RESUMO
Various space missions have measured the total solar irradiance (TSI) since 1978. Among them the experiments Precision Monitoring of Solar Variability (PREMOS) on the PICARD satellite (2010-2014) and the Variability of Irradiance and Gravity Oscillations (VIRGO) on the mission Solar and Heliospheric Observatory, which started in 1996 and is still operational. Like most TSI experiments, they employ a dual-channel approach with different exposure rates to track and correct the inevitable degradation of their radiometers. Until now, the process of degradation correction has been mostly a manual process based on assumed knowledge of the sensor hardware. Here we present a new data-driven process to assess and correct instrument degradation using a machine-learning and data fusion algorithm, that does not require deep knowledge of the sensor hardware. We apply the algorithm to the TSI records of PREMOS and VIRGO and compare the results to the previously published results. The data fusion part of the algorithm can also be used to combine data from different instruments and missions into a composite time series. Based on the fusion of the degradation-corrected VIRGO/PMO6 and VIRGO/DIARAD time series, we find no significant change (i.e [Formula: see text] W/m[Formula: see text]) between the TSI levels during the two most recent solar minima in 2008/09 and 2019/20. The new algorithm can be applied to any TSI experiment that employs a multi-channel philosophy for degradation tracking. It does not require deep technical knowledge of the individual radiometers.
RESUMO
Two 60-d experiments were conducted to evaluate the effects of supplementing degradable (DIP) and(or) undegradable (UIP) intake protein on the performance of lactating first-calf heifers. Diets were formulated to meet the requirements for either DIP, metabolizable protein (MP), or both when diets contained low-quality grass hay and an efficiency of microbial protein synthesis estimate of 10%. In Exp. 1, 32 individually fed first-calf heifers (avg 395 kg) were allotted to a 2 x 2 factorial arrangement of treatments (main effects of DIP, MP, and DIP x MP interaction) 1 d after calving. Cows consumed a basal diet of chopped crested wheat grass hay (4.3% CP, 67% DIP) ad libitum. Supplemental DIP and UIP were supplied by varying the ratios of soybean meal (75% DIP) and a heat-treated, protected soybean meal (70% UIP). Cow weight gain was better (P < 0.01) when adequate DIP was supplied than when DIP was deficient. However, calf weight gain was not increased by supplementing the cow with DIP. Supplemental UIP did not (P > 0.40) improve cow or calf weight gain. Blood urea N levels were higher (P < 0.01) for cows receiving supplemental DIP and UIP. However, milk production estimates were similar among treatments, as were digestibilities of OM and ADF. Nitrogen digestibility was greater when supplemental DIP was fed, but providing additional UIP did not (P = 0.15) change N digestibilities. Experiment 2 evaluated similar supplements using the same experimental design to determine changes in cow and calf weight gain, body condition score, and pregnancy rate. Seventy-two first-calf heifers (avg 441 kg) were allotted to supplement treatments 1 d after calving and were fed grass hay (5% CP, 53% DIP, 10% microbial efficiency) for ad libitum consumption for 60 d. Supplements were individually fed three times/week. Varying the ratios of soybean meal, heat-treated soybean meal, and corn gluten meal provided additional DIP and UIP. Unlike in Exp. 1, supplemental UIP improved (P < 0.05) cow weight gain. Calves from dams supplemented with DIP gained 5 kg more weight after 60 d than calves from dams deficient in DIP. Pregnancy rates in the fall were similar (P = 0.90) among treatments. These data suggest that DIP was more limiting in Exp. 1 than was UIP. Supplementing UIP in Exp. 2 improved cow weight gains but did not improve calf gains. Data suggest that the efficiency of microbial protein synthesis for this forage-based diet was probably less than 10%.