A near infrared spectroscopy calibration for the prediction of fresh grass quality on Irish pastures

The aim of this study was to develop near infrared spectroscopy(NIRS)calibrations to predict quality parameters,dry matter(DM,g kg1)and crude protein(CP,g kg1 DM),in fresh un-dried grass.Knowledge of these parameters would enable more precise allocation of quality herbage to grazing livestock.Perennial ryegrass samples(n=1615)were collected over the 2017 and 2018 grazing seasons at Teagasc Moorepark to develop a NIRS calibration dataset.Additional samples were collected for an independent validation dataset(n=197)during the 2019 grazing season.Samples were scanned using a FOSS 6500 spectrometer at 2 nm intervals in the range of 1100~2500 nm and absorption was recorded as log 1/Reflectance.Reference wet chemistry analysis was carried out for both parameters and the resultant data were calibrated against spectral data by means of modified partial least squares regression.A range of mathematical spectral treatments were examined for each calibration,which were ranked in order of standard error of prediction(SEP)and ratio of percent deviation(RPD).Best performing calibrations achieved high predictive precision for DM(R2=0.86 SEP=9.46 g kg1,RPD=2.60)and moderate precision for CP(R2=0.84 SEP=20.38 g kg1 DM,RPD=2.37).These calibrations will aid the optimisation of grassland management and the development of precision agricultural technologies.

Effect of introducing weather parameters on the accuracy of milk production forecast models

The objective of this study was to analyze the effect of adding meteorological data to the training process of two milk production forecast models.The two models chosen were the nonlinear auto-regressive model with exogenous input(NARX)and the multiple linear regression(MLR)model.The accuracy of these models were assessed using seven different combinations of precipitation,sunshine hours and soil temperature as additional model training inputs.Lactation data(daily milk yield and days in milk)from 39 pasture-based Holstein-Friesian Irish dairy cows were selected to compare to the model outputs from a central database.The models were trained using historical milk production data from three lactation cycles and were employed to predict the total daily milk yield of a fourth lactation cycle for each individual cow over short(10-day),medium(30-day)and long-term(305-day)forecast horizons.The NARX model was found to provide a greater prediction accuracy when compared to the MLR model when predicting annual individual cow milk yield(kg),with R2 values greater than 0.7 for 95.5%and 14.7%of total predictions,respectively.The results showed that the introduction of sunshine hours,precipitation and soil temperature data improved the prediction accuracy of individual cow milk prediction for the NARX model in the short,medium and long-term forecast horizons.Sunshine hours was shown to have the largest impact on milk production with an improvement of forecast accuracy observed in 60%and 70%of all predictions(for all 39 test cows from both groups).However,the overall improvement in accuracy was small with a maximum forecast error reduction of 4.3%.Thus,the utilization of meteorological parameters in milk production forecasting did not have a substantial impact on forecast accuracy.