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

Abstract

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.

Recent advances in decision support for beef and dairy farming: modeling approaches and opportunities

“…A frequent problem in this regard is how to balance onfarm land use with livestock production requirements for increasing economic performance (see Flaten et al, 2019a, and other studies occurring under both "crops, forage, pasture" and "feed" in Table 8). Less frequently, but worthy of mention, are problems on decisions about what type of farm to run (Mosnier et al, 2017;Diakité et al, 2019aDiakité et al, , 2019bDominati et al, 2019;Rendel et al, 2020;Kokemohr et al, 2022), biosecurity measures (Shrestha et al, 2018), the use of technology (Kuhn et al, 2019;Worden and Hailu, 2020;Kleftodimos et al, 2022), and how to dispose or make use of materials that represent an incidental side effect of primary production or byproducts (Kuhn et al, 2019;Taifouris and Martin, 2021;Cecchini et al, 2022;Kokemohr et al, 2022). Another topic worthy of mention is how to improve animal welfare through decisions relating to cow-calf contact, as exemplified by Asheim et al (2016) (see, e.g., Hansen et al, 2023, for further insight to the topic).…”

“…Cecchini et al (2022) and Rojas-Downing et al ( 2018) consider climate variability, while Worden and Hailu (2020) consider uncertainty in genomic selection and breeding. Furthermore, many of the models are static (see, e.g., Klootwijk et al, 2016;Notte et al, 2016;Dominati et al, 2019;Bellingeri et al, 2020;Rendel et al, 2020;Salinas-Martínez et al, 2020;Allison et al, 2021;Quintero-Herrera et al, 2021), but there are also many models that are dynamic (see, e.g., Castelán-Ortega et al, 2016;Cortez-Arriola et al, 2016;Romera and Doole, 2016;Shrestha et al, 2018;Paul et al, 2020;Ouellet et al, 2021;Taifouris and Martin, 2021;Kokemohr et al, 2022). The dynamic nature reflects that many of the decisions in farming rely on how some factors change over time, for example, the milk yield or weight of an animal.…”

AgYields - a national database for collation of past, present and future pasture and crop yield data

“…1) The emergence of models which require these fundamental data, for wider regional locations, which are being applied to land use evaluation and planning such as AgInform (Rendel et al, 2020) and APSIM (Vogeler et al, 2016). 2) Emerging challenges to crop and pasture productivity and persistence (e.g., climate change; environmental regulations) which will require data on a range of species.…”

How Does Network Infrastructure Construction Affect Livestock Carbon Emissions?

“…Research has revealed that the NIC facilitates the mitigation of factor misallocation and fosters progress in technology innovation [15]. Furthermore, the reduction of factor misallocation and the improvement of technology innovation in the livestock industry are conductive to reducing LCEs [16,17]. However, no literature combines the two aspects of research to explore whether NIC can affect LCEs and the potential transmission pathways.…”