Environmental characterization and yield gap analysis to tackle genotype-by-environment-by-management interactions and map region-specific agronomic and breeding targets in groundnut

Hajjarpoor, Amir; Kholová, Jana; Janila, Pasupuleti; Soltani, Afshin; Burridge, James; Degala, Subhash Babu; Gattu, S.; Murali, T. V.; Garin, Vincent; Radhakrishnan, T.; Vadez, Vincent

doi:10.1016/j.fcr.2021.108160

Cited by 19 publications

(11 citation statements)

References 56 publications

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“…Here we consider selection strategies that incorporate applications of genomic prediction ( Meuwissen et al., 2001 ; Bernardo and Yu, 2007 ; Heffner et al., 2009 ; Cooper et al., 2014a ; Voss-Fels et al., 2019 ). Selection pressure is implemented by applying truncation selection to the distributions of observed or predicted values for one or more traits within the reference population of genotypes (RPG) of a breeding program; for example, selection to increase crop yield, improve grain quality and improve abiotic and biotic stress tolerances to reduce the extent of yield losses due to the occurrence of the frequent stresses in the TPE ( Chenu et al., 2011 ; Kholová et al., 2013 ; Hajjarpoor et al., 2021 ; Messina et al., 2022a ). The structure of the breeder’s equation has a long history in animal and plant breeding ( Lush, 1937 ; Hallauer and Miranda, 1988 ; Nyquist and Baker, 1991 ; Comstock, 1996 ; Moose and Mumm, 2008 ) and is frequently used as a quantitative framework for the design and optimization of crop breeding programs ( Araus and Cairns, 2014 ; Araus et al., 2018 ; Cobb et al., 2019 ; Voss-Fels et al., 2019 ; Kholová et al., 2021 ; Cooper and Messina, 2023 ).…”

Section: Theoretical Developmentmentioning

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: Theoretical Developmentmentioning

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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“…Similarly, crop improvement programs have to breed for the same traits in order to enhance the economics of peanut farming. This is currently difficult because evaluation of these traits in both processes (i.e., market and breeding) relies on manual assessment which is time-consuming and potentially error-prone (e.g., [ 3 , 44 , 51 , 53 , 54 ]. Therefore, assessing the potential of emerging X-ray technology options and advanced data analytics to close these gaps was the main motivation of the presented study.…”

Section: Discussionmentioning

confidence: 99%

X-ray driven peanut trait estimation: computer vision aided agri-system transformation

et al. 2022

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Background In India, raw peanuts are obtained by aggregators from smallholder farms in the form of whole pods and the price is based on a manual estimation of basic peanut pod and kernel characteristics. These methods of raw produce evaluation are slow and can result in procurement irregularities. The procurement delays combined with the lack of storage facilities lead to fungal contaminations and pose a serious threat to food safety in many regions. To address this gap, we investigated whether X-ray technology could be used for the rapid assessment of the key peanut qualities that are important for price estimation. Results We generated 1752 individual peanut pod 2D X-ray projections using a computed tomography (CT) system (CTportable160.90). Out of these projections we predicted the kernel weight and shell weight, which are important indicators of the produce price. Two methods for the feature prediction were tested: (i) X-ray image transformation (XRT) and (ii) a trained convolutional neural network (CNN). The prediction power of these methods was tested against the gravimetric measurements of kernel weight and shell weight in diverse peanut pod varieties1. Both methods predicted the kernel mass with R2 > 0.93 (XRT: R2 = 0.93 and mean error estimate (MAE) = 0.17, CNN: R2 = 0.95 and MAE = 0.14). While the shell weight was predicted more accurately by CNN (R2 = 0.91, MAE = 0.09) compared to XRT (R2 = 0.78; MAE = 0.08). Conclusion Our study demonstrated that the X-ray based system is a relevant technology option for the estimation of key peanut produce indicators (Figure 1). The obtained results justify further research to adapt the existing X-ray system for the rapid, accurate and objective peanut procurement process. Fast and accurate estimates of produce value are a necessary pre-requisite to avoid post-harvest losses due to fungal contamination and, at the same time, allow the fair payment to farmers. Additionally, the same technology could also assist crop improvement programs in selecting and developing peanut cultivars with enhanced economic value in a high-throughput manner by skipping the shelling of the pods completely. This study demonstrated the technical feasibility of the approach and is a first step to realize a technology-driven peanut production system transformation of the future.

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“…However, taking aim at the TPE as a mixture of environment-types (Podlich et al, 1999; Cooper et al, 2014a,b; Gaffney et al, 2015; Rogers et al, 2021; Smith et al, 2021a, b; Messina et al, 2022a,b) is much less common than taking aim at specific environment-types. Taking aim at the TPE requires detailed consideration of the mixture of target environment-types within the TPE (Chapman et al, 2000; Chenu et al, 2011; Kholová et al, 2013; Cooper et al, 2014a, b; Lobell et al, 2015; Hajjarpoor et al, 2021; Resende et al, 2021), the extent of GxE interactions between environment-types (Figure 2) and the details of the genetic variance-covariance structure among the environment-types, and appropriate attention to weighting the sources of G2P information for traits, that is available from the environment-types sampled in the MET training data sets, by their frequencies of occurrence and relative importance in the TPE (Podlich et al, 1999; Cooper et al, 2014a, b; Gaffney et al, 2015; Messina et al, 2018; Smith et al, 2021b).…”

Section: Discussionmentioning

confidence: 99%

“…Here we consider selection strategies that incorporate applications of genomic prediction (Heffner et al, 2009; Cooper et al, 2014a; Voss-Fels et al, 2019). Selection pressure is implemented by applying truncation selection to the distributions of observed or predicted values for one or more traits within the reference population of genotypes (RPG) of a breeding program; for example, selection to increase crop yield, improve grain quality and improve abiotic and biotic stress tolerances to reduce the extent of yield losses due to the occurrence of the frequent stresses in the TPE (Chenu et al, 2011; Kholová et al, 2013; Hajjarpoor et al, 2021; Messina et al, 2022a). The structure of the breeder’s equation has a long history in animal and plant breeding (Lush, 1937; Hallauer and Miranda, 1988; Nyquist and Baker, 1991; Comstock, 1996) and is frequently used as a quantitative framework for the design and optimisation of crop breeding programs (Araus and Cairns, 2014; Cobb et al, 2019; Kholová et al, 2021; Cooper and Messina, 2022).…”

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

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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 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

Environmental characterization and yield gap analysis to tackle genotype-by-environment-by-management interactions and map region-specific agronomic and breeding targets in groundnut

Cited by 19 publications

References 56 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

X-ray driven peanut trait estimation: computer vision aided agri-system transformation

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

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