# Effect of Climatic and Non-Climatic Factors on Cassava Yields in Togo: Agricultural Policy Implications

## Abstract

**:**

## 1. Introduction

- Analyze the yield response of cassava to changes in climatic and non-climatic factors in Togo; and
- Inform policy and investment decisions on the measures needed to boost productivity of cassava in the country.

## 2. Evolution of Cassava Production, Area and Yields in Togo

## 3. Yield Response of Cassava: A Review

## 4. Methods

#### 4.1. Study Area

#### 4.2. Changing Climatic Conditions for the ‘Cassava Belt’ and Yield of Cassava in Togo

#### 4.3. Analytical Framework

#### 4.3.1. Model

- Estimation of a specified model based either on automatically selected lags of the dependent variables and regressors or based on fixed lags by the researcher. Appropriate number of lags and best model among evaluated models under the automatic selection are based on one of four model selection criteria, namely Akaike information criterion (AIC), Schwarz criterion (SIC), the Hannan–Quinn criterion (HQ), and selection based on adjusted R-squared.
- After estimating the base model, a Bounds test is carried out to test the null hypothesis of non-existence of a long-run relationship among the variables in the base model. Rejecting the null (based on F-test and critical value Bounds for I(0) (lower bound) and I(1) (upper bound)) indicates the existence of long-run relationship among the variables, irrespective of the order of integration of the variables. The null hypothesis is rejected only if the F-statistics lies above the upper bound at the 5% significance level (although 10% could be used in some cases). Failing to reject the null implies the non-existence of co-integration. By the Granger representation theorem [41], a confirmation of cointegration among variables implies the existence of an error correction model (ECM) that describes short-run dynamics and/or adjustment of the cointegrated variables towards their long-run equilibrium values. The existence of cointegration is validated by a significant negative coefficient of an error correction term in the ECM. In the absence of cointegration, output for the base estimation is synonymous with output of a simple Ordinary Least Squares estimation of the specified model with the inclusion of stated lags.
- Having confirmed the existence of long-run relationships after the Bounds test, short-run (cointegrating form) and long-run coefficients are estimated from the base model using an error correction mechanism that ensures appropriate adjustment towards long-run equilibrium whenever deviations are observed in the system.
- The efficiency of the estimated coefficients is assessed based on diagnostic tests for the classical Gaussian assumptions of linear regression models (emphasizing normally distributed errors, lack of serial correlation and lack of heteroskedasticity). The appropriateness of the model specification is also assessed using a Ramsey RESET test, while the reliability and stability of the coefficients are assessed using CUSUM and CUSUM of Squares tests.

#### 4.3.2. Pairwise Granger Causality Test

#### 4.3.3. Data

## 5. Results and Discussion

#### 5.1. Unit Root Test of Variables

#### 5.2. Short- and Long-Term Relationships

^{2}, and estimates for Akaike information criterion (AIC), Schwarz information criterion (SIC), and Hanna–Quinn criterion (HQ), an ARDL (3,0,0,0,0,0,0,0,0,0,0,0,0) was selected as the best model. From Table A3 in the appendix, a total of about 91.46% of the variations in cassava yields in Togo is explained by regressors in the base model. To ascertain whether the observed estimates in the base model reflect the true relationships, a Bounds test was performed. As shown in Table 2, the null hypothesis of no long-term relationships is rejected at the 5% significance level. This indicates a need to incorporate short-term dynamics and correct for deviations from the long-term equilibrium through incorporation of an error correction mechanism in the model.

#### 5.3. Causality

## 6. Conclusions and Policy Recommendations

## Acknowledgments

## Conflicts of Interest

## Appendix A

#### Appendix A.1. Sections

#### Appendix A.1.1. Section AE 1

#### Appendix A.1.2. Section AE 2

- (i)
- The cause happens prior to its effects and
- (ii)
- The cause has unique information about the future values of its effect.

- Null: ${X}_{t}$ does not Granger cause ${Y}_{t}$${H}_{0}:{\propto}_{1}={\propto}_{2}=\dots ={\propto}_{j}=0$${H}_{1}:{\propto}_{1}={\propto}_{2}=\dots ={\propto}_{j}\ne 0$
- Null: ${Y}_{t}$ does not Granger cause ${X}_{t}$${H}_{0}:{\delta}_{1}={\delta}_{2}=\dots ={\delta}_{j}=0$${H}_{1}:{\delta}_{1}={\delta}_{2}=\dots ={\delta}_{j}\ne 0$

_{0}be rejected in either or both cases, Granger (‘predictive’) causality is said to exist between the variable. A rejection or non-rejection of the null hypothesis could lead to four possible outcomes:

- (i)
- A unidirectional Granger causality from ${X}_{t}$ to ${Y}_{t}$ (when H
_{0}is rejected in the first case); - (ii)
- A unidirectional Granger causality from ${Y}_{t}$ to ${X}_{t}$ (when H
_{0}is rejected in the second case); - (iii)
- Feedback, or bilateral causality (when H
_{0}is rejected in both cases); - (iv)
- No causality (when we fail to reject H
_{0}in both cases).

#### Appendix A.2. Tables

Variable | Period | Year Min Max | Min | Max | Mean | Std. Dev | CoV, % | Annual Growth, % |
---|---|---|---|---|---|---|---|---|

Output (tons) | 1964–1973 | 1964 1969–71 | 380,000 | 500,000 | 442,034.9 | 46,723.4 | 10.57 | 1.31 |

1974–1983 | 1977 1979 | 319,060 | 432,535 | 383,191.7 | 34,922.9 | 9.11 | −1.09 | |

1984–1993 | 1987 1990 | 355,200 | 592,867 | 445,134 | 68,100.1 | 15.3 | 0.66 | |

1994–2003 | 1994 2003 | 531,526 | 778,865 | 643,736.1 | 83,117.5 | 12.91 | 4.02 *** | |

2004–2013 | 2004 2011 | 675,475 | 998,540 | 835,525.9 | 113,564.1 | 13.59 | 4.31 *** | |

1964–2013 | 1977 2011 | 319, 060 | 998,540 | 549,924.5 | 183,975.9 | 33.45 | 1.80 *** | |

Area (Ha) | 1964–1973 | 1973 1971 | 21,000 | 33,000 | 26,700.00 | 4,056.5 | 15.19 | −0.52 |

1974–1983 | 1976 1982 | 20,630 | 108,700 | 43,980.00 | 32,923.0 | 74.86 | 21.05 *** | |

1984–1993 | 1987 1984 | 45,104 | 79,600 | 62,461.10 | 10,270.2 | 16.44 | −0.19 | |

1994–2003 | 1994 2003 | 90,403 | 132,943 | 108,771.2 | 15,246.7 | 14.02 | 4.10 *** | |

2004–2013 | 2005 2012 | 113,470 | 155,000 | 136,691.1 | 14,565.7 | 10.66 | 3.26 *** | |

1964–2013 | 1976 2012 | 20,630 | 155,000 | 75, 720.68 | 44,906.9 | 59.31 | 4.51 *** | |

Yield (tons/ha) | 1964–1973 | 1971 1973 | 15.15 | 20.35 | 16.71 | 1.54 | 9.24 | 1.84 * |

1974–1983 | 1982 1974 | 3.38 | 19.63 | 12.61 | 6.36 | 50.47 | −18.29 *** | |

1984–1993 | 1984 1987 | 5.58 | 7.88 | 7.18 | 0.69 | 9.62 | 0.85 | |

1994–2003 | 2000 1997 | 5.65 | 6.23 | 5.93 | 0.21 | 3.61 | −0.08 | |

2004–2013 | 2006 2011 | 5.62 | 6.56 | 6.10 | 0.25 | 4.17 | 1.02 ** | |

1964–2013 | 1982 1973 | 3.38 | 20.35 | 9.70 | 5.16 | 33.45 | −2.60 *** |

Regions | Population in 2010 | Area (km^{2}) | Main Crops/Livestock |
---|---|---|---|

Coastal zone/Maritime | 2,599,955 | 6100 | Corn, cassava, cotton, oil palm, peri-urban livestock farming (poultry, pigs) market gardening |

Western/Plateaux forest | 1,375,165 | 16,975 | Diversified farming: coffee, cocoa, oil palm to the southeast (Kpalimé), corn, cassava, yams, lowland rice, fruits, small ruminants, traditional poultry |

Eastern Plateaux | Cotton, corn, black-eyed peas, peanuts, lowland rice, cattle, small ruminants, traditional poultry | ||

Centrale | 617,871 | 13,317 | Cotton, corn, sorghum, millet, rice, cassava, yams, black-eyed peas, peanuts, soya, cattle, small ruminants, traditional poultry |

Kara | 769,940 | 11,738 | Cotton, corn, sorghum, yams, tomatoes, rice, black-eyed peas, soya, peanuts, cassava, millet, cattle, sheep, goats, traditional poultry, bees, etc. |

Savanes | 828,224 | 8470 | Cotton, sorghum, millet, rice, yams, peanuts, black-eyed peas, cattle, small ruminants, traditional poultry |

**Table A3.**Estimation equation (base model). Selected ARDL (3,0,0,0,0,0,0,0,0,0,0,0,0); Maximum dependent lags:3 (Automatic selection); Model selection method: Schwarz criterion (SIC); Trend specification: Unrestricted intercept; Included Observations: 29 after adjustments.

Coefficient | Std. Error | Prob. | |
---|---|---|---|

ln YCass (−1) | 0.2521 ** | 0.0906 | 0.0155 |

ln YCass (−2) | −0.2572 ** | 0.1030 | 0.0267 |

ln YCass (−3) | 0.1551 ** | 0.0584 | 0.0197 |

ln ACass | −0.9377 *** | 0.0972 | 0.0000 |

ln Rulpop | 1.2054 *** | 0.1817 | 0.0000 |

ln RPMaiCass | 0.1479 * | 0.0793 | 0.0849 |

ln RPYamCass | 0.5397 ** | 0.1877 | 0.0130 |

ln RPBeaCass | −0.3604 *** | 0.1156 | 0.0082 |

ln Exr | 0.3038 *** | 0.0985 | 0.0087 |

ln MSavprec | 0.2403 *** | 0.0763 | 0.0077 |

ln LSavprec | 0.0093 | 0.0609 | 0.8808 |

ln MSavprec _Var | 0.0355 | 0.0400 | 0.3913 |

ln LSavprec _Var | −0.1143 ** | 0.0518 | 0.0458 |

ln MSavtemp | 2.4065 * | 1.3262 | 0.0927 |

ln LSavtemp | −4.6325 ** | 1.8269 | 0.0249 |

Intercept | 6.7240 | 5.3919 | 0.2344 |

Adj. R-squared | 0.9146 | Log likelihood | 52.565 |

F-statistic | 20.993 | Akaike info criterion | −2.522 |

Prob (F-statistic) | 0.0000 | Schwarz criterion | −1.767 |

Durbin-Watson | 2.0046 | Hannan–Quinn criter. | −2.285 |

## References

- Lobell, D.B.; Burke, M.B.; Tebaldi, C.; Mastrandrea, M.D.; Falcon, W.P.; Naylor, R.L. Prioritizing climate change adaptation needs for food security in 2030. Science
**2008**, 319, 607–610. [Google Scholar] [PubMed] - Schlenker, W.; Lobell, D.B. Robust negative impacts of climate change on African agriculture. Environ. Res. Lett.
**2010**, 5, 014010. [Google Scholar] - Tatsumi, K.; Yamashiki, Y.; Valmir da Silva, R.; Takara, K.; Matsuoka, Y.; Takahashi, K.; Maruyama, K.; Kawahara, N. Estimation of potential changes in cereals production under climate change scenarios. Hydrol. Process.
**2011**, 25, 2715–2725. [Google Scholar] [CrossRef] - Jarvis, A.; Ramirez-Villegas, J.; Herrera Campo, B.V.; Navarro-Racines, C. Is cassava the answer to African climate change adaptation? Trop. Plant Biol.
**2012**, 5, 9–29. [Google Scholar] [CrossRef] - Rosenthal, D.M.; Ort, D.R. Examining cassava’s potential to enhance food security under climate change. Trop. Plant Biol.
**2012**, 5, 30–38. [Google Scholar] [CrossRef] - Adhikari, U.; Nejadhashemi, A.P.; Woznicki, S.A. Climate change and Eastern Africa; A review of impact on major crops. Food Energy Secur.
**2015**, 4, 110–132. [Google Scholar] - Burns, A.; Gleadow, R.; Cliff, J.; Zacarias, A.; Cavagnaro, T. Cassava: The drought, war and famine crop in a changing world. Sustainability
**2010**, 2, 3572–3607. [Google Scholar] - Food and Agriculture Organization. Why Cassava? Food and Agricultural Organization of the United Nations, FAO: Rome, 2013. Available online: http://www.fao.org/ag/agp/agpc/gcds/index_en.html (accessed on 18 November 2015).
- Nweke, F. New Challenges in the Cassava Transformation in Nigeria and Ghana; EPTD Discussion Paper No. 118; Environment and Production Technology Division, International Food Policy Research Institute: Washington, DC, USA, 2004. [Google Scholar]
- Statistical, Economic and Social Research and Training Centre for Islamic Countries. Food Security and Poverty Alleviation Initiative in the OIC Member States of Sub-Saharan Africa: A Preamble to Cassava Integrated Project; Statistical, Economic and Social Research and Training Centre for Islamic Countries (SESRTCIC): Ankara, Turkey, 2007. [Google Scholar]
- Sanginga, N.; Mbabu, A. Root and tuber crops (Cassava, yam, potato and sweet potato). In Proceedings of the An Action Plan for African Agricultural Transformation Conference, Dakar, Senegal, 21–23 October 2015. [Google Scholar]
- African Center for Economic Transformation. The hidden potential of cassava: The future of West Africa’s most undervalued crop. West Africa Trends
**2013**, 2, 1–14. [Google Scholar] - Ortiz, R. Improving cassava for enhancing yield, minimizing pest losses and creating wealth in sub-Saharan Africa. Gene Conserve
**2006**, 21, 301–319. [Google Scholar] - Aerni, P. Mobilizing science and technology for development: The case of the Cassava Biotechnology Network (CBN). In Proceedings of the 9th ICABR International Conference on Agricultural Biotechnology: Ten years later, Ravello, Italy, 6–10 July 2005. [Google Scholar]
- El-Sharkawy, M.A. Drought-tolerant cassava for Africa, Asia and Latin-America. Bioscience
**1993**, 43, 441–451. [Google Scholar] [CrossRef] - El-Sharkawy, M.A. Cassava biology and physiology. Plant Mol. Biol.
**2004**, 56, 481–501. [Google Scholar] [CrossRef] [PubMed] - Taylor, N.J.; Fauquet, C. Transfer of rice and cassava gene biotechnologies to developing countries. Biotechnol. Int.
**1997**, 1, 239–246. [Google Scholar] - Hillocks, R.J. Cassava in Africa. In Cassava: Biology, Production and Utilization; Hillocks, R.J., Thresh, J.M., Bellotti, A.C., Eds.; CABI Publishing: Oxon, UK, 2002; pp. 41–54. [Google Scholar]
- Tittonell, P.; Giller, K.E. When yield gaps are poverty traps: The paradigm of ecological intensification in African smallholder agriculture. Field Crops Res.
**2013**, 143, 76–90. [Google Scholar] [CrossRef] - De Vries, J.; Toenniessen, G. Securing the Harvest: Biotechnology, Breeding and Seed Systems for African Crops; CABI Publishing: Oxon, UK, 2001. [Google Scholar]
- Government of Togo and United Nations. MDG Acceleration Framework: Improving the Agricultural Productivity of Small-Holder Farmers, Togo; Ministry of Agriculture, Animal Breeding and Fisheries: Lome, Togo, 2011.
- Ministry of Food and Agriculture. Agriculture in Ghana: Facts and Figures 2012; Statistics, Research and Information Directorate; Ministry of Food and Agriculture: Accra, Ghana, 2013.
- Banito, A.; Kpémoua, K.E.; Wydra, K.; Rudolph, K. Bacterial blight of cassava in Togo: Its importance, the virulence of the Pathogen and the resistance of varieties. In Plant Pathogenic Bacteria; De Boer, S.H., Ed.; Kluwer Academic Press: Dordrecht, The Netherlands, 2001; pp. 259–264. [Google Scholar]
- Banito, A.; Verdier, V.; Kpémoua, K.K.; Wydra, K. Assessment of major cassava diseases in Togo in relation to agronomic and environmental characteristics in a systems approach. Afr. J. Agric. Res.
**2007**, 2, 418–428. [Google Scholar] - Adjata, K.D.; Muller, E.; Aziadekey, M.; Gumedzoe, Y.M.D.; Peterschmitt, M. Indicence of cassava viral diseases and first identification of East African cassava mosaic virus and Indian cassava mosaic virus by PCR in cassava (Manihot esculenta Crantz) fields in Togo. Am. J. Plant Physiol.
**2008**, 3, 73–80. [Google Scholar] [CrossRef] - West Africa Seed Network. West Africa Seed and Planting Material; The Newsletter of the West Africa Seed Network, No. 8; GTZ: Accra, Ghana, 2001. [Google Scholar]
- Blanc, E. The impact of climate change on crop yields in Sub-Saharan Africa. Am. J. Clim. Chang.
**2012**, 1, 1–13. [Google Scholar] [CrossRef] - Issahaku, Z.A.; Maharjan, K.L. Crop substitution behavior among food crop farmers in Ghana: An efficient adaptation to climate change or costly stagnation in traditional agricultural system? Agric. Food Econ.
**2014**, 2, 1–14. [Google Scholar] [CrossRef] - Ayanlade, A.; Odekunle, T.O.; Orimoogunje, O.O.I. Impacts of climate variability on Tuber crops in Guinea savanna part of Nigeria: A GIS approach. J. Geogr. Geol.
**2010**, 2, 27–35. [Google Scholar] [CrossRef] - Emaziye, P.O. The influences of temperature and rainfall on the yields of maize, yam and cassava among rural households in Delta State, Nigeria. J. Biol. Agric. Healthc.
**2015**, 5, 63–69. [Google Scholar] - Mbanasor, J.A.; Nwachukwu, I.N.; Agwu, N.M.; Onwusiribe, N.C. Impact of climate change on the productivity of cassava in Nigeria. J. Agric. Environ. Sci.
**2015**, 4, 138–147. [Google Scholar] - Ajetomobi, J.O. Sensitivity of crop yield to extreme weather in Nigeria. In Proceedings of the African Association of Agricultural Economists (AAAE) Fifth International Conference, Addis Ababa, Ethiopia, 23–26 September 2016. [Google Scholar]
- Fermont, A.M.; van Asten, P.J.A.; Tittonell, P.; van Wijk, M.T.; Giller, K.E. Closing the cassava yield gap: An analysis from smallholder farms in East Africa. Field Crops Res.
**2009**, 112, 24–36. [Google Scholar] - Ogundari, K. Maize supply response to price and nonprice determinants in Nigeria: Bounds testing approach. Int. Trans. Op. Res.
**2016**. [Google Scholar] [CrossRef] - World Bank. Togo: Vulnerability, Risk Reduction, and Adaptation to Climate Change. Climate Risk and Adaptation Country Profile; The World Bank Group: Washington, DC, USA, 2011. [Google Scholar]
- Kahsay, G.A.; Hansen, L.G. The effect of climate change and adaptation policy on agricultural production in Eastern Africa. Ecol. Econ.
**2016**, 121, 54–64. [Google Scholar] [CrossRef] - Agwu, N.M.; Nwachukwu, I.N.; Anyanwu, C.I. Climate variability: Relative effect on Nigeria’s cassava productive capacity. Rep. Opin.
**2012**, 4, 11–14. [Google Scholar] - Mythili, G. Acreage and Yield Response for Major Crops in the Pre- and Post-Reform Periods in India: A Dynamic Panel Data Approach; Indira Gandhi Institute of Development Research: Mumbai, India, 2008. [Google Scholar]
- Ozkan, B.O.; Ceylan, R.F.; Kizilay, H. Supply response for wheat in Turkey: A vector error correction approach. New Medit
**2011**, 3, 34–38. [Google Scholar] - Paltasingh, K.R.; Goyari, P. Supply response in rainfed agriculture of Odisha, Eastern India: A vector error correction approach. Agric. Econ. Rev.
**2013**, 14, 89–104. [Google Scholar] - Engle, R.F.; Granger, C.W.J. Cointegration and error correction representation: Estimation and testing. Econometrica
**1987**, 55, 251–276. [Google Scholar] [CrossRef] - Johansen, S.; Juselius, K. Maximum likelihood estimation and inference on co-integration with application to the demand for money. Oxf. Bull. Econ. Stat.
**1990**, 52, 170–209. [Google Scholar] - Johansen, S. Estimation and hypothesis testing of cointegrating vectors in Gaussian vector autoregressive models. Econometrica
**1991**, 59, 1551–1580. [Google Scholar] - Pesaran, H.M.; Shin, Y.; Smith, R.J. Bounds testing approaches to the analysis of level relationships. J. Appl. Econ.
**2001**, 16, 289–326. [Google Scholar] [CrossRef] - Harris, R.; Sollis, R. Applied Time Series Modelling and Forecasting; John Wiley & Sons: Hoboken, NJ, USA, 2003. [Google Scholar]
- Irefin, D.; Yaaba, B.N. Determinants of foreign reserves in Nigeria: An autoregressive distributed lag approach. CBN J. Appl. Stat.
**2011**, 2, 63–82. [Google Scholar] - Granger, C.W.J. Investigating causal relations by econometric models and cross-spectral methods. Econometrica
**1969**, 37, 424–438. [Google Scholar] [CrossRef] - Granger, C.W.J. Testing for causality: A personal viewpoint. J. Econ. Dyn. Control
**1980**, 2, 329–352. [Google Scholar] [CrossRef] - Eichler, M. Causal inference in time series analysis. In Causality: Statistical Perspectives and Applications; Berzuini, C., Dawid, P., Bernadinelli, L., Eds.; John Wiley & Sons Ltd.: Hoboken, NJ, USA, 2012. [Google Scholar]
- EViews user’s guide: Basic data analysis: Granger causality. Available online: http://www.eviews.com/help/helpintro.html#page/content/groups-Granger_Causality.html (accessed on 29 March 2017).
- theGlobalEconomy.com. Annual Data on Main Economic Indicators. Available online: http://www.theglobaleconomy.com/indicators_list.php (accessed on 29 March 2017).
- Newey, W.K.; West, K.D. A simple positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica
**1987**, 55, 703–708. [Google Scholar] [CrossRef] - Feder, G. The farm size and farm productivity: The tole of family labour, supervision and credit constraints. J. Dev. Econ.
**1985**, 18, 297–313. [Google Scholar] - Rosset, P.M. The multiple functions and benefits of small farm agriculture. In The Context of Global Trade Negotiations; Food First, The Institute for Food and Development Policy 398 60th Street: Oakland, CA USA; Transnational Institute Paulus Potterstraat 20 1071 DA: Amsterdam, The Netherlands, 1999. [Google Scholar]
- Agbaje, G.L.; Akinlosotu, T.A. Influence of NPK fertilizer on tuber yield of early and late-planted cassava in a forest alfsoil of southwestern Nigeria. Afr. J. Biotechnol.
**2004**, 3, 547–551. [Google Scholar] [CrossRef] - Lahai, M.T.; Ekanayake, I.J.; Koroma, J.P.C. Influence of canopy structure on yield of cassava cultivars at various toposequences of an inland valley agro ecosystem. J. Agric. Biotechnol. Sustain. Dev.
**2013**, 5, 36–47. [Google Scholar] [CrossRef] - Alves, A.A.C. Chapter 5: Cassava botany and physiology. In Cassava: Biology, Production and Utilization; Hillocks, R.J., Thresh, J.M., Bellotti, A.C., Eds.; CABI Publishing: Oxon, UK, 2002; pp. 67–89. [Google Scholar]

**Figure 1.**Decadal changes in cassava yields across selected West and Central African countries. Source: Author’s construct with data from FAOSTAT (2014). Annual growth rates for the period 1964–2013: Benin (2.34% ***), Burkina Faso (−3.43%, ***), Cameroon (2.08%, ***), Chad (3.00%, ***), Côte d’Ivoire (2.14%, ***), Ghana (1.61%, ***), Guinea (0.27%, ***), Mali (1.75%, ***), Niger (2.50%, ***), Nigeria (0.49%, ****), Senegal (1.28%, ***), Sierra Leone (1.42%, ***), Togo (−2.60%, ***).

**Figure 3.**Rainfall and temperature trends in the ‘Cassava belt’ of Togo. Source: Author’s construct with data from the National Meteorological Service. NB: MSavprec—rainfall for the main season; LSavprec—rainfall for the lean season; MSavtemp—mean temperature for the main season; LSavtemp—mean temperature for the lean season; YCass—Yield of cassava.

Variable | Phillips–Perron Test (Adj. t-Stat) | ||
---|---|---|---|

Level | First Diff. | Status | |

ln YCass | −6.1981 *** | I(0) | |

ln ACass | −3.2168 ** | I(0) | |

ln Rulpop | −3.7992 *** | I(0) | |

ln RPMaiCass | −4.9087 *** | I(0) | |

ln RPYamCass | −3.6271 ** | I(0) | |

ln RPBeaCass | −3.6846 *** | I(0) | |

ln Exr | −1.8033 | −4.6475 *** | I(1) |

ln MSavprec | −5.3801 *** | I(0) | |

ln LSavprec | −4.9648 *** | I(0) | |

ln MSavprec_Var | −5.4896 *** | I(0) | |

ln LSavprec_Var | −13.638 *** | I(0) | |

ln MSavtemp | −5.0881 *** | I(0) | |

ln LSavtemp | −3.1981 ** | I(0) |

Test Statistic | Value | K |
---|---|---|

F-statistic | 5.8722 | 12 |

Critical Value Bounds | ||

Significance | I0 Bound | I1 Bound |

10% | 4.78 | 4.94 |

5% | 5.73 | 5.77 |

2.5% | 6.68 | 6.84 |

1% | 7.84 | 4.05 |

**Table 3.**ARDL Cointegrating and long-term estimates. (Original dep. Variable: ln YCass; Selected Model: ARDL (3,0,0,0,0,0,0,0,0,0,0,0,0); Included observation: 29).

Cointegrating Form | |||

Variable | Coefficient | Std. Error | Prob |

D (ln YCass (-1)) | 0.0945 | 0.0728 | 0.2167 |

D (ln YCass (-2)) | −0.1632 ** | 0.0616 | 0.0201 |

D (ln ACass) | −0.9447 *** | 0.0675 | 0.0000 |

D (ln Rulpop) | 0.2665 | 2.6601 | 0.9218 |

D (ln RPMaiCass) | 0.1543 *** | 0.0495 | 0.0082 |

D (ln RPYamCass) | 0.6321 *** | 0.1613 | 0.0018 |

D (ln RPBeaCass) | −0.4193 *** | 0.0831 | 0.0002 |

D (ln Exr) | 0.2791 *** | 0.0915 | 0.0093 |

D (ln MSavprec) | 0.2021 *** | 0.0590 | 0.0045 |

D (ln LSavprec) | −0.0049 | 0.0554 | 0.9316 |

D (ln MSavprec _Var) | 0.0384 * | 0.0216 | 0.0988 |

D (ln LSavprec _Var) | −0.0894 *** | 0.0267 | 0.0052 |

D (ln MSavtemp) | 1.9443 * | 0.9887 | 0.0710 |

D (ln LSavtemp) | −4.7798 *** | 1.0950 | 0.0008 |

Intercept | 6.1557 *** | 1.5789 | 0.0018 |

ECT (-1) | −0.7756 *** | 0.2033 | 0.0021 |

Long-Term Coefficients | |||

Variable | Coefficient | Std. Error | Prob |

ln ACass | −1.1033 *** | 0.1963 | 0.0001 |

ln Rulpop | 1.4183 *** | 0.2902 | 0.0003 |

ln RPMaiCass | 0.1740 * | 0.0969 | 0.0958 |

ln RPYamCass | 0.6350 ** | 0.2608 | 0.0301 |

ln RPBeaCass | −0.4241 ** | 0.1600 | 0.0200 |

ln Exr | 0.3574 ** | 0.1470 | 0.0303 |

ln MSavprec | 0.2827 ** | 0.1026 | 0.0164 |

ln LSavprec | 0.0110 | 0.0723 | 0.8819 |

ln MSavprec _Var | 0.0417 | 0.0448 | 0.3681 |

ln LSavprec _Var | −0.1345 * | 0.0668 | 0.0652 |

ln MSavtemp | 2.8315 | 1.7157 | 0.1228 |

ln LSavtemp | −5.4506 ** | 2.4647 | 0.0455 |

Breusch–Godfrey LM | Breusch–Pagan–Godfrey Heteroskedasticity Test | Residual Normality Test | Ramsey RESET Test |
---|---|---|---|

F-stat (Prob) 0.0401 (0.8447) | F-stat (Prob) 1.7293 (0.1639) | Jarque–Bera (Prob) 1.3972 (0.4973) | F-statistics (Prob) 1.4922 (0.2453) |

Null Hypothesis | Obs | F-Stat | Prob |
---|---|---|---|

lnACass does not Granger Cause lnYCass | 27 | 3.74709 | 0.0195 |

lnYCass does not Granger Cause lnACass | 3.01806 | 0.0417 | |

ln Rulpop does not Granger Cause lnYCass | 27 | 4.4412 | 0.0100 |

lnYCass does not Granger Cause ln Rulpop | 2.4099 | 0.0824 | |

ln RPMaiCass does not Granger Cause lnYCass | 27 | 2.31083 | 0.0925 |

lnYCass does not Granger Cause ln RPMaiCass | 0.83260 | 0.5453 | |

ln RPYamCass does not Granger Cause lnYCass | 27 | 3.23633 | 0.0330 |

lnYCass does not Granger Cause ln RPYamCass | 0.94357 | 0.4798 | |

ln RPBeaCass does not Granger Cause lnYCass | 27 | 2.15389 | 0.1112 |

lnYCass does not Granger Cause ln RPBeaCass | 0.76413 | 0.5888 | |

D (ln Exr) does not Granger Cause lnYCass | 26 | 0.06309 | 0.9968 |

lnYCass does not Granger Cause D (ln Exr) | 1.17795 | 0.3653 | |

ln MSavprec does not Granger Cause lnYCass | 27 | 5.46868 | 0.0040 |

lnYCass does not Granger Cause ln MSavprec | 1.17301 | 0.3649 | |

ln LSavprec does not Granger Cause lnYCass | 27 | 0.83759 | 0.5422 |

lnYCass does not Granger Cause ln LSavprec | 1.76667 | 0.1768 | |

ln MSavprec _Var does not Granger Cause lnYCass | 27 | 2.0656 | 0.1234 |

lnYCass does not Granger Cause ln MSavprec _Var | 0.9859 | 0.4565 | |

ln LSavprec _Var does not Granger Cause lnYCass | 27 | 0.2441 | 0.9368 |

lnYCass does not Granger Cause ln LSavprec _Var | 1.9943 | 0.1344 | |

ln MSavtemp does not Granger Cause lnYCass | 27 | 2.42447 | 0.0810 |

lnYCass does not Granger Cause ln MSavtemp | 1.86988 | 0.156 | |

ln LSavtemp does not Granger Cause lnYCass | 27 | 3.98566 | 0.0154 |

lnYCass does not Granger Cause ln LSavtemp | 2.04236 | 0.1269 |

© 2017 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Boansi, D.
Effect of Climatic and Non-Climatic Factors on Cassava Yields in Togo: Agricultural Policy Implications. *Climate* **2017**, *5*, 28.
https://doi.org/10.3390/cli5020028

**AMA Style**

Boansi D.
Effect of Climatic and Non-Climatic Factors on Cassava Yields in Togo: Agricultural Policy Implications. *Climate*. 2017; 5(2):28.
https://doi.org/10.3390/cli5020028

**Chicago/Turabian Style**

Boansi, David.
2017. "Effect of Climatic and Non-Climatic Factors on Cassava Yields in Togo: Agricultural Policy Implications" *Climate* 5, no. 2: 28.
https://doi.org/10.3390/cli5020028