Abstract: This article compares the theoretical and functional specification of production in partial equilibrium (PE) and computable general equilibrium (CGE) models of the global agricultural and food system included in the AgMIP model comparison study. The two model families differ in their scopepartial versus economy-wideand in how they represent technology and the behavior of supply and demand in markets. The CGE models are deep structural models in that they explicitly solve the maximization problem of consumers and producers, assuming utility maximization and profit maximization with production/cost functions that include all factor inputs. The PE models divide into two groups on the supply side: (1) shallow structural models, which essentially specify area/yield supply functions with no explicit maximization behavior, and (2) deep structural models that provide a detailed activity-analysis specification of technology and explicit optimizing behavior by producers. While the models vary in their specifications of technology, both within and between the PE and CGE families, we consider two stylized theoretical models to compare the behavior of crop yields and supply functions in CGE models with their behavior in shallow structural PE models. We find that the theoretical responsiveness of supply to changes in prices can be similar, depending on parameter choices that define the behavior of implicit supply functions over the domain of applicability defined by the common scenarios used in the AgMIP comparisons. In practice, however, the applied models are more complex and differ in their empirical sensitivity to variations in specificationcomparability of results given parameter choices is an empirical question. To illustrate the issues, sensitivity analysis is done with one global CGE model, MAGNET, to indicate how the results vary with different specification of technical change, and how they compare with the results from PE models.

Abstract: The mid-term meeting was held in Sassari, Sardinia, 1-4 April 2014. The meeting was attended by 120 researchers and stakeholders from 16 countries (Fig. 1). After a day of looking back on the achievements during the first two years and presenting results to stakeholders, researchers focused on fine-tuning the planning of remaining work for the project till May 2015 and preparations for a follow-up project (MACSUR2) till May 2017. On an excursion, scientists and stakeholders visited farms in the Oristano region, one of the regional case studies of MACSUR. The meeting was a unique opportunity in this pan-European project for discussing in person common issues with and among stakeholders of different regions and how to approach the impact of climate change to producing food in Europe in a world with a growing population. A report in La Nueva Sardegna  highlighted the conference. Excursion: dairy sheep farm “Su Pranu” (Siamanna), dairy cattle farm “Sardo Farm” (Arborea), Arborea Cooperative Recordings of the presentations are available on YouTube: https://www.youtube.com/channel/UCrjoXlUIJNBW8cWOgh0_g The presentations are available on the conference website: http://ocs.macsur.eu/index.php/Hub/Mid-term/schedConf/presentations Short papers derived from the presentations are available on the conference website and in FACCE MACSUR Reports vol 5. The food consumed during lunches at the conference originated mostly from the Oristano region. Remaining food in good condition was donated to a charity organisation for needy people. Fig. 1. Number of participants per country.

Abstract: Report D-L1.4.1 provided an overview of the data and related resources available online and through EU funded projects, relating to soil organic carbon (SOC), and carbon sequestration in grasslands in particular. Building on D-L1.4.1, the report presented here discusses how meta-data describing these types of data (and experimental data more generally) can best be presented in an online resource useful to grassland modellers requiring data to use in their modelling work. Identifying the useful categories of meta-data is a necessary precursor to providing such a resource, which could facilitate better communication between modelling and experimental research groups, allowing researchers to more efficiently locate relevant data and to link up with other scientists working on similar topics. A survey among grassland modelling teams and an assessment of online meta-data resources was used to produce recommendations about the meta-data categories that should be included in an online resource. The categories are generic, so that the recommendations can be followed in the design of meta-data resources for the more general agricultural modelling community. No Label