GRASSLAND BIOPHYSICAL PARAMETERS ESTIMATION USING REMOTE SENSING PRODUCTS TO SUPPORT PASTURE INSURANCE
Notice bibliographique
Résumé
As a source of food and employment, livestock farming is an important activity for many societies around the world. In a global context, livestock farming has significant differences due to cultural and environmental aspects. A common goal of livestock producers is for the herd to gain weight and to do so it is necessary to have food available for the animals throughout the year. Unfortunately, pasture growth is susceptible to risks such as overgrazing and climate variation, which makes productivity highly variable over time, affecting ecosystem sustainability and bringing economic losses to producers. To mitigate economic losses, one of the alternatives for producers is to resort to agricultural insurance programs. Within the agricultural insurance market, the index-based approach, which considers remotely obtained variables such as precipitation data and vegetation indices, has gained notoriety for having greater geographical coverage, lower operating costs, and faster premium payments. In this context, the main objective of this research is to investigate and identify inputs that can increase the accuracy of remote monitoring of pastures. The inputs/products derived from remote sensing addressed in this research can contribute to characterizing the canopy of a given area of interest, validate gridded precipitation data that can replace weather stations, and estimate biomass production. The results of this research show that remote sensing is effective for estimating biophysical parameters of native grasslands and identifying differences between vegetation conditions in different ecoregions of the Canadian Prairies. Despite having identified indices such as the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI) as indicators of vegetative growth, and the plant senescence reflectance index (PSRI) as an indicator of senescence, the leaf area index (LAI) proved to be an interesting parameter to be used for monitoring native grasslands because it presented significant correlation with other biophysical parameters. Good results were obtained for differentiating grassland/forage types at a more detailed level, especially in the Moist-Mixed and in the Mixed Ecoregions. Evidence was also gathered that gridded data can be important to estimate precipitation and identify atmospheric events that may affect plant development, especially in regions with few meteorological stations or with gaps in the time series. It was concluded that the dry matter productivity model (DMP), despite the short data history, is a better biomass production estimator than the NDVI and the enhanced vegetation index (EVI2), especially when combined with other parameters such as annual average NDVI and the annual average temperature. The results obtained in this research showed solid evidence that remote sensing can improve the accuracy of pasture monitoring and be a valuable support tool for the agricultural insurance market, especially for the index-based approach, making the relationship between insurance companies and rural producers clearer and fairer.
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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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 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) | 0,001 | 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 ».