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The impact of extreme events (such as prolonged droughts, heat waves, cold shocks and frost) is poorly represented by most of the existing yield forecasting systems. Two new model-based approaches that account for the impact of extreme weather events on crop production are presented as a way to improve yield forecasts, both based on the Crop Growth Monitoring System (CGMS) of the European Commission. A first approach includes simple relations – consistent with the degree of complexity of the most generic crop simulators – to explicitly model the impact of these events on leaf development and yield formation. A second approach is a hybrid system which adds selected agro-climatic indicators (accounting for drought and cold/heat stress) to the previous one. The new proposed methods, together with the CGMS-standard approach and a system exclusively based on selected agro-climatic indicators, were evaluated in a comparative fashion for their forecasting reliability. The four systems were assessed for the main micro- and macro-thermal cereal crops grown in highly productive European countries. The workflow included the statistical post-processing of model outputs aggregated at national level with historical series (1995–2013) of official yields, followed by a cross-validation for forecasting events triggered at flowering, maturity and at an intermediate stage. With the system based on agro-climatic indicators, satisfactory performances were limited to microthermal crops grown in Mediterranean environments (i.e. crop production systems mainly driven by rainfall distribution). Compared to CGMS-standard system, the newly proposed approaches increased the forecasting reliability in 94% of the combinations crop × country × forecasting moment. In particular, the explicit simulation of the impact of extreme events explained a large part of the inter-annual variability (up to +44% for spring barley in Poland), while the addition of agro-climatic indicators to the workflow mostly added accuracy to an already satisfactory forecasting system. [ABSTRACT FROM AUTHOR]

We document the release of webXTREME , a new online tool for the evaluation of indices of climatic extremes (extreme temperatures and aridity) having impact on agricultural production. The tool is globally available and can be operated with either observed weather data or time series representing future climatic conditions. It is thus suitable for risk evaluation under climate change. webXTREME was implemented using Shiny , an open-source programming framework for creating web applications on the basis of the R Statistical Language. [ABSTRACT FROM AUTHOR]

Spring wheat is the principal crop in the steppe zone of Russia and Kazakhstan, but wheat productivity levels are currently low and susceptible to weather and climate anomalies. Water scarcity during the growing season represents a major stress factor and is expected to negatively affect wheat production in the future as well. In this paper we present a simple mechanistic model for assessing the impact of climate variability on spring wheat productivity in the steppe zone of Russia and Kazakhstan. The novel aspect of the model development is represented by the adoption of an adaptive approach for the formulation of growth partitioning. In spite of simplifying assumptions the model is shown to satisfactorily reproduce yield levels observed both at the local scale under controlled conditions as well as at the regional scale. The model is able to capture a significant percentage of the observed year-to-year variability of wheat yields. Results of the model application indicate that, for the steppe zone of Russia and Kazakhstan, seasonal water shortage is likely to cause yield deficits of 20–25%, with deficits of up to 40% in extreme years, and an increase in the coefficient of variation of yields. [Copyright &y& Elsevier]