Krzysztof Józef Jankowski, Wojciech Stefan Budzyński, Bogdan Dubis 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: 10.5604/12338273.1194980
Correlations between the yield components
and grain yield of winter wheat (Triticum aestivum
ssp. vulgare L.) grown after winter rapeseed
Związek pomiędzy komponentami plonu a plonem ziarna
pszenicy ozimej (Triticum aestivumssp. vulgare L.)
uprawianej po rzepaku ozimym
Keywords: winterwheat, winter rapeseed, forecrop, correlation analysis, path analysis.Abstract
A three-year field experiment (2006–2009) was conducted at the Agricultural Experiment Station in Bałcyny (N = 53°35'49''; E = 19°51'20.3''), owned by the University of Warmia and Mazury in Olsztyn (Poland). The relationships between the yield components (number of spikes per m2, number of kernels per spike and 1000 kernel weight) of winter wheat grown after winter rapeseed or after winter wheat (continuous cropping) were determined (based on linear correlation coefficients). Forecrop species had no significant influence on the correlations between the yield components of winter wheat. A strong positive correlation was observed between the number of spikes per m2 and the remaining yield components of winter wheat in both treatments. A weak correlation was noted only between the number of kernels per spike and 1000 kernel weight. The effect of yield components on the grain yield of winter wheat grown after different forecrops was quantified in a path analysis. The yield of winter wheat grown after winter rapeseed was directly influenced by two yield components – the number of spikes per m2 and 1000 kernel weight. The yield of winter wheat grown in a continuous cropping system was determined directly only by 1000 kernel weight.
Słowa kluczowe: pszenica ozima, rzepak ozimy, przedplon, analiza korelacji, analiza ścieżek Streszczenie
Badania realizowano w pełnym 3-letnim cyklu (2006–2009) na polach doświadczalnych stacji badawczej w Bałcynach (N = 53°35'49''; E = 19°51'20,3''), należącej do Uniwersytetu Warmińsko-Mazurskiego w Olsztynie. W pracy oceniono związek pomiędzy komponentami plonu (liczba kłosów na 1 m2, liczba ziaren w kłosie oraz masa 1000 ziarniaków) pszenicy ozimej uprawianej po rzepaku ozimym lub po sobie (wykorzystując wskaźniki korelacji prostej). Wybór przedplonu nie wpływał znacząco na relację pomiędzy składowymi plonu pszenicy ozimej. W obu warunkach uprawy stwierdzono silną dodatnią korelację prostą pomiędzy liczbą kłosów na 1 m2 a pozostałymi
składowymi plonu ziarna pszenicy ozimej. Tylko liczba ziarniaków w kłosie i masa 1000 ziarniaków były słabo skorelowane ze sobą. Dodatkowo skwantyfikowano rolę składowych plonu w kształ-towaniu plonu ziarna pszenicy ozimej, w zależności od wyboru przedplonu (wykorzystując
współczynniki analizy ścieżek). Za kształtowanie plonu pszenicy ozimej uprawianej po rzepaku ozimym bezpośrednio odpowiedzialne były dwa elementy struktury plonu, tj. liczba kłosów na 1 m2 oraz masa 1000 ziarniaków. Plonowanie pszenicy ozimej uprawianej po sobie było bezpośrednio determinowane tylko przez masę 1000 ziarników.
Introduction
The biological diversity of agricultural ecosystems in Europe has been decreasing steadily since the 1980s. Monocotyledonous cereal species have a predominant share of the crop structure. In European crop rotation systems, the average share of cereals is estimated at 77%, ranging from 70–74% in Germany, France, Great Britain and Poland to nearly 90% in Finland and Slovenia (Majewski 2010). Monocultures significantly reduce wheat yield, which is determined by soil quality and the duration of monoculture. The yield of winter wheat grown in long-term monoculture (21–44 years) at the Department of Agricultural Systems of the University of Warmia and Mazury in Olsztyn was analyzed by Budzyński (2012). In the above study, the grain yield of short-term monocultures (6–8 years) of winter wheat was 22% lower in comparison with the yield of wheat plants grown in a crop rotation scheme. In monocultures grown for 21–25 years, grain yield was reduced by 42%, and in monocultures grown for another 10 years – by 54%. The ranking of winter wheat forecrops developed by Dmowski (1993) is topped by mixed cereal and legume crops, clover, alfalfa, sugar beets and medium-early potatoes. Annual legumes and winter rapeseed are satisfactory forecrops, and the yield of wheat grown after those species reaches 92% of that reported for the best forecrops. The yield volume of successive crops (winter wheat) grown after winter rapeseed is approximately 0.76–0.98 t ha-1 higher than the yield of crops grown in monoculture (Weber 2000, Sieling et al. 2005, Wesołowski et al. 2007, Bednarek et al. 2009). From the environmental point of view, the yield of winter wheat grown in monoculture (short-term or long-term) is reduced mainly due to nutrient depletion, weed competition, pest and disease pressures, changes in soil structure, reduced activity of soil microorganisms and the release of phytotoxic substances from roots and postharvest residues. From the biological point of view, the decrease in the grain yield of winter wheat grown in monoculture relative to the yield of winter wheat grown after winter rapeseed results from a reduction in the number of productive spikes per m2 and lower 1000 kernel weight (Sieling et al. 2005). The decrease in winter wheat yield resulting from a higher share of cereals in crop rotation can be minimized through the application of intensive production techniques, mainly fertilization and weed, pest and disease control (Widdowson et al. 1985, Prew et al. 1986, Christen et al. 1992, Sieling et al. 2005, Adamiak et al. 2009, Adamiak et al. 2011).
From the biological perspective, the aim of intensive production processes is to minimize the decrease in the values of the main yield components. It should be noted, however, that yield components do not affect yield independently. Compensation is often observed between yield components due to their sequential development during ontogenesis and competition for resources in a given environment (Kozak 2011). In a study of winter wheat, Klepper et al. (1998) observed that the number of spikes per unit area is shaped between sowing and the beginning of heading, the number of kernels per spike – between stem elongation and the end of flowering, and single kernel weight – between heading and the dough stage. Various statistical methods are deployed in analyses of correlations between yield volume and yield components. The most popular method is multiple linear regression, in particular path analysis (Mądry et al. 2003). Path coefficients reflect the quantitative effects of yield components (independent variables) on yield (dependent variable), and they can be compared to rank the contribution of different yield components to the yield. Unlike correlation coefficients that are commonly used in agronomic research, path analysis accounts for the indirect effects of variables on the analyzed trait (Seiler and Stafford 1985, Kozak and Mądry 2006, Gozdowski and Mądry 2008, Kozak and Verma 2009, Kozak 2011). Path analysis is popularly applied to describe causal relationships in wheat breeding (Hussain et al. 1985, Simane et al. 1993, Denčić et al. 2000, Donaldson et al. 2001, Ługowska et al. 2004, Yagdi 2009, Mohsin et al. 2009, Khokhar at el. 2010). It is also a valuable instrument that explains wheat's biological responses to growing conditions, including production intensity (Akanda and Mundt 1996, García del Moral et al. 2003, Gaj 2008, Rymuza et al. 2012a and b, Khan and Naqi 2012). In addition to basic yield components, breeding studies also evaluate other yield-related traits. Agronomic studies focus on major yield components that directly influence the yield (their product is generally equal to the weight of harvested grain) (Gozdowski and Mądry 2008).
The objective of this study was to determine the effect of winter wheat grown in a continuous cropping system on correlations between yield components. The role of yield-forming traits on the grain yield of winter wheat grown after winter wheat (continuous cropping) and after winter rapeseed was also determined. A knowledge of the effects of different yield components on winter wheat yield is required to develop production technologies that effectively minimize yield loss in continuous cropping systems.
Materials and methods
Plant material and experimental design: A three-year field experiment (2006–
2009) was conducted at the Agricultural Experiment Station in Bałcyny (N = 53°35'49'', E = 19°51'20.3''), owned by the University of Warmia and Mazury
in Olsztyn (Poland). The experimental soil conditions are given in Table 1. The experiment had a randomized block design with three replications. Winter wheat was grown after two different forecrops, winter wheat (continuous cropping) and winter rapeseed. The production technology was not affected by the choice of forecrop.
Table 1 Soil conditions during the experiment — Charakterystyka warunków glebowych prowadzenia
badań (2006−2009) Item Wyszczególnienie Forecrop — Przedplon winter rapeseed rzepak ozimy winter wheat pszenica ozima 2006/07 2007/08 2008/09 2006/07 2007/08 2008/09 Soil textural group
Typ gleby gray-brown podsolic soil — gleba płowa
Soil quality class
Gatunek gleby light loam — glina lekka
Organic carbon content of soil
Zawartość substancji organicznej (%)
1.48 1.78 1.67 1.73 1.26 1.37 Soil pH — Odczyn gleby
(1 MKCl) 6.44 5.16 5.06 6.50 5.76 5.25
Concentrations of available nutrients — Zawartość przyswajalnych składników (mg kg-1soil)
P 88 82 48 80 82 41
K 166 168 100 125 145 100
Mg 120 80 42 117 80 43
Winter wheat cv. Olivin (RAGT 2n, France) was sown each year in the second half of September with the density of 450 dressed kernels per m2 of plot area. Plot size was 18 m2. The following pre-sowing fertilization treatment was applied: 30 kg N ha-1 (ammonium nitrate), 17 kg P ha-1 (triple superphosphate) and 100 kg ha-1 K (60% potash salt). In spring, two nitrogen fertilization treatments were applied at the rate of 90 ha-1 (BBCH 29) and 50 ha-1 (BBCH 32). Herbicide treatment was applied at the first leaf unfolded stage (BBCH 11) with 1000 g ha-1 of pendimethalin and 500 g ha-1 of isoproturon. Each year, disease control involved seed dressing (30 g of triadimenol, 4 g of imazalil and 3.6 g of fuberidazole per 100 kg of grain), and two chemical treatments applied at stages BBCH 32 (picoxystrobin 150 g ha-1; flusilazole 125 g ha-1; carbendazim 62.5 g ha-1; proquinazid 30 g ha-1) and BBCH 39 (picoxystrobin 100 g ha-1; flusilazole 75 g ha-1; carbendazim 37.5 g ha-1; famoxate 50 g ha-1; flusilazole 53.35 g ha-1). Each year, winter wheat was harvested in the first half of August.
The number of productive tillers (spikes) per 3 m2 (3 × 1 m2) of plot area was counted before winter wheat harvest. The number of kernels per spike was determined in spikes collected from an area of 0.5 m2 (2 × 0.25 m2) in each plot. A total of 215–242 spikes per plot were collected. Thousand kernel weight was determined on 5 000 kernels (5 × 1000) sampled from grain that had been harvested from each plot and cleaned.The grain yield of winter wheat and 1000 kernel weight were adjusted to 14% moisture content.
Statistical analysis: Partial regression coefficients for standardized variables,
i.e. path coefficients, were calculated according to the method described by Wright (1921, 1934). The diagrams of path coefficients for components that influence the grain yield of winter wheat are presented in Figures 1 and 2. One-way arrows (→) are elementary pathways that illustrate the direct effects of causal variables (yield components), and two-way arrows (↔) denote linear correlations between those variables. Every elementary path was assigned a number (path coefficient). The path coefficient for causal variable X1 (number of spikes per m2) to causal variable Y (grain yield) is marked as Py1 in the diagram. A similar approach was used to mark path coefficients for the remaining yield components – number of kernels per spike (Py2) and 1000 kernel weight (Py3). The diagram also presents residual variance values (Pe), which indicate that in addition to random factors, the yield of winter wheat could also be affected by other factors that were not evaluated in the experiment. The coefficients of linear correlation between the analyzed yield components were marked as r1,2, r1,3, r2,3 (where r1,2 denotes a correlation between variables X1 and X2, etc.).
Variance, correlation and regression analyses were performed in the Statistica 10.0 PL® application. Path analyses were carried out based on the computer script presented by Idźkowska et al. (1993). The remaining calculations were performed in the Excel® application.
Results
Correlations between yield components (correlation analysis): The correlations
between the yield components of winter wheat were not significantly influenced by the choice of forecrop species (Fig. 1 and 2). Significant positive correlations between the number of spikes per m2 and the remaining yield components, i.e. number of kernels per spike (r1,2) and 1000 kernel weight (r1,3), were observed in winter wheat grown after winter rapeseed and after winter wheat. The value of the coefficient of linear correlation between the number of spikes per m2 and 1000 kernel weight was 2–3 fold higher than between the number of spikes per m2 and the number of kernels per spike, regardless of the forecrop. From the practical point of view, an increase in the number of productive spikes per m2 (resulting from the
X1 – liczba kłosów na 1 m2; X2 – liczba ziarn w kłosie, X3 – liczba masa 1000 ziarn, Y – plon ziarna z 1 ha
Fig. 1. Path coefficients of yield components influencing the grain yield of winter wheat grown after winter rapeseed (results for a three-year study) — Diagram współczynników
ścieżek komponentów warunkujących plon ziarna pszenicy ozimej uprawianej po rzepaku ozimym (wyniki z 3 lat badań)
X1 – liczba kłosów na 1 m2; X2 – liczba ziarn w kłosie, X3 – liczba masa 1000 ziarn, Y – plon ziarna z 1 ha
Fig. 2. Path coefficients of yield components influencing the grain yield of winter wheat grown in a continuous cropping system (results for a three-year study) — Diagram
współczynników ścieżek komponentów warunkujących plon ziarna pszenicy ozimej uprawianej po sobie (wyniki z 3 lat badań)
impacts of environmental and weather conditions and the applied production technology on productive tillering) can increase the number of kernels per spike and improve grain plumpness. Such an improvement can be observed regardless of the applied forecrop species. The number of kernels per spike and 1000 kernel weight were weakly correlated in both analyzed treatments (Fig. 1 and 2).
Correlations between yield components and grain yield (path analysis): After
the standardization of variables, the multiple regression equation for the yield of winter wheat grown after winter rapeseed was expressed as: Y = 0.1095X1 – 0.0102X2 + 0.7988X3 (R
2
= 0.72) (Fig. 1). A path analysis clearly demonstrates that the grain yield of winter wheat grown after winter rapeseed was influenced mainly by 1000 kernel weight (X3) and, to a lesser (but statistically significant) degree, by the number of spikes per m2 (X1) (Fig. 1). Thousand kernel weight had a significant direct effect on crop yield (Py3 = 0.7988**) (Fig. 1). The above yield component had a weak indirect effect on the yield of winter wheat (Table 2). The overall effect (coefficient of phenotypic correlation) of the number of spikes per m2 was more influenced by the indirect effect of 1000 kernel weight (0.2904) than by the direct effect (0.1095) (Fig. 1, Table 2).
Table 2 The influence of forecrop species on the indirect effects of yield components of winter wheat grown as a successive crop (results for a three-year study) — Wpływ wyboru
przedplonu na efekty pośrednie składowych plonu pszenicy ozimej uprawianej jako rośliny następczej (wyniki z 3 lat badań)
Yield components
Elementy struktury plonu X1 X2 X3
Phenotypic correlation with grain yield
Korelacja fenotypowa z plonem ziarna
Winter wheat grown after winter rapeseed — Pszenica ozima uprawiana po rzepaku ozimym
X1 – -0.0014 0.2904 0.3984**
X2 0.0153 – -0.1102 -0.1051
X3 0.0398 0.0014 – 0.8400**
Winter wheat grown after winter wheat (continuous cropping) — Pszenica ozima uprawiana po sobie
X1 – 0.0050 0.4410 0.5124**
X2 0.0199 – 0.1046 0.1412
X3 0.0344 0.0021 – 0.8868**
X1…X4 – refer to Fig. 1 and 2− jak na rys. 1, 2
After the standardization of variables, the multiple regression equation for the grain yield of winter wheat grown in a continuous cropping system was expressed as: Y = 0.0664X1 + 0.0167X2 + 0.8504X3 (R2 = 0.79) (Fig. 2). The yield of winter wheat grown in a 2-year continuous cropping system was influenced directly
only by 1000 kernel weight (Py3 = 0.8504**) (Fig. 2). The direct effects of the remaining yield components were positive, although weak and statistically unproven (Table 2). The significant phenotypic correlation between the number of spikes per m2 (0.5124**) and winter wheat yield was determined mainly by the indirect effect of 1000 kernel weight (0.4410) (Fig. 2, Table 2).
Discussion
Correlation analysis: The yield-forming traits of plants and stands can be
shaped simultaneously during ontogenesis or sequentially, i.e. in a specific sequence, in the same or different stages of ontogenesis. Yield components are generally correlated to a varied extent and in different directions. The above can be attributed to the impacts of complex environmental and climate conditions and the influence of the applied production technology on the yield-forming stages of plant development and growth. The correlations between yield components can also be determined by the strong physiological mechanism of mutual compensation (Mądry et al. 2003). For this reason, the results of studies investigating the correlations between the yield components of winter wheat are inconclusive. In a study by Saleem et al. (2006), all of the examined wheat yield components (number of productive tillers, number of spikelets per spike and 1000 kernel weight) were positively correlated. According to Gaj (2008), the only correlated yield components of winter wheat were the number of spikes per m2 and the number of kernels per spike. Ługowska et al. (2004) and Zečević et al. (2004) demonstrated that the number of spikes per m2/number of productive tillers per plant were negatively correlated with the number of kernels per spike and 1000 kernel weight. Hussain et al. (1985), Khan et al. (2005) and Khokhar at el. (2010) observed weak (statistically non-significant) correlations between the yield components of wheat.
The results of published studies indicate that production technology can considerably influence the correlations between the yield components of winter wheat. Simane et al. (1993) did not report significant correlations between yield components in durum wheat plants grown under optimal water conditions. A negative correlation between the number of spikes per m2and the number of kernels per spike was noted in a water deficit (regardless of the season in which drought occurred). Grain weight was not significantly correlated with the remaining yield components regardless of water supply levels. In a study by Rymuza et al. (2012b), the correlations between yield components of winter wheat grown in monoculture of intercropped species. In treatments where winter wheat was intercropped with white mustard, positive correlations were noted between 1000 kernel weight vs. the number of spikes per m2 and the number of kernels per
spike. The correlations between the number of kernels per spike and 1000 kernel weight of spring wheat were weaker when Lacy phacelia was intercropped (only the correlation between the number of spikes per m2 and 1000 kernel weight was statistically significant). In our study, the choice of forecrop species did not have a major influence on the strength or direction of correlations between the yield components of winter wheat. Positive correlations between the number of spikes per m2 vs. the number of kernels per spike and 1000 kernel weight were observed in winter wheat grown after winter wheat (continuous cropping) and after winter rapeseed. Rymuza et al. (2012a) observed strong positive correlations between the number of kernels per spike and 1000 kernel weight and between the number of spikes per m2 and the number of kernels per spike in a conventional tillage (plough-based) system. In a direct seeding system, a strong positive correlation was noted only between the number of spikes per m2 and 1000 kernel weight. García del Moral et al. (2003) investigated the effect of irrigation on the correlations between yield components of durum wheat grown in two different climates in Spain. In the warm Mediterranean climate of southern Spain, the number of spikes per m2 was negatively correlated with kernel weight (under irrigated conditions) and the number of kernels per spike (under rainfed conditions). In the cooler regions of northern Spain that remains under the influence of the continental climate, the correlations between the yield components of durum wheat were generally weak, but a strong negative correlation was reported between the number of kernels per spike and kernel weight. Akanda and Mundt (1996) studied the correlations between the yield components of four winter wheat cultivars that were sown in pure and blended (two cultivars) forms. The plants were subjected to different chemical treatments against stripe rust. The evaluated correlations were much more complex in treatments not affected by stripe rust and in cultivar mixtures. In mono-cultivar treatments infected with Pucciniastriiformis, a positive correlation was observed between the number of spikes per m2 and individual seed weight. In the absence of stripe rust, a significant negative correlation was found between individual seed weight and the remaining yield components (number of spikes per m2 and the number of kernels per spike). In rust-infected cultivar mixtures, the number of spikes per m2 and individual seed weight were positively correlated. In fungicide-treated cultivar mixtures, the number of spikes per m2 was significantly correlated with the number of kernels per spike (positive correlation) and individual seed weight (negative correlation). The number of kernels per spike compensated for or stimulated individual seed weight subject to habitat conditions (experimental site).
Path analysis: In natural sciences, path analysis is one of the most popular
methods for analyzing causal relationships between variables. Causality generally implies as a set of traits that are involved in the evaluated biological process. In path analysis, interpretation is based on three types of effects between variables:
indirect, direct and total (Kozak 2011). In a study by Ługowska et al. (2004), the main yield components directly responsible for variations in the grain yield of winter wheat were the number of spikes per m2 and 1000 kernel weight. The direct effects of the above components on wheat yield were also demonstrated by Yagdi (2009). In the cited study, the direct positive effect of plant density on yield was compensated by the remaining yield components (the phenotypic correlation between plant density and grain yield was low). According to Mohsin et al. (2009), the number of kernels per spike directly determined the yield of synthetic elite lines of wheat. The number of spikes per m2 was highly phenotypically correlated with grain yield due to the significant indirect effect of the number of kernels per spike. Studies of common wheat (Donaldson et al. 2001) and durum wheat (Simane et al. 1993) also demonstrated that grain yield was directly affected by the number of spikes per m2 and the number of kernels per spike. In the work of Hussain et al. (1985), the number of kernels per spike and 1000 kernel weight had the greatest influence on wheat yield (these yield components delivered maximum direct effects). Khokharet et al. (2010) observed a negative direct effect of yield components (number of productive tillers, number spikelets per spike and 1000 kernel weight) on grain yield. Positive phenotypic correlations between the above components and grain yield were attributed to the indirect effects of yield-related traits.
The results of agricultural research suggest that the influence of individual yield components on yield is determined not only by genetic traits of a cultivar, but also by production intensity. Khan and Naqi (2012) observed strong direct effects of the number of spikes per m2 and the number of kernels per spike on the grain yield of wheat grown under different irrigation conditions. Rymuza et al. (2012b) evaluated the influence of yield components on the grain yield of spring wheat grown in monoculture and with different catch crop species (white mustard, Lacy phacelia). The yield of spring wheat grown after white mustard was determined mainly by the number of spikes per m2 and 1000 kernel weight. The yield of spring wheat grown after Lacy phacelia was most highly influenced by three components: number of spikes per m2, number of kernels per spike and 1000 kernel weight. In this study, forecrop species also significantly modified the effects of yield components on the grain yield of winter wheat. The yield of winter wheat grown in a continuous cropping system was directly determined only by 1000 kernel weight. The yield of winter wheat grown after winter rapeseed was dependent on two components: the number of spikes per m2 and 1000 kernel weight. According to García del Moral et al. (2003), in irrigated treatments, 1000 kernel weight contributed most (direct positive effect) to the yield of durum weight, regardless of climate conditions. The remaining yield components (number of spikes per m2 and number of kernels per spike) were highly correlated with wheat yield only in non-irrigated treatments in a cooler climate. In a study by Denčić et al. (2000), none of the evaluated yield components had a significant direct effect on the grain
yield of winter wheat grown under optimal moisture conditions. In drought conditions, wheat yield was directly determined by the number of kernels per spike. Akanda and Mundt (1996) performed a path analysis to determine the influence of yield components on wheat grain yield and observed that all direct effects were similar for all yield components in both monoculture and mixed (two cultivar) treatments, regardless of experiment location and the presence of stripe rust. The direct effects of the number of spikes per unit area and the number of kernels per spike were somewhat more pronounced in healthy plants than in plants infected with Puccinia striiformi, whereas the direct effects of kernel weight were greater in inoculated plants.
Conclusions: A strong positive correlation was noted between the number of spikes per m2 and the remaining yield components of winter wheat. The strength of linear relationships (measured by the value of the correlation coefficient) between those components was greater in winter wheat grown in a continuous cropping system. The yield of winter wheat grown after winter rapeseed was directly influenced by two yield components: the number of spikes per m2 and 1000 kernel weight. The yield of winter wheat grown in a continuous cropping system was determined only by 1000 kernel weight, i.e. the yield component that was strongly modified by environmental conditions and the applied production technology. A significant correlation between grain yield and a single yield component could pose a high risk of yield variability (observed in winter wheat grown after winter wheat). The productivity of winter wheat in continuous cropping systems can be improved by sowing cultivars characterized by high kernel weight and introducing cultivation practices that stimulate grain ripening.
Acknowledgements:
The results presented in this paper were obtained as part of a comprehensive study financed by the Polish Ministry of Science and Higher Education (grant No. N310 031 32/167).
References
Adamiak E., Adamiak J., Przybylski R. 2011. Significance of crop rotation on the regulation weed infestation in winter cereals. Progress in Plant Protection, 51 (2): 817-820 [in Polish].
Adamiak J., Adamiak E., Bruderek A. 2009. The effect of protection with fungicides on the yield of winter wheat cultivated in crop rotation and long-term monoculture. Progress in Plant Protection 49 (2): 591-594 [in Polish].
Akanda S.I., Mundt C.C. 1996. Path coefficient analysis of the effects of stripe rust and cultivar mixtures on yield and yield components of winter wheat. Theor Appl Genet, 92: 666-672. Bednarek W., Tkaczyk P., Dresler S. 2009. Yields of winter wheat in dependence on some soil
Budzyński W. 2012. Wheat – common wheat, spelt and durum wheat. Cultivation and utilization. PWRiL, Poznań [in Polish].
Christen O., Sieling K., Hanus H. 1992. The effect of different preceding crops on the development, growth and yield of winter wheat. Eur. J. Agron., 1: 21-28.
Denčić S., Kastori R., Kobiljski B., Duggan B. 2000. Evaluation of grain yield and its components in wheat cultivars and landraces under near optimal and drought conditions. Euphytica, 113: 43-52. Dmowski Z. 1993. The effect of cultivation region, soil and production technology on the yield of rye
and winter wheat. Publishing House of the Wrocław University of Environmental and Life Sciences. Dissertations and Monographs, 114: 1-63 [in Polish].
Donaldson E., Schillinger W.F., Dofing S.M. 2001. Straw production and grain yield relationships in winter wheat. Crop Sci., 41: 100-106.
Gaj R. 2008. Sustainable management of phosphorus in soil and plants in condition of intensive plant production. Fertilizers and fertilization, 33: 1-143 [in Polish].
García del Moral L.F., Rharrabti Y., Villegas D., Royo C. 2003. Evaluation of grain yield and its components in durum wheat under Mediterranean conditions: an ontogenic approach. Agronomy Journal, 92 (2): 266-274.
Gozdowski D., Mądry W. 2008. Characteristics and empirical comparison of simple and complex path analysis in grain yield determination by yield – related traits Part I. Presentation of methods. Biul. IHAR, 249: 109-124 [in Polish].
Hussain D., Chowdbhry A.R., Alam K. 1985. Path coefficient analysis in wheat. Pak J. Agri. Sci., 22 (4): 260-264.
Idźkowska M., Gołaszewski J., Koczowska I., Grabowski S. 1993. Analysis of path coefficients in winter rye (Secalecereale 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].
Khan A.J., Azam F., Ali A., Tariq M., Amin M. 2005. Inter-relationship and path coefficient analysis for biometric traits in drought tolerant wheat (Triticum aestivum L.). Asian J. Plant Sci., 4 (5): 540-543.
Khan N., Naqvi F.N. 2012. Correlation and path coefficient analysis in wheat genotypes under irrigated and non-irrigated conditions. Asian J. Agric. Sci., 4 (5): 346-351.
Khokhar M.I., Hussain M., Anwar J., Muzaffar Iqbal M., Sher Baz Khan, Arif Khan Abdul Qayyum M., Mehmood S. 2010:Correlation and path analysis for yield and yield contributing characters in wheat (Triticum aestivum L.). Acta Agriculturae Serbica, 29: 19-24.
Klepper B., Rickman R., Waldman S., Chevalier P. 1998. The physiological life cycle of wheat: Its use in breeding and crop management. Euphytica, 100: 341-347.
Kozak M. 2011. Analysis of cause-and-effect relationships in agronomy and plant breeding. Biul. IHAR, 259: 3-21 [in Polish].
Kozak M., Mądry W. 2006. Note on field component analysis. Cereal Research Communications, 34 (2-3): 933-940.
Kozak M., Verma M.R. 2009. Multiplicative field component analysis: what does it offer to cereal agronomists and breeders. Plant Soil Environ., 55 (3): 134-138.
Ługowska B., Banaszak Z., Wójcik W., Grzmil W. 2004. The effect of yield components on seed yield of semi-dwarf winter wheat. Biul. IHAR, 231: 5-10 [in Polish].
Majewski E. 2010. Selected production, economic and environmental aspects of crop rotations. Roczniki Nauk Rolniczych, 97 (3): 159-169 [in Polish].
Mądry W., Laudański Z., Kozak M., Rozbicki J. 2003. An empirical comparison of sequential yield component analysis and single path analysis for winter triticale grain yield and its components. Biul. IHAR, 230: 146-156 [in Polish].
Mohsin T., Khan N., Naqvi F.N. 2009. Heritability, phenotypic correlation and path coefficient studies for some agronomic characters in synthetic elite lines of wheat. Journal of Food, Agriculture & Environment, 7 (3-4): 278-282.
Prew R.D., Beane J., Carter N., Church B.M., Dewar A.M., Lacey J., Penny A., Plumb R.T., Thorne G.N., Todd A.D. 1986. Some factors affecting the growth and yield of winter wheat grown as a third cereal with much or negligible take-all. J. Agric. Sci. 107: 639–671.
Rymuza K., Turska E., Wielogórska G., Wyrzykowska M., Bombik A. 2012a. Use of principal component analysis for the assessment of spring wheat characteristics. Acta Sci. Pol., Agricultura, 11 (1): 79-90.
Rymuza K., Turska E., Wielogórska G., Wyrzykowska M., Bombik A. 2012b. 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.
Saleem U., Khalig I., Mahmood T., Rafique M. 2006. Phenotypic and genotypic correlation coefficients yield and yield components in wheat. J. Agric. Res., 44 (1): 1-6.
Seiler G.J., Stafford R.E. 1985. Factor analysis of components of yield in guar. Crop. Sci., 25: 905-908.
Sieling K., Stahl K., Winkelmann C., Christen O. 2005. Growth and yield of winter wheat in the first 3 years of a monoculture under varying N fertilization in NW Germany. Europ. J. Agronomy, 22: 71-84.
Simane B., Struik P.C., Nachit M.M., Peacock J.M. 1993. Ontogenetic analysis of yield components and yield stability of durum wheat in water-limited environments. Euphytica, 71: 211-219. Weber R. 2000. Variations in the yield of selected winter wheat cultivars subject to production
technology and forecrop species. Pam. Puł., 120: 501-509 [in Polish].
Wesołowski M., Dąbek-Gad M., Maziarz P. 2007. The effect of forecrop species and herbicide treatment on the yield of winter wheat. Fragm. Agron., 4: 240-246 [in Polish].
Widdowson F.V., Penny A., Gutteridge R.J., Darby R.J., Hewitt M.V. 1985. Tests of amounts and times of application of nitrogen and sequential sprays of aphicides and fungicides on winter wheat, and the effects of take-all (Gaeumannomyces graminis var. tritici) on two varieties at Saxmundham, Suffolk 1980-3. J. Agric. Sci., 105: 97–122.
Wright S. 1934. The method of path coefficients. Ann. Mathem. Stat., 5: 161-215. Wright S. 1921. Correlation and causation. J. Agric. Res., 20: 557-585.
Yagdi K. 2009. Path coefficient analysis of some yield components in durum wheat (Triticum durum Desf.). Pak. J. Bot., 41 (2): 745-751.
Zečecić V., Knežević D., Mićanović D. 2004. Genetic correlations and path-coefficient analysis of yield and quality components in wheat (Triticum aestivum L.). Genetika, 36 (1): 13-21.
