Sixteen Months of Mutton

Sixteen Months of Mutton: Meat-Eating Journeys through Kazakhstan, Kyrgyzstan , and Mongolia [Stu Lamb, James Baker] on leondumoulin.nl *FREE* shipping on.
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With a sheep population of 2. The value chain of Namibia's mutton production is shown diagrammatically in Figure 2. From this value chain map it is clear that there are various value chains, markets, and linkages between the various role players in the Namibian mutton industry. The on-farm production - as well as the marketing through various marketing channels to local and other various export markets - is depicted in Figure 2. The production figures of these channels are displayed in Figure 1. As a result of the contribution of livestock production to the country's economy, research and planning in this sector are of paramount importance for a healthy, sustainable industry.

This study will focus on the supply side of mutton production in Namibia with the aid of a supply response model that will be used to test the hypotheses of certain economic phenomena. Two hypotheses are tested in this study. The first is that climate factors play a major role in determining the supply response in the mutton industry. The second is that price-related factors also play a major role in determining the supply of mutton in the industry.

Supply response studies are also useful for evaluating production policies and incentives. The first studies on supply response were done in order to understand the price mechanism.

The approach to their study was based on the fundamentals of the work done by Nerlove [9]. Seleka [11] researched the short-run supply of small ruminants including sheep and goats in Botswana, using pooled data for six agricultural regions. The research also found that producer prices have no impact on small ruminant sales. The most recent research was conducted by Ogundeji et al. With the aid of an error correction model ECM , the supply response of beef production in South Africa was investigated. The independent variables in their supply model were rainfall; the real producers' price of beef, lamb, pork, chicken, and yellow maize; imports; and cattle populations that represented the climatic, economic, trade, and demographic factors.

The production variables were modelled respectively to cattle marketed for slaughtering dependent variable. Results showed that beef producers in South Africa respond to these production variables in the long-run. In the short-run, the results showed that the beef marketed is only responsive to climatic factors and the importing of beef.

To measure agricultural output supply responses to price and other non-price factors , two broad approaches can be followed: Programming models, usually linear programming, involve the creation of a linear production model that represents the typical production system of a specific product or various products. An objective function is usually specified that is related to profit maximisation. Other objectives such as risk minimisation can also be defined. By solving the model using various sets of data, and assuming that the profit is maximised, the supply-price relationship can be established for a specific product.

The advantages of this approach are that linear programming is capable of handling complex multi- relationships at farm level in a production system. The complex multi-relationships involve recognition of all the effects of supply on product prices, input prices, and technological and physical restrictions. However, the data requirements are extensive: Due to the restricted data and resources that are available, this approach is not widely used by researchers when supply-response studies are conducted.

Production in agriculture is not instantaneous, and is dependent on post-investment decisions and expectations. From a practical perspective, the production in any period or season is affected by past decisions. The partial adjustment model used by Nerlove [9] is an early version of an econometric approach used to measure agricultural supply-response for a single commodity. Nerlove's partial adjustment model is used to capture agricultural supply response to price incentives. The general static supply function can be mathematically presented as: The dynamic adjustment of the supply response equation is based on Nerlove's hypothesis that "each year farmers revise the output level they expect to prevail in the coming year in proportion to the error they made in predicting the output level of this period".

This is presented as: By substituting equation 1 in equation 2, we obtain: According to Abou-Talb et al. First, it displays an inability to distinguish between short-run and long-run elasticities. Second, the model uses integrated non-stationary series that pose the danger of spurious regression results.

So it can be concluded that the partial adjustment model - used as a framework by many previous studies on supply response analysis - is less appropriate for the study of supply response on agricultural output due to its limitations, and due to the improvement in other methods. Empirical dynamics of supply can also be described by error correction models ECM. The ECM offers a means of re-incorporating levels of variables alongside their differences, and hence of modelling long-run and short-run relationships between integrated series. In addition to this, economic time series data contain trends over time.

Although regression analysis shows significant results with high R 2 , the results may be spurious. ECM and co-integration analysis are used to overcome the problem of spurious regression [17]. The ECM approach is used to analyse non-stationary time series data that are known to be co-integrated. This method also assumes co-movement of the variables in the long-run. The general form of the ECM method is: Co-integration techniques received much attention because they solved the statistical problems associated with non-stationary data series leading to spurious regression results.

Various co-integration approaches are available - all with some limitations and assumptions. The ARDL model has the capacity as mentioned earlier to eliminate spurious regression results and to distinguish between long-run and short-run elasticities. Unlike other co-integration techniques for example Engle-Granger and Johansen, the ARDL model does not impose restrictive assumptions that all the variables in the study must be integrated to the same order.

The effect of this is that the ARDL approach can be applied regardless of whether the underlying variables are stationary, non-stationary, or mutually integrated [19]. Another difficulty avoided by the ARDL approach concerns decisions about the number of endogenous and exogenous variables to be included, as well as the lags within these variables. The ARDL approach makes it possible to include in the supply model different variables that have a different optimal number of lags [20].

Due to these problems, researchers propose the direct estimation of the long-run parameters using unrestricted error correction models UECM that specify the inclusion of dynamics [21]. Due to the dynamic nature of production and market equilibrium, the dynamics arising from both dependent and independent variables need to be taken into account. Unrestricted dynamic models incorporating lagged and current values of both dependent and independent variables then become an autoregressive distributed lag model.

The bounds-testing approach to the level relationship, together with the ADRL modelling approach to co-integration analysis developed by Persaran et al. The existence of a long-run level relationship in an ECM framework between the dependent variable Y t and the independent variable X t can be tested when it is not known whether the underlying independence is stationary, non- stationary, or mutually co-integrated with the ARDL approach.

The ARDL approach to co-integration analysis involves 2 stages. Here the joint significance is tested by testing the null hypothesis of no co-integration by setting all the lagged level variables equal to zero; or against the alternative hypothesis that the coefficients of all the lagged variables in the model are not equal to zero [22].

Considering the supply model in equation 5, the null hypothesis is: Whether the F-statistic is significant is determined using critical values developed by Persaran et al. These are bounds containing a band of critical values with upper and lower limits for different significance levels. If the F-statistic lies above the upper bound for a specific significance level, a non-spurious long-run relationship exists among the variables in the ADRL model.

Sixteen Months of Mutton: Meat-Eating Journeys Through Kazakhstan, Kyrgyzstan, and Mongolia

If the F-statistic lies below the lower bound critical value, there is no long-run relationship among the variables in the ARDL model [23]. If the long-run relationship is confirmed with the Wald test among the variables, the second stage of the ARDL approach can be conducted. The second stage involves estimation of the long-run and short-run elasticities of the ADRL model. After the long-run relationship is confirmed among the variables, ordinary least square OLS regression is used to estimate the long-run and short-run elasticity coefficient of supply.

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The model used for this study - based on economic theory and previous work done in this field of the livestock industry - selects the variables influencing mutton supply. However, as mentioned earlier, it is not always possible to construct a model suggested by theory because we cannot include all the variables initiated by theory due to the non-availability of data and quantification problems. The ideal would have been to include forward-looking factors of production in the supply model, in order to consider future risks in producers' decision-making regarding production.

This was not taken into account due to quantification challenges. Therefore, the following unrestricted error correction type of ARDL model for mutton supply in Namibia was hypothesised in equation 7: In this case, lnYt is the dependent variable of the mutton supply model, representing mutton marketed per month, and is measured in sheep carcass units.

The latter is included as an economic factor competing with mutton in the red meat market. Theory rarely provides a basis for specifying the lag lengths in distributed lag models. In this ARDL model it is sensible to start at a maximum lag length of 12 months.

A quantitative analysis of supply response in the Namibian mutton industry

This is the maximum lag length that is appropriate to the supply dynamics of sheep production in a production year. According to Sarmiento et al. Only a few studies on supply response go beyond the Durbin- Watson test in reporting the performance of their model specification. Emphasis is given to model specification tests to ensure that the hypothesised model is statistically significant.

Specification tests include those for serial correlation, heteroscedasticity, model stability, a test for normality in the model residuals, and model specification RESET test. A satisfactory result on the specification tests assures reliable results from the supply model, and is therefore an important part of the study. The data required for the supply response analysis was obtained from the Meat Board of Namibia. Livestock marketed and livestock producer prices were obtained from monthly published reports.

The rainfall was obtained from the Meteorological Service of Namibia. Data from three weather stations Grootfontein, Windhoek, and Keetmanshoop was used to calculate the country's monthly average that was used in the model. The Namibian price data was deflated to real price data by dividing the monthly nominal prices of the selected price variables by the monthly consumer price index CPI obtained from the Central Bureau of Statistics in Namibia. The response analysis time span covers the period January to December The time series data of the selected variables first has to undergo analytical statistical tests before it can be used to compute short-run and long-run elasticities.

The first test on the data is for seasonality. The most common approach is to use the method of dummy variables [25]. According to the goodness of fit, R 2 , and the significance of the regression coefficients, sheep marketed [Y t ] and monthly rainfall [RF t ] are most likely to contain seasonal factors. The 'deseasonalisation' of the data by the dummy variable method is used to eliminate the seasonal component.

Stationarity properties of the supply model variables are determined.


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The ARDL approach followed in this study avoids the pre-testing requirement on the time series properties. However, the stationarity properties are needed to test for long-run relationship among the specified variables in the Engle-Granger and Johansen approach to co-integration. The Wald test incorporates the long-run relationship among variables, whether variables are non- stationary, stationary, or mutually co-integrated. The unit root test is therefore not applicable in the ARDL approach. However, it is still essential to complement the estimation process with a unit root test in order to be sure that the variables to be included in the analysis are not integrated to a higher order - i.

From Table 1 we can conclude that none of the integrated variables in the mutton supply function are of an order higher than one With the stationarity properties of the data known, the next step of the supply response analysis, using the ARDL approach, can be conducted. Once the ECM is specified and estimated, the next step is to test for the joint null hypothesis of no long-run level relationship.


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    • As mentioned in Section 3. The results obtained from the Bounds test are presented in Table 2. Therefore it is important to determine the stationarity properties of the data. The computed F-statistics for the mutton supply model in equation 7, based on Wald's test, are This result clearly exceeds the lower bound value I 0 , of 4. Thus the null hypothesis of no co-integration is rejected for the supply model, and a non-spurious long-run relationship is confirmed among the monthly mutton marketed, the real Namibian mutton producer price, the real Namibian beef producer price, and the monthly rainfall for mutton supply in Namibia.

    6 months baby with mutton piece

    This result implies that these variables move together and so cannot move 'too far away' from each other independently [22]. From this result we can conclude that any disequilibrium among the variables in the supply model is a short-run phenomenon. Misspecification in the regression is possible, making it is important to test the assumptions of the statistical model. These tests include those for normality, those for heteroscedasticity, and the regression specification error test RESET that was introduced by Ramsey in [26].

    The validity of the specific mutton supply model is therefore confirmed by using the relevant diagnostic tests. The Jarque-Bera statistic confirmed the normality behaviour of the residuals of the estimated mutton supply model refer to Table 3. The Breusch-Godfrey LM test statistic rejects the first, second, and third order serial correlation in the mutton supply model. The ARCH tests verify that residuals are homoscedastic in the supply model.

    The Ramsey RESET test shows no evidence of functional form misspecification in rejecting the hypothesis of misspecification. According to Ogazi [20], the null hypothesis i. The long-run elasticities of the production variables included in the mutton supply model, and which influence the supply, are calculated from the computed coefficients of the respective lag level independent variables LNPt-1, LPBt-1, RFt-1 , divided by the coefficient of the lag level dependent variable LYt-1 of the specific ECM mutton supply model in Table 4.

    The results are given a negative sign to obtain the long-run supply elasticities of the different variables in mutton supply. In explaining the long-run elasticities of equation 7, Table 5 shows the elasticities obtained from the analysis. All the long-run variables contain the expected signs and are statistically significant. The average real Namibian mutton price elasticity of supply is shown to be elastic by the expected positive sign. Related to this factor is the positive relationship between the mutton producer price and the number of sheep marketed.

    That is, the mutton producer's decision to market sheep for slaughter is positively influenced by price in the long-run. The average real Namibian beef producer price elasticity of supply is shown to be inelastic by a negative sign. In practical terms this is because when the beef price increases, mutton supply decreases in the long-run, due to the fact that mutton and beef are competing products.

    The reason for this behaviour is that producers start to reduce mutton production because beef production is more profitable as the beef price increases. The average real Namibian beef producer price elasticity of supply is The long-run price elasticity of competing products can be compared with those of Ogundeji et al. Their research obtained a competing product price elasticity of supply of This is less than the results obtained in this study. The difference between these elasticities can be attributed to the fact that their work incorporated the price of various competing products pork and beef and not only one product in this case, beef.

    The long-run rainfall elasticity of supply is inelastic and has a positive sign. As expected, the positive sign of rainfall has a positive effect on mutton supply in the long-run. Lists with This Book. This book is not yet featured on Listopia. Keith Baron rated it really liked it Dec 13, Sarah rated it really liked it Sep 03, Chris marked it as to-read May 05, Dasha marked it as to-read Feb 26, Melissa Lindsey marked it as to-read Mar 14, Stephanie marked it as to-read Apr 15, Jennifer Goins marked it as to-read May 09, Forza marked it as to-read Sep 11, Paulo Jan marked it as to-read Feb 05, Kris marked it as to-read May 15, Leah Hanley marked it as to-read May 28, Shakeela marked it as to-read Jul 17, There are no discussion topics on this book yet.

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