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Research, Policy and Practice

Table 5 Geographical and historical factors associated with feeding strategy cluster

From: Ranchers or pastoralists? Farm size, specialisation and production strategy amongst cattle farmers in south-eastern Kazakhstan

Variable 1: small sedentary 2: medium mobile 3: medium fodder purchaser 4: medium fodder producer 5: large mobile fodder purchaser 6: large mobile fodder producer Total farms F or Pearson’s χ2# βlncattleα
N 40 56 25 32 27 20 200   β
Worked on sovkhoz (1/0) 0.5 0.54 0.36 0.59 0.22-cv 0.65 0.48 13.3** −0.38**
Agricultural education (1/0) 0.03 0.2 0 0.13 0.07 0.25 0.12 14.1** 0.29
District of residence Enbek (1/0) 0.15 0.09 0.44+cv 0.25 0.26 0.20 0.20 14.7** 0.30
Kegen (1/0) 0.15 0.07-cv 0.24 0.13 0.56+cv 0.25 0.20 29.4*** 0.40**
Raiymbek (1/0) 0.7 0.84 0.32 0.63 0.19 0.55 0.59 44.7*** −0.50***
Population of home village† 47073 48433 915212 6254 7530 5849 6043 4.3*** 660*
Distance from Almaty (km)†† 213 22953 1722 211 1722 200 205 5.6*** −12.88***
  1. From 2009 census
  2. ††Generated by straight line GIS distance tool—as crow flies
  3. #Continuous variables: OLS regressions with group as the categorial predictor. Statistic given is the F ratio for the overall model (***p < 0.01, **p < 0.05, *p < 0)
  4. Superscripts 1, 2, 3, 4, 5 and 6 indicate groups to which difference is significantly different from zero at **p < 0.05 or lower using pairwise comparisons with Bonferroni correction
  5. #Binary variables: Pearson’s chi-squared test: ***p < 0.01, **p < 0.05, * p < 0
  6. Superscript +/-cv for binary variables indicates those cells which have Pearson’s residuals greater than the critical value with Bonferroni correction (+ or − 2.86), indicating contribution to χ2
  7. αContinuous variables: OLS regression on log of cattle ownership; for binary variables: logistic regression