1. RICE PRODUCTION IN GHANA: PAST, PRESENT AND FUTURE “DRIVING FORCES AND REQUIRED ACTIONS” SUPERVISORS: PROF. IMRE FERTO PROF. THOMAS HECKELEI TUTOR: DR. ZOLTAN BAKUCS BY: BOANSI DAVID

2. 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

3. PROBLEM STATEMENT Source: Author’s construct with data from IRRI- (USDA data)

4. 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 al (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)

5.  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)? RESEARCH QUESTIONS

6.  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 OBJECTIVES

7. RICE PLANNING: YIELD GAP ANALYSIS RICE MARKET STRUCTURE Source: Author’s construct with data from IRRI and MoFA (2011) Source: Author’s construct Yield gap = 1- (Actual / Potential) (Licker et al, 2010) 0 – on production frontier 1- No productivity

8. REVIEW ON THEORY AND METHODOLOGY 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 THEORY 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))

9. MODEL(S) AND DATA VERIFICATION MODEL 1: AREA, OUTPUT, RPRice, RPMaize, PUreaFert, IrrigArea AREA = f (RPRice, RPMaize, PUreaFert, IrrigArea) MODEL 2: YIELD, OUTPUT, RPRice, RPMaize, PUreaFert, IrrigArea YIELD = f (RPRice, RPMaize, PUreaFert, IrrigArea) OUTPUT = f (RPRice, RPMaize, PUreaFert, IrrigArea)……∆ AREA, ∆YIELD(-1) 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)

10. Series ADF-Test stat. Level Critical V 5% Lags-SIC Max 10 ADF-Test stat. First diff. Lag-SIC Max 10 ln OUTPUT-0.5055-2.862-3.9335***1 ln AREA-1.2765-2.862-3.7344***4 ln YIELD-0.5183-2.862-5.0828***1 ln RPRice-2.0214-2.861-7.8427***0 ln RPMaize-1.9360-2.868-6.3838***6 ln PUreaFert-1.7549-2.860-3.6625***0 ln IrrigArea-1.9566-2.860-3.4396**0 UNIT ROOT TEST **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

11. RESULTS Variable Short-run effects Coefficients standard error Long-run effects Coefficients Standard error ∆ ln AREA (-1)0.198126** 0.085398 ∆ ln OUTPUT0.513069*** 0.060127 ln RPRice ∆ ln RPRice (-1)-0.153275* 0.085922 1.001215*** 0.30893 ln RPMaize ∆ ln RPMaize (-1)0.017401 0.062808 0.422904 0.30507 ln PUreaFert ∆ ln PUreaFert (-1)0.110798 0.075536 -0.361652** 0.13471 ln IrrigArea ∆ ln IrrigArea (-1)0.191501 0.334006 -0.303422 0.56694 Intercept-0.816620 2.05212 Residual (-1)-0.795483*** 0.136411 Adj. R-squared Log-Likelihood Durbin -Watson Stat Jarque-Bera B-G LM F. Stat ARCH : F-stat Q-stat (1, 2) 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 ACREAGE RESPONSE ***1%, **5%, *10%

12. YIELD RESPONSE Variable Short-run effects Coefficients standard error Long-run effects Coefficients Standard error ∆ ln YIELD (-1)0.634719** 0.266484 ∆ ln OUTPUT (-1)-0.379290** 0.142923 ln RPRice ∆ ln RPRice (-1)0.146638 0.158645 0.715598*** 0.09426 ln RPMaize ∆ ln RPMaize (-1)-0.027016 0.116106 -0.088320 0.09307 ln PUreaFert ∆ ln PUreaFert (-1)0.037984 0.128934 -0.166075*** 0.04110 ln IrrigArea ∆ ln IrrigArea (-1)0.351536 0.565586 0.981747*** 0.17300 Intercept-5.723128*** 0.62647 Residual (-1)-1.210480*** 0.347171 Adj. R-squared Log-Likelihood Durbin -Watson Stat Jarque-Bera B-G LM F. Stat ARCH : F-stat Q-stat (1,2) 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 ***1%, ** 5%, * 10%

13. OUTPUT RESPONSE Variable Short-run effects Coefficients standard error Long-run effects Coefficients Standard error ∆ ln YIELD (-1)0.547137** 0.255680 ∆ ln AREA1.336831*** 0.176942 ∆ ln OUTPUT (-1)-0.382781*** 0.135847 ln RPRice ∆ ln RPRice (-1)0.202232 0.153547 1.721109*** 0.35074 ln RPMaize ∆ ln RPMaize (-1)-0.029260 0.110425 0.335122 0.34636 ln PUreaFert ∆ ln PUreaFert (-1)-0.051844 0.554640 -0.530982*** 0.15295 ln IrrigArea ∆ ln IrrigArea (-1)0.098784 0.554640 0.677121 0.64367 Intercept-6.548208*** 2.32988 Residual (-1)-0.929189*** 0.247273 Adj. R-squared Log-Likelihood Durbin -Watson Stat Jarque-Bera B-G LM F. Stat ARCH : F-stat Q-stat 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

14. IMPULSE RESPONSE AREA HARVESTED

15. YIELD RESPONSE

16. OUTPUT RESPONSE

17. 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

18. RECOMMENDATIONS FOR POLICY MAKERS  Initiation of measures to resolve the price transmission problem – eg. road infrastructure development, supply of timely market information  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 – to help train farmers on sustainable soil fertility management, to enhance sustainable yield and output  Improvement in the current fertilizer subsidy structure – to help mitigate the significant adverse impacts of increments in fertilizer prices on yield, output and area harvested of rice

19. 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

20. STABILITY TEST OF ESTIMATES ACREAGE RESPONSE MODEL YIELD RESPONSE MODEL OUTPUT RESPONSE MODEL

21. REFERENCES Aker, J.C., Block, S., Ramachandran, V. and Timmer, P.C. (2011). West African Experience with the World Rice Crisis, 2007-2008. CGD Working Paper 242. Washington, D.C.: Center for Global Development. http://www.cgdev.org/content/publications/detail/1424823http://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. Annals Food Science and Technology, 12 (1): 39-44 Engle, R.F. and Granger, C.W.J. (1987). Cointegration and Error Correction: Representation, Estimation and Testing. Econometrica, 55:251-276 Johansen, R. and Juselius, K. (1998). Testing structural hypothesis in multivariate cointegration of the PPP and the UIP for UK. Econometrics, 53:211-244 Licker, R., Johnston, M., Barford C., Foley, J.A., Kucharik, C.J., Monfreda, C. and Ramankutty, N. (2010). Mind the Gap: How do climate and agricultural management explain the “yield gap” of croplands around the world? A Journal of Macroecology. Global Ecology and Biogeography (Global Ecol. Biogeogra.) 2010. Blackwell Publishing Ltd. DOI:10.1111/j.1466-8238.2010.00563.x Molua, E.L. (2010). Price and non-price determinants and acreage response of rice in Cameroon. ARPN Journal of Agricultural and Biological Science. Vol. 5, No. 3 May 2010. ISSN 1990-6145. © 2006-2010 Asian Research Publishing Network (ARPN). Mulwanyi, A., Hutagaol, P. and Sinaga, B.M. (2011). Impact of rice purchasing policy on welfare of both producers and consumers in Indonesia. J. ISSAAS Vol.17, No.2:48-57(2011) Mythili, G. (2008). ‘Acreage and yield response for major crops in the pre- and post -reform periods in India: A dynamic panel data approach’. Report prepared for IGIDR-ERS/USDA project: Agricultural markets and policy, Indira Gandhi Institute of Development Research, Mumbai Nerlove, M. (1958). The Dynamics of Supply: Estimation of Farm Supply Response to Price. John Hopkins University Press, Baltimore Olaf, K. and Emmanuel, D. (2009). Global Food Security Response: Ghana Rice Study. Attachment I to the Global Food Security Response West African Rice Value Chain Analysis. microREPORT#156, USAID MoFA (2011). Agriculture in Ghana: Facts and Figures (2010). Statistical, Research and Information Directorate (SRID), Ministry of Food and Agriculture, Accra, Ghana

22. Pesaran, M.H., Shin, Y. and Smith, R. (2001). “Bounds testing approaches to the analysis of level relationships”. Journal of Applied Econometrics 16(3), 289-326 Sachchamarga, K. and Williams, G.W. (2004). Economic factors affecting rice production in Thailand. Texas Agribusiness Market Research Center (TAMRC) International Research Report No. IM-03-04. March 2004 Tomlins, K.I., Manful, J.T., Larwer, P. and Hammond, L. (2005). Urban consumer preferences and sensory evaluation of locally produced and imported rice in West Africa. Food Quality and Preference, 16(1): 79-89 THANK YOU