Cilj ovoga istraživanja bio je biološkim testom i uobičajenim fizikalno-kemijskim ana-lizama utvrditi pogodnost komposta i vermikomposta za upotrebu kao komponenata supstrata za uzgoj presadnica četiriju različitih biljnih vrsta (kres salata — Lapidium sativum L., krastavac — Cucumis sativus L., ječam — Hordeum vulgare L. i pšenoraž — xTriticosecale Wittmack). Dodatni cilj bio je utvrditi hoće li se dodatkom različitih koncentracija amonijskoga dušika (400 i 600 mg/L NH4-N) postići fitostimulativni ili fitotoksični učinak na istraživane testne biljke. Osnovna hipoteza bila je da postoji razlika u pogodnosti uzgoja različitih presadnica na kompostu, vermikompostu i njihovim smjesama s komercijalnim supstratom te da postoji razlika u pogodnosti testnih biljaka za evaluaciju kvalitete komposta. Istraživani kompost i vermikompost proizvedeni su iz biljnih ostataka nakon održavanja i uređivanja zelenih javnih povr-šina. Analizirani kompost i vermikompost su stabilna i zrela organska gnojiva s povi-šenim pH vrijednostima i specifičnom gustoćom. Snižavanje pH vrijednosti i nasipne gustoće može se postići miješanjem s kiselim tresetnim supstratom. Utvrđene su značajne razlike između testnih biljaka, pri čemu je ječam bio dominantan u pogledu visine i nadzemne mase, a krastavac je imao nešto duži korijen. S druge strane, kres salata bila je značajno inferiorna glede navedenih svojstava. Istraživani kompost i vermikompost mogu uspješno poslužiti kao komponenta za smjesu s komercijal-nim supstratom u omjeru 1:1, to jest kao zamjena 50 % komercijalnoga supstrata. Zalijevanje presadnica otopinama amonijskoga dušika (otopine amonijeva karbona-ta) povećalo je nadzemnu masu i visinu ječma i pšenoraži, a učinak na masu i visinu krastavca i kres salate nije bio konzistentan. Suprotan je učinak na duljinu korijena, uz redukciju korijena krastavca i pšenoraži, a bez značajnoga učinka na korijen ječma i kres salate. U biološkim testovima trebalo bi se koristiti ječmom i kres sala-tom, a u nekim slučajevima i krastavcem, dok se nije potrebno koristiti pšenoražju ako se koristimo ječmom kao testnom biljkom. Ječam je najpogodnija testna biljka za utvrđivanje pozitivnoga učinka otopine amonijaka, a krastavac je najpogodniji za testiranje niskih (nedovoljnih) količina amonijskoga oblika dušika. Kres salata najpogodnija je biljna vrsta za utvrđivanje fitotoksičnosti supstrata.

A research on the influence of a sowing date on the soybean yield and the yield of its components was carried out at the Experimental Site of the Faculty of Agriculture of the University of Zagreb during 2019 and 2020. The study involved two soybean varieties, the AFZG Ana (maturity group 0) and Gabriela (maturity group 00-0), sown on the two sowing dates (i.e., an optimal and a late one) and three sowing rates: 40, 65, and 90 germinating seeds m-2. The sowing time did not have a significant effect on the soybean seeds’ yield and the yield of their components. In both research years, the Gabriela variety achieved a higher seed yield when compared to the AFZG Ana variety, which is a result of a higher number of nodes on the main stem, number of fertile nodes on the main stem, number of pods, number of seeds, and the seed weight per plant. The Gabriela variety achieved a significantly higher harvest index in both years. The seed yield increased with an increasing sowing density up to 65 seeds m-2. However, the differences were statistically significant only in 2019. The values of the yield components decreased with an increasing sowing density.

Wheat production faces several challenges, including climate change, expensive inputs, rising market demand, and environmental pollution, which necessitates alternative solutions. This study aimed to analyze the impact of applying the antifungal chemical protection and biostimulators on the winter wheat. The field experiment was set up during the 2022–23 growing season according to the RCBD in four replicates on the surface area of the Agricultural Institute Osijek. In total, three treatments (control, antifungal chemical protection, and antifungal chemical protection + biostimulator) and six varieties of wheat origin from Croatia (Žitarka, Golubica, and Felix) and Poland (Venecja, Plejada, and Opoka) were applied, and a total of sixteen properties were analyzed. The temperatures and precipitation data during the vegetation season were monitored. According to the analysis of variance, variety was significant for fourteen traits, treatment for six traits, and their interaction for all sixteen traits. Generally, average wheat yield was low (4.60 t ha-1) but the quality, in context of protein content, was quite high (14.8%). The biostimulator had a positive effect on the yield, kernel number per ear, hectoliter mass and wet gluten/protein ratio. Varieties originating from Poland showed slightly higher values of agro-morphological properties while Croatian varieties showed slightly higher values of grain quality.

A high–quality DNA has crucial importance for enzymatic applications in plant molecular biology, including agricultural research. The researchers often need to store the extracted DNA, which has to be suitable for the downstream enzymatic applications, such as a real–time PCR, even after months and years. Storing the DNA as a precipitate in ethanol at –80°C is a commonly used method of preservation. The aim of this research was to determine whether the same was applicable to a temperature of –20°C. In this study, a high-quality DNA from soybean and maize seed was extracted by the widely used CTAB method. The DNA samples were stored at three regimes: water-diluted at +4°C, water-diluted at –20°C, and an undiluted DNA in ethanol at –20°C. The concentration and yield were measured using a spectrophotometer. The impurities and fragmentation were estimated on agarose gel by electrophoresis. Additionally, the DNA was assessed amplifying by the real–time PCR. The quantity and quality of the extracted DNA were assessed prior and subsequent to an annual storage. The results indicated that ethanol provided excellent protection for a short– term DNA storage at –20°C and probably for a long–term storage due to a reduced water content and slightly basic conditions. Thus, such a storage regime could be an appropriate solution when other options are not available.

This study’s main objectives were (i) to determine the stability of oat genotypes for the grain yield (t ha-1), (ii) to investigate the relationship of stability parameters with the grain yield in the analyzed set of genotypes, and (iii) to identify the high-yielding, stable, and adaptive oat genotypes for breeding purposes. The study was conducted during the 2015-22 period and involved fourteen winter oat genotypes maintained in the Bulgarian Seed Gene Bank. The trial was laid out in a randomized block design of variants in three replications and an experimental plot size of 10 m2. Sixteen grain yield stability parameters were determined. The AMMI stability value (ASV), the yield stability index (YSI), and the genotype selection index (GSI) were also calculated. A year and genotype x year interaction had an almost equally dominant effect on the grain yield per hectare. The Spearman’s rank correlation coefficients indicated that grain yield had significant positive associations with Thennarasu’s non-parametric statistics—the NP(2), NP(3), and NP(4), Kang’s rank-sum (KR), and the YSI. The yield stability parameters estimated the G11, G12, G13, and the G1 genotypes as the most stable. The ASV identified the G14, G8, and the G12 as the most stable genotypes, while the YSI detected the G14, G12, and G11, respectively. The GSI classified the G14, G12, G8, and the G11 as genotypes with the broadest adaptability to adverse climatic conditions. Thus, they could serve as a source material in winter oat breeding programs.

The three–year trials of the impact of the planting technology on the maize yield were carried out at the trial sites Jakšić and Tenja. The KWS Intelligens and KWS Kollegas hybrids were used in the research, planted at a standard row spacing of 70 cm, and in the twin rows with a spacing of 48 × 22 cm. In 2020, the highest average grain yield at the site Jakšić in standard planting, with the KWS Intelligens hybrid, was 12.064 t ha–1, and in the twin row planting an increase in yield of +4.17% was recorded. The KWS Kollegas hybrid had the highest difference in grain yield between a standard and twin row planting in 2021 of +7.52%, in favor of the twin row. At the site Tenja, slightly higher grain yields were recorded in all three years of research. The major difference in yield was recorded in 2020 with the hybrid KWS Kollegas, of +8.22%, or 1.115 t more grain, when planting in the twin rows in comparison with a standard planting. The statistical tests indicate that planting in the twin rows at both trial sites obtained statistically significant results in all three years of research concerning the grain yield ha–1, grain weight per ear, and grain moisture content.

Maize (Zea mays) is one of the most important crops in the world, with nitrogen (N) fertilization being the most expensive input in its production. Based on a favorable variability, production costs can be reduced by the means of selection for nitrogen use efficiency (NUE). This study aims to analyze the variability in the collection of maize lines in terms of nitrogen use efficiency (NUE) and to identify the loci associated with the yield and grain moisture. The Agricultural Institute Osijek diversity panel comprises of 1,346 maize inbred lines genes with 56,000 SNP (Single Nucleotide Polymorphism) markers. The experiment was carried out with 109 selected inbreds, based on their performance in the long–term trials. The selected inbreds were testcrossed to a common tester from the Iodent heterotic group. The experiments in three nitrogen environments were carried out in 2023: N0 (no nitrogen fertilization), N69 (low nitrogen supply), and Nfull (sufficient nitrogen supply). Association mapping revealed varying associations between the SNPs and the yield/moisture under different nitrogen treatments, highlighting the influence of nitrogen stress on genetic mechanisms governing these traits. The research contributes valuable insights into maize adaptability to nitrogen levels, aiding in the development of strategies for stable and high–yield maize production.

This research aspires to demonstrate how the stage of lactation influences the content of macroelements in the Lacaune sheep milk. The research was conducted with 30 Lacaune sheep in their fourth lactation during the early (60 days), middle (120 days), and late lactation stages (180 days). The macroelement concentration (Ca, K, Na, and Mg) of the Lacaune sheep milk was determined by the ICP. As lactation progressed, a significant increase in the concentrations of Mg and Na and a significant decrease in K were observed in the Lacaune sheep milk. A stability of Ca and P concentrations during lactation was demonstrated by the lack of significant variation in the Lacaune sheep milk, based on the stage of lactation. A significant positive correlation was detected between Ca:Mg, Ca:P, and K:P, as well as a significant negative correlation between Mg:K and K:Na. The obtained results prove a significant effect of lactation stage on the content of most macroelements in the Lacaune sheep milk, except Ca and P, which should be included in the research scheme.

To evaluate the potential of a machine-learning approach in the assessment of quantitative changes in the tractor diesel-engine oil during exploitation, this study aspired to propose a machine-learning regression method to reduce the frequency of expensive and time–consuming engine oil sampling. The input engine-oil datasets with fresh engine oil (Samples A) and with the engine oil subsequent to 250 working hours (Samples B) were sampled for twelve elements in a two–year exploitation study at the Belje company. The field data collection was performed having deployed six heavy, four–wheel-drive FENDT 930 Vario agricultural tractors, each monitored for 1,500 working hours, during which an engine oil was sampled every 250 working hours. The evaluated machine-learning prediction methods, based on a tenfold cross–validation, achieved a moderately high prediction accuracy, with a slightly higher coefficient of determination (R2), in the range of 0.51–0.73, for the Samples B, than those in the range of 0.49–0.64 for the Samples A. These results strongly suggest that none of the machine learning methods constantly achieved high prediction accuracy and that the selection of optimal machine-learning models should be mandatory, having also confirmed a high potential of machine-learning methods in the detection of quantitative changes in tractor diesel-engine oil during exploitation.