Development of management zones for site-specific fertilization in potato fields
Notice bibliographique
Résumé
Soil variability and the resultant lower potato tuber yield can be mitigated through precision agricultural practices. This study has quantified variability of soil and crop properties, identified significant factors responsible for fluctuations in tuber yield, and delineated management zones (MZs) for site-specific soil fertility characterization of potato fields through proximal sensing of fields. The field experiments were conducted during potato growing seasons of 2017 and 2018 in the region of Souris alongside the La Pierre Lane in Prince Edward Island (PEI), Canada. Grids of 25 m x 25 m were established, and soil was sampled from each grid and analyzed for soil chemical properties. Time Domain Reflectometry (TDR), DualEM-2 sensor, and GreenSeekerTM were used to collect each grid’s moisture content (θ), horizontal coplanar geometry (HCP) of the apparent ground electrical conductivity, and normalized difference vegetation index (NDVI), respectively, at various plant growth stages during the cropping seasons. The soil samples were collected from the same grids to determine soil organic matter content (SOM), pH, Lime Index (LI), phosphorous (P), potassium (K), calcium (Ca), iron (Fe), cation exchange capacity (CEC) and %P/Al (P aluminum ratio) using standard methods. Potato tuber yield was collected manually from 0.91 x 3 m strips at the same grids. Results suggested that most of the parameters had moderate to high variability in both fields. Tuber yield was high in both fields due to high HCP, θ and SOM. Results from semivariograms revealed that the selected soil and crop properties showed a low, moderate, and high spatial dependence within the fields. Tuber yield had highly significant (p < 0.001) correlations with HCP, θ, SOM, and P during first sampling in the beginning of the growing season. However, during the second sampling, the tuber yield was significantly correlated with HCP, θ, NDVI, SOM, P, and K. Stepwise regression (through backward elimination at α = 0.01) excluded the low important variables, namely P and K, leaving HCP, θ, SOM, and NDVI as the most influential variables for tuber yield. Stepwise regression shortlisted the major properties of soil and crop that explained 71 to 86% of within-field variability. The cluster analysis grouped the soil and crop data into three zones, termed as excellent, medium, and poor, at a 40% similarity level. The coefficient of variation and the interpolated maps characterized least to moderate variability of soil fertility parameters except for HCP and K, which were highly variable. The results of multiple means comparison indicated that the tuber yield and HCP were significantly different in all MZs. The significant relationship of HCP and yield suggested that the ground conductivity data can be used to develop MZs for site‐specific fertilization in potato fields like those used in this study. The soil and crop variability data helped establishing MZs that can facilitate site-specific precision nutrient management for improving soil fertility and optimizing potato tuber yield. Moreover, this study suggested that managing the crop inputs based on HCP, θ and NDVI has significant potential to enhance tuber yield. The delineation of MZs has been suggested as a solution to mitigate adverse impacts of soil variability on potato tuber yield.
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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) | 0,000 | 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 | 0,001 | 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 ».