• agriculture;
  • cassava;
  • GIS;
  • regression kriging;
  • sampling design;
  • spatial distribution

Cassava mosaic disease (CMD) seriously affects cassava yields in Africa. This study compared the spatial distribution of CMD using three independent surveys in Rwanda and Burundi. Geostatistical techniques were used to interpolate the point-based surveys and predict the spatial distributions of different measures of the disease. Correlative relationships were examined for 35 environmental and socio-economic spatial variables of which 31 were correlated to CMD intensity, with the highest correlation coefficients for latitude (−0·47), altitude (−0·36) and temperature (+0·36). The most significant explanatory variables were entered in separate linear regression models for each of the surveys. The models explained 54%, 44% and 22% of the variation in CMD. The residuals of the regression models were interpolated using kriging and added to the regression models to map CMD across both countries. Significant differences were calculated in some areas after correcting for interpolation error. An important explanation of the differences is interaction between the CMD pandemic and the dates of the three surveys. Large relative prediction errors obtained in the regression kriging procedure show the need to improve the survey design and decrease measurement error. Improved maps of crop diseases such as CMD could aid targeting of control interventions and thereby contribute to increasing crop yields. This study validated the unique character of each of the survey approaches adopted and underlines the importance of specific interpretation of results for CMD management. The study emphasizes the need for optimization of sampling designs and survey protocols to maximize the potential of regression kriging.