Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/288227 
Year of Publication: 
2023
Citation: 
[Journal:] Australian Journal of Agricultural and Resource Economics [ISSN:] 1467-8489 [Volume:] 68 [Issue:] 1 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2023 [Pages:] 125-145
Publisher: 
Wiley, Hoboken, NJ
Abstract: 
The dissemination of conservation agriculture (CA) technologies has become the objective of a growing number of projects aimed at reducing food insecurity in vulnerable areas of the world. While many studies have found that CA increases farm productivity, little is known about the components of the productivity gains related to CA adoption. CA is a knowledge‐intensive technology, and it is expected to affect both technical efficiency (TE) and input productivity positively. Using cross‐sectional farm‐level data of 220 maize farmers in Bangladesh, we measure the impact of CA on farmers' TE. We first apply propensity score matching (PSM) to create comparable counterfactual groups of CA and non‐CA farmers. Then, we use a stochastic frontier with correction for self‐selection bias to analyse TE. Finally, we fit a stochastic meta‐frontier (SMF) model to the data and use it to compare TE across the two farmer groups. The analysis showed that CA farmers exhibit greater TE levels than non‐CA farmers. This can be attributed to enhancements in farm management, leading to 8% and 9% increases in their productivity and TE, respectively. Thus, there is a case for policymakers to strengthen programs delivering CA technologies that improve food security in Bangladesh.
Subjects: 
conservation agriculture
meta‐frontier analysis
self‐selection bias
South Asia
technical efficiency
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article
Document Version: 
Published Version

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