Estimation of Total Factor Productivity Growth of Major Pulse Crops in Rajasthan, India
Bibliographic record
Abstract
Background: Pulses, supplemented with cereals, provide a perfect mix of vegetarian protein of high biological value. The productivity of pulses in India is less than half of the productivity levels in the USA and Canada. Present investigation was aimed to Total Factor Productivity (TFP) growth in three pulse crops in the state of Rajasthan from 2000-01 to 2017-18. Methods: In the current study, the Tornqvist Theil Index was used to compute the total output index, total input index and total factor productivity index. The Tornqvist Index is exact for the homogenous translog production function that can deliver a second order approximation to an arbitrary twice differentiable homogenous production function. The translog function does not require perfect substitutes for inputs. If the relative price of input increases, the producer decreases its use (substituting other inputs) until all marginal productivities are proportional to the new prices. Result: The results of this study has indicates low TFP in Gram (0.98%) despite a 59.23 per cent share in the total pulse output of the state. The annual compound growth rate of TFP of black gram increased at the rate of 1.11 per cent per annum (moderate growth) and the contribution of TFP to output growth was low; at about 41.63. Whereas, the compound growth rate of TFP of annual green gram crop increased at the 2.38 per cent per annum (high growth) while its TFP to output growth was about 66.43 per cent. The real cost of production of gram, black gram and green gram crop increased by 0.77, 1.49 and 1.57 per cent per annum, respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".