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Hutchings, N., & Kipling, R. (2014). Inventory of farm-scale models within LiveM (Vol. 3).
Abstract: The aim of WP3 is to improve the assessment of the impact of climate change on livestock and grassland systems at the farm-scale. The first step in this process is to understand the current state of the art in farm-scale modelling, and the resources available within the MACSUR knowledge hub. Here, an inventory of the farm-scale models available within LiveM is presented, along with a summary of the types of model represented. Thirteen farm-scale models were identified, three of which focus on environmental aspects of farm systems (GHG emissions etc.) and ten of which focus on management strategies (productivity, economics etc.).
Keywords: LiveM;
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Wallach, D., Thorburn, P., Asseng, S., Challinor, A. J., Ewert, F., Jones, J. W., et al. (2016). Overview paper on comprehensive framework for assessment of error and uncertainty in crop model predictions (Vol. 8).
Abstract: Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. Several ways of quantifying prediction uncertainty have been explored in the literature, but there have been no studies of how the different approaches are related to one another, and how they are related to some overall measure of prediction uncertainty. Here we show that all the different approaches can be related to two different viewpoints about the model; either the model is treated as a fixed predictor with some average error, or the model can be treated as a random variable with uncertainty in one or more of model structure, model inputs and model parameters. We discuss the differences, and show how mean squared error of prediction can be estimated in both cases. The results can be used to put uncertainty estimates into a more general framework and to relate different uncertainty estimates to one another and to overall prediction uncertainty. This should lead to a better understanding of crop model prediction uncertainty and the underlying causes of that uncertainty. This study was published as (Wallach et al. 2016)
Keywords: MACSUR_ACK; CropM
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Calanca, P. (2016). Modelling the impacts of seasonal drought on herbage growth under climate change (Vol. 8).
Abstract: Conference presentation PDF
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Biewald, A. (2016). Representative Agricultural Pathways for Europe (Vol. 9 C6 -).
Abstract: Agricultural aspects have been covered in the scenario process on shared socio-economic pathways (SSPs), but only to a limited extent. In order to analyze the future dynamics of agricultural development they need to be complemented and specified by Representative Agricultural Pathways (RAPs), which cover different aspects of agricultural development as for example European agricultural and domestic policy, environmental policies, different livestock management systems, cropping systems or irrigation efficiencies.In this paper we will develop a general framework for RAPs where we define for each SSP the corresponding specific agricultural development. Some aspects of the above mentioned specifics can be derived from the definitions in the SSPs, as for example irrigation efficiencies which are linked to technological development. Agricultural policies on the other hand are not included in the SSP definitions. Here we will define agricultural and environmental policies, including the available funding in each area of the common agricultural policy (CAP) (pillars 1 and 2). As RAPs can only to a small degree be developed as European guidelines and implemented unilaterally, it is important to translate the overall storylines into specific scenario parameterization at national levels. Concerned by this are 1. national policies, as well as the agri-environmental schemes of the CAP in Pillar II, 2. livestock efficiencies and the development of extensive and intensive farm management, and 3. crop management systems.Additionally we will define which respresentative concentration pathways (RCPs) will match best the future agricultural and agro-economic trajectories. The following 5 preliminary RAPs for Europe will be further developed in our analysis:EU-RAP1 (Sustainable Europe) : strong CAP, strong shift on environmental regulation, no producer support, green CAP with strong mititgation componentEU-RAP2 (Middle of the road): BAU or things will stay as they are.EU-RAP3 (Fragmented Europe): Europe breaks up, rich countries support farmers with national subsidies, poor countries do not. There is no CAP anymoreEU-RAP4 (Two Europes): Europe is divided in a poor and a rich part. In the rich part a green and environmental friendly CAP will be implemented, in the poor part of Europe, the CAP will cease to existEU-RAP5(Fossil fueled Europe): free market world, strong institutions, weak on enviromental regulations, low domestic polices? Local green CAP without mitigation
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Brilli, L., Ferrise, R., Dibari, C., Bindi, M., & Bellocchi, G. (2017). Needs on model improvement (Vol. 10).
Abstract: The need to answer new scientific questions can be satisfied by an increased knowledge of physiological mechanisms which, in turn, can be used for improving the accuracy of simulations of process-based models. In this context, this report highlights areas that need to be further improved to facilitate the operational use of simulation models. It describes missing approaches within simulation models which, if implemented, would likely improve the representation of the dynamics of processes underlying different compartments of crop and grassland systems (e.g. plant growth and development, yield production, GHG emissions), as well as of the livestock production systems. The following rationale has been used in the organization of this report. We first briefly introduced the need to improve the reliability of existing models. Then, we indicated climate change and its influence on the global carbon balance as the main issue to be addressed by existing crop and grassland (section 2), and livestock (section 3) models. In section 2, among the major aspects that if implemented may reduce the uncertainty inherent to model outputs, we suggested: i) quantifying the effects of climate extremes on biological systems; ii) modelling of multi-species sward; iii) coupling of pest and disease sub-models; iv) improvement of the carry-over effect. In section 3, as the most important aspects to consider in livestock models we indicated: i) impacts and dynamics of pathogens and disease; ii) heat stress effects on livestock; iii) effects on grassland productivity and nutritional values; iv) improvement of GHG emissions dynamics. In Section 4, remarks are made concerning the need to implement the suggested aspects into the existing models.
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