Assessment of Precision Irrigation on Potatoes in Southern Alberta
Notice bibliographique
Résumé
Precision irrigation, in which water is applied at different times and rates across and within farm fields according to environmental and soil conditions, offers a promising solution for effective water resource management. Excess irrigation can lead to disease and lower yields while under irrigation leads to a deficit often resulting in reduced yields and quality. Understanding water requirements is key to making informed irrigation decisions. Optimizing water usage for potato crops in southern Alberta is important in the face of water scarcity challenges. This project evaluates precision irrigation scheduling performance, including creation of management zones within a field, to determine which field variables have the most effect on potato yield, and analyze the effectiveness of predictive software. The data collected from five irrigated potato fields in Southern Alberta from 2019 to 2022, as well as from the Integrated Agriculture Technology Center (IATC) in 2021 and 2022, were analyzed. Annually, soil parameters, topography, moisture usage, and yield were evaluated at 5-6 monitoring points per field to represent variations within that field. Soil moisture at each point was monitored using moisture sensors and the Alberta Irrigation Management Model (AIMM) software was used to estimate evapotranspiration (ET) and soil moisture changes at the IATC site. It was revealed that topographic complexity had the most significant influence on soil moisture dynamics, resulting in significant effects on potato yield. Soil moisture had a significant positive effect on yield during the tuber bulking stage, especially at a depth of 0-35cm, but a significant negative impact at a depth of 35-60cm. While variations in growing degree days and soil complexity did not consistently affect yield, there was a tendency towards a negative effect. Moisture content variations among points at the IATC sites had no significant relationship with yield, indicating success in the ability of predictive scheduling and VRI in reducing this source of yield variability. The AIMM model demonstrated higher reliability in prediction of irrigation requirements in 2022 than 2021, possibly due to differences in factors such as soil organic matter, bulk density of the soil, soil texture, weather, topography, and subsoil constraints, which affect model performance, but were not measured in this study. Precision irrigation offers a potential solution to address water scarcity challenges by optimizing water use efficiency and enhancing crop yield and quality through informed irrigation practices and technology integration.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 ».