Moleculaire evolutie van het ziekteresistentiegen Rx in Solanum

Aardappel (Solanum tuberosum ssp. tuberosum) is het vierde voedselgewas ter wereld met een jaarlijkse opbrengst van onderveer vierhonderd miljoen ton. De geschiedenis van de domesticatie van de aardappel toont aan dat ziekteverwekkers het spoor van de aardappel volgen, wat tot omvangrijke productieverliezen leidt. Planten, en dus ook de aardappel, hebben verdedigingsmechnismen ontwikkeld die samen met de potentiële ziekteverwekkers zijn geëvolueerd. Zeer effectief is een systeem dat gebruik maakt van resistentiegenen (R-genen). De eiwitten waarvoor de R-genen coderen zijn in staat om specifieke eiwitten afkomstig van een pathogeen te herkennen. Deze laatste eiwitten worden aangeduid als avirulentieproducten (Avr-genen). Het mechanisme waarin één R-genproduct (direct of indirect) specifiek een interactie aangaat met één Avr-gen wordt ook wel gen-om-gen-interactie genoemd. Tijdens de zoektocht naar duurzame resistentie in voedselgewassen is een nog steeds toenemend aantal R-genen geïdenticifeerd en gekarakteriseerd. Dit heeft belangrijke informatie over de genomische organisatie en de evolutionaire dynamiek opgeleverd. R-genen zijn bijvoorbeeld van georganiseerd in complexe clusters in het genoom; zogenaamde 'hotspots' voor resistenti

Nitrous oxide emissions from irrigated cotton soils of northern Australia

An automated gas sampling methodology has been used to estimate nitrous oxide (N2O) emissions from heavy black clay soil in northern Australia where split applications of urea were applied to furrow irrigated cotton. Nitrous oxide emissions from the beds were 643 g N/ha over the 188 day measurement period (after planting), whilst the N2O emissions from the furrows were significantly higher at 967 g N/ha. The DNDC model was used to develop a full season simulation of N2O and N2 emissions. Seasonal N2O emissions were equivalent to 0.83% of applied N, with total gaseous N losses (excluding NH3) estimated to be 16% of the applied N

Parameter-induced uncertainty quantification of soil N 2 O, NO and CO 2 emission from Höglwald spruce forest (Germany) using the LandscapeDNDC model

Assessing the uncertainties of simulation results of ecological models is becoming increasingly important, specifically if these models are used to estimate greenhouse gas emissions on site to regional/national levels. Four general sources of uncertainty effect the outcome of process-based models: (i) uncertainty of information used to initialise and drive the model, (ii) uncertainty of model parameters describing specific ecosystem processes, (iii) uncertainty of the model structure, and (iv) accurateness of measurements (e.g., soil-atmosphere greenhouse gas exchange) which are used for model testing and development. The aim of our study was to assess the simulation uncertainty of the process-based biogeochemical model LandscapeDNDC. For this we set up a Bayesian framework using a Markov Chain Monte Carlo (MCMC) method, to estimate the joint model parameter distribution. Data for model testing, parameter estimation and uncertainty assessment were taken from observations of soil fluxes of nitrous oxide (N2O), nitric oxide (NO) and carbon dioxide (CO2) as observed over a 10 yr period at the spruce site of the Höglwald Forest, Germany. By running four independent Markov Chains in parallel with identical properties (except for the parameter start values), an objective criteria for chain convergence developed by Gelman et al. (2003) could be used. Our approach shows that by means of the joint parameter distribution, we were able not only to limit the parameter space and specify the probability of parameter values, but also to assess the complex dependencies among model parameters used for simulating soil C and N trace gas emissions. This helped to improve the understanding of the behaviour of the complex LandscapeDNDC model while simulating soil C and N turnover processes and associated C and N soil-atmosphere exchange. In a final step the parameter distribution of the most sensitive parameters determining soil-atmosphere C and N exchange were used to obtain the parameter-induced uncertainty of simulated N2O, NO and CO2 emissions. These were compared to observational data of an calibration set (6 yr) and an independent validation set of 4 yr. The comparison showed that most of the annual observed trace gas emissions were in the range of simulated values and were predicted with a high certainty (Root-mean-squared error (RMSE) NO: 2.4 to 18.95 g N ha−1 d−1, N2O: 0.14 to 21.12 g N ha−1 d−1, CO2: 5.4 to 11.9 kg C ha−1 d−1). However, LandscapeDNDC simulations were sometimes still limited to accurately predict observed seasonal variations in fluxes

Nitrogen as a threat to the European greenhouse balance

Reactive nitrogen (N_r) is of fundamental importance in biological and chemical processes in the atmosphere-biosphere system, altering the Earth's climate balance in many ways. These include the direct and indirect emissions of nitrous oxide (N2O), atmospheric N_r deposition and tropospheric ozone formation (O3), both of which alter the biospheric CO2 sink, N_r supply effects on CH4 emissions, and the formation of secondary atmospheric aerosols resulting from the emissions of nitrogen oxides (NOx) and ammonia (NH3). Human production and release of N_r into the environment is thus expected to have been an important driver of European greenhouse balance. Until now, no assessment has been made of how much of an effect European N_r emissions are having on net warming or cooling