Predicting Response of Potato and Barley to Climate Change in Maine Using the Crop Model DSSAT
Notice bibliographique
Résumé
Climate change has the potential to impact yield and yield stability, and thus, sustainability in agriculture. Farmers are confronted seasonally with the challenges and unpredictability weather can bring. Current climate change projections anticipate an overall rise in temperature, precipitation and CO2 for the Northeast with weather increasing in variability in the forms of heatwaves, drought and heavy rain events. Using the computer simulation model DSSAT (Decision Support Systems for Agrotechnology), we aimed to assess the vulnerability and potential climate adaptation strategies for potato and barley in Maine. Chapter 1, “Assessing the Vulnerability of Potato and Barley to Climate Change using the Crop Model DSSAT”, encompasses the calibration and evaluation of the crop model DSSAT for two varieties each of potato (an early-season and late-season) and barley (a 2-row variety and 6-row variety) in Maine. The growth and development of each variety was assessed across numerous planting dates under a baseline weather scenario (1989-2018) and four future weather scenarios for 2050-2079, varying by emissions scenario and CO2 concentration. An additional assessment was conducted looking at yield stability under less variable and more variable weather. Following any necessary adjustments, model evaluations found the calibrated model to adequately simulate all four varieties under various management and growing conditions in the state. Subsequent simulations revealed that the late-season variety of potato and the 6-row barley variety may be more stable with climate change in Maine, while the early-season variety of potato may be more vulnerable, particularly with increased weather variability. The late-season variety of potato and both varieties of barley performed best with the earliest possible planting, while the early-season variety of potato performed better with late planting. Crop growth and development improved with climate change and projected elevated CO2 for all four varieties in terms of biomass and final yield. Crop quality could not be evaluated. Chapter 2, “Investigating Soil Health as a Climate Resilience Strategy for Potato and Barley in Maine Using the DSSAT Crop Model”, evaluates adaptive management strategies for potato and barley in Maine. Adaptive management strategies included improved soil health in a manure-based system (amended) and irrigation in a fertilizer-based system, both compared to a conventional fertilizer-based system (nonamended). Here, the model was evaluated for a set of data containing many rotations of potato and barley in a nonamended fertilizer-based system and an amended manure-based system. Following minor changes and a successful evaluation, simulations were conducted using a feasible planting date and the five weather scenarios from Chapter 1. Results found the irrigated system to perform best under all five weather scenarios for potato with the amended system a close second in production performance, while the performance of barley in the amended system was equal to that of the irrigated system. While irrigation may not be the most viable option for all Maine farmers, this study illustrated the importance soil health both now and in the future in improving or maintaining current crop production.
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) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 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 | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».