Winter wheat straw management on subsequent processing tomato yield, quality, economics and nitrogen dynamics
Bibliographic record
Abstract
Van Eerd, L. L., Loewen, S. A. and Vyn, R. J. 2015. Winter wheat straw management on subsequent processing tomato yield, quality, economics and nitrogen dynamics. Can. J. Plant Sci. 95: 273–283. The removal of crop residues to meet the anticipated demand for the bioeconomy sector may impact subsequent crop productivity. A field experiment was designed to evaluate the response of processing tomato (Solanum lycopersicum L.) on winter wheat (Triticum aestivum L.) straw management practices of: (1) retaining straw, (2) removing straw, or (3) retaining straw with a fall application of calcium ammonium nitrate at 34 kg N ha−1 to enhance straw decomposition. At two locations in 2006–2009, a split-plot design within a randomized complete block experiment, with wheat straw management as main-plot factor and nitrogen fertilizer (0 and 145 or 224 kg N ha−1) to the tomato crop as split-plot factor. At Ridgetown, marketable and total yield and profit margins were significantly higher with straw retained compared with straw removed treatments (total yield of 74.4 vs. 66.3 Mg ha−1 yr−1, respectively) but the straw retained plus fall N treatment (total yield 72.8 Mg ha−1 yr−1) was not different. However, at Leamington, straw management had no effect on yield or profit margins likely due to the rye (Secale cereale L.) cover crop used in the production system. At both locations, tomato quality (color, pH, soluble solids) was not influenced by straw or N management. Nitrogen fertilizer application to tomato had a significant effect on total processing yield, soil mineral N, and plant N, but wheat straw management had no effect on these parameters and there were no N fertilizer by straw management interactions. Winter wheat straw management did not impact soil N fertility for subsequent crop production. Thus, there may be significant undesired effects of removing crop residues on a subsequent crop yield; however, planting a cover crop may mitigate subsequent yield losses associated with biomass removal.
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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) | 0.001 | 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".