|Title||QTL-based physiological modelling of leaf photosynthesis and crop productivity of rice (Oryza sativa L.) under well-watered and drought environments|
|Source||University. Promotor(en): Paul Struik; H. Wang, co-promotor(en): Xinyou Yin; Tjeerd-Jan Stomph. - S.L. : s.n. - ISBN 9789461735300 - 181|
Crop and Weed Ecology
Centre for Crop Systems Analysis
|Publication type||Dissertation, internally prepared|
|Keyword(s)||oryza sativa - fotosynthese van het kroondak - gewasproductie - stress omstandigheden - loci voor kwantitatief kenmerk - genetische merkers - plantenveredeling - simulatiemodellen - canopy photosynthesis - crop production - stress conditions - quantitative trait loci - genetic markers - plant breeding - simulation models|
|Categories||Rice / Plant Physiology / Plant Breeding and Selection Methods|
Key words: Drought, ecophysiological crop modelling, GECROS, genotype, G×E interaction, modelling, Oryza sativa L., photosynthesis, quantitative trait locus, rice.
Improving grain yield of rice (Oryza sativa L.) crop for both favourable and stressful environments is the main breeding objective to ensure food security. The objective of this study was to amalgamate crop modelling and genetic analysis, to create knowledge and insight useful in view of this breeding objective.
Photosynthesis is fundamental to biomass production, but the process is very sensitive to abiotic stresses, including drought. Upland rice cv. Haogelao, lowland rice cv. Shennong265, and 94 of their introgression lines (ILs) were studied under drought and well-watered conditions to analyse the genetics of leaf photosynthesis. After correcting for microclimate fluctuations, significant genetic variation was found in this population, and 1-3 quantitative trait loci (QTLs) were detected per photosynthesis-related trait. A major QTL was mapped near marker RM410 on Chromosome 9 and was consistent for phenotyping at flowering and grain filling, under drought and well-watered conditions, and across field and greenhouse experiments. These results suggest that photosynthesis at different phenological stages and under different environmental conditions is, at least to some extent, influenced by the same genetic factors.
To understand the physiological regulation of genetic variation and resulting QTLs for photosynthesis detected in the first study, 13 ILs were carefully selected as representatives of the population, based on the QTLs for leaf photosynthesis. These 13 ILs were studied under moderate drought and well-watered conditions in the experiment where combined gas exchange and chlorophyll fluorescence data were collected to assess CO2 and light response curves. Using these curves, seven parameters of a photosynthesis model were estimated to dissect photosynthesis into stomatal conductance (gs), mesophyll conductance (gm), electron transport capacity (Jmax), and Rubisco carboxylation capacity (Vcmax). Genetic variation in light saturated photosynthesis and the major QTL of photosynthesis on Chromosome 9 were mainly associated with variation in gs and gm. Furthermore, relationships between these parameters and leaf nitrogen or dry matter per unit area were shown valid for variation across genotypes and across water treatments. In view of these results and literature reports, it was argued that variation in photosynthesis due to environmental conditions and to genetic variation shares common physiological mechanisms.
QTL analyses were further extended to other physiological parameters of rice. Molecular marker-based estimates of these traits from estimated additive allele effects were used as input tothe mechanistic crop model GECROS. This marker/QTL-based modelling approach showed the ability of predicting genetic variation of crop performance within ILs for a diverse set of field conditions. This approach also showed the potential of extrapolating to a large population of recombinant inbred lines from the same parents. Most importantly, this model approach may improve the efficiency of marker-assisted selection, as it provides a tool to rank the relative importance of the identified markers in determining final yield under specific environmental conditions.
To examine the extent to which natural genetic variation in photosynthesis can contribute to increasing biomass production and yield of rice, the GECROS crop model was used again to analyse the impact of genetic variation in photosynthesis on crop biomass production. It was shown that in contrast to other studies a genetic variation in photosynthesis of 25% can be scaled up equally to crop level, resulting in an increase in biomass of 22-29% across different locations and years. The difference with earlier studies seems related to the fact that variation in both Rubisco-limited and electron transport-limited photosynthesis were observed in our IL population.
This thesis has contributed to closing the gap between genotype and phenotype by integrating crop physiology and genetics through an innovative QTL/marker-based modelling approach. This approach can contribute to making the use of genomics much more efficient in practical plant breeding.