Transcript Document
RICE PRODUCTION IN GHANA: PAST, PRESENT AND FUTURE “DRIVING FORCES AND REQUIRED ACTIONS”
BY: BOANSI DAVID
SUPERVISORS: PROF. IMRE FERTO PROF. THOMAS HECKELEI TUTOR: DR. ZOLTAN BAKUCS
OUTLINE PROBLEM STATEMENT AND OBJECTIVES
RICE PLANNING AND MARKET STRUCTURE
REVIEW ON THEORY AND METHODOLOGY
MODEL(S) AND DATA VERIFICATION
RESULTS
ACREAGE RESPONSE YIELD RESPONSE OUTPUT RESPONSE IMPULSE RESPONSES
SYNTHESIS OF RESULTS
RECOMMENDATIONS
REFERENCES
PROBLEM STATEMENT Performance of Ghana in supply of rice
Prod. Milled (000 t) Total Supply (000 t) Share of Prod in Supply, % 400 300 200 100 0 800 700 600 500
Year Source: Author’s construct with data from IRRI- (USDA data)
120 100 80 60 40 20 0
Suggestions on bridging the gap
Doubling of area under cultivation, increasing yield by at least 50% and investing in vital areas (Olaf and Emmanuel (2009); Aker
et a
l (2011)) Improving the quality of local rice on the market (Diako
et al
. (2011); Tomlins
et al
(2005))
Past studies:
Focus mainly on the quality aspect with close to nothing on supply side at the national level
Current study
: Estimating the supply and impulse response of rice for Ghana (1973-2009)
RESEARCH QUESTIONS
What is the yield gap of rice production in Ghana for the scope of study (1973-2009)?
What is the characteristics of the current rice market structure of Ghana?
What are the factors driving area harvested, yield and output of rice in Ghana?
How would area harvested, yield and output of rice respond to future impulses (shocks in selected determinants)?
OBJECTIVES
Analysis of rice planning: yield gap
Review of the rice market structure for Ghana
Estimation of supply response of rice – acreage, yield and output responses, 1973-2009
Tracing of response of harvested area, yield and output to impulses for the next ten years
RICE PLANNING: YIELD GAP ANALYSIS Source: Author’s construct with data from IRRI and MoFA (2011) RICE MARKET STRUCTURE Yield gap = 1- (Actual / Potential) (Licker et al, 2010) 0 – on production frontier 1 No productivity Source: Author’s construct
REVIEW ON THEORY AND METHODOLOGY REVIEW ON THEORY
Farmers production decisions are influenced by both price and non-price determinants
Price determinants
Real producer price of rice , real producer price of maize , price of urea fertilizer , world price of rice and maize with important indirect effects to producers (Molua (2010); Mulwanyi
et al
(2011)
Non-price determinants
Irrigation , area of land cultivated, agricultural labor availability, agro-climatic conditions, access to capital and credit, rural infrastructure (Bingxen and Shenggen (2009); Sachcharmarga and Williams (2004); Mythili (2008))
REVIEW ON METHODOLOGY
OLS estimation of static models, the Nerlove framework (Nerlove, 1958), the Autoregressive Distributed Lag or bound test (Pesaran
et al
, 2001), Co-integration analysis (Engle and Granger (1987), Phillips and Ouliaris (1988), and
Johansen and Juselius
, 1998))
MODEL(S) AND DATA VERIFICATION MODEL 1: AREA , OUTPUT , RPRice , RPMaize , PUreaFert , IrrigArea
AREA =
f
(RPRice, RPMaize, PUreaFert, IrrigArea) OUTPUT =
f
(RPRice, RPMaize, PUreaFert, IrrigArea)……∆ AREA, ∆YIELD(-1)
MODEL 2: YIELD , OUTPUT , RPRice , RPMaize , PUreaFert , IrrigArea
YIELD =
f
(RPRice, RPMaize, PUreaFert, IrrigArea)
RPRice
– Real producer price of rice
RPMaize
– Real producer price of maize
PUreaFert
– Price of urea fertilizer (World Price as proxy)
IrrigArea
Irrigated Area (Irrigated agricultural area as proxy)
Series ln OUTPUT UNIT ROOT TEST ADF-Test stat.
Level
-0.5055
Critical V 5%
-2.86
Lags-SIC Max 10
2
ln AREA ln YIELD ln RPRice ln RPMaize ln PUreaFert ln IrrigArea
-1.2765
-0.5183
-2.0214
-1.9360
-1.7549
-1.9566
-2.86
-2.86
-2.86
-2.86
-2.86
-2.86
0 0 2 2 1 8
ADF-Test stat.
First diff.
-3.9335*** -3.7344*** -5.0828*** -7.8427*** -6.3838*** -3.6625*** -3.4396**
Lag-SIC Max 10
1 4 1 0 6 0 0 **5%, ***1% -
JMulTi software for time series analysis
Asymptotic critical values: Davidson, R. and Mackinnon, J. (1993). “Estimation and Inference in Econometrics “ pp 708, table 20.1. Oxford University Press, London
RESULTS
ACREAGE RESPONSE Variable
∆ ln AREA (-1) ∆ ln OUTPUT ln RPRice ∆ ln RPRice (-1) ln RPMaize ∆ ln RPMaize (-1) ln PUreaFert ∆ ln PUreaFert (-1) ln IrrigArea ∆ ln IrrigArea (-1) Intercept Residual (-1) Adj. R-squared Log-Likelihood Durbin -Watson Stat Jarque-Bera B-G LM F. Stat ARCH : F-stat Q-stat (1, 2)
***1%, **5%, *10% Coefficients Short-run effects standard error 0.198126**
0.085398
0.513069***
0.060127
Long-run effects Coefficients Standard error 1.001215***
0.30893
-0.153275*
0.085922
0.422904 0.30507
0.017401
0.062808
-0.361652**
0.13471
0.110798
0.075536
-0.303422 0.56694
0.191501 0.334006
-0.816620 2.05212
-0.795483*** 0.136411
0.820894 Akaike info. criterion -1.408542
32.64948 Schwarz criterion -1.053034
2.063097 Hannan-Quinn criter. -1.285821
0.931728
Prob.: 0.6276
1.556548 Prob. F(2,25): 0.2306
1.145194 Prob.:F(2,30): 0.3317
0.3374 Prob.: 0.561 ; 3.2678 Prob.:0.195
YIELD RESPONSE Variable
∆ ln YIELD (-1) ∆ ln OUTPUT (-1) ln RPRice ∆ ln RPRice (-1) ln RPMaize ∆ ln RPMaize (-1) ∆ ∆ ln PUreaFert ln PUreaFert (-1) ln IrrigArea ln IrrigArea (-1) Intercept Residual (-1) Adj. R-squared Log-Likelihood Durbin -Watson Stat Jarque-Bera B-G LM F. Stat ARCH : F-stat Q-stat (1,2)
***1%, ** 5%, * 10% Coefficients Short-run effects standard error 0.634719**
0.266484
-0.379290**
0.142923
Long-run effects Coefficients Standard error 0.715598***
0.09426
0.146638 0.158645
-0.088320 0.09307
-0.027016 0.116106
-0.166075***
0.04110
0.037984 0.128934
0.981747***
0.17300
0.351536 0.565586
-5.723128***
0.62647
-1.210480*** 0.347171
0.443448 Akaike info. criterion -0.314312
13.50046 Schwarz criterion 0.041196
2.223571 Hannan-Quinn criter. -0.191591
1.472917
Prob.: 0.4788
1.157555 Prob.: F(2,25): 0.3305
0.180656 Prob.: F(2,30): 0.8356
1.0164 Prob.: 0.313 ; 2.1711 Prob.: 0.338
OUTPUT RESPONSE Variable
∆ ln YIELD (-1) ∆ ln AREA ∆ ln OUTPUT (-1) ln ∆ ln RPRice RPRice (-1) ln RPMaize ∆ ln RPMaize (-1) ln ∆ ln PUreaFert PUreaFert (-1) ln ∆ ln IrrigArea IrrigArea (-1) Intercept Residual (-1) Adj. R-squared Log-Likelihood Durbin -Watson Stat Jarque-Bera B-G LM F. Stat ARCH : F-stat Q-stat
Coefficients Short-run effects standard error 0.547137**
0.255680
Long-run effects Coefficients Standard error 1.336831*** -0.382781***
0.176942
0.135847
1.721109***
0.35074 0.202232
0.153547
0.335122 0.34636
-0.029260 0.110425
-0.530982***
0.15295
-0.051844 0.554640
0.677121 0.64367
0.098784 0.554640
-6.548208***
-0.929189*** 0.247273
0.755176 Akaike info. criterion -0.395319
15.91809 Schwarz criterion 0.004627
2.152159 Hannan-Quinn criter. -0.257258
2.414789 Prob.: 0.2990
1.916749 Prob. F(2,24): 0.1689
0.770464 Prob. F(2,30): 0.4717
0.7981 Prob.: 0.372 ; 3.4234 Prob.: 0.181
2.32988
.20
.15
.10
.05
.00
-.05
-.10
1 2 Response of LNAREA to Cholesky One S.D. Innovations
IMPULSE RESPONSE AREA HARVESTED
Accumulated Response of LNAREA to Cholesky One S.D. Innovations 1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
1 2 3 4 5 LNAREA LNRPRICE LNPUREAFERT 6 7 8 LNOUTPUT LNRPMAIZE LNIRRIGAREA 9 10 3 4 5 LNAREA LNRPRICE LNPUREAFERT 6 7 8 LNOUTPUT LNRPMAIZE LNIRRIGAREA 9 10
Response of LNYIELD to Cholesky One S.D. Innovations
YIELD RESPONSE
Accumulated Response of LNYIELD to Cholesky One S.D. Innovations 1.0
.20
.15
0.8
0.6
.10
0.4
.05
0.2
.00
0.0
-.05
-0.2
-.10
1 2 3 4 5 LNYIELD LNRPRICE LNPUREAFERT 6 7 8 LNOUTPUT LNRPMAIZE LNIRRIGAREA 9 10 -0.4
1 2 3 4 5 LNYIELD LNRPRICE LNPUREAFERT 6 7 8 LNOUTPUT LNRPMAIZE LNIRRIGAREA 9 10
.28
.24
.20
.16
.12
.08
.04
.00
-.04
-.08
1 1.6
1.2
0.8
0.4
0.0
-0.4
1 2 Response of LNOUTPUT to Cholesky One S.D. Innovations
OUTPUT RESPONSE
Response of LNOUTPUT to Cholesky One S.D. Innovations .30
.25
.20
.15
.10
.05
.00
-.05
-.10
3 4 5 6 7 8 9 10 LNAREA LNRPRICE LNPUREAFERT LNOUTPUT LNRPMAIZE LNIRRIGAREA 1 2 3 4 5 LNYIELD LNRPRICE LNPUREAFERT 6 7 8 LNOUTPUT LNRPMAIZE LNIRRIGAREA 9 Accumulated Response of LNOUTPUT to Cholesky One S.D. Innovations Accumulated Response of LNOUTPUT to Cholesky One S.D. Innovations 2.0
1.6
1.2
0.8
0.4
0.0
10 -0.4
2 3 4 5 LNAREA LNRPRICE LNPUREAFERT 6 7 8 LNOUTPUT LNRPMAIZE LNIRRIGAREA 9 1 2 3 4 5 LNYIELD LNRPRICE LNPUREAFERT 6 7 8 LNOUTPUT LNRPMAIZE LNIRRIGAREA 9 10 10
SYNTHESIS OF RESULTS Rice farmers currently face structural and bio-physical constraints
Poor transmission of price increments by virtue of the market structure Limited storage facilities and market for their produce Soil fertility challenges (high price of fertilizer and low usage by the poor farmers)
Failure of importance of current irrigation systems to reflect in the short-run due to
Centeredness of investments on the major public schemes rather than been focused on initiating low cost irrigation schemes to reach the small-holders who dominate rice production High cost of accessing and managing irrigation systems and to Poor management of the already developed public irrigation schemes and areas in the country
RECOMMENDATIONS
FOR POLICY MAKERS
Initiation of measures to resolve the price transmission problem – eg development, supply of timely market information . road infrastructure Pursuing of area expansion and intensification Measure to improve storage facilities in the country, promote consumption of local rice and ensure ready market for farmer’s produce Rehabilitation and effective management of irrigation systems in the country, and ensuring a diffusion of low cost irrigation and water control systems in the major rice producing areas Making of investment in research and extension – management, to enhance sustainable yield and output to help train farmers on sustainable soil fertility Improvement in the current fertilizer subsidy structure – of increments in fertilizer prices on yield, output and area harvested of rice to help mitigate the significant adverse impacts
FOR FARMERS
Strengthening of farmer’s organization – to help improve negotiation for subsidies on inputs like fertilizer Development of formal contract swith buyers through farmer’s organization – to ensure secure, quality and more regular sales of produce and to help minimize post-harvest losses. This will as well improve the bargaining power of local rice producers in securing fair prices
16 12 8 4 0 -4 -8 -12 -16 84 86 88 90 92 94 CUS UM
STABILITY TEST OF ESTIMATES
96 02
ACREAGE RESPONSE MODEL
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
84 86 88 90 92 04 06 08 98 00 5% S ignific anc e 94 96 CUS UM of S quares 98 00 02 04 5% S ignific anc e 06 08 16 12 -4 -8 -12 -16 8 4 0 15 10 5 0 -5 -10 -15 84 84 86 86 88 88 90 90 92 94 CUS UM 96 98 00 02
YIELD RESPONSE MODEL
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
84 86 88 04 06 08 90 5% S ignific anc e 92 94 96 CUS UM of S quares 98 00 02 04 5% S ignific anc e 92 94 CUSUM 96 98 00
OUTPUT RESPONSE MODEL
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
02 04 06 08 84 86 88 90 5% Signific anc e 92 94 96 CUS UM of S quares 98 00 02 04 5% S ignific anc e 06 06 08 08
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http://www.cgdev.org/content/publications/detail/1424823 Bingxin, Y. and Shenggen, F. (2009). Rice production response in Cambodia.
Contributed Paper prepared for presentation at the International Association of Agricultural Economists Conference
, Beijing, China, August 16-22, 2009 Diako, C., Sakyi-Dawson, E., Bediako-Amoa, B., Saalia, F.K., and Manful, J.T. (2011). Cooking characteristics and variations in nutrient content of some new scented rice varieties in Ghana.
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PPP
and the
UIP
for UK.
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