Technical efficiency and risk of smallholders’ rubber production in South Sumatra, Indonesia

In Indonesia, particularly in South Sumatra, rubber production is characterised by risks that result production variability. The presence of risks not only affected rubber output but also farmer’s decision with regard to input use. Another problem is that the productivity of smallholders’ rubber was lowest compared to that of private and government estates. The general objective of this study is to determine the technical efficiency and to estimate production risk in smallholders’ rubber in South Sumatra. The specific objectives are: (1) to estimate the level of technical efficiency of smallholders’ rubber production by applying Parametric (Stochastic Frontier Analysis integrated with Just and Pope Model) and Non-parametric (Bootstrap DEA) approaches; (2) to identify and determine factors affecting technical efficiency of smallholders’ rubber production; and (3) to estimate the level of production risk in smallholders’ rubber production The data collected covered the yields obtained of rubber, technological inputs and farmers’ demographic/socio-economic characteristics. The selection of 384 respondents was conducted through the combination of the purposive, multi-stage and random sampling techniques. The study was carried out in South Sumatra Province, Indonesia. This study employed the parametric approach (Stochastic Frontier Model integrated with Just-Pope model) and the non-parametric approach (bootstrap DEA analysis). Results of Stochastic Frontier Analysis indicated that, on average, the sampled farms in this study had the technical efficiency score at 0.72 while when applied to data envelopment analysis (DEA), the mean technical efficiency estimate was 0.80. Then, bootstrap DEA was applied to correct bias estimation of DEA. Under bootstrap DEA, the mean technical efficiency reduced to 0.76. The finding also showed that production input factors of Weighted Rubber Trees (WRT), fertiliser, herbicide and labour were essential in rubber production development. All those production inputs increased the mean output in the production process. The sum of all of inputs elasticities indicated that rubber farms had decreasing return to scale (0.82). In this study, production risk was jointly explained by fertiliser, herbicide and labour. Fertiliser and herbicide were not statistically significant concerning production risk. Meanwhile, labour was estimated statistically significant to be risk reducing-input. It implies that an increase in labour input will reduce output risk. Farmer’s age, family size and education were found to be not significant concerning inefficiency effect model. Extension visit, farming experience, and recommended tapping system were significants to increase production efficiency. Lastly, adopting planting material of rubber clone was also evident to increase production efficiency. This study concludes that production efficiency was explained by technical inefficiency and production risk. But the effect of inefficiency was greater than production risk. This implies that it is necessary to pay more attention to farmers’ practices and the existence of production risk. In particular, the estimation of technical efficiency without accounting the risk effect on production output may cause incorrect estimation results. The results of the study showed that labor was risk-reducing input and some factors (experience, extension visit, recommended tapping system and planting material of rubber clone) had significant contributions to increase technical efficiency. Thus, it is suggested that farmers are encouraged to use more labour input on rubber farm to reduce risk. Then, the farmers should also be encouraged to apply recommended tapping system S/2 d2 and rubber clone. The government should also intensify research and development activities to produce new high yielding rubber clones and intensify extension visit by involving experienced farmers in order to improve farmer’s knowledge on farming management.

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