T he revolution in sequencing technologies has enabled fast and relatively inexpensive genome information (Metzker, 2010 Abbreviations: AMMI, additive main effect and multiplicative interaction; a top10 , mean relatedness of the top10 individuals in the validation set to those in the training set; AYT, advanced yield trial; BLUE, best linear unbiased estimator; BLUP, best linear unbiased predictor; CMD, cassava mosaic disease; CMDI, cassava mosaic disease incidence; CV-CR, cross-validation close relatives; CV-GE, crossvalidation genotype × environment; CV-Random, random crossvalidation; CV-Random_Half, cross-validation scheme in which a randomly chosen half of the observations are used; CV-noCR, cross-validation no close relatives; DM, root dry matter content; GS, genomic selection; G×E, genotype × environment; GBS, genotyping by sequencing; MAS, marker-assisted selection; MCBBI, mean cassava bacterial blight incidence; PYT, preliminary yield trial; RCBD, randomized complete-block design; RKHS, reproducing kernel Hilbert spaces; SNP, single nucleotide polymorphism; top10, 10 most closely related individuals; UYT, uniform yield trial.

Food insecurity and malnutrition are major challenges facing rural populations in sub‐Saharan Africa. A total of 150 quality protein maize (Zea mays L.) (QPM) hybrids generated from 30 early‐maturing QPM inbreds plus six checks were evaluated under drought, low soil N, and Striga [Striga hermonthica (Delile) Benth.]‐infested environments in Nigeria for 2 yr. The objectives were to (i) examine the gene action conditioning the traits in the inbreds, (ii) classify them into heterotic groups using two methods, (iii) identify the best QPM inbred testers across environments, and (iv) identify stable and high‐yielding hybrids. General and specific combining ability (GCA and SCA, respectively) mean squares were significant (P < 0.01) for grain yield and other traits across environments, indicating that additive and nonadditive gene actions were important in the inheritance of most traits of the inbreds. Preponderance of SCA sum of squares over GCA for most measured traits across environments indicated that nonadditive gene action largely modulated inbred trait inheritance. The GCA effects of multiple traits (HGCAMT) method classified the inbreds into three heterotic groups each under drought and across environments and four groups under low N and Striga‐infested environments. Single nucleotide polymorphism (SNP)‐based method placed the inbreds into three groups across environments and was more efficient. TZEQI 6 and TZEQI 55 were identified as testers across environments. TZEQI 44 × TZEQI 4, TZEQI 35 × TZEQI 39, TZEQI 35 × TZEQI 59, TZEQI 6 × TZEQI 35, and TZEQI 45 × TZEQI 33 were the most stable and highest‐yielding hybrids across environments and should be commercialized for improved nutrition and food security in sub‐Saharan Africa.

a b s t r a c tFood insecurity and malnutrition are two major challenges facing rural populations in sub-Saharan Africa (SSA). Hybrids of quality protein maize (QPM) have a crucial role here to play because QPM contains increased lysine and tryptophan concentrations and has a higher biological value than the normal maize. Information on the combining ability and heterotic patterns of QPM inbreds is crucial for the success of hybrid programs in the sub-region. Ninety-one diallel crosses derived from 14 early maturing yellowendosperm QPM inbreds were evaluated from 2010 to 2012 under Striga infested, drought, low-N and optimal environments in Nigeria. The objectives were to (i) examine the combining ability of the set of early yellow QPM inbreds, (ii) classify the inbreds into heterotic groups and identify the best testers (iii) compare the efficiencies of the heterotic grouping methods in classifying the inbreds and (iv) determine the grain yield and stability of the inbreds in hybrid combinations under the research environments. General (GCA) and specific (SCA) combining ability effects were important in the inheritance of grain yield and other traits of the inbreds. However, GCA was more important than SCA under each contrasting environment and across environments suggesting that the additive gene action was more important than the non-additive in the set of inbreds. The SCA effects of grain yield and the heterotic group's SCA and GCA of grain yield (HSGCA) methods classified the inbreds into three groups each, while the heterotic grouping based on GCA of multiple traits (HGCAMT) and the SNP-based genetic distance (GD) methods had two groups each across research environments. There was close correspondence among the classifications of all the grouping methods in terms of placement of inbreds into the same heterotic groups. The SNP-based method was the most efficient and was used to identify TZEQI 87 and TZEQI 91 as the best testers for the SNP-based heterotic groups 1 and 2. The hybrids, TZEQI 87 × TZEQI 93, TZEQI 77 × TZEQI 91 and TZEQI 80 × TZEQI 91 were identified as the most stable and high yielding across research environments and should be commercialized.

Maize production in West and Central Africa (WCA) is constrained by a wide range of interacting stresses that keep productivity below potential yields. Among the many problems afflicting maize production in WCA, drought, foliar diseases, and parasitic weeds are the most critical. Several decades of efforts devoted to the genetic improvement of maize have resulted in remarkable genetic gain, leading to increased yields of maize on farmers’ fields. The revolution unfolding in the areas of genomics, bioinformatics, and phenomics is generating innovative tools, resources, and technologies for transforming crop breeding programs. It is envisaged that such tools will be integrated within maize breeding programs, thereby advancing these programs and addressing current and future challenges. Accordingly, the maize improvement program within International Institute of Tropical Agriculture (IITA) is undergoing a process of modernization through the introduction of innovative tools and new schemes that are expected to enhance genetic gains and impact on smallholder farmers in the region. Genomic tools enable genetic dissections of complex traits and promote an understanding of the physiological basis of key agronomic and nutritional quality traits. Marker-aided selection and genome-wide selection schemes are being implemented to accelerate genetic gain relating to yield, resilience, and nutritional quality. Therefore, strategies that effectively combine genotypic information with data from field phenotyping and laboratory-based analysis are currently being optimized. Molecular breeding, guided by methodically defined product profiles tailored to different agroecological zones and conditions of climate change, supported by state-of-the-art decision-making tools, is pivotal for the advancement of modern, genomics-aided maize improvement programs. Accelerated genetic gain, in turn, catalyzes a faster variety replacement rate. It is critical to forge and strengthen partnerships for enhancing the impacts of breeding products on farmers’ livelihood. IITA has well-established channels for delivering its research products/technologies to partner organizations for further testing, multiplication, and dissemination across various countries within the subregion. Capacity building of national agricultural research system (NARS) will facilitate the smooth transfer of technologies and best practices from IITA and its partners.

Since 1996, tsetse (Glossina spp.) control operations, using odor-baited traps, have been carried out in the Luke area of Gurage zone, southwestern Ethiopia. Glossina morsitans submorsitans Newstead was identified as the dominant species in the area, but the presence of Glossina fuscipes Newstead and Glossina pallidipes Austen also was recorded. Here, we refer to the combined number of these three species and report the work undertaken from October 2002 to October 2004 to render the control system more efficient by reducing the number of traps used and maintaining the previously reached levels of tsetse occurrence and trypanosomiasis prevalence. This was done by the design and implementation of an adaptive tsetse population management system. It consists first of an efficient community-participatory monitoring scheme that allowed us to reduce the number of traps used from 216 to 127 (107 monitoring traps and 20 control traps). Geostatistical methods, including kriging and mapping, furthermore allowed identification and monitoring of the spatiotemporal dynamics of patches with increased fly densities, referred to as hot spots. To respond to hot spots, the Luke community was advised and assisted in control trap deployment. Adaptive management was shown to be more efficient than the previously used mass trapping system. In that context, trap numbers could be reduced substantially, at the same time maintaining previously achieved levels of tsetse occurrences and disease prevalence.