Krzysztof Józef Jankowski, Wojciech Stefan Budzyński, Paweł Hulanicki Department of Agrotechnology, Agricultural Production Management and Agribusiness, University of Warmia and Mazury in Olsztyn
Corresponding author`s email: krzysztof.jankowski@uwm.edu.pl DOI: 10.5604/12338273.1194979
Relationship between yield components
and seed yield obtained from oil crops
of the family Brassicaceae
Związek pomiędzy komponentami plonu
a plonem nasion roślin oleistych z rodziny Brassicaceae
Keywords: oil crops, yield, components of yield, correlation analysis, path analysisAbstract
This paper reports a complete, three-year cycle of field experiments conducted in 2005–2008, in the experimental fields located at the Experimental Station in Bałcyny (N = 53°35'49''; E = 19°51'20,3''), which belongs to the University of Warmia and Mazury in Olsztyn, Poland. In the present article the authors examine the relationship between yield components (number of plants per 1 m2, number of siliques per plant, number of seeds in a silique, and 1000 seeds weight) of winter oilseed rape, spring oilseed rape, white mustard and Indian mustard, submitted to analysis by the simple correlation method. Additionally, the contribution of individual yield components to the total seed yield of the analyzed Brassicaceae crops was examined by the path analysis method. In spring and winter oilseed rape a significant phenotype correlation was demonstrated between the number of siliques per plant (positive for both varieties) as well as the number of seeds in a silique (negative for winter oilseed rape and positive for spring one) versus the yield. In white mustard, a strong positive phenotype correlation was found between the seed yield and the number of siliques per plant as well as the number of seeds in a silique, while the 1000 seeds weight was correlated negatively with the seed yield. In Indian mustard, a positive phenotype correlation with the seed yield was confirmed for the number of siliques per plant and number of seeds in a silique, while a negative correlation was detected between the seed yield and the number of plants per m2 as well as a negative one – for the 1000 seeds weight.
Słowa kluczowe: rośliny oleiste, plon, komponenty plonu, korelacja, analiza ścieżek
Streszczenie
Badania zrealizowano w pełnym 3-letnim cyklu (2005–2008) na polach doświadczalnych uniwer-syteckiej stacji badawczej w Bałcynach (N = 53°35'49''; E = 19°51'20,3''). W pracy oceniono związek pomiędzy komponentami plonu (liczba roślin na 1 m2, liczba łuszczyn na roślinie, liczba nasion
w łuszczynie oraz masa 1000 nasion) rzepaku ozimego, rzepaku jarego, gorczycy białej oraz gorczycy sarepskiej z wykorzystaniem analizy korelacji prostej. Dodatkowo skwantyfikowano rolę składowych plonu w kształtowaniu plonu nasion badanych gatunków z rodziny Brassicaceae z wyko-rzystaniem analizy ścieżek. U rzepaku jarego oraz ozimego udowodniono istotną korelację
feno-typową liczby łuszczyn na roślinie (u obu form dodatnią) oraz liczby nasion w łuszczynie (u rzepaku ozimego – ujemną, rzepaku jarego – dodatnią) z plonem. U obu form rzepaku wysoka wartość korelacji fenotypowej liczby łuszczyn na roślinie z plonem nasion była efektem bezpośredniego oddziaływania tej składowej plonu. U gorczycy białej stwierdzono silną dodatnią korelację fenotypową z plonem liczby łuszczyn na roślinie oraz liczby nasion w łuszczynie oraz ujemną – masy 1000 nasion. U gorczycy sarepskiej korelację fenotypową z plonem nasion udowodniono w odniesieniu do liczby łuszczyn na roślinie i liczby nasion w łuszczynie (dodatnia) oraz liczby roślin na 1 m2 i masy 1000 nasion (ujemna).
Introduction
One of the most important traits contributing to qualitative assessments in investigations on the productivity of agricultural technologies or individual treatments is the yield of seeds, grains, tubers, etc. Most frequently, the modification of yields induced by agronomic practice is examined in the context of yields being conditioned by classical yield components. For oilseed rape or mustard, these are the number of plants per 1 m2, number of siliques per plant, number of seeds per silique and the 1000 seeds weight. The course of a production process (its quantitative and qualitative elements) differentiates yields of crops by the direct impact of yield-forming traits of plants, that is yield components (Kozak 2011). However, yield components never affect the final yield independently of one another. Contrary to that, their mutual compensation is a frequent development, caused by pleiotropy and/or gene feedback, or else by the sequential formation of yield components during the ontogenesis and competition for nutrients available in a given habitat. Such compensation is manifested, for example, by negative correlations between yield components (Mądry et al. 2007). It is especially important to know what compensation occurs between yield components when these traits are taken into consideration as criteria for a successful selection of factors indirectly affecting yields. Plant breeders often employ path analysis to investigate yield components. Path analysis supplies the information that enables them to design a multi-component, genetically conditioned plant ideotype which will be broadly adaptable to a chosen cultivation region (Özer et al. 1999; Wielebski 2005, Marjanović-Jeromela et al. 2007, 2011, Tunçtürk and Çiftçi 2007, Naderi and Emam 2010, Sadat et al. 2010, Sabaghnia et al. 2010, Rameeh 2011, Khayat et al. 2012, Wójtowicz 2013).In studies on agricultural technologies, the power of an effect produced by agritechnical factors on yield components is most often presented by regression equations and/or correlation coefficients (Wójtowicz and Muśnicki 2001ab). However, employing these tools to an appraisal of the role of individual yield structure components in the creation of final yield may not necessarily ensure satisfying results (Idźkowska et al. 1993). In their study, Konys and Wiśniewski (1984) compared correlation coefficients with path analysis coefficients, finding
essential differences (the two types of coefficients achieved different values and sometimes even opposite signs). Such discrepancies were due to the fact that correlation coefficients, unlike path analysis ones, do not account for indirect effects on the analyzed trait (Seiler and Stafford 1985). Determination of the relationship between yield components and yield may be useful in both plant breeding and the development of precise methods in agritechnological experimentation. Our knowledge of mutual relationships between yield components and yields of crops may also enable us to improve the efficiency of production technologies in agricultural practice (Budzyński and Jankowski 2003, Jankowski 2007, Grzebisz et al. 2010, Rymuza et al. 2012, Golba et al. 2013, Wójtowicz 2013). Path coefficients reflect the effects of quantitative ratios of individual yield components (independent variables) on yield (dependent variable) and are therefore mutually comparable, which shows their rank of importance in the formation of yield. They can help to identify those yield structure elements which deserve special attention when planning a production technology, that is the ones whose stimulation will make it possible to secure the biggest benefits, such as the highest yield increments. The path analysis coefficients also enable researchers to describe mutual relationships within agricultural plant communities between a given crop (ecological dominant) and weeds (Bijanzadeh et al. 2010).
Budzyński and Jankowski (2003) used path analysis to describe the role of single yield structure components in the creation of spring oilseed rape depending on the nitrogen fertilization level. In their experiment, the level of yields produced by oilseed rape nourished with small doses of nitrogen (40–80 kg ha-1) was directly dependent on the number of seed-containing siliques per 1 m2 of field and the number of seeds per silique. Under a higher nitrogen fertilization level (120–160 kg ha-1), there was only one trait, i.e. the number of seeds per silique, that played a major role in shaping the yield of spring oilseed rape. The effect of the number of siliques per 1 m2 (positive) and weight of 1000 seeds (negative) on yield was achieved indirectly, via the number of seeds per silique. In a study of Wójtowicz (2013) an increase of nitrogen fertilization levels stabilized seed yield by increasing 1000 seeds weight and number of seeds per silique. Also, Grzebisz et al. (2010) applied the path analysis method to their assessment of the role of yield components (number of siliques per plant, number of seeds per silique, weight of 1000 seeds) in forming the seed yield of winter oilseed rape under different mineral fertilization regimes (NPK; NPK + CuZnMnMo; NPK + CuZnMn). That research demonstrated that the weight of 1000 seeds and number of siliques per plant were the decisive factors influencing the seed yield of oilseed rape. Inclusion of micronutrients to a fertilization system for oilseed rape limited the role of the number of siliques per plant to the advantage of the 1000 seeds weight trait.
Gaining better knowledge of mutual relationships between crop yield components as well as the cause and effect relations between yield components
and yield can be helpful in designing more efficient crop growing technologies. The objective of this study has been to identify mutual relationships between yield components (number of plants per 1 m2, number of siliques per plant, number of seeds per silique and 1000 seeds weight) and to recognize the role of individual yield components in the creation of seed yields from major oil plants which belong to the family of Brassicaceae (winter oilseed rape, spring oilseed rape, white mustard, Indian mustard).
Materials and methods
Plan material and experiment. A controlled field experiment was run in the
experimental fields at the Experimental Station in Bałcyny (N = 53°35'49''; E = 19°51'20,3''), in 2005–2008. The experiment was set up in a random block design with 3 replications, testing 3 oil crops which belong to the Brassicaceae: winter oilseed rape (Brassica napus L. var. oleifera subvar. biennis), spring oilseed rape (Brassica napus L. var. oleifera subvar. annua), white mustard (Sinapis alba L.) and Indian mustard (Brassica juncea L./Czern. and Coss.).
Plot size was 18 m2. Each year the experiment was established on Haplic Luvisol developed from boulder clay (IUSS 2006). The soil had a slightly acidic pH ranging from 5.75 to 6.39 in 1 M KCl. Soil nutrient levels were as follows: 1.47–1.75% Corg (Kurmies method); 85–143 mg P kg
-1
(Egner-Riehm method), 104–133 mg K kg-1 (Egner-Riehm method), 51–103 mg Mg kg-1 (atomic absorption spectrometry – AAS), 3.3–8.3 mg S kg-1 (Bardsley and Lancaster method), 2.8–4.4 mg Cu kg-1 (AAS), 11–23 mg Zn kg-1 (AAS) and 180–235 mg Mn kg-1 (AAS) (Houba et al. 1995). The preceding crop was spring barley.
Winter oilseed rape was fertilized prior to sowing with 30 kg N ha-1, 22 kg P ha-1 and 166 kg K ha-1. In spring, nitrogen was applied in a split dose: 120 kg ha-1 at the stage of resumed vegetative growth (BBCH 20) and 80 kg ha-1 at the early budding phase (BBCH 50). The spring varieties of oil crops were fertilized before sowing with 70 kg N ha-1, 17 kg P ha-1 and 100 kg K ha-1 (spring oilseed rape and white mustard) or 70 kg N ha-1, 13 kg P ha-1 and 66 kg K ha-1 (Indian mustard). An adidtional 30 kg ha-1 dose of nitrogen was applied at the early budding of spring oilseed rape and white mustard (BBCH 50). The crops were nourished with sulphur supplied in doses: 60 kg ha-1 (winter oilseed rape), 40 kg ha-1 (spring rape and white mustard) or 25 kg ha-1 (Indian mustard). Phosphorus was introduced to soil in the form of triple superphosphate; potassium as highly concentrated (60%) potassium salt; nitrogen as ammonia nitrate and ammonia sulphate; sulphur as ammonia sulphate. Seeds of the winter rape cultivar Californium (breeder: Monsanto SAS, France) were sown from 1st to 10th or from 10th to 20th of August, using 90 germinating seeds per 1 m2 of a plot. The spring crops were sown in April,
using 140 (spring oilseed rape cultivar Hunter; breeder: RAPS GbR Saatzucht Lundsgaard, Germany), 130 (white mustard cv. Borowska, breeder: Małopolska Hodowla Roślin – HBP sp. z o.o., Poland) and 250 (Indian mustard cv. Mało-polska, breeder: Małopolska Hodowla Roślin – HBP sp. z o.o., Poland) germinating seeds per 1 m2 of a plot. The oil crops were sown at about 19-cm inter-row spacing. After the oilseed rape had been sown, metazachlor and quinmerac were applied, in the doses: 1 166 + 290.5 g ha-1 (winter form) or 999 + 249 g ha-1 (spring form). White and Indian mustards were weeded by applying 105 g ha-1 chlopyralid at the BBCH 14 stage. Pest infestation was controlled chemically, by spraying the plants with insecticides: once to four times (winter rape), 3 to 4 times (white and Indian mustard) and 4 to 6 times (spring rape). Disease-causing pathogens were controlled chemically only on plots under winter oilseed rape, using for that purpose 100 g ha-1 dimoxystrobin and 100 g ha-1 boscalid at the end of flowering (BBCH 66–67), when petals are being shed from the main raceme. All the oil crops were harvested when they had reached full technological maturity.
Immediately before the harvest of oil crops (BBCH 86–87), the major yield components were determined (on a sample of 20 plants from each plot): number of plants per 1 m2 of a plot, number of siliques per plant, number of seeds per silique and 1000 seeds weight (g), recalculated into the 13% moisture content. The number of seeds per silique was determined on a sample of 20 siliques from the middle part of the main stem and lateral branches of a single plant. The density of oil plants per 1 m2 before harvest was determined at 5 randomly selected sites on each plot. The volume of yields of oilseed crops from each plot was determined by weight after treshing and then corrected according to the standard moisture content (13%). The results were converted to a field surface area equal 1 ha.
Statistical analysis. Partial regression coefficients for standardized variables, i.e.
path analysis coefficients, were calculated according to the method described by Wright (1921, 1934). Figures 1 to 4 present diagrams of path coefficients for the components which condition the seed yield of the analyzed oil plants. One-sided arrows in these diagrams are elementary paths illustrating the direct effect of a causal variable (yield structure elements), while double-sided arrows stand for simple correlations between these variables. Each elementary path has a subordinated number (path coefficient). The path coefficient for causal variable X1 (number of plants per 1 m2) to the dependent variable (seed yield) is marked as Py1 in diagrams. Analogous symbols were assigned to the path coefficients of the other yield structure elements, i.e. number of siliques per plant (Py2), number of seeds in a silique (Py3) and 1000 seeds weight (Py4). The diagrams also give the value of residual variation (Pe), which indicates that yields obtained from the examined oil plants, apart from the analyzed random variables, may have been affected by some other factors, not included in our research. The simple correlation coefficients between the analyzed yield components were marked as r1,2, r1,3, r1,4, r2,3, r2,4, r3,4
(where, for example, r1,2 stands for the correlation between traits X1 and X2, etc.). In all our analyses of correlation and regression relationships we were supported by the statistical software package Statistica 10.1 PL software (StatSoft, Inc. 2011). The path analysis relied on a computer programme script included in Idźkowska et al. (1993). All other calculations were done on EXCEL® spreadsheets.
Results
Winter oilseed rape
Correlation analysis. Our analysis of the data illustrated in Figure 1 suggests that
the correlation between yield components of winter oilseed rape was small and statistically insignificant. However, a tendency has been discovered for a negative correlation between the number of plants per area unit and number of siliques on a plant (r1,2) as well as 1000 seeds weight (r1,4). Thus, increasing the density of winter rape plants may not necessarily lead to the desirable end such as a higher yield, because the potential yield stimulating effect of this solution could be effectively counteracted by some loss in the number of siliques per plant and 1000 seeds weight. It is worthwhile to notice that negative correlations were also found between the number of siliques on a plant (r2,4) and number of seeds in a silique (r3,4) versus the weight of 1000 seeds (Fig. 1). Thus, a higher number of siliques per plant or number of seeds in a silique may have an unfavourable influence on the weight of winter rape seeds.
Path analysis. The multiple regression equation for the yield of winter rape seeds,
following standardization of the variables, looked as follows: Y = 0.0797X1 + 0.5655X2 – 0.2983X3 + 0.4181X4 (R
2
= 0.62). The analysis of path coefficients shows, without any doubt, that the final yield of winter rape seeds was predominatly shaped by a small number of siliques per plant (Py2) and 1000 seeds weight (Py4), that is the traits negatively correlated with each other (r2,4) (Figure 1). Consequently, the phenotype correlation of the 1000 seeds weight with the seed yield was weak (not significant) because the positive direct effect of the 1000 seeds weight (Py4 = 0.4181*) was weakened by the negative effect of the indirect influence produced by the number of siliques per plant (-0.1248). Noteworthy is the strong (significant) negative phenotype correlation of the number of seeds in a silique with the seed yield of winter oilseed rape (-0.5232*) (Table 1). In this case, the weak negative effect of the number of seeds in a silique on the winter rape seed yield (Py3 = -0.2983) was reinforced by the likewise negative direct effects of the number of siliques per plant (-0.1074) and the 1000 seeds weight (-0.1511) (Table 1). Thus, a high number of seeds in a silique may be a trait implying low yielding potential due to the poor setting of siliques and small weight of seeds.
X1 – liczba roślin na 1 m2, X2 – liczba łuszczyn na roślinie, X3 – liczba nasion w łuszczynie,
X4 – masa 1000 nasion, Y – plon nasion z 1 ha
Fig. 1. Diagram of path coefficients of the winter oilseed rape seed yield components (means from 3 years) — Diagram współczynników ścieżek komponentów warunkujących
plon nasion rzepaku ozimego (średnio z 3 lat badań)
Spring oilseed rape
Correlation analysis. The spring variety of oilseed rape presented two strong
correlations: between the number of plants per area unit versus the number of siliques per plant (r1,2), and between the number of siliques per plant versus number of seeds per silique (r2,3) (Fig. 2). This means that a higher number of plants on a field of spring oilseed rape before harvest may have positive influence on the number of set productive siliques on a plant, which in turn might be beneficial for the number of seeds in a silique (stimulating its increase). It was only the weight of 1000 seeds that was negatively correlated with the remaining yield components. A particularly strong negative impact on the 1000 seeds weight was produced by a higher plant density per 1 m2. In general, our analysis of the data shown in Figure 2 implies that a single spring oilseed rape plant may generate more siliques per plant and a higher number of seeds in response to a higher plant density per area unit. However, it should be remembered that under such conditions created for the growth and development of spring oilseed rape, a marked (significant) decrease in the weight of 1000 seeds should be expected. Therefore, when the plant density is higher, agronomic treatments aimed at stimulating a higher mass of spring oilseed rape should be intensified (Fig. 2).
Table 1 Indirect effects of oil crop seed yield components (means for 3 years of the experiment)
Efekty pośrednie składowych plonu nasion roślin oleistych (średnio z 3 lat badań) Yield structure components Komponenty struktury plonu X1 X2 X3 X4 Phenotype correlation with yield Korelacja fenotypowa z plonem Winter oilseed rape Rzepak ozimy
X1 – 0.2291 -0.1257 -0.0731 -0.3482
X2 -0.0322 – 0.0566 -0.0921 0.4987*
X3 0.0336 -0.1074 – -0.1511 -0.5232*
X4 -0.0139 -0.1248 0.1561 – 0.4354
Spring oilseed rape Rzepak jary
X1 – 0.5226 0.0216 -0.2797 -0.0125
X2 -0.1733 – 0.0896 -0.2064 0.5451*
X3 -0.0419 0.5238 – -0.1002 0.5247*
X4 0.1706 -0.3796 -0.0315 – 0.2135
White mustard Gorczyca biała
X1 – -0.0543 -0.0040 -0.0763 -0.3109
X2 0.0303 – 0.1689 0.1989 0.7142**
X3 0.0028 0.2114 – 0.2393 0.7061**
X4 -0.0431 -0.2013 -0.1935 – -0.7503**
Indian mustard Gorczyca sarepska
X1 – -0.0032 -0.1423 -0.3274 -0.5969**
X2 0.0704 – 0.2169 0.4866 0.7795**
X3 0.0558 0.0039 – 0.4239 0.8000**
X4 -0.0749 -0.0051 -0.2474 – -0.8695**
X1…X4 – cf. figs 1–4 — jak na rys. 1–4
Path analysis. The relationship between the analyzed yield structure components
and the seed yield of spring rape was as follows: Y = -0.2770X1 + 0.8352X2 + 0.1429X3 + 0.4541X4 (Fig. 2). The high value of the determination ratio (R2 = 0.63) proves that the analyzed yield components made a large contribution to the seed yield. The dominant direct effect on spring oilseed rape yields was produced by the number of siliques per plant (Py2 = 0.8352**). The effect of the direct impact of the number of siliques per plant (Fig. 2) was higher than the value of the coefficient of phenotype correlation between this trait and seed yield (Table 1). This was a consequence of a weaker direct effect of the number of siliques per plant by the negative indirect effect via the 1000 seeds weight. A significant positive phenotype correlation was observed between the number of seeds in a silique and seed yield (0.5247**). In that case, the high value of this coefficient resulted primarily from the positive indirect effect (0.5238) via the number of siliques on a plant (Fig. 2, Table 1).
X1 – liczba roślin na 1 m 2
, X2 – liczba łuszczyn na roślinie, X3 – liczba nasion w łuszczynie,
X4 – masa 1000 nasion, Y – plon nasion z 1 ha
Fig. 2. Diagram of path coefficients of the spring oilseed rape seed yield components (means from 3 years) — Diagram współczynników ścieżek komponentów warunkujących
plon nasion rzepaku jarego (średnio z 3 lat badań)
White mustard
Correlation analysis. The number of siliques per plant was positively correlated
with the number of seeds in a silique (r2,3). Unfortunately, these two yield structure components (number of siliques per plant and number of seeds per silique) were strongly negatively correlated with the weight of 1000 seeds (r2,4 and r3,4), which means that any agronomic treatments stimulating the number of siliques per plant may also be the cause of a small weight of seeds. Consequently, when white mustard plants are found to go through a very abundant budding stage and produce a high number of set siliques, agritechnical measures should be applied to stimulate a higher weight of 1000 seeds. Noteworthy is the weak relationship between the plant density prior to harvest and the other white mustard seed yield components (Fig. 3).
Path analysis. The relationship between the analyzed yield structure components
and yield of white mustard seeds can be represented by the following function: Y = -0.1764X1 + 0.3162X2 + 0.2525X3 – 0.3124X4, at R2 = 0.69 (Fig. 3). Our analysis of path coefficients did not show any significant direct impact of the
X1 – liczba roślin na 1 m2, X2 – liczba łuszczyn na roślinie, X3 – liczba nasion w łuszczynie,
X4 – masa 1000 nasion, Y – plon nasion z 1 ha
Fig. 3. Diagram of path coefficients of the white mustard seed yield components (means from 3 years) — Diagram współczynników ścieżek komponentów warunkujących plon nasion
gorczycy białej (średnio z 3 lat badań)
analyzed yield components on yield volume (Fig. 3). This effect was achieved through the indirect influence on other yield components (Table 1). Very strong positive phenotype correlations were proven between the number of siliques on a plant (0.7142**), number of seeds in a silique (0.7061**) as well as the 1000 seeds weight (-0.7503**) and the seed yield of white mustard (Table 1). Weak positive effects of the indirect influence of the number of siliques per plant (Py2 = 0.3162) and number of seeds in a silique (Py3 = 0.2525) on white mustard seed yield (Fig. 3) were reinforced by the positive indirect effects via the weight of 1000 seeds (Table 1). A reverse phenomenon was observed in the case of the phenotype correlation of the 1000 seeds weight with yield, where a weak negative indirect effect (Py4 = -0.3124) (Fig. 3) was reinforced by the likewise negative indirect effects of the other yield components, especially the number of siliques per plant (-0.2013) and number of seeds in a silique (-0.1935) (Table 1).
Indian mustard
Correlation analysis. In Indian mustard noteworthy are very high negative values
of coefficients of the simple correlation between the number of plants per 1 m2 versus the number of siliques per plant (r1,2), the number of siliques per plant vs the 1000 seeds weight (r2,4) or the number of siliques per plant vs the number of seeds per silique (r2,3) (Fig. 4). A higher density of plants per 1 m
2
can lead to a lower number of productive siliques on a plant and a lower number of seeds in a silique. It was only the 1000 seeds weight that was able to increase at a higher plant density. Thus, an excessive density of plants per area unit can be extremely disadvantegous to the conformation of white mustard plants capable of ensuring good yields, because it may lead to lower numerical values of very important yield components, whose stimulation through agrotechnical treatments is rather difficult (i.e. number of siliques per plant or number of seeds per silique).
Path analysis. The multiple regression analysis for the seed yield of brown
mustard, following standardization of variables, was as follows: Y = -0.1241X1 + 0.0056X2 – 0.0316X3 – 0.5421X4 (R2 = 0.80). No significant direct effect of any of the Indian mustard yield structure components on the crop’s seed yield was proven (in which the plant was similar to white mustard) (Fig. 4). High (significant) values of coefficients of the phenotype correlation between the analyzed yield components and yield were the result of large indirect effects brought about by other yield components (Table 1). Positive phenotype correlations with the Indian mustard seed yield were determined for the number of siliques per plant (X2) and number of seeds in a silique (X3). Reversely, the number of plants per 1 m2 (X1) and weight of 1000 seeds (X4) were negatively correlated with the seed yield of this mustard species. A weak effect of the indirect impact of the number of plants per 1 m2 (Py1 = -0.1241) on mustard seed yields was reinforced by a likewise negative effect of the indirect influence of the 1000 seeds weight (-0.3274) (Fig. 4, Table 1). The negative phenotype correlation of the 1000 seeds weight with seed yield (-0.8695**) was the consequence of the negative indirect effect (Py4 = -0.5421) reinforced by the indirect (also negative) effect of the number of seeds in a silique (-0.2474) (Fig. 4, Table 1). The high value of the phenotype correlation of the number of siliques on a plant (0.7795**) and number of seeds in a silique (0.8000**) resulted from the strong positive indirect effect of 1000 seeds weight (0.4866 and 0.4239, respectively) (Table 1).
X1 – liczba roślin na 1 m2, X2 – liczba łuszczyn na roślinie, X3 – liczba nasion w łuszczynie,
X4 – masa 1000 nasion, Y – plon nasion z 1 ha
Fig. 4. Diagram of path coefficients of the Indian mustard seed yield components (means from 3 years) — Diagram współczynników ścieżek komponentów warunkujących plon
nasion gorczycy sarepskiej (średnio z 3 lat badań)
Discussion
Correlation analysis. The investigations carried out by Başalma (2008) showed
a positive correlation between the number of siliques (on lateral branches and on the main stem) versus the number of seeds in a silique, and a negative one between the number of seeds in a silique and the 1000 seeds weight of winter oilseed rape. Also, Marjanović-Jeromela et al. (2007) demonstrated a significant (albeit very weak) correlation between the number of siliques per plant and number of seeds in a silique of winter oilseed rape. Jankowski (2007) detected sigificant differences in mutual relationships of yield components between different breeding types of winter oilseed rape (a population linaege and a restored hybrid variety). In the lineage variety, only one trait, namely the number of siliques per plant, was positively correlated with the number of seeds in a silique. As for the hybrid variety, there were two other correlations, strong and negative ones: the number of siliques per plant vs the weight of 1000 seeds and the number of seeds in a silique vs the 1000 seeds weight. The studies conducted by Wielebski (2005) showed that the yield
of composite hybrids was significantly correlated with the number of plants per area unit and the weight of 1000 seeds. The number of seeds per silique was positively correlated with the yield of restored hybrids. Ali et al. (2002) did not demonstrate significant correlations between the analyzed winter oilseed rape yield components (number of siliques on a plant, number of seeds in a silique and 1000 seeds weight). Weak (not significant) correlations between yield components in this species are also indicated by the results of the current experiment.
Rameeh (2011) showed a positive correlation between the number of siliques per plant and number of seeds per silique in spring oilseed rape. The studies by Tunçtürk and Çiftçi (2007) conducted on 16 cultivars of spring oilseed rape showed a positive correlation of the number of siliques per plant with the number of seeds in a silique, as well as a negative one of the number of siliques per plant with the 1000 seeds weight. Also, in the experiment reported by Sadat et al. (2010) the weight of 1000 seeds weight was significantly (negatively) correlated with the number of siliques per plant and number of seeds per silique. A negative correlation between the 1000 seeds weight and the other yield components in this crop was also demonstrated in our current experiment. In turn, the number of plants per 1 m2, number of siliques per plant and number of seeds per silique were positively correlated with one another. Partly contradictory results were achieved by Özer et al. (1999) oraz Khayat et al. (2012). In their studies, all these traits, namely the number of siliques, number of seeds and 1000 seeds weight, were mutually positively (statistically insiginificantly) correlated. It is worthwhile to point to the report by Khan et al. (2000), where no significat correlations were shown between yield components in spring oilseed rape (number of siliques per plant, number of seeds per silique and 1000 seeds weight). The mutual relationships between spring rape seed yield components can be strongly modified by stress growth conditions (biotic and abiotic stresses). Budzyński and Jankowski (2003) demonstrated experimentally that in spring oilseed rape treated with insecticides only the 1000 seeds weight was negatively correlated with the number of siliques per 1 m2 or the number of seeds in a silique. With respect to spring oilseed rape grown without pest control, an extremely high (positive) simple correlation coefficient occurred between the number of siliques per area unit and number of seeds in a silique. Similarly to the oilseed rape treated with insecticides, the 1000 seeds weight was negatively correlated to the remaining yield components. Naderi and Emam (2010) found a strong positive relationship between all yield components of spring oilseed rape only under water stress. In non-stress conditions, however, the analyzed yield components were not significantly correlated with each other. Sabaghnia et al. (2010) reported contrary results. These authors analyzed 49 genotypes of spring oilseed rape and discovered a weak (although significant) simple correlation between the number of seeds per silique and the 1000 seeds weight for plants exposed to water stress. In a non-stressed
environment, all the analyzed yield components were mutually strongly and positively correlated.
Jankowski and Budzyński (2003) showed a very strong negative correlation between the number of seeds per silique and the 1000 seeds weight of white mustard. The current research also demonstrated negative correlations between the 1000 seeds weight and number of siliques per plant as well as number of seeds per silique. In the study of Jankowski and Budzyński (2003), the relationship between the other yield components (i.e. number of siliques per 1 m2 and number of seeds) was weak (simple correlation coefficients were low and statistically not significant). Contrary results were achieved in the current experiment, where the number of siliques per plant was positively correlated with the number of seeds per silique.
The investigations by Jankowski and Budzyński (2003) or Gangapur et al. (2009) showed strong positive correlations between the analyzed components of the seed yield structure of Indian mustard (such as the number of siliques per plant, number of seeds per silique and 1000 seeds weight). Tahira et al. (2011) reported strong positive correlation only between the number of seeds per silique and 1000 seeds weight. Contrary results were obtained in the experiment reported herein, where most of the yield components were mutually correlated (number of plants per 1 m2 ↔ number of siliques per plant; number of siliques per plant ↔ 1000 seeds weight as well as the number of seeds per silique ↔ 1000 seeds weight). There were only two positive correlations: between the number of plants per 1 m2 and 1000 seeds weight, and the number of siliques per plant and number of siliques per plant. It is worth quoting the studies by Akbar et al. (2007) and Singh et al. (2012), in which no relationships between yield components of Indian mustard were proven, that is the relevant simple correlation coefficients were not significant.
Path analysis. Marjanović-Jeromela et al. (2007) showed strong positive
phenotype correlation of the number of siliques per plant and 1000 seed weight versus the seed yield of winter oilseed rape. It is worth underlining that these relationships were the result of a strong indirect influence of yield components on seed yield via plant heights. The effects of the direct impact of the number of siliques and 1000 seeds weight were rather weak. Another study by Jankowski (2007) conducted on different breeding types of winter oilseed rape cultivars also suggested a considerable role of the effects produced by the indirect impact of yield components on winter oilseed rape seed yield. The analysis of the path coefficients in the population line and hybrid variety of winter oilseed rape showed a strong cause and effect relationship (indirect effect) between all the yield structure compnents and seed yield. The dominant role in yield creation (in both varieties) was played by the number of plants per unit area ˃ number of siliques per plant ˃ number of seeds per silique ˃ 1000 seeds weight. The indirect effect of yield components on yield was strongly modified by indirect effects, most often
negative ones. Therefore, having considered the indirect effects, it was proven that only the number of siliques per plant was significantly phenotype correlated with the seed yield of both breeding varieties of winter oilseed rape. This study has also verified the important role of effects of the indirect influence of yield components via other traits on yields produced by the analyzed form of oilseed rape. The results discussed in this paper provide evidence for the significant phenotype correlation between the phenotype number of siliques per plant and the seed yield. The value of the phenotype correlation coefficient for this feature was primarily conditioned by the effect of the direct impact. For the other yield components (i.e. number of seeds per silique and 1000 seeds weight), the effects of the indirect impact via other components had a substantial influence on the total effect (the value of the coefficient of a phenotype correlation).
In the experiment of Rameeh (2011), the number of siliques per plant and 1000 seeds weight had a strong direct effect on seed yield of spring oilseed rape. Tunçtürk and Çiftçi (2007) as well as Sadat et al. (2010) performed a path analysis as well, demonstrating a high positive direct effect of the number of siliques per plant, 1000 seeds weight and number of seeds per silique on seed yield of spring oilseed rape. In turn, studies by Özer et al. (1999) oraz Tunçtürk and Çiftçi (2007) showed a large contribution of indirect effects to the phenotype correlations of the analyzed yield components with seed yields of this type of oilseed rape. In the reported experiment Tunçtürk and Çiftçi (2007) showed that the positive effect of the direct influence of the number of siliques per plant was reinforced by the likewise positive indirect effect via the number of seeds per silique. The positive effect of the direct influence of the 1000 seeds weight on yield was weakened by a negative indirect influence of the number of siliques per plant. In turn, Özer et al. (1999) found out that the effect of the direct influence of the number of siliques per plant was weakened by the indirect negative effect of the 1000 seeds weight. In the current study, the number of siliques per plant was the only trait which determined indirectly the spring oilseed rape yield (effects of the indirect influence of this yield component via other ones were weak). The number of seeds per silique influenced seed yield through the effect of the number of siliques (a strong indirect effect). In constrast to the results of other authors cited above, in our experiment the 1000 seeds weight was not one of these yield cmponents which performed a direct or indirect role in shaping the seed yield of spring oilseed rape. Noteworthy are the reports by Budzyński and Jankowski (2003), Naderi and Emam (2010) and Sabaghnia et al. (2010), in which the researchers determined the relationship between elements of the yield structure and seed yield of spring oilseed rape grown under abiotic and biotic stress. Naderi and Emam (2010) demonstrated a strong effect of the direct influence of the number of seeds per silique on seed yield irrespective of the water supply of the crops. A sequential path analysis done by Sabaghnia et al. (2010) helped them to identify the 1000 seeds weight as the yield component that most
potently determined the seed yield of spring oilseed rape under the optimal water supply conditions and under water stress. In turn, another experiment by Budzyński and Jankowski (2003) revealed that plant pest control treatments could differentiate the role of individual yield ocmponents in the formation of spring oilseed rape yields. In the case of rape plants protected from insects chemically, all the analyzed components (number of siliques per 1 m2, number of seeds in a silique and 1000 seeds weight) were strongly phenotype correlated with seed yields. High values of the phenotype correlation coefficients of yield components with seed yield resulted from large direct effects. When oilseed rape plantations were not treated with insecticides, the effects of the direct influence of yield components were weak (not significant). High values of phenotype correlations of individual yield structure elements with seed yields were achieved owing to large indirect effects.
Jankowski and Budzyński (2003) proved that white mustard yields were mainly affected by a small plant density before harvest per 1 m2 and the number of siliques per plant. However, the significant effect of the direct influence of the number of plants per area unit was levelled by the negative indirect effects of the other yield structure components (the phenotype correlation coefficients for these traits were weak and not significant). This effect was realized through the indirect influence. A weak positive effect of the contribution of the number of siliques per plant and number of seeds per silique to white mustard seed yield was strengthened by positive indirect effects via the 1000 seeds weight. A reverse phenomenon was observed for the phenotype correlation of the 1000 seeds weight with seed yield, where a weak negative direct effect was reinforced by likwise negative indirect effects via the other yield components.
Khayat et al. (2012) imply a very strong phenotype correlation of the number of siliques on a plant and the 1000 seeds weight with the seed yield of Indian mustard. However, regarding the weight of one thousand seeds, the relationship of this trait with seed yield was achieved through an indirect effect via the number of siliques per plant. Shabana et al. (1990) and Akbar et al. (2007) showed a strong positive phenotype correlation of the number of siliques per plant with seed yield, being primarily the consequence of the direct effect of this trait on the dependent variable. Ghosh and Chatterjee (1988) showed the maximum direct effect of the number of siliques per 1 m2 on Indian mustard seed yield. In another study, by Gangapur et al. (2009), all the analyzed yield structure elements (number of siliques per plant, number of seeds per silique and 1000 seeds weight) were postively phenotype correlated with seed yields of Indian mustard. It is worth underlining that the above relationship was weaker when the crop was not given chemical disease and pest protection. On the other hand, in the research by Tahira et al. (2011), there was only one trait, i.e. the 1000 seeds weight, that was significantly positively correlated with the seed yield of Indian mustard. In our study, a significant phenotype correlation with seed yield was evidenced for the
number of siliques per plant and number of seeds per silique (positive) as well as the number of plants per 1 m2 and 1000 seeds weight (negative). It is worhtwhile to add that significant values of a phenotype correlation between the analyzed yield structure components versus seed yield were the result of large indirect effects via other yield components (effects of the direct impact were weak).
Conclusions
The above research has demonstrated that seed yields obtained from oil crops which belong to the family Brassicaceae are formed with a varied contribution of individual yield structure components. This proves that actual yields of these species may depend on changeable production technology factors. Our recognition of the cause and effect relationship between yield structure components and yields of annual oil crops could be helpful in designing and developing more efficient technologies for production of these species.Acknowledgement
The results presented in this article originate from a complex research project funded by the Polish Misnitry of Science and Higher Education, under the framework of the reseach grant no N310 031 32/167.
References
Akbar M., Saleem U., Tahira Yaqub M., Iqbal N. 2007. Utilization of genetic variability, correlation and path analysis for seed yield improvement in mustard (Brassica juncea). J. Agric. Res., 45 (1): 25-31.
Ali N., Javidfar F., Attary A.A. 2002. Genetic variability, correlation and path analysis of yield and its components in winter rapeseed (Brassica napus L.). Pak. J. Bot., 34 (2): 145-150.
Başalma D. 2008. The correlation and path analysis of yield and yield components of different winter rapeseed (Brassica napus ssp. oleifera L.) cultivars. Research Journal of Agriculture and Biological Sciences, 4 (2): 120-125.
Bijanzadeh E., Naderi R., Behpoori A. 2010. Interrelationships between oilseed rape yield and weeds population under herbicides application. Australian Journal of Crop Science, 4 (3): 155-162. Budzyński W., Jankowski K. 2003. Relations between spring oilseed rape yield and its components
at the different levels of inputs for production. Biul. IHAR, 228: 161-174 (in Polish).
Gangapur D.R., Prakash B.G., Salimath P.M., Ravikumar R.L., Rao M.S.L. 2009. Correlation and path analysis in Indian mustard (Brassica juncea L.Czern and Coss). Karnataka J. Agric. Sci., 22 (5): 971-977.
Ghosh R.K., Chatterjee B.N. 1988. Path analysis of important growth functions of Indian mustard (Brassica juncea L. Czern and Coss). J Agron Crop Sci., 160 (2): 116-121.
Golba J. Rozbicki J., Gozdowski D., Sas D, Mądry W., Piechociński M., Kurzyńska L., Studnicki M., Derejko A. 2013. Adjusting yield components under different levels of N applications in winter wheat. Int. J. Plant Product., 7 (1): 139-150.
Grzebisz W., Łukowiak R., Biber M., Przygocka-Cyna K. 2010. Effect of multi-micronutrient fertilizers applied to foliage on nutritional status of winter oilseed rape and development of yield forming elements. J. Elementol., 15 (3): 477-491.
Houba V.J.G., van der Lee J.J., Novozamsky I. 1995. Soil and plant analysis. Part 5B. Soil analysis procedure other procedure. Wageningen Agric. Univ., Wageningen, the Netherlands.
Idźkowska M., Gołaszewski J., Koczowska I., Grabowski S. 1993. Analysis of path coefficients in winter rye (Secale cereale L.). Part II. Analysis of path coefficients in evaluating the correlation of characteristics determining the grain weight per plant. Acta Acad. Agricult. Tech. Olst., Agricultura, 56: 25-30 (in Polish).
IUSS Working Group WRB. 2006. World Reference Base for Soil Resources. 2nd edition. World Soil Resources Reports No. 103. FAO, Rome: 132.
Jankowski K. 2007. Habitat, agrotechnological and economic conditions of winter rapeseed production for consumption and energy purposes. University Publisher University Warmia and Mazury in Olsztyn. Disseratations and Monographs, 131: 1-174 (in Polish).
Jankowski K., Budzyński W. 2003. The role of components yield in management yielding of some spring oilseed crops. Rośliny Oleiste – Oilseed Crops, XXIV (2): 443-454 (in Polish).
Khan S., Farhatullah, Khalil I.H. 2008. Phenotypic correlation analysis of elite F3:4 brassica
populations for quantitative and qualitative traits. Journal of Agricultural and Biological Science, 3: 38-42.
Khayat M., Lack Sh., Karami H. 2012. Correlation and path analysis of traits affecting grain yield of canola (Brassica napus L.) varieties. J. Basic. Appl. Sci. Res., 2 (6): 5555-5562.
Konys I., Wiśniewski P. 1984. Path analysis. Rocz. AR w Poznaniu, 102 (20): 37-57 (in Polish). Kozak M. 2011. Analysis of cause-and-effect relationships in agronomy and plant breeding. Biul.
IHAR, 259: 3-21 (in Polish).
Mądry W., Gozdowski D., Rozbicki J., Pojmaj M., Samborski S. 2007. Relationships between grain yield and its components in winter triticale advanced lines across three locations. Biul. IHAR, 245: 77-94 (in Polish).
Marjanovic-Jeromela A., Marinkovic R., Ivanovska S., Jankulovska M., Mijic A., Hristov N. 2011. Variability of yield determining components in winter rapeseed (Brassica napus L.) and their correlation with seed yield. Genetika, 43 (1): 51-66.
Marjanović-Jeromela A., Marinković R., Mijić, A. Zdunić, Z. Ivanovska, S. Jankulovska M. 2008. Correlation and path analysis of quantitative traits in winter rapeseed (Brassica napus L.). Agriculturae Conspectus Scientificus, 73 (1): 13-18.
Naderi R., Emam Y. 2010. Interrelationships among grain yield and related characters of four oilseed rape (Brassica napus L.) cultivars under drought stress conditions. J. Desert, 15: 133-138. Özer H., Oral E., Dogru Ü. 1999. Relationships between yield and yield components on currently
improved spring rapeseed cultivars. Turk. J. Agric. For., 23: 603-607.
Rameeh V. 2011. Correlation and path analysis in advanced lines of rapeseed (Brassica napus) for yield components. Journal of Oilseed Brassica, 2 (2): 56-60.
Rymuza K., Turska E., Wielogórska G., Wyrzykowska M., Bombik A. 2012. Evaluation of yield determination of spring wheat grown in monoculture interrupted with stubble crop growth by means of path analysis. Acta Sci. Pol., Agricultura, 11 (2): 53-61.
Sabaghnia N., Dehghani H., Alizadeh B., Mohghaddam M. 2010. Interrelationships between seed yield and 20 related traits of 49 canola (Brassica napus L.) genotypes in non-stressed and water-stressed environments. Span. J. Agric. Res., 8 (2): 356-370.
Sadat H.A., Nematzadeh G.A., Jelodar N.B., Chapi O.G. 2010. Genetic evaluation of yield and yield components at advanced generations in rapeseed (Brassica napus L.). Afr. J. Agric. Res., 5 (15): 1958-1964.
Seiler G.J., Stafford R.E. 1985. Factor analysis of components of yield in guar. Crop. Sci., 25: 905-908. Shabana R., Shrief S.A., Ibrahim A.F., Geisler G. 1990. Correlation and path coefficient analysis for
some new released (00) spring rapeseed cultivars grown under different competitive systems. J. Agron. Crop Sci., 165 (2-3): 138–143.
Singh V.V., Singh M., Chauhan J.S., Kumar S., Meena M.I., Singh B.K., Singh K., Singh U.B. 2012. Development and evaluation of half sib progenies for morpho-physiological characters in Indian mustard (Brassica juncea L.) under rainfed conditions. SABRAO J. Breed. Genet., 44 (2): 229-239. StatSoft Inc. 2011. Statistica (data analysis software system), version 10. www.statsoft.com.
Tahira, Mahmood T., Tahir M.S., Saleem U., Hussain M., Saqib M. 2011. The estimation of heritability, association and selection criteria for yield components in mustard (Brassica juncea). Pak. J. Agri. Sci., 48 (4): 251-254.
Tunçtürk M., Çiftçi, V. 2007. Relationships between yield and some yield components in rapeseed (Brassica napus ssp. oleifera l.) cultivars by using correlation and path analysis. Pak. J. Bot., 39 (1): 81-84.
Wielebski F. 2005. Share of yield components in the creation of yield of winter oilseed rape hybrids. Rośliny Oleiste – Oilseed Crops, XXVI (1): 87-98 (in Polish).
Wójtowicz M. 2013. Effect of environmental and agronomical factors on quantity and quality of yield of winter oilseed rape (Brassica napus L.). Publisher IHAR-PIB Radzików, Poland. Monographs and Dissertations, 45, 1-111 (in Polish).
Wójtowicz M., Muśnicki Cz. 2001a. Udział komponentów struktury w kształtowaniu plonu nasion podwójnie ulepszonych odmian rzepaku ozimego. Rocz. AR w Poznaniu, CCCXXXV, Roln., 61: 105-121.
Wójtowicz M., Muśnicki Cz. 2001. Zmienność i współzależność pomiędzy plonem nasion podwójnie ulepszonych odmian rzepaku ozimego a komponentami jego struktury. Rocz. AR w Poznaniu, CCCXXXV, Roln., 61: 123-137.
Wright S. 1921. Correlation and causation. J. Agric. Res., 20: 557-585.
