Agricultural Innovations for Sustainable Crop Production Intensification
Bibliographic record
Abstract
Sustainable crop production intensification should be the first strategic objective of innovative agronomic research for the next 40 years. A range of options exist (often very location specific) for farming practices, approaches and technologies that ensure sustainability, while at the same time improving crop production. The main challenge is to encourage farmers in the use of appropriate technologies,  and  to  ensure that  knowledge  about  sound  production  practices  is  increasingly accepted and applied by farmers. There is a huge, but underutilized potential to link farmers’ local knowledge with science-based innovations, through favourable institutional arrangements.  The same  holds  for  the  design,  implementation and  monitoring  of  improved  natural  resource management that links community initiatives to external  expertise.  It is also suggested that a comprehensive effort be undertaken to measure different stages of the innovation system, including technological adoption and diffusion at the farm level, and to investigate the impact of agricultural policies on technological change and technical efficiency. This paper provides a brief review of agronomic management practices that support sustainable crop production system and evidence on developments  in the selection of crops and cultivars; describes farming systems for crop which take a predominantly ecosystem approach; discusses the scientific application of ecosystem principles for the management of pest and weed populations; reviews the improvements in fertilizer and nutrient management that explain productivity growth; describes the benefits and constraints of irrigation technologies; and suggests a way forward. Seven changes in the context for agricultural development are proposed that heighten the need to examine how innovation occurs in the agricultural sector.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".