Various maize yield losses and their dynamics triggered by drought thresholds based on Copula-Bayesian conditional probabilities
Graphical Abstract
Introduction
Global climate change has become one of the most studied topics in recent decades due to its adverse impacts on social economy, environment and biology (IPCC, 2014). Climate change is increasing the frequency, severity and duration of droughts in many regions of the world (Liu et al., 2020, Peng et al., 2020, Vogt et al., 2021). Over the past few decades, droughts have caused significant impacts on the agricultural sector, leading to food insecurity especially in developing countries (Chi et al., 2020, Randell et al., 2021). Despite significant research breakthroughs in the technology and crop yield potential, food production and security remain highly dependent on weather and climate change, as temperature and precipitation are key drivers of crop growth (Petritsch and Hasenauer, 2014; Zou et al., 2019). Thus, studying the response of crop yield to climate change is essential to ensure food security (Leng and Hall, 2019, Feng et al., 2019).
As a result, food security issues caused by extreme weather events, such as drought, have generated research and public interest in climate change analysis (Hameed et al., 2019, Guo et al., 2019, Fang et al., 2019, Cui et al., 2019, Zhu et al., 2021, Zhou et al., 2021). A number of studies have explored the effect of drought on crop yield (Liu et al., 2018, Marina et al., 2019, Leng and Hall, 2019; Venkatappa et al., 2021). The mechanism of crop yield reduction caused by drought has been explored based on the knowledge of crop physiology (Shah et al., 2017; Feng et al., 2019; Leng and Hall, 2019). Temperature anomalies were found to cause crop yield changes during the growing season (Deryng et al., 2014). High temperature can reduce production by shortening the growing season and destroying plant cells (Zampieri et al., 2016, Zampieri et al., 2018).
In order to detect and quantify droughts, the standardized precipitation evapotranspiration index (SPEI) was proposed by Vicente-Serrano et al. (2010) to account for the effects of both temperature and precipitation. SPEI has been proved to be an effective drought indicator and been widely used in a range of studies (e.g., Liu et al., 2018). Maize is the crop with the largest planting area and yield in China (Tian et al., 2019). The three provinces of Northeast China (Heilongjiang, Jilin and Liaoning provinces) are an important commodity grain production base, in which maize is one of the most important crops. Therefore, the present study focuses on the investigation of drought impacts on maize yield in these three provinces.
The GAR Special Report on Drought 2021 states that while droughts may develop gradually over several months, they can also trigger sudden and dramatic famines and subsequent food riots when a social tipping point is crossed (Vogt et al., 2021). Tipping points occur in nonlinear dynamic systems and the effects of droughts on the food system are highly nonlinear (Vogt et al., 2021). It is critical to assess the tipping point of drought impacts on food systems, namely to quantify drought thresholds that induce grain yield reduction. Some studies have analyzed drought thresholds and their effects on crops based on field experiments (Liu et al., 2021). However, few studies have explored and quantified, through probabilistic statistics, the drought thresholds that can cause different levels of food yield reduction at the provincial level. Probabilistic methods for comprehensive assessment of drought impacts on crop production are helpful to capture the multivariate characteristics of agricultural drought risk (Ribeiro et al., 2019). It has been widely recognized that copula functions are becoming quite popular among multivariate analysis approaches (e.g. Lee et al., 2013; Li et al., 2015; Ribeiro et al., 2019). The application of Copula theory in agrometeorological research is also relatively new. Madadgar et al. (2017) and Bokusheva et al. (2016) have recently modeled the joint distribution of agricultural crops and drought conditions with copula-model. The advantage of using Copula in multivariate modeling is that the probability distributions of individual variables are not necessarily the same (Nelsen, 1999; Ribeiro et al., 2019). Existing studies have assessed grain yield reduction under different drought levels (e.g. mild, moderate, severe, and extreme drought), but did not accurately quantify the drought thresholds that can cause dynamic food production losses. The impact of climate on yields may vary over time due to global warming (and related extremes) and changes in agricultural practices (Trnka et al., 2016, Zipper et al., 2016, Feng et al., 2021). Accurate estimates of drought thresholds triggering corresponding food yield losses and their dynamics over different periods can help individuals and societies to appropriately predict these impacts and adjust agriculture practices to maximize agricultural yields.
Therefore, the purpose of this study is to assess the probability response of maize yield to drought conditions based on a copula-Bayesian framework in three provinces of Northeast China. A model of multivariate distribution is employed to jointly study the behavior of drought and maize yields. The conditional probability distribution framework for maize yield loss under different drought conditions was then used to quantify the drought thresholds that induce different yield losses. The main objectives of this study are: (1) to evaluate the vulnerability of maize yield in different provinces under drought stress at different periods; (2) to quantify the drought thresholds inducing maize yield loss and their dynamics in different provinces.
Section snippets
Study area and data
This study selects three provinces in Northeast China (118°50′−135°20′E, 38°43′−53°33′N) as the research area, which includes Heilongjiang, Jilin and Liaoning provinces covering an area of 7.87 × 105 km2 with complex topography of plains and mountains (Fig. 1). Located in the northernmost part of China, this region is the country's largest granary, the largest grain output, and an important forestry base in China. The Changbai Mountains, the Greater Khingan Mountains and the Lesser Khingan
Drought index calculation
In terms of meteorological drought indices, the Standardized Precipitation Evapotranspiration Index (SPEI) at growing season (from May to September, average growing season of maize in the three northeastern provinces) at scales of 5 (SPEI5) during 1980–2018 is used in this study. The detailed steps for the calculation of the SPEI are as follows: first, the monthly potential evapotranspiration were estimated based on the climate data (precipitation and temperature) using the method of
Impact of drought on maize yield during the growing season
Fig. 3A shows the variation of maize yield per unit area in the three Provinces of Northeast China during 1980–2018. In general, during this period of time, strong human interventions, such as the improvement of agricultural practice, investment and technological progress, have led to a steady increase in maize yield. Nevertheless, the trend of sharp decline, and significant decline in maize yield were found during severe drought years (as shown in the gray vertical shadow in Fig. 3). Fig. 3B
Changes of meteorological factors and irrigation conditions in three northeastern provinces
To understand the reasons for the different maize yield losses under the influence of drought in different provinces of the Northeast China, precipitation (P), evapotranspiration (E), the difference between precipitation and evapotranspiration (P-E) in different periods, and the aridity index (AI). AI is defined by the ratio of precipitation (P) to reference crop evapotranspiration (ET0) (UNEP, 1992), and is considered an important indicator of climate regionalization. It is widely used in
Conclusions
Based on the Copula-Bayesian Probability Assessment theory, this study developed a bivariate probabilistic framework, which facilitates the quantification of crop vulnerability to drought stress at multiple levels and the identification of drought-vulnerable farmland ecosystems in a more informative way. In the proposed framework, drought conditions are characterized by the SPEI, which can be flexibly replaced by any other yield-related climate variable as part of an effort to understand the
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
This study was jointly funded by Supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA28060100), the National Key Research and Development Program of China (2017YFC0405900), and the National Natural Science Foundation of China (51709221).
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