Impacts of extreme weather on wheat and maize in France: evaluating regional crop simulations against observed data

Extreme weather conditions can strongly affect agricultural production, with negative impacts that can at times be detected at regional scales. In France, crop yields were greatly influenced by drought and heat stress in 2003 and by extremely wet conditions in 2007. Reported regional maize and wheat yields where historically low in 2003; in 2007 wheat yields were lower and maize yields higher than long-term averages. An analysis with a spatial version (10x10 km) of th EPIC crop model was tested with regards to regional crop yield anomalies of wheat and maize resulting from extreme weather events in France in 2003 and 2007, by comparing simulated results against reported regional crops statistics, as well as using remotely sensed soil moisture data. Causal relations between soil moisture and crop yields were specifically analyzed. Remotely sensed (AMSR-E) JJA soil moisture correlated significantly with reported regional crop yield for 2002-2007. The spatial correlation between JJA soil moisture and wheat yield anomalies was positive in dry 2003 and negative in wet 2007. Biweekly soil moisture data correlated positively with wheat yield anomalies from the first half of June until the second half of July in 2003. In 2007, the relation was negative the first half of June until the second half of August. EPIC reproduced observed soil dynamics well, and it reproduced the negative wheat and maize yield anomalies of the 2003 heat wave and drought, as well as the positive maize yield anomalies in wet 2007. However, it did not reproduce the negative wheat yield anomalies due to excessive rains and wetness in 2007. Results indicated that EPIC, in line with other crop models widely used at regional level in climate change studies, is capable of capturing the negative impacts of droughts on crop yields, while it fails to reproduce negative impacts of heavy rain and excessively wet conditions on wheat yield, due to poor representations of critical factors affecting plant growth and management. Given that extreme weather events are expected to increase in frequency and perhaps severity in coming decades, improved model representation of crop damage due to extreme events is warranted in order to better quantify future climate change impacts and inform appropriate adaptation responses

Do automatic self-associations relate to suicidal ideation?

Dysfunctional self-schemas are assumed to play an important role in suicidal ideation. According to recent information-processing models, it is important to differentiate between 'explicit' beliefs and automatic associations. Explicit beliefs stem from the weighting of propositions and their corresponding 'truth' values, while automatic associations reflect more simple associations in memory. Both types of associations are assumed to have different functional properties and both may be involved in suicidal ideation. Thus far, studies into self-schemas and suicidal ideation focused on the more explicit, consciously accessible traces of self-schemas and predominantly relied on self-report questionnaires or interviews. To complement these 'explicit' findings and more directly tap into self-schemas, this study investigated automatic self-associations in a large scale community sample that was part of the Netherlands Study of Depression and Anxiety (NESDA). The results showed that automatic self-associations of depression and anxiety were indeed significantly related to suicidal ideation and past suicide attempt. Moreover, the interactions between automatic self-depressive (anxious) associations and explicit self-depressive (anxious) beliefs explained additional variance over and above explicit self-beliefs. Together these results provide an initial insight into one explanation of why suicidal patients might report difficulties in preventing and managing suicidal thoughts. © 2009 The Author(s)