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Record W3134951635

Adoption of Precision Agriculture in Alberta Irrigation Districts with Implications for Sustainability

2021· article· en· W3134951635 on OpenAlexvenueaboutno aff
Lorraine A. Nicol, Christopher J. Nicol

Bibliographic record

VenueJournal of rural and community development · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSmart Agriculture and AI
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureSustainabilityCroppingIrrigationProduction (economics)BusinessAgricultural economicsAgricultural scienceEmerging technologiesGeographyEconomicsEnvironmental science
DOInot available

Abstract

fetched live from OpenAlex

To fulfill worldwide food requirements in the future, agriculture production will need to increase significantly. However, given the stress agriculture places on the environment, production must also be sustainable. A new suite of agricultural technologies commonly referred to as precision agriculture (PA) has been found to improve farming efficiency and environmental sustainability. This study focuses on the adoption of PA on irrigated farms in southern Alberta. Through irrigation, southern Alberta has become amongst the most fertile and productive agricultural regions in Canada. Alberta is also recognized for its entrepreneurial and progressive farm culture and practices. This economic and cultural environment may provide fertile ground for the adoption of PA technologies. In a survey of farmers in three irrigation districts in Alberta, we explore whether we see high rates of PA adoption. In exploring farmer and farm characteristics, we expect to find: (a) adoption leaning towards more advanced PA technologies; (b) the use of PA technologies leaning towards specialty crop production; (c) PA technology adoption to be negatively related to age; and (d) PA technology adoption to be positively related to both farm size and education. Finally, we expect there to be significant differences in farmer and farm characteristics across the three irrigation districts, owing to differences in cropping patterns and climatic variables. Our findings show no district embodies all the farm and farmer characteristics we expected. But, as expected, within districts and across districts, there are statistically significant differences in many of the characteristics studied. Consolidating and comparing the results leads to interesting profiles of the districts, where one district is relatively distinct and the two others are relatively similar; and consistent across all districts are the positive indicators for agricultural sustainability relative to farmers’ estimates of reduced inputs of irrigation water, fertilizer, herbicides, and pesticides, as a result of the use of PA technologies. Keywords: precision agriculture, irrigation, agriculture, technology, Alberta

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

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

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 head0.232
Teacher spread0.216 · 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 teacher head, 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

Citations7
Published2021
Admission routes2
Has abstractyes

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