Integrated farm management systems to improve nutrient management using semi-virtual Farmlets: agronomic responses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 N 2 O 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it