Energy, carbon and water footprints on agricultural machinery

Mantoam, Edemilson José; Angnes, Graciele; Mekonnen, Mesfin; Romanelli, Thiago Libório

doi:10.1016/j.biosystemseng.2020.08.019

Cited by 53 publications

(20 citation statements)

References 38 publications

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“…As a typical model of China's agricultural development in the new era, ecological agriculture has been widely recognized and accepted in theory and practice. It is considered as an inevitable choice to solve the plight of modern agriculture, reduce the pressure on population, resources, and environment, and the basis for the marketization and internationalization of China's agricultural products [6]. Agricultural data has the characteristics of large amount, multidimensional, dynamic, incomplete, and uncertain.…”

Section: Introductionmentioning

confidence: 99%

Data Mining Technology for Agricultural Equipment Machinery and Information Network Data Resources

Fan

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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In order to realize the automation and intelligence of agricultural equipment mechanical state detection and improve the efficiency and accuracy of agricultural machinery equipment detection technology, this paper proposes an intelligent agricultural machinery equipment state detection method based on computer data mining. This method uses support vector machine to classify the equipment status and uses ant colony algorithm to solve the optimization problem. Its basic logic is to collect the status data of agricultural machinery equipment, extract the status detection characteristics of agricultural machinery equipment, and use computer data mining to establish an automatic state detection model of agricultural machinery equipment. The application results show that the correct rate of the state detection of agricultural machinery equipment is more than 95%, and the false detection rate of the state of agricultural machinery equipment is far lower than that of other current state detection methods of agricultural machinery equipment. Conclusion. This method can realize high-precision real-time detection of agricultural machinery equipment and has higher practical value.

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Section: Introductionmentioning

confidence: 99%

Data Mining Technology for Agricultural Equipment Machinery and Information Network Data Resources

Fan

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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“…Population growth has caused a sharp increase in food consumption, and agricultural mechanisation has become a key strategy for enhancing grain yield and quality ( Mantoam et al, 2020 ). The machinery of agriculture is becoming a fundamental and important equipment in modern farming ( Han et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices

Abd Wahab,

Hasikin,

Wee Lai

et al. 2024

PeerJ Computer Science

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Background Maintaining machines effectively continues to be a challenge for industrial organisations, which frequently employ reactive or premeditated methods. Recent research has begun to shift its attention towards the application of Predictive Maintenance (PdM) and Digital Twins (DT) principles in order to improve maintenance processes. PdM technologies have the capacity to significantly improve profitability, safety, and sustainability in various industries. Significantly, precise equipment estimation, enabled by robust supervised learning techniques, is critical to the efficacy of PdM in conjunction with DT development. This study underscores the application of PdM and DT, exploring its transformative potential across domains demanding real-time monitoring. Specifically, it delves into emerging fields in healthcare, utilities (smart water management), and agriculture (smart farm), aligning with the latest research frontiers in these areas. Methodology Employing the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria, this study highlights diverse modeling techniques shaping asset lifetime evaluation within the PdM context from 34 scholarly articles. Results The study revealed four important findings: various PdM and DT modelling techniques, their diverse approaches, predictive outcomes, and implementation of maintenance management. These findings align with the ongoing exploration of emerging applications in healthcare, utilities (smart water management), and agriculture (smart farm). In addition, it sheds light on the critical functions of PdM and DT, emphasising their extraordinary ability to drive revolutionary change in dynamic industrial challenges. The results highlight these methodologies’ flexibility and application across many industries, providing vital insights into their potential to revolutionise asset management and maintenance practice for real-time monitoring. Conclusions Therefore, this systematic review provides a current and essential resource for academics, practitioners, and policymakers to refine PdM strategies and expand the applicability of DT in diverse industrial sectors.

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“…This study combines practical experience in production and relevant research results. Also, it takes into account that the decarbonization of agriculture mainly manifests itself in the reduction of inputs of high-carbon agricultural materials and high-carbon agricultural production activities, and concludes that the carbon emissions from agriculture mainly originate from the following aspects: (1) direct or indirect carbon emissions from the production and utilize of fertilizers, pesticides, and agricultural films (Long and Tang, 2021); (2) carbon emissions from the direct ordiesel fuel's indirect consumption by agrarian machinery (Mantoam et al, 2020);…”

Section: Explained Variables: Agricultural Carbon Emissions (Ace)mentioning

confidence: 99%

Robotics, environmental regulation, and agricultural carbon emissions: an examination of the environmental Kuznets curve theory and moderating effects

Li,

Chen

2024

Front. Sustain. Food Syst.

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IntroductionReducing carbon emissions from agriculture is essential to ensuring food security and human prosperity. As a country with approximately 20% of the global population, China has begun actively practicing the low-carbon agricultural development conception. Against the backdrop of disruptive technologies that continue to be integrated into various industries, the massive application of agricultural robots has opened the way to intelligent agriculture. This paper tries to answer whether there is some non-linear nexus between the application of agricultural robots and agricultural carbon emissions in China. As an essential tool for carbon emission reduction in China, does environmental regulation moderate the nexus between agricultural robot applications and agricultural carbon emissions? If so, how does this effect manifest itself?MethodsThis work takes China as an example by collecting macro-regional panel data from 30 provinces from 2006 to 2019. The environmental Kuznets curve theory is extended to agricultural carbon emissions, and we carried out empirical tests utilizing the panel fixed effects model and the moderating effects model.ResultsThis study verifies the inverted U-shaped nexus between agricultural robotics applications and agricultural carbon emissions in Chinese provinces, i.e., the agricultural carbon emissions (ACE)-Kuznets curve holds. The higher the level of formal environmental regulation, the larger the peak of the ACE-Kuznets curve and the more the inflection point is pushed back. The higher the level of informal environmental regulation, the lower the peak of the ACE-Kuznets curve and the later the inflection point.DiscussionThe findings in this paper represent the first exploration of the environmental Kuznets curve in agricultural carbon emissions. It is noteworthy that the moderating effect of formal environmental regulation does not lower the peak of the curve as we expect. This appearance is attributed to the reality that China is still in a phase of rising agricultural carbon emissions, which is exacerbated by the overlapping positive effects of agricultural robotics applications and formal environmental regulations. Informal environmental regulation is more effective than formal environmental regulation in reducing agricultural carbon emissions at this stage.

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Energy, carbon and water footprints on agricultural machinery

Cited by 53 publications

References 38 publications

Data Mining Technology for Agricultural Equipment Machinery and Information Network Data Resources

Data Mining Technology for Agricultural Equipment Machinery and Information Network Data Resources

Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices

Robotics, environmental regulation, and agricultural carbon emissions: an examination of the environmental Kuznets curve theory and moderating effects

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