Sensitivity analysis of the INRA 2018 feeding system for ruminants by a one-at-a-time approach: Effects of dietary input variables on predictions of multiple responses of dairy cattle

Abstract

In the INRA 2018 feeding system for ruminants, the prediction of multiple animal responses is based on the integration of the characteristics of the animal and the available feedstuff characteristics, as well as the rationing objectives. In this framework, the characterization of feedstuffs in terms of net energy, digestible protein, and fill units requirs information on their chemical composition, digestibility, and degradability. Despite the importance of these feed characteristics, a comprehensive assessment of their impact on the responses predicted by the INRA 2018 feeding system has not been carried out. Thus, our study investigated how variables predicted by the INRA feeding system (i.e., outputs) for dairy cows are affected by variation in feed characterization (i.e., inputs). Five input variables were selected for the sensitivity analysis (SA): CP, OM apparent digestibility (OMd), GE, effective degradability of nitrogen assuming a passage rate of 6%/h (ED6_N) and true intestinal digestibility (dr_N) of nitrogen. A one-at-a-time SA was performed on predicted digestive, productive and environmental output variables for dairy cows with 6 contrasted diets. These 6 diets were formulated to meet 95% of the potential daily milk production (37.5 kg) of a multiparous cow at wk 14 of lactation. Then, the values of the 5 key input variables of each feedstuff were randomly sampled around the INRA 2018 feed table values (reference point). The response of the output variable to the variation of the input variable was quantified and compared using the tangent value at the reference point and the normalized sensitivity coefficient. Among the major final output variables, CP and dr_N had the greatest impact on nitrogen (N) excretion in urine (as a proportion of total fecal and urinary N excretion, UN/TN), OMd and GE had the greatest impact on N utilization efficiency (N in milk as proportion of intake N, NUE), and ED6_N had the greatest impact on milk protein yield (MPY). Additionally, CP, GE, and dr_N had the least effect on methane emission, OMd had the least effect on UN/TN, and ED6_N had the least effect on NUE. The responses of most output variables to ED6_N and dr_N variations were highly dependent on diet, and were related to the ratio between PDI (i.e., metabolizable protein) and UFL (i.e., NE_L) at the reference point of each diet. In conclusion, we were able to analyze the response of output variables to the variations of the input variables, using the tangent and its normalized value at the reference point. The predicted final outputs were more impacted by variations in CP, GE, and OMd. The other 2 input variables, ED6_N and dr_N, had a smaller effect on the final output variables, but the responses varied between the diets according to their PDI/UFL ratio. Among the final output variables affected by ED6_N, MPY was the most impacted, but when quantified this impact was at an acceptable level. Our present study was conducted using 6 representative diets for dairy cattle fed at their potential, but should be completed by the analysis of more diverse conditions.

Keywords: INRAtion®V5; dairy cattle; normalized sensitivity coefficient; sensitivity analysis.