Simulating synergistic effects of climate change and conservation practices on greenhouse gas emissions and crop growth in long-term maize cropping systems
Bibliographic record
Abstract
Understanding the impacts of future climate change and long-term agronomic practices on environmental quality and agricultural productivity is critical to the development of sustainable agronomic management approaches. Given these requirements, the present study’s objective was to evaluate the potential impacts of climate change and long-term conservation practices on greenhouse gas (GHG) emissions and crop growth. To project potential future (2065–2084) climatic conditions for a field under a long-term maize cropping system situated in Nebraska (USA), 12 different combinations of regional climate models × global climate models (RCMs-GCMs) were generated under representative concentration pathway 8.5 (RCP8.5) and a heightened atmospheric carbon dioxide concentration ([CO 2 ] atm = 714.1 ppm). Then, employing a well-calibrated instance of the Root Zone Water Quality Model (RZWQM2), the effects of four long-term conservation practices were simulated under the 12 RCMs-GCMs. Compared to other climatic factors ( e.g. , shortwave radiation, wind run, and relative humidity), temperature, precipitation, and [CO 2 ] atm played more important roles for future carbon dioxide (CO 2 ) and nitrous oxide (N 2 O) emissions, global warming potential (GWP), soil organic carbon (SOC), crop yield, and total crop biomass. The sum of their relative contributions to GHG emissions and crop growth exceeded 83.9 % across all treatments. Under future climatic conditions, CO 2 and N 2 O emissions increased significantly — 19.4 % ± 5.8 % and 26.6 % ± 8.9 %, respectively — compared to those under historical baseline conditions. Likewise, the GWP increased by 19.8 ± 5.8 %. Although rising [CO 2 ] atm afforded limited benefits in terms of crop photosynthesis rates, rising future temperatures shortened crop growth cycles, resulting in a net decrease of 9.1 % ± 1.9 % in maize yield and 4.2 % ± 1.6 % in total biomass. SOC saw a net increase of 4.8 % ± 0.4 % under the future ( vs. baseline) climate. Compared to residue removal with no-till treatment, annual CO 2 and N 2 O emissions in the long-term maize cropping system were predicted to increase by 26.8 % and 27.9 % between 2065 and 2084 under residue retention with tillage treatment, respectively. Whereas crop yield and biomass were not significantly affected by residue or tillage management practices. The simulation method provided valuable evidence for management decisions to assess the synergistic effects of climate change and long-term agronomic practices on the environment and agricultural productivity. Further investigation is needed on the effects of other climate RCPs and agronomic management for sustainable agricultural production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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 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".