An Empirical Analysis of the effect of Agricultural Input on Agricultural Productivity in Nigeria

The main object of this study is to investigate the effect of Agricultural input on Agricultural productivity in Nigeria from 1990 to 2016 using secondary annual time series data sourced from World Bank database (2016) and Central Bank of Nigeria Statistical Bulletin (2016). The methodology adopted for the study was fi rst and foremost unit root test by Augmented Dickey-Fuller (ADF) approach; a test for longrun relationship (Johansen cointegration), Granger causality test and then the Ordinary Least Squares (OLS) multiple regression method. Variables in the model were both stationary as well as exhibited longrun equilibrium relationship. Empirical OLS regression result revealed an inverse relationship between government expenditure and agricultural output. Deriving from the fi ndings, the study recommended the following for policy implementation: The Nigerian government should put in place policies and modalities that will encourage existing banks (both commercial and agricultural banks) to make credit facilities readily available to farmers with personnel assigned to monitor and ensure that such funds are judiciously used for the purpose which it is taken; Government must provide funds to acquire sophisticated farm tools (harvesters, tractors, herbicides, fertilizer etc.) and as well build irrigation, dams, storage facilities and establish food processing industries across the country to enable farmers increase productivity, process and preserve their food stuff; Finally, government spending on agricultural sector must of a necessity be increased. The present lackluster and uninspiring attitude of government to management of appropriated funds must change. Corrupt civil servants, contractors and bureaucrats who divert and misappropriate allocated funds for the growth of the sector must be punished to serve as a deterrent to other intending treasury looters. The various fi nancial crimes commissions such as EFCC and ICPC should be strengthen to do this. Research Article


Background to the study
Agricultural development is one of the most powerful tools to end extreme poverty, boost shared prosperity and feed a projected 9.7 billion people by 2050. Growth in the agriculture sector is two to four times more effective in raising incomes among the poorest compared to other sectors. 2016 analyses found that 65% of poor working adults made a living through agriculture. Agriculture is also crucial to economic growth: in 2014, it accounted for one-third of global gross-domestic product (GDP) [1]. Agriculture is the science or practice of farming, including cultivation of the soil for the growing of crops and the rearing of animals to provide food, wool, and other products while agricultural productivity is increase in per capita output of agricultural produce (Stamp 1970). To meet the needs of a world population expected to reach nine billion by 2050, agricultural production will need to increase by at least 60 percent [2]. Due to its relative importance and future gains, it is known to be a major source of raw materials for processing industries in the manufacturing of fi nished goods and services. It produces about 80% of all manufacturing industries' raw materials used in the production of fi nished goods in most economies of the world. For many years, productivity has been a key issue of agricultural development strategies because of its impact on economic growth and development. It is also a known fact that the easiest means through which mankind can get out of poverty to a condition of relative material affl uence is by increasing agricultural productivity. Productivity improvements create the wealth that can be used to meet the needs of the future. the New Nigerian Agricultural Policy etc. [3]. In addition to these measures, fi nancial measures such as the establishment of agricultural credit scheme were introduced by successive governments. Inspite of these measures, the development of the agricultural sector has been slow and the impact of this sector on economic growth and development has been minimal.
In fact, the former Minister of Agriculture, Dr. Akinwunmi Adesina once lamented that the import bill for food in Nigeria is exceptionally high and it is growing at an unsustainable rate of 11% per annum. Ironically, Nigeria is importing what it can produce in abundance. This trend is hurting Nigerian farmers and displacing local production [4]. In the same vein, Senator Ibikunle Amosun once lamented the high rate of importation of food in Nigeria, describing it as a shame that the giant of Africa imports what it eats [5]. It is in view of the foregoing that the present paper intends to examine the effect of agricultural inputs on agricultural productivity in Nigeria between 1990 to 2016, using an econometric approach of Ordinary Least Squares Regression.
Study Objectives: Specifi cally, the study is designed to achieve the following objectives in addition to the broad objective earlier stated.
(i) Examine the effect of Agricultural machinery (tractors) on agricultural productivity in Nigeria; (ii) Determine the impact of Agricultural credit (loans) on agricultural productivity in Nigeria; (iii) Examine the causality effect of government expenditure on agriculture on agricultural productivity.

Justifi cation for the study:
The study is justifi ed because it will provide an insight into how effective both fi scal and monetary instruments designed by the Central Bank of Nigeria and the Nigerian government helped in achieving the overall objectives of the nation's agricultural policy which is fi rst and foremost tailored towards achieving food security and exportable surplus for enhanced economic growth and development. Furthermore, the study is expected to serve as a reference material for future research as well as guide government in its future policy designs towards achieving country-wide expected goals.
The remainder of this study is sectionalized as follows: Part two is dedicated to theoretical and empirical review. In part three, the data and methodology adopted for the study is discussed. Part four presents the empirical fi ndings, while part fi ve provides the conclusion and policy recommendations of the study.

Agricultural productivity
According to Fulginiti and Perrin [6], as cited in Amire and Arigbede [2], agricultural productivity refers to the output produced by a given level of inputs in the agricultural sector of a given economy. More formally, it can be defi ned as "the ratio of value of total farms outputs to the value of total inputs used in farm production" [7], as cited in [8]. Put differently, agricultural productivity is measured as the ration of fi nal output, in appropriate units to some measure of inputs.

An overview of agricultural policies in Nigeria
In the view of Nwagbo [9], agricultural policy-making in Nigeria has been through changes over time. During each phase, the characteristics of policy have refl ected the roles expected of the sector and the relative endowment of resources. Institutions were created while others were disbanded depending on the exigencies of the time. Hence the marketing Boards gave way to commodity boards and production companies; the River Basin development Authorities have been modifi ed to meet changing objectives; small-scale irrigation schemes are receiving more attention than the earlier large versions; agricultural extension by the State Ministries of Agriculture has given way to extension by the Agricultural Development Project (ADP). Other measures include, National Accelerated Food Production (NAFP), Operation Feed the Nation (OFN), Green Revolution (GRP) and the Directorate for Food, Roads and Rural Infrastructure and fi nally, the New Nigerian Agricultural Policy. The fi rst national policy on agriculture was adopted in 1988 and was expected to remain valid for about fi fteen years, that is, up to year 2000. Nigeria's agricultural policy is the synthesis of the framework and action plans of government designed to achieve overall agricultural growth and development. The policy aims at the attainment of self-sustaining growth in all the sub-sectors of agriculture and the structural transformation necessary for the overall socio-economic development of the country as well as the improvement in the quality of life of Nigerians.
According to ARCN (2016) [10], the broad policy objectives Include: • Attainment of self-suffi ciency in basic food commodities With particular reference to those which consume considerable shares of Nigeria's foreign exchange and for which the country has comparative advantage in local production; • Increase in production of agricultural raw materials to meet the growth of an expanding industrial sector;

Theoretical framework
The theoretical framework of this study is built on the Cobb-Douglas production function. This theoretical model was applied in extant literature including Ekwere [11]. In economics, the Cobb-Douglas functional form of production function is widely used to represent the relationship of an output to input. In agricultural production, effi cient allocation of farm resources helps farmers to attain their objectives. It avails farmers the opportunity of improving their productivity and income. At the microeconomic level effi cient allocation of farm resources (farmland, credit facilities, fertilizer, tractors and labour, among others) help farmers to contribute to food production, employment creation, industrial raw materials and export product for foreign exchange earnings. According to Olayide and Heady [7], agricultural productivity is synonymous with resource productivity which is the ratio of total output to the resource/inputs being considered. According to Olujenyo (2008), the production function could be expressed in different functional forms such as Cobb Douglas, linear, quadratic, polynomials and square root polynomials, semilog and exponential functions. However, the Cobb Douglas functional form is commonly used for its simplicity and fl exibility coupled with the empirical support it has received from data for various industries and countries.

Materials and Methods
This study adopts a non-experimental research design approach. The data used were obtained from secondary sources and therefore, no sampling was done neither was any sampling technique adopted in the process of research.

Sources of data collection
The data for this study were secondary in nature and sourced from the publication of World Bank Database and Central Bank of Nigeria (CBN) [13], statistical bulletin for various issues. The data spans the period 1990 to 2016 (26 years). The data from this period present a considerable degree of freedom that is necessary to capture the effect of explanatory variables on the dependent variables. Furthermore, data sourced from the World Bank can be reliable because many studies have employed the data published by this institution for econometric purposes due to its reliability.

Variables adopted for the study
Variables adopted for the study are Ag-output (proxy for agricultural productivity) used as dependent variable to be regressed against Ag-Machine (proxy for Agricultural machinery, tractors per 100sq.km of arable land), Ag-Exp (proxy for government expenditure on agriculture) and gross domestic product as independent variables respectively. Gross Domestic Product is included as a control variable to avoid the challenge of variable omission and model misspecifi cation.

Method of data analysis
The method of data analysis include fi rst and foremost unit root test using Augmented Dickey-Fuller (ADF); a test The model for the ADF unit root framework is as follows: In the above equation,  is the constant and  is the coeffi cient of time trend. X is the variable under consideration.
In this study, the variables include log(FDI), log(GDP-pc), log(INVT), and log(MAN). Δ is the fi rst-difference operator; t is a time trend; and  t is a stationary random error. The test for a unit root is conducted on the coeffi cient of Xt-1 in the above regression. If the coeffi cient, , is found to be signifi cantly different from zero ( ≠ 0), the null hypothesis that the variable X contains a unit root problem is rejected, implying that the variable does not have a unit root. The optimal lag length is also determined in the ADF regression and is selected using Akaike information criterion (AIC).

Johansen cointegration test:
This paper attempts to use the Johansen maximum likelihood cointegration test (Johansen, 1988) to determine long-run relationships among the variables being investigated. In examining causality, the Granger causality analysis is also performed. In order to obtain good results from the test, selecting the optimal lag length is so important. The Johansen cointegration framework takes its starting point in the vector autoregressive (VAR) model of order p given by: where yt is a vector of endogenous variables and A represents the autoregressive matrices. xt is the deterministic vector and B represents the parameter matrices.  t is a vector of innovations and p is the lag length. The VAR can be re-written as: The matrix ∏ contains the information regarding the long-  The mathematical form of the model is specifi ed below Ag-output = f(Ag-Machine + Ag-Credit + Ag-Exp + Gdp)... In order to capture the stochastic term μ t of the variables, the explicit form of the models is given in econometric form below: The estimated models are further transformed into loglinear form. This is aimed at reducing the problem of multicollinearity among the variables in the models. Thus the loglinear models are specifi ed as shown below: Where,

Data description and sources
This paper used secondary data (time series data).
Empirical investigation was carried out on the basis of the Below is the data presentation (Table 1).

ADF unit root test results
In order to begin the dynamic (long-term) regression analysis, the study begins with the unit root test for the stationarity of the variables in each of the models using the Augmented Dickey-Fuller (ADF) since it adjusts properly for autocorrelation ( Table 2).
The results of the unit root test using the Augmented Dickey-Fuller (ADF) test as shown above revealed that no variable was stationary at levels. Hence, the null hypothesis of non-stationarity cannot be rejected at levels. However, at fi rst difference, all variables were stationary. That means at fi rst difference the variables were integrated of order I (1).

Co-Integration tests
This is used to test for the existence of long-run relationship between dependent and independent variables. The Johansen co-integration test was conducted on the selected variables.
The result is as tabulated in table 3. OLS output (Table 4) Here, Ag-machine, Ag-credit and GDP entered the model with a positive sign. Only the coeffi cient of government expenditure  on agriculture was inversely related to the dependent variable.

Granger causality test
Below is the output of the Pairwise Granger causality test.
To reject the null hypothesis formulated, the probability value of the F-statistic must be less than 0.05. If the probability value of the F-statistic is greater than 0.05 signifi cance level, the null hypothesis is not rejected, thus concluding that the variable under consideration does not Granger cause the other.
The extract below is in conformity with the above stated rules (i.e. the F-statistic p-value is less than 0.05% signifi cance level) ( Table 5).
The variable of interest here is AG_EXP (government expenditure on agriculture) and AG_OUTPUT (agricultural productivity). From the extracts above, it is revealed that there is a unidirectional (one-way) causation between agriculture output and government spending on the sector within the period studied. The result of CUSUMQ stability test indicates that the model is stable. This is because the CUSUMQ lines fall in-between the two 5% lines. Finally, the normality test adopted is the

Decision P-value
There is unidirectional causality between AG_MACHINE and AG_OUTPUT 0.0301 There is unidirectional causality between AG_OUTPUT and AG_CREDIT 0.0236 There is unidirectional causality between AG_OUTPUT and AG_EXP 0.0190 There is bidirectional causality between GDP and AG_OUTPUT 0.0127 / 0.0000 There is unidirectional causality between AG_CREDIT and AG_MACHINE 0.0017 There is unidirectional causality between AG_MACHINE and AG_EXP 0.0091 There is bidirectional causality between GDP and AG_CREDIT 0.0334 / 0.0000 There is bidirectional causality between GDP and AG_EXP 0.0436 . 0.0142   (Figures 1,2).

Conclusion / Recommendations
The role of agriculture in any economy is indeed signifi cant and cannot be over-emphasized. It is one of the most dominant sectors in any economy as the very survival of every nation depends on how well or bad its agricultural sub-sector is managed. It is indeed not just a major source of livelihood for its citizens but a source of foreign exchange earner to the nation. This is because apart from providing food for the give priority to the sector in terms of funding. A country's future in terms of food security is a function of government's commitment to making its agriculture work, and working, very effectively and effi ciently towards delivering expected dividends.
The following is therefore recommended for policy implementation: (a) If the Nigeria government really want to attain the objective of self-suffi ciency in food production, the government need to put in place policy and modalities that will encourage existing banks (both commercial and agricultural banks) to make credit facilities readily available to farmers with personnel assigned to monitor and ensure that such funds are judiciously used for the purpose which it is taken.
(b) Furthermore, government must provide funds to acquire sophisticated farm tools (harvesters, tractors, herbicides, fertilizer etc) and as well build irrigation, dams, storage facilities and establish food processing industries across the country to enable farmers increase productivity, process and preserve their food stuff.