CHAPTER 7 THE POTENTIAL OF INNOVATION PLATFORMS AND ICTS IN
7.3 Results
Page 162 of 231 categories (1=full adoption, 2=partial adoption, and 3=non adoption). Non adoption was taken as a reference category, while the choice of explanatory variables and expected sign of influence is largely guided by empirical literature that includes studies by Hassan and Nhemachena (2008) and Ahmed (2016). The same model was used for both AI and fodder production since the two dependent variables are affected by almost the same variables. Table 7.1 summarizes the explanatory variables used for empirical estimation, together with their expected influence on farm level adaptations.
Table 7.1: Description of explanatory variables and expected signs Explanatory
variable
Description Expected sign for
CSA adoption
Age Age of household head (years) +
Gender Gender of household head (1 if male, 0 otherwise) +/- Educ Number of years of formal education of household head + Agrictraining Household head completed agricultural training (1=yes, 0=no) + Stocktype Dominant herd stock type (1=indigenous, 0=otherwise) -
Herdsize Size of the dairy herd +
Lactcows Total number of lactating cows +
Dairyincome Estimated annual income from dairy activities ($) +
ICTuse Use of ICTs in dairy activities (1=yes, 0=no) +
Innovation Farmer participation in innovation platforms (1=yes, 0=no) +
Page 163 of 231 21 to 88 years. The average number of years spend in formal education is eight, which is consistent with national statistics which show literacy levels of over 90% (ZIMSTAT, 2014).
However, less than half of the interviewed households (41%) completed agricultural training (Table 7.2).
Table 7.2: Descriptive statistics for variables selected for the MNL regression model (N=227)
Explanatory Variable Minimum Maximum M SD
Total number of animals in dairy herd 1 48 4.30 5.01
Number of lactating cows 0 8 1.45 1.43
Gender of HH Head 0 1 0.79 0.41
Age of HH Head 21 88 56.41 13.88
HH completed agricultural training 0 1 0.41 0.49
Participation in innovation platforms 0 1 0.57 0.50
Fodder production adoption 1 3 1.78 0.83
HH using ICTs in dairy 0 1 0.72 0.45
AI Adoption 1 3 2.02 0.85
Stock type 0 1 0.35 0.48
Estimated Total Annual Dairy Income ($) 0.00 33,600 1,346.00 2,850.00
Years of formal education for HH Head 0 22 8.10 4.08
The results are similar to the findings from previous studies that also highlighted the numerous socio-economic variables affecting smallholder dairying in Zimbabwe (Hanyani-Mlambo et al., 1998). The study findings also reflect the characteristics found in other typical mixed crop- livestock systems (Somda et al., 2004). More than half the surveyed households (57%) participated in innovation platforms, while most households (72%) used ITCs to guide dairy production and marketing. Most smallholder dairy producing households had also either partially or fully-adopted fodder production and/or use of artificial insemination in crossbreeding programmes.
Page 164 of 231 7.3.2 Factors influencing the adoption of artificial insemination
The MNL regression model is estimated using the maximum likelihood method. MNL model assessments found the Log-likelihood Ratio (LR) to be significant (p<0.01) (Table7.3). This means that the independent variables selected into the model statistically significantly improved the model in predicting the influence on smallholder dairy producers’ adoption of artificial insemination. This entails that the choice of variables is good. In addition, the measure of Goodness-of-fit shows that the model specification is good. Pseudo-R2 measures also show that a greater proportion of the variation in the dependent variable is being explained by the given explanatory variables. The conclusion is that the MNL model employed is reliable and appropriate. Results show that the dairy herd size, the number of lactating cows, estimated annual dairy income, ICT use in dairying, and the stock type are statistically significant in explaining the adoption of AI. The result implies that the decision to fully, partially or not adopt at all is mostly explained by the five factors. The results of the MNL regression analysis of factors influencing the adoption of artificial insemination as a CSA innovation are presented in Table 7.3.
Factors that are statistically significant, for comparisons of the level of adoption between full adoption and non adoption, are dairy herd size, number of lactating cows, participation in innovation platforms, ICT use and stock type. The implication is that smallholder dairy producers are more likely to fully adopt artificial insemination if the herd size is limited, have a large number of lactating cows, are participating in innovation platforms, are using ICTs, and the dairy stock type is not indigenous. For partial adopters, it is likely that they will partially adopt when compared to non-adopters when there is a high number of lactating cows, they participate in innovation platforms, are using ICTs in dairying, and that the stock type is not indigenous.
Page 165 of 231 Table 7.3 : MNL regression for factors influencing artificial insemination adoption (N=227)
Category Variables ß SE Wald Df Sig. Exp(ß)
Fully Adopted
Intercept -5.382 2.722 3.909 1 0.048
Herdsize -0.270 0.150 3.245 1 0.072* 0.763
Age 0.026 0.030 0.713 1 0.399 1.026
Lactcows 1.877 0.536 11.126 1 0.001*** 6.535
Dairyincome 0.000 0.000 0.716 1 0.397 1.000
Educ -0.056 0.085 0.436 1 0.509 0.945
Gender 1.101 0.955 1.329 1 0.249 3.008
Agrictraining 0.277 0.805 0.119 1 0.730 1.320
Innovation -1.258 0.756 2.768 1 0.096* 0.284
ICTuse -3.144 0.893 12.395 1 0.000*** 0.043
Stocktype 5.356 1.033 26.896 1 0.000*** 211.819
Partially Adopted
Intercept -3.685 2.489 2.192 1 0.139
Herdsize -0.153 0.149 1.056 1 0.304 0.858
Age 0.022 0.028 0.595 1 0.441 1.022
Lactcows 1.725 0.553 9.710 1 0.002*** 5.611
Dairyincome -0.001 0.000 1.644 1 0.200 0.999
Educ -0.061 0.077 0.633 1 0.426 0.941
Gender 0.608 0.889 0.469 1 0.494 1.837
Agrictraining 0.524 0.778 0.453 1 0.501 1.688
Innovation -1.332 0.715 3.468 1 0.063* 0.264
ICTuse -2.433 0.812 8.964 1 0.003*** 0.088
Stocktype 4.731 0.844 31.441 1 0.000*** 113.416
-2 Log Likelihood 233.807 Cox and Snell .685
χ2 262.034 Nagelkerke .772
Df 20 McFadden .528
p-value 0.000
***, ** and * significant at P<0.01, P<0.05 and P<0.1 respectively.
Page 166 of 231 7.3.3 Factors influencing the adoption of fodder production
The Log-likelihood Ratio (LR) is significant at the 1% level. Again, this shows that the model statistically significantly predicts the dependent variable better than the intercept-only model, thus the choice of explanatory variables is good. Other preliminary assessments highlight the χ2 result as showing that the selected factors are significantly different from zero at P<0.01 for the adoption of fodder production. The McFadden’s R-square or Pseudo R2 is 0.310. This implies that up to 31% of the variations in probabilities of adopting fodder production by the sampled smallholder dairy producers was explained by the selected explanatory variables. Results show that the factors that are significant in explaining the adoption of fodder production are the dairy herd size, estimated annual dairy income, participation in innovation platforms, and the use of ICTs. The other factors are not significant enough to explain the adoption of fodder production.
The results of MNL regression on determinants of fodder production adoption are presented in Table 7.4 using non adoption as a reference category.
Results in Table 7.4 show that for full adoption, the major determining factors are the number of lactating cows, the dairy herd size, participation in innovation platforms and ICT use. This means that the sampled smallholder dairy producers are likely to be full adopters than a non- adopter of fodder production if the household has a high number of lactating cows, have a large dairy herd, if it is participating in innovation platforms, and are using ICTs in dairy activities.
Similarly, when compared to a non-adopters, households partially adopt fodder production when the dairy herd size is larger, dairy income is high, are participating in innovation platforms and are using ICTs in dairy activities. As before, the other factors are insignificant.