Long-term straw return improves cooked indica rice texture by altering starch structural, physicochemical properties in South China.

Straw return can improve rice eating quality by modifying starch formation from long-term field trials, whereas the relevant mechanisms are still unknown. A long-term field experiment, including straw removal (CK), straw burning return (SBR), and straw return (SR) was conducted to investigate the starch structure, physicochemical properties, and cooked rice textures of indica early- and late-rice. Compared with CK, SBR and SR enhanced relative crystallinity, amylopectin long chains in both rice seasons, and gelatinization temperatures in late rice. Compared to SBR, SR decreased protein content and amylopectin short chains but increased starch branching degree, breakdown, and stickiness, ultimately contributing to improved starch thermal and pasting properties. Meanwhile, SR decreased hardness, cohesiveness, and chewiness, resulting in cooked texture meliorated, which was mainly attributed to amylopectin chain length and starch granule size. The results suggest that SR increased cooked texture of indica rice by altering starch structural and physicochemical properties.

[Relationships between climate change and rice development and its yield formation: a simulation study].

With the application of mechanistic model (RICAM 1.3, RIce growth Calendar Model), this paper simulated the rice development and its yield formation under different climatic conditions at multi-locations of Asia. A three-stage Beta model (3s-Beta) was developed to predict the flowering stage of rice and to describe its three successive phases of photo-thermal response, i.e., basic vegetative phase, photoperiod-sensitive phase, and post photoperiod-sensitive phase. The 1980-1989 multi-location data of Morioka (Japan, 39 degrees 43'N), Nanchang (China, 28 degrees 36'N) and Los Banos (Philippines, 14 degrees 11'N) were used to verify the suitability of the model in studying ecosystem change. Comparisons of simulated results with observed data showed that this model could generally predict the eco-physiological processes of rice, and performed very well over a wide range of environments.

Model analysis of flowering phenology in recombinant inbred lines of barley.

A generic model for flowering phenology as a function of daily temperature and photoperiod was applied to predict differences of flowering times among 96 individuals (including the two parents) of a recombinant inbred line population in barley (Hordeum vulgare L.). Because of the large number of individuals to study, there is a need for simple ways to derive model parameters for each genotype. Therefore the number of genotype-specific parameters was reduced to four, namely f(o) (the minimum number of days to flowering at the optimum temperature and photoperiod), (1) and (2) (the development stages for the start and the end of the photoperiod-sensitive phase, respectively), and delta (the photoperiod sensitivity). Values of these parameters were estimated using a newly described methodological framework based on data from a photoperiod-controlled experiment where plants were mutually transferred between long-day and short-day environments at regular intervals. This modelling approach was tested in eight independent field environments of different sowing dates in two growing seasons. The four-parameter model predicted 37-67% of observed phenotypic variation in an environment, 76% of variation in across-environment mean days to flowering among the genotypes, and 96% of variation in across-genotype mean among the eight environments. When all the observations of the 96 genotypes across the eight environments were pooled, the model explained 81% of the total variation. Sensitivity analysis showed that all four model parameters were important for predicting differences in flowering time among the genotypes; but their relative importance differed and the ranking was in the order of f(o), delta, theta1, and theta2. This study highlighted the potential of using ecophysiological models to assist the genetic analysis of quantitative crop traits whose phenotype is often environment-dependent.