Mitigation of Salinity and Drought with Soil Amendments for Sustainable of Rice Production
Bibliographic record
Abstract
Compost, biochar and a mixture of compost biochar and soil microbes can reduce impact of drought and salinity stress.800 and 10000 ppm NaCl (11.2 and 14.0 dSm -1 ) watering at Inpari 45 agritan and AU 11 Sigupai super green rice, for 3 days at reproductive phase.Irrigated with tab water, until field capacity (aerobic cultured) until harvest.Nutrient uptake, growth and yield of rice were varying.Reduce the salinity level in the reproductive phase from 10000 ppm (14 dSm -1 ) to 387.22 μS cm -1 , while from 8000 ppm (11.2 dSm -1) to 519.22 in the Inpari 42 SGR variety while in the AU 11 Sigupai variety was decreased to (459.89, to 542.33 μS cm -1 at S1, S2.Biochar reduces soil salinity of S1 and S2 to 594.17, 476 μS cm -1 by using biochar (A1) Meanwhile, compost can reduce salinity S1 and S2 to 433.50 μS cm -1 , to 527.00 μS cm -1 .While the mixed amendment (A3) can reduce the salinity to 441.00 μS cm -1 , 392.33 μS cm -1 at harvest time.Change on nutrient uptake, chlorophyll content, leave drying score and shoot dry weight of rice.Soil amendments were effective in reducing drought and salinity.There is a close relationship between the parameters studied.Mixed soil amendment (biochar 25 g + compost 25 g + 40 g mychorizha + 25 g tricoderma) per 10 kg of soil were effective increasing rice yield.The highest yields were found in rice on soil mix amendment (A3) followed by A2 and A1 with the yield potential 4.72, 4,69, 4.19 t ha -1 .While the rice at S2 a yield potential of A3, A2, A1 of 3.2, 2.8 and 2.9 in t ha - 1 respectively.Other mechanisms need to be investigated that can increase yields and reduce salinity with the three types of organic amendments.
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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 | 0.000 |
| 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 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".