Prediction of Drought Damage using Meteorological Factor Analysis in Corn
Bibliographic record
Abstract
In Korea, cultivation of corn and soybeans has been attempted to improve food self-sufficiency by using reclaimed land created in the 1980s.[1] It was well known that crops suffered from early growth and development inhibition, photosynthesis and yield reduction due to drought stress.[2] In this study, the crop evapotranspiration (ETc) was calculated through the analysis of climatic factors in the reclaimed land, and the vulnerability to drought stress was evaluated for each growing season of corn.Corn was cultivated (April 28 to August 31, 2021) in the Saemangeum reclaimed land (Gimje-si, Jeollabuk-do).During the corn cultivation period, the weather station measuring device (Watch model 2900ET) was installed to collect soil moisture content and meteorological data at 1-hour intervals.Standard evapotranspiration (ETo) was calculated using Specware Pro 9 software (FAO-Penman Montrith equation) [3], and crop evapotranspiration (ETc=ETo x Kc) was applied by crop coefficients (Rural Development Administration) for each growing season of corn.In this study, the mean value of crop evapotranspiration (ETc) during the corn cultivation period of the Saemangeum reclaimed land was 6.3mm/day, and the precipitation was 9.2mm/day.The precipitation was higher than the crop evapotranspiration during the corn cultivation period, but in May and June, the crop evapotranspiration (284mm) was higher than the precipitation (190mm).It meaned that irrigation was required during this period.In addition, the soil moisture content in June was also low at 12.6%, which was confirmed to be less than the effective moisture content (0.2 bar to 0.5 bar).It was determined that water shortage would occur in the early stages of growth (G1~G2) during corn cultivation (sowing on April 28) depending on natural rainfall in reclaimed land.Therefore, it was considered to be effective to delay the sowing time to prevent damage to corn grown on reclaimed land.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".