Key message Historical data from breeding programs can be efficiently used to improve genomic selection accuracy, especially when the training set is optimized to subset individuals most informative of the target testing set. Abstract The current strategy for large-scale implementation of genomic selection (GS) at the International Maize and Wheat Improvement Center (CIMMYT) global maize breeding program has been to train models using information from full-sibs in a “test-half-predict-half approach.” Although effective, this approach has limitations, as it requires large full-sib populations and limits the ability to shorten variety testing and breeding cycle times. The primary objective of this study was to identify optimal experimental and training set designs to maximize prediction accuracy of GS in CIMMYT’s maize breeding programs. Training set (TS) design strategies were evaluated to determine the most efficient use of phenotypic data collected on relatives for genomic prediction (GP) using datasets containing 849 (DS1) and 1389 (DS2) DH-lines evaluated as testcrosses in 2017 and 2018, respectively. Our results show there is merit in the use of multiple bi-parental populations as TS when selected using algorithms to maximize relatedness between the training and prediction sets. In a breeding program where relevant past breeding information is not readily available, the phenotyping expenditure can be spread across connected bi-parental populations by phenotyping only a small number of lines from each population. This significantly improves prediction accuracy compared to within-population prediction, especially when the TS for within full-sib prediction is small. Finally, we demonstrate that prediction accuracy in either sparse testing or “test-half-predict-half” can further be improved by optimizing which lines are planted for phenotyping and which lines are to be only genotyped for advancement based on GP.

Abstract Adoption of rice varieties that perform well under high iron-associated (HIA) stress environments can enhance rice production in West Africa. This study reports the genetic characterization of 323 rice accessions and breeding lines cultivated in West Africa using genotyping-by-sequencing and their phenotypic response to HIA treatments in hydroponic solution (1500 mg l−1 FeSO4·7H2O) and hot-spot fields. The germplasm consisted of four genetic subpopulations: Oryza glaberrima (14%), O. sativa-japonica (7%), O. sativa-indica Group 1 (45%), and O. sativa-indica Group 2 (25%). Severe versus mild stress in the field was associated with a reduced SPAD value (12%), biomass (56%), and grain yield (57%), with leaf bronzing explaining 30% and 21% of the variation for biomass and grain yield, respectively. Association mapping using 175 indica genotypes identified 23 significant single nucleotide polymorphism (SNP) markers that mapped to 14 genomic regions. Genome-wide association study (GWAS) signals associated with leaf bronzing, a routinely used indicator of HIA stress, differed in hydroponic compared with field conditions. Contrastingly, six significant SNPs on chromosomes 8 and 9 were associated with the SPAD value under HIA stress in both field and hydroponic experiments, and a candidate potassium transporter gene mapped under the peak on chromosome 8. This study helps define criteria for assessing rice performance under HIA environments.

BackgroundGroundnut is an important legume crop in Togo. However, groundnut yield has been steadily decreasing for decades as a result of lack of organized breeding program to address production constraints. Though, low yielding varieties and late leaf spot have been often reported as the most important constraints, there is no documented evidence. Identifying and documenting the major production constraints is a prerequisite for establishing a good breeding program with clearly defined priority objectives and breeding strategies. Thus, the objectives of this study were to identify groundnut production constraints and assess farmers’ preferred traits.MethodsA participatory rural appraisal approach was used to collect data on agronomic practices, farmers’ preferences, and possible threats to production through individual and group interviews. Three regions and three villages per region were selected based on the representativeness of groundnut production systems. In each village, 20 farmers were randomly selected and interviewed; thus, a total of 180 farmers were interviewed. Content analysis was carried out for qualitative data and for quantitative data generated within and across regions, comparative descriptive statistics were carried out. Differences in perception and preferences were assessed using chi-square tests.ResultsThe study has revealed that, though there were some variation across the regions, traits pertaining to yield such as pod yield (66.66%) and pod size (12.12%) were the most important. Leaf spot diseases, rosette and peanut bud necrosis (37.77%) and insects such as pod sucking bug and bruchid (27.77%) were considered to be the most important constraints limiting groundnut production. Among diseases, farmers in all the three regions indicated that late leaf spot is of economic importance which they associated to various causes such as maturity, drought, or insects. No gender differences were observed for the perception of constraints and groundnut traits preferences. Land size is significantly influenced by age and gender. Besides, farmers have pointed the lack of improved varieties and the unavailability of groundnut seeds highlighting the necessity of a sustainable groundnut seed system linked with a strong breeding program.ConclusionThis study has enabled understanding of the farming practices, constraints, and farmers preferred characteristics, thus providing the basis for a participatory breeding program in Togo which should consider that farmers perceive low yielding varieties and diseases as major constraints to production.

To enable a scalable sparse testing genomic selection (GS) strategy at preliminary yield trials in the CIMMYT maize breeding program, optimal approaches to incorporate genotype by environment interaction (GEI) in genomic prediction models are explored. Two cross-validation schemes were evaluated: CV1, predicting the genetic merit of new bi-parental populations that have been evaluated in some environments and not others, and CV2, predicting the genetic merit of half of a bi-parental population that has been phenotyped in some environments and not others using the coefficient of determination (CDmean) to determine optimized subsets of a full-sib family to be evaluated in each environment. We report similar prediction accuracies in CV1 and CV2, however, CV2 has an intuitive appeal in that all bi-parental populations have representation across environments, allowing efficient use of information across environments. It is also ideal for building robust historical data because all individuals of a full-sib family have phenotypic data, albeit in different environments. Results show that grouping of environments according to similar growing/management conditions improved prediction accuracy and reduced computational requirements, providing a scalable, parsimonious approach to multi-environmental trials and GS in early testing stages. We further demonstrate that complementing the full-sib calibration set with optimized historical data results in improved prediction accuracy for the cross-validation schemes.

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Striga hermonthica can cause as high as 100% yield loss in maize depending on soil fertility level, type of genotype, severity of infestation and climatic conditions. Understanding the mode of inheritance of Striga resistance in maize is crucial for introgression of resistance genes into tropical germplasm and deployment of resistant varieties. This study examined the mode of inheritance of resistance to Striga in early‐maturing inbred line, TZdEI 352 containing resistance genes from Zea diploperennis. Six generations, P1, P2, F1, F2, BC1P1 and BC1P2 derived from a cross between resistant line, TZdEI 352 and susceptible line, TZdEI 425 were screened under artificial Striga infestation at Mokwa and Abuja, Nigeria, 2015. Additive‐dominance model was adequate in describing observed variations in the number of emerged Striga plants among the population; hence, digenic epistatic model was adopted for Striga damage. Dominance effects were higher than the additive effects for the number of emerged Striga plants at both locations signifying that non‐additive gene action conditioned inheritance of Striga resistance. Inbred TZdEI 352 could serve as invaluable parent for hybrid development in Striga endemic agro‐ecologies of sub‐Saharan Africa.

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Cowpea ( Vigna unguiculata L. Walp) is an important legume crop, especially in sub-Saharan Africa. Poor soil fertility is among the major abiotic factors that contribute to this crop's low yield. Phosphorus (P)-based fertilizers significantly increase cowpea yields but these fertilizers are not well adopted by smallholder cowpea farmers. To understand why, we surveyed 420 farmers across three major cowpea-producing states in Nigeria: first, we assessed the cowpea farmers' knowledge and perception of the need for fertilizers, especially P fertilizers; and, second, we identified factors that determine the use – or non-use – of P-based fertilizers. Although over 80% of farmers surveyed were aware of the value of fertilizers as a yield-increasing factor and were able to identify crops suffering from nutrient deficiency, only 10% used P-based fertilizers like single super phosphate (SSP) and another 11% used combinations of nitrogen, phosphorus, and potassium compound fertilizers and SSP for cowpeas. Reasons for not using P-containing fertilizers included high cost, poor availability in rural markets, and lack of awareness on the need to use P fertilizers. Additionally, many growers believed that cowpeas do not require fertilizers, especially if the previous crop had been maize. Our findings are important for strategies to increase the productivity of cowpeas among smallholder growers especially in the northern regions of Nigeria and beyond, where subsistence farming systems are prevalent. Increased cowpea production through the adequate use of inputs like P fertilizers will support Nigeria's effort to reduce its large imports of cowpea grain from neighboring countries. Our survey further demonstrated that P-containing fertilizers are crucial production inputs for increased cowpea production in these regions and in areas with similar traditional farming practices. Our results will benefit breeders, development partners, extension personnel, and other stakeholders in cowpea value chains.