Forage Yield and Quality Indices of Silage-Corn Following Organic and Inorganic Phosphorus Amendments in Podzol Soil under Boreal Climate
Bibliographic record
Abstract
Dairy and livestock industry drives the economy and food security through sustainable supply of dairy products and meat across the globe. Dairy farm operations produce a large quantity of manure, which is a cheap and abundant plant nutrient source. However, insufficient forage production with low quality matrix are the current challenges of dairy industry in boreal climate due to extreme weather conditions. To address these challenges, a field experiment was conducted for three years to determine the effects of organic (dairy manure-based phosphorus (DMP)) and inorganic phosphorus (P) amendments on forage yield and quality indices of silage-corn cultivated in boreal climate. Experimental treatments were: (i) DMP with high P concentration (DMP1); (ii) DMP with low P concentration (DMP2) and (iii) inorganic P, also designated as control; and five silage-corn genotypes (Fusion-RR, Yukon-R, A4177G3-RIB, DKC23-17RIB, DKC26-28RIB). Results revealed that DMP1 amendment produced significantly higher forage yield compared to inorganic P, whereas non-significant effects were shown on quality indices except P mineral, available and crude protein. Yukon-R and DKC26-28RIB showed superior agronomic performance and produced significantly higher forage yield, whereas A4177G3-RIB produced lowest forage yield but exhibited superior nutritional quality; higher minerals, protein, total digestible nutrients, net energy for gain, net energy for maintenance and calculated milk production compared to other genotypes. Yukon-R not only produced higher forage, but also displayed good forage quality indices which were very close to A4177G3-RIB genotype. Therefore, we conclude that Yukon-R cultivation following DMP as organic amendment could be a sustainable production practice to attain high forage yield with optimum nutritional quality to meet the forage needs of growing dairy industry in boreal climate.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".