Radish Planting Date and Nitrogen Rate for Cover Crop Production and the Impact on Corn Yields in Upstate Missouri
Bibliographic record
Abstract
Cover crops can increase yield of a marketable crop, offer greater yield stability, reduce fertilizer inputs, suppress weeds, and interrupt disease or pest cycles. Producers increasingly look to radish (Raphanus sativus L.) as a cover crop, but integrating it into a cropping system with corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] has been difficult. Radish following wheat (Triticum aestivum L.) may provide more options for farmers. Few studies have tested the effects of nitrogen (N) rates on radish production for a dual purpose cover/forage crop or whether additional N is needed because radish is often used to trap N. This study evaluated radish planting date (1 Aug., 15 Aug., 29 Aug., and 11 Sep. in 2012 and 2013) and N fertilizer amount (0, 17, 33, and 67 kg N ha-1) on radish heights, chlorophyll content, and biomass production as well as their impact on corn yields the next year (2013 and 2014). Across planting dates and years, non-fertilized controls had shorter radishes than any N rate, while radish chlorophyll content increased as N rates increased. Chlorophyll and biomass content generally did not significantly increase above 33 kg N ha-1. Radish foliage dry biomass production was 820 to 1670 kg ha-1 greater at the first planting date (1 Aug.) in 2012 and 2013 than the other planting dates. Winter annual weed suppression was greatest at the first planting date in 2012. No differences in winter annual weed suppression were observed in 2013 compared to the other planting dates. Radish planting date did not affect corn yield the following year (2013 and 2014). This study demonstrates the benefits of planting radishes in late summer prior to 1 Sep. and fertilizing with 33 kg N ha-1 for optimum radish biomass production following wheat in upstate Missouri.
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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 | 0.001 |
| 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".