Prediction of Lactation Milk Yield in Simmental Cattle Milked with Robotic Milking System Using CHAID and CART Algorithms

Abstract

Animal husbandry has long been a key component of agriculture, fulfilling essential nutritional needs. Technological advancements have gradually replaced human labor with machines, particularly in dairy farming, where the milking process is vital for income generation. Robotic milking systems have emerged as significant innovations, allowing for efficient, hygienic, and automated milking while reducing dependence on labor.This study aims to predict lactation milk yield (LMY) in Simmental cows during their first lactation period in robotic milking farms by using various factors, including Days in Milk (DIM), Status (S), Number of Inseminations (IN), Milk Flow Rate (MFR), Robot Rejection Rate (RRR), Rumination Time (RT), Time Spent in the Robot (TSR), Feed Amount in the Robot (FAR), Feed Consumption Rate in the Robot (FCRR), and Milking Frequency (MF). The analysis incorporates Classification and Regression Trees (C&RT) and Chi-squared Automatic Interaction Detector (CHAID) algorithms, identifying DIM as the primary predictor.The CHAID analysis revealed that newly calved cows (DIM < 30) had an average LMY of 5,692 L, while those receiving over 5.09 kg of feed achieved an average of 8,426 L. For cows in the 30 to 81 days of lactation, higher feed allocation correlated with increased milk yield. The CART algorithm confirmed these findings, establishing DIM as the most influential factor. Overall, robotic milking systems facilitate individualized management of dairy cows, optimizing factors such as feed allocation and milking frequency. By leveraging advanced algorithms to analyze these variables, this study highlights the potential for improving milk yield and animal welfare in modern dairy farming practice.

Keywords

• Robotik sağım
• Süt verimi
• Süt sığırı
• Veri madencilği
• CART
• CHAID

Robotik Sağım Sistemiyle Sağılan Simental Sığırlarının Laktasyon Süt Veriminin CHAID ve CART Algoritmaları ile Tahmini

Abstract

Hayvancılık, uzun süredir tarımın temel bir bileşeni olup, temel beslenme ihtiyaçlarını karşılamaktadır. Teknolojik gelişmeler, özellikle sütçülükte, insan emeğini makinelerle kademeli olarak değiştirmiştir; burada sağım süreci gelir elde etmede hayati bir öneme sahiptir. Robotik sağım sistemleri, verimli, hijyenik ve otomatik sağımı mümkün kılarak, iş gücüne olan bağımlılığı azaltan önemli yenilikler olarak ortaya çıkmıştır. Bu çalışma, robotik sağım çiftliklerinde Simental ineklerinin birinci laktasyon döneminde laktasyon süt verimini (LSV), Days in Milk (DIM), Statü (S), Dölleme Sayısı (IN), Süt Akış Hızı (MFR), Robot Reddi Oranı (RRR), Ruminasyon Süresi (RT), Robotta Geçirilen Süre (TSR), Robotta Verilen Yem Miktarı (FAR), Robotta Yem Tüketim Oranı (FCRR) ve Sağım Sıklığı (MF) gibi çeşitli faktörleri kullanarak tahmin etmeyi amaçlamaktadır. Analiz, Sınıflandırma ve Regresyon Ağaçları (C&RT) ve Ki-Kare Otomatik Etkileşim Algılama (CHAID) algoritmalarını içererek, DIM'i birincil tahmin edici olarak belirlemiştir. CHAID analizi, yeni doğum yapmış ineklerin (DIM < 30) ortalama LSV'sinin 5.692 L olduğunu, 5.09 kg'dan fazla yem verilenlerin ise ortalama 8.426 L'lik süt verimi elde ettiğini ortaya koymuştur. 30 ile 81 gün arasındaki laktasyon dönemindeki ineklerde, daha yüksek yem tahsisi ile artan süt verimi arasında bir ilişki bulunmuştur. CART algoritması, bu bulguları doğrulamış ve DIM'i en etkili faktör olarak belirlemiştir. Genel olarak, robotik sağım sistemleri, süt ineklerinin bireysel yönetimini kolaylaştırarak, yem gibi faktörlerin optimizasyonunu sağlar. Bu çalışmada, bu değişkenleri analiz etmek için gelişmiş algoritmalar kullanılarak, modern sütçülük pratiğinde süt verimi ve hayvan refahını iyileştirme potansiyeli vurgulanmaktadır.

Keywords

• Robotic milking; milk yield; cattle
• data minig
• CART
• CHAID

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