Perception of tools for assessing on-farm nutrient losses and mitigation options
Bibliographic record
Abstract
This research focuses on the prominent issue of degraded water quality in New Zealand caused by the intensification of agricultural land use, resulting in increased levels of diffuse pollutants such as sediment, nitrogen and phosphorus in waterways (Duncan, 2017). Degraded water quality is a critical issue that needs to be addressed both socially and scientifically. It needs to be addressed socially as human behaviour is influencing this degradation, and the science is needed to further our understanding and implementation of the best mitigation solutions. The aim of this study was to evaluate how information surrounding potential nutrient mitigation measures provided by decision support tools is understood and interpreted by farmers facing tightening environmental regulations and a changing social outlook on environmental sustainability. To achieve this aim, the following activities were conducted: (i) A review of current theories and tools available to understand and encourage pro-environmental behaviour. (ii) A case study using the Land Utilisation Capability Indicator (LUCI) model to determine stakeholder engagement was carried out through interviews with 6 farmers in the Mangatarere Catchment. The review showed that while information alone does not drive behaviour change, it is an essential component that when used in collaboration with other methods and incentives, can be very successful (Kennedy, 2010; Mackenzie-Mohr, 2000; Stern, 2000). From this review a method that was identified as having huge potential in terms of managing water quality was the use of land use models alongside targeted on-farm advice (Bouraoui & Grizzetti, 2014). To understand the stakeholder perception and uptake of the information this method provides a case study was carried out using the LUCI model with 6 farmers in the Mangatarere Catchment. The results showed that LUCI proved to be a valuable tool for both the case study farmers and the wider farming community. Farmer feedback highlighted the importance of ensuring that information provided by such tools is communicated in a consolidated manner. This thesis shows that land use models such as LUCI have the potential to be a beneficial method of engaging stakeholders in prominent issues such as degrading water quality.
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 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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".