Attempts to lower the environmental footprint of milk production needs a sound understanding of the genetic and nutritional basis of methane (CH4) emissions from the dairy production systems. This in turn requires accurate and reliable techniques for the measurement of CH4 output from individual cows. Many of the available measurement techniques so far are either slow, expensive, labor intensive and are unsuitable for large-scale individual animal measurements. The main objectives of this study were to examine and validate a non-invasive individual cow CH4 measurement system that is based on photoacoustic IR spectroscopy (PAS) technique implemented in a portable gas analysis equipment (F10), referred to as PAS-F10 method and to estimate the magnitude of between-animal variations in CH4 output traits. Data were collected from 115 Nordic Red cows of the Minkiö experimental dairy farm, at the Natural Resources Institute Finland (Luke). Records on continuous daily measurements of CH4, milk yield, feed intake and BW measurements over 2 years period were compiled for data analysis. The daily CH4 output was calculated using carbon dioxide as a tracer method. Estimates from the non-invasive PAS-F10 technique were then tested against open-circuit indirect respiration calorimetric chamber measurements and against estimates from other widely used prediction models. Concordance analysis was used to establish agreement between the chamber and PAS-F10 methods. A linear mixed model was used for the analysis of the large continuous data. The daily CH4 output of cows was 555 l/day and ranged from 330 to 800 l/day. Dry matter intake, level of milk production, lactation stage and diurnal variation had significant effects on daily CH4 output. Estimates of the daily CH4 output from PAS-F10 technique compared relatively well with the other techniques. The concordance correlation coefficient between combined weekly CH4 output estimates of PAS-F10 and chamber was 0.84 with lower and upper confidence limits of 0.65 and 0.93, respectively. Similarly, when chamber CH4 measurements were predicted from PAS-F10 measurements, the mean of two separate weekly PAS-F10 measurements gave the lowest prediction error variance than either of the separate weekly PAS-F10 measurements alone. This suggests that every other week PAS-F10 measurements when combined would improve the estimation of CH4 output with PAS-F10 technique. The repeatability of daily CH4 output from PAS-F10 technique ranged from 0.40 to 0.46 indicating that some between-animal variation exist in CH4 output traits.

In the global South, dairying is often promoted as a means of poverty alleviation. Yet, under conditions of climate warming, little is known regarding the ability of small-scale dairy producers to maintain production and/or the robustness of possible adaptation options in meeting the challenges presented, particularly heat stress. The authors created a simple, deterministic model to explore the influence of breed and heat stress relief options on smallholder dairy farmers in Odisha, India. Breeds included indigenous Indian (non-descript), low-grade Jersey crossbreed and high-grade Jersey crossbreed. Relief strategies included providing shade, fanning and bathing. The impact of predicted critical global climate parameters, a 2°C and 4°C temperature rise were explored. A feed price scenario was modelled to illustrate the importance of feed in impact estimation. Feed costs were increased by 10% to 30%. Across the simulations, high-grade Jersey crossbreeds maintained higher milk yields, despite being the most sensitive to the negative effects of temperature. Low-capital relief strategies were the most effective at reducing heat stress impacts on household income. However, as feed costs increased the lower-grade Jersey crossbreed became the most profitable breed. The high-grade Jersey crossbreed was only marginally (4.64%) more profitable than the indigenous breed. The results demonstrate the importance of understanding the factors and practical trade-offs that underpin adaptation. The model also highlights the need for hot-climate dairying projects and programmes to consider animal genetic resources alongside environmentally sustainable adaptation measures for greatest poverty impact.