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Record W4206501483 · doi:10.13031/ja.14582

Dry Bean (<i>Phaseolus vulgaris</i> L.) Crop Water Production Functions and Yield Response Factors in an Arid to Semi-Arid Climate

2022· article· en· W4206501483 on OpenAlexaff
Vivek Sharma, Abhijit Rai

Bibliographic record

VenueJournal of the ASABE · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant pathogens and resistance mechanisms
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsIrrigationPhaseolusAgronomyEvapotranspirationEnvironmental scienceWater contentCropAridYield (engineering)Dry beanDeficit irrigationCrop yieldRandomized block designSoil waterSemi-arid climateMoisture stressIrrigation managementSoil scienceBiology

Abstract

fetched live from OpenAlex

Highlights Deficit irrigation negatively affected dry bean yield and yield components. Excess irrigation increased crop ETc but not dry bean yield. Soil moisture fluctuation was greater in the top 0.3 m of the soil profile compared to deeper depths. Crop water production function had a slope of 18.9 kg ha-1 mm-1 and threshold crop evapotranspiration of 171 mm. Dry bean crop was found to be sensitive to water stress (yield response factor Ky = 1.94). Abstract. Under changing climate conditions and declining water resources, understanding crop response to water stress is critical for effective irrigation management. The objectives of this study were to quantify dry bean (Phaseolus vulgaris L., cv. Othello) soil moisture dynamics, crop evapotranspiration (ETc), and yield response factor and to develop dry bean irrigation and crop water production functions (IWPF and CWPF). Five irrigation treatments, i.e., full irrigation (FIT), 75% FIT, 50% FIT, 25% FIT, and 125% FIT, were evaluated using a randomized complete block design (RCBD) with three replications for three years (2017, 2018, and 2019) in the arid to semi-arid intermountain region of Powell, Wyoming. The results showed a significant influence of irrigation on dry bean soil moisture dynamics and ETc. The dry bean crop showed a greater soil moisture fluctuation in the top 0.3 m of the soil profile compared to 0.6 m and at 0.9 m. ETc ranged from 187 to 438 mm, from 190 to 409 mm, and from 217 to 398 mm in the 2017, 2018, and 2019 growing seasons, respectively. A positive two-segment relationship was observed between dry bean seed yield and cumulative irrigation water applied. The average cumulative seasonal irrigation of 310 mm resulted in maximum seed yield. For all three years, the seed yield increased linearly with ETc. Combining the data from the three years resulted in a CWPF with a slope of 18.9 kg ha-1 mm-1 and an offset of 171 mm of ETc (i.e., the ETc required for crop establishment before any seed yield is produced, or threshold ETc). Moreover, the dry bean crop was found to be sensitive to water stress (Ky = 1.94). These results indicated that under the typical semi-arid to arid climate conditions of the intermountain region of Wyoming, deficit irrigation of dry bean may not be a viable strategy because the yield loss outweighs water-saving benefits. Keywords: Dry bean, Crop evapotranspiration, Crop production function, Irrigation water production function.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

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 head0.020
GPT teacher head0.215
Teacher spread0.194 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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