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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue X, October 2025
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Factors Promoting Utilisation of Climate-Smart Agricultural
Practices in Northern Ghana
Seidu Nanundow 1, Hudu Zakaria 2, Osman Tahidu Damba 3 and Samuel Allottey 2
1 Department of Food Security and Climate Change, Faculty of Food and Consumer Sciences, University for Development
Studies (UDS), Tamale, Ghana.
2 Department of Agricultural Innovation Communication, Faculty of Food and Consumer Sciences, University for
Development Studies (UDS), Tamale, Ghana
3 Department of Agricultural and Food Economics, Faculty of Food and Consumer Sciences, University for Development
Studies (UDS), Tamale, Ghana
DOI: https://doi.org/10.51583/IJLTEMAS.2025.1410000119
Received: 02 November 2025; Accepted: 08 November 2025; Published: 17 November 2025
Abstract: This study examined the factors driving the adoption of Climate Smart Agricultural (CSA) practices in Northern Ghana.
In nine selected communities, 318 smallholder farmers were interviewed, comprising 115 adopters and 203 non-adopters of CSA
practices. Descriptive statistics were used to analyse the most common CSA practices in the area. Chi-square analysis and binary
logistic regression were performed to examine which factors most likely influenced the use of the selected CSA practices. The
study found that age, educational level, household size, access to extension and farmer-based organisations (FBO) membership of
all smallholder farmers surveyed directly influenced smallholder farmers’ utilisation of CSA practices. As revealed in the study, up
to 90% of the smallholder farmers in the study area were aware of the various CSA practices being examined. The study also
showed that smallholder farmers frequently used on-farm composting, with the Chi-square test showing that access to extension
services, household size, and educational attainment all had an impact on smallholder farmers' usage of on-farm composting. The
study recommends that the Ministry of Food and Agriculture (MoFA) intensify extension service delivery on CSA practices to
promote their adoption by smallholder farmers in the face of climate variability and change.
Key Works: Climate Change, Climate Smart Agriculture, Utilisation, Greenhouse Gas and Smallholder.
I. Background
Africa has been identified as the continent most vulnerable to adverse effects of climate change because of its heavy reliance on
natural resources and rain-fed agriculture. Smallholder farmers who depend largely on natural resources for their livelihoods are
enduring significant repercussions from climate change, as noted by Batisani (2012). Due to existing climate-related pressures like
drought, floods, erratic rainfall, and a lack of adaptive capability, African countries are particularly exposed and vulnerable to the
adverse effects of climate change (FAO, 2021).
Several studies have explored the potential of CSA to enhance raid-fed agriculture in Africa. For example, a study by Karg et al.
(2020) examined the effectiveness of soil and water conservation measures in improving crop yields and reducing erosion in raid-
fed agriculture systems in Burkina Faso. The study found that farmers who adopted these practices could increase their crop yields
by up to 54% while reducing erosion by up to 67%.
Another study by Kamara et al. (2019) investigated the potential of agroforestry in improving raid-fed agriculture systems in the
Sahel region. The study found that integrating trees into raid-fed agriculture systems can improve soil fertility, provide shade for
crops, and enhance biodiversity, leading to higher yields and improved resilience to climate change.
Overall, these studies suggest that CSA can be a practical approach to enhancing raid-fed agriculture in Africa. By adopting climate-
resilient and environmentally sustainable practices, farmers can improve their yields and livelihoods while reducing their
vulnerability to climate change.
However, the general lack of involvement of farmers and other implementing stakeholders in the developmental stages of CSA
technologies has been blamed for the low adoption and scaling up of climate-smart agriculture and other sustainable agriculture
practices. At a conference hosted by Ghana Climate Change, Agriculture and Food Security (CCAFS) Science Policy Platform to
create a technical report on CSA, participants consisting of community leaders and farmers and agribusiness entrepreneurs
bemoaned the lack of coordination among the institutions involved in the development and promotion of CSA and the absence of
successful project implementation (Essegbey et al., 2016).
In response, the Food and Agriculture Organization called for the involvement of farmers in the development of climate-smart
technologies to consider indigenous knowledge in formulating sustainable agriculture practices in the face of climate change (FAO,
2021).
Involving farmers in the development of CSA practices is crucial for ensuring that the approach is tailored to the needs and priorities
of local communities. Participatory approaches to CSA development can help to build trust and ownership among farmers, leading
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to more successful and sustainable outcomes. Several studies have highlighted the importance of involving farmers in the
development of CSA. A study by Sova et al. (2017) examined the role of participatory research in developing CSA practices in
Mali. The study found that involving farmers in the research process led to the identification of locally appropriate practices, such
as intercropping and the use of organic fertilizers, that was more effective than externally imposed interventions. The study also
highlighted the importance of building partnerships with local organisations to ensure the sustainability of CSA practices.
Also, a study by Vanlauwe et al. (2014) emphasised the importance of involving farmers in the testing and adaptation of CSA
practices in Uganda. The study found that farmers could identify the most promising CSA practices, such as intercropping with
legumes and using crop residues as mulch and were more likely to adopt these practices when involved in the research process.
Similarly, a study by Baudron et al. (2017) emphasised the importance of involving farmers in the development of CSA practices
in Malawi. The study found that participatory approaches, such as farmer field schools and participatory varietal selection, led to
higher adoption rates of CSA practices, such as intercropping and agroforestry, and greater yields and soil fertility improvements.
These studies suggest that involving farmers in the development of CSA practices can lead to more effective and sustainable
outcomes. By building partnerships with local organisations and using participatory approaches, researchers and development
practitioners can ensure that CSA practices are tailored to the needs and priorities of local communities.
Northern Ghana depends mainly on cultivating food crops and animal rearing for the livelihoods and sustenance of its people amidst
mounting challenges (GSS, 2020). According to MOFA (2022), poor soils, poor infrastructure, limited market access, and absence
of access to new and improved agricultural inputs are among the problems threatening improved livelihoods in northern Ghana and
harming food security. However, agriculture still holds many prospects for the area in eradicating the endemic poverty and chronic
food insecurity amongst the majority of the inhabitants, mainly smallholder farmers. However, the fragile nature of northern Ghana
in terms of vegetation, climate and ecology exposes agricultural production in the area to climate variability and changes. In view
of this, it is important to employ agricultural strategies to enhance food production by reducing vulnerability and resilience to
climate change while minimising its carbon footprint.
This can be achieved through the adoption of CSA practices. However, Agrawal (2008) has long observed that as climate change
continues to worsen northern Ghana farmers' vulnerability, the success of the uptake of CSA techniques in the area would rely on
the level of organisational and farmer awareness and network building among stakeholders. Also, Abegunde et al. (2020) reviewed
a decade's CSA-related scientific material between 2010 and 2020. The main conclusions were that there are differences between
macro-areas in the pattern and degree of adoption of CSA methods and the reaction to climate change. Significant drivers of CSA
adoption include resources, institutional tools, climatic and ecological environments, and farmer attributes like experience and
access to extension services. Dethier and Effenberger (2012) also believe that while extension services and research and
development are essential, establishing strong institutions is crucial for technology-driven agricultural development. Similarly,
Imran et al. (2019), among many other researchers, looked at how the implementation of CSA would affect farmers' incomes. They
concluded that CSA increases agricultural output and farm revenue on a sustainable basis, improves the efficiency of how nutrients
and water are used, makes crops more tolerant to climatic shocks, and reduces Greenhouse Gas (GHG) emissions to a minimum
level. Anuga et al. (2019) concluded that institutional, economic, environmental, and sociocultural factors were the most critical
factors influencing CSA utilisation.
Even with the positive effects of CSA practices on farmers and their long impact on reducing climate change vulnerability among
farmers while reducing the agricultural carbon footprint, the practice is rare in northern Ghana. This situation is of research concern
because both government and non-governmental organisations have implemented programmes and projects to promote the adoption
of CSA among farmers in the area. A recent study by Agbenyo et al. (2022) which examined income and economic impacts through
the adoption of CSA practices, found a positive coefficient between the adoption of CSA and the income accruing to farmers.
Through the Ghana Ministry of Food and Agriculture, programmes such as the Ghana Agriculture Sector Investment Programme
(GASIP) through sponsorship from the International Fund for Agricultural Development (IFAD) had implemented projects in
northern Ghana to promote and build the capacity of smallholder farmers on climate-smart agricultural techniques (GASIP 2022).
The Ministry of Food and Agriculture is implementing the project, and its main objective is to aid in the reduction of poverty in
rural Ghana (Ambler, 2020). Some activities comprise the Climate Change Resilience sub-component of GASIP: advancing CSA
by extension and raising value chain players' understanding of climate change resilience (IFAD, 2019).
The project has introduced CSA practices to farmers, built their capacity to uptake those practices, and helped establish small-scale
irrigation schemes. Nine (9) agrarian communities have so far benefited from the project’s training programmes and provision of
improved crop varieties as well as highlight-quality fertilizers, crop protection products and small-scale irrigation schemes to ensure
uptake and utilisation of CSA in the area. This paper, therefore, examines the factors influencing the utilisation and uptake of CSA
among farmers in the areas where these projects were implemented. This is to investigate the factors affecting the adoption of CSA
practices from academic perspectives beyond the usual project evaluation studies.
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II. Research Methodology
The Study Area
The study was done in Northern Ghana, which has a land area of 97702 km2 and is located between 8° and 11° north latitudes.
According to GSS (2020), Northern Ghana is situated in the Sudan Savannah and Guinea Savannah, which are sub-humid to semi-
arid and receive 400 to 1200 mm of precipitation annually. The Guinea-Savannah zone makes up around 90% of the territory in
Northern Ghana, and the Sudan-Savannah zone makes up the remaining 10% (GSS, 2020). These areas experience unimodal rainfall
with an average rainfall of 1100mm (FAO, 2005). These regions have a long dry season of almost seven months (October to April),
followed by a single cycle of rainfall of about five months (May to September), sufficient for agricultural productivity. During these
five months of the cropping season, the zones occasionally record sporadic floods or droughts. A minimum temperature of 22°C is
experienced yearly, ranging from 33 to 35°C. In the rainy season, humidity is at its maximum and is only moderately high in the
dry season (FAO, 2005). Crop farming activities are only permitted once a year due to the rainfall pattern.
The soils of Ghana's Sudan and Guinea savannah regions are recognised to be the lowest, with low levels of some crucial nutrients
like calcium, phosphorus, nitrogen, and potassium. In these regions, various crops are grown, including maize, rice, sorghum, yam,
cassava, cowpea, groundnut, and tree crops such as mango, cashew including shea and dawadawa, which are wild commercial trees
(FAO, 2009). For farmers to get a good yield of their crops, particularly cereals, the soil must be amended with both macro and
micro soil nutrients. Rain-fed agriculture is the region's dominant farming system. Irrigation covers a minuscule portion of the
region's arable land. Farmers in the area are unemployed for the seven months of the dry season, which causes food and income
insecurity (MOFA, 2020).
Sampling Procedure and Sample Size
Multi-stage sampling technique was used in this study. In the first stage, the district and communities of the study were purposively
selected. Districts and communities which have benefited from the GASIP CSA project were targeted for inclusion in the study. In
the next stage, stratified sampling procedure was used to select respondents based on their involvement in the project or otherwise.
Respondents were grouped based on communities and their involvement and non-involvement in the GASIP CSA project. A
sampling fraction of ½ was used for each stratum. Simple random sampling was used in the third stage to select respondents for the
household interviews. The random number method was used to assign every individual a number.
A total of 641 households were identified in the 9 selected communities, 231 farm households being adopters of CSA while 410
households were non-adopters of CSA (MoFA, 2022). According to Louangrath (2013), the Yamane 1965 formula for sample size
should be used for research like this where the study population is known or finite. In the formula, n = N/(1+N(e)2 where (n is
sample size, N represent Sample frame and e representing margin of error). The sample size calculated was 246, this is based on
Yamane's 1965 formula for determining sample size. However, for more accuracy a total of 318 farmers, were interviewed, 115
adopters and 203 non-adopters.
Table 3.1: Sampling Frame
Communities CSA Farmers Sample Non-CSA
Farmers
Sample Total Sample
Dulzugu 20 10 122 61 71
Dalun-Kukuo 20 10 13 6 16
Gingani 20 10 20 10 20
Kprum 40 20 19 9 29
Cheshegu 35 17 57 28 45
Gbullun-Nyoring 36 18 10 5 23
Voggu-Kushibo 20 10 20 10 20
Zangballin 20 10 123 61 71
Zugu 20 10 26 13 23
Total 231 115 410 203 318
Source: MoFA, (2022).
The selection of participants for the focus group discussions was made with a blend of farmers in the project and farmers who were
not in the project. However separate meetings were also held for the adopters and non-adopters because Sim (1998) states that, the
more homogeneous the membership of the group, in terms of social background, level of education, knowledge, and experience, the
more confident individual group members are likely to be in voicing their views. Nine (9) focus group discussions (FGD) were held
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in all the selected communities. Each focus group comprised 10 farmers. The groups were also segregated based on sex. This is
because when both genders are present, women are much less inclined to talk, and more men would take the lead in the conversation
(Morgan, 1997).
Data Collection and Sources
Both primary and secondary data sources were relied on to obtain information from the respondents. Primary data for this study
was collected directly from smallholder farmers in the selected communities in the district using questionnaires and focus group
discussions. Data for the study was gathered from smallholder farmers through face-to-face interviews using semi-structured
questionnaires. The questionnaire obtained information about the respondents' perspectives on the types of CSA practices accessible
in the study area, as well as their awareness, uptake, and years of use of the CSAs. Secondary data were sourced from books,
articles, conference papers, dissertations and web reports.
Method of Data Analysis
Semi-structured questionnaires that had been administered were scrutinised to find and remove faults by ensuring that the questions
answered were correct and complete. Data input and analysis were done using the Statistical Package for Social Sciences (SPSS),
which also provided test statistics and descriptive analysis. All graphs were created using Microsoft Excel. After data is acquired,
appropriate tools and techniques should be employed for classification and analysis, according to Panneerselvam (2004) and
Yelfaanibe (2011). To report and interpret this study’s findings, descriptive and inferential tools and techniques were employed.
Whether a given phenomenon is simple or complicated, descriptive tools and procedures of inquiry describe the nature of the
phenomenon. However, the requirement for methods of determining a condition means that it is more than a complex one (Osuala,
2005).
Using STATA, the Chi-square analysis was performed to test the level of association between adoption and selected socio-
demographic and institutional factors found in literature as determinants of adoption of CSA practices among smallholder farmers.
These factors included gender, educational status, household size, FBO membership, access to market, and access to extension
services. The 95% confidence interval was used to determine the level of significance, which is expressed as:
χ2 = ∑
(��1−��1)
2
��1
) …………………………………………………………… (1).
where Oi is the observed value, Ei is the expected value, and c is the degree of freedom.
A binary logistic regression was conducted to determine which of the variables influenced the adoption of the chosen CSA practices.
These variables were chosen based on hypotheses and prior empirical discoveries in literature. This is expressed as:
Y = B0 + B1X1 + . . . + BKXK ……………………………………………… (2)
where each Xi is a predictor
Bi is the regression coefficient.
III. Results and Discussion
Existing Climate-Smart Agriculture Practices Utilised by Smallholder Farmers
This section of the results lists the smallholder farmers' current methods for practicing climate-smart agriculture. As shown in Table
1 below, between 60 % and 100 % of the smallholder farmers in the research area are aware of the various CSA techniques. These
results resonate with previous studies suggesting that smallholder farmers in dryland farming systems employ a host of practices to
manage climate risks and sustain livelihood and food security (Fagariba et al., 2018 & Rahut et al., 2021). Also, the most (over
65.0%) utilised CSA practices in the study area were on-farm composting, crop rotation, mulching, improved crop varieties, use of
minimum tillage, improved livestock, and land bunding. Additionally, Figure 1 further compares the level of awareness and
utilisation of CSA practices. The result revealed a less than 65.0% utilisation of some of the CSA practices by smallholder farmers.
These CSA practices utilised were agroforestry (55.0%), small-scale irrigation (63.0%), rainwater harvesting (58.0%), crop-
livestock integration (50.0%) and residue retention (48.0%). The result is similar to the findings made by Fagariba et al. (2018)
when they reported that planting early maturing varieties of crops, agroforestry and woodlot schemes, water management and water
harvesting, earth bunding, crop residue mulching, and zero tillage/minimum tillage as standard CSA practices among respondents
in the Sissala West district of the Upper West region. Residue retention was utilised by only 48% of the smallholder farmers who
indicated during FGDs that bye laws are not enforced on animal grazing and bush burning hence making it difficult to leave crop
residue after harvest. Again, they stated that in the wake of non-enforcement of these bye laws the alternative is to fence their fields
which is expensive for them. Also, only 50% of the respondents use crop-livestock integration which the smallholder farmers
indicated that aside from the cost involved in using the practice, they have also been advised that animals contribute to greenhouse
gas emissions which affects agriculture considerably.
Generally, the various CSA practices were adequately promoted by institutions such as MoFA, and NGOs through community
radio and other media. The high utilisation level among smallholder farmers is supported by studies such as Kangogo, Dentoni and
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Bijman, 2021; Andersson and D'Souza, 2014; Birner and Resnick, 2010), who all reported a high level of adoption of CSA practices
among farmers. All these studies further revealed that a high level of CSA practices leads to enhanced food security, improved soil
fertility, carbon sequestration and improvement of farmers' livelihoods. For instance, if utilized by smallholder farmers, improved
crop varieties would increase crop yield and further reduce post-harvest losses. Since food insecurity is a significant concern to
policymakers and farmers, utilisation of these CSA practices would significantly reduce the incidence of food insecurity among
rural households.
Table 1: Climate-smart Agriculture practices utilised by Smallholder Farmers
Practices Aware practice (%) Currently using
practice (%)
Willingness to
introduce (%)
Agroforestry 63 55 29
On-farm Composting 96 79 65
Crop Rotation 100 77 30
Mulching 99 67 33
Intercropping 100 64 36
Improved Crop Varieties 99 83 33
Use of Minimum Tillage 93 68 43
Small Scale Irrigation 85 63 39
Improved Livestock 99 68 47
Rain Water Harvesting 95 58 30
Land Bunding 96 67 64
Crop-Livestock Integration 95 50 44
Residue Retention 64 48 12
Source: Field Survey Data, 2022
Figure 1: CSA Practices Awareness and Current Utilisation
Source: Field Survey Data, 2022.
0
10
20
30
40
50
60
70
80
90
100
63
96
100 99 100 99
93
85
99
95 96 95
64
55
79 77
67 64
83
68
63
68
58
67
50 48
P
er
en
ta
ge
CSA Practices
Aware practice (%) Currently using practice (%)
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Factors Likely to Influence Uptake of CSA Practice of On-Farm Composting
Results of the study shown in Table 2 revealed that on-farm composting was widely utilised among smallholder farmers. Also, the
Chi-square test revealed that educational level (χ2 = 11.110, p = 0.025), household size (χ2 = 56.539, p = 0.000) and access to
extension (χ2 =3.220, p = 0.073) all affected smallholder farmers’ utilisation of on-farm composting.
The logistic regression model, which was conducted to assess the determinants of adoption of CSA practices, shown in Table 3,
was found to be significant, with the selected explanatory variables jointly accounting for 62.9% (Nagelkerke R Square = 0.629) of
the variation in the utilisation of on-farm composting as a CSA practice. This suggests that approximately 62.9% of the variation
in the utilisation of on-farm composting is explained by the model.
Also, the educational level of smallholder farmers was found to have a positive influence on smallholder farmers' utilisation of on-
farm compositing with a coefficient of 0.2760. This implies that educated smallholder farmers are more likely to utilise on-farm
composting as a CSA practice than non-educated smallholder farmers if everything else remains the same. Furthermore, access to
extension among smallholder farmers was found to have a positive influence on smallholder farmers' utilisation of on-farm
compositing with a coefficient of 3.220. This implies that smallholder farmers with access to extension services are more likely to
utilise on-farm composting as CSA practices compared to smallholder farmers without access to extension services if everything
remains the same. On-farm composting has been a long tradition among farmers to increase soil fertility. The high level of on-farm
composting utilisation could be attributed to loss of soil fertility combined with adverse effects of climate change and vulnerability
and the high cost of inorganic fertilizers as a result of the reduction of government subsidies on the product. In this study, educational
level and access to extension all influenced smallholder farmers' utilisation of on-farm composting, which is supported by Akram
et al. (2019); they reported that vegetable farmers accessing extension services are always encouraged to use compost on their farms
as means of boosting yield. Also, the findings support the work of Abegunde et al. (2020), who found that farmers’ characteristics,
such as farmers’ experience and access to extension services, are significant determinants of CSA adoption.
The smallholder farmers generally attributed their utilisation of on-farm composting to the high cost of inorganic fertilizers. In a
FGD, they argued that for the past three years government has reduced subsidy on inorganic fertilizers making the product expensive
and sometimes unavailable which makes them shift their focus to producing their own organic fertilizers.
Table 2: Chi-Square test Analysis of factors likely to influence utilisation of on-farm composting
Attributes Degree of Freedom Chi-Square test
χ2 Sig
Sex 1 2.380 0.123
Age 4 2.540 0.637
Educational level 4 11.110 0.025
Household size 15 56.539 0.000
Access to market 1 0.254 0.614
Access to extension 1 3.220 0.073
FBO membership 1 0.471 0.492
Source: Field Survey Data, 2022
Table 3: Factors influencing utilisation of on-farm composting as a CSA practice
Attributes On-Farm Composting
B SE Z P>|z|
Sex 0.5450 0.3355 1.62 0.104
Age -0.0370 0.1906 -0.19 0.846
Educational level 0.2760 0.1110 2.48 0.013*
Household size 0.0227 0.0460 0.49 0.621
Access to market 0.1259 0.3372 0.37 0.709
Access to extension 0.7903 0.4659 1.70 0.090
FBO membership -0.6082 0.5033 -1.21 0.227
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Pseudo R2
Log-likelihood
Prob > chi2
Nagelkerke R Square
0.0534
-152.95835
0.0536
0.629
Source: Field Survey Data, 2022
Factors Influencing Utilisation of Improved Crop Varieties
The results in Table 4 below revealed that improved crop varieties were widely utilised among smallholder farmers. However, the
Chi-square test showed that access to extension (χ2 = 6.816, p = 0.009) affected smallholder farmers' utilisation of improved crop
varieties. Also, the extent of utilisation, age of smallholder farmers, household size and access to extension all influence smallholder
farmers' utilisation of improved crop varieties.
The logistic regression model that included these variables explained around 50.5% of the variances. Furthermore, the age of
smallholder farmers was found to have a positive influence on smallholder farmers' utilisation of improved crop varieties with a
coefficient of 0.3847. This implies that older smallholder farmers are more likely to utilise improved crop varieties as CSA practices
compared to young smallholder farmers if everything remains the same. Also, household size was found to negatively influence
smallholder farmers’ utilisation of improved crop varieties with a coefficient of 0.1375. This implies that smallholder farmers with
smaller household sizes are more likely to utilise improved crop varieties as CSA practices compared to smallholder farmers with
large household sizes if everything remains the same. Smallholder farmers with smaller household size utilise improved crop
varieties because of the perceived increase in yield. They usually cultivate fewer acreages due to the unavailability of family labour
and want to maximise yield from the small farm.
Finally, access to extension among smallholder farmers was found to have a positive influence on smallholder farmers’ utilisation
of improved crop varieties, with a coefficient of 1.142. This implies that smallholder farmers with access to an extension are more
likely to utilise improved crop varieties as CSA practices compared to smallholder farmers without access to an extension if
everything remains the same. During focus group discussions, the farmers stated that they use improved crop varieties to adapt to
climate change and variability. It was summarised in one of the focus group meetings by participants as follows:
“We receive early maturing seeds and drought tolerant varieties from our Agric. Officer to be able to get good yields because the
cropping pattern is changing”.
Generally, farmers are encouraged to adopt improved crop varieties to increase yield. Also, most of the improved crop varieties are
bred to withstand drought, and some are early maturing; these features are desirable in the face of the erratic rainfall pattern being
experienced in the study area. The usage of improved crop varieties is influenced by age, household size and access to extension.
This finding is in line with Belay et al. (2017), who reported that farmers with access to extension services and large households
use improved crop varieties for improved yield and well-being. Moreover, farmers utilised improved crop varieties because of the
demand for a particular crop variety.
Table 4: Chi-Square test Analysis of factors likely to influence utilisation of improved crop varieties
Attributes Degree of Freedom Improved Crop Varieties
χ2 Sig
Sex 1 .014 0.906
Age 4 1.412 0.842
Educational level 4 6.812 0.146
Household size 15 20.837 0.142
Access to market 1 2.068 0.150
Access to extension 1 6.816 0.009
FBO membership 1 1.221 0.269
Source: Field Survey Data, (2022).
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Table 5: Factors influencing utilisation of improved crop varieties
Attributes Improved crop varieties
B SE Z P>|z|
Sex -0.0293 0.3837 -0.08 0.939
Age 0.3847 0.2036 1.89 0.059*
Educational level 0.1277 0.1220 1.05 0.295
Household size -0.1375 0.0506 -2.72 0.007**
Access to market 0.4650 0.3510 1.32 0.32
Access to extension 1.142 0.5820 1.96 0.050*
FBO membership -0.3876 0.6120 -0.63 0.527
Pseudo R2
Log-likelihood
Prob > chi2
Nagelkerke R Square
0.0714
-133.05416
0.0047
0.505
Source: Field Survey Data, (2022).
Factors Influencing Utilisation of Crop-Livestock Integration
The study in Table 6 below revealed that crop-livestock integration was among the least utilised CSA practice among smallholder
farmers. However, the Chi-square test revealed that none of the variables influenced smallholder farmers' utilisation of crop-
livestock integration as a CSA practice. The logistic regression model was able to explain 60.1% of the variances. Additionally,
only FBO membership negatively influenced smallholder farmers’ utilisation of crop-livestock integration with a coefficient of
1.067. This implies that smallholder farmers who are not members of FBO are more likely to utilise crop-livestock integration as
CSA practices compared to smallholder farmers who are members of FBO if everything remains the same. FBO members according
to discussions from the focus groups sometimes follow more standardized or specialized farming practices promoted by their
sponsored organizations, which in most cases do not emphasize crop-livestock integration to the same extent. Again, FBO members
in most cases have access to alternative CSA practices and they turn to adopt those that are more suitable to them. Additionally,
FBO membership according to the farmers in some cases involve social or economic conditionalities that shape the types of
practices adopted, potentially limiting some farmers' inclination or flexibility to integrate crop and livestock components
comprehensively.
In this study, FBO membership was only the variable that influenced smallholder farmers’ decision to utilise crop-livestock
integration. According to Reddy (2016), farmers utilised or adopted crop-livestock integration to reduce the cost of chemical
fertilizer usage on their farms since the animal dropping would serve as a fertilizer for the farm. Also, the animal dropping is
purported to improve soil nutrition, limit soil nutrient leaching and promote higher yields. Furthermore, the current government
initiative of rearing food and jobs only targets farmers in groups. Hence, FBO membership utilisation of crop-livestock integration
is not surprising for influencing this study since members of FBOs always stand the chance of benefiting from projects and
interventions such as climate-related interventions.
Table 6: Chi-Square test Analysis of Factors likely to influence utilisation of crop-livestock integration
Attributes Degree of Freedom Chi-Square test
χ2 Sig
Sex 1 0.151 0.697
Age 4 1.397 0.845
Educational level 4 2.964 0.564
Household size 15 8.510 0.902
Access to market 1 1.110 0.292
Access to extension 1 0.003 0.955
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FBO membership 1 0.069 0.792
Source: Field Survey Data, 2022
Table 7: Factors likely to influence utilisation of crop-livestock integration
Attributes Crop-livestock integration
B SE Z P>|z|
Sex -0.1088 0.2890 -0.38 0.707
Age 0.1954 0.1540 1.27 0.205
Educational level 0.0086 0.0893 0.10 0.923
Household size -0.0365 0.0386 -0.94 0.345
Access to market 0.2521 0.2786 0.90 0.366
Access to extension 0.2913 0.3405 0.86 0.392
FBO membership -1.067 0.3697 -2.89 0.004***
Pseudo R2
Log-likelihood
Prob > chi2
Nagelkerke R Square
0.0410
-211.35936
0.0116
0.601
Source: Field Survey Data, (2022).
Factors Likely to Influence Utilisation of Residue Retention
The results in Table 8 below revealed that crop residue retention was least utilised among smallholder farmers. However, the Chi-
square test showed that educational level (χ2 = 9.561, p = 0.049), access to extension (χ2 = 6.063, p = 0.014) and FBO membership
(χ2 = 9.517, p = 0.002) all affected smallholder farmers’ utilisation of residue retention. Moreover, the extent of utilisation, age of
smallholder farmers, educational level, access to extension, and FBO membership all influenced farmers’ utilisation of residue
retention. Combining these factors in a logistic regression model could explain 50.8% of the variances.
Moreover, the age of smallholder farmers was found to have a positive influence on smallholder farmers’ utilisation of crop residue
retention with a coefficient of 0.2865. This implies that a one-year increase in the age of a farmer will influence their likelihood of
utilising crop residue retention as a CSA practice compared to young smallholder farmers, all things being equal. Also, the
educational level of smallholder farmers was found to have a positive influence on smallholder farmers' utilisation of crop residue
retention with a coefficient of 0.2558. This implies that farmers with a high level of education are more likely to utilise crop residue
retention as a CSA practice than non-educated smallholder farmers, all things being equal. According to Fagariba et al. (2018), soil
and land management practices such as crop residue mulching and zero tillage improve the microclimate, boost soil fertility, and
reduce the high intensity of direct sunlight on the crops and soil nutrients. This could be the reason why educated smallholder
farmers use the practice.
Additionally, household size was found to have a negative influence on smallholder farmers’ utilisation of crop residue retention
with a coefficient of 0.1342. This implies that smallholder farmers with smaller household sizes are more likely to utilise crop
residue retention as CSA practices compared to smallholder farmers with large household sizes. Small-size farm families at the
smallholder level usually have small farm sizes. So, it can therefore be argued that on one hand, smaller farm sizes can easily be
protected from grazing livestock and wild bushfires after harvest as against larger households with larger farms. On the other hand,
smaller farm families might also lack the required labour to protect their crop residue from grazing livestock and/or wild bushfires.
Finally, access to extension among smallholder farmers was found to have a positive influence on smallholder farmers’ utilisation
of crop residue retention with a coefficient of 1.107. This implies that smallholder farmers with access to an extension are more
likely to utilise crop residue retention as CSA practice compared to smallholder farmers without access to an extension if everything
remains the same. Despite the benefits of residue retention in mitigating the adverse effects of climate change and climate
vulnerability, the low utilisation of crop residue retention could be attributed to farmers’ inability to protect their farms, after harvest,
from grazing livestock and wild bushfires. During FGDs, smallholder farmers bemoaned their difficulty in leaving residues on their
field thereby losing out in getting the benefits associated with it to their crops. It was stated in the meetings by the respondents as
follows:
“Here cattle will graze all the stalks if you leave them on the field or bushfire will
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burn all. Cattle owners are not restricted to where to graze and they burn the bush
haphazardly to get green grasses. It is really a concern and we wish the authorities do
something about it”.
The low level of usage of crop residue retention has been found in many studies to be attributed to the perennial bushfires and
uncontrolled grazing by livestock in the study area. This is because the practice of crop residue retention requires protecting leftovers
after harvest to be decomposed on the field and thereby releasing their nutrient back into the soil for subsequent planting (Branca
et al., 2020). This confirms the findings of Harvey et al. (2014), who found that weak regulatory frameworks and poor tenure rights
and land use and management frameworks can hinder the propensity of farmers to adopt various CSA practices. Low crop residue
usage could result in low crop yield in the study area. Hence, to boost crop yield, there is a need to promote the effective use of
crop residues on farmland rather than allowing animals to feed on it. This required strong local laws and regulatory framework
regarding wild bushfires control, effective land tenure systems and proper land use management systems with clear demarcation
for crops and livestock farming.
Table 8: Chi-Square test Analysis of factors likely to influence utilisation of residue retention
Attributes Degree of Freedom Chi-Square test
χ2 Sig
Sex 1 0.057 0.811
Age 4 0.698 0.952
Educational level 4 9.561 0.049
Household size 15 15.049 0.448
Access to market 1 0.195 0.659
Access to extension 1 6.063 0.014
FBO membership 1 9.517 0.002
Source: Field Survey Data, 2022
Table 9: Factors likely to influence utilisation of residue retention
Attributes Residue retention
B SE Z P>|z|
Sex -0.0071 0.3011 -0.02 0.981
Age 0.2865 0.1664 1.72 0.085*
Educational level 0.2558 0.0921 2.78 0.005***
Household size -0.1342 0.0429 -3.13 0.002***
Access to market 0.0571 0.2954 0.19 0.847
Access to extension 1.107 0.3482 0.001 0.001***
FBO membership 0.3036 0.3759 0.419 0.419
Pseudo R2
Log-likelihood
Prob > chi2
Nagelkerke R Square
0.1142
-195.11305
0.0000
0.508
Source: Field Survey Data, (2022).
IV. Conclusion and Recommendation
In analysing the existing climate-smart agriculture practices utilised by smallholder farmers in the area, the study concluded that
majority of the smallholder farmers were using various CSA practices. The study established that most smallholder farmers utilised
on-farm composting, crop rotation, mulching, improved crop varieties and minimum tillage. However, only some of the smallholder
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farmers utilised crop residue retention. Generally, several factors influence smallholder farmers' utilisation of CSA practices among
smallholder farmers. However, this study concluded that age, educational level, household size, access to extension and FBO
membership all directly influenced smallholder farmers’ utilisation of CSA practices.
This study recommends that the district assemblies, in collaboration with their departments of agriculture, should embark on an
educational campaign to promote the adoption of CSA practices among smallholder farmers as a climate change mitigation and
adaptive strategy.
Also, the district assemblies, in collaboration with the traditional authorities, should ensure the institutionalization of enforceable
laws and regulatory frameworks regarding wild bushfires control, effective land tenure systems and proper land use management
systems with clear demarcation for crops and livestock farming.
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