Greener and climate resilient dairy production

Vic Drought Hub - Farmland 1
  • The University of Melbourne
  • Climate Resilience

To explore the effects of feeding conserved forage in typical dairy diet on enteric CH4 emissions and its intensity.

Exploring impact of feeding conserved forage on dairy production & greenhouse-gas emissions

 

Introduction

In Victoria, dairy production heavily relies on supplementary conserved forage, such as silage, hay, and straw, to meet the feed requirements during grazing pasture gaps. Winter cereals, planted in autumn and harvested in spring/early summer for conserved fodder, have become a vital feed source for dairy cows. These cereals, unlike traditional perennial ryegrass, exhibit higher water use efficiency and can thrive in dryland conditions.

Presently, the evaluation of winter cereal species primarily focuses on their grain production potential and impact on dairy milk production. However, there is a lack of comprehensive information regarding how feeding different types and qualities of conserved cereal forages can influence enteric methane (CH4) emissions in diverse dairy production systems. Since CH4 is a significant contributor to greenhouse gas (GHG) emissions from dairy farms, understanding and quantification of such impact is crucial.

Objective

The primary objective of this activity was to explore the effects of feeding conserved forage in typical dairy diet on enteric CH4 emissions and its intensity.

Methodology

This study aimed to assess the influence of feeding high-quality and low-quality hay and silage on enteric CH4emissions and emission intensity in lactating dairy cows (excluding dry cows and replacement cattle). Two distinct northern Victoria case-study farming systems were selected for this study (Table 1).

 

Table 1: Dairy production systems of two case-study farms.

Farm A Farm B
Farm systems System 4 (hybrid system) System 2 (pasture based)
Milking herd size 720 276
Breed Holstein, Jersey Holstein, Jersey, Aussie Red
Concentrate (t DM/cow.year) 2.5 2.2
Calving pattern Split calving Split calving
Average liveweight (kg/head) 579 535
Average winter milk production (kg/head.day) 28.6 22.6
Average spring milk production (kg/head.day) 28.9 28.9
Average summer milk production (kg/head.day) 26.7 21.6
Average autumn milk production (kg/head.day) 25.4 19.0
Average annual milk production (kg/head.day) 27.4 23.0

 

Baseline farm data required for the analysis was obtained from Munidasa et al. (2023). Hay metabolisable energy (ME; MJ ME/kg DM) data for the low and high-quality feed scenarios was obtained from the Murray Dairy Fodder for the Future project, comprising field data collected from 6 different locations in Victoria and across two seasons. First quartile (Q1) ME of each hay type considered for the low-quality scenario and fourth quartile (Q4) ME of each feed considered for high quality feed can be found in Table 2. These ME values were used to replace the Munidasa et al. (2023) hay ME values from baseline farm dataset, to develop scenarios. Further, the standard deviation of each feed from the Fodder for the Future project was calculated and applied to the Figures 1 and 2 as an error bar to understand the uncertainty of the outputs (emission and emission intensity) from each scenario.

 

Table 2: Metabolisable energy (ME; MJ/kg DM) content of the hay used in the analysis.

  Barley Oats Wheat Lucerne Vetch
Baseline farm A N/A 10.0 6.0 10.0 N/A
Baseline farm B 6.5 N/A 10.0 N/A 11.4
Low quality hay 8.4 8.4 8.4 8.5 7.3
High quality hay 10.8 10.8 10.3 11.1 10.8

 

 

Results and Discussion

Enteric CH4 is recognised as the most significant individual contributor to on-farm GHG emissions (Charmley et al., 2016). As a result, it is crucial to closely monitor, assess, and find ways to reduce enteric CH4 emissions from lactating dairy cows. Such efforts are of utmost importance for effective emissions management on farms. This study assesses the impact of the quality of the hay on CH4 emission and its intensity.

Hay represented on average ~40% in diet in Farm B, while only ~6% in Farm A. Because of the substitution of hay was the only variable in this modelling exercise, this led to less changes in Farm A emission intensity across all scenarios than in Farm B. The quality changes (high ME vs0 low ME vs baseline) in hays did not change the absolute emission and emission intensity in Farm A and B, when the standard deviation (95% probability) error bar was considered (Figure 1 and 2).

 

Table 3: Absolute emission (kg CO2-e/head.day) from offering hays with different quality in the diet.

Farm A Winter Spring Summer Autumn Average
Baseline farm hay 13.22 13.22 12.84 12.30 12.89
High quality hay 13.12 13.18 12.64 12.27 12.80
Low quality hay 13.16 13.28 12.80 12.32 12.89
Farm B
Baseline farm hay 11.35 13.32 11.16 10.47 11.57
High quality hay 11.08 13.16 11.06 10.31 11.40
Low quality hay 11.23 13.62 12.12 10.89 11.96

 

Table 4: Methane emission intensities (CO2-e g/kg fat and protein corrected milk) from offering hays with different quality in the diet.

Farm A Winter Spring Summer Autumn Average
Baseline farm diet 0.470 0.480 0.440 0.440 0.458
High quality hay diet 0.471 0.474 0.431 0.437 0.453
Low quality hay diet 0.473 0.477 0.437 0.438 0.456
Farm B
Baseline farm diet 0.530 0.460 0.470 0.470 0.483
High quality hay diet 0.516 0.452 0.466 0.468 0.475
Low quality hay diet 0.523 0.468 0.510 0.495 0.499

 

Farm A had higher absolute emission and lower emission intensity compared to Farm B (Figure 1, Figure 2). According to the National Greenhouse Gas Inventory methodology, enteric CH4 emissions are primarily influenced by the DMI of the animals. In the case of lactating cows in this project, their DMI is influenced by factors such as liveweight, live weight gain, and milk production within the model. As a result of this, Farm A, which employed more intensive feeding system had a larger and higher-producing cows, generated a greater amount of CH4 emissions (measured in kg CO2e/head.day) compared to the pasture-based system Farm B. However, Farm A’s emissions were less per unit of product (i.e., emission intensity) when compared with Farm B, due to the higher milk production.

 

Figure 1 Absolute emission (CO2-e g:head.day) of feeding different quality hay on Farm A (A) and Farm B (B). Error bar indicates 2 standard deviations of the mean

 

 

Figure 1: Absolute emission (CO2-e g/head.day) of feeding different quality hay on Farm A (A) and Farm B (B). Error bar indicates 2 standard deviations of the mean.

 

 

 

 

 

Figure 2 Emission intensity (CO2-e g:kg FPCM) of feeding different quality hay on Farm A (A) and Farm B (B). Error bar indicates 2 standard deviations of the mean

  

Figure 2: Emission intensity (CO2-e g/kg FPCM) of feeding different quality hay on Farm A (A) and Farm B (B). Error bar indicates 2 standard deviations of the mean.

 

 

 

 

 


References

Munidasa, S., Cullen, B., Eckard, R., Talukder, S., Barnes, L., & Cheng, L. (2023). Comparative enteric-methane emissions of dairy farms in northern Victoria, Australia. Animal Production Science. https://www.publish.csiro.au/AN/AN22330

Charmley, E.S.R.O., Williams, S.R.O., Moate, P.J., Hegarty, R.S., Herd, R.M., Oddy, V.H., Reyenga, P., Staunton, K.M., Anderson, A., & Hannah, M.C. (2016). A universal equation to predict methane production of forage-fed cattle in Australia. Animal Production Science, 56(3), 169-180.