Abstract: The first TradeM workshop was held at Haifa University (Israel), 3-5 March 2013. It was a  state-of-the-art Workshop ‘Economic Modelling on Agriculture with Climate Change for  Food Security’. Sixteen papers are presented, following a call for abstracts submitted in  December 2012.  Presented, reviewed and discussed models, their inputs, outputs and main results of  case-study analyses let indicate of how the model can be used to analyze the  impacts of climate change on food security, how the model can contribute to, and  benefit from other economic and/or crop and livestock models and what input is  needed from CropM and LiveM. There were explored ideas for closer integration and  linkage between agriculture and economic models and between economic models at  different levels, addressing issues of model structure, scale and data processing. Focus was  on model comparison, gap analysis, scientific advancements and improvements. We also  addressed the key challenges of the economic models (macro- versus micro-economics;  uncertainty versus risks; variability and distribution), and identified ways to cope with  scaling, uncertainty, risks. The workshop let identify the requirements from CropM and  LiveM, find policy questions that MACSUR is going to address, start with the content of the  case studies and plan for publication of scientific papers.  The sessions were broadcast live via the internet. Twenty-four registered participants and  about 65 local visitors attended the workshop.This work was co-financed by NCBiR, Contract no. FACCE JPI/04/2012 – P100 PARTNER No Label

Abstract: The size of arable crop yields depends on many weather factors, such as precipitation and air temperature during the vegetation period. When studying the relation between yields and precipitation, not only the total amount of precipitation, but also the occurrence of long periods without precipitation must be taken into account. The paper [Bojar et al., 2014] demonstrated that barley yield significantly statistically depends on the length of the series of days without precipitation. This paper attempts to analyse the statistical data on daily precipitation totals recorded during the January – December periods in the years 1971 – 2013 at the weather station of the University of Science and Technology in Bydgoszcz, Faculty of Agriculture and Biotechnology, in the Research Centre located in an agricultural area in the Mochle township, situated 17 kilometres from Bydgoszcz. The primary statistical operation in the study is an attempt to estimate the Markov chain order. To this end, two criteria of chain order determination are applied: BIC (Bayesian information criterion, Schwarz 1978) and AIC (Akaike information criterion, Akaike 1974). Both are based on the log-likelihood functions for transition probability of the Markov chain constructed on certain data series. Statistical analysis of precipitation totals data leads to the conclusion that both AIC and BIC indicate the 2nd order for the studied Markov chain. The proposed method of estimating the variability of precipitation occurrence in the future will be utilised to improve region-related bio-physical and economical models, and to assess the risk of extreme events in the context of growing climate hazards. It will serve as basis for a search in agriculture for solutions mitigating those hazards.