Molecular Breeding for Complex Adaptive Traits: How Integrating Crop Ecophysiology and Modelling Can Enhance Efficiency

Progress in crop improvement is limited by the ability to identify favourable combinations of genotypes (G) and management practices (M) in relevant target environments (E) given the resources available to search among the myriad of possible combinations. To underpin yield advance we require prediction of phenotype based on genotype. In plant breeding, traditional phenotypic selection methods have involved measuring phenotypic performance of large segregating populations in multi-environment trials and applying rigorous statistical procedures based on quantitative genetic theory to identify superior individuals. Recent developments in the ability to inexpensively and densely map/sequence genomes have facilitated a shift from the level of the individual (genotype) to the level of the genomic region. Molecular breeding strategies using genome wide prediction and genomic selection approaches have developed rapidly. However, their applicability to complex traits remains constrained by gene-gene and gene-environment interactions, which restrict the predictive power of associations of genomic regions with phenotypic responses. Here it is argued that crop ecophysiology and functional whole plant modelling can provide an effective link between molecular and organism scales and enhance molecular breeding by adding value to genetic prediction approaches. A physiological framework that facilitates dissection and modelling of complex traits can inform phenotyping methods for marker/gene detection and underpin prediction of likely phenotypic consequences of trait and genetic variation in target environments. This approach holds considerable promise for more effectively linking genotype to phenotype for complex adaptive traits. Specific examples focused on drought adaptation are presented to highlight the concepts.

Exploring profit - Sustainability trade-offs in cropping systems using evolutionary algorithms

Models that implement the bio-physical components of agro-ecosystems are ideally suited for exploring sustainability issues in cropping systems. Sustainability may be represented as a number of objectives to be maximised or minimised. However, the full decision space of these objectives is usually very large and simplifications are necessary to safeguard computational feasibility. Different optimisation approaches have been proposed in the literature, usually based on mathematical programming techniques. Here, we present a search approach based on a multiobjective evaluation technique within an evolutionary algorithm (EA), linked to the APSIM cropping systems model. A simple case study addressing crop choice and sowing rules in North-East Australian cropping systems is used to illustrate the methodology. Sustainability of these systems is evaluated in terms of economic performance and resource use. Due to the limited size of this sample problem, the quality of the EA optimisation can be assessed by comparison to the full problem domain. Results demonstrate that the EA procedure, parameterised with generic parameters from the literature, converges to a useable solution set within a reasonable amount of time. Frontier ‘‘peels’’ or Pareto-optimal solutions as described by the multiobjective evaluation procedure provide useful information for discussion on trade-offs between conflicting objectives.

Development and testing of an evaluation procedure for commercial manure additive products.

Manure additive products can be used to reduce odour emissions (OE) from livestock farms. The standardised evaluation of these manure additive products under specific farm conditions is important. In this study, the efficacy of a manure additive (WonderTreat(TM), CKLS, Inc., Hong-Kong) was assessed under Australian conditions utilising a combination of laboratory and field-scale evaluation techniques. As a first step, the efficacy of the manure additive was assessed in a laboratory-scale trial using a series of uniformly managed digesters and standard odour, liquor ammonia and hydrogen sulphide concentration measurement procedures. This showed that the addition of WonderTreat(TM) at the 'low dose rate' (LDR) (102.6 g m-2) used during the trial significantly, but only marginally (30%; P = 0.02) reduced the OE rate (mean 13.9 OU m-2 s-1) of anaerobic pig liquor relative to an untreated control (UC) (19.9 OU m-2 s-1). However, the 'high dose rate' (HDR) (205.3 g m-2) also assessed during the trial preformed similarly (19.7 OU m-2 s-1) to the UC. No statistically significant difference in the concentrations of a range of measured water quality variables at the 5% level was observed between the treatments or controls digesters. As a second step, a field-scale assessment of the manure additive was undertaken at a commercial piggery. Two piggery manure lagoons (each with approximately 2500 m2 surface area) were included in the study; one was treated with WonderTreat(TM) while the other was used as control. The efficacy of the treatment was assessed using olfactometric evaluation of odour samples collected from the surface of the pond using a dynamic wind tunnel and ancillary equipment. No statistically significant reduction in OE rate could be demonstrated (P = 0.35), partially due to the limited number of samples taken during the assessment. However, there was a numerical reduction in the average OE rate of the treatment pond (29 OU m-2 s-1 at 1 m s-1) compared to the control lagoon (38 OU m-2 s-1 at 1 m s-1).

Surveillance protocols for management of invasive plants: modelling Chilean needle grass (Nassella neesiana) in Australia

Aim: To develop a surveillance support model that enables prediction of areas susceptible to invasion, comparative analysis of surveillance methods and intensity and assessment of eradication feasibility. To apply the model to identify surveillance protocols for generalized invasion scenarios and for evaluating surveillance and control for a context-specific plant invasion. Location: Australia. Methods: We integrate a spatially explicit simulation model, including plant demography and dispersal vectors, within a Geographical Information System. We use the model to identify effective surveillance protocols using simulations of generalized plant life-forms spreading via different dispersal mechanisms in real landscapes. We then parameterize the surveillance support model for Chilean needle grass [CNG; Nassella neesiana (Trin. & Rupr.) Barkworth], a highly invasive tussock grass, which is an eradication target in south-eastern Queensland, Australia. Results: General surveillance protocols that can guide rapid response surveillance were identified; suitable habitat that is susceptible to invasion through particular dispersal syndromes should be targeted for surveillance using an adaptive seek-and-destroy method. The search radius of the adaptive method should be based on maximum expected dispersal distances. Protocols were used to define a surveillance strategy for CNG, but simulations indicated that despite effective and targeted surveillance, eradication is implausible at current intensities. Main conclusions: Several important surveillance protocols emerged and simulations indicated that effectiveness can be increased if they are followed in rapid response surveillance. If sufficient data are available, the surveillance support model should be parameterized to target areas susceptible to invasion and determine whether surveillance is effective and eradication is feasible. We discovered that for CNG, regardless of a carefully designed surveillance strategy, eradication is implausible at current intensities of surveillance and control and these efforts should be doubled if they are to be successful. This is crucial information in the face of environmentally and economically damaging invasive species and large, expensive and potentially ineffective control programmes.

Australia takes prickly acacia biocontrol search to India

A project co-funded by Meat & Livestock Australia and the Queensland Government is putting new life into the search for biocontrol agents for prickly acacia (Acacia nilotica), a Weed of National Significance in Australia.

Drought stress characterization of post-rainy season (rabi) sorghum in India

During the post-rainy (rabi) season in India around 3 million tonnes of sorghum grain is produced from 5.7 million ha of cropping. This underpins the livelihood of about 5 million households. Severe drought is common as the crop grown in these areas relies largely on soil moisture stored during the preceding rainy season. Improvement of rabi sorghum cultivars through breeding has been slow but could be accelerated if drought scenarios in the production regions were better understood. The sorghum crop model within the APSIM (Agricultural Production Systems sIMulator) platform was used to simulate crop growth and yield and the pattern of crop water status through each season using available historical weather data. The current model reproduced credibly the observed yield variation across the production region (R2=0.73). The simulated trajectories of drought stress through each crop season were clustered into five different drought stress patterns. A majority of trajectories indicated terminal drought (43%) with various timings of onset during the crop cycle. The most severe droughts (25% of seasons) were when stress began before flowering and resulted in failure of grain production in most cases, although biomass production was not affected so severely. The frequencies of drought stress types were analyzed for selected locations throughout the rabi tract and showed different zones had different predominating stress patterns. This knowledge can help better focus the search for adaptive traits and management practices to specific stress situations and thus accelerate improvement of rabi sorghum via targeted specific adaptation. The case study presented here is applicable to other sorghum growing environments. © 2012 Elsevier B.V.