Net Biome Productivity of Irrigated and Rainfed Maize–Soybean Rotations: Modeling vs. Measurements
Bibliographic record
Abstract
Estimates of agricultural C sequestration require an understanding of how net ecosystem productivity (NEP) and net biome productivity (NBP) are affected by land use. Such estimates will most likely be made using mathematical models that have undergone well‐constrained tests against field measurements of CO 2 exchange as affected by management. We tested a hydraulically driven soil–plant–atmosphere C and water transfer scheme in ecosys against CO 2 and energy exchange measured by eddy covariance (EC) over irrigated and rainfed no‐till maize–soybean rotations at Mead, NE. Correlations between modeled and measured fluxes ( R 2 > 0.8) indicated that <20% of variation in EC fluxes could not be explained by the model. Annual aggregations of modeled fluxes indicated that NEP of irrigated and rainfed soybean in 2002 was −30 and −9 g C m −2 yr −1 (net C source) while NEP of irrigated and rainfed maize in 2003 was 615 and 397 g C m −2 yr −1 (net C sink). These NEPs were within the range of uncertainty in annual NEP estimated from gap‐filled EC fluxes. When grain harvests were subtracted from NEP to calculate NBP, both the modeled and measured maize–soybean rotations became net C sources of 40 to 80 g C m −2 yr −1 during 2002 and 2003. Long‐term model runs (100 yr) under repeated 2001–2004 weather sequences indicated that a rainfed no‐till maize–soybean rotation at Mead would lose about 30 g C m −2 yr −1 . Irrigating this rotation would raise SOC by an average of 6 g C m −2 yr −1 over rainfed values. Modeled and measured results indicated only limited opportunity for long‐term soil C storage in irrigated or rainfed maize–soybean rotations under the soil, climate, and management typical of intensive crop production in the U.S. Midwest.
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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 | 0.000 | 0.001 |
| 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 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".