MAINA W. N. – A56-8003/2017

Table of Contents
TOC o “1-3” h z u 1INTRODUCTION PAGEREF _Toc523680411 h 11.1.BACKGROUND PAGEREF _Toc523680412 h 11.2.STATEMENT OF THE PROBLEM PAGEREF _Toc523680415 h 31.3.STUDY OBJECTIVES PAGEREF _Toc523680416 h 52.LITERATURE REVIEW PAGEREF _Toc523680422 h 62.1.Soil organic carbon PAGEREF _Toc523680423 h 62.2.The depletion of Soil organic carbon PAGEREF _Toc523680424 h 72.3.Land degradation in SSA and across Kenya and Ethiopia PAGEREF _Toc523680425 h 82.4.Reversing land degradation PAGEREF _Toc523680426 h 92.5.Increasing soil organic carbon content in the soil PAGEREF _Toc523680427 h 102.6.Sustainable land management practices in Kenya and Ethiopia PAGEREF _Toc523680428 h 102.7.Factors affecting the adoption of sustainable land management practices PAGEREF _Toc523680429 h 112.8.Trade-offs in agriculture PAGEREF _Toc523680430 h 122.9.Extent and intensity of adoption of sustainable land management practices PAGEREF _Toc523680431 h 133.METHODOLOGY PAGEREF _Toc523680432 h 143.1Study area PAGEREF _Toc523680434 h 143.1.1Western Kenya PAGEREF _Toc523680435 h 143.1.2Ethiopia PAGEREF _Toc523680436 h 173.2Determination of Sample size PAGEREF _Toc523680437 h 203.3Sampling Procedure PAGEREF _Toc523680438 h 213.4Data Collection PAGEREF _Toc523680439 h 223.5Analysis of data PAGEREF _Toc523680440 h 223.6Desktop modelling using GIS PAGEREF _Toc523680441 h 253.7Validation PAGEREF _Toc523680442 h 27EXPECTED OUTPUTS PAGEREF _Toc523680443 h 27WORK PLAN PAGEREF _Toc523680444 h 28BUDGET PAGEREF _Toc523680445 h 284REFERENCES PAGEREF _Toc523680446 h 295APPENDICES PAGEREF _Toc523680447 h 38Appendix 1: HOUSEHOLD SURVEY QUESTIONNAIRE PAGEREF _Toc523680448 h 38Appendix 2: FOCUS GROUP DISCUSSION QUESTIONNAIRE PAGEREF _Toc523680449 h 46
The World Bank (2013) acknowledges that agriculture is the most important economic activity for the growth of Sub-Saharan Africa (SSA) because it contributes to about 65% of the labour force and 32% of the SSA gross domestic product (GDP). In addition, it has been indicated as the most significant way of reducing poverty, hunger, and the continued degradation of the natural environment in SSA (World Bank, 2013). However, increasing food production is a major challenge facing SSA, as the common farming systems are known to accelerate land degradation and soil fertility loss (Mwangana, 2016). Soil organic carbon (SOC) is the foundation of soil fertility (Chan, 2008). It releases nutrients for plant growth, promotes the soil structure, enhances the health of soil and is a defence against harmful substances. SOC influences the processs of soil formation like leaching of cations, soil acidification and gleying (including iron-reduction and podzolization) (Gaiser & Stahr, 2013). SOC accounts for less than 5% of the weight of upper soil layers on average and reduces with depth (Haynes, 2005). According to The Commonwealth Scientific and Industrial Research Organisation of Australia (CSIRO), rain forests or area with good soils, exhibit SOC levels greater than 10%, compared to poorer or heavily exploited soils where the SOC levels are likely to be less than 1% (Mackey et al., 2008). Soil carbon levels are influenced by factors such as temperature, rainfall, land management, soil nutrition, and soil type (Futurefarmers, 2008).

Land degradation is the major environmental problem that causes the constant depletion of SOC (Smith et al., 2016). It is is attributed to poor agricultural practices. It leads to loss of soil carbon, erosion, nutrient depletion, and other land degradation forms resulting in low agricultural yields and a reduction of suitable land for agriculture (Smith, 2016). Research has documented that land degradation trends can be reversed by applying land use types that encourage land restoration and by the adoption of sustainable land management practices (Lal, 2015).
Agricultural management practices that encourage stubble retention and direct additions of organic materials (Chan, 2008) significantly increases the SOC content over the years ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1111/gcb.13068”, “ISBN” : “1365-2486”, “ISSN” : “13652486”, “PMID” : “26301476”, “abstract” : “Soils are subject to varying degrees of direct or indirect human disturbance, constituting a major global change driver. Factoring out natural from direct and indirect human influence is not always straightforward, but some human activities have clear impacts. These include land use change, land management, and land degradation (erosion, compaction, sealing and salinization). The intensity of land use also exerts a great impact on soils, and soils are also subject to indirect impacts arising from human activity, such as acid deposition (sulphur and nitrogen) and heavy metal pollution. In this critical review, we report the state-of-the-art understanding of these global change pressures on soils, identify knowledge gaps and research challenges, and highlight actions and policies to minimise adverse environmental impacts arising from these global change drivers. Soils are central to considerations of what constitutes sustainable intensification. Therefore, ensuring that vulnerable and high environmental value soils are considered when protecting important habitats and ecosystems, will help to reduce the pressure on land from global change drivers. To ensure that soils are protected as part of wider environmental efforts, a global soil resilience programme should be considered, to monitor, recover or sustain soil fertility and function, and to enhance the ecosystem services provided by soils. Soils cannot, and should not, be considered in isolation of the ecosystems that they underpin and vice versa. The role of soils in supporting ecosystems and natural capital needs greater recognition. The lasting legacy of the International Year of Soils in 2015 should be to put soils at the centre of policy supporting environmental protection and sustainable development. This article is protected by copyright. All rights reserved.”, “author” : { “dropping-particle” : “”, “family” : “Smith”, “given” : “Pete”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “House”, “given” : “Joanna I.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Bustamante”, “given” : “Mercedes”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Sobocku00e1”, “given” : “Jaroslava”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Harper”, “given” : “Richard”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Pan”, “given” : “Genxing”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “West”, “given” : “Paul C.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Clark”, “given” : “Joanna M.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Adhya”, “given” : “Tapan”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Rumpel”, “given” : “Cornelia”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Paustian”, “given” : “Keith”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Kuikman”, “given” : “Peter”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Cotrufo”, “given” : “M. Francesca”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Elliott”, “given” : “Jane A.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Mcdowell”, “given” : “Richard”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Griffiths”, “given” : “Robert I.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Asakawa”, “given” : “Susumu”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Bondeau”, “given” : “Alberte”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Jain”, “given” : “Atul K.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Meersmans”, “given” : “Jeroen”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Pugh”, “given” : “Thomas A.M.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Global Change Biology”, “id” : “ITEM-1”, “issue” : “3”, “issued” : { “date-parts” : “2016” }, “page” : “1008-1028”, “title” : “Global change pressures on soils from land use and management”, “type” : “article-journal”, “volume” : “22” }, “uris” : “”, “” } , “mendeley” : { “formattedCitation” : “(Smith et al., 2016)”, “plainTextFormattedCitation” : “(Smith et al., 2016)”, “previouslyFormattedCitation” : “(Smith et al., 2016)” }, “properties” : { }, “schema” : “” }(Smith et al., 2016). Their adoption leads to the accumulation of SOC in agricultural soils (Lal, 2008). The promotion of agricultural management practices has been widely carried out in SSA in order to reclaim degraded lands and improve productivity (Mango et al., 2017). Governments, private entities and non-governmental organizations have introduced various campaigns on soil, land and water conservation. For example, in Kenya, the National Soil and Water Conservation Project (NSWCP), supported by the Swedish International Development Agency (SIDA), selected Machakos district in 1974 to carry out soil and water conservation. The campaign commenced in 1979, becoming a national campaign with support from local government as well as the Ministry of Agriculture. Wolka (2014), recognizes the efforts that have been underway for nearly four decades in Ethiopia through soil conservation measures. Amid these efforts and studies that have been carried out focusing on land management practices that enhances soil carbon (Gomiero, 2013), little have been done to inform these entities on the extent to which these land management practices have been adopted and the constraints to adoption.
Therefore, this study aims at establishing the extent to which land management practices that enhance soil organic carbon has been adopted by small-scale farmers in the Western parts of Kenya and two representative watersheds of Ethiopia. The study will employ both descriptive and econometric analysis. This will involve household surveys and multivariate probit econometric analysis to ascertain the factors that constrain adoption of sustainable land management (SLM) practices that enhance soil organic carbon, the trade-offs associated with these practices and the extent of farmer’s use of these SLM practices. It will also utilize GIS for further development of spatial relationships between the existing carbon stocks layers and the anticipated factors affecting adoption as well as the spatial representation of the study findings.

Land degradation is a global concern but is mostly severe in the tropics and sub-tropics due to limited land resources, highly increased population, conversion of land-use to urban and industrial uses, and the continued failure to address hunger and malnutrition (Lal, 1998). It is a problem threatening the sustainability of agriculture in Kenya and Ethiopia. It depletes soil organic carbon and this directly affects soil fertility, productivity and the overall quality of these soils (Dlamini et al., 2014). Land degradation can be reversed by the adoption of sustainable land management practices which add nutrients to the soil, rebuilds topsoil through soil amendments, re-establishes vegetation, and buffers soil acidity (Scherr ; Yadav, 1996). Research has documented that SLM practices have been widely used in the SSA to improve soil and crop productivity (Mango et al., 2017). This has been done by the governments, non-governmental organizations (NGO’s) as well as individual farmers. For example, Western Kenya being the grain basket of the Kenyan economy (Mukoye et al., 2016) boasts the highest coverage of agricultural projects from the government and development NGO’s. Ethiopia on the other hand, according to Miheretu ; Yimer (2017), has been in a continuous struggle to introduce sustainable land management practices with the aim of increasing agricultural production and ensuring sustainable utilization of natural resources since 1970s. The early 1980s saw the introduction of new land conservation technologies through food for work incentives by the Ethiopian authorities (Shiferaw and Holden 1998). Despite the efforts of the government and the NGOs to increase the uptake of SLM practices, adoption of these measures by the small-scale farmers remains low (Nigussie et al., 2017). This is because the adoption of the SLM practices is influenced by some biophysical and socio-economic characteristics which are plot specific, specific to every farmer and in other cases location based (Bisaro et al., 2011). Gauny, (2016) reported that these factors act as barriers in the adoption of Soil carbon enhancing land management practices (SCELMP). This is in addition to the fact that the farmers’ decision to adopt or not to adopt to certain management practices is dictated by trade-offs. This is more evident in the small-scale farms where crop and livestock production are integrated and both are contributing to the household economy.

The government, policy makers, NGO’s and funding entities in both Kenya and Ethiopia lack the information regarding the extent to which farmers have adopted to SCELMP. They also lack the information on the factors that influence the extent of adoption of various land management practices. This is because even though restoration interventions are being implemented, the achieved positive results are less than expected and proper studies have not been clearly carried out and understood before the implementation of these interventions (Mugume, 2014). Very few studies have been conducted in Kenya and Ethiopia to analyze the factors influencing the extent of adoption of SCELMP. Therefore, this study seeks to fill this gap and hence, bring about a better understanding of the factors influencing the negative or positive extent of adoption of SCELMP as well as their spatial distribution in Western Kenya and Ethiopia.
Land degradation can be reduced significantly if appropriate and large-scale land management interventions are carried out (Amdihun et al., 2014). This will increase soil fertility and improve agricultural productivity (Thomas, 1997) which will address the food security of the study areas. However, the adoption of these land management interventions is low and requires immediate attention. This study will be significant as it will attempt to establish the extent to which farmers have adopted SCELMP as well as the factors that constrain their adoption. These will inform decision makers on which land management practices to put emphasis on and the problems to solve before encouraging farmers to adopt these practices. It will also instruct policy makers on the most suitable intervention programmes to implement on the study areas and guide funding entities on the process by which to engage farmers in the study areas in a bid to find permanent solutions to soil fertility depletion and in general soil degradation. Based on the constraints to adoption of SCELMPs, solutions will be sought. These will be disseminated to farmers to encourage uptake of these practices and therefore, increased adoption of SCELMPs.
STUDY OBJECTIVESThe study seeks to generate information for use by policy makers and decision makers in making informed decisions in encouraging the adoption of soil organic carbon enhancement technologies among small-scale farmers in Kenya and Ethiopia.

The specific objectives will include;
To assess the extent of adoption of soil organic carbon enhancement technologies among small-scale farmers in Kenya and Ethiopia.

To establish the key socio-economic and biophysical constraints to adoption of soil carbon enhancement technologies.
To map the barriers and tradeoffs of the adoption of soil carbon intervention activities.

There’s a very low extent of adoption of soil organic carbon interventions among the small-scale farmers in Kenya and Ethiopia.

Site specific biophysical and socioeconomic factors do not influence farmer’s decisions to practice the technologies that increase soil organic carbon.

There exists no barriers and trade-offs to the adoption of land management practices that increase soil organic carbon by small-scale farmer’sLITERATURE REVIEWSoil organic carbonSoil organic matter (SOM) is the fraction of soil consisting of plant, animal, and microbial residues, both living and dead and at various stages of decomposition (Mugume, 2014). It is a source of nutrients for plants and a sink for carbon dioxide and nitrogen gases. It plays a very important role in maintaining soil fertility and plant nutrients status (Bationo et al., 2006; Piccolo, 2012). SOM improves the soil structure and therefore reduces soil erosion while improving water infiltration (Brady & Weil, 2010). According to Young & Young (2001), about 58-60% of SOC is SOM. Briggs & Twomlow, (2002) noted that changes in the SOM content of the soil indicate changes in productivity levels as well as the change in soil organic carbon (SOC) content of the soil. SOC is the most important component of the SOM as it influences the quality of the soil by influencing the chemical, physical and biological factors of the soil (Bullock, 2005). The factors that influence SOC content are divided into natural and human-induced factors (Piccolo, 2012). Natural factors include climate, soil parent material, land cover and/or vegetation and topography while human-induced factors are; land use, management, and degradation (FAO, 2005). A high percentage of SOC is found on the topsoil (15–25 cm) as plant residues (Lal, 2004; Bot, 2005). SOC stocks up to a depth of 1 m ranges between 30 tons per ha in arid environments to 800 tons per ha in humid and cool environments (Lal, 2004). The global soil carbon (SC) pool consists of 2500 gigatons (Gt) with about 1550 Gt of SOC and 950 Gt of soil inorganic carbon (SIC). The soil carbon pool is estimated to be 3 times the size of the atmospheric pool (Lal, 2004).
The depletion of Soil organic carbonContinued conversion of protected areas (forests and rangelands) to farmlands, coupled with poor land management (inappropriate farming methods and poor residues management) has led to reduced soil organic carbon and soil fertility (Wiesmeier et al., 2016; Godde et al., 2016). Montgomery (2007) reported that inappropriate farming methods exacerbates soil erosion and loss of SOM leading to a decline in soil fertility and undermines crop production. Lal, (2003) states that mineralization and soil erosion causes a severe depletion of the soil organic carbon pool. The author also estimates the amount of total carbon displaced by erosion assuming a delivery ratio of 10% and SOC content of 2–3%, to be 4.0–6.0 Pg/year and this loss may be followed by a 20% emission resulting from mineralization of the displaced Carbon. This translates to additional 0.8–1.2 Pg C/year loss on earth.
One of the major causes of soil organic carbon depletion is the conversion of grasslands and forests to agricultural production. Lal, (2004) documents that this conversion encourages depletion of SOC with up to 60% in temperate soils and 75 % in tropical soils. As a solution to the above, adoption of sustainable land management practices is expected to reduce the risks of C emission and sequester C in soil and biota.
Land degradation in SSA and across Kenya and EthiopiaLand degradation is generally a reduction in soil quality (Lal, 2009). This could be physical, chemical, biological or ecological (Lal, 2015). The process of land degradation is started mostly by anthropogenic factors of land misuse and soil mismanagement which aggravates the process of soil erosion and other land degradation processes. This leads to a decline in ecosystem services. Organic matter and vegetation cover improve the resistance of soils to erosion by stabilizing it (Berhe et al., 2007). Land degradation undermines agricultural productivity which studies documents that 50-70% of the Africa’s population depends on it for their livelihood (Jama et al. 2011).

Many areas in western Kenya are densely populated with a population of about 500-1200 inhabitants per square kilometer. As this population depends on subsistence farming, they have opted to increasing land under cultivation and in the process encouraging land degradation (Morera, 2010). Inappropriate practices that affect the environment include; deforestation, increased cultivation of marginal areas and overgrazing. These practices encourage soil erosion and subsequently soil degradation, loss of biodiversity and water degradation.

One of the most serious environmental setbacks of Ethiopia is land degradation. Land degradation in Ethiopia is attributed to intensive farming on fragile and steep farms using inappropriate farming methods and population pressure (Tadesse ; Belay, 2004). The major cause of land degradation being soil erosion. Soil erosion occurs in the highlands of Ethiopia in areas above 1500 m.a.s.l which constitutes 45% of the whole country (Tadesse ; Belay, 2004) and supports 80% of the population with 95% of the cultivated land and 75% of the livestock population (Shiferaw and Holden, 1998).
Reversing land degradationDuring the early agrarian periods, soil organic matter could be regenerated by the use of periodic fallows in the course of shifting cultivation (Snapp et al., 1998). However, due to increased population which translated to reduced land for cultivation, shifting cultivation and fallows are no longer possible (Nkamleu, 1999). Therefore, methods that assist in reversing land degradation and increasing soil organic matter should be practiced (Gockowski et al. 2000). Monique and Sasha (2016) recognizes the need to stop and reverse land degradation trends, while using hands on and cost-effective methods, such as the adoption of sustainable land management (SLM) practices.
Land degradation can be reversed through the adoption of the most suitable soil and water conservation (SWC) practices with significant incentives for effective implementation and the availability of adequate policies (Koning et al., 2001). Lal (2015), documents that soil degradation trends which includes depletion of soil organic carbon can be reversed by the use of recommended land management practices. Increasing soil organic carbon pool consequently improves soil quality and therefore reducing the risks of land degradation (Lal, 2015).

Measures such as conservation agriculture, ensuring continuous vegetation cover such as planting cover crops and residue mulching, integrated soil fertility management, and controlled grazing at appropriate stocking rates on a specific site lead to increase in SOC and restoring soil quality (Lal, 2015). Montgomery (2007), states that residues, which forms the organic matter part of the soil, once left on the ground surface as mulch enhances water infiltration reducing runoff, erosion and the residues later forms the organic carbon part of the soil. Agricultural practices such as no-till agriculture or minimum tillage, and organic farming can help to reduce soil loss and restore soil fertility (Lal, 2010; Carr et al.,2013)
Increasing soil organic carbon content in the soilResearch has documented that management practices help in reducing erosion and restoring soil carbon (Franzluebbers, 2010). In addition to preventing soil degradation, these practices restore soil carbon stock leading to enhanced soil carbon sequestration potential (Silver et al., 2007). These practices are not limited to contours, plantation of xerophytic plants, controlling livestock to avoid overgrazing and liming to regulate soil pH in these acidic soils.

Several soil and crop management technologies are known to increase soil carbon sequestration. Dahal and Bajracharya, (2010) documents these technologies as no-till (NT) farming with residue mulch and cover cropping, integrated nutrient management (INM), balancing nutrient application with the use of organic manures and inorganic fertilizers, crop rotations (including agroforestry), use of soil amendments (biochar and compost), and improved pastures with controlled stocking rates.

Sustainable land management practices in Kenya and Ethiopia
In sustainable land management the best-known practices are divided into three principles which are: improve livelihoods, increase productivity and improve ecosystems (Liniger et al., 2011). The adoption of Sustainable land management practices in SSA started during colonization era and continued after as the government put efforts to reduce erosion by using soil and water conservation measures. For example, in Lesotho, the highlands had been protected by buffer strips by 1960; in Malawi approximately 118 000 km of bunds had been constructed on 416 000 ha by the period between 1945 and 1960; and in Zambia, half of the native land in the eastern province had been protected by contour strips by 1950 (Beinart, 1984). As explained in Pretty et al. (1995), Kenya has a long history of government intervention in SWC and land management. Since 1930’s, the colonial government had already introduced these measures but they were not easily absorbed as they were so tainted by forced labor that no administrator dared to popularize it (ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “ISSN” : “0044-7447”, “abstract” : “For some 50 years, conservation programs in Kenya have produced only patchy and unsustainable conservation of soil and water resources. In 1988, the Ministry of Agriculture adopted the Catchment Approach. At first seen as a way of concentrating technical effort, this approach has evolved to be interdisciplinary and community mobilizing. Conservation coverage must be complete if it is to be sustainable, and this will only happen if there is the full participation of all actors at a local level. Catchment committees articulate local priorities and provide a link with external agencies. A self-evaluation conducted in six districts found the most significant impact where there has been interactive participation. In these communities, crop yields are increasing, farmers are growing a greater diversity of crops, there are more trees and ground cover, groundwater resources are being recharged, land prices and labor rates are increasing, and communities are actively replicating successes to neighboring communities.”, “author” : { “dropping-particle” : “”, “family” : “Pretty”, “given” : “Jules N”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Thompson”, “given” : “J”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Kiara”, “given” : “J K”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Ambio”, “id” : “ITEM-1”, “issue” : “1”, “issued” : { “date-parts” : “1995” }, “page” : “7-15”, “title” : “Agricultural regeneration in Kenya: the catchment approach to soil and water conservation”, “type” : “article-journal”, “volume” : “24” }, “uris” : “”, “” } , “mendeley” : { “formattedCitation” : “(Pretty, Thompson, & Kiara, 1995)”, “manualFormatting” : “Pretty et al., 1995)”, “plainTextFormattedCitation” : “(Pretty, Thompson, & Kiara, 1995)”, “previouslyFormattedCitation” : “(Pretty, Thompson, & Kiara, 1995)” }, “properties” : { }, “schema” : “” }Pretty et al., 1995). It has been noted that, the health and resilience of our ecosystems is highly influenced by the management practices that we adopt as well as the environmental changes (Monique and Sasha, 2016).

Recent studies indicate that SLM practices, are able to boost yields by improving food security and preventing land degradation in future (Branca et al., 2013). Snapp et al. (1998) documented that land management practices must be highly effective in the farmers’ land so as to enhance food security, reduce risks and increase soil fertility.

Factors affecting the adoption of sustainable land management practicesThe farmers decision to adopt or reject to new land water management technologies at any given time and space is influenced by a set of biophysical and socioeconomic factors ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “author” : { “dropping-particle” : “”, “family” : “Tadesse”, “given” : “M”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Belay”, “given” : “K”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “id” : “ITEM-1”, “issue” : “1”, “issued” : { “date-parts” : “2004” }, “page” : “49-62”, “title” : “Factors Influencing Adoption of Soil Conservation Measures in Southern Ethiopia : The Case of Gununo Area”, “type” : “article-journal”, “volume” : “105” }, “uris” : “”, “”, “” } , “mendeley” : { “formattedCitation” : “(Tadesse & Belay, 2004)”, “plainTextFormattedCitation” : “(Tadesse & Belay, 2004)”, “previouslyFormattedCitation” : “(Tadesse & Belay, 2004)” }, “properties” : { }, “schema” : “” }(Tadesse ; Belay, 2004). These factors vary from one region to another and therefore, the adoption rate of these practices varies. According to Rezvanfar et al., (2009), there are numerous factors that determine the farmers’ adoption to any land management practice. These factors are divided into two categories which include the farmers characteristics e.g. age, education, farming experience, level of knowledge, access to information etc. and farm characteristics such as plot size, land tenure, income etc. A study carried out in Cameroon on the status of adoption of agroforestry practices and factors that affect adoption (Nkamleu & Manyong, 2005), identified gender of farmer, household family size, level of education, farmer’s experience, farmers’ groups membership, access to extension, security of land tenure, agro ecological zone, distance to nearest town/market, and income from off farm sources as some of the factors affecting its adoption. A study carried out in Brazil ( HYPERLINK “” l “!” De Souza Filho et al., 1999) showed that there was a high probability for a farmer to adopt to a sustainable agricultural practice if they were involved in farmers’ organizations, had links with nongovernmental organizations, had sufficient family labor, and their farm was located in an area with favorable soil conditions. Marenya ; Barrett (2007) found out that the adoption of sustainable land management practices aimed at increasing soil fertility and managing natural resources are limited by resource constraints such as size of the farm owned by a household, livestock value, off-farm income, family labor supply, educational attainment and gender of the household head.
Trade-offs in agricultureDecision making involves the selection of various alternatives and in the process includes trade-offs in our day to day operations and in agriculture (Barrowclough, 2016). Klapwijk et al. (2015) describes trade-offs as exchanges that occur as compromises and are pervasive especially when land is managed with multiple value chains. Trade-offs are known to be very consistent when resources are limited and when the stakeholders’ goals conflict (Giller et al., 2008).
In agriculture, there are various types of trade-offs (Erenstein, 2015). Examples include trade-offs associated with the crop (grains or residues), (milk or meat), the field (food production or groundwater water quality associated with nitrate leaching), the farm (production of different competing crops) and the landscape (agricultural production versus land for nature (Klapwijk et al., 2015). The key determinants of trade-offs and adoption are divided into either agronomic or environmental impacts of practices or technologies (Tittonell et al., 2015). Agronomic impacts include the effort to increase yields while environmental impacts include efforts to reduce degradation. Rainfed areas are known to experience scarcity of natural resources and therefore, small-scale crops and livestock farmers face many trade-offs associated with crop residue biomass (Tittonell et al., 2015). These include what percentage of residues should be fed to livestock as fodder, increase soil fertility, as fuel and in some cases as construction material or sold for profit. Gauny (2016) documents that biomass trade-offs influence farmers’ decisions and they can translate into the adoption land management practices associated with retention of crop residues on farms.

Extent and intensity of adoption of sustainable land management practicesSharma et al. (2011), interpreted the number of technologies adopted as a measure of the intensity and the diversity of adoption. In studying intensity of adoption of land management practices Lohr and Park (2002) and Isgin et al. (2008) noted the relationship of various technologies and also that there is no limit to the number of technologies adopted as long as the last one adopted is profitable. Extent is based on the size of the area covered by a chosen practice. At the farm level it is the proportion of the land covered by a SCELMP in which a farmer decides to adopt based on some biophysical and socioeconomic factors.
Study areaThe study will be conducted in both Kenya and Ethiopia. In Kenya, the study will be carried out in western Kenya region focusing on two counties; Vihiga and Kakamega, while in Ethiopia it will be carried out in the Yasir and Azuga Suba watersheds.

Western Kenya
The study areas in Kenya which will be Kakamega and Vihiga counties are located in the western parts of Kenya North of the equator. This is as shown on figure 3.1 below.

Figure 3.1: the location and sub-counties of Kakamega and Vihiga counties (Source: IEBC 2014)
Vihiga County
Vihiga County lies between longitudes 340 30′ and 350 0′ E and latitudes 00 and 00 15′ N and borders Siaya County to the West, Nandi County to the East, Kakamega County to the North, and Kisumu County to the South. The County has 5 sub-counties, 25 wards and 129 villages. The altitude of the county ranges between 1,300 m and 1,800 m above sea level and slopes gently from west to east. The county covers a total area of 531 km2 with the equator cutting across the southern part of the County. It receives an annual rainfall range of between 1,800 and 2,000 mm with a mean of 1900mm. The temperature ranges between 14ºC to 32ºC, with a mean of 23ºC.
The soil types of this county are comprised of Acrisols especially Haplic Acrisols. Traces of Nitisols and Ferralsols are also found in the county based on FAO soil taxonomy classes.

The County is categorized into two agro-ecological zones, the upper and lower midlands. The upper midland zone comprises of Hamisi, Sabatia and parts of Vihiga Sub counties. The lower midland zone comprising Emuhaya and Luanda Sub counties has mainly the red loamy sand soils derived from sedimentary and basalt rocks.

According to 2009 National Census, the county has a population of about 554,622 with 262,716 males and 291,906 females and a population density of 1,045/km². Agriculture is the main economic activity and contributes about 64 per cent to the County’s income. Crops planted include tea, maize, beans millet, bananas, avocado, papaya, sweet potatoes, cassava, and mushroom. Livestock rearing especially dairy and zebu cattle and poultry is also practiced in the county. Due to continuous subdivisions of land, farm sizes have shrunk over the years with the current status seeing the average farm size owned by a farmer as 0.5 hectares (Marenya ; Barrett, 2007).

It has a good amount of forest cover covering an approximate area of 783.57 km² such as Kibiri forest and Maragoli forest, which is an extension of Kakamega forest.
Kakamega County
Kakamega County lies along longitude 340 45′ 0′ E and latitude 00 and 00 17′ N and borders the Counties of Bungoma to its North, Vihiga to the South, Busia to the West, Nandi and Uasin Gishu to the East, Trans Nzoia to its North East and Siaya to the South West. The county administrative units comprise 12 Sub-counties, 60 wards, 187 Village Units and 400 Community Areas. The altitude of the county ranges from 1,240 to 2,000 metres above sea level. The southern part of the county is hilly and is made up of rugged granites rising in places to 1,950 metres above sea level. The Nandi Escarpment forms a prominent feature on the county’s eastern border, with its main scarp rising from the general elevation of 1,700 metres to 2,000 metres. The County covers an area of about 3,225 square kilometres. It receives an annual rainfall range of between 1280 to 2214 mm with a mean of 1747 mm. The rainfall pattern is evenly distributed all year round with March and October receiving heavy rains while December and February receive light rains. The temperatures range from 180C to 290C, with a mean of 240C. The county has an average humidity of 67 percent.

The soil types of this county are comprised mainly of Acrisols in the main area and Ferralsols in Lugari and Likuyani sub counties. Traces of Nitisols and Cambisols are also found in the county based on FAO soil taxonomy classes.

There are two main agro-ecological zones in the county namely; the Upper Midland (UM) and the Lower Midland (LM) zones.
According to 2009 National Census, the county has a population of about 1,660,651 consisting of 797,112 males and 863,539 females and a population density of 544.3/km². Average land sizes have reduced to between 0.5 and 2 acres due to subdivisions.

Agriculture is the main economic activity and contributes about over 65% of the total earnings of the County and over 80% of employment. Crops planted include maize, beans, millet, sweet potatoes, cassava, bananas, avocado, papaya, tea, and mushroom. Livestock rearing especially dairy and zebu cattle and poultry is also practiced in the county.

EthiopiaThe study areas in Ethiopia will be Yasir and Azuga Suba watersheds. Their locations are as shown on figure 3.2 below.

Figure 3.2: the locations of Yasir and Azuga Suba watersheds
Yasir watershed
Yasir watershed is found on the Mirab Gojjam zone of the Amhara region (also known as West Gojjam Zone) of Ethiopia. It lies between longitudes 37° 3’47.32″E and 37° 6’38.20″E and latitudes 10°48’0.83″N and 10°35’45.58″N. Yasir watershed covers an area of about 115.8km2.

Mirab Gojjam is bordered on the north by Lake Tana and the Abay river which separates it from the Debub Gondar, on the south by the Abay River which separates it from the Oromia region and Benishangul-Gumuz region, on the west by Agew Awi, on the northwest by Semien Gondar, and on the east by Misraq Gojjam. It receives an annual average of about 1500mm an average temperature of 20ºC.

The major soil types of the area are mainly Vertisols to the South of the watershed, Luvisols to the North of the watershed and Leptosols. Nitisols are also found in small traces to the south of Wan based on FAO soil taxonomy classes. The main agroecological zone of Yasir watershed is Tropic cool-sub humid zone.

According to the 2007 census conducted by the Central Statistical Agency of Ethiopia (CSA), this zone has a total population of 2,106,596 where, 1,058,272 are men and 1,048,324 women; with an approximate area of 13,311 square kilometers, Mirab Gojjam has a population density of 158/km².
According to the World Bank (2004), on average the rural household has 1.1 hectare of land (compared to the national average of 1.01 hectare of land and an average of 0.75 for the Amhara Region) and an equivalent of 0.7 heads of livestock. 19.5% of the population is in non-farm related employment, compared to the national average of 25% and a Regional average of 21%.

Azuga Suba
Azuga Suba watershed is found in the Kembata Tembaro zone of the Ethiopian Southern Nations, Nationalities, and Peoples’ Region (SSNPR) with a small part of it lying in the Hadiya zone to the North. Kembata Tembaro zone is bordered on the north by Gurage, on the south by Wolayita, on the southeast by an exclave of the Hadiya Zone, on the southwest by Dawro, on the northwest by Hadiya, and on the east by the Alaba special woreda. It lies between longitudes 37°53’46.75″E and 37°50’30.52″E and latitudes 7°25’51.42″N and 7°15’57.34″N. Azuga Suba watershed covers an area of about 88.7 km2.
Kembata Tembaro is located at about 260 km South of Addis Ababa and 2 km south to Angacha town. It receives an annual average rainfall of about 1656 mm. The mean annual maximum temperature is 24°C and monthly values range between 23 and 24 0C. The mean annual minimum temperature is 14°C and monthly values range between 13 and 14°C. The coldest months are June and August, whereas February is the hottest month.
The major soil types of the area are mainly Vertisols to the North and Luvisols to the south of the watershed. Other types found in the area based on FAO soil taxonomy classes include; Nitisols and andosols. There are two main agro-ecological zones in the watershed namely; Tropic cool-humid zone which covers the larger part of the watershed and Tropic cool-sub humid zone at the northern most part of the watershed.

According to the 2007 census done by the Central Statistical Agency of Ethiopia (CSA), this zone has a total population of 1,080,837, where, 536,676 are men and 544,161 women. Kembata Tembaro Zone has a population density of 502.13/km2.
According to the World Bank (2004), on average the rural household has 0.6 hectare of land (compared to the national average of 1.01 hectare of land and an average of 0.89 ha for the SNNPR) and an approximate of 0.5 heads of livestock. 10.7% of the population is in non-farming related employment, compared to the national average of 25% and a Regional average of 32%.

Determination of Sample sizeThe principal objective of sampling is to enable a researcher obtain a subset of a population from which to obtain information (Nyariki, 2009). This sample is what will be used to generalize about the entire population of the study area. However, this will only be possible and accurate if the sample is a representative of the target population (Nyariki, 2009). It should be noted that, the distribution of the population affects the sampling design to be adopted during the study and therefore, should be put into consideration while calculating the sample size. Mugenda and Mugenda (2003) caution that time and resources available for the researcher should also be considered. The following formula will be used to determine the sample size for the research in Western Kenya, Vihiga and Kakamega counties and Ethiopian Yasir and Azuga Suba watersheds. The equation is based on Berenson,, (2006). This formula estimates population proportion with infinite population.

n0 = Z2pqe2
Where: n0 is the sample size, Z is standard score based on the assumed confidence level,
(which is 1.96 for commonly used 95% confidence interval), e is the margin of error (in decimal), p is the assumed proportion in decimal, and q is 1-p.

In this study z, p, q, and e are assumed to be 1.96, 0.5, 0.5 and 0.08 respectively based on the assumption that at least half of the population in the study area has adopted the SLM practices. Using these values gives 150 respondents.
This will be taken as 150 for each area i.e. for Vihiga county, Kakamega county, Yasir watershed and Azuga Suba watershed. To cater for possible errors associated with data collection, an additional 10 respondents will be added to the calculated sample. Therefore, the sampling size will be taken as 160 for each area i.e. Vihiga, Kakamega, Yasir and Azuga Suba. This will therefore give a total of 320 respondents for Kenya and 320 respondents for Ethiopia.

Sampling Procedure
The study will adopt both purposive selection and multi-stage random sampling. The first stage will involve the purposive selection of 2 counties in Kenya i.e. Vihiga and Kakamega and 2 watersheds in Ethiopia which are, Yasir and Azuga Suba. The multi-stage sampling procedure will be applied as follows: In Kenya, the first stage will involve the selection of 5 sub-counties from Kakamega county to form primary sampling units. In Vihiga county all the sub counties (5 in number) will be studied.
The second stage will involve the selection of 3 wards from each selected subcounty and 4 wards in the largest sub county of the selected sub counties will have 4 wards. The wards will be selected based on geographical distribution to ensure proper coverage of the area. Using simple random sampling method, 10 farm households will be selected from each ward to reach 160 farmers per county.
In Ethiopia, a similar procedure will be used with the first stage involving random selection of Pastoral areas (PAs) followed by a random selection of villages from each selected PA. The number of sample households will be selected randomly from an existing list of farmers. The list will be obtained from the local agricultural administrators. This will be a representative of the biophysical and socioeconomic conditions experienced by the communities in these watersheds.

Soil organic carbon enhancing land management practices
The practices that will be considered for this study include manure/compost, intercropping, fertilizer application, mulching/residues, agroforestry, minimum tillage, terraces and grass strips. These are considered as practices which enhance soil organic carbon based on Lal (2006) and Ingram et al., (2014).

Data Collection
Both primary and secondary data will be collected.
Primary data will be obtained using a structured questionnaire, with both open and closed questions (See appendix 1). The data to be collected will be on: demographic information, socio-economic attributes of the respondents’, land degradation in this case soil erosion, land management practices employed by farmers, residue and labour trade-offs.

Focus group discussions (FGDs) will be carried out to help the researcher understand the social realities of the farmers in the study areas. The FGDs will involve approximately 20 farmers for each FGD with both sexes involved. From the selected 5 sub counties, 4 farmers 2 male and 2 females will be selected with each expected to come from different wards. An FGD questionnaire will be used during this set up (See appendix 2). Probing questions will be asked at the end of every FGD to reveal more in-depth information or to clarify earlier responses.
Secondary data will be obtained from literature review and existing data collected and analysed from previous researches. This data will include community level information of the study area as well as GIS layers of SOC stocks, slope, elevation, climatic data, distance to markets, roads and social infrastructure, education level, wealth etc. The GIS data will be used in desktop modelling of factors affecting the adoption of soil carbon enhancement technologies by comparing the values of these factors with the existing SOC stocks at a fine resolution.
Analysis of data
The study proposes that a farmer given a set of soil carbon enhancing land management practices (SCELMPs) has to decide which SCELMP to give a higher priority while selecting the practices to adopt on their farm. This decision is affected by various biophysical and socioeconomic activities. In most cases, a farmer will divide his farm into plots. During data collection, the proportion of a plot under each SCELMP will be obtained. With the size of the plots known, the area of each plot under a specific practice will be obtained. The area of the plots under a specific practice will be summed to come up with the area of the whole farm under the practice. This proportion of the farm area under a specific SCELMP will be the extent of adoption of the practice on that farm. In Kenya during the study, an administrative ward randomly selected from a sub-county will have 10 randomly selected farms/households representing the area. To represent the extent of adoption of the selected SCELMPs, the proportion of land under the SCELMP will be considered in the case of a specific farm. To make assumption for the selected sub-county, the areas of the representative farms will be taken and their total area computed. Similarly, area under each SCELMP under study will be calculated for each farm and total area computed for each SCELMP. The extent will then be calculated as the percentage of the total farm area for each SCELMP. In Ethiopia, a similar method to represent the extent of adoption will be carried out for specific farms and at the PA level. Using GIS techniques, the extent to which farmers have adopted to SCELMPs will be spatially represented on a map by the use of charts and graphs showing the proportion of each farmers land under various SCELMPs. This will be followed by a continuous map representation of the total proportion of the area covered by each SCELMP at the sub-county level in the case of Kenya and PAs in the case of Ethiopia.

Key socio-economic and biophysical constraints to adoption of soil carbon enhancement technologies will be obtained by employing the Multivariate probit model (MVP). An MVP model will be used to analyse the interdependent investment decisions of the SCELMPs by small-scale farmers. Investment decisions by small-scale farmer are naturally multivariate, therefore, the most suitable modelling procedure must in its place consider the interactions and possible simultaneity of the investment decision and so, it should not be univariate. This is because, there is a higher probability of farmers investing in a combination of technologies than in a singular technology in order to cope with multiple agricultural production constraints (Kassie et al., 2013).

The MVP model models the effects of the set of explanatory variables on each of the various practices concurrently, while allowing the errors to be freely correlated (Greene, 2008). In comparison to MVP models, univariate probit models normally ignore the potential correlation among the unobserved disturbances in the investment equations as well as the relationships between the investments of different SCELMPs. Farmers might consider a combination of practices as complementary and others as substitution. Failure to capture unobserved factors and inter- relationships among investment decisions regarding different practices will lead to bias and inefficient estimate (Greene, 2008).

The MVP econometric model is described by a set of binary dependent variables Yij. The model is specified as follows:
?*ij = Xij? + ?ij j = 1,……, m and (1)
?ij = 1 if ?*ij;0 0 0therwise (2)
Where: ?ij for j=1, 2,…, m represents an unobserved latent variable of the SCELMPs j invested by farmer i, X is a matrix of independent variables reflecting plot specific and household characteristics, ? is a vector of parameter estimates and ?ij are error terms. Error terms have a standard normally distribution with mean vector zero and a covariance matrix with diagonal elements equal to 1.
The dependent variables (SCELMPs) in the econometric analysis will be manure/compost, intercropping, fertilizer application, mulching/residues, agroforestry, minimum tillage, terraces and grass strips.
The most significant factors affecting farmers’ adoption of land management practices will be obtained from the analysis of the data that will have been obtained from household surveys using multivariate Probit (MVP) model. Therefore, mapping of factors that constrain farmers adoption of SCELMP will be done based on the most significant factor per area represented by the sample farms in the household survey at a subcounty level in Kenya and PA level in Ethiopia.
Desktop modelling using GIS
To support these findings, GIS layers of SOC will be reclassified to high, medium and low carbon stock levels. These classes will be used in calculating the values of biophysical and socio-economic data related to field survey’s findings within the classes (zones) of the SOC dataset so as to bring about relationships existing in these areas using the zonal statistics tool. The biophysical and socioeconomic data will be in GIS layer formats. Statistical data per selected class and using administrative wards will be obtained for further analysis and conclusions. These will be such as mean, maximum, minimum, sum, standard deviation etc.
To further support these findings spatial autocorrelation will also be done using Local Moran’s I statistic. Spatial autocorrelation in GIS assists us in understanding the degree to which one object is similar to other objects in its vicinity. Moran’s I (Index) determines spatial autocorrelation. Moran’s I (Moran, 1950) examines spatial autocorrelation of continuous spatial data. Based on cross-products of the deviations from the mean it is calculated for n observations on a variable x at locations i, j as:
I = nS0ijwij(xi-x)(xj-x)i(xi-x)2 ‘
Where x is the mean of the x variable, wij are the elements of the weight matrix, and S0 is the sum of the elements of the weight matrix: S0=ijwijMoran’s I is similar to the correlation coefficient nonetheless, it is not equivalent. It diverges from -1 to +1. Without autocorrelation and irrespective of having the specified weight matrix, Moran’s I statistic expects , which inclines to zero with the increase in sample size. For a row-standardized spatial weight matrix, the normalizing factor is equal to (since each row adds up to 1), and the statistic simplifies to a ratio of a spatial cross product to a variance. A Moran’s I coefficient greater than indicates positive spatial autocorrelation, and a Moran’s I smaller than indicates negative spatial autocorrelation. The variance is:
Var (1) = nn2-3n+3S1-nS2+3S02-k{nn-1S1-2nS2+6S02(n-1)(n-2)(n-3)S02 – 1(n-1)2Where,
S1 = 12i?j(wij+wij)2 = 2S0 for symmetric w containing 0’s and 1’s.

S2 = 12(wi0+w0i)2 Where wi0= jwij and w0i= jwij
The method that will be used will be the Bivariate local Moran’s I, which will be run on the GeoDa software testing two variables; the dependent variable which will be Soil organic carbon layers and the independent variables representing the factors that are anticipated to affect SOC stocks.
ValidationAfter the mapping of biophysical and socioeconomic factors and mapping of other factors in relation to the modelling using GIS, validation will be required so as to support these findings. This will involve participatory mapping involving farmers and key informants of the study areas to ascertain the accuracy of the maps and also to help include any additional information that might have been missed in the field surveys.

3 journal papers
One data paper
WORK PLANJune July Aug Sep Oct Nov Dec Jan Feb March April May
Proposal writing Proposal presentation Field work for data collection Data analysis Mapping of barriers and tradeoffs Field work for Validation Thesis writing
Activity Description Quantity Cost per unit Amount
Focus group discussions in Western Kenya (Vihiga and Kakamega) Transport to and fro Nairobi to Vihiga and Kakamega 1(return) 4000
Subsistence 6 days 1500 8000
Accommodation 5 days 2000 10000
Car hire 2 days 7000 14000
Venue (with breakfast and lunch) 2 days 45000 90000
Farmers transport reimbursement 40 1500 60000
Sub Total 186000
Data collection (Western Kenya) Transport to Western Kenya to and fro 2 2000 4000
Subsistence 12 days 1500 18000
Accommodation 12 days 2000 24000
Airtime 12 days 500 6000
Field guide/ local Agric officer 12 days 1000 12000
Enumerators 2 sites each 6 days 5 4500 270000
Car hire (fuel inclusive) 14 days 7000 98000
Sub Total 432000
Data collection for Yasir and Azuga Suba watersheds in Ethiopia Enumerators for 2 sites each 8 days 6 3000 288800
Car hire 20 days 8000 160000
Fuel 60900
Air fare to Addis Ababa return ticket 2 11000 22000
Trip to the airport both ways in Kenya 2 4000 8000
Trip to the airport both ways in Ethiopia 2 4000 8000
Accommodation for 25 days 1 3000 75000
Subsistence for 26 days 1 2000 52000
Sub Total 674700
Internet 12 3000 36000
Total 1328700
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The Department of LARMAT, University of Nairobi in collaboration with CIAT are conducting a research on Soil Carbon Enhancement Practices (SCEP) Western Kenya; Vihiga and Kakamega Counties and Ethiopia; Yasir and Azuga Suba watersheds. This questionnaire is meant to collect data on SCEP and impact of adopting the practices. The respondents for this survey shall be the main decision makers regarding production and other agricultural activities in the household who must be at least 18 years old. Participation in this survey is voluntary. Information obtained is strictly for academic and research purposes only and responses obtained will be treated with confidentiality. This interview is voluntary and will take approximately 1-1.5 hours. Your participation will be highly appreciated.

Informed Voluntary Consent to Participate in Research Study
By signing this form, I agree that;
I am voluntarily taking part in this survey. I understand that I don’t have to take part, and I can stop the interview at any time;
I don’t expect to receive any benefit or payment for my participation;
I have been able to ask any questions that I have, and I understand that I am free to contact the researcher with any questions I may have in the future.
I agree to participate in this research (tick one box) FORMCHECKBOX Yes FORMCHECKBOX No_________

______________________________ _________________________________ ________
Name of Participant Signature of Participant Date
______________________________ _________________________________ ________
Name of Researcher Signature of Researcher Date
Section 1: General information
Date____/______/_____ (Date/Month/Year)
Enumerator’s Name ________________
Interview: start time_______________
Section 1: Site characteristics and GPS coordinates
1.4Sub-location _______________
1.5Village name_______________
1.7Name of PA_______________
1.9Name of the village /Gotte/ _______________
GPS coordinates:
Northing: _____________ Easting: ______________ Altitude _____________ masl
Section 2: Household respondent
2.1Name of the household head________________ _________________
2.2Year of birth of the Household head_______________
2.3What is the level of education of the household head? _________
Codes: 0=No Education1=Primary, 2=Secondary, 3= Technical/Vocational Training, 4=University

2.4Genderof the household head_______________ 1=Male. 2= Female
2.5How many years has the household head been involved in farming ___________ years?
NB: Question 2.6 to 2.9 need to be answered if and only if the household head is NOT the respondent
2.6Name of the respondent _____________ _________________
2.7How many years have you been involved in farming ___________ years?
Section 3: Household demographic characteristics
3.1 How many people including you live and eat in this household? Please provide us with details to fill the following table
3.1.1 3.1.2 3.1.3 3.1.4 3.1.5 3.1.6 3.1.7 3.1.8
No. list name of HH members (first name only) What is “__” relationship to HH head (See Codes) Gender 1=Male, 2=Female Age (years) Level of education? (see Codes) Occupation (See Codes) Does the person participate in Farming activities (Provide Labour)?
0= No
Codes for: Relation to HH Codes for: occupation Code for: education level
1=Wife / Husband
2=Son/ Daughter
4=Brother/ Sister
1=Farming (Crop and Livestock)
2=Employed (Informal Sector)
3=Employed (Formal Sector)
6=Others…Specify 0=No Education
3= Technical/Vocational Training
3.2 What is the distance in walking minutes of the household to the infrastructure listed in the table below: (fill in yourself if you know, ask only if appropriate)
3.2.1 3.2.2
Infrastructures Distance from the household in walking minutes to
Motorable road Tarmac road Local market Nearest livestock market Nearest urban market Nearest electricity Nearest clinic 3.3 Please provide us with the following information about your household to enable us fill this simple wealth scorecard
3.3.1 3.3.2 3.3.3
Indicator Values Points
How many people in the family are aged 0 to 17? 5 or more 3 or 4 1 or 2 zero 0 7 16 27 Does the family own a gas stove or gas range? No Yes 0 13 How many television sets does the family have Zero 1 2 or more 0 9 18 What are the house’s outer walls made of? Mud, bamboo, sticks iron, aluminum, concrete, brick, stone, wood, asbestos 0 4 How many radios does the family own? Zero 1 2 or more 0 3 10 Does the family own a sofa set? No Yes 0 9 What is the house’s roof made of? Light (Salvaged, makeshift Strong (Galvanized iron, aluminum tile, concrete, brick, stone, or asbestos) 0 2 What kind of toilet facility does the family have None, open pit, closed pit, or other Water sealed 0 3 Do all children in the family of ages 6 to 11 go to school? No Yes No children ages 6-11 0 4 6 Do any family members have salaried employment? No Yes 0 6 Section 4: Plot characteristics, perception on soil erosion and fertility, yields and inputs
4.1Land access of the household and Plot Characteristics
4.1.1 4.1.2 4.1.3 4.1.4 4.1.5 4.1.6 4.1.7 4.1.8 4.1.9 4.1.10 4.1.11
Plot ID Plot Description Plot size/area
(acres) Tenure system (See Code) Who manages the plot (See Code) If rented, rent value (KSh/year) Distance from the house in to the plot (in walking minutes) Do you perceive the plot to be fertile?
1= Yes 0= No Plot Slope (See Code) Soil type (See Code) Perceive soil erosion as a problem
1= Yes 0= No
Plot description: 1= Homestead, 2= Cash crop, 3= Food crop, 4= Fodder crop, 5= Grazing land, 6=Others, please specify ______
Tenure system: 1= Owned with title, 2= Owned without title, 3= Communal/public, 4= Rented in, 5= Rented out
Who manages the plot: 1= HH head, 2= Spouse, 3= Joint (HH head ; spouse), 4= Other male, 5= Other female, 6= Others, please specify ______
Plot Slope: 1=flat, 2=moderate steep, 3=very steep
Soil type: 1=Clay, 2=Loam, 3=Sandy
Note hectare = 2.471 acres
4.2 Do you see any importance of implementing soil management practices that improves soil fertility in any of the plot ____ (1= Yes 0= No).
If the answer is yes in question 4.4 above, please indicate the specific plot where you deem it necessary to
implement soil fertility enhancing practices (including providing the reason why) in the Table below
4.2.1 4.2.2 4.2.3 4.2.4 4.2.5 4.2.6
Soil enhancing practices
(use Practice codes below) On which plots (use plot IDs in 4.1) is this practice implemented? What is the plot proportion under the (4.4.1) practices (In percentage) How many years have you been implementing this practice What is the reason for implementing this practice?
(use code A) Which are the main challenges associated with implementing this practice
(use code B)
Practice: 1=Agroforestry, 2=Terraces, 3=Minimum tillage, 4= Mulching, 5=Intercropping, 6=Improved Fallowing, 7=Grass Strips, 8=Farm Yard Manure, 9=Inorganic Fertilizer, 10= Maize with Legumes.
A: Reasons for implementing the practice: 1=Increase fertility, 2=Enhances sequestration of soil carbon, 3=Was recommended by extension officers, 4= Because neighbours are using it, 5= Reduces soil erosion, 6=Conserves water, 7=Was recommended within my social groups, 9=Others, please specify _________
B: The main challenges 1=Labour requirements, 2=Competition for straw with animals, 3=Expensive inputs, 4=Lack of knowledge, 5=Limited access to input market, 6=Lack of capital, 7= Others, please specify _________
4.3 For the different plots on your farm, could you please indicate the main crops, their yield per year (by seasons) in the Table below
4.1.1 4.3.1 4.3.2 4.3.3 4.3.4 4.3.5 4.3.6
Plot ID The main crop(s) or crop names % occupied by the crop How much quantity was harvested during the 1st season 2017 (use yield codes) How much quantity was harvested during 2nd season in 2017 (use yield codes) What proportion of residue was left in the plot
For what purpose was the rest of the residue used for (use residue purpose codes)
Quantity Unit (See Codes) Quantity Unit (See Codes)) (In percentage) (See Codes)
unit: 1=90kg bag, 2=70kg bag, 3=50kg bag, 4= others, please specify _____
Season 1 is equivalent to long rain season
Season 2 is equivalent to short rain season
Residue: 1=Animal Feed. 2=Fuel, 3=Cooking Vegetable, 4=Soil fertility enhancement, 5 Others, please specify _____
4.4 Did you sell any produce from the last cropping season (2017)? _____ (1=Yes, 0=No).

If yes proceed fill the table below if NO skip to question 5.1
4.4.1 4.4.2 4.4.3
Crop names Please indicate the quantity of crop yield that was sold
(Use the unit codes) What is the price in (KSh) per unit (use the unit codes below)
Unit: 1=90kg bag, 2=70kg bag, 33=50kg bag, 4= others please specify _____
4.5 For the different crops grown on the farm could you please indicate the if you use the following inputs and their costs, per year in the Table below
4.1.1 4.5.1 4.5.2 4.5.3. 4.5.4 4.5.5 4.5.6 4.5.7 4.5.8 4.5.9
Plot ID Did you use inorganic fertilizer on this plot last year?
1= Yes 0= No Please specify the fertilizer type (use codes) Specify the quantity of fertilizer that you use (see codes for markets below) Source of fertilizer (use Fertilizer source code) Price per Unit (if bought) Did you use manure on this plot in the last one year?
1= Yes 0= No How much manure did you use? (see codes for manure unit below) Source of Manure (use manure source code) Price per unit of manure
Qty unit Qty unit Codes:
Fertilizer Type: 1=CAN, 2=NPK, 3=DAP, 4=Urea, 5=Liming, 6 others, please specify _____
Fertilizer Unit: 1=50kg bag, 2= 25 Kg 3=10Kg 4= others, please specify _____
Fertilizer Source: 1=NGO’s, 2=Government, 3= Agrovet Store 4= others, please specify ____
Manure Source: 1=Own farm, 2, Purchased, 3= others, please specify _____
Manure Unit: 1=Debe, 2= Wheelbarrow, 3= Bucket, 4= Suck, 5= others, please specify _____
4.6 Where do you source your labor for farm activities? ______ use the codes below
Codes 1= Family labor only; 2=Hired Labor only; 3=Family and Hired Labor.
4.7 Do you provide labour to other farms during the start of the season before working on your farmland for the purpose of getting income? ____ (1= Yes 0= No)
4.8 Do you own any livestock? ______ (1= Yes 0= No)
If YES fill the table. IF No move skip to Section 5
4.8.1 4.8.2 4.8.3
Type of Livestock (use the livestock type codes below) The number of Livestock owned. Purpose for Keeping Livestock (Use purpose code below)
Livestock Type: 1=Cattle, 2=Goat, 3=Sheep, 4=Donkey, 5=Poultry 6= others, please specify _____
Purpose: 1=Source of Food, 2=Source of Income, 3= Source of Manure, 4= Form of Saving (Asset), 5= others, please specify _____
4.9 Did you sell any of you Livestock in the last 12 months? ______ (1= Yes 0= No)
If Yes fill the table below. IF No Skip to Section 5
4.9.1. 4.9.2 4.9.3
Type of Cattle Sold (Use of the livestock type codes below) The number Sold Unit Price in KSHs
Livestock Type: 1=Cattle, 2=Goat, 3=Sheep, 4=Donkey, 5=Poultry, 6= others, please specify _____
Section 5: Social capital
5.1 Did you belong to a farmer groups or organization in your community during the last 12 months? ______ (1=Yes, 0=No).

If Yes fill the table below. IF No skip to question 5.2
5.1.1 5.1.2 5.1.3 5.1.4 5.1.5
Type of Group/Organization (See the Type of Group codes below) What is the most important function of the group or organization?
(See the function codes below) Is there a Membership fee?
1=Yes 0=No If yes in 5.15
How much in Ksh. Role in the Group
(See the Role codes below
Type of Group: 1=Women Group, 2=SACCO/Credit Group, 3=Farmer Cooperative, 4Input Supply Group, 5=Producer and Marketing Group, 6= Youth Group, 7=Others, please specify _________
Function:1=Produce marketing, 2=Input access, 3=Savings and credit, 4=Farmer trainings, 5=Transport services, 6= Share Inputs (Labor, Capital), 7=Other, please specify _________
Role: 1= Administrative, 2= Ordinally Member, 3= Other, please specify _________
5.2 If you have NOT been a member of any community group/organization for the last 12 months, why? _____ use codes below
Codes: 1=They are not available, 2=They are time wasting, 3=I am not interested in being a member, 4= The group/organization are corrupt/ poorly managed, 5=Gender restriction, 6=Other reason, please specify ______________________
Section 6: Access to credit
6.1 Did you acquire loan during the last 12 months ____ (1=Yes, 0=No).

6.2 If No in 6.1 Why NO ____ use the codes below
Codes 1=No need, 2=Not aware of the availability of credit, 3=Lack of enough collateral to secure a facility, 4=High interests for the credit 5=Long credit application procedures. 5. Other, please specify
If Yes, fill the table below: If No skip to question 6.2
6.2.1 6.2.2 6.2.3
Loan type
(use codes below) The main
purpose for which the credit was acquired (use codes below) AmountReceived
Loan type: 1=Formal bank, 2=Micro finance institution, 3=SACCO, 4=Community groups, 5= Informal Sources (e.g. Neighbour / Family), 6=Mobile money, 7=Others, please specify _________
Loan purpose: 1=Farm inputs, 2=School fees, 3=Food, 4=Land, 5=Livestock, 6= Expand business, 7=Farm implements/equipment
8=Other, please specify _________
Section 7: Access extension services
7.1 Did you receive extension services in the last 12 months ____ (1=Yes, 0=No).
If Yes, fill the table below: If No skip to question 7.2
7.1.1 7.1.2 7.1.3 7.1.4 7.1.5
Source of extension services (use the extension source code) What kind of information did you receive from this source (use the codes below)
Did you receive this information at the appropriate time?
(1= Yes ,0=No) Did you apply this information?
(1= Yes, 0=No) What were the terms of provision of the extension services (use the codes below)
Extension Source: 1= Researchers, 2=Farmer to farmer, 3=Media (Magazine, TV/radio, 5=Out grower (seed companies), 6=County Government, 7=NGO, 8=Development Organization, 9=Online Groups (Facebook, WhatsApp), 10=Religious Group (Churches, church committee), 11=Others (specify)Kind of information: 1=Pests and diseases, 2=Markets & prices, 3=Government initiatives, 4= Good agricultural practices, 5= Post-harvest 6= Other, please specify __________
Terms of provision of the extension services: 1=Free, 2= Paid, 3=Others, please specify __________
Section 8: Information on other incomes and their sources
8.1 For the different sources of income specified in the table below, please specify whether the household earned any income, and if so how much income was generated per month during the last 3 months
8.1.1 8.1.2
Income Source Did anyone in the household earn income from this source
(1= Yes, 0=No)
Formal salaried employment (e.g., civil servant, private sector employee) Informal Salaried Employment Business – Trade or services Sale of natural resources products (e.g., Sand harvesting, Mining, Timber) Pensions Renting out land Remittances Others, please (specify) ______________________ Section 9: Follow up
9.1 If you don’t mind, could you please share your phone number, so that I can call if I need a clarification in any of the responses that you have provided ___________
Concluding information
Interview: end time_______________
Thank you very much for your time.

Appendix 2: FOCUS GROUP DISCUSSION QUESTIONNAIREAssessment of the application of soil carbon enhancing practices in Western Kenya
The purpose of this FGD is to obtain exploratory insights from farmers and various stakeholders’ in western Kenya on various soil management practices applied by households in the area. It is also intended to give a broader understanding on the importance of these practices, and the constraints or challenges and opportunities revolving around the same.

Checklist for discussion;
What are the economic activities practised by most households in this area? (Hint: crop or livestock production, small business, informal or formal employment)
What are the crop varieties commonly grown in the area (Hint: food crop, cash crop, folder crops)? What are the benefits of growing these crops to households (Hint: income, food, forage, enhance soil fertility)?
What soil types and their characteristics (Hint:|example Clay or loam and their soil colour and size of particles) are found in this area?
How would you compare soil fertility now and 5-10 years ago (Hint: Has it improved, declined or remained the same, Explain)?
Have the changes (4 above) affected the environment (e.g. water pollution, soil erosion), crop yields (e.g. increase or decrease), household income and food security (e.g. Food availability and variety) over the years?
What are some of the soil management practices employed by households to improve fertility? (Hint: List all the practices mentioned by farmers)
Of the practices listed (in 6 above) which are the four most important? (Hint as practiced by a majority (more than 60%) of farmers)?
Can you assess the benefits (other than enhanced soil fertility) of the practices listed in 7 above?
What are some of the challenges faced in implementing the practice in 7 above (from the male and female perspective)?
Do you or have you received any information or training on implementing practices listed in 6 in the last 2 years? (Will be a yes or no answer, Record the percentage of farmers)
Where to you get this information (Hint NGOs, Extension officer (govt), Researchers, Other Farmers, Tv, Radio, Religious Groups) (We will follow up on farmers that said Yes in 8)
And are there challenges faced in accessing such information? (Hint: reliability, accessibility, availability, timeliness)
Which actors (e.g. extension officers, NGOs, community groups, other experienced farmers) do you think would be important in ensuring more households adopt soil fertility management practices?
Apart from the practices named in 6 above, would you be willing to implement other soil fertility management practice of introduced to you (Hint: we will ask about other practices that we are aware of but the farmers are not practising or have not mentioned)
What are main sources of cooking fuel? (Hint: crop residue, firewood, kerosene, gas: record percentage of farmers using different sources)
Do you work in other people farms for income before working on your farm? (Yes or No answer Capture labour trade-off)
Thank You