Integrative Assessment of Nitrogen and Irrigation Regimes on Chickpea Productivity Using Treatment-by-Trait Biplot Modeling
Bibliographic record
Abstract
This research aimed to study the interactive impacts of nitrogen fertilization and irrigation regimes on the performance of chickpea (Cicer arietinum L.). A split-plot layout based on a randomized block scheme with five replications was done during the 2024–2025 growing season. The main plots consisted of four irrigation treatments: well-watered (I1), rainfed (I2), and supplementary irrigation applied at flowering (I3) or at both flowering and seed formation stages (I4). The sub-plots included three levels (N1, N2, and N3) of nitrogen starter fertilizer (0, 20, and 40 kg ha-1). The first and second components of treatment-by-trait interaction model accounted for 87% of variation, allowing for reliable graphical interpretation. Biplot analysis revealed that well-watered combined with nitrogen application at 20 or 40 kg ha-1 (I1-N2 and I1-N3) significantly enhanced seed yield and yield components. However, supplementary irrigation during key reproductive stages (I3-N2, I4-N2, and I4-N3) also produced favorable results, offering a water-efficient alternative to full irrigation. Traits such as yield performance, pods and seeds of plant, plant height, and chlorophyll content were identified as highly representative and responsive, while root depth, harvest index, and water use efficiency showed greater variability across treatments. The findings confirm that nitrogen fertilization is essential not only for improving early growth and establishment in semi-arid soils, but also for maximizing yield when synchronized with strategic irrigation. These results support the development of integrated nutrient and irrigation management strategies tailored to semi-arid agroecosystems to improve legume yield, resilience, and resource use efficiency.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".