1. Guiding crop improvement (genetics-agronomy) in diverse environments: a sorghum case study
ABSTRACT:
Postrainy sorghum productivity is limited by drought but this limitation varies with time and geographical scale and so the breeding target moves. We developed a cross-disciplinary approach to maximize crop productivity returns within the boundaries of these environmental constraints. This approach is based on a thorough understanding of the socio-economic context and farmer demand, and of the biological and environmental constraints to crop production, in the target environment. It is backed up by genetic efforts to harness critical traits and use of crop simulation to represent what are the best options, either genetic traits or agronomic modifications. This strategy is purposefully designed to set priorities on breeding and agronomic management decisions and strategy
step 3
step 4
Trade-off associated with intervention
(example; smaller canopy)
step 5
Demand-specific crop design & management
step 1
defining target agro-ecology
step 2
envirotyping
INTRODUCTION Crop production is the consequence of crop x management x environmental (GxExM) interactions with water accounting for ~50% yield reductions.
This complexity hampers breeding progress but it is an opportunity if it is deciphered and understood. Methods are needed to decide on the most promising breeding and agronomic management opportunities.
The approach consists in: (i) characterizing the constraint patterns in the target environment; (ii) deciphering genetic and agronomic modifications that can improve crop productivity in these different stress patterns; (iii) Phenotyping and genotyping to harness key genetic traits; (iv) Testing the effects of these modifications with crop simulation; (v) Assessing the socio-economic impact of these interventions and start designing best-option ideotypes targeted to specific sub-environments.
This approach is applicable to virtually every crop
“STOVER type”
Livestock feed
Water stress scenarios; effect on yield
(Kholová et al. 2013)
Grain yield
Pre-flowering
Flowering
Post-flowering
Post-flowering relieved
No stress
Rabi-sorghum-INDIA
(Kholová et al. 2013)
12.3
0.5
Phenotyping for adaptations
(poster;xy)
“GRAIN type”
Sorghum bread
RESULTS
(Kholová et al. 2014)
(Vadez et al. 2015)
(Vikramana et al. in prep.)
DISCUSSIONS: With an understanding of the socio-economic drivers in the target region, tools techniques and approaches were developed to understand the system production limitations and to enhance the economic gains from the crop in target agroecology. This involved:
Using crop modelling to cluster target production environments constraints into smaller more predictable units
Deciphering and designing genetic and agronomic solution to specific constraints and developing phenotyping techniques towards the harnessing of key genetic traits
Using crop modelling to evaluate effects and trade-offs associated with particular crop-genetics and management interventions
Designing crop ideotypes for site-specific demand for crop commodities to set priorities on the breeding and agronomic management investments
MATERIALS AND METHODS
Step 1: Envirotyping involves combining soil data (NBSS & LUP, Bangalore; ISRIC), daily weather records (IMD, Pune) and coefficients expressing crop attributes (maldandi sorghum; Ravi Kumar et al. 2009) using a system modelling software (APSIM platform; Hammer et al 2010) to represent a crop virtually.
Step 2: System model (APSIM) allowed us to output seasonal water stress trajectories across 100’s of year-by-location combinations. These trajectories were clustered into main stress types and the grain yield associated with particular stress scenarios analyzed (Kholová et al. 2013).
Step 3: The adaptive mechanisms and their variability within germplasm were assessed at various levels of biological integration and methods developed to assay these trait by high-throughput phenotyping (eg LeasyScan poster, Vadez et al. 2015)
Step 4: Grain and stover yield trade-offs associated with alterations in the crop genetics and management practice combinations was analyzed using APSIM (Kholová et al. 2014)
Step 5: Knowing the demand for particular crop commodities (grain/stover) + environmental constrains + range of environment-adaptations available + agro-ecology specific trade-offs associated with particular crop-management intervention, we can design the agro-ecology specific demand driven crop type with higher probability to enhance the economic gains (Vikraman et al in prep.).
REFERENCES:
Hammer, van Oosterom, McLean, Chapman, Broad, Hadland, Muchow 2010 J. Exp. Bot., 1–18.
Kholová, McLean, Vadez, Craufurd, Hammer 2013 Field Crops Res 141;38-46
Kholová, Murugesan, Kaliamoorthy, Malayee, Baddam, Hammer, McLean, Deshpande, Hash, Craufurd, Vadez. Functional Plant Biol. DOI: /FP
Vadez, Kholová, Hummel, Zhokhavets, Gupta, Hash. J. Exp. Bot doi: /jxb/erv
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