QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Stefanon, B. Articles by Gruber, L. Search for Related Content PubMed PubMed Citation Articles by Stefanon, B. Articles by Gruber, L.

---

Articles

Using Artificial Neural Networks to Model the Urinary Excretion of Total and Purine Derivative Nitrogen Fractions in Cows

Bruno Stefanon^*1, Valentino Volpe^*, Stefano Moscardini^*,2 and Leonhard Gruber

^* Dipartimento di Scienze della Produzione Animale, Facoltà di Medicina Veterinaria, Università degli Studi di Udine, 33010 Pagnacco (Ud), Italy and Federal Research Institute for Agriculture in the Alpine Regions, A 8952, Irdning, Austria

¹To whom correspondence should be addressed. E-mail: bruno.stefanon{at}dspa.uniud.it.

A dataset of 177 individual nitrogen balances from dry and lactating cows was split in two independent groups: training dataset (n = 130) and challenge dataset (n = 47). The training dataset was used to develop multiple linear regressions (MLR) and artificial neural networks (ANN) aimed at predicting the urinary excretion of total (NURI) and that of purine derivative nitrogen (PDN). Input variables for the prediction of NURI were crude protein (CP) intake, effective degradability of non-protein dry matter (DM), neutral detergent fiber (NDF) content of the diet, live weight and milk yield. Live weight, total carbohydrate intake, the ratio of non-protein DM degraded to CP degraded and milk yield corrected for DM intake were entered to predict PDN. The regression between predicted and observed values for the training dataset showed a better statistical accuracy of ANN than did MLR models, especially for PDN. The evaluation of the two models on the challenge dataset showed similar determination coefficients, either when predicting total nitrogen excretion (0.623 and 0.614 for ANN and MLR, respectively) or PDN (0.688 and 0.666, for ANN and MLR, respectively). Moreover, both approaches were affected by a tendency to under-predict both targets at high levels of NURI and PDN. However, with the ANN approach, it is possible to study the response of the model to modifications of individual inputs by the so-called response analysis. This unique feature could be used to study the effect of different physiological situations as well as providing hypotheses for additional research.

KEY WORDS: • modeling • neural networks • nitrogen excretion • purine derivatives • cows