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Influence of surface ozone on crop yield of maize in China
Journal of Integrative Agriculture 2020, 19(2): 578–589 Available online at www.sciencedirect.com
ScienceDirect
RESEARCH ARTICLE
Influence of surface ozone on crop yield of maize in China YI Fu-jin1, FENG Jia-ao1, WANG Yan-jun2, JIANG Fei3 1
College of Economics and Management, Nanjing Agricultural University, Nanjing 210095, P.R.China Institute for Disaster Risk Management/School of Geographical Science, Nanjing University of Information Science and Technology, Nanjing 210044, P.R.China 3 Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing 210023, P.R.China 2
Abstract This study investigated the adverse effect of surface ozone on the maize yield using a unique panel from 880 counties in China. To identify the impact of elevated surface ozone concentrations, we constructed an econometric model by controlling the impact of climate variables and related economic variables. This study also considered the potential spatial correlation in the measurement of the impact of surface ozone on maize yield. Results confirmed that the increase of ozone concentration decreased the maize yield. Moreover, maize was found to be the most sensitive to ozone at the end of the second month of the growing season. The average annual loss of maize caused by ozone pollution is about 4.234 million tons in 2013–2015, accounting for 1.9% of the average output. Keywords: ozone pollution, maize, yield, food security
1. Introduction Many factors, such as air pollution, affect grain supply. This paper focuses on the impacts of increasingly prominent surface ozone pollution on the maize yield. Surface ozone is a toxic air pollutant from photochemical reactions among anthropogenic nitrogen oxide and volatile organic compounds emissions under light in the stratosphere (Chameides et al. 1999). Ozone has strong oxidizability
Received 25 March, 2019 Accepted 20 September, 2019 Correspondence YI Fu-jin, E-mail: [email protected] © 2020 CAAS. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/). doi: 10.1016/S2095-3119(19)62822-4
and can oxidize polyunsaturated fatty acids directly with protein, amine, and mercaptan to form free radicals, which affect the health of organisms (Mehlman and Borek 1987). Typically, surface ozone exceeding a certain threshold decreases plants’ chlorophyll content, reduces plant leaf area, damages leaf membrane system, slows down the net photosynthetic rate of leaves, and inhibits the root growth, thereby inhibiting the plant growth and reducing crop outputs eventually (Feng et al. 2003; Ashmore 2005; Pleijel et al. 2006; Zheng et al. 2006). Current surface ozone pollution level in China is much higher than that in developed economies, such as the United States and the European Union (Li et al. 2015). Over the past few decades, anthropogenic ozone emissions in North America and Europe have fallen as air quality controls have tightened, while that in Asia is on the rise (Tiwari and Agrawal 2018). Our data show that 7-h ozone average concentration (9:00 am–4:00 pm) in China in recent years has reached 57.6 μg m–3 (about 29 ppb). According to the EU standard,
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the ozone concentration in China has exceeded the limit over 70% (Li et al. 2015). Hence, we expect that severe surface ozone pollution is likely to reduce crop outputs that will lower China’s domestic food supply. This study adds literatures in the empirical evidence of the adverse effects of surface ozone on maize. A large number of studies have conducted laboratory research on the response of various food crops (e.g., rice, wheat and soybean) to ozone stress, which proved that ozone pollution indeed reduces the crop outputs (Zheng et al. 2009). Maize is moderately sensitive to ozone in all crops (Mills et al. 2007) from limited studies. Wang and Mauzerall (2004) used Weibull function to simulate the effect of ozone on the yield of maize and predicted the relative maize loss of 16% in China caused by ozone pollution in the future. From the view of methodology, there are also some other studies that incorporate economic variables into the response model of crop outputs with respect to ozone concentration. Yi et al. (2016) and Carter et al. (2017) found that the high surface ozone concentration has a significant negative influence on winter wheat and rice productivity in China. Furthermore, Yang et al. (2016) found that the decrease in crop output leads peasants to reduce the inputs of winter wheat production in short term, and then exacerbate the reduction of the wheat yield. Similar results are found from India’s empirical analysis that shows two potent short-lived climate pollutants, namely, surface ozone and black carbon, have direct effects on crop yields beyond their indirect effects through climate (Burney and Ramanathan 2014). It is believed that the estimation in this study based on field observations will deepen the understanding of the impact of surface ozone on crop production. The study is organized as follows. Section 2 mainly introduces the economic status of maize production and consumption in China. Section 3 details the model used in this article. Section 4 discusses the model results and evaluates aggregated output losses caused by ozone pollution. Section 5 summarizes the conclusions.
2. Maize production in China Maize is an important food crop and plays an essential role in economic development in China. Domestic production of maize in China has expanded from 23 million hectares in 2000 to 35.4 million hectares in 2017, and the maize output accounts for about 38% of the national grain output (Chen et al. 2016). China has dominated the planting areas of maize in the world, and its output reached 216 million tons accounting for 21% of global output in 2017 (FAO 2019). However, China is also a principal buyer of maize in the world market. Fig. 1 shows China’s maize total production, imports, exports and feed production (FAO 2019). Due to the diet transition with rapid economic development,
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China has become a net importer of maize since 2009. Meanwhile, distiller’s dried grains (DDGS), which are important secondary product formed by the processing of maize ethanol, are mainly used as feed substitute for maize. Over all, China’s imports of maize and DDGS have reached more than 30% of the world supply in 2013 . As maize is mainly used for forage in China, the demand is expected to increase further. In 2012–2013, maize consumption of forage reached about 147.45 million tons, accounting for 67.3% of the total consumption of maize in China (Chen and Wang 2016). With the development of the economy, China’s maize deep-processing enterprises continue to expand their capacity to satisfy the demand increase driven by the development of breeding industries (Xi et al. 2018). The feed industry has maintained a growing trend in China in recent decades (Fig. 1). Furthermore, a plan of expanding the production of bio-ethanol has been issued in 2017, stating that bio-ethanol will be promoted nationwide to achieve full coverage by 2020. Therefore, we could foresee that the rapidly growing bio-ethanol processing industry will boost the demand of maize.
3. Empirical strategy and data sources 3.1. Empirical designs Baseline model An econometric model is constructed to estimate the adverse effect of surface ozone on the maize yield from confounding factors, such as climate variables and related economic variables. The relationship between ozone and logarithm of maize yield is described as linear based on the study by Wang et al. (1997). The empirical model is shown as follows: log( yit ) =Oit β+Cit´γ+Xit´δ+et +ui +εit (1) where yit is the maize yield for county i in year t. Oit is ozone concentration for county i in year t, and vector Cit contains the information of weather, such as temperature and precipitation. Let vector Xit denote socioeconomic variables. et and ui are fixed effects for controlling temporal and regional effects, respectively. Parameter vector (β, γ, δ) are for estimated values. This study allows spatial correlation among error terms (εit). First, adjacent counties often choose similar production methods or the same seed varieties, which leads to a regional correlation of maize yields. Second, agricultural policies of local provincial government often have a regional effect that may drive adjacent counties to have similar production decisions. In addition, neighboring counties may have similar regional characteristics, such as soil conditions, pests, and disease characteristics. Ignoring these factors may lead to a significant overestimate of the true inference (Chen et al. 2016). Although none of these
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Fig. 1 Maize output, trade, and forage production in China in 1998–2015. Source: FAO (2019).
spatial correlations can be directly observed, the error (ε) can be simply written as: (2) ε=λWε+μ where λ is the parameter of spatial correlation, W is a spatial weight matrix associated with the spatial autoregressive process of explained variables, and μ is an error term. In actual regional analysis, the selection of the matrix contains exogenous information about the spatial connection between region r and region r´, which only need to be calculated by weight (LeSage et al. 2009). Two kinds of weighting matrices are used in this paper: first-order matrix and second-order matrix. In the first-order matrix, the (r, r´) element of the matrix is unity if counties r and r´ share a common boundary, and 0 otherwise. Meanwhile, in the second-order matrix, if county r is adjacent to county r´ and county r´´ is adjacent to county r´, then (r, r´´)= (r, r´)=1 and the rest elements are 0. Nonlinearity Besides the linear assumption between ozone concentration and maize response, this study examined the nonlinear effect of ozone on maize yield as well. The increase of ozone concentration can slow the development of wheat and rice in early stage, accelerate senescence in later stage, shorten the filling period, and decrease plant height (Bai et al. 2002). However, it is unknown whether maize has the similar responses. This study pioneers in detecting the nonlinear effects of surface ozone on maize yields in both temporal and accumulated ways. We constructed a nonlinear ozone exposure model to estimate the influence of ozone on maize yield, and the new flexible model can detect nonlinearities and breakpoints in the effect of ozone pollution on the yield. First, according to Yang and Tu (2003), the growing period of maize is divided into three phases including seedling stage, panicle stage and anthesis maturity period, and
each stage lasts about 30 days. Therefore, we intend to identify the differential responses of maize with respect to the ozone concentration variations by dividing the whole growing period into corresponding stages to determine the most sensitive phase. Eq. (1) can be modified to: log( yit )=β1 O
+β2 O
it, p1
+β3 O
it, p2
+
it, p3
C´it γ+X´it δ+et +ui +εit
(3)
where Oit, pm (m=1, 2, 3) is the ozone concentration in period m, and each period contains 30 days according to the above definition. For example, Oit, p1 represents the average daily ozone concentration at seedling stage, etc. Hence, we would expect parameter β to capture the most sensitive period for maize yield with respect to ozone exposure. Second, we construct daily ozone concentration bins to identify the effects of extreme values of ozone exposure. Each bin counts the days of ozone concentration calculated by a specific measurement falling into a pre-determined range. To the best of our knowledge, there is no relevant literature for determining the length of range; hence, we referred to the mean value and standard deviation of ozone data to establish the following range to make the results more intuitive: log( yit )=β1 O
+β2 O
it, s1
+β3 O
it, s2
+β4 O
it, s3
+
it, s4
Cit´γ+Xit´δ+et +ui +εit Oit, s1 = ∑ 1{Oit, d ∈[30,40)} d
Oit, s2 = ∑ 1{Oit, d ∈[40,50)} d
Oit, s3 = ∑ 1{Oit, d ∈[50,60)} d
Oit, s4 = ∑ 1{Oit, d ∈[60,∞)} d
(4)
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where Oit, d is the ozone concentration (measured in ppb) for county i of day d in year t, and Oit, sn (n=1, 2, 3, 4) represents the number of days that a specific ozone concentration level falls in each corresponding interval.
3.2. Data We complied a three-year panel data set at county level. The maize production data for 880 maize-producing counties were obtained from the Agricultural Information Institute of the Chinese Academy of Agricultural Sciences1. The air pollution data in 2013–2015 were obtained from the China National Environmental Monitoring Center because the data made available to the public started from 2013. Weather data were obtained from 825 monitoring stations provided by the China Meteorological Data Service Center (CMDC). County-level socioeconomic variables data are replaced by provincial data from China Compilation of Cost-benefit Information on Agricultural Products due to the availability of the data. The descriptive statistics of the panel data used in this paper are shown in Table 1. Socioeconomic variables Socioeconomic variables include maize planted area, output, price, and production price index. Both maize price and input factor price affect the maize yield by changing producers’ planting decision including crop mix and inputs. Due to the availability of
data, we use the producer price index to represent the input price for maize production. Considering that peasants usually make their planting decision according to the price of output of the previous year, lagged maize price is used as a proxy variable for farmers’ expected prices. A summary of statistics for socioeconomic variables can be found in Table 1. The average planting area and yield over 2013–2015 in the main maize-producing area are shown in Appendix A. Obviously, North and Northeast China are the major maize production areas. However, the maize yield in those areas is not as high as that in Northwest China. Air pollutants Air quality data used in this paper are collected from 1 412 monitoring stations of China. We interpolated station-level data into 0.10412°×0.10412° grids by using inverse distance weighted (IDW) method. Then, the average ozone level in each county is calculated over all grids in each specific county. Five widely used ozone measurements are used to represent the relationship between surface ozone and maize yield. 1 i i M7= n ∑ni=1 CO3 (CO3 from 9:00 to 16:00) (5) 1 i i M12= ∑ni=1 CO3 (CO3 from 8:00 to 20:00) (6) n i i SUM06= ∑ni=1 (CO3 –60) (when CO3≥60 ppb) (7) i
AOT40=∑ni=1 (CO3 –40) i
(when CO3>40 ppb from 9:00 to 16:00)
(8)
Table 1 Summary statistics Variable1) Maize sown area (1 000 ha) Maize yield (t ha–1) Lagged maize price (CNY kg–1) Production price index M7 (ppb) M12 (ppb) SUM06 (ppm) AOT40 (ppm) W126 (ppm) GDD8–32°C (1 000°C) GDD34+°C (°C) Precipitation (dm) PM2.5 (μg m–3) PM10 (μg m–3) Number of counties
2013 Mean 24.140 6.071 2.267 107.900 40.740 38.550 2.657 6.192 11.241 1.506 0.266 3.638 50.500 98.360
2014 Std. 34.090 2.044 0.311 1.315 7.617 7.401 2.675 3.674 8.377 0.360 1.288 1.454 14.790 26.710
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Mean 26.660 6.001 2.249 106.900 43.360 41.050 3.183 7.218 13.331 1.388 0.002 3.295 49.860 92.440
2015 Std. 45.580 2.125 0.272 1.959 7.397 7.431 3.138 3.794 9.161 0.307 0.024 1.942 12.250 20.640
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Mean 28.120 5.926 2.300 106.800 45.320 42.900 3.003 7.708 13.565 1.402 0.003 3.540 39.640 75.770
Std. 43.760 2.134 0.196 2.307 7.586 7.351 2.992 3.906 8.915 0.286 0.101 2.226 13.710 34.470 880
1)
M7 and M12 refer to the daily average ozone concentration from 9:00 to 16:00 and that from 8:00 to 20:00, respectively. SUM06 represents cumulative ozone concentrations when the hourly ozone concentration exceeds 60 ppb. AOT40 is the cumulative ozone concentration when the hourly ozone concentration exceeds 40 ppb and the light intensity is more than 50 W m−2. W126 is expressed as a sum of weighted hourly concentrations, cumulated over the 12-h daylight period from 8:00 to 20:00. GDD, growing degree days. GDD8–32°C is calculated by the accumulated temperature between 8 and 32°C in the growing period of maize. GDD34+°C refers to the accumulated temperature above 34°C in maize growth period.
1
Due to the limitation in availability of ozone data, some samples from southern China provinces were dropped, and the total maize output of the rest samples accounted for more than 75% of the total maize production in China.
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in the baseline analysis (for comparison, estimates using
i
W126= ∑ni=1 Wi CO3 where(9) 1 i Wi = (CO3 8:00 to 20:00) i 1+4 403exp(–0.126CO3 ) M7 and M12 refer to the daily average ozone concentration from 9:00 to 16:00 and that from 8:00 to 20:00, respectively. SUM06 represents cumulative ozone concentrations when the hourly ozone concentration exceeds 60 ppb. AOT40 is the cumulative ozone concentration when the hourly ozone concentration exceeds 40 ppb and the light intensity is more than 50 W m−2. Due to the lack of light intensity data, we accumulate the ozone concentration between 9:00 and 16:00. The W126 is expressed as a sum of weighted hourly concentrations, cumulated over the 12-h daylight period from 8:00 to 20:00. Since ozone has cumulative effects on plant damage, and European Open-top Chamber Project found that ozone would affect plants when exceeded the concentration 40 ppb, we choose AOT40 as the basic index
SUM06 are shown and discussed in Appendix B, and the other measures are used for robustness checks. Although some ecological studies find evidences that an exposure index based on stomatal flux could improve the accuracy of measurement (e.g., Goumenaki et al. 2007; Pleijel et al. 2007), limited data prohibit us from implementing these methods. Following the sets in literature (e.g., Van Dingenen et al. 2009 ), we define the growing season as the three months before harvest. The crop calendar information, including planting and harvesting dates, were obtained from Major World Crop Areas and Climatic Profiles released by the United States Department of Agriculture (USDA). All these ozone concentration measurements were calculated during the maize growing season. By applying the five types of ozone measurements, Fig. 2-A shows average ozone concentration distribution in the maize planting regions in 2013–2015 measured by
A
AOT40 (ppm) No test [0, 4) [4, 8) [8, 10) [10, ∞)
B
AOT40 (2013–2014) (ppm)
AOT40 (2014–2015) (ppm)
No test
No test
[–∞, 0)
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[0, 2)
[0, 1)
[2, 3)
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[3, ∞)
Map inspection number: GS(2019)5858
Fig. 2 Average ozone concentrations in China in 2013–2015 measured by AOT40 (A) and ozone concentration variations over time measured by AOT40 (B). AOT40 is the cumulative ozone concentration when the hourly ozone concentration exceeds 40 ppb and the light intensity is more than 50 W m−2.
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AOT40, and other ozone measures are shown in Appendices C–E due to limited space. These figures consistently show that East China and North China are the major ozonepolluted regions. Combined with Appendix A, the relative high ozone concentrations are expected to adversely affect the maize production. Fig. 2-B shows the variations of ozone concentration from 2013 to 2014 and from 2014 to 2015, which is helpful for identifying the adverse effect of surface ozone on the maize yield. Weather Temperature and precipitation are considered in the statistical analysis. Growing degree days (GDD) refers to the accumulated temperature value when the temperature is higher than the plant growth threshold (Schlenker et al. 2006). Specifically, GDD8–32°C is calculated by the accumulated temperature between 8 and 32°C in the growing period of maize. GDD34+°C refers to the accumulated temperature above 34°C in maize growth period that is considered harmful for the yield (Ritchie and NeSmith 1991). Moreover, this study does not include irrigation as maize is mainly fed by rain in China. Alternatively, an aridity index is used to capture the effect of humidity on maize yield, which is defined as follows. Aridity index=
R 1.07H
(10)
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where R and H are the average monthly rainfall and average temperature, respectively (Paltasingh et al. 2012). Expectedly, high temperature and low rainfall will lead to high drought degrees. Therefore, a smaller value of aridity index indicates greater drought degree. Following the study by Yi et al. (2016), an aridity index of less than 20 signifies drought.
4. Results and discussion After introducing the baseline results for estimating the adverse effect of surface ozone pollution on maize yield, a set of robustness checks are processed by using different ozone measurements and controlling alternative weather variables and air pollutants. Then, the nonlinear effect of surface ozone on maize yield is investigated. Moreover, an estimated output loss of maize driven by surface ozone is reported.
4.1. Baseline results Table 2 reports the basic results obtained using AOT40 as ozone concentration measurement in the empirical discussion. Columns (1) and (2) rule out time and countyfixed effect, and all the signs for the covariates are consistent
Table 2 Baseline results Variable1) AOT40
Fixed effect (FE) (1) –0.0059*** (0.0020)
Maize sown area Lagged maize price Production index Precipitation Precipitation squared GDD8–32°C GDD8–32°C GDD34+°C Year fixed effect Lambda Number of counties 1)
0.0602*** (0.0175) –0.0044*** (0.0017) –0.3950 (0.5610) 0.0393 (0.1620) 0.0032 (0.0078) Yes
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(2) –0.0062*** (0.0019) –0.0046*** (0.0003) 0.0395 (0.0738) –0.0007 (0.0050) 0.0728*** (0.0162) –0.0055*** (0.0016) –0.1130 (0.5150) –0.0229 (0.1490) 0.0039 (0.0074) Yes
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FE, second-order spatial correlation (3) (4) –0.0060*** –0.0061*** (0.0017) (0.0016) –0.0047*** –0.0002 0.0512 (0.0637) –0.0015 (0.0043) 0.0599*** 0.0720*** (0.0145) (0.0135) –0.0044*** –0.0054*** (0.0014) (0.0013) –0.3450 –0.0788 (0.4730) (0.4390) 0.0301 –0.0214 (0.1370) (0.1270) 0.0029 0.0028 (0.0063) (0.0060) Yes Yes 0.1450*** 0.0999** (0.0401) (0.0412) 880 880
FE, first order spatial correlation (5) (6) –0.0060*** –0.0062*** (0.0017) (0.0015) –0.0046*** (0.0002) 0.0392 (0.0621) –0.0014 (0.0042) 0.0599*** 0.0727*** (0.0145) (0.0134) –0.0044*** –0.0055*** (0.0014) (0.0013) –0.3490 –0.0822 (0.4680) (0.4300) 0.0282 –0.0286 (0.1350) (0.1240) 0.0031 0.0037 (0.0063) (0.0060) Yes Yes 0.0583** 0.0638** (0.0260) (0.0265) 880 880
AOT40 is the cumulative ozone concentration when the hourly ozone concentration exceeds 40 ppb and the light intensity is more than 50 W m−2. GDD, growing degree days. GDD8–32°C is calculated by the accumulated temperature between 8 and 32°C in the growing period of maize. GDD34+°C refers to the accumulated temperature above 34°C in maize growth period. ** and ***, significant at 5 and 1%, respectively. Standard errors are in parentheses. Lamda is the autoregressive coefficient of the spatial error term.
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with expectations. Columns (3)–(6) in Table 2 consider both fixed effects and spatial correlations. The difference is that columns (3) and (4) considered second-order correlation, whereas columns (5) and (6) incorporated first-order correlation. Overall, the results of Table 2 show that ozone has significant negative influence on maize yield. Given spatial correlation is considered, the standard errors for ozone concentration from Columns (4) and (6) in Table 2 are relatively smaller than the result in Column (2). Specifically, 1 ppm increase of ozone exposure measured by AOT40 leads to a decrease in maize yield by 0.61% in column (4) in Table 2. Moreover, the standard error in Column (4) is extremely close to the one in Column (6). This finding is consistent with the conclusion by Chen et al. (2016) that the key results are not sensitive to spatial weighting matrix.
4.2. Robustness checks To show the robustness of the above results, Tables 3–5
present a set of robustness tests by incorporating various confounding variables. Table 3 reports the effects of surface ozone exposure on maize yield using other four ozone measurements. We consistently found the adverse impacts of ozone on maize yield, and all of them are statistically significant at 1% level. Overall, one unit (ppm for AOT40, SUM06 and W126; ppb for M7 and M12) increase of ozone exposure will reduce the maize yield by 0.29–0.61%. Technically, the adverse effect of ozone using M12 is greater than that using M7, and AOT40 is greater than that using SUM06 that warns ozone seems having less adverse effect on maize at night. Tables 4 and 5 show the results obtained by controlling different confounding variables. Column (1) in Table 4 replaced climate variables by the aridity index. Obviously, aridity index is positively correlated with maize yield given that a more humid climatic condition tends to result in higher maize yields. Compared to columns (4) and (6) in Table 2 controlling for all climate variable, however, the coefficients of AOT40 are extremely close.
Table 3 Effects of ozone on maize yield with different ozone exposure measures Variable1) M7
(1) M7 –0.0036*** (0.0010)
M12
(2) M12
(3) SUM06
–0.0041*** (0.0008)
SUM06
–0.0056*** (0.0021)
W126 Maize sown area Lagged maize price Production index Precipitation Precipitation squared GDD8–32°C GDD8–32°Csquared GDD34+°C Year fixed effect Lambda Number of counties 1)
(4) W126
–0.0046*** (0.0003) 0.0333 (0.0737) –0.0011 (0.0050) 0.0728*** (0.0162) –0.0055*** (0.0162) –0.2030 (0.5150) 0.0122 (0.1490) 0.0043 (0.0074) Yes
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–0.0047*** (0.0002) 0.0405 (0.0636) –0.0025 (0.0043) 0.0713*** (0.0135) –0.0054*** (0.0013) –0.1440 (0.4380) 0.0087 (0.1270) 0.0033 (0.0060) Yes 0.1410*** (0.0413) 880
–0.0047*** (0.0002) 0.0584 (0.064) –0.0013 (0.0043) 0.0743*** (0.0135) –0.0055*** (0.0013) 0.0536 (0.4420) –0.0608 (0.1280) 0.0024 (0.0061) Yes 0.1490*** (0.0412) 880
–0.0029*** (0.0007) –0.0047*** (0.0002) 0.0552 (0.0637) –0.0020 (0.0043) 0.0729*** (0.0135) –0.0054*** (0.0013) 0.0358 (0.4400) –0.0554 (0.1280) 0.0028 (0.0060) Yes 0.1450*** (0.0412) 880
M7 and M12 refer to the daily average ozone concentration from 9:00 to 16:00 and that from 8:00 to 20:00, respectively. SUM06 represents cumulative ozone concentrations when the hourly ozone concentration exceeds 60 ppb. W126 is expressed as a sum of weighted hourly concentrations, cumulated over the 12-hour daylight period from 8:00 to 20:00. GDD, growing degree days. GDD8–32°C is calculated by the accumulated temperature between 8 and 32°C in the growing period of maize. GDD34+°C refers to the accumulated temperature above 34°C in maize growth period. *** , significant at 1%. Standard errors are in parentheses. Lamda is the autoregressive coefficient of the spatial error term.
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Table 4 Robustness checks considering different air pollutants Variables AOT40 Aridity index
(1) –0.0061*** (0.0016) 0.0035*** (0.0006)
(2) –0.0058*** (0.0016) 0.0049 (0.0035)
PM10
(3) –0.0058*** (0.0016)
–0.0004** (0.0002)
PM2.5 Maize sown area Lagged maize price Production index
–0.0046*** (0.0002) 0.0895 (0.0609) –0.0037 (0.0042)
Precipitation Precipitation squared GDD8–32°C GDD8–32°Csquared GDD34+°C Year fixed effect Lambda Number of counties
(4) –0.0058*** (0.0016)
Yes 0.1570*** (0.0404) 880
–0.0046*** (0.0002) 0.0461 (0.0639) –0.0020 (0.0043) 0.0322 (0.0313) –0.0046*** (0.0014) –0.0314 (0.4400) –0.0026 (0.1280) 0.0023 (0.0061) Yes 0.1460*** (0.0412) 880
–0.0047*** (0.0002) 0.0254 (0.0647) –0.0024 (0.0043) 0.0740*** (0.0135) –0.0056*** (0.0013) –0.0391 (0.4390) –0.0354 (0.1270) 0.0029 (0.0060) Yes 0.1420*** (0.0413) 880
–0.0010** (0.0005) –0.0047*** (0.0002) 0.0168 (0.0655) –0.0026 (0.0043) 0.0734*** (0.0135) –0.0055*** (0.0013) –0.0319 (0.4390) –0.0436 (0.1280) 0.0030 (0.0060) Yes 0.1420*** (0.0413) 880
1)
AOT40 is the cumulative ozone concentration when the hourly ozone concentration exceeds 40 ppb and the light intensity is more than 50 W m−2. GDD, growing degree days. GDD8–32°C is calculated by the accumulated temperature between 8 and 32°C in the growing period of maize. GDD34+°C refers to the accumulated temperature above 34°C in maize growth period. ** and ***, significant at 5 and 1%, respectively. Standard errors are in parentheses. Lamda is the autoregressive coefficient of the spatial error term.
Considering that China has been seriously affected by PM2.5 and PM10 in recent decade, we added these
during the growth period maize yield is decreased by surface ozone significantly, and the result is robust.
pollutants into columns (3) and (4) in Table 4 to control the impacts of air particles. PM2.5 and PM10 have significant
4.3. Nonlinear damage
negative effects on maize yield, which is consistent with the findings by Zhou et al. (2017). Predictably, the addition of
Table 6 identifies the most sensitive stage of maize with
air particles mitigated the adverse effect of AOT40 on maize
respect to ozone pollution. The first column shows that the
yield. Since the unit of AOT40 is different from the one of
second period in maize growing season (panicle stage) is
air particle, we calculated their standardized coefficients
the most sensitive period that drives a significant reduction of
to make their coefficients comparable (Appendix F). We
maize yield. Specifically, 1 ppm increase of AOT40 causes
found that adding a standard deviation of AOT40 will result
a decrease in maize yield by 2.1%, which is about 3.5 times
in 1.06% reduction of maize yield, while an increase in one
higher than the coefficient in column (7) in Table 2. Columns
standard deviation of PM2.5 and PM10 will result in 0.69%
(2)–(5) in Table 6 report the estimates using alternative
and 0.56% reduction of maize yield, respectively, which are
measurements, and similar results are obtained that the
about half of ozone damage on maize yield.
impacts of ozone concentration in the second period all
Meanwhile, considering that the degree of drought is
have negative signs. Although the coefficient of SUM06 in
likely to change the breath rate of maize that would indirectly
period 2 marginally missed the statistical significance, other
affect the effects of ozone on maize yield, Table 5 examines
ozone measures showed that maize was very sensitive to
the interactions between ozone exposure and drought index,
ozone exposure in period 2.
but no significant effect was found in the results. Overall,
Furthermore, Fig. 3 describes the nonlinear effect
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Table 5 Robustness checks with weather interactions Variable1) AOT40
(1) –0.0092*** (0.0021)
(2) –0.0061** (0.0030)
Aridity index Drought AOT40×Drought
–0.0660** (0.0261) 0.0060** (0.0025)
AOT40×Aridity index Maize sown area Lagged maize price Production index Precipitation Precipitation squared GDD8–32°C GDD8–32°Csquared GDD34+°C Year fixed effect Lambda Number of counties
(3) –0.0050* (0.0029) 0.0038*** (0.0010)
–0.0046*** (0.0002) 0.0398 (0.0639) –0.0017 (0.0043) 0.0603*** (0.0172) –0.0044*** (0.0015) –0.0390 (0.4390) –0.0159 (0.1270) 0.0020 (0.0061) Yes 0.1430*** (0.0414) 880
(4) –0.0066*** (0.0018)
0.0009 (0.0015) 0.0001 (0.0001) –0.0047*** (0.0002) 0.0512 (0.0638) –0.0015 (0.0043) 0.0720*** (0.0154) –0.0054*** (0.0013) –0.0788 (0.4400) –0.0214 (0.1280) 0.0028 (0.0061) Yes 0.1450*** (0.0412) 880
0.0001 (0.0001) –0.0046*** (0.0002) 0.0875 (0.0611) –0.0039 (0.0042)
Yes 0.1570*** (0.0404) 880
–0.0047*** (0.0002) 0.0530 (0.0639) –0.0016 (0.0043) 0.0773*** (0.0159) –0.0058*** (0.0014) –0.0908 (0.4400) –0.0174 (0.1280) 0.0027 (0.0061) Yes 0.1470*** (0.0412) 880
1)
AOT40 is the cumulative ozone concentration when the hourly ozone concentration exceeds 40 ppb and the light intensity is more than 50 W m−2. GDD, growing degree days. GDD8–32°C is calculated by the accumulated temperature between 8 and 32°C in the growing period of maize. GDD34+°C refers to the accumulated temperature above 34°C in maize growth period. * ** , and ***, significant at 10, 5 and 1%, respectively. Standard errors are in parentheses. Lamda is the autoregressive coefficient of the spatial error term.
of ozone on maize yield and the standard error of the coefficients under two kinds of ozone measurement (M7, M12) based on eq. (4). Since AOT40, W126 and SUM06 are accumulation indicators, they cannot be used in this step. Fig. 3 shows that the higher the ozone concentration, the greater the impact on maize yield. Specifically, a decrease in maize yield of approximately 0.1% is expected with the time experiencing M7 (M12) between 30 and 40 ppb, whereas one more day with M7 (M12) over 60 ppb is associated with more than 0.2% decrease in maize yield. Considering there are more than 15% days having ozone concentration higher than 60 ppb during the growing season, the negative effects of ozone exposure on maize output is not trivial.
4.4. Output loss of maize yield We use the results in the baseline model to estimate maize output losses under observed ozone exposure from 2013
to 2015. To approximate the total loss driven by surface ozone pollution, we first multiply the coefficient of AOT40 in the baseline model (see Column (4) in Table 2) by the AOT40 concentration level and sown area in 2013–2015 to obtain yearly maize output loss over all counties, then calculate the annual average loss in the whole country. The average annual output of maize caused by ozone is 4.234 million tons in 2013–2015, accounting for about 1.9% of the average total output. This value is about 10 times the volume of average maize import in 2013–2015. Specifically, we chose the top eight major maize production regions in China to report the damages driven by surface ozone pollution, as shown in Fig. 4. The highest loss in Shandong Province is more than 1 million tons, which is around 0.2% of the domestic supply because of two reasons. First, Shandong is a major maize production province in China with annual sown area exceeding 8% of national sown area. Second, Shandong has a relatively high surface ozone concentration level, as shown in Fig. 2.
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Table 6 Identify sensitive periods of maize yield in ozone exposure1) (1) AOT40 0.0015 (0.0040) –0.0214*** (0.0058) 0.0028 (0.0060) –0.0046*** (0.0002) 0.0597 (0.0625) 0.0015 (0.0042) 0.0704*** (0.0136) –0.0056*** (0.0013) –0.0382 (0.4310) –0.0410 (0.1250) 0.0053 (0.0061) 0.0659* (0.0347) 857
Variable2) Period 1 (day 1–30) Period 2 (day 31–60) Period 3 (day 60–90) Maize sown area Lagged maize price Production index Precipitation Precipitation squared GDD8–32°C GDD8–32°Csquared GDD34+°C Lambda Number of counties
(2) SUM06 0.0001 (0.0044) –0.0132 (0.0087) –0.0090 (0.0080) –0.0046*** (0.0002) 0.0513 (0.0624) 0.0004 (0.0042) 0.0765*** (0.0136) –0.0057*** (0.0013) 0.1570 (0.4320) –0.0801 (0.1260) 0.0041 (0.0061) 0.0617* (0.0347) 857
(3) W126 –0.0001 (0.0017) –0.0069** (0.0027) –0.0009 (0.0026) –0.0046*** (0.0002) 0.0529 (0.0624) 0.0003 (0.0042) 0.0733*** (0.0136) –0.0056*** (0.0013) 0.1370 (0.4320) –0.0836 (0.1250) 0.0047 (0.0061) 0.0645* (0.0346) 857
(4) M12 0.0008 (0.0037) –0.0175*** (0.0042) 0.0001 (0.0050) –0.0164*** (0.0010) 0.3190 (0.3010) 0.0159 (0.0204) 0.3530*** (0.0646) –0.0274*** (0.0062) –1.8290 (2.0710) 0.5610 (0.6030) 0.0157 (0.0291) 0.0776** (0.0339) 857
(5) M7 0.0035 (0.0035) –0.0180*** (0.0040) 0.0005 (0.0047) –0.0163*** (0.0010) 0.3610 (0.3010) 0.0196 (0.0203) 0.3490*** (0.0647) –0.0274*** (0.0062) –2.1250 (2.0770) 0.6320 (0.6050) 0.0158 (0.0290) 0.0772** (0.0339) 857
1)
AOT40 is the cumulative ozone concentration when the hourly ozone concentration exceeds 40 ppb and the light intensity is more than 50 W m−2. SUM06 represents cumulative ozone concentrations when the hourly ozone concentration exceeds 60 ppb. W126 is expressed as a sum of weighted hourly concentrations, cumulated over the 12-h daylight period from 8:00 to 20:00. M7 and M12 refer to the daily average ozone concentration from 9:00 to 16:00 and that from 8:00 to 20:00, respectively. 2) Period 1 begins at 3 months before maize harvest; A part of the county was removed where SUM06, W126 and AOT40 were 0 in each period. GDD, growing degree days. GDD8–32°C is calculated by the accumulated temperature between 8 and 32°C in the growing period of maize. GDD34+°C refers to the accumulated temperature above 34°C in maize growth period. * ** , and ***, significant at 10, 5 and 1%, respectively. Standard errors are in parentheses. Lamda is the autoregressive coefficient of the spatial error term.
0.1 Change in yield (%)
Change in yield (%)
0
–0.1
0
–0.1
–0.2
–0.2 –0.3
–0.3 20
30
40 50 M7 (ppb)
60
70
20
30
40 50 M12 (ppb)
60
70
Fig. 3 Nonlinear effect of surface ozone on maize yield. M7 and M12 refer to the daily average ozone concentration from 9:00 to 16:00 and that from 8:00 to 20:00, respectively.
According to our estimation results, the annual average loss of maize caused by ozone pollution was over 7.2 billion CNY from 2013 to 2015, accounting for about 50% of China’s total import in 2015. Hence, the control of ozone pollution has potential economic benefits.
5. Conclusion The influence of air pollution on social economy has been given increasing research attention. However, due to the limitation in availability of data and scientific research
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2014
2015
Output change (million tons)
ng H
ei
lo
i hu An
xi an Sh
er In n
ng
jia
on M
ng ao Li
Ji lin
ni
nx i aa Sh
an en H
0
Sh
an
do
ng
go
lia
2013
–0.2 –0.4 –0.6 –0.8 –1.0 –1.2
Fig. 4 Distribution of maize loss in major producing areas in 2013–2015.
methods, empirical research remains insufficient, particularly for maize production. This study provides a rigorous analysis of the effect of ozone exposure on maize yield based on a unique county-specific panel, and a spatial error model is used into the empirical model because of the spatial correlation. Our analysis shows that maize yield is negatively related to surface ozone concentration, which is consistent with previous ecological and economic studies. Moreover, the nonlinear regression result indicates that maize is the most sensitive to ozone in the second month of the growing season. According to previous ecological research, the influence of ozone on plant seedlings was not obvious, but it will be significantly affected as the plants mature. Meanwhile, we use the empirical results to demonstrate that ozone pollution decreased the average annual output of maize by 4.234 million tons per year in 2013–2015, accounting for about 1.9% of the total domestic output. As the main maize-producing province, maize output loss in Shandong is reduced by nearly 1 million tons every year, which is higher than the other regions. To achieve food security, policies to control ozone concentration are necessary to reduce the ozone damage to maize yield. China has taken several ozone control policies, such as raising prices to limit ozone precursors. However, compared with the standards of ozone concentrations implemented in developed economies, our ozone pollution control needs to be strengthened. Typically, a more targeted and effective policy should be set by taking different measures considering regional environmental differences. For example, the high ozone concentration in eastern regions of China may be due to precursors, whereas that in western regions may be due to higher illumination.
Acknowledgements Authors gratefully acknowledge the financial support by the National Natural Science Foundation of China (71673137), the Nanjing Agricultural University, China (Y0201400037, SKCX2015004), the Education Department of Jiangsu Province, China (2014SJD069), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), the China Center for Food Security Studies at Nanjing Agricultural University, Jiangsu Rural Development and Land Policy Research Institute, and Jiangsu Agriculture Modernization Decision Consulting Center, China. Appendices associated with this paper can be available on http://www.ChinaAgriSci.com/V2/En/appendix.htm
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G. 2006. Differential ozone sensitivity in an old and a modern Swedish wheat cultivar—grain yield and quality, leaf chlorophyll and stomatal conductance. Environmental & Experimental Botany, 56, 63–71. Ritchie J T, NeSmith D S. 1991. Temperature and crop development. Botanical Gazette, 74, 341–342. Schlenker W, Hanemann W M, Fisher A C. 2006. The impact of global warming on U.S. agriculture: An econometric analysis of optimal growing conditions. Review of Economics & Statistics, 88, 113–125. Tiwari S, Agrawal M. 2018. Ozone concentrations in troposphere: Historical and current perspectives. In: Tiwari S, Agrawal M. eds. Tropospheric Ozone and its Impacts on Crop Plants. Springer International Publishing, Switzerland. Wang C, Guan J, Zheng Y. 1997. Carbon Dioxide, Ozone, Ultraviolet Radiation and Crop Production. China Meteorological Press, China. (in Chinese) Wang X, Mauzerall D L. 2004. Characterizing distributions of surface ozone and its impact on grain production in China, Japan and South Korea: 1990 and 2020. Atmospheric Environment, 38, 4383–4402. Xi Y, Yang L, Wu T. 2018. Analysis of maize market situation in 2017 and outlook for 2018. Agricultural Outlook, 14, 10–15. (in Chinese) Yang W, Tu N. 2003. Each Theory of Crop Cultivation. (Southward Version). China Agriculture Press, China. (in Chinese) Yang Y, Yi F, Hu H. 2016. The new challenge of agricultural environment to grain production revenue: A case study of winter wheat planting under surface ozone pollution. China’s Rural Economy, 9, 72–82. (in Chinese) Yi F, Jiang F, Zhong F, Zhou X, Ding A. 2016. The impacts of surface ozone pollution on winter wheat productivity in China - An econometric approach. Environmental Pollution, 208(Pt B), 326–335. Zheng F, Wang X, Zhang W, Duan X, Hou P. 2009. Effects of ozone stress on photosynthesis and yield of rice. Chinese Journal of Agricultural and Environmental Sciences, 11, 2217–2223. (in Chinese) Zheng Q, Wang X, Xie J, Feng Z, Feng Z, Ni X. 2006. Effects of exogenous ascorbate acid on membrane protective system of in situ rice leaves under O3 stress. Journal of Ecology, 26, 1131–1137. (in Chinese) Zhou L, Chen X, Tian X. 2017. The impact of fine particulate matter (PM2.5) on China’s agricultural production from 2001 to 2010. Journal of Cleaner Production, 178, 133–141.
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RESEARCH ARTICLE
Influence of surface ozone on crop yield of maize in China YI Fu-jin1, FENG Jia-ao1, WANG Yan-jun2, JIANG Fei3 1
College of Economics and Management, Nanjing Agricultural University, Nanjing 210095, P.R.China Institute for Disaster Risk Management/School of Geographical Science, Nanjing University of Information Science and Technology, Nanjing 210044, P.R.China 3 Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing 210023, P.R.China 2
Abstract This study investigated the adverse effect of surface ozone on the maize yield using a unique panel from 880 counties in China. To identify the impact of elevated surface ozone concentrations, we constructed an econometric model by controlling the impact of climate variables and related economic variables. This study also considered the potential spatial correlation in the measurement of the impact of surface ozone on maize yield. Results confirmed that the increase of ozone concentration decreased the maize yield. Moreover, maize was found to be the most sensitive to ozone at the end of the second month of the growing season. The average annual loss of maize caused by ozone pollution is about 4.234 million tons in 2013–2015, accounting for 1.9% of the average output. Keywords: ozone pollution, maize, yield, food security
1. Introduction Many factors, such as air pollution, affect grain supply. This paper focuses on the impacts of increasingly prominent surface ozone pollution on the maize yield. Surface ozone is a toxic air pollutant from photochemical reactions among anthropogenic nitrogen oxide and volatile organic compounds emissions under light in the stratosphere (Chameides et al. 1999). Ozone has strong oxidizability
Received 25 March, 2019 Accepted 20 September, 2019 Correspondence YI Fu-jin, E-mail: [email protected] © 2020 CAAS. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/). doi: 10.1016/S2095-3119(19)62822-4
and can oxidize polyunsaturated fatty acids directly with protein, amine, and mercaptan to form free radicals, which affect the health of organisms (Mehlman and Borek 1987). Typically, surface ozone exceeding a certain threshold decreases plants’ chlorophyll content, reduces plant leaf area, damages leaf membrane system, slows down the net photosynthetic rate of leaves, and inhibits the root growth, thereby inhibiting the plant growth and reducing crop outputs eventually (Feng et al. 2003; Ashmore 2005; Pleijel et al. 2006; Zheng et al. 2006). Current surface ozone pollution level in China is much higher than that in developed economies, such as the United States and the European Union (Li et al. 2015). Over the past few decades, anthropogenic ozone emissions in North America and Europe have fallen as air quality controls have tightened, while that in Asia is on the rise (Tiwari and Agrawal 2018). Our data show that 7-h ozone average concentration (9:00 am–4:00 pm) in China in recent years has reached 57.6 μg m–3 (about 29 ppb). According to the EU standard,
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the ozone concentration in China has exceeded the limit over 70% (Li et al. 2015). Hence, we expect that severe surface ozone pollution is likely to reduce crop outputs that will lower China’s domestic food supply. This study adds literatures in the empirical evidence of the adverse effects of surface ozone on maize. A large number of studies have conducted laboratory research on the response of various food crops (e.g., rice, wheat and soybean) to ozone stress, which proved that ozone pollution indeed reduces the crop outputs (Zheng et al. 2009). Maize is moderately sensitive to ozone in all crops (Mills et al. 2007) from limited studies. Wang and Mauzerall (2004) used Weibull function to simulate the effect of ozone on the yield of maize and predicted the relative maize loss of 16% in China caused by ozone pollution in the future. From the view of methodology, there are also some other studies that incorporate economic variables into the response model of crop outputs with respect to ozone concentration. Yi et al. (2016) and Carter et al. (2017) found that the high surface ozone concentration has a significant negative influence on winter wheat and rice productivity in China. Furthermore, Yang et al. (2016) found that the decrease in crop output leads peasants to reduce the inputs of winter wheat production in short term, and then exacerbate the reduction of the wheat yield. Similar results are found from India’s empirical analysis that shows two potent short-lived climate pollutants, namely, surface ozone and black carbon, have direct effects on crop yields beyond their indirect effects through climate (Burney and Ramanathan 2014). It is believed that the estimation in this study based on field observations will deepen the understanding of the impact of surface ozone on crop production. The study is organized as follows. Section 2 mainly introduces the economic status of maize production and consumption in China. Section 3 details the model used in this article. Section 4 discusses the model results and evaluates aggregated output losses caused by ozone pollution. Section 5 summarizes the conclusions.
2. Maize production in China Maize is an important food crop and plays an essential role in economic development in China. Domestic production of maize in China has expanded from 23 million hectares in 2000 to 35.4 million hectares in 2017, and the maize output accounts for about 38% of the national grain output (Chen et al. 2016). China has dominated the planting areas of maize in the world, and its output reached 216 million tons accounting for 21% of global output in 2017 (FAO 2019). However, China is also a principal buyer of maize in the world market. Fig. 1 shows China’s maize total production, imports, exports and feed production (FAO 2019). Due to the diet transition with rapid economic development,
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China has become a net importer of maize since 2009. Meanwhile, distiller’s dried grains (DDGS), which are important secondary product formed by the processing of maize ethanol, are mainly used as feed substitute for maize. Over all, China’s imports of maize and DDGS have reached more than 30% of the world supply in 2013 . As maize is mainly used for forage in China, the demand is expected to increase further. In 2012–2013, maize consumption of forage reached about 147.45 million tons, accounting for 67.3% of the total consumption of maize in China (Chen and Wang 2016). With the development of the economy, China’s maize deep-processing enterprises continue to expand their capacity to satisfy the demand increase driven by the development of breeding industries (Xi et al. 2018). The feed industry has maintained a growing trend in China in recent decades (Fig. 1). Furthermore, a plan of expanding the production of bio-ethanol has been issued in 2017, stating that bio-ethanol will be promoted nationwide to achieve full coverage by 2020. Therefore, we could foresee that the rapidly growing bio-ethanol processing industry will boost the demand of maize.
3. Empirical strategy and data sources 3.1. Empirical designs Baseline model An econometric model is constructed to estimate the adverse effect of surface ozone on the maize yield from confounding factors, such as climate variables and related economic variables. The relationship between ozone and logarithm of maize yield is described as linear based on the study by Wang et al. (1997). The empirical model is shown as follows: log( yit ) =Oit β+Cit´γ+Xit´δ+et +ui +εit (1) where yit is the maize yield for county i in year t. Oit is ozone concentration for county i in year t, and vector Cit contains the information of weather, such as temperature and precipitation. Let vector Xit denote socioeconomic variables. et and ui are fixed effects for controlling temporal and regional effects, respectively. Parameter vector (β, γ, δ) are for estimated values. This study allows spatial correlation among error terms (εit). First, adjacent counties often choose similar production methods or the same seed varieties, which leads to a regional correlation of maize yields. Second, agricultural policies of local provincial government often have a regional effect that may drive adjacent counties to have similar production decisions. In addition, neighboring counties may have similar regional characteristics, such as soil conditions, pests, and disease characteristics. Ignoring these factors may lead to a significant overestimate of the true inference (Chen et al. 2016). Although none of these
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Maize export (left axis)
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Fig. 1 Maize output, trade, and forage production in China in 1998–2015. Source: FAO (2019).
spatial correlations can be directly observed, the error (ε) can be simply written as: (2) ε=λWε+μ where λ is the parameter of spatial correlation, W is a spatial weight matrix associated with the spatial autoregressive process of explained variables, and μ is an error term. In actual regional analysis, the selection of the matrix contains exogenous information about the spatial connection between region r and region r´, which only need to be calculated by weight (LeSage et al. 2009). Two kinds of weighting matrices are used in this paper: first-order matrix and second-order matrix. In the first-order matrix, the (r, r´) element of the matrix is unity if counties r and r´ share a common boundary, and 0 otherwise. Meanwhile, in the second-order matrix, if county r is adjacent to county r´ and county r´´ is adjacent to county r´, then (r, r´´)= (r, r´)=1 and the rest elements are 0. Nonlinearity Besides the linear assumption between ozone concentration and maize response, this study examined the nonlinear effect of ozone on maize yield as well. The increase of ozone concentration can slow the development of wheat and rice in early stage, accelerate senescence in later stage, shorten the filling period, and decrease plant height (Bai et al. 2002). However, it is unknown whether maize has the similar responses. This study pioneers in detecting the nonlinear effects of surface ozone on maize yields in both temporal and accumulated ways. We constructed a nonlinear ozone exposure model to estimate the influence of ozone on maize yield, and the new flexible model can detect nonlinearities and breakpoints in the effect of ozone pollution on the yield. First, according to Yang and Tu (2003), the growing period of maize is divided into three phases including seedling stage, panicle stage and anthesis maturity period, and
each stage lasts about 30 days. Therefore, we intend to identify the differential responses of maize with respect to the ozone concentration variations by dividing the whole growing period into corresponding stages to determine the most sensitive phase. Eq. (1) can be modified to: log( yit )=β1 O
+β2 O
it, p1
+β3 O
it, p2
+
it, p3
C´it γ+X´it δ+et +ui +εit
(3)
where Oit, pm (m=1, 2, 3) is the ozone concentration in period m, and each period contains 30 days according to the above definition. For example, Oit, p1 represents the average daily ozone concentration at seedling stage, etc. Hence, we would expect parameter β to capture the most sensitive period for maize yield with respect to ozone exposure. Second, we construct daily ozone concentration bins to identify the effects of extreme values of ozone exposure. Each bin counts the days of ozone concentration calculated by a specific measurement falling into a pre-determined range. To the best of our knowledge, there is no relevant literature for determining the length of range; hence, we referred to the mean value and standard deviation of ozone data to establish the following range to make the results more intuitive: log( yit )=β1 O
+β2 O
it, s1
+β3 O
it, s2
+β4 O
it, s3
+
it, s4
Cit´γ+Xit´δ+et +ui +εit Oit, s1 = ∑ 1{Oit, d ∈[30,40)} d
Oit, s2 = ∑ 1{Oit, d ∈[40,50)} d
Oit, s3 = ∑ 1{Oit, d ∈[50,60)} d
Oit, s4 = ∑ 1{Oit, d ∈[60,∞)} d
(4)
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where Oit, d is the ozone concentration (measured in ppb) for county i of day d in year t, and Oit, sn (n=1, 2, 3, 4) represents the number of days that a specific ozone concentration level falls in each corresponding interval.
3.2. Data We complied a three-year panel data set at county level. The maize production data for 880 maize-producing counties were obtained from the Agricultural Information Institute of the Chinese Academy of Agricultural Sciences1. The air pollution data in 2013–2015 were obtained from the China National Environmental Monitoring Center because the data made available to the public started from 2013. Weather data were obtained from 825 monitoring stations provided by the China Meteorological Data Service Center (CMDC). County-level socioeconomic variables data are replaced by provincial data from China Compilation of Cost-benefit Information on Agricultural Products due to the availability of the data. The descriptive statistics of the panel data used in this paper are shown in Table 1. Socioeconomic variables Socioeconomic variables include maize planted area, output, price, and production price index. Both maize price and input factor price affect the maize yield by changing producers’ planting decision including crop mix and inputs. Due to the availability of
data, we use the producer price index to represent the input price for maize production. Considering that peasants usually make their planting decision according to the price of output of the previous year, lagged maize price is used as a proxy variable for farmers’ expected prices. A summary of statistics for socioeconomic variables can be found in Table 1. The average planting area and yield over 2013–2015 in the main maize-producing area are shown in Appendix A. Obviously, North and Northeast China are the major maize production areas. However, the maize yield in those areas is not as high as that in Northwest China. Air pollutants Air quality data used in this paper are collected from 1 412 monitoring stations of China. We interpolated station-level data into 0.10412°×0.10412° grids by using inverse distance weighted (IDW) method. Then, the average ozone level in each county is calculated over all grids in each specific county. Five widely used ozone measurements are used to represent the relationship between surface ozone and maize yield. 1 i i M7= n ∑ni=1 CO3 (CO3 from 9:00 to 16:00) (5) 1 i i M12= ∑ni=1 CO3 (CO3 from 8:00 to 20:00) (6) n i i SUM06= ∑ni=1 (CO3 –60) (when CO3≥60 ppb) (7) i
AOT40=∑ni=1 (CO3 –40) i
(when CO3>40 ppb from 9:00 to 16:00)
(8)
Table 1 Summary statistics Variable1) Maize sown area (1 000 ha) Maize yield (t ha–1) Lagged maize price (CNY kg–1) Production price index M7 (ppb) M12 (ppb) SUM06 (ppm) AOT40 (ppm) W126 (ppm) GDD8–32°C (1 000°C) GDD34+°C (°C) Precipitation (dm) PM2.5 (μg m–3) PM10 (μg m–3) Number of counties
2013 Mean 24.140 6.071 2.267 107.900 40.740 38.550 2.657 6.192 11.241 1.506 0.266 3.638 50.500 98.360
2014 Std. 34.090 2.044 0.311 1.315 7.617 7.401 2.675 3.674 8.377 0.360 1.288 1.454 14.790 26.710
880
Mean 26.660 6.001 2.249 106.900 43.360 41.050 3.183 7.218 13.331 1.388 0.002 3.295 49.860 92.440
2015 Std. 45.580 2.125 0.272 1.959 7.397 7.431 3.138 3.794 9.161 0.307 0.024 1.942 12.250 20.640
880
Mean 28.120 5.926 2.300 106.800 45.320 42.900 3.003 7.708 13.565 1.402 0.003 3.540 39.640 75.770
Std. 43.760 2.134 0.196 2.307 7.586 7.351 2.992 3.906 8.915 0.286 0.101 2.226 13.710 34.470 880
1)
M7 and M12 refer to the daily average ozone concentration from 9:00 to 16:00 and that from 8:00 to 20:00, respectively. SUM06 represents cumulative ozone concentrations when the hourly ozone concentration exceeds 60 ppb. AOT40 is the cumulative ozone concentration when the hourly ozone concentration exceeds 40 ppb and the light intensity is more than 50 W m−2. W126 is expressed as a sum of weighted hourly concentrations, cumulated over the 12-h daylight period from 8:00 to 20:00. GDD, growing degree days. GDD8–32°C is calculated by the accumulated temperature between 8 and 32°C in the growing period of maize. GDD34+°C refers to the accumulated temperature above 34°C in maize growth period.
1
Due to the limitation in availability of ozone data, some samples from southern China provinces were dropped, and the total maize output of the rest samples accounted for more than 75% of the total maize production in China.
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in the baseline analysis (for comparison, estimates using
i
W126= ∑ni=1 Wi CO3 where(9) 1 i Wi = (CO3 8:00 to 20:00) i 1+4 403exp(–0.126CO3 ) M7 and M12 refer to the daily average ozone concentration from 9:00 to 16:00 and that from 8:00 to 20:00, respectively. SUM06 represents cumulative ozone concentrations when the hourly ozone concentration exceeds 60 ppb. AOT40 is the cumulative ozone concentration when the hourly ozone concentration exceeds 40 ppb and the light intensity is more than 50 W m−2. Due to the lack of light intensity data, we accumulate the ozone concentration between 9:00 and 16:00. The W126 is expressed as a sum of weighted hourly concentrations, cumulated over the 12-h daylight period from 8:00 to 20:00. Since ozone has cumulative effects on plant damage, and European Open-top Chamber Project found that ozone would affect plants when exceeded the concentration 40 ppb, we choose AOT40 as the basic index
SUM06 are shown and discussed in Appendix B, and the other measures are used for robustness checks. Although some ecological studies find evidences that an exposure index based on stomatal flux could improve the accuracy of measurement (e.g., Goumenaki et al. 2007; Pleijel et al. 2007), limited data prohibit us from implementing these methods. Following the sets in literature (e.g., Van Dingenen et al. 2009 ), we define the growing season as the three months before harvest. The crop calendar information, including planting and harvesting dates, were obtained from Major World Crop Areas and Climatic Profiles released by the United States Department of Agriculture (USDA). All these ozone concentration measurements were calculated during the maize growing season. By applying the five types of ozone measurements, Fig. 2-A shows average ozone concentration distribution in the maize planting regions in 2013–2015 measured by
A
AOT40 (ppm) No test [0, 4) [4, 8) [8, 10) [10, ∞)
B
AOT40 (2013–2014) (ppm)
AOT40 (2014–2015) (ppm)
No test
No test
[–∞, 0)
[–∞, 0)
[0, 2)
[0, 1)
[2, 3)
[1, 3)
[3, ∞)
[3, ∞)
Map inspection number: GS(2019)5858
Fig. 2 Average ozone concentrations in China in 2013–2015 measured by AOT40 (A) and ozone concentration variations over time measured by AOT40 (B). AOT40 is the cumulative ozone concentration when the hourly ozone concentration exceeds 40 ppb and the light intensity is more than 50 W m−2.
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AOT40, and other ozone measures are shown in Appendices C–E due to limited space. These figures consistently show that East China and North China are the major ozonepolluted regions. Combined with Appendix A, the relative high ozone concentrations are expected to adversely affect the maize production. Fig. 2-B shows the variations of ozone concentration from 2013 to 2014 and from 2014 to 2015, which is helpful for identifying the adverse effect of surface ozone on the maize yield. Weather Temperature and precipitation are considered in the statistical analysis. Growing degree days (GDD) refers to the accumulated temperature value when the temperature is higher than the plant growth threshold (Schlenker et al. 2006). Specifically, GDD8–32°C is calculated by the accumulated temperature between 8 and 32°C in the growing period of maize. GDD34+°C refers to the accumulated temperature above 34°C in maize growth period that is considered harmful for the yield (Ritchie and NeSmith 1991). Moreover, this study does not include irrigation as maize is mainly fed by rain in China. Alternatively, an aridity index is used to capture the effect of humidity on maize yield, which is defined as follows. Aridity index=
R 1.07H
(10)
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where R and H are the average monthly rainfall and average temperature, respectively (Paltasingh et al. 2012). Expectedly, high temperature and low rainfall will lead to high drought degrees. Therefore, a smaller value of aridity index indicates greater drought degree. Following the study by Yi et al. (2016), an aridity index of less than 20 signifies drought.
4. Results and discussion After introducing the baseline results for estimating the adverse effect of surface ozone pollution on maize yield, a set of robustness checks are processed by using different ozone measurements and controlling alternative weather variables and air pollutants. Then, the nonlinear effect of surface ozone on maize yield is investigated. Moreover, an estimated output loss of maize driven by surface ozone is reported.
4.1. Baseline results Table 2 reports the basic results obtained using AOT40 as ozone concentration measurement in the empirical discussion. Columns (1) and (2) rule out time and countyfixed effect, and all the signs for the covariates are consistent
Table 2 Baseline results Variable1) AOT40
Fixed effect (FE) (1) –0.0059*** (0.0020)
Maize sown area Lagged maize price Production index Precipitation Precipitation squared GDD8–32°C GDD8–32°C GDD34+°C Year fixed effect Lambda Number of counties 1)
0.0602*** (0.0175) –0.0044*** (0.0017) –0.3950 (0.5610) 0.0393 (0.1620) 0.0032 (0.0078) Yes
880
(2) –0.0062*** (0.0019) –0.0046*** (0.0003) 0.0395 (0.0738) –0.0007 (0.0050) 0.0728*** (0.0162) –0.0055*** (0.0016) –0.1130 (0.5150) –0.0229 (0.1490) 0.0039 (0.0074) Yes
880
FE, second-order spatial correlation (3) (4) –0.0060*** –0.0061*** (0.0017) (0.0016) –0.0047*** –0.0002 0.0512 (0.0637) –0.0015 (0.0043) 0.0599*** 0.0720*** (0.0145) (0.0135) –0.0044*** –0.0054*** (0.0014) (0.0013) –0.3450 –0.0788 (0.4730) (0.4390) 0.0301 –0.0214 (0.1370) (0.1270) 0.0029 0.0028 (0.0063) (0.0060) Yes Yes 0.1450*** 0.0999** (0.0401) (0.0412) 880 880
FE, first order spatial correlation (5) (6) –0.0060*** –0.0062*** (0.0017) (0.0015) –0.0046*** (0.0002) 0.0392 (0.0621) –0.0014 (0.0042) 0.0599*** 0.0727*** (0.0145) (0.0134) –0.0044*** –0.0055*** (0.0014) (0.0013) –0.3490 –0.0822 (0.4680) (0.4300) 0.0282 –0.0286 (0.1350) (0.1240) 0.0031 0.0037 (0.0063) (0.0060) Yes Yes 0.0583** 0.0638** (0.0260) (0.0265) 880 880
AOT40 is the cumulative ozone concentration when the hourly ozone concentration exceeds 40 ppb and the light intensity is more than 50 W m−2. GDD, growing degree days. GDD8–32°C is calculated by the accumulated temperature between 8 and 32°C in the growing period of maize. GDD34+°C refers to the accumulated temperature above 34°C in maize growth period. ** and ***, significant at 5 and 1%, respectively. Standard errors are in parentheses. Lamda is the autoregressive coefficient of the spatial error term.
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with expectations. Columns (3)–(6) in Table 2 consider both fixed effects and spatial correlations. The difference is that columns (3) and (4) considered second-order correlation, whereas columns (5) and (6) incorporated first-order correlation. Overall, the results of Table 2 show that ozone has significant negative influence on maize yield. Given spatial correlation is considered, the standard errors for ozone concentration from Columns (4) and (6) in Table 2 are relatively smaller than the result in Column (2). Specifically, 1 ppm increase of ozone exposure measured by AOT40 leads to a decrease in maize yield by 0.61% in column (4) in Table 2. Moreover, the standard error in Column (4) is extremely close to the one in Column (6). This finding is consistent with the conclusion by Chen et al. (2016) that the key results are not sensitive to spatial weighting matrix.
4.2. Robustness checks To show the robustness of the above results, Tables 3–5
present a set of robustness tests by incorporating various confounding variables. Table 3 reports the effects of surface ozone exposure on maize yield using other four ozone measurements. We consistently found the adverse impacts of ozone on maize yield, and all of them are statistically significant at 1% level. Overall, one unit (ppm for AOT40, SUM06 and W126; ppb for M7 and M12) increase of ozone exposure will reduce the maize yield by 0.29–0.61%. Technically, the adverse effect of ozone using M12 is greater than that using M7, and AOT40 is greater than that using SUM06 that warns ozone seems having less adverse effect on maize at night. Tables 4 and 5 show the results obtained by controlling different confounding variables. Column (1) in Table 4 replaced climate variables by the aridity index. Obviously, aridity index is positively correlated with maize yield given that a more humid climatic condition tends to result in higher maize yields. Compared to columns (4) and (6) in Table 2 controlling for all climate variable, however, the coefficients of AOT40 are extremely close.
Table 3 Effects of ozone on maize yield with different ozone exposure measures Variable1) M7
(1) M7 –0.0036*** (0.0010)
M12
(2) M12
(3) SUM06
–0.0041*** (0.0008)
SUM06
–0.0056*** (0.0021)
W126 Maize sown area Lagged maize price Production index Precipitation Precipitation squared GDD8–32°C GDD8–32°Csquared GDD34+°C Year fixed effect Lambda Number of counties 1)
(4) W126
–0.0046*** (0.0003) 0.0333 (0.0737) –0.0011 (0.0050) 0.0728*** (0.0162) –0.0055*** (0.0162) –0.2030 (0.5150) 0.0122 (0.1490) 0.0043 (0.0074) Yes
880
–0.0047*** (0.0002) 0.0405 (0.0636) –0.0025 (0.0043) 0.0713*** (0.0135) –0.0054*** (0.0013) –0.1440 (0.4380) 0.0087 (0.1270) 0.0033 (0.0060) Yes 0.1410*** (0.0413) 880
–0.0047*** (0.0002) 0.0584 (0.064) –0.0013 (0.0043) 0.0743*** (0.0135) –0.0055*** (0.0013) 0.0536 (0.4420) –0.0608 (0.1280) 0.0024 (0.0061) Yes 0.1490*** (0.0412) 880
–0.0029*** (0.0007) –0.0047*** (0.0002) 0.0552 (0.0637) –0.0020 (0.0043) 0.0729*** (0.0135) –0.0054*** (0.0013) 0.0358 (0.4400) –0.0554 (0.1280) 0.0028 (0.0060) Yes 0.1450*** (0.0412) 880
M7 and M12 refer to the daily average ozone concentration from 9:00 to 16:00 and that from 8:00 to 20:00, respectively. SUM06 represents cumulative ozone concentrations when the hourly ozone concentration exceeds 60 ppb. W126 is expressed as a sum of weighted hourly concentrations, cumulated over the 12-hour daylight period from 8:00 to 20:00. GDD, growing degree days. GDD8–32°C is calculated by the accumulated temperature between 8 and 32°C in the growing period of maize. GDD34+°C refers to the accumulated temperature above 34°C in maize growth period. *** , significant at 1%. Standard errors are in parentheses. Lamda is the autoregressive coefficient of the spatial error term.
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Table 4 Robustness checks considering different air pollutants Variables AOT40 Aridity index
(1) –0.0061*** (0.0016) 0.0035*** (0.0006)
(2) –0.0058*** (0.0016) 0.0049 (0.0035)
PM10
(3) –0.0058*** (0.0016)
–0.0004** (0.0002)
PM2.5 Maize sown area Lagged maize price Production index
–0.0046*** (0.0002) 0.0895 (0.0609) –0.0037 (0.0042)
Precipitation Precipitation squared GDD8–32°C GDD8–32°Csquared GDD34+°C Year fixed effect Lambda Number of counties
(4) –0.0058*** (0.0016)
Yes 0.1570*** (0.0404) 880
–0.0046*** (0.0002) 0.0461 (0.0639) –0.0020 (0.0043) 0.0322 (0.0313) –0.0046*** (0.0014) –0.0314 (0.4400) –0.0026 (0.1280) 0.0023 (0.0061) Yes 0.1460*** (0.0412) 880
–0.0047*** (0.0002) 0.0254 (0.0647) –0.0024 (0.0043) 0.0740*** (0.0135) –0.0056*** (0.0013) –0.0391 (0.4390) –0.0354 (0.1270) 0.0029 (0.0060) Yes 0.1420*** (0.0413) 880
–0.0010** (0.0005) –0.0047*** (0.0002) 0.0168 (0.0655) –0.0026 (0.0043) 0.0734*** (0.0135) –0.0055*** (0.0013) –0.0319 (0.4390) –0.0436 (0.1280) 0.0030 (0.0060) Yes 0.1420*** (0.0413) 880
1)
AOT40 is the cumulative ozone concentration when the hourly ozone concentration exceeds 40 ppb and the light intensity is more than 50 W m−2. GDD, growing degree days. GDD8–32°C is calculated by the accumulated temperature between 8 and 32°C in the growing period of maize. GDD34+°C refers to the accumulated temperature above 34°C in maize growth period. ** and ***, significant at 5 and 1%, respectively. Standard errors are in parentheses. Lamda is the autoregressive coefficient of the spatial error term.
Considering that China has been seriously affected by PM2.5 and PM10 in recent decade, we added these
during the growth period maize yield is decreased by surface ozone significantly, and the result is robust.
pollutants into columns (3) and (4) in Table 4 to control the impacts of air particles. PM2.5 and PM10 have significant
4.3. Nonlinear damage
negative effects on maize yield, which is consistent with the findings by Zhou et al. (2017). Predictably, the addition of
Table 6 identifies the most sensitive stage of maize with
air particles mitigated the adverse effect of AOT40 on maize
respect to ozone pollution. The first column shows that the
yield. Since the unit of AOT40 is different from the one of
second period in maize growing season (panicle stage) is
air particle, we calculated their standardized coefficients
the most sensitive period that drives a significant reduction of
to make their coefficients comparable (Appendix F). We
maize yield. Specifically, 1 ppm increase of AOT40 causes
found that adding a standard deviation of AOT40 will result
a decrease in maize yield by 2.1%, which is about 3.5 times
in 1.06% reduction of maize yield, while an increase in one
higher than the coefficient in column (7) in Table 2. Columns
standard deviation of PM2.5 and PM10 will result in 0.69%
(2)–(5) in Table 6 report the estimates using alternative
and 0.56% reduction of maize yield, respectively, which are
measurements, and similar results are obtained that the
about half of ozone damage on maize yield.
impacts of ozone concentration in the second period all
Meanwhile, considering that the degree of drought is
have negative signs. Although the coefficient of SUM06 in
likely to change the breath rate of maize that would indirectly
period 2 marginally missed the statistical significance, other
affect the effects of ozone on maize yield, Table 5 examines
ozone measures showed that maize was very sensitive to
the interactions between ozone exposure and drought index,
ozone exposure in period 2.
but no significant effect was found in the results. Overall,
Furthermore, Fig. 3 describes the nonlinear effect
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Table 5 Robustness checks with weather interactions Variable1) AOT40
(1) –0.0092*** (0.0021)
(2) –0.0061** (0.0030)
Aridity index Drought AOT40×Drought
–0.0660** (0.0261) 0.0060** (0.0025)
AOT40×Aridity index Maize sown area Lagged maize price Production index Precipitation Precipitation squared GDD8–32°C GDD8–32°Csquared GDD34+°C Year fixed effect Lambda Number of counties
(3) –0.0050* (0.0029) 0.0038*** (0.0010)
–0.0046*** (0.0002) 0.0398 (0.0639) –0.0017 (0.0043) 0.0603*** (0.0172) –0.0044*** (0.0015) –0.0390 (0.4390) –0.0159 (0.1270) 0.0020 (0.0061) Yes 0.1430*** (0.0414) 880
(4) –0.0066*** (0.0018)
0.0009 (0.0015) 0.0001 (0.0001) –0.0047*** (0.0002) 0.0512 (0.0638) –0.0015 (0.0043) 0.0720*** (0.0154) –0.0054*** (0.0013) –0.0788 (0.4400) –0.0214 (0.1280) 0.0028 (0.0061) Yes 0.1450*** (0.0412) 880
0.0001 (0.0001) –0.0046*** (0.0002) 0.0875 (0.0611) –0.0039 (0.0042)
Yes 0.1570*** (0.0404) 880
–0.0047*** (0.0002) 0.0530 (0.0639) –0.0016 (0.0043) 0.0773*** (0.0159) –0.0058*** (0.0014) –0.0908 (0.4400) –0.0174 (0.1280) 0.0027 (0.0061) Yes 0.1470*** (0.0412) 880
1)
AOT40 is the cumulative ozone concentration when the hourly ozone concentration exceeds 40 ppb and the light intensity is more than 50 W m−2. GDD, growing degree days. GDD8–32°C is calculated by the accumulated temperature between 8 and 32°C in the growing period of maize. GDD34+°C refers to the accumulated temperature above 34°C in maize growth period. * ** , and ***, significant at 10, 5 and 1%, respectively. Standard errors are in parentheses. Lamda is the autoregressive coefficient of the spatial error term.
of ozone on maize yield and the standard error of the coefficients under two kinds of ozone measurement (M7, M12) based on eq. (4). Since AOT40, W126 and SUM06 are accumulation indicators, they cannot be used in this step. Fig. 3 shows that the higher the ozone concentration, the greater the impact on maize yield. Specifically, a decrease in maize yield of approximately 0.1% is expected with the time experiencing M7 (M12) between 30 and 40 ppb, whereas one more day with M7 (M12) over 60 ppb is associated with more than 0.2% decrease in maize yield. Considering there are more than 15% days having ozone concentration higher than 60 ppb during the growing season, the negative effects of ozone exposure on maize output is not trivial.
4.4. Output loss of maize yield We use the results in the baseline model to estimate maize output losses under observed ozone exposure from 2013
to 2015. To approximate the total loss driven by surface ozone pollution, we first multiply the coefficient of AOT40 in the baseline model (see Column (4) in Table 2) by the AOT40 concentration level and sown area in 2013–2015 to obtain yearly maize output loss over all counties, then calculate the annual average loss in the whole country. The average annual output of maize caused by ozone is 4.234 million tons in 2013–2015, accounting for about 1.9% of the average total output. This value is about 10 times the volume of average maize import in 2013–2015. Specifically, we chose the top eight major maize production regions in China to report the damages driven by surface ozone pollution, as shown in Fig. 4. The highest loss in Shandong Province is more than 1 million tons, which is around 0.2% of the domestic supply because of two reasons. First, Shandong is a major maize production province in China with annual sown area exceeding 8% of national sown area. Second, Shandong has a relatively high surface ozone concentration level, as shown in Fig. 2.
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Table 6 Identify sensitive periods of maize yield in ozone exposure1) (1) AOT40 0.0015 (0.0040) –0.0214*** (0.0058) 0.0028 (0.0060) –0.0046*** (0.0002) 0.0597 (0.0625) 0.0015 (0.0042) 0.0704*** (0.0136) –0.0056*** (0.0013) –0.0382 (0.4310) –0.0410 (0.1250) 0.0053 (0.0061) 0.0659* (0.0347) 857
Variable2) Period 1 (day 1–30) Period 2 (day 31–60) Period 3 (day 60–90) Maize sown area Lagged maize price Production index Precipitation Precipitation squared GDD8–32°C GDD8–32°Csquared GDD34+°C Lambda Number of counties
(2) SUM06 0.0001 (0.0044) –0.0132 (0.0087) –0.0090 (0.0080) –0.0046*** (0.0002) 0.0513 (0.0624) 0.0004 (0.0042) 0.0765*** (0.0136) –0.0057*** (0.0013) 0.1570 (0.4320) –0.0801 (0.1260) 0.0041 (0.0061) 0.0617* (0.0347) 857
(3) W126 –0.0001 (0.0017) –0.0069** (0.0027) –0.0009 (0.0026) –0.0046*** (0.0002) 0.0529 (0.0624) 0.0003 (0.0042) 0.0733*** (0.0136) –0.0056*** (0.0013) 0.1370 (0.4320) –0.0836 (0.1250) 0.0047 (0.0061) 0.0645* (0.0346) 857
(4) M12 0.0008 (0.0037) –0.0175*** (0.0042) 0.0001 (0.0050) –0.0164*** (0.0010) 0.3190 (0.3010) 0.0159 (0.0204) 0.3530*** (0.0646) –0.0274*** (0.0062) –1.8290 (2.0710) 0.5610 (0.6030) 0.0157 (0.0291) 0.0776** (0.0339) 857
(5) M7 0.0035 (0.0035) –0.0180*** (0.0040) 0.0005 (0.0047) –0.0163*** (0.0010) 0.3610 (0.3010) 0.0196 (0.0203) 0.3490*** (0.0647) –0.0274*** (0.0062) –2.1250 (2.0770) 0.6320 (0.6050) 0.0158 (0.0290) 0.0772** (0.0339) 857
1)
AOT40 is the cumulative ozone concentration when the hourly ozone concentration exceeds 40 ppb and the light intensity is more than 50 W m−2. SUM06 represents cumulative ozone concentrations when the hourly ozone concentration exceeds 60 ppb. W126 is expressed as a sum of weighted hourly concentrations, cumulated over the 12-h daylight period from 8:00 to 20:00. M7 and M12 refer to the daily average ozone concentration from 9:00 to 16:00 and that from 8:00 to 20:00, respectively. 2) Period 1 begins at 3 months before maize harvest; A part of the county was removed where SUM06, W126 and AOT40 were 0 in each period. GDD, growing degree days. GDD8–32°C is calculated by the accumulated temperature between 8 and 32°C in the growing period of maize. GDD34+°C refers to the accumulated temperature above 34°C in maize growth period. * ** , and ***, significant at 10, 5 and 1%, respectively. Standard errors are in parentheses. Lamda is the autoregressive coefficient of the spatial error term.
0.1 Change in yield (%)
Change in yield (%)
0
–0.1
0
–0.1
–0.2
–0.2 –0.3
–0.3 20
30
40 50 M7 (ppb)
60
70
20
30
40 50 M12 (ppb)
60
70
Fig. 3 Nonlinear effect of surface ozone on maize yield. M7 and M12 refer to the daily average ozone concentration from 9:00 to 16:00 and that from 8:00 to 20:00, respectively.
According to our estimation results, the annual average loss of maize caused by ozone pollution was over 7.2 billion CNY from 2013 to 2015, accounting for about 50% of China’s total import in 2015. Hence, the control of ozone pollution has potential economic benefits.
5. Conclusion The influence of air pollution on social economy has been given increasing research attention. However, due to the limitation in availability of data and scientific research
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2014
2015
Output change (million tons)
ng H
ei
lo
i hu An
xi an Sh
er In n
ng
jia
on M
ng ao Li
Ji lin
ni
nx i aa Sh
an en H
0
Sh
an
do
ng
go
lia
2013
–0.2 –0.4 –0.6 –0.8 –1.0 –1.2
Fig. 4 Distribution of maize loss in major producing areas in 2013–2015.
methods, empirical research remains insufficient, particularly for maize production. This study provides a rigorous analysis of the effect of ozone exposure on maize yield based on a unique county-specific panel, and a spatial error model is used into the empirical model because of the spatial correlation. Our analysis shows that maize yield is negatively related to surface ozone concentration, which is consistent with previous ecological and economic studies. Moreover, the nonlinear regression result indicates that maize is the most sensitive to ozone in the second month of the growing season. According to previous ecological research, the influence of ozone on plant seedlings was not obvious, but it will be significantly affected as the plants mature. Meanwhile, we use the empirical results to demonstrate that ozone pollution decreased the average annual output of maize by 4.234 million tons per year in 2013–2015, accounting for about 1.9% of the total domestic output. As the main maize-producing province, maize output loss in Shandong is reduced by nearly 1 million tons every year, which is higher than the other regions. To achieve food security, policies to control ozone concentration are necessary to reduce the ozone damage to maize yield. China has taken several ozone control policies, such as raising prices to limit ozone precursors. However, compared with the standards of ozone concentrations implemented in developed economies, our ozone pollution control needs to be strengthened. Typically, a more targeted and effective policy should be set by taking different measures considering regional environmental differences. For example, the high ozone concentration in eastern regions of China may be due to precursors, whereas that in western regions may be due to higher illumination.
Acknowledgements Authors gratefully acknowledge the financial support by the National Natural Science Foundation of China (71673137), the Nanjing Agricultural University, China (Y0201400037, SKCX2015004), the Education Department of Jiangsu Province, China (2014SJD069), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), the China Center for Food Security Studies at Nanjing Agricultural University, Jiangsu Rural Development and Land Policy Research Institute, and Jiangsu Agriculture Modernization Decision Consulting Center, China. Appendices associated with this paper can be available on http://www.ChinaAgriSci.com/V2/En/appendix.htm
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