Assessment of an indirect technique to predict hay and silage storage dry matter losses through Monte Carlo simulation

Abstract

Control of dry matter losses (DML) is a major concern of forage conservation systems. Measuring DML during hay and silage storage is difficult and time-consuming, so it is usually limited to experimental conditions. The lack of a practical way of measuring DML to monitor forage conservation efficiency has contributed to the poor adoption of good practices. The availability of a practical, easy, and economic technique capable of estimating on-farm DML would facilitate advisory and extension work. The objective of this study was to assess the accuracy and precision of an indirect technique based on compositional changes to estimate storage DML for silages and hays. Data were generated through a Monte Carlo simulation developed to test the effects of type of data distribution (normal or log-normal), variability (5 and 10% coefficient of variation), and sample size (1000, 30, 20, and 10). Results indicated that potential markers (acid detergent fibre and acid detergent lignin were explored) had log-normal distribution and that a coefficient of variation of ~10% was reasonable. Summary statistic analysis showed that means and medians were coherent for different sample sizes. It was concluded that changes in marker concentrations could lead to a reasonably robust system of predicting DML during hay or silage storage.