Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Barriers to scaling sustainable land and water management in Uganda: a cross-scale archetype approach
Stockholm University, Faculty of Science, Stockholm Resilience Centre.ORCID iD: 0000-0002-1600-5450
Show others and affiliations
Number of Authors: 62021 (English)In: Ecology and Society, E-ISSN 1708-3087, Vol. 26, no 3, article id 6Article in journal (Refereed) Published
Abstract [en]

In African small-scale agriculture, sustainable land and water management (SLWM) is key to improving food production while coping with climate change. However, the rate of SLWM adoption remains low, suggesting a gap between generalized SLWM advantages for rural development across the literature, and the existence of context-dependent barriers to its effective implementation. Uganda is an example of this paradox: the SLWM adoption rate is low despite favorable ecological conditions for agriculture development and a large rural population. A systemic understanding of the barriers hindering the adoption of SLWM is therefore crucial to developing coherent policy interventions and enabling effective funding strategies. Here, we propose a cross-scale archetype approach to identify and link barriers to SLWM adoption in Uganda. We performed 80 interviews across the country to build cognitive archetypes, harvesting stakeholders’ perceptions of different types of barriers. We complemented this bottom-up perspective with a spatial archetype analysis to contextualize these results across different social-ecological regions. We found poverty trap, overpopulation, risk aversion, remoteness, and post-conflict patriarchal systems as cognitive archetypes that synthesize the different dynamics of barriers to SLWM adoption in Uganda. Our results reveal both specific and cross-cutting barriers. Ineffective extension services emerges as a ubiquitous barrier, whereas gender inequality is a priority barrier for large supported farms and farms in drier lowlands in northern Uganda. The combination of cognitive and spatial archetypes proposed here can help to overcome ineffective “one-size-fits-all” solutions and support context-specific policy plans to scale up SLWM, rationing resources to support sustainable intensification of agriculture.

Place, publisher, year, edition, pages
2021. Vol. 26, no 3, article id 6
Keywords [en]
archetype analysis, barriers to adoption, sustainability science, sustainable land and water management, Uganda
National Category
Social and Economic Geography
Identifiers
URN: urn:nbn:se:su:diva-199865DOI: 10.5751/ES-12531-260306ISI: 000708519300015OAI: oai:DiVA.org:su-199865DiVA, id: diva2:1625939
Available from: 2022-01-10 Created: 2022-01-10 Last updated: 2024-07-04Bibliographically approved
In thesis
1. Sustainable Land and Water Management for a Greener Future: Large-scale insights in support of Agroecological Intensification
Open this publication in new window or tab >>Sustainable Land and Water Management for a Greener Future: Large-scale insights in support of Agroecological Intensification
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The challenge of producing more food in times of climate change, degraded land and scares water resources is calling for a radical transformation of agriculture. Sustainable agricultural intensification is the process of increasing the productivity of farms while preserving functional ecosystems. A range of sustainable land and water management (SLWM) practices and approaches to sustainable intensification have been successfully implemented at the local scale during the last decades, but adoption rate remains low due to a variety of barriers and lack of effective approaches from authorities at larger scales (national to global). Despite the wealth of local successes, promoting and realizing the widespread uptake of SLWM requires large scale understanding of the potential and challenges of adoption of SLWM, which is currently lacking. This thesis bridges outcomes of successful implementation of SLWM from local cases to large scale social-ecological patterns, showing where and what is the potential of SLWM to contribute to sustainable agricultural intensification and the barriers to achieve it. The methodological approach and the results presented in this thesis aim at providing insights to improve current assessments of sustainable intensification of agriculture and practical guidance to planning, policy making and funding interventions to promote the widespread adoption of SLWM.

Place, publisher, year, edition, pages
Stockholm: Stockholm Resilience Centre, Stockholm University, 2020. p. 37
National Category
Environmental Sciences Agricultural Science Climate Science
Research subject
Sustainability Science
Identifiers
urn:nbn:se:su:diva-185721 (URN)978-91-7911-310-0 (ISBN)978-91-7911-311-7 (ISBN)
Public defence
2020-11-20, rum 306, hus 2 B, Roslagsvägen 101, Kräftriket, Stockholm, 13:00 (English)
Opponent
Supervisors
Available from: 2020-10-27 Created: 2020-10-05 Last updated: 2025-02-01Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records

Piemontese, LuigiJaramillo, Fernando

Search in DiVA

By author/editor
Piemontese, LuigiJaramillo, Fernando
By organisation
Stockholm Resilience CentreDepartment of Physical Geography
In the same journal
Ecology and Society
Social and Economic Geography

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 144 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf