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International Journal of Agricultural Science and Research (IJASR) ISSN 2250-0057 Vol. 2 Issue 4 Dec 2012 99-110 © TJPRC Pvt. Ltd.,

ANALYSIS OF AGRICULTURAL PRODUCTIVITY GROWTH, INNOVATION AND TECHNOLOGICAL PROGRESS IN AFRICA AJAO A.O & SALAMI. O Agricultural Economics Department, Ladoke Akintola University of Technology, Ogbomoso, Nigeria

ABSTRACT Over the last 5 decades agricultural research in Africa has made substantial progress in the development of new technologies. However, due to ineffective technology transfer mechanisms and a poor linkage between agricultural research and the farmers, the impact on the ground has been so far minimal. This paper reviews the evolution and the present state of agricultural productivity and technology in Africa; drivers of technology adoption in agriculture in Africa; and the impact of this technology on the performance of the agricultural sector. Utilizing data envelopment analysis (DEA) for time series data for a 39-year period (1971-2009), the study measures Malmquist Index of total factor productivity and examines changes in agricultural productivity in African countries in the context of diverse institutional arrangements, human capital, natural factors and food price volatility. The total factor productivity is further decomposed to determine whether the performance of factors productivity is due to technological change or technical efficiency change over the reference period and it was observed that technological change contributes more to the productivity growth than efficiency change which showed a decline. The findings suggests that the slow growth in agricultural productivity experienced by the continent lies with the institutions, human capital development, policies and natural factors such as access to the sea and land quality

KEYWORDS: Data Envelopment Analysis, Innovation, Productivity, Africa INTRODUCTION Background During 2000 – 07, Africa experienced its highest economic growth since the 1970s with the rate of gross domestic product (GDP) growth averaging 5.6% annually in 2001-08. The continent economic growth dropped to 2.5 % and 4.7% in 2009 and 2010 as a result of the global financial crisis. The uprising in North Africa may depress the continent’s growth to 3.7% in 2011. However, Africa´s average growth is estimated to rise to 5.8 % in 2012. Until recently, the African agricultural landscape was characterized by sluggish growth and low factor productivity. Some recent agricultural growth accelerations notwithstanding (e.g., Ethiopia), the sector’s growth (at a rate of 2 to 5 percent a year) remained insufficient to reduce rural poverty, attain food security, and facilitate structural transformation that the continent needs. Considering that agriculture is the foundation of most African economies and African peoples’ livelihoods, IFAD (2003) and Diao et al (2007)) concluded that greater agricultural and rural development is the key to solving the problems of hunger and poverty. Although agriculture accounts for 70 percent of full-time employment in Africa, 33 percent of its total gross domestic product (GDP), and 40 percent of its total export earnings, productivity has stalled. Per capita output of staple foods continues to fall, and the continent is steadily losing its world market shares for major export crops like coffee, tea, and cocoa (IFPRI, 2002; Salami et al, 2010). While Africa accounts for more than one-quarter of the world’s arable land, it only generates about 10 percent of global agricultural output. Hence, the sector has a huge potential for growth. Over the last 5 decades agricultural research in Africa has made substantial progress in the development of new technologies.


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However, due to ineffective technology transfer mechanisms and a poor linkage between agricultural research and the farmers, the impact on the ground has been so far minimal. Agricultural productivity is one of the determinants of high and sustained agricultural growth. Faster agricultural growth has put countries on the path of a much broader transformation process: rising farm incomes raise demand for industrial goods; lowered food prices curb inflation and induce non-farm growth increase the demand for unskilled workers. Rising on-farm productivity also encourages broad entrepreneurial activities through diversification into new products, the growth of rural service sectors, the birth of agro-processing industries, and the exploration of new export market (Harvey, 2006; World Bank, 2008). Harvey (2006) clearly emphasized the strong linkage between agricultural productivity and economic growth. He noted the impact of agricultural productivity to the transformation of poor countries to a prosperous nation and concluded that increasing agricultural productivity is a necessary condition for poverty alleviation. The relative importance of the potential implications of improvements in agricultural productivity varies among African countries based on their resource endowments, demographic characteristics, marketing opportunities, and labour supply and demand. However, agricultural output grew slower than overall population growth between 1965 and 2010. Moreover, the countries are achieving their agricultural growth more through expansion of cultivated areas than through yield increases, while in other parts of the world almost all growth is the result of yield increases. We reviewed the evolution of agricultural productivity and technology and the impact of the technology on the performance of the agricultural sector in Africa. The paper highlights recent innovations and successes in the acceleration of agricultural productivity in some African countries.

In particular, key challenges to technological progress and

agricultural productivity in Africa; and appropriate measures needed to break the barriers to technological advancement and productivity growth in Africa’s agriculture are outlined. The study measures total factor productivity and examines changes in agricultural productivity in African countries in the context of diverse institutional arrangements and food price volatility. Since output from agriculture can be broadly classified into two main groups: Crop and Livestock, the nonparametric DEA approach provides an attractive option. Therefore, this study used Data Envelopment Analysis (DEA) technique to calculate Malmquist TFP index numbers, hence, aims to provide understanding of agricultural productivity growth in Africa.

MATERIALS AND METHODS The Malmquist TFP Indices DEA is linear-programming methodology, which uses data on input and output quantities of a Decision Making Units (DMU) such as individual firms of a specific sectors to construct a piece-wise linear surface over data points. In this study, the countries were used as the DMU. The DEA method is closely related to Farrell’s original approach (1957) and it is widely being regarded in the literature as an extension of that approach. This approach was initiated by Charnes et al.; (1978) and related work by Fare, et al,. (1985). The frontier surface is constructed by the solution of a sequence of linear programming problems. The degree of technical inefficiency of each country, which represents the distance between the observed data point and the frontier, is produced as a by-product of the frontier construction method. DEA can either be input or output oriented depending on the objectives. The input-oriented method, defines the frontier by seeking the maximum possible proportional reduction in input usage while the output is held constant for each country. The output-oriented method seeks the maximum proportional increase in output production with input level held fixed. These two methods, that is, input-output oriented methods provide the same technical efficiency score when a


Analysis of Agricultural Productivity Growth, Innovation and Technological Progress in Africa

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constant return to scale (CRS) technology applies but are unequal when variable returns to scale (VRS) is assumed (Coelli and Rao, 2001). In this study, the output-oriented method was used by assuming that in agriculture, it is common to assume output maximization from a given sets of inputs. The interpretation of CRS assumption has attracted a lot of critical discussion e.g. Ray and Desli (1997), Lovell (2001) but also monotonicity and convexity are debatable e.g. Cherchye, et al.,(2000). Fare et al., (1994) used Data Envelopment Analysis (DEA) methods to estimate and decompose the Malmquist productivity index. The DEA method is a non-parametric approach in which the envelopment of decision-making units (DMU) can be estimated through linear programming methods to identify the “best practice” for each DMU. The efficient units are located on the frontier and the inefficient ones are enveloped by it. Four linear programs (LPs) must be solved for each DMU in this study (Country) to obtain the distances defined in equation (iii) and they are:

[d

t o

( xt , y t )

]

−1

= Max φ ,λ φ ,

(i)

− φy it + Yt λ ≥ 0 xi ,t − X t λ ≥ 0 s.t λ ≥ 0

[d

t +1 o

( x t +1 , y t +1 )

]

−1

= Max φ , λ φ ,

− φ y i ,t +1 + Y t +1 λ ≥ 0

st

x i , t + 1 − X t +1 λ ≥ 0

λ ≥0

[d

t o

(ii)

( x t +1 , y t +1 )

]

−1

= Max φ ,λ φ ,

(iii)

− φ y i ,t +1 + Y t λ ≥ 0 x i ,t +1 − X t λ ≥ 0

st λ ≥ 0

[d st

t +1 o

( xt , yt )

]

−1

= Max

φ ,λ

φ,

− φ y i ,t + Y t + 1 λ ≥ 0 x i ,t − X

t +1

λ ≥ 0

λ ≥ 0 Where

(iv)

λ is a N X 1 vector of a constant and φ is a scalar wit h φ ≥ 1 Over time best practice are natural and to include frontier shifts, that is, technical change, the Malmquist

productivity index is a well-established measure. The Malmquist productivity index, as proposed by Caves, et al., (1982), allows one to describe multi-input, multi-output production without involving explicit price data and behavioral assumptions. The Malmquist Productivity Index identifies TFP growth with respect to two time periods through a quantitative ratio of distance functions (Malmquist, 1953). Distance functions can be classified into input distance functions and output distance functions. Input distance functions look for a minimal proportional contraction of an input vector, given an output vector, while output distance functions look for maximal proportional expansion of an output vector, given an input vector. By using distance functions, the Malmquist Productivity Index can measure TFP growth without cost data, only with quantity data from multi-input and multi-output representations of technology.


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In this study, we use output distance functions. According to Hjalmarson and Veiderpass (1992), The Malmquist (quantity) index was originally introduced in a consumer theory context as a ratio between two deflation or proportional scaling factor deflating two quantity vectors onto the boundary of a utility possibility set. This deflation or distance function approach was later applied to the measurement of productivity in Caves, et al., (1982) in a general production function framework and in a non-parametric setting by Fare, et al., (1992). The productivity change, that is TFP change (TFPCH) using technology of period t as reference is as follows:

 d t (x , y )  M ot ( xt , y t , xt +1 , y t +1 ) =  o t t +1 t +1   d o ( xt , yt )  ………………………………………………………

(v)

Similarly, we can measure Malmquist productivity index with period t+1 as references as follows:

M

t +1 o

 d ot +1 ( xt +1 , y t +1 )  ( xt , y t , xt +1 , y t +1 ) =  t +1   d o ( xt , y t )  …………………………………………………

(vi)

in order to avoid choosing arbitrary period as reference, Fare et al., (1994) specifies the Malmquist productivity index as the geometric mean of the above two indices

 d t ( x , y ) d t +1 ( x , y )  M o ( xt , y t , xt +1 , y t +1 ) =  o t t +1 t +1 o t +1 t +1 t +1   d o ( x t , y t ) d o ( xt , y t ) 

1/ 2

…………………………………

(vii)

equation (vii) can be decomposed into the following two components namely efficiency change index (EFFCH) which measures the catching up components measuring efficiency change in relation to the frontier at different time. The second component is the geometric average of both components and measures technical change (TECHCH) which measure the technology shift between period t and t+1. The first component in TECHCH measures the position of unit t+1 with respect to the technologies in both periods. The second component also estimates this for unit t. If the TECHCH is greater (or less) than one, then technological progress (or regress) exists.

dot +1 ( xt +1 , yt +1 ) EFFCH = dot ( xt , yt ) ……………………………………………………………

(viii)

and

 d 0t ( xt +1 , , yt +1 ) d ot ( xt , y t )  TECHCH =  t +1 X t +1   d o ( xt +1 , y t +1 ) d o ( xt , yt ) 

1/ 2

……………………………………….…

(ix)

Econometric Model and Variable Definition The productivity growth and its components were achieved by solving equation (v)-(ix) and an Ordinary Least Square (OLS) estimation techniques was used to examine the effect of selected variables on agricultural productivity growth in the equation below.

The endogenous variable, Y is the Malmquist productivity index for each of the countries. The twelve exogenous variables (X1…X12) are research and development; life expectancy; urbanization, institutional quality index; land quality;


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Analysis of Agricultural Productivity Growth, Innovation and Technological Progress in Africa

trade openness; access to sea port; education;. Economically active population growth rate, average GDP growth and inflation

While

β is a vector of unknown parameters to be estimated.

Data Sources The study was based on the data that were drawn from the FAO web site (AGROSTAT) and it covers a period of 39 years (1971-2009) for 42 African countries. We dropped Eritrea and Ethiopia along with eight other countries due to data-related problems. The following are some of the main features of the data series that were used for the study. The data consists of information on agricultural value production and means of production such as total rural population; land area; fertilizer; and herbicide for each of the selected countries were drawn from FAO statistic database. To examine the drivers of TFP, the study considered the following variables: institutional quality index (Center for Instutitional Reform and Informal Sector); Land Quality (Peterson, 1987); Education which was used as a proxy for quality of labor (UNDP); access to the sea port (Sachs and Warner 1997); Research and Development (ASTI); life expectancy (UNDP); average annual growth of economically active population (Wirold Bank); Inflation (World Bank) trade openness (Sach and Warner 1995) Urbanization

RESULTS AND DISCUSSIONS The Malmquist index of TFP was constructed for 42 countries for a period of 39 years. The obtained index was decomposed into technological change and efficiency change over the sample period using DEA. The Malmquist TFP Indices The productivity growth and its components over the sample period are presented in table 1. The average TFP growth over the period was found to be 0.2 percent per annum with large variation in growth rate observed for various countries. Recall that the value greater than one indicates an improvement in the productivity, pure efficiency and technology while less than one implies decline. It was observed from table 2 that twenty two countries which represents about 52 percent of the total sample experienced productivity growth within the reference period. The observed growth is accounted for largely by technological change with the exception of Mali, Senegal and Liberia in which efficiency change accounts for the observed growth rather than technological progress. Other countries including Congo and Somalia experienced on the average a decline in growth. Overall, the results showed there have been evidence of technological improvement over the reference period; this indicates advances in technology as represented by an upward shift in the production frontier (2.1percent) which have not been fully exploited by African farmers and further suggests that technology is an important driver of African agricultural growth while 1.8 percent decline in efficiency was observed. The obtained technical efficiency was further decomposed into scale and pure technical efficiencies; it was observed as shown in Table 2 that there is a decline in pure efficiency change and scale efficiency change with a value of 0.987 and 0.995 respectively, this contributes to decline of overall efficiency change. This findings support the earlier findings by Nkamleu et al., 2008; Allen, 2010, Block 2010 and NiPratt and Yu 2011. Table 1: Malmquist Index Summary of Country Means Firm Algeria

Effch 1.019

techch 1.055

pech 1.019

sech 1.000

tfpch 1.075

Angola

0.927

1.028

0.927

1.000

0.953

Benin

0.959

1.105

0.959

1.000

1.060


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Table 1: Malmquist Index Summary of Country Means – Contd., Firm Effch techch pech sech tfpch Botswana 0.973 1.106 0.973 1.000 1.076 Burkina Faso Burundi Cape Verde Central African Republic Chad Comoros Congo Côte d'Ivoire Djibouti

1.004 0.989 1.001 0.994

1.043 1.002 1.071 1.098

1.004 0.989 1.001 0.994

1.000 1.000 1.000 1.000

1.047 0.991 1.072 1.091

0.997 0.998 0.945 0.998 0.996

1.113 1.042 1.044 0.995 1.026

0.997 0.998 0.945 1.000 0.996

1.000 1.000 1.000 0.998 1.000

1.110 1.039 0.986 0.993 1.022

Egypt

0.989

1.025

0.989

1.000

1.014

Gabon

1.006

1.001

1.006

1.000

1.006

Ghana

0.918

1.083

0.918

1.000

0.995

Guinea

0.922

0.972

0.922

1.000

0.896

Kenya

0.938

0.974

0.938

1.000

0.914

Lesotho

0.969

1.011

0.963

1.007

0.980

Liberia

1.017

0.987

1.017

1.000

1.004

Libya

1.000

0.983

1.000

1.000

0.983

Madagascar Malawi

1.018 0.896

1.010 0.950

1.018 0.901

1.000 0.995

1.028 0.851

Mali

1.024

0.996

1.024

1.000

1.019

Mauritius

0.967

1.020

0.965

1.002

0.986

Morocco

1.000

0.993

1.000

1.000

0.993

Mozambique Namibia

1.005 1.030

1.007 1.005

1.029 1.022

0.977 1.007

1.012 1.035

Niger

0.952

1.048

0.984

0.967

0.997

Nigeria

0.897

0.980

0.932

0.962

0.879

Rwanda

1.000

0.999

1.000

1.000

0.999

Sao Tome and Principe Senegal

0.960

0.995

1.000

0.960

0.955

1.031

0.980

1.001

1.030

1.011

Seychelles

1.011

0.991

1.000

1.011

1.002

Sierra Leone Somalia

1.004 0.920

0.958 0.991

1.001 1.000

1.003 0.920

0.963 0.911

South Africa

0.947

0.949

1.000

0.947

0.898

Swaziland

1.026

1.019

1.026

1.000

1.046

Tunisia

1.018

1.046

1.019

0.999

1.065

united republic of Tanzania Zambia

0.998

1.085

0.998

1.000

1.083

0.889

1.108

0.889

1.000

0.984

Zimbabwe

1.097

0.984

1.097

1.000

1.079

Mean

0.982

1.021

0.987

0.995

1.002

The mean productivity growth was computed for each region namely Southern Africa, East Africa, West Africa, Central Africa and Northern Africa. The East, South and North Africa experienced growth of 3.3 percent, 2.6 percent and


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3.6 percent respectively with 0.5 percent and 2 percent growth in efficiency change and technological change accounted for the observed growth in North Africa. Meanwhile, the observed growth observed in other two regions is accounted for by a decline in efficiency and improvement in the technology. It should be noted here that though productivity decline was experienced in West and southern Africa region which is largely accounted for poor efficiency change, the region still enjoy a modest technological growth of 1.8 percent and 1.6 percent respectively. This finding further support the opinion that improves performance with expenditure on R and D enhanced agricultural productivity growth. Table 2: Annual Mean TFP by Regions 1971-2009 Region

EffCh

TeCh

TFPCh

West Africa

0.977

1.018

0.995

Southern Africa

0.979

1.016

0.994

East Africa

0.978

1.055

1.033

North Africa

1.005

1.020

1.026

Central Africa

0.978

1.055

1.032

The overall performance as earlier shown was poor but this simple average did not show the variation across time. Fig 2 reports the TFP change decomposition by decades and it showed to have varied widely over time. Much of the fluctuation lies in the efficiency and technological changes; it could be observed that when technological change was growing, the efficiency change was declining. This implies African farmers have not been efficient in the use of the technology and this further re-emphasizes the need to adopt bottom up technological transfer approach. The TFP showed a decline from the first decades until 2002 when the recovery starts and extends to 2009 with agricultural TFP growing at 2.1 percent

Attempts was made to examine the agricultural performance of Fragile States and it was observed from Table 4 that nine out of the fourteen fragile states considered had decline growth with Guinea having 10.4 decline in growth. It should be pointed out that despite experiencing decline growth; Burundi and Congo still have technological progress of 0.4 and 4.4 percent respectively. On the average, the fragile states had 0.5 percent decline in growth with 1.1 percent improvement in the technological progress and 1.6 percent decline in efficiency. Comparing these with the overall Africa average, the findings further re-emphasizes the importance of stability in the polity to productivity growth.


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Table 3: Malmquist Index Summary of Fragile State Means Fragile State Chad Central African Republic Zimbabwe Comoros Liberia Rwanda C么te d'Ivoire Burundi Congo Sierra Leone Sao Tome and Principe Djibouti Somalia Guinea Mean

EFFCh 0.997 0.994

TechCh 1.113 1.098

PECh 0.997 0.994

SECh 1.000 1.000

TFPCh 1.110 1.091

1.097 0.998 1.017 1.000 0.998 0.989 0.945 1.004 0.960

0.984 1.042 0.987 0.999 0.995 1.002 1.044 0.958 0.995

1.097 0.998 1.017 1.000 1.000 0.989 0.945 1.001 1.000

1.000 1.000 1.000 1.000 0.998 1.000 1.000 1.003 0.960

1.079 1.039 1.004 0.999 0.993 0.991 0.986 0.963 0.955

0.938 0.920 0.922 0.984

0.974 0.991 0.972 1.011

0.938 1.000 0.922 0.993

1.000 0.920 1.000 0.992

0.914 0.911 0.896 0.995

Sources of Agricultural Productivity Growth Attempt was made to understand factors that influence growth across the countries as this can be used to formulate policies that will enhance productivity. This study therefore examined the determinants of productivity growth or otherwise using factors such as human capital; natural factors such as access to the sea port and land quality; institutional quality and macroeconomic variables. The TFP was regressed on the explanatory variables defined earlier after we tested for multicollinearity using the tolerance factor and variance inflation factor, all the explanatory variables followed a priori expectation. The OLS estimates of these explanatory variables are reported in table 4. The R-square was found to be 0.77 suggests that about 77 percent of the variation in the TFP was explained by the explanatory variables in the model, with the F statistic which shows the overall significance of all the explanatory variables included in the model was significant at 1 percent probability level. The study further found out as expected, R and D which was used as a proxy for the technology is positive and significant. This finding further support the argument that the extension services should be strengthened for a proper and effective dissemination of the products of R and D to the end users. The coefficients of life expectancy and education which are proxy for labour availability and its quality have positive sign with

life expectancy having significantly impact on the productivity growth, education, though not

significant agrees with a priori expectation and similar to Shih-Hsun (2003) which found education to be an important varaible in determining TFP growth in the Chinese agricultural sector. The effect of land quality and trade openness was examined and was found to be major determinants of TFP growth in terms of magnitude and significant at 1 percent. This showed evidence of a positive result from various reforms such as trade liberalization of some African countries in recent years. This finding agrees with Caselli and Wilson (2004) that noted that trade openness is a channel of transmission of foreign technology. Land quality has positive significant impact on the agricultural productivity growth, this reflect the importance of land quality to the productivity growth The effect of access to the sea port on the TFP was also examined and it was observed that its coefficient agreed with the a priori expectation and have significant impact on the TFP. This finding further support the importance of a


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functioning port to international trade activities and similar to the other findings in the literature (e.g. Sachs and Warners 1997). Past studies have not made a conscious effort to examine the effect of urbanization, average annual growth in real GDP, average annual growth of economically active population and inflation could have in transforming and improve agricultural productivity. This study found a negative but significant relationship between TFP and urbanization; this suggests the release of agricultural labour to other sectors of the economy which is not accompanied by comparative agricultural progress and further showed failure of various policies by successive African countries to prevent rural-urban migration. Also, inflation was found to be significantly negative, this did not come as surprise due to the effect inflation on agricultural key inputs especially, fertilizer which often times beyond the reach of an average African farmers. The study further found out as expected that open public institution governance will enhance growth as captured by institutional quality index. This study found that a unit change in this index is associated with about 3 percent change in growth, this findings is supported by Pardey (1996), Sachs and Warner (1997) and Adkin et al (2002). The effect of growth in economically active population was found to be negative and significant. This might be due to high child and maternal mortality rate in the continent as well as decreased efforts to eradicate major tropical diseases such as malaria which have attendants effect on labour availablilty Overall, the findings from the study suggests that the slow growth in agricultural productivity experiencing by the continent lies with the institutions, human capital development and natural factors such as access to the sea and land quality. Table 4: Determinants of TFP Variable Urbanization R and D LifeExp Edu Instquality Landquality Trade openness econpopgrowth access to sea inflation avGDPgrowth Constant R-square F

Coefficient -0.001 0.002 0.046 0.005 0.030 0.145 0.567 -0.176 0.025 -0.002 0.006 1.600 0.77 14.37

t-ratio 3.23* 1.83*** 2.70** 1.15 2.09*** 4.73* 3.27* -3.02* 0.82 -2.47** 0.50 3.5

CONCLUSIONS AND WAY FORWARD In this paper we have discussed evolution of agricultural productivity, challenges and recent innovations. We also empirically investigated the trends of agricultural productivity in Africa over the period of 1971 and 2009 by applying twostage estimation procedure. In the first stage, we computed the output-orientated Malmquist productivity indexes and its decompositions using non-parametric DEA approach. In the second stage, linear regression was used to identify the major determinants of TFP growth. The results show mean annual growth in total factor productivity growth of 0.2 percent, with performance in growth attributable rather to technological change than efficiency change. In terms of sub-regional performance evaluation, on the average, It was found that only North, East and Central Africa regions of the continent on the average experienced productivity growth with technological change accounted for the productivity growth experienced during the study period. The highest growth was observed for the Central and North Africa.


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In determining the source of the observed growth, it was observed that the coefficients of education which was used as proxy for labour quality have positive sign and significantly impact on the productivity growth. The coefficient of R and D which was used as a proxy for the technology is also positive and significant. This finding further support the argument that the extension services should be strengthened for a proper and effective dissemination of the products of R and D to the end users. Also trade openness and land quality have positive significant impact on the agricultural productivity growth with trade openess ranks highest in influencing TFP. Generally, the findings from the study suggests that the slow growth in agricultural productivity experiencing by the continent lies with the institutions, human capital development and natural factors such as access to the sea port and land quality. One main contribution and new findings from this study reveals that on the average, there is productivity growth but looking at the trend over the years, it was observed that the growth fluctuates over the years suggesting the effects of instability in the region. Also, the study revealed negative but significant relationship between TFP and urbanization; this suggests the release of agricultural labour to other sectors of the economy which is not accompanied by comparative agricultural progress and further showed failure of rural-urban migration policies. The main reasons for the poor performance of agricultural productivity in Africa have been a combination of unclear land rights, weak market linkages, infrastructure gap and lack of skills. The inadequate access to financing has constrained investment, expansion, and technology and adoption of innovations. The importance of removing infrastructure bottlenecks and adoption of innovation cannot be overstated. In particular, the diffusion and adoption of new technology is crucial for agricultural productivity. In the research and development component of the agricultural development agenda, non-governmental organizations would find relevance in the offering of supportive services as extension and distribution of inputs to farmers with adequate back up by the relevant public agencies to forestall undue contradictions and duplication of efforts. Similarly, private sector has a great role to play in transformation of agriculture from the current state of underdevelopment into a viable venture. The donor agencies have a great role in revitalizing African agriculture and enhanced increased agricultural productivity. First they should encourage African governments to adopt preventive and mitigation measures to food price volatility; two, they can play a great role in encouraging African countries to be committed to Maputo Declaration, in which African governments have resolved to devote at least 10 percent of their budgets to the agricultural sector. This will help to reverse the recent decline in the share of agriculture’s in the national budget. Three, they should continue to facilitate regional integration and trade in the continent for enhanced trade and investment flows. Four, they should monitor the policies and performance of the RMCs and identify countries with outstanding policy, programs and practice for adoption by others.

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