• Modeling restricted feeding conditions on cows' feeding behavior on pasture-based milk production systems to develop a decision support system

      Shafiullah, AZM; Werner, J; Kennedy, E; Leso, L.; O'Brien, B; Umstätter, C; Science Foundation Ireland; Teagasc Walsh Scholarship; 13/IA/1977 (The Organising Committee of the 9th European Conference on Precision Livestock Farming (ECPLF), Teagasc, 2019)
      The aim of this study was to identify a set of feeding behaviour and activity related variables that could potentially detect a shortage of feed for the individual cow on pasture. A group of lactating cows was offered 100% of their intake capacity as herbage allowance throughout a 10-week experimental period, while another group was offered 60% of their intake allowance, either for a two week or six week period in springtime. Each cow was equipped with an automated noseband sensor. The data was analyzed by using a binomial generalized lineal model (GLM). The GLM was examined for the classification of full or restricted herbage allowance as a function of a previously identified set of characteristics. The model was further refined by including additional characteristics, which achieved higher prediction performance. The refined model achieved 77% accuracy, 75% sensitivity, 78% specificity and F-score 0.76 towards a decision support system for grass utilization in pasture based milk production.
    • Predicting the dry matter intake of grazing dairy cows using infrared reflectance spectroscopy analysis

      Lahart, B; McParland, S; Kennedy, E; Boland, T.M.; Condon, T; Williams, M; Galvin, N; McCarthy, B; Buckley, F; Department of Agriculture, Food and the Marine; et al. (Elsevier, 2019-10-31)
      The objective of this study was to compare mid-infrared reflectance spectroscopy (MIRS) analysis of milk and near-infrared reflectance spectroscopy (NIRS) analysis of feces with regard to their ability to predict the dry matter intake (DMI) of lactating grazing dairy cows. A data set comprising 1,074 records of DMI from 457 cows was available for analysis. Linear regression and partial least squares regression were used to develop the equations using the following variables: (1) milk yield (MY), fat percentage, protein percentage, body weight (BW), stage of lactation (SOL), and parity (benchmark equation); (2) MIRS wavelengths; (3) MIRS wavelengths, MY, fat percentage, protein percentage, BW, SOL, and parity; (4) NIRS wavelengths; (5) NIRS wavelengths, MY, fat percentage, protein percentage, BW, SOL, and parity; (6) MIRS and NIRS wavelengths; and (7) MIRS wavelengths, NIRS wavelengths, MY, fat percentage, protein percentage, BW, SOL, and parity. The equations were validated both within herd using animals from similar experiments and across herds using animals from independent experiments. The accuracy of equations was greater for within-herd validation compared with across-herds validation. Across-herds validation was deemed the more suitable method to assess equations for robustness and real-world application. The benchmark equation was more accurate [coefficient of determination (R2) = 0.60; root mean squared error (RMSE) = 1.68 kg] than MIRS alone (R2 = 0.30; RMSE = 2.23 kg) or NIRS alone (R2 = 0.16; RMSE = 2.43 kg). The combination of the benchmark equation with MIRS (R2 = 0.64; RMSE = 1.59 kg) resulted in slightly superior fitting statistics compared with the benchmark equation alone. The combination of the benchmark equation with NIRS (R2 = 0.58; RMSE = 1.71 kg) did not result in a more accurate prediction equation than the benchmark equation. The combination of MIRS and NIRS wavelengths resulted in superior fitting statistics compared with either method alone (R2 = 0.36; RMSE = 2.15 kg). The combination of the benchmark equation and MIRS and NIRS wavelengths resulted in the most accurate equation (R2 = 0.68; RMSE = 1.52 kg). A further analysis demonstrated that Holstein-Friesian cows could predict the DMI of Jersey × Holstein-Friesian crossbred cows using both MIRS and NIRS. Similarly, the Jersey × Holstein-Friesian animals could predict the DMI of Holstein-Friesian cows using both MIRS and NIRS. The equations developed in this study have the capacity to predict DMI of grazing dairy cows. From a practicality perspective, MIRS in combination with variables in the benchmark equation is the most suitable equation because MIRS is currently used on all milk-recorded milk samples from dairy cows.