Quantifying farm sustainability through the lens of ecological theory

Abstract

ABSTRACTThe achievements of the Green Revolution in meeting the nutritional needs of a growing global population have been won at the expense of unintended consequences for the environment. Some of these negative impacts are now threatening the sustainability of food production through the loss of pollinators and natural enemies of crop pests, the evolution of pesticide resistance, declining soil health and vulnerability to climate change. In the search for farming systems that are sustainable both agronomically and environmentally, alternative approaches have been proposed variously called ‘agroecological’, ‘conservation agriculture’, ‘regenerative’ and ‘sustainable intensification’. While the widespread recognition of the need for more sustainable farming is to be welcomed, this has created etymological confusion that has the potential to become a barrier to transformation. There is a need, therefore, for objective criteria to evaluate alternative farming systems and to quantify farm sustainability against multiple outcomes. To help meet this challenge, we reviewed the ecological theories that explain variance in regulating and supporting ecosystem services delivered by biological communities in farmland to identify guiding principles for management change. For each theory, we identified associated system metrics that could be used as proxies for agroecosystem function. We identified five principles derived from ecological theory: (i) provide key habitats for ecosystem service providers; (ii) increase crop and non‐crop habitat diversity; (iii) increase edge density: (iv) increase nutrient‐use efficiency; and (v) avoid extremes of disturbance. By making published knowledge the foundation of the choice of associated metrics, our aim was to establish a broad consensus for their use in sustainability assessment frameworks. Further analysis of their association with farm‐scale data on biological communities and/or ecosystem service delivery would provide additional validation for their selection and support for the underpinning theories.