Geostatistical Analysis of Yield Monitor Data for Precision Agriculture
Notice bibliographique
Résumé
It is long known that yield and other crop and soil characteristics vary across a farm field with measurements in the neighborhood being more similar than those far apart. However, such in-field spatial variability has been generally ignored because uniformity is required as a convenient means of operating modern farm equipment for most farming practices such as crop inputs and harvest. Moreover, until recently, the ability to detect and assess the in-field spatial variability has been limited. The situation is now changing with the recent advent of geomatic technologies such as yield monitors equipped with GPS on combine harvesters. The objective of this research was the geostatistical analysis of data from one such technology (yield monitor data). The focus was investigating the utility of multi-year yield monitor data from the same farm field located in southern Alberta for identifying patterns and stability of spatial variability. In this 125 ha field, three crops were grown in four years: wheat (Triticum aestivum L.) in 2008, canola (Brassica napus L.) in 2009, wheat in 2010 and barley (Hordeum vulgare L.) in 2011. Yield readings were cleaned using Yield Editor version 2.0 and normalized to remove scaling effect over different crops and years. The cleaned and normalized data were analyzed to fit three variogram models (exponential, Gaussian, and spherical) that are commonly used in geostatistical applications. The model fitting indicated that the similarity between yield readings were best described by an exponential function of the distance separating the readings, but with the similarity disappearing at different distances in all four crop years, ranging from 39.6 m (2008) to 99.6 m (2009). The spatial stability of yield patterns over the years was measured by Pearson’s correlations using interpolated yields mapped to a common grid. The apparent lack of spatial stability over the years suggests that recommended inputs or farm-level decisions such as variable rate applications cannot be based just on ‘eyeballing’ yield/soil maps from raw data at one farm in one year. Instead, these recommendations or decisions should be based on the maps or information derived from predicted data at multiple farms/locations over multiple years under tested, statistically sound spatial models for precise and profitable management of farm fields.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| 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 | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| 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 tête enseignante, 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 ».