Title
Classification trees versus multinomial models in the analysis of urban farming systems in Central Africa Classification trees versus multinomial models in the analysis of urban farming systems in Central Africa
Author
Faculty/Department
Faculty of Medicine and Health Sciences
Publication type
article
Publication
Barking ,
Subject
Economics
Biology
Source (journal)
Agricultural systems. - Barking
Volume/pages
80(2004) :2 , p. 133-149
ISSN
0308-521X
ISI
000220877900002
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Abstract
This study was aimed at the identification of location and household characteristics influencing the choice of keeping livestock or practising crop production in a post-conflict region in Central Africa. Two thousand eight hundred randomly selected families from Brazzaville (Congo) were surveyed. From these, 6% are both keeping livestock and producing crops, 3% are keeping livestock only, 24% are producing crops only and 67% are not producing crops nor keeping livestock. From these four groups, respectively, 135, 84, 246 and 245 households were interviewed to collect further data on household and location characteristics. Non-parametric and parametric techniques were compared as tools to analyse the groups. In the non-parametric classification tree method CART following variables were identified as being important for the engagement in keeping livestock or urban agriculture: keeping livestock before 1997, practising agriculture before 1997, property size, locality, income, availability of water, professional activity and level of instruction. Including surrogate variables resulted in extra variables: age, availability of electricity and sex. In the multinomial regression only the most important variables from the classification tree were withheld and other insights were obtained. The results of this research highlighted the shortcomings of multinomial regression. Fitting a full model containing all possible interactions becomes an impossible task with 20 explanatory variables. Using the classification tree information in a multinomial model appears the most appropriate solution, and this method is a useful tool for further work in the analysis of livestock and crop production systems. The importance of the historical component in the decision to practise crop production and keep livestock was demonstrated. Being involved in crop production develops roots, which cannot be destroyed by impediments such as periods of conflict.
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