Agung B. Santoso, Evawaty S. Ulina, Siti F. Batubara, Novia Chairuman, Sudarmaji, Siti D. Indrasari, Arlyna B. Pustika, Nana Sutrisna, Yanto Surdianto, Rahmini, Vivi Aryati, Erpina D. Manurung, Hendri F. P. Purba, Wasis Senoaji, Noldy R. E. Kotta, Dorkas Parhusip, Widihastuty, Ani Mugiasih, Jeannette M. Lumban Tobing
{"title":"印度尼西亚稻农是否准备好采用精准农业技术？","authors":"Agung B. Santoso, Evawaty S. Ulina, Siti F. Batubara, Novia Chairuman, Sudarmaji, Siti D. Indrasari, Arlyna B. Pustika, Nana Sutrisna, Yanto Surdianto, Rahmini, Vivi Aryati, Erpina D. Manurung, Hendri F. P. Purba, Wasis Senoaji, Noldy R. E. Kotta, Dorkas Parhusip, Widihastuty, Ani Mugiasih, Jeannette M. Lumban Tobing","doi":"10.1007/s11119-024-10156-7","DOIUrl":null,"url":null,"abstract":"<p>Precision agriculture technologies (PATs) are believed to be able to ensure the sustainability of rice production. However, the adoption of PATs in developing countries is much lower than in developed countries. The basic question of our research is how Indonesian rice farmers are ready to adopt precision agriculture since they are smallholder farmers. Data was collected from 521 rice farmers in five Indonesian provinces, i.e. North Sumatra, West Java, Yogyakarta, South Sulawesi, and East Nusa Tenggara, in 2023. Farmers were interviewed face to face using structured questionnaires. The data were analysed using Partial Least Squares-Structural Equation Modelling (PLS-SEM) through the Python software. The results showed that Indonesian rice farmers have a moderate level of readiness. The mean value of the capabilities and opportunities indicators were 2.54 to 3.8, while the range for the opportunity’s indicator is 3.23 to 4.11, larger than the capabilities indicators. The level of precision agriculture implementation on Indonesian rice farmers was significant influenced by management (β = 0.42, t = 7.11, <i>p</i> < 0.05), environment (β = 0.17, t = 3.63, <i>p</i> < 0.05), readiness (β = 0.14, t = 2.51, <i>p</i> < 0.05), and technology (β = 0.10, t = 2.12, <i>p</i> < 0.05), economy (β = 0.09, t = 3.63, <i>p</i> < 0.05), and technology<sup>2</sup> (β = -0.072, t = 3.5, <i>p</i> < 0.05). Meanwhile, farmer readiness was significantly influenced by opportunity (β = 0.39, t = 6.64, <i>p</i> < 0.05) and capabilities (β = 0.43, t = 6.82, <i>p</i> < 0.05). This research provides information on the status of human resource capacity in exploiting opportunities for implementing precision agriculture and technical policy advice. The Indonesian government should improve farmers’ skills in information technology, Global Positioning Systems (GPS), and sensor technology in agricultural sectors, and facilitate access to technology and resources in order to increase rice farmers’ readiness to adopt PATs. For opportunity indicators, however, further research is needed to determine which components require immediate attention for construction or development.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"182 1","pages":""},"PeriodicalIF":5.4000,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Are Indonesian rice farmers ready to adopt precision agricultural technologies?\",\"authors\":\"Agung B. Santoso, Evawaty S. Ulina, Siti F. Batubara, Novia Chairuman, Sudarmaji, Siti D. Indrasari, Arlyna B. Pustika, Nana Sutrisna, Yanto Surdianto, Rahmini, Vivi Aryati, Erpina D. Manurung, Hendri F. P. Purba, Wasis Senoaji, Noldy R. E. Kotta, Dorkas Parhusip, Widihastuty, Ani Mugiasih, Jeannette M. 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The mean value of the capabilities and opportunities indicators were 2.54 to 3.8, while the range for the opportunity’s indicator is 3.23 to 4.11, larger than the capabilities indicators. The level of precision agriculture implementation on Indonesian rice farmers was significant influenced by management (β = 0.42, t = 7.11, <i>p</i> < 0.05), environment (β = 0.17, t = 3.63, <i>p</i> < 0.05), readiness (β = 0.14, t = 2.51, <i>p</i> < 0.05), and technology (β = 0.10, t = 2.12, <i>p</i> < 0.05), economy (β = 0.09, t = 3.63, <i>p</i> < 0.05), and technology<sup>2</sup> (β = -0.072, t = 3.5, <i>p</i> < 0.05). Meanwhile, farmer readiness was significantly influenced by opportunity (β = 0.39, t = 6.64, <i>p</i> < 0.05) and capabilities (β = 0.43, t = 6.82, <i>p</i> < 0.05). This research provides information on the status of human resource capacity in exploiting opportunities for implementing precision agriculture and technical policy advice. The Indonesian government should improve farmers’ skills in information technology, Global Positioning Systems (GPS), and sensor technology in agricultural sectors, and facilitate access to technology and resources in order to increase rice farmers’ readiness to adopt PATs. 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Are Indonesian rice farmers ready to adopt precision agricultural technologies?
Precision agriculture technologies (PATs) are believed to be able to ensure the sustainability of rice production. However, the adoption of PATs in developing countries is much lower than in developed countries. The basic question of our research is how Indonesian rice farmers are ready to adopt precision agriculture since they are smallholder farmers. Data was collected from 521 rice farmers in five Indonesian provinces, i.e. North Sumatra, West Java, Yogyakarta, South Sulawesi, and East Nusa Tenggara, in 2023. Farmers were interviewed face to face using structured questionnaires. The data were analysed using Partial Least Squares-Structural Equation Modelling (PLS-SEM) through the Python software. The results showed that Indonesian rice farmers have a moderate level of readiness. The mean value of the capabilities and opportunities indicators were 2.54 to 3.8, while the range for the opportunity’s indicator is 3.23 to 4.11, larger than the capabilities indicators. The level of precision agriculture implementation on Indonesian rice farmers was significant influenced by management (β = 0.42, t = 7.11, p < 0.05), environment (β = 0.17, t = 3.63, p < 0.05), readiness (β = 0.14, t = 2.51, p < 0.05), and technology (β = 0.10, t = 2.12, p < 0.05), economy (β = 0.09, t = 3.63, p < 0.05), and technology2 (β = -0.072, t = 3.5, p < 0.05). Meanwhile, farmer readiness was significantly influenced by opportunity (β = 0.39, t = 6.64, p < 0.05) and capabilities (β = 0.43, t = 6.82, p < 0.05). This research provides information on the status of human resource capacity in exploiting opportunities for implementing precision agriculture and technical policy advice. The Indonesian government should improve farmers’ skills in information technology, Global Positioning Systems (GPS), and sensor technology in agricultural sectors, and facilitate access to technology and resources in order to increase rice farmers’ readiness to adopt PATs. For opportunity indicators, however, further research is needed to determine which components require immediate attention for construction or development.
期刊介绍：
Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming.
There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to:
Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc.
Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc.
Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc.
Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc.
Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc.
Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.
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