## 3 Results  
Table 4 shows that the average of the technological innovation (TI) level was 46.3% with an uneven behavior between areas, and four different groups were configured (\(p<0.01\)) (Table 4). The highest TI level was viewed in A5 animal health (72%). On the opposite side, the lowest levels were found in A2 animal feeding and A4 reproductive management with 28.3% and 27.4% respectively. In addition, these two areas showed high coefficients of variation, 52.2% and 70.9% respectively. Similar patterns of technological adoption in animal health and reproduction were found by [9] in Ecuador, [10; 39] with smallholders in Mexico and [26] in Ethiopia. The efforts in improvement of animal health are based on the use of technologies for parasite control, sanitary governmental programs and the quality requirements for the dairy industry.  
Low levels of Spearman correlations were identified (\(p<0.05\)), and were significant (\(p<0.01\)) between the 10 pairs of areas. The highest levels of correlations were found between A5 health and A1 management, and A2 feeding and A4 reproduction, with \(r=0.35\), \(r=0.36\), and \(r=0.35\), respectively. Moreover, A1 management showed high correlation levels regarding A2 feeding, and A5 health, with \(r=0.40\), and \(r=0.35\), respectively (Table 5).  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Technological Areas** & **Innovation (\(n\))** & **Innovation Level (\%)** & **Q1** & **Q3** & **CV** \\ \hline Global innovation & 45 & 46.3 \(\pm\) 0.3 & 40.0 & 50.1 & 24.5 \\ A1. Management & 8 & 61.2 \(\pm\) 0.4 b & 50.0 & 75.0 & 24.2 \\ A2. Animal feeding & 14 & 28.3 \(\pm\) 0.4 d & 14.3 & 35.7 & 52.2 \\ A3. Genetics & 9 & 59.5 \(\pm\) 0.4 c & 55.6 & 66.7 & 25.9 \\ A4. Reproductive management & 7 & 27.4 \(\pm\)0.5 d & 14.3 & 28.6 & 70.9 \\ A5. Animal health & 6 & 72.0 \(\pm\) 0.4 a & 57.1 & 85.7 & 22.8 \\ \hline \end{tabular}
\begin{tabular}{l l l l l} \hline
**SE: standard error. Q1: first quartile. Q3: third quartile. CV: coefficient of variation. Different letters within column (\(p<0.01\)).** \\ \end{tabular}
\end{table}
Table 4: Livestock innovation level (mean \(\pm\) standard error) for each area (A).  
Of the producers, 56% have implemented the PMP and 44% did not. The results of the binary logistic regression are shown in Table 6 and confirm the positive association between PMP and the economic viability of the mixed system. The model estimates that the likelihood of being viable economically is 5.059 times higher when a PMP system has been implemented. The Hosmer and Lemeshow (\(p\) = 0.837) test does not proof the absence of fit of the model, which was correctly classified in 70% of the cases. The Nagelkerke (0.176) determination coefficient and the Hosmer- Lemeshow statistic (2.7649) suggest that a PMP program explains 21% of the economic viability of farms.  
The second estimated model for the farms that implements a PMP (Table 7) is explained by the five innovation areas with a 62% accuracy in prediction and a Nagelkerke R\({}^{2}\) of 0.235 and an H-L Statistic of 7.540 (\(p\)-value Chi-Sq = 0.480). This indicates that the model is accepted to describe the probability curve of the variability of the farms with PMP. In the suggested model, the innovation area of feeding (\(p\) < 0.05) is shown as the explanatory variable of the viability of PMP farms; the estimated OR value for A2 feeding is 1.080, meaning that when a farm adopts a technology from A2 feeding, it has an 80% of possibility of being viable.