Integrating Record Keeping with Whole Farm Nutrient Mass Balance: A Case Study
Bibliographic record
Abstract
A whole farm nutrient mass balance (NMB) assessment gathers information on farm characteristics, nutrient imports and exports; provides indicators of farm production efficiency, potential environmental footprint; and helps identify opportunities for management improvements. Simplifying the data collection process facilitates wider NMB adoption. Our objectives were to: (1) integrate the NMB assessment into “Fields and Crops Manager”, an on-farm crop management program; (2) evaluate existing, on-farm, software packages for their ability to supply data for the assessment; and (3) document farmer response prior to and after conducting an NMB with the integrated record system. Software evaluated included the DRMS DHI-202 Herd Summary report, Centerpoint Accounting, PCDART and Quickbooks Accounting software, FeedWatch and TMR Tracker feeding systems, and Dairy Comp 305 herd management software. Six dairy farmers participated in the evaluation. The NMB results were presented and discussed in group meetings with each farmer. The records in the Fields and Crops Manager program and other software packages were not complete enough to allow for automated transfer of data into the NMB tool. Instead, the new NMB function within Fields and Crops Manager was used as a platform to enter, calculate, and store the NMB. Despite initial hesitation about participating, all six farmers concluded that (1) the NMB assessment was worth the data collection effort; and (2) meetings with farm advisors (crop planner, nutritionist) greatly improved the value of the NMB. The biggest obstacle to calculating NMBs can be overcome if purchases are recorded in the farm’s accounting software as invoices are entered.
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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 | 0.027 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".