*4.3. Econometric Results (Structural Equation Modeling)*

The ADRI as an outcome variable was regressed using Equation (6) at the aggregate level (general) and Equation (7) at disaggregate level against the explanatory variables to the determinants of smallholder livestock farmers' household resilience to food insecurity. A structural equation modeling approach was applied to empirically assess smallholder livestock farmers' resilience to food insecurity in Northern Cape Province of South Africa. The results in Table 6 (aggregated) and Table 7 (disaggregated) show that assets, adaptive capacity, safety nets, and climate change indicators significantly impacted households' resilience to food insecurity. ADC (β = 0.171), ASS (β = 0.150), CH (β = 0.053), and SSF (β = 0.001) contributed to the regression model. Asset, SSF, and adaptive capacity indicators positively impacted households' resilience to food insecurity and were significant at 5%. The variance inflation factor (VIF) statistics indicated that there was no multicollinearity problem in the analysis.

**Table 6.** Structural equation modeling results (aggregated).


\*\* Significant at 5%. Source: Authors' estimation based on survey (2020).

Households' resilience to food insecurity in the Northern Cape was empirically assessed in detail (Table 7). The results indicated that HFS (β = 0.333), AA (β = 0.089), and NAA (β = −0.019) influenced households' resilience to food insecurity. Herd/flock size (HFS) and AA indicators positively impacted households' resilience to food insecurity. The HFS was the most crucial dimension compared to the other components of assets. Smallholder farmers used livestock as a coping and adaptation mechanism, because they sold livestock during agricultural drought to enhance their resilience.

Four dummy variables were used to estimate the resilience impact of adaptive capacity on food insecurity. The results in Table 7 showed that migration indicators positively impacted households' resilience to food insecurity. Migration (β = 0.037), credit (β = −0.250), perception (β = −0.181), and income source (β = −0.122) contributed to the regression model.

The results in Table 7 showed that all the social safety net indicators had a positive and significant impact on households' resilience to food insecurity. Cash (β = 0.044), training (β = 0.124), food support (β = 0.075), water rights (β = 0.111), garden equipment (β = 0.195), sanitary latrines (β = 0.037), and farm input (β = 0.145) contributed to the regression model.

The two variables that were included under climate change, focusing on drought, namely, drought occurrence and drought intensity, had a negative and significant impact at 10% on household resilience to food insecurity (Table 7). Drought occurrence (β = −0.118) and drought intensity (β = −0.021) contributed to the regression model.


**Table 7.** Structural equation modeling results (disaggregated).

\*\*\* Significant at 1%; \*\* significant at 5%; \* significant at 10%. Source: Authors' estimation (2020).
