An optimization model of a New Zealand dairy farm

Combined mechanistic and genetic modelling to benchmark body reserve traits as proxies of dairy cows' lifetime efficiency in grass-based production systems

Improving lifetime lactation efficiency of dairy cows by selection is difficult due to the complexity of this trait and the existence of genotype-by-environment interactions. This study aimed at assessing the relevance of traits derived from body reserves as lifetime efficiency indicators under contrasting nutritional environments. Given the absence of large-scale datasets covering a panel of feeding regimes, phenotypes were simulated for populations of 20 000 dairy cows using a mechanistic bioenergetic model. Ten phenotypes were computed for third-lactation cows. Analysed phenotypes comprised total milk production, lactation efficiency, BW at calving (BWcalv), DM intake (DMI) and interval between first insemination and conception. Five traits described levels and changes of body reserves at different periods during lactation. Lifetime lactation efficiency was computed for all cows (Life_Eff). Three nutritional environments were defined considering a grass-based production system with seasonal calving: a high non-limiting scenario (HS) mimicking ad libitum access to feed and two limiting environments with moderate (MS) and low (LS) feed offer. Variance components were estimated for all traits within and between environments using REML. Heritabilities estimated for milk production, lactation efficiency, BWcalv and DMI were moderate in the different environments (0.27-0.35 ± 0.04). The heritability of body reserve levels and dynamics were moderate in the HS and MS scenarios (0.23-0.30 ± 0.03) and lower in the LS scenario (0.14-0.25 ± 0.03). The heritability of Life_Eff was low in the HS environment (0.07 ± 0.01) and slightly increased in the limiting environments. All genetic correlations estimated between environments were moderate to high (≥0.66 ± 0.07), suggesting low to moderate genotype-by-environment interactions. Estimated genetic correlations were moderate between Life_Eff and body reserve levels (from 0.39 to 0.51 ± 0.08) and moderate but negative between Life_Eff and change in body reserves traits (-0.27 to -0.37 ± 0.09) in the HS environment. The genetic correlations between Life_Eff and body reserve levels increased to higher values in the limiting environments. In contrast, genetic correlations between Life_Eff and the changes in body reserves were closer to zero. In conclusion, this study showed that body reserve levels were relevant proxies of lifetime irrespective of the environment. In contrast, changes in body reserves that reflected energy mobilisation in early lactation were less informative about lifetime efficiency in environments with severe feed restrictions.

Implementation of Technical and Technological Progress in Dairy Production

The involvement of people and technical devices is a characteristic feature of technological processes in agriculture. Human access to modernized and more efficient technical equipment determines the differentiation of the proportions of the contributions of human labor and technical equipment to the implementation of production technology on farms. Taking into account the data on manual and machine work inputs, the methodology of determining the technological index level (TL) was presented. The aim of the present study was to present the scope of use of the technological index level to assess the effects of technological progress in the dairy production system, with particular emphasis on cow milking. For the value range of the technological index level (0–100%), changes in the milkman’s work efficiency were presented based on research carried out on farms equipped with milking equipment at different levels of technical advancement. Moreover, the course of changes in electricity and water consumption per liter of milk was determined in association with the technological index level. The issue of simultaneous implementation of various forms of progress was developed based on the example of milking cows with a milking robot. Five categories (ranges) of cows’ milk yield were distinguished and compared with the current yields of cows in the European Union. On this basis, a discussion was initiated on the factors that facilitate and limit the implementation of technical and technological progress in dairy production.

The assessment of supplementation requirements of grazing ruminants using nutrition models

This paper was aimed to summarize known concepts needed to comprehend the intricate interface between the ruminant animal and the pasture when predicting animal performance, acknowledge current efforts in the mathematical modeling domain of grazing ruminants, and highlight current thinking and technologies that can guide the development of advanced mathematical modeling tools for grazing ruminants. The scientific knowledge of factors that affect intake of ruminants is broad and rich, and decision-support tools (DST) for modeling energy expenditure and feed intake of grazing animals abound in the literature but the adequate predictability of forage intake is still lacking, remaining a major challenge that has been deceiving at times. Despite the mathematical advancements in translating experimental research of grazing ruminants into DST, numerous shortages have been identified in current models designed to predict intake of forages by grazing ruminants. Many of which are mechanistic models that rely heavily on preceding mathematical constructions that were developed to predict energy and nutrient requirements and feed intake of confined animals. The data collection of grazing (forage selection, grazing behavior, pasture growth/regrowth, pasture quality) and animal (nutrient digestion and absorption, volatile fatty acids production and profile, energy requirement) components remains a critical bottleneck for adequate modeling of forage intake by ruminants. An unresolved question that has impeded DST is how to assess the quantity and quality, ideally simultaneously, of pasture forages given that ruminant animals can be selective. The inadequate assessment of quantity and quality has been a hindrance in assessing energy expenditure of grazing animals for physical activities such as walking, grazing, and forage selection of grazing animals. The advancement of sensors might provide some insights that will likely enhance our understanding and assist in determining key variables that control forage intake and animal activity. Sensors might provide additional insights to improve the quantification of individual animal variation as the sensor data are collected on each subject over time. As a group of scientists, however, despite many obstacles in animal and forage science research, we have thrived, and progress has been made. The scientific community may need to change the angle of which the problem has been attacked, and focus more on holistic approaches.

Methane and nitrous oxide emissions from Canadian dairy farms and mitigation options: An updated review

This review examined methane (CH4) and nitrous oxide (N2O) mitigation strategies for Canadian dairy farms. The primary focus was research conducted in Canada and cold climatic regions with similar dairy systems. Meta-analyses were conducted to assess the impact of a given strategy when sufficient data were available. Results indicated that options to reduce enteric CH4from dairy cows were increasing the dietary starch content and dietary lipid supplementation. Replacing barley or alfalfa silage with corn silage with higher starch content decreased enteric CH4per unit of milk by 6%. Increasing dietary lipids from 3% to 6% of dry matter (DM) reduced enteric CH4yield by 9%. Strategies such as nitrate supplementation and 3-nitrooxypropanol additive indicated potential for reducing enteric CH4by about 30% but require extensive research on toxicology and consumer acceptance. Strategies to reduce emissions from manure are anaerobic digestion, composting, solid–liquid separation, covering slurry storage and flaring CH4, and reducing methanogen inoculum by complete emptying of slurry storage at spring application. These strategies have potential to reduce emissions from manure by up to 50%. An integrated approach of combining strategies through diet and manure management is necessary for significant GHG mitigation and lowering carbon footprint of milk produced in Canada.

Optimising the interrelationship between intake per cow and intake per hectare

Production and profit in grazed systems remain inherently constrained by the fundamental trade-off between maximising individual herbage intake and pasture utilisation. The primary objective of this paper is to investigate the trade-off between herbage intake per cow and intake per hectare, from the perspective of economic optimisation, for an intensive pasture-based dairy farm in New Zealand (NZ). A detailed optimisation model of a dairy farm is applied, to allow the complex animal–plant–supplement dynamics underlying this relationship to be explicitly considered. Model output confirmed the existence of the fundamental inverse relationship between individual cow intake and herbage utilisation, which arises from the underlying biophysical dynamics within a grazing system, in the context of pasture-based NZ dairy farms. Results indicated that profitable management relies on increasing total pasture eaten (grazing plus pasture silage harvested on farm) through the use of a relatively high stocking rate and moderate levels of pasture intake per cow. Indeed, for 450 kg liveweight cows, optimal pasture intake per cow is 5 t dry matter (DM) per cow for per hectare intakes of 12–16 t DM/ha and 4.5 t DM/cow for a per hectare intake of 17 t DM/ha. Thus, a goal to maximise both individual intake and herbage utilisation in grazing systems is misinformed; it is the latter that is principally important to farm profit. Indeed, seeking to maximise both simultaneously is not possible, and trying to maximise individual intake can impose an enormous cost. However, while high herbage utilisation increases profit, this can also be associated with greater nutrient leaching, reinforcing the importance of considering the environmental impacts of grazing management.

Cost-effective mitigation of greenhouse gas emissions from different dairy systems in the Waikato region of New Zealand

The New Zealand dairy industry produces approximately 17% of this country's total greenhouse gas emissions (GHG-e) and it is also this nation's largest export industry. The industry needs to reduce GHG-e under proposed policy directives and for ongoing market security. Given these pressures, there is the need to identify cost-effective management strategies to reduce on-farm GHG-e. The objective of this study was to investigate how the management of dairy farms in the Waikato region of New Zealand could change to minimise the abatement costs associated with GHG-e mitigation. Three typical farm systems importing low (less than 10%), medium (10-20%), and high (more than 20%) amounts of supplement are modelled using a non-linear optimisation model. A reduction in nitrogen fertiliser application was the production factor that changed the most to achieve the cap in all of the simulated systems, followed by a reduction in stocking rate. With the prices used in this study, decreasing farming intensity by reducing nitrogen fertiliser by 21-42% and stocking rate by 8-10% represented a cost of $68-$119/ha and a production reduction of 54-117 kg MS/ha for the three systems studied. Improving reproductive performance proved to be effective in reducing GHG-e, allowing for fewer replacement cows to be supported. However, it did not have a significant effect on profit when emissions were unconstrained. Nitrification inhibitors and stand-off pads were not identified as useful mitigation options, given their high cost relative to de-intensification.