Winter cover crops on processing tomato yield, quality, pest pressure, nitrogen availability, and profit margins
Bibliographic record
Abstract
Much of cover crop research to date focuses on key indicators of impact without considering the implications over multiple years, in the absence of a systems-based approach. To evaluate the effect of three years of autumn cover crops on subsequent processing tomato (Solanum lycopersicum L.) production in 2010 and 2011, a field split-split-plot factorial design trial with effects of cover crop type, urea ammonium nitrate fertilizer rate (0 or 140 kg N ha-1 preplant broadcast incorporated) and tomato cultivar (early vs. late) was conducted. The main plot factor, cover crop, included a no cover crop control, oat (Avena sativa L.), winter cereal rye (hereafter referred to as rye) (Secale cereale L.), oilseed radish (OSR) (Raphanus sativus L. var. oleiferus Metzg Stokes), and mix of OSR and rye (OSR + rye) treatments. Cover crop biomass of 0.5 to 2.8 and 1.7 to 3.1 Mg ha-1 was attained in early Oct. and the following early May, respectively. In general, OSR increased soil mineral N during cover crop growth and into the succeeding summer tomato growing season, while the remaining cover crops did not differ from the no cover crop control. The lack of a cover crop by N rate interaction in soil and plant N analyses at harvest suggests that growers may not need to modify N fertilizer rates to tomatoes based on cover crop type. Processing tomato fruit quality at harvest (rots, insect or disease damage, Agtron colour, pH, or natural tomato soluble solids (NTSS)) was not affected by cover crop type. In both years, marketable yield in the no cover crop treatment was lower or not statistically different than all planted cover crops. Partial profit margins over both years were 1320 $ ha-1 higher with OSR and $960 higher with oat compared to the no cover crop control. Thus, results from a systems-based approach suggest that the cover crops tested had no observed negative impact on processing tomato production and have the potential to increase marketable yield and profit margins.
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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 | 0.000 |
| 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) | 0.002 | 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".