# Examining Yam Production in Response to Climate Change in Nigeria: A Co-Integration Model Approach

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. The Study Area

#### 2.2. Data Collection

#### 2.3. Data Analysis and Model Specification

#### 2.3.1. Production Function Model Specification:

_{1}, X

_{2})

- Y = output of yam (kg)
- f = functional form
- X
_{1}= rainfall (mm) - X
_{2}= temperature (°C)

^{a}+ T

^{β}

_{0}+ β

_{1}Log (R) + β

_{2}Log (T) + e

- Y = output of yam (tonnes)
- R = rainfall (mm)
- T = temperature (°C)
- β = coefficients to be estimated
- e = stochastic variable

_{1}X

_{1}+ b

_{2}X

_{2}+ b

_{3}X

_{3}+ U

- Y = the dependent variable (output of yam)
- a = intercept of Y (constant)
- b = slope or coefficient of X
- X
_{1}= rainfall (in mm) - X
_{2}= temperature (in °C) - U = error term

#### 2.3.2. Unit Root Test

#### 2.3.3. The Co-Integration Test

_{1}and Y

_{1}co-integrate, a linear combination of them must be stationary, i.e., Y

_{1}= U

_{t}, where U

_{t}is stationary.

#### 2.3.4. Error Correction Model

#### 2.3.5. Multivariate Model Specification

_{0}+ β

_{1}InX

_{1}+ β

_{2}InX

_{2}+ u

- β = regression coefficient
- Q = output of yam
- X
_{1}= mean annual temperature (degree centigrade) - X
_{2}= mean annual rainfall (millimeter) - U = random error term

_{t}= f(X

_{1}, X

_{2})

_{t}= a

_{0}+ a

_{1}X

_{1}+ a

_{2}X

_{2}

_{t}) = a

_{0}+ a

_{1}Log X

_{1}+ a

_{2}Log X

_{2}+ U

_{t}

- Y
_{t}= output of yam - X
_{1}= annual temperature (°C) - X
_{2}= annual rainfall (mm) - U
_{t}= stochastic error term

## 3. Results and Discussion

#### 3.1. Trend Analysis of Yam Output and Climate Variables

#### 3.1.1. The Trend of Yam Production

#### 3.1.2. The Trend of Annual Rainfall

#### 3.1.3. The Trend of Annual Temperature

#### 3.2. Theoretical Overview and Evaluation of the Climate Change Variables on Yam Production

#### 3.2.1. Stationarity and Non-Stationarity of Time Series

#### 3.2.2. Unit Root (Stationarity) Test

_{t}= δY

_{t−1}+ ϵ

_{t}

_{t}denotes a serially uncorrected white noise error term with an average of zero and a constant variance.

#### 3.2.3. Co-Integration Test (Long Term Co-Integrating Relationship)

_{t}~I (1) and climate variable that is integrated in order zero is denoted by ΔC

_{t}~I (0). In a case where time series are discovered to be non-stationary at levels, but stationary in first difference, co-integration procedures will probably be implemented (Natanelov et al. 2013). After completion of the unit root test on the time series, and with the assumption that the time series are integrated in the same order, a multivariate Johansen test was conducted on all the variables to investigate co-integration.

#### 3.2.4. Johansen Co-Integration Test Result

#### 3.2.5. The Outcome from the Error Correction Model

#### 3.2.6. Post Regression Test

## 4. Summary and Conclusions

## 5. Recommendations

- Given the observed adverse effect of temperature on yam production, policymakers need to create an enabling environment for independent researchers as well as institutes to develop pest- and disease-tolerant yam varieties.
- The agricultural policy to support farmers in Nigeria to concentrate more on the bottom-top participatory approach so that the existing and the developing adaptation practices and technologies could be focused at the farm level since the impact of climate change is crop and location-specific.
- The government should take appropriate steps to provide an effective weather forecast system that can facilitate the extension of weather information to local farmers to help them know when to expect temperature increase and thus take necessary adaptation action.

## 6. Limitations of the Study

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Map of Cross River State showing the 18 Local Government Areas. Source: Geospatial Analysis Mapping and Environmental Research Solutions (2018).

**Figure 2.**Trend analysis plot for yam output in Cross River State. Source: Author’s computation from data obtained from NPAFS and FMW&WR.

**Figure 3.**Trend analysis plot for annual rainfall (MM) in Cross River State. Source: Author’s computation from data obtained from CBN.

**Figure 4.**Trend analysis plot for annual temperature (°C) in Cross River State. Source: Author’s computation from data obtained from CBN.

Variables | @ Level | 1st Difference | Decision | ||||
---|---|---|---|---|---|---|---|

ADF Statistics | p-Value | Order of Integration | ADF Statistics | p-Value | Order of Integration | ||

Yam output | −2.262092 | 0.4335 | I(1) | −2.576955 | 0.2927 | I(1) | Non-stationary |

Rainfall | −3.526359 | 0.0623 | I(1) | −4.725198 | 0.0069 *** | I(0) | Stationary |

Temperature | −3.000557 | 0.1550 | I(1) | −4.583192 | 0.0091 *** | I(0) | Stationary |

Hypothesized No. of CE(s) | Eigenvalue | Trace Statistic | 0.05 Critical Value | Prob. |
---|---|---|---|---|

None * | 0.749 | 56.98 | 47.856 | 0.005 |

At most 1 * | 0.562 | 30.72 | 29.797 | 0.039 |

At most 2 | 0.444 | 15.04 | 15.495 | 0.068 |

At most 3 * | 0.184 | 3.881 | 3.841 | 0.048 |

Variable | Coefficient | Std. Error | t-Statistics | Prob. |
---|---|---|---|---|

C | 14.428 | 3.553 | 4.060 | 0.000 |

LOG(AVRAINFALL) | 0.150 | 0.149 | 1.010 | 0.326 |

LOG(AVTEMP) | −3.420 | 0.991 | −3.452 | 0.003 |

R-Squared | 0.981 | Mean dependent variable | 7.730 | |

Adjusted R-Squared | 0.979 | S.D. dependent variable | 0.550 | |

SE of Regression | 0.080 | Akaike info criterion | −2.047 | |

Sum Squared Residual | 0.116 | Schwarz criterion | −1.848 | |

Log-Likelihood | 26.512 | Hannan–Quinn criterion | −1.999 | |

F-Statistic | 323.743 | Durbin–Watson statistic | 1.188 | |

Prob(F-Statistic) | 0.000 |

Variable | Coefficient | Std. Error | t-Statistic | Prob. |
---|---|---|---|---|

ECM(−1) | −0.779 | 0.231 | −3.372 | 0.003 |

D(ECM(−1)) | 0.353 | 0.204 | 1.729 | 0.100 |

R-Squared | 0.386 | Mean dependent variable | 0.003 | |

Adjusted R-Squared | 0.352 | S.D. dependent variable | 0.079 | |

SE of Regression | 0.063 | Akaike info criterion | −2.572 | |

Sum Squared Residual | 0.073 | Schwarz criterion | −2.472 | |

Log-Likelihood | 27.72 | Hannan–Quinn criterion | −2.552 |

Variable | Coefficient | Std. Error | t-Statistic | Prob. |
---|---|---|---|---|

C | 0.012 | 0.023 | 0.552 | 0.588 |

DLOG(AVRAINFALL) | 0.119 | 0.104 | 1.140 | 0.271 |

DLOG(AVTEMP) | −2.875 | 1.159 | −2.479 | 0.025 ** |

ECM(−1) | −0.477 | 0.269 | −1.771466 | 0.0955 * |

R-Squared | 0.405 | Mean dependent variable | 0.064 | |

Adjusted R-Squared | 0.256 | S.D. dependent variable | 0.085 | |

SE of Regression | 0.073 | Akaike info criterion | −2.181 | |

Sum Squared Residual | 0.086 | Schwarz criterion | −1.933 | |

Log-Likelihood | 27.909 | Hannan–Quinn criterion | −2.128 | |

F-Statistic | 2.718 | Durbin–Watson statistics | 1.287 | |

Prob(F-Statistic) | 0.067 |

Variable | Coefficient | Std. Error | t-Statistic | Prob. |
---|---|---|---|---|

C | 0.018701 | 0.019414 | 0.963238 | 0.3518 |

DLOG(AVRAINFALL) | −0.034880 | 0.081811 | −0.426342 | 0.6763 |

DLOG(AVTEMP) | 0.118822 | 0.900055 | 0.132016 | 0.8968 |

ECM(−1) | −1.274533 | 0.451685 | −2.821732 | 0.0136 |

RESID(−1) | 1.673803 | 0.482709 | 3.467521 | 0.0038 |

RESID(−2) | 0.387120 | 0.312556 | 1.238562 | 0.2359 |

R-squared | 0.476140 | Mean dependent variable | 7.23 × 10^{−18} | |

Adjusted R-squared | 0.251629 | SD dependent varvariable | 0.065641 | |

SE of Regression | 0.056785 | Akaike info criterion | −2.637900 | |

Sum Squared Residual | 0.045143 | Schwarz criterion | −2.289726 | |

Log-Likelihood | 34.69795 | Hannan–Quinn criterion | −2.562337 | |

F-Statistic | 2.120784 | Durbin–Watson statatistic | 1.762265 | |

Prob(F-Statistic) | 0.115641 |

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**MDPI and ACS Style**

Angba, C.W.; Baines, R.N.; Butler, A.J.
Examining Yam Production in Response to Climate Change in Nigeria: A Co-Integration Model Approach. *Soc. Sci.* **2020**, *9*, 42.
https://doi.org/10.3390/socsci9040042

**AMA Style**

Angba CW, Baines RN, Butler AJ.
Examining Yam Production in Response to Climate Change in Nigeria: A Co-Integration Model Approach. *Social Sciences*. 2020; 9(4):42.
https://doi.org/10.3390/socsci9040042

**Chicago/Turabian Style**

Angba, Cynthia W., Richard N. Baines, and Allan J. Butler.
2020. "Examining Yam Production in Response to Climate Change in Nigeria: A Co-Integration Model Approach" *Social Sciences* 9, no. 4: 42.
https://doi.org/10.3390/socsci9040042