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Decidable Regression Techniques for Statistical Modelling with Sustainable Agriculture Operations

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Abstract

One of the powerful statistical techniques is the regression analysis that is used for modelling the relationship of a response variable with one or more explanatory variables. In addition, a variety of ways to estimate agricultural production have been developed in order to help farmers, researchers, and policy makers in several practical problems. It is well-known that the regression models have a wide applications in the sector of agriculture, and help decision makers by revealing information regarding crop productivity, resource optimization, and risk management. These models support sustainable and effective agricultural practices by exploring the relationships between different factors and outcomes. In this paper, the modelling and analysis are developed to evaluate the growth pattern based on total cultivated area, agriculture production, temperature and humidity. In this study, several regression procedures are comprehensively analyzed, and the goodness of fit is examined. Both univariate and multivariate regression models are designed for the study purpose. In addition, the univariate regression models and the multivariate regression models are found to fit the data significantly in this investigation. The bootstrap technique is also performed to construct the confidence intervals for the estimates of the regression models to test the validity of the estimates. Both univariate and multivariate regression estimates are found to be significant. Also, the estimators for all univariate and multivariate regression models are bounded within 95% bootstrapping confidence limits.

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References

  1. Ii E, Session TA, Shimoshimizu M. Review of OLS Estimator. 2019;1–14.

  2. Sulewski P, Majewski E, Wąs A. The importance of agriculture in the renewable energy production in Poland and the EU. Probl Agric Econ. 2017;350(1):50–74.

    Google Scholar 

  3. Njegomir V, Pejanovic L, Kekovic Z. Agricultural entrepreneurship, environmental protection and insurance. Ekon Poljopr. 2017;64(3):1035.

    Article  Google Scholar 

  4. Dielman TE, Box RO, Worth F. Least absolute value regression : recent contributions. J Stat Comput Simul. 2005;75(4):263.

    Article  MathSciNet  Google Scholar 

  5. Sumberg J. Future agricultures: the promise and pitfalls of a (re)turn to nature. Outlook Agric. 2022;51(1):3–10.

    Article  Google Scholar 

  6. Dincă G, Netcu IC, El-Naser A. Analyzing EU’s agricultural sector and public spending under climate change. Sustain. 2024;16(1):72.

    Article  Google Scholar 

  7. Sudarsono RRSM, Wandebori H. Machine learning predictive modeling of agricultural sustainability indicators. Indones J Appl Stat. 2024;6(1):69.

    Article  Google Scholar 

  8. Ramsey JB. Tests for specification errors in classical linear least-squares regression analysis. J R Stat Soc Ser B. 1969;31(2):350.

    Article  MathSciNet  Google Scholar 

  9. Tibshirani R. Regression Shrinkage and Selection via the Lasso. J R Stat Soc Ser B [Internet]. 1996;58(1):267–88. http://www.jstor.org/stable/2346178.

  10. Blundell R, Duncan A. Kernel regression in empirical microeconomics. J Hum Resour. 1998;33:62–87.

    Article  Google Scholar 

  11. Kim TH, White H. James-stein-type estimators in large samples with application to the least absolute deviations estimator. J Am Stat Assoc. 2001;96(454):697–705.

    Article  MathSciNet  Google Scholar 

  12. Zhang H, Mei C. Local least absolute deviation estimation of spatially varying coefficient models: robust geographically weighted regression approaches. Int J Geogr Inf Sci. 2011;25(9):1467–89.

    Article  Google Scholar 

  13. Romano JP, Wolf M. Resurrecting weighted least squares. J Econom. 2017;197(1):1–19.

    Article  MathSciNet  Google Scholar 

  14. Parsons DJ, Rey D, Tanguy M, Holman IP. Regional variations in the link between drought indices and reported agricultural impacts of drought. Agric Syst. 2019;173:119.

    Article  Google Scholar 

  15. Kim J, Cho E, Okafor CE, Choi D. Does environmental, social, and governance drive the sustainability of multinational corporation’s subsidiaries? Evidence From Korea. Front Psychol. 2022. https://doi.org/10.3389/fpsyg.2022.899936.

    Article  Google Scholar 

  16. Lechene V, Pendakur K, Wolf A. Ordinary least squares estimation of the intrahousehold distribution of expenditure. J Polit Econ. 2022;130(3):681.

    Article  Google Scholar 

  17. Majewski S, Mentel G, Dylewski M, Salahodjaev R. Renewable energy, agriculture and CO2 emissions: empirical evidence from the middle-income countries. Front Energy Res. 2022. https://doi.org/10.3389/fenrg.2022.921166.

    Article  Google Scholar 

  18. Zulkifli R, Aimran N, Deni SM, Badarisam FN. A comparative study on the performance of maximum likelihood, generalized least square, scale-free least square, partial least square and consistent partial least square estimators in structural equation modeling. Int J Data Netw Sci. 2022;6(2):391.

    Article  Google Scholar 

  19. Omodara OD, Ige OA, Oluwasola O, Oyebanji AT, Afape OO. Factors influencing cassava farmers’ choice of climate change adaption practices and its effect on cassava productivity in Nigeria. Heliyon. 2023;9(3):e14563.

    Article  Google Scholar 

  20. Eddamiri S, Bassine FZ, Ongoma V, Epule Epule T, Chehbouni A. An automatic ensemble machine learning for wheat yield prediction in Africa. Multimed Tools Appl. 2024;83:66433.

    Article  Google Scholar 

  21. Basil N, Marhoon HM, Ibrahim AR. A new thrust vector-controlled rocket based on JOA using MCDA. Meas Sens. 2023;26:100672.

    Article  Google Scholar 

  22. Basil N, Alqaysi ME, Deveci M, Albahri AS, Albahri OS, Alamoodi AH. Evaluation of autonomous underwater vehicle motion trajectory optimization algorithms. Knowl Based Syst. 2023;276:110722.

    Article  Google Scholar 

  23. Basil N, Marhoon HM, Gokulakrishnan S, Buddhi D. Jaya optimization algorithm implemented on a new novel design of 6-DOF AUV body: a case study. Multimed Tools Appl. 2022. https://doi.org/10.1007/s11042-022-14293-x.

    Article  Google Scholar 

  24. Mohammed AF, Basil N, Abdulmaged RB, Marhoon HM, Ridha HM, Ma’arif A, et al. Selection and evaluation of robotic arm based conveyor belts (RACBs) motions: NARMA (L2)-FO (ANFIS) PD-I based Jaya Optimization Algorithm. Int J Robot Control Syst. 2024;4(1):262.

    Article  Google Scholar 

  25. Basil N, Marhoon HM. Selection and evaluation of FOPID criteria for the X-15 adaptive flight control system (AFCS) via Lyapunov candidates: optimizing trade-offs and critical values using optimization algorithms e. -Prime Adv Electr Eng Electron Energy. 2023;6:100305.

    Article  Google Scholar 

  26. Basil N, Marhoon HM. Towards evaluation of the PID criteria based UAVs observation and tracking head within resizable selection by COA algorithm. Results Control Optim. 2023;12:100279.

    Article  Google Scholar 

  27. Mohamadwasel NB. Rider Optimization Algorithm implemented on the AVR Control System using MATLAB with FOPID. In: IOP Conference Series: Materials Science and Engineering. 2020.

  28. Marhoon HM, Basil N, Mohammed AF. Medical defense nanorobots (MDNRs): a new evaluation and selection of controller criteria for improved disease diagnosis and patient safety using NARMA(L2)-FOP + D(ANFIS)µ – Iλ-based Archimedes Optimization Algorithm. Int J Inf Technol. 2024. https://doi.org/10.1007/s41870-023-01724-7.

    Article  Google Scholar 

  29. Basil N, Marhoon HM, Hayal MR, Elsayed EE, Nurhidayat I, Shah MA. Black-hole optimisation algorithm with FOPID-based automation intelligence photovoltaic system for voltage and power issues. Aust J Electr Electron Eng. 2024;21(2):115.

    Article  Google Scholar 

  30. Mohammed AF, Marhoon HM, Basil N, Ma’arif A. A new hybrid intelligent fractional order proportional double derivative+ Integral (FOPDD+ I) controller with ANFIS simulated on automatic voltage regulator system. Int J Robot Control Syst. 2024;4(2):463.

    Article  Google Scholar 

  31. Pandit P, Dey P, Krishnamurthy K. Comparative assessment of multiple linear regression and fuzzy linear regression models. SN Comput Sci. 2021. https://doi.org/10.1007/s42979-021-00473-3.

    Article  Google Scholar 

  32. Adamek R, Adamek R, Smeekes SW. Lasso inference for high-dimensional time series. J Econom. 2023;235(2):1114.

    Article  MathSciNet  Google Scholar 

  33. Kanapka L, Ivanova A. A frequentist design for basket trials using adaptive lasso. Stat Med. 2024;43(1):156.

    Article  MathSciNet  Google Scholar 

  34. Moersdorf J, Rivers M, Denkenberger D, Breuer L, Jehn FU. The fragile state of industrial agriculture: estimating crop yield reductions in a global catastrophic infrastructure loss scenario. Glob Challen. 2024. https://doi.org/10.1002/gch2.202300206.

    Article  Google Scholar 

  35. Müller M. Generalized linear models BT. In: Gentle JE, Härdle WK, Mori Y, editors. Handbook of computational statistics: concepts and methods. Berlin Heidelberg: Springer; 2012. p. 681–709.

    Chapter  Google Scholar 

  36. Baayen RH, Linke M. An introduction to the generalized additive model. A Pract Handb corpus Linguist. New York: Springer; 2020. p. 563–91.

    Google Scholar 

  37. Burnham KP, Anderson DR. Multimodel inference: understanding AIC and BIC in model selection, vol. 33. Sociological Methods and Research. 2004

  38. Hansen BE. Least squares model averaging. Econometrica. 2007;75(4):1175.

    Article  MathSciNet  Google Scholar 

  39. Chaurasia V, Pal S. COVID-19 pandemic: ARIMA and regression model-based worldwide death cases predictions. SN Comput Sci. 2020;1(5):288.

    Article  Google Scholar 

  40. Montgomery DC, Peck EA, Vining GG. Introduction to linear regression analysis. 6th ed. New York: John Wiley Sons Inc; 2021.

    Google Scholar 

  41. Huang FL. Multilevel modeling and ordinary least squares regression: how comparable are they? J Exp Educ. 2018;86(2):265.

    Article  Google Scholar 

  42. Koenker R, Machado JAF, Skeels CL, Welsh AH. Amemiya’s form of the weighted least squares estimator. Aust J Stat. 1993;35(2):155.

    Article  MathSciNet  Google Scholar 

  43. Morgenthaler BYS. Least-absolute-deviations fits for generalized linear models. Biometrika. 1992;79:747–54.

    Article  Google Scholar 

  44. Oksanen EH. A simple approach to teaching generalized least squares theory. Am Stat [Internet]. 1991;45(3):229–33. http://www.jstor.org/stable/2684297

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Acknowledgements

The authors acknowledge their appreciation for invaluable assistance in proofreading by Mr. Francis Andrew, Polyglot Institute, Ibri, Oman. This work has been supported by the SERB, Govt. of India under grant # SUR/2022/004482.

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Correspondence to Debasis Gountia.

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Nair, S.B., Al-Hemyari, Z.A. & Gountia, D. Decidable Regression Techniques for Statistical Modelling with Sustainable Agriculture Operations. SN COMPUT. SCI. 5, 1159 (2024). https://doi.org/10.1007/s42979-024-03518-5

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