Abstract: Agricultural systems models are complex and tend to be over-parameterized with respect to observational datasets. Practical identifiability analysis based on local sensitivity analysis has proved effective in investigating identifiable parameter sets in environmental models, but has not been applied to agricultural systems models. Here, we demonstrate that identifiability analysis improves experimental design to ensure independent parameter estimation for yield and quality outputs of a complex grassland model. The Pasture Simulation model (PaSim) was used to demonstrate the effectiveness of practical identifiability analysis in designing experiments and measurement protocols within phe-notyping experiments with perennial ryegrass. Virtual experiments were designed combining three factors: frequency of measurements, duration of the experiment. and location of trials. Our results demonstrate that (i) PaSim provides sufficient detail in terms of simulating biomass yield and quality of perennial ryegrass for use in breeding, (ii) typical breeding trials are insufficient to parameterize all influential parameters, (iii) the frequency of measurements is more important than the number of growing seasons to improve the identifiability of PaSim parameters, and (iv) identifiability analysis provides a sound approach for optimizing the design of multi-location trials. Practical identifiability analysis can play an important role in ensuring proper exploitation of phenotypic data and cost-effective multi-location experimental designs. Considering the growing importance of simulation models, this study supports the design of experiments and measurement protocols in the phenotyping networks that have recently been organized.

Abstract: ► Introduces the special issue on Agricultural prediction using climate model ensembles. ► Discuss remaining scientific challenges. ► Develops distinction between projection- and utility-based ensemble modelling. ► Recommendations made RE modelling and the analysis and reporting of uncertainty. Significant progress has been made in the use of ensemble agricultural and climate modelling, and observed data, to project future productivity and to develop adaptation options. An increasing number of agricultural models are designed specifically for use with climate ensembles, and improved methods to quantify uncertainty in both climate and agriculture have been developed. Whilst crop–climate relationships are still the most common agricultural study of this sort, on-farm management, hydrology, pests, diseases and livestock are now also examined. This paper introduces all of these areas of progress, with more detail being found in the subsequent papers in the special issue. Remaining scientific challenges are discussed, and a distinction is developed between projection- and utility-based approaches to agro-climate ensemble modelling. Recommendations are made regarding the manner in which uncertainty is analysed and reported, and the way in which models and data are used to make inferences regarding the future. A key underlying principle is the use of models as tools from which information is extracted, rather than as competing attempts to represent reality.

Abstract: Europe’s ability to feed its population depends on the balance of agricultural productivity (yields and land suitability) and demand which are affected by future climate and socio-economic change (arising from changing food demand; prices; technology change etc).  Land use under 2050 climate change and socio-economic scenarios can be rapidly and systematically quantified with a modelling system that has been developed from meta-models of optimal cropping and crop and forest yields derived from the outputs of the previously developed complex models (Audsley et al; 2015). Profitability of each possible land use is modelled for every soil in every grid across the EU. Land use in a grid is then allocated based on profit thresholds set for intensive agriculture extensive agriculture, managed forest and finally unmanaged forest or unmanaged land.  The European demand for food as a function of population, imports, food preferences and bioenergy, is a production constraint, as is irrigation water available.   The model iterates until demand is satisfied (or cannot be met at any price).  Results are presented as contour plots of key variables. For example, given a 40% increase in population from the baseline socio-economic scenario, adapting by increasing crop yields by 40% will leave a 38% probability that the 2050 future climate will be such that we cannot feed ourselves – considering “all” the possible climate scenarios. No Label