Growth analysis of winter wheat cultivars as affected by nitrogen fertilization / Wachstumsanalyse von Winterweizensorten in Abhängigkeit von Stickstoffdüngung

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Summary

Growth analysis helps explain the differences in yield and growth potential between cultivars in response to management practices and environmental conditions. The aim of the research was: (i) to investigate the effect of nitrogen fertilization on the growth and growth parameters of different wheat (Triticum aestivum L.) cultivars and (ii) to study the relationship between yield and growth parameters at the individual plant and plant stand level. In the two-factorial, split-plot experiment, the main plot was the nitrogen (N) treatment and the sub-plot was the cultivar. In response to N fertilization, the values of growth rate parameters increased up to the N160 treatment. The mean values of crop growth rate (g m-2 day-1) in the treatments were as follows: N0: 10.4, N80: 15.4, N160: 17.2 and N240: 16.3. The leaf area index, leaf area duration and especially the duration of the flag-leaf gave a good reflection of the effect of N fertilization. Multiple regression analysis demonstrated the significant effect of growth rates, size and duration of leaf area, biomass distribution and yield components on the yield. The results showed that understanding the growth of plants is important for optimizing management decisions.

Summary

Growth analysis helps explain the differences in yield and growth potential between cultivars in response to management practices and environmental conditions. The aim of the research was: (i) to investigate the effect of nitrogen fertilization on the growth and growth parameters of different wheat (Triticum aestivum L.) cultivars and (ii) to study the relationship between yield and growth parameters at the individual plant and plant stand level. In the two-factorial, split-plot experiment, the main plot was the nitrogen (N) treatment and the sub-plot was the cultivar. In response to N fertilization, the values of growth rate parameters increased up to the N160 treatment. The mean values of crop growth rate (g m-2 day-1) in the treatments were as follows: N0: 10.4, N80: 15.4, N160: 17.2 and N240: 16.3. The leaf area index, leaf area duration and especially the duration of the flag-leaf gave a good reflection of the effect of N fertilization. Multiple regression analysis demonstrated the significant effect of growth rates, size and duration of leaf area, biomass distribution and yield components on the yield. The results showed that understanding the growth of plants is important for optimizing management decisions.

1 Introduction

Identifying the physiological, biochemical or morphological characteristics responsible for inherent or environmentally induced variation in plant growth or yield requires careful growth analysis. Plant growth analysis is an explanatory, holistic and integrative approach for interpreting plant form and function. It uses simple primary data in the form of weights, areas, volumes and contents of plant components to investigate the processes within and involving the whole plant (Causton and Venus, 1981; Hunt, 1982). Two distinct approaches to the growth analysis of plants have evolved. In the classical approach, parameters are calculated using various formulae. The functional approach involves fitting curves to experimental data, and the instantaneous values of growth parameters are calculated from the first derivative of the function fitted. Growth analysis helps to explain differences in growth potential between species and cultivars in response to environmental conditions and management practices (Lambers et al., 1998). Understanding the growth of plants is important for optimizing management decisions. Plant growth analysis can provide data to calibrate crop models and to test the effects of climatic factors on photosynthesis and partitioning (Boote et al., 2016). Combined with reduced rates of yield improvement, the increasing global population has led to reduced productivity per capita, hence the need to increase the grain yield by at least 50% over the next few decades (Reynolds et al., 2009; Slafer et al., 2014). A better understanding of crop yield physiology would help to achieve the rates of yield improvement required in the near future.

Various authors have published the results of growth analysis on various crops in terms of different management practices and cultivar comparisons, including maize (e.g., Bullock et al., 1993), wheat (Davidson and Campbell, 1984; Barneix, 1990; Karimi and Siddique, 1991; Ozturk et al., 2006; Neugschwandtner et al., 2015), triticale (Royo and Blanco, 1999), Bermuda grass (Silva et al., 2016), soybean (Clawson et al., 1986; Yusuf et al., 1999; Hu and Wiatrak, 2012), potato (Oliveira et al., 2016), sugar beet (Hoffman and Kluge-Severin, 2011) and peas (Silim et al., 1985; Munier-Jolain et al., 2010; Neugschwandtner et al., 2013). However, few studies appear to have been made on the effect of agronomic treatments on the growth and productivity of wheat at both the individual plant and plant stand levels.

The aim of the research was: (i) to investigate the effect of nitrogen fertilization on the growth and growth parameters of different wheat cultivars and (ii) to study the relationship between yield and growth parameters at both the individual plant and plant stand level in several years.

2 Materials and methods

2.1 Field experiments and growing conditions

The effect of nitrogen fertilization on the yield and yield components of various wheat cultivars was studied in a small-plot long-term experiment, with two factors arranged in a split-plot design in four replications. The experiment was carried out in the years 2006/2007, 2007/2008 and 2008/2009 at the Agricultural Institute of the Centre for Agricultural Research in Martonvásár. In the long-term crop rotation experiment, the crop sequence was pea, winter wheat, maize and spring barley. The dose of N fertilizer formed the main plot and wheat cultivar the subplots. The doses of N fertilizer (calcium ammonium nitrate) were 0, 80, 160 and 240 kg ha-1 (designated as N0, N80, N160 and N240, respectively) and were applied in two splits: one-third before sowing and the other two-thirds in early spring at tillering. All the plots were given the same dosage of phosphorus and potassium (120 kg ha-1 of each). The three Martonvásár wheat genotypes sown in the subplots were Mv Toborzó (extra early), Mv Palotás (early) and Mv Verbunkos (mid-early). The ploughed layer of the chernozem soil, a humus-containing loam, was slightly acidic with moderate supplies of phosphorus and good supplies of potassium.

In the dry year of 2007, the total rainfall during the growing season was only one-third (200 mm) of that in 2008 and 2009 (638 and 617 mm, respectively). The rainfall distribution was also unfavorable in 2007, while in 2008 and 2009 both the quantity and distribution of rainfall were satisfactory (with the exception of lack of rain in April 2009). The mean temperature during the growing season was higher in 2007 (12°C) than in the other two years (10°C), which could be attributed partly to the very mild winter.

2.2 Sampling procedures

The sampling area for each treatment was 13.5 m2 (9 m × 1.5 m). At each sampling date, destructive samples consisting of 5 plants were taken randomly from a 0.5 m2 area once a week on a total of 25 occasions in 2007, 21 in 2008 and 17 in 2009, covering the whole growing season. Sampling was begun when the wheat reached the two-leaf stage. Leaf area was estimated by measuring the green leaf area of all the leaves with a leaf area meter (Model AM 300, BioScientific Ltd, UK). The dry mass of leaves, stems and spikes was determined after drying in a drying cabinet at 60°C for 48 h. The harvest index was derived from a 0.18 m2 subplot. The plants were cut at the soil surface, bundles were weighed and threshed, and grain weights were recorded.

2.3 Growth analysis

The Hunt-Parsons program (HP curves) (Hunt and Parsons, 1974), which fits first-, second- or third-order polynomial exponential curves to the trends in lnY (dry weight) versus t (time) and lnZ (leaf area) versus t, was used for functional growth analysis. A polynomial exponential function is a polynomial function of the natural logarithm of a growth attribute in relation to time (Causton and Venus, 1981). The output consisted of observed and fitted values of lnY and lnZ and the values of dY/dt, dZ/dt, (1/Y) (dY/dt), (1/Z)(dZ/dt), Z/Y and (1/Z)(dY/dt), together with their standard errors and 95% confidence intervals. The absolute growth rate (AGR), absolute growth rate of leaf area (ALGR), relative growth rate (RGR), net assimilation rate (NAR), leaf area ratio (LAR), crop growth rate (CGR) and leaf area index (LAI) were calculated using the Hunt-Parsons program, while the method of classical growth analysis (Evans, 1972; Hunt, 1982) was used to calculate the harvest index (HI), leaf area duration (LAD), leaf area duration of the flag-leaf (LADflag-leaf) and biomass duration (BMD). The growth analysis indices (parameters) were characterized in terms of dynamics over time and average (mean) and maximum (max) values (Causton and Venus, 1981; Hunt, 1982).

2.4 Statistical analysis

The split-plot design from the General Analysis of Variance menu of the GenStat 18 program was applied to analyze the growth parameter data sets, while the relationships between growth parameters were studied by linear regression analysis. Multiple linear regression analysis was used to determine relationships between the yield per plant (g plant-1) and yield per unit area (t ha-1) (as dependent variables), and the yield components and growth indices (as independent variables) for all the data (n = 36). The individual and joint effects of independent variables on the yield were determined using the All Subsets Regression menu of multiple regression. Relationships were analyzed between the yield per plant and the following eight independent variables: grain number (GN) per spike, thousand kernel weight (TKW), RGRmean, AGRmean, ALGRmean, NARmean, LARmean and LADflag_leaf. The relationships between yield per unit area (t ha-1) and the following seven variables were analyzed: GN per m2, TKW, CGRmean, LAImax, LADLAI, HI and BMD. The following indices: R2 (adjusted multiple correlation coefficient), R2 (multiple correlation coefficient), Mallows Cp and AIC (Akaike’s information criterion) were used as criteria in selecting the variables (Afifi et al., 2004).

3 Results

3.1 Above-ground dry matter and leaf area

The dynamics of dry matter accumulation per plant over time was expressed by a third-degree exponential function (Figure 1), the only exceptions being the dry matter accumulation of Mv Toborzó and Mv Verbunkos in the N0 treatment in 2007, to which a quadratic exponential function was fitted. In all cases, the functions gave a good fit to the measurement data (R2 = 94.7–99.3%). The dynamics of dry matter accumulation gave a good reflection of the effect of nitrogen treatments. In response to N fertilizer, the dry matter production increased up to the N240 treatment in 2007 and 2008, and up to N160 in 2009, the greatest differences generally being observed between the N0 and N80 treatments. Averaged over the cultivars and years, the maximum values were as follows: N0: 3.03, N80: 4.01, N160: 4.38, N240: 4.37 g plant-1.

Figure 1

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Figure 1

Effect of N treatment on the above-ground dry matter (g plant-1) of wheat cultivars (mean values for the years 2007-2009)

Error bars are LSD (p<0.05) separating means of different fertilization treatments.

Abbildung 1. Einfluss der N-Düngung auf die oberirdische Trockenmasse (g Pflanze-1) der Weizensorten (Mittelwerte für die Jahre 2007-2009)

Die Fehlerbalken zeigen Grenzdifferenzen (p<0,05), welche die Mittelwerte der Düngebehandlungen abgrenzen.

Citation: Die Bodenkultur: Journal of Land Management, Food and Environment 68, 1; 10.1515/boku-2017-0005

In all cases, the dynamics of leaf area growth was depicted with a third-degree exponential function (Figure 2) (R2 = 83.6–97.0%). The dynamics in the N0 and N80 treatments was quite distinct from that in the N160 and N240 treatments. The maximum value of leaf area per plant was smallest in the N0 treatment and significantly greater in the N80 treatment, while the highest values were obtained in the N160 and N240 treatments. Averaged over years and cultivars, the maximum leaf area in the N treatments was as follows (cm2 plant-1): N0: 84.8, N80: 134.9, N160: 160.7, N240: 169.5. The maximum leaf area was achieved by the plants immediately before heading. The dynamics of leaf area gave a clear indication of the different maturity dates of the cultivars. As a function of year and N treatment, the maximum leaf area was recorded 173–187 days after sowing (DAS) for Mv Toborzó, 180–194 DAS for Mv Palotás and 187–194 DAS for Mv Verbunkos. Averaged over years and N treatments, the leaf area of Mv Verbunkos was the greatest (144 cm2), followed by Mv Toborzó (135 cm2) and Mv Palotás (134 cm2). The maximum leaf area per plant (averaged over cultivars and N treatments) was considerably lower in 2009 (115 cm2) than in the other two years (2007: 135 cm2, 2008: 155 cm2).

Figure 2

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Figure 2

Effect of N treatment on the leaf area (cm2 plant-1) of wheat cultivars (mean values for the years 2007-2009)

Error bars are LSD (p<0.05) separating means of different fertilization treatments.

Abbildung 2. Einfluss der N-Düngung auf die Blattfläche (cm2 Pflanze-1) der Weizensorten (Mittelwerte für die Jahre 2007-2009)

Die Fehlerbalken zeigen Grenzdifferenzen (p<0,05), welche die Mittelwerte der Düngebehandlungen abgrenzen.

Citation: Die Bodenkultur: Journal of Land Management, Food and Environment 68, 1; 10.1515/boku-2017-0005

3.2 Growth parameters of individual plants

Absolute growth rate (AGR), the rate of change in size per unit time, is the simplest index of growth. Figure 3 shows the dynamics of AGR of Mv Palotás in 2008. The dynamics of growth parameters were similar in each year. Tables 1-3 give the detail data of the parameters. The dynamics of AGR could be characterized by a bell-shaped curve. The maximum values were obtained in the period around flowering (ca. 203 days after sowing). In the treatment where no N fertilizer was given, the dynamics of AGR was clearly distinct from that of the fertilized treatments. The value of AGRmean (g day-1 10-2) rose to the N160 treatment (Table 1), with the following values in the individual treatments: N0: 2.26, N80: 3.17, N160: 3.54, N240: 3.51. There was little difference in the AGRmean values of the different wheat cultivars: Mv Verbunkos and Mv Palotás: 3.13, Mv Toborzó: 3.09. The mean value of AGR was higher in 2008 and 2009 (3.16 and 3.55, respectively) than in 2007 (2.64).

Figure 3

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Figure 3

Effect of N treatment on the dynamics of the AGR, RGR, LAI, ALGR, NAR and LAR of Mv Palotás in 2008

Error bars are LSD (p<0.05) separating means of different fertilization treatments.

Abbildung 3. Einfluss der N-Düngung auf die Dynamik von AGR, RGR, LAI, ALGR, NAR und LAR der Sorte Mv Palotás in 2008

Die Fehlerbalken zeigen Grenzdifferenzen (p<0,05), welche die Mittelwerte der Düngebehandlungen abgrenzen.

Citation: Die Bodenkultur: Journal of Land Management, Food and Environment 68, 1; 10.1515/boku-2017-0005

Table 1

Effect of N fertilization on the mean values of the growth parameters and the maximum leaf area index (LAI) of wheat cultivars, using the functional method of growth analysis (2007-2009)

Tabelle 1. Einfluss der N-Düngung auf die Mittelwerte der Wachstumsparameter und den maximalen Blattflächenindex (LAI) der Weizensorten nach der funktionellen Methode der Wachstumsanalyse (2007-2009)

200720082009

N rateToborzóPalotásVerbunkosToborzóPalotásVerbunkosToborzóPalotásVerbunkos
AGRmean [g day−1 10−2]
N02.172.042.282.332.072.612.142.402.26
N802.502.632.642.983.253.523.823.873.29
N1602.792.952.843.283.403.634.604.423.92
N2402.882.983.013.493.523.834.093.993.76
LSD values
N rate (N)0.21***0.15***0.26***
Cultivar (C)0.14NSNS0.14***0.17***
N × C0.30*0.26***0.36*

ALGRmean [cm2 day−1]
N00.350.830.821.351.131.250.670.991.17
N801.151.511.351.691.912.281.501.861.76
N1601.571.851.442.341.902.482.322.361.84
N2401.492.021.652.321.862.782.502.082.61
LSD values
N rate (N)0.10***0.09***0.13***
Cultivar (C)0.10***0.08***0.08***
N × C0.18***0.15**0.17***

RGRmean [g g−1 day−110−2]
N02.342.732.722.943.153.242.452.872.54
N802.452.822.723.203.363.193.403.542.97
N1602.592.822.753.443.533.453.633.793.29
N2402.542.732.843.393.533.343.273.333.07
LSD values
N rate (N)0.13**0.11***0.34***
Cultivar (C)0.18***0.13NSNS0.30NSNS
N × C0.31*0.22***0.58**

NARmean [g m−2 day−1]
N02.172.312.131.922.812.643.843.833.50
N802.222.392.351.932.981.984.563.913.03
N1602.062.432.292.243.302.204.063.813.14
N2402.162.182.572.173.132.113.233.533.06
LSD values
N rate (N)0.18***0.15***"0.23***
Cultivar (C)0.11***0.16**0.23**
N × C0.24***0.30***0.42NSNS

LARmean[cm2 g −1]
N075.284.888.1107.782.893.074.475.264.1
N8084.590.888.6113.285.0113.281.679.685.8
Nl6095.291.692.9112.880.6115.388.088.997.1
N24090.598.789.1113.482.7117.185.883.887.3
LSD values
N rate (N)2.4***3.0**6.0***
Cultivar (C)2.6NSNS1.5***5.5*
N × C4.7***3.7***10.4NSNS

CGRmean [g m day−1]
N010.39.310.912.510.113.09.88.49.6
N8014.812.614.415.316.116.817.316.115.2
Nl6017.215.415.317.216.318.320.118.116.9
N24016.614.714.916.317.018.215.817.915.6
LSD values
N rate (N)1.2***0.9***0.9***"
Cultivar (C)1.0***0.6***0.8***
N × C1.9**1.3***1.5*

LAImax [m2 m−2]
N02.953.854.435.774.535.133.712.633.43
N807.496.797.066.887.649.394.764.955.59
Nl6010.498.677.509.707.5110.566.596.385.74
N2409.589.237.648.517.3810.937.135.266.88
LSD values
N rate (N)0.37***0.34***0.25***
Cultivar (C)0.60**0.23***0.30*
N × C1.03***0.48***0.53**
Table 2

Effect of N fertilizer treatments on the biomass duration (BMD) and the leaf area duration (LADLAI, LADflag-ieaf) of wheat cultivars, using the classical method of growth analysis (2007-2009)

Tabelle 2. Einfluss der N-Düngung auf die Biomassedauer (BMD) und die Blattflächendauer (LADLAI, LADflagdeaf) der Weizensorten nach der klassischen Methode der Wachstumsanalyse (2007-2009)

200720082009

N rateToborzóPalotásVerbunkosToborzóPalotásVerbunkosToborzóPalotásVerbunkos
BMD (g day)
N0200178194149132156133131129
N80249221229181199200186182171
N160273250249201205208213197187
N240288256264217213219209195187
LSD values
N rate (N)6***4***4***
Cultivar (C)3***2***3***
N × C8**5***6***

LADLAI (day)
N0193243287325235265265191229
N80472408436376386450310288365
N160639520475488383502366349378
N240606554465437389511352328388
LSD values
N rate (N)18***8***11***
Cultivar (C)12***7***6***
N × C25***13***14***

LADflag_leaf (cm2 day)
N0533487488499437559368349412
N80574568549627685678477568553
N160667572623907825858483654675
N240650624637806826925602611777
LSD values
N rate (N)18***30***27***
Cultivar (C)19***21***38***
N × C35NSNS43***66**
Table 3

Variables significantly influencing yield per plant (g plant−1) alone or in combination, based on the stepwise method of multiple regression analysis (n = 36)

Tabelle 3. Variablen, die den Ertrag der Einzelpflanzen (g Pflanze−1) signifikant beeinflussen, allein oder in Kombinationen, nach der schrittweisen Methode der Mehrfach-Regressionsanalyse (n = 36)

R2: multiple correlation coefficient, R2 : adjusted R2, Cp: Cp criterion, AIC: Akaike’s information criterion

No. of variablesVariableR2R2CpAIC
1GN spike−193.693.4161197
1RGRmean70.870.0848884
1AGRmean63.462.310721108
1ALGRmean58.557.312191255
1LADflag_leaf30.928.820542090
2GN spike−1 TKW98.298.124.560.5
2GN spike−1 RGRmean94.694.2134170
2GN spike−1 AGRmean94.393.9143179
3GN spike−1 TKW RGRmean98.798.611.047.0
3GN spike−1 LARmean NARmean95.595.i107143
4GN spike−1 TKW RGRmean LADflag_leaf98.998.78.444.4
4GN spike−1 TKW ALGRmean LADflag_leaf98.798.514.750.7
4GN spike−1 TKW LARmean NARmean98.598.418.154.1

Relative growth rate (RGR) expresses growth in terms of the rate of increase in size per unit of size. After the plants reached the 4-leaf stage (ca. 110 days after sowing), the dynamics of RGR exhibited an initial, relatively rapid increase. The maximum value was reached between mid-March and mid-April (around 154 days), at the beginning of shooting, after which it gradually declined, dropping to 0 when the foliage withered completely in mid-June (236 days) (Figure 3). The value of RGRmean (g g-1 day-1 10-2) was the greatest in the N160 treatment (Table 1), with the following values per N treatment: N0: 2.78, N80: 3.07, N160: 3.25, N240: 3.12. There was no significant difference between the RGR values of the cultivars: Mv Toborzó: 2.97, Mv Palotás: 3.08, Mv Verbunkos: 3.01. In the wetter years of 2008 and 2009, the RGRmean value (g g-1 day-1 10-2) was considerably higher (3.31 and 3.18, respectively) than in 2007 (2.67), when the rainfall supplies were less favorable.

The absolute growth rate of the leaf area (ALGR) was characterized by two successive bell-shaped curves (Figure 3), the first describing the growth of leaf area and the second describing the dynamics of leaf withering. The maximum value of ALGR was achieved at the end of tillering (174 days after sowing), immediately prior to shooting, with a difference of around a week between the cultivars due to differences in their maturity dates. The mean values of ALGR (cm2 day-1 10-2) in each N treatment were as follows: N0: 0.95, N80: 1.67, N160: 2.01, N240: 2.15 (Table 1). Among the cultivars Mv Verbunkos exhibited a higher value of ALGRmean (1.79) than Mv Toborzó (1.60) or Mv Palotás (1.69). The mean value of ALGR (cm2 day-1 10-2) was lowest in 2007 (1.34) and substantially higher in 2008 (1.94) and 2009 (1.81).

Net assimilation rate (NAR) is an index of the productive efficiency of plants, calculated in relation to total leaf area. Starting from the early growth stage (111 days after sowing), NAR increased rapidly for a few weeks, until the side-tillers had developed (Figure 3), after which the rate slowed and remained more or less constant until the foliage was fully developed. Then, as the leaf area decreased (about 174 days), NAR accelerated up to the end of the vegetation period. The mean value of NAR (Table 1) was smallest in the N0 treatment (2.79), rising with the application of N fertilizer and reaching the highest value in the N80 (2.82) or N160 treatment (2.84), depending on the cultivar and year. The NAR values of Mv Palotás (3.05) and Mv Toborzó (2.71) exceeded that of Mv Verbunkos (2.58). The mean value of NAR was highest in 2009 (3.63), while there was little difference between the values recorded in 2007 (2.27) and 2008 (2.45).

Leaf area ratio (LAR) is a morphological index expressing the leafiness of the plant as the ratio between total leaf area per plant and total dry weight per plant. The LAR values reached a maximum at the end of tillering (160-168 days after sowing), after which they declined steeply until flowering, followed by a further, slower decrease (Figure 3). The mean values of LAR provided a good illustration of the effects of N treatments (Table 1), and exhibited the following values in the individual N treatments: N0: 82.8, N80: 91.4, N160: 95.8, N240: 94.3 cm2 g-1. Among the wheat cultivars, the LARmean values of Mv Toborzó (93.5) and Mv Verbunkos (94.3) were higher than that of Mv Palotás (85.4) . The highest value of LARmean was recorded in 2008 (101.4), while the values in 2007 and 2009 were similar (89.2 and 82.6, respectively).

3.3 Growth parameters of the crop stand

Crop growth rate (CGR) is an index of agricultural productivity of land in terms of the plant biomass produced per unit area. The dynamics of CGR was similar to that of AGR, being characterized by a bell-shaped curve with maximum values during the flowering period. The mean value of CGR (CGRmean: g m-2 day-1) was lowest in the N0 treatment, increasing significantly in the N80 treatment (15.4) and achieving the highest value in the N160 treatment (17.2), after which it dropped slightly (N240: 16.3) (Table 1). Among the wheat cultivars, Mv Toborzó had the highest CGRmean value (15.3), followed by Mv Verbunkos (14.9) and Mv Palotás (14.4). CGRmean exhibited the lowest value in 2007 (13.9), with higher values in 2008 (15.6) and 2009 (15.1).

Leaf area index (LAI) is the ratio between the total leaf area of the crop and the total ground area on which it stands. The dynamics of LAI in response to N fertilization was similar to that of the leaf area (Figure 3). The maximum LAI values (Table 1) clearly reflected the effect of N fertilization (m2 m-2): N0: 4.05, N80: 6.73, N160: 8.13, N240: 8.06. Averaged over N treatments and years, the LAImax values were lower for Mv Palotás (6.24) than for Mv Toborzó (6.96) and Mv Verbunkos (7.02). LAImax was highest in 2008 (7.83), somewhat lower in 2007 (7.14) and much lower in 2009 (5.25).

Leaf area duration (LAD) is a quantitative expression of the length of time over which the plant stand maintains an active photosynthesizing leaf area. Both N fertilization and cultivar had a highly significant effect on the value of LADLAI in all three years, and there was also a significant N fertilizer × cultivar interaction (Table 2). The value of LADLAI (days) was lowest in the N0 treatment (248), significantly higher in N80 (388) and N160 (448), and the highest in N240 (455). Averaged over the years and N treatments, the greatest value of LADLAI was obtained for Mv Toborzó (402), followed by Mv Verbunkos (296) and Mv Palotás (356); though in the favorable years of 2008 and 2009, Mv Verbunkos had the highest values. In the individual years, the LADLAI value was significantly higher in 2007 (442) than in 2008 (396) or 2009 (317).

The leaf area duration of the flag-leaf (LADflag_leaf) differed in terms of both N treatments and cultivars (Table 2). The lowest values were recorded in the N0 treatment, rising with increases in N rate. The highest LADflag_leaf values were found in the N160 treatment in 2007 and 2008 (423 and 864 cm2 day, respectively) and in the N240 treatment in 2009 (664 cm2 day). In 2008 and 2009, the LADflag_leaf values of Mv Verbunkos exceeded those of the other two cultivars. In terms of the years, the highest LADflag_leaf value (cm2 day) was recorded in 2008 (719), with a significantly lower value in 2007 (581) and the lowest in 2009 (544).

The biomass duration (BMD) takes into account not only how much dry weight develops, but also how long it lasts. The effects of both N fertilizer and cultivar on BMD were significant in all the years, and there was also a significant N fertilizer × cultivar interaction (Table 2). The value of BMD (g day) was lowest in the N0 treatment (156), rising significantly with increasing N rates to 201.9 in N80, 220 in N160 and 227 in N240 (Table 2). Averaged over the N treatments, Mv Toborzó had the highest BMD in 2007 and 2009 (252 and 185 g day, respectively), while in the favorable year of 2008, the highest value was recorded for Mv Verbunkos (196 g day). The value of BMD (g day) was the highest in the dry year of 2007 (248), with significantly lower values in 2008 (191) and 2009 (177).

3.4 Regression between growth parameters and yield

Significant linear regression was found between leaf area duration (LAD) and biomass duration (BMD) based on the data of three years (Y = 75.9 + 0.324BMD). The R2 value showed that LAD explained 75.9% of the variance in BMD. On the basis of the 3-year data, linear regression was significant at P < 0.1% level between the absolute leaf area growth rate (ALGRmax) and the maximum value of the leaf area index (LAImax) (Y = 1.97 + 1.56ALGRmax, R2 = 79.6%). In each year, significant linear regression was detected between the mean absolute growth rate of dry matter (AGRmean) and the biomass duration (BMD). Based on R2 AGRmean accounted for 75% of the variance in BMD in 2007, for 95.7% in 2008 and for 95.3% in 2009. Based on the three-year data (n = 36), there was a significant relationship between RGRmean and its two components, NARmean and LARmean. The two components explained 62.7% of the variance in RGRmean at P < 0.1% level. The two parameters had similar effects on the RGRmean. In all three years and averaged over three years, significant linear regression (P < 0.1%) was found between CGRmax and its components, NARmean and LAImax, which together determined 71.2% of the variance in CGRmax. In all three years, the effect of LAImax was decisive, being more than three times as great as that of NARmean.

Relationships were investigated between the yield per plant (g plant-1), as a dependent variable, and eight independent variables (Table 3). In decreasing order of R2, the individual variables having the greatest significant effect on the yield per plant were GN per spike, RGRmean, AGRmean, ALGRmean and LADflag_leaf. The two independent variables that had the greatest joint influence on the yield were GN spike-1 and TKW, with the regression equation: Y = −1.133 + 0.04386GN + 0.02486TKW. This was followed (in decreasing order of R2) by the GN spike-1 and RGRmean and the GN per spike and AGRmean. The three independent variables with the greatest joint effect on the yield per plant were the GN spike-1, the TKW and RGRmean, with the regression equation: Y = −1.494 + 0.03971GN + 0.02381TKW + 0.1709RGRmean. The four independent variables with the greatest combined effect on yield per plant were GN spike-1, TKW, RGRmean and LADflag_leaf. In this case, the regression equation was: Y = −1.029 + 0.04299GN + 0.02438TKW + 0.0896ALGRmean + 0.000341LADflag_leaf.

The influence of seven independent variables was examined on the crop yield (t ha-1) (Table 4). The independent variables that individually had a separate significant influence on the crop yield (in decreasing order of R2) were GN m-2, CGRmean, LAImax, HI, TKW and LADLAI. The two independent variables having the greatest effect on the yield in combination were GN m-2 and LADLAI. The regression equation was: Y = 4.072 + 0.00009843 GN m-2 + 0.002947 LADLAI. This was followed (in decreasing order of R2) by GN m-2 and LAImax, GN m-2 and CGR, GN m-2 and BMD and GN m-2 and HI. The three independent variables having the greatest combined influence on the yield (t ha-1) were the GN m-2, LAImax and HI, with the regression equation: Y = 1.08 + 0.0000802 GN m-2 + 0.1398LAImax + 0.0825HI.

Table 4

Variables significantly influencing crop yield (t ha−1) alone or in combination, based on the stepwise method of multiple regression analysis (n = 36)

Tabelle 4. Variablen, die den Ertrag des Pflanzenstandes (t ha−1) signifikant beeinflussen, allein oder in Kombinationen, nach der schrittweisen Methode der Mehrfach-Regressionsanalyse (n = 36)

R2: multiple correlation coefficient, R2 : adjusted R2, Cp: Cp criterion, AIC: Akaike’s information criterion

No. of variablesVariableR2R2CpAIC
1GN m−271.470.516.552.5
1CGRmean54.353.045.381
1LAImax32.430.482118
1HI25.323.194130
1TKW19.417104140
1LADLAI17.815.4107143
2GN m−2 LADLAI80.679.42.938.9
2GN m−2 LAImax78.377.06.842.8
2GN m−2 CGRmean76.975.59.145.1
2GN m−2 BMD76.374.810.246.2
2GN m−2 HI74.673.112.948.9
3GN m−2LAImax HI81.780.02.938.9

4 Discussion

Growth analysis demonstrated significant relationships between growth rates and yield at both individual plant and plant stand level. This is in agreement with the results showing a significant relationship between growth rate and yield in maize (Tollenaar et al., 1992) and wheat (Serrago et al., 2013). The effect of N fertilization and cultivar on the yield was significant in all the years (Sugár et al., 2016). The grain yield was lowest in treatment N0 (averaging 5.45 t ha-1), with a significant increase from the N80 treatment in 2007 and 2008 (6.45 and 7.99 t ha-1, respectively) and the N160 treatment in 2009 (7.44 t ha-1). Higher N doses had no further significant yield-increasing effect. Averaged over the treatments, the grain yield was significantly higher in 2008 and 2009 (7.28 and 7.11 t ha-1, respectively) than in 2007 (6.11 t ha-1).

Nitrogen fertilization had a significant effect on the GN per spike (except in 2007) and the TKW. The GN per spike was highest in treatments N160 and N240, while TKW dropped significantly in the N160 and N240 treatments (Sugár et al., 2016).

In response to N fertilization, the growth rates (AGR, RGR, CGR) rose up to the N160 level, in harmony with the increase in dry matter and yield. NAR and LAR made different contributions to RGR depending on the genotype and the environmental conditions. Breaking down the growth rates into their components demonstrated that at the individual plant level, NAR and LAR had similar effects; whereas at plant stand level, the effect of LAI was decisive and that of NAR only secondary. In studies on the interspecific variation in relative growth rate, Poorter (1990) concluded that in general, 80–90% of an inherently higher RGR was explained by higher LAR and only 10–20% by higher NAR.

Higher values of dry matter productivity due to better N supplies have been associated with higher values of LAI and LAD. Better nitrogen supplies generally result in greater leaf area growth which, in turn, leads to better light absorption and further carbon fixation. Thorne (1973) mentioned the great dependence of grain yield on leaf area index. Positive associations between green leaf area duration and grain yield have been observed in a range of cereals, including wheat (Evans et al., 1975), maize (Tollenaar and Daynard, 1978; Wolfe et al., 1988), oats (Helsel and Frey, 1978) and sorghum (Borrell et al., 2000).

Flag-leaf photosynthesis in wheat contributes about 30–50% of the assimilates for grain filling (Shearman et al., 2005), and the onset and rate of senescence are clearly important factors for determining resistance to abiotic stress. Hansen et al. (2005) studied 20 spring wheat cultivars and found that modern cultivars tended to have higher yields and later senescing flag leaves. Blake et al. (2007) also reported that prolonged photosynthesis in the flag-leaf increased yield in a population of recombinant inbred lines. In the present experiments, the value of LADflag_leaf, like that of the growth rates, gave a good reflection of the effects of N fertilization, cultivar and year. LADLAI and the cumulative value of BMD, like the other parameters, clearly demonstrated the influence of mineral fertilization. The linear relationship between LAD and BMD pointed to the importance of size and duration of the leaf area (the major photosynthesizing organ of the plant) in biomass formation. The linear relationships between leaf area growth rate and LAImax, and between AGR and BMD indicated the importance of growth rates in the formation of leaf area and biomass.

The positive effect of N fertilization up to N160 was demonstrated most consistently by the dynamics and mean (maximum and cumulative) values of growth parameters, in agreement with the yield response data (Sugár et al., 2016). Similarly, in spring wheat experiments performed by Farmaha et al. (2015), increasing N fertilization from low to medium generally increased the grain yield and above-ground dry matter, but no significant increases were observed when N fertilization increased from medium to high. In the present work, the growth parameters of wheat cultivars exhibited little difference, though in most cases, those of Mv Palotás and Mv Verbunkos had more similar values and were usually somewhat higher than those of Mv Toborzó. As regards the year effect, the growth rates and growth parameters were generally lowest in 2007, the year with unfavorable rainfall supplies, and higher in the favorable years of 2008 and 2009.

Multiple regression analysis demonstrated the significant effects of growth rates (AGR, RGR, ALGR, CGR), size and duration of leaf area (LAImax, LADflag_leaf, LADLAI), the size and distribution of biomass (BMD, LAR, HI) and the yield components on the size of the yield (per plant and per hectare). In agreement with the present results, Heggenstaller et al. (2009) showed that across systems (sole-crop and double-crop corn), variation in yield was positively related to maximum crop growth rate, maximum leaf area index and leaf area duration, but was not associated with maximum or seasonal net assimilation rate. It can be concluded from the present experiments that if higher temperature during the vegetative growth stage is accompanied by rainfall deficit, substantial yield losses can be expected despite the increase in vegetative growth. This is particularly important in the light of climate change (Hatfield et al., 2011; Kimball et al., 2016). The results showed that the value of many agricultural experiments could be greatly enhanced if data were available on plant growth and the partitioning of this growth.

References

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  • Barneix, A.J. (1990): Yield variation in wheat: nitrogen accumulation, light interception and harvest index. In: Lambers, H. et al. (Eds.): Causes and consequences of variation in growth rate and productivity of higher plants. SPB Academic Publishing, The Hague, The Netherlands, pp. 87–100.

  • Blake, N., Lanning, S.P., Martin, J.M., Sherman, J.D. and L.E. Talbert (2007): Relationship of flag leaf characteristics to economically important traits in two spring wheat crosses. Crop Science 47, 491–496.

  • Boote, K.J., Jones, J.W., Tollenaar, M., Dzotsi, K.A., Prasad, P.V.V. and J.I. Lizaso (2016): Testing approaches and components in physiologically based crop models for sensitivity to climatic factors. In: Hatfield, J.L. and D. Fleischer (Eds.): Improving Modeling Tools to Assess Climate Change Effects on Crop Response. Advances in Agricultural Systems Modeling 7. ASA, CSSA, SSSA, Madison, WI, pp. 1–31.

  • Borrell, A.K., Hammer, G.L. and R.G. Henzell (2000): Does maintaining green leaf area in sorghum improve yield under drought? I. Leaf growth and senescence. Crop Science 40, 1026–1037.

  • Bullock, D.G., Simons, F.W, Chung, I.M. and G.I. Johnson (1993): Growth analysis of corn grown with or without starter fertilizer. Crop Science 33, 112–117.

  • Causton, D.R. and J.C. Venus (1981): The Biometry of Plant Growth. Edward Arnold, London.

  • Clawson, K.L., Specht J.E. and B.L. Blad (1986): Growth analysis of soybean isolines differing in pubescence density. Agrononomy Journal 78, 164–172.

  • Davidson, H.R. and C.A. Campbell (1984): Growth rates, harvest index and moisture use of Manitou spring wheat as influenced by nitrogen, temperature and moisture. Canadian Journal of Plant Science 64, 825–839.

  • Evans, L.T., Wardlaw, I.E. and R.A. Fischer (1975): Wheat. In: Evans, L.T. (Ed.): Crop Physiology: Some Case Histories. Cambridge University Press, Cambridge, UK, pp. 101–150.

  • Farmaha, B.S., Sims, A.L. and J.J. Wiersma (2015): Impact of nitrogen fertility on the production performance of four hard red spring wheat cultivars. Agronomy Journal 107, 864–870.

  • Hansen, K.A., Martin, J.M., Lanning, S.P. and L.E. Talbert (2005): Correlation of genotype performance for agronomic and physiological traits in space-planted versus densely seeded conditions. Crop Science 45, 1023–1028.

  • Hatfield, J.L., Boote, K.J., Kimball, B.A., Ziska, L.H., Izaurralde, R.C., Ort, D., Thomson, A.M. and D. Wolfe (2011): Climate impacts on agriculture: Implications for crop production. Agronomy Journal 103, 351–370.

  • Heggenstaller, A.H., Liebman, M. and R.P. Anex (2009): Growth analysis of biomass production in sole-crop and double-crop corn systems. Crop Science 49, 2215–2224.

  • Helsel, D.B. and K.J. Frey (1978): Grain yield variations in oats associated with differences in leaf area duration among oat lines. Crop Science 18, 765–769.

  • Hoffmann, C.M. and S. Kluge-Severin (2011): Growth analysis of autumn and spring sown sugar beet. European Journal of Agronomy 34, 1–9.

  • Hu, M. and P. Wiatrak (2012): Effect of planting date on soybean growth, yield, and grain quality: review. Agronomy Journal 104, 785–790.

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  • Serrago, R.A., Alzueta, I., Savin, R. and G.A. Slafer (2013): Understanding grain yield responses to source-sink ratios during grain filling in wheat and barley under contrasting environments. Field Crops Research 150, 42–51.

  • Shearman, V.J., Sylvester-Bradley, R., Scott, R.K. and M.J. Foulkes (2005): Physiological processes associated with wheat yield progress in the UK. Crop Science 45, 175–185.

  • Silim, S.N., Hebblethwaite, P.D. and M.C. Heath (1985): Comparison of the effects of autumn and spring sowing date on growth and yield of combining peas (Pisum sativum L.). The Journal of Agricultural Science 104, 35–46.

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  • Tollenaar, M. and T.B. Daynard (1978): Leaf senescence in short-season maize hybrids. Canadian Journal of Plant Science 58, 869–874.

  • Tollenaar, M., Dwyer, L.M. and D.W. Stewart (1992): Ear and kernel formation in maize hybrids representing three decades of grain yield improvement in Ontario. Crop Science, 432–438.

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  • Yusuf, R.I., Siemens, J.C. and D.G. Bullock (1999): Growth analysis of soybean under no-tillage and conventional tillage systems. Agronomy Journal 91, 928–933.

Footnotes

***

p<0.001

***

p<0.001

***

p<0.001

NS

NS=non-significant;

***

p<0.001

***

p<0.001

*

p<0.05

***

p<0.001

*

p<0.05

ALGR values are for the leaf area increasing period

***

p<0.001

***

p<0.001

***

p<0.001

***

p<0.001

***

p<0.001

***

p<0.001

***

p<0.001

**

p<0.01

***

p<0.001

**

p<0.01

***

p<0.001

***

p<0.001

***

p<0.001

NS

NS=non-significant;

NS

NS=non-significant;

*

p<0.05

***

p<0.001

**

p<0.01

***

p<0.001

***

p<0.001

***

p<0.001

***

p<0.001

**

p<0.01

**

p<0.01

***

p<0.001

***

p<0.001

NS

NS=non-significant;

***

p<0.001

**

p<0.01

***

p<0.001

NS

NS=non-significant;

***

p<0.001

*

p<0.05

***

p<0.001

***

p<0.001

NS

NS=non-significant;

***

p<0.001

***

p<0.001

***

p<0.001

***

p<0.001

***

p<0.001

***

p<0.001

**

p<0.01

***

p<0.001

*

p<0.05

***

p<0.001

***

p<0.001

***

p<0.001

**

p<0.01

***

p<0.001

*

p<0.05

***

p<0.001

***

p<0.001

**

p<0.01

***

p<0.001

***

p<0.001

***

p<0.001

***

p<0.001

***

p<0.001

***

p<0.001

**

p<0.01

***

p<0.001

***

p<0.001

***

p<0.001

***

p<0.001

***

p<0.001

***

p<0.001

***

p<0.001

***

p<0.001

***

p<0.001

***

p<0.001

***

p<0.001

***

p<0.001

***

p<0.001

***

p<0.001

***

p<0.001

***

p<0.001

***

p<0.001

NS

NS=non-significant

***

p<0.001

**

p<0.01

GN, grain number

TKW, thousand kernel weight

GN, grain number

TKW, thousand kernel weight

Afifi, A., Clark, V.A. and S. May (2004): Computer-aided multivariate analysis. Chapman & Hall/CRC, Boca Raton, FL.

Barneix, A.J. (1990): Yield variation in wheat: nitrogen accumulation, light interception and harvest index. In: Lambers, H. et al. (Eds.): Causes and consequences of variation in growth rate and productivity of higher plants. SPB Academic Publishing, The Hague, The Netherlands, pp. 87–100.

Blake, N., Lanning, S.P., Martin, J.M., Sherman, J.D. and L.E. Talbert (2007): Relationship of flag leaf characteristics to economically important traits in two spring wheat crosses. Crop Science 47, 491–496.

Boote, K.J., Jones, J.W., Tollenaar, M., Dzotsi, K.A., Prasad, P.V.V. and J.I. Lizaso (2016): Testing approaches and components in physiologically based crop models for sensitivity to climatic factors. In: Hatfield, J.L. and D. Fleischer (Eds.): Improving Modeling Tools to Assess Climate Change Effects on Crop Response. Advances in Agricultural Systems Modeling 7. ASA, CSSA, SSSA, Madison, WI, pp. 1–31.

Borrell, A.K., Hammer, G.L. and R.G. Henzell (2000): Does maintaining green leaf area in sorghum improve yield under drought? I. Leaf growth and senescence. Crop Science 40, 1026–1037.

Bullock, D.G., Simons, F.W, Chung, I.M. and G.I. Johnson (1993): Growth analysis of corn grown with or without starter fertilizer. Crop Science 33, 112–117.

Causton, D.R. and J.C. Venus (1981): The Biometry of Plant Growth. Edward Arnold, London.

Clawson, K.L., Specht J.E. and B.L. Blad (1986): Growth analysis of soybean isolines differing in pubescence density. Agrononomy Journal 78, 164–172.

Davidson, H.R. and C.A. Campbell (1984): Growth rates, harvest index and moisture use of Manitou spring wheat as influenced by nitrogen, temperature and moisture. Canadian Journal of Plant Science 64, 825–839.

Evans, L.T., Wardlaw, I.E. and R.A. Fischer (1975): Wheat. In: Evans, L.T. (Ed.): Crop Physiology: Some Case Histories. Cambridge University Press, Cambridge, UK, pp. 101–150.

Farmaha, B.S., Sims, A.L. and J.J. Wiersma (2015): Impact of nitrogen fertility on the production performance of four hard red spring wheat cultivars. Agronomy Journal 107, 864–870.

Hansen, K.A., Martin, J.M., Lanning, S.P. and L.E. Talbert (2005): Correlation of genotype performance for agronomic and physiological traits in space-planted versus densely seeded conditions. Crop Science 45, 1023–1028.

Hatfield, J.L., Boote, K.J., Kimball, B.A., Ziska, L.H., Izaurralde, R.C., Ort, D., Thomson, A.M. and D. Wolfe (2011): Climate impacts on agriculture: Implications for crop production. Agronomy Journal 103, 351–370.

Heggenstaller, A.H., Liebman, M. and R.P. Anex (2009): Growth analysis of biomass production in sole-crop and double-crop corn systems. Crop Science 49, 2215–2224.

Helsel, D.B. and K.J. Frey (1978): Grain yield variations in oats associated with differences in leaf area duration among oat lines. Crop Science 18, 765–769.

Hoffmann, C.M. and S. Kluge-Severin (2011): Growth analysis of autumn and spring sown sugar beet. European Journal of Agronomy 34, 1–9.

Hu, M. and P. Wiatrak (2012): Effect of planting date on soybean growth, yield, and grain quality: review. Agronomy Journal 104, 785–790.

Hunt, R. (1982): Plant Growth Curves: The Functional Approach to Plant Growth Analysis. Edward Arnold, London, UK.

Hunt, R. and I.T. Parsons (1974): A computer program for deriving growth-functions in plant growth-analysis. Journal of Applied Ecology 11, 297–307.

Karimi, M.M. and K.H.M. Siddique (1991): Crop growth and relative growth rates of old and modern wheat cultivars. Australian Journal of Agricultural Research 42, 13–20.

Kimball, B.A., White, J.W., Wall, G.A. and M.J. Ottman (2016): Wheat responses to a wide range of temperatures: The hot serial cereal experiment. In: Hatfield, J.L. and D. Fleischer (Eds.): Improving Modeling Tools to Assess Climate Change Effects on Crop Response. Advances in Agricultural Systems Modeling 7. ASA, CSSA, SSSA, Madison, WI, pp. 33–44.

Lambers, H., Poorter, H. and M.M.I. Van Vuuren (1998): Inherent Variation in Plant Growth. Backhuys Publisher, Leiden, The Netherlands.

Munier-Jolain, N., Biarnes, V., Chaillet, I., Lecoeur, J. and M.-H. Jeuffroy (2010): Physiology of the Pea Crop. CRC Press, Enfield, New Hampshire.

Neugschwandtner, R.W., Wichmann, S., Gimplinger, D.M., Wagentristl, H. and H.-P. Kaul (2013): Chickpea performance compared to pea, barley and oat in Central Europe: Growth analysis and yield. Turkish Journal of Field Crops 18, 179–184.

Neugschwandtner, R.W., Böhm, K., Hall, R.M. and H.P. Kaul (2015): Development, growth, and nitrogen use of autumn- and spring-grown facultative wheat. Acta Agriculturae Scandinavica, Section B — Soil & Plant Science 65, 6–13.

Oliveira, J.S., Brown, H.E., Gash, A. and D.J. Moot (2016): An explanation of yield differences in three potato cultivars. Agronomy Journal 108, 1434–1446.

Ozturk, A., Caglar, O. and S. Bulu (2006): Growth and yield response of facultative wheat to winter sowing, freezing sowing and spring sowing at different seeding rates. Journal of Agronomy and Crop Science 192, 10–16.

Poorter, H. (1990): Interspecific variation in relative growth rate: On ecological causes and physiological consequences. In: Lambers et al. (Eds.): Causes and Consequences of Variation in Growth Rate and Productivity of Higher Plants. SPB Academic Publishing, The Hague, The Netherlands, pp. 35–44.

Reynolds, M., Foulkes, M.J., Slafer, G.A., Berry, P., Parry, M.A.J., Snape, J.W. and W.J. Angus (2009): Raising yield potential in wheat. Journal of Experimental Botany 60, 1899–1918.

Royo, C. and R. Blanco (1999): Growth analysis of five spring and five winter triticale genotypes. Agronomy Journal 91, 305–311.

Serrago, R.A., Alzueta, I., Savin, R. and G.A. Slafer (2013): Understanding grain yield responses to source-sink ratios during grain filling in wheat and barley under contrasting environments. Field Crops Research 150, 42–51.

Shearman, V.J., Sylvester-Bradley, R., Scott, R.K. and M.J. Foulkes (2005): Physiological processes associated with wheat yield progress in the UK. Crop Science 45, 175–185.

Silim, S.N., Hebblethwaite, P.D. and M.C. Heath (1985): Comparison of the effects of autumn and spring sowing date on growth and yield of combining peas (Pisum sativum L.). The Journal of Agricultural Science 104, 35–46.

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Journal Information


CiteScore 2017: 0.25

SCImago Journal Rank (SJR) 2017: 0.148
Source Normalized Impact per Paper (SNIP) 2017: 0.105

Figures

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    Effect of N treatment on the above-ground dry matter (g plant-1) of wheat cultivars (mean values for the years 2007-2009)

    Error bars are LSD (p<0.05) separating means of different fertilization treatments.

    Abbildung 1. Einfluss der N-Düngung auf die oberirdische Trockenmasse (g Pflanze-1) der Weizensorten (Mittelwerte für die Jahre 2007-2009)

    Die Fehlerbalken zeigen Grenzdifferenzen (p<0,05), welche die Mittelwerte der Düngebehandlungen abgrenzen.

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    Effect of N treatment on the leaf area (cm2 plant-1) of wheat cultivars (mean values for the years 2007-2009)

    Error bars are LSD (p<0.05) separating means of different fertilization treatments.

    Abbildung 2. Einfluss der N-Düngung auf die Blattfläche (cm2 Pflanze-1) der Weizensorten (Mittelwerte für die Jahre 2007-2009)

    Die Fehlerbalken zeigen Grenzdifferenzen (p<0,05), welche die Mittelwerte der Düngebehandlungen abgrenzen.

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    Effect of N treatment on the dynamics of the AGR, RGR, LAI, ALGR, NAR and LAR of Mv Palotás in 2008

    Error bars are LSD (p<0.05) separating means of different fertilization treatments.

    Abbildung 3. Einfluss der N-Düngung auf die Dynamik von AGR, RGR, LAI, ALGR, NAR und LAR der Sorte Mv Palotás in 2008

    Die Fehlerbalken zeigen Grenzdifferenzen (p<0,05), welche die Mittelwerte der Düngebehandlungen abgrenzen.

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