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Record W7022384401

Dissertation

2019· dissertation· en· W7022384401 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureContext (archaeology)Profitability indexGovernment (linguistics)Production (economics)IrrigationBest practiceWater conservationAgricultural productivity
DOInot available

Abstract

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Primary agriculture and the agri-food sector contributed $112 billion to Canada’s economy in 2017. Within Canada, Ontario and Québec are the two largest fruit and vegetable producing provinces. Irrigation and drainage practices are necessary to produce these high value horticultural crops. With an increasing population, and a decrease in water resources brought forward by climate change and competing uses from other sectors, there is a concern that water for irrigation purposes might be less readily available in the future. This situation can pose serious economic risks to agricultural producers and environmental risks to habitats and ecosystems. Farmers’ adoption of improved water management practices or technologies can be both a mitigation of environmental impacts strategy and an adaptation to a changing environment. Across Canada, efforts have been made to develop and implement improved technologies and management practices, beneficial management practices (BMPs) that could be implemented with government help (such as through cost-share programs) or without them. Such BMPs are intended to minimize the negative impacts of agricultural production on the environment. For farmers to be favourably disposed to their adoption, financial gains are important. To understand the profitability of specific BMPs and factors that influence their uptake, it is important to examine these factors within the socio-economic and environmental context of interest. Building on existing literature, this study aimed at filling certain research gaps. Namely, it was interested in understanding social, economic and environmental effects of improved water management practices and technologies in the context of Canadian fruit and vegetable production. Furthermore, this study aimed to contribute to the adoption of agricultural innovation literature selecting factors that foster or limit adoption of BMPs, together with building on the discussion on the importance of technology characteristics in the adoption process. This study evaluated different irrigation and drainage practices (BMPs) within the context of three farms by comparing the situation when the BMP was adopted and that when it was not. To evaluate the financial desirability of investments, production budgets were developed for each of the scenarios. Results were converted into two indicators – net present value (NPV) and benefit-cost ratio (BCR), in order to assess the financial desirability of investments. The profitability of proposed BMPs was realized using three case study farms. The first one was a field grown tomato-producing farm, located in Leamington, Southern Ontario, where under the baseline scenario surface drip irrigation was evaluated, and the proposed BMP was subsurface drip irrigation. A cranberry farm, located in Saint-Louis-de-Blandford, Québec represented the second case study. The baseline scenario reflected the effects of growing cranberries under a relatively wet water management strategy, without water table control. The BMP scenario represented a drier water table management strategy, where tensiometers were used to assess water needs. The third case study was a dry onion production farm, located in Saint-Patrice-de Sherrington, in Montérégie, Québec. The baseline scenario was that of no irrigation and no water table management, and the BMP scenario was sprinkler irrigation system together with the use of a tensiometer to help determine crop water needs. The second major objective of this study was to understand regional growers’ views on the proposed BMPs and the influence of different factors in their intention to adopt the proposed BMPs. To address this objective, regional producers were surveyed. There were 70 growers who completed the survey – 39 tomato growers, 19 cranberry growers and 12 onion farmers. To identify key determinants of adoption and perception of the BMP, two econometric models were used – a logistic regression model to explain adoption, and an ordered logistic model to determine influential factors in perception formation. This thesis findings show that for all three commodities, the proposed improved water management BMP financially outperformed the baseline technology. The robustness of these results was reaffirmed through sensitivity analyses. In terms of environmental effects, it is more difficult to make more certain conclusions. One of the environmental effects of interest was GHG emission levels coming from the different BMPs. These data were collected only over two growing seasons and showed a large variability over the two periods. This resulted in a lack of statistical significance in the differences between each of the two water management systems. Findings from Edwards (2014), Grant (2014) and Lloyd (2016), showed that even though the differences were not statistically significant, on average, over two growing seasons the proposed BMPs produced less GHG emissions, these results need to be further verified. The regional survey results showed that half of the sampled farmers were in favor of adopting BMPs, whereas the other half were not. However, when different farm groups were analyzed, the majority of onion growers were interested in the proposed BMP, cranberry producers were also predominately in favor of adopting sub-irrigation, whereas tomato growers were not interested in adopting a subsurface drip irrigation system. When compared to non-adopters, adopters had attained higher education levels, had a higher share of income coming from agricultural activities, and had less faming experience and primarily financial goals from farming. Adopters also had a higher share of sales coming from the selected crop (tomato, cranberry or onion) and owned a higher share of their farmed land than non-adopters own. Producers perceived a BMP as a better alternative if it provided an added economic benefit, as well as reduced costs or added benefits to the local and global community. Farmers perceived the proposed BMPs as being profitable, but expensive, capable of improving crop yields and having the potential to reduce water use on their farms. Related to BMPs perceptions, several differences were identified between adopters and non-adopters. When compared to non-adopters, adopters perceived the BMPs as a better alternative than their current water management systems. BMPs were perceived as better alternatives if they were profitable, capable of increasing crop yields, reducing GHG emissions, reducing water use, fertilizer and chemical run-off from their farms, and benefiting the local community and society at large. Economic factors predominantly influenced decisions of producers for adoption of the BMPs. Among these, influencing factors included BMP’s capacity to increase yields, the profitability of investment, and ability to perform a trial of the technology. In addition to these factors, adopters also found non-financial factors like demonstrating environmental stewardship, important. Main factors identified as reasons to not adopt the BMPs, in the order of their importance were: market stability, profitability of investment, initial cost of the system, and the risk of investment. Different combinations of factors can explain the likelihood of adopting the proposed BMPs. Based on this thesis, the models that best explain variations in likelihood of adoption should contain a mixture of farm, farmer and BMP characteristics related factors. Producers’ perception that a BMP is better than the one they are currently using (degree to which a BMP is being perceived as a better alternative), explained most of the adoption outcomes (Variable ADOPT). A specialized grower, with higher education, who also perceived the BMP as a better alternative, and whose farming goals were mainly financial ones, was indicated to more likely adopt the proposed BMPs. Given that one of the most important characteristics of a BMP in the adoption process is whether farmers perceive the BMP as a better alternative than the current practice, factors affecting this variable were identified using an ordered logistic function. Farmers with higher order farming goals (financial and lifestyle or social goals) and with higher education levels were less likely to find the alternative better than their current practice. Whereas, more specialized farmers perceiving the BMP as providing benefits to society, and who thought that making best use of scarce resources is important, along with the belief that the proposed BMP would reduce water use on their farm, were more likely to perceive the practice as a better alternative.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.573
Threshold uncertainty score0.818

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.001
Scholarly communication0.0070.004
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.4270.222

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 head0.243
Teacher spread0.239 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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