Analysis of Ergot (Claviceps purpurea) Alkaloids in Grain and Effect on Cow-Calf Performance
Notice bibliographique
Résumé
Ergot contamination of cereal crops and grasses has been of increasing concern for both grain and animal producers. The overall objective was to examine ergot contamination from an analytical, regulatory and biological perspective. The objectives of the first study were to determine the correlation between number of ergot sclerotia and weight compared to the total ergot alkaloid concentration, to evaluate the effect of particle size (PS) on ergot alkaloid analysis and to determine the impact of sample volume on analytical variability. This study demonstrated that correlations existed between both ergot sclerotia count and weight compared to the total alkaloid concentration (P’s < 0.001) but did not exist for either, at ergot alkaloid concentrations below 350 µg/kg (P = 0.956 and 0.769 respectively). This study also determined that a finer grind (PS = 192 µm) produced a lower variability (P = 0.041) than a coarser grind (PS = 516 µm). The coefficient of variation (CV) was also numerically reduced as sample volume increased (97% CV for 75 mL to 64% CV for 1000 mL; mean of all concentrations) but increased as sample concentration declined (17% CV for 81678 µg/kg to 284% for 35 µg/kg; mean of all sample volumes). An analytical approach with fine grinding of a minimum sample volume of 1000 mL (if not the entire sample available) should be used to assess ergot contamination to reduce variability. The objective of the second study was to determine if the percentages of individual ergot alkaloids were similar across different cereal grains collected from across Western Canada over different years. Ergocristine was the predominant alkaloid accounting for half of the total alkaloids in all grain types. Ergocornine (6% ± 1; P = 0.201), ergocristine (48% ± 2; P = 0.939), ergocryptine (17% ± 2; P = 0.302) and ergosine (5% ± 0.5; P = 0.239) were of similar proportions in barley (n = 39), rye (n = 7), triticale (n = 9) and wheat (n = 94). However, small differences were found between grain types for both ergometrine and ergotamine (P = 0.027 and 0.011 respectively). There were no yearly alkaloid proportion differences between the six alkaloids in barley and wheat 2015/2016 samples (P = 0.969, 0.680, 0.572, 0.080, 0.119 and 0.189 for ergocornine, ergocristine, ergocryptine, ergometrine, ergosine and ergotamine respectively). However, ergocornine was higher in wheat (P = 0.017) and ergometrine was higher in barley (P = 0.002) when comparing the 2015/2016 barley and wheat samples. With the overall proportions of ergot alkaloids comparable among the four grain types collected across Western Canada a maximum total ergot alkaloid concertation, as opposed to individual ergot alkaloid concentrations, can be considered acceptable. The objective of the third study was to evaluate the impact of ergot consumption in cow-calf performance. Overall, ergot alkaloid concentrations up to 820 µg/kg for 9 weeks had no effect on cow weight, calf weight, prolactin concentration, rectal temperature, progesterone concentration or timing of first progesterone rise (P = 0.931, 0.077, 0.298, 0.163, 0.792, 0.376). There was also no effect on the size of the first and second follicle to ovulate postpartum (P = 0.403 and 0.414 respectively) or the number of days until the first and second postpartum corpus luteum appearance (P = 0.949 and 0.984 respectively). The maximum size of the corpus luteum was 4 mm larger in the 820 µg/kg ergot treatment group compared to the control treatment (P = 0.028) for the first ovulation postpartum, however no differences were observed between the control and 820 µg/kg groups by the second ovulation (P = 0.113). A revised ergot tolerance concentration of 820 µg/kg for beef cows is suggested based on the reproduction/hormone endpoints.
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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 | 0,001 | 0,002 |
| 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 | 0,001 | 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 ».