Analyzing the Trend, Forecast, and Supply Response of Cassava Production in Nigeria from 1961 to 2014.

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Analyzing the Trend, Forecast, and Supply Response of Cassava Production in Nigeria from 1961 to 2014.


Cassava holds immense importance as a staple food in Nigeria, making it crucial to employ simulation models to enhance its production. This study aimed to estimate the trend, forecast, and supply response of cassava production in Nigeria using secondary data spanning from 1961 to 2014. The data encompassed various factors such as prices, yield, output, hectarage of cassava, as well as climatic data like rainfall, number of rain days, onset and cessation of rain, temperature, and relative humidity. The data sources included the Food and Agricultural Organization (F.A.O.), International Institute of Tropical Agriculture (I.I.T.A.), Nigeria Bureau of Statistics (N.B.S.), and Nigeria Meteorological Agency.

To achieve the study’s objectives, four techniques were employed for data analysis: growth rate analysis, grafted technique, partial adjustment hypothesis, and adaptive expectation hypothesis. Additionally, simulations were conducted to assess the impact of macro-level policy changes (pre-SAP, SAP, post-SAP, and A.T.A. periods) on cassava production.

The overall findings for the entire study period showed encouraging growth rates of 4.1%, 0.1%, and 4.2% for hectarage, yield, and output, respectively. Polynomial spline models were found to be the most effective forecasting method compared to linear, semi-log, and growth models. The study’s forecasts for the period from 2015 to 2035 indicated that cassava cultivation would cover 11,200,000 hectares, yielding 98,000 kg/ha and producing 11,000,000 tonnes of cassava by 2035.

The study revealed that farmers’ partial adjustment coefficient had a mean value of 4.69E-07, while the adaptive expectation coefficient was -0.256186542. This indicated that farmers made fewer errors in making hectarage decisions than in forming price expectations. The elasticity of supply results indicated relatively inelastic responses in both the short run and long run. Specifically, the short-run and long-run elasticities were -8.41E-15 and -2.74E-08, respectively, for the partial adjustment hypothesis, and -3.97E-02 and 0.217803916, respectively, for the adaptive expectation hypothesis. These findings suggested that farmers’ response to price changes was not very encouraging.

In conclusion, cassava production in Nigeria experienced increased growth rates from 1961 to 2014, and polynomial spline models were identified as the most suitable forecasting approach. However, farmers showed limited responsiveness to price changes and economic incentives. Recommendations include establishing short-term and long-term cassava needs based on the present population growth rate, employing the estimated models to determine required hectarage, yield, and output. Furthermore, the estimated cassava supply elasticities can serve as a useful guide for studying farmer responsiveness to price changes and can be utilized in relevant research. It is also advisable for the government to reintroduce minimum and maximum pricing policies with sufficient resources for monitoring. Additionally, promoting modern farming technology through training by the government and private sector could significantly boost cassava output.

Analyzing the Trend, Forecast, and Supply Response of Cassava Production in Nigeria from 1961 to 2014.

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