The project was implemented in 2023–2025 at the Lithuanian Research Centre for Agriculture and Forestry with the aim of investigating the relationships between crop development and agroclimatic factors, and developing tools to improve farm management efficiency. The main objective was to create an algorithm enabling accurate prediction of crop growth stages (according to the BBCH scale) based on agroclimatic indicators such as air and soil temperature, moisture, and precipitation. Methodology. The study was based on extensive field experiments involving major crops grown in Lithuania, including wheat, barley, rye, rapeseed, maize, oats, beans, and peas. Both conventional and organic farming systems were included, along with different varieties and soil types. Crop growth stages were recorded according to the BBCH scale, and their dynamics were analysed using cumulative growing degree days (GDD). Meteorological data, soil temperature and moisture (via SoilNet), and the standardized precipitation index (SPI) were also evaluated. Data were processed using statistical and mathematical modelling, including the Gompertz growth model. Key results. The study demonstrated a strong relationship between crop development and cumulative GDD, explaining approximately 93–97% of phenological variation. The most intensive development phase occurred within a range of about 400–1800 °C (depending on crop type). Total GDD required to reach maturity varied significantly between crops, with maize requiring about 2400–2700 °C and spring cereals about 1800–2000 °C. Weather conditions significantly influenced growth duration. The year 2023 was relatively dry, 2024 warm, and 2025 wetter and cooler. Drier conditions tended to accelerate plant development but did not always result in higher yields, while wetter conditions slowed development. Crop yields varied widely depending on species and environmental conditions, for example: oats 0.68–3.9 t/ha, maize 6.2–9.7 t/ha, wheat 3.0–4.9 t/ha, barley 1.0–4.9 t/ha, rapeseed 1.05–3.1 t/ha, beans 1.0–2.1 t/ha, and peas up to 4.1 t/ha. The study also highlighted the importance of soil parameters. Soil temperature and moisture had a direct impact on germination, early development stages, and yield formation. Conclusions. Cumulative GDD is the key factor determining crop development and enables accurate prediction of growth stages. GDD-based models are more reliable than predictions based on calendar days. Meteorological conditions, especially temperature and moisture, influence development rates but do not fundamentally alter overall growth patterns. Soil parameters are essential and should be integrated into crop development prediction models. Practical relevance. The developed algorithm and models allow prediction of crop development stages with a 3–7 day horizon, enabling better planning of agronomic practices such as sowing, fertilisation, plant protection, and harvesting. This contributes to reduced input costs, optimized use of agrochemicals, increased yields, and improved adaptation to climate change. The results are highly relevant for farmers, agronomists, agricultural advisors, and policymakers aiming to implement data-driven and sustainable agricultural practices.