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Mapping farmer perceptions, Conservation Agriculture practices and on-farm measurements: The role of systems thinking in the process of adoption

Mapping farmer perceptions, Conservation Agriculture practices and on-farm measurements: The role of systems thinking in the process of adoption

Lalani, Baqir ORCID: 0000-0001-8287-3283, Aminpour, Payam, Gray, Steven, Williams, Meredith, Büchi, Lucie ORCID: 0000-0002-1935-6176, Haggar, Jeremy ORCID: 0000-0002-4682-4879, Grabowski, Philip and Dambiro, José (2021) Mapping farmer perceptions, Conservation Agriculture practices and on-farm measurements: The role of systems thinking in the process of adoption. Agricultural Systems, 191:103171. ISSN 0308-521X (doi:https://doi.org/10.1016/j.agsy.2021.103171)

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Abstract

CONTEXT:
Conservation Agriculture (CA) usage, particularly in Southern Africa, has remained low with lower yield, higher weed pressure and lower soil quality cited as reasons for ‘disadoption’.

OBJECTIVE:
Using a detailed case study of 50 farmers in two villages in Cabo Delgado (Northern Mozambique), this study seeks to test the hypothesis that farmers’ perceptions of CA are associated with distinctly different ‘mental models’ and if these “ways of thinking” overlap with farmers’ identified/self-identified groupings (e.g. CA users, ‘disadopters’ and conventional tillage users). Secondly, we examine whether these different mental models (perceptions) are associated with actual differences in on-farm measurements. Finally, we explore the hypothesis that ‘systems thinking’ (i.e., understanding nonlinear causal relationships and internal feedback loops that drive a complex system) and CA usage are positively associated.

METHODS:
Fuzzy Cognitive Mapping (FCM) was used to elicit representations of farmers’ mental models. To explore the association between farmers’ mental models of CA/conventional practices and on-farm measurements we evaluated cowpea aboveground biomass, yield, weed cover, and soil quality parameters from the farmer’s main plot. We drew on network analysis to measure structural metrics of cognitive maps that provide important information about a person’s mental model (perceptions) of causal interdependencies of farming dynamics.

RESULTS AND CONCLUSIONS:
We find evidence of two data-driven distinct clusters of farmers’ mental models that are in relative alignment with farmers’ identified/self-identified groupings. Cluster 1 mainly consists of conventional users and cluster 2 mainly consists of CA users/disadopters. While no significant differences in socio-demographic variables were observed, clusters of mental models were associated with key differences in on-farm measurements. Importantly, cluster 1, who tended to be conventional users, had lower yields, lower soil cover, significantly lower carbon stock and higher weed coverage than cluster 2. Soil quality indicators were higher in cluster 2 as were farmers’ overall revenue per hectare. Moreover, cluster 2 had significantly higher degrees of ‘systems thinking’ (measured through complex network analysis of graphical mental models) than cluster 1 which had higher forms of linear thinking. We argue that higher forms of experiential learning and practice of CA relate to higher degrees of systems thinking and stronger positive perceptions of CA, even among the CA ‘disadopters’.

SIGNIFICANCE:
Our findings highlight the importance of systems thinking abilities and the need to consider detailed biophysical, socio-economic and mental modelling variables rather than simple binary measurements which may have led to erroneous conclusions on CA and thus has implications for how CA is understood and promoted in future.

Item Type: Article
Uncontrolled Keywords: Conservation Agriculture; Decision-making; Mental models; Cropping systems
Subjects: S Agriculture > S Agriculture (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Natural Resources Institute
Faculty of Engineering & Science > Natural Resources Institute > Development Studies Research Group
Faculty of Engineering & Science > Natural Resources Institute > Food & Markets Department
Last Modified: 07 Jun 2021 21:33
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/32997

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