Soil mineral nitrogen benefits derived from legumes and comparisons of the apparent recovery of legume or fertiliser nitrogen by wheat
Why this work is in the frame
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Bibliographic record
Abstract
Nitrogen (N) contributed by legumes is an important component of N supply to subsequent cereal crops, yet few Australian grain-growers routinely monitor soil mineral N before applying N fertiliser. Soil and crop N data from 16 dryland experiments conducted in eastern Australia from 1989–2016 were examined to explore the possibility of developing simple predictive relationships to assist farmer decision-making. In each experiment, legume crops were harvested for grain or brown-manured (BM, terminated before maturity with herbicide), and wheat, barley or canola were grown. Soil mineral N measured immediately before sowing wheat in the following year was significantly higher (P < 0.05) after 31 of the 33 legume pre-cropping treatments than adjacent non-legume controls. The average improvements in soil mineral N were greater for legume BM (60 ± 16 kg N/ha; n = 5) than grain crops (35 ± 20 kg N/ha; n = 26), but soil N benefits were similar when expressed on the basis of summer fallow rainfall (0.15 ± 0.09 kg N/ha per mm), residual legume shoot dry matter (9 ± 5 kg N/ha per t/ha), or total legume residue N (28 ± 11%). Legume grain crops increased soil mineral N by 18 ± 9 kg N/ha per t/ha grain harvested. Apparent recovery of legume residue N by wheat averaged 30 ± 10% for 20 legume treatments in a subset of eight experiments. Apparent recovery of fertiliser N in the absence of legumes in two of these experiments was 64 ± 16% of the 51–75 kg fertiliser-N/ha supplied. The 25 year dataset provided new insights into the expected availability of soil mineral N after legumes and the relative value of legume N to a following wheat crop, which can guide farmer decisions regarding N fertiliser use.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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