An APSIM-powered framework for post-rainy sorghum-system design in India

Ronanki, Swarna; Pavlík, Jan; Masner, Jan; Jarolímek, Jan; Stočes, Michal; Subhash, Degala; Talwar, H. S.; Tonapi, Vilas A.; Mallayee, Srikanth; Baddam, Rekha; Kholová, Jana

doi:10.1016/j.fcr.2021.108422

Cited by 7 publications

(14 citation statements)

References 45 publications

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“…Breeding objectives are framed to develop product outcomes ( Fehr, 1987a ; Fehr, 1987b ; varieties, hybrids, clones, populations). These products are to be used by farmers within the Genotype-by-Environment-by-Management (GxExM) context of agricultural systems of the target population of environments (TPE); which includes the biophysical environment and the agronomic management practices adopted by farmers ( Ceccarelli, 1989 ; Ceccarelli, 1994 ; Duvick et al., 2004 ; Chenu et al., 2011 ; Persley and Anthony, 2017 ; van Etten et al., 2019 ; Ceccarelli and Grando, 2020 ; Cooper et al., 2020 ; Cooper et al., 2021 , Cooper et al., 2023 ; Kholová et al., 2021 ; Ronanki et al., 2022 ; Zhao et al., 2022 ). Through successful adoption and use of the improved products by farmers, together with appropriate agronomic management practices, breeding programs can improve food productivity and so contribute to enhanced global food security.…”

Section: Introductionmentioning

confidence: 99%

Extending the breeder’s equation to take aim at the target population of environments

Cooper

Powell²,

Gho³

et al. 2023

Front. Plant Sci.

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A major focus for genomic prediction has been on improving trait prediction accuracy using combinations of algorithms and the training data sets available from plant breeding multi-environment trials (METs). Any improvements in prediction accuracy are viewed as pathways to improve traits in the reference population of genotypes and product performance in the target population of environments (TPE). To realize these breeding outcomes there must be a positive MET-TPE relationship that provides consistency between the trait variation expressed within the MET data sets that are used to train the genome-to-phenome (G2P) model for applications of genomic prediction and the realized trait and performance differences in the TPE for the genotypes that are the prediction targets. The strength of this MET-TPE relationship is usually assumed to be high, however it is rarely quantified. To date investigations of genomic prediction methods have focused on improving prediction accuracy within MET training data sets, with less attention to quantifying the structure of the TPE and the MET-TPE relationship and their potential impact on training the G2P model for applications of genomic prediction to accelerate breeding outcomes for the on-farm TPE. We extend the breeder’s equation and use an example to demonstrate the importance of the MET-TPE relationship as a key component for the design of genomic prediction methods to realize improved rates of genetic gain for the target yield, quality, stress tolerance and yield stability traits in the on-farm TPE.

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Section: Introductionmentioning

confidence: 99%

Extending the breeder’s equation to take aim at the target population of environments

Cooper

Powell²,

Gho³

et al. 2023

Front. Plant Sci.

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“…For weather, we used the synthetic data provided by Nasapower (Sparks, 2021) which has been shown to sufficiently represent the observed weather data (Hajjarpoor et al 2019, Ronanki et al 2022). The selected data contained daily minimum and maximum temperature, precipitation, and solar radiation that was also used as the CSM input.…”

Section: Methodsmentioning

confidence: 99%

“…A second objective of this article was to test the ability of CM to reflect the dynamics of the pearl millet production systems in India and incorporate some of these components into the TPE analysis, which can be a basis for future system optimization (e.g. Ronanki et al (2022)). Here, we shall emphasize that the APSIM CM do not simulate important biotic stresses like pest and disease attack.…”

Section: K the Use Of Crop Model For Pearl Millet Envirotyping And It...mentioning

confidence: 99%

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Characterization of the pearl millet cultivation environments in India: status and perspectives enabled by expanded data analytics and digital tools

Garin¹,

Choudhary²,

Murugesan³

et al. 2023

Preprint

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The cultivation of pearl millet in India is experiencing important transformations due to changes in weather, socio-economic trends, and technological progress. In this scope, we propose a new characterization of the pearl millet production environment in India using the latest available data and methodology. For that, we constructed a database incorporating data on various aspects of pearl millet cultivation at the district level from 1998 to 2017. We complemented this analysis using extensive pearl millet agri-system simulations to evaluate crop models' abilities to reconstruct and analyse the system at an unprecedented scale. We also proposed a new method to infer system parameters from crop model data. Our results show important differences compared to the characterization currently used. The East part of the pearl millet tract (East Rajasthan, Haryana, Uttar Pradesh, and Madhya Pradesh) emerges as the only region where pearl millet cultivation has grown with potential surplus that is likely exported. Important reductions of pearl millet cultivated area in Gujarat, Maharashtra and Karnataka are potentially due to economy-driven transition to other more pro table crops like cotton, maize, or castor bean. The data used also point toward a constant increase of the rain during the growing season which could have major consequences on the future of this crop, with potential positive effects like extra yield but also negative like extra pressure due to more intense and erratic rainfall or transition to more pro table crops requiring more water. Despite difficulties to predict pearl millet yield in rapidly changing environments, the tested crop models reflected reasonably well the pearl millet production system, thus, setting the base for effective system design in future climatic scenarios. Our data and results have been gathered in an open-source interactive online application.

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“…Breeding objectives are framed to develop product outcomes (varieties, hybrids, clones, populations). These products are to be used by farmers within the Genotype-by-Environment-by-Management (GxExM) context of agricultural systems of the target population of environments (TPE); which includes the biophysical environment and the agronomic management practices adopted by farmers (Cooper et al, 2020(Cooper et al, , 2021Kholová et al, 2021;Ronanki et al, 2022). Through successful adoption and use of the improved products by farmers, breeding programs can improve food productivity and so contribute to enhanced global food security.…”

Section: ) Introductionmentioning

confidence: 99%

Extending the breeder’s equation to take aim at the Target Population of Environments

Cooper

Powell

Gho

et al. 2022

Preprint

View full text Add to dashboard Cite

A major focus for genomic prediction has been on improving trait prediction accuracy using combinations of algorithms and the training data sets available from plant breeding multi-environment trials (METs). Any improvements in prediction accuracy are viewed as pathways to improve traits in the reference population of genotypes and product performance in the target population of environments (TPE). To realise these breeding outcomes there must be a positive MET-TPE relationship that provides consistency between the trait variation expressed within the MET data sets that are used to train the genome-to-phenome (G2P) model for applications of genomic prediction and the realised trait and performance differences in the TPE for the genotypes that are the prediction targets. The strength of this MET-TPE relationship is usually assumed to be high, however it is rarely quantified. To date investigations of genomic prediction methods have not given adequate attention to quantifying the structure of the TPE and the MET-TPE relationship and its potential impact on training the G2P model for applications of genomic prediction to accelerate breeding outcomes for the on-farm TPE. We provide a perspective on the importance of the MET-TPE relationship as a key component for the design of genomic prediction methods to realize improved rates of genetic gain for the target yield, quality, stress tolerance and yield stability traits in the on-farm TPE.

show abstract

An APSIM-powered framework for post-rainy sorghum-system design in India

Cited by 7 publications

References 45 publications

Extending the breeder’s equation to take aim at the target population of environments

Extending the breeder’s equation to take aim at the target population of environments

Characterization of the pearl millet cultivation environments in India: status and perspectives enabled by expanded data analytics and digital tools

Extending the breeder’s equation to take aim at the Target Population of Environments

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