This study aims to explore the impact of climate change, technology, and agricultural policy on rice production in South Korea. In the presence of a long-run relationship among variables, the results show that an increase in CO2 emissions increases rice production by 0.15%. The mean temperature raises rice production by 1.16%. The rainfall has an adverse impact on rice production which shows improper irrigation systems and weather forecasting reports. Similarly, for technical factors, the area under rice and fertilizer used in the study has a direct effect on rice production. The study suggests that the Korean government needs to implement new policies and acquire advanced technology for weather forecasting. The concerned authorities need to inform rice growers about future weather and climate changes. We recommend that Korea needs to provide virgin arable undivided land to deserving rice growers based on ownership and/or lease for future food security. Finally, the study recommends that legislators should recommend policies for sustainable food security with the introduction of new agricultural technologies and subsidies, along with the provision of new varieties of seeds that can absorb the adverse shock of climate change and ensure a suitable amount of food.

  • Carbon dioxide emission improves the process of photosynthesis due to which rice production increases in Korea.

  • Rice production increases 1.16% with an increase in mean temperature in the long-run.

  • Adverse shock of rainfall on rice production shows the improper irrigation system and weather forecasting reports.

  • Cultivated area under rice has a noteworthy direct effect on rice production both in the short- and long-run.

Graphical Abstract

Graphical Abstract
Graphical Abstract

Agricultural researchers pay great attention to crop production due to variations in climate from the past to the present and from the present to the future (Lobell et al. 2011; Butler & Huyber 2012; Urban et al. 2012; Asseng et al. 2013; Wheeler & von Braun 2013). These variations in climate have an impact on agricultural production, but the variation in production over time has received less attention (Chen et al. 2013; Osborne & Wheeler 2013). The variations in agricultural production destabilize farmers' income (Reidsma et al. 2010; Li et al. 2013; Mottaleb et al. 2013), food supply (Slingo et al. 2005; Lobell & Burke 2010), and increase the price, which are negative effects of climate change (Ray et al. 2012, 2013). Climate change easily influences agricultural production due to meteorological variables that control the basic process in crop development and growth (Meza & Silva 2009). Climate has a positive or negative impact on agricultural production worldwide (Reilly et al. 2003; Tao et al. 2006, 2008; li et al. 2009; Lobell & Burke 2010; Özdoğan 2011; Siddiqui et al. 2012; Licker et al. 2013; Mishra et al. 2013; Janjua et al. 2014; Saadi et al. 2015; Ben-Ari et al. 2016), although these effects are unclear and their spatial pattern, driving mechanism, and severity are unidentified (Tao & Zhang 2013; Tao et al. 2014).

The Republic of Korea is situated in East Asia at 37° north, 127° 30 east with a total area of 96,920 square kilometers with 17,360 square kilometers of agricultural land. The share of agriculture in the GDP of South Korea fell from 27% to 3.3% from 1970 to 1999 (CIA 2007). In 2017, the total contribution of agriculture to GDP was 1.9%, and the contribution of rice in agriculture to GDP was 13.1% (KOSTAT 2017). The most grown crops in Korea are rice, barley, millet, corn, sorghum, buckwheat, etc. The production of rice in Korea decreased by 2.6% from the last year's production reported by KOSTAT (2019). Korea mostly depends on imports of agricultural products but these can be affected by various factors (Nasrullah et al. 2020). The most important Korean crop is rice, and is 90% of the country's total grain. Korean farmers cannot be competitive rice producers in the international market, but produce enough rice to fulfill domestic demand. The Korean agricultural policy in 1990 hugely disturbed farming communities due to removal of subsidies from agricultural inputs (fertilizer, pesticides, farm equipment, machinery, etc.). This policy not only caused a declines in agricultural production but also increased the demand for international agri-products (OECD 1999).

Mostly, two methods are used in natural science to work out the impact of climate changes on agriculture. In the first approach, there are crop simulation models (Chami & Daccache 2015; Li et al. 2015; Rotich & Mulungu 2017) (CERES, C-CAM, EPIC, and others) and climate change scenario (Özdoğan 2011; Jalota et al. 2014; Wilcox & Makowski 2014), while the second approach is climate chamber experiments or field experiments (Leadley & Drake 1992). The most used approach in natural science is the crop simulation model with a climate change scenario. However, this model is difficult and laborious because of its dependency on many inputs such as rainfall, temperature, nutrition, atmospheric circulation, carbon circulation, economics factors, etc. Researchers are trying to find the uncertainties in parameter values because of a lack of understanding during model projection, which misleads the required predicted results (Lobell & Burke 2010). For field experiments, enough time and funds are required to get more sample results (Guo 2015).

In the field of socioeconomics, researchers mostly use empirical models (regression model, panel data model) (Huang et al. 2010; Conradt et al. 2016; Gornott & Wechsung 2016) and economic models (Ricardian model, yield function) (Yang 2007; Zhou 2012) to cover the impact of climate variation based on statistical data. These approaches are used to diagnose the uncertainty in the model and decrease the dependency on field data (Lobell & Burke 2010). The statistical approach depends on historical data which are collected from different experimental stations (Siddiqui et al. 2012; Gornott & Wechsung 2016), or advanced technology (Tian & Wan 2000), but there is less evidence of empirical analysis related to climate changes and technology used in agricultural production (Zhang & Huang 2012; Tao & Zhang 2013).

The autoregressive distributed lag (ARDL) bound test, proposed by Pesaran et al. (2001), allows determination of the long-run relation existing in series. The ARDL approach has recently become more known in some empirical studies for exploring the relation of climate change with other agricultural factors in several countries (Ghana (Asumadu-Sarkodie & Owusu 2016), Pakistan (Arshed & Abduqayumov 2016), and Europe (Acaravci & Ozturk 2010)) because of its difference in the ability to identify long-/short-run relationships among variables compared to the previous approach. The ARDL is applied respectively to find the integrations of variables, which is also a good fit for small sample data. Therefore, this study was organized to find the short- and long-run impact of climatic factors, technical factors, and agricultural policy (1990) on rice production of Korea by using the ARDL model. Based on the results, the study will also provide some possible suggestions.

Data collection

The study uses important factors that are responsible for affecting rice production in South Korea (Republic of Korea). Previous studies of Yang (2007), Zhou (2012), and Guo (2015) stated that natural factors and agricultural technology significantly affect agricultural production. Hence, the study jointly uses agricultural technology (e.g., area and fertilizer) and natural factors (e.g., carbon dioxide (CO2) emission, mean temperature, and rainfall) along with an additional variable of agricultural policy as an explanatory variable and rice production is used as an explained variable. The annual data covering the period from 1973 to 2018 for rice production, CO2, mean temperature, rainfall, area under rice, and fertilizers were gathered from the Korean Statistical Information System (KOSIS 2019), as shown in Table 1. The study highlights the 1990 agricultural policy of Korea in the model which hugely affects domestic rice production. The data are converted into log form before applying the ARDL bound test.

Table 1

Variable description and data source

VariablesDescriptionsMeasurement UnitsSource
Rpro Rice production in South Korea Thousands of tons KOSIS 
CO2 Carbon dioxide emission Thousands of kilotons KOSIS 
MT Mean temperature Degree Celsius KOSIS 
MRF Mean rainfall Millimeters KOSIS 
Area Area under rice Thousands of hectares KOSIS 
Fert Fertilizer used Thousands of tons KOSIS 
D Dummy for agricultural policy (1990) D = 1 after 1990, otherwise 0  
VariablesDescriptionsMeasurement UnitsSource
Rpro Rice production in South Korea Thousands of tons KOSIS 
CO2 Carbon dioxide emission Thousands of kilotons KOSIS 
MT Mean temperature Degree Celsius KOSIS 
MRF Mean rainfall Millimeters KOSIS 
Area Area under rice Thousands of hectares KOSIS 
Fert Fertilizer used Thousands of tons KOSIS 
D Dummy for agricultural policy (1990) D = 1 after 1990, otherwise 0  

Methodology

The study applied a well-known approach by Pesaran et al. (2001) called the autoregressive distributed lag (ARDL) approach. The ARDL model is considered as the best econometric method compared to others in a case when the variables are stationary at I(0) or integrated of order I(1). Based on the study objectives, it is a better model than others to catch the short-run and long-run impact of independent variables on rice production.

The ARDL approach is appropriate for generating short-run and long-run elasticities for a small sample size at the same time and follow the ordinary least square (OLS) approach for cointegration between variables (Duasa 2007). ARDL affords flexibility about the order of integration of the variables. ARDL is suitable for the independent variable in the model which is I(0), I(1), or mutually cointegrated (Frimpong & Oteng 2006), but it fails in the presence I(2) in any variables. To find the relation between dependent and independent, the following model was constructed as:
(1)
By converting all variables of Equation (1) into the natural log, the model is designed below:
(2)
where RPro represents rice production, while t represents the time period from 1973 to 2018. represents the constant while to are the coefficients of variables and CO2, MT, MRF, Area, Fert and D are the CO2 emission, mean temperature, mean rainfall, area under rice, fertilizer use, and the dummy (dummy = 0 before 1990, above 1990 = 1) used for agricultural policy, while represents the error term. Equation (2) can be written in ARDL form as follows:
(3)
where represents drift component while shows the first difference, shows the white noise. The study uses the Akaike information criterion (AIC) for choosing the lag length. After finding the long-run association existing between variables, the study uses the error correction model (ECM) to find the short-run dynamics. The ECM general form of Equation (3) is formulated below in Equation (4):
(4)
where Δ represents the first difference while is the coefficients of ECM for short-run dynamics. ECM shows the speed of adjustment in long-run equilibrium after a shock in the short run.

Estimation procedure

After analyzing data through Equation (2), the long-run association among all variables is verified by using the Wald test. The null hypothesis of the Wald test suggests the existence of no cointegration, while the alternative hypothesis shows the existence of cointegration. The calculated F-statistics are compared to lower and upper bound values (Pesaran & Shin 1999). If the estimated F-statistic value is larger than the lower and upper bound then there will be cointegration.

CUSUM and CUSUMSQ test

By confirming that the long-run associations exist between variables, the study applies the cumulative sum (CUSUM) and cumulative sum of square (CUSUMSQ) tests (Brown et al. 1975). Previous studies (Pesaran & Shin 1999; Pesaran et al. 2001) suggested these tests portray the good fitness of the ARDL model. These tests are used to plot the residual of ECM. If the statistics in the plot fall in critical bounds at a 5% significant value, the results suggest that the coefficients of the ARDL model are stable.

Descriptive statistics

The empirical study uses the time series data to find the effects of climate variation, technology variation, and agricultural policy on rice production in South Korea. The descriptive statistics of the important variables stated in Table 2 specified that the Jarque–Bera test for entire variables used in the study is insignificant, which implies that all the selected variables are normally distributed. The trend in rice production shows that the rice production was high in 1988 but after 1990 it shows a continuous reduction untill 2018, as shown in Figure 1. The descriptives statistics show that the CO2 emission during the time period was 345.243 thousand kilotons with the lowest emission of 73.09 kilotons in 1973, while the highest emission was noted as 592.50 kilotons in 2018, as shown in Table 2. The trend line in Figure 2 shows that the CO2 emission continuously increases at a rate of 0.05% each year. Similarly, the mean temperature observed during the study period was 17.67 °C ranging from 16.35 °C (in 1980) to 18.93 °C (in 2015). The observed trend line in Figure 3 shows a continuous variation in mean temperature going upward with a speed of 0.002% each year. The mean rainfall during the study area was observed as 1,142.51 millimeters, ranging from 729.8 to 1,697.4 millimeters. The trend line in rainfall shows high volatility during the time period with an upward movement of 0.002% each year, as shown in Figure 4. The estimated trend in rainfall is similar to the previous study of Alahmadi & Rahman (2019), which stated that climate change causes extreme rainfall. The mean area under rice and fertilizer used for rice is 1,062.46 thousand hectares and 766.5 thousand tons. The trend lines of area and fertilizer are observed going downward, as shown in Figures 5 and 6.

Table 2

Descriptive statistics of the variable used

VariablesRProCO2MTMRFAreaFertD
Median 5,029.956 369.802 17.763 1,161.150 1,062.565 821.500 1.000 
Mean 4,947.337 345.243 17.669 1,142.507 1,062.460 766.500 0.609 
Std. Dev 626.493 180.803 0.586 229.723 159.855 206.856 0.493 
Maximum 6,053.482 592.499 18.925 1,697.400 1,262.324 1,104.000 1.000 
Minimum 3,550.257 73.094 16.350 729.800 737.673 423.000 0.000 
Skewness −0.207 −0.062 −0.326 0.217 −0.416 −0.451 −0.445 
Kurtosis 2.114 1.533 2.840 2.605 2.078 1.870 1.198 
Jarque–Bera 1.833 4.156 0.866 0.662 2.957 4.010 7.742 
Prob. 0.400 0.125 0.648 0.718 0.228 0.135 0.021 
Observations 46 46 46 46 46 46 46 
VariablesRProCO2MTMRFAreaFertD
Median 5,029.956 369.802 17.763 1,161.150 1,062.565 821.500 1.000 
Mean 4,947.337 345.243 17.669 1,142.507 1,062.460 766.500 0.609 
Std. Dev 626.493 180.803 0.586 229.723 159.855 206.856 0.493 
Maximum 6,053.482 592.499 18.925 1,697.400 1,262.324 1,104.000 1.000 
Minimum 3,550.257 73.094 16.350 729.800 737.673 423.000 0.000 
Skewness −0.207 −0.062 −0.326 0.217 −0.416 −0.451 −0.445 
Kurtosis 2.114 1.533 2.840 2.605 2.078 1.870 1.198 
Jarque–Bera 1.833 4.156 0.866 0.662 2.957 4.010 7.742 
Prob. 0.400 0.125 0.648 0.718 0.228 0.135 0.021 
Observations 46 46 46 46 46 46 46 

Note: The results are taken before using Logarithm. RPro, CO2, MT, MRF, Area, Fert, D represent rice production, CO2 emission, mean temperature, mean rainfall, area under rice, fertilizer used for rice, and agricultural policy in 1990.

Figure 1

The trend in rice production of South Korea (1973–2018).

Figure 1

The trend in rice production of South Korea (1973–2018).

Close modal
Figure 2

The trend in carbon dioxide emission of South Korea (1973–2018).

Figure 2

The trend in carbon dioxide emission of South Korea (1973–2018).

Close modal
Figure 3

The trend in mean temperature of South Korea (1973–2018).

Figure 3

The trend in mean temperature of South Korea (1973–2018).

Close modal
Figure 4

The trend in rainfall of South Korea (1973–2018).

Figure 4

The trend in rainfall of South Korea (1973–2018).

Close modal
Figure 5

The trend in area under rice of South Korea (1973–2018).

Figure 5

The trend in area under rice of South Korea (1973–2018).

Close modal
Figure 6

The trend in fertilizer used for rice of South Korea (1973–2018).

Figure 6

The trend in fertilizer used for rice of South Korea (1973–2018).

Close modal

Unit root test

It is important to check the unit root of each variable before applying the ARDL bound test. For finding the bound F-statistic test, all the variables must be stationary at I(0), I(1), or both. To check the integration order of each variable, the study incorporates the augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root test as used by the previous study of Rizwanullah et al. (2020). The result of ADF and PP reflects that there is no unit root in the series. Table 3 shows that rice production, CO2 emission, mean temperature, and mean rainfall is stationary at order I(0), while the area under rice, fertilizer used, and agricultural policy is stationary at order I(1).

Table 3

Unit root test

ADF
PP
VariableLevels1st differencesLevels1st differencesOrder of integration
LnRPro −2.861c −8.658a −2.765c −10.808a I (0) 
LnCO2 −3.505b −5.427a −4.003a −5.476a I (0) 
LnMT −4.548a −7.660a −4.564a −34.036a I (0) 
LnMRF −5.501a −18.209a −5.501a −18.209a I (0) 
LnArea 0.869 −5.169a 0.676 −5.161a I (1) 
LnFert −0.848 −9.278a −0.402 −9.989a I (1) 
D −1.232 −6.633a −1.232 −6.633a I (1) 
ADF
PP
VariableLevels1st differencesLevels1st differencesOrder of integration
LnRPro −2.861c −8.658a −2.765c −10.808a I (0) 
LnCO2 −3.505b −5.427a −4.003a −5.476a I (0) 
LnMT −4.548a −7.660a −4.564a −34.036a I (0) 
LnMRF −5.501a −18.209a −5.501a −18.209a I (0) 
LnArea 0.869 −5.169a 0.676 −5.161a I (1) 
LnFert −0.848 −9.278a −0.402 −9.989a I (1) 
D −1.232 −6.633a −1.232 −6.633a I (1) 

Note: Critical values at level are −3.585, 2.928, and 2.602 at 1, 5, and 10% level, while at first difference the critical values are −3.589, −2.930, and −2.603 at 1, 5, and 10% level. a, b, and c represent the 1, 5, and 10% significance level. RPro, CO2, MT, MRF, Area, Fert, D represent rice production, CO2 emission, mean temperature, mean rainfall, area under rice, fertilizer used for rice, and agricultural policy in 1990.

Structural break unit root test

Due to numerous policy and macroeconomic shifts, earlier unit root tests did not reflect a break in a series thus leading to biases in regression results. Therefore, Narayan & Popp (2010) proposed a structural break unit root test with two breaks in the level and slope with the supposition of unknown timing. Employing Narayan & Popp's (2010) structural break method demonstrates stable power, correct size, and identifies structure break more clearly than the previous study of Zivot & Andrews (1992). The estimated results of the structural break unit root test are reported in Table 4.

Table 4

Unit root test with two structural breaks (Narayan & Popp 2010)

Break in intercept (M1)
Break in intercept and trend (M2)
Variablest-statisticsTB1TB2t-statisticsTB1TB2
LnRPro −4.583 1987 2009 −4.719 1987 2009 
LnCO2 −0.831 1987 1997 −0.742 1987 1997 
LnMT −5.290 1987 1997 −7.485 1987 2012 
LnMRF −5.745 1997 2012 −6.122 1997 2014 
LnArea −1.092 1996 2002 −1.958 1996 2009 
LnFert −4.308 2005 2012 −5.287 1987 2005 
D −6.178 1989 1990 −5.604 1989 1990 
Break in intercept (M1)
Break in intercept and trend (M2)
Variablest-statisticsTB1TB2t-statisticsTB1TB2
LnRPro −4.583 1987 2009 −4.719 1987 2009 
LnCO2 −0.831 1987 1997 −0.742 1987 1997 
LnMT −5.290 1987 1997 −7.485 1987 2012 
LnMRF −5.745 1997 2012 −6.122 1997 2014 
LnArea −1.092 1996 2002 −1.958 1996 2009 
LnFert −4.308 2005 2012 −5.287 1987 2005 
D −6.178 1989 1990 −5.604 1989 1990 

Note: Critical values for both Model M1 (−4.735, −4.194, −3.863) and Model M2 (−5.151, −4.644, −4.376) are at 1, 5, and 10%, respectively. M1 and M2 are the first and second model while TB1 and TB2 are the first time and second time break. RPro, CO2, MT, MRF, Area, Fert, D represent rice production, CO2 emission, mean temperature, mean rainfall, area under rice, fertilizer used for rice, and agricultural policy in 1990.

Lag selection criteria

Before applying the ARDL bound test for checking cointegration exists or not among rice production, carbon dioxide emission, mean temperature, rainfall, area under rice, fertilizer used, and agricultural policy, it is important to select an appropriate lag order of the variable. The study employed the optimal lag order of the vector autoregression (VAR) model for the selection of appropriate lag order. The observed results in Table 5 show the entire lag selection criteria for employing the ARDL bound test which implies that the model gives better results at lag 1 as compared to lag 2 and 3.

Table 5

Lag order criteria by using VAR (vector autoregression)

LagLogLLRFPEAICSCHQ
203.2938 NA 2.56 × 10−13 −9.129945 −8.843238 −9.024216 
412.4829 340.5403* 1.53 × 10−16−16.67829* −14.28694* −15.73477* 
457.7475 58.94933 2.19 × 10−16 −16.58060 −12.10626 −14.82093 
512.5832 53.56040 2.84 × 10−16 −16.40686 −10.37073 −13.35226 
LagLogLLRFPEAICSCHQ
203.2938 NA 2.56 × 10−13 −9.129945 −8.843238 −9.024216 
412.4829 340.5403* 1.53 × 10−16−16.67829* −14.28694* −15.73477* 
457.7475 58.94933 2.19 × 10−16 −16.58060 −12.10626 −14.82093 
512.5832 53.56040 2.84 × 10−16 −16.40686 −10.37073 −13.35226 

*Represents the criterion selecting the lag order. LR, FPE, AIC, SC, and HQ represent the sequential modified LR test statistic, final prediction error, Akaike information criterion, Schwarz information criterion, and Hannan–Quinan information criterion, respectively.

Additionally, the polynomial graph is also used for the confirmation of appropriate lag length under the VAR method, as shown in Figure 7. The graph shows that the dots inside the circle confirm the validation of good results at lag 1.

Figure 7

Inverse roots of AR characteristic polynomial.

Figure 7

Inverse roots of AR characteristic polynomial.

Close modal

ARDL bound test for cointegration

Before finding the long- and short-run relations that exist between variables, it is important to use the ARDL bound test (Pesaran et al. 2001) for the confirmation of cointegration. The estimated results shown in Table 6 portray that the value of F-statistics is larger than lower and upper bound at 1% significance level when rice production, mean temperature, rainfall, area, fertilizer, and agricultural policy are the dependent variables. Hence, the alternative hypothesis of cointegration is accepted and the ARDL bound test approves the existence of long-run association among rice production, CO2, mean temperature, mean rainfall, the area, fertilizer used for rice, and agricultural policy. Furthermore, the study also applies the cointegration approach of Johansen & Juselius (1990) to check the robustness of existing long-run association among variables. The empirical results of Johansen's cointegration, shown in Table 7, provide the evidence of robustness and effective long-run association among the variables.

Table 6

ARDL bound test for cointegration

EquationLagF-statisticsP-value
RPro=ʃ(CO2, MT, MRF, Area, Fert, D) (1, 0, 0, 0, 0, 1, 0) 5.048a 0.000 
Critical value 10% 5% 1% 
Lower bound I(0) 2.12 2.45 3.15 
Upper bound I(1) 3.23 3.61 4.43 
EquationLagF-statisticsP-value
RPro=ʃ(CO2, MT, MRF, Area, Fert, D) (1, 0, 0, 0, 0, 1, 0) 5.048a 0.000 
Critical value 10% 5% 1% 
Lower bound I(0) 2.12 2.45 3.15 
Upper bound I(1) 3.23 3.61 4.43 

aRepresents the 1% significance level.

Table 7

Johansen cointegration estimation

HypothesisTest statistics5% critical valueP-value
Trace statistics 
 r ≤ 0 163.9797a 125.6154 0.0000 
 r ≤ 1 111.9238a 95.75366 0.0024 
 r ≤ 2 68.55279 69.81889 0.0628 
 r ≤ 3 41.18989 47.85613 0.1827 
 r ≤ 4 24.36719 29.79707 0.1854 
 r ≤ 5 11.80508 15.49471 0.1665 
 r ≤ 6 1.568229 3.841466 0.2105 
Maximum eigenvalue 
 r ≤ 0 52.05589a 46.23142 0.0107 
 r ≤ 1 43.37100b 40.07757 0.0206 
 r ≤ 2 27.36290 33.87687 0.2444 
 r ≤ 3 16.82270 27.58434 0.5952 
 r ≤ 4 12.56211 21.13162 0.4933 
 r ≤ 5 10.23685 14.26460 0.1968 
 r ≤ 6 1.568229 3.841466 0.2105 
HypothesisTest statistics5% critical valueP-value
Trace statistics 
 r ≤ 0 163.9797a 125.6154 0.0000 
 r ≤ 1 111.9238a 95.75366 0.0024 
 r ≤ 2 68.55279 69.81889 0.0628 
 r ≤ 3 41.18989 47.85613 0.1827 
 r ≤ 4 24.36719 29.79707 0.1854 
 r ≤ 5 11.80508 15.49471 0.1665 
 r ≤ 6 1.568229 3.841466 0.2105 
Maximum eigenvalue 
 r ≤ 0 52.05589a 46.23142 0.0107 
 r ≤ 1 43.37100b 40.07757 0.0206 
 r ≤ 2 27.36290 33.87687 0.2444 
 r ≤ 3 16.82270 27.58434 0.5952 
 r ≤ 4 12.56211 21.13162 0.4933 
 r ≤ 5 10.23685 14.26460 0.1968 
 r ≤ 6 1.568229 3.841466 0.2105 

a and b represent the 1 and 5% significance level.

Short- and long-run estimation of parameters

After verifying the existence of a long- and short-run association between variables from the ARDL bound test, the study finds the short- and long-run parameters of the variables. Rice is a major staple food crop in South Korea which is highly affected by the various factors of climate change. The empirical results of climatic factors are shown in Table 8 for a long-run association which implies that an increase in carbon dioxide emission in South Korea can significantly increase rice production. The estimated outcomes imply that a rise of 1% in CO2 emission can increase rice production up to 0.15%. Chunhua et al. (2020) stated that elevated atmospheric CO2 can increase rice production in the future because elevated CO2 improves the photosynthesis process in rice. Wang et al. (2015) also stated that elevated CO2 increases rice yield by 20%. Similarly, the mean temperature in South Korea has a significant positive long-run association with rice production at a 1% level while the mean rainfall has a substantial negative long-run association with rice at a 5% significant level. This outcome implies that a rise of 1% in mean temperature can increase rice production by 1.16% while an increase in 1% of rainfall can decrease rice production by 0.13%. Minasny et al. (2012), Shakoor et al. (2015) stated that due to severe cold weather in Korea, rice production decreases. It is also reported from the results that the mean temperature (17.67 °C) of Korea during rice growth is less than the previous study of Kashyap & Agarwal (2020) and Chandio et al. (2018), which is 23.46 °C and 20.27 °C. Therefore, an increase in mean temperature can increase rice production. Korres et al. (2017) stated that increase in temperatures from 25 to 35 °C can reduce the growth as well as rice yield. Chandio et al. (2020) and Mahmood et al. (2012) specified that in the long run, increase in rainfall can decrease rice production. Rotich & Mulungu (2017) reported that due to climate change the rain pattern was not stable, which is a serious threat for agricultural production and food security. Similarly, Mosammam et al. (2016) stated that a change in the frequency of rainfall can decrease agricultural production. The trend line of rainfall also shows a continuous variation due to which rice production decreases in Korea. Kakumanu et al. (2019) stated that heavy rainfall is the major constraint for rice productivity. The study also observed a significant impact of the area under rice on the production of rice at a 1% level in the long run. Rice production increases 0.73% with the increase of 1% cultivated area under rice in the long run, which is similar to the previous study of Hussain (2012). Similarly, fertilizer also plays an important role in soil fertility and crop nutrients. Any adverse shock in fertilizer consumption can decrease rice production. The study shows that in the long run, 1% increase in fertilizers can significantly increase rice production by 0.19%. The estimated findings of the study coincide with a previous study of Rehman et al. (2017). Agricultural policy plays a significant role in the development of agricultural and rural communities. The Korean government has implemented a policy to withdraw agricultural subsidies for the up-gradation of the environment. The current study implies that in the long run, the agricultural policy in 1990 significantly decreases rice production by 0.11%. The estimated results are similar to the previous study of Bala et al. (2014), which stated that removing agricultural subsidies not only discourages the farming community but also increases the prices of agricultural commodities.

Table 8

Long-run estimation of parameters from ARDL models (34 observations from 1973 to 2018).

VariablesCoefficientStd. errort-statisticsProb.
LnCO2 0.152a 0.048 3.144 0.003 
LnMT 1.162a 0.452 2.569 0.014 
LnMRF −0.129b 0.060 −2.141 0.039 
LnArea 0.728a 0.196 3.379 0.000 
LnFert 0.189b 0.086 2.186 0.035 
D −0.110b 0.0489 −2.261 0.029 
C −1.037 1.852 −0.559 0.579 
VariablesCoefficientStd. errort-statisticsProb.
LnCO2 0.152a 0.048 3.144 0.003 
LnMT 1.162a 0.452 2.569 0.014 
LnMRF −0.129b 0.060 −2.141 0.039 
LnArea 0.728a 0.196 3.379 0.000 
LnFert 0.189b 0.086 2.186 0.035 
D −0.110b 0.0489 −2.261 0.029 
C −1.037 1.852 −0.559 0.579 

a and b represent 1, 5, and 10% significance levels. Dependent variable is rice production. ARDL (1, 0, 0, 0, 0, 1) based Akaike information criteria.

In the short run, the coefficient of climatic factors such as CO2, mean temperature, and rainfall significantly influence rice productivity. The results shown in Table 9 indicate that in the short run an increase of 1% carbon dioxide emission and mean temperature in the Republic of Korea can increase rice production by 0.15% and 1.11%. The study also finds that a 0.12% reduction in rice production occurs due to an increase of 1% in rainfall in the short run. In technical factors, rice cultivated area is significant at a 1% level which shows that in the short run the rice productivity increases by 0.69% with a 1% increase in a cultivated area. The study also finds that fertilizer used in the short run has no impact on rice production. This result is opposite to a previous study by Saddozai et al. (2015), which implies that inappropriate use of fertilizer can decrease the production level, while Nasrullah et al. (2019) stated that fertilizer used has an insignificant effect on rice production, and Zulfiqar et al. (2020) found a significant positive impact of fertilizer on production. Similarly, Minasny et al. (2012) stated that the Korean government removed the subsidy on fertilizer during 1990 because of the continuous increase in soil organic component (SOC). Therefore, due to removing the subsidy the fertilizer use in Korea decreased showing no impact on rice production. Likewise, Nasrullah et al. (2020) stated that various trade barriers and distances significantly decrease the flow of trade due to which the use of chemical fertilizer in Korea is inappropriate for rice. The dummy variable used for agricultural policy significantly reduces agriculture production by 0.11%. The results are similar to existing studies of Chandio et al. (2018, 2020); Korres et al. (2017), Janjua et al. (2014), Mahmood et al. (2012), and Hussain (2012).

Table 9

Short-run estimation of parameters from ARDL models (34 observation from 1973 to 2018)

VariablesCoefficientStd. errort-statisticsProb.
DLnCO2 0.145a 0.052 2.781 0.009 
DLnMT 1.105a 0.406 2.721 0.010 
DLnMRF −0.122b 0.053 −2.313 0.026 
DLnArea 0.693a 0.207 3.341 0.002 
DLnFert −0.079 0.094 −0.844 0.404 
D −0.105b 0.049 −2.149 0.036 
ECM(-1) −0.951 0.129 −7.375 0.000 
VariablesCoefficientStd. errort-statisticsProb.
DLnCO2 0.145a 0.052 2.781 0.009 
DLnMT 1.105a 0.406 2.721 0.010 
DLnMRF −0.122b 0.053 −2.313 0.026 
DLnArea 0.693a 0.207 3.341 0.002 
DLnFert −0.079 0.094 −0.844 0.404 
D −0.105b 0.049 −2.149 0.036 
ECM(-1) −0.951 0.129 −7.375 0.000 

a and b represent 1, 5, and 10% significance level. Dependent variable is rice production. ARDL (1, 0, 0, 0, 0, 1) based Akaike information criteria.

The estimated coefficient of ECM is negative and significantly verifies the existence of cointegration among variables. ECM shows the speed of adjustment in long-run equilibrium after short-run shocks. The ECM coefficient of rice production for South Korea is −0.95 and significant at a 1% level, showing that any deviation from the short-run equilibrium between variables and rice production can be adjusted and recovered each year at 0.95% in the long run, as shown in Table 9.

Diagnostic tests

Numerous diagnostic tests are used to find the errors in the model and are shown in Table 10. The estimated value of R-square and adjusted R-square is greater than 76 showing the model is a good fit. The projected Ramsey reset test (χ2 Ramsey reset) illustrates that the functional form of the estimated model is correct. Similarly, the expected result of χ2 Arch and χ2 B-G shows that there is no heteroscedasticity problem in the model. The estimated scores of the Jarque–Bera test (χ2 normality) and serial correlation (χ2SC) imply that the existing model is normal and finds no serial correlation.

Table 10

Diagnostic tests

R-square 0.783 
Adjusted R-square 0.734 
Durbin–Watson statistics 1.975 
χ2 Ramsey reset 0.298 (0.767) 
χ2 ARCH 0.229 (0.635) 
χ2 B-G 0.441 (0.647) 
χ2 Normality 2.217 (0.330) 
χ2SC 0.441 (0.647) 
R-square 0.783 
Adjusted R-square 0.734 
Durbin–Watson statistics 1.975 
χ2 Ramsey reset 0.298 (0.767) 
χ2 ARCH 0.229 (0.635) 
χ2 B-G 0.441 (0.647) 
χ2 Normality 2.217 (0.330) 
χ2SC 0.441 (0.647) 

Note: For Ramsey reset, the null is the correct functional form. For the Arch test, the null is no heteroscedasticity. For the Breusch-Pagan-Godfrey (B-G) test, the null is no serial correlation. For the J-B test, the null is normality. SC stands for no serial correlation. P-values are presented in parentheses.

Stability check

Due to the presence of structural changes in all variables because of single or multiple structure breaks, the study using cumulative sum (CUSUM) and cumulative sum of squares (CUSUMSQ) tests for checking stability in the short-run and long-run coefficients proposed by Brown et al. (1975). The CUSUM and CUSUMSQ lines of rice production are at the 5% significance level over time, confirming the stability and good fitness of the ARDL model. The results of CUSUM and CUSUMSQ are shown in Figures 8 and 9. The results of the ARDL model are based on AIC. Figures 10 and 11 show 20 computed ARDL models based on AIC and SC.

Figure 8

Plot of CUSUM for coefficients' stability of ARDL model. Source: authors' estimates from data 1973–2018.

Figure 8

Plot of CUSUM for coefficients' stability of ARDL model. Source: authors' estimates from data 1973–2018.

Close modal
Figure 9

Plot of CUSUMSQ for coefficients' stability of ARDL model. Source: authors' estimates from data 1973–2018.

Figure 9

Plot of CUSUMSQ for coefficients' stability of ARDL model. Source: authors' estimates from data 1973–2018.

Close modal
Figure 10

Akaike information criteria graph. Source: authors' estimates from data 1973–2018.

Figure 10

Akaike information criteria graph. Source: authors' estimates from data 1973–2018.

Close modal
Figure 11

Schwarz criteria graph. Source: authors' estimates from data 1973–2018.

Figure 11

Schwarz criteria graph. Source: authors' estimates from data 1973–2018.

Close modal

Rice is the main staple food in South Korea but its production decreases gradually each year and is not sufficient to fulfill the domestic demand. This decline in rice production not only pressurizes the local farmers but also attracts the attention of policymakers. Therefore, the study's main aim is to scrutinize the short- and long-run relationship among rice production, technical factors, climatic factors, and agricultural policy of South Korea using annual data from 1973 to 2018. The ADF and PP are applied to check the stationarity before using the ARDL model which can mislead the desired results. The estimated results of ADF and PP proved that all the variables are stationary at I(0) and I(1). The result of the ARDL bound test verified the presence of long- and short-run relationships among variables. The estimated short- and long-run elasticity of the ARDL model discovers a significant direct impact of CO2 and mean temperature. It is concluded that elevated atmospheric CO2 in the respondent area during the study period increases rice production. The increase in CO2 emission in Korea increases the photosynthesis process, which is highly valuable for the production of rice. Likewise, the climatic factor, mean temperature also increases rice production. It is concluded that the mean temperature has a valuable effect on the vegetation and production process. On the other hand, the climatic factor rainfall is not stable during the study period, and has an adverse shock on the production. The technical factors (area under rice and fertilizer) have a direct positive effect on rice production, which implies that an increase in area and fertilizer can boost rice production. The agricultural policy against subsidies on agricultural inputs is also responsible for the reduction in rice production. Similarly, the various stability and diagnostic tests verify that the model is a good fit, functional form is correct, the model is normal, and there are no problems of heteroscedasticity and serial correlation in the model.

The trend line in Figure 1 shows a significant decline in rice production after the withdrawal of agricultural subsidies. Similarly, the trend line of fertilizer used also shows a continuous decline. Therefore, to avoid food shortage in the near future the government needs to avoid this kind of policy which discourages the farmers. The estimated elasticity of rainfall significantly decreases rice production; therefore, it is suggested that the Korean government needs to implement new policies and acquire advanced technology for weather forecasting. The government also needs to reinforce and develop a better irrigation system. The concerned authority needs to inform rice growers about future weather and climate changes. The study also specifies that the area under rice has a significant effect on rice production, but the trend line shows that the agricultural area continuously decreases. Therefore, it is recommended that the Korean government needs to provide virgin arable undivided land to deserving rice growers based on ownership/lease for future food security. In short, the study specifies that the concerned authorities and policymakers should spot the aggressive effect of climate change on the main food crops. Therefore, the legislators should recommend some strong policies regarding sustainable food security by introducing new agricultural technologies, subsidies on agricultural inputs, and a new variety of seeds that absorb the adverse shock of climate and ensure a suitable amount of food for the massive population of Korea. The study also suggests that further research is needed to discover the impact of climate change and other factors that are responsible for the decrease in agricultural production in Korea and worldwide.

Scope and limitation of the study

The study provides evidence to the research community of how much climate change and other factors are responsible for the decrease in rice production in South Korea. This study provides a significant pathway to understanding the climatic changes and their various impacts on rice production. This study begins and helps to develop a strong understanding of current and potential impacts that will affect the agriculture of today and in coming decades worldwide. This understanding is crucial because it allows decision-makers to place climate change in the context of other large challenges facing the nation and the world.

The study is limited to finding out the impact climatic factors (CO2, mean temperature, and rainfall), technical factors (area under rice and fertilizer used for rice), and agricultural policy (anti-subsidy policy for agricultural inputs in 1990) have on rice production from 1973 to 2018 in South Korea.

The authors extend heartfelt thanks to the editorial board and the anonymous reviewers for their valuable suggestions. This research was sponsored by the Innovation Platform Open Fund Project of Hunan Education Department, China (19K087).

All relevant data are available from an online repository or repositories (http://kosis.kr.eng/).

Acaravci
A.
Ozturk
I.
2010
On the relationship between energy consumption, CO2 emissions and economic growth in Europe
.
Energy
35
(
12
),
5412
5420
.
https://doi.org/10.1016/j.energy.2010.07.009
.
Alahmadi
F. S.
Rahman
N. A.
2019
Climate change impacts on extreme rainfall frequency prediction
.
Journal of Water and Climate Change
11
(
1
).
https://doi:10.2166/wcc.2019.138
Arshed
N.
Abduqayumov
S.
2016
Economic impact of climate change on wheat and cotton in major districts of Punjab
.
International Journal of Economics and Financial Research
2
(
10
),
183
191
.
Asseng
S.
Ewert
F.
Rosenzweig
C.
Jones
J. W.
Hatfield
J. L.
Ruane
A. C.
Boote
K. J.
Thorburn
P. J.
Rötter
R. P.
Cammarano
D.
Brisson
N.
Basso
B.
Martre
P.
Aggarwal
P. K.
Angulo
C.
Bertuzzi
P.
Biernath
C.
Challinor
A. J.
Doltra
J.
Gayler
S.
Goldberg
R.
Grant
R.
Heng
L.
Hooker
J.
Hunt
L. A.
Ingwersen
J.
Izaurralde
R. C.
Kersebaum
K. C.
Müller
C.
Kumar
S. N.
Nendel
C.
O'Leary
G.
Olesen
J. E.
Osborne
T. M.
Palosuo
T.
Priesack
E.
Ripoche
D.
Semenov
M. A.
Shcherbak
I.
Steduto
P.
Stöckle
C.
Stratonovitch
P.
Streck
T.
Supit
I.
Tao
F.
Travasso
M.
Waha
K.
Wallach
D.
White
J. W.
Williams
J. R.
2013
Uncertainty in simulating wheat yields under climate change
.
Nature Climate Change
3
,
827
832
.
https://doi.org/10.1038/nclimate1916
.
Asumadu-Sarkodie
S.
Owusu
P. A.
2016
The relationship between carbon dioxide and agriculture in Ghana: a comparison of VECM and ARDL model
.
Environmental Science and Pollution Research
23
(
11
),
10968
10982
.
https://doi:10.1007/s11356-016-6252-x
.
Bala
B. K.
Alias
E. F.
Arshad
F. M.
Noh
K. M.
Hadi
A. H. A.
2014
Modelling of food security in Malaysia
.
Simulation Modelling Practice and Theory
47
,
152
164
.
https://doi:10.1016/j.simpat.2014.06.001
.
Ben-Ari
T.
Adrian
J.
Klein
T.
Calanca
P.
Van der Velde
M.
Makowski
D.
2016
Identifying indicators for extreme wheat and maize yield losses
.
Agricultural and Forest Meteorology
220
,
130
140
.
https://doi:10.1016/j.agrformet.2016.01.009
.
Brown
R. L.
Durbin
J.
Evans
J. M.
1975
Techniques for testing the constancy of regression relationships over time
.
Journal of the Royal Statistical Society. Series B (Methodological)
37
(
2
),
149
192
.
Butler
E. E.
Huybers
P.
2012
Adaptation of US maize to temperature variations
.
Nature Climate Change
3
(
1
),
68
72
.
https://doi:10.1038/nclimate1585
.
Chandio
A. A.
Jiang
Y.
Magsi
H.
2018
Climate change impact on rice production in Pakistan: an ARDL-bounds testing approach to cointegration
.
Preprints
.
https://doi:10.20944/preprints201812.0095.v1
.
Chandio
A. A.
Jiang
Y.
Rehman
A.
Rauf
A.
2020
Short and long-run impacts of climate change on agriculture: an empirical evidence from China
.
International Journal of Climate Change Strategies and Management
12
(
2
),
201
221
.
https://doi.org/10.1108/IJCCSM-05-2019-0026
.
Chen
C.
Baethgen
W. E.
Robertson
A.
2013
Contributions of individual variation in temperature, solar radiation and precipitation to crop yield in the North China Plain, 1961–2003
.
Climatic Change
116
,
767
788
.
https://doi.org/10.1007/s10584-012-0509-2
.
Chunhua
L.
Huang
Y.
Sun
W.
Yu
L.
Zhu
L.
2020
Response of rice yield and yield components to elevated [CO2]: a synthesis of updated data from FACE experiments
.
European Journal of Agronomy
112
.
https://doi.org/10.1016/j.eja.2019.125961
.
CIA Factbook
.
2007
Central Intelligence Agency
.
Conradt
T.
Gornott
C.
Wechsung
F.
2016
Extending and improving regionalized winter wheat and silage maize yield regression models for Germany: enhancing the predictive skill by panel definition through cluster analysis
.
Agricultural and Forest Meteorology
216
,
68
81
.
https://doi.org/10.1016/j.agrformet.2015.10.003
.
Duasa
J.
2007
Determinants of Malaysian trade balance: an ARDL bound testing approach
.
Global Economic Review
36
(
1
),
89
102
.
https://doi:10.1080/12265080701217405
.
Frimpong
M. J.
Oteng
E. F.
2006
Bound Testing Approach: An Examination of Foreign Direct Investment, Trade and Growth Relationships
.
MPRA Paper No. 352
, pp.
1
19
.
Gornott
C.
Wechsung
F.
2016
Statistical regression models for assessing climate impacts on crop yields: a validation study for winter wheat and silage maize in Germany
.
Agricultural and Forest Meteorology
217
,
89
100
.
https://doi.org/10.1016/j.agrformet.2015.10.005
.
Guo
J. P.
2015
Advances in impacts of climate change on agricultural production in China
.
Journal of Applied Meteorological Science
1
,
1
11
. .
Huang
W.
Deng
X. Z.
He
S. J.
Lin
Y. Z.
2010
An econometric analysis on the impacts of climatic change on grain production at counties of China
.
Progress in Geography
29
,
677
683
.
https://doi.org/10.11820/dlkxjz.2010.06.006
.
Hussain
A.
2012
Impact of credit disbursement, area under cultivation, fertilizer consumption and water availability on rice production in Pakistan (1988–2010)
.
Sarhad Journal of Agriculture
28
(
1
),
95
101
.
Jalota
S. K.
Vashisht
B. B.
Harsimran
K.
Samanpreet
K.
Prabhjyot
K.
2014
Location specific climate change scenario and its impact on rice and wheat in Central Indian Punjab
.
Agricultural Systems
131
(
C
),
77
86
.
https://doi.org/10.1016/j.agsy.2014.07.009
.
Janjua
P. Z.
Samad
G.
Khan
N.
2014
Climate change and wheat production in Pakistan: an autoregressive distributed lag approach
.
NJAS-Wageningen Journal of Life Sciences
68
,
13
19
.
https://doi.org/10.1016/j.njas.2013.11.002
.
Kakumanu
K. R.
Kotapati
G. R.
Nagothu
U. S.
Kuppanan
P.
Kallam
S. R.
2019
Adaptation to climate change and variability: a case of direct seeded rice in Andhra Pradesh India
.
Journal of Water and Climate Change
10
(
2
),
419
430
.
https://doi:10.2166/wcc.2018.141
.
Kashyap
D.
Agarwal
T.
2020
Temporal trends of climatic variables and water footprint of rice and wheat production in Punjab, India from 1986 to 2017
.
Journal of Water and Climate Change
.
https://doi:10.2166/wcc.2020.093
Korean Statistical Information System (KOSIS)
.
2017
Available from: http://kosis.kr.eng/
Korean Statistical Information System (KOSIS)
.
2019
Available from: http://kosis.kr.eng/
Korres
N. E.
Norsworthy
J. K.
Burgos
N. R.
Oosterhuis
D. M.
2017
Temperature and drought impacts on rice production: an agronomic perspective regarding short- and long-term adaptation measures
.
Water Resources and Rural Development
9
,
12
27
.
https://doi:10.1016/j.wrr.2016.10.001
.
Leadley
P. W.
Drake
B. G.
1992
Open top chambers for exposing plant canopies to elevated CO2 concentration and for measuring net gas exchange
.
Plant Ecology
104–105
,
3
15
.
https://doi.org/10.1007/BF00048141
.
Li
T. X.
Zhao
G. Q.
Li
Y.
2009
Climate change and its impacts on duration of winter wheat overwintering stage in Henan Province
.
Chinese Journal of Agrometeorology
30
,
143
146
.
Li
Y.
Conway
D.
Wu
Y.
Gao
Q.
Rothausen
S.
Xiong
W.
Ju
H.
Lin
E.
2013
Rural livelihoods and climate variability in Ningxia, Northwest China
.
Climatic Change
119
(
3–4
),
891
904
.
https://doi:10.1007/s10584-013-0765-9
.
Li
Z.
Jin
X.
Zhao
C.
Wang
J.
Xu
X.
Yang
G.
Lee
C.
Shen
J.
2015
Estimating wheat yield and quality by coupling the DSSAT-CERES model and proximal remote sensing
.
European Journal of Agronomy
71
,
53
62
.
https://doi.org/10.1016/j.eja.2015.08.006
.
Licker
R.
Kucharik
C. J.
Doré
T.
Lindeman
M. J.
Makowski
D.
2013
Climatic impacts on winter wheat yields in Picardy, France and Rostov, Russia: 1973–2010
.
Agricultural and Forest Meteorology
176
,
25
37
.
https://doi:10.1016/j.agrformet.2013.02.010
.
Lobell
D. B.
Burke
M. B.
2010
On the use of statistical models to predict crop yield responses to climate change
.
Agricultural and Forest Meteorology
150
(
11
),
1443
1452
.
https://doi:10.1016/j.agrformet.2010.07.008
.
Lobell
D. B.
Schlenker
W.
Costa-Roberts
J.
2011
Climate trends and global crop production since 1980
.
Science
333
(
6042
),
616
620
.
https://doi:10.1126/science.1204531
.
Mahmood
N.
Ahmad
B.
Hassan
S.
Bakhsh
K.
2012
Impact of temperature ADN precipitation on rice productivity in rice-wheat cropping system of Punjab province
.
Journal of Animal and Plant Sciences
22
,
993
997
.
Meza
F. J.
Silva
D.
2009
Dynamic adaptation of maize and wheat production to climate change
.
Climatic Change
94
(
1–2
),
143
156
.
https://doi:10.1007/s10584-009-9544-z
.
Minasny
B.
McBratney
A. B.
Hong
S. Y.
Sulaeman
Y.
Kim
M. S.
Zhang
Y. S.
Kim
Y. H.
Han
K. H.
2012
Continuous rice cropping has been sequestering carbon in soils in Java and South Korea for the past 30 years
.
Global Biogeochemical. Cycles
26
,
GB3027
.
https://doi:10.1029/2012GB004406
.
Mishra
A.
Singh
R.
Raghuwanshi
N. S.
Chatterjee
C.
Froebrich
J.
2013
Spatial variability of climate change impacts on yield of rice and wheat in the Indian Ganga Basin
.
Science of the Total Environment
468–469
,
S132
S138
.
https://doi:10.1016/j.scitotenv.2013.05.080
.
Mosammam
H. M.
Mosammam
A. M.
Sarrafi
M.
Nia
J. T.
Esmaeilzadeh
H.
2016
Analyzing the potential impacts of climate change on rainfed wheat production in Hamedan Province, Iran, via generalized additive models
.
Journal of Water and Climate Change
7
(
1
),
212
223
.
https://doi:10.2166/wcc.2015.153
.
Mottaleb
K. A.
Mohanty
S.
Hoang
H. T. K.
Rejesus
R. M.
2013
The effects of natural disasters on farm household income and expenditures: a study on rice farmers in Bangladesh
.
Agricultural Systems
121
,
43
52
.
Narayan
P. K.
Popp
S.
2010
A new unit root test with twostructural breaks in level and slope at unknown time
.
Journal of Applied Statistics
37
(
9
),
1425
1438
.
https://doi:10.1080/02664760903039883
.
Nasrullah
M.
Chang
L.
Saddozai
K. N.
Khalid
A. O.
Bayisenge
R.
Hameed
G.
2019
Cost and net return of tobacco growers – a case study of district Mardan (KP-Pakistan)
.
Sarhad Journal of Agriculture
35
(
2
),
565
571
.
Nasrullah
M.
Chang
L.
Khan
K.
Rizwanullah
M.
Zulfiqar
F.
Ishfaq
M.
2020
Determinants of forest product group trade by gravity model approach: a case study of China
.
Forest Policy and Economics
113
,
102117
.
https://doi:10.1016/j.forpol.2020.102117
.
OECD
1999
Review of Agricultural Policies in Korea
. .
Osborne
T. M.
Wheeler
T. R.
2013
Evidence for a climate signal in trends of global crop yield variability over the past 50 years
.
Environmental Research Letters
8
(
2
),
024001
.
https://doi:10.1088/1748-9326/8/2/024001
.
Özdoğan
M.
2011
Modeling the impacts of climate change on wheat yields in Northwestern Turkey
.
Agriculture, Ecosystems & Environment
141
(
1–2
),
1
12
.
https://doi:10.1016/j.agee.2011.02.001
.
Pesaran
M.
Shin
Y.
1999
An Autoregressive Distributed Lag Modelling Approach to Cointegration Analysis, Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium
.
Chapter 11
,
Cambridge University Press
,
Cambridge
.
https://doi:10.12691/jfe-4-6-4
Pesaran
M. H.
Shin
Y.
Smith
R. J.
2001
Bounds testing approaches to the analysis of level relationships
.
Journal of Applied Econometrics
16
(
3
),
289
326
.
https://doi:10.1002/jae.616
.
Ray
D. K.
Ramankutty
N.
Mueller
N. D.
West
P. C.
Foley
J. A.
2012
Recent patterns of crop yield growth and stagnation
.
Nature Communications
3
(
1
).
https://doi:10.1038/ncomms2296
Ray
D. K.
Mueller
N. D.
West
P. C.
Foley
J. A.
2013
Yield trends are insufficient to double global crop production by 2050
.
PLoS ONE
8
(
6
),
e66428
.
https://doi:10.1371/journal.pone.0066428
.
Rehman
A.
Chandio
A. A.
Hussain
I.
Jingdong
L.
2017
Fertilizer consumption, water availability and credit distribution: major factors affecting agricultural productivity in Pakistan
.
Journal of the Saudi Society of Agricultural Sciences
.
https://doi:10.1016/j.jssas.2017.08.002
Reilly
J.
Tubiello
F.
McCarl
B.
Abler
D.
Darwin
D.
Fuglie
K.
Hollinger
S.
Izaurralde
C.
Jagtap
S.
Jones
J.
Mearns
L.
Ojima
D.
Paul
E.
Paustian
K.
Riha
S.
Rosenberg
N.
Rosenzweig
N.
2003
U.S. Agriculture and climate change: new results
.
Climatic Change
57
,
43
67
.
https://doi.org/10.1023/A:1022103315424
.
Rizwanullah
M.
Liang
L. Z.
Yu
X. Y.
Zhou
J. N.
Nasrullah
M.
Ali
M. U.
2020
Exploring the cointegration relation among top eight Asian Stock Markets
.
Open Journal of Business and Management
8
,
1076
1088
.
https://doi.org/10.4236/ojbm.2020.83068
.
Rotich
S. C.
Mulungu
D. M. M.
2017
Adaptation to climate change impacts on crop water requirements in Kikafu catchment Tanzania
.
Journal of Water and Climate Change
8
(
2
),
274
292
.
https://doi:10.2166/wcc.2017.058
.
Saadi
S.
Todorovic
M.
Tanasijevic
L.
Pereira
L. S.
Pizzigalli
C.
Lionello
P.
2015
Climate change and Mediterranean agriculture: impacts on winter wheat and tomato crop evapotranspiration, irrigation requirements and yield
.
Agricultural Water Management
147
,
103
115
.
https://doi.org/10.1016/j.agwat.2014.05.008
.
Saddozai
K. N.
Nasrullah
M.
Khan
N. P.
2015
Stochastic frontier production analysis of tobacco growers in district Mardan, Pakistan
.
Pakistan Journal of Agricultural Research
28
(
4
),
346
353
.
Shakoor
U.
Saboor
A.
Baig
I.
Afzal
A.
Rahman
A.
2015
Climate variability impacts on rice crop production in Pakistan
.
Pakistan Journal of Agricultural Research
28
(
1
),
19
27
.
Siddiqui
R.
Samad
G.
Nasir
M.
Jalil
H. H.
2012
The impact of climate change on major agricultural crops: evidence from Punjab, Pakistan
.
The Pakistan Development Review
4
(
51
),
261
274
.
Slingo
J. M.
Challinor
A. J.
Hoskins
B. J.
Wheeler
T. R.
2005
Introduction: food crops in a changing climate
.
Philosophical Transactions of the Royal Society B: Biological Sciences
360
(
1463
),
1983
1989
.
https://doi:10.1098/rstb.2005.1755
.
Tao
F.
Zhang
Z.
2013
Climate change, wheat productivity and water use in the North China Plain: a new super-ensemble-based probabilistic projection
.
Agricultural and Forest Meteorology
170
,
146
165
.
https://doi:10.1016/j.agrformet.2011.10.003
.
Tao
F.
Yokozawa
M.
Xu
Y.
Hayashi
Y.
Zhang
Z.
2006
Climate changes and trends in phenology and yields of field crops in China, 1981–2000
.
Agricultural and Forest Meteorology
138
(
1–4
),
82
92
.
https://doi:10.1016/j.agrformet.2006.03.014
.
Tao
F. L.
Yokozawa
M.
Liu
J. Y.
Zhang
Z.
2008
Climate–crop yield relationships at provincial scales in China and the impacts of recent climate trends
.
Climate Research
38
,
83
94
.
https://doi.org/10.3354/cr00771
.
Tao
F.
Zhang
Z.
Xiao
D.
Zhang
S.
Rötter
R. P.
Shi
W.
Liu
Y.
Wang
M.
Liu
F.
Zhang
H.
2014
Responses of wheat growth and yield to climate change in different climate zones of China, 1981–2009
.
Agricultural and Forest Meteorology
189–190
,
91
104
.
https://doi:10.1016/j.agrformet.2014.01.013
.
Tian
W.
Wan
G. H.
2000
Technical efficiency and its determinants in China's grain production
.
Journal of Productivity Analysis
13
,
159
174
.
https://doi.org/10.1023/A:1007805015716
.
Urban
D.
Roberts
M. J.
Schlenker
W.
Lobell
D. B.
2012
Projected temperature changes indicate significant increase in interannual variability of U.S. maize yields
.
Climatic Change
112
(
2
),
525
533
.
https://doi:10.1007/s10584-012-0428-2
.
Wang
J.
Wang
C.
Chen
N.
Xiong
Z.
Wolfe
D.
Zou
J.
2015
Response of rice production to elevated [CO2] and its interaction with rising temperature or nitrogen supply: a meta-analysis
.
Climatic Change
130
(
4
),
529
543
.
https://doi:10.1007/s10584-015-1374-6
.
Wheeler
T.
von Braun
J.
2013
Climate change impacts on global food security
.
Science
341
(
6145
),
508
513
.
https://doi:10.1126/science.1239402
.
Wilcox
J.
Makowski
D.
2014
A meta-analysis of the predicted effects of climate change on wheat yields using simulation studies
.
Field Crops Research
156
,
180
190
.
https://doi.org/10.1016/j.fcr.2013.11.008
.
Yang
W.
2007
A Study on Technological Change Pattern of Grain in China
.
Doctoral thesis
,
Chinese Academy of Agricultural Sciences
,
Beijing, China
.
Zhang
T.
Huang
Y.
2012
Estimating the impacts of warming trends on wheat and maize in China from 1980 to 2008 based on county level data
.
International Journal of Climatology
33
(
3
),
699
708
.
https://doi:10.1002/joc.3463
.
Zhou
W. K.
2012
Impact of Climate Change on Chinese Food Production and its Countermeasures
.
Doctoral thesis
,
Nanjing Agricultural University
,
Nanjing, China
.
Zivot
E.
Andrews
D. W. K.
1992
Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis
.
Journal of Business Economic Statistics
10
(
3
),
251
270
.
https://doi.org/10.1080
.
Zulfiqar
F.
Shang
J.
Nasrullah
M.
Rizwanullah
M.
2020
Allocative efficiency analysis of wheat and cotton in district Khanewal, Punjab, Pakistan
.
GeoJournal
.
https://doi.org/10.1007/s10708-020-10228-x
.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).