Abstract: This study investigates the use of bioenergy for achieving stringent climate stabilization targets and it analyzes the economic drivers behind the choice of bioenergy technologies. We apply the integrated assessment framework REMIND-MAgPIE to show that bioenergy, particularly if combined with carbon capture and storage (CCS) is a crucial mitigation option with high deployment levels and high technology value. If CCS is available, bioenergy is exclusively used with CCS. We find that the ability of bioenergy to provide negative emissions gives rise to a strong nexus between biomass prices and carbon prices. Ambitious climate policy could result in bioenergy prices of 70 $/GJ (or even 430 $/GJ if bioenergy potential is limited to 100 EJ/year), which indicates a strong demand for bioenergy. For low stabilization scenarios with BECCS availability, we find that the carbon value of biomass tends to exceed its pure energy value. Therefore, the driving factor behind investments into bioenergy conversion capacities for electricity and hydrogen production are the revenues generated from negative emissions, rather than from energy production. However, in REMIND modern bioenergy is predominantly used to produce low-carbon fuels, since the transport sector has significantly fewer low-carbon alternatives to biofuels than the power sector. Since negative emissions increase the amount of permissible emissions from fossil fuels, given a climate target, bioenergy acts as a complement to fossils rather than a substitute. This makes the short-term and long-term deployment of fossil fuels dependent on the long-term availability of BECCS.

Abstract: A retrospective study on seasonal variations in the characteristics of cow’s milk and temperature-humidity index (THI) relationship was conducted on bulk milk data collected from 2003 to 2009. The THI relationship study was carried out on 508 613 bulk milk data items recorded in 3328 dairy farms form the Lombardy region, Italy. Temperature and relative humidity data from 40 weather stations were used to calculate THI. Milk characteristics data referred to somatic cell count (SCC), total bacterial count (TBC), fat percentage (FA%) and protein percentage (PR%). Annual, seasonal and monthly variations in milk composition were evaluated on 656 064 data items recorded in 3727 dairy farms. The model highlighted a significant association between the year, season and month, and the parameters analysed (SCC, TBC, FA%, PR%). The summer season emerged as the most critical season. Of the summer months, July presented the most critical conditions for TBC, FA% and PR%, (52 054 ± 183 655, 3.73% ± 0.35% and 3.30% ± 0.15%, respectively), and August presented higher values of SCC (369 503 ± 228 377). Each milk record was linked to THI data calculated at the nearest weather station. The analysis demonstrated a positive correlation between THI and SCC and TBC, and indicated a significant change in the slope at 57.3 and 72.8 maximum THI, respectively. The model demonstrated a negative correlation between THI and FA% and PR% and provided breakpoints in the pattern at 50.2 and 65.2 maximum THI, respectively. The results of this study indicate the presence of critical climatic thresholds for bulk tank milk composition in dairy cows. Such indications could facilitate the adoption of heat management strategies, which may ensure the health and production of dairy cows and limit related economic losses.

Abstract: The use of biophysical models in agroecology has increased in the last few decades for two main reasons: the need to formalize empirical knowledge and the need to disseminate model-based decision support for decision makers (such as farmers, advisors, and policy makers). The first has encouraged the development and use of mathematical models to enhance the efficiency of field research through extrapolation beyond the limits of site, season, and management. The second reflects the increasing need (by scientists, managers, and the public) for simulation experimentation to explore options and consequences, for example, future resource use efficiency (i.e., management in sustainable intensification), impacts of and adaptation to climate change, understanding market and policy responses to shocks initiated at a biophysical level under increasing demand, and limited supply capacity. Production concerns thus dominate most model applications, but there is a notable growing emphasis on environmental, economic, and policy dimensions. Identifying effective methods of assessing model quality and performance has become a challenging but vital imperative, considering the variety of factors influencing model outputs. Understanding the requirements of stakeholders, in respect of model use, logically implies the need for their inclusion in model evaluation methods. We reviewed the use of metrics of model evaluation, with a particular emphasis on the involvement of stakeholders to expand horizons beyond conventional structured, numeric analyses. Two major topics are discussed: (1) the importance of deliberative processes for model evaluation, and (2) the role computer-aided techniques may play to integrate deliberative processes into the evaluation of agroecological models. We point out that (i) the evaluation of agroecological models can be improved through stakeholder follow-up, which is a key for the acceptability of model realizations in practice, (ii) model credibility depends not only on the outcomes of well-structured, numerically based evaluation, but also on less tangible factors that may need to be addressed using complementary deliberative processes, (iii) comprehensive evaluation of simulation models can be achieved by integrating the expectations of stakeholders via a weighting system of preferences and perception, (iv) questionnaire-based surveys can help understand the challenges posed by the deliberative process, and (v) a benefit can be obtained if model evaluation is conceived in a decisional perspective and evaluation techniques are developed at the same pace with which the models themselves are created and improved. Scientific knowledge hubs are also recognized as critical pillars to advance good modeling practice in relation to model evaluation (including access to dedicated software tools), an activity which is frequently neglected in the context of time-limited framework programs.