Effect of Some Preceded Peanut Cultivars on Wheat Yield and Agro – Economic Feasibility Under Two Cropping Systems in Sandy Soil
Bibliographic record
Abstract
The cropping system is an approach to help farmer in decision making to remain sustainable in an ever-changing agricultural environment. A two-year study was carried out at Ismailia Agricultural Experiments and Research Station, ARC, Ismailia governorate, Egypt during 2011/2012 and 2012/2013 seasons to study the effect of preceded peanut cultivars on yield and profitability of wheat under two cropping systems in sandy soil. This experiment included six treatments which were the combinations of three peanut cultivars (Giza 4, Giza 5 and Ismailia 1) as preceding cultivars in the summer season and two cropping systems (conventional and intensive). A split plot design replicated thrice was used. The results indicated that peanut cv. Ismailia 1 increased available soil nitrogen (N) content which affected positively number of grains per spike and grain weight per spike and finally the economic yield. Growing fahlberseem in transition period between peanut and wheat enhanced available soil N content that increased grain yield per ha by about 1.00 percent as compared with those of conventional cropping system. Accordingly, intensive cropping system increased wheat grain yield and its attributes as compared with conventional cropping system. Peanut cultivars × cropping systems interaction had a significant effect on available soil N content and all the studied wheat traits except number of spikes/m2. Intensive cropping system increased total and net returns as compared with conventional cropping system. The Egyptian farmers could achieve an increase in their income by $ 2603.2 per ha when using intensive cropping system which included peanut cv. Ismailia 1
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".