A close-up of a plant Description automatically generated with medium confidence

Sorghum

LEK 431 Term Project Daniel van Heerden & Kyla Schoeman

Sorghum is the fifth most important crop in the world. In South Africa it is the fourth most important crop. Sorghum can be grown in semi- arib regions. Requires less rainfall then maize. Sorghum is a popular grain as it has many uses A few uses for sorghum: Used as a breakfast meal The malt of sorghum is used to make beer It can be used as a type of rice As sorghum has high nutritional value and the ability to grow in harsher environments, it should be a good competing crop.

Background

Why Sorghum?

It should be a good competing crop Maize is a substitute for sorghum It was observed that sorghum had a declining trend The hectares drop from 400 000ha in 1987 to less than 30 000ha in 2018 This is an interesting observation as South Africa can easily export to African countries

Sorghum Ha 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 254000 388000 401000 326000 228000 196000 166000 191000 239000 227000 180000 174000 161000 131277 98900 142200 88300 75250 95497 130000 86500 37150.000000000007 69000 86800 85500 86675 69200 48550.000000000007 62620 78850 70500 48500 42350 28800 Area Maize MZ Ha 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 4420625.0377986087 4599178.7118234048 4695847.8983973386 4239796.794677956 4327367.704868461 3983907.7713940125 3647271.5452071363 3965711.2186271544 4164736.0145146661 4442233.4442092534 3357263.9854853339 3761000 4023065 3559750 3566683 4012843 3189215 3533459 3650904 3204110 3223440 2032446 2897066 3296980 2896682.8 3263339.7686200002 2858759.7686200002 3141113.7686200002 3238099.7395161288 3096000 3048050 2212880 2995250 2633685 Price Maize Average MZ R/ton 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 216.57499999999999 216.625 232.81 312 285.5 265.5 302.67 357.62 452.81 417 387.02 598.62 615.65499999999997 590 566.20000000000005 674.41499999999996 547.57500000000005 873.94 1365.9099999999999 930.32 848.67 623.16999999999996 978.36500000000001 1499.0250000000001 1593.7950000000001 1371.355 1114.7049999999999 1663.9 2181.3150000000001 2075.27 2141.2350000000001 2370.42 3582.7950000000001 1942.605 Price Sorghum Average SG R/ton 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 197.36 180.52 189.49 192.4 205.21 205.47 231.83 295 475 466.37 357 482 475 520 550 730 520 760 1500 1450 900 450 1191.4100000000001 1483.43 1774.43 1494.65 1383.5 1671.61 2675.01 2691.62 2626.78 2380.9 3434.39 2638.27 Indices of producer prices 2010=100 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 16.8 18.399999999999999 19.8 21 22.9 25.5 29.1 38.200000000000003 37.4 36.1 45.3 47.2 46.3 49.1 54.2 51.2 61.6 85.7 88.7 74.599999999999994 54.4 72.3 103.2 123.5 109.4 100 128.6 158.5 160.6 167.2 183.3 244.5 170.7 161.80000000000001

The Data

Time Series 1985 to 2018 Sorghum area planted Sorghum price Maize area planted Maize price Producer price index Annual Precipitation Price where converted to real prices

Several models where built Linear models Log-linear models Trend models

The Models

Model 1 Data Sorghum Area planted Sorghum Real Price Maize Area Planted Maize Real Price Linear model The model was a good fit, however the statistic was not as significant. Model 2 Data Sorghum Area planted Sorghum Real price Maize Real price Maize Area planted Trend variable Linear model The model showed good fit but the statistics was not satisfactory.

Model 3 Data Sorghum Area planted Sorghum Real price Maize Area planted Maize Real price Trend Variable Log-Linear model The model show the best fit with the most satisfactory statistics. Model 4 Data Sorghum Area planted Sorghum Real price Maize Area planted Maize Real price Annual Precipitation Trend Variable Log-Linear model The model had a good fit, however the models was not as good as the previous models.

Model 3 Economic evaluation: Economically as the area planted of sorghum declines area planted maize should increases as they are interchangeable crops. Expected positive relationship between the sorghum area planted and the maize area planted in the previous period. The coefficient = 2.1168275 thus it is positive, as expected. The mean of the dependent variable rises as the independent variable rises. Expected negative relationship between the sorghum area planted and the real maize price in the previous period. The coefficient = -0.24959018 thus it is negative, as expected. The mean of the dependent variable decreases as the independent variable rises. Expected negative relationship between the sorghum area planted and the real sorghum price in the previous period. The coefficient = -0.06779827 thus it is negative, as expected. The mean of the dependent variable decreases as the independent variable rises.

Expected negative relationship between the sorghum area planted and the trend in the previous period. The coefficient = -0.29596112 thus it is negative, as expected. The mean of the dependent variable decreases as the independent variable rises. As a result, the variables relationships are consistent with the economic theory. Thus, making the model economically significant. Statistical evaluations of Model 3: Significance of independent variables ROT: t-stats > 1.96 LN (MZ HA) t-stats = 5.54798438 5.54798438 > 1.96 Therefore, LN (MZ HA) is statistically significant.

ROT: t-stats > 1.96 LN(RMZP) t-stats = -0.68367151 -0.68367151 < 1.96 Therefore, LN(RMZP) is statistically insignificant. ROT: t-stats > 1.96 LN(RSGP) t-stats = -0.25616294 -0.25616294 < 1.96 Therefore, LN(RSGP) is statistically insignificant.

ROT: t-stats > 1.96 LN(Trend) t-stats = 3.10271285 3.10271285 > 1.96 Therefore, LN(Trend) is statistically significant. ROT: p-value < 0.05 LN (MZ HA) p-value = 0.00000554 0.00000554 < 0.05 Therefore, LN (MZ HA) is statistically significant .

ROT: p-value < 0.05 LN(RMZP) p-value = 0.499609296 0.499609296 > 0.05 Therefore, LN(RMZP) is statistically insignificant. ROT: p-value < 0.05 LN(RSGP) p-value = 0.799633469 0.799633469 > 0.05 Therefore, LN(RSGP) is statistically insignificant.

ROT: p-value < 0.05 LN(Trend) p-value = 0.004248963 0.004248963 < 0.05 Therefore, LN(Trend) is statistically significant. Significance of the model: ROT: f-stats > 4 f-stats =51.77429709 51.77429709 > 4 Therefore, statistically significant.

Model fit: ROT: Adjusted R ² > 0.7 Adjusted R ² = 0.86022705 0.86022705 > 0.7 Thus, it is a good fit. The trend variable is significant thus it can be concluded that there is a trend. In this case it is a downward trend as the coefficient’s sign is negative.

In conclusion of the evidence above and the statistics generated by the model, there are many other factors that contributed to the decline in sorghum area planted. This is seen as the price of sorghum does not increase as the area planted decreases. This can be concluded that the demand for sorghum is low, which in turn keeps the price of sorghum low. The low price of sorghum contributes to it being unprofitable to produce, therefore the sorghum area planted decreases over time. It can also be due to the substitute product maize becoming cheaper as the yield increases, as the same input is used to generate a larger amount of maize. For this reason, maize becomes inferior to sorghum and leads to a decline in demand and producers adjusting their sorghum area planted to less sorghum and to more maize. It could be suggested that more research on sorghum must be done such as improved yield as it will be able to compete with maize. The consumers could also be made aware of its nutritional value and its benefits as to increase demand and stimulate production as more demand will increase the price that could push sorghum back into a positive profit margin for producers. This could lead to more variety available to consumers as well as more competitive food prices.