Correction to: High-throughput method for ear phenotyping and kernel weight estimation in maize using ear digital imaging.

[This corrects the article DOI: 10.1186/s13007-018-0317-4.].

Yield gains and associated changes in an early yellow bi-parental maize population following genomic selection for Striga resistance and drought tolerance.

BACKGROUND: Maize yield potential is rarely maximized in sub-Saharan Africa (SSA) due to the devastating effects of drought stress and Striga hermonthica parasitism. This study was conducted to determine the gains in grain yield and associated changes in an early-maturing yellow bi-parental maize population (TZEI 17 x TZEI 11) F3 following genomic selection (GS) for improved grain yield, Striga resistance and drought tolerance. Fifty S1 lines were extracted from each of cycles C0, C1, C2 and C3 of the population and crossed to a tester TZEI 23 to generate 200 testcrosses. The testcrosses were evaluated under drought, artificial Striga-infested and optimal (free from Striga infestation and without limitation of water and nitrogen) environments in Nigeria, 2014-2017. RESULTS: Gains in grain yield of 498 kg ha- 1 cycle- 1 (16.9% cycle- 1) and 522 kg ha- 1 cycle- 1 (12.6% cycle- 1) were obtained under Striga-infested and optimal environments, respectively. The yield gain under Striga-infested environments was associated with increased plant and ear heights as well as improvement in root lodging resistance, husk cover, ear aspect and Striga tolerance. Under optimal environments, yield gain was accompanied by increase in plant and ear heights along with improvement of husk cover and ear rot resistance. In contrast, genomic selection did not improve grain yield under drought but resulted in delayed flowering, poor pollen-silk synchrony during flowering and increased ear height. Genetic variances and heritabilities for most measured traits were not significant for the selection cycles under the research environments. Ear aspect was a major contributor to grain yield under all research environments and could serve as an indirect selection criterion for simultaneous improvement of grain yield under drought, Striga and optimal environments. CONCLUSION: This study demonstrated that genomic selection was effective for yield improvement in the bi-parental maize population under Striga-infested environments and resulted in concomitant yield gains under optimal environments. However, due to low genetic variability of most traits in the population, progress from further genomic selection could only be guaranteed if new sources of genes for Striga resistance and drought tolerance are introgressed into the population.

High-throughput method for ear phenotyping and kernel weight estimation in maize using ear digital imaging.

BACKGROUND: Grain yield, ear and kernel attributes can assist to understand the performance of maize plant under different environmental conditions and can be used in the variety development process to address farmer's preferences. These parameters are however still laborious and expensive to measure. RESULTS: A low-cost ear digital imaging method was developed that provides estimates of ear and kernel attributes i.e., ear number and size, kernel number and size as well as kernel weight from photos of ears harvested from field trial plots. The image processing method uses a script that runs in a batch mode on ImageJ; an open source software. Kernel weight was estimated using the total kernel number derived from the number of kernels visible on the image and the average kernel size. Data showed a good agreement in terms of accuracy and precision between ground truth measurements and data generated through image processing. Broad-sense heritability of the estimated parameters was in the range or higher than that for measured grain weight. Limitation of the method for kernel weight estimation is discussed. CONCLUSION: The method developed in this work provides an opportunity to significantly reduce the cost of selection in the breeding process, especially for resource constrained crop improvement programs and can be used to learn more about the genetic bases of grain yield determinants.

Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize.

BACKGROUND: Recent developments in unmanned aerial platforms (UAP) have provided research opportunities in assessing land allocation and crop physiological traits, including response to abiotic and biotic stresses. UAP-based remote sensing can be used to rapidly and cost-effectively phenotype large numbers of plots and field trials in a dynamic way using time series. This is anticipated to have tremendous implications for progress in crop genetic improvement. RESULTS: We present the use of a UAP equipped with sensors for multispectral imaging in spatial field variability assessment and phenotyping for low-nitrogen (low-N) stress tolerance in maize. Multispectral aerial images were used to (1) characterize experimental fields for spatial soil-nitrogen variability and (2) derive indices for crop performance under low-N stress. Overall, results showed that the aerial platform enables to effectively characterize spatial field variation and assess crop performance under low-N stress. The Normalized Difference Vegetation Index (NDVI) data derived from spectral imaging presented a strong correlation with ground-measured NDVI, crop senescence index and grain yield. CONCLUSION: This work suggests that the aerial sensing platform designed for phenotyping studies has the potential to effectively assist in crop genetic improvement against abiotic stresses like low-N provided that sensors have enough resolution for plot level data collection. Limitations and future potential uses are also discussed.