Abstract

CONTEXTRuminant livestock make an important contribution to global food security by converting feed that is unsuitable for human consumption into high value food protein, demand for which is currently increasing at an unprecedented rate because of increasing global population and income levels. Factors affecting production efficiency, product quality, and consumer acceptability, such as animal fertility, health and welfare, will ultimately define the sustainability of ruminant production systems. These more complex systems can be developed and analysed by using models that can predict system responses to environment and management. OBJECTIVEWe present a framework that dynamically models, using a process-based and mechanistic approach, animal and grass growth, nutrient cycling and water redistribution in a soil profile taking into account the effects of animal genotype, climate, feed quality and quantity on livestock production, greenhouse gas emissions, water use and quality, and nutrient cycling in a grazing system. METHODSA component to estimate ruminant animal growth was developed and integrated with the existing components of the SPACSYS model. Intake of herbage and/or concentrates and partitioning of the energy and protein contained in consumed herbage and/or concentrates were simulated in the component. Simulated animal growth was validated using liveweight data from over 200 finishing beef cattle and 900 lambs collected from the North Wyke Farm Platform (NWFP) in southwest England, UK, between 2011 and 2018. Annual nitrous oxide (N2O), ammonia, methane and carbon dioxide emissions from individual fields were simulated based on previous validated parameters. RESULTS AND CONCLUSIONSA series of statistical indicators demonstrated that the model could simulate liveweight gain of beef cattle and lamb. Simulated nitrogen (N) cycling estimated N input of 190 to 260 kg ha−1, of which 37–61% was removed from the fields either as silage or animal intake, 15–26% was lost through surface runoff or lateral drainage and 1.14% was emitted to the atmosphere as N2O. About 13% of the manure N applied to the NWFP and excreta N deposited at grazing was lost via ammonia volatilisation. SIGNIFICANCEThe extended model has the potential to investigate the responses of the system on and consequences of a range of agronomic management and grazing strategies. However, modelling of multi-species swards needs to be validated including the dynamics of individual species in the swards, preferential selection by grazing animals and the impact on animal growth and nutrient flows.

Highlights

  • We are at a critical juncture for global livestock production as competing requirements for maximal productivity and minimal pollu­ tion have driven the requirement for sustainable intensification (Springmann et al, 2018)

  • About 13% of the manure N applied to the North Wyke Farm Platform (NWFP) and excreta N deposited at grazing was lost via ammonia volatilisation

  • Modelling of multi-species swards needs to be validated including the dynamics of individual species in the swards, preferential selection by grazing animals and the impact on animal growth and nutrient flows

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Summary

Introduction

We are at a critical juncture for global livestock production as competing requirements for maximal productivity and minimal pollu­ tion have driven the requirement for sustainable intensification (Springmann et al, 2018). Sustainable intensification of ruminant livestock may be applied to pastoral grazing, mixed-cropping, feedlot, and housed production systems All these systems have associ­ ated environmental impacts such as water and air pollution where greenhouse gas (GHG) emissions, soil degradation and erosion are all of particular concern. Factors affecting production efficiency, product quality, and consumer acceptability, such as reduced animal fertility, health and welfare, impact on the sustainability of rumi­ nant production systems. These challenges necessitate multidisciplinary solutions that can only be properly researched, implemented and tested in real-world production systems (Eisler et al, 2014). These more complex systems can be developed and analysed by using models that can predict system responses to environment and management

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