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J. Catalão , “ Computer simulation of wind power systems: power electronics and transient stability analysis ,” in Int. Conf. Power Sys. Trans. , Kyoto , Japan . 2009 . [3] B. Sun , H. Zhengyou , Y. Jia , and K. Liao , “ Small

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Pr. Dam design , Hassania School of Public Works , 2013 . [5] Xu B. , Wang Y. Stability analysis of the Lingshan

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Euphytica 1981 30 835 840 Becker, H.C., Léon, J., 1988. Stability analysis in plant breeding

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, “ Gabion walls and their use ,” Int. J. Adv. Mech. Civil Eng. , vol. 3 , no. 4 , pp. 2394 – 2827 , 2016 . [13] D. I. Mazni , A. Hakam , J. Tanjung , and F. A. Ismail , “ Stability analysis of concrete block retaining wall based on a scaled

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control law and adaptive laws of adaptive controller are developed based on Lyapunov stability analysis to guarantee asymptotic stability of adaptive controlled-system. In the work, design of classic and adaptive synergetic control schemes have been

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control on wind–diesel-photovoltaic hybrid system. The organization of this work is shown in Fig. 1 . Fig. 1. Organization of work Objectives and Contribution: (a) A novel hybrid system is proposed for transient stability analysis on grid connected Wind

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181 186 Becker, H. C., Leon, J. (1988): Stability analysis in plant breeding. Plant Breed. , 101 , 1–23. Leon J

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Cereal Research Communications
Authors:
M. Oyekunle
,
A. Menkir
,
H. Mani
,
G. Olaoye
,
I.S. Usman
,
S.G. Ado
,
U.S. Abdullahi
,
H.O. Ahmed
,
L.B. Hassan
,
R.O. Abdulmalik
, and
H. Abubakar

. Application of AMMI model in the stability analysis of spring wheat in rainfed area . Acta Ecol. Sin. 26 : 3677 – 3684 . Crossa , J. 1990 . Statistical analyses of

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and Plant Sci. 1988 1 1 4 Becker, H. C., Leon, J. (1988): Stability analysis in plant

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In a long-term experiment set up in Martonvásár (N 47°21′, E 18°49′), Hungary in 1960 on a humous loam soil of the chernozem type, the effect of five crop production factors in increasing maize yields was studied in seven treatments. The factors studied were soil cultivation, fertilisation, plant density, variety and weed control. All the factors had a favourable and an unfavourable level. Yield data recorded over 42 years were evaluated using analysis of variance and stability analysis. The highest yield (8.59 t ha −1 ) was obtained when all the production factors were favourable and lowest (2.09 t ha −1 ) when these factors were unfavourable. When only one factor was unfavourable and all the other factors were favourable the following yields were obtained (t ha −1 ): soil tillage: 8.32, fertilisation: 5.21, genotype: 4.98, plant density: 6.31 weed control: 7.01. The crop production factors contributed to the increase in maize yield in the following ratios (%): fertilisation 30.6, variety 32.6, plant density 20.2, weed control 14.2, soil cultivation 2.4. The highest value of the coefficient of variation (CV%) was obtained when all the production factors were at the unfavourable level (45.7%) and when weed control or fertilisation were unfavourable (36.6% and 34.8%, respectively), while the lowest value was recorded when all the factors were favourable (19.5%). The significant treatment × year interaction could be attributed principally to treatments in which weed control, fertilisation, genotype or all the factors were unfavourable. The regression coefficient of linear regression analysis provided a satisfactory characterisation of the stability of the treatments in different environments, while the distance between the straight lines expressed the yield differences between the treatment pairs. The AMMI (Additive Main Effect and Multiplicative Interaction) model proved to be a valuable approach for understanding agronomic treatment × environment interactions and assessing the mean performance and yield stability of treatments.

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