Adoption of Precision Agriculture Systems Smart Farm (HandySense) of Nam Dok Mai Mango farmers, Bang Phli District, Samut Prakan Province

Abstract

      The objectives of this research were to study 1) personal factors, economic factors, and exposure to news about smart farms; 2) compare the adoption of a precision agriculture and smart farms system (HandySense) classified according to personal factors, economic factors, and news exposure factors; 3) compare personal factors, economic factors, media exposure about smart farm and acceptance of precision agriculture and smart farm system (HandySense) of farmers; and  4) problems and suggestions. The sample consisted of 223 Nam Dok Mai mango farmers. Accidental sampling and data were collected using questionnaires. Statistics employed included mean, standard deviation, frequency, percentage, minimum and maximum. Hypotheses were tested using t-tests and F-tests. The results of the study revealed that the farmers were female, had an average age of 57.1 years old, attended secondary school for educational level, were married, had a family of 4 people, had an average cultivation experience with Nam Dok Mai mangoes of 21.7 years, had an average planting area of ​​2.2 rai, had an average yield of 446.4 kilograms/rai/year, had an average income from sales Nam Dok Mai mangoes at 33,569.5 baht/year/person, had an average production cost of 3,337.3 baht/year, and had an average labor of 2 people. Farmers received information about Smart Farming from personal media via agricultural extension officers, from activity media via training, from mass media via television, and from social media via YouTube. Farmers' acceptance of the precision agriculture system (HandySense) overall average is at a high level. The hypotheses testing found that age, educational level, marital status, experience in growing Nam Dok Mai mangoes, and media exposure to smart farming from personal media, mass media, and social media were different in terms of farmers' adoption of the precision farming system and smart farming (HandySense) at a statistically significant level (P<0.05). The problem and suggestion are that HandySense requires a high investment and is suitable for growing mangoes in the early stages. Thus, the government should provide guidance on its use so farmers can better understand and correctly and consistently employ the system.