The characterisation of agricultural activity depends on what firms produce. In this article, we introduce the importance of the vegetative cycle in the prediction of bankruptcy of agricultural firms, analysing the financial ratios proposed by the classical models of Altman (1983), Ohlson (1980) and Zmijewski (1984). We consider a total of 2 228 Portuguese firms, with 83 failing between 2015 and 2019. The findings confirm that the differences between healthy and bankrupt firms depend on their vegetative cycle. Although predictors based on liquidity are helpful only in predicting the bankruptcy of non-perennial crop firms, activity predictors are better in identifying healthy perennial crop firms. In addition, we show substantial statistical differences in terms of liquidity and profitability, but only in healthy firms. The results encourage the topic of the vegetative cycle to be more present in the construction of more accurate bankruptcy prediction models.
Altman E. (1968): Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23: 589–609.
https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
Altman E. (1983): Corporate Financial Distress: A Complete Guide to Predicting, Avoiding, and Dealing with Bankruptcy. New York, US, John Wiley & Sons: 368.
Barry P.J., Ellinger P.N. (1989): Credit scoring, loan pricing, and farm business performance. Western Journal of Agricultural Economics, 14: 45–55.
Bureau van Dijk (2021): Amadeus – A database of comparable financial information for public and private companies across Europe. Available at https://www.bvdinfo.com (accessed Oct 22, 2021).
Burke M., Emerick K. (2016): Adaptation to climate change: Evidence from US agriculture. American Economic Journal: Economic Policy, 8: 106–140.
https://doi.org/10.1257/pol.20130025
Chen J., Katchova A.L., Zhou C. (2021): Agricultural loan delinquency prediction using machine learning methods. International Food and Agribusiness Management Review, 24: 797–812.
https://doi.org/10.22434/IFAMR2020.0019
De Jager F., Swanepoel V. (1994): Factors associated with farm financial failure in the northern springbok flats. Agrekon, 33: 242–247.
https://doi.org/10.1080/03031853.1994.9524789
Dinterman R., Katchova A.L., Harris J.M. (2018): Financial stress and farm bankruptcies in US agriculture. Agricultural Finance Review, 78 : 441–456.
https://doi.org/10.1108/AFR-05-2017-0030
Dorohan-Pysarenko L., Rębilas R., Yehirova O., Yasnolob I., Kononenko Z. (2021): Methodological peculiarities of probability estimation of bankruptcy of agrarian enterprises in Ukraine. Agricultural and Resource Economics: International Scientific E-Journal, 7: 20–39.
https://doi.org/10.51599/are.2021.07.02.02
European Commission (2008): NACE Rev. 2 statistical classification of economic activities in the European community. Available at https://ec.europa.eu/eurostat/documents/3859598/5902521/KS-RA-07-015-EN.PDF (accessed July 15, 2022).
European Commission (2021): On an action plan for the development of organic production (Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions COM 141 final/2). [Dataset]. Available at https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX%3A52021DC0141 (accessed Sept 10, 2022).
Faello J. (2015): Understanding the limitations of financial ratios. Academy of Accounting and Financial Studies Journal, 19: 75.
Hardy W.E., Weed J.B. (1980): Objective evaluation for agricultural lending. Journal of Agricultural and Applied Economics, 12 : 159–164.
https://doi.org/10.1017/S0081305200015429
Hossari G., Rahman S.F., Ratnatunga J. (2007): An empirical evaluation of sampling controversies n ratio-based modelling of corporate collapse.The Journal of Applied Research in Accounting and Finance (JARAF), 2: 15–26.
Jägermeyr J., Müller C., Ruane A.C., Elliott J., Balkovic J., Castillo O., Faye B., Franke J.A. et al. (2021): Climate impacts on global agriculture emerge earlier in new generation of climate and crop models. Nature Food, 2: 873–885.
https://doi.org/10.1038/s43016-021-00400-y
Johnson R.B., Hagan A.R. (1973): Agricultural loan evaluation with discriminant analysis. Journal of Agricultural and Applied Economics, 5: 57–62.
https://doi.org/10.1017/S0081305200011249
Klepáč V., Hampel D. (2017): Predicting financial distress of agriculture companies in EU. Agricultural Economics – Czech, 63: 347–355.
https://doi.org/10.17221/374/2015-AGRICECON
Krause K.R., Williams P.L. (1971): Personality characteristics and successful use of credit by farm families. American Journal of Agricultural Economics, 53: 619–624.
https://doi.org/10.2307/1237826
Laitinen E.K. (1993): Financial predictors for different phases of the failure process. Omega, 21: 215–228.
https://doi.org/10.1016/0305-0483(93)90054-O
Lanz B., Dietz S., Swanson T. (2017): Global population growth, technology, and Malthusian constraints: A quantitative growth theoretic perspective. International Economic Review, 58: 973–1006.
https://doi.org/10.1111/iere.12242
Limsombunchai V., Gan C., Lee M. (2005): An analysis of credit scoring for agricultural loans in Thailand. American Journal of Applied Sciences, 2: 1198–1205.
https://doi.org/10.3844/ajassp.2005.1198.1205
Lukason O. (2014): Why and how agricultural firms fail: evidence from Estonia. Bulgarian Journal of Agricultural Science, 20: 5–11.
Mann H.B., Whitney D.R. (1947): On a test of whether one of two random variables is stochastically larger than the other. The Annals of Mathematical Statistics, 18: 50–60.
https://doi.org/10.1214/aoms/1177730491
Ohlson J.A. (1980): Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18: 109–131.
https://doi.org/10.2307/2490395
Özerol G., Bressers H. (2017): How do farmers align with the agri-environmental changes in irrigated agriculture? A case study from the Harran Plain, Turkey. Irrigation and Drainage, 66: 45–59.
https://doi.org/10.1002/ird.2064
Römer U., Mußhoff O. (2018): Can agricultural credit scoring for microfinance institutions be implemented and improved by weather data? Agricultural Finance Review, 78: 83–97.
https://doi.org/10.1108/AFR-11-2016-0082
Rusiana H., Brewer B., Escalante C. (2017): Effects of business maturity, experience, and size on farms economic vitality: A credit migration analysis of farm service agency borrowers. Agricultural Finance Review, 77: 153–163.
https://doi.org/10.1108/AFR-03-2016-0026
Valaskova K., Durana P., Adamko P., Jaros J. (2020): Financial compass for Slovak enterprises: Modeling economic stability of agricultural entities. Journal of Risk and Financial Management, 13: 92.
https://doi.org/10.3390/jrfm13050092
Whittington G. (1980): Some basic properties of accounting ratios. Journal of Business Finance & Accounting, 7: 219–232.
Zech L., Pederson G. (2002): Predictors of farm performance and repayment ability as factors for use in risk-rating models. Agricultural Finance Review, 63: 41–54.
https://doi.org/10.1108/00214990380001140
Zmijewski M.E. (1984): Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22: 59–82.
https://doi.org/10.2307/2490859
, Raquel Medeiros Gaspar