Predicting pasture and sheep production in the Victorian Mallee with the decision support tool, GrassGro
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Bibliographic record
Abstract
The GrassGro decision support tool was designed to quantify sheep and pasture production in response to management and climate variability in temperate Australia, and has been tested in temperate but not low-rainfall Australian conditions. Data from field experiments and from on-farm monitoring was used to test GrassGro predictions of annual and perennial pasture production, and sheep production at 4 locations throughout the Victorian Mallee, which is a low-rainfall area (275–375 mm annually). Predictions of long-term pasture production were then made. Predictions of the herbage biomass of annual pastures closely matched observed data for both a sandy loam (1991–2002 data) and a whole paddock (combining sandy loam and loam and sand) (2001–2002 data) soil type, at several locations across the Victorian Mallee. Linear regression between observed and simulated (April to September) data produced coefficients, significance and root mean square error of r2 = 0.81, P<0.001, 217 kg DM/ha, respectively, for sandy loam soil types and r2 = 0.94, P<0.001, 72 kg DM/ha, respectively, for whole paddock soil types. A series of simulations for individual years from 1970 to 2002 quantified the large impact of climate variability and demonstrated that seedbank and location, but not soil fertility, had a large influence on annual pasture production. However, GrassGro underestimated the production of the perennial pasture, lucerne (r2 = 0.2). GrassGro was also unable to adequately predict sheep production because it failed to take into account the sparse, clumpy structure of the low biomass pastures typical of this region. Methods to improve GrassGro were identified and included: (i) the need to adjust sheep intake from low biomass, sparse pastures, (ii) the ability to predict summer growing and autumn growing plant species, (iii) the ability to graze crop stubbles and (iv) refinements to the coefficients of equations used to model lucerne growth.
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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 | 0.000 | 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) | 0.000 | 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 it