Estimation of phenological development and fractional leaf area of canola (Brassica napus L.) from temperature
Notice bibliographique
Résumé
Argentine Canola (Brassica napus L.) is an economically successful crop on the Canadian Prairies. The 1999 growing season had a record area seeded of 5,598,700 hectares, declining slightly to 4,894,600 hectares in 2000. Since 1997. canola has been ranked as Manitoba's most valuable agricultural commodity. Although canola is an important contributor to the Canadian economy, little research has been conducted at the field level to determine how crop phenological stage and ground cover respond to weather variables such as temperature. Such basic agronomic knowledge is essential for successful agrometeorological modeling. The objectives of this project were to develop a methodology for estimating phenological development and fractional leaf area of canola using temperature, and to further evaluate the accuracy of top-zone (10 cm depth) soil moisture modeled from a Canola Phenology and Water-Use Model. Five test sites within Agro-Manitoba were used during the 1999 growing season, while three test sites were used in 2000. Weekly field observations were conducted during the growing season to determine the phenological stage of the crop, the amount of ground cover, and near surface soil moisture. Daily maximum and minimum temperatures and rainfall data were obtained from the nearest Environment Canada weather station. The fungal infection Sclerotinia (Sclerotinia sclerotiorum (Lib.) de Bary) costs prairie canola producers approximately $260 million annually as a result of yield loss and management techniques requiring expensive fungicide applications. The current model for predicting sclerotinia stem rot on the Canadian Prairies estimates the risk of infection based on crop phenological stage and soil moisture estimates derived from the Raddatz model. However, the current model is regional in nature and is limited because crop stage is estimated using a simple growing degree-day (GDD) above 5oC, while soil moisture is modeled using a coupled atmosphere-crop-soil agrometeorological model, which utilizes the simple GDD relationship to estimate fractional leaf area. This study determined that a GDD above 5oC was an inadequate estimator of crop phenology and that the P-Days system, utilizing base, optimal, and maximum temperature thresholds of 5, 17, and 30oC, respectively was an overall better estimator of crop phenology. Further results indicated that fractional leaf area was better estimated from P-Days(5,17,30) than from GDD used in the original model. Observed and modeled top-zone soil moisture values were compared. The relatively low R2 of 0.60 suggests that poor estimates of soil moisture was linked to the use of off-site precipitation data and perhaps linked to the inaccurate estimation of fractional leaf area from GDD above 5oC.
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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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 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 ».