Multivariate probabilistic estimates of heat stress for rice across China

Zhang, Lei; Yang, Banghua; Guo, Anhong; Huang, Dapeng; Huo, Zhiguo

doi:10.1007/s00477-018-1572-7

Cited by 8 publications

(2 citation statements)

References 37 publications

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“…This is mainly because most of the copulas in Archimedean copula family admit an explicit formula, while the elliptical copula family does not comply. Archimedean copula family has been studied in numerous research fields, for example, rainfall frequency analysis (Zhang & Singh, 2007;Zhang & Singh, 2012), intensity-duration-frequency relationship (Ariff et al, 2012), modelling wind speed dependence (Xie et al, 2012), modelling option pricing (Cherubini & Luciano, 2002) and probabilistic estimates of heat stress for rice (Zhang et al, 2018).…”

Section: Types Of Copulas Usedmentioning

confidence: 99%

Modelling High Dimensional Paddy Production Data using Copulas

Roslan¹,

Shinyie²,

Ling³

2021

JST

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As the climate change is likely to be adversely affecting the yield of paddy production, thence it has brought a limelight of the probable challenges on human particularly regional food security issues. This paper aims to fit multivariate time series of paddy production variables using copula functions and predicts the next year event based on the data of five countries in southeast Asia. In particular, the most appropriate marginal distribution for each univariate time series was first identified using maximum likelihood parameter estimation method. Next, we performed multivariate copula fitting using two types of copula families, namely, elliptical copula family and Archimedean copula family. Elliptical copula family studied are normal and t copula, while Archimedean copula family considered are Joe, Clayton and Gumbel copulas. The performance of marginal distribution and copula fitting was examined using Akaike information criterion (AIC) values. Finally, we used the best fitted copula model to forecast the succeeding event. In order to assess the performance of copula function, we computed the forecast means and estimation errors of copula function with a generalized autoregressive conditional heteroskedasticity model as reference group. Based on the smallest AIC, the majority of the data favoured the Gumbel copula, which belongs to Archimedean copula family as well as extreme value copula family. Likewise, applying the historical data to forecast the future trends may assist all relevant stakeholders, for instance government, NGO agencies, and professional practitioners in making informed decisions without compromising the environmental as well as economical sustainability in the region.

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Section: Types Of Copulas Usedmentioning

confidence: 99%

Modelling High Dimensional Paddy Production Data using Copulas

Roslan¹,

Shinyie²,

Ling³

2021

JST

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“…Previous studies have documented that increases in temperatures have accelerated rice phenology development, thus decreasing crop yield [6][7][8][9]. Furthermore, many research works have also emphasized the compound climate risk that occurs during the rice reproductive stage, such as heading, flowering, and early grain filling stage, which would normally cause a great loss in yield [10][11][12]. High temperature during flowering is well known to cause spikelet sterility and reduce yield in rice [13], and temperature at grain filling is a significant factor influencing spikelet fertility, 1000-grain weight [14], and rice quality [15].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Rice Sowing Dates for High Yield and Climate Adaptation in Central China

Liu,

Song,

et al. 2023

Agronomy

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Optimizing the sowing date of rice can change the seasonal patterns and distributions of climate factors during the crop growing season, making it one of the most effective ways to adapt to climate change and achieve high yield. A four–year field experiment (2018–2021) was conducted at Jingzhou Agricultural Meteorological Experiment Station, central China, with four different sowing dates (SD) each year, late April (SD1), early May (SD2), mid–May (SD3) and late May (SD4). Dry matter accumulation, grain yield and climate conditions were observed across sowing dates. Our findings revealed that delaying the sowing date from early May to mid or late May could increase grain yield by 5.6% to 8.6%. However, sowing too early could increase heat stress, decrease the net effective accumulated temperature, inhibit rice growth, and reduce grain yield. On the other hand, sowing too late could increase the risk of low temperatures after flowering. From the perspective of increasing net effective accumulated temperature, reducing heat stress and low temperature after flowering, mid to late May was the most favorable sowing date to ensure high yield. It is suggested that optimizing rice sowing dates can effectively avoid the threat of heat stress and better match thermal resources, thereby increasing rice productivity.

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