ABSTRACT
Horticulture farmers continue to experience climate change-related problems despite advancement in technologies such as mobile phones. Currently, mobile phone is the most commonly used tool in communication. Previous studies have shown that application of mobile phones in farming helps to reduce information asymmetry and improve productivity. However, there is little evidence on whether farmers are using their phones to build resilience and improve horticultural productivity within the context of climate change, commonly referred to as climate-smart horticulture (CSH). This study analyzed the extent and effect of mobile phone use on productivity of climate-smart horticulture farmers in Taita-Taveta County. Primary data was collected from a random sample of 403 green gram and tomato farmers. Paired t-test statistics were used to characterize the adoption of climate-smart horticulture practices between users of mobile phone and non-users. Binary logit model was applied to examine the factors influencing mobile phone use on climate-smart horticulture. Negative binomial regression was applied to assess the determinants of adoption of the number of climate-smart horticulture practices. Productivity was measured using partial factor productivity. Tobit model (censored from below) was applied to analyze the effect of mobile phone use on productivity of climate-smart horticulture farmers. Results show that a significantly higher percentage of mobile phone users adopted climate-smart horticulture practices than non-users. Trust on the information transmitted through mobile phones, access to electricity (hydro-electricity and solar power), access to credit and the number of CSH practices adopted significantly influenced the use of mobile phone on climate-smart horticulture. Gender (being a male farmer), education, farming experience, mobile phone use on CSH and CSH awareness positively determined the number of CSH practices adopted.
However, farm size and climate change awareness negatively affected the number of CSH practices adopted by farmers. Partial factor productivity scores showed that farmers who produced tomatoes were more productive than green gram and both crop producers. Tobit regression (censored from below) results showed that mobile phone use improved productivity of climate- smart horticulture farmers by 90%. Other factors including education, gender, farming experience and climate-smart horticulture awareness positively influenced productivity. There is need to develop a mobile phone supported digital hub that will provide specific climate-smart horticulture information to farmers to build resilience to climate change and improve productivity. The County government of Taita-Taveta should also collaborate with other development partners such as Kenya climate smart agriculture project (KCSAP) to build the capacity of agricultural extension workers to improve dissemination of climate-smart horticulture knowledge and skills to farmers.
Key words: Mobile phone, climate-smart horticulture, productivity, tomato, green gram.
TABLE OF CONTENTS
DECLARATION ii
DEDICATION iii
ACKNOWLEDGEMENTS iv
LIST OF FIGURES ix
LIST OF TABLES x
LIST OF ABBREVIATIONS AND ACRONYMS xii
ABSTRACT xiv
CHAPTER ONE
1.0 INTRODUCTION
1.1 Background information 1
1.2 Research problem statement 4
1.3 Objectives of the study 6
1.4 Research hypotheses 7
1.5 Justification 7
1.6 Study area 8
1.7 Organization of the thesis 10
CHAPTER TWO
2.0 LITERATURE REVIEW
2.1 The climate-smart horticulture concept 11
2.2 Review of mobile phone use in agriculture 11
2.3 A review of factors influencing mobile phone use on climate-smart horticulture 13
2.4 Knowledge gaps on the effect of mobile phone use on horticulture productivity 16
2.5 Conceptual framework 19
2.6 Theoretical framework 21
2.6.1 Diffusion of innovation theory 22
2.6.2 Theory of planned behavior 24
2.6.3 Random utility theory 26
CHAPTER THREE
3.0 ADOPTION OF CLIMATE-SMART HORTICULTURE PRACTICES AND USE OF MOBILE PHONES BY SMALLHOLDER FARMERS
3.1 Abstract 28
3.2 Introduction 29
3.3 Methodology 30
3.3.1 Data sources and sampling procedure 30
3.3.2 Test for multicollinearity 32
3.3.3 Test for heteroscedasticity 33
3.3.4 Test for endogeneity 33
3.3.5 Data analysis 34
3.4 Results and discussion 36
3.4.1 Climate-smart horticulture adoption characteristics 36
3.4.2 Climate-smart horticulture adoption behavior exhibited by farmers 39
3.4.3 Evolution and key drivers of mobile phone use among farming community in Taita-Taveta County 41
3.4.4 Mobile phone use characteristics among climate-smart horticulture farmers 43
3.4.5 Correlation between the type of mobile phone used and the number of climate-smart horticulture practices adopted 46
3.4.6 Differences in climate-smart horticulture adoption characteristics between mobile phone users and non-users 47
CHAPTER FOUR
4.0 FACTORS INFLUENCING MOBILE PHONE USE AND ADOPTION OF CLIMATE- SMART HORTICULTURE PRACTICES
4.1 Abstract 50
4.2 Introduction 51
4.3 Methodology 52
4.3.1 Data analysis 52
4.3.2 Expected signs for variables in the binary logit and negative binomial regression models 55
4.4 Results and discussion 59
4.4.1 Characteristics of mobile phone users and non-users on climate-smart horticulture 59
4.4.2 Factors influencing the use of mobile phone and adoption of climate-smart horticulture practices 62
CHAPTER FIVE
5.0 EFFECT OF MOBILE PHONE USE ON PRODUCTIVITY OF CLIMATE-SMART HORTICULTURE FARMERS
5.1 Abstract 68
5.2 Introduction 69
5.3 Methodology 70
5.4 Results and discussion 77
5.4.1 Technical efficiency scores for tomato and green gram farmers 77
5.4.2 Productivity score for climate-smart horticulture farmers in Taita-Taveta County 78
5.4.2 Effect of mobile phone use on productivity of climate-smart horticulture farmers 79
CHAPTER SIX
6.0 CONCLUSION AND RECOMMENDATIONS
6.1 Conclusion 85
6.2 Recommendations 86
6.2.1 Policy recommendations 86
6.2.2 Recommendations for further research 87
REFERENCES 88
APPENDICES 113
Appendix 1: Focus group discussion guide 113
Appendix 2: Household survey questionnaire 114
Appendix 3: Variance inflation factor(s) (VIFs) 125
Appendix 4: Partial correlation coefficients for all variables 127
LIST OF FIGURES
Figure 1.1: A map of the research site (Taita-Taveta County) 9
Figure 2.1: A framework on determinants of mobile phone use on CSH and its effects on productivity 21
Figure 2.2: Innovation adopters’ characteristics 22
Figure 2.3: A diagrammatic presentation of the theory of planned behavior 25
Figure 3.1: Percentage of farmers who adopted climate smart horticulture practices in Taita- Taveta County 38
Figure 3.2: Climate-smart horticulture practice(s) adoption pattern among farmers in Taita- Taveta County 40
Figure 3.3: Extent to which climate-smart horticulture farmers use their mobile phones 46
Figure 3.4: Pearson’s correlation analysis of type of phone used against the number of climate- smart horticulture practices adopted 47
LIST OF TABLES
Table 2.1: Categorization of selected CSH practices 24
Table 3.1: One-way analogous ANOVA for the three groups of farmers 35
Table 3.2: Climate-smart horticulture practices adopted by different types of farmers 37
Table 3.3: Evolution of mobile phone use and key drivers from 1980 – 2021 41
Table 3.4: Farmer classification based on type of phone used in climate-smart horticulture 43
Table 3.5: Mobile phone use characteristics among different crop farmers 45
Table 3.6: Mean differences in adoption of climate-smart horticulture practices between mobile phone users and non-users 48
Table 4.1: Variables included in the binary logit, negative binomial regression model and their expected signs 55
Table 4.2: Mean differences in socio-economic and institutional characteristics between mobile phone users and non-users on climate-smart horticulture 61
Table 4.3: Binary logit regression results on factors influencing mobile phone use on climate- smart horticulture 63
Table 4.4: Negative binomial regression results for determinants of the number of CSH practices adopted 67
Table 5.1: Variables included in the Tobit model and expected signs 77
Table 5.2: Technical efficiency scores for green grams and tomato farmers 78
Table 5.3: Productivity scores (Kgs per acre) for green grams and tomato farmers 79
Table 5.4: Productivity scores (Kgs per acre) for green grams and tomatoes in the three sub- counties 79
Table 5.5: Tobit regression results for climate-smart horticulture farmers based on the type of crop produced 81
Table 5.6: Tobit regression results for climate-smart horticulture farmers in three different sub- counties 82
LIST OF ABBREVIATIONS AND ACRONYMS
ANOVA Analysis of Variance
CAK Communication Authority of Kenya
CSA Climate-smart Agriculture
CSAP Climate-smart Agriculture Practices
CSH Climate-smart Horticulture
FAO Food and Agriculture Organization of the United Nations
GB Gigabyte
HCDA Horticultural Crops Directorate Authority
HFCS Household Food Consumption Score
ICT Information and Communication Technology
IPCC Intergovernmental Panel on Climate Change
KALRO Kenya Agricultural and Livestock Research Organization
KCSAP Kenya Climate Smart Agriculture Project
KNBS Kenya National Bureau of Statistics
MoALF Ministry of Agriculture Livestock and Fisheries
NBRM Negative Binomial Regression Model
NGO Non-governmental Organization
PBC Perceived Behavioral Control
PFP Partial Factor Productivity
PSM Propensity Score Matching
RAM Random Access Memory
RUM Random Utility Model
RUT Random Utility Theory
SMS Short Messaging Service
SSA Sub-Saharan Africa
TFP Total Factor Productivity
TPB Theory of Planned Behavior
TTCIDP Taita-Taveta County Integrated Development Plan
UNDP United Nations Development Programme
WHO World Health Organization
CHAPTER ONE
INTRODUCTION
1.1 Background information
Globally, the development of horticulture production contributes to improved household nutrition and diversification of incomes (Davies, 2015). In Kenya, the horticulture sector (flowers, vegetables and fruits) is the largest foreign exchange earner that contributes 30% of all domestic exports (Kenya national Bureau of Statistics (KNBS), 2022). It is largely concentrated among fifteen Counties (located in Coast, South Eastern, Central, South Rift and Western parts of Kenya) that contribute about 74% of the total national horticultural output (HCDA, 2018). Between the year 2019 and 2020, the total value of domestic exports of horticulture, in Kenya, grew by 5%, compared to the previous year, due to increased area under production and demand for flowers and vegetables (KNBS, 2022).
However, horticulture productivity is directly influenced by variability in climate (rainfall and temperatures) patterns which lead to low quantity and quality of output (Hirpo and Gebeyehu, 2019). This sector is mainly dominated by small-scale farmers who own less than 10 acres of land and contribute between 50 and 60% of total horticultural output (UNEP, 2015; World Bank and CIAT, 2015). Climate change has continued to be the main challenge affecting horticulture in Africa. It is likely to prolong severe effects on; soil health, water availability, disease control and production planning (AgriProFocus and Verbos Business Development, 2018; Patrick et al., 2020). Horticulture farmers are at risk of climate change incidences such as prolonged drought, poor spatial and temporal rainfall distribution and increased temperature variability.
This is likely to cause damage of between 15% and 50% decline in crop productivity (Nhemachena et al., 2020). In addition, Kenya is likely to suffer from severe food insecurity by the year 2100 if considerable adaptation and mitigation measures on climate change are not put in place. This is due to a significant decline in maize, beans, millet and sorghum yields that is likely to be experienced (Kabubo-Mariara and Kabara, 2015). The Northern and Eastern regions will need humanitarian food assistance and livelihood support throughout the year 2022 (FEWSNET, 2021). In Taita-Taveta County, production of most horticultural crops (such as green grams, onions and tomatoes) contribute on average 10% and 90% of household food requirements and income, respectively. However, production is projected to decline by between 37% and 46%, respectively due to climate change (Mohamed and Chege, 2019; Osano et al., 2018).
In an effort to reduce adverse effects of climate change on agriculture, the government of Kenya initiated Kenya climate-smart agriculture project (KCSAP) covering 24 Counties, including Taita- Taveta in 2017. Climate-smart agriculture (CSA) is a system that seeks to improve adaptation to climate change, productivity, improve food security and decrease emission of greenhouse gases (Government of Kenya, 2017). Therefore, the approaches (new and indigenous) applied by farmers to build resilience and adapt farming to local climate variabilities are included in considered, in this study, as ‘climate-smart’. Climate-smart horticulture (CSH) draws from this definition but confines it to horticulture (Sahu, 2016).
There are four main categories of CSA approaches that entail innovative ways of: managing field, crop management, reducing farm risk and conserving the soil (Thornton et al., 2018; Wekesa et al., 2018). Crop management methods include innovative; integrated pest management, crop irrigation, use of improved seed varieties that are well adapted to local climate, crop rotation, matching planting dates to climate conditions and efficient use of inorganic fertilizers (Pooniya et al., 2015; Shah and Wu, 2019).
General field management practices include use of terraces, agroforestry and use of live barriers – which are strips of crops (such as grass) planted along a contour to prevent soil erosion (Caulfield et al., 2020; Hellin and Haigh, 2002). Soil conservation practices entail the use of organic fertilizers, cover crops, composting, mulching and conservation agriculture (Baumhardt and Blanco-Canqui, 2018). On the other hand, farm risk reduction practices include crop diversification, use of farm water ponds, use of information technologies to guide farm activities and crop insurance (Filan and Fake, 2012; FAO, 2018).
Previous studies have shown that farmers who adopted all the four practices had higher household food consumption scores (HFCS) than non-adopters (Wekesa et al., 2018). Specifically, adopting crops that are well adapted to local climate, sustainable water-use and management practices and technology use in production planning are important strategies for horticulture farmers facing climate change problems (FAO, 2017).
Another way of strengthening the capacity of horticulture farmers to deal with climate-related risks is through giving them information that is accurate, reliable, and timely to enable them to make informed decisions. This is because farm productivity and agricultural transformation have been traditionally suppressed by information asymmetry, inadequate access to markets, low use of improved technologies, low access to relevant infrastructure, high costs of production and transport (Government of Kenya, 2019; Ogutu, et al., 2014).
Mobile phone use in horticulture enables the transmission of knowledge and information on CSH practices (Mehar and Mittal, 2014). The use of mobile phone on CSH means that the farmer uses the phone to make and receive payments for inputs and output, respectively, search for horticulture-related information and weather information. For example, rapid growth in application of mobile phones in agriculture is reducing information deficit by making it possible for farmers to obtain relevant information about weather, credit, farm inputs and output market at lower costs than traditional agricultural extension services (Etwire et al., 2017; Kirui et al., 2013).
In Kenya, mobile phone penetration rate was estimated to be 95% in 2019 (CAK, 2019). About 53% of farmers own smartphones (a mobile phone that has capacity to support other applications apart from voice calls and short message services (SMS)) while 47% have basic feature phone with SMS (Geopoll, 2018). However, the extent and effect of use of mobile phone on CSH is not well known (Mittal and Hariharan, 2018; Government of Kenya, 2017).
1.2 Research problem statement
Climate change effects including prolonged droughts, unpredictable rainfall pattern and floods are causing damage to the world food system (IPCC, 2020). For example, between the years 2006 and 2016 prolonged droughts caused 30% of total agricultural losses in the world (costing over USD 29 billion). Specifically, 83% of these losses were reported in Africa (FAO, 2018). In the past 100 years, Sub-Saharan Africa (SSA) surface temperatures rose by 0.5 to 20C and drought and floods have also become more frequent (Government of Kenya, 2018). It is further projected that temperatures will rise by 4.50C by the year 2100 in Kenya if climate change measures are not implemented (WHO, 2016).
Consequently, incidences of pests and diseases will increase, while there will be little natural water available for irrigation; hence reducing the quantity and quality of crop produce (especially for fruits and vegetables) (Azam et al., 2017). This means that the livelihoods of 80% of rural and 70% of the total populations in Kenya and Taita-Taveta County, respectively, will be adversely affected as they rely mainly on agriculture (Taita-Taveta County Integrated Development Plan, TTCIDP, 2018).
Further, it is anticipated that climate change will cause an increase in prices of basic foods such as maize, rice and wheat by 4%, 7% and 15%, respectively, in SSA and between 1 and 29% globally by the year 2050 hence negatively affecting household food security (IPCC, 2019). To control these consequences of climate change, the Government of Kenya has been promoting CSA practices in 24 counties since the year 2017. However, farmers in Taita-Taveta County are still facing climate change problems (Mohamed and Chege, 2019). Elsewhere, low uptake of CSA technologies in Tanzania and South Africa have been attributed to lack of information (Abegunde et al, 2019; Jha et al., 2020).
There is a growing empirical evidence that mobile phones can be utilized to obtain and share information on CSA technologies hence contributing to solve the problem of climate change among farmers (Chhachhar et al., 2016; Tadesse and Bahiigwa, 2015). For instance, in Taita- Taveta county, mobile phone penetration rate was estimated to be over 80% in the year 2018 with farmers using it to access information online (TTCIDP, 2018).
Also, Etwire et al. (2017) and Ogbeide and Ele (2015) showed that Ghanaian and Nigerian farmers applied mobile phone technology to obtain timely weather and market information. This underscores the importance of mobile phone as an enabler of agricultural development.
In addition, studies such as Amir et al. (2016), Baba (2017), Jairath and Yadav (2012) and Ogutu et al. (2014) showed positive effect of mobile phones on agricultural marketing, use of fertilizers and improved seeds in Kenya and Ethiopia. However, these studies did not focus on CSH and economic implication of the mobile phone on CSH farmers. Also, most impact studies concentrate on projects and often ignore farmers’ decisions which are mostly dependent on self-innovation and information gathered from other farmers (Mehar and Mittal, 2014).
This affects sustainability of such project impacts (Jha et al., 2020). In addition, the government of Kenya suggests that there is need to integrate ICTs in climate smart farming systems (Government of Kenya, 2017 and 2018). But, there is still a gap in existing literature on whether farmers are using their phones to access information on CSH and if such use has any effect on adoption of CSH practices and crop productivity. Therefore, this study sought to provide insights on mobile phone use and its effects on productivity of CSH farmers in Taita-Taveta County with specific attention to green grams and tomato farmers.
1.3 Objectives of the study
The study sought to evaluate the extent and effects of mobile phone use on productivity of climate- smart horticulture farmers in Taita-Taveta County. The following specific objectives were pursued:
i. To characterize adoption of climate-smart horticulture practices and use of mobile phones in accessing related information.
ii. To examine the factors influencing the use of mobile phones on climate-smart horticulture.
iii. To analyze the determinants of extent of adoption of climate-smart horticulture practices.
iv. To evaluate the effect of mobile phone use on productivity of climate-smart horticulture farmers.
1.4 Research hypotheses
i. There are no differences in climate-smart practices between mobile phone users and non- users.
ii. Infrastructural, socio-economic and institutional factors do not affect mobile phone use on climate-smart horticulture.
iii. Socio-economic, infrastructural and institutional factors do not affect the extent of adoption of climate-smart horticulture practices.
iv. Mobile phone use does not affect the productivity of climate-smart horticulture farmers.
1.5 Justification
This study examined the use of mobile phone on CSH. This will help the Kenyan national government and other stakeholders to develop policies and interventions that will benefit farmers through knowledge transfer and real time weather information. This is in line with recommendation(s) 1 and 2 of eTransform Africa: Agricultural sector study report, 2012 (Deloitte, 2012). This is because mobile phone use helps to reduce information gaps and cost hence improving adoption of climate-smart horticulture practices (Jha et al., 2020; Mittal and Hariharan, 2018).
Information on the factors affecting mobile phone use in climate-smart horticulture will assist the county government of Taita-Taveta, non-governmental organizations (NGOs) and private entities (such as Microsoft) to address the specific challenges that affect the farming population in Taita- Taveta County hence saving on extension costs.
It will also contribute to achievement of Kenya agricultural sector growth and transformation strategy 2019-2029 flagships 8 and 9 on strengthening digital and data use cases for improved decision making and sustainable and climate smart natural resource management (MoALFI, 2019).
Information on the influence of mobile phone use on CSH productivity will benefit Taita-Taveta county government by enhancing agricultural service delivery hence improving livelihoods of the community through agriculture (TTCIDP, 2018). It will contribute towards achieving the African Union agenda 2063 aspiration one – section(s) 9, 13 and 16 on eradicating poverty, modernize agriculture and address climate change challenges through technological transformation (African Union, 2015). It also contributes to attainment of the sustainable development goals number 1 and 2 on ending extreme poverty and achieving zero hunger, respectively (UNDP, 2015).
1.6 Study area
The study was done in Taita-Taveta County, Kenya, because it has been implementing CSA project since the year 2018 and has different agro-ecological zones; lower highland zone (altitude of above 1680 m) to lowland zone (altitude of below 610 m). The area is also susceptible to climate changes including high temperatures and unpredictable rainfall (MoALF, 2016; Motaroki et al., 2021). The main economic activity in the region is agriculture, however, poverty levels range between 50 and 70% (TTCIDP, 2018). Taita-Taveta County is among the six counties of the Coast region. It is the top County in horticulture production relative to all counties in the Coast region. The county has four sub-counties and twenty administrative wards as shown in Figure 1.1.
The study covered Wundanyi, Taveta and Mwatate sub-counties because they have high output and acreage of land under tomato and green grams (Mohamed and Chege, 2019; Moranga, 2016).
This study focused on green grams and tomato crops because they are widely grown for both subsistence and commercial purposes in Taita-Taveta County.
In addition, green gram is a drought tolerant seed vegetable prioritized in CSA project within the county (Balasubramanian, 2014; Blair et al., 2016; Goodman, 2004; KCSAP, 2018). The average land area under green grams in the county is 843 hectares and contributes an average of 0.6% of total output in Kenya (International Trade Centre, ITC, 2016; Osano et al., 2018).
On the other hand, tomato production covers 755 hectares of the County’s crop land and contributes, on average, 6% of total value of tomatoes sold in Kenya (HCDA, 2018). About 90% of green grams and tomatoes produced in Taita-Taveta are sold for income thus contributing to household poverty alleviation (MoALF, 2014; Mohamed and Chege, 2019).
1.7 Organization of the thesis
This thesis includes six chapters. Chapter one provides background information, research problem statement, objectives of the study, hypotheses, justification and describes the study area. Chapter two provides a review of climate-smart horticulture concept, application of mobile phones in farming, factors influencing mobile phone use in climate-smart horticulture and knowledge gaps on the influence of mobile phone use on horticulture productivity. This chapter also presents the conceptual and theoretical frameworks. Chapter three, four and five present the methodology, results and conclusions of the respective specific objectives. Finally, chapter six provides the conclusion and recommendations derived from the study.
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