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Madry W.,Warsaw University of Life Sciences | Gacek E.S.,Research Center for Cultivar Testing | Paderewski J.,Warsaw University of Life Sciences | Gozdowski D.,Warsaw University of Life Sciences | Drzazga T.,Maopolska Plant Breeding Company
International Journal of Plant Production | Year: 2011

The objective of the paper was to illustrate using and usefulness of a joint AMMI and cluster analyses to assess the grain yield adaptive response of Polish and foreign 31 winter wheat cultivars in a range of 20 environments (locations) and across 3 years (2005-2007) under integrated crop management, using data obtained in the post-registration variety testing trials (called PDO trials), to identify those entries with specific and wide adaptation. Two-stage combined analysis of variance for data in the three-way GLY classification was carried out according to a mixed model (cultivar and location as fixed factors and years as random factor). GL repeated (across years) interaction effects were modeled by (a) joint regression and (b) additive main effects and multiplicative interaction (AMMI). The thirty one cultivar adaptive responses, expressed by nominal yields based on significant AMMI-1 model, accounting for 27.8% of SS for GL interactions, were divided into six homogenous groups by Ward's method of cluster analysis. Group-mean cultivar adaptive responses indicated clearly the wide adaptation of cultivars in groups 1 and 2 including mostly German and United Kingdom entries and also two Polish ones. Cultivars from group 6, including three Polish cultivars and three foreign ones, were among at most four top-ranking entries at all locations excluding one environment (Wyczechy at Pomerania region). Cultivars from group 3, including seven Polish cultivars and one from United Kingdom and France, showed extremely specific adaptation characterized by nominal yield responses being positively related to GL interaction PC 1 scores of the locations. However, cultivars from group 5, including five Polish ones and a French one were poor adapted to the growing area. Presented the joint AMMI and cluster analyses were effective to distinguish adaptive responses of studied cultivars on the basis of data from PDO trials and could be seen as a better alternative, based more on probability-approached methodology, to common pattern analysis. Source

Studnicki M.,Warsaw University of Life Sciences | Madry W.,Warsaw University of Life Sciences | Noras K.,Warsaw University of Life Sciences | Wojcik-Gront E.,Warsaw University of Life Sciences | Gacek E.,Research Center for Cultivar Testing
Spanish Journal of Agricultural Research | Year: 2016

The main objectives of multi-environmental trials (METs) are to assess cultivar adaptation patterns under different environmental conditions and to investigate genotype by environment (G×E) interactions. Linear mixed models (LMMs) with more complex variance-covariance structures have become recognized and widely used for analyzing METs data. Best practice in METs analysis is to carry out a comparison of competing models with different variance-covariance structures. Improperly chosen variance-covariance structures may lead to biased estimation of means resulting in incorrect conclusions. In this work we focused on adaptive response of cultivars on the environments modeled by the LMMs with different variance-covariance structures. We identified possible limitations of inference when using an inadequate variance-covariance structure. In the presented study we used the dataset on grain yield for 63 winter wheat cultivars, evaluated across 18 locations, during three growing seasons (2008/2009-2010/2011) from the Polish Post-registration Variety Testing System. For the evaluation of variance-covariance structures and the description of cultivars adaptation to environments, we calculated adjusted means for the combination of cultivar and location in models with different variance-covariance structures. We concluded that in order to fully describe cultivars adaptive patterns modelers should use the unrestricted variance-covariance structure. The restricted compound symmetry structure may interfere with proper interpretation of cultivars adaptive patterns. We found, that the factor-analytic structure is also a good tool to describe cultivars reaction on environments, and it can be successfully used in METs data after determining the optimal component number for each dataset. © 2016 INIA. Source

Samborski S.M.,Warsaw University of Life Sciences | Gozdowski D.,Warsaw University of Life Sciences | Walsh O.S.,University of Idaho | Lamb D.W.,University of New England of Australia | And 3 more authors.
Agronomy Journal | Year: 2015

Active optical sensors (AOSs) measure crop reflectance at specific wavelengths and calculate vegetation indices (VIs) that are used to prescribe variable N fertilization. Visual observations of winter wheat (Triticum aestivum L.) plant greenness and density suggest that VI values may be genotype specific. Some sensor systems use correction coefficients to eliminate the effect of genotype on VI values. This study was conducted to assess the effects of winter wheat cultivars and growing conditions on canopy reflectance, as measured by red or amber normalized difference vegetative indices (NDVIs) derived from AOSs. Variations in NDVI values among three wheat cultivars were measured at three growth stages (Zadoks 31, 37, and 65) during 3 yr at three sites in Poland. GreenSeeker Model 505 and Crop Circle ACS-210 sensors were utilized to measure red and amber NDVIs, respectively. Significant (p < 0.05) differences in both forms of NDVI associated with wheat genotypes were observed across years and sites at Zadoks 31, the time when canopy sensing and N fertilization decisions are oft en made. Lack of a genotype × site interaction for both red and amber NDVIs and the presence of a significant genotype × year interaction for both VIs suggested that (i) canopy greenness and density of the same genotype measured at the same growth stage are likely to be stable across different growing conditions, and (ii) NDVI values for a particular genotype tend to vary more across years than across sites. Because developing temporally variable correction coefficients is not practical, we strongly recommend that an in situ calibration (based on in-field or a virtual reference strip) is utilized to normalize NDVI across genotypes, years, and sites. © 2015 by the American Society of Agronomy 5585 Guilford Road, Madison, WI 53711 USA All rights reserved. Source

Bujak H.,Wroclaw University of Environmental and Life Sciences | Tratwal G.,Research Center for Cultivar Testing | Weber R.,Institute of Soil Science and Plant Cultivation | Kaczmarek J.,Wroclaw University of Environmental and Life Sciences | Gacek E.,Research Center for Cultivar Testing
Zemdirbyste | Year: 2013

The study makes use of yields of winter wheat (Triticum aestivum L.) cultivars obtained in a series of Post-Registration Variety Testing System, experiments conducted in 2006-2008 at 12 locations of diverse edaphic and climatic conditions of Western Poland. The experiments were carried out at two intensity levels in two replications. The paper presents an analysis pertaining to the intensive level of cultivation, which differed from the standard cultivation in nitrogen fertilization higher by 40 kg ha-1, full chemical protection against fungal diseases, application of growth regulator and in foliar spray of the plants with multi-nutrient preparation. The experiments were set out in a split-block design. The trials involved the following 23 cultivars of winter wheat: 'Bogatka', 'Dorota', 'Finezja', 'Flair', 'Fregata', 'Kobiera', 'Kris', 'Legenda', 'Mewa', 'Muza', 'Nadobna', 'Nutka', 'Rapsodia', 'Rywalka', 'Sakwa', 'Sukces', 'Smuga', 'Tonacja', 'Trend', 'Turnia', 'Wydma', 'Zawisza', 'Zyta'. The statistical study started with an analysis of the original matrix of correlation between 12 experimental sites, consisting in formation of cores composed of pairs of locations described by the highest correlation coefficients. Next, factor analysis was performed, the aim of which was to diminish the dimensions of the correlation matrix. Based on the Kaiser criterion and the Cattell scree test, factors were selected whose Eigen values exceeded 1. The analysis of the primary correlation matrix and the designated cores for pairs of locations indicate existence of three large subregions: a southern, northern and seaside subregion, as well as of four microregions around Nowa Wieś Ujska, Wyczechy, Krościna Mała and Masłowice. Geographically, the subregions stretch from the south-west in the northeastward direction. The factor analysis has allowed us to divide Western Poland into three subregions: southern, northern and the microregion of Nowa Wieś Ujska, differing with respect to the variability and ranking of yields of the winter wheat cultivars. It has been suggested that when choosing a cultivar, the farmer should take into account both the list of varieties recommended for a given area and their performance at the experimental station within the pertinent subregion, a site which is characterized by soil and climatic conditions similar to those prevailing at the target area. The soil type proved to be the environmental factor of paramount importance in determining winter wheat yields. Source

Bakinowska E.,University of Life Sciences in Poznan | Pilarczyk W.,University of Life Sciences in Poznan | Pilarczyk W.,Research Center for Cultivar Testing | Osiecka A.,Research Center for Cultivar Testing | Wiatr K.,Research Center for Cultivar Testing
Journal of Plant Protection Research | Year: 2012

The logistic model is commonly used for analysis of discrete, multinomial data. Such a model was used for the statistical evaluation of data concerning infection of field pea varieties by downy mildew, in two series of field trials. Each series consisted of experiments performed in locations spread over the whole of Poland in the time period from 2002 to 2005. Varieties cultivated on light soils were compared in the first series, and varieties cultivated on rich soils in the second. The most resistant varieties were identified (Sokolik - light soils, Terno - rich soils) and significant differences among varieties were detected. Estimators of model parameters were found using the Fisher scoring method implemented in logistic glm procedure of the SAS system. Source

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