Bernard Rolland

Comparing the performance of cereal varieties in organic and non-organic cropping systems in different European countries

Top ranking varieties are tested in multiple environments before and after registration in order to assess their value for cultivation and use (VCU testing). Recently, interest has increased in obtaining varieties specifically adapted to organic farming conditions. This raised the question if an independent system of trials may be required for this purpose. To help answering this question, through the exchange network of European cereal researchers SUSVAR (www.cost860.dk), a number of data sets of agronomic traits from barley, wheat and winter triticale, from trials performed in Denmark, Sweden, The Netherlands, France, Switzerland, UK and Germany, were made available and analysed using an approach based on mixed models involving parameters describing genetic correlation between the two types of experiments, i.e., organic and non-organic (high or low input). Estimated variance components and correlations were used to evaluate response to selection and index selection. The response to index selection was analysed as a function of the fraction of available trials assigned to the organic system. The genetic correlations were interpreted in terms of ranking agreement. We found high genetic correlations between both systems for most traits in all countries. Despite high genetic correlations, the chances of very good agreement in observed rankings were moderate. Combining information from both organic and non-organic systems is shown to be beneficial. Further, ignoring information from non-organic trials when making decisions regarding performance under organic conditions is a sub-optimal strategy.

Genome-wide association mapping of three important traits using bread wheat elite breeding populations

The exponential development of molecular markers enables a more effective study of the genetic architecture of traits of economic importance, like test weight in wheat (Triticum aestivum L.), for which a high value is desired by most end-users. The association mapping (AM) method now allows more precise exploration of the entire genome. AM requires populations with substantial genetic variability of the traits of interest. The breeding lines at the end of a selection cycle, characterized for numerous traits, represent a potentially useful population for AM studies. Using three elite line populations, selected by several breeders and genotyped with about 2,500 Diversity Arrays Technology markers, several associations were identified between these markers and test weight, grain yield and heading date. To minimize spurious associations, we compared the general linear model and mixed linear model (MLM), which adjust for population structure and kinship differently. The MLM model with the kinship matrix was the most efficient. Finally, elite lines from several breeding programs had sufficient genetic variability to allow for the mapping of several chromosomal regions involved in the variation of three important traits.

Bread wheat milling behavior: effects of genetic and environmental factors, and modeling using grain mechanical resistance traits

Key messageGenetic (Pinb-D1 alleles) and environment (through vitreousness) have important effects on bread wheat milling behavior. SKCS optimal values corresponding to soft vitreous or hard mealy grains were defined to obtain the highest total flour yield.AbstractNear-isogenic lines of bread wheat that differ in hardness, due to distinct puroindoline-b alleles (the wild type, Pinb-D1a, or the mutated forms, Pinb-D1b or Pinb-D1d), were grown in different environments and under two nitrogen fertilization levels, to study genetic and environmental effects on milling behavior. Milling tests used a prototype mill, equipped with two break steps, one sizing step, and two reduction steps, and this enabled 21 individual or aggregated milling fractions to be collected. Four current grain characters, thousand grain weight, test weight, grain diameter, and protein content, were measured, and three characters known to influence grain mechanical resistance, NIRS hardness, SKCS hardness index, and grain vitreousness (a character affecting the grain mechanical behavior but generally not studied). As expected, the wild type or mutated forms of Pinb-D1 alleles led to contrasted milling behavior: soft genotypes produced high quantities of break flour and low quantities of reduction flour, whereas reverse quantities were observed for hard genotypes. This different milling behavior had only a moderate influence on total flour production. NIRS hardness and vitreousness were, respectively, the most important and the second most important grain characters to explain milling behavior. However, contrary to NIRS hardness, vitreousness was only involved in endosperm reduction and not in the separation between the starchy endosperm and the outer layers. The highest flour yields were obtained for SKCS values comprised between 30 and 50, which corresponded either to soft vitreous or hard mealy grains. Prediction equations were defined and showed a good accuracy estimating break and reduction flours portions, but should be used more cautiously for total flour.

IPSIM-Web, An Online Resource for Promoting Qualitative Aggregative Hierarchical Network Models to Predict Plant Disease Risk: Application to Brown Rust on Wheat

A qualitative pest modeling platform, named Injury Profile Simulator (IPSIM), provides a tool to design aggregative hierarchical network models to predict the risk of pest injuries, including diseases, on a given crop based on variables related to cropping practices as well as soil and weather environment at the field level. The IPSIM platform enables modelers to combine data from various sources (literature, survey, experiments, and so on), expert knowledge, and simulation to build a network-based model. The overall structure of the platform is fully described at the IPSIM-Web website ( www6.inra.fr/ipsim ). A new module called IPSIM-Wheat-brown rust is reported in this article as an example of how to use the system to build and test the predictive quality of a prediction model. Model performance was evaluated for a dataset comprising 1,788 disease observations at 13 French cereal-growing regions over 15 years. Accuracy of the predictions was 85% and the agreement with actual values was 0.66 based on Cohens κ. The new model provides risk information for farmers and agronomists to make scientifically sound tactical (within-season) decisions. In addition, the model may be of use for ex post diagnoses of diseases in commercial fields. The limitations of the model such as low precision and threshold effects as well as the benefits, including the integration of different sources of information, transparency, flexibility, and a user-friendly interface, are discussed.