An integrated pasture biomass and beef cattle liveweight predictive model under weather forecast uncertainty: An application to Northern Australia
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Published:2023-03-03
Issue:3
Volume:12
Page:
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ISSN:2048-3694
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Container-title:Food and Energy Security
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language:en
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Short-container-title:Food and Energy Security
Author:
Masoud Mahmoud1ORCID,
Hsieh Jeff2,
Helmstedt Kate2,
McGree James2,
Corry Paul2
Affiliation:
1. Department of Information Systems & Operations Management, Business School King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia
2. School of Mathematical Sciences Queensland University of Technology Brisbane Queensland Australia
Abstract
AbstractBeef production drives a significant proportion of Australia's agricultural economy, representing AUD11 billion in agricultural production value annually. The profitability of cattle farms across seasons varies greatly and is particularly influenced by weather and associated uncertainty through pasture growth. Furthermore, producers must be careful in managing stocking rates to avoid overgrazing which has a significant environmental and economic impact. Predictive modelling of pasture and cattle liveweight can help producers to better manage this difficult balancing act. In this paper, we integrate a model for pasture growth and cattle weight gain to formulate predictions of liveweight production volume and pasture biomass. Monte‐Carlo simulation was used to understand the impact of uncertainty in model inputs relating to weather. Through such an approach, a distribution of possible pasture outcomes and beef production is formed which can be inspected to determine, for example expected production volume and risk of overgrazing. The methodology is demonstrated and validated through application to case study data sourced from Australian beef producers. Results demonstrate that it is possible to obtain reasonable predictions of liveweight gain and highlight the importance of considering the associated uncertainty. The accuracy of the proposed models has been examined by calculating statistical parameters MSE, MAE and R2. This approach can provide insight into the distribution of possible production outcomes given the long‐term rainfall outlook. The use of such predictive models and simulations by producers will lead to better‐informed planning of stocking rates, with benefits to productivity and sustainable utilisation of pasture.
Funder
Ernst & Young Foundation
Meat and Livestock Australia
Subject
Agronomy and Crop Science,Renewable Energy, Sustainability and the Environment,Food Science,Forestry
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