Sulkava, Mika

Tauriainen, Jukka

Yli-Heikkilä, Maria

Jukuri

Predicting profitability of dairy farmswith a learning ensemble modelMaria Yli-Heikkila¨, Jukka Tauriainen, Mika SulkavaMTT Agrifood Research FinlandEuropean Union’s (EU) agricultural policy aims at viable food produc-tion, sustainable management of natural resources and balanced develop-ment across all Europe’s rural areas. The Farm Accountancy Data Network(FADN) is an instrument for evaluating the income of agricultural holdingsand the impact of the EU’s agricultural policy. Every year, the memberstates collect accountancy data from a sample of the agricultural holdings.The Finnish accountancy data is also anonymously available for academicresearchers. Thus, we have been able to use an extensive set of variables andobservations for modeling. Our aim has been to develop a tool for individ-ual farmers in the Finnish dairy sector to estimate the profitability of theirbusiness.Finnish family farms are not subject to an accounting obligation. Theirincome statement is typically based on cash-based single-entry bookkeepingand is prepared for tax purposes only. Estimating the financial performanceof a business requires, in addition, determining the monetary value of farmsassets, liabilities, and capital. In a typical case, with a limited amount ofaccounting data, the farmer cannot calculate financial indicators, such asprofitability ratios. Therefore, the farmer cannot assess the sustainability ofthe business over time or in comparison to other farms in the sector. Toaddress this issue, we have built a model that predicts the profitability ratiofrom such variables that are at hand for a farmer.The response variable profitability ratio indicates how operative costs in-cluding family factors, meaning the wage claim and the interest claim ofagriculture, are covered by family farm income. As a relative concept, prof-itability ratio is well suited for comparisons between years, as well as forfarms representing different size classes and production sectors.In this study, we applied a learning ensemble method (e.g. Friedmanet al., 2008), which combines several prediction models based on differentlearning algorithms. Ensemble prediction is then a linear combination of thepredictions of each of the ensemble members.We extracted unbalanced panel data from the FADN database. We com-bined data from the years 2000-2011, resulting in 4228 observations of 334variables on Finnish dairy farms. We split the data into a training set and atest set by 2/3. The training set was first used for variable selection.Random forest (RF), first introduced by Breiman (2001), is a supervisedlearning algorithm based on decision trees. RF combines tree predictors suchthat each tree depends on the values of a random vector sampled indepen-dently and with the same distribution for all trees in the forest. Internal esti-mates of the model are also used to measure variable importance. (Breiman,2001.)In the variable selection, we applied the backwards selection wrapper algo-rithm, as described in Kuhn (2014). The algorithm produces several orderingsof variables by RF’s computation of importance measures on each trainingset of a 10-fold cross-validation. The procedure was repeated five times tosmooth out the variability. Based on the mean difference of prediction ac-curacies observed for each tree in terms of mean squared error before andafter random permutation of a predictor variable, RF identified 20 variablesas the most informative predictor set. The final model was then validatedwith the test set data. The predictive performance was measured in termsof root mean squared error RMSE = 0.25 and adjusted R2 = 0.68. The pre-dictors included in the final model indicated productivity (annual cattle careworkload per produced milk), scale of operations (total income in relation toexpenses, profit/loss, advance payment of tax), indebtedness (interest costs),and level of investment (tax deductions on production facilities and supportpayment entitlements).In the second phase, five other prominent predictive model methods weretested. The models were the generalized linear model (GLM), generalizedboosted regression model (GBM), multivariate adaptive regression splines(MARS), projection pursuit regression (PPR), and support vector machine(SVM) with a Gaussian radial basis function. The models produced RMSEmetrics on the test set as follows: 0.26 (GLM), 0.23 (GBM), 0.22 (MARS),0.23 (PPR), and 0.26 (SVM). Corresponding adjusted R2 metrics were: 0.67(GLM), 0.73 (GBM), 0.75 (MARS), 0.73 (PPR), and 0.67 (SVM). RF, GBM,MARS, PPR, and SVM were then combined into a linear regression ensem-ble. The prediction accuracy outperformed the individual models by havingRMSE = 0.22 and adjusted R2 = 0.76.We have previously used a profitability prediction tool based on randomforest, in a web application serving farmers. As the ensemble predictionmodel outperforms our earlier model based on random forest, we will replaceour existing tool with the new model.Keywords: Predictive modelling, learning ensembles, dairy farms, prof-itability.References:Breiman, L. (2001). Random Forests Machine Learning, Volume 45(1), 5-32.Friedman, J. H., Popescu, B. E. (2008). Predictive learning via rule ensembles.The Annals of Applied Statistics 2 (2008), no. 3, 916–954.Kuhn, M. (2014). Contributions from J. Wing, S. Weston, A. Williams, C. Keefer,A. Engelhardt, T. Cooper, Z. Mayer and the R Core Team. caret: Classifi-cation and Regression Training. R package version 6.0-24.

Predicting profitability of dairy farms with a learning ensemble model

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