Impacts of Heat and Drought on Gross Primary Productivity in China

Heat and drought stress, which often occur together, are the main environmental factors limiting the survival and growth of vegetation. Studies on the response of gross primary production (GPP) to extreme climate events such as heat and drought are highly significant for the identification of ecologically vulnerable regions, ecological risk assessments, and ecological environmental protection. We got 1982–2017 climatic data from the University of East Anglia Climatic Research Unit, Norwich, England, and GPP data from National Earth System Science Data Sharing Service Platform, Beijing, China. Using Theil–Sen median trend analysis and the Mann–Kendall test, we analyzed trends in temperature and the standardized precipitation/standardized precipitation evapotranspiration indices in the eight vegetation regions of China. Additionally, the response of GPP to the single and combined impacts of heat and drought were analyzed using multidimensional copula functions, and GPP reduction probabilities were estimated under different drought levels and heat intensities. The results showed that the probability of a drastic GPP reduction increases with increasing drought levels and heat intensities. The combined impacts of heat and drought on vegetation productivity is greater than the impacts of either drought or heat alone and presents a nonlinear superposition of the two extremes. The impact of heat on GPP is not evident when the drought level is high. The temperate grassland and warm temperate deciduous broad-leaved forest regions are the most sensitive regions to drought and heat in China. This study provides a scientific basis for the comprehensive evaluation of the risk of GPP reduction under the single and combined impacts of heat stress and drought stress.

Download Full-text

Important Physiological Changes Due to Drought Stress on Oat

Frontiers in Ecology and Evolution ◽

10.3389/fevo.2021.644726 ◽

2021 ◽

Vol 9 ◽

Author(s):

Hongying Xie ◽

Mengran Li ◽

Youjun Chen ◽

Qingping Zhou ◽

Wenhui Liu ◽

...

Keyword(s):

Drought Stress ◽

Drought Resistance ◽

Comprehensive Evaluation ◽

Photochemical Efficiency ◽

Damage Index ◽

Principal Component ◽

Drought Tolerant ◽

Gray Correlation ◽

Relative Electrical Conductivity ◽

The Impact

As temperatures rise and water availability decreases, the water decit is gaining attention regarding future agricultural production. Drought stress is a global issue and adversely affects the productivity of different crops. In this study, drought-tolerant varieties of oats were screened to determine drought-tolerant varieties that may be employed in drought-prone areas to achieve sustainable development and mitigate the impact of climate change. To do so, the growth and stress adaptive mechanism of 15 domestic and overseas oat cultivars at the seedling stage were analyzed. Water stress was simulated using 20% polyethylene glycol (PEG-6000). The results showed that the soluble protein content and superoxide dismutase activity of variety DY2 significantly increased under drought stress, whereas the photochemical efficiency and relative water content decreased slightly. The relative electrical conductivity (REC) and drought damage index of the QH444 and DY2 varieties increased the least. The peroxidase content of Q1 and DY2 significantly increased, and the catalase activity of Q1, QH444, and DY2 also substantially increased. Principal component analysis revealed that nine physiological and biochemical parameters were transformed into three independent comprehensive indexes. The comprehensive evaluation results showed that DY2, LN, and Q1 exhibited a strong drought resistance capacity and could be used as a reference material for a drought-resistant oat breeding program. The gray correlation analysis also indicated that Fv/Fm, chlorophyll, REC, and malondialdehyde could be used as key indexes for evaluating the drought resistance of oat.

Download Full-text

The Impact of Drought Stress on some Morpho-Physiological Traits and RAPD Markers in Wheat Genotypes

Journal of Plant Production Sciences ◽

10.21608/jpps.2015.7396 ◽

2015 ◽

Vol 4 (1) ◽

pp. 27-37 ◽

Cited By ~ 1

Author(s):

Bayoumi Y. ◽

Amal Abd EL-Mageed ◽

Enas Ibrahim ◽

Soad Mahmoud ◽

I. El-Demardash ◽

...

Keyword(s):

Drought Stress ◽

Rapd Markers ◽

Physiological Traits ◽

Wheat Genotypes ◽

The Impact

Download Full-text

Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

Journal Of Big Data ◽

10.1186/s40537-021-00488-w ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Yahya Albalawi ◽

Jim Buckley ◽

Nikola S. Nikolov

Keyword(s):

Social Media ◽

Deep Learning ◽

Comprehensive Evaluation ◽

Classification Problem ◽

Data Sets ◽

Word Embeddings ◽

Data Set ◽

Lower Accuracy ◽

Health Related ◽

The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

Download Full-text

Improving Drought Tolerance in Mungbean (Vigna radiata L. Wilczek): Morpho-Physiological, Biochemical and Molecular Perspectives

Agronomy ◽

10.3390/agronomy11081534 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1534

Author(s):

Chandra Mohan Singh ◽

Poornima Singh ◽

Chandrakant Tiwari ◽

Shalini Purwar ◽

Mukul Kumar ◽

...

Keyword(s):

Drought Stress ◽

Drought Tolerance ◽

Crop Production ◽

Mitigation Strategies ◽

Theoretical Research ◽

Whole Genome Sequence ◽

Complex Nature ◽

Drought Tolerant ◽

Breeding Programmes ◽

The Impact

Drought stress is considered a severe threat to crop production. It adversely affects the morpho-physiological, biochemical and molecular functions of the plants, especially in short duration crops like mungbean. In the past few decades, significant progress has been made towards enhancing climate resilience in legumes through classical and next-generation breeding coupled with omics approaches. Various defence mechanisms have been reported as key players in crop adaptation to drought stress. Many researchers have identified potential donors, QTLs/genes and candidate genes associated to drought tolerance-related traits. However, cloning and exploitation of these loci/gene(s) in breeding programmes are still limited. To bridge the gap between theoretical research and practical breeding, we need to reveal the omics-assisted genetic variations associated with drought tolerance in mungbean to tackle this stress. Furthermore, the use of wild relatives in breeding programmes for drought tolerance is also limited and needs to be focused. Even after six years of decoding the whole genome sequence of mungbean, the genome-wide characterization and expression of various gene families and transcriptional factors are still lacking. Due to the complex nature of drought tolerance, it also requires integrating high throughput multi-omics approaches to increase breeding efficiency and genomic selection for rapid genetic gains to develop drought-tolerant mungbean cultivars. This review highlights the impact of drought stress on mungbean and mitigation strategies for breeding high-yielding drought-tolerant mungbean varieties through classical and modern omics technologies.

Download Full-text

Assessing the Sensitivity of Main Crop Yields to Climate Change Impacts in China

Atmosphere ◽

10.3390/atmos12020172 ◽

2021 ◽

Vol 12 (2) ◽

pp. 172

Author(s):

Yuan Xu ◽

Jieming Chou ◽

Fan Yang ◽

Mingyang Sun ◽

Weixing Zhao ◽

...

Keyword(s):

Climate Change ◽

Crop Yield ◽

Food Production ◽

Crop Yields ◽

Climatic Factors ◽

Climatic Data ◽

The South ◽

The North ◽

Output Elasticity ◽

The Impact

Quantitatively assessing the spatial divergence of the sensitivity of crop yield to climate change is of great significance for reducing the climate change risk to food production. We use socio-economic and climatic data from 1981 to 2015 to examine how climate variability led to variation in yield, as simulated by an economy–climate model (C-D-C). The sensitivity of crop yield to the impact of climate change refers to the change in yield caused by changing climatic factors under the condition of constant non-climatic factors. An ‘output elasticity of comprehensive climate factor (CCF)’ approach determines the sensitivity, using the yields per hectare for grain, rice, wheat and maize in China’s main grain-producing areas as a case study. The results show that the CCF has a negative trend at a rate of −0.84/(10a) in the North region, while a positive trend of 0.79/(10a) is observed for the South region. Climate change promotes the ensemble increase in yields, and the contribution of agricultural labor force and total mechanical power to yields are greater, indicating that the yield in major grain-producing areas mainly depends on labor resources and the level of mechanization. However, the sensitivities to climate change of different crop yields to climate change present obvious regional differences: the sensitivity to climate change of the yield per hectare for maize in the North region was stronger than that in the South region. Therefore, the increase in the yield per hectare for maize in the North region due to the positive impacts of climate change was greater than that in the South region. In contrast, the sensitivity to climate change of the yield per hectare for rice in the South region was stronger than that in the North region. Furthermore, the sensitivity to climate change of maize per hectare yield was stronger than that of rice and wheat in the North region, and that of rice was the highest of the three crop yields in the South region. Finally, the economy–climate sensitivity zones of different crops were determined by the output elasticity of the CCF to help adapt to climate change and prevent food production risks.

Download Full-text

Comparing transcriptional responses to Fusarium crown rot in wheat and barley identified an important relationship between disease resistance and drought tolerance

BMC Plant Biology ◽

10.1186/s12870-020-02818-1 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Z. Y. Su ◽

J. J. Powell ◽

S. Gao ◽

M. Zhou ◽

C. Liu

Keyword(s):

Drought Stress ◽

Drought Tolerance ◽

Crown Rot ◽

Differentially Expressed ◽

Transcriptional Responses ◽

Drought Treatment ◽

Crop Species ◽

Fusarium Crown Rot ◽

Important Relationship ◽

The Impact

Abstract Background Fusarium crown rot (FCR) is a chronic disease in cereal production worldwide. The impact of this disease is highly environmentally dependant and significant yield losses occur mainly in drought-affected crops. Results In the study reported here, we evaluated possible relationships between genes conferring FCR resistance and drought tolerance using two approaches. The first approach studied FCR induced differentially expressed genes (DEGs) targeting two barley and one wheat loci against a panel of genes curated from the literature based on known functions in drought tolerance. Of the 149 curated genes, 61.0% were responsive to FCR infection across the three loci. The second approach was a comparison of the global DEGs induced by FCR infection with the global transcriptomic responses under drought in wheat. This analysis found that approximately 48.0% of the DEGs detected one week following drought treatment and 74.4% of the DEGs detected three weeks following drought treatment were also differentially expressed between the susceptible and resistant isolines under FCR infection at one or more timepoints. As for the results from the first approach, the vast majority of common DEGs were downregulated under drought and expressed more highly in the resistant isoline than the sensitive isoline under FCR infection. Conclusions Results from this study suggest that the resistant isoline in wheat was experiencing less drought stress, which could contribute to the stronger defence response than the sensitive isoline. However, most of the genes induced by drought stress in barley were more highly expressed in the susceptible isolines than the resistant isolines under infection, indicating that genes conferring drought tolerance and FCR resistance may interact differently between these two crop species. Nevertheless, the strong relationship between FCR resistance and drought responsiveness provides further evidence indicating the possibility to enhance FCR resistance by manipulating genes conferring drought tolerance.

Download Full-text

Assessing Impact of Climate Variability in Southwest Coastal Bangladesh Using Livelihood Vulnerability Index

Climate ◽

10.3390/cli9070107 ◽

2021 ◽

Vol 9 (7) ◽

pp. 107

Author(s):

Sabrina Mehzabin ◽

M. Shahjahan Mondal

Keyword(s):

Climate Variability ◽

Linear Regression Analysis ◽

Kendall Test ◽

Vulnerability Index ◽

Precipitation Anomaly ◽

Primary Data ◽

Precipitation Concentration Index ◽

Coastal Bangladesh ◽

Present Decade ◽

The Impact

This study analyzed the variability of rainfall and temperature in southwest coastal Bangladesh and assessed the impact of such variability on local livelihood in the last two decades. The variability analysis involved the use of coefficient of variation (CV), standardized precipitation anomaly (Z), and precipitation concentration index (PCI). Linear regression analysis was conducted to assess the trends, and a Mann–Kendall test was performed to detect the significance of the trends. The impact of climate variability was assessed by using a livelihood vulnerability index (LVI), which consisted of six livelihood components with several sub-components under each component. Primary data to construct the LVIs were collected through a semi-structed questionnaire survey of 132 households in a coastal polder. The survey data were triangulated and supplemented with qualitative data from focused group discussions and key informant interviews. The results showed significant rises in temperature in southwest coastal Bangladesh. Though there were no discernable trends in annual and seasonal rainfalls, the anomalies increased in the dry season. The annual PCI and Z were found to capture the climate variability better than the currently used mean monthly standard deviation. The comparison of the LVIs of the present decade with the past indicated that the livelihood vulnerability, particularly in the water component, had increased in the coastal polder due to the increases in natural hazards and climate variability. The index-based vulnerability analysis conducted in this study can be adapted for livelihood vulnerability assessment in deltaic coastal areas of Asia and Africa.

Download Full-text