# Saudi Fertilizers and Their Impact on Global Food Security: Present and Future

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## 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:$$\Delta {X}_{t}={C}_{t1}+{B}_{1}{X}_{t-1}+{e}_{t1}$$$$\Delta {X}_{t}={C}_{t2}+{b}_{t}+{B}_{2}{X}_{t-1}+{e}_{t2}$$
_{1}and B_{2}are ADF coefficients to be valued, b is the trend, C is the constant, e is error term, and t is the time selected. In testing the null hypothesis (H_{0}), X has a unit root against alternative hypothesis (H1), X has a stationary. The series variables are stationary if the t-statistic of the ADF coefficient is greater than t-critical values.

- 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]:$$\Delta {X}_{t}={C}_{1}+\sum _{t-1}^{p}{a}_{1}\Delta {Y}_{t-1}+{b}_{1}{X}_{t-1}+{b}_{2}{Y}_{t-1}+{E}_{1}$$$$\Delta {Y}_{t}={C}_{2}+{\sum}_{t-1}^{p}{a}_{2}\Delta {X}_{t-1}+{b}_{3}{Y}_{t-1}+{b}_{4}{X}_{t-1}+{E}_{2}$$
_{1}, a_{2}—coefficients of the difference of lag independent variables; b_{1}, b_{3}—coefficients of lag dependent variable; b_{2}, b_{4}—coefficients of lag independent variable, respectively; E_{1}, E_{2}—error terms.

_{1}= b

_{2}= 0 against alternative hypothesis b

_{1}≠ b

_{2}≠ 0 in Equation (3). A similar test was applied for the Y variable as dependent variable (null hypnosis: b

_{3}= b

_{4}= 0 against alternative hypothesis b

_{3}≠ b

_{4}≠ 0 in Equation (4)).

- 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]:$$\Delta \left(\mathrm{SU}\right)=\Delta {b}_{5}{\mathrm{SU}}_{-1}+\Delta {b}_{6}{\mathrm{WU}}_{-1}+{b}_{7}{V}_{1(t-1)}+{U}_{1}$$$$\Delta \left(\mathrm{WU}\right)=\Delta {b}_{8}{\mathrm{WU}}_{-1}+\Delta {b}_{9}{\mathrm{SU}}_{-1}+{b}_{10}{V}_{2(t-1)}+{U}_{2}$$
_{5}and b_{8}: coefficients of difference of lag dependent variable; b_{6}and b_{9}: coefficients of difference of lag independent variable; b_{7}and b_{10}: speed of adjustment (this necessarily must be negative and significant to accurately show model instability); SU: KSA urea export quantities; WU: world urea export quantities; V_{1}and, V_{2}: error correction terms; U_{1}and U_{2}: error terms.

#### 2.2.2. Cointegration Tests: Testing Long-Run Association (Engle–Granger Test)

- The regression equations are as follows:$${X}_{t}={a}_{1}+{b}_{11}{Y}_{t}+{z}_{t1}$$$${Y}_{t}={a}_{2}+{b}_{12}{X}_{t}+{z}_{t2}$$
_{11}and b_{12}are the slopes coefficient estimate from Equations (7) and (8), respectively. - The ADF test is applied on the residuals (z
_{t1}and z_{t2}) 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 b_{11}and b_{12}exit (b_{11}and b_{12}≠ 0) and the series are cointegrated. Accordingly, the series is integrated of order 1,1. - Error correction model (ECM)

_{13}and b

_{16}: coefficients of difference of lag dependent variable; b

_{14}and b

_{17}: coefficients of difference of lag independent variable; b

_{15}and b

_{18}: speed of adjustment (necessity be negative and significant to accurate model instability); SD: KSA urea export quantities; WD: world urea export quantities; V

_{3}and V

_{4}: error correction terms; U

_{3}and U

_{4}: error terms.

#### 2.2.3. Regression Test

_{19}and b

_{20}= coefficients to valued; C = constant and e = the error term; L = logarithm.

#### 2.2.4. Forecasting Analysis

_{t}represent growth rate between two sequential years. F represents KSA quantities of fertilizer export (urea and DAP) and t = year. Then, the growth rate of urea and DAP was enumerated for the period from 2002 to 2018 and for the period 2019 to 2026 as following [62]:

#### 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

_{t1}) and (z

_{t2}) in Equations (7) and (8), respectively (Table 10). The table shows that ADF statistics are 4.67 and –4.83 and statistically significant at 1% level of significance. The results direct us to adopt the alternative hypothesis of integration, signifying that the KSA DAP prices and world DAP prices have a long-run association.

#### 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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**Figure 1.**Top countries exported urea and DAP fertilizer (00 tonnes) in 2020. Source: Constructed by author.

**Figure 2.**Stability diagnostic (SU as dependent variable). Source: Author calculations based on collected data.

**Figure 3.**Stability diagnostic (WU as dependent variable). Source: Author calculations based on collected data.

**Figure 6.**Forecasting graph (KSA urea export quantities). Source: Author calculations based on collected data. Figure accuracy indicators: mean square error= 1.5, alpha= 0.9, beta= 0.0, gamma= 0.0.

**Figure 7.**Forecasting graph (KSADAP export quantities). Source: Author calculations based on collected data. Figure accuracy indicators: mean square error = 2.5, alpha = 1.0, beta = 0.25, gamma = 0.0.

**Figure 8.**Top countries importing fertilizer from KSA (000 USD) in 2018. Source: Constructed by author.

**Figure 9.**Top counties producing wheat in the world (2018 million tonnes). Source: Author calculations based on collected data. Red color = country importing fertilizer from KSA.

**Figure 10.**Top countries producing sorghum in the world (2018 million tonnes). Source: Author calculations based on collected data. Red color = country importing fertilizer from KSA.

**Figure 11.**Top countries producing maize in the world (2018 million tonnes). Source: Author calculations based on collected data. Red color = country importing fertilizer from KSA.

**Figure 12.**Top countries producing rice in the world (2018 million tonnes). Source: Author calculations based on collected data. Red color = country importing fertilizer from KSA.

**Figure 13.**Top countries producing millet in the world (2018 million tonnes). Source: Author calculations based on collected data. Red color = country importing fertilizer from KSA.

**Figure 14.**Share of countries (importers of KSA fertilizers) to the total global production of main grains (percentage). Source: Author calculations based on collected data. Red color= the share of countries importing fertilizer from KSA.

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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## Share and Cite

**MDPI and ACS Style**

Emam, A.
Saudi Fertilizers and Their Impact on Global Food Security: Present and Future. *Sustainability* **2023**, *15*, 7614.
https://doi.org/10.3390/su15097614

**AMA Style**

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 Style**

Emam, 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