Roles of instrumented farm-scale trials in trade-off assessments of pasture-based ruminant production systems

For livestock production systems to play a positive role in global food security, the balance between their benefits and disbenefits to society must be appropriately managed. Based on the evidence provided by field-scale randomised controlled trials around the world, this debate has traditionally centred on the concept of economic-environmental trade-offs, of which existence is theoretically assured when resource allocation is perfect on the farm. Recent research conducted on commercial farms indicates, however, that the economic-environmental nexus is not nearly as straightforward in the real world, with environmental performances of enterprises often positively correlated with their economic profitability. Using high-resolution primary data from the North Wyke Farm Platform, an intensively instrumented farm-scale ruminant research facility located in southwest United Kingdom, this paper proposes a novel, information-driven approach to carry out comprehensive assessments of economic-environmental trade-offs inherent within pasture-based cattle and sheep production systems. The results of a data-mining exercise suggest that a potentially systematic interaction exists between ‘soil health’, ecological surroundings and livestock grazing, whereby a higher level of soil organic carbon (SOC) stock is associated with a better animal performance and less nutrient losses into watercourses, and a higher stocking density with greater botanical diversity and elevated SOC. We contend that a combination of farming system-wide trials and environmental instrumentation provides an ideal setting for enrolling scientifically sound and biologically informative metrics for agricultural sustainability, through which agricultural producers could obtain guidance to manage soils, water, pasture and livestock in an economically and environmentally acceptable manner. Priority areas for future farm-scale research to ensure long-term sustainability are also discussed.     

MELODIE: a whole-farm model to study the dynamics of nutrients in dairy and pig farms with crops

In regions of intensive pig and dairy farming, nutrient losses to the environment at farm level are a source of concern for water and air quality. Dynamic models are useful tools to evaluate the effects of production strategies on nutrient flows and losses to the environment. This paper presents the development of a new whole-farm model upscaling dynamic models developed at the field or animal scale. The model, called MELODIE, is based on an original structure with interacting biotechnical and decisional modules. Indeed, it is supported by an ontology of production systems and the associated programming platform DIESE. The biotechnical module simulates the nutrient flows in the different animal, soil and crops and manure sub-models. The decision module relies on an annual optimization of cropping and spreading allocation plans, and on the flexible execution of activity plans for each simulated year. These plans are examined every day by an operational management sub-model and their application is context dependent. As a result, MELODIE dynamically simulates the flows of carbon, nitrogen, phosphorus, copper, zinc and water within the whole farm over the short and long-term considering both the farming system and its adaptation to climatic conditions. Therefore, it is possible to study both the spatial and temporal heterogeneity of the environmental risks, and to test changes of practices and innovative scenarios. This is illustrated with one example of simulation plan on dairy farms to interpret the Nitrogen farm-gate budget indicator. It shows that this indicator is able to reflect small differences in Nitrogen losses between different systems, but it can only be interpreted using a mobile average, not on a yearly basis. This example illustrates how MELODIE could be used to study the dynamic behaviour of the system and the dynamic of nutrient flows. Finally, MELODIE can also be used for comprehensive multi-criterion assessments, and it also constitutes a generic and evolving framework for virtual experimentation on animal farming systems.