Plant nutrient removal and soil residual chemical properties as impacted by maize planting date and density
Bibliographic record
Abstract
This study aimed to measure maize (Zea mays) plant nutrient content and nutrient removal in grain, and to evaluate the residual soil nitrogen, phosphorus, and potassium as impacted by planting date and density. Field experiments were conducted to evaluate six plant densities and seven planting dates using a split-split plot design with three replications. Besides the crop growth and yield parameters, six plants were collected at the maturity and soil was sampled from each plot for nutrient analysis. Plant N, P, and K concentrations varied with planting date and density and within the ranges of 0.6-1.024%, 0.054-0.127%, and 0.75-1.71%, respectively. Grain N, P, and K concentrations decreased with plant density and varied from 1.059 to 1.558%, 0.20 to 0.319%, and 0.29 to 0.43%, respectively. Soil residual nutrient varied with depth, planting density and date. Residual N concentration in the topsoil varied from 0.6 to 37.2 mg kg-1 in 2019 and from 1.5 to 11.2 mg kg-1 in 2020 and was high under the last two planting dates. Soil residual N concentration was higher in the second layer than in the topsoil. The N concentration in the third layer varied from 0.1 to 33.2 mg kg-1 and was impacted by plant density. Topsoil P did not vary among planting dates and densities. The second and third soil layers P concentration was not affected. There was 83% increase in topsoil K in 2020 compared to 2019, and a decrease of 65 and 23% in soil K was observed in the second and third soil layers, respectively. For maize production system sustainability, future research should use a holistic approach investigating the impact of planting date, plant density on crop growth, yield, nutrient uptake and remobilization, and soil properties under different fertilizer rates to develop the fertilizer recommendation for maize while reducing the environmental impact of the production system.
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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 | 0.000 | 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) | 0.000 | 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".