Stanisław Samborski

Winter triticale grain yield, a comparative study of 15 genotypes

Abstract In the paper, we studied genotypic differences in the process of winter triticale grain yield formation, in terms of yield determination by its components. We applied yield component analysis on four dwarf and 11 traditional (conventional stature) genotypes; an ontogenetic approach was employed to provide the appropriate view of this process. Using the k-means clustering procedure, the genotypes were grouped subject to similar yield structure (i.e., mean values and coefficients of variation of grain yield and its components, first clustering), similar correlation matrices among grain yield and its components (second clustering), and similar results of yield component analysis (third clustering). Based on the analysis, we attempted to provide an overall general view of determining winter triticale grain yield by its components. Thus, we elucidate that the pattern of influencing winter triticale grain yield by its components, as well as the pattern of co-relationships among these traits, cannot be explained by yield structure, and hence these two processes are determined by genotypic factors. The correlation among the traits (grain yield and its components) and the results of yield component analysis, on the other hand, were significantly related in the study, so they have similar genotypic sources.

Does nitrogen uptake affect nitrogen uptake efficiency, or vice versa?

The aim of the paper is to enter into a discussion concerning the title question. In our opinion it is N uptake efficiency that affects N uptake, but not vice versa, mainly because the former is a genotypic characteristic, and as such is not influenced by N uptake. To support the conclusions we also show a similarity between the model used for the problem in question and a yield component model.

Prediction accuracy of selected spatial interpolation methods for soil texture at farm field scale

Abstract Soil texture was examined in four crop fields with areas of 10 to 45 ha located in northern and central Poland. In each field, from 21 to 60 soil samples were collected using stratified sampling. The content (%) of soil particles, i.e., sand, silt and clay, was then evaluated using laboratory methods. The apparent electrical conductivity (ECa) was measured and used as ancillary data for the interpolation of soil texture. The obtained data were used to compare selected spatial interpolation methods according to the accuracy of prediction. The examined methods were evaluated based on the results of cross-validation tests. Two methods of validation were used: leave-one-out cross-validation and validation based on a test set of points, with approximately 30% randomly selected. The smallest root mean square error (RMSE) for the prediction of sand, silt and clay was observed for ordinary cokriging in which ECa was used as a covariate. The other three methods, i.e., inverse distance weighting, radial basis functioning and ordinary kriging, had very similar RMSE values, which were approximately 10% higher compared to ordinary cokriging

Determination of the Most Relevant Soil Properties for the Delineation of Management Zones in Production Fields

The proper delineation of management zones (MZs) is very important in site-specific crop management. The aim of this study was to determine the soil attributes that are most relevant in the delineation process of MZs. Two fields of 22 ha and 45 ha located in northern Poland were examined. The analyses were based on MZs delineated using standardized multiyear yield data and logistic regression, which was applied to select the variables that discriminate the yield-based MZs to the greatest degree. The effects of several variables, including soil chemical and physical properties and topographic attributes, on the discrimination of yield-based MZs were investigated. The sand and organic carbon content in the soil produced the most relevant delineation of MZs. Highly correlated variables should be not included in analyses for MZ delineation because they can strongly affect the results of cluster analyses that are used for the delineation of MZs.

Sensitivity of sensor-based nitrogen rates to selection of within-field calibration strips in winter wheat

Abstract. Active optical sensors (AOSs) are used for in-season variable-rate application of nitrogen (N). The sensors measure crop reflectance expressed as vegetative indices (VIs). These are transformed into N recommendations during on-site calibration of AOSs—‘familiarising’ the sensors with the crop N status of the representative part of a field. The ‘drive-first’ method is often used by growers to calibrate AOSs. Due to large spatial variation of crop N status within fields, it is difficult to identify the most representative sample strip for AOS calibration. Seven site-years were used to evaluate the sensitivity of sensor-based N prescriptions for winter wheat (Triticum aestivum L.) to selection of sample strips for AOS calibration that fall into extreme, very low or very high values of 95th percentiles of amber normalised difference VI (NDVI) values. A Crop Circle ACS-210 sensor was used to collect canopy reflectance values, expressed as amber NDVI, at the beginning of wheat stem elongation. Our study showed that the sample-strip selection significantly affected sensor-based N prescriptions. The drive-first method may result in under- or over-applications of N and in lower N-use efficiency. One way to overcome this problem is to collect whole field NDVI values during pesticide application before sensor-based N application. The NDVI values from the entire field then can be used to choose the most representative sample strips for AOS calibration.

Assessing grain yield and quality traits stability of spring wheat cultivars at different crop management levels

Eleven spring wheat cultivars were compared in terms of the stability of their grain yield and grain quality. The cultivars’ stability was evaluated separately at two different crop management levels – moderate-input management and high-input management. Three stability models were used for the two crop management levels based on a linear mixed model framework with restricted maximum likelihood. The Shukla model was the most appropriate for the evaluation of stability of tested spring wheat cultivars. The thousand-grain weight, starch content, Zeleny sedimentation value and test weight were characterized, and the stability ranking cultivars at moderate-input management level was mostly consistent with the rank of cultivars 24 for high-input management level. For grain yield, grain protein content and wet gluten content, the stability rankings were not consistent. Cultivars ‘Monsun’ and ‘Parabola’ are the most stable cultivars for grain yield in moderate-input management and high-input management, respecti...

Within-Field Variability of Winter Wheat Yield and Grain Quality versus Soil Properties

ABSTRACT Within-field variability in wheat grain yield and its quality always exists in production fields and depends, among other factors, mainly on various soil properties related to nutrients and water availability. The aim of the research was to examine the relationships between selected soil properties such as texture; pH; content of the available phosphorus (P), potassium (K), magnesium (Mg), and organic carbon; and winter wheat grain yield and quality under rainfed conditions. Six crop fields with winter wheat (Triticum aestivum, L.) in three sites located in different regions of Poland were examined during two seasons. The grain yield was mainly determined by the soil texture, and the majority of the chemical soil properties did not have a significant effect on grain yield. The grain quality traits were determined by the examined soil properties to a smaller degree than grain yield. The relationships were not consistent across sites and years.