Notice bibliographique
Résumé
Abstract Variability in seasonal precipitation, potential climate change impacts, competition for water among users, rising population and increasing food demands are putting pressure on agricultural water demands. For irrigated agriculture in Canada to play a major role in addressing current and future global food supply problems, more innovative and sustainable irrigation management approaches are required. In this context a decision support system that ensured more effective irrigation water allocation, application and optimisation was developed. Crop water requirements and irrigation schedules for bell pepper (Capsicum annuum L.) were obtained from greenhouse and field studies. Greenhouse experiments were conducted to determine appropriate irrigation water applications, agronomic and physiological response to water stress for peppers grown on clay and loamy sand soils. These studies involved four irrigation levels -120% (T120), 100% (T100), 80% (T80) and 40% (T40) of pan evaporation (Epan). The results showed that highest yields and water use efficiency were obtained with 120% Epan replenishment on loamy sand compared to clay soil. The corresponding crop water stress index (CWSI) at T120 was 0.18 to 0.20 on clay, and 0.09 to 0.11 on loamy sand. The fruit total soluble solids content was highest in the T40, and least in the T120 treatments.Given that the greenhouse results were obtained under controlled conditions, it was necessary to extend the research in the field. Experiments were conducted to determine the level of available soil water at which irrigation should be applied to prevent water stress and yield loss for peppers on a clay soil. Four irrigation thresholds, as a percentage of available water content, were investigated. These were: 85% (T1), 75% (T2), 50% (T3), and 25% (T4) available water content. A control of no irrigation (T5) was implemented. The crop water stress index (CWSI) and effects of elevated CO2 on the stomatal conductance and water applied were also investigated. The three CO2 levels studied were: ambient CO2 (~400 ppm), predicted CO2 for the year 2050 (550 ppm), and predicted CO2 for the year 2100 (750 ppm). Optimum marketable yields were achieved when 50% (T3) of the available water content had been depleted with a corresponding CWSI of 0.3 to 0.4. A decrease in stomatal conductance with increasing CO2 was observed. Irrigation water requirements decreased by 6-42% under elevated CO2 of 550 ppm, and 28-58% for elevated CO2 of 750 ppm. An integrated agricultural water demand model (IAWDM) was developed using a graphical user interface (GUI) in Matlab to estimate irrigation water requirements (IWR). A pre-requisite for the model development was to ensure that solar radiation (Rs) input data were of good quality. The suitability of nine (Rs) estimation methods, and their effects on reference evapotranspiration (ETo) were evaluated using data from eight weather stations across Canada. Based on Root mean square error (RMSE) of 1-6%, the Hargreaves and Samani (H-S) method gave best results for locations that did not have reliable, long term, observed Rs and sunshine duration data. Output from the IAWDM was compared with CROPWAT simulations, and metered irrigation water-use. IWR from IAWDM deviated from field data by 7 to 28%, while CROPWAT deviated by 7 to 42%. Future IWR was estimated using Agriculture and Agri-food Canada (AAFC) generated climate change data for 2040 to 2069. Results showed that IWR of bell peppers will increase by 19 to 27% in the future. A sensitivity analysis showed that IWR is most sensitive to air temperature, reference evapotranspiration (ETo), and crop coefficients, followed by solar radiation and precipitation.Overall the findings from this study led to a more sustainable greenhouse and field production of vegetable. The improved management practices increased irrigation water use efficiency thereby leading to a more beneficial use of agricultural water.
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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,022 | 0,006 |
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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».