Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,007 | 0,004 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,427 | 0,222 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».