Short‐Range Spatial Variability of Nitrogen Fixation by Field‐Grown Chickpea
Bibliographic record
Abstract
Biological N fixation (BNF) by legumes under field conditions is known to vary widely across landscapes and between agroecosystems. A poor correlation between the 15 N Natural Abundance ( 15 NA) and 15 N Enriched ( 15 NE) approaches for estimating BNF across the landscape has been observed by many. These observations led some to conclude that the two approaches are measuring different processes and can not be compared. Others argue that short‐range spatial variability of BNF is very high, thereby obscuring any relationships between experimentally measured estimates of BNF. Our study, which quantifies spatial variability of BNF using the 15 NA approach, provides evidence that short‐range spatial variability of BNF is very high. In a field study, BNF of chickpea ( Cicer arietinum L.) was measured at 0.3‐m intervals on a 33‐m transect, using wheat ( Triticum aestivum ‘Katepwa’) as reference crop. Each crop was sampled at 110 points along the transect. Estimates of BNF in the grain varied from 36 to 70%, with a mean value of 55%. The variogram for BNF had a range of 3.2 m and a relative nugget effect of 71%. Using a simulation study, we calculated r 2 of 0.12 and 0.02 for BNF at sites spaced 1 m and 2 m apart, respectively. We concluded that short‐range spatial variability of BNF across the landscape is the likely cause of reported discrepancies between the 15 NA and 15 NE approaches used in other studies. Moreover, if such a high spatial variability in BNF is the norm rather than the exception, comparisons between 15 NA and 15 NE approaches for estimating BNF under field conditions will be unreliable.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".