Towards understanding the traits contributing to performance of pearl millet open-pollinated varieties in phosphorus-limited environments of West Africa

Aims Pearl millet [Pennisetum glaucum (L.) R. Br.] open-pollinated varieties, which are the predominant cultivars, have never been systematically evaluated for adaptation to low-soil phosphorus (P), a major constraint on pearl millet production in West Africa (WA). Methods We evaluated grain yield (GY), flowering time (FLO), harvest index (HI), and residual grain yields (RGY) of 102 open-pollinated varieties from WA under low-P (−P) and high-P (+P) field conditions in six environments of WA. In addition, PE-related traits of the varieties were evaluated at early growth stage in a pot experiment. Results Significant genetic variation was observed for GY, FLO, HI and PE-related traits. P-efficient varieties had higher yield under −P conditions. Varietal performance under −P varied across environments depending on FLO, relative flowering delay under −P (FD) and RGY measured in the field. Low-P-susceptible varieties had higher FLO, lower HI than low-P-tolerant varieties. Response to direct selection under −P field conditions was 20.1 g m−2, whereas indirect selection response under +P was 16.3 g m−2. Conclusions Selection under −P field conditions while taking into account seasonal variations for FLO, FD and PE is expected to be important for improving GY specifically targeting −P environments in WA

Association analysis of low-phosphorus tolerance in West African pearl millet using DArT markers

Pearl millet [Pennisetum glaucum (L.) R. Br.] is a food security crop in the harshest agricultural regions of the world. While low soil phosphorus (P) availability is a big constraint on its production, especially in West Africa (WA), information on genomic regions responsible for low-P tolerance in pearl millet is generally lacking. We present the first report on genetic polymorphisms underlying several plant P-related parameters, flowering time (FLO) and grain yield (GY) under P-limiting conditions based on 285 diversity array technology markers and 151 West African pearl millet inbred lines phenotyped in six environments in WA under both high-P and low-P conditions. Nine markers were significantly associated with P-related traits, nine markers were associated with FLO, whereas 13 markers were associated with GY each explaining between 5.5 and 15.9 % of the observed variation. Both constitutive and adaptive associations were observed for FLO and GY, with markers PgPb11603 and PgPb12954 being associated with the most stable effects on FLO and GY, respectively, across locations. There were a few shared polymorphisms between traits, especially P-efficiency-related traits and GY, implying possible colocation of genomic regions responsible for these traits. Our findings help bridge the gap between quantitative and molecular methods of studying complex traits like low-P tolerance in WA. However, validation of these markers is necessary to determine their potential applicability in marker-assisted selection programs targeting low-P environments, which are especially important in WA where resource-poor farmers are expected to be the hardest hit by the approaching global P crisis

Performance of cowpea varieties under Striga gesnerioides (Willd.) Vatke infestation using biplot analysis

Published online: 10 October 2017Striga gesnerioides (Willd) Vatke, is a major destructive parasitic weed of cowpea (Vigna unguiculata (L.) Walp.) which causes substantial yield reduction in West and Central Africa. The presence of different virulent races within the parasite population contributes to significant genotype × environment interaction, and complicates breeding for durable resistance to Striga. A 3-year study was conducted at three locations in the dry savanna agro-ecology of Nigeria, where Striga gesnerioides is endemic. The primary objective of the study was to identify cowpea genotypes with high yield under Striga infestation and yield stability across test environments and to access suitability of the test environment. Data collected on grain yield and yield components were subjected to analysis of variance (ANOVA). Means from ANOVA were subjected to the genotype main effect plus genotype × environment (GGE) biplot analysis to examine the multi-environment trial data and rank genotypes according to the environments. Genotypes, environment, and genotypes × environment interaction mean squares were significant for grain yield and yield components, and number of emerged Striga plants. The environment accounted for 35.01%, whereas the genotype × environment interaction accounted for 9.10% of the variation in grain yield. The GGE biplot identified UAM09 1046-6-1 (V7), and UAM09 1046-6-2 (V8), as ideal genotypes suggesting that these genotypes performed relatively well in all study environments and could be regarded as adapted to a wide range of locations. Tilla was the most repeatable and ideal location for selecting widely adapted genotypes for resistance to S. gesnerioides