Inability to efficiently implement high-throughput field phenotyping is increasingly perceived as a key component that limits genetic gain in breeding programs. Field phenotyping must be integrated into a wider context than just choosing the correct selection traits, deployment tools, evaluation platforms, or basic data-management methods. Phenotyping means more than conducting such activities in a resource-efficient manner; it also requires appropriate trial management and spatial variability handling, definition of key constraining conditions prevalent in the target population of environments, and the development of more comprehensive data management, including crop modeling. This review will provide a wide perspective on how field phenotyping is best implemented. It will also outline how to bridge the gap between breeders and ‘phenotypers’ in an effective manner.

Maize is a major source of food security and economic development in sub-Saharan Africa (SSA), Latin America, and the Caribbean, and is among the top three cereal crops in Asia. Yet, maize is deficient in certain essential amino acids, vitamins, and minerals. Biofortified maize cultivars enriched with essential minerals and vitamins could be particularly impactful in rural areas with limited access to diversified diet, dietary supplements, and fortified foods. Significant progress has been made in developing, testing, and deploying maize cultivars biofortified with quality protein maize (QPM), provitamin A, and kernel zinc. In this review, we outline the status and prospects of developing nutritionally enriched maize by successfully harnessing conventional and molecular marker-assisted breeding, highlighting the need for intensification of efforts to create greater impacts on malnutrition in maize-consuming populations, especially in the low-and middle-income countries. Molecular marker-assisted selection methods are particularly useful for improving nutritional traits since conventional breeding methods are relatively constrained by the cost and throughput of nutritional trait phenotyping.

Monitoring of genetic gain in crop genetic improvement programs is necessary to measure the efficiency of the program. Periodic measurement of genetic gain also allows the efficiency of new technologies incorporated into a program to be quantified. Genetic gain within the International Maize and Wheat Improvement Centre (CIMMYT) breeding program for eastern and southern Africa were estimated using time series of maize (Zea mays L.) hybrids. A total of 67 of the best‐performing hybrids from regional trials from 2000 to 2010 were selected to form an era panel and evaluated in 32 trials in eight locations across six countries in eastern and southern Africa. Treatments included optimal management, managed and random drought stress, low‐nitrogen (N) stress and maize streak virus (MSV) infestation. Genetic gain was estimated as the slope of the regression of grain yield on the year of hybrid release. Genetic gain under optimal conditions, managed drought, random drought, low N, and MSV were estimated to have increased by 109.4, 32.5, 22.7, 20.9 and 141.3 kg ha−1 yr−1, respectively. These results are comparable with genetic gain in maize yields in other regions of the world. New technologies to further increase the rate of genetic gain in maize breeding for eastern and southern Africa are also discussed.

Key message Intensive public sector breeding efforts and public-private partnerships have led to the increase in genetic gains, and deployment of elite climate-resilient maize cultivars for the stress-prone environments in the tropics. Abstract Maize (Zea mays L.) plays a critical role in ensuring food and nutritional security, and livelihoods of millions of resource-constrained smallholders. However, maize yields in the tropical rainfed environments are now increasingly vulnerable to various climate-induced stresses, especially drought, heat, waterlogging, salinity, cold, diseases, and insect pests, which often come in combinations to severely impact maize crops. The International Maize and Wheat Improvement Center (CIMMYT), in partnership with several public and private sector institutions, has been intensively engaged over the last four decades in breeding elite tropical maize germplasm with tolerance to key abiotic and biotic stresses, using an extensive managed stress screening network and on-farm testing system. This has led to the successful development and deployment of an array of elite stress-tolerant maize cultivars across sub-Saharan Africa, Asia, and Latin America. Further increasing genetic gains in the tropical maize breeding programs demands judicious integration of doubled haploidy, high-throughput and precise phenotyping, genomics-assisted breeding, breeding data management, and more effective decision support tools. Multi-institutional efforts, especially public–private alliances, are key to ensure that the improved maize varieties effectively reach the climate-vulnerable farming communities in the tropics, including accelerated replacement of old/obsolete varieties.

Abstract:In the crop breeding process, the use of data collection methods that allow reliable assessment of crop adaptation traits, faster and cheaper than those currently in use, can significantly improve resource use efficiency by reducing selection cost and can contribute to increased genetic gain through improved selection efficiency. Current methods to estimate crop growth (ground canopy cover) and leaf senescence are essentially manual and/or by visual scoring, and are therefore often subjective, time consuming, and expensive. Aerial sensing technologies offer radically new perspectives for assessing these traits at low cost, faster, and in a more objective manner. We report the use of an unmanned aerial vehicle (UAV) equipped with an RGB camera for crop cover and canopy senescence assessment in maize field trials. Aerial-imaging-derived data showed a moderately high heritability for both traits with a significant genetic correlation with grain yield. In addition, in some cases, the correlation between the visual assessment (prone to subjectivity) of crop senescence and the senescence index, calculated from aerial imaging data, was significant. We concluded that the UAV-based aerial sensing platforms have great potential for monitoring the dynamics of crop canopy characteristics like crop vigor through ground canopy cover and canopy senescence in breeding trial plots. This is anticipated to assist in improving selection efficiency through higher accuracy and precision, as well as reduced time and cost of data collection.

Open‐pollinated varieties (OPVs) still represent a significant proportion of the maize (Zea mays L.) seed system in many countries of sub‐Saharan Africa. The International Maize and Wheat Improvement Centre (CIMMYT) has been breeding improved maize varieties for the stress‐prone environments experienced by most smallholder farmers in eastern and southern Africa for over 30 yr. Hybrid breeding is now the major focus of the CIMMYT breeding pipeline. However, OPVs are generated within the hybrid pipeline. This is the first study to document genetic gain for maize grain yield under both optimal and stress (random and managed drought, low nitrogen [N], and maize streak virus [MSV]) conditions within the CIMMYT eastern and southern African OPV breeding pipeline. Genetic gain was estimated using the slope of the regression on the year of OPV release in regional trials over a 12‐yr period (1999–2011). Open‐pollinated varieties were separated into two maturity groups, early (<70 d to anthesis) and intermediate‐late (>70 d to anthesis). Genetic gain in the early maturity group under optimal conditions, random drought, low N, and MSV was 109.9, 29.2, 84.8, and 192.9 kg ha−1 yr−1. In the intermediate‐late maturity group, genetic gain under optimal conditions, random drought, low N, and MSV was 79.1, 42.3, 53.0 and 108.7 kg ha−1 yr−1. No significant yield gains were made under managed drought stress for both maturity groups. Our results show continued improvement in OPVs for both yield potential and stress tolerance.

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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.