Early lactation dairy cows: Development of equations to predict intake and milk performance at grazing

Low-Intensity, High-Frequency Grazing Strategy Increases Herbage Production and Beef Cattle Performance on Sorghum Pastures

Simple Summary

Finding smart management targets to improve livestock production and make it sustainable are very important for livestock in the tropics. We assessed the effects of high-intensity and low-frequency (HILF) vs. low-intensity and high-frequency (LIHF) grazing on herbage production and performance of beef cattle grazing sorghum pastures. The LIHF resulted in shorter rest periods when compared with the HILF. The greater leaf lamina mass in LIHF allowed greater sward light interception at post-grazing, resulting in greater total herbage production than HILF. The average daily gain (ADG) was greater for the LIHF than for the HILF treatment; however, even with a greater stocking rate in the HILF, there was no difference for LW gain per ha. Our findings demonstrated that the LIHF strategy that is based on offering to the animals an optimal sward structure to favor the herbage intake rate fosters greater herbage production, harvesting efficiency, and ADG without compromising LW gain per area, despite the lower herbage harvested per stocking cycle. Therefore, we conclude that the classic trade-off between animal performance and forage production could be offset on tropical grasses grazed by beef cattle only by adjusting grazing management according to a LIHF grazing management strategy.

Abstract

We assessed the effects of high-intensity and low-frequency (HILF) vs. low-intensity and high-frequency (LIHF) grazing on herbage production and performance of beef cattle grazing sorghum pastures. The experimental design was a complete randomized block with two treatments and four replicates (paddocks), carried out in 2014/15. The management target of 50 and 30 cm for pre- and post-grazing, respectively, a LIHF grazing management strategy oriented to maximize beef cattle herbage intake per unit time, was compared with a HILF grazing management strategy of 80 and 20 cm for pre- and post-grazing, respectively, aiming to maximize herbage accumulation and harvest efficiency. Sixteen Brangus steers of 15-month-old and 265 ± 21 kg of live weight (LW) were randomly distributed to paddocks (experimental units). The LIHF resulted in shorter rest periods when compared with the HILF. The greater leaf lamina mass in LIHF allowed greater sward light interception at post-grazing, resulting in greater total herbage production than HILF (7581 and 4154 kg DM/ha, respectively). The average daily gain (ADG) of steers was greater for the LIHF than for the HILF treatment (0.950 and 0.702 kg/animal, respectively); however, even with a greater stocking rate in the HILF, there was no difference for LW gain per ha, with an average of 4 kg LW/ha/day. Our findings demonstrated that the LIHF strategy that is based on offering to the animals an optimal sward structure to favor the maximum herbage intake rate fosters greater herbage production, harvesting efficiency, and ADG without compromising LW gain per area of beef steers, despite the lower herbage harvested per stocking cycle.

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.

Herbage dry matter intake estimation of grazing dairy cows based on animal, behavioral, environmental, and feed variables

Information about the individual herbage DMI (HDMI) of grazing dairy cows is important for an efficient use of pasture herbage as an animal feed with a range of benefits. Estimating HDMI, with its multifaceted influencing variables, is difficult but may be attempted using animal, performance, behavior, and feed variables. In our study, 2 types of approaches were explored: 1 for HDMI estimation under a global approach (GA), where all variables measured in the 4 underlying experiments were used for model development, and 1 for HDMI estimation in an approach without information about the amount of supplements fed in the barn (WSB). The accuracy of these models was assessed. The underlying data set was developed from 4 experiments with 52 GA and 50 WSB variables and one hundred thirty 7-d measurements. The experiments differed in pasture size, herbage allowance, pregrazing herbage mass, supplements fed in the barn, and sward composition. In all the experiments, cow behavioral characteristics were recorded using the RumiWatch system (Itin and Hoch GmbH, Liestal, Switzerland). Herbage intake was estimated by applying the n-alkane method. Finally, HDMI estimation models with a minimal relative prediction error of 11.1% for use under GA and 13.2% for use under WSB were developed. The variables retained for the GA model with the highest accuracy, determined through various selection steps, were herbage crude protein, chopped whole-plant corn silage intake in the barn, protein supplement or concentrate intake in the barn, body weight, milk yield, milk protein, milk lactose, lactation number, postgrazing herbage mass, and bite rate performed at pasture. Instead of the omitted amounts of feed intake in the barn and, due to the statistical procedure for model reduction, the unconsidered variables postgrazing herbage mass and bite rate performed at pasture, the WSB model with the highest accuracy retained additional variables. The additional variables were total eating chews performed at pasture and in the barn, total eating time performed at pasture, number of total prehension bites, number of prehension bites performed at pasture, and herbage ash concentration. Even though behavioral characteristics alone did not allow a sufficiently accurate individual HDMI estimation, their inclusion under WSB improved estimation accuracy and represented the most valid variables for the HDMI estimation under WSB. Under GA, the inclusion of behavioral characteristics in the HDMI estimation models did not reduce the root mean squared prediction error. Finally, further adaptation, as well as validation on a more comprehensive data set and the inclusion of variables excluded in this study such as body condition score or gestation, should be considered in the development of HDMI estimation models.

Restricting dairy cow access time to pasture in early lactation: the effects on milk production, grazing behaviour and dry matter intake

One of the main aims of pasture-based systems of dairy production is to increase the proportion of grazed grass in the diet. This is most easily achieved by increasing the number of grazing days. However, periods of inclement weather conditions can reduce the number of days at pasture. The two objectives of this experiment were: (i) to investigate the effect of restricting pasture access time on animal production, grazing behaviour and dry matter intake (DMI) of spring calving dairy cows in early lactation; and (ii) to establish whether silage supplementation is required when cows return indoors after short grazing periods. In all, 52 Holstein-Friesian spring calving dairy cows were assigned to a four-treatment study from 25 February to 26 March 2008. The four treatments were: full-time access to pasture (22H; control); 4.5-h- pasture access after both milkings (2 × 4.5H); 3-h pasture access after both milkings (2 × 3H); 3-h pasture access after both milkings with silage supplementation by night (2 × 3SH). All treatments were offered 14.4 kg DM/cow per day herbage from swards, with a mean pre-grazing yield of 1739 kg DM/ha above 4 cm, - and were supplemented with 3 kg DM/cow per day of concentrate. The 2 × 3SH treatment was offered an additional 4 kg DM/cow of grass silage by night. Restricting pasture access time (2 × 3H, 2 × 3SH and 2 × 4.5H) had no effect on milk (28.3 kg/cow per day) and solids-corrected milk (27.2 kg/cow per day) yield when compared with the treatment grazing full time. Supplementing animals with grass silage did not increase milk production when compared with all other treatments. Milk protein concentration tended to be lower (P = 0.08; 32.2 g/kg) for the 2 × 3SH animals when compared with the 22H animals (33.7 g/kg). The grass DMI of the 2 × 3SH treatment was significantly lower (-2.3 kg DM/cow per day) than all other treatments (11.9 kg DM/cow per day), yet the total DMI of these animals was highest (16.6 kg DM/cow per day). The 22H cows grazed for 481 min/cow per day, which is significantly longer than all other treatments. The 2 × 3H animals grazed for 98% of the time, whereas the 2 × 3SH grazed for 79% of their time at pasture. Restricting pasture access time did not affect end body weight or body condition score. The results of this study indicate that restricting pasture access time of dairy cows in early lactation does not affect milk production performance. Furthermore, supplementing cows with grass silage does not increase milk production but reduces grazing efficiency.