Saudi Fertilizers and Their Impact on Global Food Security: Present and Future
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Description
2.2. Analysis Methods
2.2.1. Cointegration Tests: Testing Long-Run Association (ARDL Bounds Test)
- Unit root test: [61,62] state the following equation to test the order of integration of the series under study, using the ADF test:
- 2.
- The autoregressive distributed lag (ARDL bounds test): To test long-run association between the series, the ARDL model test was applied. The ARDL test is efficient for small observations of time series and used irrespective of the series order I(0) and I(1) but not I(2). The following equations were used [62,63]:
- 3.
- Error correction model (ECM): The ECM test is applied to measure the speed parameter of the short-run relationship between two series [66]. It is run when the cointegration test appears to show a long-run association between the series. The ECM equations are the followings [62]:
2.2.2. Cointegration Tests: Testing Long-Run Association (Engle–Granger Test)
- The regression equations are as follows:
- The ADF test is applied on the residuals (zt1 and zt2) examining whether the series are connected. If the ADF statistics are negative and greater than the critical t-value (of order 1), then it is predictable that the coefficient b11 and b12 exit (b11 and b12≠ 0) and the series are cointegrated. Accordingly, the series is integrated of order 1,1.
- Error correction model (ECM)
2.2.3. Regression Test
2.2.4. Forecasting Analysis
2.2.5. The Impact of KSA Fertilizer on Global Food Security: Qualitative Analysis
3. Results
3.1. Part One: Cointegration Test Analysis Results (KSA Urea and World Urea)
3.1.1. The Results of Unit Root Tests
3.1.2. Results of ARDL Tests
3.1.3. Long-Run Confirmation
3.1.4. Results of ECM
3.2. Cointegration Analysis Results (KSA DAP and World DAP)
3.2.1. The Results of Unit Root Tests
3.2.2. Results of Engle–Granger Test
3.2.3. Results of ECM
3.3. Regression Analysis Results
3.3.1. World Urea Exported Quantities (LWUQ—Independent Variable)
3.3.2. World DAP Export Quantities (LWDQ—Independent Variable)
3.4. Forecasting Analysis Result
3.5. The Impact of KSA Fertilizer on Global Food Security
4. Discussion, Conclusions, and Recommendations
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Sources | Unit | Variable (Export) |
---|---|---|
-KSA urea prices (SU) | (USD/tonne) | https://www.worldbank.org/en/research/commodity-markets (accessed on 16 August 2022). |
-World urea prices (WU) | ||
-KSA DAP * prices (SD) | ||
-World DAP prices (WD) | ||
-KSA urea quantities (SUQ) | (tonne) | https://www.fao.org/faostat/ar/#data/RFB (accessed on 17 August 2022). |
-world urea quantities (WUQ) | ||
-KSA DAP quantities (SDQ) | ||
-world DAP quantities (WDQ) |
Time Series | Intercept | Intercept and Trend | Stationarity | Intercept | Intercept and Trend | Stationarity |
---|---|---|---|---|---|---|
At Level | At First Difference | |||||
KSA urea prices | −2.901 *** | −2.676 | Non stationary | −5.172 * | −5.644 * | Stationary |
World urea prices | −2.698 | −2.431 | Non stationary | −4.940 * | −4.748 * | Stationary |
KSA DAP prices | −2.906 | −2.761 | Non stationary | −7.098 * | −7.391 * | Stationary |
World DAP prices | −2.739 | −2.442 | Non stationary | −5.637 * | −5.092 * | Stationary |
Model 1 | Model 2 | ||
---|---|---|---|
SU (Dependent Variable) Selected ARDL Model (4, 4) | WU (Dependent Variable) Selected ARDL Model (4, 4) | ||
Independent V. | Coefficient | Independent V. | Coefficient |
SU (−1) | −0.037 (0.86) | WU (−1) | −0.091 (0.72) |
SU (−2) | −0.015 (0.94) | WU (−2) | 0.096 (0.61) |
SU (−3) | −0.145 (0.611) | WU (−3) | −0.280 (0.30) |
SU (−4) | −0.408 (0.207) | WU (−4) | −0.355 (0.27) |
WU | 0.994 (0.000) | SU | 0.981 (0.00) |
WU (−1) | 0.139 (0.586) | SU (−1) | 0.011 (0.96) |
WU (−2) | −0.072 (0.708) | SU (−2) | −0.019 (0.92) |
WU (−3) | 0.216 (0.442) | SU (−3) | 0.215 (0.44) |
WU (−4) | 0.428 (0.171) | SU (−4) | 0.329 (0.32) |
C | 43.186 (0.222) | C | −35.313 (0.33) |
-R-squared 0.99 -Adj. R-squared 0.98 -F-statistics 87.03 prob. 0.000 | -R-squared 0.99 -Adj. R-squared 0.98 -F-statistics 24.32 prob. 0.000 | ||
-Jarque–Bera test: 0.19 prob. 0.91 -autocorrelation LM Test: Breusch–Godfrey 0.024 prob. 0.88 -Breusch–Pagan–Godfrey Heteroscedasticity test: 0.29 prob. 0.95 | -Jarque–Bera test: 0.40 prob. 0.82 -autocorrelation LM Test: Breusch–Godfrey 0.055 prob. 0.8 3-Breusch–Pagan–Godfrey Heteroscedasticity test: 0.23 prob. 0.97 |
Dependent-Independent | F-Statistic of Bound Test | ||
---|---|---|---|
SU-WU | 14.970 | ||
WU-SU | 12.067 | ||
-Significance | 1% | 5% | 10% |
-Lower Bound | 4.94 | 3.62 | 3.02 |
-Upper Bound | 5.58 | 4.16 | 3.51 |
SU (Dependent Variable) | WU (Dependent Variable) | ||
---|---|---|---|
Independent V. | Coefficient | Independent V. | Coefficient |
WU | 0.895 (0.000) | SU | 1.050 (0.00) |
C | 78.256 (0.22) | C | −65.286 (0.04) |
-R-squared 0.92 -Adj. R-squared 0.91 | -R-squared 0.92 -Adj. R-squared 0.91 | ||
-Jarque–Bera test: 0.19 prob. 0.91 -autocorrelation LM Test: Breusch–Godfrey 0.024 prob. 0.88 -Breusch–Pagan–Godfrey Heteroscedasticity test: 0.29 prob. 0.95 | -Jarque–Bera test: 0.40 prob. 0.82 -autocorrelation LM Test: Breusch–Godfrey 0.055 prob. 0.83 -Breusch–Pagan–Godfrey Heteroscedasticity test: 0.23 prob. 0.97 |
SU (Dependent Variable) | WU (Dependent Variable) | ||
---|---|---|---|
Independent V. | Coefficient | Independent V. | Coefficient |
WU | 0.878 (0.000) | SU | 1.045 (0.00) |
C | 82.906 (0.07) | C | −62.695 (0.23) |
-R-squared 0.93 -Adj. R-squared 0.90 | -R-squared 0.93 -Adj. R-squared 0.90 | ||
-Jarque–Bera test: 0.19 prob. 0.91 -autocorrelation LM Test: Breusch–Godfrey 0.024 prob. 0.88 -Breusch–Pagan–Godfrey Heteroscedasticity test: 0.29 prob. 0.95 | -Jarque–Bera test: 0.40 prob. 0.82 -autocorrelation LM Test: Breusch–Godfrey 0.055 prob. 0.83 -Breusch–Pagan–Godfrey Heteroscedasticity test: 0.23 prob. 0.97 |
Dependent-Independent | ARDL | FMOLS | DOLS |
---|---|---|---|
SU-WU | 0.994 * | 0.895 * | 0.878 * |
WU-SU | 0.981 * | 1.050 * | 1.045 * |
Lag | LogL | LR | FPE | AIC | SC | HQ |
---|---|---|---|---|---|---|
0 | −189.30 | NA * | 5838845. | 21.26 | 21.36 * | 21.27 |
1 | −184.31 | 8.32 | 5262283 * | 21.15 * | 21.44 | 21.19 * |
Error Correction | D (SU) | D (WU) | |||
---|---|---|---|---|---|
CointEq1 | 1.805 [2.218] | 1.329 [1.608] | |||
D(SU (−1)) | 0.820 [0.809] | 0.640 [0.621] | |||
D(WU (−1)) | −0.826 [−0.819] | −0.734 [−0.716] | |||
C | 3.125 [0.137] | 6.346 [0.273] | |||
ECM residual autocorrelation LM tests | Lags | LM Stat | prob. | ||
1 | 7.92 | 0.10 | |||
VEC residual heteroscedasticity tests | Chi-sq. 23.70 | prob. 0.79 | |||
Jarque–Bera statistic | 7.44 | prob. = 0.11 |
World DAP Prices | KSA DAP Prices | |
---|---|---|
KSA DAP Prices (LSD) | 4.67 * | |
World DAP Prices (LWD) | −4.83 * |
Lag | LogL | LR | FPE | AIC | SC | HQ |
---|---|---|---|---|---|---|
0 | −22.12 | NA | 0.05 | 2.68 | 2.78 | 2.69 |
1 | −15.25 | 11.46 * | 0.037 * | 2.36 * | 2.66 * | 2.40 * |
Error Correction | D (LSD) | D (LWD) | |||
---|---|---|---|---|---|
CointEq1 | 0.040 [0.23] | 0.289 [2.71] | |||
D(LSD(−1)) | −0.495 [−2.52] | −0.123 [−1.02] | |||
D(LWD(−1)) | 1.058 [2.54] | 0.050 [0.20] | |||
C | 0.001 [0.01] | 0.037 [0.46] | |||
ECM residual autocorrelation LM tests | Lags | LM stat | Prob. | ||
1 | 6.83 | 0.145 | |||
VEC residual normality tests | Chi-sq. = 1.57 | prob. = 0.46 | |||
Jarque–Bera statistic | 5.43 | prob. = 0.25 |
Variable | Coefficient | t-Statistic | Prob. |
---|---|---|---|
LSUQ | 0.82 | 7.39 | 0.000 |
C | 5.28 | 3.20 | 0.005 |
R-squared = 0.76 Adjusted R-squared = 0.75 F-statistic = 54.54 prob. (F-statistic) = 0.000 LM statistics (Breusch–Godfrey autocorrelation of residual) F = 10.67116 with prob. = 0.0013 Heteroscedasticity test: Breusch–Pagan–Godfrey: F = 0.021344 with prob. = 0.8856 |
Variable | Coefficient | t-Statistic | Prob. |
---|---|---|---|
SDQ | 1.95 | 7.25 | 0.000 |
C | 12359677 | 31.55 | 0.000 |
R-squared = 0.76 Adjusted R-squared = 0.74 F-statistic = 52.58803 prob. (F-statistic) = 0.0000 Collinearity statistics: VIF = 1.000 LM statistics (Breusch–Godfrey autocorrelation of residual) F = 3.394586 with prob. = 0.084 Heteroscedasticity test: Breusch–Pagan–Godfrey: F = 2.444359 with prob. = 0.1364 |
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Emam, A. Saudi Fertilizers and Their Impact on Global Food Security: Present and Future. Sustainability 2023, 15, 7614. https://doi.org/10.3390/su15097614
Emam A. Saudi Fertilizers and Their Impact on Global Food Security: Present and Future. Sustainability. 2023; 15(9):7614. https://doi.org/10.3390/su15097614
Chicago/Turabian StyleEmam, Abda. 2023. "Saudi Fertilizers and Their Impact on Global Food Security: Present and Future" Sustainability 15, no. 9: 7614. https://doi.org/10.3390/su15097614