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 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.

In this study, we focus on the mitigation of the negative impact of drought using the application of superabsorbent polymers (SAPs) to seed. One way to monitor drought and quantify its impact on crops in field conditions is the nondestructive measurement of physiological processes of the crops using spectral indexes LAI and PRI during vegetation. Therefore, during 2018 and 2019, the increase in biomass and intensity of photosynthetic activity was monitored, and the effect of the SAPs application on the yield parameters of the sugar beet was evaluated in the trial conditions (control, SAPs) at the end of the vegetation period. Through statistical analysis, the significant impact (α ≤ 0.01) of SAPs application on the values of spectral indexes LAI and PRI as well as root and white sugar yields was found. Although the sugar content difference between SAPs and control conditions was not statistically significant, SAPs had a positive influence on the value of this parameter. It was found through periodic monitoring of spectral indexes during the growing period that the crop in the SAPs condition showed higher values of PRI at the beginning of vegetation, which was caused by the accumulation of moisture in the vicinity of the seed and subsequent faster growth of roots and photosynthetic apparatus. Moreover, the values of LAI were significantly higher (α ≤ 0.01) in the SAPs condition throughout the vegetation period. In the interaction evaluation, we confirmed that in both years the values of LAI were higher in the condition with SAPs compared with the control. In contrast, the PRI values were significantly different across conditions. The interaction of conditions with variety showed that the variety Brian obtained higher values of LAI and PRI in the SAPs condition. The correlation analysis found a positive correlation between spectral indexes LAI:PRI (r = 0.6184**), and between LAI:RY (r = 0.6715**), LAI:WSY (r = 0.5760**), and PRI:RY (r = 0.5038*), which confirms the close relationship between physiological processes in the plant and the size of its yield.

The work summarizes changes, which involved the sugar and sugar beet production in Turkey and in Poland, in the years 1995-2014. Sugar beet area in Turkey has decreased by 7% and in Poland by 48%. Harvest has increased by 51% in Turkey and has remained unchanged in Poland. Sugar beet yield has increased: in Turkey by 63% and in Poland by 97%. 71% farmers have stopped cropping sugar beet, while in Poland this share has been 87%. Mean plantation size has increased in Turkey by over a factor of 2 (compared to 1997), while in Poland it has been by a factor of almost 4. Sugar production in Turkey has dropped by 13% (compared to 1997) and in Poland it has grown by 29%. Three new sugar plants started operation in Turkey in the years 1995-2014. In Poland 58 facilities shut down. Turkey’s securing position of major sugar producer is to be expected, under the conditions of increased pace of production concentration and implementing new production technologies. Withdrawal of sugar production quotas in the EU resulted in increased sugar beet spring sowing in several member states, including Poland. Further developments in 2018 and in the following years shall depend on the profitability of sugar beet crops and of sugar production. Significant influence is also to be expected from the isoglucose competition, production of which is to enjoy quotas withdrawal, as well.