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There is some difference in the moving from and left out groups, though it seems clear that overall, the left out group is less mobile than the other groups.
The staying with and left out groups are the least flexible with adapting sales and purchase rates to changing conditions as sales rates are essentially invariant to the period; the other two groups show the anticipated pattern of sales rates being higher in the drought than in the recovery phase.
Perhaps, the left out groups are less able to bargain as immediate consumption needs take priority as suggested by the results of Turner and Williams (2002), or possibly, the animals are in qualitatively worse condition due to the limited mobility and limited access to inputs revealed in the data analysis presented above.
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Combining the findings presented above paints a stark picture for the left out group in particular.
In Figure 4, the left out group is clearly the most sensitive to the overall impact of the drought event.
In contrast, the left out group that is also more sedentary is the hardest hit by the drought death rate.
In both of these measures, the least resilient are the left out group and the most resilient are the combining group.
Further, for all but the left out group, the average price per TLU increases in the recovery phase compared to the drought phase.
The main finding to stress with regard to the left out group is they are left out in a further dimension; prices of livestock for the other groups recover as the drought ends, but they remain relatively stagnant for the left out group.
However, in relative terms as captured by the coefficient of variation, the staying with and combining groups are significantly less sensitive to fluctuations in milk than the left out group, while the moving from group is not statistically different from the groups with the larger herds.
While all the income values in Table 2 are categorized as 'poor' by any conventional threshold, it is still evident that the left out group is relatively worse off in terms of mean income and the combining group is relatively better off by this measure.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com