Sustainability Assessment and Agricultural Supply Chains Evidence-Based Multidimensional Analyses as Tools for Strategic Decision-Making—The Case of the Pineapple Supply Chain in Benin

Sustainability

Freddy Padonou¹  Doriane Desclee²  David Sohinto³ 

[1] Department of Agricultural Economics, University of Abomey-Calavi, 01 BP 526 Godomey, Benin;ERAIFT-UNESCO, University of Kinshasa, BP 15373 Kinshasa, Democratic Republic of Congo;Faculty of Letters, Arts and Humanities, University of Abomey-Calavi, 01 BP 526 Godomey, Benin;

关键词: sustainable development;    assessment;    analytical framework evidence-based diagnosis;    pineapple;    Benin;    agricultural supply chain;   

DOI  :  10.3390/su13042060

来源: DOAJ

【 摘 要 】

Contributing to Sustainable Development Goals and Agenda 2030 is a shared objective of all institutions and people. The challenges differ according to the characteristics of every context. In developing countries, strongly dependent on the agricultural sector, agricultural supply chains are recognized as crucial for economic growth and enablers for livelihood improvement. Moreover, sustainable development issues are correlated and can meet in agricultural supply chains. For several decades, parallel to decision-makers, the research community has elaborated sustainability assessment tools. Such tools evolved to fit with actuality, but it is challenging to find decision-making support tools for sustainable development adequate in agricultural supply chains and developing countries contexts. There is a necessity to define evidence-based tools and exhaustive analytical frameworks according to sustainability multidimensionality and strategical tradeoffs necessity. The VCA4D method aims to go beyond the limits of previous methods. It proposes a combination of multidisciplinary analytical tools applied empirically to analyze agricultural supply chains in their context. It provides evidence-based analytical results allowing to identify enablers for strategic sustainable and inclusive interventions. However, to even better meet contextual exhaustiveness’s expectations and indicators’ robustness to lead to relevant interventions, we should insist on a stricter framing of contextual data collection processes.

【 授权许可】

Unknown