Economic and financial viability of wheat production in Cameroon
DOI:
https://doi.org/10.25081/jsa.2025.v9.9411Keywords:
Economic Profitability, Financial Profitability, Wheat Production, CameroonAbstract
This study evaluated the economic and financial profitability of wheat production in Cameroon using data from 300 individuals in Adamawa, North-West, and West regions. Key factors influencing profitability were identified through correlation heatmaps, pair-plot diagrams, and modeling algorithms (Generalized Least Squares, Random Forest, and Least Absolute Shrinkage and Selection Operator). Positive factors included production volume, packaging, and transportation costs, while negative factors included production workforce, experience, and fertilizer costs. The net margin for wheat production was positive at 76,691,000 FCFA, but financial profitability was low, with an import-to-export ratio of 0.16. The study highlights the need to enhance wheat production to reduce importation.
Downloads
References
Alshqaq, S. S., & Abuzaid, A. H. (2023). An Efficient Method for Variable Selection Based on Diagnostic-Lasso Regression. Symmetry, 15(12), 2155. https://doi.org/10.3390/sym15122155
Andriopoulos, V., & Kornaros, M. (2023). LASSO Regression with Multiple Imputations for the Selection of Key Variables Affecting the Fatty Acid Profile of Nannochloropsis oculata. Marine Drugs, 21(9), 483. https://doi.org/10.3390/md21090483
Becker, G. S. (1991). A Treatise on the Family. Cambridge, MA: Harvard University Press.
Bhrolcháin, M. N., & Beaujouan, É. (2012). Fertility Postponement is Largely due to rising Educational Enrolment. Population Studies, 66(3), 311-327. https://doi.org/10.1080/00324728.2012.697569
Biau, G., & Scornet, E. (2015). A Random Forest Guided Tour. arXiv, arXiv:1511.05741. https://doi.org/10.48550/arXiv.1511.05741
Bir-Jmel, A., Douiri, S. M., Bernoussi, S. E., Maafiri, A., Himeur, Y., Atalla, S., Mansoor, W., & Al-Ahmad, H. (2024). GFLASSO-LR: Logistic Regression with Generalized Fused LASSO for Gene Selection in High-Dimensional Cancer Classification. Computers, 13(4), 93. https://doi.org/10.3390/computers13040093
Boström, H. (2023). Example-Based Explanations of Random Forest Predictions. arXiv, arXiv:2311.14581. https://doi.org/10.48550/arXiv.2311.14581
Broutin, N., Devroye, L., Lugosi, G., & Oliveira, R. I. (2024). Subtractive random forests. arXiv, arXiv:2210.10544. https://doi.org/10.48550/arXiv.2210.10544
Browne, M. W. (1973). Generalized Least Squares Estimators in the Analysis of Covariance Structures. ETS Research Bulletin Series, 1973(1), i-36. https://doi.org/10.1002/j.2333-8504.1973.tb00197.x
Buhl, S., & Klüppelberg, C. (2018). Generalised least squares estimation of regularly varying space-time processes based on flexible observation schemes. arXiv, arXiv:1704.05656. https://doi.org/10.48550/arXiv.1704.05656
Cattaneo, M. D., Klusowski, J. M., & Underwood, W. G. (2024). Inference with Mondrian Random Forests. arXiv, arXiv:2310.09702. https://doi.org/10.48550/arXiv.2310.09702
Chang, Q., & Goplerud, M. (2023). Generalized Kernel Regularized Least Squares. arXiv, arXiv:2209.14355. https://doi.org/10.48550/arXiv.2209.14355
Chen, L., & Wang, D. (2024). Cost Estimation and Prediction for Residential Projects Based on Grey Relational Analysis–Lasso Regression–Backpropagation Neural Network. Information, 15(8), 502. https://doi.org/10.3390/info15080502
Cheng, S.-W., & Wong, M. T. (2024). A Fast Method for Lasso and Logistic Lasso. arXiv, arXiv:2402.02463. https://doi.org/10.48550/arXiv.2402.02463
Chintalapudi, N., Angeloni, U., Battineni, G., di Canio, M., Marotta, C., Rezza, G., Sagaro, G. G., Silenzi, A., & Amenta, F. (2022). LASSO Regression Modeling on Prediction of Medical Terms among Seafarers’ Health Documents Using Tidy Text Mining. Bioengineering, 9(3), 124. https://doi.org/10.3390/bioengineering9030124
Christakis, I., Sarri, E., Tsakiridis, O., & Stavrakas, I. (2024). Investigation of LASSO Regression Method as a Correction Measurements’ Factor for Low-Cost Air Quality Sensors. Signals, 5(1), 60-86. https://doi.org/10.3390/signals5010004
Chronopoulos, I., Chrysikou, K., & Kapetanios, G. (2023). High Dimensional Generalised Penalised Least Squares. arXiv, arXiv:2207.07055. https://doi.org/10.48550/arXiv.2207.07055
Cui, Z., & Gong, G. (2018). The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features. NeuroImage, 178, 622-637. https://doi.org/10.1016/j.neuroimage.2018.06.001
Curth, A., Jeffares, A., & van der Schaar, M. (2024). Why do Random Forests Work? Understanding Tree Ensembles as Self-Regularizing Adaptive Smoothers. arXiv, arXiv:2402.01502. https://doi.org/10.48550/arXiv.2402.01502
Darbelet, M., & Laugine, J. M. (1990). Economie d’entreprise. Foucher, 374-375.
Emmert-Streib, F., & Dehmer, M. (2019). High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection. Machine Learning and Knowledge Extraction, 1(1), 359-383. https://doi.org/10.3390/make1010021
Esping-Andersen, G. (2009). The Incomplete Revolution: Adapting to Women’s New Roles. Cambridge, UK: Polity Press.
FAO. (2011). L’État de l’insécurité alimentaire dans le monde 2011. Quelles sont les conséquences de l’instabilité des cours internationaux pour l’économie et la sécurité alimentaire des pays ? Rome.
FAO. (2016). Produire plus avec moins en pratique le maïs, le riz et le blé. ORGANISATION DES NATIONS UNIES POUR L’ALIMENTATION ET L’AGRICULTURE. Rome.
Fontanarosa, J. B., & Dai, Y. (2011). Using LASSO regression to detect predictive aggregate effects in genetic studies. BMC Proceedings, 5, S69. https://doi.org/10.1186/1753-6561-5-S9-S69
Freijeiro-González, L., Febrero-Bande, M., & González-Manteiga, W. (2020). A critical review of LASSO and its derivatives for variable selection under dependence among covariates. arXiv, arXiv:2012.11470. https://doi.org/10.48550/arXiv.2012.11470
Gafarov, B. (2023). Generalized Automatic Least Squares: Efficiency Gains from Misspecified Heteroscedasticity Models. arXiv, arXiv:2304.07331. https://doi.org/10.48550/arXiv.2304.07331
Gaïffas, S., Merad, I., & Yu, Y. (2023). WildWood: A new Random Forest algorithm. arXiv, arXiv:2109.08010. https://doi.org/10.48550/arXiv.2109.08010
GIZ/PARSE-AD. (2017). Etude sur les métiers rentables : Projet d’Appui à la Résilience Socio-économique des jeunes vulnérables (Extrême-Nord, Nord et Adamaoua). Ministère Fédéral de la Coopération économique et du Développement (BMZ), 2-100
Hiabu, M., Mammen, E., & Meyer, J. T. (2023). Random Planted Forest: A directly interpretable tree ensemble. arXiv, arXiv:2012.14563. https://doi.org/10.48550/arXiv.2012.14563
Hong, C., Xiong, Y., Xia, J., Huang, W., Xia, A., Xu, S., Chen, Y., Xu, Z., Chen, H., & Zhang, Z. (2024). LASSO-Based Identification of Risk Factors and Development of a Prediction Model for Sepsis Patients. Therapeutics and Clinical Risk Management, 20, 47-58. https://doi.org/10.2147/TCRM.S434397
Hsu, C.-L., Wu, P.-C., Wu, F.-Z., & Yu, H.-C. (2024). LASSO-derived model for the prediction of lean-non-alcoholic fatty liver disease in examinees attending a routine health check-up. Annals of Medicine, 56(1), 2317348. https://doi.org/10.1080/07853890.2024.2317348
Hu, X. J., Lagakos, S. W., & Lockhart, R. A. (2009). Generalized least squares estimation of the mean function of a counting process based on panel counts. Statistica Sinica, 19, 561-580.
Izza, Y., & Marques-Silva, J. (2021). On Explaining Random Forests with SAT. arXiv, arXiv:2105.10278. https://doi.org/10.48550/arXiv.2105.10278
Jones, A. M., Bosler, P. A., Kuberry, P. A., & Wright, G. B. (2023). Generalized moving least squares vs. Radial basis function finite difference methods for approximating surface derivatives. arXiv, arXiv:2309.04035. https://doi.org/10.48550/arXiv.2309.04035
Khanna, A., Lu, F., Raff, E., & Testa, B. (2023). Sparse Private LASSO Logistic Regression. arXiv, arXiv:2304.12429. https://doi.org/10.48550/arXiv.2304.12429
Kim, Y., Kim, S. Y., & Kim, H. (2024). Heterogeneous Random Forest. arXiv, arXiv:2410.19022. https://doi.org/10.48550/arXiv.2410.19022
Lai, B., & Bernstein, D. S. (2024). Generalized Forgetting Recursive Least Squares: Stability and Robustness Guarantees. arXiv, arXiv:2308.04259. https://doi.org/10.48550/arXiv.2308.04259
Batsch, L. (2003). Rentabilité Économique, Linéarité de l’investissement et Retour Sur Dépenses Cumulées : Quelques Problèmes de Mesure. Cereg, Université Paris-Dauphine Mai 2003.
Lee, J. H., Shi, Z., & Gao, Z. (2021). On LASSO for Predictive Regression. arXiv, arXiv:1810.03140. https://doi.org/10.48550/arXiv.1810.03140
Lee, T.-F., Chao, P.-J., Ting, H.-M., Chang, L., Huang, Y.-J., Wu, J.-M., Wang, H.-Y., Horng, M.-F., Chang, C.-M., Lan, J.-H., Huang, Y.-Y., Fang, F.-M., & Leung, S. W. (2014). Using Multivariate Regression Model with Least Absolute Shrinkage and Selection Operator (LASSO) to Predict the Incidence of Xerostomia after Intensity-Modulated Radiotherapy for Head and Neck Cancer. Plos One, 9(2), e89700. https://doi.org/10.1371/journal.pone.0089700
Lenoir, B. (2013). A general approach of least squares estimation and optimal filtering. arXiv, arXiv:1305.6324. https://doi.org/10.48550/arXiv.1305.6324
Li, J., & Sonthalia, R. (2024). Generalization for Least Squares Regression With Simple Spiked Covariances. arXiv, arXiv:2410.13991. https://doi.org/10.48550/arXiv.2410.13991
Li, J., Pollinger, F., & Paeth, H. (2020). Comparing the Lasso Predictor-Selection and Regression Method with Classical Approaches of Precipitation Bias Adjustment in Decadal Climate Predictions. Monthly Weather Review, 148(10), 4339-4351. https://doi.org/10.1175/MWR-D-19-0302.1
Li, R., Yan, Q., & Jiang, S. W. (2024). Generalized Moving Least-Squares for Solving Vector-valued PDEs on Unknown Manifolds. arXiv, arXiv:2406.12210. https://doi.org/10.48550/arXiv.2406.12210
Li, Y., Lu, F., & Yin, Y. (2022). Applying logistic LASSO regression for the diagnosis of atypical Crohn’s disease. Scientific Reports, 12, 11340. https://doi.org/10.1038/s41598-022-15609-5
Li, Y., Yang, R., Wang, X., Zhu, J., & Song, N. (2023). Carbon Price Combination Forecasting Model Based on Lasso Regression and Optimal Integration. Sustainability, 15(12), 9354. https://doi.org/10.3390/su15129354
Liu, L., Gao, J., Beasley, G., & Jung, S.-H. (2023a). LASSO and Elastic Net Tend to Over-Select Features. Mathematics, 11(17), 3738. https://doi.org/10.3390/math11173738
Liu, M., Li, Q., Zhang, J., & Chen, Y. (2023b). Development and Validation of a Predictive Model Based on LASSO Regression: Predicting the Risk of Early Recurrence of Atrial Fibrillation after Radiofrequency Catheter Ablation. Diagnostics, 13(22), 3403. https://doi.org/10.3390/diagnostics13223403
Lloyd-Jones, L. R., Nguyen, H. D., & McLachlan, G. J. (2016). A globally convergent algorithm for lasso-penalized mixture of linear regression models. arXiv, arXiv:1603.08326. https://doi.org/10.48550/arXiv.1603.08326
Louppe, G. (2015). Understanding Random Forests: From Theory to Practice. arXiv, arXiv:1407.7502. https://doi.org/10.48550/arXiv.1407.7502
Lupton-Smith, C., Stuart, E. A., McGinty, E. E., Dalcin, A. T., Jerome, G. J., Wang, N.-Y., & Daumit, G. L. (2022). Determining Predictors of Weight Loss in a Behavioral Intervention: A Case Study in the Use of Lasso Regression. Frontiers in Psychiatry, 12, 707707. https://doi.org/10.3389/fpsyt.2021.707707
Maggin, D. M., Swaminathan, H., Rogers, H. J., O’Keeffe, B. V., Sugai, G., & Horner, R. H. (2011). A generalized least squares regression approach for computing effect sizes in single-case research: Application examples. Journal of School Psychology, 49(3), 301-321. https://doi.org/10.1016/j.jsp.2011.03.004
Makelele, L. (2014). Analyse de la Performance Financière d’une Entreprise Hôtelière : cas de l’Hôtel Mangwe/Uvira. TFC, UCB.
Mcdonald, P. (2000). Gender Equity, Social Institutions and the Future of Fertility. Journal of the Australian Population Association, 17, 1-16. https://doi.org/10.1007/BF03029445
McEligot, A. J., Poynor, V., Sharma, R., & Panangadan, A. (2020). Logistic LASSO Regression for Dietary Intakes and Breast Cancer. Nutrients, 12(9), 2652. https://doi.org/10.3390/nu12092652
Mei, Z., & Shi, Z. (2024). On LASSO for High Dimensional Predictive Regression. arXiv, arXiv:2212.07052. https://doi.org/10.48550/arXiv.2212.07052
Meng, L., Zheng, T., Wang, Y., Li, Z., Xiao, Q., He, J., & Tan, J. (2021). Development of a prediction model based on LASSO regression to evaluate the risk of non-sentinel lymph node metastasis in Chinese breast cancer patients with 1–2 positive sentinel lymph nodes. Scientific Reports, 11, 19972. https://doi.org/10.1038/s41598-021-99522-3
MINADER/AFD/IRAM. (2015). Etude de Préfaisabilité d’un Programme de Gestion des Risques Agricoles, Rapport Final, Décembre 2015.
Mokrišová, M., & Horváthová, J. (2023). Domain Knowledge Features versus LASSO Features in Predicting Risk of Corporate Bankruptcy—DEA Approach. Risks, 11(11), Article 199. https://doi.org/10.3390/risks11110199
Silue, N. Z., Dao, D., Valerie Kouame, H. K., & Kone, M. (2019). Analyse de la Rentabilité Economique des Systemes de Production a Base d’Igname : cas des Sites de Leo et Midebdo au Burkina Faso. Institut de Gestion Agropastorale/Université Péléforo Gon Coulibaly Côte d’Ivoire Agronomie Africaine, 31(1), 1-14.
Ng, T. L., & Newton, M. A. (2022). Random weighting in LASSO regression. arXiv, arXiv:2002.02629. https://doi.org/10.48550/arXiv.2002.02629
Nobari, K. S., & Gibberd, A. (2024). ℓ1-Regularized Generalized Least Squares. arXiv, arXiv:2405.10719. https://doi.org/10.48550/arXiv.2405.10719
Paraïso, A., Sossou, A., Yègbémey, R., & Biaou, G. (2010). Analyse de la Rentabilité de la Production du Fonio (Digitaria Exilis) dans la Commune de Boukombe au Bénin. IVème Journée Scientifique de l’Université de Lomé, Togo, 25-28 octobre 2010.
Peri-Rotem. (2020). Écarts de fécondité en fonction du niveau d’instruction: le rôle de la religion en Grande-Bretagne Et En France. Population, 75, 9-38.
Pirou, J. P. (2005). Mesure de la Rentabilité des Entreprise. Bulletin de la Banque de France, N° 134 de Février 2005.
Pourhamidi, R., & Moslemi, A. (2023). Using the Precision Lasso for gene selection in diffuse large B cell lymphoma cancer. Journal of the Egyptian National Cancer Institute, 35(1), 19. https://doi.org/10.1186/s43046-023-00172-5
Probst, P., Wright, M., & Boulesteix, A.-L. (2019). Hyperparameters and Tuning Strategies for Random Forest. arXiv, arXiv:1804.03515. https://doi.org/10.48550/arXiv.1804.03515
Rajaratnam, B., Roberts, S., Sparks, D., & Yu, H. (2019). Influence Diagnostics for High-Dimensional Lasso Regression. Journal of Computational and Graphical Statistics, 28(4), 877-890. https://doi.org/10.1080/10618600.2019.1598869
Reid, S., Tibshirani, R., & Friedman, J. (2014). A Study of Error Variance Estimation in Lasso Regression. arXiv, arXiv:1311.5274. https://doi.org/10.48550/arXiv.1311.5274
Rhodes, J. S., Cutler, A., & Moon, K. R. (2023). Geometry- and Accuracy-Preserving Random Forest Proximities. arXiv, arXiv:2201.12682. https://doi.org/10.48550/arXiv.2201.12682
Roch, S., & Rohe, K. (2017). Generalized least squares can overcome the critical threshold in respondent-driven sampling. arXiv, arXiv:1708.04999. https://doi.org/10.48550/arXiv.1708.04999
Shah, I., Naz, H., Ali, S., Almohaimeed, A., & Lone, S. A. (2023). A New Quantile-Based Approach for LASSO Estimation. Mathematics, 11(6), 1452. https://doi.org/10.3390/math11061452
Sion, M. (2020). Ratio de rentabilité économique : quelle est son importance ?. Par Michel Sion le 31 mars 2020, Analyse financière.
Surve, T., & Pradhan, R. (2024). Example-based Explanations for Random Forests using Machine Unlearning. arXiv, arXiv:2402.05007. https://doi.org/10.48550/arXiv.2402.05007
Tiama, D., Kaboré, B., Nanéma, K. R., Dabiré, M., & Sawadogo, N. (2018). Systèmes de culture et caractérisation paysanne des ignames du Passoré au Burkina Faso. International Journal of Innovation and Scientific Research, 38(2), 203-211.
Utkin, L. V., & Konstantinov, A. V. (2022). Attention and Self-Attention in Random Forests. arXiv, arXiv:2207.04293. https://doi.org/10.48550/arXiv.2207.04293
van der Wurp, H., & Groll, A. (2023). Introducing LASSO-type penalisation to generalised joint regression modelling for count data. AStA Advances in Statistical Analysis, 107, 127-151. https://doi.org/10.1007/s10182-021-00425-5
van Egmond, M. B., Spini, G., van der Galien, O., IJpma, A., Veugen, T., Kraaij, W., Sangers, A., Rooijakkers, T., Langenkamp, P., Kamphorst, B., van de L’Isle, N., & Kooij-Janic, M. (2021). Privacy-preserving dataset combination and Lasso regression for healthcare predictions. BMC Medical Informatics and Decision Making, 21, 266. https://doi.org/10.1186/s12911-021-01582-y
Waltz, N. (2024). Grafting: Making Random Forests Consistent. arXiv, arXiv:2403.06015. https://doi.org/10.48550/arXiv.2403.06015
Wang, J., Xu, Y., Liu, L., Wu, W., Shen, C., Huang, H., Zhen, Z., Meng, J., Li, C., Qu, Z., he, Q., & Tian, Y. (2023). Comparison of LASSO and random forest models for predicting the risk of premature coronary artery disease. BMC Medical Informatics and Decision Making, 23, 297. https://doi.org/10.1186/s12911-023-02407-w
Wong, A., Kramer, S. C., Piccininni, M., Rohmann, J. L., Kurth, T., Escolano, S., Grittner, U., & Domenech de Cellès, M. (2023). Using LASSO Regression to Estimate the Population-Level Impact of Pneumococcal Conjugate Vaccines. American Journal of Epidemiology, 192(7), 1166-1180. https://doi.org/10.1093/aje/kwad061
Wyss, R., van der Laan, M., Gruber, S., Shi, X., Lee, H., Dutcher, S. K., Nelson, J. C., Toh, S., Russo, M., Wang, S. V., Desai, R. J., & Lin, K. J. (2024). Targeted learning with an undersmoothed LASSO propensity score model for large-scale covariate adjustment in health-care database studies. American Journal of Epidemiology, 193(11), 1632-1640. https://doi.org/10.1093/aje/kwae023
Yang, S., Yan, Y., Zhu, X., & Sun, Q. (2023). Online Linearized LASSO. arXiv, arXiv:2211.06039. https://doi.org/10.48550/arXiv.2211.06039
Zhang, Q., & Drissi-Habti, M. (2024). Electric Cable Insulator Damage Monitoring by Lasso Regression. Machines, 12(1), 50. https://doi.org/10.3390/machines12010050
Zhang, Y., Ji, W., & Bradic, J. (2024). Adaptive Split Balancing for Optimal Random Forest. arXiv, arXiv:2402.11228. https://doi.org/10.48550/arXiv.2402.11228
Zhao, Y., & Huo, X. (2023). A Survey of Numerical Algorithms that can Solve the Lasso Problems. arXiv, arXiv:2303.03576. https://doi.org/10.48550/arXiv.2303.03576
Published
How to Cite
Issue
Section
Copyright (c) 2025 Adama Farida, Ngonkeu Mangaptche Eddy Leonard, Jean Marie Gankou
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
.