Search

Leaf-color modification can affect canopy photosynthesis, with potential effects on rice yield and yield components. Modulating source–sink relationships through crop management is often used to improve crop productivity. This study investigated whether and how modifying leaf color alters source–sink relationships and whether current crop cultivation practices remain applicable for leaf-color modified genotypes. Periodically collected data of total biomass and nitrogen (N) accumulation in rice genotypes of four genetic backgrounds and their leaf-color modified variants (greener or yellower) were analyzed, using a recently established modelling method to quantify the source–sink (im)balance during grain filling. Among all leaf-color variants, only one yellower-leaf variant showed a higher source capacity than its normal genotype. This was associated with greater post-flowering N-uptake that prolonged the functional leaf-N duration, and this greater post-flowering N-uptake was possible because of reduced pre-flowering N-uptake. A density experiment showed that current management practices (insufficient planting density accompanied by abundant N application) are unsuitable for the yellower-leaf genotype, ultimately limiting its yield potential. Leaf-color modification affects source–sink relationships by regulating the N trade-off between pre-and post-flowering uptake, as well as N translocation between source and sink organs. To best exploit leaf-color modification for improving crop productivity, adjustments of crop management practices are required.

To study the performance of genotypes under different growing conditions, plant breeders evaluate their germplasm in multi-environment trials. These trials produce genotype environment data. We present statistical models for the analysis of such data that differ in the extent to which additional genetic, physiological, and environmental information is incorporated into the model formulation. The simplest model in our exposition is the additive 2-way analysis of variance model, without genotype environment interaction, and with parameters whose interpretation depends strongly on the set of included genotypes and environments. The most complicated model is a synthesis of a multiple quantitative trait locus (QTL) model and an eco-physiological model to describe a collection of genotypic response curves. Between those extremes, we discuss linear-bilinear models, whose parameters can only indirectly be related to genetic and physiological information, and factorial regression models that allow direct incorporation of explicit genetic, physiological, and environmental covariables on the levels of the genotypic and environmental factors. Factorial regression models are also very suitable for the modelling of QTL main effects and QTL environment interaction. Our conclusion is that statistical and physiological models can be fruitfully combined for the study of genotype environment interaction.

Crop modelers and geneticists have developed a vision of their roles in plant breeding from their own perspective. However, to improve breeding efficiency, interdisciplinary collaboration becomes increasingly important. The objective of this paper is to explore opportunities for collaboration between modelers and geneticists in ideotype breeding for high crop yield. The advent of molecular markers enables variation of a complex trait to be dissected into the effects of quantitative trait loci (QTL) and assists the transfer of these QTL into desired cultivars or lines. A recent study in which QTL information was linked to crop modeling has shown that QTL analysis removes part of random errors of measured model input parameters and that QTL information can successfully be coupled with crop models to replace measured parameters. The QTL‐based modeling overcomes the limitations in designing ideotypes by using models that ignore the inheritance of model input traits. On the other hand, crop modeling can potentially be a powerful tool to resolve genotype × environment interactions and to dissect yield into characters that might be under simpler genetic control. Based on the complementary aspects of crop modeling and QTL mapping, we propose an approach that integrates marker‐assisted selection into model‐based ideotype framework to support breeding for high crop yield. For this approach to be effective, there is a need to develop crop models that are capable of predicting yield differences among genotypes in a population under various environmental conditions.

Crop models can support plant breeding if they can predict differences in performance of different genotypes. In this study, the ability of a crop model to explain yield differences among genotypes in a recombinant inbred line (RIL) population of two‐row barley (Hordeum vulgareL.) was explored. Yield and model‐input traits of 94 RILs and their parents, ‘Prisma’ and ‘Apex’, were measured in field experiments conducted in Wageningen, Netherlands, in 1996 at low and in 1997 at high N levels. The major gene, denso, with the dwarfing allele from Prisma, was segregating in this population. Short densoRILs outyielded tall types in both years, and this yield advantage was stronger in 1997, largely because the tall genotypes lodged. A crop model based on existing routines for biomass production explained only 26 to 38% of the yield variation among genotypes. The model, using input traits measured from the 1997 data, did not accurately predict growth of genotypes in 1996 because some traits varied with plant N status, which the model did not account for. Model analysis in the high‐N environment showed that of the seven model‐input traits examined, only lodging score, preflowering duration, and fraction of biomass partitioned to spikes had a significant effect on yield. When these three traits were used while fixing others at their across‐genotype means, the model explained 65% of yield variation. To allow effective use of crop modeling in breeding, the ability of crop models to explain yield differences among genotypes has to be improved.

The time interval between sowing and flowering in rice (Oryza sativaL.) comprises three successive phases: a basic vegetative phase (BVP), a photoperiod‐sensitive phase (PSP), and a post‐PSP phase (PPP). The objective of this study was to estimate the length of the three phases in diverse rice cultivars to provide the basis for accurately predicting rice flowering dates. In greenhouse experiments, plants of 20 cultivars were transferred from short day (SD) (10 h d−1) long day (LD) (12.5 or 14 h d−1) or from LD to at various tim es aft er sowing. The duration of BVP varied greatly among cultivars ranging from 16.7 to 45.4 d. The indicacultivars exhibited an apparently longer BVP than the japonica cuitivars. The duration of PPP also varied among cultivars, from 18.0 to 37.2 d. For all cultivars, the length of the intervening PSP was shorter under SD conditions (3.6–24.1 d) than under LD conditions (10.5–76.5 d); the difference in PSP between SD and LD depended on the photoperiod sensitivity of the cultivar. The PSP under both SD and LD conditions did not necessarily end at panicle initiation (PI), but, on average, 4 to 5 d after PI. This study confirms the necessity to divide the entire preflowering period into the three phases for modeling purposes. Further studies are needed to elucidate the phenomenoinn some cultivars of a significant delay of flowering by early SD‐to‐LD transfers before PI.

To accurately predict the time of flowering, it is essential to determine the effects of environmental factors on crop development at different stages. The objective of this study was to examine the variation in flowering responses to day temperature (TD) and night temperature (TN) during the preflowering period (PFP) in rice (Oryza sativaL.). Three controlled‐environment experiments were conducted in which plants of three contrasting cultivars, CO36, Shan You 63, and Nipponbare, were transferred at different times after sowing between two constant temperatures (26 and 21°C), between two with the same TD(TD/TN: 26/26 and 26/16°C), or between two TDwith the same TN(28/19 and 19/19°C). In all experiments, the relationship between the time to flowering and the time of transfer was defined by separate linear phases, indicating that the sensitivity of flowering responses to both Tn and TN varies during PFP. The entire PFP can be divided into three consecutive phases. Temperature sensitivity during the second phase was greater than that during the first and third phases, which did not differ significantly. The length of the second phase varied greatly, depending on both temperature and cultivar. For all cultivars, the second phase was shorter at HT (5.4–17.0 d) than at LT (20.7 to more than 78.7 d). The duration of the first phase ranged from about 20 d in the japonicacv. Nipponbare to 32.4 to 64.2 d in the two indicacultivars. The length of the third phase also varied among cultivars, but to a lesser extent, from 16.4 to 38.1 d. Any significant effects of temperature on main‐stem leaf number occurred in the second phase. For accurate modeling of rice flowering dates, functions describing the effects of both TDand TNin the second phase have to be different from those in the first and third phases.