Assessing the yield potential of soybean maturity groups in different Ukrainian climatic zones
Bibliographic record
Abstract
In the context of global climate change, increasing demands for food security, and the need to expand sources of plant-based protein, soybean is gaining particular importance as a highly productive and valuable agricultural crop. Purpose. The study aimed to evaluate the yield potential of soybean cultivars from different maturity groups under various agro-climatic conditions of Ukraine by analysing their adaptability, productivity, and stability. The objective was to justify the selection of maturity groups best suited for specific regions to ensure sustainable soybean production. Methods. Field experiments were conducted in 2023–2024 across three agro-climatic zones: Odesa (Steppe), Cherkasy (Forest-Steppe), and Zhytomyr (Polissia). A total of 26 early- and mid-maturing soybean cultivars of Ukrainian and foreign origin were evaluated. Adaptive variability was assessed using standard statistical methods. Results. Among early-maturing cultivars, Taverna, Eri, and Calgary showed superior individual productivity, surpassing the standard by 9–13% in seed weight per plant and reaching yields up to 3.15 t ha⁻¹ in Polissia. These cultivars demonstrated high plasticity and stability across environments. Among mid-maturing cultivars, ES Visitor and ES Collector delivered consistently high yields across all zones, exceeding the standard by 0.09–0.26 t ha⁻¹. Alicia also showed high productivity in the Forest-Steppe and Polissia, making it suitable for regions with moderate moisture. The highest average yield for early-maturing cultivars was recorded in Polissia (2.50 t ha⁻¹), and for mid-maturing ones - in the Forest-Steppe (2.68 t ha⁻¹). Regardless of the zone, Taverna, Eri, Calgary, ES Visitor, and ES Collector demonstrated stable and high productivity. Conclusions. The findings provide a basis for optimising cultivar selection and soybean production technologies, tailored to regional climatic conditions and challenges posed by climate change.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".