Grain yield data of winter wheat (Triticum aestivum L.) trials in Poland had a four‐way factorial design of 24 genotypes by 20 locations by two managements by 3 yr. The experimental design had genotype–management strip plots with two replications for genotypes, with somewhat more genotypes than the 24 having no missing data. The research objectives were to extend additive main effects and multiplicative interactions (AMMI) analysis from two‐way to higher‐way datasets to reduce spurious complexity originating from noise, delineate wheat mega‐environments in Poland, and make genotype recommendations within each mega‐environment. Statistical analysis began with adjusting the yield estimates using the strip‐plot experimental design and then combining the results in a genotype × location × management × year (GLMY) table. This table was analyzed by a four‐way ANOVA mixed model. Next the GLMY dataset was reorganized into a two‐way classification, namely a genotype × environment (G × E) dataset, where the 120 environments were defined as combinations of location, management, and year. This two‐way dataset was analyzed by AMMI, with practical limitations of working with only a few mega‐environments focusing interest on the AMMI1 member of this model family. The first principal component had an evident geographical interpretation, contrasting northeast Poland (colder climate) and southwest Poland (warmer climate). Suitable genotypes were recommended within each of these two mega‐environments. The methodological significance of this paper is the extension of AMMI analysis from the customary two‐way G × E datasets to higher‐way datasets, such as the present four‐way GLMY dataset.

The response of some photosynthetic parameters (CO 2 assimilation, transpiration rate, stomatal conductance, intercellular CO 2 concentration, water-use efficiency, and chlorophyll content), shoot development, and the morphological features of the root system to differentiated conditions of nitrogen supply was tested in festulolium (Festulolium braunii K. Richert A. Camus) varieties (Felopa and Sulino). Nitrogen fertilization with no nitrogen added [0 g(N)], single dosage [0.23 g(N)], and double dosage [0.46 g(N)] per pot and per year was applied. Lack of nitrogen resulted in formation of longer and finer roots and lowered chlorophyll content, CO 2 assimilation, and water-use efficiency, resulting in lower dry matter accumulation. Application of both dosages of nitrogen resulted in improved aboveground features, while root features were enhanced without nitrogen fertilization. Dependence between physiological parameters and morphological traits was significant and positively correlated in the case of the aboveground parts of plants and negatively correlated to the belowground parts.

A proper understanding of cultivar adaptation to different environments is of great relevance in agronomy and plant breeding. As wheat is the most important crop in Poland, with a total of about 22% of the total sown area, the study of its performance in environments with different productivity levels for consequent cultivar recommendation is of major importance. In this paper, we assess the relative performance of winter wheat cultivars in environments with different productivity and propose a method for cultivar recommendation, by considering the information of environmental conditions and drought stress. This is performed in the following steps: (1) calculation of expected wheat productivity, depending on environmental factors, (2) calculation of relative productivity of cultivars in the environments, and (3) recommendation of cultivars of a specific type and range of adaptation. Soil and weather conditions were confirmed as the most important factors affecting winter wheat yield. The weather factors should be considered rather in shorter (e.g., 10 day) than longer (e.g., 60 day) time periods and in relation to growth stages. The ANCOVA model with genotype and management intensity as fixed factors, and soil and weather parameters as covariates was proposed to assess the expected wheat productivity in particular environments and the expected performance of each genotype (cultivar). The recommendation of cultivars for locations of specified productivity was proposed based on the difference between the expected cultivar yield and the mean wheat productivity, and compared with the Polish official cultivar recommendation list.

In the majority of research on models for multienvironment trials, evaluation of the prediction accuracy of models with different variance–covariance structures is focused on predicting the means for cultivar × location (C × L) combinations. In cultivar recommendation, however, it is often more important to evaluate prediction accuracy in modeling cultivar × region (C × R) combinations. The aim of this paper was to evaluate the prediction accuracy of two single‐stage linear mixed models (LMMs) with different variance–covariance structures, emphasizing factor‐analytic (FA) structures. One of the models was used to predict means for C × L combinations and the other one for C × R combinations. Additionally, we assessed implications of model choice for consistency in cultivar ranking. The data used for the analysis performed in this study were obtained from 42 locations and 47 winter wheat (Triticum aestivum L.) cultivars during three growing seasons within the Polish Post‐Registration Variety Testing System. The data were assigned to six agroecological regions. For evaluating the prediction accuracy of LMMs, we used cross validation based on a modified equation for the mean squared error of prediction. Yield rankings modeled by different variance–covariance structures were compared by Spearman's rank correlation. For each model with a different variance–covariance structure, we calculated the correlation coefficients between estimated and observed data. The model with the highest predictability for means of the C × L classification was the FA(2) variance–covariance structure. In the case of C × R means, the compound symmetry structure fared favorably, and using more complex variance–covariance structures (including heterogeneous covariances) did not increase prediction accuracy.

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Constrained principal component analysis (C‐PCA) describes a two‐dimensional data table and assumes a linear dependence of the principal component scores on known additional parameters (i.e., explanatory matrices). In this study, we used C‐PCA to generalize the additive main effects and multiplicative interaction (AMMI) model and propose the constrained AMMI model. The constrained AMMI model is interpreted and illustrated when (i) only the environmental principal component parameters have an explanatory data matrix, (ii) only the genotype principal component parameters have an explanatory data matrix, and (iii) both types of parameters have explanatory data matrices. The cross‐validation procedure is adapted for model diagnosis. Data for winter wheat (Triticum aestivum L.) genotype × location × management × year grain yield, recorded in Poland from multienvironment trials conducted in the post‐registration variety testing system, were analyzed and used for model comparison.

Traditional husbandry fostered rich semi-open oakwood communities composed of forest and non-forest species. In the eastern Carpathian region, silvo-pastoralism was commonplace by the mid-1900s. This study aimed to determine the state of the preservation of the ecotonal character of grassland-woodland interfaces in formerly pastured cultural landscapes of SE-Polish Carpathian foothills and W-Ukrainian Ciscarpathia in the context of land-use change. In the first region, despite the long-lasting history of forest grazing amongst mainly arable land, the post-WWII collapse of husbandry and the imposed ban on forest grazing, has led to swift development of dense undergrowth and establishment of impermeable ecological woodland-open habitat barrier. As a result, former silvo-pastoral oakwoods developed the features of the Tilio-Carpinentum forest community although some forest species have not yet moved in due to their poor dispersibility. The much younger oakwoods in the Ukrainian study region are remnants of the sparsely treed grasslands, some of which had been ploughed in the mid 20th century. Their semi-open canopy structure, maintained through repetitive grass burning, contributes to the communities ecotonal character, but without regular livestock-led plant “spill-over” from the grassland, the oakwoods remain species-poor. The restoration of species-rich semi-open oak woods requires “unsealing” the forest-grassland interface, reducing the degree of canopy closure, and opening that zone up to extensive grazing—an important seed dispersal vector.