Soil Moisture Based Estimation of Length of Growing Period for Efficient Crop Planningin Micro Landformsof Chaka Watershed, Purulia, India
Bibliographic record
Abstract
Estimation of length of growing period (LGP) is essential in any cropping system as it determines crop selection, yield potential, different farming practices and has direct impact on agricultural productivity and sustainability.Considering this, an agro-topo pedological study has been carried out for Chaka watershed, Purulia, West Bengal, India, to assess length of growing period based on soil moisture availability.It is observed that along with the influence of climatic parameters length of growing period varies with change of micro landform depending on available water holding capacity (AWC) and actual soil moisture storage (ASMS).Soil samples from different landforms have been collected at regular incremental depth to get values of available water holding capacity and actual soil moisture storage.Soil moisture storage is observed to increase sequentially from the undulating ridge top to back slope, foot slope, and finally the valley fill, which are locally referred to as tarn, baid, kanali, and bahal respectively.Accordingly, length of growing period varies in different topographical situation from 158 to 184 days.The span of growing period is 150-160 days in tarn, 160-170 days in baid, 170-180 days in kanali and 180-190 days in bahal.Based on this aspect, a cropping system with alternate crops and a cropping pattern suited to the actual growing period in different micro-landforms has been suggested.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".