Understanding the potential of existing models to characterize animal health conditions and estimate greenhouse gas emissions

The primary objective of this study was to assess the status and priorities for future development in modelling of the impacts of animal health on greenhouse gas (GHG) emissions. It also aimed to facilitate communication between experimental researchers and modellers by defining a list of parameters that are needed to model livestock health and disease, and the impact of health conditions on GHG emissions. The summary presented here provides a brief overview of ongoing work, which the L2.1/L2.2 partners, with support from the Global Research Alliance Animal Health Network (GRA AHN), is currently developing into a paper for publication in a peer reviewed journal

A review of interventions and parameters used to address milk quality in eastern and southern Africa

In the last two decades, there has been abundant research directed at improving milk quality and safety all around the world. While some studies limit milk quality to a limited number of bacteriological parameters, it is not unusual to come across papers where quality is assumed or not quantified. The relevant information on milk quality is rather scattered in sub-Saharan Africa. In this study, we conducted a comprehensive review of studies published in eastern and southern Africa in the past two decades, referring to cow milk quality associated with an intervention. This study reports a systematic categorization of the quality parameters related to various interventions where quality was referred to directly and indirectly. It also shows the variation in number and type of parameters used in assessing milk quality in different countries. The microbial quality of milk was the most common quality parameter examined (19 studies), followed by the milk composition (n = 7), then acidity (n = 6) and adulteration with water (n = 4). However, there was no consistency in the quality parameters used to indicate a change in quality associated with these interventions. It is advisable that future studies use the list of parameters presented in this study to build foundation for comparative assessments of change in milk quality for the respective intervention categories.</p

How do farm models compare when estimating greenhouse gas emissions from dairy cattle production?

The European Union Effort Sharing Regulation (ESR) will require a 30% reduction in greenhouse gas (GHG) emissions by 2030 compared with 2005 from the sectors not included in the European Emissions Trading Scheme, including agriculture. This will require the estimation of current and future emissions from agriculture, including dairy cattle production systems. Using a farm-scale model as part of a Tier 3 method for farm to national scales provides a more holistic and informative approach than IPCC (2006) Tier 2 but requires independent quality control. Comparing the results of using models to simulate a range of scenarios that explore an appropriate range of biophysical and management situations can support this process by providing a framework for placing model results in context. To assess the variation between models and the process of understanding differences, estimates of GHG emissions from four farm-scale models (DairyWise, FarmAC, HolosNor and SFARMMOD) were calculated for eight dairy farming scenarios within a factorial design consisting of two climates (cool/dry and warm/wet)×two soil types (sandy and clayey)×two feeding systems (grass only and grass/maize). The milk yield per cow, follower:cow ratio, manure management system, nitrogen (N) fertilisation and land area were standardised for all scenarios in order to associate the differences in the results with the model structure and function. Potential yield and application of available N in fertiliser and manure were specified separately for grass and maize. Significant differences between models were found in GHG emissions at the farm-scale and for most contributory sources, although there was no difference in the ranking of source magnitudes. The farm-scale GHG emissions, averaged over the four models, was 10.6 t carbon dioxide equivalents (CO2e)/ha per year, with a range of 1.9 t CO2e/ha per year. Even though key production characteristics were specified in the scenarios, there were still significant differences between models in the annual milk production per ha and the amounts of N fertiliser and concentrate feed imported. This was because the models differed in their description of biophysical responses and feedback mechanisms, and in the extent to which management functions were internalised. We conclude that comparing the results of different farm-scale models when applied to a range of scenarios would build confidence in their use in achieving ESR targets, justifying further investment in the development of a wider range of scenarios and software tools

Effect of supplementing sheep diets with macroalgae species on in vivo nutrient digestibility, rumen fermentation and blood amino acid profile

In this study, a brown macroalgae species, Saccharina latissima, processed to increase its protein concentration, and a red macroalgae species, Porphyra spp., were used to evaluate their in vivo digestibility, rumen fermentation and blood amino acid concentrations. Four castrated rams were used, whose diets were supplemented with a protein-rich fraction of S. latissima, a commercial Porphyra spp. and soybean meal (SBM). Our results show that the protein digestibility of a diet with S. latissima extract was lower (0.55) than those with Porphyra spp. (0.64) and SBM (0.66). In spite of the higher nitrogen (N) intake of diets containing Porphyra spp. and SBM (20.9 and 19.8 g N/day, respectively) than that with S. latissima (18.6 g N/day), the ratio of N excreted in faeces to total N intake was significantly higher in the diet with S. latissima than those with Porphyra spp. and SBM. This reflects that the utilization of protein in S. latissima was impaired, possibly due to reduced microbial activity. The latter statement is corroborated by lower volatile fatty acid composition (25.6, 54.8 and 100 mmol/l for S. latissima, Porphyra spp. and SBM, respectively) and a non-significant tendency for lower ammonia concentration observed in diets with S. latissima and Porphyra spp. compared to SBM. It is important to note that the S. latissima used in this trial was rinsed during processing to remove salt. This process potentially also removes other water-soluble compounds, such as free amino acids, and may have increased the relative fraction of protein resistant to rumen degradation and intestinal absorption. Furthermore, the phlorotannins present in macroalgae may have formed complexes with protein and fibre, further limiting their degradability in rumen and absorption in small intestines. We recommend that further studies explore the extent to which processing of macroalgae affects its nutritive properties and rumen degradability, in addition to studies to measure the intestinal absorption of these macroalgae species

Does collaborative farm-scale modelling address current challenges and future opportunities?

Resources required increasing, resources available decreasingFarm-scale modellers will need to make strategic decisionsSingle-owner modelsMay continue with additional resourcesRisk of ‘succession’ problemCommunity modelling is an alternativeNeed to continue building a community of farm modellersThe results will be published as a peer-reviewed article

Challenges and research gaps in the area of integrated climate change risk assessment for European agriculture and food security

Priorities in addressing research gaps and challenges should follow the order of im­por­tance, which in itself would be a matter of defining goals and metrics of importance, e.g. the extent, impact and likelihood of occurrence. For improving assessments of cli­mate change impacts on agriculture for achieving food security and other sustainable develop­ment goals across the European continent, the most important research gaps and challen­ges appear to be the agreement on goals with a wide range of stakeholders from policy, science, producers and society, better reflection of political and societal prefer­ences in the modelling process, and the reflection of economic decisions in farm manage­ment within models. These and other challenges could be approached in phase 3 of MACSUR

Stakeholder engagement and the perceptions of researchers: how agricultural modellers view challenges to communication

Conference presentation PD

How does the projected climate change impact on dry matter yields, greenhouse gas emissions and economics in Norwegian dairy farming systems

Future climate projections showing increases in the air temperature and the number of rainydays in Norway will require changes in feed-base to adapt to climate change. A large number ofstudies have used single models to quantify the effects of management-related changes onproductivity, greenhouse gas (GHG) emissions and profitability. Here, we combined four models:BASGRA and CSM-CERES-Wheat, HolosNor and JORDMOD to estimate the impacts of projectedclimate conditions on grass and wheat dry matter (DM) yields, farm level GHG emissions andprofits. Simulations were carried out for baseline (1961-1990) and future (2046-2065) climateconditions projected based on two climate models and for production conditions with andwithout a milk quota. We compared four locations with different climate conditions for low, andmedian and high yielding years. The spring wheat grain DM yields simulated for the sameweather conditions within each climate projection varied between 2200 kg and 6800 kg DM perha. The GHG emissions intensities (kilogram carbon dioxide equivalent: kgCO2e emissions per kgfat and protein corrected milk: FPCM) varied between 0.82 kg and 1.25 kg CO2e per kg FPCM,with the lowest and highest emissions found in central Norway and south-east Norway,respectively. The farm profitability expressed by total national land rents varied from 1900 millionNorwegian krone (NOK) for median yields under baseline climate conditions up to 3900 millionNOK for median yields under future projected climate conditions. The projected future changein climate evaluated here decelerated the production of GHG emissions from dairy production inthe locations assessed due to higher milk yields per cow and partly to higher crop yields

Understanding the potential of existing models to characterize animal health conditions and estimate greenhouse gas emissions

The primary objective of this study was to assess the status and priorities for future development in modelling of the impacts of animal health on greenhouse gas (GHG) emissions. It also aimed to facilitate communication between experimental researchers and modellers by defining a list of parameters that are needed to model livestock health and disease, and the impact of health conditions on GHG emissions. The summary presented here provides a brief overview of ongoing work, which the L2.1/L2.2 partners, with support from the Global Research Alliance Animal Health Network (GRA AHN), is currently developing into a paper for publication in a peer reviewed journal