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Dynamic Adjustment of Irrigation Technology/Water Management in Western U.S. Agriculture: Toward a Sustainable Future

2010· article· en· W2081676625 on OpenAlexvenueno aff
Glenn D. Schaible, C. S. Kim, Marcel P. Aillery

Bibliographic record

VenueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomie · 2010
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityAgricultureWater useIrrigationIrrigated agricultureProduction (economics)Water resource managementNatural resource economicsBusinessEconomicsEnvironmental scienceGeography

Abstract

fetched live from OpenAlex

Changing water demands induced through climate change and a growing biofuel energy sector throughout the western States are expected to increase pressures on the present allocation mechanisms for an increasingly scarce resource, raising uncertainty about the sustainability of irrigated agriculture in the West. In this paper, we first present the policy motivation for examining continued producer adoption of water conserving irrigation production systems as a foundation for providing a sustainable future for western irrigated agriculture. Second, we summarize the historical transitions that help to define the adjustment path to increased sustainability for the sector. While western irrigated agriculture is on a path toward greater sustainability, evidence suggests that the sustainability goal has not been fully attained. Third, we develop a new conceptual framework for groundwater management that endogenizes both per acre applied water and an acreage‐based technology adoption relationship within a normative, dynamic‐optimization model for groundwater irrigated agriculture. The framework models producer adoption decisions under uncertainty while accounting for the influence of irrigation technology as a quasi‐fixed input, i.e., the influence of asset fixity on producer adoption decisions. In this model, total crop production is based on consumptive use of irrigation water while the cost side is based on total applied water . L’évolution de la demande en eau que suscitent le changement climatique et l’essor du secteur des biocarburants dans l’Ouest américain devrait faire monter les pressions sur les mécanismes actuels d’allocation d’une ressource de plus en plus limitée, soulevant ainsi de l’incertitude quant à la viabilité de l’agriculture irriguée dans cette région. Dans le présent article, nous avons tout d’abord présenté la motivation politique pour examiner l’adoption soutenue, de la part des producteurs agricoles, de systèmes de production irriguée axés sur l’économie de l’eau comme élément permettant d’assurer un avenir durable pour l’agriculture irriguée. Nous avons ensuite résumé les transitions historiques qui aident à définir les mesures à prendre pour accroître la viabilité du secteur. Bien que l’agriculture irriguée dans l’Ouest américain soit sur la voie d’une viabilité accrue, les données disponibles autorisent à penser que l’objectif de la viabilité n’a pas été pleinement atteint. Enfin, nous avons élaboré un nouveau cadre conceptuel pour la gestion de l’eau souterraine qui endogénise le lien entre l’eau utilisée à l’acre et l’adoption d’une technologie fondée sur la superficie dans le cadre d’un modèle d’optimisation dynamique normatif pour l’agriculture irriguée à partir des eaux souterraines. Le cadre conceptuel modélise les décisions d’adoption du producteur en présence d’incertitude tout en tenant compte de l’influence des technologies d’irrigation comme intrants quasi fixes, c’est‐à‐dire, l’influence de la fixité des actifs sur les décisions d’adoption du producteur. Dans ce modèle, la production végétale totale est fondée sur l’évapotranspiration d’eau d’irrigation tandis que l’aspect coût est fondé sur la quantité totale d’eau appliquée .

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.004
GPT teacher head0.140
Teacher spread0.136 · 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 designSimulation or modeling
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

Citations16
Published2010
Admission routes1
Has abstractyes

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