Macronutrient Fertilization on the Yield Components and Nutrition of Lima Bean
Bibliographic record
Abstract
Lima bean stands out in the agricultural sector as a source of income for small and medium farmers, being an important protein source for the population. However, there few studies related to the adequate management of fertilization that subsidize the maximization of crop production. In this context, the objective of this research was to evaluate the effect of macronutrient doses on the yield components and nutritional status of lima bean. The treatments were chosed based on the statistical arrangement of the Baconian method. Six nutrients (nitrogen, phosphorus, potassium, calcium, magnesium and sulfur) were applied in three different doses along with two additional treatments, one with reference doses and the other with no addition of nutrients, totaling 20 treatments that were arranged in a completely randomized design with four replications. The pod length, pod number per plant, pod weight, number of grains per pod, number of grains per plant, grain weight per plant and macronutrient leaf contents were evaluated. The addition of macronutrients leads to increases in yield components and nutritional status of lima bean s when cultivated in Entisol Quartzipsamment. The doses of macronutrients that promote higher productive yield and adequately supply the nutritional needs of lima beans are: 25 mg N dm-3; 157 mg P2O5 dm-3; 90 mg K2O dm-3; 1.27 cmolc Ca dm-3; 0.50 cmolc Mg dm-3 and 30 mg S dm-3.
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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 | 0.000 | 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".