size, we distinguished only two classes of developmental stage. Following Plénet and Lemaire (1999), one could have alternatively defined three classes by splitting postsilking further into two classes (up to 25 d past silking and from there to silage maturity), which was not possible for the present study due to a limited data set. After having established the validity of the model for the extended range by an analysis of covariance, we estimated the parameters for Eq. [2] using PROC NLIN.
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Validation of the Critical Nitrogen Dilution Curve
To study the performance of the derived Ncrit-to-biomass relationship, we applied our critical N dilution curve to an independent validation set (Ostenfeld experiment). The DM yields ranged between 1 and 22 t DM ha–1, depending on fertilization level, sampling date, hybrid, and year. Dry matter yield data were evaluated by an analysis of variance and revealed consistently lower yields for the 0 and the 50 kg N ha–1 treatments compared with the 150 kg N ha–1 fertilization level (data not presented). Assuming that the 97 data points of the 0- and 50-kg N treatments were N limited while the 41 data points of the 150-kg N treatment were generally non-N-limited, we examined the number of data points misclassified by the critical N dilution curve. Overall, the limited vs. the nonlimited categories were well discriminated by the curve, as Fig. 5
illustrates. In the nonlimiting group, the instances of misclassification were moderate to low and, in the limiting group, nearly absent. Table 4 displays the number of incorrect classifications with corresponding relative frequencies calculated for each hybrid and category separately. Larger discrepancies for Helix, Helmi, and Naxos in the nonlimiting category suggest that the accuracy of prediction depends to some degree on the hybrid involved. In total, for 14.6%, i.e., for 6 out of 41 hybrid x fertilizer combinations, growth was misleadingly classified as N limited. One should, however, take into account that the highest N treatment of the Ostenfeld experiment was in the range of optimum rather than excess N fertilization, which might be the reason for most of these misclassifications. Despite the fact that the 150 kg N ha–1 treatment resulted in significantly higher DM yield for all hybrids than the two lower N treatments did, it is still conceivable to assume that Helix, Helmi, and Naxos did not achieve maximum DM yield with the 150 kg N ha–1 treatment. In addition, one should consider that the observed rate of 14.6% wrong classifications drops to only 2.4% if we allow for an uncertainty of 5% with respect to Ncrit estimation. In the N-limited category, only 6.2%, i.e., 6 out of 97 data points, were mispredicted. This rate drops to 3.1% if an uncertainty of 5% is included. The outliers all belonged to the 50 kg N ha–1 treatment, and contrary to the high-N group, the dependency from hybrid did not show up, except for Alarik.
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Among the crop-related indicators available for assessing N supply of forage maize, the Ncrit is accentuated by several advantageous features. The work of Plénet and Lemaire (1999) and the present study have proven the concept of Ncrit to be valid for nearly the whole vegetation period of silage maize, i.e., form early growth stages (>1 t DM ha–1 biomass) until silage maturity. Other indicators do not allow for such wide time frames, for instance, the end-of-season corn stalk test (Hooker and Morris, 1999), whose reliability is limited in earlier growth stages. Another important advantage of the Ncrit method relies in its potential to detect N deficiency as well as overfertilization. The popular chlorophyll meter test, in contrast, is a poor predictor of excess N supply (Schröder et al., 2000). The critical N content does not require any reference plots, which, however, are recommended for other crop-related indicators (Schröder et al., 2000). Finally, data on N content of maize silages are widely available to German farmers, i.e., only the corresponding DM yield has to be determined to apply Ncrit as an indicator of N status.
Impact of Source–Sink Ratio on Carbon and Nitrogen Allocation during the Grain-Filling Period
The experimental verification of the extendibility of the critical N curve until the stage of silage maturity suggests that the dilution effect of starch accumulation on N concentration of the whole crop is not as pronounced as stated in Plénet and Lemaire (1999). To understand this observation, a closer look on the source–sink relationships during grain filling may be helpful.
The partitioning and remobilization of C and N into the grain is a function of the specific source–sink ratio of the crop, which itself depends on genotype x environment interactions and can be influenced by crop management factors, e.g., by fertilization (Uhart and Andrade, 1995). The sink capacity of the grains strongly depends on the growing conditions during the early stages of kernel growth (Jones et al., 1996). In climates with high temperatures and with high light intensity, the kernel growth is primarily fuelled by photosynthesis of leaves and stalk, and yields are mainly limited by sink capacities (Uhart and Andrade, 1995). In cool-temperate climates with low irradiation intensities, as in northern Europe, northern USA, and Canada, maize production is more likely to be source limited, i.e., assimilate demand for kernel development exceeds the supply by photosynthesis of the stover (Coors et al., 1997).
While sink limitation is indicated by greater accumulation of nonstructural carbohydrates (CH) in the stalk and less N remobilization from vegetative plant parts (Uhart and Andrade, 1995), source limitation, on the other hand, is characterized by a greater remobilization of N as well as nonstructural carbohydrates from stalk (Reed et al., 1988). In our experiments, those treatments with N concentrations close to the Ncrit showed about 32 to 43% of kernel N originating from vegetative parts (with 5 to 24% from leaf and 19 to 27% from stalk, data not presented). These results are in agreement with the study by Ma et al. (1999), who reported that for highly fertilized crops, vegetative parts contribute to kernel N between 20 to 50%. Somewhat higher values were obtained by Ta and Weiland (1992), with N remobilization of 63 to 73% and 83 to 85% for leaf and stalk, respectively. For leaf, stalk, and roots, they reported contributions to kernel N of 47, 45, and 9%, respectively. According to Ruget (1993), carbohydrates remobilized from stover may account for less than 10% under favorable growing conditions and up to 50% in less-favored, northern areas. In the present study, the treatments with N concentrations close to the Ncrit showed losses of stalk DM ranging between 30 and 34% (data not shown), which is an indication of source-limited grain growth conditions.
There is a general consensus that the whole-crop N level depends to some extent on source and sink limitations. However, evidence gathered in several studies suggests that in the case of forage maize, the influence of source–sink restrictions on the relationship between Ncrit and biomass is not that pronounced. Coors et al. (1997) studied the effects of ear fill on yield and forage quality and found that a 50% decrease in ear fill resulted in a 0.5% (0.8 g N kg–1 DM) increase of N content of the whole crop. Shading experiments of Reed et al. (1988) indicate also very modest effects of source limitation on the N content of the whole crop. A reduction of irradiation by 50% resulted in only slight decreases of whole-crop N concentration during the grain-filling stage. The 50% ear fill scenario of Coors et al. (1997) as well as the 50% shading treatments of Reed et al. (1988), however, have to be regarded as beyond the scope of sink or source limitations usually encountered in northern Europe.
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CONCLUSIONS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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ACKNOWLEDGMENTS |
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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) postsilking growth stages; the table enclosed presents the effects of log-transformed biomass (lnW), developmental phase (dev), and the corresponding interaction.
: 50 kg N ha–1) denote N-limiting growing conditions and the closed symbol (
: 150 kg N ha–1) non-N-limiting conditions. The continuous line (—) represents the mononomial critical N dilution curve Ncrit = 34.12·W–0.391, describing the relationship between dry matter (DM) yield (W, t DM ha–1) and the critical N concentration (Ncrit, g N kg–1 DM).