COMPETITION EFFECTS IN PLANT BREEDING FIELD TRIALS WITH PEA (PISUM SATIVUM L.), FABA BEAN (VICIA FABA L.) AND YELLOW LUPIN (LUPINUS LUTEUS L.)

Janusz Gołaszewski, Aneta Stawiana-Kosiorek, Dariusz Załuski, Anna Zaręba
Department of Plant Breeding and Seed Production, University of Warmia and Mazury, Olsztyn, Poland

ABSTRACT

In plant breeding field trials plant competition effects can seriously distort the treatment contrasts. To detect the effects, the special designs, known as competition designs, should be applied. In the study the so-called competition diallel with 5 genotypes was used to estimate the competition effects in pea, faba bean and yellow lupin. For each species the two trials with 1- and 3-row plots were established. Soil samples were taken to assess spatial soil variation. The semivariances of soil properties (pH, P, K, Mg) were calculated and semivariogram models were fitted. Finally, kriging was applied to predict the values of the soil properties for each plot. The general linear models were applied to estimate the magnitude of neighbor effects and the spatial effects of soil variation. The competition effects were calculated according to the competition diallel model using original data and residuals after elimination of soil spatial effects. The competition effects in yield of grain legumes depended on the plot size as well as on the species and its growth habit. The competition effects in 1-row plot experiment ranged from 10 to 40% in pea, 10 to 30% in faba bean and 10 to 100% in yellow lupin. For the 3-row plot experiment the effects ranged from 0 to 10% in pea and faba bean and 5 to 50% in yellow lupin. The competition analysis with additional information on soil variation in comparison with the analysis using the original plot data may extend the interpretation of specific competition relations between the genotypes of a given crop.

Key words: pea, faba bean, yellow lupin, field trials, plant competition, geostatistical methods.

INTRODUCTION

In plant breeding field trials soil variability and competition between plants are the two potential sources affecting the quality of data. The heterogeneous soils and high inter-plot interference can inflate the experimental error and bias the estimation of treatment contrasts [5,12]. From the beginning of rational experimentation both of the sources were distinguished but the development of statistical techniques to improve cost-effectiveness and efficiency of plant breeding experiments was mainly related to the elimination of the soil variability. Successively, the advances in the theory of randomization, application of much more effective experimental designs up to the recently developed alternative approaches by spatial statistics taking into account the neighborhood of plants on adjacent plots, have enabled one to control, at least partially, the influence of the environment [1,10,15,20]. The methods deal with spatial variation but do not estimate the competition effects.

Some methodic approaches focused on the problem of plant competition enable one to assess the magnitude of competition effects and estimate their impact on the results from field experiments [3,4,11,17,18,21]. The methodic problem of such the studies is that the effects are often very tiny and to size them, special experimental designs, called ‘competition designs’ should be applied [9,13]. The sources of competition in plant breeding trials arise from genetic background of the genotype and the specificity of the experiment. The first one results from interdependence of plants grown adjacent to each other as their natural need for limited sunshine, soil nutrients, moisture, carbon dioxide, oxygen, etc, referred to as competition effects, as well as the inherent competition ability (aggressiveness, adaptability). The second one is connected with the research process in plant breeding: (i) the necessity for simultaneous test of many genotypes, (ii) small quantity of seeds at the initial stages of a breeding program what makes that the small, borderless, often 1-row plots in the limited number of replications are applied, (iii) differentiated susceptibility of genotypes to pathogens and pests and (iv) new genotypes have often lower vitality and germinate poorly or do not germinate at all (losses in the plot stand) and (v) in different environments (conducted in years and/or locations) there are different competition effects.

Even though there are many experimental designs that take into account mutual neighborhood of studied cultivars and methods of spatial analysis they are mostly projected to estimate the significance of differences between cultivars. To assess the effects of competition, the mathematical model should be primarily based on neighbor balanced design with the additional property to reduce maximally environmental variation, i.e. soil variability.

The objective of the study was to estimate the competition effects in grain legumes: pea (Pisum sativum L.) faba bean (Vicia faba L.) and yellow lupin (Lupinus luteus L.) as well as an attempt to explain the competition effects in the context of spatial variation estimated through some soil properties, soil acidity and available macronutrients – phosphorus, potassium and magnesium.

MATERIAL AND METHODS

In 2000, the six experiments with pea, faba bean and yellow lupin were established in Tomaszkowo, the Research Station of the University of Warmia and Mazury in Olsztyn. The soils of the Station are very heterogeneous, which causes high variability of the traits but it also enables one to find one-block field with the suitable soil for various crops. In our case, the one-block field for the three different species allowed to obtain the relatively dense measurement net of soil samples to comply with the requirements for the minimum number of observations in calculation of the semivariances. Prior to the establishment of the experiments, a total of 72 soil samples were taken in the regular net (single mesh 4 x 4 m) to determine soil acidity (pH) and the content of available macronutrients: phosphorus (P), potassium (K) and magnesium (Mg).

For each experiment, the ‘competition diallel’ was applied. It was the design with five cultivars allocated to each plot in a two-stage process of randomization, at first, to allocate the cultivars in central part of the plot and the second, to allocate the neighbor cultivars on the outer parts of the plot (Fig. 1). Thus, each treatment represents pair of cultivars; i.e. one in the centre and the other as a neighbor, giving 25 treatments. For each of the species the two experiments were established, one with single central row and single neighbor rows and the other one with three central rows and two neighbor rows. The second experiment was an extension of the first one. The rows were 3 m long, 0.3 m apart.

Fig. 1. Field layout of the experiments (a) and scheme of randomization (b) (a capital letter – cultivar in central row/rows, a lower case letter – cultivar in the adjacent row/rows)

The experimental material covers the sets of five cultivars for each species differed in their physiological and morphological traits. The studied species of grain legumes differ in their morphology and growth habits, which directly determines their competition ability.

The cultivars of pea cover a broad spectrum of variation. In the aspect of varietal competition, the plants are differentiated in their earliness, leaf structure (bipinnate leaves, semi-leafless) and type of growth (tall and short stemmed). In the study the cultivars differed in their height, ‘Albatros’ and ‘Kormoran’ were the tallest, ‘Mazurek’ and ‘Pelikan’ were medium and the shortest stem was found in ‘Żuraw’.

Faba bean is the species with indeterminate or determinate growth habit. In the specified environmental conditions, like field experiments, plants can set lateral branches. All these kinds of variation as well as experimental conditions of the experiment can affect the competition. Cultivars ‘Titus’ and ‘Tim’ are short stemmed with determinate growth habit, while ‘Kodam’, ‘Nadwislanski’ and ‘Tom’, are tall with indeterminate growth habit.

Yellow lupin is the species with slow germination of seeds and its forms differ in the type of growth during later stages of growth. Maximum vegetative growth rate occurs during flowering. The main stem and each branch usually terminate in an inflorescence, which is a simple raceme with varying numbers of flowers. Even after the main stem flowering has ceased, the plant can develop lateral secondary as well as tertiary flower sets from a sequence of lateral branches. Cultivars differ in ability to set pods on these secondary and tertiary branches. The process of setting pods is highly influenced by environmental conditions, which very often limits the potential yield. The cultivars in the study differed in height and the number of lateral branches created. ‘Juno’ and ‘Parys’ were relatively tall with tertiary branches while ‘Piast’, ‘Polo’ and ‘Popiel’ were lower and resistant to lodging.

Taking into account all the rows of the two experiments for each species, the average competition effect for a cultivar was estimated according to the general linear model with dummy variables [14]. The scheme of coding, i.e. for cultivar A: 1 – cultivar A is adjacent to cultivar A, -1 – cultivar A is adjacent to the other cultivars, 0 – in other cases:

eq. (1)

Because of the single lattice design, only the direct and interference competition effects for the 1- and 3-row plot were calculated according to the ANOVA model:

eq. (2)

where:
y_ij – an observation from a single plot,
µ – the overall mean,
v_i – the direct effect,
c_j – the interference (neighbor) effect,
ε_ij – the error term.

As competition effects can be masked by spatial variation accross the experimental field, an attempt to assess the differences in estimation before and after elimination of spatial variation was undertaken. In order to assess the spatial effects of soil variation, the semivariances for pH, P, K, and Mg were calculated and variogram models for these properties were fitted. Prior to calculation of semivariance, the data were detrended with the median technique [2]. Using the variogram parameters, kriging was made to predict the values of chemical properties for each row of the experiments [7,19]. It should be underlined that the soil properties were used just only to estimate spatial variation of the experimental field. To eliminate the spatial effects of soil variation, the linear model was applied (3) and the adjusted plot values (4) were analyzed according to the eq. (2):

eq. (3)

eq. (4)

Environmental conditions. Generally, the vegetation season in 2000 coincided with favorable weather conditions. The only disadvantage was at the ripening period of pea when high precipitation made plants drying difficult and extended the ripening period.

RESULTS

Basal statistics for soil properties across the experimental field, given in Table 2, show the low variation in pH and the medium variation in the content of available magnesium, phosphorus and potassium. The similar mean values and the similar level of variation for the three crops were stated for the potassium content. The soil acidity and magnesium and phosphorus content changed across the experimental field. From the experiments with yellow lupin through the experiments with pea and faba bean, the values of pH increased successively while the content of phosphorus decreased successively. For the magnesium content the changes in the mean values were not so explicitly directional.

Table 1. Cultivars of faba bean, pea, and yellow lupin

Pea	Faba bean	Yellow lupin
Albatros (Alb)	Kodam (Kod)	Juno (Jun)
Kormoran (Kor)	Nadwiœlański (Nad)	Parys (Par)
Mazurek (Maz)	Tim (Tim)	Piast (Pia)
Pelikan (Pel)	Titus (Tit)	Polo (Pol)
Żuraw (Żur)	Tom (Tom)	Popiel (Pop)

Table 2. Basal statistics for soil properties of the experimental field (crops were set up according to their position in the field)

Specification	Soil properties
	pH	Mg	P	K
	mg·100 g-1 of soil
Yellow lupin
	6.8	9.9	31.2	21.7
CV, %	2.8	18.3	8.2	16.0
Pea
	7.2	9.3	29.2	20.8
CV, %	4.4	11.1	15.8	13.8
Faba bean
	7.4	10.7	24.7	21.2
CV, %	4.3	11.1	20.4	15.0
Whole experimental field
	7.1	10.0	28.4	21.2
CV, %	5.0	14.8	17.5	14.9

– mean, CV, % – coefficient of variation, %

The semivariograms are presented in Figure 2. Except for the potassium content with random variation, all the other soil properties analyzed showed strong effects of spatial correlation along (width of field) and across (length of field) the experimental ranges. Along the ranges there were linear models in pH, magnesium and phosphorus content while across the ranges the semivariogram models were linear for pH and magnesium content, and quadratic for phosphorus content. The maps of the soil properties in Figure 3, based on semivariogram parameters, visualize the changes in soil properties in the field.

Fig. 2. Semivariograms of soil properties of the experimental field at Tomaszkowo, 2000 (dashed lines – along experimental ranges, solid lines – across experimental ranges)

Fig. 3. Variability of soil acidity and available macronutrients Mg, P, K across the experimental field (in the field from left to right: yellow lupin, pea, and faba bean)

Table 3 provides statistics for plant height and seed yield calculated using the data from the total number of rows in the two experiments for a given species. The plant height was quite a stable trait, while seed yields showed high (pea, faba bean) and very high variation (yellow lupin). The mean excess of plant height and seed yield for a given cultivar when the other cultivars were the neighbors, in relation to pure stand with the same cultivar on central and adjacent rows, was significant only in the case of pea cultivars: ‘Żuraw’ (41% in seed yield) and ‘Kormoran’ (6% in plant height and 53% in seed yield). The mean excesses in plant height and seed yield for faba bean and yellow lupin, although high, were not significant.

Table 3. Mean and coefficient of variation (%) for plant height and seed yield per row of yellow lupin, pea, and faba bean

Specification	Pea	Faba bean	Yellow lupin
Plant height, cm
	72.6	96.7	69.1
CV,%	8.6	15.8	17.3
Seed yield per row, g
	353.7	355.6	117.4
CV,%	35.9	41.2	68.5

The comparison of the results on variation (Table 3) and the competition effects (Table 4) suggests some remarks. With the scale of variation for a given trait corresponding to the magnitude of competition effects, the lower the variation, the lower the competition effects and vice versa. However, when the variation of the trait is high and highly modified by environmental conditions, the competition effects are hardly detectable.

Table 4. Mean excess of percentage for plant height and seed yield of a given cultivar when the other cultivars were neighbors for the pure stand when the same cultivar was found in central and neighbor rows (cultivars set up in ascending order of their height)

Specification	Pea cultivars
Trait	Mazurek (70)	Pelikan (71)	Albatros (72)	Żuraw (75)	Kormoran (78)
Plant height, cm	0	0	1	1	-6*
Seed yield, g per row	-10	-7	15	41*	53*
	Faba bean cultivars
Trait	Titus (82)	Tim (83)	Tom (103)	Kodam (111)	Nadwiœlański (112)
Plant height, cm	0	1	3	2	-1
Seed yield, g per row	-16	-2	-10	19	11
	Yellow lupin cultivars
Trait	Popiel (62)	Piast (65)	Juno (66)	Polo (68)	Parys (85)
Plant height, cm	9	-5	4	1	-4
Seed yield, g per row	-37	-46	2	-30	41

* significant at a = 0.05

The competition effects in yields in relation to the plant height were much more distinguishable and univocal for pea (Table 5) than for faba bean (Table 6) and yellow lupin (Table 7). In pea, the direct and interference effects for 3-rows plot experiments were relatively lower than for 1-row plot experiments. Generally, the pea cultivar with taller plants was more competitive than the cultivar with lower plants, although the cultivars differed in their ability to utilize the neighborhood of other cultivars. Some of them, like ‘Mazurek’ cultivar, the lowest one in the study, yielded better in the vicinity of taller neighbor plants while the others with similar plant height yielded worse. It can be related to the different aggressiveness of the cultivar and its adaptability to the environmental conditions of a given experiment. Often, the lower plants have shadow and better moisture conditions in the vicinity of taller plants, especially under insufficient precipitation during the season. The direct effects were linearly related to the interference effects. In the faba bean experiments the direct effects can be attributed to the growth habit of the cultivar. The cultivars with determinate growth, ‘Titus’ and ‘Tim’, in 1-row plot experiment demonstrated higher effects than the cultivars with indeterminate growth, ‘Nadwislanski’ and ‘Tom’, except for ‘Kodam’ with the highest direct effect in the experiment. In the 3-row plot experiment, the direct effects of cultivars with determinate growth habit were relatively high, positive for ‘Titus’ and negative for ‘Tom’, while the effects of cultivars with indeterminate growth habit were close to zero. It suggests that in the experiment with 3-row plots, the competition effects of the cultivar with indeterminate growth habit tend to balance. In the experiments with yellow lupin, the direct effects in seed yield were not connected with plant height but rather with the yield potential of the cultivars. ‘Piast’ the best yielding cultivar in the study, showed very high positive direct effects in the two experiments (1-row and 3-row plots) and ‘Parys’, the highest one in the study and with the lowest yield had very high negative direct effects. The positive interference effects of ‘Piast’ in the two experiments confirm that the cultivar is not susceptible to the neighborhood of other cultivars.

Table 5. Pea yield components for the competition diallel with 1- and 3-row plots

Cultivar in the center of the plot	Neighbor cultivar					Direct effect
	Mazurek	Pelikan	Albatros	Żuraw	Kormoran
1-row plots (SE = 25.9)
Mazurek	366	311	384	193	400	-52
Pelikan	461	261	351	171	263	-82
Albatros	263	344	367	184	249	-102
Żuraw	619	517	475	565	543	161
Kormoran	478	605	447	364	395	75
Interference effect	54	25	22	-88	-13
3-row plots, 3-row mean (SE = 15.4)
Mazurek	311	359	293	332	317	-4
Pelikan	306	393	226	342	212	-30
Albatros	173	464	280	291	270	-31
Żuraw	486	285	421	251	412	44
Kormoran	383	352	408	276	317	21
Interference effect	6	44	-1	-28	-21

Table 6. Faba bean yield components for the competition diallel with 1- and 3-row plots

Cultivar in the center of the plot	Neighbor cultivar					Direct effect
	Titus	Tim	Tom	Kodam	Nadwislanski
1-row plot (SE = 22.1)
Titus	240	227	197	502	227	4
Tim	263	376	253	294	256	14
Tom	262	363	115	278	195	-32
Kodam	338	278	285	304	305	27
Nadwiœlański	309	244	300	252	201	-13
Interference effect	8	23	-44	51	-38
3-rows plot, 3-row mean (SE = 28.1)
Titus	477	592	341	515	508	26
Tim	407	421	515	248	562	-30
Tom	562	719	442	455	129	1
Kodam	520	648	286	368	496	3
Nadwiœlański	223	477	625	596	389	1
Interference effect	-23	110	-19	-24	-44

Table 7. Yellow lupin yield components for the competition diallel with 1- and 3-row plots

Cultivar in the center of the plot	Neighbor cultivar					Direct effect
	Popiel	Piast	Juno	Polo	Parys
1-row plot (SE = 13.3)
Popiel	55	86	89	21	130	-29
Piast	187	273	117	238	91	76
Juno	175	104	69	162	97	16
Polo	101	121	23	179	55	-9
Parys	82	30	29	79	29	-55
Interference effect	15	18	-40	31	-25
3-row plots, 3-row mean (SE = 13.7)
Popiel	171	209	84	154	132	14
Piast	156	259	233	95	180	49
Juno	141	246	158	185	136	38
Polo	128	51	171	156	162	-2
Parys	26	49	30	48	32	-99
Interference effect	-11	27	0	-8	-8

The direct effects calculated using the residuals after elimination of soil variation were related to the original data in pea and yellow lupin experiments with smaller values in pea and similar effects in yellow lupin (Fig. 4). The species with the distinct distortion of the effects obtained from the two analyses was the faba bean with effects in 1-row plot experiment. The effects in yield calculated with the corrected data were stronger than the ones calculated with the original data. The comparison of the effects from the two analyses confirmed that the plot size and environmental conditions play an important role in the analysis of the results from ‘competition experiments’. The increase in the plot size leads to a decrease in inter-plot competition effects and taking into account soil variation may keep the effects at the same level (yellow lupin) or modify the effects by decreasing them (pea) or even changing their structure (faba bean).

Fig. 4. Direct effect of cultivars as a percentage of general mean calculated using the experimental data (experiment) and the residuals after the elimination of soil variation (kriging)

To summarize the results on competition effects in grain legumes, it can be said that the competition effects in 1-row plot experiment ranged from 10 to 40% in pea, 10 to 30% in faba bean and 10 to 100% in yellow lupin. For the 3-row plot experiment the effects ranged from 0 to 10% in pea and faba bean and from 5 to 50% in yellow lupin.

DISSCUSION

Due to the scarcity of seeds, the screening field trials conducted at the initial stages of plant breeding programs are usually planned as one-row plot experiments that cover a broad spectrum of breeding cultivars in small number of replications. In such the experiments direct vicinity of different breeding forms causes plant competition that can be related to the competitive ability of the breeding forms and their adaptability to the environmental conditions of a given experiment. Special experiments, say ‘competition experiments’, enable one to assess the competition effects. The experiments established in the study were balanced neighbor designs where each genotype occurs as a neighbor to every other genotype an equal number of times. According to Kempton and Fox [9], the number of plots required for such a design makes them impracticable for a high number of genotypes and in such a case they suggest using a partially balanced design instead.

There are a few references concerning the competition in breeding trials with grain legumes analyzed here. In the previous study with some pea genotypes Gołaszewski [4] stated that in the pea experiments the competition effects can extend to 30% and one-buffer row on each side of the plots guarantees unbiased estimates of genotype effects. Kempton and Lokwood [11] considered the problem of inter-plot competition in faba bean depending on the height of plant of neighbor genotypes. In the study the effect of yield in one-single trial was 0.4% per cm of the total height of neighbors and 0.25% for 4-row plots. The authors suggested that in 4-row plots trial the buffer rows are not necessary when the height difference between neighbor plants is less than 20 cm. Gomez [6] in rice and Rich [16] in wheat showed that in the similar experiments the border effects, although smaller, extend up to the second and further rows of the plot. The purposefulness of buffer rows application in the small plots experiments with soybeans was pointed out by Gedge et al. [3]. The other aspects of competition effects in field experiments were considered by Jenkyn and Banbridge [7]. The authors showed that in the experiment with disease factor, the competition effects were observed not only in the adjacent plot but also in further plots.

The competition effects in yield of grain legumes depend on the plot size. The larger the plots, the lower the competition effects. The neighbor interference between plots depends on the crop and its growth habit. In breeding experiments with pea, yield is decreased when a taller cultivar is a neighbor although the yield dropping magnitude is connected with the competition ability of a given cultivar and its adaptability to the specific micro-environmental conditions in the neighborhood of other cultivars. In faba bean experiments the competition effects distribute across the cultivars according to their determinate or indeterminate growth. The cultivars with determinate growth habit are susceptible to the neighborhood of the cultivars with indeterminate growth habit. For yellow lupin, the most variable species in yield, the yield potential of a given cultivar affects the competition effects. The cultivar with high yield potential as a result of its growth habit can utilize environmental sources better than the other cultivars, so it is much more competitive than the others.

The competition analysis with additional information on soil variability in comparison with the analysis on the original plot data can change the specific competition relations between the cultivars for a given crop by decreasing the effects (pea), modifying them (faba bean) or keeping them at the same level.

The results of the study can suggest some remarks of general nature. First, it seems to be sensible that in the analysis of competition experiment, the environmental conditions should be precisely assessed and included into the analysis. On the other hand, as the competition effect of a given cultivar is the varietal trait like plant height or seed yield, the competition experiment should value the cultivars according to their competitive ability. It could be important when testing new forms in the screening trials. With the marks susceptible, neutral or unsusceptible, it is feasible to plan rationally further experimental activities such as the technical arrangements of cultivars in blocks of cultivars similar in competitive ability.

CONCLUSIONS

The study was based on the results from the three one-year experiments established with the same experimental method and each experiment located in the near vicinity of each other in the field. Thus, the below stated conclusions should be treated as preliminary and further studies on the topic are needed.

1. The same competition designs applied in the three experiments allowed assessing significant competition effects in pea, while in faba bean and yellow lupin the effects, even if high, were not significant. The magnitude of competition effects was different and affected by inherent variability of the species.

2. The competition effects in 1-row plot experiment ranged from 10 to 40% in pea, 10 to 30% in faba bean and from 10 to 100% in yellow lupin. For the 3-row plot experiment the effects ranged from 0 to 10% in pea and faba bean and from 5 to 50% in yellow lupin.

3. The varietal competitiveness in the context of intra- and inter-plot interference is less important when the plots in plant breeding experiments with a given plant are getting larger than 1-row plots.

4. The competition analysis with additional information on soil variability, in comparison with the analysis on the original plot data, can change the interpretation on specific competition relations between the cultivars for a given crop by decreasing the effects (pea), modifying them (faba bean) or keeping them at the same level.

5. Including information on spatial variation of the experimental field into the analysis of competition effects may provide additional information to interpret the results on the background of spatial variation of the experimental field.

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Janusz Gołaszewski
Department of Plant Breeding and Seed Production,
University of Warmia and Mazury, Olsztyn, Poland
Pl. Łodzki 3, 10-724 Olsztyn, Poland
email: janusz.golaszewski@uwm.edu.pl

Aneta Stawiana-Kosiorek
Department of Plant Breeding and Seed Production,
University of Warmia and Mazury, Olsztyn, Poland
Pl. Łodzki 3, 10-724 Olsztyn, Poland
email: anetastko@poczta.onet.pl

Dariusz Załuski
Department of Plant Breeding and Seed Production,
University of Warmia and Mazury, Olsztyn, Poland
pl. Łodzki 3, 10-724 Olsztyn, Poland
email: dariusz.zaluski@uwm.edu.pl

Anna Zaręba
Department of Plant Breeding and Seed Production,
University of Warmia and Mazury, Olsztyn, Poland
Pl. Łodzki 3, 10-724 Olsztyn, Poland

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