ENSO–climate fluctuation–crop yield early warning system—A case study in Jilin and Liaoning Province in Northeast China

Accepted Manuscript ENSO-Climate Fluctuation-Crop Yield Early Warning System—A Case Study in Jilin and Liaoning Province in Northeast China Zhao Zhang, Boyan Feng, Jiabin Shuai, Peijun Shi PII:

S1474-7065(15)00116-3

DOI:

10.1016/j.pce.2015.09.015

Reference:

JPCE 2420

To appear in:

Physics and Chemistry of the Earth

Received Date: 16 April 2015 Revised Date:

10 August 2015

Accepted Date: 30 September 2015

Please cite this article as: Zhang, Z., Feng, B., Shuai, J., Shi, P., ENSO-Climate Fluctuation-Crop Yield Early Warning System—A Case Study in Jilin and Liaoning Province in Northeast China, Physics and Chemistry of the Earth (2015), doi: 10.1016/j.pce.2015.09.015. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Title: ENSO-Climate Fluctuation-Crop Yield Early Warning System—A Case Study in Jilin and Liaoning Province in Northeast China Authors: Zhao Zhang*1; Boyan Feng1; Jiabin Shuai1; Peijun Shi1 Affiliations: 1: Academy of Disaster Reduction and Emergency Management, Ministry of Civil Affairs& Ministry of Education/ State Key Laboratory of Earth Surface Process and Resource Ecology, Beijing Normal University Full Address for correspondence: Zhang Zhao, Beijing Normal University, No.12, Xueyuannan Road, Haidian District, Beijing, 100875, China. E-mail: [email protected]

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Abstract

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Crop yield is very sensitive to climate variability. The El Niño–Southern Oscillation (ENSO) is one of the most important contributors to global climate fluctuation, and therefore has a major impact on agricultural production. In this study, we structure an ENSO–climate fluctuation–crop yield early warning system to model the maize yield in Jilin and Liaoning Provinces in Northeast China. The system, which consists of a weather generator and a Model to capture the Crop Weather relationship over a Large Area (MCWLA) , is not only capable of simulating the maize yield both at the provincial (regional) scale and the grid scale, but can also provide the exceedance probability of yield. Simulation results show maize yields in El Niño years to be higher on average than those in neutral years, while yields in La Niña years are the lowest. Spatially, the central part of the study area always shows a higher yield than other parts of the study, while yields in the northeast and northwest parts are relatively lower, no matter how high or low the exceedance probability and whatever the ENSO phase. Our study strongly implies that such a warning system shows considerable potential for application in other areas of China.

Key words: Crop yield; Early warning system; ENSO; Maize

1 Introduction

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Agricultural problems and food security have deep implications for the survival of humankind and the stability of nations, and should be addressed as necessary first steps for most local governments in China. However, agriculture is very sensitive to climatic variability, and is vulnerable to agro-meteorological disasters (AMDs) such as flooding and droughts (Wang et al., 2014; Zhang et al., 2014a, b). In China, the agricultural area affected by AMDs every year accounts on average for 31.1% of the total cultivated area. The areas affected by drought (since 1950), flooding (since 1950) and cold damage (since 1978) are 56.2%, 24.2% and 5.8% respectively of the total area affected by AMDs (Shuai et al., 2013; Wang et al., 2010; Tao and Zhang, 2011). Many previous studies have demonstrated that AMDs have occurred more frequently and violently against the background of global warming, especially during recent decades, and China has become one of the countries most severely affected by AMDs (Tao et al., 2012; Wang et al., 2014; Zhang et al., 2014a, b, c). However, as an efficient mitigation and adaptation measure for AMDs, crop early warning systems have not been widely applied or even well developed in China. There are a wide range of AMDs and their frequencies and intensities are driven by multiple factors (Xin et al., 2007; Liu et al., 2011). Among these, the El Niño–Southern Oscillation (ENSO), which refers to fluctuations in both sea-surface temperature in the tropical Pacific and sea-level pressure in the southern Pacific, is generally regarded as one of the most important contributors to interannual variability in weather (Hong et al., 2001). More and more studies suggest that ENSO has a major impact on global atmospheric circulation and regional climatic anomalies, although it is not directly defined as a meteorological disaster (Alexander et al., 2009; Kenyon and Hegerl, 2008; Schubert et al., 2008). More recently, many studies have revealed a strong relationship between different ENSO phases (El Niño and La Niña) and extreme weather events such as extreme rainfall (Grimm and Tedeschi, 2009; 2

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Ropelewski and Bell, 2008; Zhang et al., 2010). Several studies in China have already proved that the interannual fluctuations in temperature and precipitation caused by ENSO show a significant impact on China’s agriculture production (Gong et al., 1998; Liu et al., 1995; Park et al., 2014; Zhang et al., 2007). Thus, understanding the associations between ENSO and crop yield and the potential application of such information to crop production has gained more and more attention in China. Maize, one of China’s three staple crops, has a very high planting proportion in the North China Plain and the Northeast Plain. Many researchers have studied the response of maize yield to temperature and precipitation variation. For example, Ma (2012) analyzed the impact of high temperatures and drought on maize yield in Northeast China via a Decision Support System for Agrotechnology Transfer (DSSAT) model; Liu et al. (2013) conducted an infrared temperature-increasing simulation experiment and verified that changes in temperature and moisture have a significant effect on maize yield. Therefore, taking account of the significant impact of temperature and moisture on maize production and the interannual fluctuations of temperature and precipitation caused by ENSO, it is urgent and critical to investigate the relationship between ENSO and maize yield and develop an early warning system for reducing climatic risk based on this relationship. Many studies have already revealed that ENSO is an important signal for climatic characteristics. In other words, the variability of climatic conditions in the near future in a region can be forecast when ENSO forecasting information has been provided (Intergovernmental Panel on Climate Change (IPCC), 2012). Following studies on the impact of ENSO on crop systems, some researchers have made further attempts to apply ENSO forecasting information in order to direct agricultural production. For example, Messina et al. (1999) have developed a system to explore the potential for tailoring land allocation among crops to ENSO phases in the Pampas region of Argentina, with the aim of optimizing decisions either to mitigate expected adverse conditions or to take advantage of favourable conditions. Meza et al. (2003) evaluated the value of a perfect forecast of El Niño phases for selected agricultural locations in Chile. All these studies have strongly substantiated the idea that if forecasting information on climatic anomalies is available, an early warning signal should be released to relevant departments, allowing farmers to adjust their planting plans to minimize their potential losses. Accordingly, ENSO conditions are closely monitored by major meteorological institutes and many stochastic models have been developed to predict the beginning and the intensity of ENSO episodes and direct agricultural production worldwide (Food and Agriculture Organization (FAO), 2014). However, studies on the relationship of ENSO and crop yield in China are not extensive, and some results are inconsistent. For example, Tao et al. (2004) investigated the relationship between ENSO and three staple crop yields during developing ENSO years using data from seven provinces, and found that there was large variability in production associated with ENSO. Other studies found strong connections between ENSO and crop yields in Yunnan (Tian et al., 2000), Sichuan (Xiao et al., 1994), and mid-eastern China (Meng et al., 2009). However, a study concerning rice yields in Jiangxi did not agree with the above findings, and implied that the relationship between ENSO and staple crop yields in China varied across crops and regions (Deng et al., 2010). Therefore, the effect of ENSO on primary crop yields in China remains uncertain. According to previous studies, we could infer that the impact of ENSO on crop yield in China varies by crop type and region. So far, there has been no study focused on forecasting yield by integrating the crop model and ENSO forecasting information in China, which has weakened the potentially valuable utility of ENSO signals for directing crop production. The main purposes of this study are: 1) to construct an original framework for an ENSO–climate fluctuation–crop yield early warning system; 2) to apply the early warning system for maize in Jilin and 3

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2 Data Sources and Methodology 2.1 Data

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2.2 Weather Generator

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Data for maize yields at prefectural and provincial scales were obtained from China Statistical Yearbook and China Planting Information (http://zzys.agri.gov.cn/nongqing.aspx). Historic climate data in China were obtained from the China Meteorological Data Sharing Service System (http://www.escience.gov.cn/metdata/page/index.html). Monthly maximum temperature (Tmax), minimum temperature (Tmin), mean temperature (Tmin), precipitation (P), and sunshine duration (S) data are available from 43 weather stations in the study area that provide continuous records from October 1962 to the present. Since solar radiation data are not available due to the cost, maintenance and calibration requirements of the measuring equipment, here we use S as a substitute, as measures of this have been performed regularly (Shuai et al., 2013; Zhang et al., 2010). The soil and hydrologic datasets were obtained from the FAO database (http://www.fao.org/waicent/faoinfo/economic/faodef/faodefe.htm).

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In this study, we use a weather generator (WG), which can generate stochastic synthetic weather time series using Monte Carlo simulation algorithms based on historic meteorological observation data (Wilks and Wilby, 1999). The generator can reproduce the main statistical characteristics of the weather from which the climatic parameters were fit at different ENSO phases. Three different ENSO phases (El Niño year, La Niña year and neutral year) were extracted according to the COAPS (Center for Ocean-Atmospheric Prediction Studies) method (http://coaps.fsu.edu/). The use of ENSO indices to predict likely seasonal to interannual climate conditions has reached a point at which there is a strong possibility of better addressing the effects of climate variability when combined with WGs. Please see Wilks and Wilby (1999) and Wilks (2002) for details on how to reproduce reasonable weather time series for each ENSO phase. The daily outputs from the WG are the most important sources of temporal variability for crop growth and development, and will be used as inputs for the crop model. We include key climate variables such as temperature, precipitation, solar radiation, wind speed and relative humidity at each ENSO phase in order to guarantee the accuracy of crop simulation.

2.3 Crop Model The process-based general MCWLA (Model to capture the Crop–Weather relationship over a Large Area) developed by Tao et al. (2009) is applied in this study. The details on model development, parameter optimization and uncertainty analysis are described in Tao et al. (2009). Briefly, the MCWLA is designed to investigate the impacts of weather and climate variability on crop growth, development 4

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and productivity at a large spatial scale. So far, three main crop yields (rice, wheat and maize) have been simulated successfully by this model. Here, we select the MCWLA-Maize model to simulate the maize yield in the study area. Growing degree-days provide the driving force for the processes of canopy development, flowering and maturity. The daily growth of the crop leaf area is simulated using heat-dependent potential growth rate and stresses from water, improved by the General Large-Area Model (GLAM) (Challinor et al., 2004). Soil hydrology is modelled following the semi-empirical approach of Haxeltine and Prentice (1996a), a simplification of the model developed by Neilson (1995). The MCWLA adopts the robust, process-based representation of coupled CO2 and H2O exchanges in the Lund–Potsdam–Jena (LPJ) dynamic global vegetation models (Bondeau et al., 2007; Haxeltine and Prentice 1996a, b; Sitch et al., 2003). Impacts on yield due to factors other than weather (e.g., pests, disease and management factors) are modelled in a simplified way via GLAM (Challinor et al., 2004). Biomass is accumulated from the photosynthate and further transferred into crop yield by harvest index. Furthermore, MCWLA has succeeded in significantly capturing not only the interannual variability in crop yield at four provinces from 1985 to 2002, but also crop response to elevated CO2 and extreme temperatures by adopting photosynthesis–stomatal conductance coupling, which agreed well with controlled-environment experiments, suggesting its power for predicting crop yield in a future climate (Tao et al., 2009).

2.4 Study Area

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The study area (Fig. 1) was selected because Jilin and Liaoning are two key provinces for maize production in China. Specifically, there are a total of 147 grid squares planting maize in study area at 0.5° × 0.5° grid resolution, the proportion of maize planting area in most grid squares being over 50%. Furthermore, among the three granary provinces (Heilongjiang, Jilin and Liaoning), the proportion of maize yield and planting area in Jilin is much higher than those of the other two provinces. Taking 2012 as an example, the proportions of maize yield in Jilin, Liaoning and Heilongjiang are 83%, 74% and 51% respectively, while the proportions of planting area are 82%, 77% and 61% respectively. Tao et al. (2009) simulated the maize yield in Jilin and Liaoning from 1963 to 2008 using the MCWLA-Maize model, showing an excellent simulation capability for which the confidence level reached 99% in both provinces.

2.5 Analysis Processes The framework of the early warning system was firstly constructed. Second, the mean maize yield during different ENSO phases was analyzed to understand a general view of the associations between yield and ENSO phases. Spatial differences in responding to ENSO signal were diagnosed thirdly to direct agricultural production. Finally, the drivers controlling the yield fluctuations between ENSO phases were identified, and the potential adaptations measures were also mentioned in the discussion part. Among these, we also conducted the ANOVA analyses to investigate the differences between different ENSO phases.

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3 Results 3.1 The framework of the early warning system

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The early warning system is constructed according to the chain effect among three components (ENSO, climate variability, and crop yield), as another form of ENSO–climate variability–crop yield linkage. In other words, ENSO definitely shows impact to some climate variables in some degree and in some land areas, and consequently, the variability in such variables are certainly associated with crop yield. The main components of the system include the WG and the MCWLA-Maize model. The basic operational processes of the system are shown in Fig. 2. First, ENSO phases were classified by COAPS methods (http://coaps.fsu.edu/), specifically the monthly sea surface temperature anomaly (SSTA) over the tropical Pacific at 4°N–4°S, 150°W–90°W. If SSTA values are 0.5°C or greater for six consecutive months, the ENSO year of October through to the following September is categorized as an El Niño phase. La Niña phases, on the other hand, refer to a year when SSTA values equal or exceed −0.5°C. Years which are not within the above two classifications are defined as neutral years. Secondly, monthly probability distributions of climatic variables were simulated based on each ENSO phase. Since monthly climatic variables were not accurate enough to simulate the maize yield by the MCWLA-Maize model, we generated the daily variables by using random number generation and interpolation using the WG. Thirdly, stochastic samples of each ENSO phase over 1000 years were generated to characterize fully the features of each ENSO phase. Thus, such sampled datasets, covering 3000 years in total were able to describe all kinds of historic ENSO scenarios. The yield could then be simulated by the MCWLA-Maize model, combined with soil, hydrological and other maize-phonological data. Finally, a statistical analysis was carried out to construct an exceedance probability curve for the mean maize yield on the grid scale. In order to show the spatial patterns of maize yield during different ENSO phases, yields with levels exceeding 10%, 40%, 50%, 60% and 90% probabilities were depicted separately at the grid scale during El Niño and La Niña year, respectively. The system was calibrated and validated repeatedly during all simulation processes, making it more credible for potential application in other areas.

3.2 Regional mean maize yield in different ENSO phases Tao et al. (2009) and Shuai et al. (2013) used the MCWLA-Maize model to simulate the maize yield successfully in Northeast China. Based on this calibrated model, the maize yield in Jilin and Liaoning Provinces were simulated in different ENSO phases. For each ENSO phase, the yield simulation was simulated for 1000-year periods, and the mean yields in the two provinces were obtained. According to the histogram for these 3000 results (Fig. 3), the yields showed normal distributions for each of the three ENSO phases, with the left curve showing La Niña years and the middle and right curves showing neutral and El Niño years, respectively. Specifically, the mean yield in La Niña years was 4400 kg/ha and the maximum was 7600 kg/ha; in the neutral years, the mean and maximum yields amounted to 4800 kg/ha and 7800 kg/ha, with 4900 kg/ha and 8000 kg/ha for the El Niño years. The fitting parameters (µ and σ) from the normal distributions for the three phases further confirmed the above 6

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3.3 Spatial distribution of maize yield

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results: µ, denoting the mean yield, was 4316.6, 4639.3 and 4769.8 kg/ha for La Niña years, neutral years and El Niño years, respectively; σ, denoting standard deviation, was 1033.7, 1035.3 and 995.8 kg/ha for La Niña years, neutral years and El Niño years, respectively. Exceedance probability curves were further obtained based on the fitting distributions in three ENSO years (Fig. 4). Curves for La Niña years (blue), neutral years (green) and El Niño years (red) were located from left to right in sequence. For each ENSO phase, a lower exceedance probability corresponded to higher maize yield, and vice versa. Additionally, the data clearly indicated that the maize yield ranged from 1500 to 8000 kg/ha, suggesting a huge variability in maize yield. Furthermore, comparing yields in El Niño years and La Niña years to neutral years, the production gains in El Niño years were far below the losses in La Niña years, suggesting more severe impact due to La Niña compared to El Niño.

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The exceedance probability curves described the possible maize yield in different ENSO phases at provincial scale. However, since Jilin and Liaoning covered a large area for planting maize, spatial differences at a smaller scale were masked by the provincial pattern. Therefore, it was necessary to analyze the spatial distribution of maize yield at a smaller scale. We further analyzed the spatial distribution at the 0.5° × 0.5° grid scale with different exceedance probability levels for the different ENSO phases (Fig. 5). Here, we selected only five exceedance probabilities: 10%, 40%, 50%, 60% and 90%. Maize yields on these five levels were representative for describing the yield variation during different climatic conditions. Such analysis at a higher resolution was also significant, indeed essential, for constructing an early warning system to direct real agriculture production for local governors. Higher exceedance probabilities generally correspond to lower maize yield, a finding strongly substantiated by the two columns (left column Fig.5-(a-e)-(1) and middle column Fig.5-(a-e)-(2)) with more red color from 10% to 90%. For El Niño, almost all grid squares reached 7000 kg/ha at the 10% level, with 5% of grid squares showing a yield over 7500 kg/ha (the bright yellow areas, Fig. 5-a-1). With an exceedance probability of 90%, 80% of grid squares had yields ranging from 2500 to 3000 kg/ha (the dark red areas, Fig. 5-e-1), indicating a distinct decrease in yield. Specifically, according to the left column (Fig.5-(a-e)-1), the middle, southern and southwest parts of the study area (or the middle area of Jilin and most areas of Liaoning) with a light color demonstrated a significantly higher yield in the El Niño phase. By contrast, the northeast and northwest parts (the southeast and northwest areas of Jilin), with a dark color, showed a relatively lower yield (left column in Fig. 5). Taking the 50% level of exceedance probability as an example, mean yield in these light-colored areas reached over 5500 kg/ha. Among these, 10% showed a yield over 7000 kg/ha in the middle and northern part of Liaoning, while 20% of grid squares in the east of Jilin showed less than 4000 kg/ha (Fig. 5-c-1). In La Niña years, the central part of the study area (the middle of Jilin and Liaoning), was less affected and maintained a high level of yield. However, a yield reduction risk was predicted for the northeast and northwest areas (the northeast and northwest of Jilin and west of Liaoning). Still taking an exceedance probability of 50% as an example, over 40 grid squares in the east and south of Jilin had a yield of less than 3500 kg/ha, while only a few grid squares in the west, middle and north of Liaoning reached a yield of 5500 kg/ha. Comparing yields in El Niño years with those in La Niña years, there was a yield gap between these two ENSO phases no matter how high or how low the exceedance probability (right column in Fig. 5). 7

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Moreover, most grid squares at each exceedance probability level were colored red, meaning that yields in El Niño years were higher than those in La Niña years. These results further confirmed the yield differences described above. Generally, increasing with the exceedance probability level, the dark-colored areas associated with lower yield enlarged gradually and encircled the central areas, but in different degrees during the two ENSO phases. Consequently, the different gaps at the same exceedance probability level ultimately caused the spatial change in yield differences during the two ENSO phases (right column in Fig. 5). For example, the proportion of yield difference less than ±200 kg/ha was nearly 15% at 90% exceedance probability level, but less than 5% at 10% exceedance probability level. Additionally, two areas including the northwestern and southwestern corners, with the exception of the 50% exceedance probability level, always indicated the strongly positive effect of the El Niño phase on maize yield. Overall, maize yield in the study area showed highly similar responses to ENSO phases at different exceedance probability levels, while there were different spatial distributions at grid level. Nevertheless, the early warning system for maize yield could be applied in these areas to determine targeted solutions to climatic variability.

4 Discussion

4.1 Adaptation measures for ENSO according to primary controlling climatic

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A clear yield gap between different ENSO phases was demonstrated both in this study and in previous studies (e.g., Shuai et al., 2013). Corresponding measures are expected to lower potential losses from ENSO, especially during La Niña years. In order to make these measures more targeted, it is important first to understand which key variable is controlling the yield gap. Figure 6 shows the variation of precipitation and temperature in different ENSO phases. According to the classification of ENSO phase by the COAPS method, we averaged the precipitation, three temperature variables (Tmean, Tmin, Tmax) (Fig. 6) of maize’s growing season in study area to find which variable primarily contributed to yield gaps during different ENSO phases. Significant differences (p < 0.05) in precipitation (Fig.6-a) were found between ENSO phases. The maximum, mean and minimum precipitation amounts in El Niño years were 1345 mm, 751 mm and 429 mm respectively, which were all higher than those in neutral years (900 mm, 695 mm, 416 mm) and in La Niña years (926 mm, 636 mm, 359 mm). However, the differences of the temperature variables between ENSO phases were not detected (p > 0.1). The above results suggested that the precipitation is the main factor controlling the yield gap between ENSO phases. Taking account of the higher yields in El Niño years compared to La Niña years in the above results and the previous study of Shuai et al. (2013), as well as the significant difference in precipitation between ENSO phases, it follows that some adaptive measures should be taken into consideration to mitigate the potential yield loss from drought when La Niña signals occur. Among all measures, optimal irrigation has been accepted as the most effective method to fight drought caused by La Niña events (Han, 2004; Zheng et al., 2010; Zhang, 2014). However, the time, quantity and type of irrigation should 8

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be chosen according to soil properties, evaporation intensity, maize varieties and the forecasting information of precipitation (Zhang, 2014). Sprinkler irrigation, subsurface irrigation, furrow irrigation, border irrigation and micro-irrigation are more efficient and water-economical than conventional irrigation methods. Moreover, a combination of furrow and border irrigation has been already applied in western Jilin Province, indicating a high capacity for soil water storage and considerable adaptability to drought (Han, 2004; Zhang et al., 2014; Zheng et al., 2010). An additional alternative approach is to adjust sowing time for maize to satisfy the demands of temperature, water and illumination (Ye, 2012). As drought and heat stresses will occur more frequently under the background influence of global warming in the study area, a change in sowing time has been proven to be an effective way to redistribute the light, temperature and water resources, and consequently to reduce potential yield losses from meteorological disasters (Luo et al., 2000; Ye, 2012; Yuan et al., 2012).

4.2 Potential application of the early warning system for other crops and other areas

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Many previous studies have pointed out that there are strong relationships between ENSO signals and climatic variables in other regions of China. Additionally, climatic variables, especially precipitation and temperature, have been shown to have a significant effect on crop yield in major crop planting areas in China (Tao et al., 2012; Wang et al., 2014; Zhang et al., 2015). Not only MCWLA-Maize, but also other series of MCWLA models, including for rice and wheat, have been developed by Tao et al., and MCWLAs have been proven highly sensitive to climate change, and have succeeded in capturing interannual variability in crop yield (Tao et al., 2009; Tao and Zhang, 2013a, b). Therefore, a similar early warning system could be applied for other crops in other regions of China. For example, as a major rice-producing area, especially for double-cropping rice, the Middle-Lower Yangtze Plain has a critical role in producing this staple food for the Chinese people. In this area, many studies have revealed that climate change has had a great impact on the rice yield during recent decades (Yang et al., 2010; Zhao, 2006). Furthermore, some studies have indicated that extreme precipitation events in this area had a strong relationship with ENSO (Xiao et al., 2000; Wen et al., 2011). Therefore, it is entirely possible to construct similar early warning systems for other crops in other areas, such as rice in the Yangtze Plain, which can be used to direct real crop production. Meanwhile, plenty of studies have increasingly concerned about the impact of climate change on agricultural production. They had stated a large range of yield variation could be ascribed to climate change, and such researches were generally based on the relationship between historical statistical yield and climate observation for a long-term (at least 30 years) (Tao and Zhang, 2011; Tao et al., 2012; Zhang et al., 2015). Due to its power of the early warning system in the study, similarly, it would be very practical and realizable to identify the dynamics of correlation between crop yield and ENSO in different areas, with taking account of the management and adaptation measures. Therefore, such issues would also be our main focus in the future.

5 Conclusion An ENSO–climate fluctuation–crop yield early warning system for regional agricultural production has been constructed to simulate the maize yield response to ENSO, with Jilin and Liaoning Provinces taken 9

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There was no conflict of interest.

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Acknowledgements

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Conflict of interest

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as an example. The system took into account climate fluctuations during different ENSO phases and the degree of their impact on crop yield at both regional and grid scales. It was found that maize yield during each ENSO phase exhibited a normal distribution. Mean yields at regional scale were 4400, 4800 and 4900 kg/ha in La Niña, neutral and El Niño years, respectively. The results suggest that the El Niño phase has a positive effect on maize yield, with a negative one for La Niña years. Spatial distribution at grid scale further demonstrated the yield gap between El Niño and La Niña years, differentiated significantly according to levels of exceedance probability and the specific areas of focus. Nevertheless, the central part of the study area always shows a higher yield than other parts in the study, while yields in the northeast and northwest parts are relatively lower, no matter how high or low the exceedance probability and whatever the ENSO phase. Precipitation was the main climatic factor controlling significant differences during different ENSO phases. Finally, some adaptation measures and potential applications of the system have been proposed to reduce possible losses in La Niña years.

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This study was funded by the Fund for Creative Research Groups of the National Natural Science Foundation of China (no. 41321001), and the State Key Laboratory of Earth Surface Processes and Resource Ecology of Beijing Normal University (2014-ZY-06).

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Figure List Figure. 1 The study area (colours represent the proportion of maize planting).

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Figure. 2 The basic framework of the early warning system. Figure. 3 Histogram of simulated maize yield in different ENSO phases. Blue, green and brown bars represent La Niña, neutral and El Niño years, respectively. Light blue, light green and red curves are fitted curves of these simulation results in La Niña, neutral and El Niño years, respectively.

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Figure. 4 Exceedance probability curves for maize yield in different ENSO phases, and blue for La Niña years, green for neutral years and red for El Niño years.

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Figure. 5 Spatial distribution of maize yields (unit: kg/ha) in Jilin and Liaoning Provinces during El Niño and La Niña years at different exceedance probabilities. Rows from top to bottom correspond to exceedance probabilities of 10%, 40%, 50%, 60% and 90%, respectively (a–e); columns from left to right show maize yield in El Niño years and La Niña years and the yield gap between them (yields in El Niño years minus yields in La Niña years).

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Figure.6 Boxplots of four climatic variables in different ENSO phases during the period 1980–2008 in Jilin and Liaoning Provinces; (a–d) represent precipitation (mm), mean temperature (0.1°C), minimum temperature (0.1°C) and maximum temperature (0.1°C), respectively.

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Highlights

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·We firstly structure an early warning system of maize yield in China. ·The system simulates well the impact of each ENSO phase on maize yield. ·Significant difference in spatial response of maize yield is well presented. ·The study implies the potential usage of the system in other areas of China.