Abstract
This article discusses the potential of BNs to complement the analytical toolkit of agricultural extension. Statistical modelling of the adoption of agricultural practices has tended to use categorical (logit/probit) regression models focusing on a single technology or practice, explained by a number of household and farm characteristics. Here, a Bayesian network (BN) is used to model household-level data on adoption of agrosilvopastoral practices in Tiby, Mali. We discuss the advantages of BNs in modelling more complex data structures, including (i) multiple practices implemented jointly on farms, (ii) correlation between probabilities of implementation of those practices and (iii) correlation between household and farm characteristics. This paper demonstrates the use of BNs for ‘deductive’ reasoning regarding adoption of practices, answering questions regarding the probability of implementation of combinations of practices, conditional on household characteristics. As such, BNs is a complementary modelling approach to logistic regression analysis, which facilitates exploring causal structures in the data before deciding on a reduced form regression model. More uniquely, BNs can be used ‘inductively’ to answer questions regarding the likelihood of certain household characteristics conditional on certain practices being adopted.
Similar content being viewed by others
Notes
FunciTree: Functional Diversity. An ecological framework for sustainable and adaptable agroforestry systems in landscapes of semi-arid and arid ecoregions. Co-funded by the EU 7th Frame Programme.
See http://funcitree.hugin.com/for examples of other online BN models.
References
Adesina AA, Mbila D, Nkamleu GB, Endamana D (2000) Econometric analysis of the determinants of adoption of alley farming by farmers in the forest zone of southwest. Cameroon Agric Ecosyst Environ 80:255–265
Aguilera PA, Fernández A, Fernández R, Rumi R, Salmeron A (2011) Bayesian networks in environmental modelling. Environ Modell Softw 26:1376–1388. doi:10.1016/j.envsoft.2011.06.004
Akinwumi AA, Mbila D, Nkamleu GB, Dominique E (2000) Econometric analysis of the determinants of adoption of alley farming by farmers in the forest zone of southwest. Cameroon Agric Ecosyst Environ 80:255–265
Ayuk IT (1997) Adoption of agroforestry technology: the case of live hedges in the central plateau of Burkina Faso. Agric Syst 54:189–206
Barton DN, Saloranta T, Moe SJ, Eggestad HO, Kuikka S (2008) Bayesian belief networks as a meta-modelling tool in integrated river basin management—Pros and cons in evaluating nutrient abatement decisions under uncertainty in a Norwegian river basin. Ecol Econ 66:91–104
Barton DN et al (2012) Bayesian networks in environmental and resource management. Integr Environ Assess Manag 8:418–429
Baynes J, Herbohn J, Russell I, Smith C (2011) Bringing agroforestry technology to farmers in the Philippines: Identifying constraints to the success of extension activities using systems modelling. Small-Scale For 10:357–376. doi:10.1007/s11842-010-9153-8
Berthe A, Traoré SF, Traoré B, Kanrissoko CD, Guinda B, Kablan RA, Yost R (1999) Improving food crop production in subsistence farming in Fansirakoro ami N’tétoukoro. In: Badiane AN (ed) Improving and sustaining Food and Raw Material production in West Africa : Reversing Soil Acidification, Loss of Orgauic Matter, and Erosive Runoff in Food production Systems West group: Cape verde, Gambia, Mali, and Senegal Proceedings of the West Group Workshop, Kaolack Senegal, January 2–14
Chianu JN, Tsujii H (2004) Determinants of farmers’ decision to adopt or not adopt inorganic fertilizer in the savannas of northern Nigeria. Nutr Cycl Agroecosyst 70:293–301
Cisse Y et al (2013) Facteurs déterminant l’adoption des technologies agro forestières en zone soudano-sahélienne au Mali: cas des communes rurales de Dioro et de Farakou Massa dans la région de Ségou. FunciTree Project
Cramb RA (2005) Farmers’ strategies for managing acid upland soils in Southeast Asia: an evolutionary perspective. Agric Agric Ecosyst Environ 106:69–87
Frayer J, Sun ZL, Muller D, Munroe DK, Xu JC (2014) Analyzing the drivers of tree planting in Yunnan, China, with Bayesian networks. Land Use Policy 36:248–258. doi:10.1016/j.landusepol.2013.08.005
Garcia YT (2001) Analysis of farmer decisions to adopt soil conservation technology in Argao. World Agroforestry Centre (ICRAF). Transforming lives and landscapes
Gret-Regamey A, Brunner SH, Altwegg J, Christen M, Bebi P (2013) Integrating expert knowledge into mapping ecosystem services trade-offs for sustainable forest management. Ecol Soc. doi:10.5751/Es-05800-180334
Haines-Young R (2011) Exploring ecosystem service issues across diverse knowledge domains using Bayesian Belief Networks. Prog Phys Geogr 35:681–699
Hugin (2014) Manual. Hugin Release 8.0, March 2014. Hugin Expert A/S
Joshi L, Wibawa G, Sinclair FL (2001) Local ecological knowledge and socio-economic factors influencing farmers’ management decisions in jungle rubber agroforestry systems in Jambi, Indonesia. DFID Project R7264 Forestry Research Programme
Kuikka S, Hildén N, Gislason H, Hansson S, Sparholt H, Varis O (1999) Modeling environmentally driven uncertainties in Baltic cod (Gadus morhua) management by Bayesian influence diagrams. Can J Fish Aquat Sci 56:629–641
Landuyt D, Broekx S, Dhondt R, Engelen G, Aertsens J, Goethals PLM (2013) A review of Bayesian belief networks in ecosystem service modelling. Environ Modell Softw 46:1–11
Lapar MLA, Pandey S (1999) Adoption of soil conservation: the case of Philippine uplands. Agric Econ 21:241–256
Levasseur V, Olivier A, Steven F (2009a) Facteurs d’adoption de la haie vive améliorée au Mali. Cahiers Agric 18:1–6
Levasseur V, Olivier A, Steven F (2009b) Facteurs d’adoption de la haie vive améliorée au Mali. Cahiers Agric 18(4):350–355
López F, Gómez R, Harvey C, López M, Sinclair FL (2007) Toma de decisiones de productores ganaderos sobre el manejo de los árboles en potreros en Matiguás, Nicaragua Agroforestería en las Américas
Marcot BG (2012) Metrics for evaluating performance and uncertainty of Bayesian network models. Ecol Model 230:50–62
Marcot BG, Steventon JD, Sutherland GD, McCann RK (2006) Guidelines for developing and updating Bayesian belief networks applied to ecological modeling and conservation. Can J For Res Revue Canadienne De Recherche Forestiere 36:3063–3074. doi:10.1139/X06-135
McCann RK, Marcot BG, Ellis R (2006) Bayesian belief networks: applications in ecology and natural resource management. Can J For Res Revue Canadienne De Recherche Forestiere 36:3053–3062. doi:10.1139/x06-238
McVittie A, Norton L, Martin-Ortega J, Siameti I, Glenk K, Aalders I (2015) Operationalizing an ecosystem services-based approach using Bayesian Belief Networks: an application to riparian buffer strips. Ecol Econ 110:15–27. doi:10.1016/j.ecolecon.2014.12.004
Sadoddin A, Letcher RA, Jakeman AJ, Newham LTH (2005) A Bayesian decision network approach for assessing the ecological impacts of salinity management. Math Comput Simul 69:162–176
Scherr SJ (1995) Economic factors in farmer adoption of agroforestry: patterns observed in Western Kenya. World Dev 23:787–804
Uusitalo L (2007) Advantages and challenges of Bayesian networks in environmental modelling. Ecol Model 203:312–318. doi:10.1016/j.ecolmodel.2006.11.033
Varis O (1997) Bayesian decision analysis for environmental and resource management. Environ Modell Softw 12:177–185
Villanueva C, Ibrahim M, Harvey CA, Sinclair FL, Muñoz D (2003) Decisiones claves que influyen sobre la cobertura arbórea en fincas ganaderas de Cañas, Costa Rica Agroforestería en las Américas 10
Acknowledgments
This research has been supported by the FunciTree Project (http://funcitree.nina.no/) Grant No. 227265 co-funded by the European Commission, Directorate General for Research, within the 7th Framework Programme of RTD, Theme 2—Biotechnology, Agriculture & Food.
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Barton, D.N., Cisse, Y., Kaya, B. et al. Diagnosing agrosilvopastoral practices using Bayesian networks. Agroforest Syst 91, 325–334 (2017). https://doi.org/10.1007/s10457-016-9931-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10457-016-9931-1