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Efficient planning of sorghum production in South Africa – Application of the Box-Jenkin’s method

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Shoko R. R., Belete A.

Journal of Agribusiness and Rural Development

2017

vol. 46

issue 4

836-841

Estimation and forecasting of crop production are crucial in supporting policy decisions regarding food security and development issues. The present study examines the current status of sorghum production in South Africa. Univariate time series modelling using ARIMA model was developed for forecasting sorghum production. Box and Jenkins linear time series model, which involves autoregression, moving average, and integration, also known as ARIMA (p, d, q) model was applied. The annual production series of sorghum from 1960 to 2014 exhibited a decreasing trend while prediction of sorghum production between 2017 and 2020 showed an increasing trend. The study has shown that the best-fitted model for sorghum production series is ARMA (1, 0, 4). The model revealed a good performance in terms of explaining variability and forecasting power. This study has also shown that sorghum could contribute to the household and national food security because of its drought-tolerant properties.

Maize yield sensitivity to climate variability in South Africa: application of the ardl-ecm approach

81%

Shoko R. R., Belete A., Chaminuka P.

Journal of Agribusiness and Rural Development

2019

vol. 54

issue 4

363-371

Climate affects crop production decisions and outcomes in agriculture. From very short-term decisions about which crops to grow, when to plant or harvest a field, to longer-term decisions about farm investments, climate can positively or negatively affect agricultural systems. Although the general effects of climate change on agriculture are broadly understood, there are limited studies that model the relationship between specific crops and climate variables. The study uses the Autoregressive Distributed Lag (ARDL) model to analyze the sensitivity of maize yield to climate variables, fertilizer use and other non-climate variables. This paper uses annual time-series data of 47 observations spanning from 1970 to 2016. The results reveal that rainfall and temperature are important maize yield drivers in South Africa. However, if excessive, they will produce negative effects. The findings of this analysis are relevant for designing long-term interventions to mitigate the effects of climate change on maize production.

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2 Journal of Agribusiness and Rural Development

2 Belete A.

2 Shoko R. R.

1 Chaminuka P.

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1 2017

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Efficient planning of sorghum production in South Africa – Application of the Box-Jenkin’s method

Maize yield sensitivity to climate variability in South Africa: application of the ardl-ecm approach