Posts Tagged ‘phenotyping’

The list of STMA-supported research publications from Eastern and Southern African region for the year 2019 is out

Posted on Eastern Africa Publications, Published Journals, Research Publication, Seed System Publication, Southern Africa Publications, November 17, 2019

The list of research publications supported by the STMA project for the year 2019 has been compiled. Breakthrough research findings cover adoption studies, evaluation of new maize breeding methodologies, identification of genetic markers for accelerated maize improvement for various stresses including maize lethal necrosis resistance, drought and heat tolerance,…

Amondo et al, 2019. Productivity and production risk effects of adopting drought-tolerant maize varieties in Zambia published in the International Journal of Climate Change Strategies and Management is accessible here

Awata et al, 2019. Maize lethal necrosis and the molecular basis of variability in concentrations of the causal viruses in co-infected maize plant, published in the Journal of General and Molecular Virology is accessible here

Awondo, S.N. et al, 2019. Multi-Site Bundling of Drought Tolerant Maize Varieties and Index Insurance, published in the Journal of Agricultural Economics is accessible here.

Buchaillot MB et al, 2019. Evaluating maize genotype performance under low nitrogen conditions using RGB UAV phenotyping techniques, published in Sensors. Accessible here

Chaikam V et al, 2019. Doubled haploid technology for line development in maize: technical advances and prospects, published in Theoretical and Applied Genetics. Accessible here.

Chaikam, V. et al, 2019. Genome-wide association study to identify genomic regions influencing spontaneous fertility in maize haploids, published in Euphytica, accessible here.  

Katengeza, S.P. et al, 2019. Adoption of Drought Tolerant Maize Varieties under Rainfall Stress in Malawi, published in the Journal of Agricultural Economics. Accessible here.  

Lunduka, R.W. et al, 2019. Impact of adoption of drought-tolerant maize varieties on total maize production in south Eastern Zimbabwe, published in the Climate and Development. Accessible here.  

Nair S et al, 2019. Genetic dissection of maternal influence on in vivo haploid induction in maize, published in The Crop Journal (in press)

Simtowe, F. et al, 2019. Impacts of drought-tolerant maize varieties on productivity, risk, and resource use: Evidence from Uganda, published in the Land Use Policy Journal. Accessible here.  

Simtowe, F. et al, 2019. Heterogeneous seed access and information exposure: implications for the adoption of drought-tolerant maize varieties in Uganda, published in the Agricultural and Food Economics Journal. Accessible here.  

Sitonik, C. et al, 2019. Published in Theoretical and Applied Genetics. Genetic architecture of maize chlorotic mottle virus and maize lethal necrosis through GWAS, linkage analysis and genomic prediction in tropical maize germplasm. Accessible here.

Tigist Mideksa Damesa et al, 2019. Comparison of weighted and unweighted stage-wise analysis for genome-wide association studies and genomic selection, to be published in Crop Science. The PhD dissertation is accessible here.

Wegary, D. et al, 2019. Molecular diversity and selective sweeps in maize inbred lines adapted to African highlands, published in Nature Scientific Reports. Accessible here.  

Wender et al 2019. Performance and Yield Stability of Maize Hybrids in Stress-prone Environments in Eastern Africa,  published in the Crop Journal. Accessible here.  

Not reported but published in 2018

Araus JL et al, 2018. Phenotyping: New Crop Breeding Frontier. In: R. A. Meyers (ed.), Encyclopedia of Sustainability Science and Technology, Springer Science+Business Media, LLC, part of Springer Nature 2018.

Das B et al, 2018. Identification of low N tolerant donors for maize breeding in sub-Saharan Africa. Published in Euphytica and accessible here.

Yuan Y et al., 2018. Genome-wide association mapping and genomic prediction analyses reveal the genetic architecture of grain yield and flowering time under drought and heat stress conditions in maize. Published in Frontiers in Plant Science and accessible here.

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

Posted on , November 17, 2019

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.

Maize Ear Digital Imaging for Yield Components Assessment

Posted on , April 8, 2019

In Sub-Saharan Africa, many food security programs aim to increase crop yields by developing and disseminating better seeds and agronomy to millions of smallholder farmers. In the case of maize, research and development organizations use key indicators like grain yield, or maize cob number and size to understand the performance of the maize plant under different environmental conditions. But these indicators are still labor-intensive and expensive to measure. CIMMYT has developed a digital imaging tool called Maize Ear Analyzer that collects maize cob and grain parameters 90% faster than traditional methods (Makanza et al, 2018). This imaging tool can be adapted to other crops.

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