Regional Weather Variations and Yields Achieved in Soybean Crops
Bibliographic record
Abstract
Monitoring weather conditions during soybean cultivation is essential in agricultural planning. The variation of these conditions, such as temperature, precipitation, relative humidity and soil moisture directly influence the productive performance of crop.With this, the objective of the work was to verify the effects of weather conditions on the soybean yield, carrying out the survey of the minimum, maximum and average temperature and the total precipitation during the cultivation of the soybean and collecting the data of productivity reached in the agricultural harvests of 2017/2018, 2018/2019 and 2019/2020 of soybeans in a commercial area with 15.5 ha, located in the Céu Azul City, Paraná State, Brazil. Regarding the results for the three soybean harvests, the air temperature remained adequate for the development of the crop in most of the cycle. And the values observed for precipitation indicated the occurrence of well-distributed rainfall in the 2019/2020 harvest, and in the 2017/2018 harvest there was irregular rainfall distribution, however there were no periods without precipitation. However, the large precipitation deficit occurred in the 2018/2019 harvest, where the lack of rain occurred in 28 days, between 12/03/2018 and 12/30/2018, indicating a drought in this period. The soybean yield obtained in the area in the 2019/2020 harvest was 3.727 t ha-1, higher than the other two soybean harvests, being that 2018/2019 harvest reaching the lowest value, 2.394 t ha-1, indicating the influence of the weather in the soyben yield achieved.
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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 | 0.001 | 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 | 0.000 | 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".