Application of Probit Analysis in the Decision of Youths to Participate in Vegetable Production

Abstract

Youths are successor farming generation and therefore the future of food security. At present, they constitute about 60% of Nigeria’s population and have over the years contributed significantly to national development. Unfortunately, the present environment makes it difficult to explore their full potentials in production through participation in agriculture. The ageing smallholder farmers are less likely to increase capacity needed to sustainably expand agricultural production. There is therefore a pressing need to engage the youth in ways that they can see a promising future in agriculture as well as influence them to build capacity through effective involvement in agricultural production. Several factors however, have continued to hinder capacity building and effective participation of youths in vegetable farming. An empirical study was conducted to estimate the factors affecting the willingness of youth to participate in small scale waterleaf production. The representative waterleaf producers were selected using the multi stage sampling procedures.With the aid of questionnaire, primary data were obtained from 100 farmers. Univariate probit regression model was used to analyze the data. Results of analysis indicated that the most critical factors affecting the participation of youths in waterleaf production were age, educational qualification, size of household members, and farm income. Results indicated that youths who have acquired some form of education were more willing to be involved in waterleaf production. Findings further indicated that youth in families with higher income from farming activities were more willing to participate in waterleaf production.

Youth Participation in Agricultural Enterprises as Rural Job Creation Work and Its Determinants in Southern Ethiopia

“…For instance, as education increases by 1 grade (level), the probability of youth participation in an agriculture enterprise decreases by 18%, holding other factors constant, while compared to those youths who have access to initial capital, the probability of youth participation in an agriculture enterprise decreases by 36% for those youth who lacked initial capital, holding other factors constant. A study by Etim et al [22] found similar results in Nigeria. However, studies by Martinson et al [34] from Ghana and Gitore et al [15] from Ethiopia found unalike results that indicated that the likelihood of youth participation in agriculture enterprises increased as the education level increased.…”

“…Even though the binary models have a quite alike cumulative normal function (probit) and the logistic function (logit) [13,20], Gujarati [21] has noted the main difference by demonstrating that the logistic function has a slightly fatter tail as compared to probit model distribution. In addition, different studies suggested that the use of the probit model is more advantageous due to its normal distributional nature of latent error terms [18,20,22,23]. Since our data resembles a normal distribution, the probit model was used among the alternative logit model to estimate the probability of youth participation in agricultural enterprises.…”

Religious Institutions and Poverty Reduction: Evidence from Akwa Ibom State, Nigeria

“…This might be related to the fact that younger individuals are more willing to try new things than older ones. It is consistent with findings by Ayamga (2006), Etim andEdet (2013a,2013b), Etwire et al,(2013), Etwire et al,(2017), Etim et al,(2020aEtim et al,( , 2020b, who discovered that teenagers were risk takers, responsive, and more inclined to accept innovations quicker than older individuals. However, this conclusion is in contrast to that of Asante et al (2011), who found that age had a favorable influence on farmers' decision-making.…”

Determinants of Market Participation among Dairy Producers in Southwestern Ethiopia

“…(3) For econometric estimation, linear probability model (LPM), Logit, Probit models suggested regression models by numerous for binary choice depended on variables [6,23,[31][32][33][34] . Since our dependent variable is dummy, the alternative binary models such as logit and probit models were used for this study.…”