Abstract:The development of information technology has encouraged farmers to be able to use cellular phones to support agricultural activities. However, only a few farmers use their cellphones for marketing activities. This study aims to analysis factors influence farmers to use cell phones in marketing activities, and whether cell phone use has an impact on farmers' welfare. The analytical method used is the logit regression model and Propensity Score Matching (PSM). The results showed that what influenced farmers' de… Show more
“…The fundamental problem that is often encountered when using the PSM method is not being able to measure the potential outcomes of households using mobile phones (Y 1i ) and the control group (Y 0i ) at the same time (Feryanto & Rosiana, 2021). In this regard, only one can be observed, using an estimation model that allows seeing the average value of the impact of cell phone usage.…”
Section: Analysis Methodsmentioning
confidence: 99%
“…Several previous studies have identified the determinants of using information and communication technology in the form of mobile phones by farmers. Several factors, including age, influence farmers' decisions to use mobile phones, length of education, income, marital status, home ownership status, and land area (Aminou et al, 2018;Folitse et al, 2019;Umaroh & Afifah, 2020;Feryanto & Rosiana, 2021;William et al, 2021). Folitse et al (2019), in their study using logistic regression analysis it was found that the coefficient of the age variable had a negative and significant relationship with the use of mobile phones as a communication tool for agricultural information.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Other factors, such as education level and land area, also positively and significantly influence decisions to use mobile phones (Feryanto & Rosiana, 2021). The higher the education level, the higher the probability of farmers using mobile phones.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Several previous studies examine the effect of mobile phones on agricultural productivity. Feryanto & Rosiana (2021) analyzed the impact of mobile phone use on farmers' welfare in Java and non-Java as measured by farm income. Using the Propensity Score Matching (PSM) method, the results show that the impact of the average income of farmers who use mobile phones is more significant, Rp-7,430,000 per year, compared to farmers who do not use mobile phones for marketing activities.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Catur Yuantari et al (2016) also stated that the level of knowledge of farmers about technology, one of which is in marketing agricultural products, is still not optimal. Simple technology, such as mobile phones, has not been used optimally for farming activities (Feryanto & Rosiana, 2021).…”
This study examines the use of mobile phones on agricultural productivity in East Java Province and its comparison with peer provinces. This study uses a Propensity Score Matching (PSM) approach with a probit model to analyze the impact of using mobile phones for agricultural business activities on the amount of rice output in the form of rice. The data used in this study is IFLS wave 5 2014. The estimation results show that, in general, age, education, marital status, the natural logarithm of income, the number of paddy fields, and location affect the probability of using mobile phones for agricultural purposes. On the other hand, this study also found results that the probability of using a cell phone by individuals in East Java Province is influenced by their level of education. Individual education level in East Java Province positively relates to mobile phone use. It means that the level of education can increase the probability of using mobile phones for agricultural business purposes. Furthermore, the results of the impact of mobile phone use on the amount of rice output found that, on average, individuals who use mobile phones for farming purposes in East Java will get a higher amount of rice output by 841.3 kg compared to individuals who do not use mobile phones for business agriculture.
“…The fundamental problem that is often encountered when using the PSM method is not being able to measure the potential outcomes of households using mobile phones (Y 1i ) and the control group (Y 0i ) at the same time (Feryanto & Rosiana, 2021). In this regard, only one can be observed, using an estimation model that allows seeing the average value of the impact of cell phone usage.…”
Section: Analysis Methodsmentioning
confidence: 99%
“…Several previous studies have identified the determinants of using information and communication technology in the form of mobile phones by farmers. Several factors, including age, influence farmers' decisions to use mobile phones, length of education, income, marital status, home ownership status, and land area (Aminou et al, 2018;Folitse et al, 2019;Umaroh & Afifah, 2020;Feryanto & Rosiana, 2021;William et al, 2021). Folitse et al (2019), in their study using logistic regression analysis it was found that the coefficient of the age variable had a negative and significant relationship with the use of mobile phones as a communication tool for agricultural information.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Other factors, such as education level and land area, also positively and significantly influence decisions to use mobile phones (Feryanto & Rosiana, 2021). The higher the education level, the higher the probability of farmers using mobile phones.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Several previous studies examine the effect of mobile phones on agricultural productivity. Feryanto & Rosiana (2021) analyzed the impact of mobile phone use on farmers' welfare in Java and non-Java as measured by farm income. Using the Propensity Score Matching (PSM) method, the results show that the impact of the average income of farmers who use mobile phones is more significant, Rp-7,430,000 per year, compared to farmers who do not use mobile phones for marketing activities.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Catur Yuantari et al (2016) also stated that the level of knowledge of farmers about technology, one of which is in marketing agricultural products, is still not optimal. Simple technology, such as mobile phones, has not been used optimally for farming activities (Feryanto & Rosiana, 2021).…”
This study examines the use of mobile phones on agricultural productivity in East Java Province and its comparison with peer provinces. This study uses a Propensity Score Matching (PSM) approach with a probit model to analyze the impact of using mobile phones for agricultural business activities on the amount of rice output in the form of rice. The data used in this study is IFLS wave 5 2014. The estimation results show that, in general, age, education, marital status, the natural logarithm of income, the number of paddy fields, and location affect the probability of using mobile phones for agricultural purposes. On the other hand, this study also found results that the probability of using a cell phone by individuals in East Java Province is influenced by their level of education. Individual education level in East Java Province positively relates to mobile phone use. It means that the level of education can increase the probability of using mobile phones for agricultural business purposes. Furthermore, the results of the impact of mobile phone use on the amount of rice output found that, on average, individuals who use mobile phones for farming purposes in East Java will get a higher amount of rice output by 841.3 kg compared to individuals who do not use mobile phones for business agriculture.
Landak Regency has the potential to develop rice with a production surplus of 52,296 tonnes. This increase in production must be balanced with a good marketing system so that farmers and marketing agencies can receive more favorable prices. The purpose of this study is to analyze the channel, function and operational efficiency of rice marketing (marketing margin, farmer's share, and profit to cost ratio) in Landak District. This study involved 45 respondent farmers as a sample which was obtained through a purposive sampling method in which the respondent farmers were selected with consideration of certain characteristics according to the research objectives. Marketing agency respondents were obtained using the snowball method based on information flow from farmers and obtained 13 marketing agency respondents. Qualitative data is used to analyze marketing channels and agencies. Quantitative data is used to analyze marketing margins, farmer's share and profit to cost ratio. The results showed that there were 5 marketing channels and 4 marketing agencies involved. The marketing channel is distinguished from the products sold by farmers, namely channels 1, 2 and 3, farmers sell unhusked rice, while channels 4 and 5 sell rice. Channel 1 consists of farmer-gatherer-factory-retailer-end consumer, channel 2 consists of farmer-collector-mill-retailer-end consumer, channel 3 consists of farmer-mill-retailer-end consumer, channel 4 consists of farmer-retailer -end consumers and channel 5 farmers sell rice directly to end consumers. The results of the operational efficiency analysis show that a relatively efficient marketing channel is channel 1 based on the profit to cost ratio having a value of >1 (profitable). Increasing the institutional role of farmers (poktan and gapoktan) such as collective transactions can help farmers receive more profitable prices..
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