The development of precision livestock farming (PLF) technologies for application in grazing systems is rapidly evolving. PLF technologies that facilitate the spatial and temporal management of variability in landscapes, pastures and animals promise to improve the efficiency, profitability and sustainability of livestock farming. However, such technologies as a complete package do not yet exist in grazing systems and the question of impacts at the farm system level remains unresolved. Other potential benefits, including the impacts of PLF technologies on the externalities of production, such as reductions in net greenhouse gas (GHG) emission intensity and other pollutants, also remain unclear.

To determine the net benefits of autonomous PLF technologies for grazing systems, particularly in the management of rotational grazing systems, supplementary feeding and livestock selling, an ex ante analysis was undertaken using the Sustainable Grasslands Model (SGM). The SGM is a stochastic dynamic bioeconomic model that simulates daily livestock production under climate risk at a mechanistic level, and integrates the results into a whole farm economic framework. The SGM is calibrated using data from Rothamsted Research’s North Wyke Farm Platform and is applied to a typical lowland sheep meat production system in the UK. It stochastically embeds climate risk in the simulations using climate data from 2000-2020. A fractional factorial design is used to test 5 temporal data densities (1, 8, 15, 22, 29 days) against 5 levels of livestock liveweight / pasture biomass assessment error (+0/0%, +0/10%, +10/20%, +20/30%, +30/40%) with 250 iterations per treatment. It assumes that management decisions regarding rotations (livestock movements), supplementary feeding and livestock selling are made each day data becomes available. This design allows for the comparison of current management practice, with a human assessment error of +20%/30% at 30-day decision-making intervals, to a complete ‘smart’ system (+0/0% at a daily decision interval) that hypothetically maximises the possible benefits from autonomous and integrated PLF technologies. The approach predicts the marginal productive, environmental and economic benefits from the full adoption of the PLF technology package with varying degrees of precision.

The modelling results indicate that the adoption of PLF technologies to improve precision in precision grazing systems increases system productivity and reduces the negative externalities of sheep meat production. Increasing the temporal density of data and its precision for decision making was found to reduce the inherent risk of the system, as well as reduce the impact of large climate shocks on the systems performance. However, Net Present Value as an annuity was found not to be maximised with daily data. In combination, the results indicate that the value of increasing temporal data density is higher than the value of reducing assessment error, but with diminishing marginal returns from increased data and decision-making resolution. The results also indicate that significant benefits from the adoption of advanced PLF technologies in precision grazing systems may be gained in terms of maintaining ground-cover/biodiversity in all parts of the landscape, improving the management of animal health and welfare, and reducing externalities, such as soil erosion and GHG emissions.