• Energy Research

European livestock agriculture is extraordinarily diverse, and so are the challenges it faces. This diversity has contributed to the development of a fragmented set of research communities. As a result, livestock research is often under-represented at policy level, despite its high relevance for the environment and food security. Understanding livestock systems and how they can sustainably adapt to global change requires inputs across research areas, including grasslands, nutrition, health, welfare and ecology. It also requires experimental researchers, modellers and stakeholders to work closely together. Networks and capacity building structures are vital to enable livestock research to meet the challenges of climate change. They need to maintain shared resources and provide non-competitive arenas to share and synthesize results for policy support. Long term strategic investment is needed to support such structures. Their leadership requires very different skills to those effective in scientific project coordination.

European livestock agriculture is extraordinarily diverse, and so are the challenges it faces. This diversity has contributed to the development of a fragmented set of research communities. As a result, livestock research is often under-represented at policy level, despite its high relevance for the environment and food security. Understanding livestock systems and how they can sustainably adapt to global change requires inputs across research areas, including grasslands, nutrition, health, welfare and ecology. It also requires experimental researchers, modellers and stakeholders to work closely together. Networks and capacity building structures are vital to enable livestock research to meet the challenges of climate change. They need to maintain shared resources and provide non-competitive arenas to share and synthesize results for policy support. Long term strategic investment is needed to support such structures. Their leadership requires very different skills to those effective in scientific project coordination.

Abstract Several process-based models for simulating the growth of perennial grasses have been developed but few include the simulation of regrowth. The model CATIMO simulates the primary growth of timothy ( Phleum pratense L.), an important perennial forage grass species in northern regions of Europe and North America. Our objective was to further develop the model CATIMO to simulate timothy regrowth using the concept of reserve-dependent growth. The performance of this modified CATIMO model in simulating leaf area index (LAI), biomass dry matter (DM) yield, and N uptake of regrowth was assessed with data from four independent field experiments in Norway, Finland, and western and eastern Canada using an approach that combines graphical comparison and statistical analysis. Biomass DM yield and N uptake of regrowth were predicted at the same accuracy as primary growth with linear regression coefficients of determination between measured and simulated values greater than 0.79, model simulation efficiencies greater than 0.78, and normalized root mean square errors (14–30% for biomass and 24–34% for N uptake) comparable with the coefficients of variation of measured data (1–21% for biomass and 1–25% for N uptake). The model satisfactorily simulated the regrowth LAI but only up to a value of about 4.0. The modified CATIMO model with its capacity to simulate regrowth provides a framework to simulate perennial grasses with multiple harvests, and to explore management options for sustainable grass production under different environmental conditions.

Abstract Several process-based models for simulating the growth of perennial grasses have been developed but few include the simulation of regrowth. The model CATIMO simulates the primary growth of timothy ( Phleum pratense L.), an important perennial forage grass species in northern regions of Europe and North America. Our objective was to further develop the model CATIMO to simulate timothy regrowth using the concept of reserve-dependent growth. The performance of this modified CATIMO model in simulating leaf area index (LAI), biomass dry matter (DM) yield, and N uptake of regrowth was assessed with data from four independent field experiments in Norway, Finland, and western and eastern Canada using an approach that combines graphical comparison and statistical analysis. Biomass DM yield and N uptake of regrowth were predicted at the same accuracy as primary growth with linear regression coefficients of determination between measured and simulated values greater than 0.79, model simulation efficiencies greater than 0.78, and normalized root mean square errors (14–30% for biomass and 24–34% for N uptake) comparable with the coefficients of variation of measured data (1–21% for biomass and 1–25% for N uptake). The model satisfactorily simulated the regrowth LAI but only up to a value of about 4.0. The modified CATIMO model with its capacity to simulate regrowth provides a framework to simulate perennial grasses with multiple harvests, and to explore management options for sustainable grass production under different environmental conditions.

Increasing demand of fossil-free fuels in the transport sector drives towards using new biomass sources in fuel production. Municipal waste as a substrate is used in many countries in biomethane production, but the amount of waste can cover only a small portion of the fuel used. In Europe, the new renewable energy directive (RED II) was established December 2018 to ensure the sustainability of renewable fuels. The directive includes typical and default greenhouse gas (GHG) emissions for several potential substrates, such as biogas from manure or maize silage, which the biogas plants can use to verify their emissions directly or to calculate their emissions using the methods provided. However, such default value for grass silage as biogas substrate is lacking. We defined the conditions needed to fulfil the sustainability criteria of the directive when producing biomethane for vehicle fuel using grass silage as the feedstock in Finland. The emission reduction targets are not easy to achieve in Finland when using grass cultivated exclusively for energy production. The reduction targets can be achieved, however, if the grass is cultivated due to an improved crop rotation, where the grass is co-digested with manure and/or energy sources with zero emissions for the process can be applied.

Increasing demand of fossil-free fuels in the transport sector drives towards using new biomass sources in fuel production. Municipal waste as a substrate is used in many countries in biomethane production, but the amount of waste can cover only a small portion of the fuel used. In Europe, the new renewable energy directive (RED II) was established December 2018 to ensure the sustainability of renewable fuels. The directive includes typical and default greenhouse gas (GHG) emissions for several potential substrates, such as biogas from manure or maize silage, which the biogas plants can use to verify their emissions directly or to calculate their emissions using the methods provided. However, such default value for grass silage as biogas substrate is lacking. We defined the conditions needed to fulfil the sustainability criteria of the directive when producing biomethane for vehicle fuel using grass silage as the feedstock in Finland. The emission reduction targets are not easy to achieve in Finland when using grass cultivated exclusively for energy production. The reduction targets can be achieved, however, if the grass is cultivated due to an improved crop rotation, where the grass is co-digested with manure and/or energy sources with zero emissions for the process can be applied.