Meeting the world's requirement for rice production in the future needs a dual approach: 1. the theoretical yield potential of rice should increase and 2.theyield gaps should be reduced especially in marginal areas where stresses such as salinity, low supply of plant nutrients, droughts and flooding often limit yields. Rice breeding used to be geared towards developing cultivars for high-input management conditions and these cultivars may not be suited to low input environments. In regions such asAfrica, where most of the rice is produced under low-input conditions, breeding should be targeted at rice cultivars that are tolerant to the main stresses encountered in the rice agro-ecosystems. Soil salinity and inadequate supply of plant nutrients (especially nitrogen) are two major stresses limiting rice yields in The Gambia where this study was undertaken. The aim of this study was to determine appropriate selection methods for rice across a range of environments and to identify options at the level of yield components and physiological traits to increase rice yield potentials under both high and low input conditions. For this purpose, a segregating population of rice comprising Recombinant Inbred Lines (RILs) developed from the cross of a high-yielding, semi-dwarf, salt-sensitive cultivar, IR29 and a tall, traditional, salt-tolerant cultivar, Pokkali, was grown in fresh water (EC of 0.15 dS m-1) and saline (EC of 8 dS m-1) conditions with 0 or 100 kg ha-1nitrogen.
Analyses of variance revealed significant genotype × environment interaction for yield and for the four yield components, number of panicles m-2, total number of grains per panicle, thousand grainweightand percent spikelet fertility, across the range of test environments. Inter-environmental correlations for grain yield between the 0 and 100 kg ha-1N fertilizer regimes were high and significant in both fresh and saline water signifying that in the lowland, rice can be bred for general adaptability to different N fertilizer levels. However, the inter-environmental correlations for yield between fresh water and saline conditions, especially for the highest yielding cultivars in either environment, were low. This suggests that different sets of rice cultivars should be bred for cultivation in fresh water or saline environments. Analysis of the relationships between yield and yield components by means of regression revealed that generally in fresh waterenvironments,yield was sink-limited and that grain number attributes (comprising number of panicles m-2and total number of grains per panicle) of rice should be enhanced in order to boost yield potential under fresh water conditions. In saline environments, however, salt stress strongly limits assimilate production and translocation. To increase rice yield potential in saline environments, cultivars with better grain filling attributes (comprising grain weight and spikelet fertility) should be developed. The N fertilizer regime influenced the relative importance of panicles m-2and total number of grains per panicle in fresh water and of individual grain weight and spikelet fertility in saline environments, for yield determination.
Through molecular marker analysis, putative quantitative trait loci (QTLs) were detected for grain yield and yield components in all four test environments. Overall, markers accounted for 23%-60% of the variation in yield and yield components. Markers associated with more than one trait had either similar or opposite effects on the traits. For all five traits studied, most markers were expressed in only one environment implying strong environmental specificity in expression of the QTLs for rice yield and yield components. Marker-assisted selection, based on AFLP markers, was successfully conducted for grain yield of rice in all four test environments over two years.
A study of the physiological basis of yield formation recognized the importance of high biomass production in all test environments for high yielding ability. High biomass production in fresh water environments was associated with high leaf area index and high leaf N concentration while in salineenvironments,high biomass production was better associated with leaf area index than with leaf N concentration. Salt stress reduced leaf area index, biomass production and yield but increased leaf N concentration and prolonged growth. In saline environments, differences in leaf area index and biomass between salt-tolerant and salt-sensitive genotypes were larger during the pre-flowering growth phase than after anthesis.Around flowering time, high stem weights and allocation of more dry matter to shoots instead of roots, reduced yields in fresh water environments but increased yields in saline water environments.High leaf weights and late flowering increased yields in fresh water but in saline environments high leaf weights decreased yields in the zero N fertilizer regime and increased yields in the high N fertilizer regime. In saline environments, late flowering generally reduced yields.
The ORYZA1 model gave good predictions of yield and biomass in all test environments although grain yield was better predicted than biomass production and predictions were better in fresh water than in saline environments. The model revealed, through sensitivity analysis, the yield increment that can be achieved by improving leaf area index, specific leaf N and fraction of dry matter allocated to panicles during grain filling, for diverse rice genotypes in different environments. For saline environments, inclusion of effects of salinity on specific leaf N and spikelet fertility in the model are expected to improve its performance.
A thorough understanding of the genetic and physiological basis of yield formation in different environments would help breeders develop rice cultivars with high yield potentials under low- and/or high input cultivation environments. Integrating knowledge from different scientific disciplines such as statistics, physiology, biotechnology and systems modelling would facilitate this process.
|Qualification||Doctor of Philosophy|
|Award date||29 Mar 2004|
|Place of Publication||[S.l.]|
|Publication status||Published - 2004|
- oryza sativa
- simulation models
- plant physiology