Nguyen Thi Lang

Diversity of Global Rice Markets and the Science Required for Consumer-Targeted Rice Breeding

With the ever-increasing global demand for high quality rice in both local production regions and with Western consumers, we have a strong desire to understand better the importance of the different traits that make up the quality of the rice grain and obtain a full picture of rice quality demographics. Rice is by no means a ‘one size fits all’ crop. Regional preferences are not only striking, they drive the market and hence are of major economic importance in any rice breeding / improvement strategy. In this analysis, we have engaged local experts across the world to perform a full assessment of all the major rice quality trait characteristics and importantly, to determine how these are combined in the most preferred varieties for each of their regions. Physical as well as biochemical characteristics have been monitored and this has resulted in the identification of no less than 18 quality trait combinations. This complexity immediately reveals the extent of the specificity of consumer preference. Nevertheless, further assessment of these combinations at the variety level reveals that several groups still comprise varieties which consumers can readily identify as being different. This emphasises the shortcomings in the current tools we have available to assess rice quality and raises the issue of how we might correct for this in the future. Only with additional tools and research will we be able to define directed strategies for rice breeding which are able to combine important agronomic features with the demands of local consumers for specific quality attributes and hence, design new, improved crop varieties which will be awarded success in the global market.

Addressing the dilemmas of measuring amylose in rice.

ABSTRACT Amylose content is a parameter that correlates with the cooking behavior of rice. It is measured at the earliest possible stages of rice improvement programs to enable breeders to build the foundations of appropriate grain quality during cultivar development. Amylose is usually quantified by absorbance of the amylose-iodine complex. The International Network for Quality Rice (INQR) conducted a survey to determine ways that amylose is measured, reproducibility between laboratories, and sources of variation. Each laboratory measured the amylose content of a set of 17 cultivars of rice. The study shows that five different versions of the iodine binding method are in use. The data show that repeatability was high within laboratories but reproducibility between laboratories was low. The major sources of variability were the way the standard curve was constructed and the iodine binding capacity of the potato amylose used to produce the standard. Reproducibility is much lower between laboratories using ...

Quantitative Trait Loci Associated with Heat Tolerance in Rice (Oryza sativa L.)

A total of 310 BC2F2 lines derived from the cross of OM5930/N22 were evaluated for heat stress at flowering. Genetic map was set up with 264 polymorphic SSRs to detect linkage to the target traits. The map covers 2,741.63 cM with an average interval of 10.55 cM between two marker loci. Markers associated with heat tolerance were located mostly on chromosomes 3, 4, 6, 8, 10 and 11. The proportion of phenotypic variation explained by each QTL ranged from 17.1% for RM160 to 36.2% for RM3586. Four QTLs were detected for filled grains per panicle on chromosome 4 at the interval of RM468 RM7076 and RM241 RM26212, explaining 13.1 and 31.0% of the total phenotypic variation, respectively. Two QTLs controling unfilled grain percentage was also detected at loci RM554 and RM3686 on chromosome 3 explaining 25.0 and 11.2% of the total phenotypic variance. One QTL was detected for 1,000-grain weight located at the locus RM103 on chromosome 6, explaining 30.6% of the total phenotypic variance. Also, a QTL at the locus RM5749 on chromosome 4 was identified which explained 10.8% of the total phenotypic variance of grain yield. A single QTL at the interval of RM3586RM160 on chromosome 3 was detected in conformity with the QTL findings for heat tolerance in previous studies.

Identifying QTLs Associated and Marker-Assisted Selection for Salinity Tolerance at the Seedling, Vegetative and Reproductive Stages in Rice (Oryza Sativa L.)

Salinity affects rice growth in all growth stages, with the seedling and reproductive stages being the most sensitive. Genetically improving salt tolerance of rice is an important objective of rice breeding programs. Hence, mapping quantitative trait loci (QTL) will be useful for marker-assisted selection in rice breeding programs. An advanced backcross population (BC2F2) was developed with the parents included OM5629 as a donor of salt tolerance and OM7347 as a recurrent parent with good quality traits and drought tolerance. Molecular markers associated with both qualitative and quantitative trait loci (QTL) salt tolerance were identified by using 416 polymorphic SSR markers. QTLs, associated with stress tolerance at EC = 15 dS/m at seedling stage, detected from the BC2F2 population of OM7347/OM5629, were located on chromosomes 1 and 3. Three QTLs were identified at the intervals of RM3252-S1-1 - RM10694, RM3740-RM5336 and RM11125-RM9 with genetic distance of 4.4, 4.5 and 18 cM on chromosome 1, respectively. Two QTLs at the intervals of RM3867-RM6959 and RM6876-RM4425 with genetic distance of 4.5 and 18.0 cM on chromosome 3, respectively. One QTL on chromosome 5 was detected at the interval of RM874 - RM10359, it was associated with salt stress tolerance under EC = 8dS/m at vegetative stage. Three QTLs at the regions of RM1324-RM2412, RM1185-RM24, and RM1282-RM2560 on chromosome 1, and one QTL of RM453-RM511 on chromosome 12, were related to salt tolerance under EC = 8dS/m at reproductive stage. Two tightly linked markers as RM3252-S1-1 and RM3867, were exhibited their effectiveness in identification of salt tolerance genotypes in BC3F6 population of OMCS2000/ Pokkali. The identification of new QTLs associated with salt tolerance will provide important information for the functional analysis of rice salinity stress.