To what extent is climate change adaptation a novel challenge for agricultural modellers?

Modelling is key to adapting agriculture to climate change (CC), facilitating evaluation of the impacts and efficacy of adaptation measures, and the design of optimal strategies. Although there are many challenges to modelling agricultural CC adaptation, it is unclear whether these are novel or, whether adaptation merely adds new motivations to old challenges. Here, qualitative analysis of modellers' views revealed three categories of challenge: Content, Use, and Capacity. Triangulation of findings with reviews of agricultural modelling and Climate Change Risk Assessment was then used to highlight challenges specific to modelling adaptation. These were refined through literature review, focussing attention on how the progressive nature of CC affects the role and impact of modelling. Specific challenges identified were: Scope of adaptations modelled, Information on future adaptation, Collaboration to tackle novel challenges, Optimisation under progressive change with thresholds, and Responsibility given the sensitivity of future outcomes to initial choices under progressive change.

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.

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.