Lactation performance and rumen fermentation in dairy cows fed a diet with alfalfa hay replaced by corn stover and supplemented with molasses

Overexpression of a Voltage-Dependent Anion-Selective Channel (VDAC) Protein-Encoding Gene, MsVDAC, from Medicago sativa Confers Cold and Drought Tolerance to Transgenic Tobacco

Voltage-dependent anion channels (VDACs) are highly conserved proteins that are involved in the translocation of tRNA and play a key role in modulating plant senescence and multiple pathways. However, the functions of VDACs in plants are still poorly understood. Here, a novel VDAC gene was isolated and identified from alfalfa (Medicago sativa L.). MsVDAC localized to the mitochondria, and its expression was highest in alfalfa roots and was induced in response to cold, drought and salt treatment. Overexpression of MsVDAC in tobacco significantly increased MDA, GSH, soluble sugars, soluble protein and proline contents under cold and drought stress. However, the activities of SOD and POD decreased in transgenic tobacco under cold stress, while the O2- content increased. Stress-responsive genes including LTP1, ERD10B and Hxk3 were upregulated in the transgenic plants under cold and drought stress. However, GAPC, CBL1, BI-1, Cu/ZnSOD and MnSOD were upregulated only in the transgenic tobacco plants under cold stress, and GAPC, CBL1, and BI-1 were downregulated under drought stress. These results suggest that MsVDAC provides cold tolerance by regulating ROS scavenging, osmotic homeostasis and stress-responsive gene expression in plants, but the improved drought tolerance via MsVDAC may be mainly due to osmotic homeostasis and stress-responsive genes.

Predicting Daily Dry Matter Intake Using Feed Intake of First Two Hours after Feeding in Mid and Late Lactation Dairy Cows with Fed Ration Three Times per Day

Simple Summary

It is difficult to obtain feed intake of dairy cows in experiments since all cows are raised in a free-stall barn in commercial dairy farms nowadays. Therefore, it is necessary to develop a simple, accurate, and reliable feed intake prediction model to replace direct measurement. In this study, we generated a forecasting model to predict daily dry matter intake (DMI) of dairy cows in mid and late lactation based on the feed intake of first 2 h after feeding (DMI-2h). The proposed prediction equation was: DMI (kg/day) = 8.499 + 0.2725 × DMI-2h (kg/day) + 0.2132 × Milk yield (kg/day) + 0.0095 × Body weight (kg/day) (R² = 0.46). Compared with NRC model (2001), our model shows higher accuracy and precision on predicting daily DMI of dairy cows with fed ration three times per day in mid and late lactation period. This prediction model could be used as an alternative approach for researchers who have difficulty in measuring DMI in dairy cows’ experiments.

Abstract

The objective of this study was to evaluate the feasibility of using the dry matter intake of first 2 h after feeding (DMI-2h), body weight (BW), and milk yield to estimate daily DMI in mid and late lactating dairy cows with fed ration three times per day. Our dataset included 2840 individual observations from 76 cows enrolled in two studies, of which 2259 observations served as development dataset (DDS) from 54 cows and 581 observations acted as the validation dataset (VDS) from 22 cows. The descriptive statistics of these variables were 26.0 ± 2.77 kg/day (mean ± standard deviation) of DMI, 14.9 ± 3.68 kg/day of DMI-2h, 35.0 ± 5.48 kg/day of milk yield, and 636 ± 82.6 kg/day of BW in DDS and 23.2 ± 4.72 kg/day of DMI, 12.6 ± 4.08 kg/day of DMI-2h, 30.4 ± 5.85 kg/day of milk yield, and 597 ± 63.7 kg/day of BW in VDS, respectively. A multiple regression analysis was conducted using the REG procedure of SAS to develop the forecasting models for DMI. The proposed prediction equation was: DMI (kg/day) = 8.499 + 0.2725 × DMI-2h (kg/day) + 0.2132 × Milk yield (kg/day) + 0.0095 × BW (kg/day) (R² = 0.46, mean bias = 0 kg/day, RMSPE = 1.26 kg/day). Moreover, when compared with the prediction equation for DMI in Nutrient Requirements of Dairy Cattle (2001) using the independent dataset (VDS), our proposed model shows higher R² (0.22 vs. 0.07) and smaller mean bias (−0.10 vs. 1.52 kg/day) and RMSPE (1.77 vs. 2.34 kg/day). Overall, we constructed a feasible forecasting model with better precision and accuracy in predicting daily DMI of dairy cows in mid and late lactation when fed ration three times per day.