Comparison of forecast models of production of dairy cows combining animal and diet parameters

Abstract

We study the effect of nutritional diet characteristics on the lactating Holstein-Friesian dairy cows in Brittany, France from 36 individuals. An analysis of the relations between fat/protein content and milk yield was implemented for our dataset. The fat and protein production increase at a slower rate as milk yield increases. The importance of chemical composition on milk production is studied using the linear model. The data analysis confirms the importance of Starch, crude fiber, and protein which have a positive effect on milk production. This analysis also confirms the previous study on the effect of parity on the production. After that, the milk production forecasting is investigated using both linear models and machine learning approaches (support vector machine, random forest, neural network). We study the performance of multiple linear regression and machine learning-based models in both non-autoregressive and autoregressive cases at the individual level. The autoregressive models, which take into account the previously observed milk yield, have proven to significantly outperform the non-autoregressive approaches. Moreover, the computational cost of each approach is presented in the paper. While the random forest algorithm gives the best performance in both non-autoregressive and autoregressive approaches. The support vector machine algorithm gives a very close performance with a substantial less computing time. The support vector machine

Predicting daily milk yield for primiparous cows using data of within-herd relatives to capture genotype-by-environment interactions

“…Test-day (TD) models have been developed to describe the lactation curve (Wood, 1967;Wilmink, 1987;Olori et al, 1999;Mayeres et al, 2004), and in the case of primiparous cows, these models typically describe the general shape of the curve for fixed subsets of cows grouped by age at calving, season of calving, breed composition, or herd (Jamrozik and Schaeffer, 1997;Druet et al, 2003;Macciotta et al, 2004;Strabel and Jamrozik, 2006;Daltro et al, 2019). Recently, machine-learning algorithms have been used to forecast milk production with high accuracy, with the ability to adapt to different management conditions and capacity to provide predictions for individual cows (Murphy et al, 2014;Zhang et al, 2016;Nguyen et al, 2020). However, the lack of performance data from prior lactations makes prediction of TD yields and lactation curves for individual primiparous cows very challenging.…”

Classification and prediction of milk yield level for Holstein Friesian cattle using parametric and non-parametric statistical classification models

“…ANN is network computation formed of a dense mesh of computing units and connections. The strength of the connection is numerically phrased as a weight or synaptic weight, as stated by Abo Elfadl and Abdalla [ 11 ] and Nguyen et al [ 14 ]. The incommensurate node numbers in the entry and exit layers are prescribed due to data structure.…”

“…Artificial neurons are doing their job as summing and nonlinear mapping junctions [ 11 ]. ANN made up of three units or layers, a layer of “input” units which receive the measurement vector X and attached to a layer of “hidden” units, in which there is splitting for the input zone into two quasi spaces, which is related to a layer of “output” units [ 13 , 14 ]. By incorporating such semispaces, the units of the output layer can form any polygonal partition of the input space, as stated by Teshnizi and Ayatollahi [ 13 ].…”

Impact of Climate Change on Dryland Agricultural Systems: A Review of Current Status, Potentials, and Further Work Need

“…They suggested ML processes could be used for irrigation planning as well as for yield and disease prediction. Furthermore, ML and AI can be used for gully erosion mapping (Tien Bui et al, 2019), groundwater mapping (Arabameri et al, 2020), drip irrigation (Klyushin & Tymoshenko, 2021), optimization of irrigation and application of pesticides and herbicides (Talaviya et al, 2020), dairy farm management (Cockburn, 2020;Shine et al, 2018), milk production forecasting (Nguyen et al, 2020), livestock farming (García et al, 2020), selection of suitable crop traits (Shekoofa et al, 2014) and seasonal rainfall forecasting (Feng et al, 2020;van Ogtrop et al, 2014).…”