Strengths and limitations of small and medium-sized maize seed companies in Latin America

Towards the end of 2010, the International Maize and Wheat Improvement Center (CIMMYT) promoted the formation of a public-private partnership in Mexico that linked the main maize research and improvement centers (such as CIMMYT, INIFAP, Chapingo Autonomous University, among others), with the private seed production and marketing sector. Dubbed the Maize Seed Consortium, one of its main objectives was to evaluate, develop and/or deploy new high-yielding maize hybrids for distribution to maize grain producers, and at the same time contribute to the strengthening and diversification of the seed sector in Mexico. However, despite having had access to improved germplasm from research institutions such as CIMMYT, and having adopted new competitive hybrids, significant scope still remains to improve rainfed maize yields in Mexico. This document seeks to describe the strengths and limitations of Latin American small- and medium-sized seed enterprises (“seed SMEs”), with a particular focus on Mexican companies that colaborated with CIMMYT between 2011 and 2021, by studying a representative sample consisting of 10 companies whose marketing data and product portfolio were analyzed in depth. The study serves as the basis for recommending actionsthat can further strengthen the Latin American seed sector, and interventions that are required to overcome its present weaknesses. As appropriate, these findings and recommendations can be extrapolated and applied to other Latin American SMEs such as those in Central America, Ecuador, Bolivia, Venezuela, and Colombia, for which information is also included

Molecular mapping across three populations reveals a QTL hotspot region on chromosome 3 for secondary traits associated with drought tolerance in tropical maize

Identifying quantitative trait loci (QTL) of sizeable effects that are expressed in diverse genetic backgrounds across contrasting water regimes particularly for secondary traits can significantly complement the conventional drought tolerance breeding efforts. We evaluated three tropical maize biparental populations under water-stressed and well-watered regimes for drought-related morpho-physiological traits, such as anthesis-silking interval (ASI), ears per plant (EPP), stay-green (SG) and plant-to-ear height ratio (PEH). In general, drought stress reduced the genetic variance of grain yield (GY), while that of morpho-physiological traits remained stable or even increased under drought conditions. We detected consistent genomic regions across different genetic backgrounds that could be target regions for marker-assisted introgression for drought tolerance in maize. A total of 203 QTL for ASI, EPP, SG and PEH were identified under both the water regimes. Meta-QTL analysis across the three populations identified six constitutive genomic regions with a minimum of two overlapping traits. Clusters of QTL were observed on chromosomes 1.06, 3.06, 4.09, 5.05, 7.03 and 10.04/06. Interestingly, a ~8-Mb region delimited in 3.06 harboured QTL for most of the morpho-physiological traits considered in the current study. This region contained two important candidate genes viz., zmm16 (MADS-domain transcription factor) and psbs1 (photosystem II unit) that are responsible for reproductive organ development and photosynthate accumulation, respectively. The genomic regions identified in this study partially explained the association of secondary traits with GY. Flanking single nucleotide polymorphism markers reported herein may be useful in marker-assisted introgression of drought tolerance in tropical maize

Evaluating Maize Genotype Performance under Low Nitrogen Conditions Using RGB UAV Phenotyping Techniques

Maize is the most cultivated cereal in Africa in terms of land area and production, but low soil nitrogen availability often constrains yields. Developing new maize varieties with high and reliable yields using traditional crop breeding techniques in field conditions can be slow and costly. Remote sensing has become an important tool in the modernization of field-based high-throughput plant phenotyping (HTPP), providing faster gains towards the improvement of yield potential and adaptation to abiotic and biotic limiting conditions. We evaluated the performance of a set of remote sensing indices derived from red-green-blue (RGB) images along with field-based multispectral normalized difference vegetation index (NDVI) and leaf chlorophyll content (SPAD values) as phenotypic traits for assessing maize performance under managed low-nitrogen conditions. HTPP measurements were conducted from the ground and from an unmanned aerial vehicle (UAV). For the ground-level RGB indices, the strongest correlations to yield were observed with hue, greener green area (GGA), and a newly developed RGB HTPP index, NDLab (normalized difference Commission Internationale de I´Edairage (CIE)Lab index), while GGA and crop senescence index (CSI) correlated better with grain yield from the UAV. Regarding ground sensors, SPAD exhibited the closest correlation with grain yield, notably increasing in its correlation when measured in the vegetative stage. Additionally, we evaluated how different HTPP indices contributed to the explanation of yield in combination with agronomic data, such as anthesis silking interval (ASI), anthesis date (AD), and plant height (PH). Multivariate regression models, including RGB indices (R2 > 0.60), outperformed other models using only agronomic parameters or field sensors (R2 > 0.50), reinforcing RGB HTPP's potential to improve yield assessments. Finally, we compared the low-N results to the same panel of 64 maize genotypes grown under optimal conditions, noting that only 11% of the total genotypes appeared in the highest yield producing quartile for both trials. Furthermore, we calculated the grain yield loss index (GYLI) for each genotype, which showed a large range of variability, suggesting that low-N performance is not necessarily exclusive of high productivity in optimal conditions

Genomic analysis of resistance to fall armyworm (Spodoptera frugiperda) in CIMMYT maize lines

The recent invasion, rapid spread, and widescale destruction of the maize crop by the fall armyworm (FAW; Spodoptera frugiperda (J.E. Smith)) is likely to worsen the food insecurity situation in Africa. In the present study, a set of 424 maize lines were screened for their responses to FAW under artificial infestation to dissect the genetic basis of resistance. All lines were evaluated for two seasons under screen houses and genotyped with the DArTseq platform. Foliar damage was rated on a scale of 1 (highly resistant) to 9 (highly susceptible) and scored at 7, 14, and 21 days after artificial infestation. Analyses of variance revealed significant genotypic and genotype by environment interaction variances for all traits. Heritability estimates for leaf damage scores were moderately high and ranged from 0.38 to 0.58. Grain yield was negatively correlated with a high magnitude to foliar damage scores, ear rot, and ear damage traits. The genome-wide association study (GWAS) revealed 56 significant marker–trait associations and the predicted functions of the putative candidate genes varied from a defense response to several genes of unknown function. Overall, the study revealed that native genetic resistance to FAW is quantitative in nature and is controlled by many loci with minor effects

Marker-Assisted Breeding of Improved Maternal Haploid Inducers in Maize for the Tropical/Subtropical Regions

For efficient production of doubled haploid (DH) lines in maize, maternal haploid inducer lines with high haploid induction rate (HIR) and good adaptation to the target environments is an important requirement. In this study, we present second-generation Tropically Adapted Inducer Lines (2GTAILs), developed using marker assisted selection (MAS) for qhir1, a QTL with a significant positive effect on HIR from the crosses between elite tropical maize inbreds and first generation Tropically Adapted Inducers Lines (TAILs). Evaluation of 2GTAILs for HIR and agronomic performance in the tropical and subtropical environments indicated superior performance of 2GTAILs over the TAILs for both HIR and agronomic performance, including plant vigor, delayed flowering, grain yield, and resistance to ear rots. One of the new inducers 2GTAIL006 showed an average HIR of 13.1% which is 48.9% higher than the average HIR of the TAILs. Several other 2GTAILs also showed higher HIR compared to the TAILs. While employing MAS for qhir1 QTL, we observed significant influence of the non-inducer parent on the positive effect of qhir1 QTL on HIR. The non-inducer parents that resulted in highest mean HIR in the early generation qhir1+ families also gave rise to highest numbers of candidate inducers, some of which showed transgressive segregation for HIR. The mean HIR of early generation qhir1+ families involving different non-inducer parents can potentially indicate recipient non-inducer parents that can result in progenies with high HIR. Our study also indicated that the HIR associated traits (endosperm abortion rate, embryo abortion rate, and proportion of haploid plants among the inducer plants) can be used to differentiate inducers vs. non-inducers but are not suitable for differentiating inducers with varying levels of haploid induction rates. We propose here an efficient methodology for developing haploid inducer lines combining MAS for qhir1 with HIR associated traits

Evaluating maize genotype performance under low nitrogen conditions using RGB UAV phenotyping techniques

Maize is the most cultivated cereal in Africa in terms of land area and production, but low soil nitrogen availability often constrains yields. Developing new maize varieties with high and reliable yields using traditional crop breeding techniques in field conditions can be slow and costly. Remote sensing has become an important tool in the modernization of field-based high-throughput plant phenotyping (HTPP), providing faster gains towards the improvement of yield potential and adaptation to abiotic and biotic limiting conditions. We evaluated the performance of a set of remote sensing indices derived from red–green–blue (RGB) images along with field-based multispectral normalized difference vegetation index (NDVI) and leaf chlorophyll content (SPAD values) as phenotypic traits for assessing maize performance under managed low-nitrogen conditions. HTPP measurements were conducted from the ground and from an unmanned aerial vehicle (UAV). For the ground-level RGB indices, the strongest correlations to yield were observed with hue, greener green area (GGA), and a newly developed RGB HTPP index, NDLab (normalized difference Commission Internationale de I´Edairage (CIE)Lab index), while GGA and crop senescence index (CSI) correlated better with grain yield from the UAV. Regarding ground sensors, SPAD exhibited the closest correlation with grain yield, notably increasing in its correlation when measured in the vegetative stage. Additionally, we evaluated how different HTPP indices contributed to the explanation of yield in combination with agronomic data, such as anthesis silking interval (ASI), anthesis date (AD), and plant height (PH). Multivariate regression models, including RGB indices (R2 > 0.60), outperformed other models using only agronomic parameters or field sensors (R2 > 0.50), reinforcing RGB HTPP’s potential to improve yield assessments. Finally, we compared the low-N results to the same panel of 64 maize genotypes grown under optimal conditions, noting that only 11% of the total genotypes appeared in the highest yield producing quartile for both trials. Furthermore, we calculated the grain yield loss index (GYLI) for each genotype, which showed a large range of variability, suggesting that low-N performance is not necessarily exclusive of high productivity in optimal conditions.This research and APC was funded by Bill & Melinda Gates Foundation and USAID Stress Tolerant Maize for Africa program, grant number [OPP1134248], and the MAIZE CGIAR research program. The CGIAR Research Program MAIZE receives W1&W2 support from the Governments of Australia, Belgium, Canada, China, France, India, Japan, Korea, Mexico, Netherlands, New Zealand, Norway, Sweden, Switzerland, U.K., U.S., and the World Bank

Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction

The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support

Genetic trends for yield and key agronomic traits in pre-commercial and commercial maize varieties between 2008 and 2020 in Uganda

Estimating genetic gains is vital to optimize breeding programs for increased efficiency. Genetic gains should translate into productivity gains if returns to investments in breeding and impact are to be realized. The objective of this study was to estimate genetic gain for grain yield and key agronomic traits in pre-commercial and commercial maize varieties from public and private breeding programs tested in (i) national performance trials (NPT), (ii) era trial and, (iii) compare the trends with the national average. The study used (i) historical NPT data on 419 improved maize varieties evaluated in 23 trials at 6-8 locations each between 2008 and 2020, and (ii) data from an era trial of 54 maize hybrids released between 1999 and 2020. The NPT data was first analyzed using a mixed model and resulting estimate for each entry was regressed onto its first year of testing. Analysis was done over all entries, only entries from National Agricultural Research Organization (NARO), International Maize and Wheat Improvement Center (CIMMYT), or private seed companies. Estimated genetic gain was 2.25% or 81 kg ha-1 year-1 from the NPT analysis. A comparison of genetic trends by source indicated that CIMMYT entries had a gain of 1.98% year-1 or 106 kg ha-1 year-1. In contrast, NARO and private sector maize entries recorded genetic gains of 1.30% year-1 (59 kg ha-1 year-1) and 1.71% year-1 (79 kg ha-1 year-1), respectively. Varieties from NARO and private sector showed comparable mean yields of 4.56 t ha-1 and 4.62 t ha-1, respectively, while hybrids from CIMMYT had a mean of 5.37 t ha-1. Era analysis indicated significant genetic gain of 1.69% year-1 or 55 kg ha-1 year-1, while a significant national productivity gain of 1.48% year-1 (37 kg ha-1 year-1) was obtained. The study, thus, demonstrated the importance of public-private partnerships in development and delivery of new genetics to farmers in Uganda