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Farms clustered (circled) together represent a farm type.
Labour productivity was highest on Farm Type 2 due to more family labour use and lowest on Farm Type 3 due to the small operational holding.
Conversely, farm type was a significant predictor of native species richness.
Farm Type 3 was least efficient in all the resources considered, emphasizing the negative effect of low resource endowments.
Statistical analysis of infection levels clearly showed no significant effect according to farm type (p = 0.71) or treatment (p = 0.81).
The r-squared for the individual farm type models are only slightly below the r-squared for the full sample model.
The multivariate approach (algorithm) followed for the farm type delineation may also be used for the development of software or web-based decision support system.
This suggests that multi-year standard deviation is an appropriate variability measurement even when a single farm type with a smaller sample size is analysed.
Results showed that participation profits were heterogeneous, depending on (i) the sector (higher for papaya and chicken meat), on (ii) contract scheme, and on (iii) farm type.
Factors associated with the presence or characteristics of individual EbA practices included the size of coffee plantations, farmer experience, farmer education, land tenure, landscape and farm type.
The typological characterisation of farm diversity then makes it possible to define a set of constraints (SOC) for each farm type.
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