Comparing decision-support systems in adopting sustainable intensification criteria.

Bouda Vosough Ahmadi,Dominic Moran,Andrew P. Barnes,Philippe V. Baret

doi:10.3389/fgene.2015.00023

Bouda Vosough Ahmadi, Dominic Moran + Show 2 more

Open Access

https://doi.org/10.3389/fgene.2015.00023

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Abstract

Sustainable intensification (SI) is a multifaceted concept incorporating the ambition to increase or maintain the current level of agricultural yields while reduce negative ecological and environmental impacts. Decision-support systems (DSS) that use integrated analytical methods are often used to support decision making processes in agriculture. However, DSS often consist of set of values, objectives, and assumptions that may be inconsistent or in conflict with merits and objectives of SI. These potential conflicts will have consequences for adoption and up-take of agricultural research, technologies and related policies and regulations such as genetic technology in pursuit of SI. This perspective paper aimed at comparing a number of frequently used socio-economic DSS with respect to their capacity in incorporating various dimensions of SI, and discussing their application to analyzing farm animal genetic resources (FAnGR) policies. The case of FAnGR policies was chosen because of its great potential in delivering merits of SI. It was concluded that flexible DSS, with great integration capacity with various natural and social sciences, are needed to provide guidance on feasibility, practicality, and policy implementation for SI.

Highlights

The growing human population, and growing global demand for food, are major challenges that will need to be addressed in a world with a potentially dramatically changing climate, and with diminishing natural resources such as farm animal genetic resources (FAnGR; Tilman et al, 2011; Tscharntke et al, 2012)
Decision-support systems (DSS) are considered as set of scientific and analytical tools and approaches that are used in interpreting research results into policy relevant outcomes
Technical aspects of farming practices could be included to some extend in these methods but less than mechanistic models. Both structural equation modeling (SEM) and cognitive mapping (CM) are strong in looking at social aspects of socio-ecological farming systems including farmers, behavior, perceptions, and goals

Summary

Introduction

The growing human population, and growing global demand for food, are major challenges that will need to be addressed in a world with a potentially dramatically changing climate, and with diminishing natural resources such as farm animal genetic resources (FAnGR; Tilman et al, 2011; Tscharntke et al, 2012). Farming practices are considered as business activities that generate products and income for farmers (private good) but at the same time could generate positive (e.g., ecosystem services) and negative externalities affecting environmental and ecological systems (public good). To incorporate SI’s criteria including social, ecological, and environmental externalities in DSS that enhance agricultural policies, greater integration of social and technical aspects of farming practices is needed.

Results

Conclusion