The Impact of Climate Change on China’s Crop Production: a CMIP5 Ensemble Assessment

Chaochao Gao Department of Environmental Science

Zhejiang University Hangzhou, China gaocc@zju.edu.cn

Abstract—Accurate evaluation and prediction of the potential impact of climate change on crop production is essential to the sustainable development of a country’s agriculture. Model variations plus uncertainties in the future climate change scenarios create a big challenge for such evaluation. In this work, we developed a statistical crop model using the historical yield data between 1981-2010 from the National Bureau of Statistics; and analyzed the impact of temperature and precipitation change on the crop yields in six different regions of China based on ensemble climate predictions from CMIP5 (the Coupled Model Intercomparison Project Phase 5). Our results suggest that crop productionss per unit sown area in Eastern, Centural-South, and Southwest China were all negetively impacted by increasing temperature, while in Northwest China the yield was positively correlated with both temperature and precipitation change. The sensitivity of crop yield to climate warming tends to increase from north to south and from inner land to coast regions. Future projections with a medium greenhouse gas mitigation scenario (RCP4.5) showed that without sufficient adaptation measures the crop yield in China would experience a 3.4-8.0% reduction in 2030s. Our approach used over 20 ensemble predictions from six state-of-the-art climate models thus reduced the overall uncertainty and provided a consistant albeit conservative assessment of the climate impact.

Keywords-climate change; China; crop production; CMIP5; ensemble assessment

I. INTRODUCTION Accurate evaluation and prediction of the potential impact

of climate change on crop production is essential to the sustainable development of a country’s agriculture. This is of particular importance in China since China hosts about 22% of the world population with only 7% of the global supply of arable land.

Three approaches have been used to evaluate the impacts of climate change on crop. The first method is to synthesize existing studies [1]. However, currently available studies are insufficient and too diverse to make a reasonable assessment for China. The second and commonly used approach is to

apply process-based model driven by climate observation and prediction. Le et al [2] compared model-based assessments over the past 10 years and pointed out that the results vary substantially depending on the region, crop type, climate change scenarios, and the time frame studied. The third approach is to develop statistic models of production in response to climate change. For example, using statistic model Lobell et al [1] found that the global maize and wheat production has decreased by 3.8% and 5.5%, respectively, from 1980 to 2008. The same study also reported a reduction of 12% and 2% for maize and wheat production in China, and a slight increase of rice productivity. Very few studies focused on China has taken this approach, except for Liu and Lin [3] who derived the statistic models between crop production vs. temperature and agricultural investments using historical data. However, this study was not able to differentiate the impact of sown area on crop production and did not provide estimate of future productivity under climate variations.

Despite of the differences in these approaches, one needs predictions of future climate variation to project crop productivity and consequently food security. Most studies applied a single or few GCM predictions with some coupled with a regional climate model; only a few studies used the multi-model ensembles (for instance the Coupled Model Intercomparison Project Phase 3 - CMIP3) to reduce model uncertainties. The predictions were mostly driven by the SRES-A2 and B2 scenarios. In preparation for the forthcoming IPCC AR5 report, a new phase of model intercomparison project (CMIP5) was conducted. Compared to CMIP3 the models have undergone a few changes and improvements: spatial and vertical resolutions have been increased, additional features and parameters have been added. In addition, new climate change scenarios such as the family of Representative Concentration Pathways [4] have been included to reflect the impact of potential GHGs emission mitigations [5].

In this study, we have investigated the impact of future climate change on China’s crop (namely rice, wheat, maize, beans, and potatoes) productions using the most up-to-date CMIP5 model simulations, and focused our analysis on the impact of temperature and precipitation change on the productivities in six different regions of China. The data and model experiments are described in section II, results are shown in section III, and in section IV we presented our discussion and conclusions.

This work is supported by the Fundamental Research Funds for the Central Universities (2012QNA6003 and 2012XZZX012), and Zhejiang Provincial Natural Science Foundation of China (LY12D03001)

II. DATA AND METHODS

A. Crop Yield Datasets The annual crop production statistics, fertilizer usage, and

sown areas from 1980 to 2010 for different provinces in China were obtained from the China Rural Statistical Yearbook (1985-2011, [6]) as well as National Statistical Data Bank (1978-2010, [7]) of National Bureau of Statistics of China. We divided the mainland China into six regions based on the geographic distribution: the region of Northern China (NC), Northeast China (NE), Eastern China (EC), Central-South China (CS), Southwest China (SW), and Northwest China (NW). Table I listed detailed information for these regions. Regional time series of the total grain production (defined as the sum of rice, wheat, maize, bean, and potato productions) and the corresponding harvested area and fertilizer application (defined as the sum of the nitrogen, phosphatic, potassic, and compound fertilizer applications) for the period of 1981-2010 were summed from the provincial statistics within each study area.

TABLE I. REGIONS EVALUATED IN THIS STUDY AND SELECTED STATISTIC SUMMAIES.

Regions Cities and providences

included

Average crop yield in

2000a

Climate related yield

change in 2030b

Northern China

Beijing, Tianjing, Hebei, Shanxi, Inner Mongolia

3.55 N/A

Northeast China

Liaoning, Jilin, Heilongjiang 4.42 N/A

Eastern China

Shanghai, jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, Shandong

5.08 -8.0

Central-South China

Henan, Hubei, Hunan, Guangdong, Hainan

4.98 -6.2

Southwast China

Chongqing, Guangxi, Sichuan, Guizhou, Yunnan, Xizang

4.14 -3.4

Northwest China

Shanxi, Gansu, Ningxia, Xinjiang 3.42 -6.0

a Refer to the decadal average yield during the period of 1996-2005, unit: ton/ha. b Refer to the yield change in percentage with respect to the decadal average calculated in a.

B. Climate Experiments The climate variables including monthly near surface air

temperatures (Tas), their maximum and minimum values (Tasmax & Tasmin), and precipitation flux (P) analyzed in this study were obtained from the World Climate Research Programme’s CMIP5 [5]. We restricted model analysis to those models that had at least three ensemble members for both the 1980-2010 hindcast simulations and the 10 or 30-year decadal projection runs initialized at 2005. As a result, a total of six models and 28 ensemble outputs were available for this evaluation. A brief description of these models’ basic characteristics was given in Table II together with the references.

All model simulations were standardized according to the protocol set by CMIP5 for easy comparison: models were initiated with ocean conditions representative of the observed anomalies in some or full fields toward the end of 1980 and 2005, respectively; and forced with natural and anthropogenic forcing for the next 10 to 30 years [5]. The major external forcings such as solar, greenhouse gases, and land use for the 1980 decadal hindcasts were standardized based on the most recent observational databases. Those for the 2005 decadal predictions were forced by the RCP 4.5, a mid-range scenario that corresponding to a radiative forcing of +4.5W/m2 at stabilization in 2100 [4]. This scenario is equivalent to an increase of the atmospheric GHGs concentration of 850 CO2-equivalent ppm by 2100.

TABLE II. MODELS USED IN THIS STUDY AND THEIR BASIC CHARACTERISTICS

Model Name

Atmospheric Resolution

N. of ens. members

Climate Variables

HadCM3[8] 2.5°Lat×3.75°Lon 10 Tas, P, Tasmax, Tasmin

MRI-CGCM3[9] T159 3 Tas, P, Tasmax,

Tasmin MPI-ESM-

LR[10] T63 3 Tas, P, Tasmax, Tasmin

IPSL-CM5A-LR[11] 1.875°Lat×3.75°Lon 6 Tas, P, Tasmax,

Tasmin

CFSv2[12] T126 3 Tas, P, Tasmax, Tasmin

MIROC5-CGCM[13] T85 3 Tas, P, Tasmax,

Tasmin

C. Methods The climate variables were averaged spatially over the

geographic locations for each region; and temporally over the calendar year, summer (i.e., June, July, and August), and winter (i.e., December, January, and February) for Tas, Tasmax, and Tasmin, respectively. Afterward, the spatial-temporal mean variables were averaged over the 28 ensemble simulations to give the regional climate time series. Results obtained in this matter are relatively more reliable albeit conservative than individual model projection.

Crop yield (defined as the production per unit sown area, unit ton/ha) was chosen over production in order to exclude the variation caused by the nonlinear and sometimes large shifts in the harvested areas. The crop and climate time series were then combined to generate the statistical model for each region via the following steps: (1) yield and unit fertilizer usage were computed by dividing the total crop production and fertilizer application by the corresponding sown areas; (2) the multi-variable regression analysis was conducted for the whole region of China with yield as the response variable and unit fertilizer usage, temperatures (including Tas, Tasmax, Tasmin) and precipitation as the predictors; (3) linear impact of fertilizer on yield (obtained from the above step, R2 = 0.916) was removed from each yield time series; (4) a statistical model was established with the residual yield as the response and climate time series as the predictors, using stepwise regression method.

III. RESULTS Fig. 1 shows the annual time series of near surface air

temperature, crop production, fertilizer application, and sown area over mainland China during the past three decades. As can be seen, the overall crop production in general has been growing through most of this period, along with the increasing fertilizer usage and temperature. The steep reduction during 2000-2003 and another small decline during 1985-1990 were most likely caused by the decrease of sown area. The two temperature drops seen in early 1980s and 1990s were due to the 1982 El Chichón and 1991 Pinatubo eruption, none of which appeared to cause significant impact on crop productivity.

1980 1985 1990 1995 2000 2005 2010300

350

400

450

500

550

Cro

p P

roduct

ion (

MT

)

Changes of Crop Production, Temperature, Fertilizer Usage, and Sown Area in China

1980 1985 1990 1995 2000 2005 20103.1

3.3

3.5

3.7

3.9

4.1

Year

Near

Surf

ace

Air T

em

pera

ture

(degre

e C

)

1980 1985 1990 1995 2000 2005 2010

15

25

35

45

55

Fert

ilize

r U

sage (

MT

)

1980 1985 1990 1995 2000 2005 2010

97.5

102.5

107.5

112.5

117.5

Sow

n A

rea (

Mha)

Figure 1. Changes of grain yield, fertilizer usage, sown area, and model simulated temperature for mainland China during 1981-2010: (black) grain yield, (blue) fertilizer usage, (green) sown area, and (red) near surface air

temperature.

Regression analysis between crop yield and the climate time series as well as the unit fertilizer usage showed that the yield was strongly correlated with the fertilizer usage (y = 2.7796+4.2184x with R2 = 0.916, p = 0.0000). In order to focus our analysis on climate variables, we removed this fertilizer-impact by assuming a linear relationship between grain yield and fertilizer usage and subtracting this impact from each regional yield time series. After that we applied the stepwise regression analysis on the residual yields and climate variables, and the results showed that the regional crops responded differently in different regions which could be classified into three types:

A. Northern and Northeast China No statistically significant (defined at p <0.05) relationship

was found between the crop yield and climate variables in both North China (NC) and Northeast China (NE), different from Liu and Lin [3] who reported a negative and positive impact of temperature on crop productions in NC and NE, respectively. This discrepancy may be caused by the different crop and climate time series applied in the two studies. Liu and Lin [3] used the total crop production which was not recommended for cases with large and nonlinear variations in

the sown area. Whereas in this study we used the yield to remove the impact from sown area, and one plausible reason for the low sensitivity of yield response could be the cancelation of positive and negative impacts warming of the past three decades had on the rice vs. wheat and maize yields, as reported by several studies [14-15]. On the other hand, the model simulated temperature changes applied in this study (0.25– 0.35 ºC/10a, Fig.2) were much smaller than the observations (0.4– 0.8 ºC/10a) used in [3], which may also contribute to the crops’ low response to temperature change.

Figure 2. Model simulated average near surface air temperature for the six regions in China during 1981-2010, and the decade of 2026-2035.

B. Northwest China A statistical model was obtained between the crop yield and

temperature as well as precipitation, as shown by the follow equation.

y = -2.5348 + 0.4014**t + 3.3902*pr R2 = 0.46 (1)

where y represents crop yield in ton/ha, t represents temperature in degree C, and pr represents precipitation in mm/day. “**” refers to 0.01 significant level while “*” refers to 0.05 significant level. The model suggests that the temperature increasing at this region has a positive impact on crop yield, so does the precipitation change.

C. Eastern, Central-South, and Southwest China A statistical model between the crop yield and temperature

was proposed for the regions of EC, CS, and SW as shown by the following three equations, respectively.

y = 13.8093 - 0.7006**t R2 = 0.25 (2)

y = 11.7806 - 0.5450**t R2 = 0.23 (3)

y = 3.5222 - 0.1972*t R2 = 0.17 (4)

As can be seen from the above statistic models, the crop yields in these three regions were all negatively affected by temperature change. Among the three regions, the adverse impact was more pronounced in Eastern China, followed by Central-South and Southwest China. The p value for the last

equation was 0.05, indicating the model for CS was statistically less significant than the other two models.

D. Impact on crop yield in 2030 The CMIP5 30-year (2005-2035) predictions forced with

the RCP4.5 scenario was used to project climate changes for the six regions. RCP 4.5 is a median greenhouse gas mitigation scenario that aims to control the radiation forcing at 4.5 W/m2 by the end of this century. The projected average temperature change from 1996-2005 to 2026-2035 was roughly 0.5 – 0.7ºC based on 28 ensemble outputs; whereas the projected precipitation change was around –12% in Northern and Northwest China, gradually increased towards the Southern and coastal regions and reached –20.3% in East China (Fig. 3). The decade of 2026-2035 was chosen because it was a time period that’s most relevant to agricultural adaption and investments that would take place today, as it typically takes 15 or more years for these actions to realize full returns [1].

Figure 3. Projections of temperature change (blue), precipitation change (red) and yield impacts (green) in 2030s. Values represented the actual change for

temperature and percentage change for yield and precipitation, with respect to the 1996-2005 averages.

Applying these climate variations to the statistical models obtained above, we evaluated the impact of future climate change on crop production in the four regions (Table II). The results, expressed as the percentage change in the decadal average crop yield during 2026-2035 with respect to that during 1996-2005 (hereafter refer to as the temperature change in 2030 w.r.t 2000), indicated that all of the four regions would experience negative climatic impact in 2030s. Among the four regions, Eastern China was projected to be hit the most (8% yield lose) by climate change due to its relatively high temperature sensitivity, followed by Central-South China (6.2% reduction). In Northwest China, the enhancement of crop yield by increasing temperature was canceled by a larger reduction due to the diminishing precipitation, resulting in an overall 6% yield reduction (Fig. 3).

IV. DISCUSSION AND CONCLUSIONS Despite of its importance, the linkage between crop

production and climate variation especially at sub-national

scale are not well understood. China’s large area and wide range of agro-ecological conditions rendering the exposure and sensitivity to climate change vary considerably across the nation, leading to complex spatial pattern.

Using historical crop yield records and 28 CMIP5 ensemble simulations and predictions we studied the potential risk future climate change may post on the crop yields in six different regions of China. After removing the effects of sown area and fertilizer usage, statistic model between crop yield and climate variables were obtained for four out of the six regions. The results indicated that crop yield in the regions of Eastern, Central-South, and Southwest China were all negatively correlated with temperature change, and the temperature sensitivities were higher for regions with warmer climate to begin with (e.g., East and Central-South China). The latter is in agreement with Lobell et al [14] which examined the spatial patter at global scale and derived the same conclusion. Yield in Northwest China was found to be positively correlated with both temperature and precipitation change. No statistically significant relationship was found between yield and climate variations for Northern and Northeast China. Besides the reasons mentioned above, this low sensitivity may also be caused by the observation and/or model errors which were suggested to bias estimates of their effects towards zero, a phenomenon known as regression dilution [1]. Overall, the results suggested a couple of general patterns: the impact associated with temperature change was more pronounced than that of precipitation at the sub-national scale; the crop sensitivities to climate variation tend to increase from north to south as well as from inner plains to coastal regions.

Projection of the potential climate impacts into 2030s demonstrated that yields in all of the four regions would be suppressed by 3.4% to 8.0% with respect to the 2000s values (Fig. 3). Eastern China appeared to suffer the most under climate change, mainly due to its high sensitivity since the predicted temperature increase is relatively low among the regions studied. In Southwest China, the yield reduction from projected precipitation change outweighed the temperature-induced enhancement. Nevertheless, with sufficient irrigation supply, crop productivity in this region could benefit from future climate change. At global scale, Lobell et al [1] found that the combined impact of climate change and elevated CO2 likely enhanced the yield of rice (particularly at high latitudes) and soybean, while suppressed that of maize and wheat. The latter were the two dominant grains grown in Northern and Northeast China. The above result indicated that crop production in these two regions may benefit from future warming if we switch from maize and wheat intense agriculture to introduce more rice or winter-wheat productions.

All available models submitted to the CMIP5 archive as of February 2012 that had a reasonably realistic representation of climate forcings, and a minimum of three 1980 and 2005 decadal ensemble runs have been included in this study. This approach minimized the uncertainties resulting from model differences, as well as the shift from observational to model-projected climate, thus provided a more systematic and consistent way to elucidate the climatic impacts. Nevertheless, the impacts at the sub-national scale (as defined in this study) likely masked differences at the local or provincial level, which

are probably more relevant to adaptation measures. Future work with finer scale of both agricultural and climate variables, as well as combination of validated process-based model such as DSSAT simulations, will improve the evaluation.

Studies have also proposed that changes in extreme may have a more adverse impact on crop production than that in mean climate along [16-17]. For example, short episodes of extreme temperature at critical stages of crop growing season could cause sterility and consequently yield reduction, regardless of substantial mean climate variations. Additional studies that focus on the impacts of climate change during the growing seasons for individual crop and region, for example the impact of heat events during the month of April to October on rice production or the occurrence of the extreme rainfall during May to August on spring wheat and maize yields, will help to identify the climatic effects that otherwise may be obscured by the mean values.

In summary, with historical crop yield records and 28 climate model ensembles we analyzed the statistical relationship between yield and climate change for the six different regions of China. The obtained relationships varied among different regions with vulnerability increasing from north to south and from inland to the coastal regions. Future climate change into 2030s with a median GHGs mitigation plan was projected to reduce the crop yields by 3.4~8.0% with respect to the past decade. Adaptation strategies such as improving the irrigation infrastructure in Northwest China or increasing the rice production in Northeast China would overcome these adverse impacts. The use of CMIP5 ensemble results provided a more reliable and consistent way to evaluate the climatic impacts by minimizing the uncertainties resulting from model differences and the shift from observational to model-projected climates. Nevertheless, further study at finer spatial-temporal scales as well as for individual crop is needed to develop a specific and systematic adaptation plan.

ACKNOWLEDGMENT The author would like to thank the two anonymous

reviewers for helpful comments, and the modeling groups, the Program for Climate Model Diagnosis and Intercomparison, and the WCRP’s Working Group on Coupled Modeling for their work in making the CMIP5 data available.

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