Agroforestry has potential for strengthening the climate change resilience of smallholder farmers in Southeast Asia. In Indonesia, the food security challenges faced by smallholders will likely worsen due to climate change impacts. Agroforestry provides and option for strengthening climate change resilience, while contributing to food access, income, health, and environmental stability. To evaluate the evidence for such benefits, this systematic review identifies 22 peer-reviewed articles published between 2000 and 2019 which assess agroforestry’s contributions to food security in Indonesia, mostly in Java or Sumatra. Analysis of the studies indicate that traditional and commercial agroforestry contribute to food security in diverse ways: for example, traditional homegardens offer 20% more dietary diversity than commercial counterparts, while commercial homegardens may contribute up to five times more income. Agri-silviculture contributions fall along a timber versus non-timber forest product continuum that displays a similar tradeoff between diversity and income. Those systems with a commercial focus may receive 54% of their income from a single commodity crop such as coffee, while traditional systems allow greater access to plants with medicinal benefits. Nearly all agroforestry systems offered indirect benefits for food security, such as allowing more off-farm work than traditional agriculture and contributing to environmental stability: users of agroforestry were found by one study to collect 83% less fuelwood from natural forests. One study highlighted that agroforestry options have up to 98% greater net present value (for periods over 30 years) compared to slash and burn style agriculture. However, very few studies of Indonesian agroforestry focused explicitly on financial analysis and food security, indicating the need for further research. Given the similar situations faced by many Southeast Asia countries, our findings contribute to emerging trends throughout the region regarding the relationship between agroforestry and food security.

Aim This research study focused on exploring the impact of resilience on COVID‐19 phobia (C19P) among individuals from different nations including a cluster of European countries, India, Indonesia, Pakistan and the United States of America (USA). Method We recruited research participants via disseminating an electronic survey on Facebook Messenger (FM) that included 812 participants. The electronic survey assessed unidentifiable demographic information, the COVID‐19 Phobia Scale (C19P‐S; Arpaci et al., 2020) and the Brief Resilience Scale (BRS; Smith et al, 2008). Results Based on simple linear regression, resilience had a statistically significant negative affect on all four C19P factors including psychological, psychosomatic, economic and social factors ( p < .001). Resilience showed a statistically significant difference for at least two nations ( p < .001) investigated in this research, as shown by using the Kruskal–Wallis test. Utilising linear regression analysis showed that age affects the resilience score positively significantly ( p < .001). Based on the Kruskal–Wallis test, we found no statistically significant differences in resilience scores between genders, but found statistically significant differences in resilience scores based on marital status, educational level and professional status ( p = .001). Conclusion We concluded that the higher the resilience level, the lower the level of C19P. The level of resilience was highest in the USA, followed by Europe, Pakistan, India and Indonesia. Age affected the resilience level positively and resilience differed based on marital status, education levels, and professional status but not between genders. Implications are offered for effective counselling interventions during this COVID‐19 pandemic and the aftermath.

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The maximum likelihood parameter estimation method with Newton Raphson iteration is used in general to estimate the parameters of the logistic regression model. Parameter estimation using the maximum likelihood method cannot be used if the sample size and proportion of successful events are small, since the iteration process will not yield a convergent result. Therefore, the maximum likelihood method cannot be used to estimate the parameters. One way to resolve this un-convergence problem is using the score function modification. This modification is used to obtain the parameters estimate of logistic regression model. An example of parameter estimation, using maximum likelihood method with small sample size and proportion of successful events equals 0.1, showed that the iteration process is not convergent. This non-convergence can be solved with modifications on a score function. Modification on score function is to change a score function, a matrix of the first derivative of the log likelihood function, to the first derivative matrix itself minus multiplication of information matrix and biased vector. The modification of the score function can quickly yield values of parameter estimates, especially when the sample sizes are larger, and convergence was reached before the 10th iteration.