Are Indonesian rice farmers ready to adopt precision agricultural technologies?

IF 5.4 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Precision Agriculture Pub Date : 2024-06-14 DOI:10.1007/s11119-024-10156-7

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":"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. Lumban Tobing","doi":"10.1007/s11119-024-10156-7","DOIUrl":null,"url":null,"abstract":"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.","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":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Precision Agriculture","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1007/s11119-024-10156-7","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

Abstract

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.

Abstract Image

查看原文本刊更多论文

印度尼西亚稻农是否准备好采用精准农业技术？

精准农业技术（PATs）被认为能够确保水稻生产的可持续性。然而，发展中国家采用精准农业技术的比例远远低于发达国家。我们研究的基本问题是，由于印尼稻农是小农，他们准备如何采用精准农业技术。我们于 2023 年从印尼五个省份（即北苏门答腊、西爪哇、日惹、南苏拉威西和东努沙登加拉）的 521 位稻农那里收集了数据。采用结构化问卷对农民进行了面对面的访谈。通过 Python 软件使用偏最小二乘法-结构方程模型（PLS-SEM）对数据进行分析。结果显示，印尼稻农的准备程度适中。能力和机会指标的平均值为 2.54 至 3.8，而机会指标的范围为 3.23 至 4.11，大于能力指标。印尼稻农的精准农业实施水平受管理（β = 0.42，t = 7.11，p < 0.05）、环境（β = 0.17，t = 3.63，p < 0.05）、准备度（β = 0.14，t = 2.51，p <；0.05）和技术（β = 0.10，t = 2.12，p <；0.05）、经济（β = 0.09，t = 3.63，p <；0.05）和技术2（β = -0.072，t = 3.5，p <；0.05）。同时，农民的准备程度受机会（β = 0.39，t = 6.64，p < 0.05）和能力（β = 0.43，t = 6.82，p < 0.05）的显著影响。这项研究提供了有关人力资源在利用实施精准农业的机会和技术政策建议方面的能力状况的信息。印尼政府应提高农民在信息技术、全球定位系统（GPS）和农业部门传感器技术方面的技能，并促进技术和资源的获取，以提高稻农采用 PATs 的意愿。不过，对于机会指标，还需要进一步研究，以确定哪些组成部分需要立即关注建设或开发。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Precision Agriculture 农林科学-农业综合

CiteScore

12.30

自引率

8.10%

发文量

103

审稿时长

>24 weeks

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.