No abstract

Fertilization is one of the most important components of precision agriculture, ensuring high and stable crop yields. The process of spatial interpolation of soil sample data is recognized as a reliable method of determining the prescription rates for precise fertilization. However, the application of a free open-source geographic information system (GIS) software was often overlooked in the process. In this study, a method of precise fertilization prescription map creation was developed using an open-source GIS software to enable a wider and cheaper availability of its application. The study area covered three independent locations in Osijek-Baranja County. A method was developed for the fertilization of sugar beet with phosphorous pentoxide, but its application is universal with regard to the crop type. An ordinary kriging was determined as an optimal interpolation method for spatial interpolation, with the mean RMSE of 1.8754 and R2of 0.6955. By comparing the precision fertilization prescription rates to a conventional approach, the differences of 4.1 kg ha-1 for Location 1, 15.8 kg ha-1 for Location 2, and 11.2 kg ha-1 for Location 3 were observed. These values indicate a general deficit in soil phosphorous pentoxide, and precise fertilization could ensure its optimal content in the future sowing seasons.

The paper presents the result of a triennial field experiment (2013‒15), aiming to determine the influence of irrigation, nitrogen fertilization, and cultivars, as well as their interactions on the yield and chemical properties of the soybean seeds. Four soybean cultivars (Lucija, Vita, Ika and Tena) of different maturity groups were investigate as a sub‐subplot factor (C). The main plot factor (A - irrigation) resulted in a statistically very significant (P≤0.01) seed yield in all three years, and it was found out by an analysis of variance. The subplot factor (B - nitrogen fertilization) had an impact on the grain yield depending on the research year, while sub‐subplot factor (C-cultivar) significantly affected all examined traits. The factor interactions and their significance varied by the research years. The seed yield achieved in 2013 (3883 kg ha-1) indicated a great importance of all factors’ interaction. The correlations between a seed yield and a protein and oil concentration were determined during the research.

Adverse storage conditions impair the quality of stored grain, contribute to the growth and propagation of the fungi leading to an even greater deterioration of the grain quality. Antifungal biopreparations can help preserve grain quality. Lactic acid bacteria (LAB) and their metabolites with antifungal properties can potentially be used in storage conditions or for the decontamination of stored products. The aim of this study was to determine the antifungal activity of Lactobacillus casei, Lactobacillus brevis and Leuconostoc mesenteroides on the inhibition of mycelial growth of pathogenic fungi Aspergillus niger, Penicillium chrysogenum and Fusarium graminearum. The biocontrol potential of LAB against fungi was tested under laboratory conditions in culture media as an ideal substrate for fungal growth and wheat grains as a model of natural substrate. A liquid culture media (MRS broth) was inoculated with cells or cell-free supernatants (CFS) of each LAB species and the fungal spores. The fungal growth was evaluated by measuring the increase in mycelial biomass after 7, 14, 21 and 28 days of incubation. The second antifungal assay was performed on the wheat grain treated with LAB CFS and inoculated with fungal spores. Biopreparation containing cells or CFS of all LAB species significantly inhibited the fungal growth of P. chrysogenum (mean inhibition 69-75% LAB cells and 80-81% LAB CFS) and F. graminearum (mean inhibition 60-83% LAB cells and 83-88% LAB CFS) in the culture media. In the treatment with wheat grain, the CFS of all the tested LAB species significantly inhibited only the growth of F. graminearum (83-90% mean inhibition). There is inconsistency in the efficiency of LAB preparations when comparing assays. The results indicate that media used in the experiment affect the activity or tolerance of the tested bacteria and fungi. L. casei, L. brevis and L. mesenteroides proved their antifungal properties in the culture media and natural substrate. F. graminearum was the most susceptible, and A. niger was the most tolerant to treatments with LAB cells and LAB CFS. More research is needed to reveal the mode of action of the LAB against phytopathogenic fungi in different conditions for their application on stored grains.

With the emergence of machine learning methods during the past decade, alternatives to conventional geostatistical methods for soil mapping are becoming increasingly more sophisticated. To provide a complete overview of their performance, this study performed cost–benefit analysis of four soil mapping methods based on five criteria: accuracy, processing time, robustness, scalability and applicability. The evaluated methods were ordinary kriging (OK), regression kriging (RK), random forest (RF) and ensemble machine learning (EML) for the prediction of total soil carbon and nitrogen. The results of these mechanisms were objectively standardized using the linear scaling method, and their relative importance was quantified using the analytic hierarchy process (AHP). EML resulted in the highest cost–benefit score of the tested methods, with maximum values of accuracy, robustness and scalability, achieving a 55.6% higher score than the second-ranked RF method. The two geostatistical methods ranked last in the cost–benefit analysis. Despite that, OK could retain its place as the most frequent method for soil mapping in recent studies due to its widespread, user-friendly implementation in GIS software and its univariate character. Further improvement of machine learning methods with regards to computational efficiency could additionally improve their cost–benefit advantage and establish them as the universal standard for soil mapping.