Household Determinants of Tree Planting on Farms in Rural Rwanda

J.D. Ndayambaje, W.J.M. Heijman, G.M.J. Mohren

Research output: Contribution to journalArticleAcademicpeer-review

29 Citations (Scopus)

Abstract

In Rwanda, trees on farms are widely recognized for increasing and diversifying farm productivity while releasing pressure on existing forests. However, the motivation of rural households to plant trees on farms is often unclear. This study evaluates rural households demographic and socio-economic characteristics, as well as their attitudes, that influence the presence of trees on farms. Data used in this study were collected from a survey of 480 households across three altitude regions of Rwanda. Binary logistic regression analysis using PASW Statistics was applied to determine relevant predictor variables for the presence of trees on farms. The results show regional variation in explaining the presence of trees on farms. When data from the three regions were analysed together, significant predictor variables comprise the gender of head of the household, the number of salaried members of the households, the amount of farm fuelwood, the number of meals per day, the geographical location of the households and the selling of tree products. The presence of different tree species on farms was driven by economic factors, of which availability of food, firewood, and poles, and total income were most common. The results of the study imply that policy measures that target food security and income diversification in rural areas may, at the same time, enhance tree planting. Moreover, it is concluded that rural development and extension in agriculture should be site specific, to account for biophysical conditions and specific rural household motivations to plant trees on farms.
Original languageEnglish
Pages (from-to)477-508
JournalSmall-scale Forestry
Volume11
Issue number4
DOIs
Publication statusPublished - 2012

Keywords

  • discriminant-analysis
  • agroforestry practices
  • logistic-regression
  • adoption
  • management
  • systems
  • gender
  • nepal
  • technology
  • prediction

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