CINXE.COM
{"title":"Climate Change in Albania and Its Effect on Cereal Yield","authors":"L. Basha, E. Gjika","volume":206,"journal":"International Journal of Environmental and Ecological Engineering","pagesStart":35,"pagesEnd":47,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10013492","abstract":"<p>This study is focused on analyzing climate change in Albania and its potential effects on cereal yields. Initially, monthly temperature and rainfalls in Albania were studied for the period 1960-2021. Climacteric variables are important variables when trying to model cereal yield behavior, especially when significant changes in weather conditions are observed. For this purpose, in the second part of the study, linear and nonlinear models explaining cereal yield are constructed for the same period, 1960-2021. The multiple linear regression analysis and lasso regression method are applied to the data between cereal yield and each independent variable: average temperature, average rainfall, fertilizer consumption, arable land, land under cereal production, and nitrous oxide emissions. In our regression model, heteroscedasticity is not observed, data follow a normal distribution, and there is a low correlation between factors, so we do not have the problem of multicollinearity. Machine learning methods, such as Random Forest (RF), are used to predict cereal yield responses to climacteric and other variables. RF showed high accuracy compared to the other statistical models in the prediction of cereal yield. We found that changes in average temperature negatively affect cereal yield. The coefficients of fertilizer consumption, arable land, and land under cereal production are positively affecting production. Our results show that the RF method is an effective and versatile machine-learning method for cereal yield prediction compared to the other two methods: multiple linear regression and lasso regression method.<\/p>","references":"[1]\tD. R. Easterling, J. L. Evans, P. Y. Groisman, T. R. Karl, K. E. Kunkel, P. Ambenje, \u201cObserved variability and trends in extreme climate events: a brief review\u201d. Bulletin of the American Meteorological Society, vol. 81, no. 3, 2000, pp. 417\u2013425. https:\/\/doi.org\/10.1175\/1520-0477(2000)081<0417:OVATIE>2.3.CO;2\r\n[2]\t\tD. Gerten, S. Rost, W. von Bloh, W. Lucht, \u201cCauses of change in 20th century global river discharge\u201d. Geophysical Research Letters vol.35, no. 20, 2008, pp. 1\u20135. https:\/\/doi.org\/10.1029\/2008GL035258\r\n[3]\tM. Rusticucci, B. Tencer, \u201cObserved changes in return values of annual temperature extremes over Argentina\u201d, Journal of Climate, vol. 21, no. 21, 2008, pp. 5455\u20135467,DOI: https:\/\/doi.org\/10.1175\/2008JCLI2190.1\r\n[4]\tB. Yan, Z. Xi, F. Huang, L. Guo, X. Zhang, (2016) \u201cClimate Change Detection and Annual Extreme Temperature Analysis of the Amur River Basin Hindawi Publishing Corporation\u201d, Advances in Meteorology, vol. 2016, Article ID 6268938, 14 pages, http:\/\/dx.doi.org\/10.1155\/2016\/6268938\r\n[5]\t\tS. A. Rahmstorf, J. A. Cazenave, J. E. Church, R. F. Hansen, D. E. Keeling, R. C. Parker, J. Somerville, \u201cRecent climate observations compared to projections\u201d. Science Vol. 316, no 5825,2007. pp. 709 DOI: 10.1126\/science.1136843\r\n[6]\tM. R. Rahman, H. Lateh, \u201cClimate change in Bangladesh: a spatio-temporal analysis and simulation of recent temperature and rainfall data using GIS and time series analysis model\u201d. Theor Appl Climatol vol.128, 2017, pp. 27\u20134. https:\/\/doi.org\/10.1007\/s00704-015-1688-3 \r\n[7]\tA. Asfaw, B. Simane, A. Hassen, A. Bantider, \u201cVariability and time series trend analysis of rainfall and temperature in northcentral Ethiopia: A case study in Woleka sub-basin\u201d. Weather and Climate Extremes, vol. 19, 2018, pp. 29-41, ISSN 2212-0947, https:\/\/doi.org\/10.1016\/j.wace.2017.12.002\r\n[8]\t\tH. Park, G. Chung,\u201cA Nonparametric Stochastic Approach for Disaggregation of Daily to Hourly Rainfall Using 3-Day Rainfall Patterns\u201d, Water, 2020, vol. 12, 2306; https:\/\/doi.org\/10.3390\/w12082306\r\n[9]\tB. McKevith, \u201cNutritional aspects of cereals\u201d. Nutr. Bull. 2004, vol, 29, pp. 111\u2013142. https:\/\/doi.org\/10.1111\/j.1467-3010.2004.00418.x\r\n[10]\tA. Ortiz-Bobea, T. R Ault, C. M. Carillo, R. G. Chambers, D. B. Lobell, \u201cAnthropogenic climate change has slowed global agricultural productivity growth\u201d. Nat. Clim. Chang. 2021, vol. 11, pp. 306\u2013312. https:\/\/doi.org\/10.1038\/s41558-021-01000-1\r\n[11]\tQ. Mi, X. Li, J. Gao, \u201cHow to improve the welfare of smallholders through agricultural production outsourcing: Evidence from cotton farmers in Xinjiang, Northwest China\u201d, Journal of Cleaner Production, vol. 256, 2020, https:\/\/doi.org\/10.1016\/j.jclepro.2020.120636\r\n[12]\tS. Fahad, A. A. Bajwa, U. Nazir, S. A. Anjum, A. Farooq, A. Zohaib, et al. \u201cCrop production under drought and heat stress: Plant responses and management options\u201d, Frontiers in Plant Science, vol. 8, 2017, pp.1\u201316. https:\/\/doi.org\/10.3389\/fpls.2017.01147\\\r\n[13]\tY. Su, S. He, K. Wang, A. R. Shahtahmassebi, L. Zhang, J. Zhang, M. Zhang, M. Gan, \u201cQuantifying the sustainability of three types of agricultural production in China: An emergy analysis with the integration of environmental pollution\u201d. Journal of Cleaner Production, 2020, https:\/\/doi.org\/10.1016\/j.jclepro.2019.119650\r\n[14]\tS. Achli, T. E. Epule, D. Dhiba, A. Chehbouni, S. Er-Raki, \u201cVulnerability of Barley, Maize, and Wheat Yields to Variations in Growing Season Precipitation in Morocco\u201d. Appl. Sci. 2022, vol. 12, 3407. https:\/\/doi.org\/10.3390\/app12073407\r\n[15]\tD. B. Lobell, M. B. Burke, \u201cOn the use of statistical models to predict crop yield responses to climate change\u201d, Agricultural and Forest Meteorology, vol. 50, pp. 1443\u20131452, 2010, doi:10.1016\/j.agrformet.2010.07.008 \r\n[16]\tSumiati, Musdalipa, A. T. Darhyati, A. T. Fitriyah, F. Baharuddin, \u201cThe impact of climate change on agricultural production with a cases study of Lake Tempe, district of Wajo, south Sulawesi\u201d. Eurasia J Biosci vol. 14, 2020, pp. 6761-6771\r\n[17]\tB. Liu, S. Asseng, C. M\u00fcller, et al \u201cSimilar estimates of temperature impacts on global wheat yield by three independent methods\u201d. Nature Clim Change vol. 6, pp. 1130\u20131136, 2016, https:\/\/doi.org\/10.1038\/nclimate3115 \r\n[18]\tS. Ujjainia, P. Gautam, S. Veenadhari, (2020) \u201cCrop Yield Prediction using Regression Model\u201d, International Journal of Innovative Technology and Exploring Engineering (IJITEE). vol. 9, no. 10, 2020, DOI: 10.35940\/ijitee.J7491.0891020 \r\n[19]\tD. J. Olive, Multiple linear regression. In Linear Regression, pp. 17-83. Springer, Cham, 2017.\r\n[20]\tV. S. Konduri, T. J. Vandal, S. Ganguly, A. R. Ganguly, \u201cData Science for Weather Impacts on Crop Yield\u201d. Front. Sustain. Food Syst. Vol. 4, no. 52, 2020. doi: 10.3389\/fsufs.2020.00052 \r\n[21]\tJ. H. Jeong, J. P. Resop, N. D. Mueller, D. H. Fleisher, K. Yun, E. E. Butler, et al. (2016) \u201cRandom Forests for Global and Regional Crop Yield Predictions\u201d. PLoS ONE vol. 11, no. 6, 2016. https:\/\/doi.org\/10.1371\/journal.pone.0156571\r\n[22]\tS. Vincenzi, M. Zucchetta, P. Franzoi, M. Pellizzato, F. Pranovi, G. A. De Leo, et al., \u201cApplication of a Random Forest algorithm to predict spatial distribution of the potential yield of Ruditapes philippinarum in the Venice lagoon, Italy\u201d. Ecol Model. 2011; 222(8):1471\u20138.\r\n[23]\tO. Mutanga, E. Adam, M. A. Cho, \u201cHigh density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm\u201d. Int Journal Appl Earth Obs. vol. 18, 2012, pp. 399\u2013406.\r\n[24]\tS. Fukuda, W. Spreer, E. Yasunaga, K. Yuge, V. Sardsud, J. Muller, \u201cRandom Forests modelling for the estimation of mango (Mangifera indica L. cv. Chok Anan) fruit yields under different irrigation regimes\u201d. Agric Water Manage. vol. 116, 2013, pp.142\u201350. \r\n[25]\tS. Ben Mariem, D. Soba, B. Zhou, I. Loladze, F. Morales, I. Aranjuelo, \u201cClimate Change, Crop Yields, and Grain Quality of C3 Cereals: A Meta-Analysis of (CO2), Temperature, and Drought Effects\u201d. Plants 2021, vol. 10, 1052. https:\/\/doi.org\/10.3390\/plants10061052\r\n[26]\tD. Garc\u00eda-Le\u00f3n, R. L\u00f3pez-Lozano, A. Toreti, M. Zampieri, \u201cLocal-Scale Cereal Yield Forecasting in Italy: Lessons from Different Statistical Models and Spatial Aggregations\u201d. Agronomy, 2020, vol. 10, 809. https:\/\/doi.org\/10.3390\/agronomy10060809\r\n[27]\tF. Giorgi, P. Lionello, \u201cClimate change projections for the Mediterranean region\u201d. Glob. Planet. Chang. 2008, vol. 63, pp. 90\u2013104. https:\/\/doi.org\/10.1016\/j.gloplacha.2007.09.005\r\n[28]\tA. C. Russo, C. M. Gouveia, R. M. Trigo, M. L. R. Liberato, C. DaCamara, C. \u201cThe influence of circulation weather patterns at different spatial scales on drought variability in the Iberian Peninsula\u201d. Front. Environ. Sci. 2015, vol. 3, pp. 1\u201315. https:\/\/doi.org\/10.3389\/fenvs.2015.00001\r\n[29]\tT. Porja, \u201cHeat Waves Affecting Weather and Climate over Albania\u201d. J Earth Sci Clim Change vol.4, 2013, 149. Doi: 10.4172\/2157-7617.1000149 \r\n[30]\tS. Dervishi, V. Picari, \u201cAnalysis of Urban Heat Island Phenomenon and Mitigation Strategies for Tirana, Albania\u201d, Proceedings of the 16th IBPSA Conference Rome, Italy. 2019. https:\/\/doi.org\/10.26868\/25222708.2019.211334\r\n[31]\tE. Gjika, L. Basha, A. Ferrja, A. Kamberi, \u201cAnalyzing Seasonality in HPP Energy Production and External Variables\u201d, ITISE2021-International Conference on Time Series. Granada, 19th-21th July, 2021. Gran Canaria (SPAIN), https:\/\/www.mdpi.com\/2673-4591\/5\/1\/15 \r\n[32]\tE. Gjika, L. Basha, Energy production and consumption relying on climacteric variables (Albania case study), Finance and Accounting towards Sustainable Development Goals International Conference, Faculty of Economy, University of Tirana 26 November 2021. https:\/\/feut.edu.al\/lajmerime\/1167-international-conference-finance-and-accounting-towards-sustainable-development-goals-november-26-2021-tirana-albania-2 \r\n[33]\tD. M. Bates, D. G. Watts, Nonlinear Regression Analysis and Its Applications. Wiley,1988. \r\n[34]\tT. Hastie, R. Tibshirani, J. H. Friedman, The Elements of Statistical Learning: Data mining, Inference, and Prediction. 2nd ed. New York: Springer, 2009\r\n[35]\tS. M. Quiring, T. N. Papakryiaokou, \u201cAn evaluation of agricultural drought indices for the Canadian prairies\u201d. Agric. For. Meteorol. 2003, 168, pp. 49\u201362. https:\/\/doi.org\/10.1016\/S0168-1923(03)00072-8\r\n[36]\tL. Michel, D. Makowski, D. \u201cComparison of statistical models for analyzing wheat yield time series\u201d. PLoS ONE, 2013, 8, e78615. https:\/\/doi.org\/10.1371\/journal.pone.0078615 \r\n[37]\tD. Wallach, D. Makowski, J. Jones, F. Brun, \u201cWorking with Dynamic Crop Models\u2014Methods, Tools and Examples for Agriculture and Environment\u201d Academic Press: Cambridge, MA, USA, 2014.\r\n[38]\tW. H. Beyer, CRC Standard Mathematical Tables, 31st ed. Boca Raton, FL: CRC Press, pp. 536 and 571, 2002.\r\n[39]\tS. Kotz, C. B. Read, N. Balakrishnan, B. Vidakovic, Encyclopedia of Statistical Sciences, 16 Volume Set, 2nd Edition, Wiley, 9686 Pages\r\n[40]\tL. Breiman, \u201cRandom Forests\u201d. Machine Learning, vol. 45, 2001. pp. 5-32, https:\/\/doi.org\/10.1023\/A:1010933404324","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 206, 2024"}