Selection for wheat (Triticum aestivum L.) grain quality is often costly and time-consuming since it requires extensive phenotyping in the last phases of development of new lines and cultivars. The development of high-throughput genotyping in the last decade enabled reliable and rapid predictions of breeding values based only on marker information. Genomic selection (GS) is a method that enables the prediction of breeding values of individuals by simultaneously incorporating all available marker information into a model. The success of GS depends on the obtained prediction accuracy, which is influenced by various molecular, genetic, and phenotypic factors, as well as the factors of the selected statistical model. The objectives of this article are to review research on GS for wheat quality done so far and to highlight the key factors affecting prediction accuracy, in order to suggest the most applicable approach in GS for wheat quality traits.

Drought is a significant constraint in bean production. In this study, we used high-throughput phenotyping methods (chlorophyll fluorescence imaging, multispectral imaging, 3D multispectral scanning) to monitor the development of drought-induced morphological and physiological symptoms at an early stage of development of the common bean. This study aimed to select the plant phenotypic traits which were most sensitive to drought. Plants were grown in an irrigated control (C) and under three drought treatments: D70, D50, and D30 (irrigated with 70, 50, and 30 mL distilled water, respectively). Measurements were performed on five consecutive days, starting on the first day after the onset of treatments (1 DAT–5 DAT), with an additional measurement taken on the eighth day (8 DAT) after the onset of treatments. Earliest detected changes were found at 3 DAT when compared to the control. D30 caused a decrease in leaf area index (of 40%), total leaf area (28%), reflectance in specific green (13%), saturation (9%), and green leaf index (9%), and an increase in the anthocyanin index (23%) and reflectance in blue (7%). The selected phenotypic traits could be used to monitor drought stress and to screen for tolerant genotypes in breeding programs.

Given that global amounts of waste are growing rapidly, it is extremely important to determine what amount of waste will be generated in the near future. Accurate waste forecasting is also important for planning and designing a sustainable municipal solid waste (MSW) management system. For that reason, there is a need to build a model to predict the amount of MSW generated in the near future. Based on previous research, artificial neural networks (ANN) show better results in predicting waste generation compared to other mathematical models. In this research, an ANN model using the iterative algorithm Broyden–Fletcher–Goldfarb–Shanno (BFGS) for the prediction of MSW fractions, based on the socio-demographic characteristics, economic and industrial data obtained in Croatia and summarized data of the member states of EU (EU-27 from 2020), showed good predictive capabilities. The coefficient of determination during the training cycle for the output variables; household and similar waste (HHS), paper and cardboard waste (PCW), wood waste (WW), textile waste (TW), plastic waste (PW) and glass waste (GW) were 0.993; 0.997; 0.999; 0.997; 0.998; and 0.998, respectively, while reduced chi-square, mean bias error, root mean square error, mean percentage error, average absolute relative deviation and sum of squared errors were found low. In this paper, Yoon′s method of interpretation shows the relationships between socio-demographic data and the amount of generated waste. The results indicate that the lowest level of education shows a negative impact on observed waste-types calculations, with a relative impact between −9.889 and −4.467%. The most pronounced positive impact on the calculation of HHS, PCW, WW, TW, PW and GW was observed for year variable, gross domestic product, exports of goods and services, imports of goods and services, wages and salaries, secondary income, arrivals in collective accommodation establishments, overnight stays in collective accommodation establishments and exports of petroleum and petroleum products to partner countries, with a relative influence of 4.063–7.028; 2828–4851; 5240–6197; 5.308–6.341; 4290–4810; 4533–5805; and 4.345–4.493, respectively. The obtained results indicate that the amount of HHS waste at the EU-27 level in 2025 will decrease by approximately 18% compared to the data from 2018. The quantities of other observed recyclable types of waste will increase by 34% for PCW, 310% for WW, 40% for TW, 276% for PW and about 67% for GW. The amount of waste generated provides the basic information needed to plan, operate and optimize the waste management system. It could also help in the transition to an environmentally friendly and economically profitable circular economy. The model created in this research could also help with the system of separate waste collection, which would lead to more efficient recycling and the achievement of the set goals for recycling 55% of municipal waste by 2025.

Landraces represent valuable genetic resources for breeding programmes to produce high-yielding varieties adapted to stressful environmental conditions. Although the common bean (Phaseolus vulgaris L.) is an economically important food legume for direct human consumption worldwide, common bean production in Croatia is based almost exclusively on landraces and there is no common bean breeding program. Information on phaseolin type and results of population structure and genetic diversity obtained by analysis of SSR and SNP markers, in combination with the morphological characterization of 174 accessions of 10 common bean landraces (morphotypes), enabled thorough classification of accessions. The accessions were classified into phaseolin type H1 (“S”) of Mesoamerican origin and phaseolin types H2 (“H” or “C”) and H3 (“T”) of Andean origin. By applying distance- and model-based clustering methods to SSR markers, the accessions were classified into two clusters at K = 2 separating the accessions according to the centres of origin, while at K = 3, the accessions of Andean origin were further classified into two clusters of accessions that differed in phaseolin type (H2 and H3). Using SNP markers, model-based analysis of population structure was performed, the results of which were consistent with those of SSR markers. In addition, 122 accessions were assigned to 14 newly formed true-type morphogenetic groups derived from three different domestication events: (1) Mesoamerican (H1A) (“Biser”, “Kukuruzar”, “Tetovac”, “Trešnjevac”), (2) Andean—indeterminate type (H2B1) (“Dan noć”, “Sivi”, “Puter”, ”Sivi prošarani”, “Trešnjevac”) and (3) Andean—determinate type (H3B2) (“Bijeli”, “Dan noć”, “Puter”, “Trešnjevac”, “Zelenčec”). The rest of the accessions could represent putative hybrids between morphogenetic groups. The differences between the true-type groups of accessions were further analysed based on nine quantitative traits, and the subsets of traits that best distinguish among centres of origin (A: Mesoamerican, B: Andean) and genetic groups (H1A, H2B1, H3B2) were proposed.

Combining experimental studies on grain yield variability with crop model simulations in maize could assist in choosing the optimum maturity group for a certain location, counteracting the effect of climate change. However, studies considering specificities in Southeast Europe are lacking. The objectives were to put various environmental covariates including stress degree days (SDD) into FAO maturity settings to determine the impact of climate change on maize growing in Southeast Europe and to compare trends for grain yields over twenty years of maize experimental and simulation data grouped in five FAO maturity groups (FAO 200–FAO 600). Pre-registration yield trials of maize planted in one location in Croatia grown from 1996 to 2015 were used to determine “potential yield”. Correlation coefficients between 12 climate covariates and grain yield (GY) across the maturity groups revealed the tightest negative associations between SDD and GY that were weakened by later-maturity groups. Similar trends in GY were obtained by both experimental and simulation data, highlighting FAO 600 as a nearly no yield-reducing FAO group over the two decades. Our results indicate that choosing early maize hybrids in Southeast Europe does not seem to be an optimum option in the future, since these hybrids are more sensitive to omnipresent heat stress than late hybrids.