Nigerian agricultural economy was characterized by low capacity utilization of resources that was further worsened by declined allocation functions and poor performance of Major agricultural facilities. Monetary authorities were unable to stimulate, direct and stabilize agricultural sector performance. Given the prolonged food crisis in Nigeria, it was not known whether the observed performance of the agricultural sector should be attributed to either inefficient monetary policy or technical inefficiency. Secondary data collected were reviewed, lagged and analyzed. The hypothesis was tested using F-statistics and T-test.
The outcome of the test of hypothesis showed that the observed F-value (54.47955*) was statistically significant at all distributions (F* > F0.01), hence the conclusion that monetary policy did influence farm outputs and the overall agricultural sector performance. Monetary aggregates shifted resources out of agricultural sector and the results showed that farm output and income velocity moved in opposite directions, hence improvements in farm outputs and income velocity were not able to defray inflationary pressures. This provided a strong reason why banks were reluctant to lend to farmers. Money broadly defined (M­2) shifted investment portfolios out of agriculture and the impact was statistically significant at 1% level.The incidence of persistent low cash reserve requirement increased bank lending rates volatility.

Keywords:Monetary aggregates, economic indicators, farm outputs, money narrowly and broadly defined, trade and non-trade sectors.
Nigerian economy was characterized by poor performance of the agricultural sector and government commitment in terms of budgetary expenditure on agriculture per fiscal year over the studied period, was insignificant when compared to aggregate government expenditure. The economy also experienced ineffective distribution functions, declining agricultural capacity utilization and poor performance of major agricultural facilities. Nigerian economy grossly displayed a weak agricultural base and low quality physical and social services (Garba,2000Ebo, 1995; Evbuomwan, 1996 2000 and CBN, 2000c).

Economic policy is a course of action intended to achieve or mitigate socioeconomic problems (McConnel and Brue, 1999). Resources flow and allocation to the agricultural sector were highly influenced by monetary policies. Monetary policy actions in Nigeria varied considerably between 1970 and 2010. Monetary Authorities in Nigeria were unable to stimulate, direct and stabilize agricultural sector performance both at short and long-run (Ajayi and Ojo, 1981; Aham, 1992 and Afolabi, 1998 ). Sustainable consolidation in agriculture was difficult to achieve in Nigeria because public policy options failed to address fundamental problems of the rural farmers (Mark, 1999; CBN, 2000a). Effort made to pursue economic policy measures that can capture and improve the performance of the agricultural sector failed, generating profound policy implications (Oran et al, 1979; UNFPA, 1986; IRRI, 1988 and Ahan, 1992). Hence, basic agricultural policy problems were in existence over the period and the policy regimes were such that showed persistent long-term profile of policy interruption, discontinuation and volatility making agricultural sector prone to both empirical investigation and distributive judgment (Drabenstott and Tim, 1996; Colander, 1998).

The key policy questions therefore were centered on whether farm outputs were in harmony with monetary policy intents and purpose? The structure and conduct of the policy actions measured in terms of emphasis, policy mix and commitment determined the efficiency of the policy instruments. Government had the aptitude and latitude to manipulate selected monetary aggregates in pursuance of her policy objectives and often without knowing in advance the precise impact on the desired targets. Given the prolonged Nigeria’s food crisis and the insignificant agricultural share of the nation’s GDP and exports, it was not known whether the observed performance indices of the agricultural sector should be attributed to inefficient and misconceived monetary policies and or inefficient agricultural resources allocation and utilization. Hence, this work was intended to capture the response of farm outputs to monetary policies in Nigeria and to recommend appropriate policy options that can reposition agricultural sector gainfully in the scheme of both domestic and international commodity markets. Therefore, it was possible to hypothize that monetary aggregates and economic indicators did not influence farm outputs and the overall performance of the agricultural sector in Nigeria. To enhance agricultural sector performance, monetary policies that discouraged under employment and or low capacity utilization of agricultural resources were necessary (Lipton, 1977; Gardner, 1981; Dennis and Alfred 1997; Mark, 1999 and McCallum, 2000).
The results provided an in-depth policy options for modeling agriculture into future policy process in Nigeria .The  policy options offered were such that have the potency to drive agricultural productivity forward and turn Nigerian agricultural economy into market-oriented. (Garba,2000; Evbuomwan, 1996 2000 ; CBN, 2000c and Tweeten, 1980, 1983).

It is imperative to investigate and capture the farm outputs response to monetary policy mix but given the research period of four decades (1970-2010) the study involved the collection and analysis of large volume of data of diverse nature and sources. These informed the purposive choice of crop sub-sector for  the study and as the index of analysis (CBN, 2000b ; McClave and Sincich,2000).

Food production took considerable time each year and farmers’ output adjustment with respect to monetary aggregates and economic indicators adjustments were not instantaneous. This implied that farm outputs supply adjustments were perceptible in the commodity markets only after a period not less than one year (Henderson and Qundt, 1980). Therefore, the quantity of farm output demanded in a particular year was a function of the price in the said year but the quantity supplied in the same said year was a function of the price in the previous year. Therefore, the lagged values of the exogenous variables that took into account, the length of time in the overall adjustment process of economic aggregates were used for regression analysis (Christopher, 1992). The use of the lagged values of the variables was the most efficient method to render economic behaviours dynamic and as partial adjustment mechanism, it reflected the actual change in farm outputs in the period (t). Therefore the response of farm outputs to lagged monetary aggregates was implicitly expressed as:
Yht = F (Xt-1, Xt-2, ……….., Xt-12) + U                                                     …… Eq(1)
Where Yht was farm output contributions to GDP measured in tonnes in the period (t) and (F) was the functional relationship and (U) was the error term. Then, Xt-1, Xt-2, ……….., Xt-12 were money narrowly defined (M1), money broadly defined (M2), broader monetary aggregate (M3), cash reserve requirement, Discount rates, exchange rates, banks’ CBN balances, lending interest rates, banks subscribed government bonds, inflation rates, agricultural credit supplied and banks liquidity ratio respectively. The data was collected from the Central Bank of Nigeria and Federal Bureau of Statistics (CBN, 2010; FBOS, 2010).

Equation (1) was explicitly expressed as:
a.                  Linear function
Yht= a + b1 X t-1 + ………………… + b12 Xt-12 + U                                    …. Eq(2)
b.         Semi-log function
Yht= a + b1 logX t-1 + …………… + b12 log Xt-12 + U                     …. Eq(3)
c.         Double-log function
            logYht= a + b1 logX t-1 + …………… + b12 log Xt-12 + U                                …. Eq(4)
Where the a priori expectations were b1< 0, b2<0, b3>0, b4<0, b5<0, b6<0, b7>0, b8<0, b9<0, b10<0, b11>0 and b12>0 and (a) was the intercept while the b5 were the parameter estimates of the independent variables (John and David, 1986).The hypothesis was tested using F-statistics and T-test (Christopher,1992 ; Murray and Larry, 1999 ;Kerry, 2000; Rangaswamy, 2007 and Upender, 2008)

Results and Discussion

The new innovations in global monetary economics steered up various economic reforms and adjustments in Nigeria. Monetary Authorities in Nigeria therefore, influenced the economy, using monetary policy instruments to stimulate, stabilize and direct resources endowment flow and growth. Major policy targets that were captured included production, consumption, income distribution and trade among others. In Nigeria, various economic sectors and units were differently captured by government policy actions and in most cases, the outcome were not in harmony with government policy intent and directions. Hence, monetary policies were strongly believed to have shifted resource out of the agricultural sector in Nigeria. Farm outputs appeared highly volatile, prompting such key policy questions as whether agriculture did receive adequate prudent policy attention. Hence this research effort and the results that follows, as shown in Table, 1.
Table, 1: Goodness of fit Statistics of Farm output Response to Monetary Aggregates and Economic Indicators
Parameters                 lin(Eq.2)                                lin-log(Eq.3)                         log-log(Eq.4)
R                                 0.98651                                  0.99088                                  0.98540
R2                                0.97320                                  0.98184                                  0.97102
                                    (97%)                                     (98%)                                     (97%)
R-2                               0.95534                                  0.96974                                  0.95170
                                    (96%)                                     (97%)                                     (95%)
SEE                             6931.09                                  5705.20                                  0.13098
                                    (2.7%)                                                (2%)                                       (3%)
DW                             1.62311                                  2.20964                                  2.32094
Observed F-value     54.47955*                             81.12112*                             50.25456*
Sig. F-value               0.0000                                                0.0000                                                0.0000
Regression DF                      12                                            12                                            12
Residual DF              18                                            18                                            18
Tabular F-value(1%)           3.37                                        3.37                                        3.37
Source: Estimates from multiple regression analysis of field data, 2012.
Notes: * = significant at 1% level of confidence.

In Table 1, the goodness of fit statistics of output response to monetary aggregates and economic indicators was presented in lin (Eq.2), lin-log (Eq.3) and log-log (Eq.4). In lin-log (Eq.3) the explanatory power (98%) was ample and standard error estimates (2%) inconsiderable relative to the parameters of lin(Eq.2) and log-log (Eq.4). The size of the adjusted R-Square (97%) obtained for lin-log (Eq.3) implied that the explanatory power (R2) of 98% was not magnified. However, in conducting the test of the null hypothesis and subsequent discussions of the results, lin (Eq.2) was chosen based on its consistency with plausible econometric attributes and it gave in addition, a good fit. The choice of lin (Eq.2) for the test of hypothesis was further prompted by its greater number of variables that were statistically significant and consistent with a priori expectations. Its explanatory power (97%) was also considered profuse and the standard error estimate (2.7%) was infinitesimal. The adjusted R-Square of about 96% confirmed that the R-Square was not exaggerated (Christopher, 1992; Upender, 2008; Kerry, 2000; Murry and Larry, 1999 ;Rangaswamy,2007).
The general direction of change in the regression analysis of lin (Eq.2) was an increase since the intercept was positive. The goodness of fit statistics in Table 1, showed that farm outputs response to the combined impact of monetary aggregates and economic indicators exhibited a linear supply function. The multiple correlation coefficient (0.98651) and the R-Square (0.97320) were statistical estimates subject to error and were therefore tested for reliability and degree of confidence (Table, 1). The outcome of the test showed that the observed F-value (54.47955*) was statistically significant at 1% level of confidence, contrary to the theoretical assumptions. Specifically, the F-value was significant at 0.0000, which implied that it was statistically significant at all distributions. Hence the rejection of the null hypothesis that monetary aggregates and economic indicators did not influence farm outputs and the overall performance of the agricultural sector in Nigeria.
The alternative hypothesis was therefore accepted with the understanding that there was only one chance per hundred that the null hypothesis would be accepted when it should be rejected. Thus there was 99% confidence that rejecting the null hypothesis was a right decision and that the acceptance of the alternative hypothesis was right.
The Durbin-Watson d-statistics test and multicollinearity diagnostics test conducted did not show any strong evidence of autocorrelation and multicollinearity respectively. Equation (2) showed the parameters and standard error estimates of variables specified to test farm outputs response to monetary aggregates    and economic indicators as follows:
Yht = 419 – 0.0001Xt-1 – 0.081Xt-2 + 0.082Xt-3*** + 22X+t-4 – 244Xt-5 – 261Xt-6 – 1.12Xt-7 – 279Xt-8 -         
        (963)   (0.162)         (0.15)            (0.048)      (252)    (179)                (620)       (0.674)     (123)      
0.714Xt-9*** + 81Xt-10 + 4.47Xt-11* + 227Xt-12***
(0.37)           (112)      (1.47)          (127)                       …… Eq. (2)
R2 = 0.97320 (97%), R-2 = 0.95534, SEE = 6931.09 (2.7%), DW = 1.62311, F-value = 54.47955*,
* = Significant at 1%, ** = Significant at 5%, *** = Significant at 10% level of Confidence.
Where b1<0, b2<0, b3>0, b4<0, b5<0, b6<0, b7>0, b8<0, b9<0 b10<0, b11>0,and b12>0.
The intercept of lin (Eq.2) was positive but not statistically significant. This implied that the direction of change in the regression was an increase and that the average value of farm output contributions to GDP when the explanatory variables were set at zero was 419 tonnes of farm outputs.
The negative correlation (-0.001) of money narrowly defined (Xt-1) conformed to the theoretical expectations. This implied that farm output and money narrowly defined moved in opposite directions. The impact of money narrowly defined on farm outputs contributions to GDP was not statistically significant but farm output performance was more consistent compared to the supply of money narrowly defined (M1). The money narrowly defined (M1) was inelastic with respect to changes in farm output. The negative correlation of farm output response to changes in money narrowly defined (M1) implied that income velocity and farm output did not increase or decrease simultaneously in the same direction. Thus improvements in income velocity and farm output were infinitesimal to defray inflationary pressures and this provided a strong evidence why commercial banks were reluctant to lend to farmers as farmers were discouraged from borrowing from banks. This result therefore confirmed the general low investments in agricultural sector across the research period.
The M2, money broadly defined (Xt-2) and M - broader monetary aggregates (Xt-3) were expectedly in conformity with a priori expectations. The M2 was statistically insignificant while M3 was statistically significant at (F*>F0.10). Money broadly defined changed simultaneously in opposite direction with respect to farm output while broader monetary aggregates changed in the same direction. The negative correlation coefficient of money broadly defined (M2) was not statistically significant but it shifted investment portfolios from the high risky, less liquid and less profitable agricultural sector to other lucrative sectors. Banks invested immensely on government bonds and Treasury bills that were more profitable compared to investments in agriculture.
Farm output was more consistent with respect to money supply (M2 and M3) but money supply elasticity of farm output was inelastic. Broader monetary aggregate (M3) gave the overall liquidity of the economy and its impact was statistically significant at (F*>F0.10) level.
The cash reserve requirement ratio (Xt-4) was positively correlated with farm output contrary to a priori expectations but its impact was statistically insignificant. Cash reserve requirement ratio elasticity of farm output was inelastic but farm output was more consistent compared to cash reserve requirement. Cash reserve requirement appeared to have stimulated bank lending to agriculture but it complicated monetary policy operations by removing distortion tax on depository banks as low cash reserved requirement ratio prevailed and consequently increased bank lending rates volatility. The cash reserve requirement ratio differed behaviorally with the CBN bank balances (Xt-7) which unexpectedly was negatively correlated with farm output contrary to theoretical expectations. Its impact was statistically insignificant but it created two policy problems. Though farm output was consistent with respect to CBN bank balances and the bank balances elasticity of farm output was inelastic. Bank balances arising from recapitalization created long term adverse effect following its spurious relationship with other monetary aggregates. Accordingly, this work agreed with Sellon and Weiner (1997) that bank recapitalization arise from payment needs and timing rather than a mandated linkage to deposit liabilities. This consequently, re-engineered the payment system structure as an important factor in monetary policy formulation and implementation. Thus changes in the payment system affected the demand for settlement balances and complicated monetary policy actions. Traditionally, CBN could have lowered the cash reserved requirement ratio to avoid limiting the volume of investment portfolio to less liquid agricultural sector and thus expand aggregate money supply.
Contrary to theoretical expectations, discount rate (Xt-5) was positively correlated to farm outputs. This appeared to suggest that CBN encouraged and eased money and agricultural credit supply to enhance farm outputs. Discount rate elasticity of farm output was inelastic and it had greater variability compared to farm outputs. Changes in discount rates were not supported by appropriate changes in monetary policy actions; hence the changes did not make impacts that were statistically significant. In conformity with a priori expectations, the exchange rate (Xt-6) regimes were negatively correlated to farm outputs. The exchange rates impacts were statistically insignificant and volatile. The response of farm output to exchange rates was inelastic but more consistent. The magnitude of exchange rate negative correlations adversely affected agricultural production and trade volumes. Nigerian economy experienced positive excess demand for foreign exchanged arising from overvalued domestic currency, hence trade in goods and non-factor services were negative. Consequently, this constrained Nigeria to over draw from her foreign reserves and then turned to borrow from world capital markets to maintain balance of trade. The result was low opportunity cost of resources employed in Nigeria’s rural economy and preference of Nigerians to place more savings in foreign assets. The situation would have been worse, if the impact of the exchange rate was statistically significant.
Expectedly and in conformity with a priori expectations, the bank lending rate (Xt-8) had negative correlation with farm outputs. That is, when lending rates increased, farm outputs decreased and vice versa. The impact of lending rate on farm output was not statistically significant and farm output had greater variability compared to lending rates. Banks lending rate elasticity of farm output was less than unity. Banks subscribed government bonds (Xt-9) had negative correlation with farm output and was thus, in harmony with theoretical expectations. The impact of bank subscribed government bonds on farm output was significant at 10% but it had greater variability compared to farm outputs. Government bonds elasticity of farm output was inelasticity. However, its negative correlation implied that banks shifted their portfolio investments from agricultural financing to highly liquid government securities and bonds. Contrary to theoretical expectations, inflation rate (Xt-10) was positively correlated with farm outputs,though its impact was not statistically significant. Farm output was however more consistent with respect to inflation rate. Inflation rate elasticity with respect to farm output was less than unity.
Agricultural credit supply (Xt-11) had positive correlation with farm output and this was in conformity with a priori expectations. The impact of agricultural credit supply on farm outputs was statistically significant at 1% level but farm output was more consistent with respect to agricultural credit supplied. Agricultural credit supply elasticity of farm output was about unity. That is, banks expanded agricultural credit supply proportionally by the same percentage change in farm outputs. Banks liquidly ratio (Xt-12) had positive correlation with farm output in conformity with theoretical expectations. Its impact on farm output was statistically significant at (F*<F0.10) level but banks liquidity elasticity of farm output was less than unity.

Significant share of the adjustments burden created by volatile monetary aggregates and economic indicators were borne by the agricultural sector. Given the prevailed exchange rates regime, changes in monetary policies had significant policy implications that were reflected in changes in real exchange rates and not in real interest rates. This policy implication was a burden of adjustment passed to the trade sectors which included export sectors and those sectors such as agriculture that competed with imports (CBN 2000a, Barkema and Doye 1985, Falk et al, 1986).
Nigeria’s agricultural sector was a trade sector, given the nation’s food imports and exports of agricultural commodities. In this context, the results showed that adjustments in monetary policies shifted to non-trade activities. Therefore, the farmers’ supply function in Nigeria showed precisely the response of farm outputs to monetary aggregates volatility and how farmers adjusted their farm outputs to the prevailing food prices (Ojwang, 1996).Therefore, Nigerian agricultural sector should be restructured to oligopolistic economy and government distribution function policy overhauled to ensure distributional equity.


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