Basmati Rice Quality Enhancement by Zinc Fertilization and Green Manuring on a Sub-tropical Inceptisol in Indo-Gangetic Plains of India
Bibliographic record
Abstract
Basmati (aromatic) rice is premier rice grown in north-western India and Pakistan. This rice is preferred for their long and slender kernels which expand 3-4 times in length and remain fluffy and are well known all over the world, especially in the Middle East and South Asia for their long fluffy grains on cooking. Paddy soils are usually deficient in organic matter because of high temperature and moisture, which causes rapid decomposition of organic matter. The importance of leguminous green manure crops in improving soil fertility, and soil physical properties received increasing attention. Also, the zinc (Zn) deficiency in soils is prevalent worldwide, especially in high pH calcareous soils. No reports were available on combining green manuring crops and Zn fertilization on productivity, Zn content and kernel quality of Basmati rice. Therefore, the current investigation was undertaken to quantify the combined effects of summer green manuring crops and zinc fertilization on productivity, Zn content and kernel quality of Basmati rice in summer green manuring-Basmati rice cropping system. A field study was therefore conducted for two years (2009 and 2010) on a sandy clay-loam soil (typic Ustochrept) at the research farm of the ICAR-Indian Agricultural Research Institute, New Delhi, India. The experiments were conducted in split plot design, keeping three green manuring crops viz. Sesbania aculeata (Dhaincha), Crotalaria juncea (Sunhemp), and Vigna unguiculata (Cowpea) and one summer fallow treatment as main-plot treatments and six Zn sources viz. control (no Zn application), ZnSO4∙7H2O (21% Zn), ZnSO4∙H2O (33% Zn), ZnO (82% Zn), ZnSO4∙7H2O + ZnO (50% + 50%) and EDTA-chelated Zn (12% Zn) in sub-plots and was replicated thrice. The experiments in both the years were conducted with a fixed lay-out plan on the same site. The results showed that incorporation of green manures along with zinc (Zn) fertilization increased grain and straw yield, enhanced Zn concentrations and improved the kernel quality before and after cooking in Basmati rice ‘Pusa Basmati 1’. The application of EDTA-chelated Zn (12% Zn) was the best in terms of grain and straw yield and Zn concentrations in grain and straw and kernel quality before and after cooking Basmati rice. Application of ZnSO4∙7H2O (21% Zn) was the second-best treatment followed by ZnSO4∙H2O (33% Zn) and ZnSO4∙7H2O + ZnO (50% + 50%). Application of ZnO (82% Zn) had least effect in increasing the studied parameters. The lowest values were observed with control (no Zn application). Among the summer green manuring crops, incorporation of Sesbania aculeata (Dhaincha) was found to be the best over Crotalaria juncea (Sunhemp), Vigna unguiculata (Cowpea) and summer fallow in terms of grain and straw yield, Zn concentrations in grain and straw and kernel quality before and after cooking in Basmati rice. Zn fertilization with EDTA-chelated Zn (12% Zn) lead to 25.91 and 21.26% higher grain yield; 60.66 and 82.14% Zn-denser grains; with 13.33 and 10.92% increase in head rice recovery in Basmati rice over control (no Zn application) during 2009 and 2010, respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".