Integrated farm management systems to improve nutrient management using semi-virtual Farmlets: agronomic responses
Bibliographic record
Abstract
Abstract With increasing demand for land and food, there is growing interest in sustainable intensification of agricultural production. Here we investigated sustainable intensification of grass and corn production for dairy farms using a system of semi-virtual farmlets that combine replicated field research plots with feed modelling. We improved manure N capture by spreading separated liquid fraction with a low emission sliding shoe applicator on grass, and manure P capture by precision injecting separated sludge into corn. Reducing the number of annual harvests (5 to 3) increased grass yield and inter-seeding Italian ryegrass in early maturing corn increased fall growth of the cover crop, thus helping to protect soil over winter and providing additional high quality herbage in spring. Irrigation improved yield and potentially yield stability of corn and grass, and adding a nitrification inhibitor to reduce N2O emission may help reduce pollution swapping especially from injected manure. Overall, allocating more land to corn than grass will increase farm productivity but effectiveness of measures to reduce pollution and pollution swapping need to be evaluated. Results show that good practices ensuring vigorous crops are challenging to implement but critical for achieving sustainable intensification. The semi-virtual farmlet system is very helpful for developing and evaluating sustainable production measures for corn and grass.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".