Abstract

Sustainable intensification (SI) of agriculture is a promising strategy for boosting the capacity of the agricultural sector to meet the growing demands for food and non-food products and services in a sustainable manner. Assessing and quantifying the options for SI remains a challenge due to its multiple dimensions and potential associated trade-offs. We contribute to overcoming this challenge by proposing an approach for the ex-ante evaluation of SI options and trade-offs to facilitate decision making in relation to SI. This approach is based on the utilization of a newly developed SI metrics framework (SIMeF) combined with agricultural systems modelling. We present SIMeF and its operationalization approach with modelling and evaluate the approach’s feasibility by assessing to what extent the SIMeF metrics can be quantified by representative agricultural systems models. SIMeF is based on the integration of academic and policy indicator frameworks, expert opinions, as well as the Sustainable Development Goals. Structured along seven SI domains and consisting of 37 themes, 142 sub-themes and 1128 metrics, it offers a holistic, generic, and policy-relevant dashboard for selecting the SI metrics to be quantified for the assessment of SI options in diverse contexts. The use of SIMeF with agricultural systems modelling allows the ex-ante assessment of SI options with respect to their productivity, resource use efficiency, environmental sustainability and, to a large extent, economic sustainability. However, we identify limitations to the use of modelling to represent several SI aspects related to social sustainability, certain ecological functions, the multi-functionality of agriculture, the management of losses and waste, and security and resilience. We suggest advancements in agricultural systems models and greater interdisciplinary and transdisciplinary integration to improve the ability to quantify SI metrics and to assess trade-offs across the various dimensions of SI.

Highlights

  • To assess the extent to which SI metrics framework (SIMeF) metrics can be quantified using agricultural systems modelling for the ex-ante assessment of Sustainable intensification (SI) options, we used the set of models from the research project ‘Assessing options for the SUSTainable intensification of Agriculture for integrated pro­ duction of food and non-food products at different scales’ (SUSTAg)

  • Our study proposes that a holistic, generic, and policy-relevant SI metrics framework, such as SIMeF, together with agricultural systems modelling for the ex-ante assessment of SI options is a powerful approach to aid SI-related decision making

  • In this paper, we present the SI metrics framework SIMeF, which proposes a holistic, generic, and policy-relevant approach for quantifying and assessing SI options over thematic areas and model types

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Summary

Introduction

Sustainable Intensification (SI) of agriculture is being promoted by a growing number of international organizations (FAO, 2017; 2012; 2011; 2004;; UN, 2012; USAID, 2012) and other commissions and focus groups concerned with the sustainability of agricultural production (e.g., Baulcombe et al, 2009; Beddington et al, 2012; Buckwell et al, 2014; Elliott et al, 2013; Foresight, 2011) as a promising strategy to increase the productivity and sustainability of the agricultural sector simulta­ neously. The increasing consensus that wider con­ siderations should be encompassed in the definition of SI (De Koeijer et al, 2002; Elliott et al, 2013; FAO, 2004; Garnett et al, 2013; Petersen and Snapp, 2015; Pretty and Bharucha, 2014; Reardon et al, 1999) permits the evolution towards a broader definition This definition in­ corporates social and economic aspects, such as social equity and nutrition (Smith et al, 2017), rural development (Pasakarnis et al, 2013), the economic viability of agriculture (Ruben and Lee, 2000; The Montpellier Panel, 2013), and the quality of life of society (National Research Council, 2010). It reflects the multi-functionality of agricultural systems by considering diverse agricultural production outputs, including food and non-food products (Harvey and Pilgrim, 2011; National Research Council, 2010) and ecosystem services (Elliott et al, 2013; Pretty et al, 2011; Wezel et al, 2015)

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