The growing interest and momentum surrounding the regenerative agriculture (RA) movement has generated considerable debate about motivations, definitions, stakeholders, and the role that scientific research plays. Amidst the sometimes-emotive debate there are some incontrovertible facts and drivers.
The first of these is that most human activity is exploitative of the natural resource base upon which it relies. Agriculture is no different to other sectors of the economy in that its ability to sustain production and profits relies explicitly or implicitly on cycles of renewal and exploitation of natural resources, and no matter how diverse, the ecology of an agricultural landscape bears little resemblance to the native ecosystem it has replaced.
The second issue is that one manifestation of the renewal-exploitation cycle, land degradation, is particularly acute in Australia and arresting it is an existential issue for mainstream agriculture. Thirdly, there is growing interest and activity in carbon abatement opportunities for agriculture, reinforced by injection of capital investment. In some countries 'nature-based' farming is becoming the primary expectation of society towards agriculture.
The emergence of these three drivers, signifying economic, ecological and social dimensions, will continue to require science-based evidence and enquiry to support discussion and decision making. Otherwise, RA will devolve to an unhelpful mix of fact, belief and sometimes mischievous political motives, with the consequence that beneficial practices may be ignored by many producers.
The aim of this paper is to propose a role for scientific activity in working with the RA movement. This is not to imply that regenerative agriculture is simply defined by on-the-ground practice and factors related to sustaining productivity and provision of ecosystem services. The authors recognise that regenerative agriculture also includes recognising increase in wellbeing and empowerment of practitioners. The intent in this paper is to look specifically at those questions where agricultural or data sciences have salience.