• Soybean germplasm evaluation: Nutritive grain quality under climate change conditions
• Genotypic selection of peas under climate change
• Innovative strategies for early stress detection and increasing maize tolerance to cold stress
• Legumes as a source of biologically active phytochemicals
• Breeding development of alfalfa and red clover germplasm adapted to climate changes
• Predictive modelling of agronomic traits in maize using modern technologies
• Assessment of quantitative traits in small grain cereals germplasm considering climate change
• The role of genetic specificity, plant growth regulators, and biostimulants in increasing the resistance to abiotic and biotic stress, yield, and quality of sour cherry fruit
• The influence of climate changes on the expression of the most important agronomic, physiological and biochemical traits of sunflower under conditions of different plant density
• Influence of combined abiotic stresses and climatic oscillations on production and quality of seed
• Water, soil and cultivar – fundamental elements of sustainable agriculture in climate change

Full project title

Innovative strategies for early stress detection and increasing maize tolerance to cold stress

Project acronym

CSI-Maize

Lead researcher

Maja Mazur, PhD, Scientific Associate

Project team members

1. Antun Jambrović, PhD, Senior Scientific Advisor, Agricultural Institute Osijek
2. Tatjana Ledenčan, PhD, Senior Scientific Advisor, Agricultural Institute Osijek
3. Andrija Brkić, PhD, Senior Scientific Associate, Agricultural Institute Osijek
4. Vlatko Galić, Scientific Assosciate, Agricultural Institute Osijek
5. Mirna Volenik, PhD, Technologist, Agricultural Institute Osijek
6. Lovro Vukadinović, MSc, Assistant/PhD student, Agricultural Institute Osijek

Project financing source

National Recovery and Resilience Plan 2021-2026 within the framework of the Program Agreement with the Ministry of Science, Education and Youth

Project budget

47.250,00 EUR

Project summary

Frequent occurrences of extreme meteorological conditions during vegetation pose a challenge in maize breeding and cultivation. Climate change necessitates the shifting of planting dates earlier in the spring, which may expose maize to adverse effects caused by low temperatures during or shortly after planting, during germination, and in early growth stages. Therefore, it is essential to investigate maize tolerance to cold stress, which is crucial for achieving high productivity and better adaptation to weather conditions. Special emphasis is placed on the impact of earlier short (priming) stress and memory on the physiological response of plants to repeated stress in early developmental stages as a new approach to examining tolerance to abiotic stress. Additionally, the project aims to apply machine learning models in detecting stress caused by low temperatures, representing an innovative approach in analyzing datasets and represents one of the initial steps in developing and implementing machine learning models in scientific and breeding work at the Agricultural Institute Osijek. To achieve the set goal, research will be conducted under controlled conditions on inbred maize lines. The project results will lead to new insights into the impact of low temperatures on the growth and development of maize lines during early vegetative stages. Implementation of the project will significantly contribute to the recognition and excellence of the Agricultural Institute Osijek and strengthen collaboration within the Department of Maize Breeding and Genetics by enhancing human capacity for scientific work.

Project aims

The main objective of the project is to investigate tolerance of maize lines to cold stress, with a particular emphasis on the impact of earlier short (priming) stress and memory on the physiological response of plants to repeated stress in early developmental stages, as well as the application of machine learning models in detecting stress caused by low temperatures.

Additional objectives include establishing a research team and enhancing the skills of its members, as well as disseminating research results through the publication of 3 scientific papers categorized as A1 and indexed in the WoS database, along with presentations at international conferences.