# Prediction of daily nutritional requirements of gestating sows based on their behaviour and machine learning methods

Maëva Durand, Christine Largouët, Louis Bonneau de Beaufort, Jean-Yves Dourmad, Charlotte Gaillard

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## ► To cite this version:

Maëva Durand, Christine Largouët, Louis Bonneau de Beaufort, Jean-Yves Dourmad, Charlotte Gaillard. Prediction of daily nutritional requirements of gestating sows based on their behaviour and machine learning methods. ESPHM - 14th European symposium of porcine health management, May 2023, Thessalokini, Greece. pp.1-1, 2023. [hal-04119697](https://hal.inrae.fr/hal-04119697)

HAL Id: hal-04119697

https://hal.inrae.fr/hal-04119697

Submitted on 6 Jun 2023

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# RÉPUBLIQUE FRANÇAISE

Liberté
Égalité
Fraternité

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maeva.durand@inrae.fr

INRAO

L'INSTITUT
agro Rennes
Angers

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#DigitAg

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14th EUROPEAN SYMPOSIUM
OF PORCINE HEALTH MANAGEMENT

2023

GREECE
May 31 - June 2

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## Prediction of daily nutritional requirements of gestating sows based on their behaviour and machine learning methods

M. DURAND, C. LARGOUËT, L. BONNEAU, J.Y. DOURMAD, C. GAILLARD

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## BACKGROUND & OBJECTIVE

* **Precision Feeding** aims to define the right feeding strategy according to individual's nutrient requirements, to reduce feed cost and environmental losses.

* Usually, the nutrient requirements of gestating sows are calculated by a **mechanistic nutritional model** requiring input data such as sows and herd characteristics.

* **Aim of this study:** Prediction of nutritional requirements using machine learning methods and sensor data.

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## CONCLUSION

* **Machine learning methods** using sensor data and **behavioural data** can accurately predict the sows daily requirements (error under 7% for energy and 12% for lysine) which could simplify the application of precision feeding on farms.

* **Sow's activity, feeding behaviour,** and **body weight** are the best predictors. **Adding sow and housing characteristics** significantly improves the results.

* **Gradient Tree Boosting** is the most accurate ML algorithm.

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## MATERIAL AND METHODS

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## RESULTS

SID Lys: Standard ileal digestible lysine; ME: Metabolisable energy; RMSE: Root Mean Square Error; MAPE: Mean Absolute Percentage Error; R²: coefficient of determination

<figure><img src="image_16.png" /><figcaption>Lower MAPE obtained using scenarios SP with **automatic weighting system + feeder** for lysine (5.31%) and with **feeder + activity sensors** for energy (3.88%).</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>R² values were higher with **Gradient Tree Boosting** (0.95 for energy and 0.99 for lysine) compared to those obtained with **linear regression** (0.52 and 0.83).</figcaption></figure>