Fig 1.
Map of the study area: Lundazi, Chipata and Katete districts (in violet), Eastern Province of Zambia.
Table 1.
Main farming characteristics of three districts of Eastern Province of Zambia, Lundazi, Chipata and Katete.
Table 2.
Surveyed variables from the Eastern Province of Zambia and for the three districts (Lundazi, Chipata and Katete) and the variables used for the three Eastern Zambia typologies (T1, T2 and T3).
Fig 2.
Typology 1: Representation of the six farm types of resulting from the Principal Component Analysis and clustering analysis on the planes defined by the first four principal components.
The red colour variables are the most explanatory of the horizontal axis (PC1); those in blue are the most explanatory variables of vertical axes (PC2, PC3 and PC4), thus defining the gradients.
Fig 3.
Typology 2: Representation of the five farm types of resulting from the Principal Component Analysis and clustering analysis on the planes defined by the first four principal components.
The red colour variables are the most explanatory of the horizontal axis (PC1); those in blue are the most explanatory variables of vertical axes (PC2, PC3 and PC4), thus defining the gradients.
Fig 4.
Typology 3: Representation of the farm types of resulting from the Principal Component Analysis and clustering analysis on the planes defined by the first four principal components, for the districts Lundazi, Chipata and Katete.
The red coloured variables are the most explanatory of the horizontal axis (PC1); those in blue are the most explanatory variables of vertical axes (PC2) and those in violet are variables correlated with both PC1 and PC2.
Fig 5.
(a) Comparison of the two dendrograms from the resource-based typology (T1) and the crop-based typology (T2), and (b) cross-tabulation of numbers of farms of T1 allocated to different types of T2; the intensity of the red colouring indicates the percentage of overlap.
The ‘unclassified’ farms are farms that were included in T1 but were detected as outliers for T2. Fig 6a illustrates the overlapping between T1 and T2, comparing the individual position each farm in the two dendrogram of the two typologies, while Fig 6b quantifies the percentage of overlap between the two typologies.
Fig 6.
Cross-tabulations of numbers of farms of typology T2 allocated to different types of typologies for districts Lundazi (T3-Lundazi; a), Chipata (T3- Chipata; b) and Katete district (T3- Katete; c).
The intensity of the red colouring indicates the percentage of overlap.
Fig 7.
Theoretical example of a change of scale, from scale 1 to scale 2 (e.g. from province to district).
Distribution of observations of a quantitative variable (e.g. farm area) at the province level (level 1) and at the district level (level 2). The different colours are associated with different values classes within the variable. Zooming in from scale 1 to scale 2, magnifies the variation within the district, potentially revealing new classes.
Fig 8.
General framework of the typology process, where expert knowledge is combined with statistical techniques (PCA: Principal Component Analysis; MCA: Multiple Correspondence Analysis; MFA: Multiple Factorial Analysis).