Future contributions of crop modelling - from heuristics and supporting decision making to understanding genetic regulation and aiding crop improvement

Crop modelling has evolved over the last 30 or so years in concert with advances in crop physiology, crop ecology and computing technology. Having reached a respectable degree of acceptance, it is appropriate to review briefly the course of developments in crop modelling and to project what might be major contributions of crop modelling in the future. Two major opportunities are envisioned for increased modelling activity in the future. One opportunity is in a continuing central, heuristic role to support scientific investigation, to facilitate decision making by crop managers, and to aid in education. Heuristic activities will also extend to the broader system-level issues of environmental and ecological aspects of crop production. The second opportunity is projected as a prime contributor in understanding and advancing the genetic regulation of plant performance and plant improvement. Physiological dissection and modelling of traits provides an avenue by which crop modelling could contribute to enhancing integration of molecular genetic technologies in crop improvement. Crown Copyright (C) 2002 Published by Elsevier Science B.V. All rights reserved.

Trait physiology and crop modelling as a framework to link phenotypic complexity to underlying genetic systems

New tools derived from advances in molecular biology have not been widely adopted in plant breeding for complex traits because of the inability to connect information at gene level to the phenotype in a manner that is useful for selection. In this study, we explored whether physiological dissection and integrative modelling of complex traits could link phenotype complexity to underlying genetic systems in a way that enhanced the power of molecular breeding strategies. A crop and breeding system simulation study on sorghum, which involved variation in 4 key adaptive traits-phenology, osmotic adjustment, transpiration efficiency, stay-green-and a broad range of production environments in north-eastern Australia, was used. The full matrix of simulated phenotypes, which consisted of 547 location-season combinations and 4235 genotypic expression states, was analysed for genetic and environmental effects. The analysis was conducted in stages assuming gradually increased understanding of gene-to-phenotype relationships, which would arise from physiological dissection and modelling. It was found that environmental characterisation and physiological knowledge helped to explain and unravel gene and environment context dependencies in the data. Based on the analyses of gene effects, a range of marker-assisted selection breeding strategies was simulated. It was shown that the inclusion of knowledge resulting from trait physiology and modelling generated an enhanced rate of yield advance over cycles of selection. This occurred because the knowledge associated with component trait physiology and extrapolation to the target population of environments by modelling removed confounding effects associated with environment and gene context dependencies for the markers used. Developing and implementing this gene-to-phenotype capability in crop improvement requires enhanced attention to phenotyping, ecophysiological modelling, and validation studies to test the stability of candidate genetic regions.

Trait physiology and crop modelling to link phenotypic complexity to underlying genetic systems

New tools derived from advances in molecular biology have not been widely adopted in plant breeding because of the inability to connect information at gene level to the phenotype in a manner that is useful for selection. We explore whether a crop growth and development modelling framework can link phenotype complexity to underlying genetic systems in a way that strengthens molecular breeding strategies. We use gene-to-phenotype simulation studies on sorghum to consider the value to marker-assisted selection of intrinsically stable QTLs that might be generated by physiological dissection of complex traits. The consequences on grain yield of genetic variation in four key adaptive traits – phenology, osmotic adjustment, transpiration efficiency, and staygreen – were simulated for a diverse set of environments by placing the known extent of genetic variation in the context of the physiological determinants framework of a crop growth and development model. It was assumed that the three to five genes associated with each trait, had two alleles per locus acting in an additive manner. The effects on average simulated yield, generated by differing combinations of positive alleles for the traits incorporated, varied with environment type. The full matrix of simulated phenotypes, which consisted of 547 location-season combinations and 4235 genotypic expression states, was analysed for genetic and environmental effects. The analysis was conducted in stages with gradually increased understanding of gene-to-phenotype relationships, which would arise from physiological dissection and modelling. It was found that environmental characterisation and physiological knowledge helped to explain and unravel gene and environment context dependencies. We simulated a marker-assisted selection (MAS) breeding strategy based on the analyses of gene effects. When marker scores were allocated based on the contribution of gene effects to yield in a single environment, there was a wide divergence in rate of yield gain over all environments with breeding cycle depending on the environment chosen for the QTL analysis. It was suggested that knowledge resulting from trait physiology and modelling would overcome this dependency by identifying stable QTLs. The improved predictive power would increase the utility of the QTLs in MAS. Developing and implementing this gene-to-phenotype capability in crop improvement requires enhanced attention to phenotyping, ecophysiological modelling, and validation studies to test the stability of candidate QTLs.

Application of biotechnology in breeding lentil for resistance to biotic and abiotic stress

Lentil is a self-pollinating diploid (2n = 14 chromosomes) annual cool season legume crop that is produced throughout the world and is highly valued as a high protein food. Several abiotic stresses are important to lentil yields world wide and include drought, heat, salt susceptibility and iron deficiency. The biotic stresses are numerous and include: susceptibility to Ascochyta blight, caused by Ascochyta lentis; Anthracnose, caused by Colletotrichum truncatum; Fusarium wilt, caused by Fusarium oxysporum; Sclerotinia white mold, caused by Sclerotinia sclerotiorum; rust, caused by Uromyces fabae; and numerous aphid transmitted viruses. Lentil is also highly susceptible to several species of Orabanche prevalent in the Mediterranean region, for which there does not appear to be much resistance in the germplasm. Plant breeders and geneticists have addressed these stresses by identifying resistant/tolerant germplasm, determining the genetics involved and the genetic map positions of the resistant genes. To this end progress has been made in mapping the lentil genome and several genetic maps are available that eventually will lead to the development of a consensus map for lentil. Marker density has been limited in the published genetic maps and there is a distinct lack of co-dominant markers that would facilitate comparisons of the available genetic maps and efficient identification of markers closely linked to genes of interest. Molecular breeding of lentil for disease resistance genes using marker assisted selection, particularly for resistance to Ascochyta blight and Anthracnose, is underway in Australia and Canada and promising results have been obtained. Comparative genomics and synteny analyses with closely related legumes promises to further advance the knowledge of the lentil genome and provide lentil breeders with additional genes and selectable markers for use in marker assisted selection. Genomic tools such as macro and micro arrays, reverse genetics and genetic transformation are emerging technologies that may eventually be available for use in lentil crop improvement.

The role of physiological understanding in plant breeding; From a breeding perspective

The role of physiological understanding in improving the efficiency of breeding programs is examined largely from the perspective of conventional breeding programs. Impact of physiological research to date on breeding programs, and the nature of that research, was assessed from (i) responses to a questionnaire distributed to plant breeders and physiologists, and (ii) a survey of literature abstracts. Ways to better utilise physiological understanding for improving breeding programs are suggested, together with possible constraints to delivering beneficial outcomes. Responses from the questionnaire indicated a general view that the contribution by crop physiology to date has been modest. However, most of those surveyed expected the contribution to be larger in the next 20 years. Some constraints to progress perceived by breeders and physiologists were highlighted. The survey of literature abstracts indicated that from a plant breeding perspective, much physiological research is not progressing further than making suggestions about possible approaches to selection. There was limited evidence in the literature of objective comparison of such suggestions with existing methodology, or of development and application of these within active breeding programs. It is argued in this paper that the development of outputs from physiological research for breeding requires a good understanding of the breeding program(s) being serviced and factors affecting its performance. Simple quantitative genetic models, or at least the ideas they represent, should be considered in conducting physiological research and in envisaging and evaluating outputs. The key steps of a generalised breeding program are outlined, and the potential pathways for physiological understanding to impact on these steps are discussed. Impact on breeding programs may arise through (i) better choice of environments in which to conduct selection trials, (ii) identification of selection criteria and traits for focused introgression programs, and (iii) identifying traits for indirect selection criteria as an adjunct to criteria already used. While many breeders and physiologists apparently recognise that physiological understanding may have a major role in the first area, there appears to be relatively Little research activity targeting this issue, and a corresponding bias, arguably unjustified, toward examining traits for indirect selection. Furthermore, research on traits aimed at crop improvement is often deficient because key genetic parameters, such as genetic variation in relevant breeding populations and genetic (as opposed to phenotypic) correlations with yield or other characters of economic importance, are not properly considered in the research. Some areas requiring special attention for successfully interfacing physiology research with breeding are discussed. These include (i) the need to work with relevant genetic populations, (ii) close integration of the physiological research with an active breeding program, and (iii) the dangers of a pre-defined or narrow focus in the physiological research.

Evaluating plant breeding strategies by simulating gene action and dryland environment effects

Functional genomics is the systematic study of genome-wide effects of gene expression on organism growth and development with the ultimate aim of understanding how networks of genes influence traits. Here, we use a dynamic biophysical cropping systems model (APSIM-Sorg) to generate a state space of genotype performance based on 15 genes controlling four adaptive traits and then search this spice using a quantitative genetics model of a plant breeding program (QU-GENE) to simulate recurrent selection. Complex epistatic and gene X environment effects were generated for yield even though gene action at the trait level had been defined as simple additive effects. Given alternative breeding strategies that restricted either the cultivar maturity type or the drought environment type, the positive (+) alleles for 15 genes associated with the four adaptive traits were accumulated at different rates over cycles of selection. While early maturing genotypes were favored in the Severe-Terminal drought environment type, late genotypes were favored in the Mild-Terminal and Midseason drought environment types. In the Severe-Terminal environment, there was an interaction of the stay-green (SG) trait with other traits: Selection for + alleles of the SG genes was delayed until + alleles for genes associated with the transpiration efficiency and osmotic adjustment traits had been fixed. Given limitations in our current understanding of trait interaction and genetic control, the results are not conclusive. However, they demonstrate how the per se complexity of gene X gene X environment interactions will challenge the application of genomics and marker-assisted selection in crop improvement for dryland adaptation.

Improving drought tolerance in rainfed lowland rice: An example from Thailand

A large portion of the world's poor farm in rainfed systems where the water supply is unpredictable and droughts are common. In Thailand there are approximately 6.2 million ha of rain fed lowland rice, which account for 67% of the country's total rice-growing area. This rice system is often characterised by too much and too little water in the same season. Farmers' estimates of their annual losses to drought are as high as 45% in the upper parts of the toposequence. In contrast to irrigated rice systems, gains from crop improvement of rainfed rice have been modest, in part because there has been little effort to breed and select for drought tolerance for the target rainfed environments. The crop improvement strategy being used in Thailand considers three mechanisms that influence yield in the drought prone targets: yield potential as an important mechanism for mild drought (where yield loss is less than 50%), drought escape (appropriate phenology) and drought tolerance traits of leaf water potential, sterility, flower delay and drought response index for more severe drought conditions. Genotypes are exposed to managed drought environments for selection of drought tolerant genotypes. A marker assisted selection (MAS) scheme has been developed and applied for selection of progenies in the backcrossing program. The plant breeding program uses rapid generation advance techniques that enable early yield testing in the target population of environments (TPE) through inter-station (multi-location yield testing) and on-farm trials. A farmer participatory approach has been used to identify the TPE for the breeding program. Four terrace paddy levels have been identified, upper (drought), middle (drought prone to favorable) and lower (flooded). This paper reports the change in the breeding program for the drought prone tainted lowland rice environments of North and Northeast Thailand by incorporating our knowledge on adaptation and on response of rice to drought. (c) 2005 Elsevier B.V. All rights reserved.

Improving drought tolerance in rainfed lowland rice: an example from Thailand

A large portion of the world’s poor farm in rainfed systems where the water supply is unpredictable and droughts are common. In Thailand there are approximately 6.2 million ha of rain fed lowland rice which account for 67% of the country’s total rice-growing area. This rice system is often characterised by too much and too little water in the same season. Farmers’ estimates of their annual losses to drought are as high as 45% in the upper parts of the toposequence. In contrast to irrigated rice systems, gains from crop improvement of rainfed rice have been modest, in part because there has been little effort to breed and select for drought tolerance for the target rainfed environments. The crop improvement strategy being used in Thailand considers three mechanisms that influence yield in the drought prone targets: yield potential as an important mechanism for mild drought (where yield loss is less than 50%), drought escape (appropriate phenology) and drought tolerance traits of leaf water potential, sterility, flower delay and drought response index for more severe drought conditions. Genotypes are exposed to managed drought environments for selection of drought tolerant genotypes. A marker assisted selection (MAS) scheme has been developed and applied for selection of progenies in the backcrossing program. The plant breeding program uses rapid generation advance techniques that enable early yield testing in the target population of environments (TPE) through inter-station (multi-location yield testing) and on-farm trials. A farmer participatory approach has been used to identify the TPE for the breeding program. Four terrace paddy levels have been identified, upper (drought), middle (drought prone to favorable) and lower (flooded). This paper reports the change in the breeding program for the drought prone rainfed lowland rice environments of North and Northeast Thailand by incorporating our knowledge on adaptation and on response of rice to drought.

Coconut revival: new possibilities for the 'tree of life'

Proceedings of the International Coconut Forum held in Cairns, Australia, 22-24 November 2005. Coconut is one of the most important crops grown in the humid tropics, with more than 11 million farmers, mostly smallholders with low income, growing the palm in 90 countries. These proceedings document the vast range of topics covered in the forum, including R&D, business and government, and regional and international agency interests.

Using crop simulation to generate genotype by environment interaction effects for sorghum in water-limited environments

Multi-environment trials (METs) used to evaluate breeding lines vary in the number of years that they sample. We used a cropping systems model to simulate the target population of environments (TPE) for 6 locations over 108 years for 54 'near-isolines' of sorghum in north-eastern Australia. For a single reference genotype, each of 547 trials was clustered into 1 of 3 'drought environment types' (DETs) based on a seasonal water stress index. Within sequential METs of 2 years duration, the frequencies of these drought patterns often differed substantially from those derived for the entire TPE. This was reflected in variation in the mean yield of the reference genotype. For the TPE and for 2-year METs, restricted maximum likelihood methods were used to estimate components of genotypic and genotype by environment variance. These also varied substantially, although not in direct correlation with frequency of occurrence of different DETs over a 2-year period. Combined analysis over different numbers of seasons demonstrated the expected improvement in the correlation between MET estimates of genotype performance and the overall genotype averages as the number of seasons in the MET was increased.