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Land dedicated to livestock contributes at least 40% of the global agricultural output. While advances in the application of geospatial information systems and remote sensing technologies offer much to agriculture, capturing and using that rich spatial biophysical information is not a feature available in most farm systems models. In this paper, we tackle this gap describing a land-based integrated grazing farm optimisation and resource allocation model (AgInform®) that departs from the use of whole farm and average data, to the integration of biological data obtained directly from each of the land units within the farm. The model allows the exploration of the dynamics of biophysical and financial performance of the farm in a steady-state, single-year approach, where the opening and closing values of the biological elements of the farm system conditions must remain the same (e.g., animal numbers, herbage mass), unless otherwise specified. The user supplies pasture growth rates, minimum and maximum acceptable pasture masses for each land management unit (LMU), differential boundary conditions to deliver defined environmental outcomes, animal performance (sheep, beef and deer), farm costs and market prices. The linear programming (LP) equations formed by AgInform® can be divided into a single objective and constraints (which accommodate the boundaries), including those placed on individual LMUs. The optimization routine uses this information to identify the mix of livestock production enterprises that maximises profit for the business. The model in maintaining the link between available pasture mass and livestock requirements for each LMU throughout all calculations, enables the livestock type and number carried, along with the pasture mass required on each LMU throughout the year to achieve the required animal performance levels to be included as model outputs. A hill land sheep and beef farm consisting of seven distinct LMUs was used as a farm-level case to assess if AgInform® (1) has sufficient flexibility to integrate biological information from each LMU; (2) could use the specified livestock performance targets to derive a feasible livestock policy that optimised resource use and farm returns; (3) can assign each fortnight animal type and number and herbage mass to each LMU; and (4) can mimic reality to produce credible solutions.

Optimisation of the Resource of Land-Based Livestock Systems to Advance Sustainable Agriculture: A Farm-Level Analysis