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.
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.
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.
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).
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:
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.
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).
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) p<0.001 p<0.001 p<0.001 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=non-significant; 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=non-significant; p<0.001 p<0.01 p<0.001 NS=non-significant; p<0.001 p<0.05 p<0.001 p<0.001 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 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) 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=non-significant p<0.001 p<0.01 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) GN, grain number TKW, thousand kernel weight R2: multiple correlation coefficient, 2007 2008 2009 N rate Toborzó Palotás Verbunkos Toborzó Palotás Verbunkos Toborzó Palotás Verbunkos N0 2.17 2.04 2.28 2.33 2.07 2.61 2.14 2.40 2.26 N80 2.50 2.63 2.64 2.98 3.25 3.52 3.82 3.87 3.29 N160 2.79 2.95 2.84 3.28 3.40 3.63 4.60 4.42 3.92 N240 2.88 2.98 3.01 3.49 3.52 3.83 4.09 3.99 3.76 LSD values N rate (N) 0.21 0.15 0.26 Cultivar (C) 0.14NS 0.14 0.17 N × C 0.30 0.26 0.36 N0 0.35 0.83 0.82 1.35 1.13 1.25 0.67 0.99 1.17 N80 1.15 1.51 1.35 1.69 1.91 2.28 1.50 1.86 1.76 N160 1.57 1.85 1.44 2.34 1.90 2.48 2.32 2.36 1.84 N240 1.49 2.02 1.65 2.32 1.86 2.78 2.50 2.08 2.61 LSD values N rate (N) 0.10 0.09 0.13 Cultivar (C) 0.10 0.08 0.08 N × C 0.18 0.15 0.17 N0 2.34 2.73 2.72 2.94 3.15 3.24 2.45 2.87 2.54 N80 2.45 2.82 2.72 3.20 3.36 3.19 3.40 3.54 2.97 N160 2.59 2.82 2.75 3.44 3.53 3.45 3.63 3.79 3.29 N240 2.54 2.73 2.84 3.39 3.53 3.34 3.27 3.33 3.07 LSD values N rate (N) 0.13 0.11 0.34 Cultivar (C) 0.18 0.13NS 0.30NS N × C 0.31 0.22 0.58 N0 2.17 2.31 2.13 1.92 2.81 2.64 3.84 3.83 3.50 N80 2.22 2.39 2.35 1.93 2.98 1.98 4.56 3.91 3.03 N160 2.06 2.43 2.29 2.24 3.30 2.20 4.06 3.81 3.14 N240 2.16 2.18 2.57 2.17 3.13 2.11 3.23 3.53 3.06 LSD values N rate (N) 0.18 0.15 0.23 Cultivar (C) 0.11 0.16 0.23 N × C 0.24 0.30 0.42NS N0 75.2 84.8 88.1 107.7 82.8 93.0 74.4 75.2 64.1 N80 84.5 90.8 88.6 113.2 85.0 113.2 81.6 79.6 85.8 Nl60 95.2 91.6 92.9 112.8 80.6 115.3 88.0 88.9 97.1 N240 90.5 98.7 89.1 113.4 82.7 117.1 85.8 83.8 87.3 LSD values N rate (N) 2.4 3.0 6.0 Cultivar (C) 2.6NS 1.5 5.5 N × C 4.7 3.7 10.4NS N0 10.3 9.3 10.9 12.5 10.1 13.0 9.8 8.4 9.6 N80 14.8 12.6 14.4 15.3 16.1 16.8 17.3 16.1 15.2 Nl60 17.2 15.4 15.3 17.2 16.3 18.3 20.1 18.1 16.9 N240 16.6 14.7 14.9 16.3 17.0 18.2 15.8 17.9 15.6 LSD values N rate (N) 1.2 0.9 0.9 Cultivar (C) 1.0 0.6 0.8 N × C 1.9 1.3 1.5 N0 2.95 3.85 4.43 5.77 4.53 5.13 3.71 2.63 3.43 N80 7.49 6.79 7.06 6.88 7.64 9.39 4.76 4.95 5.59 Nl60 10.49 8.67 7.50 9.70 7.51 10.56 6.59 6.38 5.74 N240 9.58 9.23 7.64 8.51 7.38 10.93 7.13 5.26 6.88 LSD values N rate (N) 0.37 0.34 0.25 Cultivar (C) 0.60 0.23 0.30 N × C 1.03 0.48 0.53 2007 2008 2009 N rate Toborzó Palotás Verbunkos Toborzó Palotás Verbunkos Toborzó Palotás Verbunkos N0 200 178 194 149 132 156 133 131 129 N80 249 221 229 181 199 200 186 182 171 N160 273 250 249 201 205 208 213 197 187 N240 288 256 264 217 213 219 209 195 187 LSD values N rate (N) 6 4 4 Cultivar (C) 3 2 3 N × C 8 5 6 N0 193 243 287 325 235 265 265 191 229 N80 472 408 436 376 386 450 310 288 365 N160 639 520 475 488 383 502 366 349 378 N240 606 554 465 437 389 511 352 328 388 LSD values N rate (N) 18 8 11 Cultivar (C) 12 7 6 N × C 25 13 14 N0 533 487 488 499 437 559 368 349 412 N80 574 568 549 627 685 678 477 568 553 N160 667 572 623 907 825 858 483 654 675 N240 650 624 637 806 826 925 602 611 777 LSD values N rate (N) 18 30 27 Cultivar (C) 19 21 38 N × C 35NS 43 66 No. of variables Variable R2 Cp AIC 1 GN 93.6 93.4 161 197 1 RGRmean 70.8 70.0 848 884 1 AGRmean 63.4 62.3 1072 1108 1 ALGRmean 58.5 57.3 1219 1255 1 LADflag_leaf 30.9 28.8 2054 2090 2 GN spike−1 TKW 98.2 98.1 24.5 60.5 2 GN spike−1 RGRmean 94.6 94.2 134 170 2 GN spike−1 AGRmean 94.3 93.9 143 179 3 GN spike−1 TKW RGRmean 98.7 98.6 11.0 47.0 3 GN spike−1 LARmean NARmean 95.5 95.i 107 143 4 GN spike−1 TKW RGRmean LADflag_leaf 98.9 98.7 8.4 44.4 4 GN spike−1 TKW ALGRmean LADflag_leaf 98.7 98.5 14.7 50.7 4 GN spike−1 TKW LARmean NARmean 98.5 98.4 18.1 54.1
The
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).
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
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
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) GN, grain number TKW, thousand kernel weight R2: multiple correlation coefficient, No. of variables Variable R2 C AIC 1 GN 71.4 70.5 16.5 52.5 1 CGRmean 54.3 53.0 45.3 81 1 LAImax 32.4 30.4 82 118 1 HI 25.3 23.1 94 130 1 TKW 19.4 17 104 140 1 LADLAI 17.8 15.4 107 143 2 GN m−2 LADLAI 80.6 79.4 2.9 38.9 2 GN m−2 LAImax 78.3 77.0 6.8 42.8 2 GN m−2 CGRmean 76.9 75.5 9.1 45.1 2 GN m−2 BMD 76.3 74.8 10.2 46.2 2 GN m−2 HI 74.6 73.1 12.9 48.9 3 GN m−2LAImax HI 81.7 80.0 2.9 38.9
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.