Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 200

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Climate smart agriculture rapid appraisal (CSA-RA): A tool for prioritizing context-specific climate smart agriculture technologies

Open access
Mwongera, Caroline, Shikuku, Kelvin M., Twyman, Jennifer, Läderach, Peter, Ampaire, Edidah, Van Asten, Piet, Twomlow, Steve, Winowiecki, Leigh A.·2017·Agricultural Systems

Abstract

Approaches that aim to identify and prioritize locally appropriate climate smart agriculture (CSA) technologies will need to address the context-specific multi-dimensional complexity in agricultural systems. The climate smart agriculture rapid appraisal (CSA-RA) is a mixed method approach that draws on participatory bottom-up, qualitative, and quantitative tools to assess the heterogeneity of local contexts, and prioritize context-specific CSA options. This is an imperative if countries are to respond to the COP21 agreement and meet their intended nationally determined contributions (INDCs). The CSA-RA is designed to assess biophysical including climatic, socio-cultural, economic and technological characteristics at the household, farm and community/regional level. The CSA-RA employs gender-disaggregated methods, including gender differences in perceptions of climate change and its impacts. The CSA-RA combines common participatory rural appraisal (PRA) and rapid rural appraisal (RRA) tools into one methodology, that disaggregates the gender dimension, and includes resource mapping; climate calendars; historical calendars; cropping calendars; organization mapping; transect walks; key informant interviews; farmer interviews; and pairwise ranking matrix. The tool collects qualitative and quantitative data from various stakeholders (farmers, local leaders, researchers, local-level agricultural experts, private sector actors, donor organizations, and policy implementers), allowing expansive analysis, triangulation and validation. Application of the CSA-RA in Tanzania and Uganda reveals heterogeneity across the sites in terms of vulnerability, constraints and CSA priorities among different social groups (gender) and agro-ecological zones. Thus, the CSA-RA allows stakeholders to simultaneously take into account biophysical and socio-economic aspects to target and implement CSA. • CSA-RA: tool to prioritize context specific climate smart agriculture technologies • Novel mixed method approach employing participatory, qualitative, and quantitative tools • Innovative combination of gender-disaggregated methods and climate analysis • Bottom-up approach, incorporating multiple scales and actors • Rapid and comprehensive

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.agsy.2016.05.009

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 1 references

Cited by

See all 200

Metadata

Authors
Mwongera, C., Shikuku, K. M., Twyman, J., Läderach, P., Ampaire, E., Van Asten, P., Twomlow, S., & Winowiecki, L. A.
Venue
Agricultural Systems
Publisher
Elsevier BV
Published
2017-02
Volume
151
Pages
192-203
ISSN
0308-521X (ISSN-L)
OpenAlex
W2411248342
Cited by
200
References
1
FWCI
12.24
Type
journal article
Language
en
Open access
hybrid

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Climate smart agriculture rapid appraisal (CSA-RA): A tool for prioritizing context-specific climate smart agriculture technologies. https://app.cochat.ai/papers/10.1016/j.agsy.2016.05.009
The paper
Mwongera, C., Shikuku, K. M., Twyman, J., Läderach, P., Ampaire, E., Van Asten, P., Twomlow, S., & Winowiecki, L. A. (2017). Climate smart agriculture rapid appraisal (CSA-RA): A tool for prioritizing context-specific climate smart agriculture technologies. Agricultural Systems. https://doi.org/10.1016/j.agsy.2016.05.009

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.