Offstream watering points for cattle as a method for riparian restoration
Notice bibliographique
Résumé
The cattle grazing industry contributes to land and water degradation both in Australia and internationally, particularly in countries such as the United States of America and Canada. Currently, 50% of the world’s cattle production is set to occur in tropical and subtropical climates by 2050, but research of sustainable management practices for cattle grazing in these areas is limited. Historically, environmental research conducted in agricultural areas have focused on the negative impacts on land and water resources, while the identification, implementation and evaluation of best management practices (BMP) to counter environmental impacts while sustaining agricultural production is an evolving field of research, particularly in the Australian context. River and floodplain habitats are vital ecosystems that bridge terrestrial and aquatic ecosystems, and cattle grazing land is often collocated with these environments. By implementing BMPs, riverine ecosystem services can be maintained or improved to provide benefits to both graziers and the environment. An example of a best management practice designed to protect riverine environments is the provision of an offstream watering point (OSWP, more commonly known as a water trough) to cattle as an alternative water source to instream water, thereby reducing the time cattle spend in streams. However, previous research investigating the effectiveness of OSWPs at reducing the time cattle spend in nearby river, stream or riparian habitats has delivered mixed results. This study investigated OSWP utilisation by cattle in the Fitzroy Basin, Queensland, Australia to better understand why the effectiveness of OSWPs is not uniform. This research investigated how utilisation of OSWPs by cattle may vary with environmental factors (e.g. temperature, water quality, etc.) by initially conducting a systematic literature review and then exploring relationships among key factors identified particularly relevant ii to the beef grazing industry. Subsequently, I formally examined relationships among variables of water quality, temperature humidity index, rainfall, OSWP distance to the stream or river, vegetation, and stocking density on the results utilisation of OSWPs by cattle. Cattle’s utilisation of OSWPs was measured as a function of water consumption (WC) and visitation to OSWPs using real-time field data collected between the months of September 2017 to February 2018 from 33 paddocks in the major beef grazing region of the Fitzroy Basin. A controlled experiment was then conducted to assess cattle’s drinking preference of OSWP water quality where they were given a choice between good and lower quality water, compared to the volume of water consumed when there was no choice, and the subsequent effects this may have on cattle production. The results of the systematic literature review indicated that the time cattle spent on riverbanks (in the presence of an OSWP) was associated with paddock slope and distance between the OSWP and the river. Significant relationships were identified among factors including water quality in OSWPs, rainfall events, vegetation greenness, riparian condition, and water consumption volume (which included Temperature Humidity Index as a co-variable). Field observations on commercial beef grazing properties revealed that cattle consume approximately half of their daily water requirement from OSWPs when they have a choice to drink from either a river or an OSWP, with most OSWP visitations occurring from 8 am to 10 am. The controlled experiment indicated that cattle do not necessarily prefer water of good quality (filtered municipal water) over lower quality water when given a choice, as they consumed them equally. Instead cattle drank greater volumes when they were offered only lower quality water compared to when they were offered only good quality water. Consumption of good quality water additionally resulted in improved feed conversion rates by cattle. Findings of this study will contribute to improved water resource management for graziers and is likely to improve animal 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 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 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,001 |
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 ».