Plant Abiotic Stress: Temperature Extremes

Bahuguna, Rajeev Nayan; Jagadish, Krishna S.V.; Coast, Onoriode; Wassmann, R.

doi:10.1016/b978-0-444-52512-3.00172-8

Cited by 13 publications

(4 citation statements)

References 38 publications

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“…A comprehensive geographical mapping exercise on a global scale indicated regions with higher vulnerability to HNT or HDT or a combined HNDT stress (Laborte et al, 2012), substantiating the need for systematic investigation of the response of rice genotypes to these conditions. HNT of 30 o C coinciding with flowering and extended to a few days after flowering did not affect seed-set/spikelet fertility negatively under growth chamber and field conditions Coast et al, 2014;Jagadish et al, 2015) and this is largely supported by our findings. However, the present results indicate clear differential responses of rice genotypes to HDT and HNT, with a greater impact of HDT on seed-set compared to HNT (Tables A5.2 and A5.3).…”

Section: Discussionsupporting

confidence: 87%

“…The reduced time spent for translocation of assimilates from current assimilate production via photosynthesis in leaves and remobilization of the stored assimilates in stem and sheath to the grains, leads to partially filled grains (Bahuguna et al, 2014). However, Kim et al (2011) have shown an earlier termination of grain filling in temperate rice under high temperature than leaf senescence, indicating that poor grain filling was not the result of lack of assimilates.…”

Section: Heat Waves and Grain Yieldmentioning

confidence: 99%

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Physiological responses of rice to increased day and night temperatures

Shi¹

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Section: Discussionsupporting

confidence: 87%

Section: Heat Waves and Grain Yieldmentioning

confidence: 99%

Physiological responses of rice to increased day and night temperatures

Shi¹

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“…In contrast to high day temperature (HDT) stress, rice lacks an escape or avoidance mechanism under HNT stress ( Bahuguna et al., 2014 ; Bahuguna et al., 2015 ; Hirabayashi et al., 2015 ). However, rice may have an enhanced ability to meet the increased carbon demand under increased night respiration, minimizing the negative impact of HNT on grain yield and quality ( Impa et al., 2020 ).…”

Section: Physiological and Molecular Implications Of Combined Abiotic...mentioning

confidence: 99%

Physiological and molecular implications of multiple abiotic stresses on yield and quality of rice

et al. 2023

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Abiotic stresses adversely affect rice yield and productivity, especially under the changing climatic scenario. Exposure to multiple abiotic stresses acting together aggravates these effects. The projected increase in global temperatures, rainfall variability, and salinity will increase the frequency and intensity of multiple abiotic stresses. These abiotic stresses affect paddy physiology and deteriorate grain quality, especially milling quality and cooking characteristics. Understanding the molecular and physiological mechanisms behind grain quality reduction under multiple abiotic stresses is needed to breed cultivars that can tolerate multiple abiotic stresses. This review summarizes the combined effect of various stresses on rice physiology, focusing on grain quality parameters and yield traits, and discusses strategies for improving grain quality parameters using high-throughput phenotyping with omics approaches.

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“…With the world population expected to reach more than 9 Â 10 9 people by 2050, the food demand must increase by 70% in a situation where yield average of several staple crops is expected to decline [1]. More than 95% of our food is produced on soil [2].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning, Compositional and Fractal Models to Diagnose Soil Quality and Plant Nutrition

Parent¹,

Natale²,

Brunetto³

2022

Soil Science - Emerging Technologies, Global Perspectives and Applications

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Soils, nutrients and other factors support human food production. The loss of high-quality soils and readily minable nutrient sources pose a great challenge to present-day agriculture. A comprehensive scheme is required to make wise decisions on system’s sustainability and minimize the risk of crop failure. Soil quality provides useful indicators of its chemical, physical and biological status. Tools of precision agriculture and high-throughput technologies allow acquiring numerous soil and plant data at affordable costs in the perspective of customizing recommendations. Large and diversified datasets must be acquired uniformly among stakeholders to diagnose soil quality and plant nutrition at local scale, compare side-by-side defective and successful cases, implement trustful practices and reach high resource-use efficiency. Machine learning methods can combine numerous edaphic, managerial and climatic yield-impacting factors to conduct nutrient diagnosis and manage nutrients at local scale where factors interact. Compositional data analysis are tools to run numerical analyses on interacting components. Fractal models can describe aggregate stability tied to soil conservation practices and return site-specific indicators for decomposition rates of organic matter in relation to soil tillage and management. This chapter reports on machine learning, compositional and fractal models to support wise decisions on crop fertilization and soil conservation practices.

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Plant Abiotic Stress: Temperature Extremes

Cited by 13 publications

References 38 publications

Physiological responses of rice to increased day and night temperatures

Physiological responses of rice to increased day and night temperatures

Physiological and molecular implications of multiple abiotic stresses on yield and quality of rice

Machine Learning, Compositional and Fractal Models to Diagnose Soil Quality and Plant Nutrition

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