Intra-specific competition in maize: Contribution of extreme plant hierarchies to grain yield, grain yield components and kernel composition

Field Crops Research 97 (2006) 155–166 www.elsevier.com/locate/fcr

Intra-specific competition in maize: Contribution of extreme plant hierarchies to grain yield, grain yield components and kernel composition G.A. Maddonni *, M.E. Otegui Dpto. de Produccio´n Vegetal, Facultad de Agronomı´a, Universidad de Buenos Aires, Av. San Martı´n 4453 (C1417DSE), Buenos Aires, Argentina Received 18 May 2005; accepted 16 September 2005

Abstract Maize (Zea mays L.) cropping conditions that promote high intra-specific competition pressure generate an increased plant-to-plant variability within the stand, and the appearance of individuals with different ability to capture scarce resources (i.e. dominant and dominated plants). The objectives of this paper were to analyze (i) stand density effects on plant biomass at physiological maturity (R6), grain yield per plant (GYP), GYP components (KNP: kernel number per plant; KW: kernel weight), and kernel composition (starch, oil and protein contents per kernel) of the mean plant of the stand (i.e. considering all individuals) and of the dominant (D) and dominated (d) individuals; and (ii) the contribution of these extreme plant hierarchies to GYP, GYP components and kernel composition of the mean plant of a stand. Four maize hybrids of contrasting KW (small and large KW) were cropped at a wide range of stand densities (3–15 pl m
2) during 1999/2000 and 2001/ 2002 in Argentina. The mean value of measured variables declined as plant density increased from 3 to 15 pl m
2, and plant-to-plant variability (CV: coefficient of variation) of the same variables increased with enhanced crowding. The magnitude of the reduction in mean plant values differed among variables: plant biomass at R6, GYP and KNP underwent a larger reduction (ca. 66%) than KW (ca. 14–19%) or kernel contents (ca. 22% for oil and protein contents, and 13% for starch content). Similarly, the increase in CVs was larger for plant biomass at R6 (from ca. 13 to 40%) and GYP (from ca. 30 to 58 and 15 to 38% for small and large KW hybrids, respectively) than for KW (ca. from 7 to 20%). Only a slight increase in CVs of oil (6–17%) and protein (9–12%) concentrations of large KW hybrids was recorded. The CV of KNP followed a trend similar to that for GYP. Differences between plant categories increased when mean GYP and KNP of all individuals of the stand were smaller than 157 g pl
1 and 649 kernel pl
1, respectively. Below these thresholds, the d/D ratio dropped from 0.76 to 0.30 (small KW hybrids) or to 0.40 (large KW hybrids) for GYP (r2 = 0.76, P < 0.001), and from 0.75 to 0.38 (small KW hybrids) or to 0.46 (large KW hybrids) for KNP (r2 = 0.59, P < 0.001). In contrast, the d/D ratio for KW varied always from 1 to 0.80 in response to decreased mean KW (r2 = 0.39, P < 0.01). The concentration of kernel contents did not differ between plant types. Results indicate that grain yield of maize crops grown at high stand densities is composed by plants bearing very different kernel numbers, with slight differences in kernel size, and similar starch, oil and protein concentration. # 2005 Elsevier B.V. All rights reserved. Keywords: Maize; Plant grain yield; Grain yield components; Kernel composition; Plant hierarchies

1. Introduction Maize (Zea mays L.) is one of the most sensitive grass species to intra-specific competition. When plant population density is increased, both plant biomass and grain yield per plant (GYP) decline (Edmeades and Daynard, 1979a; Tetio* Corresponding author. Tel.: +54 11 45248039; fax: +54 11 45148739. E-mail address: [email protected] (G.A. Maddonni). 0378-4290/$ – see front matter # 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.fcr.2005.09.013

Kagho and Gardner, 1988). Considering GYP components, i.e., kernel number per plant (KNP) and kernel weight (KW), the former is always reduced when stand density is increased (Edmeades and Daynard, 1979a; Tetio-Kagho and Gardner, 1988; Echarte et al., 2000; Maddonni and Otegui, 2004), while KW is either not affected (Tetio-Kagho and Gardner, 1988) or is only slightly (5–30%) reduced (Edmeades and Daynard, 1979a; Echarte et al., 2000; Sangoi et al., 2002; Borra´s et al., 2003). Consequently, GYP is mainly related to

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KNP (Echarte et al., 2000), while KW contributes to GYP variations only in some hybrids (Otegui et al., 1995; Echarte et al., 2000). Grain yield per unit land area, however, shows a curvilinear response to plant population, producing a maximum value at the optimum plant density. Below this stand density, KNP increase is not compensated by the reduction in the number of plants per area, while substantial barrenness occurs above the optimum density (Tetio-Kagho and Gardner, 1988). The optimum plant density depends on resource availability (i.e., water, nutrients, irradiance) and the tolerance of a hybrid to intra-specific competition (Tollenaar et al., 1992; Tollenaar and Wu, 1999; Echarte et al., 2000; Tollenaar and Lee, 2002; Sangoi et al., 2002). Current commercial hybrids generally have an enhanced tolerance to increased stand density as compared to older hybrids (Sangoi et al., 2002; Echarte et al., 2004). Based on this fact, maize today is cultivated at higher stand densities than those commonly used in the previous decades. For example, estimated optimum plant densities for hybrids released during 1970s, 1980s and 1990s in Brazil were 71,000, 79,000 and 85,000 pl ha
1, respectively (Sangoi et al., 2002). These cropping conditions promote high intra-specific competition pressure, which may lead to an increased plant-to-plant variability within the stands (Edmeades and Daynard, 1979a; Vega and Sadras, 2003), and the appearance of individuals with different abilities to capture scarce resources. Large plants represent those individuals with an enhanced competitive ability throughout their life cycle, and could be identified as the ‘‘dominant plants’’ of the stand. They contrast with small plants having a low capacity for resource capture (i.e. ‘‘dominated plants’’). In a previous study (Maddonni and Otegui, 2004), we have analyzed the growth and kernel set of maize plants at different stand densities (6, 9 and 12 pl m
2), and have focused on the dominant and dominated individuals. In that work a plant was classified within each extreme hierarchy group when its biomass at physiological maturity (R6; Ritchie et al., 1993) was within the uppermost (dominant) or the lowermost (dominated) 33% of the data set. The study revealed that differences in shoot biomass between extreme hierarchies started very early in the cycle (at four-leaf stage V4; Ritchie et al., 1993), were maximized at V7–13, and remained constant from this stage onwards, thereby conditioning KNP. In the present paper we will broaden the previous analysis by addressing the effects of extreme plant hierarchies on GYP, KW and kernel composition (i.e. starch, oil and protein contents). Moreover, we will evaluate the contribution of different plant hierarchies to the mean values of these traits and of KNP. We expect (i) increased variation among plants in response to increased stand density for all analyzed variables, (ii) an enhanced effect of dominated individuals on mean plant values when competition for resources is increased at high stand density, and (iii) that the more limited growth of dominated individuals will promote a larger decrease in the production of rich energy compounds of kernels (i.e. protein and oil contents; Sinclair and de Wit,

1975) than in starch accumulation. For testing these hypotheses we grew four hybrids of contrasting KW (small KW: DK752 and DK4F37, large KW: DK696 and Exp980) at a wide range of stand densities (3–15 pl m
2). We analyzed the effects of these treatments on plant biomass at R6, GYP, GYP components (KNP, KW, grained ears pl
1) and kernel composition of (i) the mean plant of a stand, and (ii) individuals in the dominant and dominated plant hierarchies.

2. Materials and methods 2.1. Experimental design and growing conditions Field experiments were conducted in Argentina during the growing seasons of 1999/2000 at Salto (348330 S, 608330 W) and 2001/2002 at Pergamino (338560 S, 608340 W), both on silty clay loam soils (Typic Argiudoll). At Salto, sowing took place on 9 November, and treatments were a factorial combination of (i) five stand densities (3, 6, 9, 12 and 15 pl m
2), and (ii) four hybrids (DK696, DK752, DK4F37, and an experimental hybrid, Exp980). At Pergamino, sowing took place on 5 November, and treatments were a factorial combination of (i) three stand densities (6, 9 and 12 pl m
2), and (ii) two hybrids (DK696 and Exp980). Details of the experiment conducted at Pergamino were published in a previous paper (Maddonni and Otegui, 2004). Cultivars were classified as small (DK752 and DK4F37) and large (DK696 and Exp980) KW hybrids, based on actual KW (<250 and >250 mg kernel
1 for small and large KW, respectively) at commercial plant densities (e.g. 6–7 pl m
2). At these stand densities, Exp980 exhibits the highest plant height to the uppermost leaf (>200 cm), DK696 and DK4F37 intermediate statures (ca. 190 cm) and DK752 the shortest plant height (ca. 170 cm). Hybrids DK752, DK696 and Exp980 are single crosses, and DK4F37 is double cross. All hybrids have a similar thermal time requirement (base temperature 8 8C; Ricthie and NeSmith, 1991) from sowing to silking (ca. 900 8Cd) and physiological maturity (ca. 1800 8Cd). In all experiments, treatments were arranged in a splitplot design with three replicates. At Salto, hybrids were the main factor and stand densities the sub-factor. At Pergamino, stand densities were the main factor and hybrids the sub-factor. Plots were always five rows, 0.70 m apart and 20 m long. Rows had always an east–west orientation. Seeds (3–4) of uniform size were hand-planted, and thinned during the heterotrophic seedling phase (from germination to V3; Pommel, 1990) in all experiments. In order to avoid nitrogen (N) restrictions, urea (200 kg N ha
1) was applied at V4. Plots were kept free of weeds, insects and diseases. Total amount of rainfall in 1999/2000 (280 mm) was lower than that recorded during 2001/2002 (396 mm) but water stress was prevented by means of furrow (1999/2000) or sprinkler (2001/2002) irrigation, with soils near field capacity

G.A. Maddonni, M.E. Otegui / Field Crops Research 97 (2006) 155–166

throughout the growing season. Mean air temperature (ca. 23 8C) and solar radiation (ca. 25 MJ m
2 d
1) were similar during both growing seasons. 2.2. Measurements In this work measurements were made on an individual plant basis. Successive plants in 2 m2 (lowest stand density) or 1 m2 (all other stand densities) of the central row of each hybrid  stand density treatment combination (hereinafter referred to as treatment) were sampled at R6. Each harvested plant was oven dried at 70 8C until constant weight to determine its aboveground biomass (plant biomass at R6), GYP and GYP components. Harvest index (HI) per plant was estimated as the quotient between GYP and plant biomass at R6. Kernel number per plant was counted, and KW was calculated as the quotient between GYP and KNP. Prolificacy was determined by computing all grained ears (>10 kernels; Tollenaar et al., 1992) per each sampled plant. Kernels were analyzed for starch, protein and oil concentration by near-infrared transmittance (Infratec 1227, Tecator, Sweden). Each sample (ca. 96.7  2.2 g) was constituted by all intact kernels of each plant. Calibration of NIR instrument was performed by Monsanto Argentina, with the most representative maize hybrids cultivated in the world (Sanguinetti, pers. commun.). The concentration (g kg
1) of each kernel component was expressed on a dry weight basis. Starch, protein and oil contents of kernels (mg kernel
1) were calculated as the product between KW and the concentration (milligram of each component per milligram of grain) of each component. Plant biomass, GYP, HI, prolificacy, KNP, KW and kernel composition of the mean plant of the stand were calculated as the corresponding value obtained from all plants sampled at each treatment (i.e., mean of the whole plant population). 2.3. Extreme plant hierarchies In this work we followed the methodology described in Maddonni and Otegui (2004) for plant classification within a hierarchy group. For this purpose, all data of individual plant biomass recorded for each plot at R6 were ranked in ascending order, and the cumulative frequency was calculated for each record. A plant was classified as dominant when its biomass was ranked within the uppermost 33% of the data set. Dominated plants were those with a biomass value ranked within the lowermost 33% of the data. Mean values of all tested variables were also computed within each hierarchy group. 2.4. Statistical analyses In each experiment, differences among treatments for all analyzed variables were tested by ANOVA, and differences between dominant and dominated hierarchies for measured

157

variables were evaluated by means of a t-test (Steel and Torrie, 1960). Data set of both experiments were pooled together and several relationships and correlations analyses among variables were tested for the whole data base. Association among variables was investigated using linear and nonlinear models. Differences between groups of hybrid for the intercept and the slope of the linear regressions were tested by ANOVA and a t-test (Steel and Torrie, 1960). The relationship between HI and plant biomass at R6 was investigated using a non-linear model (Eqs. (1) and (2)) described in Echarte and Andrade (2003): HI ¼

a1 ðplant biomass
c1 Þ 1 þ b1 ðplant biomass
c1 Þ

if plant biomass > c1 (1)

HI ¼ 0

if plant biomass  c1

(2)

where a1 represents the initial slope of the relationship, b1 the degree of curvilinearity of the relationship, and c1 the threshold plant biomass for HI > 0 (i.e., minimum plant biomass that allocates dry matter in harvestable organs). Differences among hybrids for a1, b1 and c1 values were compared with the confidence interval of the parameters (P < 0.05). For all measured variables, a correlation analysis was performed between mean plant values of the whole plant population and means representative of extreme plant hierarchy groups. The contribution of extreme plant hierarchies to the whole population mean for GYP, GYP components and kernel composition were tested by regression analysis. Differences between plant hierarchies for the intercept and the slope of the linear regressions were tested by ANOVA and a t-test (Steel and Torrie, 1960). For each treatment, the ratio between dominated (d) and dominant (D) groups was calculated for all measured variables. The d/D ratio was related to the corresponding mean plant value of the population. A bilinear model with plateau (Eqs. (3) and (4)) was used for describing this relationship for all tested variables: d ¼ a2 þ b2 x for x < c2 D

(3)

d ¼ a2 þ b2 c 2 D

(4)

for x  c2

where a2 is the intercept, b2 the slope, x the mean plant value, and c2 the threshold between models (i.e., x value above which the difference between both plant hierarchies attains the minimum value, evidenced as the maximum ratio between hierarchies). The fitting of the models was performed by an optimization technique (Jandel TBLCURVE, 1992).

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3. Results 3.1. Stand density effects on the mean plant of the stand Increased stand density was accompanied by a significant (P < 0.05) reduction in mean values of plant biomass at R6, HI, GYP, prolificacy, and GYP components (Table 1). The proportion of change across stand densities, however, varied among variables. Prolificacy decreased significantly (P < 0.05) in response to stand density increase from 3 to 6 pl m
2, with a smaller reduction for DK4F37 (20%) than for the other hybrids (ca. 45%). At stand densities >6 pl m
2, only DK4F37 had grained ears pl
1 > 1. Plant biomass, GYP and KNP presented a larger reduction (ca. 66%) than HI (24% for DK752, 17% for DK4F37, 11% for Exp980 and non-affected in DK696) or KW (ca. 14% for DK752 and DK4F37, and ca. 19% for DK696 and Exp980) in response to stand density increase from 3 to 15 pl m
2. Consequently, GYP was closely related to plant biomass at R6 (r2 = 0.88, P < 0.001) and to KNP (r2 = 0.97,

P < 0.001). Kernel weight variations explained 57% of GYP variability for both small and large KW hybrids (r2 = 0.57, P < 0.001). A single relationship was fitted to GYP and KNP values of hybrids with similar kernel size. The slope of the linear model fitted to large KW hybrids was larger (GYP =
23.7 + 0.29KNP; r2 = 0.98, P < 0.001) than the slope obtained for the small KW ones (GYP =
11.7 + 0.24KNP; r2 = 0.98, P < 0.001). This difference in slope (P < 0.001) was related to the variable response of KW to KNP between both groups of hybrids. Increased stand density promoted a decrease in both grain yield components among large KW hybrids, evidenced in the positive response of KW to KNP (KW = 196.6 + 0.076KNP, r2 = 0.61, P < 0.001). In contrast, KW variation of the small KW hybrids was not related to KNP variability. Differences among hybrids in KW were mainly registered at high KNP values (i.e. at low stand densities). Oil and protein contents were more reduced (ca. 22%) than starch content (ca. 13%) in response to stand density increase from 3 to 15 pl m
2, and the percent of reduction in

Table 1 Mean values of plant biomass at R6, grain yield per plant, grain yield components, and kernel contents of four hybrids cultivated at different stand densities during 1999/2000 and 2001/2002 Experiment

1999/2000

2001/2002

Stand density (pl m
2)

Plant biomass (g pl
1)

Grain yield (g pl
1)

Grain yield components

DK696

3 6 9 12 15

466.0 299.2 214.4 156.5 155.5

aa b c d d

244.5 143.3 101.0 68.3 74.6

a b c d d

2.0 1.2 1.0 1.0 1.0

DK752

3 6 9 12 15

448.1 306.6 241.7 172.3 160.6

a b c d d

231.5 151.5 118.8 79.0 73.6

a b c d d

2.0 1.0 1.0 1.0 1.0

DK4F37

3 6 9 12 15

447.6 221.8 211.9 137.1 139.5

a b b c c

191.1 89.6 86.5 56.3 56.4

a b b c c

20a 1.6 b 1.3 bc 1.2 c 1.1c

Exp980

3 6 9 12 15

556.9 326.5 236.8 174.7 193.0

a b c d cd

275.6 151.4 112.7 76.1 84.5

a b c d d

2.0 1.1 1.1 1.1 1.0

DK696

6 9 12

342.9 a 234.6 b 197.6 c

168.6 a 115.2 b 97.3 c

1.4 a 1.0 b 1.0 b

643 a 492 b 421 c

263.1 a 234.4 b 230.7 b

170.1 a 155.2 b 155.8 b

15.1 a 13.1 b 13.0 b

20.0 a 13.1 c 14.8 b

Exp980

6 9 12

423.7 a 287.9 b 221.6 c

194.5 a 132.1 b 99.9 c

1,7 a 1.0 b 1.0 b

755 a 561 b 468 c

259.1 a 235.3 b 211.1 c

170.6 a 159.4 b 144.8 b

14.7 a 12.6 b 11.0 b

16.5 a 12.7 b 13.0 b

Hybrid

Kernel contents

Kernel number (pl
1)

Kernel weight (mg)

Starch (mg kernel
1)

Oil (mg kernel
1)

Protein (mg kernel
1)

a b c c c

921 565 428 307 339

a b c d d

266.3 253.4 238.9 223.0 217.1

a b c cd d

170.2 163.3 151.9 151.7 141.9

a b c c c

15.7 15.1 13.5 12.7 11.8

a a b b b

31.2 28.5 24.3 25.2 21.4

a b c c d

a b b b b

994 693 593 391 353

a b c d d

235.1 219.2 201.7 201.0 205.0

a b c c c

148.7 142.8 129.6 131.1 137.4

a a b b ab

15.2 14.4 12.8 11.6 12.3

a b c c c

24.7 22.8 19.2 19.5 20.9

a a b b b

793 416 373 267 266

a b b c c

244.4 216.6 235.7 210.8 202.2

a b ab bc c

151.8 141.6 150.8 138.6 138.0

a a a a b

16.1 13.8 15.8 13.2 13.6

a b a b b

27.5 24.3 25.9 21.1 23.3

a b a b b

1065 585 456 359 402

a b c d c

260.6 259.3 241.9 209.4 207.5

a a b c d

166.3 164.9 154.9 138.3 137.0

a a b c c

15.1 16.3 14.1 11.0 11.0

ab a b c c

20.0 19.1 16.6 14.5 14.7

a a b c c

Prolificacy (ears pl
1)

a b b b b

Cultivars were classified as small (DK752 and DK4F37) and large (DK696 and Exp980) KW hybrids, based on actual KW (<250 and >250 mg kernel
1 for small and large KW, respectively) at commercial plant densities (e.g. 6–7 pl m
2). a Different letters within a column, experiment and hybrid indicate significant differences (P < 0.05) among stand densities.

G.A. Maddonni, M.E. Otegui / Field Crops Research 97 (2006) 155–166

each kernel component was greater for large KW hybrids than for the small KW ones (Table 1). Variation in these traits, however, was related not only to the effect of stand density on KW. The concentration of each trait also varied in response to stand density. Starch concentration increased from ca. 625 to 684 g kg
1 in response to KW decrease due to enhanced crowding (Fig. 1a). In contrast, oil concentration remained almost constant (ca. 64 g kg
1, for small KW hybrids) or decreased (from 63 to 52 g kg
1, for large KW hybrids) in response to KW reduction (Fig. 1b). Similarly, protein concentration was reduced (from 117 to 95 g kg
1) as KW decreased in small KW hybrids and in the large KW hybrid DK696 during 1999/2000, or was not affected (ca. 67 g kg
1) by KW reduction in the other treatments (Fig. 1c). Consequently, kernel content of all components declined when KW was reduced (Fig. 1d–f), but the relative reduction rate differed among components and hybrids, due

159

to the differential response of each kernel component concentration to KW variations. 3.2. Stand density effects on plant-to-plant variability An increased plant-to-plant variability at high stand densities could be inferred from the positive and significant (P < 0.001) response of the coefficient of variation (CV) of plant biomass at R6 to the number of plants per unit land area. The CV rose from ca. 13 to 40% when plant density was increased from 3 to 15 pl m
2. In addition to stand density effects on the variation of plant biomass, a sharp drop was detected in HI per plant when plant biomass at R6 was below 200–230 g pl
1 (Fig. 2). Comparisons among hybrids revealed that the response of HI to plant biomass at R6 of small KW hybrids was larger (P < 0.05) than that of large KW hybrids. This was evident in a smaller value of a1

Fig. 1. Relationship between kernel weight and starch concentration (a), oil concentration (b), protein concentration (c), starch content (d), oil content (e), and protein content (f). Each point represents the mean plant value of a stand of small (triangles) and large (squares) KW hybrids, cropped at five stand densities (3, 6, 9, 12, and 15 pl m
2) in two experiments. Lines indicate linear regressions fitted to small (dotted line) and large (solid line) KW hybrids. Equations are included close to the fitted data set. In (c) and (f), data of one large KW hybrid was included within the data set of the small KW hybrids to fit the linear regression between variables. One single linear regression was fitted to the whole data set in (d).

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Fig. 2. Harvest index (HI) per plant as a function of plant biomass at physiological maturity (R6). Each point represents an individual plant (empty symbols: dominated individuals; full symbols: other plants of the stand) of four hybrids (DK4F37, DK752, DK696 and Exp980) cropped at five stand densities (3, 6, 9, 12, and 15 pl m
2) in two experiments. Solid lines indicate the curvilinear model (Eqs. (1) and (2)) fitted to the whole data set of each hybrid. For DK4F37: HI = 0.018(plant biomass at R6
44.72)/(1 + 0.037(plant biomass at R6
44.72)) for plant biomass at R6 > 44.72 (r2 = 0.35, n = 101, P < 0.001). For DK752: HI = 0.019(plant biomass at R6
36.3)/(1 + 0.033(plant biomass at R6
36.3)) for plant biomass at R6 > 36.3 (r2 = 0.76, n = 94, P < 0.001). For DK696: HI = 0.046(plant biomass at R6
67.7)/(1 + 0.088(plant biomass at R6
67.7)) for plant biomass at R6 > 67.7 (r2 = 0.25, n = 170, P < 0.001). For Exp980: HI = 0.055(plant biomass at R6
44.1)/(1 + 0.12(plant biomass at R6
44.1)) for plant biomass at R6 > 44.1 (r2 = 0.54, n = 175, P < 0.001).

(ca. 0.02) and b1 (ca. 0.03) parameters for the former than for the latter (a1  0.05 and b1  0.10) (Fig. 2). Thus, for the small KW hybrids, the CV of HI was larger for stand densities 12 pl m
2 (ca. 38%) than for stand densities <12 pl m
2 (ca. 16%). Contrarily, for large KW hybrids, the CV of HI was slightly increased in response to enhanced stand density (from ca. 12 to 14% at stand densities <12 and 12 pl m
2, respectively). The CVof GYP matched the response observed in HI, and rose significantly (P < 0.001) in response to enhanced plant population. The increase in plant-to-plant variability for GYP was larger for small KW hybrids (from ca. 30 to 58%) than for the large KW ones (from ca. 15 to 38%). The CV of KNP followed a similar trend to that reported for GYP. In contrast, plant-to-plant variability in KW of all tested hybrids was slightly increased (from ca. 7 to 20%) in response to increased plant population (P < 0.001). For all hybrids, the CV of starch concentration (ca. 2.5%) was not affected by increased plant density. In the same way, the CVs of oil (ca. 9%) and protein (ca. 13%) concentrations were low for small KW hybrids, and did not vary among stand densities. In contrast, inter-plant variability for the concentration of both of these traits increased significantly (P < 0.001) for large KW hybrids in response to increased

stand density (from ca. 6 to 17% for oil concentration, and from ca. 9 to 12% for protein concentration). 3.3. Contribution of contrasting plant hierarchies to GYP, GYP components and kernel composition of the mean plant of a stand Because of the criterion applied for ranking plants at R6, individuals of the dominant group always had larger (P < 0.05) shoot biomass than those in the dominated class (Fig. 3). The d/D ratio for plant biomass at R6, however, varied as the mean plant value of a stand was reduced (Fig. 4a). A bilinear model with plateau accurately (r2 = 0.66, P < 0.05) described this relationship for both small and large KW hybrids. Canopies with a mean plant biomass >352 g pl
1 (i.e. at the lowest stand density) had dominated individuals with a plant biomass 25% lower than that of the dominant group (i.e. d/D ratio close to 0.75). In contrast, for stand densities with a mean plant biomass <352 g pl
1, the d/D ratio for plant biomass dropped from 0.75 to a minimum of 0.40 (i.e. at the lowest mean plant biomass of 137.1 g pl
1, Table 1). For both small and large KW hybrids, the d/D ratio for HI was close to 1 when values of mean plant biomass at R6 were larger than 264 g pl
1 (Figs. 2 and 4b) or the mean HI was

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Fig. 3. Cumulative frequency of plant biomass at physiological maturity (R6) of four hybrids (DK4F37, DK752, DK696 and Exp980) cropped at five stand densities (triangles: 3 pl m
2; squares: 6 pl m
2; circles: 9 pl m
2; full diamonds: 12 pl m
2; empty diamonds: 15 pl m
2) during 1999/2000. Dotted lines exemplify the frequency thresholds used in this paper to identify the dominant (frequency  0.67) and dominated (frequency  0.33) plants of a stand.

above 0.48 (Fig. 4c). The ratio between extreme hierarchies for HI declined below these thresholds (r2  0.59, P < 0.001), reaching a minimum value of ca. 0.60 when small KW hybrids had a plant biomass of 140 g pl
1 (Fig. 4b) or a HI of 0.35 (Fig. 4c). Barrenness almost did not occur (1 out of 100 plants and 2 out of 93 plants for DK4F37 and DK752, respectively), because plant biomass was generally above the threshold value (c1 parameter ca. 40.5 g pl
1; Fig. 2) below which dry matter is not allocated in harvestable organs. Thus, the low mean HI values of small KW hybrids at high stand densities was mainly related to the low ear biomass of dominated plants. Consequently, the stand density increase not only determined a different plant biomass at R6 between dominant and dominated individuals, but also a diverse dry matter allocation in harvestable organs between these hierarchies. As a result, dominant plants presented higher (P < 0.05) GYP and KNP than dominated individuals at any stand density (Fig. 4d and e). Differences between plant categories, however, increased when mean GYP and KNP of all individuals of the stand were below 157 g pl
1 (Fig. 4d) and 649 kernel pl
1 (Fig. 4e), respectively. Below these thresholds, the response of the d/D ratio to mean plant values dropped from 0.76 to 0.30 (small KW hybrids) or to 0.40 (large KW hybrids) for GYP (r2 = 0.76, P < 0.001), and from 0.75 to 0.38 (small KW hybrids) or to 0.46 (large KW hybrids) for KNP (r2 = 0.59, P < 0.001). On the other hand, for both groups of hybrids, the d/D ratio

varied (i) from 1 to 0.80 in response to mean KW decrease (r2 = 0.39, P < 0.01; Fig. 4f), and (ii) did not differ from 1 for starch (1.02, CV 4%), protein (0.95, CV 8%) and oil (1.01, CV 11%) concentrations of kernels (data not shown). The contribution of extreme plant hierarchy groups to mean stand values of GYP, GYP components and kernel composition resulted similar among hybrids. At any stand density, plants of both hierarchy groups had a similar contribution to mean stand values of GYP and KNP (correlation coefficients 0.98, P < 0.001). This was evidenced in the linear regressions fitted to the response of each hierarchy group data set to mean plant values of the whole stand for these variables (Fig. 5a and b). Intercept values significantly (P < 0.01–0.001) differed from 0 and between plant hierarchies (P < 0.001), and slope values were close to 1 and did not differ between plant hierarchies. Contrarily, plant density effects on mean KW of the whole stand were mainly promoted by changes in KW of the dominated individuals (Fig. 5c). The intercept (85.9 and
82.9 for the dominant and dominated plants, respectively) and the slope values (0.67 and 1.31 for the dominant and dominated plants, respectively) of linear regressions fitted to KW data differed from 0 and 1 (P < 0.001), respectively, and between plant hierarchies (P < 0.001). Kernel weight variations of dominated plants explained more than 88% of mean KW variability across all stand densities (P < 0.001).

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Fig. 4. Response of the d/D ratio to mean plant values of the stand (i.e. considering all plants of a stand) for several studied variables. Symbols represent small (triangles) and large (squares) KW hybrids cropped at contrasting stand densities in two experiments. Studied variables were plant biomass at R6 (a), harvest index (HI, c), grain yield per plant (GYP, d), kernel number per plant (KNP, e), and kernel weight (KW, f). The relationship between the d/D ratio for HI and values of mean plant biomass at R6 is also presented (b). Lines indicate bilinear (Eqs. (3) and (4), a–e) or linear (f) models fitted to the each data set. Model parameters are for (a): a2 = 0.2, b2 = 0.0015, c2 = 351.7 (r2 = 0.66, n = 25, P < 0.001); for (b): a2 = 0.2, b2 = 0.003, c2 = 264.3 (r2 = 0.75, n = 25, P < 0.001); for (c): a2 =
0.68, b2 = 3.52, c2 = 0.48 (r2 = 0.59, n = 25, P < 0.001); for (d): a2 = 0.026, b2 = 0.0046, c2 = 157 (r2 = 0.76, n = 25, P < 0.001); for (e): a2 = 0.11, b2 = 0.001, c2 = 649 (r2 = 0.59, n = 25, P < 0.001); for (f): a = 0.26, b = 0.003 (r2 = 0.39, n = 25, P < 0.001).

The responses of some kernel components were similar to those observed for KW. Starch and oil contents in kernels of dominant plants varied less (slope < 1, P < 0.001) than those of the dominated individuals (slope > 1, P < 0.001) in response to changes observed in mean plant values of the stand (Fig. 5d and e). Moreover, variations in starch and oil contents in kernels of the dominated individuals explained more than 60–80% of the variation observed in values of the mean plant of the stand (P < 0.001). This differential contribution of extreme plant hierarchy groups to mean plant values of the stand was not observed for protein content (Fig. 5f).

4. Discussion Intra-specific competition associated with increased stand density generates an impoverished environment in terms of available resources per plant. In the present

research, this condition was clearly evidenced in the sharp decline of mean plant values observed for plant biomass at R6, prolificacy, KNP and GYP. The proportional decline in these variables exceeded that observed for other tested attributes (e.g., KW and kernel contents). These results agree with previous findings on kernel number and grain yield determination (Tollenaar et al., 1992; Andrade et al., 1999; Vega et al., 2001b; Kiniry et al., 2002), and evidence differential inter-plant competition effects on assimilate capture per plant, plant growth and biomass allocation to reproductive structures (Edmeades and Daynard, 1979a,b; Vega et al., 2001a; Echarte and Andrade, 2003). In contrast to GYP and KNP, mean KW of the stand was slightly reduced when stand density was increased. This was not an unexpected result, because kernel growth is primarily sustained by the post-flowering source–sink ratio (e.g. assimilate availability per kernel), and we have previously demonstrated (Borra´s et al., 2003) that this ratio does not vary markedly among stand densities. In other words, the

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Fig. 5. Response of each plant hierarchy (dominated plants: empty symbols; dominant plants: full symbols) to mean plant values of the stand for several studied variables in two experiments. Triangles and squares identify small and large KW hybrids, respectively. Studied variables were grain yield per plant (GYP, a), kernel number per plant (KNP, b), kernel weight (KW, c), starch content (d), oil content (e), and protein content (f). Solid lines indicate linear regressions fitted to the data set of dominated and dominant plants. Equations are included above (dominant plants) or below (dominated plants) the data sets. One single linear regression was fitted to the whole data set in (f). Dotted lines indicate the 1:1 relationship between variables.

reduction in vegetative plant size (the source) promoted by increased stand density is almost matched by the reduction in KNP (the sink) caused by enhanced crowding, yielding a very stable source–sink ratio, and KW. Plant-to-plant variability for HI, GYP and GYP components has been previously acknowledged (Echarte and Andrade, 2003; Echarte et al., 2004), but present research is the first to report the response of these attributes to early-established hierarchies among plants within a canopy of uniform stand density. In contrast to previous studies (Nafziger et al., 1991; Pommel et al., 2002; Liu et al., 2004) early-established hierarchies among plants in our research (see details in Maddonni and Otegui, 2004) cannot be attributed to temporal (e.g. differences in seedling emergence date) or spatial (e.g. within-row plant spacing)

variability among plants, neither to a non-uniform seed weight at sowing. We had previously speculated that the early detection of neighbors by some plants of a stand may modify their ability for acquiring resources (Maddonni and Otegui, 2004). Probably, dominant plants were more sensitive than dominated individuals of the stand in detecting neighbors (Maddonni et al., 2002), and quickly developed shade avoidance characteristics (Smith and Whitelam, 1997) such as a lower root/shoot ratio, longer leaves and taller stalks. We have demonstrated that during the pre-silking period, dominated plants were shorter and thinner than the dominant ones (Maddonni and Otegui, 2004). Thus, the early reaction of dominant individuals may have enhanced their ability for resource capture (e.g., improved light foraging; Ballare´ et al., 1997), and may

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explain differences in GYP and KNP among plants of extreme hierarchy groups even at the lowest stand density. Dominant plants always had more KNP than dominated individuals, but differences between plant types in plant growth rate around female flowering were significant only at 12 pl m
2 (Maddonni and Otegui, 2004). Collectively, these findings provide robust evidence of the differential abilities for resource capture among individuals of a stand, which are not only reflected in differences in plant growth, but also in contrasting biomass partitioning to reproductive structures (i.e. HI per plant). Hence, the decline in HI of a stand, commonly recorded at high population densities (Echarte and Andrade, 2003), is largely a result of a reduced ability of dominated plants for assimilation and for assimilate allocation to harvestable organs. The latter response was more pronounced in the small KW hybrids. Another important finding of the present research was the similar contribution of extreme hierarchy groups of both small and large KW hybrids to mean values of GYP and KNP (Fig. 5a and b) which contrasted with their differential contribution to mean values of KW (Fig. 5c). As described above, previous research on source–sink ratio effects on KW does not indicate remarkable differences for this trait across stand densities, but no reference was made to inter-plant variation within a stand. Differences among plant hierarchies in their contribution to mean KW of the stand may have come up from increased plant-to-plant variability in (i) the postflowering source–sink ratio at increased stand density, and/or (ii) the buffer capacity of reserves stored before silking, which may become the main determinants of final KW at low postflowering source–sink ratios (Uhart and Andrade, 1995; Borra´s et al., 2004). Thus, differences between plant hierarchies in KW could also be related to the effects of increased crowding on plant growth during the pre-flowering period. The thinner and shorter stems of dominated individuals (Maddonni and Otegui, 2004), probably had a reduced non-structural carbohydrate content for buffering a decreased source–sink ratio during kernel growth. Finally, these differences in KW among individuals within a stand at high density could be also related to early-established differences in potential KW. Evidence from previous research (Borra´s and Otegui, 2001) suggests that maize KW potential is not affected by stand density increases up to 9 pl m
2, but no evidence is available on this topic for supra-optimal stand densities, which usually promote plant barreness (Echarte et al., 2004) but also large variations in KW (Echarte and Andrade, 2003). Ear biomass at the onset of active kernel growth (i.e. 15 days after female flowering) was significantly larger for the dominant plants than for the dominated ones at stand densities above 9 pl m
2 (Maddonni and Otegui, 2004). Ovaries of dominated plants at very high stand densities, therefore, may have reached a threshold size that determines a reduction in KW potential, like the observed pre-fertilization effects directly linked to ovary size in wheat (Calderini and Reynolds, 2000). These hypothesis need to be tested in maize.

The response to stand density of kernel components of individual plants received much less attention than that of grain yield and grain yield components. The only work on this topic (Borra´s et al., 2002) covered a narrow range of stand densities (3 and 9 pl m
2), with no reference to aboveoptimum conditions that usually enhance the differences between extreme plant hierarchies (Edmeades and Daynard, 1979a; Vega and Sadras, 2003). Our results on kernel components are particularly important for high stand density environments, which are easier to find in irrigated than in dryland farming. Among the latter, however, year-dependent conditions (e.g. reduced water availability) can promote differences in grain quality of individual plants similar to those observed at supra-optimal stand densities. Inter-plant variations of this type have never been addressed previously. We expected to find differences between extreme plant hierarchies in kernel composition, especially in rich energy compounds like protein and oil (Sinclair and de Wit, 1975). Dominant and dominated plants, however, had a similar starch, protein and oil concentration, in spite of their similar or different KW. These results indicate that the dynamics of starch, oil and protein deposition in kernels of the dominant and dominated plants did not differ. Differences between plant types in the content of each kernel component were only related to differences in kernel size. Among kernel components, oil and starch contents of the mean plant of a stand were related to changes in oil and starch contents of the dominated individuals. Hence, mean oil and starch yield per kernel decreased in response to enhanced stand density due to the reduced contribution made by dominated plants. This new evidence of the determination of grain quality is relevant for some specific production goals, e.g., high oil corn that enhances the quality of feed rations of livestock and poultry, and reduces the need for expensive dietary supplements. Moreover, growers producing high oil corn under pre-established contracts receive rewards for increased oil levels (Thomison et al., 2003). The differential contribution of extreme plant hierarchies to oil content of kernels could demand a reduced stand density when the goal is maximum oil yield per unit land area rather than maximum grain yield. Finally, in previous research (Maddonni and Otegui, 2004) we defined the early temporal window when extreme plant hierarchies are established. In this paper, we added simple relationships for predicting differences between dominant and dominated individuals for several traits in response to increased stand density. This information could assist in future improvements of maize grain yield simulation, like the inclusion of plant-to-plant variability in male and female flowering dynamics at high stand density for predicting potential kernel number (Lizaso et al., 2003; Fonseca et al., 2004). Our findings on intra-specific competition effects on plant-to-plant variability may help to develop improved relationships between flowering dynamics and plant growth, with the objective of a more accurate prediction of final kernel set.

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5. Conclusions Maize crops have a natural inter-plant variability, measurable in several phenotypic traits, that is magnified as intra-specific competition is increased. We have quantified the plant-to-plant variability of a stand with the CVs of plant variables that are easy to assess at physiological maturity. One of these variables (plant biomass at R6) was used for plant classification within hierarchy groups. The CVs of almost all measured variables rose in response to stand density increase, but the magnitude of the response differed among variables: GYP = KNP > plant biomass at R6 > KW > protein concentration > oil concentration. Starch concentration was associated with the lowest CV, and was not affected by stand density increase. Thus, at high stand densities, plants within a stand differed primarily in their ability to set kernels. A more detailed analysis revealed that plant biomass at R6 and GYP of the dominant and dominated individuals were reduced as stand density increased, but the decline was sharper for the latter. In contrast, there was less variation among plants for KW, which differed between extreme hierarchies only at high stand densities (12–15 pl m
2). Moreover, kernels of both extreme plant types had similar oil, protein and starch concentrations. Changes in mean values of KW and kernel composition of a stand in response to stand density were primarily related to changes in kernel size of dominated individuals. Our findings indicate that grain yield of maize crops cultivated at high stand densities is determined by plants with very different KNP, but with slight differences in KW and kernel composition. Under irrigated and fertilized conditions we did not observe a substantial barrenness, but this phenomenon can be magnified in more impoverished growing environments and in more intolerant to crowding hybrids, due to the enhanced effect of dominated individuals on grain yield and its determinants as competition for resources increases.

Acknowledgements Authors wish to thank L. Borra´s, K. D’Andrea and A. Sanguinetti for their valuable help. This work was supported by the National Agency for Science Promotion (ANPCyT, PICT08-06608 and PEI 6537), Universidad de Buenos Aires (UBACyT G061), Dekalb-Monsanto Argentina and Fundacio´n Antorchas. G.A. Maddonni and M.E. Otegui are members of the National Council for Research (CONICET).

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