scholarly journals Factors Influencing Genomic Prediction Accuracies of Tropical Maize Resistance to Fall Armyworm and Weevils

2020 ◽  
Author(s):  
Arfang Badji ◽  
Lewis Machida ◽  
Daniel Bomet Kwemoi ◽  
Frank Kumi ◽  
Dennis Okii ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Insect Pests ◽  
Fall Armyworm ◽  
Sub Saharan Africa ◽  
Tropical Maize ◽  
Maize Weevil ◽  
Individual Traits ◽  
Maize Resistance ◽  
Sub Saharan ◽  
Training Sets

Genomic selection (GS) can accelerate variety improvement when training set (TS) size, and its relationship with the breeding set (BS) are optimized for prediction accuracies (PA) of genomic prediction (GP) models. Sixteen GP algorithms were run on phenotypic best linear unbiased predictors (BLUPs) and estimators (BLUEs) of resistance to both fall armyworm (FAW) and maize weevil (MW) in a tropical maize panel. For MW resistance, 37% of the panel was the TS, and BS was the remainder whilst for FAW, random-based training sets (RBTS) and pedigree-based training sets (PBTS) were designed. PAs achieved with BLUPs varied from 0.66 to 0.82 for MW resistance traits, and, for FAW resistance, 0.694 to 0.714 for RBTS of 37%, and 0.843 to 0.844 for RBTS of 85%, and, these were at least two-fold those from BLUEs. For PBTS, FAW resistance PAs were generally higher than those for RBTS, except for one dataset. GP models generally showed similar PAs across individual traits whilst the TS designation was determinant since a positive correlation (R=0.92***) between TS size and PAs was observed for RBTS and, for the PBTS, it was negative (R=0.44**). This study pioneers the use of GS for maize resistance to insect pests in sub-Saharan Africa.

scholarly journals Factors Influencing Genomic Prediction Accuracies of Tropical Maize Resistance to Fall Armyworm and Weevils

Plants ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 29
Author(s):  
Arfang Badji ◽  
Lewis Machida ◽  
Daniel Bomet Kwemoi ◽  
Frank Kumi ◽  
Dennis Okii ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Insect Pests ◽  
Fall Armyworm ◽  
Sub Saharan Africa ◽  
Tropical Maize ◽  
Maize Weevil ◽  
Individual Traits ◽  
Maize Resistance ◽  
Sub Saharan ◽  
Training Sets

Genomic selection (GS) can accelerate variety improvement when training set (TS) size and its relationship with the breeding set (BS) are optimized for prediction accuracies (PAs) of genomic prediction (GP) models. Sixteen GP algorithms were run on phenotypic best linear unbiased predictors (BLUPs) and estimators (BLUEs) of resistance to both fall armyworm (FAW) and maize weevil (MW) in a tropical maize panel. For MW resistance, 37% of the panel was the TS, and the BS was the remainder, whilst for FAW, random-based training sets (RBTS) and pedigree-based training sets (PBTSs) were designed. PAs achieved with BLUPs varied from 0.66 to 0.82 for MW-resistance traits, and for FAW resistance, 0.694 to 0.714 for RBTS of 37%, and 0.843 to 0.844 for RBTS of 85%, and these were at least two-fold those from BLUEs. For PBTS, FAW resistance PAs were generally higher than those for RBTS, except for one dataset. GP models generally showed similar PAs across individual traits whilst the TS designation was determinant, since a positive correlation (R = 0.92***) between TS size and PAs was observed for RBTS, and for the PBTS, it was negative (R = 0.44**). This study pioneered the use of GS for maize resistance to insect pests in sub-Saharan Africa.

scholarly journals Genomic Prediction of Tropical Maize Resistance to Fall Armyworm and Weevils: Genomic Selection Should Focus on Effective Training Set Determination

2020 ◽  
Author(s):  
Arfang Badji ◽  
Lewis Machida ◽  
Daniel Bomet Kwemoi ◽  
Frank Kumi ◽  
Dennis Okii ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Genomic Prediction ◽  
Insect Pests ◽  
Fall Armyworm ◽  
Sub Saharan Africa ◽  
Tropical Maize ◽  
Training Set ◽  
Maize Weevil ◽  
Maize Resistance ◽  
Sub Saharan

Genomic selection (GS) can accelerate variety release by shortening variety development phase when factors that influence prediction accuracies (PA) of genomic prediction (GP) models such as training set (TS) size and relationship with the breeding set (BS) are optimized beforehand. In this study, PAs for the resistance to fall armyworm (FAW) and maize weevil (MW) in a diverse tropical maize panel composed of 341 double haploid and inbred lines were estimated. Both phenotypic best linear unbiased predictors (BLUPs) and estimators (BLUEs) were predicted using 17 parametric, semi-parametric, and nonparametric algorithms with a 10-fold and 5 repetitions cross-validation strategy. n. For both MW and FAW resistance datasets with an RBTS of 37%, PAs achieved with BLUPs were at least as twice as higher than those realized with BLUEs. The PAs achieved with BLUPs for MW resistance traits: grain weight loss (GWL), adult progeny emergence (AP), and number of affected kernels (AK) varied from 0.66 to 0.82. The PAs were also high for FAW resistance RBTS datasets, varying from 0.694 to 0.714 (for RBTS of 37%) to 0.843 to 0.844 (for RBTS of 85%). The PAs for FAW resistance with PBTS were generally high varying from 0.83 to 0.86, except for one dataset that had PAs ranging from 0.11 to 0.75. GP models showed generally similar predictive abilities for each trait while the TS designation was determinant. There was a highly positive correlation (R=0.92***) between TS size and PAs for the RBTS approach while, for the PBTS, these parameters were highly negatively correlated (R=-0.44***), indicating the importance of the degree of kinship between the TS and the BS with the smallest TS (31%) achieving the highest PAs (0.86). This study paves the way towards the use of GS for maize resistance to insect pests in sub-Saharan Africa.

scholarly journals Factors Influencing Genomic Prediction Accuracies of Tropical Maize Resistance to Fall Armyworm and Weevils

2020 ◽  
Author(s):  
Arfang Badji ◽  
Lewis Machida ◽  
Daniel Bomet Kwemoi ◽  
Frank Kumi ◽  
Dennis Okii ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Insect Pests ◽  
Fall Armyworm ◽  
Sub Saharan Africa ◽  
Tropical Maize ◽  
Maize Weevil ◽  
Phenotypic Data ◽  
Best Linear Unbiased ◽  
Maize Resistance ◽  
Best Linear Unbiased Estimators

Genomic selection (GS) can accelerate variety release by shortening the variety development phase when factors that influence prediction accuracies (PA) of genomic prediction (GP) models such as training set (TS) size and relationship with the breeding set (BS) are optimized beforehand. In this study, PAs for the resistance to fall armyworm (FAW) and maize weevil (MW) in a diverse tropical maize panel composed of 341 double haploid and inbred lines were estimated using 16 parametric, semi-parametric, and nonparametric algorithms with a 10-fold and 5 repetitions cross-validation strategy. For MW resistance, 126 lines that had both genotypic and phenotypic data were used as a TS (37% of the panel) and the remaining lines, with only genotypic data, as a BS. Regarding FAW damage resistance, two TS determination strategies, namely: random-based TS (RBTS) with increasing sizes (37, 63, 75, and 85%) and pedigree-based TS (PBTS) were used. For both MW and FAW resistance datasets with an RBTS of 37%, PAs achieved with phenotypic best linear unbiased predictors were at least as twice as higher than those realized with best linear unbiased estimators. The PAs achieved with BLUPs for MW resistance traits varied from 0.66 to 0.82. The PAs with BLUPs for FAW resistance datasets ranged from 0.694 to 0.714 for RBTS of 37%, and 0.843 to 0.844 for RBTS of 85%. The PAs with BLUPs for FAW resistance with PBTS were generally high varying from 0.83 to 0.86, except for the third dataset which had the largest TS (86.22% of the panel) with PAs ranging from 0.11 to 0.75. GP models showed generally similar predictive abilities for each trait while the TS designation was determinant. There was a highly positive correlation (R=0.92***) between TS size and PAs for the RBTS approach while, for the PBTS, these parameters were highly negatively correlated (R=-0.44***), indicating the importance of the relationship between the TS and the BS with the smallest TS (31%) achieving the highest PAs (0.86). This study paves the way towards the use of GS for maize resistance to insect pests in sub-Saharan Africa.

The Spread of Fall Armyworm (faw) Spodotera Frugiperda

2018 ◽  
Vol 29 (5) ◽  
pp. 213-214 ◽  
Author(s):  
Graham Matthews
Keyword(s):  
Fall Armyworm ◽  
Sub Saharan Africa ◽  
Sub Saharan ◽  
Army Worm

The author introduces the next three articles on the invasion of Fall Army Worm into Sub-Saharan Africa and Asia describing how the pest spreads, the damage it causes and approaches to its control.

Tracing the distribution of natural enemies of non-native invasive eucalypt insect pests in sub-Saharan Africa

2021 ◽  
pp. 1-10
Author(s):  
Mesfin Wondafrash ◽  
Bernard Slippers ◽  
Birhane A Asfaw ◽  
Idea A Makowe ◽  
Herbert Jenya ◽  
...  
Keyword(s):  
Natural Enemies ◽  
Insect Pests ◽  
Sub Saharan Africa ◽  
Sub Saharan ◽  
Native Invasive

scholarly journals Genetic Basis of Maize Resistance to Multiple Insect Pests: Integrated Genome-Wide Comparative Mapping and Candidate Gene Prioritization

Genes ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 689
Author(s):  
A. Badji ◽  
D. B. Kwemoi ◽  
L. Machida ◽  
D. Okii ◽  
N. Mwila ◽  
...  
Keyword(s):  
Insect Resistance ◽  
Defense Mechanisms ◽  
Genome Wide Association Study ◽  
Insect Pests ◽  
Physical Map ◽  
Fall Armyworm ◽  
Nucleotide Polymorphisms ◽  
Multiple Traits ◽  
Maize Weevil ◽  
Genome Wide

Several species of herbivores feed on maize in field and storage setups, making the development of multiple insect resistance a critical breeding target. In this study, an association mapping panel of 341 tropical maize lines was evaluated in three field environments for resistance to fall armyworm (FAW), whilst bulked grains were subjected to a maize weevil (MW) bioassay and genotyped with Diversity Array Technology’s single nucleotide polymorphisms (SNPs) markers. A multi-locus genome-wide association study (GWAS) revealed 62 quantitative trait nucleotides (QTNs) associated with FAW and MW resistance traits on all 10 maize chromosomes, of which, 47 and 31 were discovered at stringent Bonferroni genome-wide significance levels of 0.05 and 0.01, respectively, and located within or close to multiple insect resistance genomic regions (MIRGRs) concerning FAW, SB, and MW. Sixteen QTNs influenced multiple traits, of which, six were associated with resistance to both FAW and MW, suggesting a pleiotropic genetic control. Functional prioritization of candidate genes (CGs) located within 10–30 kb of the QTNs revealed 64 putative GWAS-based CGs (GbCGs) showing evidence of involvement in plant defense mechanisms. Only one GbCG was associated with each of the five of the six combined resistance QTNs, thus reinforcing the pleiotropy hypothesis. In addition, through in silico co-functional network inferences, an additional 107 network-based CGs (NbCGs), biologically connected to the 64 GbCGs, and differentially expressed under biotic or abiotic stress, were revealed within MIRGRs. The provided multiple insect resistance physical map should contribute to the development of combined insect resistance in maize.

scholarly journals Efficacy of a cry1Ab Gene for Control of Maruca vitrata (Lepidoptera: Crambidae) in Cowpea (Fabales: Fabaceae)

2020 ◽  
Vol 113 (2) ◽  
pp. 974-979
Author(s):  
Prince C Addae ◽  
Mohammad F Ishiyaku ◽  
Jean-Batiste Tignegre ◽  
Malick N Ba ◽  
Joseph B Bationo ◽  
...  
Keyword(s):  
Insect Pests ◽  
Field Trials ◽  
Genetically Engineered ◽  
Maruca Vitrata ◽  
Sub Saharan Africa ◽  
High Dose ◽  
Cry1ab Protein ◽  
Cry1ab Gene ◽  
Pod Borer ◽  
Sub Saharan

Abstract Cowpea [Vigna unguiculata (L) Walp.] is an important staple legume in the diet of many households in sub-Saharan Africa. Its production, however, is negatively impacted by many insect pests including bean pod borer, Maruca vitrata F., which can cause 20–80% yield loss. Several genetically engineered cowpea events that contain a cry1Ab gene from Bacillus thuringiensis (Bt) for resistance against M. vitrata were evaluated in Nigeria, Burkina Faso, and Ghana (West Africa), where cowpea is commonly grown. As part of the regulatory safety package, these efficacy data were developed and evaluated by in-country scientists. The Bt-cowpea lines were planted in confined field trials under Insect-proof netting and artificially infested with up to 500 M. vitrata larvae per plant during bud formation and flowering periods. Bt-cowpea lines provided nearly complete pod and seed protection and in most cases resulted in significantly increased seed yield over non-Bt control lines. An integrated pest management strategy that includes use of Bt-cowpea augmented with minimal insecticide treatment for protection against other insects is recommended to control pod borer to enhance cowpea production. The insect resistance management plan is based on the high-dose refuge strategy where non-Bt-cowpea and natural refuges are expected to provide M. vitrata susceptible to Cry1Ab protein. In addition, there will be a limited release of this product until a two-toxin cowpea pyramid is released. Other than South African genetically engineered crops, Bt-cowpea is the first genetically engineered food crop developed by the public sector and approved for release in sub-Saharan Africa.

Molecular Improvement of Tropical Maize for Drought Stress Tolerance in Sub-Saharan Africa

2009 ◽  
Vol 28 (1-2) ◽  
pp. 16-35 ◽  
Author(s):  
Sylvester Anami ◽  
Marc De Block ◽  
Jesse Machuka ◽  
Mieke Van Lijsebettens
Keyword(s):  
Drought Stress ◽  
Stress Tolerance ◽  
Sub Saharan Africa ◽  
Tropical Maize ◽  
Drought Stress Tolerance ◽  
Sub Saharan

scholarly journals Analysis of strain distribution, migratory potential, and invasion history of fall armyworm populations in northern Sub-Saharan Africa

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Rodney N. Nagoshi ◽  
Georg Goergen ◽  
Kodjo Agbeko Tounou ◽  
Komi Agboka ◽  
Djima Koffi ◽  
...  
Keyword(s):  
Strain Distribution ◽  
Fall Armyworm ◽  
Sub Saharan Africa ◽  
Invasion History ◽  
History Of ◽  
Sub Saharan

scholarly journals Managing a Transboundary Pest: The Fall Armyworm on Maize in Africa

2021 ◽  
Author(s):  
Michael Hilary Otim ◽  
Komi Kouma Mokpokpo Fiaboe ◽  
Juliet Akello ◽  
Barnabas Mudde ◽  
Allan Tekkara Obonyom ◽  
...  
Keyword(s):  
Host Range ◽  
Sustainable Management ◽  
Fall Armyworm ◽  
Reproductive Rate ◽  
Sub Saharan Africa ◽  
Migration Ability ◽  
Rapid Spread ◽  
Sub Saharan ◽  
Lepidoptera Noctuidae ◽  
High Migration

The fall armyworm (Spodoptera frugiperda J.E Smith) (Lepidoptera: Noctuidae) invaded Africa in 2016, and has since spread to all countries in sub-Saharan Africa, causing devastating effects on mainly maize and sorghum. The rapid spread of this pest is aided by its high reproductive rate, high migration ability, wide host range and adaptability to different environments, among others. Since its introduction, many governments purchased and distributed pesticides for emergency control, with minimal regard to their efficacy. In this chapter, we review efforts towards managing this pest, highlight key challenges, and provide our thoughts on considerations for sustainable management of the pest.

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