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Record W6987074676

Rural water use decision-making, adoption of water conservation practices in southwestern Ontario

2000· dissertation· en· W6987074676 on OpenAlexaffabout

Bibliographic record

VenueThe Atrium (University of Guelph) · 2000
Typedissertation
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsBibliographical Society of Canada
Fundersnot available
KeywordsWater conservationAgricultureFarm waterWater useLivestockWater scarcityPopulationWater supply
DOInot available

Abstract

fetched live from OpenAlex

Almost all rural Ontarions use self-supplied water (e.g., private wells) for domestic, agricultural and other purposes. Shortages are increasingly common in Ontario, particularly during the drier summer months when water demand is at its highest. Water availability and related problems are expected to intensify with projected population growth and urban expansion, and with projected climate change. One approach to addressing water supply problems is the practice of water conservation. Promotion of water conservation initiatives is, however, dependent on identifying influences on certain water use decisions and subsequent behaviour. Factors associated with the adoption of conservation practices vary and include endogenous and exogenous influences, including, among others, attributes of the decision-maker, farm economic factors, and biophysical conditions. This research was an investigation into rural water conservation decision-making in southwestern Ontario. It aimed to identify and assess the importance of a range of factors influencing rural domestic and agricultural decision-making with regards to water conservation. Mail-back questionnaire responses from 291 agricultural and rural non-farm property owners revealed that most practice some form of water conservation, whether deliberately or incidentally. Analysis indicated that household water-saving was more common indoors than outdoors, and non-farm respondents were more active household conservers than agricultural respondents. Livestock operators favoured water equipment maintenance over all other livestock water-saving measures. Irrigators were more likely to adopt a series of conservation measures, most commonly scheduling of irrigation, and reducing water needs of agricultural crops. Statistical analysis revealed that adoption of water conservation measures in the home was influenced by program awareness and participation, level of formal education, and anticipation of future water shortages. Higher levels of livestock water conservation were associated with several factors, including greater farm gross sales, agriculture as the primary income source, and land tenure. Lengthy, in-depth interviews with seventeen agricultural households provided additional insight into the potential motivations and constraints for water conservation. Perceptions of limited opportunities to conserve water in the home and in agriculture were constraints to adoption. Furthermore, perceptions of water conservation, primarily conservation as a preventative measure to avoid water shortages, highlighted why experience with a water shortage did not influence water conservation behaviour. Technological fixes in response to past experiences may negate any motivation to implement preventative conservation. Awareness programs aimed at household water conservation may prove effective in rural households, although somewhat less effective in agriculture. Financial incentives to aid in the installation of water-saving equipment, along with awareness programs, may be necessary to improve water conservation within the agricultural sector.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.015
GPT teacher head0.208
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2000
Admission routes2
Has abstractyes

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