Abstract
Combine harvesters (CHs) use large amounts of diesel fuel and they contribute significantly to greenhouse gas (GHG) emissions and air pollutants. A reduction in fuel consumption of agricultural machines has immediate economic benefits but can also results in longer term environmental benefits. Savickas et al. (Precis Agric 2023. https://doi.org/10.1007/s11119-023-10060-6) introduced extensive telematics data analysis and field tests for identifying the potential sustainable use of CHs. This paper follows on from that research and discusses the need for an IT tool for the management of economic and environmental indicators of a CH. The innovative tool allows the comparison of the data of specific agricultural machines with the multi-year telematics data stored in the database. It offers opportunities to reduce environmental impact and evaluate the results of field trials, presenting them in terms of environmental impact, expressed as CO2 equivalents.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Available from the corresponding author on reasonable request.
Code availability
Available from the corresponding author on reasonable request.
References
Agarwal, A. K., Prashumn, & Chandra, K. (2021). Di-ethyl ether-diesel blends fuelled off-road tractor engine: Part-I: Technical feasibility. Fuel, 308, 121972. https://doi.org/10.1016/j.fuel.2021.121972
Bernhardt, H., Bozkurt, M., Brunsch, R., Colangelo, E., Herrmann, A., Horstmann, J., Kraft, M., Marquering, J., Steckel, T., Tapken, H., Weltzien, C., & Westerkamp, C. (2021). Challenges for agriculture through industry 4.0. Agronomy, 11(10), 1–10. https://doi.org/10.3390/agronomy11101935
Bernhardt, H., Mederle, M., Treiber, M., & Woerz, S. (2018). Aspects of digitization in agricultural logistics in Germany. Actual Tasks on Agricultural Engineering, 46, 245–251.
Bie, P., Ji, L., Cui, H., Li, G., Liu, S., Yuan, Y., He, K., & Liu, H. (2022). A review and evaluation of nonroad diesel mobile machinery emission control in China. Journal of Environmental Sciences. https://doi.org/10.1016/j.jes.2021.12.041
Bochtis, D. D., & Vougioukas, S. G. (2008). Minimising the non-working distance travelled by machines operating in a headland field pattern. Biosystems Engineering, 101(1), 1–12. https://doi.org/10.1016/j.biosystemseng.2008.06.008
Chinnasamy, C., Tamilselvam, P., & Ranjith, R. (2019). Influence of aluminum oxide nanoparticle with different particle sizes on the working attributes of diesel engine fueled with blends of diesel and waste plastic oil. Environmental Science and Pollution Research, 26(29), 29962–29977. https://doi.org/10.1007/s11356-019-06139-1
Dallmann, T., & Menon, A. (2016). Technology pathways for diesel engines used in non-road vehicles and equipment. The International Council on Clean Transportation (ICCT), 1, 47.
Eggerl, A. (2017). Optimization of combine processes using expert knowledge and methods of artificial intelligence [Technische Universitat Dresden]. PhD thesis. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-230087
European Commission. (2022). European vehicle emissions standards—Euro 7 for cars, vans, lorries and buses. https://ec.europa.eu/info/law/better-regulation/have-your-say/initiatives/12313-European-vehicle-emissions-standards-Euro-7-for-cars-vans-lorries-and-buses_en
Goltyapin, V., & Golubev, I. (2020). Global trends in the development of monitoring systems for mobile agricultural equipment. E3S Web of Conferences, 157, 1–8. https://doi.org/10.1051/e3sconf/202015701013
Gundoshmian, T. M., Ghassemzadeh, H. R., Abdollahpour, S., & Navid, H. (2010). Application of artificial neural network in prediction of the combine harvester performance. Journal of Food, Agriculture and Environment, 8(2), 721–724.
Hainsch, K., Löffler, K., Burandt, T., Auer, H., Crespo del Granado, P., Pisciella, P., & Zwickl-Bernhard, S. (2022). Energy transition scenarios: What policies, societal attitudes, and technology developments will realize the EU Green Deal? Energy. https://doi.org/10.1016/j.energy.2021.122067
He, P., Li, J., Fang, E., DeVoil, P., & Cao, G. (2019). Reducing agricultural fuel consumption by minimizing inefficiencies. Journal of Cleaner Production, 236, 117619. https://doi.org/10.1016/j.jclepro.2019.117619
Huang, R., Liu, J., He, X., Xie, D., Ni, J., Xu, C., Zhang, Y., Ci, E., Wang, Z., & Gao, M. (2020). Reduced mineral fertilization coupled with straw return in field mesocosm vegetable cultivation helps to coordinate greenhouse gas emissions and vegetable production. Journal of Soils and Sediments, 20(4), 1834–1845. https://doi.org/10.1007/s11368-019-02477-2
Janulevičius, A., Juostas, A., & Pupinis, G. (2010). Tractor engine load and fuel consumption in road construction works. Transport, 25(4), 403–410. https://doi.org/10.3846/transport.2010.50
Janulevičius, A., Juostas, A., & Pupinis, G. (2013). Engine performance during tractor operational period. Energy Conversion and Management, 68, 11–19. https://doi.org/10.1016/j.enconman.2013.01.001
Jia, Q., Zhang, H., Wang, J., Xiao, X., Chang, S., Zhang, C., Liu, Y., & Hou, F. (2021). Planting practices and mulching materials improve maize net ecosystem C budget, global warming potential and production in semi-arid regions. Soil and Tillage Research, 207, 104850. https://doi.org/10.1016/j.still.2020.104850
Johnsson, F., Kjärstad, J., & Rootzén, J. (2019). The threat to climate change mitigation posed by the abundance of fossil fuels. Climate Policy, 19(2), 258–274. https://doi.org/10.1080/14693062.2018.1483885
Kaur-Sidhu, M., Ravindra, K., Mor, S., & John, S. (2020). Emission factors and global warming potential of various solid biomass fuel-cook stove combinations. Atmospheric Pollution Research, 11(2), 252–260. https://doi.org/10.1016/j.apr.2019.10.009
Kim, S., Sim, J., Cho, Y., Sung, B., & Park, J. (2021). Numerical study on the performance and NOx emission characteristics of an 800cc MPI turbocharged SI engine. Energies, 14(7419), 1–29. https://doi.org/10.3390/en14217419
Kiniulis, V., Steponavičius, D., Andriušis, A., Kemzūraitė, A., & Jovarauskas, D. (2017). Corn ear threshing performance of filler-plate-covered threshing cylinders. Mechanika, 23(5), 714–722. https://doi.org/10.5755/j01.mech.23.5.17389
Kiniulis, V., Steponavičius, D., Kemzūraitė, A., Andriušis, A., & Juknevičius, D. (2018). Dynamic indicators of a corn ear threshing process influenced by the threshing-separation unit load. Mechanika, 24(4), 412–421. https://doi.org/10.5755/J01.MECH.4.24.20721
Kovács, I., & Husti, I. (2018). The role of digitalization in the agricultural 4.0—how to connect the industry 4.0 to agriculture? Hungarian Agricultural Engineering, 7410(33), 38–42. https://doi.org/10.17676/hae.2018.33.38
Lamb, W. F., Grubb, M., Diluiso, F., & Minx, J. C. (2021). Countries with sustained greenhouse gas emissions reductions: An analysis of trends and progress by sector. Climate Policy, 22(1), 1–17. https://doi.org/10.1080/14693062.2021.1990831
Liao, P., Sun, Y., Zhu, X., Wang, H., Wang, Y., Chen, J., Zhang, J., Zeng, Y., Zeng, Y., & Huang, S. (2021). Identifying agronomic practices with higher yield and lower global warming potential in rice paddies: a global meta-analysis. Agriculture, Ecosystems and Environment, 322, 107663. https://doi.org/10.1016/j.agee.2021.107663
Lovarelli, D., Fiala, M., & Larsson, G. (2018). Fuel consumption and exhaust emissions during on-field tractor activity: A possible improving strategy for the environmental load of agricultural mechanisation. Computers and Electronics in Agriculture, 151, 238–248. https://doi.org/10.1016/j.compag.2018.06.018
Lunner-Kolstrup, C., Hörndahl, T., & Karttunen, J. P. (2018). Farm operators’ experiences of advanced technology and automation in Swedish agriculture: A pilot study. Journal of Agromedicine, 23(3), 215–226. https://doi.org/10.1080/1059924X.2018.1458670
Macián, V., Monsalve-Serrano, J., Villalta, D., & Fogué-Robles, Á. (2021). Extending the potential of the dual-mode dual-fuel combustion towards the prospective EURO VII emissions limits using gasoline and OMEx. Energy Conversion and Management. https://doi.org/10.1016/j.enconman.2021.113927
Mehlig, D., Woodward, H., Oxley, T., & Holland, M. (2021). Electrification of road transport and the impacts on air quality and health in the UK. Atmosphere, 12(1491), 1–15. https://doi.org/10.3390/atmos12111491
Microsoft. (2022a). SQL Server. https://www.microsoft.com/en-us/sql-server/sql-server-downloads
Microsoft. (2022b). Visual Studio .NET. https://visualstudio.microsoft.com/
Microsoft. (2022c). Windows Server. https://www.microsoft.com/en-us/windows-server
Pirjola, L., Rönkkö, T., Saukko, E., Parviainen, H., Malinen, A., Alanen, J., & Saveljeff, H. (2017). Exhaust emissions of non-road mobile machine: Real-world and laboratory studies with diesel and HVO fuels. Fuel, 202(2017), 154–164. https://doi.org/10.1016/j.fuel.2017.04.029
Saiz-Rubio, V., & Rovira-Más, F. (2020). From smart farming towards agriculture 5.0: A review on crop data management. Agronomy. https://doi.org/10.3390/agronomy10020207
Savickas, D., Steponavičius, D., Kliopova, I., & Saldukaitė, L. (2020). Combine harvester fuel consumption and air pollution reduction. Water, Air, and Soil Pollution, 231(3), 1–11. https://doi.org/10.1007/s11270-020-4466-5
Savickas, D., Steponavičius, D., Špokas, L., Saldukaitė, L., & Semenišin, M. (2021). Impact of combine harvester technological operations on global warming potential. Applied Sciences (Switzerland). https://doi.org/10.3390/app11188662
Savickas, D., Steponavičius, D., & Kemzūraitė, A. (2023). A novel approach for analysing environmental sustainability aspects of combine harvester through telematics data. Part I. Evaluation and analysis of field tests. Precision Agriculture. https://doi.org/10.1007/s11119-023-10060-6
Sopegno, A., Calvo, A., Berruto, R., Busato, P., & Bocthis, D. (2016). A web mobile application for agricultural machinery cost analysis. Computers and Electronics in Agriculture, 130, 158–168. https://doi.org/10.1016/j.compag.2016.08.017
Stek, K. (2022). Personality development in higher education in the era of Industry 4.0: Comparing Educational practices and philosophies in Industry 1.0 and Industry 4.0. Advanced Series in Management, 28, 35–50. https://doi.org/10.1108/S1877-636120220000028005
Subeesh, A., & Mehta, C. R. (2021). Automation and digitization of agriculture using artificial intelligence and internet of things. Artificial Intelligence in Agriculture, 5, 278–291. https://doi.org/10.1016/j.aiia.2021.11.004
van Linden, V., & Herman, L. (2014). A fuel consumption model for off-road use of mobile machinery in agriculture. Energy, 77, 880–889. https://doi.org/10.1016/j.energy.2014.09.074
Venkatesan, V., Nallusamy, N., & Nagapandiselvi, P. (2021). Performance and emission analysis on the effect of exhaust gas recirculation in a tractor diesel engine using pine oil and soapnut oil methyl ester. Fuel, 290, 120077. https://doi.org/10.1016/j.fuel.2020.120077
Winther, M., Dore, C., Lambrecht, U., Norris, J., Samaras, Z., & Zierock, K.-H. (2019). EMEP/EEA air pollutant emission inventory guidebook 2019. EMEP/EEA Air Pollutant Emission Inventory Guidebook, 2019, 1–81.
Ximinis, J., Massaguer, A., Pujol, T., & Massaguer, E. (2021). Nox emissions reduction analysis in a diesel Euro VI Heavy Duty vehicle using a thermoelectric generator and an exhaust heater. Fuel, 301, 121029. https://doi.org/10.1016/j.fuel.2021.121029
Yu, Y., Jiang, T., Li, S., Li, X., & Gao, D. (2020). Energy-related CO2 emissions and structural emissions’ reduction in China’s agriculture: An input–output perspective. Journal of Cleaner Production, 276, 124169. https://doi.org/10.1016/j.jclepro.2020.124169
Funding
No funding was received for conducting this study.
Author information
Authors and Affiliations
Contributions
Conceptualisation—DS, DS and AK. Methodology—DS, DS and AK. Software—DS. Validation—DS, DS and AK. Formal analysis—DS, DS and AK. Investigation—DS, DS. Resources—DS, DS and AK. Data curation—DS, DS and AK. Writing—original draft preparation—DS, DS and AK. Writing—review and editing—DS, DS and AK. Visualization—DS. Supervision— DS. All authors have read and agreed to the published version of the manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Ethical approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Savickas, D., Steponavičius, D. & Kemzūraitė, A. A novel approach for analysing environmental sustainability aspects of combine harvesters through telematics data. Part II: an IT tool for comparative analysis. Precision Agric 25, 221–234 (2024). https://doi.org/10.1007/s11119-023-10065-1
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11119-023-10065-1