Introduction: Evaluating Long‐Term Impacts of Harvesting Crop Residues on Soil Quality
Bibliographic record
Abstract
Utilizing crop residues as biofuel feedstocks will involve trade‐offs between bioenergy production and agroecosystem services. Consequently, agricultural production managers and policymakers need to critically evaluate current functions of crop residues in light of increasing demands for agricultural intensification including bioenergy. At issue are the short‐ and long‐term impacts of residue harvest on the sustainability of soil resources and related food and energy production and the often disparate economic, environmental, edaphic, climatic, technological, and logistical factors involved. Although field studies cannot address all scenarios, long‐term studies can provide insights on how crop residue harvest will impact key factors of agricultural sustainability such as soil organic matter (SOM). This topic was the major theme of the 2009 International American Society of Agronomy symposium entitled “Residue Removal and Soil Quality—Findings from Long‐Term Research Plots.” The seven papers in this special Agronomy Journal section were developed from this symposium and draw on long‐term studies from Europe, Canada, Australia, and the United States to examine residue harvest impacts on SOM and factors related to long‐term sustainably. In combination, these papers conclude that residue harvest will impact SOM, although the nature of the effects is situation‐dependent. Also clear is that the assessment of harvesting residues must be placed in a farming systems context that includes an evaluation of economic and environmental trade‐offs specific for a given farm and location. Therefore, future challenges include the development of science‐based, site‐specific decision aids that enable growers to make economically sound and environmentally sustainable choices regarding residue harvest.
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 | 0.001 | 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 | 0.000 | 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) | 0.002 | 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".