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Journal of Geographical Sciences >

2018 , Vol. 28 >Issue 11: 1700 - 1714

DOI: https://doi.org/10.1007/s11442-018-1538-1

Special Issue: Land system dynamics: Pattern and process

Changes in production potentials of rapeseed in the Yangtze River Basin of China under climate change:A multi-model ensemble approach

  • TIAN Zhan 1, 2, 3 ,
  • JI Yinghao 1, 2 ,
  • SUN Laixiang 4, 5 ,
  • XU Xinliang , 7, * ,
  • FAN Dongli , 1, * ,
  • ZHONG Honglin 4 ,
  • LIANG Zhuoran 6 ,
  • FICSHER Gunther 5
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  • 1. Shanghai Institute of Technology, Shanghai 200030, China
  • 2. Shanghai Climate Center, Shanghai Meteorological Bureau, Shanghai 200030, China
  • 3. School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
  • 4. Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
  • 5. International Institute for Applied Systems Analysis (IIASA), A-2361 Laxenburg, Austria
  • 6. Hangzhou Meteorological Bureau, Hangzhou 310051, China
  • 7. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100039, China
*Corresponding author: Fan Dongli, Associate Professor, E-mail: ; Xu Xinliang, Professor, E-mail:

Author: TIan Zhan, E-mail: tianz@lreis.ac.cn

Received date: 2017-03-06

  Accepted date: 2017-09-20

  Online published: 2018-11-20

Supported by

National Natural Science Foundation of China, No.41671113, No.51761135024, No.41601049, No.41475040

China’s National Science & Technology Pillar Program, No.2016YFC0502702

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Journal of Geographical Sciences, All Rights Reserved
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Abstract

Rapeseed is one of the major oil crops in China and it is very sensitive to climate change. The Yangtze River Basin is the main rapeseed production area in China. Therefore, a better understanding of the impact of climate change on rapeseed production in the basin is of both scientific and practical importance to Chinese oil industry and food security. In this study, based on climate data from 5 General Circulation Models (GCMs) with 4 representative concentration pathways (RCPs) in 2011-2040 (2020s), 2041-2070 (2050s) and 2071-2100 (2080s), we assessed the changes in rapeseed production potential between the baseline climatology of 1981-2010 and the future climatology of the 2020s, 2050s, and 2080s, respectively. The key modelling tool - the AEZ model - was updated and validated based on the observation records of 10 representative sites in the basin. Our simulations revealed that: (1) the uncertainty of the impact of climate change on rapeseed production increases with time; (2) in the middle of this century (2050s), total rapeseed production would increase significantly; (3) the average production potential increase in the 2050s for the upper, middle and lower reaches of the Yangtze River Basin is 0.939, 1.639 and 0.339 million tons respectively; (4) areas showing most significant increases in production include southern Shaanxi, central and eastern Hubei, northern Hunan, central Anhui and eastern Jiangsu.

Key words： climate change; rapeseed production; AEZ; Yangtze River Basin

Cite this article

TIAN Zhan , JI Yinghao , SUN Laixiang , XU Xinliang , FAN Dongli , ZHONG Honglin , LIANG Zhuoran , FICSHER Gunther . Changes in production potentials of rapeseed in the Yangtze River Basin of China under climate change:A multi-model ensemble approach[J]. Journal of Geographical Sciences, 2018 , 28(11) : 1700 -1714 . DOI: 10.1007/s11442-018-1538-1

1 Introduction

Rapeseed is the third most important oilseed produced globally, and production has expanded remarkably in most of the major producing nations in recent years (FAOSTAT, 2015). Growing population, expanding affluence, rapid urbanization, and changing dietary preferences have driven the increase in global demand for edible oil products (Foley et al. 2005; Kastner et al. 2012). It has been estimated that global production of vegetable oils must nearly double by 2050 to meet FAO projections for food, fuel and industrial demands (FAO, 2003). China is the largest producer and consumer of rapeseed oil in the world, with more than 50% of the edible oil (being rapeseed oil) consumed in the country as a result of the dietary habit of the Chinese. The country is expected to import more than 15 million tons of edible oil to meet domestic consumer demand by 2030 (Tian et al., 2014a). This heavy dependence of edible oil on imports has attracted great concerns about the domestic supply capability and the associated food security risk for China. Rapeseed is mainly distributed in the Yangtze River Basin, which is the world’s largest rapeseed production area and accounts for more than 80% of national total production in China. Therefore, an in-depth study on the impact of future climate change on rapeseed production in the Yangtze River Basin is of great significance for Chinese edible oil industry and food security.
Supply capability concerns are further amplified by climate change, because rapeseed production is very sensitive to climate conditions. To address the concerns on rapeseed production capability under future climate change, this paper employs the well-known Agro-Ecological Zone (AEZ) model to simulate the impact of future climate change on rapeseed production potential in the Yangtze River Basin in China. The simulations are carried out for the climate change predictions of 5 General Circulation Models (GCMs) with 4 representative concentration pathways (RCPs) in 2011-2040 (2020s), 2041-2070 (2050s) and 2071-2100 (2080s). The ensemble outputs of the AEZ simulations give a probabilistic assessment of the changes in production potentials of rapeseed in the Yangtze River Basin under climate change.
Crop simulation models are useful tools in climate impact studies as they can integrate the soil-plant-atmosphere continuum and trace how multiple climate factors interact with crop growth and yield processed (Challinor et al., 2014). Many crop models have been developed and applied in climate impact assessment, production estimation, cultivation and management for rapeseed, including APSIM (Agricultural Production Systems Simulator), CSM-CROPGRO-Canola, EPIC (Environmental Policy Integrated Climate), and the AEZ model. The APSIM model (Holzworth et al., 2014) simulates crop growth processes and accounts for farming system management, soil processes, and climate in a dynamic way across sites and seasons. Farre et al. (2002) concluded that the APSIM-Canola model, together with long-term weather data, can be reliably used to quantify yield expectation for different cultivars, sowing dates, and locations in the grain belt of Western Australia. The APSIM-Canola model was applied to simulate the impacts of future climatic changes on the growth and yield of rapeseed in China under the regional climate model PRECIS, showing a decrease in rapeseed yields in each considered period (Zhang et al., 2011). EPIC is a cropping system model that has been widely used for estimating soil productivity in the world since it was published in 1985. Based on the EPIC model and the RegCM3 model, the impact of climate change on the major grain and oil crops in the Loess Plateau of China was analyzed and rapeseed yield in the semi-humid region of the Loess Plateau found to increase between 2001-2050 compared to the 1961-2000 baseline (Wang et al., 2011). The CSM-CROPGRO model (Boote et al., 1998) was adapted in the DSSAT (Decision Support System for Agro-technology Transfer) to simulate spring canola (Saseendran et al., 2010). The DSSAT model (Deligios et al., 2013) was used to simulate the development, growth and distribution of rapeseed in the Mediterranean environment, showing high accuracy.
However, most of the previous studies and simulation models have focused on site-level analysis and not paid sufficient attention to cultivars adaptation at a large scale. The Yangtze River Basin is vast and significant differences exist in observed climatic changes in the upper, middle and lower reaches (Tian et al., 2013). In addition, because of the spatial variability of climate and soil, there are often mismatches between the crop varieties (with their respective growth calendar) in the model and the actual varieties in the area. Existing simulations of rapeseed production potentials have shown a lack of attention to cultivars adaptation under the historical and forthcoming climate change. The AEZ model, which is an agro-ecological productivity model, has been extensively used in the impact assessment literature for agriculture. It can speedily assess the impact of climate, soil, and other factors on production potentials across grid cells of a large area. The AEZ model was used to analyze China’s rapeseed production potential in different periods (Cai, 2007; Cai et al., 2009). However, these estimations were based on the default cultivar parameters of the 2002 version of the AEZ, which represented prevailing rapeseed cultivars in the 1970s and included only one variety for winter and spring oilseed respectively. In this research, we enrich and update the rapeseed cultivars in the AEZ model based on the observation records of 10 representative sites in the Yangtze River Basin.
It should be noted that the estimates of climate change impacts on the growth and development of crops are characterized by large uncertainties, mainly because of the choice of carbon emission scenarios, climate models, and crop models (Trnka et al., 2014). By considering the uncertainties of climate change forecasting, the multi-model ensemble simulation method can be used to describe a range of possibility for future climate projection (Yang et al., 2017). Many studies have used this approach to construct a set of probability estimation, so as to express and assess the impact of climate change on agriculture (Hansen et al., 2006; Tao et al., 2009; Tebaldi et al., 2008). Tang et al. (2015) used the projections derived from four global gridded crop models (GGCropMs) to assess the effects of future climate change on the yields of major crops (i.e., maize, rice, soybean and wheat) in China. Masutomi et al. (2009) used a combined set of 49 GCMs in three emission scenarios to assess the impact of climate change on rice production. Yang et al. (2017) assessed the effects of heat stress on wheat yields in China using the ensemble method with climate projections based on 30 Atmosphere-Ocean General Circultaion Models (AOGCMs) under representative concentration pathway scenarios in the Coupled Model Inter-Comparison Project Phase 5 (CMIP5). However, most of Multi-Model climate prediction methods are applied to simulate the impact of climate change on food crops. There are still few studies on the possible impact of climate change on future rapeseed production in China with this ensemble method.
We develop a probabilistic estimation of the effects of climate change on rapeseed in the Yangtze River Basin by making use of multi-model ensemble output. More specifically, we use 5 GCMs under 4 RCP scenarios in the CMIP5 of the IPCC Fifth Assessment Report, for a total of 20 climate change scenarios in the 2020s, 2050s, and 2080s. In this way, we provide scientifically robust information for supporting the future regional planning of oilseed production in China.

2 Materials and methods

2.1 Study area

The Yangzte River Basin is located between 24º-35º and 90º-122ºE. The Qinghai-Tibet Plateau section of the Yangtze River basin is characterized by a plateau mountain climate, while the rest of the basin has a subtropical monsoon climate. Due to the rich agricultural climate resources such as sunshine, temperature, water and soil, the Yangtze River Basin has played an important role in China’s grain production and has made important contributions to China’s national economic development and social stability.
Our research area corresponds to the rapeseed dominant planting zone in China (Xiao, 2009). It is one of the most important winter rapeseed planting regions in China and includes the provinces of Sichuan, Yunnan, Guizhou, Chongqing, Shaanxi, Hubei, Hunan, Henan, Jiangxi, Anhui, Jiangsu, Zhejiang and Shanghai. It covers more than 2.2 million square kilometers and can be divided into upper, middle and lower reaches according to the climate and land resources (Figure 1). The humidity and temperature are very suitable for winter rapeseed growth.
Figure 1 Distribution of rapeseed observation stations in China

2.2 Dataset

2.2.1 Observation stations
Ten agro-meteorological observation stations are selected out of 49 observation sites based on the following criteria: (1) representative sites in each section of the Yangtze River Basin; and (2) more than 15 years observations of rapeseed crop management information. General information on these stations is shown in Table 1.
Table 1 The location information of selected 10 stations
Station Province Latitude Longitude
Neijiang Sichuan 29°35′N 105°5′E
Nanbu Sichuan 31°21′N 106°3′E
Ankang Shaanxi 32°43′N 109°2′E
Changde Hunan 29°3′N 111°41′E
Wuchang Hubei 30°21′N 114°19′E
Nanchang Jiangxi 28°33′N 115°57′E
Hefei Anhui 31°52′N 117°14′E
Gaochun Jiangsu 31°19′N 118°53′E
Longyou Zhejiang 29°2′N 119°11′E
Jiaxing Zhejiang 30°47′N 120°44′E
The crop growth records at the 10 agro-meteorological observation stations in the Yangtze River Basin are from the Chinese Meteorological Administration (CMA) and cover the period 1981-2010. The records include detailed information on crop calendar, such as sowing date, emergence date, blossom date, and harvest date. They also include yield information, such as seed weight, the ratio between seed and stem, theoretical productivity and actual yield per unit area. These records can be used to update the cultivar parameters of the AEZ model.
2.2.2 Climate data
CMIP5 used a ‘representative concentration pathways (RCPs)’ radiation forcing scenario that contains four radiation forcing concentrations, RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5, respectively (Moss et al., 2010). The RCP 2.6 scenario refers to radiation forcing reaching 2.6 W/m2 in 2100, while temperature is expected to rise between 1.6-3.6℃. RCP 4.5 is an intermediate stable path, under which the radiation forcing will stabilize at 4.5 W/m2, the equivalent of 650 ppm of CO2 concentration. Under RCP 8.5, radiation forcing will be greater than 8.5 W/m2 while CO2 concentration will exceed 1370 ppm (Taylor et al., 2012).
The climate scenario data included outputs from five global climate models (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM and NorESM1-M) driven by the above four RCPs. Each global climate model has 7 climate variables, as included in the Inter-Sectoral Impact Model Inter-comparison Project (ISI-MIP) dataset: surface air temperatures, precipitation, surface radiation (short and long wave down welling), near surface wind speed, surface air pressure, near surface relative humidity and CO2 concentration (Warszawski et al., 2014).
2.2.3 Soil data
Harmonized World Soil Database (HWSD) is employed as the soil input data into the AEZ model. The HWSD was developed by the Land Use Change and Agriculture Program of the International Institute for Applied Systems Analysis (IIASA) and the Food and Agriculture Organization of the United Nations (FAO). The HWSD provides reliable and harmonized soil information at the grid cell level for the world, with a resolution of 1 km × 1 km for China (FAO/IIASA/ISRIC/ISSCAS/JRC, 2009). The China-AEZ Model evaluates crop- specific yield reduction due to limitations imposed by soil and terrain conditions.
2.2.4 Land use data
The cultivated land distribution data comes from the National Land Use Database of Resources and Environment Data Center of Chinese Academy of Sciences. The database is a multi-temporal land use dataset of 1:10000 scale covering the whole land area of China, which is supported by the National Science and Technology Support Program and the Knowledge Innovation Project of the Chinese Academy of Sciences. With the support of many major science and technology projects, the database has been established for a number of years (Liu et al., 2000; Liu et al., 2003; Liu et al., 2005). The dataset uses Landsat terrestrial satellite remote sensing image as the main data source, and is generated by artificial visual interpretation. The land use types include six primary types (i.e., cultivated land, forest land, grassland, water area, residential land and unused land) and 25 secondary types. Through field investigation and field verification, the comprehensive evaluation accuracy of the land use type has reached 94.3% (Liu et al., 2003; Liu et al., 2010; Liu et al., 2014). In this study, the spatial distribution data of cultivated land in the Yangtze River Basin were extracted from the land use datasets in 2015, overlaid with the rapeseed dominant planting zone in China (Xiao, 2009).

2.3 Model and methodology

2.3.1 The AEZ Model
The AEZ model was jointly developed by IIASA and FAO (Fischer et al., 2002). AEZ Ver. 3.0 (IIASA/FAO, 2012) employs simple and robust crop models and provides standardized crop-modeling and environmental matching procedure to identify crop-specific limitations of prevailing climate, soil and terrain resources under assumed levels of inputs and management conditions. The standardized crop-modeling and environmental matching procedure in the AEZ makes it well suited for crop productivity assessment at regional, national and global scales. In this research, crop cultivar parameters in Land Utilization Types (LUTs) from the AEZ are enriched and updated based on the observation data.
Because of the spatial variability of climate and soil, there are often big differences between the crop varieties modelled and the actual varieties planted in the study area. A lack of attention to such differences would severely undermine the performance of the model simulations (Luo et al., 2008). We detected such differences for the AEZ model as well. To enrich the crop cultivar parameters in land utilization types of the AEZ, it is necessary to improve the accumulated temperature thresholds and temperature demand distribution equation in the AEZ. In this study we carried out the modification of physiological and ecological parameters of rapeseed, including length of growth period, harvest index, accumulated temperature threshold and temperature distribution equation. Information on these factors was directly related to the growth and yield of rapeseed cultivars under different environments.
Temperature is a major determinant of crop growth and development. In the AEZ model, the effect of temperature on crops is characterized in each grid cell by thermal regimes, and the temperature demand distribution equation is an important one in thermal regimes. Based on our detailed observations and historical climate data, we reduced the proportion of the high temperature stage in the rapeseed growth stage in the temperature demand equation, and assigned specific temperature distribution requirements to newly added Chinese subtropical rapeseed varieties. In addition, we have also made improvements in the following aspects, as shown in Table 2. First of all, we added spring rapeseed varieties in Chinese subtropical rape-producing areas. Second, the length before (Cya) and after hibernation (Cyb) for winter rapeseed varieties was adjusted and the harvest index (HI) of spring rapeseed varieties increased. Finally, in view of the consistency with the correction of the temperature distribution, we reduced the minimum threshold (TS1n) and the maximum threshold (TS1x) of the optimum accumulated temperature during the growth period, so that the demand for high temperature during the growth period is reduced and the lower temperature conditions begun to adapt to rapeseed cultivation. The above enrichments lead to significant improvement in the ability and accuracy of the AEZ simulation at the site level, as we present in more details in Section 3.1.
Table 2 Comparison of cultivar parameters
Cultivar Original parameters New parameters
Cya+Cyb HI TS1n TS1x Cya+Cyb HI TS1n TS1x
Wrs 1 35+105 0.25 1500 2100 55+85 0.25 1100 1600
Wrs 2 40+120 0.25 1600 2400 65+90 0.25 1200 1800
Wrs 3 45+135 0.25 1700 2700 75+95 0.25 1300 1950
Wrs 4 45+150 0.25 1800 3000 85+100 0.25 1400 2100
Srs 1 0+150 0.20 1400 1850 0+105 0.23 1200 2150
Srs 2 0+120 0.21 1500 2100 0+120 0.23 1300 2300
Srs 3 0+135 0.22 1600 2350 0+135 0.23 1400 2400
Srs 4 0+150 0.23 1700 2600 0+150 0.23 1500 2500
New 1 0+150 0.25 1500 2500
New 2 0+165 0.25 1650 2600
New 3 0+180 0.24 1800 2700
New 4 0+195 0.24 2000 2800
New 5 0+210 0.23 2150 2900
New 6 0+225 0.23 2300 3000

Wrs: Winter rapeseed; Srs: Spring rapeseed; New: newly added Chinese subtropical rapeseed varieties; Cya: the length before hibernation; Cyb: the length after hibernation; HI: harvest index; TS1n: the minimum threshold of the optimum accumulated temperature during the growth period; TS1x: the maximum threshold of the optimum accumulated temperature during the growth period.

2.3.2 Procedure
The physiological and ecological parameters of rapeseed including length of growth period, harvest index, accumulated temperature threshold and temperature distribution equation are calibrated and validated using the observations. Future climate impact on rapeseed production under 20 climate change projections are simulated in the 10 representative sites. Climate change in the Yangtze River Basin and its impact on rapeseed production in the 2050s is obtained with 2 global climate models under 4 RCP scenarios and used to provide scientific basis for rapeseed planting adaptation policy in the Yangtze River Basin. Figure 2 summarizes the procedure of our work.
Figure 2 Technological roadmap

3 Results

3.1 Model validation

We calibrated and validated the new AEZ rapeseed cultivar parameters under observed historical climate conditions. In order to validate the simulation capability of the AEZ model, four sites with long time series were extracted from the simulation results. By comparing the variation trend of simulated yield and observed yield with time, the simulation ability of the updated AEZ model is further evaluated. As Figure 3 shows, Changde, Wuchang, Hefei and Gaochun were selected as the verification sites in our study. The overall trend for the AEZ simulated production potential and the actual yield in 1981-2010 is basically the same, showing a significant correlation, which means that the improved AEZ model performs very well in these stations.
Figure 3 Comparison of simulated attainable yields with observed yields

3.2 Changes in the rapeseed productive potentials at the station level under the
future climate scenarios

Figure 4 shows the ensemble yield changes relative to the baseline under the rain-fed condition. In the early 21st century (2020s), Neijiang and Ankang show increasing production possibilities relative to the baseline, while Nanbu, Changde, Nanchang and Longyou have a significance probability of yield reduction. In the mid-21st century (2050s), the production of 8 stations is expected to increase relative to the 2020s, especially at the Hefei station, where the median value of rapeseed production will exceed the baseline. By the end of the 21st century (2080s), 6 of the 10 stations have a big possibility in production increasing relative to baseline. It also shows that the length of the histogram increases with time, which means that the range of fluctuation in rapeseed production changes is increasing, implying an increase in the uncertainty associated with the impact of climate change on rapeseed.
Figure 4 The ensemble yields of rain-fed winter rapeseed in the 2020s (green), 2050s (purple), and 2080s (red). The plus represents the 30-year average rain-fed rapeseed yield from 1981 to 2010 simulated with the observation data as the baseline. The horizontal line denotes the ensemble median.
Based on the rapeseed production simulated by the AEZ model with 5 GCMs and 4 RCPs under 3 future periods at the ten stations, we conduct a t-test analysis of potential impact of future climate change on rapeseed production. The results are reported in Table 3. T-test uses T-distribution theory to infer the occurrence probability of differences, to determine whether the difference between the two mean value is statistically significant or not. Based on Figure 4 and Table 3, we find that t-test results of Changde, Nanchang and Longyou are significant at the 1% level for all three periods, indicating that these three sites are significantly affected by future climate change and have a high likelihood of yield reduction. The significant level at Neijiang, Nanbu, Ankang and Jiaxing is less than 5% in the 2020s and 2050s, while not significant under the climate condition of the 2080s, the latter of which indicated statistically insignificant difference between the 2080s and baseline. Neijiang and Ankang show a yield increase in the 2020s and 2050s, whereas Nanbu and Jiaxing may experience yield decrease in these two periods. Compared to the baseline, there is no significant differences for the rapeseed yield in Hefei and Gaochun during the 2020s, while it is more likely to increase in the 2050s and 2080s. The t-test results at Wuchang station show a significance at 10% level for the 2080s only, indicating a moderately significant chance for this station to have yield increase in that period.
Table 3 T-test analysis of rapeseed yield variation in the Yangtze River Basin under future climatic conditions
Sites Baseline (kg/ha) Scenarios Sig Significance
2020s 5.37E-04 ***
Neijiang 3252 2050s 1.41E-06 ***
2080s 8.52E-01
2020s 9.59E-14 ***
Nanbu 3338 2050s 7.27E-03 ***
2080s 1.28E-01
2020s 9.75E-16 ***
Ankang 3207 2050s 1.53E-07 ***
2080s 1.04E-01
2020s 3.19E-04 ***
Changde 3317 2050s 9.57E-03 ***
2080s 7.26E-03 ***
2020s 1.05E-01
Wuchang 3125 2050s 7.30E-01
2080s 5.35E-02 *
2020s 1.65E-04 ***
Nanchang 3309 2050s 2.13E-04 ***
2080s 1.44E-04 ***
2020s 6.54E-02 *
Hefei 2992 2050s 5.69E-06 ***
2080s 1.34E-03 ***
2020s 5.92E-01
Gaochun 2975 2050s 1.71E-02 **
2080s 1.60E-03 ***
2020s 9.98E-09 ***
Longyou 3342 2050s 4.33E-08 ***
2080s 3.06E-03 ***
2020s 1.47E-03 ***
Jiaxing 3280 2050s 1.95E-02 **
2080s 5.04E-01

Note: *, **, and *** denote the level of significance at 10%, 5%, and 1%, respectively.

3.3 Changes in rapeseed production potential in the Yangtze River Basin by the 2050s

Table 4 shows the changes in total rapeseed production potential for the upper, middle and lower reaches of the Yangtze River Basin under 2 GCMs driven by 4 RCP scenarios in the 2050s relative to the baseline.
Table 4 Total rapeseed production changes for the upper, middle and lower reaches of the Yangtze River in the 2080s (million tons)
Scenarios climate models Upper reaches (mt) Middle reaches (mt) Lower reaches (mt)
RCP2.6 GFDL-ESM2M 0.826 2.528 0.507
NorESM1-M 1.911 2.583 0.385
RCP4.5 GFDL-ESM2M 0.802 3.316 0.927
NorESM1-M 1.441 2.263 0.223
RCP6.0 GFDL-ESM2M -0.122 -2.333 -0.217
NorESM1-M 0.748 1.705 0.241
RCP8.5 GFDL-ESM2M 0.779 0.240 -0.005
NorESM1-M 1.130 2.810 0.648
Average 0.939 1.639 0.339
In the upper reaches of the Yangtze River Basin, except for climate model GFDL-ESM2M driven by RCP 6.0, our simulations under all other scenarios show a yield increase compared with the results in baseline. The average increase in total production potential of the 8 combinations for the upper reaches is 0.939 million tons. The largest increase is 1.911 million tons under the NorESM1-M and RCP 2.6 scenario.
In the middle reaches of the Yangtze River Basin, our simulations under 7 of the 8 combinations show an increase. The average increase in total production potential is 1.639 million tons, while the largest increase is 3.316 million tons under the GFDL-ESM2M and RCP4.5 scenario. NorESM1-M produces an increase with all 4 RCPs scenarios.
In the lower reaches of the Yangtze River Basin, which include Jiangsu, Zhejiang and Shanghai, our simulations under 6 of the 8 combinations show an increase. The average increase is 0.339 million tons. The NorESM1-M once again produces an increase with all of the 4 RCPs scenarios. The largest increase in production potential is 0.927 million tons under the GFDL-ESM2M and RCP4.5 combination.
The above results indicate that our simulations under the GFDL-ESM2M and RCPs scenarios show more variation than under the NorESM1-M and RCPs scenarios in terms of rapeseed production in the Yangtze River Basin in the 2050s.
Figure 5 shows the changes in rapeseed yield at the grid-cell level between the baseline and the middle of the 21st century produced under the combined scenarios of RCPs and GFDL-ESM2M and NorESM1-M, respectively. In the upper reaches of the Yangtze River Basin, our simulations under almost all of the 8 combinations show a yield increase in southern Shaanxi and northern Chongqing. In contrast, except for the climate model NorESM1-M driven by RCP 2.6, our simulations under all other combinations show a slight yield decrease (0-250 kg/ha) in the east of Sichuan Province.
Figure 5 Rapeseed yield change from the baseline to the mid-21st century (2050s) under RCP2.6, RCP4.5, RCP6.0 and RCP8.5 scenarios produced with GFDL-ESM2M (a-d) and NorESM-M (e-h) climate models
In the middle reaches of the Yangtze River Basin, our simulations under all 8 combinations of GCMs and RCPs show a yield increase in central and eastern Hubei, northern Hunan and central Anhui. In Jiangxi Province, nevertheless, the rapeseed yield is expected to slightly decrease under the 8 combinations in the 2050s.
In the lower reaches of the Yangtze River Basin, the upper coastal part of Jiangsu Province shows a significant yield increase under the 8 combinations by the end of the century. In the rest of the regions, rapeseed yield may keep a relatively stable level or slight decrease, in comparison with the baseline.

4 Discussion and conclusion

In this study, the cultivar parameters of rapeseed in the AEZ model were calibrated and validated using the observations in 10 representative sites in the Yangtze River Basin. Based on the updated AEZ model, we simulated rapeseed production in the future under 20 sets of climate change projections. We first estimated the probability of change in rapeseed production potentials relative to the baseline for the 10 representative sites, and then we assessed the total rapeseed production change for the whole basin and yield changes across grid-cells of the basin in the 2050s. We found that the uncertainty of the impact of climate change on rapeseed production increases with time. There are some differences between the different scenarios and climate models, but the production change in space was overall consistent across these climate predictions. In the middle of the century, the average increase in the total production potential under the 8 scenario combinations for the upper reaches will be 0.939
million tons, mainly distributed in southern Shaanxi and northern Chongqing. For the middle reaches, there will be a significance possibility of increasing rapeseed production in central and eastern Hubei, northern Hunan and central Anhui. As for the lower reaches of the Yangtze River Basin, our simulations under 6 of the 8 scenario combinations show an increase in yield, mainly distributed in eastern Jiangsu Province along the coast. Deniel et al. (2009) examined the potential climate change impacts on the productivity of five major crops in eastern China: canola, corn, potato, rice and winter wheat. Their simulations are performed with and without the enhanced CO2-fertilization effect. Their results indicate that aggregated potential productivity increases by 8.3% for canola. However, without the enhanced CO2-fertilization effect, potential productivity declines by 2.5% to 12%. Wang et al. (2014) selected 3 experimental sites in the Yangtze River Basin, and analyzed the changes of rapeseed production potential in the future (2021-2050) relative to the reference period (1959-2009) based on the A2, B2 and A1B emission scenarios in the SRES report and the multi-year daily projected meteorological data. They showed that biomass and yield at these sites increased to different extents. Our research also showed a significant probability of increase in total production potentials for rapeseed in the Yangtze River Basin in the future.
Some limitations of this study are worth mentioning and could be overcome by future research. Firstly, the observation-based calibration method of the AEZ model on rapeseed varieties needs to be improved. Results show that the performance of the new AEZ model is not satisfactory at some sites in some years, which needs to be further improved. The crop mechanism model, which simulates the dynamic growth process of crops, can be coupled with the AEZ model to improve the AEZ varieties parameters. Secondly, improvements can be done with respect to the climate model selection. This paper uses the GCM-RCPs scenario data proposed in the IPCC AR5, which show some improvements compared to CMIP3 in mode resolution and experimental design. However, large uncertainties remain in the model simulation results, and the application of these data in agricultural production research is still far from mature. Some of the concentration pathways may need to be further explored. Finally, monthly climatic factors are used in the study (for example, monthly cumulative rainfall and monthly mean temperature) and changes in production potential do not include the effects of extreme weather events, which should also be explored in the future research.

Acknowledgement

We thank Elisa Calliari from Ca’Foscari University of Venice for providing valuable advice on the revision of the article.

The authors have declared that no competing interests exist.

References

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Xiao J, 2009. The rapeseed that conquered China.Chinese National Geography, (6): 60-73. (in Chinese)

[33]
Yang X, Chen B D, Tian Z, 2013. Uncertainty of ensemble winter wheat yield simulation in North China based on CMIP5. Progess in Geography, 32(4): 627-636. (in Chinese)The uncertainty of the influence of climate change on the North China's winter wheat yield is estimated by using the ensemble climate projections of CMIP5 and the probability of increase or reduction of the wheat yield in main production areas is analyzed.We combined 54 runs of projections from 15 global climate models of CMIP5 under different greenhouse gas emission scenarios in 2006-2030.Meanwhile,the CERES-Wheat was employed to stimulate the North China's winter wheat yield in the future.The results indicate that the projection of precipitation and solar radiation in future climate by the climate models has the largest uncertainty.Take the three representative points as an example: although in some years the yield will increases slightly,the fluctuation of winter wheat yield from year to year can be significant.An increased risk of lower yield is inevitable.And the probabilistic distributions of winter wheat yield in Middle and Eastern China during 2011-2030 over 2000,4000,6000,8000,and 10000 kg/hm2are elaborated.

DOI

[34]
Yang X, Chen B D, Tian Z, 2014. Impacts of climate change on wheat yield in China simulated by CMIP5 multi-model ensemble projections.Scientia Agricultura Sinica, 47(15): 3009-3024. (in Chinese)Objective】 By applying climate projections based on 30 Atmosphere-Ocean General Circulation Models(AOGCMs) under representative concentration pathway(RCP) scenarios in the Coupled Model Inter-comparison Project Phase 5(CMIP5), the effects of climate change on wheat yield in China were assessed in terms of ensemble method. 【Method】 The impact assessment of climate change on crops is typically based on daily data. However, significant uncertainties exist among the AOGCM outputs, particularly in daily data. In this paper, a pseudo global warming(PGW) method was assumed to be a linear coupling of contemporary weather fields and the difference component of climate perturbation signals by AOGCMs. CERES-Wheat model was employed to stimulate the wheat yield in the future and a probabilistic approach is used to address the uncertainties. 【Result】Warming is expected in all representative stations during the wheat growth period. Temperature increase under the RCP8.5 scenario is higher than that under the RCP2.6 scenario. The temperature in the representative stations of winter wheat is projected to increase by 2.7-2.9℃, and increase by 3.0-3.3℃ in the representative stations of spring wheat at the end of the 21 st century. The precipitation rate is projected to significantly increase in the future. Compared with the baseline, the observation data collected from 1996 to 2005 show that the climate-change-induced wheat yield reduced in all representative stations under irrigation conditions. The reduction probabilities increased with climate change. The irrigated yield reduction in the representative stations of spring wheat was greater than that in the representative stations of winter wheat. By the end of the 21 st century, the yield in the representative stations of winter wheat is projected to be decreased by 2% under the RCP 2.6 scenario. The yield reduction will be decreased by approximately 6% under the RCP 4.5 scenario and decreased by 9% under the RCP 8.5 scenario with a probability of 85%. In the representative stations of spring wheat, yield will be decreased by 5% under the RCP 2.6 scenario, by more than 8% under the RCP 4.5 scenario, and by more than 15% under the RCP 8.5 scenario with a probability of 90%. In comparison with the baseline, the rain-fed yield in the representative stations of winter wheat will be increased significantly. By the end of the 21 st century, the yield in winter wheat is projected to be increased by more than 21% under the RCP 2.6 scenario, more than 22% under the RCP 4.5 scenario, and more than 25% under the RCP 8.5 scenario with a probability of 90%.【Conclusion】The ensemble of daily data were obtained through the PGW method, which efficiently reserve the contemporary weather information, particularly that of extreme weather events. Effects of climate change on wheat yield under RCP2.6, RCP4.5, and RCP8.5 scenarios were assessed through the ensemble method. The results indicate that, with the increasing greenhouse gas emissions, the climate-change-induced yield-reduction probabilities of irrigated wheat in China gradually increased. Rain-fed wheat yield will be increased in the future, with large uncertainties.

DOI

[35]
Yang X, Tian Z, Sun L X, 2017. The impacts of increased heat stress events on wheat yield under climate change in China.Climatic Change, 140(3): 605-620.Abstract China is the largest wheat-producing country in the world. Wheat is one of the two major staple cereals consumed in the country and about 60% of Chinese population eats the grain daily. To safeguard the production of this important crop, about 85% of wheat areas in the country are under irrigation or high rainfall conditions. However, wheat production in the future will be challenged by the increasing occurrence and magnitude of adverse and extreme weather events. In this paper, we present an analysis that combines outputs from a wide range of General Circulation Models (GCMs) with observational data to produce more detailed projections of local climate suitable for assessing the impact of increasing heat stress events on wheat yield. We run the assessment at 36 representative sites in China using the crop growth model CSM-CropSim Wheat of DSSAT 4.5. The simulations based on historical data show that this model is suitable for quantifying yield damages caused by heat stress. In comparison with the observations of baseline 1996–2005, our simulations for the future indicate that by 2100 the projected increases in heat stress would lead to an ensemble-mean yield reduction of 617.1% (with a probability of 80%) and 6117.5% (with a probability of 96%) for winter wheat and spring wheat, respectively, under the irrigated condition. Although such losses can be fully compensated by CO2 fertilization effect as parameterized in DSSAT 4.5, a great caution is needed in interpreting this fertilization effect because existing crop dynamic models are unable to incorporate the effect of CO2 acclimation (the growth-enhancing effect decreases over time) and other offsetting forces.

DOI

[36]
Zhang H, Tian Z, Yang J, 2011. Study on Canola yield simulation in Yangtze River Region under the impact of climate change.Chinese Agricultural Science Bulletin, 27(21): 105-111. (in Chinese)It is necessary to check the potential impacts of climate change on canola production in the future.As one of the three Chinese oil-bearing crops,canola has attracted more and more attentions currently primarily due to the growing importance as a bio-energy crop.Climate change and its impacts have brought great concern to canola production,and Yangtze River region is one of the major canola growing zones in China.We assessed canola production of Yangtze River region using regional climate model(PRECIS) and APSIM-Canola crop model.Rain-fed canola growing was simulated with present climate(BS)(1961-1990),and future(2011-2100) under two climate scenarios(A 2 and B 2).Combine with multiple regression statistics method,canola production effects which climate change has caused in the past and will possible cause in the future was analyzed.The results showed that:(1) Rain-fed canola yield has a negative relation with radiation and temperature,but positive relation with precipitation during squaring-bolting stage and bolting-flowering stage.(2) Future simulations indicated that canola yields per unit field under A 2 and B 2 were both show downtrend as time going,and the lowest value appeared in 2080-2089.The variability of Rain-fed canola yields showed a growing trend from 2020 to 2089,and the variability of canola yield under A 2 was much larger than which under B 2 during the same period.(3) Adaptive measurements can be used to relieve the trend of yield reduction such as modifying sowing date and cultivation manner,or changing cultivar of canola.

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