Sustainability in our food and fiber agriculture systems is inherently knowledge intensive. It is more likely to be achieved by using all the knowledge, technology, and resources available, including data-driven agricultural technology and precision agriculture methods, than by relying entirely on human powers of observation, analysis, and memory following practical experience. Data collected by sensors and digested by artificial intelligence (AI) can help farmers learn about synergies between the domains of natural systems that are key to simultaneously achieve sustainability and food security. In the quest for agricultural sustainability, some high-payoff research areas are suggested to resolve critical legal and technical barriers as well as economic and social constraints. These include: the development of holistic decision-making systems, automated animal intake measurement, low-cost environmental sensors, robot obstacle avoidance, integrating remote sensing with crop and pasture models, extension methods for data-driven agriculture, methods for exploiting naturally occurring Genotype x Environment x Management experiments, innovation in business models for data sharing and data regulation reinforcing trust. Public funding for research is needed in several critical areas identified in this paper to enable sustainable agriculture and innovation.

This study addresses the problem of balancing the trade-offs between the need for animal production, profit, and the goal of achieving persistence of desirable species within grazing systems. The bioeconomic framework applied in this study takes into account the impact of climate risk and the management of pastures and grazing rules on the botanical composition of the pasture resource, a factor that impacts on livestock production and economic returns over time. The framework establishes the links between inputs, the state of the pasture resource and outputs, to identify optimal pasture development strategies. The analysis is based on the application of a dynamic pasture resource development simulation model within a seasonal stochastic dynamic programming framework. This enables the derivation of optimum decisions within complex grazing enterprises, over both short-term tactical (such as grazing rest) and long-term strategic (such as pasture renovation) time frames and under climatic uncertainty. The simulation model is parameterised using data and systems from the Cicerone Project farmlet experiment. Results indicate that the strategic decision of pasture renovation should only be considered when pastures are in a severely degraded state, whereas the tactical use of grazing rest or low stocking rates should be considered as the most profitable means of maintaining adequate proportions of desirable species within a pasture sward. The optimal stocking rates identified reflected a pattern which may best be described as a seasonal saving and consumption cycle. The optimal tactical and strategic decisions at different pasture states, based on biomass and species composition, varies both between seasons and in response to the imposed soil fertility regime. Implications of these findings at the whole-farm level are discussed in the context of the Cicerone Project farmlets.

Farming systems research conducted under dryland conditions is subject to the vagaries of the climate during the experimental period. Whether such an experiment experiences a representative series of climatic years must be examined in relation to the longer term climatic record. The Cicerone Project’s farmlet experiment was conducted on the Northern Tablelands of New South Wales, Australia, to investigate the profitability and sustainability of three different management systems: one managed under typical, moderate-input conditions (farmlet B); a second which employed a higher level of pasture inputs and soil fertility (farmlet A); and a third which focussed on the use of moderate inputs and intensive rotational grazing (farmlet C). The climate experienced during the 6.5-year experimental period was compared with the 118-year climatic record, using a biophysical simulation model of grazed systems. The model utilised the long-term daily climate data as inputs and provided outputs that allowed comparison of parameters known to affect grazed pastures. Modelled soil-available water, the number of soil moisture stress days (SMSDs) limiting pasture growth, and growth indices over the experimental period (2000–06) were compared with data over the climatic record from 1890 to 2007. SMSDs were defined as when the modelled available soil moisture to a depth of 300 mm was <17% of water-holding capacity. In addition, minimum temperatures and, in particular, the frequency of frosts, were compared with medium-term (1981–2011) temperature records. Wavelet transforms of rainfall and modelled available soil water data were used to separate profile features of these parameters from the noise components of the data. Over the experimental period, both rainfall and available soil water were more commonly significantly below than above the 95% confidence intervals of both parameters. In addition, there was an increased frequency of severe frosting during the dry winters experienced over the 6.5-year period. These dry and cold conditions were likely to have limited the responses to the pasture and grazing management treatments imposed on the three farmlets. In particular, lower than average levels of available soil water were likely to have constrained pasture production, threatened pasture persistence, and reduced the response of the pasture to available soil nutrients and, as a consequence, livestock production and economic outcomes. Ideally, dryland field experimentation should be conducted over a representative range of climatic conditions, including soil moisture conditions both drier and wetter than average. The drier than average conditions, combined with a higher than normal frequency of severe frosts, mean that the results from the Cicerone Project’s farmlet experiment need to be viewed in the context of the climate experienced over this 6.5-year period.