Courgette is considered as a low-calorie vegetable with health-promoting properties. However, scientific publications focused on the profile and content of bioactive compounds in courgette, as well as the potential fruit quality modulating factors, are rare. Due to the high adaptability of courgette to weather and agronomic conditions, it is produced on a global scale. The aim of this study was to analyse the impact of organic versus conventional agronomic practices on the concentration of selected antioxidants in courgette fruits. Fruits of two courgette varieties (Astra Polka and Nimba) produced in an organic and conventional system were tested by high performance liquid chromatography (HPLC) to determine the content of polyphenols (flavonoids and phenolic acids), carotenoids, chlorophylls, and vitamin C. Organic courgette fruits were characterised by their significantly higher content of phenolic acids and flavonoids when compared to the conventionally grown fruit. The organic cultivation might be a good method to increase concentration of bioactive compounds with antioxidant properties in courgette fruits. Nevertheless, the identified trends should be further confirmed, with attention paid to other courgette varieties, as well as to the potential interactions between the plant genotype, agronomic system and the location-specific growing conditions.

The ongoing climate change with increasingly frequent, prolonged drought during the vegetation period is a significant factor affecting production of field crops, including durum wheat (Triticum durum Desf.). One of the approaches to effectively protect plants from drought stress is the foliar application of bioactive substances and selection of appropriate genetic material for specific location conditions. In this study, the impacts of brown seaweed based and humic substance-based biostimulants were researched. The positive impact of bioactive substances on grain yield has been reported in many studies. However, the impact on quality components is questionable and not well investigated. In this study, a highly significant (α < 0.01) positive impact of bioactive substances on grain yield was confirmed. The highest grain yield was observed on the fertilized variant with humic substances (4.03 t ha−1). When compared to control, there was a high statistically significant difference. The biofertilization impact on quality components was weakly positive in most cases, although without statistical significance (α > 0.05). The study included evaluating the interactions biofertilization–weather conditions (BW) and biofertilization–variety (BV). According to the ANOVA results, a highly significant impact in BW on grain yield was found, and in BV, a highly significant impact on protein content, falling number, and gluten content (α < 0.01) and significant impact on grain yield and vitreousness were found (α < 0.05). Correlation analysis among the monitored parameters was performed. The results that we obtained from the multi-annual field research may contribute to sustainable arable farming in areas with a lack of rainfall during vegetation. By foliar application of bioactive substances, we achieved a significant increase in the yield of durum wheat while maintaining or increasing the quality parameters of the grain.

The yield and yield quality of sugar from the sugar beet (Beta vulgaris L.) and are determined by genotype, environment and crop management. This study was aimed at analyzing the stability of white sugar yield and the adaptation of cultivars based on 36 modern sugar beet cultivars under different environmental conditions. The compatibility of sugar beet cultivars’ rankings between the three growing seasons and between the 11 examined locations was assessed. In addition, an attempt was made to group environments to create mega-environments. From among the 11 examined locations, four mega-environments were distinguished on the basis of the compatibility of the white sugar yield rankings. The assessment of the adaptation of cultivars and the determination of mega-environments was carried out using GGE (genotype main effects plus genotype environment interaction effects) biplots and confirmed by the Spearman rank correlation test performed for cultivars between locations. The cultivars studied were characterized by a high stability of white sugar yield in the considered growing seasons. The high compliance of the sugar yield rankings between the years contributes to a more effective recommendation of cultivars.

The effect of sugar beet seed (primed and non-primed) on field emergence and root yield of sugar beet was examined. The experiment was realized in the years 2012–2014 at an Experiment Field Station of Warsaw University of Life Sciences – SGGW Faculty of Agriculture and Biology in Skierniewice (51°97'N, 20°19'E) in Poland. The experimental factor was diversified seed material of the same cultivar of sugar beet – typical seeds, traditionally prepared for sowing (non-primed seeds) and seeds before sowing, subjected to the process of priming. On average for the three years of the study, no significant effect of seed priming on the field emergence was found. On the other hand, the sugar beet emergence on plots with primed seeds was faster, more even and uniform. Seed priming, on average for the three years of the study, significantly increased the mean root mass during harvest. In contrast, priming the seeds did not cause an increase in the final plant density. No significant effect of seed priming on root yield was found, both on average for the studied period and in particular years of the study.

While evaluating plant response to biotic or abiotic stress and genotype–environment interactions and searching causes of yield gap, very often are observed data with non‐normal distributions. One of the commonly encountered types of variables with a non‐normal distribution is count data. Count data are defined as the type of observations which have a positive, non‐zero, integer value. The selection of appropriate probability distributions and model types is very important due to the risk of estimating the variance incorrectly—the phenomenon of over‐dispersion. Increasingly, biologists and agronomists have been using methods based on generalized linear models. However, sometimes, when including count data, they are not aware of or disregard their over‐dispersion. One of the solutions for over‐dispersed count data is to use probability distributions and model types which assume a more flexible mean and variance relationship. Thus, the aim of this study is to present various ways of assessing over‐dispersion. Additionally, we present alternative distributions and discuss other approaches to solve the problem of over‐dispersion in count data sets. As examples in this study are used real data sets from different agricultural experiments. In our study, in one out of the two data sets used, this phenomenon occurred. Thus, in the analysis of count data, instead of using default distribution (usually Poisson distribution), other distributions should be considered because of the possible occurrence of over‐dispersion. We also observed that there is not one universal distribution to use and each data set might need a separate assessment to choose its distribution. For an efficient and proper count data analysis, with potential over‐dispersion, it is important to explore several options, i.e. evaluate models with an alternative to Poisson probability distributions and then make an informed choice.