Drought Stress in Maize (Zea mays L.)

Aslam, Muhammad; Maqbool, M. A.; Cengiz, Rahime

doi:10.1007/978-3-319-25442-5

Cited by 85 publications

(67 citation statements)

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“…The continuous decrease in the seed moisture content from milk stage to physiological maturity indicated increase in biomass accumulation towards later stage of maturity (Aslam et al, 2015). Planting date had greater influence on seed moisture content in the drier season and late planting date than in warm season under early and mid-planting date.…”

Section: Discussionmentioning

confidence: 93%

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Planting Dates and Harvesting Stages Influence on Maize Yield under Rain-Fed Conditions

Adelabu¹,

Modi²

2017

JAS

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Understanding the challenges associated with variation in weather conditions and stages of maturity in maize are essential for farmers to achieve continuous production under climate changes. This research evaluated the interactive effect of planting date and stages of maturity at harvest on maize yield (Zea mays L.). Field trials were conducted during the 2014/15 and 2015/16 seasons at university of KwaZulu-Natal research farm Pietermaritzburg South Africa. Planting dates comprised of early (November), mid (December) and late planting dates (January). While, harvesting occurred at milk stage, dent stage and physiological maturity. A split plotdesign with four replications was used. The main plot and sub-plot consisted of planting dates and harvesting stages respectively. Response of maize to planting dates and harvesting stages was determined by variables of plant physiological growth and yield parameters. Significant differences in growth and physiological parameters were more obvious in 2015/16 season which was a drier season than 2014/15. Early and mid-planting had positive effect on parameters measured at both seasons. However, mid planting date favoured maize growth and yield more in drier season. The interaction of planting dates and harvesting stages significantly influenced grain yield, thousand seed weight, ear length and diameter. Thousand seed weight, ear length and diameter obtained at dent stage under mid planting outperformed its counterpart from physiological maturity under which late planting. With the increase in climate variability there is high risk that maize planted lately would have lower yield irrespective of its planting dates and might not attain physiological maturity. However, maize harvested at dent stage under early and mid-planting dates gave substantially high yield.

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Section: Discussionmentioning

confidence: 93%

“…This explains why researchers have previously ascribed biomass accumulation to source strength and sink's capacity which are seriously reduced by limited soil water (Aslam et al, 2015;Gambín et al, 2006).…”

Section: Discussionmentioning

confidence: 99%

Planting Dates and Harvesting Stages Influence on Maize Yield under Rain-Fed Conditions

Adelabu¹,

Modi²

2017

JAS

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“…The growth stage of the maize selected in this paper was V8 (eight visible leaves)~VT (the last spike was visible). According to some research, the water supply of maize in two weeks before and after the pollination period will determine the final yield [26]. Therefore, it is more meaningful to study and detect the phenotype of maize in this period.…”

Section: Methodsmentioning

confidence: 99%

Leaf Counting with Multi-Scale Convolutional Neural Network Features and Fisher Vector Coding

et al. 2019

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The number of leaves in maize plant is one of the key traits describing its growth conditions. It is directly related to plant development and leaf counts also give insight into changing plant development stages. Compared with the traditional solutions which need excessive human interventions, the methods of computer vision and machine learning are more efficient. However, leaf counting with computer vision remains a challenging problem. More and more researchers are trying to improve accuracy. To this end, an automated, deep learning based approach for counting leaves in maize plants is developed in this paper. A Convolution Neural Network(CNN) is used to extract leaf features. The CNN model in this paper is inspired by Google Inception Net V3, which using multi-scale convolution kernels in one convolution layer. To compress feature maps generated from some middle layers in CNN, the Fisher Vector (FV) is used to reduce redundant information. Finally, these encoded feature maps are used to regress the leaf numbers by using Random Forests. To boost the related research, a relatively single maize image dataset (Different growth stage with 2845 samples, which 80% for train and 20% for test) is constructed by our team. The proposed algorithm in single maize data set achieves Mean Square Error (MSE) of 0.32.

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“…Benth have been recognized as the most widespread stresses limiting the productivity of maize in Africa (Ejeta, 2007;Edmeades, 2013). When drought occurs during flowering of maize, it disrupts pollination and diminishes availability of photosynthate to developing kernels leading to reduction in kernel number and final yield loss of 17 to 60% (Edmeades et al, 1999;Cattivelli et al, 2008;Aslam et al, 2015). Likewise, maize plants with attached Striga plants to their roots exhibit stunted growth resulting from the withdrawal of water, nutrients and assimilate from the host by the parasite (Gurney et al, 1999) causing yield loss of up to 100% in severely infested fields in Africa (Kim et al, 2002;Ejeta, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…Over many years, breeders in the International Institute of Tropical Agriculture (IITA) have run independent breeding programs with specific emphasis on developing either drought tolerant or Striga resistant maize germplasm using reliable screening protocols established for each stress. Significant achievements have been recorded in generating maize varieties that are adapted either to drought stress (Edmeades, 2013;Aslam et al, 2015) or S. hermonthica infection (Kim, 1996: Kling et al, 2000Menkir et al, 2007). Nonetheless, such an approach will be inadequate for areas affected by concurrent presence of drought stress and S. hermonthica infestation (Mittler and Blumwald, 2010;Atkinson and Urwin, 2012).…”

Section: Introductionmentioning

confidence: 99%

Stacking Tolerance to Drought and Resistance to a Parasitic Weed in Tropical Hybrid Maize for Enhancing Resilience to Stress Combinations

et al. 2020

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environments were 0.51 and 0.57, respectively. Also, a genetic correlation between yields under Striga-infestation with that recorded in multiple rainfed environments was 0.58. These results suggest that the sequential selection scheme offers an opportunity to accumulate desirable stress-related traits in parents contributing to superior agronomic performance in hybrids across stressful and diverse rainfed field environments that are commonly encountered in the tropical savannas of Africa.

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Drought Stress in Maize (Zea mays L.)

Cited by 85 publications

References 0 publications

Planting Dates and Harvesting Stages Influence on Maize Yield under Rain-Fed Conditions

Planting Dates and Harvesting Stages Influence on Maize Yield under Rain-Fed Conditions

Leaf Counting with Multi-Scale Convolutional Neural Network Features and Fisher Vector Coding

Stacking Tolerance to Drought and Resistance to a Parasitic Weed in Tropical Hybrid Maize for Enhancing Resilience to Stress Combinations

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