Winter triticale grain yield, a comparative study of 15 genotypes

Kozak, Marcin; Samborski, Stanisław; Rozbicki, J.; Mądry, W.

doi:10.1080/09064710600931230

Cited by 13 publications

(20 citation statements)

References 10 publications

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“…The results of the present study are in conformity with the findings of Kozak et al (2007) too, who reported that two major yield components are number of ears and number of grains per ear per unit area, and more effort should be put into a good stand shaped by means of agronomic treatments, particularly at tillering and stem elongation growth stages when formation of these yield components takes place.…”

Section: Notessupporting

confidence: 92%

Spring triticale yield formation and nitrogen use efficiency as affected by nitrogen rate and its splitting

Janušauskaitė

2013

Zemdirbyste-Agriculture

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Little information exists on the pattern of nitrogen (N) uptake, remobilization and N use efficacy in spring triticale. The study was aimed to determine the effect of N rates and application regimes on the yield, yield components, as well as to ascertain optimal N fertilization regime for spring triticale. The study was based on the experiment carried out at the Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry in Central Lithuania on an Endocalcari-Endohypogleyic Cambisol (CMg-n-w-can) during the period 2008-2011. The data were analysed using ANOVA method. Nitrogen was applied as basic fertilization (N 60-180 ) shortly before sowing, and N 90 , N 150 rates were split in two or three applications. Grain yield, yield components (ears m -2 , grains ear -1 , productivity g ear -1 , and thousand grain weight), grain protein content and nitrogen use efficiency (NUE) were investigated. The analysis of variance of the data averaged over the four experimental years showed that experimental year significantly (P ≤ 0.01, P ≤ 0.05) influenced all the tested parameters on both basic fertilization backgrounds and N splitting treatments. The influence of N rate on the investigated parameters was significant (P ≤ 0.01, P ≤ 0.05) on basic fertilization backgrounds almost in all the cases; however, the influence of N splitting regime on the yield and yield components was insignificant. N splitting significantly (P ≤ 0.01) influenced protein content and all indices of NUE. Fertilization increased the grain yield by 35.7%, compared with the unfertilized control treatment. The N 90-120 rate was found to be economically and ecologically optimal for spring triticale. It resulted in the highest (4.81-4.92 Mg ha -1 ) grain yield.

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Section: Notessupporting

confidence: 92%

Spring triticale yield formation and nitrogen use efficiency as affected by nitrogen rate and its splitting

Janušauskaitė

2013

Zemdirbyste-Agriculture

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“…The relation between yield and yield components indicated that ears m -2 and grains ear -1 produced the greatest influence on grain yield. Kozak et al (2007) found that grain yield is usually positively correlated with all its components; the spikes m -2 is not correlated with grain spike -1 ; spikes m -2 is not or is negatively correlated with grain weight; and there is no significant correlation between the grain spike -1 and grain weight, or the correlation is positive. Garcia del Moral et al (2003) also found the relationship between grain and its components and path coefficients analysis helped to establish the causality of the relation.…”

Section: Tgw -Thousand Grain Weightmentioning

confidence: 93%

“…Path analysis is a basic method that enables inferences to be drawn about causal structure of data. Path coefficient is a numerical estimate of the causal relationship between two variables in the path analysis (Kozak, Kang, 2006). The yield components have either a direct or indirect effect on grain yield, or both.…”

Section: Introductionmentioning

confidence: 99%

Analysis of grain yield and its components in spring triticale under different N fertilization regimes

Janušauskaitė

2014

Zemdirbyste-Agriculture

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Grain yield is a result of an organized interplay of several yield components, which are highly susceptible to environmental fluctuations. However, there is still a lack of knowledge about the relationship between grain yield and factors governing it. Experiments were conducted during the period 2008-2011 at the Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry in Central Lithuania (55023′50″ N and 23051′40″ E) on an Endocalcari-Endohypogleyic Cambisol (CMg-n-w-can). The correlation coefficients and path coefficient analysis was done: i) to study the interrelationship between spring triticale grain yield and its components under different N fertilization regimes, ii) to establish the impact of meteorological factors on them, iii) to explain the causality of associations between the mentioned parameters through direct and indirect effects. Three nitrogen levels were used: i) without nitrogen (N 0 ), ii) N applied as basic fertilization shortly before sowing (N 60-180 ), iii) N 90 and N 150 rates split in two and three applications. Grain yield, yield components (ears m -2 , grains ear -1 , thousand grain weight) and meteorological factors were investigated. The results demonstrated that the interrelationship between grain yield and its components differed between N fertilization levels. Under single N 60-180 fertilization, grain yield of spring triticale depended on ears m -2 and a thousand grain weight. Under split N 90 , N 150 level the yield depended on ears m -2 . The correlation matrix between spring triticale grain yield and yield components did not reveal significant causality at N 0 level. The character of interaction of grain yield components influenced the grain yield by 17.2% and 28.3% at single N 60-180 and split N 90 , N 150 fertilization levels, respectively. However, such interaction was insignificant at N 0 level. The growth and development of spring triticale responded to meteorological conditions more sensitively at single N 60-180 and split N 90 , N 150 levels than at N 0 level. At all N regimes, the weather conditions caused 72.6-83.5% of ears m -2 , 46.6-94.1% of grains ear -1 and 84.5-92.7% of thousand grain weight data variation. The interaction of all meteorological factors influenced the grain yield by 25. 6% and 40.7% at N 60-180 and split N 90 , N 150 levels, respectively.

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“…The results for the latter cultivar were quite similar though the contribution of mean number of kernels per spike was two times greater than that of kernel weight, number of spikes per m 2 being unimportant. Nevertheless, in other results on the same cultivars, Kozak et al (2007b) obtained fairly different results, in which it was number of spikes per m 2 that was the main component in determining grain yield of cultivar Bogo, contributing to about 50% of yield determination. For cultivar Fidelio this difference was not so visible, but still the first component accounted for 26% of grain yield determination, while in Samborski et al (2005) study this contribution was 0%.…”

mentioning

confidence: 78%

“…One example for this is the above-mentioned research by Kozak et al (2007b), who studied 15 winter triticale genotypes in terms of yield component analysis. From that study it clearly follows that there is a strong influence of genotype on YCA, which means that the results of YCA may greatly vary among genotypes.…”

mentioning

confidence: 99%

Multiplicative yield component analysis: what does it offer to cereal agronomists and breeders

Kozak¹,

Verma²

2009

PLANT SOIL ENVIRON

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Agronomists and plant breeders are very often faced with so-called yield components, which are such plant and crop traits the product of which gives yield. Yield component analysis is a general methodology of analyzing a causal model of how yield components affect yield. In this way the information on the importance of particular yield components is extracted, which can be interesting for agronomists and plant breeders for various reasons. We discuss these reasons in this paper.Yield component analysis was applied in numerous applications, and its theoretical issues were studied quite deeply (see Kozak and Mądry 2006). Still there are some issues to be solved and discusses (for example, methodology for multiplicative yield components that develop in sequential order during ontogenesis, extracting direct and indirect effects from the analysis; see Kozak andMądry 2006 andKozak et al. 2007a). Here we will deal with one of such issues, namely how the results of yield component analysis should be used, and what kind of conclusions might be drawn based on them. Despite the fundamental importance of this issue, no account of it might be found in the literature. Applications of yield component analysis are diverse and so are the conclusions that can be drawn based on them; some conclusions are of critical importance for a given crop species, while others seem to be drawn without appropriate and desirable consideration.First, let us formally define what yield component analysis is. A yield-component model is one in which components are traits the product of which gives yield, that is (e.g., Fraser and Eaton 1983, Spaarnaij and Bos 1993, Piepho 1995), (1) where: Y stands for yield and X i , i = 1, …, k, for the i th component Owing to the multiplicative character, the model (1) should be called the multiplicative yield-component model to distinguish it from an additive yield-component model ( Jolliffe and Courtney 1984). For simplicity, henceforth we will call it yield-component model, and the whole methodology yield component analysis (instead of multi- INFORMATION

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Winter triticale grain yield, a comparative study of 15 genotypes

Cited by 13 publications

References 10 publications

Spring triticale yield formation and nitrogen use efficiency as affected by nitrogen rate and its splitting

Spring triticale yield formation and nitrogen use efficiency as affected by nitrogen rate and its splitting

Analysis of grain yield and its components in spring triticale under different N fertilization regimes

Multiplicative yield component analysis: what does it offer to cereal agronomists and breeders

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