Inmagic DB/Text WebPublisher PRO: 1 records

Your search found 1 records

1 Gennari, P.; Navarro, D. K.. 2019. The challenge of measuring agricultural sustainability in all its dimensions. Journal of Sustainability Research, 1(2):e190013. (Special issue: The Sustainable Development Goals (SDGs): Underpinning and Contributing to Sustainability Research). [doi: https://doi.org/10.20900/jsr20190013]

Sustainable agriculture ; Sustainable Development Goals ; Development indicators ; Assessment ; Monitoring ; Policies ; Farm surveys ; Economic aspects ; UN ; FAO

(Location: IWMI HQ Call no: e-copy only Record No: H049572)

https://sustainability.hapres.com/AddDownload.aspx?id=1101&type=pdf&action=JSR
https://vlibrary.iwmi.org/pdf/H049572.pdf

(0.40 MB) (404 KB)

SDG indicator 2.4.1, the “Percentage of agricultural area under productive and sustainable agriculture” aims to measure the degree of sustainability of each farm with reference to 11 distinct sustainability attributes, and hence provide an overall national assessment through a dashboard approach. Today, this indicator has an internationally agreed methodology, and dozens of countries around the world are receiving technical assistance from FAO in its measurement and implementation. However, what we may now take as a given is the result of a long and arduous process of methodological development that involved a series of difficult decisions on numerous methodological aspects. This paper reviews and provides supporting documentation for these key methodological decisions, particularly with regard to the definition of agricultural sustainability, the choice of the scale of the sustainability assessment and the data collection instrument; the sub-indicators within each dimension; the criteria to assess the sustainability level of the farm with respect to each sub-indicator; and the modality of synthesizing the information. These decisions were further encumbered by the need to faithfully capture progress towards a multidimensional SDG target determined by a political process, with negligible input from statistical experts.