Selection of oat lines for use in low-productivity environments

When crop varieties are bred for use in low-productivity environments (LPE), it must be decided whether to select directly, in LPE, or indirectly, in high-productivity environments (HPE). The relative performance of these strategies depends upon both the genetic correlation (r[subscript] G) for yields between and the heritabilities (H[superscript]2) within environments. It was hypothesized that direct selection in LPE may be more effective than indirect selection in HPE in some cases, and that such cases can be predicted on the basis of estimates of r[subscript] G and H[superscript]2;These hypotheses were tested in a population of 116 random oat lines tested in 36 yield trials. These trials were classified as LPE, MPE (medium-productivity environments), or HPE according to their mean yields. Among the 12 designated as LPE, individual trials were low yielding due to N deficiency, P deficiency, or heat stress caused by late sowing. Estimates of H[superscript]2 for grain yields within and r[subscript] G among productivity levels were used to predict expected responses in LPE to selection in LPE, MPE, and HPE. H[superscript]2 was highest in HPE, but r[subscript] G between yields in LPE and HPE was only 0.59. Estimates of r[subscript] G between nonstress and P-deficient, N-deficient, and heat-stressed environments were 0.5 ± 0.24, 1.08 ± 0.16, and 0.06 ± 0.24, respectively, indicating that P-deficient and heat-stressed environments were responsible for the low r[subscript] G between yields in LPE and HPE. For 10% selection based on line means in 2 or 4 two-replicate trials, the greatest yield gain in LPE was predicated to result from selection in MPE, but for selection in 12 six-replicate trials, direct selection in LPE was superior. These predictions were tested in three empirical selection experiments, wherein comparisons of direct and indirect selection for grain yield were made in two populations of oat lines tested in a total of three sets of environments. In two of these experiments, direct selection of LPE was superior to indirect selection in HPE. In all three, increased replication improved the efficiency of direct selection in LPE. These results confirm that neither HPE nor environments in which H[superscript]2 is greatest necessarily maximize yield gain in LPE

Assessing the impact of participatory research in rice breeding on poor rice farming households with emphasis on women farmers: a case study in eastern Uttar Pradesh, India

For the past years since the Consultative Group of International Agricultural Research (CGIAR) Systemwide Initiative on Participatory Research and Gender Analysis (PRGA) was initiated, guides for impact assessment of PRGA have been developed (Lilja and Ashby 1999; Johnson et.al., 2000; Lilja and Johnson 2001). However, according to Farnworth and Jiggins (2003) while there is rapidly growing literature on the impacts of PPB on farmers, this is not further differentiated by sex. Despite the immense literature on the impacts of production, post production technologies on women farmers, systematic studies on the impacts of PPB on women in any category, either in terms of the effects of being a participant in a participatory plant breeding process (PPB) process, or in terms of the impact of the new materials generated is few. There is practically no literature that examines the effects of PPB – either as process or in terms of the impacts of the emergent materials – on gender relations at the household, community or any other relevant social or geographic scale along the food chain. Even with women’s active involvement in rice production, post harvest and seed management, scientists who are mostly male often talk with the male farmers only. Ignoring women’s knowledge and preference for rice varieties may be an obstacle to adoption of improved varieties, particularly in areas with gender-specific tasks, and in farm activities where women have considerable influence. Feldstein (1996) cited three different ways in which gender analysis can be considered in participatory research. These are: the efficiency argument, equity oriented, and empowerment. This study attempts to fill in these research gaps. The objectives of this paper are to: a) discuss the process used in integrating participatory research and gender analysis in breeding for drought prone and submergence prone environment; b) assess how gender analysis contributed to the design and implementation of the research and development outcomes; c) assess the impacts of PVS on poor women farmers, particularly on women’s empowerment; and d) recommend strategies to further enhance women’s roles in ensuring household food (rice) food security and improving their social status within the household and the community

Identification of drought, heat and combined drought and heat tolerant donors in maize (Zea mays L.)

Low maize yields and the impacts of climate change on maize production highlight the need to improve yields in eastern and southern Africa. Climate projections suggest higher temperatures within drought-prone areas. Research in model species suggests that tolerance to combined drought and heat stress is genetically distinct from tolerance to either stress alone, but this has not been confirmed in maize. In this study we evaluated 300 maize inbred lines testcrossed to CML539. Experiments were conducted under optimal conditions, reproductive stage drought stress, heat stress and combined drought and heat stress. Lines with high levels of tolerance to drought and combined drought and heat stress were identified. Significant genotype x trial interaction and very large plot residuals were observed; consequently, the repeatability of individual managed stress trials was low. Tolerance to combined drought and heat stress in maize was genetically distinct from tolerance to individual stresses, and tolerance to either stress alone did not confer tolerance to combined drought and heat stress. This finding has major implications for maize drought breeding. Many current drought donors and key inbreds used in widely-grown African hybrids were susceptible to drought stress at elevated temperatures. Several donors tolerant to drought and combined drought and heat stress, notably La Posta Sequia C7-F64-2-6-2-2 and DTPYC9-F46-1-2-1-2, need to be incorporated into maize breeding pipelines

Effectiveness of Genomic Prediction of Maize Hybrid Performance in Different Breeding Populations and Environments

Genomic prediction is expected to considerably increase genetic gains by increasing selection intensity and accelerating the breeding cycle. In this study, marker effects estimated in 255 diverse maize (Zea mays L.) hybrids were used to predict grain yield, anthesis date, and anthesis-silking interval within the diversity panel and testcross progenies of 30 F(2)-derived lines from each of five populations. Although up to 25% of the genetic variance could be explained by cross validation within the diversity panel, the prediction of testcross performance of F(2)-derived lines using marker effects estimated in the diversity panel was on average zero. Hybrids in the diversity panel could be grouped into eight breeding populations differing in mean performance. When performance was predicted separately for each breeding population on the basis of marker effects estimated in the other populations, predictive ability was low (i.e., 0.12 for grain yield). These results suggest that prediction resulted mostly from differences in mean performance of the breeding populations and less from the relationship between the training and validation sets or linkage disequilibrium with causal variants underlying the predicted traits. Potential uses for genomic prediction in maize hybrid breeding are discussed emphasizing the need of (1) a clear definition of the breeding scenario in which genomic prediction should be applied (i.e., prediction among or within populations), (2) a detailed analysis of the population structure before performing cross validation, and (3) larger training sets with strong genetic relationship to the validation set

Selection of oat lines for use in low-productivity environments

When crop varieties are bred for use in low-productivity environments (LPE), it must be decided whether to select directly, in LPE, or indirectly, in high-productivity environments (HPE). The relative performance of these strategies depends upon both the genetic correlation (r[subscript] G) for yields between and the heritabilities (H[superscript]2) within environments. It was hypothesized that direct selection in LPE may be more effective than indirect selection in HPE in some cases, and that such cases can be predicted on the basis of estimates of r[subscript] G and H[superscript]2;These hypotheses were tested in a population of 116 random oat lines tested in 36 yield trials. These trials were classified as LPE, MPE (medium-productivity environments), or HPE according to their mean yields. Among the 12 designated as LPE, individual trials were low yielding due to N deficiency, P deficiency, or heat stress caused by late sowing. Estimates of H[superscript]2 for grain yields within and r[subscript] G among productivity levels were used to predict expected responses in LPE to selection in LPE, MPE, and HPE. H[superscript]2 was highest in HPE, but r[subscript] G between yields in LPE and HPE was only 0.59. Estimates of r[subscript] G between nonstress and P-deficient, N-deficient, and heat-stressed environments were 0.5 ± 0.24, 1.08 ± 0.16, and 0.06 ± 0.24, respectively, indicating that P-deficient and heat-stressed environments were responsible for the low r[subscript] G between yields in LPE and HPE. For 10% selection based on line means in 2 or 4 two-replicate trials, the greatest yield gain in LPE was predicated to result from selection in MPE, but for selection in 12 six-replicate trials, direct selection in LPE was superior. These predictions were tested in three empirical selection experiments, wherein comparisons of direct and indirect selection for grain yield were made in two populations of oat lines tested in a total of three sets of environments. In two of these experiments, direct selection of LPE was superior to indirect selection in HPE. In all three, increased replication improved the efficiency of direct selection in LPE. These results confirm that neither HPE nor environments in which H[superscript]2 is greatest necessarily maximize yield gain in LPE.</p

MET_GBS_data_crfilt_.75_allchrom.hmp.txt

Post-imputation GBS data for the IRRI breeding population used in Spindel et al., 2015 and Begum and Spindel et al., 2015 (with additional filtering, see papers for details). GBS dataset contains all SNPs with call rates >= .75 and all individuals with missing data < .

Data from: Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines

Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the International Rice Research Institute's (IRRI) irrigated rice breeding program and herein report the GS results. The population was genotyped with 73,147 markers using genotyping-by-sequencing. The training population, statistical method used to build the GS model, number of markers, and trait were varied to determine their effect on prediction accuracy. For all three traits, genomic prediction models outperformed prediction based on pedigree records alone. Prediction accuracies ranged from 0.31 and 0.34 for grain yield and plant height to 0.63 for flowering time. Analyses using subsets of the full marker set suggest that using one marker every 0.2 cM is sufficient for genomic selection in this collection of rice breeding materials. RR-BLUP was the best performing statistical method for grain yield where no large effect QTL were detected by GWAS, while for flowering time, where a single very large effect QTL was detected, the non-GS multiple linear regression method outperformed GS models. For plant height, in which four mid-sized QTL were identified by GWAS, random forest produced the most consistently accurate GS models. Our results suggest that GS, informed by GWAS interpretations of genetic architecture and population structure, could become an effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline

Phenotypic (Pheno) and genotypic (Geno) correlations between grain yield and senescence measured 4 (SEN4) and 6 (SEN6) weeks after flowering, the numbers of ears per plant (EPP), NDVI4, the anthesis silking interval (ASI), plant height (PHT) and anthesis.

<p>Phenotypic (Pheno) and genotypic (Geno) correlations between grain yield and senescence measured 4 (SEN4) and 6 (SEN6) weeks after flowering, the numbers of ears per plant (EPP), NDVI4, the anthesis silking interval (ASI), plant height (PHT) and anthesis.</p

Summary table of all overdominant QTL measured in the DTPpop (DTP) and LPSpop (LPS) under well-watered (WW) and drought stressed (DS) conditions showing position (Pos), bin, flanking markers, LOD scores, phenotypic variance explained by a QTL (PVE), additive and dominance effects.

<p>Traits measured are senescence 4 (SEN4) and 6 (SEN6) weeks after flowering, NDVI, grain yield (GY), ears per plants (EPP), anthesis, the anthesis silking interval (ASI) and plant height (PHT).</p