Stacking tolerance to drought and resistance to a parasitic weed in tropical hybrid maize for enhancing resilience to stress combinations

Open Access JournalMaize is a food security crop cultivated in the African savannas that are vulnerable to the occurrence of drought stress and Striga hermonthica infestation. The co-occurrence of these stresses can severely damage crop growth and productivity of maize. Until recently, maize breeding in International Institute of Tropical Agriculture (IITA) has focused on the development of either drought tolerant or S. hermonthica resistant germplasm using independent screening protocols. The present study was therefore conducted to examine the extent to which maize hybrids simultaneously expressing resistance to S. hermonthica and tolerance to drought (DTSTR) could be developed through sequential selection of parental lines using the two screening protocols. Regional trials involving 77 DTSTR and 22 commercial benchmark hybrids (STR and non-DTSTR) were then conducted under Striga-infested and non-infested conditions, managed drought stress and fully irrigated conditions as well as in multiple rainfed environments for 5 years. The observed yield reductions of 61% under managed drought stress and 23% under Striga-infestation created desirable stress levels leading to the detection of significant differences in grain yield among hybrids at individual stress and non-stress conditions. On average, the DTSTR hybrids out-yielded the STR and non-DTSTR commercial hybrids by 13–19% under managed drought stress and fully irrigated conditions and by −4 to 70% under Striga-infested and non-infested conditions. Among the DTSTR hybrids included in the regional trials, 33 were high yielders with better adaptability across environments under all stressful and non-stressful testing conditions. Twenty-four of the 33 DTSTR hybrids also yielded well across diverse rainfed environments. The genetic correlations of grain yield under managed drought stress with yield under Striga-infestation and multiple rainfed environments were 0.51 and 0.57, respectively. Also, a genetic correlation between yields under Striga-infestation with that recorded in multiple rainfed environments was 0.58. These results suggest that the sequential selection scheme offers an opportunity to accumulate desirable stress-related traits in parents contributing to superior agronomic performance in hybrids across stressful and diverse rainfed field environments that are commonly encountered in the tropical savannas of Africa

SPATIAL ANALYSIS OF YIELD TRIALS USING SEPARABLE ARIMA PROCESSES

Spatial analysis procedures based on one-dimensional and two-dimensional (separable) ARIMA (Auto Regressive Integrated Moving Average) processes were used to analyze several yield trials. Two criteria were used to determine the best spatial model: 1) standard error of the treatment difference (SED) and 2) mean squared error (MSE) of prediction based on a cross-validation approach. It is found that spatial models with two-dimensional exponential covariance functions are frequently the best models regarding SED and MSE. Differenced models are frequently the best models regarding SED and the worst with respect to MSE

Hierarchical multiple-factor analysis for classifying genotypes based on phenotypic and genetic data

A numerical classifi cation problem encountered by breeders and gene-bank curators is how to partition the original heterogeneous population of genotypes into non-overlapping homogeneous subpopulations. The measure of distance that may be defi ned depends on the type of variables measured (i.e., continuous and/or discrete). The key points are whether and how a distance may be defi ned using all types of variables to achieve effective classifi cation. The objective of this research was to propose an approach that combines the use of hierarchical multiple-factor analysis (HMFA) and the two-stage Ward Modifi ed Location Model (Ward-MLM) classifi cation strategy that allows (i) combining different types of phenotypic and genetic data simultaneously; (ii) balancing out the effects of the different phenotypic, genetic, continuous, and discrete variables; and (iii) measuring the contribution of each original variable to the new principal axes (PAs). Of the two strategies applied for developing PA scores to be used for clustering genotypes, the strategy that used the fi rst few PA scores to which phenotypic and genetic variables each contributed 50% (i.e., a balanced contribution) formed better groups than those formed by the strategy that used a large number of PA scores explaining 95% of total variability. Phenotypic variables account for much variability in the initial PA; then their contributions decrease. The importance of genetic variables increases in later PAs. Results showed that various phenotypic and genetic variables made important contributions to the new PA. The HMFA uses all phenotypic and genetic variables simultaneously and, in conjunction with the Ward-MLM method, it offers an effective unifying approach for the classifi cation of breeding genotypes into homogeneous groups and for the formation of core subsets for genetic resource conservation

Enhancing Hybrid Prediction in Pearl Millet Using Genomic and/or Multi-Environment Phenotypic Information of Inbreds

Genomic selection (GS) is an emerging methodology that helps select superior lines among experimental cultivars in plant breeding programs. It offers the opportunity to increase the productivity of cultivars by delivering increased genetic gains and reducing the breeding cycles. This methodology requires inexpensive and sufficiently dense marker information to be successful, and with whole genome sequencing, it has become an important tool in many crops. The recent assembly of the pearl millet genome has made it possible to employ GS models to improve the selection procedure in pearl millet breeding programs. Here, three GS models were implemented and compared using grain yield and dense molecular marker information of pearl millet obtained from two different genotyping platforms (C [conventional GBS RAD-seq] and T [tunable GBS tGBS]). The models were evaluated using three different cross-validation (CV) schemes mimicking real situations that breeders face in breeding programs: CV2 resembles an incomplete field trial, CV1 predicts the performance of untested hybrids, and CV0 predicts the performance of hybrids in unobserved environments. We found that (i) adding phenotypic information of parental inbreds to the calibration sets improved predictive ability, (ii) accounting for genotype-by-environment interaction also increased the performance of the models, and (iii) superior strategies should consider the use of the molecular markers derived from the T platform (tGBS)

Genetic gains in yield and yield related traits under drought stress and favorable environments in a maize population improved using marker assisted recurrent selection

The objective of marker assisted recurrent selection (MARS) is to increase the frequency of favorable marker alleles in a population before inbred line extraction. This approach was used to improve drought tolerance and grain yield (GY) in a biparental cross of two elite drought tolerant lines. The testcrosses of randomly selected 50 S1 lines from each of the three selection cycles (C0, C1, C2) of the MARS population, parental testcrosses and the cross between the two parents (F1) were evaluated under drought stress (DS) and well watered (WW) well as under rainfed conditions to determine genetic gains in GY and other agronomic traits. Also, the S1 lines derived from each selection types were genotyped with single nucleotide polymorphism (SNP) markers. Testcrosses derived from C2 produced significantly higher grain field under DS than those derived from C0 with a relative genetic gain of 7% per cycle. Also, the testcrosses of S1 lines from C2 showed an average genetic gain of 1% per cycle under WW condition and 3% per cycle under rainfed condition. Molecular analysis revealed that the frequency of favorable marker alleles increased from 0.510 at C0 to 0.515 at C2, while the effective number of alleles (Ne) per locus decreased from C0 (1.93) to C2 (1.87). Our results underscore the effectiveness of MARS for improvement of GY under DS condition

Impacto del desarrollo de híbridos de maíz en Centro América.

En este trabajo se analiza el progreso alcanzado en el desarrollo de híbridos de maíz en Centro América utilizando como fuente la base de datos regional del PCCMCA (Programa Colaborativo Centroamericano para el Mejoramiento de Cultivos y Animales) y tomando como referencia 51 ensayos realizados entre 1988 y 1990. Esta evaluación se 1Ievó a cabo utilizando una metodología que determina la confiabilidad de respuesta (RNi o probabilidad de respuesta normalizada superior a cero) de los cultivares superiores con respecto a un cultivar testigo. Se determinó la confiabilidad de los cultivares (HB83, HB85, H30, HA46, Dekalb B833, Pioneer XLH53, HRI7, MAX307, H33 y TACSA 203) como una medida que integra tanto la magnitud de respuesta como la variabilidad en rendimiento con respecto al testigo H5. Los geno tipos evaluados se clasificaron en tres clases con respecto al H5. Genotipos de confiabilidad Superior (H30 y HB85 0,9 >= RNi = RNi = RNi < 0,8). Entre los genotipos que mostraron mayor potencial como testigos regionales se encuentran el HB85 y HB83. Se concluye que se necesitan mayores estudios para correlacionar el uso de la confiabilidad con otros métodos para determinar la estabilidad genotípica

Variability in glutenin subunit composition of Mediterranean durum wheat germplasm and its relationship with gluten strength

The allelic composition at five glutenin loci was assessed by one-dimensional sodium dodecyl sulphate polyacrylamide gel electrophoresis (1D SDS–PAGE) on a set of 155 landraces (from 21 Mediterranean countries) and 18 representative modern varieties. Gluten strength was determined by SDS-sedimentation on samples grown under rainfed conditions during 3 years in north-eastern Spain. One hundred and fourteen alleles/banding patterns were identified (25 at Glu-1 and 89 at Glu-2/Glu-3 loci); 0·85 of them were in landraces at very low frequency and 0·72 were unreported. Genetic diversity index was 0·71 for landraces and 0·38 for modern varieties. All modern varieties exhibited medium to strong gluten type with none of their 13 banding patterns having a significant effect on gluten-strength type. Ten banding patterns significantly affected gluten strength in landraces. Alleles Glu-B1e (band 20), Glu-A3a (band 6), Glu-A3d (bands 6+11), Glu-B3a (bands 2+4+15+19) and Glu-B2a (band 12) significantly increased the SDS-value, and their effects were associated with their frequency. Two alleles, Glu-A3b (band 5) and Glu-B2b (null), significantly reduced gluten strength, but only the effect of the latter locus could be associated with its frequency. Only three rare banding patterns affected gluten strength significantly: Glu-B1a (band 7), found in six landraces, had a negative effect, whereas banding patterns 2+4+14+15+18 and 2+4+15+18+19 at Glu-B3 had a positive effect. Landraces with outstanding gluten strength were more frequent in eastern than in western Mediterranean countries. The geographical pattern displayed from the frequencies of Glu-A1c is discussed.R. Nazco was recipient of a Ph.D. grant from the Comissionat per Universitats i Investigació del Departament d’Innovació, Universitats i Empresa of the Generalitat of Catalonia and the Fondo Social Europeo. This study was partially funded by CICYT under projects AGL2006-09226-C02-01, AGL2009- 11187 and AGL2012-37217 and was developed within the framework of the agreement between INIA Spain and CIMMYT. The Centre UdL-IRTA is part of the Centre CONSOLIDER INGENIO 2010 on Agrigenomics funded by the Spanish Ministry of Education and Science. CRF, ICARDA and USDA Germplasm Bank are acknowledged for providing accessions for the present study.info:eu-repo/semantics/publishedVersio

Using an incomplete block design to allocate lines to environments improves sparse genome‐based prediction in plant breeding

Genomic selection (GS) is a predictive methodology that trains statistical machine-learning models with a reference population that is used to perform genome-enabled predictions of new lines. In plant breeding, it has the potential to increase the speed and reduce the cost of selection. However, to optimize resources, sparse testing methods have been proposed. A common approach is to guarantee a proportion of nonoverlapping and overlapping lines allocated randomly in locations, that is, lines appearing in some locations but not in all. In this study we propose using incomplete block designs (IBD), principally, for the allocation of lines to locations in such a way that not all lines are observed in all locations. We compare this allocation with a random allocation of lines to locations guaranteeing that the lines are allocated to the same number of locations as under the IBD design. We implemented this benchmarking on several crop data sets under the Bayesian genomic best linear unbiased predictor (GBLUP) model, finding that allocation under the principle of IBD outperformed random allocation by between 1.4% and 26.5% across locations, traits, and data sets in terms of mean square error. Although a wide range of performance improvements were observed, our results provide evidence that using IBD for the allocation of lines to locations can help improve predictive performance compared with random allocation. This has the potential to be applied to large-scale plant breeding programs