byGODFRED ANTWIISSER Conference Centre (ISSER New Building)

WASTE AND SPOILAGE TECHNOLOGY OF THE 2000s: An Empirical Study

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Outline of the presentation The Problem Development of improved rice PBV Objective of study Methodology Findings Conclusions Recommendations The way forward References Acknowledgements

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The Problem

Post-harvest losses (waste and spoilage) constitute a major drain on food production and food security in Africa

In Ghana, annual losses of cereals are between 20-30% which is mainly attributed to inadequate postharvest practices and technologies (MoFA, 2010).

The Northern Region is a major producer of rice in Ghana (37%).

Rice produced in the region is mostly parboiled due to harsh climatic conditions(high temperatures and low humidity)

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The Problem cont’d The widespread use of traditional rice-parboiling

technology in Northern Ghana leads to poor quality of final rice which often does not meet consumers’ expectations

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Development of improved rice PBV

iPBV(improved parboiling vessel)

• CPHP 2004 project(FRI and DFID)

• 2mm in thickness and has two chambers

• Parboil about max 100 kg of paddy

• Fuel savings of 30% - 50% • Reduced drudgery in the

parboiling process• Vessel allows uniform heating

(Tomlins et al., 2007).

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Objective of the Study

Assess the impact of an improved rice PBV adoption on household income in Northern region of Ghana.

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MethodologyObjectives Description of the Objective Method of analysis

Objective one Describe the differences in the characteristics of adopters and non adopters households in the Northern region of Ghana.

Descriptive statistics such as mean, minimum, maximum, standard deviation, percentages

Objective Two Determine the factors that influence the adoption of iPBV technology in the Northern region of Ghana.

Logit model(Green 2008; Baidu-Forson, 1999; Feder et al., 1985)

Objective three Estimate the impact of the adoption of iPBV technology on the household income in the Northern region of Ghana

Propensity score matching(PSM)(Rosenbaum & Rubin, 1983; Becerril & Abdulai,2010;Dehejia & Wahba, 2002)

Objective four Determine the major constraints faced by parboiling households in the Northern region

Kendall’s coefficient of concordance

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Variable descriptions of the logit modelVariables Description Measurement Expt. sign ADPT Adoption status Dummy(1= Adopted 0 = Otherwise )

Zugu Dagboni only

Parboiler stays in Zugu Dagboni D(1= stays Zugu Dagboni 0 = Otherwise )

+

Nyohini only Parboiler stays in Nyohini D(1= stays Nyohini 0=Otherwise ) -

Nyankpala only Parboiler stays in Nyankpala D(1= stays Nyankpala 0=Otherw -/+

Education Educational level of respondent D(1= had formal education 0=Otherwise

-/+

Age Age of respondent Continuous(years) -/+

Marital status Marital status D(1=married 0=otherwise) +

Household size No of person being catered for Number of person +

Member of group Member of parboiling organization D(1=member 0=otherwise) +

Experience Years of rice processing experience Years +

Production capacity

Production capacity of the rice processor per month

No of bags +

Contact Contact with developmental organisation

D(1= contact organisation 0=otherwise)

+

Household size Household size of the respondent Number of person +

Hired labour Had used hired labour D(1= hired labour 0=otherwise) +

Household income Per capita income(GHs) Continuous +

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Study Area and Sampling Technique

Region District Community # Processors Total

Purposive Purposive Simple random(lottery method)

Northern

Tamale Metro

Nyohini 50120

Zakariyeli 20

Kpalsi 30

Gbalahi 20

Tolon Kumbungu

Tolon 40120

Gbalahugu 25

Nyankpala 35

Zugu Dagboni 20

TOTAL SAMPLE SIZE = 240

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ResultsLevel of IPBV technology adoption

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ResultsObjective one:

Adopters(n=80) Non-Adopters(n=160) Total(n=240)

VariablesFreq. % Freq. % Freq. %

AgeLess than 36 25.0 31.3 39.0 24.4 64 26.7

36-45 27.0 33.8 65.0 40.6 92 38.346-55 20.0 25.0 47.0 29.4 67 27.9Above 55 8.0 10.0 9.0 5.6 17 7.1Age_Mean 42.2 42.6 42.4

GenderFemale 80 33.3 160 66.7 240 100Marital statusmarried 74 92.5 149 93.1 223 92.9

EducationNo formal 66 82.5 129 80.6 195 81.3

Education_Mean 1.15 0.68 0.83

Household Size 5.7 6.1 5.9

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ResultsObjective one:

Adopters(n=80) Non-Adopters(n=160) Total(n=240)

VariablesFreq. % Freq. % Freq. %

Residential StatusNative 68 85.0 136 85.0 204 85Migrant 12 15.0 24 15.0 36 15Other occupationYes 55 68.8 81 50.6 136 56.7

No 25 31.3 79 49.4 104 43.3EthnicityDagbomba 156 97.5 79 98.8 235 97.9Religion Muslim 74 92.5 140 87.5 214 89.2

ExperienceLess than 11 33.0 12.5 71 44.4 104 43.511-20 38.0 47.5 68 42.5 106 44.221-30 8.0 10.0 19 11.9 27 11.2Above 30 1.0 1.3 2 1.3 3 1.2

Experience_Mean 12.7 12.6 12.6

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ResultsObjective one:

Adopters(n=80) Non-Adopters(n=160) Total(n=240)

VariablesFreq. % Freq. % Freq. %

Purpose Consume &sale 60 75.0 142 88.8 202 84.2Source of waterPipe borne 65 81.3 109 68.1 174 72.5Quantity of paddy/monthLess than 21 15 19.0 56 35.1

71 29.6

21-30 22 27.6 71.0 44.3 93 38.831-40 30.0 37.5 26.0 16.4 35 14.6Above 40 13.0 16.4 7 4.4 41 17.1

Quatity_Mean 32.3 26.3 28.3Member of PBOYes 74 92.5 39 24.4 113 47.1Credit Yes 42 52.5 45 28.1 87 36.3Contact Yes

73 91.3 106 66.3 127 52.9

Aware of PBV technologyYes

80 100 48 30.0 128 53.3

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Results: Objective two T-Test

:

Variables Measurement Adopters Non-adopters Diff T-testZugu Dagboni only D(1= Zugu Dagboni 0 =

Otherwise )0.150 

0.050 0.100 2.670***

Nyohini only D(1=Nyohini 0=otherwise) 0.325 

0.150 0.002 3.201***

Nyankpala only D(1= Nyankpala 0=otherwise) 0.263 

0.088 0.175 3.709***

Education D(1=formal education 0=otherwise) 0.15 .087.

062 1.469

Age of respondent Continuous(years) 42.6 42.2 0.363 0.267

Marital status D(1=married 0=otherwise) 0.925 0.931 -0.006 -0.177

Household size Number of person 5.8 

6.1 -0.310 -0.792

Member of group Dummy (1=member,0=otherwise)

0.925 0.243 0.681 12.965***

Experience Years 12.7 

12.6 0.11 0.105

Production capacity No of bags 32.3 

26.3 6.050 4.270***

Contact D(1= contact 0=otherwise) 0.913 0.338 0.575 9.977***

Household size Number of person 5.8 

6.1 -0.31 -0.792

Hired labour D(1= hired labour 0=otherwise

0.238 

0.138 0.053 1.948*** 

Outcome variablesHousehold income GHS 2916.46 2159.99 756.47 3.705**

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Results: Obj 2 Logit results

:

Variables Coefficient Standard Error P>[Z] Marginal effect

Zugu Dagboni only 4.135*** 0.6134 0.000 0.771

Nyohini only 1.632*** 0.3530 0.000 0.250

Nyankpala only 3.413*** 0.4989 0.000 0.645

Education 0.130** 0.0595 0.029 0.014

Age of respondent 0.040*** 0.0055 0.057 0.008

Marital status -0.296 0.5925 0.617 -

Household size -0.115** 0.0549 0.037 -0.013

Member of PBO 3.948*** 0.4026 0.000 0.514

Experience -0.025** 0.0070 0.029 -0.006

Production capacity 0.032** 0.0033 0.014 0.005

Contact 2.500*** 0.3366 0.000 0.280

Household size -0.115** 0.0549 0.037 -0.127

Hired labour -0.682* 0.3819 0.074 -0.006

Access 0.333 0.3005 0.268 -

Price -0.074*** 0.0310 0.017 -0.008

Other occupation -0.661*** 0.3410 0.053 -0.075

Constant -4.245 2.4381 0.082 -

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Results two Number of observations = 240 Psuedo R2 = 0.62*** LR chi2(16) = 184.12 Prob > chi2 = 0.0000 Log likelihood = -199.7286

*, ** and *** denotes 10%, 5% and 1% significance level respectively

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ResultsObjective three and four:

Variable Matching method

Treated Controls ATT* ATU* ATE* S.E. T-stat

Household income(GHS)

NN 2916.46 2294.69 621.8 519.5 553.6 225.26 2.76***

ATT= Average treatment effect on the treatedATU= Average treatment effects on the untreated ATE= Average Treatment Effect (overall)

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Results Objective four;

Constraints Adopters Non-Adopters

Mean rank score

Rank Mean rank score Rank

Lack of funds 2.32 1st 2.97 1st

Expensive fuel (fire wood) 3.6 2nd 3.4 3rd

Lack of equipment for parboiling3.88 3rd 3.67 2nd

Expensive/unreliable water 4.08 4th 3.93 5th

Lack of drying area 4.5 5th 4.33 4th

Distance to milling place is too far 5.21 6th 4.88 6th

Low storage capacity 5.31 7th 5.36 7th

Weak knowledge of drying operations 7.1 8th 7.47 8th

Kendall's Wa 0.335*** 0.34***

Chi-Square 187.686 381.282

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Findings All of respondents were female

About 53% were aware of the existence of the technology.

Education, Age ,member of PBO, contact with developmental organisation, access to credit, targeted communities and high production capacity increases the probability of adopting the iPBV technology

Price of paddy, having other occupation, household size,using hired labour and having a lot a experience reduces the probability of adopting iPBV technology

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Findings Adopting the improved rice PBV technology increases

the annual household income by 622 GHs

The major constraints facing the parboilers were lack of funds, expensive fuel (fire wood) and lack of equipment for parboiling.

Distance to milling place , Low storage capacity and weak knowledge of drying operations had minimal impact on their operations

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Conclusion

Improved rice PBV technology increases the household income for households who adopted the technology

Constraints facing the parboilers were lack of funds, expensive fuel (fire wood) and lack of equipment for parboiling.

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Recommendations Local artisans (such as welders, etc) and other stakeholders

should pick up the technology and produce more iPBV with local resources ;this will even reduce the cost of the technology.

Also stakeholders should encourage microfinance institutions to support rice parboilers with credit (since they are now creditworthy as a result of using the new technology which increases their incomes).

Institution such as FRI, CSIR and Universities should develop alternate source of fuel (such as one the can use rice husk) to reduce the cost of fuel for processing rice.

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The way forward

In principle the new technology offer an increase in income to the parboilers but it is expensive;

I. More developmental agencies should partner these women in other to acquire more of the technology for the women which has positive impact on their livelihood.

II. Microfinance institutions should also be challenge to provide these women with credit at low interest to enable them to purchase this technology as to improve their income.

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Some References A. Smith, J., & E. Todd, P. (2005). Does matching overcome LaLonde’s critique of

nonexperimental estimators? Journal of Econometrics (Vol. 125, pp. 305–353). doi:10.1016/j.jeconom.2004.04.011

Abdulai, A., Owusu, V., & Bakang, J.-E. a. (2011). Adoption of safer irrigation technologies and cropping patterns: Evidence from Southern Ghana. Ecological Economics, 70(7), 1415–1423. doi:10.1016/j.ecolecon.2011.03.004

Abebaw, D., & Haile, M. G. (2013). The impact of cooperatives on agricultural technology adoption: Empirical evidence from Ethiopia. Food Policy, 38, 82–91. doi:10.1016/j.foodpol.2012.10.003

Diagne, M., Demont, M., & Diagne, A. (2009). Adoption and impact of an award winning post-harvest technology : The ASI rice t hresher in the Senegal River Valley.

Dehejia, R. H., & Wahba, S. (2002). Propensity Score-Matching Methods for Nonexperimental Causal Studies. Review of Economics and Statistics. doi:10.1162/003465302317331982

Dandedjrohoun, L., Diagne, A., & Biaou, G. (2012). Determinants of diffusion and adoption of improved technology for rice parboiling in Benin. Review of Agricultural and Environmental Studies, 93, 171–191. Retrieved from http://www.raestud.eu/pdf/171-191-ncho.pdf

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Thank you for your attention

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Acknowledgements

Dr. (Mrs.) Irene S. Egyir and Dr. John Baptist D. Jatoe

AfricaRice African Development Bank(AfDB) Food Research Institute(FRI) The Economy of Ghana Network (EGN)