Economic Feasibility of Management of Fertilization in the Soybean-Corn System Cultivated in Succession
Bibliographic record
Abstract
Fertilization management is one of the agricultural practices that demand high investment. Thus, the objective was to evaluate the economic viability of using combinations of fertilizers applied to soybean and corn cultivated in succession in Goiás Savanna soil. The experiment was carried out in the field, in randomized blocks. The fertilization management consisted of the combination of monoammonium phosphate fertilizers; urea; 08:40:00 +9.3%S and 3.2% Ca; simple superphosphate; potassium chloride; elemental sulfur; polyhalite (37% K2O + 9.2% S, 5.8% Ca and 1.7% Mg) and polyhalite S (14% K2O + 19.2% S, 12% Ca and 3.6% Mg) which were applied in soybean sowing and evaluated the residual effect on corn. The economic assessment took into account crop productivity and fertilizer prices in the 2018/2019 agricultural year. For each treatment, the operating cost (COi) and profitability indicators were calculated: gross revenue (GRi); net revenue (NRi); profitability index (PITi); equilibrium production (Yei) and equilibrium price (Pei). Crop productivity was not influenced by fertilizer management, showing that economic indicators can help producers choose the management with the best return and that adjusts to the financial situation of the rural company. Thus, it is concluded that the management of MAP + KCl fertilization was economically outstanding in soybean cultivation and the MAP/S + KCl applied to soybeans, associated with nitrogen fertilization in corn coverage was the most viable for off-season corn. The economic indicators showed that the MAP+KCl fertilization performed on soybean, associated with urea in corn coverage, is the most viable management system for the soybean-corn production system cultivated in succession.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".