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      An ensemble machine learning approach for forecasting credit risk of agricultural SMEs’ investments in agriculture 4.0 through supply chain finance

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          Graphic abstract

          Credit risk imposes itself as a significant barrier of agriculture 4.0 investments in the supply chain finance (SCF) especially for Small and Medium-sized Enterprises. Therefore, it is important for financial service providers (FSPs) to differentiate between low- and high-quality SMEs to accurately forecast the credit risk. This study proposes a novel hybrid ensemble machine learning approach to forecast the credit risk associated with SMEs’ agriculture 4.0 investments in SCF. Two core approaches were used, i.e., Rotation Forest algorithm and Logit Boosting algorithm. Key variables influencing the credit risk of agriculture 4.0 investments in SMEs were identified and evaluated using data collected from 216 agricultural SMEs, 195 Leading Enterprises and 104 FSPs operating in African agriculture sector. Besides the classical measures of credit risk assessment without involving SCF, the findings indicate that current ratio, financial leverage, profit margin on sales and growth rate of the agricultural SME are the upmost important variables that SCF actors need to focus on, in order to accurately and optimistically forecast and alleviate credit risk. The output of our study provides useful guidelines for SMEs, as it highlights the conditions under which they would be seen as creditworthy by FSPs. On the other hand, this study encourages the wide application of SCF in financing agriculture 4.0 investments. Due to the model’s performance, credit risk forecasting accuracy is improved, which results in future savings and credit risk mitigation in agriculture 4.0 investments of SMEs in SCF.

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          Most cited references62

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          Ensemble learning: A survey

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            Rotation forest: A new classifier ensemble method.

            We propose a method for generating classifier ensembles based on feature extraction. To create the training data for a base classifier, the feature set is randomly split into K subsets (K is a parameter of the algorithm) and Principal Component Analysis (PCA) is applied to each subset. All principal components are retained in order to preserve the variability information in the data. Thus, K axis rotations take place to form the new features for a base classifier. The idea of the rotation approach is to encourage simultaneously individual accuracy and diversity within the ensemble. Diversity is promoted through the feature extraction for each base classifier. Decision trees were chosen here because they are sensitive to rotation of the feature axes, hence the name "forest." Accuracy is sought by keeping all principal components and also using the whole data set to train each base classifier. Using WEKA, we examined the Rotation Forest ensemble on a random selection of 33 benchmark data sets from the UCI repository and compared it with Bagging, AdaBoost, and Random Forest. The results were favorable to Rotation Forest and prompted an investigation into diversity-accuracy landscape of the ensemble models. Diversity-error diagrams revealed that Rotation Forest ensembles construct individual classifiers which are more accurate than these in AdaBoost and Random Forest, and more diverse than these in Bagging, sometimes more accurate as well.
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              Climate-smart agriculture for food security

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                Author and article information

                Contributors
                Belhadi.amine@outlook.com
                Sachin.kamble@edhec.edu
                m.venkatesh@montpellier-bs.com
                imane.benkhati96@gmail.com
                f.touriki@uca.ma
                Journal
                Ann Oper Res
                Ann Oper Res
                Annals of Operations Research
                Springer US (New York )
                0254-5330
                1572-9338
                9 November 2021
                : 1-29
                Affiliations
                [1 ]GRID grid.411840.8, ISNI 0000 0001 0664 9298, Cadi Ayyad University, ; Marrakech, Morocco
                [2 ]GRID grid.462199.1, ISNI 0000 0001 2105 7899, EDHEC Business School, ; Roubaix, France
                [3 ]GRID grid.468923.2, ISNI 0000 0000 8794 7387, Montpellier Business School, ; Montpellier, France
                [4 ]GRID grid.411840.8, ISNI 0000 0001 0664 9298, ENSA-Safi, Cadi Ayyad University, ; Marrakech, Morocco
                Author information
                http://orcid.org/0000-0003-4831-4941
                http://orcid.org/0000-0003-4922-8172
                http://orcid.org/0000-0001-5291-6115
                http://orcid.org/0000-0002-3645-1538
                http://orcid.org/0000-0002-0232-9999
                Article
                4366
                10.1007/s10479-021-04366-9
                8576317
                34776573
                3055b452-1699-4ed2-98bf-a96ecf85f9b2
                © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 19 October 2021
                Categories
                Original Research

                supply chain finance,agriculture 4.0,credit risk,ensemble machine learning,african agriculture,smes

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