Using new computer based techniques to optimise energy consumption in agricultural land levelling

https://doi.org/10.17221/20/2021-RAECitation:

Almaliki S., Monjezi N. (2021): Using new computer based techniques to optimise energy consumption in agricultural land levelling. Res. Agr. Eng., 67: 149–163.

download PDF

Land levelling is one of the most energy-demanding steps in soil preparation. There are many limiting factors for a specific land levelling operation, such as fertile topsoil conservation, limited allowed slope, specific cut to fill ratio, etc. These limitations make optimisation problems of land levelling even more complicated. In this research, three computational and evolutionary methods including ICA, PSO, GA along with MLS were utilised as optimisation methods to minimise the soil cut and fill volumes and to determine the preferred levelling plane. The results indicated that ICA had the most efficient solution for the energy optimisation in the land levelling among the other investigated methods by saving 29% (17 GJ) of the total energy consumption compared with MLS. This study deals with optimising the energy consumption during land levelling projects using new computer-based techniques and compares them to the MLS method as a benchmark. All in all, ICA, PSO, and GA performed much better than MLS by saving 29, 17, and 10% of the total energy consumption in their best model (number 1 models), respectively. Nonetheless, with these great capacities for saving energy in developing countries, unfortunately, the lack of education and excess subsidies on fossil fuels nullify these potentials.

References:
Abdullaev I., Hassan M.U., Jumaboev K. (2007): Water saving and economic impacts of land leveling: The case study of cotton production in Tajikistan. Irrigation and Drainage Systems, 21: 251–263. https://doi.org/10.1007/s10795-007-9034-2
 
Ali A. (2018): Laser-land leveling adoption and its impact on water use, crop yields and household income: Empirical evidence from the rice-wheat system of Pakistan Punjab. Food Policy, 77: 19–32. https://doi.org/10.1016/j.foodpol.2018.03.018
 
Alzoubi I., Delavar R., Mirzaei F., Arrabi B.N. (2018): Effect of soil properties for prediction of energy consumption in land levelling irrigation. International Journal of Ambient Energy, 41: 1–14.
 
Anand M.K. (2014): Direct and Indirect Use of Fossil Fuels in Farming: Cost of Fuel Price Rise for Indian Agriculture. New Delhi, India, National Institute of Public Finance and Policy, 35.
 
Anderson C.L., Halderman A.D., Paul H.A., Rapp E. (1980): Land shaping requirements. Design and Operation of Farm Irrigation Systems, 281–314.
 
Anderson-Cook C.M. (2005): Practical genetic algorithms. Journal of the American Statistical Association, 471: 1099. https://doi.org/10.1198/jasa.2005.s45
 
Askew W., Al-Jibouri S.H., Mawdesley M.J., Patterson, D.E. (2002): Planning linear construction projects: Automated method for the generation of earthwork activities. Automation in Construction, 11: 643–653. https://doi.org/10.1016/S0926-5805(02)00005-5
 
Atashpaz-Gargari E., Lucas C. (2007): Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. In: IEEE Congress on Evolutionary computation, September 25–28, 2007, Singapore, The Republic of Singapore: 4661–4667.
 
Beheshti Tabar I., Keyhani A., Rafiee S. (2010): Energy balance in Iran’s agronomy (1990–2006), 14: 849–855. https://doi.org/10.1016/j.rser.2009.10.024
 
Brye K.R., Slaton N.A., Norman R.J. (2006): Soil physical and biological properties as affected by land leveling in a clayey aquert. Soil Science Society of America Journal, 70: 631–642. https://doi.org/10.2136/sssaj2005.0185
 
Cazanescu S., Mihai D., Mudura R. (2010): Modern technology for soil levelling, based on a 3d scanner. Research Journal of Agricultural Science, 42: 471–478.
 
Change I.C. (2014): Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK and New York, NY, Cambridge University Press.
 
Dauda K., Baiyeri M. (2001): Assessment of cut and fill volume for irrigated agriculture using GIS-LMS. Oasis Journal of Research and Development, 1: 28–39.
 
Demİrtaș Ç., Demİr A. (2011): The use of least square method in land leveling projects on geographic information system (GIS). Bursa, Turkey, Ziraat Fakültesi Dergisi, Uludağ Üniversitesi, 25: 27–40.
 
Easa S.M. (1989): Three-point method for estimating cut and fill volumes of land grading. Journal of Irrigation and Drainage Engineering, 115: 505–511. https://doi.org/10.1061/(ASCE)0733-9437(1989)115:3(505)
 
Eberhart R., Kennedy J. (1995): A new optimizer using particle swarm theory. In: MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, October 4–6, 1995, Nagoya, Japan: 39–43.
 
FAO. 2011. The State of the World’s Land and Water Resources for Food and Agriculture – Managing systems at risk. Rome and London, Earthscan: 309.
 
Farajian L., Moghaddasi R., Hosseini S. (2018): Agricultural energy demand modeling in Iran: Approaching to a more sustainable situation. Energy Reports, 4: 260–265. https://doi.org/10.1016/j.egyr.2018.03.002
 
Fei R., Lin B. (2017): Estimates of energy demand and energy saving potential in China’s agricultural sector. Energy, 135: 865–875. https://doi.org/10.1016/j.energy.2017.06.173
 
Gagnon P., Chrétien F., Thériault G. (2017): Land leveling impact on surface runoff and soil losses: Estimation with coupled deterministic/stochastic models for a Québec agricultural field. Journal of Hydrology, 544: 488–499. https://doi.org/10.1016/j.jhydrol.2016.11.038
 
Gebre-Selassie N., Willardson L. (1991): Application of least-squares land leveling. Journal of Irrigation and Drainage engineering, 117: 962–966. https://doi.org/10.1061/(ASCE)0733-9437(1991)117:6(962)
 
Goktepe A.B., Lav A.H. (2003): Method for balancing cut-fill and minimizing the amount of earthwork in the geometric design of highways. Journal of Transportation Engineering, 129: 564–571. https://doi.org/10.1061/(ASCE)0733-947X(2003)129:5(564)
 
Goldberg D.E., Holland J.H. (1988): Genetic algorithms and machine learning. Machine Learning, 3: 95–99. https://doi.org/10.1023/A:1022602019183
 
Holland J.H. (1962): Outline for a logical theory of adaptive systems. Journal of the ACM (JACM), 9: 297–314. https://doi.org/10.1145/321127.321128
 
International Energy Agency (IEA). (2017): World Energy Balances 2017. Paris, France, OECD Publishing: 747.
 
Jat M., Chandna P., Gupta R., Sharma S.K., Gill M.A. (2006): Laser land leveling: A precursor technology for resource conservation. Rice-Wheat Consortium Technical Bulletin Series, 7: 48.
 
Jat M.L., Gathala M.K., Ladha J.K., Saharawat Y.S., Jat A.S., Kumar V., Sharma S.K., Kumar V., Gupta R. (2009): Evaluation of precision land leveling and double zero-till systems in the rice–wheat rotation: Water use, productivity, profitability and soil physical properties. Soil and Tillage Research, 105: 112–121. https://doi.org/10.1016/j.still.2009.06.003
 
Ji Y., Borrmann A., Rank E., Seipp F., Ruzika S. (2010): Mathematical modeling of earthwork optimization problems. In: International Conference on Computing in Civil and Building Engineering (ICCCBE), June 30–July 2, 2010, Nottingham, UK.
 
Kaveh A., Talatahari S. (2010): A novel heuristic optimization method: Charged system search. Acta Mechanica, 213: 267–289. https://doi.org/10.1007/s00707-009-0270-4
 
Khan M.T.I., Ali Q., Ashfaq M. (2018): The nexus between greenhouse gas emission, electricity production, renewable energy and agriculture in Pakistan. Renewable Energy, 118: 437–451. https://doi.org/10.1016/j.renene.2017.11.043
 
Macku J. (1996): Regional Plans of Forest Development Methodology. Brandys nad Labem, Czech Republic, The Forest Management Institute, 1996: 85.
 
Ministry of Energy of the Islamic Republic of Iran (M.o.E.o.) (2018): Statistics of Energy consumption In Agricultural sector. Available at: http://panel.agriis.ir/Attachments/Files/2018/6/131732740201883892_Package.pdf (in Persian).
 
Mobtaker H.G., Keyhani A., Mohammadi A., Rafiee S., Akram A. (2010): Sensitivity analysis of energy inputs for barley production in Hamedan Province of Iran. Agriculture, Ecosystems & Environment, 137: 367–372.
 
Modarres M., Shams H. (2001): New models for land leveling optimization. In: The First National Industrial Engineering Conference, May 30–31, 2001, Tehran, Iran.
 
Moore S.R. (2010): Energy efficiency in small-scale biointensive organic onion production in Pennsylvania, USA. Renewable Agriculture and Food Systems, 25: 181–188. https://doi.org/10.1017/S1742170510000098
 
Mousavi-Avval S.H., Rafiee S., Jafari A., Mohammadi A. (2011): Optimization of energy consumption for soybean production using Data Envelopment Analysis (DEA) approach. Applied Energy, 88: 3765–3772. https://doi.org/10.1016/j.apenergy.2011.04.021
 
USDA. (1970): Land Leveling. National Engineering Handbook, U.S. Dept. of Agriculture: Soil Conservation.
 
Peter K.D., d’Oleire-Oltmanns S., Ries J.B., Marzolff I., Hssaine A.A. (2014): Soil erosion in gully catchments affected by land-levelling measures in the Souss Basin, Morocco, analysed by rainfall simulation and UAV remote sensing data. CATENA, 113: 24–40. https://doi.org/10.1016/j.catena.2013.09.004
 
Rafiee S., Mousavi Avval S.H., Mohammadi A. (2010): Modeling and sensitivity analysis of energy inputs for apple production in Iran. Energy, 35: 3301–3306. https://doi.org/10.1016/j.energy.2010.04.015
 
Reddy S.L. (1996): Optimal land grading based on genetic algorithms. Journal of Irrigation and Drainage Engineering, 122: 183–188. https://doi.org/10.1061/(ASCE)0733-9437(1996)122:4(183)
 
Rickman J. (2002): Manual for laser land leveling. Rice-Wheat Consortium for the Indo-Gangetic Plains, New Delhi, India, Technical Bulletin Series 5, 2002, 24.
 
Robertson G.P., Broome J.C., Chornesky E.A., Frankenberger J.R., Johnson P., Lipson M.J.A., Owens E.D., Pimentel D., Thrupp L.A. (2004): Rethinking the vision for environmental research in US agriculture. BioScience, 54: 61–65. https://doi.org/10.1641/0006-3568(2004)054[0061:RTVFER]2.0.CO;2
 
Samavatean N., Rafiee S., Mobli H., Mohammadi A. (2011): An analysis of energy use and relation between energy inputs and yield, costs and income of garlic production in Iran. Renewable Energy, 36: 1808–1813. https://doi.org/10.1016/j.renene.2010.11.020
 
Sattar A., Tahir A., Khan F. (2003): Impact of precision land levelling on water saving and drainage requirements. Agricultural Mechanization in Asia Africa and Latin America, 34: 39–42.
 
Scaloppi E.J., Willardson L.S. (1986): Practical land grading based on least squares. Journal of Irrigation and Drainage Engineering, 112: 98–109. https://doi.org/10.1061/(ASCE)0733-9437(1986)112:2(98)
 
Schnepf R.D. (2004): Energy use in agriculture: Background and issues. In: The Library of Congress, Congressional Research Service, Washington, DC, USA.
 
Shi Y., Eberhart R. (1998): A modified particle swarm optimizer. In: IEEE International Conference on Evolutionary Computation Proceedings, May 4–9, 1998, IEEE World Congress on Computational Intelligence, Anchorage, Alaska, USA.
 
Tieppo R.C., Romanelli T.L., Milan M., Sørensen C.A.G., Bochtis D. (2019): Modeling cost and energy demand in agricultural machinery fleets for soybean and maize cultivated using a no-tillage system. Computers and Electronics in Agriculture, 156: 282–292. https://doi.org/10.1016/j.compag.2018.11.032
 
Unger P.W., Fulton L.J., Jones O.R. (1990): Land-leveling effects on soil texture, organic matter content, and aggregate stability. Journal of Soil and Water Conservation, 45: 412–415.
 
Walker T.W., Kingery W.L., Street J.E., Cox M.S., Oldham J.L., Gerard P.D., Han F.X. (2003): Rice yield and soil chemical properties as affected by precision land leveling in alluvial soils. Agronomy Journal, 95: 1483–1488. https://doi.org/10.2134/agronj2003.1483
 
Wang G., Xue B., Yu J., Otsuki K. (2010): A GIS-based linear programming model for optimizing agricultural land levelling. Journal of the Faculty of Agriculture, Kyushu University, 55: 131–135. https://doi.org/10.5109/17814
 
Whitney R.S., Gardner R., Robertson D. (1950): The effectiveness of manure and commercial fertilizer in restoring the productivity of subsoils exposed by leveling. Agronomy Journal, 42: 239–245. https://doi.org/10.2134/agronj1950.00021962004200050006x
 
Xing B., Gao W.J. (2014): Imperialist Competitive Algorithm, in Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms. Cham, Springer International Publishing: 203–209.
 
Yilmaz I., Akcaoz H., Ozkan B. (2005): An analysis of energy use and input costs for cotton production in Turkey. Renewable Energy, 30: 145–155. https://doi.org/10.1016/j.renene.2004.06.001
 
Zhang Y., Wright J.R. (2004): Global optimization of combined region aggregation and leveling model. Journal of Computing in Civil Engineering, 18: 154–161. https://doi.org/10.1061/(ASCE)0887-3801(2004)18:2(154)
 
download PDF

© 2022 Czech Academy of Agricultural Sciences | Prohlášení o přístupnosti