The Effects of Different Tillage Systems and Cultivars on Growth, Yield and Quality of Zucchini (Cucurbita pepo L.) in a Semi-Arid Sub-Tropical Environment
Bibliographic record
Abstract
Conservation agriculture is a concept for resource-saving agricultural crop production system that serves to achieve acceptable profits and sustaining production while conserving the environment. The popularity of zucchini also known as baby marrow in the Kingdom of Eswatini has increased in recent years specifically for its economic value in the foreign market. This study was carried out at Malkerns Research Station, Malkerns in the Middleveld of the Kingdom of Eswatini to assess the effectiveness of different tillage methods and cultivars on growth, yield and quality of zucchini. The tillage methods used were zero, basin and mulch tillage. Furrow tillage was used as a control. The results showed that tillage methods had significant (P<0.05) differences in growth and yield of zucchini. Minimum tillage plants exhibited lowest number of leaves (9.5), vine length (36.4 cm), leaf area index (2.5) and number of flowers (6), number of fruits (1.3) and marketable yield/plant (4.6 ton/ha). Non-significant (P>0.05) differences were obtained from plants grown under basin, mulch and furrow tillage. The highest vine length (69.6cm), leaf number (17.0), LAI (3.6), and marketable yield (15.7 ton/ha) were obtained in zucchini plants grown under basin tillage system. However, there were no significant (P>0.05) differences in accumulation in leaves of zucchini plants of mineral content. In terms of the cultivars there were no significant (P>0.05) differences in vegetative growth. Star 8023 showed superiority in terms of number of fruits and marketable yield. It was observed that minimum tillage was less suitable in zucchini production as compared to other tillage systems. For higher production in zucchini, basin, furrow and mulch may be used. The best cultivar to use is star 8023.
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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".