Field trials with 11 maize hybrids (9 newly released and two standards) of the FAO maturity groups 300-600 were sown in four locations. The aim of the work was to recommend hybrids for individual location based on their reaction to different agroecological conditions, as well as to evaluate the possibility of replacing later hybrids with hybrids of earlier maturity groups, with certain changes in agrotechnics. ZP hybrids of the latest generation showed high grain yield potential and stability, as well as wide adaptability to different ecological conditions, degree of soil fertility and application of agrotechnical measures. A significantly higher grain yield of all hybrids compared to other locations was at the Valjevo location, that is, the location with the highest amount and favorable distribution of precipitation during the growing season. In these trials, maize hybrids of different FAO maturity groups were grown with the same number of plants per unit area. For maize hybrids with shorter vegetation, which are characterized by a lower plant height, a greater number of plants per unit area is recommended, compared to hybrids with a longer growing season. With changes in agrotechnics in this direction, along with the advantages of earlier hybrids, which are seen in avoiding critical periods for water, the grain yield of earliy hybrids could reach the yield level of later hybrids.
The growth and metabolism of plants, especially on acidic soils, largely depend on the concentration of cobalt (Co) in the soil, i.e. the rhizosphere. An optimal supply of cobalt is essential for N2 fixation of Rhizobium bacteria that are in symbiotic relationships with leguminous plants, influencing their better growth and supplying them with nitrogen. When there is a lack of Co in the plant, the organic production of legumes falls. Indirectly or directly, Co also affects other metabolic processes in plants. The aim of the work was to analyze the importance of optimal provision of forage legumes with cobalt for obtaining high and quality yields of forage and seeds.
Weeds are one of the most important factors affecting agricultural production. Environmental pollution caused by the application of herbicides over the entire agricultural land surface is becoming more and more obvious. Accurately distinguishing crops from weeds by machines and achieving precise treatment of only weed species is one possibility to reduce the use of herbicides. However, precise treatment depends on the precise identification and location of weeds and cultivated plants. The aim of the work was to describe and point out the importance of deep learning models for the detection and classification of weeds, in order to enhance their application in real conditions.
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