Impacts of Heat and Drought on Gross Primary Productivity in China

Zhu, Xiufang; Zhang, Shizhe; Liu, Tingting; Liu, Ying

doi:10.3390/rs13030378

Cited by 31 publications

(16 citation statements)

References 84 publications

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“…The standardized precipitation evapotranspiration index (SPEI) [23] was used to identify drought years in China to examine the predictive performance of the model during extreme drought conditions. The SPEI is based on the principle of water balance, considering both precipitation and potential evapotranspiration, and has been widely used in detecting drought variations during the past several decades [24][25][26]. Annual SPEI data from the SPEI Global Drought Monitor website (https://spei.csic.es/ accessed on 5 February 2021) were used in this study.…”

Section: Meteorological Datamentioning

confidence: 99%

A Machine Learning Method for Predicting Vegetation Indices in China

Yuan

Dong

2021

Remote Sensing

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To forecast the terrestrial carbon cycle and monitor food security, vegetation growth must be accurately predicted; however, current process-based ecosystem and crop-growth models are limited in their effectiveness. This study developed a machine learning model using the extreme gradient boosting method to predict vegetation growth throughout the growing season in China from 2001 to 2018. The model used satellite-derived vegetation data for the first month of each growing season, CO2 concentration, and several meteorological factors as data sources for the explanatory variables. Results showed that the model could reproduce the spatiotemporal distribution of vegetation growth as represented by the satellite-derived normalized difference vegetation index (NDVI). The predictive error for the growing season NDVI was less than 5% for more than 98% of vegetated areas in China; the model represented seasonal variations in NDVI well. The coefficient of determination (R2) between the monthly observed and predicted NDVI was 0.83, and more than 69% of vegetated areas had an R2 > 0.8. The effectiveness of the model was examined for a severe drought year (2009), and results showed that the model could reproduce the spatiotemporal distribution of NDVI even under extreme conditions. This model provides an alternative method for predicting vegetation growth and has great potential for monitoring vegetation dynamics and crop growth.

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Section: Meteorological Datamentioning

confidence: 99%

A Machine Learning Method for Predicting Vegetation Indices in China

Yuan

Dong

2021

Remote Sensing

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“…Climate variability has posed several negative impacts on forests including variations in productive traits, carbon dynamics, and vegetation shift, as well as the exhaustion of soil resources along with drought and heat stress in South Asian countries (Jhariya et al, 2019 ; Zhu et al, 2021 ). In Bangladesh, forests are vulnerable to climate variability due to increased risks of fires, rise in sea level, storm surges, coastal erosion and landslides, and ultimately reduction in forest area (Chow et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, trait-climate relationships and environmental conditions have drastically influenced structure, distribution, and forest ecology (Keenan, 2015 ). Higher rates of tree mortality and die-off have been induced in forest trees because of high temperature and often-dry events (Allen et al, 2015 ; Greenwood et al, 2017 ; Zhu et al, 2021 ). For instance, trees Sal, pine trees, and Garjan have been threatened by climate-driven continuing forest clearing, habitat alteration, and drought in South Asian countries (Wang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Impact of climate change on agricultural production; Issues, challenges, and opportunities in Asia

et al. 2022

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Agricultural production is under threat due to climate change in food insecure regions, especially in Asian countries. Various climate-driven extremes, i.e., drought, heat waves, erratic and intense rainfall patterns, storms, floods, and emerging insect pests have adversely affected the livelihood of the farmers. Future climatic predictions showed a significant increase in temperature, and erratic rainfall with higher intensity while variability exists in climatic patterns for climate extremes prediction. For mid-century (2040–2069), it is projected that there will be a rise of 2.8°C in maximum temperature and a 2.2°C in minimum temperature in Pakistan. To respond to the adverse effects of climate change scenarios, there is a need to optimize the climate-smart and resilient agricultural practices and technology for sustainable productivity. Therefore, a case study was carried out to quantify climate change effects on rice and wheat crops and to develop adaptation strategies for the rice-wheat cropping system during the mid-century (2040–2069) as these two crops have significant contributions to food production. For the quantification of adverse impacts of climate change in farmer fields, a multidisciplinary approach consisted of five climate models (GCMs), two crop models (DSSAT and APSIM) and an economic model [Trade-off Analysis, Minimum Data Model Approach (TOAMD)] was used in this case study. DSSAT predicted that there would be a yield reduction of 15.2% in rice and 14.1% in wheat and APSIM showed that there would be a yield reduction of 17.2% in rice and 12% in wheat. Adaptation technology, by modification in crop management like sowing time and density, nitrogen, and irrigation application have the potential to enhance the overall productivity and profitability of the rice-wheat cropping system under climate change scenarios. Moreover, this paper reviews current literature regarding adverse climate change impacts on agricultural productivity, associated main issues, challenges, and opportunities for sustainable productivity of agriculture to ensure food security in Asia. Flowing opportunities such as altering sowing time and planting density of crops, crop rotation with legumes, agroforestry, mixed livestock systems, climate resilient plants, livestock and fish breeds, farming of monogastric livestock, early warning systems and decision support systems, carbon sequestration, climate, water, energy, and soil smart technologies, and promotion of biodiversity have the potential to reduce the negative effects of climate change.

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“…However, drought is not only controlled by water deficit, but also closely related to temperature. Many studies have shown that high temperature will significantly exacerbate the limitation of drought on vegetation growth (Fu et al, 2020;Zhu et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Drought Variation Characteristics and Effects on Vegetation Productivity in Gansu, Northwest China

Cheng

Shuyan²

2022

Preprint

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Changes in vegetation productivity (GPP) have an important impact on the global carbon cycle. Gansu is located in the arid and semi-arid region in northwest China, and its ecological environment is fragile. The study of the impact of drought on GPP is of great significance to the global ecosystem carbon cycle. Based on the SPEI index in Gansu from 2000 to 2020, this paper analyzed the changing characteristics of drought and its impact on GPP. The results revealed that: (1) In the past 21 years, the annual SPEI-12 has been increasing at a rate of 0.3/10a, and it was divided into two distinct dry and wet stages. Before 2018, it was mainly dry, after 2018, it was mainly humid. SPEI-3 decreased at a rate of 0.1/10a in spring, fluctuated significantly in summer, and increased in autumn and winter.(2) The annual SPEI-12 of most meteorological stations was less than 0, the SPEI value increased from northwest to southeast. Except for summer, the SPEI-3 value varied greatly in other seasons, increasing from west to east in spring, decreasing from south to north in autumn, and decreasing from west to east in winter.(3) GPP displayed a significant increasing trend (R2 = 0.57, p < 0.01) in Gansu from 2000 to 2020 .The spatial variation increased from northwest to southeast.(4) Annual SPEI-12 was positively correlated with GPP, SPEI-3 was negatively correlated with GPP in spring and autumn, and positively correlated in summer and winter. The correlation between SPEI-3 and GPP in summer passed the significance test.

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Impacts of Heat and Drought on Gross Primary Productivity in China

Cited by 31 publications

References 84 publications

A Machine Learning Method for Predicting Vegetation Indices in China

A Machine Learning Method for Predicting Vegetation Indices in China

Impact of climate change on agricultural production; Issues, challenges, and opportunities in Asia

Drought Variation Characteristics and Effects on Vegetation Productivity in Gansu, Northwest China

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