This study focuses on calibrating and validating models for hyperspectral canopy reflectance data that are useful to predict the nutritive value of ryegrass-white clover mixed herbage available to the grazing cow. Hyperspectral measurements and herbage cuts were collected from 286 sampling plots from a dairy farm from July 2017 to May 2018. Hyperspectral data were pre-treated by applying a Savitzky-Golay filter followed by a Gap-segment derivative algorithm. Herbage samples were analyzed for determination of herbage nutritive value traits, digestible organic matter in dry matter (DOMD), metabolizable energy (ME), crude protein (CP), neutral detergent fiber (NDF) and acid detergent fiber (ADF). Partial least squares regression was performed to calibrate the spectra against the five nutritive value traits. Results indicate that accuracy was moderately high for the CP model (R² = 0.78) and moderate for the DOMD, ME, NDF and ADF models (0.54 < R² < 0.67). The possibility of being able to use proximal sensing for the estimation of herbage nutritive value in the field could potentially contribute to more efficient grazing management with potential economic benefits for the farm business.

The use of supplementary feeds, including concentrates, on New Zealand dairy farms has increased in recent decades and this could have mitigated methane (CH₄) emissions by decreasing the forage-to-concentrate ratio. The forage-to-concentrate ratio in the diet can be easily managed in cows that receive a total mixed ration, but under grazing conditions, the cows select their own diets by increasing or decreasing the herbage intake, likely depending on the amount of concentrates eaten. The feed selection will have a direct effect on the forage-to-concentrate ratio and the potential mitigation of CH₄ emissions. The objective of the current study was to determine the effect of feeding increasing levels of concentrates in pasutre fed early lactation dairy cows on CH₄ emissions. Thirty-two early lactation dairy cows fed fresh cut ryegrass-based herbage ad libitum were allocated to receive 0, 2, 4 and 6 kg dry matter of concentrates (n = 8 per treatment) per day in two equal meals during milking. The cows were on their respective full treatment diets for at least 14 days before CH₄ measurements started. The CH₄ emissions were measured in respiration chambers for 48 h. The CH₄ yield (g/kg dry matter intake) and intensity (g/kg milk solids) decreased linearly by 0.76 and 5.3 units per kg of concentrate in the diet (P = 0.001 and P = 0.009), respectively, with increasing the concentrate level. The variations in CH₄ emissions after each feeding increased quadratically and linearly (P = 0.023 and P = 0.038) after morning and afternoon feeding, respectively and the CH₄ emissions reached the peak more rapidly after morning feeding (linear effect; P = 0.029) when the concentrate level increased. For short-chain fatty acids, the proportion of acetate decreased by 0.72 units (P = 0.003) while the proportion of propionate increased by 0.16 units (P = 0.006) per kg of concentrate in the diet. Feeding concentrates is a simple feeding strategy to mitigate CH₄ emissions from dairy cows. CH₄ yield decreased linearly by increasing the concentrate inclusion level of the diet.

High-fiber and high-starch concentrates are used as supplements to meet the energy demand of lactating cows or to fill herbage shortage in pastoral systems. Additionally, concentrates can be a tool to mitigate enteric methane (CH₄) per unit of DMI (CH₄ yield) and per unit of milk production. This study aimed to compare the effects of supplementing herbage-fed late-lactation dairy cows with high-fiber (FIB), high-starch (STA), or a 50:50 mix of both concentrates (MIX) compared with no supplementation (CON) on CH₄ emission parameters and ruminal fermentation. Multiparous cows (Friesian × Jersey, average ± SD; 3.8 ± 0.8 yr old; 519 ± 50 kg BW, 211 ± 10 DIM; n = 32) were blocked by lactation number and then randomly allocated to receive (on a DM basis) the following: 0 kg/d of concentrate (CON), 5 kg/d of STA, 5 kg/d of FIB, or 5 kg/d of MIX (2.5 kg each of STA and FIB). After at least 14 d of diet adaptation, the CH₄ emissions, DMI, and milk production were measured in respiration chambers for 48 h, ruminal fluid was collected after chamber measurements, and short-chain fatty acids (SCFA) were analyzed. Daily milk production was recorded, and milk samples were analyzed for milk composition. The CH₄ production (g/d) trended to be greater in concentrate-fed cows than CON cows. The CH₄ yield was similar across the dietary treatments. The CH₄ per kilogram of TS (calculated as fat plus protein production) was 22% less and trended to be up to 24% less per kilogram of fat- and protein-corrected milk (FPCM) in concentrate-fed cows than CON cows mainly due to an increase in FPCM production. After morning and afternoon feeding, the time to reach peak CH₄ emissions was less in concentrate-fed cows than CON cows. The variation in the CH₄ emission rate after morning feeding was greater in concentrate-supplemented cows than CON cows. The ruminal SCFA profile was similar among treatments. Regardless of the dietary treatment, the acetate plus butyrate to propionate plus valerate ratio was positively associated with CH₄ yield, FPCM, and peak CH₄ emission rate and negatively associated with the CH₄ decline rate after peak emissions. High-starch, high-fiber, or mixed (50:50) concentrates trended to reduce the CH₄ per unit of FPCM by up to 24% in lactating cows eating fresh herbage as a basal diet.

This study aimed at determining the extent to which the deviation of daily total metabolizable energy (ME_t) requirements of individual cows from the metabolizable energy (ME) supplied per cow (DME) varied throughout the production season in a pasture-based dairy farm using proximal hyperspectral sensing (PHS). Herd tests, milk production, herbage and feed allocation data were collected during the 2016–2017 and 2017–2018 production seasons at Dairy 1, Massey University, New Zealand. Herbage ME was determined from canopy reflectance acquired using PHS. Orthogonal polynomials were used to model lactation curves for yields of milk, fat, protein and live weights of cows. Daily dietary ME supplied per cow to the herd and ME requirements of cows were calculated using the Agricultural Food and Research Council (AFRC) energy system of 1993. A linear model including the random effects of breed and cow was used to estimate variance components for DME. Daily herd ME_t estimated requirements oscillated between a fifth above or below the ME supplied throughout the production seasons. DME was mostly explained by observations made within a cow rather than between cows or breeds. Having daily estimates of individual cow requirements for ME_t in addition to ME dietary supply can potentially contribute to achieving a more precise fit between supply and demand for feed in a pasture-based dairy farm by devising feeding strategies aimed at reducing DME.

The objective of this study was to assess the relative importance of herbage nutritive value (NV), herbage quantity and climate-related factors in determining daily performance per cow in a pasture-based dairy farm. Data on milk production, live weight, body condition score, weather, herbage NV and herbage quantity were regularly collected from August 2016 to April 2017 and from July 2017 to April 2018 at Dairy 1, Massey University, Palmerston North, New Zealand. Data were analyzed using multiple linear regression. Results indicated herbage NV was of higher relative importance in explaining the variation in performance per cow than herbage quantity and climate factors. The relative importance of the interaction between herbage metabolizable energy (ME) and crude protein (CP) on explaining variation in yields of milk, fat and protein was high (0.11 ≤ R² ≤ 0.15). Herbage ME was of high relative importance in determining milk urea and body condition score, while neutral detergent fiber was a key driver of milk urea and liveweight (0.12 ≤ R² ≤ 0.16). The quantity of herbage supplied at Dairy 1 might have been high enough to not limit cow performance. Developing feeding strategies aimed at improving the efficiency of cow feeding by exploiting the daily variation in herbage NV to better match supply and demand of nutrients may be useful to improve the overall performance per cow of pasture-based dairy farms.

This study describes variation of nutritive value (NV), measured by proximal hyperspectral sensing (PHS), of herbage offered to cows on a pasture‐based dairy farm. Metabolisable energy (ME), crude protein (CP), neutral detergent fibre (NDF), acid detergent fibre (ADF) and digestible organic matter digestibility (DOMD) were predicted from PHS of canopies during the 2016–17 and 2017–18 production seasons at Dairy 1, Massey University. Mixed linear models including production seasons and months within production seasons as fixed effects and paddocks as random effects were used to estimate least‐squares means of herbage NV. Estimates of variance components were obtained with the same models but considering all factors as random effects. A simulation model, e‐Cow, was used to explore the effects of herbage NV on dairy cow performance. The NV of herbage offered to cows dropped during summer compared to spring or autumn ( P < 0.05). The contribution of the month within production season to NV variance (42.7%) was higher than production season (13.1%) or paddock (10.7%). Cow performance can be limited by ME and NDF, while CP is often found in excess of requirements. The use of canopy PHS for rapid herbage NV measurement can potentially be used to optimise grazing in pasture‐based dairy farms.