FACTORS DETERMINING ACCESS AND USE OF AGRO-METEOROLOGY ADVISORY SERVICES AMONG POTATO AGRI-ENTERPRISES IN NYERI COUNTY

GABRIEL KINYUA KINYINGI

A Research Proposal Submitted to the Graduate School in Partial Fulfillment of the Requirements for the Master of Science Degree in Agri-Enterprise development of

Egerton University.

EGERTON UNIVERSITY

JULY, 2020

DECLARATION AND RECOMMENDATION

Declaration

This research proposal is my original work and has not been submitted previously in this

University or any other for the award of a degree

Sign ……………………………………. Date…………………………………………..

Name: Gabriel Kinyua Kinyingi

Admission: KM23/14699/18

Recommendation

This research project has been submitted for examination with our approval as University Supervisors.

Sign…………………………………………… Date—————————–

Dr Edith Gathungu, PhD

Department of Agribusiness Management and agricultural Economics,

Egerton University

Sign…………………………………………… Date—————————–

Dr. Isaac Kariuki, Ph.D

Department of Agribusiness Management and Agricultural Economics,

Egerton University.

ABSTRACT

Global climate change has caused adverse effects on agricultural activities. Various stakeholders have recommended climate advisory services as prerequisite tools towards adopting climate-smart strategies to curb climate vagaries in Africa. Kenya has, however, experienced limited accessibility and application in farm decisions making against climate risks. Therefore this study will identify and characterize forms of weather prediction services accessed and utilized by potato farmers in Nyeri County. The study will also determine the intensity of use of various aspects of climate information utilized for adopting Climate-Smart adaptations among potato farmers in Nyeri County. The final objective will be to find out factors determining access and use of weather prediction services. This study will use primary data collected using semi-structured questionnaires administered among 250 households in Nyeri County.

Data will be analyzed using descriptive statistics to analyze various Agro-meteorology advisory services accessed and utilized by potato farmers. A mixed logit model will be adopted to analyze factors that determine both access and use of weather advisory services. The study will provide insights to the Nyeri County and National government policymakers in addressing the various challenges that limit farmer’s access and utilization of Agro-Meteorology advisory services. Besides, the research findings will add to the existing literature and will serve as a reference by other researchers on climate advisory services.

TABLE OF CONTENTS

DECLARATION AND RECOMMENDATION.. ii

ABSTRACT.. iii

LIST OF FIGURES. vi

LIST OF ABBREVIATIONS AND ACRONYMS. vii

CHAPTER ONE.. 1

INTRODUCTION.. 1

1.1 Background Information. 1

1.2 The statement of the problem. 3

1.3 Objectives. 3

1.3.1 Specific Objectives. 3

1.4 Research Questions. 4

1.5 Justification of Study. 4

1.6 Expected outputs. 4

1.7 Operational Definition of Terms and Concepts. 5

CHAPTER TWO.. 6

LITERATURE REVIEW… 6

2.1 Introduction. 6

2.2 Climate change. 6

2.2.1 Effect of climate change on potato Agri-Enterprises. 6

2.2. Agro-meteorology Advisory Services. 8

2.2 Climate change adaptation strategies among Potato farmers. 9

2.2 Climate-Smart Agriculture (C.S.A.) 11

2.3 Contribution of C.S.A. to sustainable Agricultural development 11

2.2.1 Sources of Agro-meteorology Services. 12

2.3.2 Use of Agro-meteorology Services to inform Enterprises’ decisions. 13

2.4 Agro-meteorology services and adaptation to climate change. 13

2.5 Review of empirical studies on the determinants of access and use of Agro-meteorology services  14

2.6 Theoretical framework. 14

2.6 Conceptual framework. 15

Figure 1. Conceptual framework on adaptation decision making. 17

CHAPTER THREE.. 18

RESEARCH METHODOLOGY.. 18

3.1 Introduction. 18

3.2 Study area. 18

3.2 Sampling technique. 20

3.4 Data analysis. 21

3.4.1 Agro-meteorology systems accessed by small-holder enterprises in Kieni Sub-County. 21

3.4.2 Weather information use. 21

3.6 Factors affecting the choice to use weather information. 22

3.9 Diagnostic tests of Heckman model 24

WORK PLAN.. 25

RESEARCH BUDGET.. 26

REFERENCES. 28

APPENDICES. 41

LIST OF TABLES

Table 1 Variables for the intensity of use of weather information 21

Table 2 Variables for computing weather information use. 23

LIST OF FIGURES

Figure 2 Map of Kieni Sub-County showing administrative wards. 19

LIST OF ABBREVIATIONS AND ACRONYMS

AAS:               Agro-meteorology Advisory Services

A.C.M.A.D.:  African Centre for Meteorological Application and Development

C.B.O.:           Community-Based Organizations

C.C.A.F.S.:    Climate Change Agriculture and Food Security

C.G.I.A.R.:    Consultative Group on International Agricultural Research

C.I.A.S.A.:     Climate-based Information on Agro-meteorology and Services for Africa

C.S.A.:           Climate-Smart Agriculture

G.D.P.:           Gross Domestic Product

G.F.C.S.:         Global Framework for Climate Services

I.C.P.A.C.:     Climate Prediction and Application Centre

I.C.T.:             Information and Communication Technology

I.G.A.D.:         Inter-Governmental Agency on Development

I.P.C.C.:          Intergovernmental Panel on Climate Change

K.C.S.A.P.:    Kenya Climate Smart Agriculture Project

K.M.D.:          Kenya Meteorological Department

N.D.M.A.:       National Drought Management Authority

N.G.O.:           Non -Governmental Organizations

N.M.A.:          National Meteorology Agencies

N.M.H.S.:       National Meteorological and Hydrological Services

S.P.S.S.:          Statistical Package for Social Sciences

S.D.G.’s:         Sustainable Development Goals

V.I.F.:             Variance Inflation Factor

WAI:               Weighted Average Index

WMO:            World Meteorological Organization

CHAPTER ONE

INTRODUCTION

1.1 Background Information

Climate change refers to the alteration of the earth’s climate due to atmospheric accumulation of greenhouse gases leading to “greenhouse effects” that sets off a process that modifies weather Patterns (Farmer, 2014). The increasing change in climate patterns causes uncertainties in Agricultural production and marketing decisions which impacts Agriculture negatively, especially in the developing countries (Filho et al., 2018). According to a report by the Food and Agriculture Organization of the United Nations, 60% of the adverse effects of climate change impact on Agriculture directly (Yadav et al., 2018). In Kenya, for example, these challenges are severe since 98% of Kenyan agriculture depend on rainwater (Zilberman, 2017). Climate change leads to extreme events such as drought and floods besides altering rainfall patterns, temperatures variations, length of growing periods and moisture levels. Such alterations in rainfall patterns affect farm management decisions. Weather patterns become unpredictable, making it even hard for farmers to choose appropriate choices. Farmers over the past years have depended on traditional weather knowledge to make production decisions; unfortunately, climate change has been rendering this knowledge obsolete (Zuma-Netshiukhwiet al., 2016)

Irish Potato (Solanumtuberosum L.) is among the crops majorly affected by climate change. Irish potato is the fourth highest produced food-crop in the world after maize, rice and wheat (Zhang et al., 2017).  In Kenya, small scale farmers have grown it as a cash and food crop, and it’s the second most consumed food crop after maize (K.M.D.P., 2013). The subsector contributes significantly to food security, generation of income and employment to many small-holder farmers in the country. Irish potato farming is an essential enterprise for small-holder farmers in Nyeri County. One of the counties in Kenya where Irish potato is a major food crop is Nyeri; with the leading producers of Irish potato being Kieni and Mathira sub-counties (K.M.D.P., 2013). Small scale farmers have dominated Irish potato production in Nyeri County, who grow it for household consumption and income generation. More than 60% of potato produce in Nyeri is mainly for sale (Muthoni et al., 2013), making these a viable enterprise in the area. The vulnerability of Irish potato production in Kieni Sub-county is due to its dependence on rain, and the area location is in the arid and semi-arid region being frequently hit by drought and famine. Irish potato production in Kieni sub-county has been made worse by climate change leading to decreased production.

Despite the threats posed by climate change in Nyeri County, adaptation strategies remain viable options to increase Irish potato production. The use of Technology in seasonal climate forecast (Agro-metrological services) and Climate Smart Agriculture offers the latest adaptation strategies that can be put in place to increase Irish potato production in Nyeri County. Agro-meteorological services entail the provision of the daily, weekly, seasonal, medium, and long-term projections on temperature and precipitation parameters, wind, and soil moisture conditions in an area (“Forecast,” 2017). Agro-meteorological advisory services enable enterprises to decide on the technologies and adaptation strategies favourable to respond to climate variability (Kaczanet al., 2013). Climate information should be accompanied by meaningful agronomic advice to enable farmers to understand and use the forecast to manage climate risks (Arslan et al., 2015). Climate-Smart Agriculture (C.S.A.) refers to agriculture that “sustainably increases productivity, enhances resilience, reduces greenhouse gas emissions, and enhances achievement of national food security and development goals” (F.A.O., 2010).

The Kenya Meteorological Department supported by Non-Governmental Organizations (N.G.O.’s), private organizations, community-based organizations, and research institutions provide information on weather services on daily, weekly, monthly, seasonal and decadal timescales. International development agencies also support it. The National Drought Management Authority (N.D.M.A.) is a local source of Agro-meteorology and prediction products for ten countries located in the Greater Horn of Africa (Pye-Smith, 2018). The Agro-meteorology from N.D.M.A. offers a prediction of onset and severity of rainfall and drought, and seasonal forecast twice a year. Various dissemination channels of weather information include radio, newspapers, television, text messages, mobile phone, online, agricultural extension agents and farmer workshops.

Agro-meteorology and advisories are considered a useful tool in influencing farmer decisions to attain sustainability in agricultural production in the presence of climate change. Possible benefits are realizable if the Agro-meteorology resources are available, reliable and applicable in decision-making. Use of weather information for smart climate strategies possible with the availability of institutional support for the provision and the practical application to managing climate risks. Although the provision of agro-meteorology services in Kenya is promising, there are limitations of accessing and applying climate risk management services (World Economic Forum, 2016). The government should not only concentrate on the development of agro-meteorology but also on the systematic implementation for farm decisions to mitigate climate risk and household vulnerability (Wood et al., 2014).  Social, technological, psychological and economic challenges threaten accessibility and usage of agro-meteorology services for climate change adaptation benefits (Oyekale, 2015). Therefore, considering socio-economic factors that determine the availability and use of climate service is vital in improving their applicability in farm decisions.

1.2 The statement of the problem.

Irish potato production in Nyeri County is vulnerable to climate change due to the dependence on rain-fed agriculture. Climate change has threatened the region’s food security and economic potential. Climate change adaptation strategies are the only viable options to increase Irish potato production in light of climate change. The Kenya Meteorological Department, National Drought Management Authority offers agro-meteorological services to Irish potato farmers in Kenya at the beginning, and during crop growing seasons, however, their access and application are not known, a gap that necessitated the current study. Thus, an understanding of the socio-economic aspects that shape enterprises’ ability to use these services in farm risk management decisions is essential. The study will further investigate Climate-Smart Agriculture strategies small scale Irish potato farmers in Nyeri County have adopted to reduce the climate change effects.

1.3 Objectives.

The overall objective of the study is to contribute to enhanced uptake of meteorological climate-smart advisory services for increased Irish Potato Productivity in Kenya.

1.3.1 Specific Objectives

1. To identify and characterize forms of agro-meteorological services accessed and utilized by potato farmers in Nyeri County
2. To determine the intensity of use of Agro-meteorology advisory services for Climate Smart adaptations among potato farmers in Nyeri County
3. To find out the determinants of access to agrometeorological services and adoption of Climate-Smart Agriculture practices among potato farmers in Nyeri County

1.4 Research Questions

1. What forms of weather prediction services do potato farmers utilize in Nyeri County?
2. What is the intensity of using agro-meteorology advisory services for Climate Smart adaptations among potato farmers in Nyeri County?

• What are the determinants for accessing agro-meteorological services and adopting Climate-Smart Agriculture among Irish potato farmers in Nyeri County?

1.5 Justification of the Study

Climate change adaptation is imperative to ensure sustainable agriculture and Agri-enterprises development in Kenya. Access to location-specific agro-meteorological services enhances Agri-enterprise’s decisions to prevent the negative impacts of climate change. Application of Agro-meteorology services in small-holder agriculture is essential in informing farmers on appropriate farming decisions to mitigate climate risks. This study will analyze the characteristics of agro-meteorological services accessed by enterprises and the two determinants, i.e. access and use of this information. The findings from this study will provide insights into the national and Nyeri County government policymakers to address the various challenges that limit enterprises’ access and utilization of Agro-meteorology services. Moreover, this study will contribute to the call by I.P.C.C. to prioritize research that improves enterprises’ adaptive capacity in Africa (Corbeels, 2018). It will help to not only attaining the Sustainable Development Goals (S.D.G.’s) of ending hunger as envisioned in the vision 2030 strategic plan but also help Kenya in its agenda on food security in the current Kenyan government. Finally, the research findings will add information to the existing literature on determinants of access and use of Agro-meteorology services. This information will serve as a reference for other researchers who will study various aspects of Agro-meteorology advisory services.

1.6 Expected outputs

1. Presentation in a scientific conference

1. Publication of at least one paper in a refereed journal
2. Write a policy brief on agro-meteorology advisory services
3. A thesis for the partial fulfilment of the requirement for the Master of Science degree in Agri-Enterprise Development.

1.7 Operational Definition of Terms and Concepts

Adaptation: This is the process of adjusting or mitigating climate change effects.

Agro-meteorology is the study of weather and use of weather and climate information in enhancing and expanding crops as well as in increasing crop production.

Agro-meteorology advisory services: Agro-meteorology with agronomic advice

Climate change: This is the alteration of the earth’s climate due to atmospheric accumulation of greenhouse gases leading to “greenhouse effects” that sets off a process that modifies weather Patterns

Climate-Smart Agriculture: is an approach that integrates to promote smart agricultural activities in the view to increase production amidst the adverse effects of climate change.

Small-holder potato farmers; are those working on land between 0.5 and 5 hectares.

The intensity of use of weather information: This is the number of times a specific attribute of weather information applies in adopting a smart climate strategy

CHAPTER TWO

LITERATURE REVIEW

2.1 Introduction

This chapter reviews the literature on climate change, climate-smart agriculture, agro-meteorology advisory services, and how these aspects interact to impact agriculture and Agri-enterprise development. It presents research findings on sources of agro-meteorology advisory services, factors that shape its access and use, and climate-smart adaptation practices that an enterprise would benefit from successful utilization of the services. The chapter reviews the empirical and theoretical framework on the benefits of agro-meteorology advisory services alongside a conceptual framework on how these factors inter-relate.

2.2 Climate change.

Climate change is a long-term alteration in the world’s weather phenomena and patterns. It comprises the significant changes in temperature levels, precipitation, wind patterns, and rainfall that have been linked to unpredictability in short and long rains, receding to difficulties in making appropriate production decisions among farmers (World Bank, 2015). Moreover, change in these climate parameters has resulted in to increase in health risks, which affects the wellbeing of farmers and the overall workforce hence reduced food production. The production of food has to be increased by 70% if we are to meet the demand for food by over 9 billion people in the year 2050(McCarthy et-al. 2011). However, climate change threatens agricultural development. For example, global warming results in various adverse weather conditions that harm the productivity of agriculture, such as crop yield and quality, and cropping system. Agriculture is the mainstream for the rural economy and is considered a crucial element in eradicating poverty, hunger, and malnutrition

2.2.1 Effect of climate change on potato Agri-Enterprises

Climate change impacts the vitality of many Agri-enterprises. Already, extreme weather conditions and gradual climate change effects are impacting Agri-enterprises operations, infrastructures, supply and distribution chains, customer traffic, profitability and employee absenteeism. Studies done in North America shows that when Agri-enterprises undergo any climate-related disaster, 43% never open again, while 29% of the rest free after two years. Experience in the United States has indicated that catastrophe can cause a drop in more than 40% of its earnings. From a logistical point of view, the more vulnerable enterprises to climate change are those that heavily depend on transportation infrastructures, energy, water and Agri-food resources. Climate change is also a threat to those businesses located in risk zones, such as coastal regions, flood plains and areas prone to landslides.

In Africa, the effect of climate change is far-reaching than in other regions of the world. Previous studies have that by 2050, there will be an increase in global temperature of about 2°C. The effects of climate change are also more severe in sub-Saharan Africa. Climate change links to many environmental problems, including floods and drought that can be devastating for the agricultural sector. The most recent study found that over half of all food production in sub-Saharan Africa was affected by climate change. In Africa, climate change affects Agri-enterprises more-so because of the limited weather monitoring facilities available to them. Africa has a severe lack of weather monitoring infrastructure and is therefore vulnerable to extreme weather events such as droughts and floods. Assess of environmental exposure to climate change have been done by the latest models, including shifting rainfall and temperature patterns and climate extremes such as floods and drought. The research identifies changes in weather patterns across Sub-Saharan Africa over the next three decades as a considerable threat to the reliability of growing conditions and the yields of economically vital exports of agricultural commodities. (Stephenson, Scott.). In East Africa, climate change has led to increased vulnerability for agriculture and food production. It is also associated with higher rates of disease transmission, malnutrition, and mortality. Climate change has become a critical constraint towards realizing sustainable agricultural development as envisioned in the vision 2030 strategic blueprint.

Kenya is highly susceptible to adverse effects of climate change due to high dependence on rain-fed agriculture alongside a low adaptive capacity. Erratic rainfall and frequent droughts are the major climate shocks that constrain agricultural productivity in Kenya. The increase in drought frequency in these areas has resulted in increased crop failures that aggravate food insecurity. These are just some of the many impacts that climate change has had on agriculture. There are many documentations in studies and research papers on the effects of climate change have been well documented on climate change in agriculture. One study done demonstrated that there is a high likelihood of farmers to produce less food when exposed to extreme weather conditions than if they did not have a climate change mitigation plan in place. Another study showed that farmers exposed to extreme weather conditions had higher levels of greenhouse gas emissions than those not exposed to such weather conditions. The explanation behind this is the amount of carbon dioxide released into the atmosphere is directly proportional to how much water it takes for a plant to grow. These findings are significant as they can help us understand the role of climate change for agricultural production and the effects of climate change on agro-business.

The use of agro-meteorological advisory services in managing impacts of climate change proofs to be effective in reducing risks associated with agriculture, as well as mitigating potential risks related to climate change. These advisory services have been used by farmers to monitor the impact of climate change on their crops and to identify potential risks that could affect them. Studies on the effectiveness of agro-meteorological advisory services assess whether they effectively manage the impacts of climate change. A report on the use of agro-meteorological advisory services published in the journal of nature communications investigates how agro-meteorological advisory services can reduce the risk of crop failure and other environmental problems. The results showed that the use of agro-meteorological advisory services has an immediate positive impact on crop failures, as well as the health of farmers and their families. The findings suggest that use of agro-meteorological advisory services can increase crop yields by reducing the risk of disease, drought, and floods. It also recommends that agro-meteorological advisory services reduce the number of risks associated with crop failures. The results were significant as they showed a positive relationship between agricultural productivity and the availability of agro-meteorological advisory services. Another study by the University of California at Berkeley showed that agriculture’s agro-meteorological advisory positively correlates with the availability of agro-meteorological advisory services.

Climate change adaptation is imperative in addressing climate change impacts. According to I.P.C.C. (2007), adaptation entails natural or human adjustments to current or future climate change to reduce the adverse effects and benefit from positive ones. As a way of enhancing climate change resilience and adaptation, the Kenyan government formed the Kenya Climate-Smart Agriculture Project (K.C.S.A.P.). This project’s primary focus is to strengthen national adaptation to climate change, risk mitigation action plans, and promote resilient Agri-enterprise development to climate change. The K.C.S.A.P. advocates for the availability and accessibility of Agro-meteorology advisory services to enhance long-term and short-term adaptation to climate change.

2.2. Agro-meteorology Advisory Services

Agro-meteorology entails the movement of agro-advisory information from the producer to the user through dissemination channels. It involves problems such as timings in crop planting.  Agro-meteorology advisory services are essential tools in initiating climate change adaptation among small-holder enterprises in Kenya and Sub-Saharan Africa (Taneja et al., 2014). Agro-meteorology entails a projection of short-term climate parameters such as daily weather forecast, monthly and seasonal forecast, and long-term projections that include decadal, multi-decadal and centennial time scales.

Agro-meteorology products include; a) rainfall anomaly maps, these could be a previous week or monthly b). Cumulative forecast rainfall weekly or monthly forecast which provides for the number of rainy days in the forecast period, c.) Rainfall Intensity Chances of dry and wet spell during the rainy season and difference variations from Last decade d.) forecast on extreme temperatures e.) Evapotranspiration maps, f.) Moisture index and Soil moisture forecast maps, g). Rangeland condition index forecast and h). Humidity levels, i). Planting dates and degree-days. Meteorology accompanied by agronomic advice, it is Agro-meteorology advisory services. According to the World Meteorological Organization (WMO, 2015), agro-meteorology advisory services is the provision of Agro-meteorology that is user-driven for risk management decisions based on scientific knowledge and sufficient access mechanism (Napolitano et al., 2015). Crop choices, market access, plant protection and climate-smart agricultural practices are different types of additional information that should accompany Agro-meteorology to enhance climate change adaptation (Taneja et al., 2013).

2.2 Climate change adaptation strategies among Potato farmers

Potato farming has had its fair share of challenges arising from climate change in the past. Estimates show that climate change destroys over half of all potato crops. Such destruction poses a significant problem for potato farmers and their families because they have no choice but to adapt. However, some solutions can be implemented by potato farmers and other food producers to help prevent these problems. One such solution is the use of climate smart adaptation strategies. A climate smart adaptation strategy is an approach to manage the effects resulting from climate change on crop production that involves adapting to changes in weather patterns and rainfall patterns. It should be noted that C.S.A. is an iterative approach that involves application of a strategy, monitoring and evaluation and done repetitively, a farmer becomes more resilient to climate changes. Agro-meteorology provides solutions to farmers that help improve efficiency, ensure continued health of livestock and crops and increased sustainability. It helps in increasing yields and market value of crops alongside solving operational problems.

Adaptation strategies include: increasing crop yields through increased irrigation and water management; reducing soil erosion and soil moisture loss; increasing crop yield through improved drainage systems and irrigation methods; and increasing crop yields through better management of soil fertility. This type of adaptation strategy has been used in many countries, including the United States, Canada and Mexico. Adaptation strategies are often implemented by large agribusiness groups or small-scale producers. The goal of adaptation strategies is to reduce the risk of disease, drought and other environmental problems associated from climate change. These strategies can be applied to a variety of agriculture sectors, including agriculture and forestry. Adaptation strategies include: improving soil fertility increasing soil fertility, reducing water use and nutrient depletion increasing crop yields for crops that need more water, increasing crop yield and food security, reducing greenhouse gas emissions through increased irrigation methods and improved irrigation practices, reducing carbon dioxide emissions in agricultural production and distribution, application of agro-meteorology in adapting to climate change. The basis of climate change adaptation strategies is on the principles of ecology, biology and geology.

The following are examples of adaptation strategies developed by different countries and regions around the world. These adaptations are based on a variety of factors including temperature, rainfall patterns, precipitation patterns and soil conditions. Adaptation strategies can be adapted to meet specific climatic conditions or environmental changes. For example, if a drought is causing severe flooding in the region of the country, it may be possible to adapt with a combination of adaptation strategies such as water conservation, irrigation and drainage. This strategy is also called adaptation to extreme weather conditions like floods. Adaptation strategies include construction of water reservoirs and other infrastructure. They are designed to reduce the amount of water needed for irrigation and drainage, planting drought-resistant varieties, and mitigating infestation by pests and diseases such as weeds, insects, and parasites. These strategies are used to prevent the spread of disease through agriculture in the area. Many adaptation strategies can be implemented to help farmers in their farming practices. Some of them are: -crop rotation it is an effective way to increase the yield of crops. This method reduces the risk of soil erosion and increases yield by 30% or more.

2.2 Climate Smart Agriculture (C.S.A.)

Climate-Smart Agriculture is an approach that integrates to promote smart agricultural activities in the view to increase production amidst the adverse effects of climate change. Climate-Smart Agriculture is multifaceted as it is an iterative, continuous process that involves planning, implementation, monitoring, evaluation, iterative learning, sharing of knowledge, and advancing towards resilient agriculture and Agri-enterprise development. Through more elaborate and innovative techniques, C.S.A. seems a more viable approach to harness extra, better quality food and alleviate hunger and poverty levels in the country. C.S.A. remains only a concept that needs further elaboration and demonstration, especially in developing countries like Kenya (Sivakumar et al.,2008). To achieve meaningful change and sustainable agri-enterprises development, C.S.A. requires support from research and development organizations, decision and policymakers, financing organizations, dedicated knowledge and practical experience worth of human resources (Lawrence, 2012).  If these stakeholders support C.S.A., then the results will be increased Agri-enterprise development, capacity building, and resilience of agriculture and food sectors in making these enterprises more adaptive to climate change and in reducing Green House Gas (GHG) emissions while promoting food security globally.

C.S.A. confronts three major approaches to achieve its objectives. The first approach is bringing together policymakers, farmers, and other stakeholders to identify, disseminate, and implement fruitful actions in light to bringing a remarkable difference. The second approach is shaping climate change and adjusting to variations by introducing smart activities like mixed cropping, introducing newer varieties, and investing in infrastructure and context-specific policies. The third approach is its concern for human wellbeing and development, in multifunctional strategies that focus on general alleviation of living standards, forest, and environmental conservation services. The emulsion of science with policies provides an appropriate framework for achieving C.S.A. goals that might defer from country to country and globally from one place to another (“Lima call for climate action,” n.d).

2.3 Contribution of C.S.A. to sustainable Agricultural development

Climate change has caused instability in agricultural production, which has directly affected enterprises that depend on agriculture as a source of their raw materials (“Climate change policy and the agricultural sector,” 2000). C.S.A. practices are intended to combat the climate change effects. C.S.A. practices will promote agricultural transformation, which will easily ensure sustainable agricultural production, achieve food security, and meet Sustainable Development Goals envisioned in the year 2030 (Kaczan et-al., 2013). C.S.A. lies at the nexus of reducing the vulnerability of agricultural systems and resolving the urgent priorities across the globe, which entail the increased incidence of adverse weather conditions. C.S.A. aims at strengthening agricultural adaptive capacity. According to Arslan et-al (2015), strengthening agriculture adaptive capacity can improve the rural people’s livelihoods, eradicate hunger, and contribute to food security. In practice, the C.S.A. approach employed in developing countries is a way of providing food security and a strategy to reduce poverty. This has been achieved through prioritizing planning and implementation of agricultural policies.

2.2.1 Sources of Agro-meteorology Services

According to a report done by the World bank (2016), Agro-meteorology advisory services in Kenya are provided and primarily dominated by the private sector, contributing to 27% of the service providers. The government agencies, N.G.O.’s and Community Based Organizations(C.B.O.) each account for 21% of the total; academia and research contribute 17%, while international organizations contribute up to 14%. Climate information is primarily a public good, and the government plays a crucial role in managing the information (Lucio and head 2012). The Kenya Meteorological Department (K.M.D.) is the national meteorological department that collects and stores climate data.

Other producers of climate data include; Food and Agriculture Organization(U.N.) Intergovernmental Authority on development climate prediction and application sector (I.C.P.A.C.), The African Center of Meteorological Applications and development (A.C.M.A.D.)., The Alliance for Green Revolution in Africa(A.G.R.A.), Association for Strengthening Agricultural Research in Eastern and Central Africa (A.S.A.R.E.C.A.), Farming and Early Warning Network (F.E.W.S.N.E.T.), International Livestock Research Institute (I.L.R.I.), Index-Based Livestock Insurance (I.B.L.I.), Consortium of International Agricultural Centers (C.G.I.A.R.), Climate Change, Agriculture and Food Security (C.C.A.F.S.), and International Crops Research Institute for the Semi-Arid Tropics (I.C.R.I.S.A.T.). K.M.D. and the National Drought Management Development Authority (N.D.M.A.) provide services for all the 11 sectors identified in the survey; Global Climate and Adaptation Partnership and Geo Envigro Limited each service six sectors; Climate Change Impacts on Ecosystem Services and Food Security in Eastern Africa (C.H.I.E.S.A.), Pan Africa Climate Justice Alliance (P.A.C.J.A.), and the Regional Centre for Mapping of Resources for Development (R.C.M.R.D.) each service five sectors. The Agricultural Sector Development Support Programme (A.S.D.S.P.), F.E.W.S.N.E.T., CARE International, and N.D.M.A. each service four sectors, whereas the rest of the service providers focus on one to three sectors.

Smallholder potato enterprises also rely on own developed indigenous knowledge on seasonal climate forecasts developed from natural indicators (McCarthy et al., 2011). Indigenous knowledge, also referred to traditional knowledge, is a body of knowledge built on observation of natural indicators by different communities over time. This information is used for decisions making in agriculture, medicine, food production and preservation, soil and water management (Arslan et al., 2015). The useful knowledge is passed from generation to generation and it varies from one community to another. Among the traditional knowledge used by local communities is the indigenous climate forecast, which was predicted by observing and interpreting natural phenomena.

2.3.2 Use of Agro-meteorology Services to inform Enterprises’ decisions

The use of A.A.S. by enterprises in decision-making against climate risks results in higher agricultural yields (Mjelde et al., 2017). Timing of forecast dissemination, psychological factors, and socio-economic factors also influence the use of climate forecast by enterprises. Enterprises in Africa do not employ climate forecast information to modify their managerial decisions against climate risks due to socio-economic factors, namely land availability, labor, access to credit, land tenure, market access, and technical information. High illiteracy level is also a major constraint towards utilization of climate forecast advisory services by small-holder enterprises. Both men and women demand Agro-meteorology services, but women face major constraints in the application of climate services in farm decisions due to limited access and control of production resources (Coulibaly et al., 2015).

2.4 Agro-meteorology services and adaptation to climate change

Favorable weather is least guaranteed prior to, during and after production seasons in Sub-Saharan Africa. Therefore, to enhance sustainable agricultural production in S.S.A. adaptation to climate change is imperative. Access to Agro-meteorology services enhances adoption of coping strategies against climate change. Altering crop, land and water management practices to cope with climate variability are various adaptation strategies (Msangi et al.,2014). Access to agro-meteorology advisory services results in the adoption of various adaptation strategies, for instance, change in cropping dates and change in crop varieties, crop diversification, use of early maturing varieties, water and soil conservation, planting fodder and use of fertilizers (Eriksen, 2017).

2.5 Review of empirical studies on the determinants of access and use of Agro-meteorology services

Various studies have analyzed factors that influence farmer access and utilization of Agro-meteorology services in farm decisions making against climate change. Oyekale (2015) analyzed factors that influence access to forecast information specific to disease and pest incidence and rainfall onset in West and East Africa using the probit model. The author established that access to radio, financial services, television ownership, previous exposure to climate shocks and education increased the likelihood of access to the forecast. However, this study used regional data for analysis whereby the sampling unit comprised of eight countries, namely; Kenya, Uganda, Tanzania, Ethiopia, Burkina Faso, Ghana, Niger, Mali, and Senegal. This study’s findings were not country-specific and therefore, cannot be used by policymakers to enhance access and use of A.A.S. in Kenya. Oyekale (2012) analyzed factors that influence climate services access in South Africa’s Limpopo basin. The author analyzed four climate forecast sources, namely radio, television, extension agents, and neighbor. Four separate Probit models were analyzed, and the results from all sources showed that the likelihood of access to climate forecasts increased with ownership of television, cars, radio, land, previous exposure of hailstorms and floods, and university education while it reduced with farming experience, large farm size, access to fertile land and household size.

A report by (adapting African agriculture to Climate Change, n.d) analyzed conditions that can improve use of Agro-meteorology in arid and semi-arid County of Baringo Kenya. The study used sensitivity analysis to establish the barriers and enablers of use of seasonal climate forecast. The results showed that conflicts and insecurity, culture, lack of information, and diversified income sources limited the uptake of seasonal forecast. Unlike the previous studies that only analyzed factors determining access or use of Agro-meteorology advisory services, this study will analyze both the factors that determine access and use of Agro-meteorology services. This is because access to climate forecast is the first step towards utilization it’s in farm decisions making. Therefore, enterprises who utilize this information are a subsample of those with access to Agro-meteorology.

2.6 Theoretical framework

Climate smart adaptations are a form of risk management tool that covers a farmer towards adverse climate risks and as such reducing marginal effects of climate change on the productivity of the farm. (Msangi, 2014) For this study, a utility maximization function given will be adopted given weather risk presence and given the choice of a certain climate smart adaptation given the choice of use of weather forecast information. The utility a farmer will be perceived to derive, in the context of climate smart practice adaptation is stability in yields and a reduction in the impacts of the risk attached to the climate smart adaptation (Maumbe, 2012). A rational farmer in this particular case chooses a risk management strategy if the benefits accrued to adaptation are more than the benefits derived without the adaptation of the strategy. Utility will henceforth be defined as;

Uy = Ey- αwy

Uy – Utility achieved from choosing a risk management strategy y

E = constant.

w= independent variable

α = coefficient of determination that covers choice of a risk aversion strategy that covers variability in yields wy.

Given the equation then we can define the coefficient as;

α = {∂U/∂Wy│∂U/∂y}

α< 0, then the farmer is more risk-averse and is likely to adapt the climate smart

α = 0, then the farmer is neutral.

α> 0, Indicates a risk taker.

Utility to implement a strategy say y(Uy) is given by the revenues accrued to the strategy less the cost of adapting the strategy.  In the view of a variety of risk aversion strategies, a farmer will choose a risk strategy X that yields higher utility than a risk aversion strategy Y. (Filho& Gomez, 2017)

E(UX) – MX> E(U.Y.)-MY

The first term expresses the utility derived from the adaptation of strategy X, and associated with the costs M, while the second term expresses the expected utility of achieving strategy Y associated with cost My. An assumption is made in this study whether the error terms are correlated or not and determines the qualitative choice model in the study.

2.6 Conceptual framework

Access and use of Agro-meteorology Services (A.A.S.) are the first steps towards adapting adverse impacts of climate change. Enterprises largely are dependent on scientific weather forecast since indigenous local indicators have been deemed ineffective and unreliable due to high climate variability. As shown in Figure 1, the Kenya Meteorological Department, community-based organizations, Non-governmental Organization (N.G.O.’s), donor-funded projects, international research organizations, and regional networks among other stakeholders provide Agro-meteorology services which entail interpretation of climate parameters to the enterprises, their implications and advice on the alternative strategies to mitigate climate risks. This information is transmitted through various dissemination channels, which include radio, television, newspapers, extension officers, local authorities, churches, community workshops, farm trials, and interaction with informed neighbors or friends. The various dissemination channels used to transmit A.A.S. should possess user-friendly attributes. For instance, enterprises will access and use A.A.S. from channels that are affordable, easily accessible, accurate, reliable, trusted, and using understandable language. These dissemination channels should also be consistent, effective, and provide timely information to enable enterprises choose appropriate coping strategies. Various factors have been hypothesized to influence both access and use of Agro-meteorology services (Figure 1). Economic factors such as employment, income, and interest rates influence both access and use of A.A.S.

Figure 1. Conceptual framework on adaptation decision making.

CHAPTER THREE

RESEARCH METHODOLOGY

3.1 Introduction

This chapter will provide a design of how the research will be conducted. It comprises the following subtopics: study area, research design sample size and sampling procedure of study and how data will be collected and analyzed.

3.2 Study area

The study will be conducted among commercialized Irish potato Agri-Enterprises in Keini sub-county. Kieni is an electoral constituency in Kenya and is one among the six constituencies of Nyeri County. Kieni has eight locations. The population density is high, and subsistence rain-fed root, tuber and cereal cultivation is the primary source of livelihood. Agricultural landholdings are usually small, with average farm size per household ranges between 0.4 and 4.0 ha. However, the zone has low rainfall levels, with 30% of the total distribution at about 1150 mm in annual precipitation. Most of the cereals crops produced are under rain-fed conditions because irrigated agriculture accounts for less than 10% of the total cultivated area resulting in land abandonment and degradation. Rainfall and soil moisture levels are usually deficient, which justifies the need for Climate Smart Agricultural (C.S.A.) practices to cover income losses in the dry region. Maize, Irish potato, cowpea are major cereal crops dominating commercial agricultural activities in the Zone. The area has vast land resources and therefore remains an essential asset for the development of Kenya.

Figure 2 Map of Kieni Sub-County showing administrative wards

Source: Egerton University geography department

3.2 Sampling technique.

Multistage sampling technique will be used to select sample residents. Kieni Sub-county is selected purposively due to its rising aridity and its characterization by two distinct Agro-ecological zones (Kieni East and Kieni West) with very potato production concentration in the middle belt of Kieni. Four wards will be selected purposively, two from each Agro-Ecologic Zone, to represent the wards with or without a weather station. As shown in the table 1 below. Small-holder potato farmers in the two regions are assumed to have different weather prediction knowledge which may result into difference in adaptive measures. Climate change may have different impacts depending on the Agro-Ecological zone. As a result, farmers may practice different climate smart adaptations, (Lema 2019) shaped by social-economics, bio physical and social-cultural context of the different areas. Four farming communities know for commercial potato production will be randomly selected. A random sampling of 250 respondents from the two regions made for the purpose of this study.

Table 1 Sample size distribution

Household data will be collected by the use of a survey using a structured questionnaire. A questionnaire pre-test will be conducted among non-sampled communities. Focus Group Discussions (F.G.D.’s), will also be used to collect data, held among male-headed and female-headed households. Key Informant Interviews (K.I.I.) will also be held with knowledgeable persons from the communities’ e.g. agricultural officers, cooperative leaders, other agricultural staff, individuals who access weather forecast information and special interviews with farmers who have already incorporated climate-smart adaptation strategies in their farming.

3.4 Data analysis

3.4.1 Agro-meteorology systems accessed by small-holder enterprises in Kieni Sub-County.

This objective will analyze the various Agro-meteorology services accessed by enterprises and the various dissemination channels. This will be achieved by the use of descriptive statistics in Statistical Packages of Social Science (S.P.S.S.) software and the results summarized by use of means and frequencies. Various sources of weather information will be analyzed. Results will henceforth be presented in charts, tables, box plots, curves, and line graphs.

3.4.2 Weather information use.

A weather information use index will be constructed to analyze the intensity of information used to adopt a climate-smart adaptation strategy. The Weighted Average Index (WAI) will help examine forecast information required to adapt to specific climate-smart agricultural adaptations. The formula below will give us the WAI.

Average Index (weighted) =

Given that WAI =

Where:

F= frequency of weather/market use

W = weight of each scale

I = weight (0 = low importance 1= moderate importance and 2= high importance;)

Weather information use index could take integer values 0, 1, 2 …n. The Weather Average Index will be used in ranking the weather information and forecast elements to explicit the necessary and valued place for market and Agro-meteorology services for adaptation by Irish potato enterprises in Kieni. Selected values for the indexes are as shown in the table 1 below.

Table 2 Variables for computing weather intensity use index

3.6 Factors affecting the choice to use weather information.

Heckman selection model will be employed to determine factors affecting the choice to use weather forecast information, for climate-smart adaptations. Two-step regressions models are used to correct the selection bias whereas the decisions process for a farmer to adopt the innovative strategy needs more than one step.

The two-step model has two-step to be estimated; access model and utilization model. The Heckman two-step procedures will be used to determine the effect of an enterprises choice to utilize certain weather-specific information for climate-smart adaptation.  The first step for model specification involves an analysis of determinants for a farmer to access weather information (selection stage) and the second is the actual adaptation conditional to the first stage of farmer accessing weather information (outcome model).

Heckman model for sample selection takes typically an assumption that there is an existing underlying relationship.

Y_j* = X_jβ + U_1J

Observation of binary outcome is only seen given the probit model as

Y_J^Probit = (y*_j>0)

The dependent variable is only observed when the observation j is made at the selection equation.

U₁~ N (0,1)

U₂~ N (0,1)

Corr(u₁,u₂) =ρ

Where U₁ and U₂ are error terms

In conclusion, to determine the effect on an enterprises decisions to use information services and adapt a climate-smart adaptation will take the marginal effects on an independent variable unit change with the probability P(Z=1| X=x). Mathematically it’s expressed as;

=  = φ(X_Iβ

Variables to include in the Heckman probit. A positive sign depicts that the variable is expected to contribute positively to access and use of Agro-meteorology services while a negative sign is expected to have no effect towards either access or use. As shown in 2 below

Table 3 Variables for computing weather information use

3.9 Diagnostic tests of Heckman model

Heteroscedasticity: This refers to the generalization of the regression model. The presence of heteroscedasticity refers to an instance where the variance of the error terms is not constant across realizations. In this case, testing the presence of heteroscedasticity, breusch pagan test will be used. (Baum, 2006).

Multicollinearity: Multicollineority occurs when two or more variables are perfectly collinear. A Variance Inflation Factor will be used to test the presence of multi-collinearity among the independent variables. (Gujarati, 2004).

Endogeneity Endogeneity entails the presence of an endogenous explanatory variable in the regression model. Endogeniety will be tested to cater for factors that affect the outcome of the variable but cannot be observed in real life and therefore forms part of an error. (Gujarati, 2004).

WORK PLAN

RESEARCH BUDGET

Source: Kenya Climate Smart Agriculture Project

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APPENDICES

Questionnaire

EGERTON UNIVERSITY

DETERMINANTS OF ACCESS AND USE OF AGRO-METEOROLOGY ADVISORY SERVICES

HOUSEHOLD SURVEY QUESTIONNAIRE

Respondent

Consent

My name is ________. In this community we are conducting a study on factors influencing access and use of agro-meteorology advisory services. I would like to request for your participation. Your participation is voluntary. Any information provided will be treated strictly confidential, used anonymously, and will not be used for any other purpose other than this study. The interview will take 20-35 minutes.

Part One

Section One: General Information

1. Enumerator        Questionnaire Number ___________

Name of Interviewer ……………………….

Date of Interview (dd/mm/yyyy) ——-/—–/——

Household information

1.1 Division………………….                       1.2 Sublocation………………….

1.3 Village……………………

Name of respondent……………………………1.4 Phone number………………….

Section 2: Respondent         

• What is your gender Օ Male      Օ Female?
• What is your educational level? Օ Pry       Օ Sec            Օ College/univ
• How old are you?                         Օ 18-35     Օ 36-60         Օ 60- Above
• How long have you been farming? Օ<15 yrs.  Օ 15-30 yrs.  Օ>30
• Do you own land? Օ Yes         Օ No
• What is the size of the land? Օ<10 ha   Օ 10-20 ha     Օ>30 ha
• Average income from farm activities?

Օ ksh – 0ksh -10000

Օ ksh. 10000-30,000

Օ ksh. 30,000-above

• What is the average income from other activities?

Օ 0ksh – ksh 10000

Օ ksh. 10000-30,000

Օ ksh. 30,000-above

Section 3 Awareness and perception on climate change

3.1 To what extent do you agree on the statements below for the past 20 years?

Section 4: Forms of weather prediction utilized by the farmer

4.1 What forms of weather prediction are you well conversant with?

1. Traditional forms
2. Conventional/Modern forms of weather prediction

If 1 above, please tick one that you are conversant with

Codes for specific weather attribute

1= On-set of rains, 2= Forecast on extreme weather (floods, heavy rains, landslides, strong winds, floods), 3= Daily weather forecast (for the next 2 to 3 days on rainfall, temperature), 4= Monthly weather forecast, 5= Long-term climate forecast (Long-term climate variability, 6=Indegenous Forecast ( Including indigenous knowledge, empirical observations), 7= Degree days, 8= Relative humidity, 9= Wind speed, 10= Wind direction

Section 5: Awareness, Access and use of Agro-meteorology advisory services

5.1 Have you heard about weather forecast and agro-meteorology advisory services? 1= Yes { },

2=NO { }

5.1.2 If yes, what is the source of access?(Please tick where appropriate).

5.1.3 Weather forecast attribute received

5.2 Is climate information received accompanied by agronomic advice and how often is the attribute applied for farm decision making? Please indicate in the table below using the codes provided

Agronomic advice codes 1= Introduce new crops/varieties, 2= Plant early maturing varieties, 3= Start irrigation, 4= Improved drainage, 5= Introduce crop cover, 6= Introduce mulching, 7= Terraces, 8= Mechanized farming, 9= Early land preparation, 10= Early planting, 11= Late planting, 12= Use of chemical fertilizers, 13 = Use of manure 14= Use of herbicides, 15= Agroforestry, 16= Plant drought resistant varieties,17=harvesting, 18=defoliation, 19=crop modeling, 20=disease risk mitigation, 21= fertilizer applications, 22= cultivation, 23=spraying, 24= transplants, 25= monitoring of growing conditions, 26= measuring plant stress, 27=Protect crops from damage, 28=freeze potential/ protection, 29=cold or warm air advection over crop areas, 30= Engage in an alternative livelihood

5.2.1 Do you trust in the weather information received 1. Yes { }     2.  No { }

If No please tell us why

• Not accurate, 2= Not well understood 3= Other (please specify)………………

6. Factors affecting choice to use weather information

Please tick where appropriate.

Thank you for your cooperation!!