What hypothesis states that there is no relationship between the dependent and independent variable?

When testing the null hypothesis H0 := 0, the Wald test statistics of 2.59 (Prob(> χ2) = 0.107), and the LR statistic of 0.437 (Prob(> χ2) = 0.51) do not allow us to reject the null hypothesis of no correlation. However, since the Wald test is close to a 10% threshold, and that outright rejection would be more convincing than inability to reject, estimation results for the individual probit models and for the RBPM are presented in Table 18.5. The two formulations are giving similar results in terms of signs of the relationship between adoption and potential explanatory variables. However, the RBPM formulation is showing higher significance of the relationship with the variables groups and training. Variables included in both equations have the same signs in the two equations, meaning the potentially indirect effects are reinforcing effects on adoption.

Table 18.5. Maximum Likelihood Estimates of Separate and Recursive Probit Models

Wald test of ρ = 0; χ2(1) = 2.59; Prob > χ2 = 0.107.

Likelihood ratio test of ρ = 0; χ2(1) = 0.59; Prob > χ2 = 0.441.

Standard errors in parentheses; *p < 0.1, **p < 0.05, ***p < 0.01.

The AME of each explanatory variable on adoption and training are presented in Table 18.6. These AME give more meaningful information as they can be interpreted in terms of impact on the probability of adoption, and also integrate the potentially indirect effects captured in the recursive system of equation. Results presented in Table 18.6 were calculated with the hypothesis of no correlations between the two errors. The variables with the strongest positive impact on Q-GAP adoption are related to farm labor available (labha), farmers' affiliations in farmer groups (groups), and farmers connections to sources of information (with an equal strength of the effects of extension contacts Govcont and other sources of information GAPcha).

Table 18.6. Average Marginal Effects of the Dependent Variables on Q-GAP Adoption

Education, measured by the number of schooling years of the household head, has a positive statistically significant relationship. This result extends to standard adoption the findings of literature on agricultural technologies adoption that the longer the farmers' schooling experience, the higher the tendency to adopt new technologies (Feder et al., 1985; Chouichom and Yamao, 2010; Liu et al., 2011). Farmers who have been through school are probably more equipped to understand the reason behind Q-GAP efforts and can follow the instructions of the program.

Besides, as the registration also requires participant to record their practices, more educated farmers are probably less impressed by this administrative exercise. However, the magnitude of the relationship is relatively limited (around 2% increase in adoption for an additional schooling year).

In the same way, farmers' experience has a positive (but not significant) relationship with Q-GAP adoption. This is in line with Knowler and Bradshaw (2007) who did not find consistent and clear impacts of experience on adoption of conservation agriculture across the studies they reviewed. If we retain the positive correlation, this indicates that experienced farmers evaluate more positively the potential of the Q-GAP program than inexperienced ones. It concurs with Chouichom and Yamao (2010) who showed that longer experience in farming and more years of education were related to conversion to organic rice farming in Surin Province in Thailand. However, this link is tenuous in our case. Farmers' participations in associations, cooperatives, and groups have positive and highly significant effects on both training and Q-GAP adoption. This confirms results found for adoption of conservation agriculture (Adesina et al., 2000). In our case, the positive effect for Q-GAP adoption is only significant under the RBPM formulation, probably as a result of the endogeneity of the rice-training variable. Common unobservable variables, such as dynamism and dedication to rice agriculture, is likely to explain both training attendance and Q-GAP adoption. Besides, AME results are showing that most of the effect of the variable group on Q-GAP adoption is indirect (via the training variable) reinforcing the possibility of a selection bias.

Frequent contacts with government and extension officers have a positive and statistically significant impact on Q-GAP adoption. This is consistent with the technology adoption literature. Feder et al. (1985), summarizing a large spectrum of adoption studies, concluded that education and extension services contacts improves farmers' ability to adjust to changes. Similarly, Moser and Barrett (2006) found that learning from extension agents influenced the decision to adopt low-input rice production methods. More recently and in a reverse relationship, low rate of adoption of sustainable agriculture in China was linked to inadequate agricultural extension efforts (Liu et al., 2011). A dual relationship may be at work: (a) more contacts are improving farmers' skills as extension officers are transmitting knowledge, but on the other hand, the farmers that maintain close contact with extension offices are probably more dedicated to agriculture. Other channels of information (variable GAPChannel) have also some positive and significant impact on Q-GAP adoption. Other channels in this case included family members, friends, village chief, community leader, experience with GAP for vegetable crops, and local soil doctors (ie, trained volunteers providing soil recommendation services to other farmers in the community).

It was expected that farmers having observed many neighbors adopting Q-GAP would be more likely to adopt: it is very common that community members decide to follow similar management patterns as each may not want to be left out (social cohesion). For example, the social cohesion factor was found to be one of the key variables of local community adoption of conservation agriculture in Laos (Lestrelin et al., 2012). However, contrary to our expectations, the number of neighbors known to be adopting Q-GAP did not have a significant relationship with adoption. Several hypotheses can be made about this counterintuitive result. First, social cohesion might be low in the agricultural zone we chose. Central plains are now cultivated by relatively larger farms and a substantial number of farms are managed by farmers that do not live permanently in the area and/or are passing orders to contracted labor. As a result, the farm-to-farm transmission is likely to be slower than expected. A second and more worrying interpretation for the Q-GAP program would be that farmers that did observe earlier adopters were not really convinced that it would fit their needs and constraints. Under such an assumption, farmer-to-farmer connections might not be efficient in spreading the program.

Labor availability is often affecting farmers' adoption decisions, especially for smallholders (White et al., 2005; Lee, 2005). Farmers adopting Q-GAP have to dedicate more time for rice cultivation. First, it requires recording all activities conducted on the farm and encourages some practices that are likely to substitute time-saving but potentially polluting practices with more knowledge and time-intensive practices. For example, using less pesticides requires more pest monitoring of the rice fields. Not surprisingly the variable labha (ie, the amount of family labor available for rice farming per ha) has a positive and highly significant effect on both adoption and training attendance. Contrary to the variable group, the influence of labha is mainly a direct effect, as the time constraints are more likely to be important once Q-GAP has been adopted. Contrary to our expectations, off-farm opportunities have a positive effect on adoption. However, this relation is not significant and cannot really be commented on.

Not all perception variables could be included in the analysis because of collinearity issues: for example, farmers who anticipated some cost-reduction potential before adopting Q-GAP were also anticipating better market access for their certified products. Among the different perceptions elicited during the interviews, we retained only the anticipations farmers had about the cost-reduction potential of Q-GAP. The relationship between expected cost reduction and adoption was both strong (25% probability increase) and significant, meaning farmers who adopted were really convinced that adopting Q-GAP would reduce their expenditures, possibly through a more rational use of chemical inputs. A nontested hypothesis here is that participation in Q-GAP could be associated by farmers with dedicated external advice leading to their more efficient use of inputs. Although not included in the model, adopters' expectations were probably high in terms of access to new markets, and price mark-up (because these variables are positively correlated to the variable on cost-reduction perceptions). We found a positive relationship between farmers attending training sessions and their perceived negative impact on the environment, but it is difficult to decide on the “direction” of the relationship. However, farmers' perception of negative impact on the environment did not translate into a significant effect on adoption.

The coefficient for training is giving unexpected results as we were expecting that farmers having attended a dedicated presentation about Q-GAP would be more likely to adopt. In fact, both individual probit and RBPM models are showing a negative relationship between training attendance and Q-GAP adoption. This should not be immediately interpreted as a sign of poorly conducted training (although this cannot be ruled out). An equivalently possible interpretation is that farmers are forming some positive expectations from the different contacts they had (justifying their training attendance) but are actually disappointed once they understand clearly the costs and benefits associated with Q-GAP adoption. On the one hand, farmers may be expecting higher “costs” in terms of labor requirements, which may prevent larger landholders from adopting (identified by the variable labha). On the other hand, some farmers may not be convinced about the potential benefits presented to them; as the government agencies are not responsible for the marketing of the Q-GAP rice, they can only suggest that the rice produced under Q-GAP will be more attractive, but cannot guarantee it. In the same way, farmers may not be confident in the capacity of the new practices in reducing production costs (for example, by using less pesticides or different types of fertilizers). In other words, farmers may well be interested by the general concept of Q-GAP but may ultimately make a rational decision related to labor issues as we showed earlier. Finally, one should also note that the negative impact is relatively small (− 5% for farmers attending the training).