ex-post impact of agra soil health project 005...
TRANSCRIPT
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
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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.
3
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
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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.
18
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
Communities Category
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
Source: Survey Data, 2012
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).
20
Table 2: Demographic Characteristics of Communities
Item
Communities Category
Overall Project
Community
Near
Project
Community
Distant
Project
Community
Communities (No.) 20 21 21 62
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
Source: Survey Data, 2012
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
Communities Category
Overall Project
Community
Near
Project
Community
Distant
Project
Community
Communities (No.) 20 21 21 62
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
Source: Survey Data, 2012
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
Source: Survey Data, 2012
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
Source: Survey Data, 2012
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
Community Categories
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
Source: Survey Data, 2012
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
Household Categories
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
Source: Survey Data, 2012
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
Community Categories
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
Source: Survey Data, 2012
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
Household Categories
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
Source: Survey Data, 2012
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
Household Categories
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
Source: Survey Data, 2012
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
Household Categories
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
Labour for manual weeding 5.00 4.00 6.00 5.00
No insect control (%) 95.24 95.65 95.52 95.48
No disease control (%) 100.00 100.00 100.00 100.00
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
Labour for manual weeding 5.00 6.00 7.00 6.00
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
No disease control (%) 95.45 97.56 86.21 93.86
Source: Survey Data, 2012
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
Household Categories
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
Source: Survey Data, 2012
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
Household Categories
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
Source: Survey Data, 2012
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
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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