EX-POST IMPACT OF AGRA SOIL HEALTH PROJECT 005...

EX-POST IMPACT OF AGRA SOIL HEALTH PROJECT 005 IN

NORTHERN GHANA

Submitting Institution

CSIR-Savanna Agricultural Research Institute,

Tamale, GHANA

FINAL DRAFT REPORT PREPARED BY:

Edward Martey1, Prince M. Etwire

1, Alexander N. Wiredu

1, John K. Bidzakin

1 and

Matthias Fosu2

April, 2013

1 Agricultural Economist, CSIR-SARI, Tamale, Ghana.

2 Soil Scientist, CSIR-SARI, Tamale, Ghana.

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Table of Contents

Contents Page

Table of Contents ............................................................................................................................ 1 Acknowledgements ......................................................................................................................... 2 List of Acronyms and Abbreviations .............................................................................................. 3 List of Tables .................................................................................................................................. 4 List of Figures ................................................................................................................................. 4

1.0 Introduction ........................................................................................................................ 5 1.1 Background and Justification of the Study .......................................................................... 5 1.2 Deliverable of the Baseline Survey...................................................................................... 6 1.4 Structure of Report ............................................................................................................... 7

2.0 Presentation on AGRA Soil Health Project 005.............................................................. 8

2.1 Concept and Justification of the AGRA Soil Health Project ............................................... 8 2.2 Project Zone and Targeted Population in Ghana ................................................................. 9

2.3 Objective of AGRA Soil Health Project ............................................................................ 10 2.4 Implementation Strategy of AGRA Soil Health Project 005 ............................................. 10

3.0 Methodology of the Study ............................................................................................... 12 3.1 Selection of Study Area ..................................................................................................... 12

3.2 Methodology of Data Collection and Types of Data Collected ......................................... 12 3.3 Method of Data Analysis ................................................................................................... 13

3.3.1 Ex-Post Impact Evaluation Methods .......................................................................... 13 3.4 Variables of the Model....................................................................................................... 16

4.0 Result and Discussions ..................................................................................................... 17

4.1 Description of Study Communities .................................................................................... 17 4.2 Characteristics of the Survey Households ......................................................................... 21 4.3 Farm Household Resources ............................................................................................... 23 4.4 Production and Sales Volume of Crops ............................................................................. 25

4.5 Adoption of Improved Technologies ................................................................................. 26 4.5.1 Land Resources, Uses and Soil Fertility ..................................................................... 26 4.5.2 Varietal Preferences and Farm Management Practices .............................................. 29 4.5.3 Adoption of Integrated Soil Fertility Management .................................................... 32

4.6 Household Access to Credit, Type and Repayment........................................................... 33

4.7 Impact of Improved Technologies ..................................................................................... 34

4.7.1 Determinants of Adoption of ISFM Technologies ..................................................... 34

4.7.2 Impact of ISFM Adoption on Income………………………………………………. 38

5.0 General Conclusion and Recommendations ................................................................ 389 References ..................................................................................................................................... 42 Appendices .................................................................................................................................... 45

2

Acknowledgements

The AGRA impact team extends its profound appreciation to the Alliance for a Green

Revolution in Africa for the financial support in carrying out this study. Our appreciation is

further extended to CSIR-Savanna Agricultural Research Institute for the logistical support and

staff time. Finally, we wish to acknowledge the farmers that took time off their busy schedule to

voluntarily participate in the survey and all who assisted in diverse ways.

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List of Acronyms and Abbreviations

AEAs Agricultural Extension Agents

AGRA Alliance for a Green Revolution in Africa

CARD Centre for Agricultural and Rural Development

CSIR Council for Scientific and Industrial Research

DD Difference-in-Difference

FAO Food and Agricultural Organisation

FBO Farmer Based Organization

FFS Farmer Field School

GSS Ghana Statistical Services

IFDC International Fertilizer Development Centre

ISFM Integrated Soil Fertility Management

IV Instrumental Variable

MoFA Ministry of Food and Agriculture

MoU Memorandum of understanding

M&E Monitoring and Evaluation

NGO Non-Governmental Organization

NPK Nitrogen Phosphorus and Potassium

p.a Per Annum

PASS Program for Africa‟s Seed Systems

PSM Propensity Score Matching

RCT Randomized Control Trial

SARI Savannah Agricultural Research Institute

SHP Soil Health Project

SRID Statistics Research and Information Directorate

UDS University for Development Studies

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List of Tables

Table 1a: Characteristics of Study Communities.......................................................................... 18 Table 1b: Characteristics of Study Communities ......................................................................... 19

Table 2: Demographic Characteristics of Communities ............................................................... 20 Table 3: Transportation Cost (GH₵) of Commodities ................................................................. 20 Table 4: Crop Calendar ................................................................................................................. 21 Table 5: Characteristics of Farm Household Heads ..................................................................... 23 Table 6: Farm Household Resources, Assets and Occupancy Status of Household Head ........... 24

Table 7: Livestock Assets of Household Head ............................................................................. 25 Table 8: Quantity of Crop Produced and Sold (MT) .................................................................... 25 Table 9: Land Resources and Use of Household Head................................................................. 27 Table 10: Crop/Land Use and Fertility ......................................................................................... 28 Table 11: Preferences of Crop Varieties ....................................................................................... 29

Table 12: Preference of Crop Varieties and Technology .............................................................. 30

Table 13: Farm Management Practices......................................................................................... 31

Table 14: Integrated Soil Fertility Management ........................................................................... 33

Table 15: Access to Credit and Repayment .................................................................................. 34

Table 16: Probit Estimates of the Determinants of ISFM Technologies in Northern Ghana...….38

Table 17: Income Framework by Adoption………………………………………………... ……39

List of Figures

Figure 1: Administrative Map of Ghana ....................................................................................... 18 Figure 2: Distribution of Income by Adoption of ISFM ………………………………………...39

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1.0 Introduction

1.1 Background and Justification of the Study

Northern, Upper East and Upper West regions, jointly referred to as Northern Ghana, accounts

for over 40 percent of agricultural land in Ghana and is considered as the breadbasket of the

country (MoFA, 2010). The area is however inundated with high levels of food insecurity and

poverty. Nearly 1 million people amounting to about half of the population of the area face

annual food deficit and are net buyers of food (GSS, 2008). This is a major concern to the

government and its development partners. About 80% of the population depends on subsistence

agriculture with very low productivity and low farm income (MoFA, 2010). Per capita income of

the area is about $200.0 p.a, which is less than 50 percent of Ghana‟s per capita income of

approximately $600.0 p.a (GSS, 2008). The main reason for the extreme poverty and high food

insecurity is the over reliance on rain-fed agriculture under low farm input conditions.

In Northern Ghana, the most important food crops are maize, rice, sorghum, millet, cassava,

groundnut, cowpea and soybean. For most farm families, cereals are the most important staples.

The importance of maize is demonstrated in its expansion to even the drier areas of Northern

Ghana where it has virtually replaced sorghum and millet which were traditional food security

crops in the region. Northern Ghana produced about 350,000 metric tons of maize in 2011 over

an area of 245,000 ha (SRID, MoFA, 2012). Nearly all production of cowpea (95%) and soybean

(97%) in the country emanates from the three Northern regions (SRID, MoFA, 2012).

Ghana is however not self-sufficient in cereal production with farmers obtaining yields that are

well below potential yields (MoFA, 2010). The low yields can be attributed to the use of

unimproved crop varieties and poor agronomic practices (low plant stand, inadequate fertilizer

application, etc.) by farmers. The soils of the major maize growing areas are low in organic

carbon (<1.5%), total nitrogen (<0.2%), exchangeable potassium (<100 ppm) and available

phosphorus (< 10 ppm, Bray 1) (Adu, 1995, Benneh et al. 1990). A large proportion of the soils

are also shallow with iron and magnesium concretions (Adu, 1969).

Despite these shortcomings, soil fertility management is sub-optimal. Fertilizer nutrient

application in Ghana is approximately 8 kg per ha (FAO, 2005) while depletion rates, which is

among the highest in Africa, range from about 40 to 60 kg of nitrogen, phosphorus, and

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potassium (NPK) per ha per year (FAO, 2005). FAO estimates show negative nutrient balance

for all crops in Ghana.

The escalating rates of soil nutrient mining are a serious threat to sustainability of agriculture and

poverty reduction in Ghana. There are also inefficiencies and bottlenecks in fertilizer distribution

networks which limit access, and add to the cost of fertilizer in farming communities. Agro-input

marketing is rudimentary and farmer-based organizations are also weak and therefore unable to

acquire credit, fertilizer and other inputs in bulk to reduce cost.

Integrated Soil Fertility Management (ISFM) is the approach advocated by AGRA to improve

the soil fertility status of African soils. AGRA has demonstrated its commitment to improving

the health of the soils in Northern Ghana by funding the Soil Health Project 005 which was

implemented by CSIR-Savanna Agricultural Research Institute between 2009 and 2011.

1.2 Deliverable of the Baseline Survey

More specifically, the ex-post study is expected to:

1. Generate inventory of information on the effectiveness of training, demonstration, credit

and radio programmes under the AGRA project.

2. Provides quantitative estimates of actual and potential adoption of ISFM technologies as

well as access to credit.

3. Evaluate adoption of improved crop varieties (maize, soy bean and cowpea) as well as

agronomic practices (row planting, transplanting etc.).

4. Assess impact of adoption of the technologies on farm level productivity and welfare

(including crop yield, crop income, and food security).

1.3 Limitations of the Study

Some of the variables in the existing baseline data were incorrectly measured and therefore will

pose a bias estimate of the variables of interest when used in the analysis. The data generated

from the end line questionnaire contained some inconsistent data which probably affected the

significance level of some of the variables adopted in the model.

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1.4 Structure of Report

This report presents results from the ex-post study for the AGRA Soil Health Project 005 in

Northern Ghana. It describes the study methodology and sample location, the socio-economic

characteristics of the respondents and the quantitative estimates of adoption of improved crop

varieties, ISFM and technologies project area.

The report is structured into four sections. Following this introductory chapter, a review of

presentation of the AGRA Soil Health Project is presented in section two. Section 3 describes the

methodology employed to achieve the objectives of the study. The empirical results are

discussed in Section 4. Finally, the summary, conclusions and policy recommendations are

distilled in Section 5.

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2.0 Presentation on AGRA Soil Health Project 005

2.1 Concept and Justification of the AGRA Soil Health Project

The Soil Health Program (SHP) aims at improving smallholder farmer productivity, through

increasing access to locally appropriate soil nutrients and promoting integrated soil fertility

management. Essentially, the program has the following three key objectives:

(i) To create in five years, physical and financial access to appropriate fertilizers for around

4.1 m African smallholder farmers in an efficient, equitable and sustainable manner.

(ii) To create in five years, access to appropriate ISFM knowledge, agronomic practice and

technology packages, for around 4.1m African smallholder farmers.

(iii) To create a national policy environment for investment in fertilizer & ISFM.

In a bid to translate the above objectives into implementable actions, three sub-programs were

established, and they constitute major strategic levers of the SHP. They include:

(i) Research and Extension: The sub-program aims to extend ISFM technology packages to

4.1m farm households by 2014. It seeks to facilitate the adoption of improved ISFM technology

packages that promote the use of both inorganic and organic fertilizer and conservation

agriculture agronomic practices. It also provides funding to African soil scientists to test various

ISFM technology options to identify and promote those that enhance small holder farmer

productivity.

(ii) Fertilizer Value Chain Development: Fertilizer sub-program aims at catalyzing local

production of fertilizer through support to local companies that are providing appropriate blends

using local phosphate rocks. Investments have been made to support identified countries to

develop and implement fertilizer quality control systems. Further grants are extended to support

the establishment and training of agro-dealers in AGRA-focused countries. The sub-program

targets to create a network of 6,500 trained agro-dealers to distribute 187,000 tons of appropriate

organic fertilizer by 2014. The initiative is expected to bring about a 15% reduction in gap

between farm gate and market prices.

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(iii) Training: Involves supporting the training of African Scientists at PhD and MSc level in 10

African Universities. The aim is to maintain a supply of soil scientists to provide research and

extension support to farmers. Soil lab technicians are also trained to improve the quality of lab

management and outputs. Training is also provided to extension staff who work directly with

farmers.

2.2 Project Zone and Targeted Population in Ghana

Northern, Upper East and Upper West were the target regions of the project. The project targeted

11 districts (Karaga, East Gonja, Central Gonja, West Mamprusi, Nanumba Northern, Namumba

South, Gushiegu, Savelugu, Tamale, Yendi and Tolon-Kumbungu) in the Northern Region, 6

districts (Builsa, Kasena-Nankana, Bolgatanga, Bawku West, Bawku East and Talensi-Nabdam)

in the Upper East Region and 5 districts (Wa, Lawra, Sissala, Lambussie and Nadowli) in the

Upper West Region. A total of 255 farming communities were targeted to be reached by the

project with Upper East and West regions contributing 55 and 50 farming communities

respectively. The project, during its life of activity, aimed to extend the ISFM technologies to

120,000 farm households resulting in the production of an additional 504,000 tonnes of maize

valued at $241,920,000.

Figure 1: Administrative Map of Ghana

Three (3) Northernern

Regions of Ghana where the

AGRA Soil Health Project

005 was implemented.

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Women play important role in agriculture in Northern Ghana. Their roles range from ownership

of farms to providing farm labour in planting, weeding, harvesting, processing and marketing.

Despite their contribution to agriculture, women are often marginalized. The project therefore

targeted 20-30 percent women participation in the Upper East and West regions and 10 -20

percent women participation in the Northern Region.

2.3 Objective of AGRA Soil Health Project

The project sought to contribute to poverty alleviation, food security, and sustainable natural

resources management in rural communities in Ghana. Generally the project sought to increase

crop productivity, food security and livelihood of small-scale maize farmers in Northern Ghana

through adoption of proven ISFM technologies and grain legume enterprises. Specifically the

project seeks:

1. To increase productivity of maize-legume cropping systems through scaling up proven ISFM

technologies

2. To strengthen farmer organizations and extension systems for wide-scale dissemination of

ISFM technologies

3. To monitor and assess impacts of ISFM technologies on small-scale agricultural productivity

and livelihood of rural people

4. To update and refine profitable fertilizer recommendations for maize and grain legumes in

Northern Ghana (Sudan and Guinea savannah zones)

2.4 Implementation Strategy of AGRA Soil Health Project 005

The project combined innovative partnership and commodity value chain approaches during

implementation in order to achieve the project objectives. Capacity building (FBOs, agro-input

dealers, among others), technical and logistic backstopping (all value chain actors especially

AEAs) and partnerships (MoFA, IFDC, UDS, Media, among others) were some of the

implementation strategies adopted.

The Ministry of Food and Agriculture and local NGOs were the main extension mechanism for

dissemination of ISFM technologies. Signing of MoUs, payment of per diem and fuel allowances

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were strategies adopted by the project to motivate Extension Agents (AEAs) tasked with the

responsibility of promoting improved varieties of maize, cowpea and soybean.

In order to avoid duplication of effort and to build synergy, the project partnered IFDC‟s Agro-

dealer program to train agro-dealers and farmer groups. The project also collaborated with

AGRA‟s PASS to produce and distribute certified seeds of new cowpea varieties to farmers.

The project also focused on strengthening farmers organizations so that they can facilitate

knowledge transfer and act as vehicles for collective action in accessing input and output

markets. Linkages between FBOs and agro-dealers were strengthened through facilitation of bulk

purchase of inputs. Through linkage with IFDC Project “Linking Farmers to Markets” FBOs

were linked to end buyers.

Project Activities undertaken include:

i. Demonstrations, FFS and On-farm testing of selected ISFM options across two different

agro-ecological zones (Guinea and Sudan savannah), focusing on soil nutrient

requirements, nutrient use efficiency, biological nitrogen fixation and crop productivity.

ii. Monitoring soil health and establishing the costs, benefits and trade-offs required for

ISFM practices involving grain legumes in small-holders‟ cereal-legume intercrops and

rotations.

iii. Field days and exchange visits to display the advantages of ISFM relative to current

practices.

iv. Radio and TV documentaries and production of technical leaflets on ISFM

v. Training in composting, fertilizer use, ISFM Technologies, FBO management,

management of demonstrations

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3.0 Methodology of the Study

3.1 Selection of Study Area

Major consultation was held with the Agricultural Extension Agents (AEAs) of the Ministry of

Food and Agriculture (MoFA) and research scientists on the project for enlightenment about the

project and the implementation areas as part of the inception phase. The consultations provided

the platform for the generation of information on the project communities and also for the

sampling process. The sample for the study was drawn from an existing database of project

implementation areas provided by the Coordinator of the AGRA SHP project. A total of 330

households, 150 from Northern region, 90 from Upper East and 90 from Upper West were

involved in the study.

3.2 Methodology of Data Collection and Types of Data Collected

The data collected for this study was based on the sampling frame. The basic sample frame for

the study was the AGRA Soil Health Project targeted districts in the three Northern regions. The

sampling procedures applied were intended to generate regionally representative sample which

also covered the targeted areas of the project. It also allowed for the determination of

“counterfactual” and “controlled” group. Generally, relevant data for the study was generated

from a cross-section of households. To determine the impact of the project, the beneficiary

communities were stratified into three strata namely project community, counterfactual

community (5km away from project community) and non-project community (5km away from

the counterfactual group) (Appendix 1). Specifically, formal household interview was conducted

for the generation of data in each of these strata per the beneficiary community of the AGRA

SHP. The community group discussion focused on details of community infrastructure, crop

calendar and community structure for the generation of community level database. The formal

household interview captured information on maize, cowpea and soybean producing households.

The information captured includes the household social capital, household resources, land

resources and use, preference of crop varieties and technologies, integrated pests and disease

management, integrated soil fertility management, post-harvest activities and income and

expenditure profile.

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The database was created in STATA software in separate tables. Quality control measures like

constant monitoring, cleaning and updating of data were put in place to ensure that the integrity

of the data is not compromised.

3.3 Method of Data Analysis

A combination of analytical tools was used for the empirical analysis of the study. Descriptive

statistics such as frequencies and means were used to describe the characteristics of the

communities and the households per region. Cross tabulations was also employed to determine

the relationship between some of the key variables of interest. A number of ex-post impact

evaluation methods are also reviewed below and the ideal one is selected for the determination of

the impact of the AGRA SHP on the livelihood of the project communities.

3.3.1 Ex-Post Impact Evaluation Methods

This section presents a review of some selected ex-post impact evaluation methods by

considering their strengths and weaknesses. The impact evaluation methods considered for this

present study are the Randomized Control Trials (RCT), Double Difference (DD), Instrumental

Variables (IV) and Propensity Score Matching (PSM).

Randomized Control Trials (RCT)

Randomized controlled trials are the most rigorous way of determining whether a cause-effect

relation exists between treatment and outcome and for assessing the cost effectiveness of a

treatment. Clinton et al. (2006), describes randomized control trials as an attempts to estimate a

program's impact on an outcome of interest. An outcome of interest is something, oftentimes a

public policy goal, that one or more stakeholders care about (e.g., unemployment rate, which

many actors might like to be lower). An impact is an estimated measurement of how an

intervention affected the outcome of interest, compared to what would have happened without

the interventions. A simple RCT randomly assigns some subjects to one or more treatment

groups (also sometimes called experimental or intervention groups) and others to a control

group. The treatment group participates in the program being evaluated and the control group

does not. After the treatment group experiences the intervention, an RCT compares what

happens to the two groups by measuring the difference between the two groups on the outcome

of interest. This difference is considered an estimate of the program's impact. Although RCTs are

14

powerful tools, their use is limited by ethical and practical concerns. Secondly, a randomised

controlled trial may be ethical but infeasible-for example, because of difficulties with

randomisation or recruitment. A third limiting factor is that RCTs are generally more costly and

time consuming than other studies.

Difference-in-Difference (DD)

The DD approach is one of the most popular non-experimental technique Easts in impact

evaluation especially where baseline data is available. The approach compares the changes in

outcomes overtime between a treatment group and control group. In a situation where the trends

are significantly greater for the treatment group (in a statistical sense), then the project is said to

have an impact. The DD estimator combines cross-sectional and over-time variation to correct

for the differences between groups when treated and controls start from different levels. The

strength of this technique controls for unobservable differences in baseline characteristics of

treatment and control households, thus minimizing potential biases in impact estimates.

However, the DD is less robust relative to the randomization technique Easts. Secondly, when

trends are parallel before the start of the intervention, bias in the estimation may still appear.

Finally, the underlying assumption of control group trend being identical to the trend that the

treated group would have had in the absence of treatment is not testable (Gertler, Martinez,

Premand, Rawlings and Vermeersch, 2011; Winters, Salazar and Maffioli, 2010)

Instrumental Variables (IV)

The IV is normally applied when a project includes some level of self-selection and there is a

concern that unobservable differences between treated and control might lead to biased estimates

of impact. This is a major problem in Agricultural projects where farmers self-select themselves

into the project. The IV technique regards the treatment variable (participating in an agricultural

project) as endogenous and therefore attempts to find an observable exogenous variable or

variables (called instruments) that influences the participation variable but do not influence the

outcome of the programme if participating (Winters, Salazar and Maffioli, 2010; Khandkler,

Shahidur, Gayatri, Koolwal, and Samad, 2009). Although the IV estimator solves the biases

generated by both time-invariant and time-variant unobservable characteristics of participants, it

only estimate a Local Average Treatment Effects (LATE) which means that its results are

relevant only for those whose behaviour is affected by the instrument (Angrist, 2001).

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Propensity Score Matching (PSM)

The PSM constructs a statistical comparison group that is based on a model of the probability in

the treatment, using observed characteristics. Participants are then matched on the basis of this

probability, or propensity score, to non-participants. The average treatment effect of the program

is then calculated as the mean difference in outcomes across these two groups. The PSM works

under the assumption that unobserved factors do not affect participation (conditional

independence) and sizable common support or overlap in propensity scores across the participant

and non-participant samples (Overlap Condition) (Winters, Salazar and Maffioli, 2010;

Khandkler, Shahidur, Gayatri, Koolwal, and Samad, 2009).

The propensity score or conditional probability of participating is calculated by using a probit or

logit model in which the dependent variable is a dummy variable equal to one if the farmer

participated in the project and zero otherwise. The vector of covariates or independent variables

should be composed of those characteristics that determined project placement in order to

replicate the selection process. Determination of these characteristics requires clearly identifying

the institutional arrangements that defined selection into the project (Caliendo and Kopening,

2008). The use of PSM alone is not appropriate when observable farmers‟ characteristics might

affect both the outcome variables and the program placement especially where farmers self-

select themselves in the programme (Winters, Salazar and Maffioli, 2010; Khandkler, Shahidur,

Gayatri, Koolwal, and Samad, 2009).

The impact of the AGRA Soil Health Project intervention on the farm level productivity and

welfare (including crop yield, crop income, and food security) was estimated by using the

propensity score matching because, it facilitates the identification of a counterfactual when the

selection bias to be addressed is clearly due to observable characteristics of the subject.

Secondly, it does not necessarily require a baseline or panel data, although in the resulting cross-

section, the observed covariates entering the logit or probit model for the propensity score would

have to satisfy the conditional independence assumption by reflecting observed characteristics X

that are not affected by participation (Winters, Salazar and Maffioli, 2010; Khandkler, Shahidur,

Gayatri, Koolwal, and Samad, 2009).

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3.4 Variables of the Model

In order to quantify the estimates of adoption of Integrated Soil Fertility Management (ISFM)

technology as well as improved crop varieties (maize, soy bean and cowpea), variables that

describe the farmers‟ characteristics, farm level characteristics and institutional characteristics

were used as explanatory variables in the model.

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4.0 Result and Discussions

The results are differentiated by the three selection sites (Project Community, Near Project and

Far from Project Community). This was necessary to capture specific socio-economic

information and adopted technologies across these selected communities.

4.1 Description of Study Communities

The study involved 62 communities from the three Northern regions which consist of 20 project

communities, 21communities near to project communities (Counterfactual) and 21 distant

communities (Control). All the communities sampled are endowed with a number of physical

amenities. Majority of the communities visited have access to feeder roads with the exception of

1 project community and 4 distant communities. Most of roads are however not tarred. The

communities have roads that enable access to input and output markets, extension services

among others. Presence of feeder roads is an incentive for vehicles to provide transportation

services to members of the communities. While some transport owners provide their services on

a daily basis, others have special days for each group of communities. The use of motorbikes and

bicycles as means of transportation is pervasive among the sampled communities. The use of

tricycles locally known as “motorking” is increasingly becoming popular as more convenient

means of transporting both goods and people.

Most of the communities (34) report that vehicles passed through their communities when it is a

market day in another community. This finding may be attributed to the market size and the level

of participation in the destination community as compared to the resident market. On the average

11 vehicles visit the communities per week. Two of the project and distant communities have

access to markets whilst 6 of the nearby communities also have access to market. Access to

market could be important in guaranteeing access to inputs, favourable output prices and social

interactions among others. The number of villages participating in project community market is

relatively lower than villages participating in near and distant communities. Members of the

communities travel an average distance of 7.95 km to participate in nearby markets in the

absence of vehicles. However, the beneficiary communities of the AGRA SHP on the average

travel a distance of 9.33 km to the nearby market relative to the near and distant communities.

Distance to markets imposes transaction cost thus creating a barrier for market participation.

Distance to nearest market also influences the type of commodity produced in the community.

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Highly perishable commodities like fruits and vegetables are produced for specific markets

especially where there are no storage structures. They usually travel on foot, by bicycle or

motorcycles. The total number of observer, participant and non-participant farm households

across the sampled communities is 120, 103 and 79 respectively.

Table 1a: Characteristics of Study Communities

Infrastructure

Communities Category

Overall Project

Community

Near

Project

Community

Distant

Project

Community

Communities (No.) 20 21 21 62

Infrastructure

Access to feeder roads

Yes

No

19

1

21

0

17

4

57

5

Access to tarred roads

Yes

No

5

15

5

16

5

16

15

47

Number of Vehicle visit per week 5 3 3 11

Special day vehicle visits

Market day in the village

Market day in another village

8

11

9

12

11

9

28

34

Access to market

Yes

No

2

18

6

15

2

19

10

52

Number of villages participating in market 9 10 11 30

Average distant to market (km) 9.33 9.19 5.32 7.95

Access to agricultural water source

Yes

No

12

8

14

7

10

11

36

26

Access to domestic water source

Yes

No

18

2

21

0

20

1

59

3

Access to school

Yes

No

13

7

19

2

15

6

47

15

Access to health post

Yes

No

0

20

8

13

4

17

12

50

Access to electricity

Yes

No

4

16

7

14

9

12

20

42

Access to administrative office

Yes

No

0

20

1

20

0

21

1

61

Source: Survey Data, 2012

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Table 1b: Characteristics of Study Communities

Infrastructure


Overall Project

Community

Near

Project

Community

Distant

Project

Community

Access to Grain mill

Yes

No

14

6

18

3

17

4

49

13

Access to telephone coverage

Yes

No

5

15

7

14

5

16

17

45

Household Category

Observer

Participant

Non-participant

16

40

44

20

57

25

84

6

10

120

103

79


Some of the sampled communities have access to domestic water source (59), agricultural water

source (36), health posts (12), schools (47) and market facility (10). Whilst twenty (20) of the

selected communities have access to electricity, 49 and 17 of the communities have access to

grain mill and telephone coverage respectively. Comparatively, the nearby communities have

more access to telephone coverage than the project and distant communities (Table 1). The

availability of mobile phone communication reception is crucial because it allows for easy

interactions. The mobile phone has over the years become a tool for developing market

strategies.

The total population of the community stands at 2,772 with females forming the majority

(1,529). Comparatively, total number of persons in the control community is higher followed by

the counterfactual community and the project community. Farming and land ownership in the

various community categories is male-dominated. The counterfactual communities were better

endowed with land relative to the other two community categories. The total number of

households across the three community categories is 299 with males forming the majority (241)

of the household head. Females become household heads in the absence of an adult male

considered capable of being the household head. This explains the largely representation of male

heads in the sample. The result is not surprising in a custom that recognizes males as heads of

household (Abatania et al. 1999). Land availability enables farmers to generate production

surpluses, overcome credit constraints, where land can be used as collateral for credit, and allow

them to adopt improved technologies that increase productivity (Olwande, 2010).

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Table 2: Demographic Characteristics of Communities

Item


Overall Project

Community

Near

Project

Community

Distant

Project

Community


Total Households

Male headed households

Female headed households

68

64

4

71

68

3

160

109

51

299

241

19

Total number of persons

Male persons

Female persons

850

417

433

899

372

527

1023

454

569

2772

1243

1529

Total Farmers

Male Farmers

Female Farmers

428

279

149

318

211

107

361

222

139

1107

712

395

Total Area of land ownership

Area of land for males

Area of land for females

305

206

99

576

373

203

335

217

118

1216

796

420


Table 3 shows the transaction cost incurred in transportation of maize, cowpea and soybean to

the market. The average charges for transporting a mini bag of maize, cowpea and soybean to the

market across the three community categories are GH₵0.67, GH₵0.58 and GH₵0.58

respectively. The average charges for transporting a maxi bag of maize, cowpea and soybean to

the market across the three community categories are GH₵1.14, GH₵1.19 and GH₵1.13

respectively. It can also be deduced from the result that community members incur the highest

transportation cost for a maxi bag of cowpea and a mini bag of maize. Locational difference may

account for this phenomenon.

Table 3: Transportation Cost (GH₵) of Commodities

Item


Overall Project

Community

Near

Project

Community

Distant

Project

Community


Transport charges per person 1.10 0.80 0.90 0.90

Transport Charge per mini bag of maize 0.60 0.70 0.70 0.67

Transport Charge per maxi bag of maize 1.16 1.27 1.00 1.14

Transport Charge per mini bag of cowpea 0.57 0.68 0.50 0.58

Transport Charge per maxi bag of cowpea 1.17 1.39 1.00 1.19

Transport Charge per mini bag of soybean 0.57 0.68 0.50 0.58

Transport Charge per maxi bag of soybean 1.00 1.39 1.00 1.13


21

Crop production was shown to be a year round pre-occupation of the households following the

rain-fall pattern which generally begins from March-April and ends in November-December.

The period between land preparation and harvesting covers a period of 10 months. Storage and

sales of harvested rice is also shown to be year-long activities. The crop calendar provides a

useful guide for timely execution of the field activities of the project. It also serves as a tool for

monitoring of farming activities and provides the targeted farmers the opportunity to fully

participate and learn from the project.

Table 4: Crop Calendar

Activities Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Main rain season

Land preparation

Planting

1st Fertilizer application

2nd

Fertilizer application

Herbicide application

Insecticide application

1st Weeding

2nd

Weeding

Harvesting

Threshing

Drying period

Storage period

Sales periods

Legend Northern (N) Upper East

(UPPER EAST)

Upper West (UPPER

WEST)

N+UPPER

EAST

N+UPPER

WEST

UPPER

EAST+UPPER

WEST

All

Source: AGRA SHP Ex-post Data, 2012

4.2 Characteristics of the Survey Households

The result indicate that sampled households in Northern Ghana were male dominated. The result

is consistent with the findings of Wiredu et al, (2010) who identified that the agricultural

production system in Ghana is dominated by men. The non-participant households were all

males. On the whole females constituted a small fraction of household heads. The average age of

a randomly selected household head in the study area was about 50 years. The non-participant

households on the average are older than the other households. This is an important indication of

22

the experience of the farm households in agriculture. On the other hand, the results suggest that

the average household head can be involved in agriculture for the next decades. This situation of

aging population of farmers is a potential threat to farm level productivity and overall production

since most of the farmers are expected to retire from active occupation within a decade. The

average experience age of farming across the sampled household categories is 30. Farming

experience is important for decision making with regard to farming activities. The total number

of household heads that were involved in AGRA SHP as observers, participants and non-

participants are 120, 103 and 79 respectively. The average year of residency by a household head

in the village is 46 years (Table 5).

The household unit is defined as the number of people who work or farm together, spend income

together, and eat from the same pot and under one authority. The average household size across

the sampled household is 9 (Table 5). The household sizes were relatively larger among the

participant household category. The average estimated household size of about 9 members in the

community was not significantly different from findings by Wiredu et al, (2010). This is true

because the household in the Northern Ghana depend largely on family labour for agricultural

operations. They are therefore motivated to manage large households.

Table 5 shows that almost all the household heads in the sampled household categories are

married (96%). The observer household category recorded the highest level of marriage (98%).

Married household heads are normally assisted by their spouses in production, processing and

marketing activities as well as decision making (Martey et al., 2012).

The levels of education of the sampled households varied across the sampled household

categories. Most of the household heads across the household categories are not educated (78%).

About 23% of the household heads within the non-participant are educated followed by

household heads within the participant household category (22%) and observer household

category (19%). Basic education forms the largest education level attained by household heads

within the education categories. The non-participant household category had the highest level of

household heads with tertiary education (Table 5). Education enables an individual to make

independent choices and to act on the basis of the decision, as well as increase the tendency to

co-operate with other people and participate in group activities.

23

Table 5: Characteristics of Farm Household Heads

Characteristics

Household Categories

Overall Observer

Household

Participant

Household

Non-

participant

Household (No.) 79 102 100 302

Male (%) 95.24 99.05 100.00 98.35

Age (Years) 49 48 51 50

Year of farming experience 28 29 31 30

Resident years in village 46 45 47 46

Household size 9 10 9 9

Marital Status (%)

Married

Divorce

Separated

Widowed

Never married

Juvenile

97.62

0.00

0.00

1.19

1.19

0.00

96.19

0.95

0.95

0.95

0.95

0.00

93.86

0.88

0.88

0.88

1.75

1.75

95.71

0.66

0.66

0.99

1.32

0.66

Education (%)

None

Basic (Primary/JHS)

Secondary(SHS/Vocational/Technical)

Tertiary (University/Polytechnic/Training)

Islamic

Adult education

80.72

7.23

7.23

1.20

1.20

2.41

78.10

14.29

5.71

0.00

0.95

0.95

77.19

15.79

4.39

2.63

0.00

0.00

78.48

12.91

5.63

1.32

0.66

0.99


4.3 Farm Household Resources

Household resources are proximate measurement of household wealth. About 54% of the

household heads across the sampled household categories resides in a mud hut with thatch roof

whilst 50% reside in mud hut with aluminium roof (Table 6). All the household heads in the

sampled household categories occupying the mud hut with thatch roof, block building with

thatch roof and block building with aluminium roof are landlords. These household heads within

these communities are able to save on rental charges.

On the average, a farm household have 1 radio, 2 mobile phones, 1 motor cycle, 9 utensils, 2

furniture, 4 mattresses, 2 bicycles and 4 water containers across the sampled household

categories. Most of the farm household do not have television and fan. The radio is one of the

major sources of information to the farmers. The average quantities of farm equipment available

to the farmers include 1 grain storage facility, 3 cutlasses, 6 hoes, 2 sickles and 1 knapsack

sprayer (Table 6).

24

Table 6: Farm Household Resources, Assets and Occupancy Status of Household Head

Household Resources

Community Categories

Overall Observer

Household

Participant

Household

Non-

participant

Mud hut with thatch roof (%) 48.28 51.38 61.67 53.78

Mud hut with aluminium roof (%) 60.92 49.54 38.33 49.60

Block building with thatch (%) 0.00 0.00 0.00 0.00

Block building with aluminium roof (%) 1.15 2.75 2.50 2.13

Television (Quantity) 0.23 0.37 0.29 0.30

Radio (Quantity) 1.00 1.00 1.00 1.00

Rifle (Quantity) 0.20 0.19 0.43 0.28

Fan (Quantity) 0.26 0.24 0.23 0.25

Mobile Phone (Quantity) 2.00 2.00 2.00 2.00

Motor cycle (Quantity) 1.00 1.00 1.00 1.00

Utensils (Quantity) 7.00 7.00 12.00 9.00

Furniture/Sofa (Quantity) 2.00 2.00 2.00 2.00

Foam mattress (Quantity) 1.00 1.00 10.00 4.00

Water containers (Quantity) 2.00 3.00 2.00 2.00

Bicycle (Quantity) 2.00 2.00 2.00 2.00

Grain Storage facility (Quantity) 1.00 1.00 2.00 1.00

Cutlass (Quantity) 3.00 4.00 3.00 3.00

Hoes (Quantity) 5.00 6.00 5.00 6.00

Sickle (Quantity) 2.00 2.00 2.00 2.00

Knapsack Sprayer (Quantity) 1.00 1.00 2.00 1.00

Occupancy status of mud hut with thatch (%)

Landlord

Tenant

100.00

0.00

100.00

0.00

100.00

0.00

100.00

0.00

Occupancy status of mud hut with aluminium (%)

Landlord

Tenant

100.00

0.00

98.15

1.85

100.00

0.00

99.38

0.62

Occupancy status of block building with thatch (%)

Landlord

Tenant

100.00

0.00

100.00

0.00

100.00

0.00

100.00

0.00

Occupancy status of block building with

aluminium (%)

Landlord

Tenant

100.00

0.00

100.00

0.00

100.00

0.00

100.00

0.00


On the average, a farm household across the sampled household categories own 3 cows, 5 goats,

4 sheep, 13 chickens, 2 fowls, 2 guinea fowls, a bull, young bull, heifer, pig and a calf (Table 7).

The livestock serves as other sources of funds and security for the households in times of risks.

25

Table 7: Livestock Assets of Household Head

Livestock Assets


Overall Observer

Household

Participant

Household

Non-

participant

Livestock Assets (Quantity)

Cow 2 3 2 3

Bull 1 1 1 1

Young Bull 1 1 1 1

Heifer 1 1 1 1

Calf 1 0.4 1 1

Goat 7 5 5 5

Sheep 4 4 5 4

Pig 1 1 2 1

Chicken 13 15 11 13

Fowl 2 2 1 2

Guinea fowl 2 2 1 2

Donkey 0.38 0.12 0.07 0.17


4.4 Production and Sales Volume of Crops

The quantity of maize, soybean and cowpea produced per hectare across the household

categories in Northern Ghana are 6.24MT, 0.24MT and 0.5MT respectively. The non-participant

households recorded the highest maize, soybean and cowpea production volume per hectare

followed by the observer and participant households. The quantity of maize, soybean and

cowpea sales is higher among the non-participant households (Table 8).

Table 8: Quantity of Crop Produced and Sold (MT)

Activities


Overall Observer

Household

Participant

Household

Non-

participant

Quantity of maize harvested (Kg) 3698.14 3348.13 10870.33 6242.44

Quantity of soybean harvested (Kg) 171.44 160.20 372.09 242.11

Quantity of cowpea harvested (Kg) 165.39 113.51 1117.20 501.14

Quantity of maize sold (Kg) 157.14 145.83 262.48 193.46

Quantity of soybean sold (Kg) 56.76 71.19 95.59 76.54

Quantity of cowpea sold (Kg) 12.95 21.63 49.15 29.75


26

4.5 Adoption of Improved Technologies

4.5.1 Land Resources, Uses and Soil Fertility

The focal crops of the AGRA SHP are maize, cowpea and soybean. Farmers in the project

intervention areas either grow one or more of these crops subject to their budget constraint. The

percentage use of tools and equipment among the three crops by household head differ across the

sampled household categories. The percentage use of tractor, tractor plough, tractor harrow,

animal plough, animal harrow, animal scotch cart wheel barrow, grain storage facility and

knapsack sprayer for maize production is higher relative to soybean and cowpea (Table 9). It can

be concluded that maize production in the sampled household categories is relatively capital

intensive. Maize is a staple crop amongst most households in Northern Ghana and is largely

grown among majority of the farmers. Generally the percentage use of tools and equipment for

maize is higher among the observer household relative to the other household categories. The

possible explanation to this phenomenon may be diffusion of the practices from the participant

households. This finding can also be attributed to the project intervention that sought to improve

livelihood through increase and/or maintaining of soil fertility for increase productivity. It is also

worth noting that the percentage use of tools and equipment in the participant household

category is higher than that of the non-participant households.

27

Table 9: Land Resources and Use of Household Head

Land Resources and Use


Overall Observer

Household

Participant

Household

Non-

participant

Use of Tractor (%)

Maize 1.49 1.56 0.92 1.28

Soybean 0.34 0.18 0.58 0.38

Cowpea 0.46 1.00 0.17 0.54

Use of Tractor Plough (%)

Maize 1.03 1.56 1.42 1.36

Soybean 0.00 0.18 0.83 0.38

Cowpea 0.11 1.00 0.33 0.51

Use of Tractor Harrow (%)

Maize 0.00 1.00 0.00 0.35

Soybean 0.00 0.64 0.00 0.22

Cowpea 13 15 11 13

Use of Animal Plough (%)

Maize 11.53 10.18 7.60 9.57

Soybean 2.68 1.88 2.39 2.29

Cowpea 0.11 1.00 0.33 0.51

Use of Animal Harrow (%)

Maize 0.64 0.46 0.00 0.33

Soybean 2.68 1.88 2.39 2.29

Cowpea 1.95 1.97 1.08 1.63

Use of Animal Scotch Cart (%)

Maize 7.24 3.02 2.54 4.00

Soybean 0.34 0.00 0.79 0.40

Cowpea 0.46 0.64 0.13 0.40

Use of Wheel Barrow (%)

Maize 3.03 2.75 0.08 1.82

Soybean 0.41 0.00 0.08 0.15

Cowpea 0.00 0.00 0.08 0.03

Use of Grain Storage Facility (%)

Maize 40.74 25.69 42.62 36.26

Soybean 2.30 0.37 3.08 1.93

Cowpea 5.23 4.95 4.64 4.91

Use of Knapsack sprayer (%)

Maize 17.43 20.12 17.13 18.24

Soybean 2.89 3.51 2.96 3.13

Cowpea 13.28 8.53 13.04 11.28


28

Maize is the most cultivated crop across the household categories in the Northern Ghana (Table

10). Almost all the households allocate part or all of their land into maize production. Among the

three focal crops of AGRA SHP, soybean is the least cultivated in terms of area. Cowpea is

mostly grown among the participant households (50%). Soil infertility is one of the challenges

faced by most of the farmers in Northern Ghana partly due to low rate of fertilizer application.

The result also shows variations in the fertility of soil cultivated for the three crops. The fertility

status of the maize plot is normal. The observer households recorded the highest percentage of

households with normal soil fertility for maize cultivation. However, 51% and 54% of the

participant and non-participant households respectively cultivate maize on rich soils. Soybean

and cowpea cultivation occur mostly on soils with low fertility. The situation is more evident

among the observer and non-participants households.

Table 10: Crop/Land Use and Fertility

Crop/Land Use


Overall Observer

Household

Participant

Household

Non-

participant

Crop Cultivation (%)

Maize 100.00 98.17 100.00 99.37

Soybean 43.68 39.45 42.98 41.96

Cowpea 47.13 50.46 47.93 48.58

Sorghum 29.89 39.45 41.32 37.54

Rice 42.53 30.28 47.93 40.38

Groundnut 65.52 61.47 52.89 59.31

Yam 44.83 56.88 50.41 51.10

Abandoned 0.00 1.83 4.13 2.21

Fallowed 0.00 1.83 6.61 3.15

Pasture 100.00 100.00 100.00 100.00

Soil Fertility for Maize Plot (%)

Low 0.00 2.75 1.65 1.47

Normal 60.92 45.87 44.63 50.47

Rich 39.08 51.38 53.72 48.06

Soil Fertility for Soybean Plot (%)

Low 70.11 61.47 58.68 63.42

Normal 13.79 20.18 19.01 17.66

Rich 16.09 18.35 22.31 18.92

Soil Fertility for Cowpea Plot (%)

Low 54.02 50.46 52.89 52.46

Normal 21.84 22.94 26.45 23.74

Rich 24.14 26.61 20.66 23.80


29

4.5.2 Varietal Preferences and Farm Management Practices

Preferences for crop varieties vary significantly across households. The obatanpa variety of

maize is mostly grown by 59% of the households in Northern Ghana followed by Okomasa and

white maize. Jenguma (23%) and black eye (16%) are the preferred soybean and cowpea

varieties among majority of the household heads across the household categories in Northern

Ghana (Table 11). Improved varieties of maize, soybean and cowpea are mostly preferred by

most of the households. The reason could be due to the collaborative effort of the major

stakeholders in the agricultural sector that promotes new technologies to farmers.

Table 11: Preferences of Crop Varieties

Preferred Variety


Overall Observer

Household

Participant

Household

Non-

participant

Maize Varieties Cultivated (%)

Obatanpa 62.18 55.96 59.30 59.24

Abrotia 0.00 0.92 2.52 1.27

Agric maize 1.16 0.92 2.52 1.59

“Dobidi” 4.65 0.92 0.00 1.59

Mamaba 1.16 1.83 5.04 2.87

Okomasa 4.65 2.75 7.56 5.10

White maize 2.33 4.59 6.72 4.78

Yellow maize 3.49 1.83 0.00 1.59

Soybean Varieties Cultivated (%)

Jenguma 25.58 21.10 23.53 23.25

Local 0.00 0.00 1.68 0.64

Maggi 1.16 1.83 0.00 0.96

Salintuya 4.65 4.59 7.56 5.73

Short type 0.00 0.00 0.84 0.32

Zangurima 0.00 0.92 0.84 0.64

Zoomi 0.00 0.00 0.84 0.32

Cowpea Varieties Cultivated (%)

Apaagbala 0.00 0.92 2.52 1.27

Bensagla 1.16 0.00 1.68 0.96

Black eye 9.30 13.76 23.53 16.24

Brown eye 1.16 0.92 1.68 1.27

Bunga 0.00 0.92 0.00 0.32

Local cowpea 3.49 9.17 3.36 5.41

Nilo 2.33 2.75 0.84 1.91

Nempagsei 0.00 0.92 0.00 0.32

Ormandau 6.98 4.59 5.04 5.41


30

The preferences of crop varieties and technology by farmers in northern Ghana are presented in

Table 12. The Kendall‟s „W‟ value of 0.258 indicates that there is 26% agreement between the

respondents in the ranking of the preferences of crop varieties and technologies by farmers in the

Northern Ghana. The low value also indicates a weak agreement among the farmers in the

ranking of their preferred crop characteristics and technologies. Among the identified

preferences, yield, demand, marketability, grain size and drought tolerance are the five most

preferred crop characteristics and technologies by majority of farmers in Northern Ghana. Yield

is the most preferred crop characteristics by most farmers due to their low output. Technology

awareness and adoption has been increasing among farmers. This awareness has come about as a

result of the constant interaction between farmers, AEAs and research institutions. Northern

Ghana is characterised by a long period of dry season thus farmers will prefer a crop that is more

tolerant to drought.

Table 12: Preference of Crop Varieties and Technology

Identified Preferences Mean Rank

Yield 5.17

Demand 7.40

Marketability 8.22

Grain Size 8.29

Drought tolerance 9.85

Earliness 11.17

Maturity 11.72

Grain Colour 12.18

Storage pest tolerance 12.25

Taste 12.53

Grain Price 12.71

Complementary technologies 12.75

Palatability 13.07

Plant Vigor 13.11

Seed availability 13.22

Striga tolerance 13.22

Field Pest Tolerance 13.59

Ease of threshing 14.01

Infertility tolerance 14.29

Pod Size 15.02

Grain Shape 15.02

Pod Colour 16.84

Pod Shape 17.57

Shattering 18.80

Seed dormancy 23.01

Number of observation 99

Kendall‟s Wa .258

Chi-square 612.681

Df 24 Assymp. Sig 0.000

31

Table 13 shows variation of farm management practices among the farm households for the

specific crops cultivated. Weed control in the maize plot is done manually during the pre-

planting period mostly by the participant farm households. Soybean and cowpea producing farm

households do not control weeds. Insect and disease control is practiced among the cowpea

producing farm households and the inputs are purchased mostly from the input dealers. On the

average 2 man-days is required for herbicide and insecticide application. Man-days for manual

weeding of maize farms are higher than that of soybean and cowpea. The plausible reason could

be due to the variation in the area under cultivation of the different crops. The application rate of

herbicide and insecticide per hectare are 1.97 litres and 5.52 litres respectively.

Table 13: Farm Management Practices

Activities


Overall Observer

Household

Participant

Household

Non-

participant

Maize Crop

Manual weed control (%) 63.22 70.64 38.33 56.33

Timing (Pre-planting) (%) 51.72 56.88 50.00 52.85

Herbicide source (Input dealer) (%) 37.93 42.20 55.83 46.20

Quantity of herbicide (l/acre) 1.67 2.20 1.97 1.97

Labour for herbicide application 2.00 2.00 2.00 2.00

Labour for manual weeding 15.00 15.00 15.00 15.00

No insect control (%) 93.65 92.75 95.52 93.97

No disease control (%) 93.18 100.00 96.55 96.49

Soybean Crop

No weed control method 57.47 64.22 60.83 61.08

Herbicide source (Non applicable) (%) 87.36 84.40 80.83 83.86


No insect control (%) 95.24 95.65 95.52 95.48


Cowpea Crop

No weed control method 52.87 49.54 55.00 52.53

Herbicide source (Non applicable) (%) 85.06 79.82 75.83 79.75


Use of inorganic insecticide for insect control (%) 33.33 43.48 61.19 46.23

Insecticide source (Input dealer) 19.05 36.23 47.76 34.67

Quantity of insecticide (l/acre) 14.22 1.08 1.92 5.52

Labour for insecticide application 1.00 2.00 2.00 2.00



32

4.5.3 Adoption of Integrated Soil Fertility Management

Integrated soil fertility management is the approach adopted to address soil fertility challenge

among smallholder farmers. ISFM practices include appropriate fertilizer and organic input

management in combination with the utilization of improved crop varieties, and adaptation to

local conditions. All (100%) the samples households in the three Northern regions use organic

and inorganic fertilizer for crop production. The ISFM is widely practiced by all the households

in the study areas. The plough-in of residue and green manure is a general practice by majority of

the farmers especially amongst the observer and non-participant household categories. The green

manure enriches the soil with nutrients and organic matter for effective plant growth. The

common compound fertilizer widely used by farmers is the Nitrogen Phosphorus and Potassium

(NPK). The average compound, urea and organic fertilizer application rate per acre for maize

production are 177kg, 102kg and 15kg respectively. The average compound, urea and organic

fertilizer application rate per acre for soybean is higher than cowpea production. Generally, the

fertilizer application rate for maize among the different household categories is high which may

be attributed to the AGRA SHP. The main application method of fertilizer is by spraying and

normally done during the pre-planting phase of the production process. Input dealers are the

main source of fertilizer for most of the household heads. The project encourages farmers to buy

inputs from reputable input dealers and also link farmers to these input dealers.

33

Table 14: Integrated Soil Fertility Management

Activities


Overall Observer

Household

Participant

Household

Non-

participant

Maize Crop

Inorganic fertilizer (%) 100.00 100.00 100.00 100.00

Organic fertilizer (%) 100.00 100.00 100.00 100.00

ISFM adoption (%) 100.00 100.00 100.00 100.00

Plough-in plant residue (%) 90.70 73.08 87.50 83.55

Plough-in green manure (%) 65.12 51.92 68.33 61.94

Fertilizer Source (%)

Input dealer 95.35 98.08 98.33 97.42

Method of Fertilizer Application (%)

Spraying 98.84 99.04 97.50 98.39

Timing of Fertilizer Application (%)

Pre-planting 98.84 99.04 96.67 98.06

Quantity of Fertilizer Application (Kg/Acre)

Qty. of Compound (NPK) Fertilizer for maize plot 194.08 173.15 169.32 177.47

Quantity of Urea Fertilizer for maize plot 111.02 109.82 88.42 101.87

Quantity of Organic Fertilizer for maize plot 22.52 5.17 18.08 14.98

Qty. of Cmpd. (NPK) Fertilizer for soybean plot 15.80 9.16 5.26 9.49

Quantity of Urea Fertilizer for soybean plot 10.19 5.06 2.11 5.34

Quantity of Organic Fertilizer for soybean plot 1.74 0.00 0.00 0.48

Qty. of Cmpd. (NPK) Fertilizer for cowpea plot 4.37 4.09 0.00 2.58

Quantity of Urea Fertilizer for cowpea plot 4.36 2.40 0.00 2.02

Quantity of Organic Fertilizer for cowpea plot 1.45 0.00 1.25 0.89


4.6 Household Access to Credit, Type and Repayment

Credit types differ among the households. Most (26%) of the household head obtains credit in

the form of fertilizer whilst 11% obtains credit in the form of service. Seed and cash credit type

are the lowest form of credit type among household heads in Northern Ghana. The observer

household heads received more credit in the form of fertilizer, service, seed and cash relative to

the other household head categories. Experience has shown that non-cash credit in the form of

inputs and services to farmers yield good result compared to cash credit. Participants‟ household

heads receive cash credit more than the other household heads.

The main source of credit for the farm household heads is Project. The AGRA SHP linked up

farmers to the Centre for Agricultural and Rural Development (CARD) who provided input

credit for the farmers. Non-governmental organizations serve as supporting players in the

34

advancement of input credit to farmers. Informal sources of credit like money lenders and

relatives were the other sources of credit to farmers. The banks‟ requirements and modalities for

accessing loan by farmers serve as a constraint to these famers and thus resort to informal

sources. Repayment of loan by household heads was mostly by sales of grains directly to CARD.

This strategy of repayment ensured that most of the household head do not default and it was

also an opportunity for a guaranteed market for household head.

Table 15: Access to Credit and Repayment

Activities


Overall Observer

Household

Participant

Household

Non-

participant

Type of Credit (%)

Cash credit 5.33 19.35 1.98 7.56

Seed credit 10.67 4.84 6.93 7.56

Fertilizer Credit 48.00 22.58 12.87 26.47

Service Credit 25.33 8.06 2.97 11.34

Source of Credit (%)

Bank 2.67 1.61 0.00 1.26

Money Lender 2.67 8.06 1.98 3.78

Neighbour 0.00 1.61 0.00 0.42

Relatives 4.00 4.84 1.98 3.36

NGOs 5.33 6.45 5.94 5.88

Project 37.33 17.74 4.95 18.49

Cooperatives 2.67 0.00 0.00 0.84

Company 0.00 3.23 0.99 1.26

Mode of Repayment (%)

Seed 2.67 1.61 0.99 0.68

Grain 41.33 30.65 18.81 28.99

Cash 12.00 11.29 0.00 6.72


4.7 Impact of Improved Technologies

4.7.1 Determinants of Adoption of ISFM Technologies

The probit model was used to estimate the parameters of the determinants of adoption of ISFM

technologies by smallholder farmers in Northern Ghana. A household head is described as an

adopter of ISFM technologies if the head uses any of the combination of ploughing in residue,

use of organic or inorganic fertilizers. The STATA SE 11 software was used to estimate these

parameters as well as the marginal effects. The relatively small value of the Pseudo R2

may be

35

due to measurement errors in the explanatory variables. The significant Wald chi-square value of

23.93 indicates that the explanatory variables jointly influence adoption of ISFM technologies

(Table 16). Adoption of ISFM is significantly determined by age of household head, farm size,

gender, years of farming experience, occupational status of farmer, participation in project and

ownership of livestock. Numerically and statistically, livestock ownership status is the most

influential determinant of ISFM adoption by farmers in Northern Ghana.

Gender is positively associated with higher probability of ISFM adoption. This indicates that

males have higher probability of adoption relative to females. The probability of a male to adopt

ISFM is 0.12 higher than that of the females. The result confirms the findings by Heyi and

Mberengwa, (2012). Agricultural production system in Northern Ghana is male-dominated

(Wiredu et al., 2011 and Wiredu et al., 2010). Male headed household are more endowed with

labour relative to female household heads and the latter are more engrossed with domestic

activities such that they do not have the luxury of time to participate in any technology transfer

that will culminate to adoption ceteris paribus.

The result indicates that conventional age square (age2) is positively related with the probability

of ISFM technologies adoption. A unit increase in the age of the household head leads to an

increase in the probability of adoption by 8.01E-05. The result confirms the respective studies of

Adesina and Forson (1995), Aklilu (2006), Asante et al. (2011) and Gbetibouo (2009) who

established a positive relationship between age and adoption of improved agricultural

technologies. Older household heads are more experienced which allows them to assess the

attributes of an improved technology relative to younger household head. Benin (2006)

concludes that most females are inhibited from making decisions regarding land management

practices even in the absence of their husband.

Education of household head is associated with a lower probability of ISFM adoption. The

probability of an educated household head to adopt ISFM technologies is 0.13 less than

uneducated household heads. Education is expected to increase depth of knowledge as well as

raise individual‟s awareness level. It also helps in the improvement of farmers‟ planning horizon.

The result is inconsistent with most findings (Asante et al., 2011; Damianos and

Giannakopoulos, 2002; Habtamu, 2006; He-Xue Feng et al 2007; Heyi and Mberengwa, 2012:

Nzomoi et al., 2007; Tambo and Abdoulaye, 2011; Udoh et al, 2008) where a positive

36

relationship is established between education and probability of adoption. Norris and Batie

(1987) and Igoden et al. (1990) also noted that higher education was likely to enhance

information access to the farmer for improved technology up take and higher farm productivity.

The possible reason for the negative effect could be as a result of competition between allocation

of time for farm and non-farm activities. It is likely for educated household heads to engage in

off-farm activities to augment household income consequently leading to a lower probability of

adopting ISFM technologies.

Farm size plays a significant positive role in the probability of adoption of ISFM in Northern

Ghana. A unit increase in the farm size of farmers leads to an increase in the probability of

adoption by 0.01. It is possible that farmers with large farm size will adopt technologies that will

enhance their land management practices due to low fertility of most soils in Northern Ghana

(FAO, 2005). Aklilu (2006) and Langyintuo and Mekuria (2005) established a positive

relationship between farm size and probability of technology adoption. However, study by

EEA/EEPRI (2002) hold a contrary view due to insecurity feelings associated with greater

landholdings.

Years of farming experience is associated with a lower probability of adoption. Less experienced

farmers are more likely to adopt ISFM technologies. A unit increase in farming experience leads

to a decrease in the probability of adoption by 0.01. It is more likely for experienced household

heads to draw on their experience in terms of agricultural activities. However, less experienced

farmers are more aggressive to adopt new technologies and are easily convinced. The result is

consistent with the findings by Wiredu et al. (2011). Contrary, to this finding, some studies like

Nhemachena and Hassan (2007) have also suggested a positive relationship between farmers

experience and adoption of technologies. According to them, years of experience enhances

farmers‟ probability of technology uptake and spreading of risk relative to less experienced

household heads.

Occupational status of farmers is negatively related to the probability of adoption. Household

heads that have farming as their primary occupation are less likely to adopt ISFM. The

probability of an individual with non-farming as the primary occupation to adopt ISFM

technology is 0.24 more than a household head with farming as the primary occupation.

37

Household heads with non-farming activities as their primary occupation are more likely to

invest the income generated from other sources into the farming business. Adoption of ISFM

technology is associated with some level of cost. Farmers with lower income may not have the

incentive to adopt.

Participation in the AGRA SHP significantly influences the adoption of ISFM negatively. The

result suggests that non-participant household heads are more likely to adopt compared to the

participant household heads. The probability of ISFM technology adoption amongst participant

household heads is 0.10 lower than non-participant household heads. It is expected that

participant household heads will have a higher adoption rate. However, participation does not

necessarily lead to adoption since individuals have different motivation for participation. It is

likely that non-participant household heads may be risk loving in terms of technology due to

their experience. Most of agricultural interventions are specifically tailored to a target group who

may initially have a strong desire to adopt and subsequently wane.

Finally, ownership of livestock is significantly associated with a lower probability of adoption.

Contrary to expectation, household heads that do not own livestock are more likely to adopt the

ISFM technology. The probability of adoption among non-livestock owners is 0.18 higher than

household head that owns livestock. The result is consistent with the findings of Aklilu (2006)

who established negative relationship between livestock holding and land management practices.

Livestock holding is normally an indication of resource endowment which has a positive

influence on technology adoption. In this study the opposite holds. Farm household heads with

no livestock holding may be using inorganic fertilizer as well as ploughing in residue into the

soil to enhance its fertility.

38

Table 16: Probit Estimates of Determinants of ISFM Technology in Northern Ghana

Variable ESTIMATED RESULT OF PROBIT MODEL

Coefficient Std Error Marginal Effect

Conventional Age square 0.0002 0.0001 8.01E-05*

Gender 0.3482 0.1923 0.1232*

Marital status of household 0.4454 0.5808 0.1698

Education status -0.3548 0.0185 -0.1302*

Years of farming experience -0.0204 0.0111 -0.0072*

Membership of association -0.0273 0.1699 -0.0096

Farm Size 0.0270 0.0561 0.0096*

Occupational status -0.8756 0.4337 -0.2379**

Household income -7.89E-06 1.62E-05 -2.79E-06

Livestock ownership status -0.5132 0.2236 -0.1815**

Participation status -0.2697 0.1604 -0.0968*

Access to credit 0.0836 0.1646 0.0294

Quantity of Maize produced 1.22E-05 1.28E-05 4.32E-06

Constant 3.5952 0.9925 3.9547

Number of Observations 300

Wald Chi-square (13) 23.93

Prob > Chi 2 0.0318

Log Pseudo likelihood -176.61042

Pseudo R-squared 0.0816

Source: Regression Estimation from Author‟s Household Survey Data (2012) **p < 0.05 and *p < 0.10

4.7.2 Impact of ISFM Adoption on Income

Table 17 shows the distribution of income across the category of respondents. According to the

survey, maize sales, other crops, livestock, trading activities, craftsmanship and remittances are

the main sources of income of farmers in Northern Ghana. The sale of maize contributes largely

to the total volume of income for both adopters and non-adopters of ISFM technology.

Generally, adopters of ISFM technologies earn higher income than non-adopters. The average

income for a household head in Northern Ghana who adopts ISFM technology is GH₵1217

(Figure 2). The ISFM technologies must be up-scaled to other districts of the three regions.

39

Table 17: Income Framework by Adoption

Activities Non-adopters

(N=95)

Adopters

(N=55) Total (N=150)

Income Profile

Total income 1063.2800 1216.8300 1119.5800

Distribution of income by sources (%)

Maize 59.9211 57.7208 59.1144

Other crops 20.0816 24.4936 21.6993

Livestock 9.8618 17.1143 12.5211

Trading activities 5.6667 6.8832 6.1126

Craftsmanship 0.5107 0.6760 0.5713

Remittance 0.2819 0.2177 0.2584

Source: Author‟s Estimation based on Household Survey Data (2012

Figure 2: Distribution of Income by Adoption of ISFM

5.0 General Conclusion and Recommendations

In addressing the low soil fertility among smallholder farmers in Northern Ghana (Upper East,

Upper West and Northern Region), the AGRA SHP was implemented. The AGRA Soil Health

Project aims at improving smallholder farmer productivity, through increasing access to locally

appropriate soil nutrients and promoting integrated soil fertility management. The approach used

by AGRA in addressing these challenges is the Integrated Soil Fertility Management (ISFM)

technique. Specifically the study compares technology adoption and improved agricultural

practices across the different communities (project, near and distant community) and household

(participant, observer and non-participant) categories.

40

The communities have roads that enable access to input and output markets, extension services

among others. Members of the communities travel an average distance of 7.95 km to participate

in nearby markets in the absence of vehicles. Crop production was shown to be a year round pre-

occupation of the households following the rain-fall pattern which generally begins from March-

April and ends in November-December. The population of the community is female dominated.

Farming and land ownership in the various community categories is male-dominated. The

average age of a randomly selected household head in the study area was about 50 years. Policies

must aim at enhancing female access to land. Government should also seek options to motivate

the youth to take up rice production thus the need to strengthen the Youth in Agriculture

Programme (YIAP).

The average household size across the sampled household is 9 and most of these household

heads are married and uneducated. About 54% of the household heads across the sampled

household categories resides in a mud hut with thatch roof. There is no significant difference in

the assets owned by the different household category. The non-participant household records the

highest volume and sales of maize, soybean and cowpea. Maize is the main crop grown among

the sampled households and its cultivation is capital intensive. Soybean and cowpea cultivation

occur mostly on soils with low fertility. Policy must aim at reducing the borrowing constraints of

farmers through reduction in the interest rate. The input credit provided by Centre for

Agricultural Development (CARD) must be replicated in the other non-beneficiary communities

in Northern Ghana.

The most preferred maize, soybean and cowpea varieties are obatampa, Jenguma and black eye

respectively. Yield and demand are the two most important attributes farmers look out for in the

adoption decision of crop variety and technology. Weeds are normally controlled manually.

Fertilizer application rate is high among maize producing household. Generally, most households

receive credit in the form of fertilizer with project as the source.

The probit model was used to estimate the parameters of the determinants of adoption of ISFM

technologies by smallholder farmers in Northern Ghana. Adoption of ISFM is significantly

determined by age of household head, farm size, gender, years of farming experience,

occupational status of farmer, participation in project and ownership of livestock. The ownership

41

of livestock was the most influential factor in the determination of adoption of ISFM

technologies in Northern Ghana. Farmer education must be intensified and technology must be

well packaged and easy to adopt.

Household heads who are adopters of ISFM technologies have higher incomes than non-

adopters. The ISFM technologies must be intensified and constraints associated with the

adoption must be addressed using a bottom-up approach.

42

References

Abatania, L., Gyasi, K.O., Terbobri, P. and Salifu, A.B. 1999. A baseline survey of cowpea

production in Northern Ghana. Technical report submitted to the PRONAF

project/IITA Benin Station.

Adesina, A.A. and Baidu-Forson, J. (1995). Farmers‟ perceptions and adoption of new

agricultural technology evidence from analysis in Burkina Faso and Guinea, West

Africa. Agricultural Economics 13:1-9.

Adu, S. V (1995). Soils of the Nasia basin. Memoir No. 6. Soil Research Institute, Kumasi.

Adu, S. V (1969). Soils of the Navrongo-Bawku area, Upper Region of Ghana. Memoir No. 5.

Soil Research Institute. Kumasi.

Akinola A. & Owombo, P. (2012). Economic analysis of adoption of mulching technology in

yam production in Osun State, Nigeria. Journal of Agriculture and Forestry, 2(1): 1-6.

Aklilu, A. (2006). Caring for the Land Best Practices in Soil and Water Conservation in Beressa

Watershed, Highlands of Ethiopia. Tropical Resource Management Papers, No. 76.

Angrist, J. (2001). “Estimation of Limited Dependent Variables Models with Dummy

Endogenous Regressors: Simple Strategies for Empirical Practice”. Journal of Business

& Economic Statistics 19(1):2-28.

Asante, O. B., Otoo, E., Wiredu, A.N., Acheampong, P., Osei-Adu, J., and Nsiah-Frimpong, B.

(2011). Willingness to adopt the vine multiplication technique in seed yam production

in the forest savanna transition agro-ecological zone, Ghana. Journal of Development

and Agricultural Economics Vol. 3(16), pp. 710-719, 26 December, 2011

Benin, S. (2002). Policies affecting land management, input use and productivity: land

redistribution and tenure in the Highlands of Amhara Region, Ethiopia. In: Benin, S.,

Pender, J. & Ehui, S. (Eds.). Policies for Sustainable Land Management in the East

African Highlands: Summary of Papers and Proceedings of Conference held at

UNCEA, Addis Ababa, Ethiopia.

Benneh, G., Agyepong, G. T., and J.A. Allotey (1990). Land degradation in Ghana.

Commonwealth Secretariat, London and University of Ghana. Legon.

Caliendo, M., and S. Kopeinig. 2008. “Some Practical Guidance for the Implementation of

Propensity Score Matching”. Journal of Economic Surveys 22(1): 31-72.

Damianos D, Giannakopoulos (2002). Farmers‟ Participation in Agri- Environmental Schemes in

Greece. Br. Food J., 104(31415): 261-273.

EEA/EEPRI (2004/05). Report on the Ethiopian Economy: Transformation of The Ethiopian

Agriculture: Potential, Constraints, and Suggested Intervention Measures, Vol. IV,

43

Addis Ababa. Food and Agriculture Organization (2005). Fertilizer use by crop in

Ghana. Rome. 39pp.

Gbetibouo, G. A. (2009). Understanding Farmers' Perceptions and Adaptations to Climate

Change and Variability: The Case of the Limpopo Basin, South Africa. IFPRI

Discussion Paper 00849 February 2009.

Gertler, P.J., Martinez, S., Premand, P., Rawlings L.B. and Vermeersch, C.M. (2011). Impact

Evaluation in Practice. The International Bank for Reconstruction and

Development/The World Bank. Washington DC: World Bank.

Ghana Statistical Service, GSS, (2008), Ghana Living Standards Survey Report of the Fifth

Round (GLSS 5), Accra, Ghana, 131pp.

Habtamu, E. (2006). Adoption of Physical Soil and Water Conservation Structures in Anna

Watershed, Hadiya Zone, Ethiopia. (Masters Thesis Addis Ababa University, 2006).

He-Xue F, Cao-Hu H, Li-Feng M (2007). Econometric analysis of the determinants of adoption

of rainwater harvesting and supplementary irrigation technology (RHSIT) in the in the

semiarid Loess Plateau of China. J. Agric. Water Manage., 89(2007): 243-250.

Heyi, D.D., and Mberengwa, I. (2012). Determinants of Farmers‟ Land Management Practices:

The Case of Tole District, South West Shewa Zone, Oromia National Regional State,

Ethiopia. Journal of Sustainable Development in Africa. Volume 14, No.1, 2012.

Igoden, C., Ohoji, P., Ekpare, J., (1990). Factors associated with the adoption of recommended

practices for maize production in the Lake Basin of Nigeria. Agricultural

Administration and Extension 29 (2), 149–156.

Khandkler, Shahidur R., Gayatri B. Koolwal, and Hussain Samad. (2009). Handbook on

Quantitative Methods of Programme Evaluation. Washington, DC: World Bank.

Langyintuo AS, Mekuria M (2005). Accounting for neighborhood influence in estimating factors

determining the adoption of improved agricultural technologies. A paper at American

Agricultural Economics Association annual meeting, providence, Rode Island, pp.1-28.

Martey, E., Annin, K., Wiredu, A. N., and Attoh, C. (2012). “Does Access to Market Information Determine the Choice of Marketing Channel among Small holder Yam Farmers in the Brong Ahafo Region of Ghana? A Multinomial Logit Regression Analysis”. Journal of Economics and Sustainable Development. Vol 3, No. 12 pp18-28

Ministry of Food and Agriculture, MoFA (2010). Agriculture in Ghana: Facts and Figures

(2009), Accra, Ghana, 53pp.

Nhemachena, C., and R. Hassan. (2007). Micro-level analysis of farmers‟ adaptation to climate

change in Southern Africa. IFPRI Discussion Paper No. 00714. International Food

Policy Research Institute, Washington, D.C.

44

Norris, E., Batie, S., (1987). Virginia farmers‟ soil conservation decisions: an application of

Tobit analysis. Southern Journal of Agricultural Economics 19 (1), 89–97.

Nzomoi JN, Byaruhanga JK, Maritim HK, Omboto PI (2007). Determinants of technology

adoption in the production of horticultural export produce in Kenya. Afr. J Bus.

Manage., 1 (5): 129-135.

Olwande, J and Mathenge, M. 2010. Market Participation among the Poor Rural Households in

Kenya. Tegemeo Institute of Agricultural Policy and Development.

Statistical Research and Information Department, SRID, Ministry of Food and Agriculture

(2012). National Crop production estimates 2011. Accra, Ghana.

Tambo JA, Abdoulaye T (2011). Climate change and agricultural technology adoption: the case

of drought tolerant maize in rural Nigeria. Mitig Adapt Strateg Glob Change. DOI

10.1007/s11027- 011-9325-7.

Udoh AJ, Idio A, Umoh E, Robson U (2008). Socioeconomic Factors Influencing Adoption of

Yam Minisett Technology in South eastern Nigeria: A Probit Analysis. Indian Res. J.

Exten. Edu., 8(2&3):1-5.

Winters, P., Salazar, L., Maffioli, A., (2010). Designing Impact Evaluations for Agricultural

Projects. Inter-American Development Bank. Impact Evaluation Guidelines. Technical

Notes, No. IDB-TN-198, December, 2010.

Wiredu AN, Mensah-Bonsu A, Andah EK, Fosu KY (2011). Hybrid Cocoa and Land

Productivity of Cocoa Farmers in Ashanti Region of Ghana, World J. Agric. Sci., 7(2):

172-178.

Wiredu AN, Gyasi KO, Marfo KA, Asuming-Brempong S, Haleegoah J, Asuming-Boakye A,

Nsiah BF (2010). Impact of improved varieties on the yield of rice producing

households in Ghana, Second Africa Rice Congress, Bamako, Mali, 22–26 March

2010.

45

Appendices

Appendix 1a: List of Communities

Community District Region Community category

Baloo Kassena-Nankana Upper East Distant Project Community

Tolli Nanumba Northern Distant Project Community

Bihinayili Tolon/Kumbungu Northern Distant Project Community

Moglaa Savelugu Northern Distant Project Community

Loho Nadowli Upper West Distant Project Community

Sakai Sissala East Upper West Distant Project Community

Nakpaar Saboba Northern Distant Project Community

Wilkambo-Boko Garu Upper East Distant Project Community

Torso Sissala East Upper West Distant Project Community

Sambol Saboba Northern Distant Project Community

Binda Nanumba Northern Distant Project Community

Jarigu Tamale Northern Distant Project Community

Passe Wa West Upper West Distant Project Community

Guno Tamale Northern Distant Project Community

Zugu Kumbungu Northern Distant Project Community

Gabiri Garu Upper East Distant Project Community

Yoguari Kassena-Nankana Upper East Distant Project Community

Bazumdi Bawku West Upper East Distant Project Community

Jang Nadowli Upper West Distant Project Community

Gusei Wa West Upper West Distant Project Community

Yong Tamale Northern Distant Project Community

Nankpawie Sissala East Upper West Near Project Community

Ankpaliga Bawku West Upper East Near Project Community

Tilli Bawku West Upper East Near Project Community

Bugri-Zambala No1 Garu Upper East Near Project Community

Yilikpani Savelugu Northern Near Project Community

Bianye Wa West Upper West Near Project Community

Ujando Daboba Northern Near Project Community

Gmantendo Nanumba South Northern Near Project Community

Yoguari Kassena-Nankana Upper East Near Project Community

Tariganga Garu Upper East Near Project Community

Kaahaa Nadowli Upper West Near Project Community

Satani Kumbungu Northern Near Project Community

Sakpe Nanumba South Northern Near Project Community

Kajelo Kassena-Nankana Upper East Near Project Community

Kotingle Tamale Northern Near Project Community

Zaari Garu Upper East Near Project Community

Gbangbaa Saboba Northern Near Project Community

Parishe Tamale Northern Near Project Community

Nyetua Savelugu Northern Near Project Community

46

Appendix 1b: List of Communities

Janbusi Wa West Upper West Near Project Community

Kulfo Sissala East Upper West Near Project Community

Azupupunga Bawku West Upper East Project Community

Zugu-Yipieligu Tolon Northern Project Community

Dunyin Tamale Northern Project Community

Buka Wa West Upper West Project Community

Bagliga Tamale Northern Project Community

Buu Nadowli Upper West Project Community

Goziesi Garu Upper East Project Community

Gbungbaliga Nanumba South Northern Project Community

Nyerigiyiligu Savelugu Northern Project Community

Gia Kassena-Nankana Upper East Project Community

Wapuli Saboba Northern Project Community

Zugu Kushibo Kumbungu Northern Project Community

Nyankani Nanumba South Northern Project Community

Mablo Tolon Northern Project Community

Sabolo Kassena-Nankana Upper East Project Community

Challu Sissala East Upper West Project Community

Kongbaloalo Sissala East Upper West Project Community

Yikurugu Bawku West Upper East Project Community

Kugnani Saboba Northern Project Community

Yiziegu Savelugu Northern Project Community

Kaleo Nadowli Upper West Near Project Community

Piise Wa West Upper West Project Community

Umbo Nadowli Upper West Project Community

47

Appendix 2: Probit Estimates of the Determinants of ISFM Technologies

(*) dy/dx is for discrete change of dummy variable from 0 to 1 credit* .0293538 .05747 0.51 0.610 -.08329 .141997 .34 mari* .1698437 .23099 0.74 0.462 -.282891 .622579 .986667 mem* -.0096219 .05984 -0.16 0.872 -.1269 .107657 .666667 income -2.79e-06 .00001 -0.49 0.626 -.000014 8.4e-06 2270.88 hav 4.32e-06 .00000 0.97 0.331 -4.4e-06 .000013 6476.58 Age2 .0000801 .00004 2.13 0.033 6.4e-06 .000154 2381.35livest~k -.1815328 .08001 -2.27 0.023 -.33834 -.024726 .16 part* -.0967592 .05796 -1.67 0.095 -.210357 .016839 .37 occu* -.2378802 .07809 -3.05 0.002 -.390924 -.084836 .93 yexp -.0072297 .00393 -1.84 0.066 -.014927 .000467 25.53 edu* -.130185 .0694 -1.88 0.061 -.26621 .00584 .24 sex .1231685 .06771 1.82 0.069 -.00954 .255877 .76 area .0095661 .00557 1.72 0.086 -.001353 .020485 6.06917 variable dy/dx Std. Err. z P>|z| [ 95% C.I. ] X = .68816378 y = Pr(isfm) (predict)Marginal effects after probit

. mfx

Note: 0 failures and 1 success completely determined. _cons .6147084 .6690682 0.92 0.358 -.6966411 1.926058 credit .0835625 .1646214 0.51 0.612 -.2390894 .4062145 mari .4453894 .580755 0.77 0.443 -.6928694 1.583648 mem -.0272651 .1698838 -0.16 0.872 -.3602313 .305701 income -7.89e-06 .0000162 -0.49 0.627 -.0000397 .0000239 hav .0000122 .0000128 0.95 0.340 -.0000129 .0000373 Age2 .0002266 .0001066 2.13 0.034 .0000176 .0004355 livestock -.5132406 .2236076 -2.30 0.022 -.9515035 -.0749777 part -.2697093 .1604163 -1.68 0.093 -.5841193 .0447008 occu -.8756153 .4336732 -2.02 0.043 -1.725599 -.0256315 yexp -.0204404 .0110787 -1.85 0.065 -.0421542 .0012734 edu -.3548376 .1845539 -1.92 0.055 -.7165567 .0068814 sex .3482294 .192369 1.81 0.070 -.0288068 .7252656 area .027046 .0157974 1.71 0.087 -.0039164 .0580084 isfm Coef. Std. Err. z P>|z| [95% Conf. Interval] Robust

Log pseudolikelihood = -176.61042 Pseudo R2 = 0.0816 Prob > chi2 = 0.0318 Wald chi2(13) = 23.93Probit regression Number of obs = 300

> e(robust) nolog. probit isfm area sex edu yexp occu part livestock Age2 hav income mem mari credit, vc

48

Appendix 3: Impact of ISFM Adoption on Incomes

Population parameters

Per capita income Total income Agricultural income

Parameter P>|z| Parameter P>|z| Parameter P>|z|

Observed sampled mean outcomes 133.4047 0.21 153.5529 0.572 100.6304 0.681

Inverse propensity score weighting estimates

ATE

49.02197 0.519 25.78654 0.912 14.48583 0.948

ATE1

124.4253 0.452 102.6918 0.743 25.45754 0.923

ATE0

5.367414 0.901 -18.73755 0.949 8.133779 0.977

LATE parametric (OLS) estimation of

population parameter

LATE

67.95958 0 137.6164 0 27.82051 0

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