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Affiliation(s)

Department of Agricultural Forest and Food Science, University of Torino, Largo Paolo Braccini 2, Grugliasco 10095, Italy

ABSTRACT

There has been increased interest in quantifying the manure production of livestock, primarily driven by public authorities, who aim to evaluate the environmental impact of livestock production, but also at the farm level, to manage manure storage and availability of fertilizer for crop production. Moreover, current manure production estimates from intensively reared beef calves are higher than actual production due to changes in farming systems, advances in animal genetics and feed efficiency. This study aims to redefine and update manure production estimates in intensively reared beef calves to predict manure production as a policy and planning tool, as there are no current models available. A trial was conducted to collect data on manure production during the growing-finishing period (243 d) of 54 Limousine calves (from 346.7 to 674.0 kg live weight, LW). Such data were used to develop two models to predict manure excretion: (1) a complex mechanistic model (CompM), and (2) a simplified empirical model (SimpM). Both models were evaluated against an independent dataset including a total of 4,692 animals on 31 farms and 5 breeds. Results from CompM require interpretation because the model does not output a single value but a range of manure production (minimum, medium and maximum), and would therefore be more suitable for professional use. The SimpM could be considered simple, reliable, and versatile for predicting manure excretion at farm level. SimpM could be refined and improved by including data from other studies on beef cattle with distinct characteristics and management.

KEYWORDS

Beef cattle, growing-finishing calves, manure production prediction, process-based model, empirical model.

Cite this paper

Biagini, D. and Lazzaroni, C. 2022. "Modelling Manure Production in Beef Calves: Development, Evaluation, and Application of a Complete vs. Simplified Prediction Model." Journal of Agricultural Science and Technology A 12 (2022): 84-95.

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