农民对温室气体排放的态度以及采用低成本传感器驱动的智能耕作来缓解温室气体排放:爱尔兰耕作和园艺农民的案例

IF 6.3 Q1 AGRICULTURAL ENGINEERING
Fredrick Otieno , Sudha-Rani N V Nalakurthi , Mahdieh Raji , Ananya Tiwari , Iulia Anton , Salem Gharbia
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引用次数: 0

摘要

目前的农业生产方式使该部门成为温室气体排放的最大贡献者,从而加剧了全球气候危机。爱尔兰农民必须在 2030 年前将农业总排放量减少 25%,以减少温室气体排放。本研究就气候风险下农民的经验、实践和挑战,调查了耕作和园艺部门的农场温室气体减排情况。研究还分析了农民参与减排实践的意愿,如采用智能耕作技术(SFT)和低成本传感器进行环境监测。此外,还确定了影响采用智能农业技术的关键农民态度和变量。对爱尔兰各地的农民(53 人)进行了问卷调查,并对农业专家进行了访谈。对获得的数据进行了探索性数据分析,以分析模式。随后,利用潜在类分析模型(LCA)进行潜在态度分析,以揭示影响采用 SFT 的潜在态度。最后,进行了逆向逐步回归分析,以确定农民的经验、实践和挑战中影响其潜在态度的重要因素(p < 0.05)。农民在耕作实践中面临多种经验和挑战,包括高度认识到气候对生产的影响(76%)和对温室气体排放源的有限认识(10%)。他们还使用化肥(67%)和杀虫剂(67%)。不过,他们表示愿意监测当地环境状况(67%),包括农场碳足迹(CF)测量(62%)。农民表现出三种态度:生产导向(21%)、智能农业导向(30%)和有机农业导向(49%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Farmer's attitudes towards GHG emissions and adoption to low-cost sensor-driven smart farming for mitigation: The case of Ireland tillage and horticultural farmers

Farmer's attitudes towards GHG emissions and adoption to low-cost sensor-driven smart farming for mitigation: The case of Ireland tillage and horticultural farmers
The current agricultural practices make the sector the single largest contributor to overall GHG emissions thus contributing to the global climate crisis. Irish farmers are required to reduce total agricultural emissions by 25% by 2030 to mitigate the emissions. This study investigates the mitigation of on-farm GHG emissions for tillage and horticulture sector with regards to farmers’ experiences, practices, and challenges under the climate risks. It also analyses farmers’ willingness to engage in emission mitigation practices such as adoption of smart farming technologies (SFT) of low-cost sensors for environmental monitoring. In addition, identifying key farmer attitudes and variables influencing adoption of the SFT. Questionnaires were administered to farmers (n = 53) across Ireland, augmented with agricultural experts’ interviews. The data obtained was subjected to exploratory data analysis to analyse patterns. This was followed by latent attitude analysis with Latent Class Analysis (LCA) model to reveal underlying attitudes influencing adoption of the SFT. Finally, a backward stepwise regression analysis was undertaken to determine significant (p < 0.05) factors in the farmer’ experiences, practices and challenges that influence their latent attitudes. The farmers have multiple experiences and challenges with their farming practices including high acknowledgement of climate impact on production (76%) and limited awareness of GHG emission sources (10%). They also practice among others use of fertilizer (67%) and pesticides (67%). Nevertheless, they showed willingness to monitor local environmental conditions (67%) including on-farm carbon footprint (CF) measurements (62%). The farmers exhibited three types of attitudes, production orientation (21%), smart farming orientation (30%), and organic farming orientation (49%).
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