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

Special Libraries, April 1919

Volume 10, Issue 3https://scholarworks.sjsu.edu/sla_sl_1919/1002/thumbnail.jp

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

50 years of rice breeding in Bangladesh: genetic yield trends

To assess the efficiency of genetic improvement programs, it is essential to assess the genetic trend in long-term data. The present study estimates the genetic trends for grain yield of rice varieties released between 1970 and 2020 by the Bangladesh Rice Research Institute. The yield of the varieties was assessed from 2001–2002 to 2020–2021 in multi-locations trials. In such a series of trials, yield may increase over time due to (i) genetic improvement (genetic trend) and (ii) improved management or favorable climate change (agronomic/non-genetic trend). In both the winter and monsoon seasons, we observed positive genetic and non-genetic trends. The annual genetic trend for grain yield in both winter and monsoon rice varieties was 0.01 t ha−1, while the non-genetic trend for both seasons was 0.02 t ha−1, corresponding to yearly genetic gains of 0.28% and 0.18% in winter and monsoon seasons, respectively. The overall percentage yield change from 1970 until 2020 for winter rice was 40.96%, of which 13.91% was genetic trend and 27.05% was non-genetic. For the monsoon season, the overall percentage change from 1973 until 2020 was 38.39%, of which genetic and non-genetic increases were 8.36% and 30.03%, respectively. Overall, the contribution of non-genetic trend is larger than genetic trend both for winter and monsoon seasons. These results suggest that limited progress has been made in improving yield in Bangladeshi rice breeding programs over the last 50 years. Breeding programs need to be modernized to deliver sufficient genetic gains in the future to sustain Bangladeshi food security

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

Identification of QTL for Early Vigor and Stay-Green Conferring Tolerance to Drought in Two Connected Advanced Backcross Populations in Tropical Maize (Zea mays L.).

We aimed to identify quantitative trait loci (QTL) for secondary traits related to grain yield (GY) in two BC1F2:3 backcross populations (LPSpop and DTPpop) under well-watered (4 environments; WW) and drought stressed (6; DS) conditions to facilitate breeding efforts towards drought tolerant maize. GY reached 5.6 and 5.8 t/ha under WW in the LPSpop and the DTPpop, respectively. Under DS, grain yield was reduced by 65% (LPSpop) to 59% (DTPpop) relative to WW. GY was strongly associated with the normalized vegetative index (NDVI; r ranging from 0.61 to 0.96) across environmental conditions and with an early flowering under drought stressed conditions (r ranging from -0.18 to -0.25) indicative of the importance of early vigor and drought escape for GY. Out of the 105 detected QTL, 53 were overdominant indicative of strong heterosis. For 14 out of 18 detected vigor QTL, as well as for eight flowering time QTL the trait increasing allele was derived from CML491. Collocations of early vigor QTL with QTL for stay green (bin 2.02, WW, LPSpop; 2.07, DS, DTPpop), the number of ears per plant (bins 2.02, 2.05, WW, LPSpop; 5.02, DS, LPSpop) and GY (bin 2.07, WW, DTPpop; 5.04, WW, LPSpop), reinforce the importance of the observed correlations. LOD scores for early vigor QTL in these bins ranged from 2.2 to 11.25 explaining 4.6 (additivity: +0.28) to 19.9% (additivity: +0.49) of the observed phenotypic variance. A strong flowering QTL was detected in bin 2.06 across populations and environmental conditions explaining 26-31.3% of the observed phenotypic variation (LOD: 13-17; additivity: 0.1-0.6d). Improving drought tolerance while at the same time maintaining yield potential could be achieved by combining alleles conferring early vigor from the recurrent parent with alleles advancing flowering from the donor. Additionally bin 8.06 (DTPpop) harbored a QTL for GY under WW (additivity: 0.27 t/ha) and DS (additivity: 0.58 t/ha). R2 ranged from 0 (DTPpop, WW) to 26.54% (LPSpop, DS) for NDVI, 18.6 (LPSpop, WW) to 42.45% (LPSpop, DS) for anthesis and from 0 (DTPpop, DS) to 24.83% (LPSpop, WW) for GY. Lines out-yielding the best check by 32.5% (DTPpop, WW) to 60% (DTPpop, DS) for all population-by-irrigation treatment combination (except LPSpop, WW) identified are immediately available for the use by breeders