Incorporation of a high potential quinone reveals that electron transfer in Photosystem I becomes highly asymmetric at low temperature

Photosystem I (PS I) has two nearly identical branches of electron-transfer co-factors. Based on point mutation studies, there is general agreement that both branches are active at ambient temperature but that the majority of electron-transfer events occur in the A-branch. At low temperature, reversible electron transfer between P700 and A1A occurs in the A-branch. However, it has been postulated that irreversible electron transfer from P700 through A1B to the terminal iron-sulfur clusters FA and FB occurs via the B-branch. Thus, to study the directionality of electron transfer at low temperature, electron transfer to the iron-sulfur clusters must be blocked. Because the geometries of the donor–acceptor radical pairs formed by electron transfer in the A- and B-branch differ, they have different spin-polarized EPR spectra and echo- modulation decay curves. Hence, time-resolved, multiple-frequency EPR spectroscopy, both in the direct-detection and pulse mode, can be used to probe the use of the two branches if electron transfer to the iron-sulfur clusters is blocked. Here, we use the PS I variant from the menB deletion mutant strain of Synechocyctis sp. PCC 6803, which is unable to synthesize phylloquinone, to incorporate 2,3-dichloro-1,4-naphthoquinone (Cl2NQ) into the A1A and A1B binding sites. The reduction midpoint potential of Cl2NQ is approximately 400 mV more positive than that of phylloquinone and is unable to transfer electrons to the iron-sulfur clusters. In contrast to previous studies, in which the iron-sulfur clusters were chemically reduced and/or point mutations were used to prevent electron transfer past the quinones, we find no evidence for radical-pair formation in the B-branch. The implications of this result for the directionality of electron transfer in PS I are discussed

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

Historical extension of operational NDVI products for livestock insurance in Kenya

Droughts induce livestock losses that severely affect Kenyan pastoralists. Recent index insurance schemes have the potential of being a viable tool for insuring pastoralists against drought-related risk. Such schemes require as input a forage scarcity (or drought) index that can be reliably updated in near real-time, and that strongly relates to livestock mortality. Generally, a long record (>25 years) of the index is needed to correctly estimate mortality risk and calculate the related insurance premium. Data from current operational satellites used for large-scale vegetation monitoring span over a maximum of 15 years, a time period that is considered insufficient for accurate premium computation. This study examines how operational NDVI datasets compare to, and could be combined with the non-operational recently constructed 30-year GIMMS AVHRR record (1981–2011) to provide a near-real time drought index with a long term archive for the arid lands of Kenya. We compared six freely available, near-real time NDVI products: five from MODIS and one from SPOT-VEGETATION. Prior to comparison, all datasets were averaged in time for the two vegetative seasons in Kenya, and aggregated spatially at the administrative division level at which the insurance is offered. The feasibility of extending the resulting aggregated drought indices back in time was assessed using jackknifed R2 statistics (leave-one-year-out) for the overlapping period 2002–2011. We found that division-specific models were more effective than a global model for linking the division-level temporal variability of the index between NDVI products. Based on our results, good scope exists for historically extending the aggregated drought index, thus providing a longer operational record for insurance purposes. We showed that this extension may have large effects on the calculated insurance premium. Finally, we discuss several possible improvements to the drought index