Agricultural Sustainability and Financial Inclusion through Warehouse Receipts: A Baseline Study from Uasin Gishu and Bungoma P4G contributes to green and inclusive growth in low- and middle-income countries by helping earlystage businesses become investment ready and matching them with supporting public-private National Platforms to enable country climate transitions in food, water and energy systems. P4G provides grants and technical assistance to startup partnerships; contributes to enabling systems improvements in partner countries through National Platforms; and shares learning on green entrepreneur ecosystems and solutions. Hosted by World Resources Institute and funded by Denmark, the Netherlands and the Republic of Korea, P4G implements in Colombia, Ethiopia, Indonesia, Kenya, South Africa and Vietnam. p4gpartnerships.org The Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT) delivers research-based solutions that harness agricultural biodiversity and sustainably transform food systems to improve people’s lives. Alliance solutions address the global crises of malnutrition, climate change, biodiversity loss, and environmental degradation. With novel partnerships, the Alliance generates evidence and mainstreams innovations to transform food systems and landscapes so that they sustain the planet, drive prosperity, and nourish people in a climate crisis. The Alliance is part of CGIAR, a global research partnership for a food-secure future. alliancebioversityciat.org cgiar.org https://p4gpartnerships.org/ https://alliancebioversityciat.org/ cgiar.org Agricultural Sustainability and Financial Inclusion through Warehouse Receipts: A Baseline Study from Uasin Gishu and Bungoma Benson Kenduiywo, Grace Koech, Chris Mwangi Ngige, Kizito Odhiambo, Lilian Ambani and Felix K. Ngetich CITATION P4G; CIAT. (2025). Agricultural Sustainability and Financial Inclusion through Warehouse Receipts: A Baseline Study from Uasin Gishu and Bungoma. Pioneering Green Partnerships (P4G), International Center for Tropical Agriculture (CIAT). 28 p. AUTHORS Benson Kenduiywo1, Grace Koech1, Chris Mwangi Ngige1, Kizito Odhiambo2, Lilian Ambani2, Felix K. Ngetich3 1 International Center for Tropical Agriculture (CIAT), part of the Alliance Bioversity & CIAT, Africa Hub 2 AgriBORA Ltd 3 Research Centre for Smallholder Farmers (RCFSF) CONTACT Benson Kenduiywo Research Specialist, CIAT b.kenduiywo@cgiar.org Design and layout: Lucelly Anaconas - Alliance of Bioversity International and CIAT Cover photo credit: Grace Koech / CIAT © P4G 2025. Some rights reserved. This work is licensed under a Creative Commons Attribution NonCommercial 4.0 International License (CC-BY-NC 4.0) https://creativecommons.org/licenses/by-nc/4.0/ October 2025 mailto:b.kenduiywo%40cgiar.org?subject= https://creativecommons.org/licenses/by-nc/4.0/ EXECUTIVE SUMMARY This baseline study assessed the implementation of the Warehouse Receipt System (WRS) and its influence on agricultural sustainability and financial inclusion among 200 smallholder farmers cultivating maize and soybean in Uasin Gishu and Bungoma counties, Kenya. The study focused on storage practices, WRS awareness and use, post-harvest losses, climate-smart agriculture (CSA) adoption, financial behaviour, and digital access. Results indicate stark contrasts between the two counties in terms of WRS awareness, adoption of CSA practices, digital literacy, and access to credit. WRS awareness stood at 37% in Uasin Gishu but was considerably lower in Bungoma (3.8% among maize farmers and 8.1% among soybean farmers). Despite this low awareness, willingness to adopt agriGHALA and CSA was notably high: 77% in Uasin Gishu, 46.2% in Bungoma maize, and 97.3% in Bungoma soybean farmers. However, actual use of the agriGHALA storage system remained minimal (only 4% of Uasin Gishu farmers and 1.4% of Bungoma soybean farmers had ever used the system). Key reasons for non-adoption included lack of awareness (82% in Uasin Gishu and 100% in Bungoma maize), storage cost, and distance to facilities. Post-harvest losses were widespread: in Uasin Gishu, losses were mainly due to pests (46%) and mould (33%), while in Bungoma, poor drying (69.2%) and pests (71.6%) were dominant. Yields also varied significantly, with Uasin Gishu farmers harvesting an average of 22.76 bags/acre of maize compared to 9.42 bags/acre in Bungoma maize and 4.04 bags/ acre in Bungoma soybean. Financial inclusion remains limited, only 1% of Uasin Gishu maize farmers accessed credit, compared to 11.5% in Bungoma maize and 4.1% in soybean. Though only 15–39% were aware that WRS could be used as collateral, 91.9% of Bungoma soybean farmers expressed willingness to take WRS-backed loans. Digital access showed disparities: smartphone ownership was 73% in Uasin Gishu, 38.5% in Bungoma maize, and 67.6% in Bungoma soybean. The cost of devices and data was cited as the primary barrier by up to 88.5% of farmers in Bungoma. Climate change awareness was high (63–73%), with CSA adoption highest in Uasin Gishu maize (52%) and Bungoma soybean (51.4%). These findings highlight the need for targeted WRS awareness campaigns, climate-smart training, and expansion of digital and financial services to build resilient, market-integrated, and climate-adaptive agricultural systems. ACKNOWLEDGMENTS We extend sincere gratitude to all individuals, as well as to the partnership of agriBORA and the Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT) through Partnering for Green Growth and the Global Goals 2030 (P4G) grant who contributed to the successful planning and execution of this exercise. We acknowledge the valuable support of hub managers, Peter Kiprop (Kuona Mbele, Uasin Gishu), Abigael Lusweti (Defana Agrovet), Jack Silali (Apex Farmpoint), Titus Saekwo (Kaptama), and Mary Goretty (Mt. Elgon Agribusiness), for mobilising farmers and facilitating enumerator recruitment. We are also grateful to the enumerators for their dedication and professionalism in the field. Special thanks go to Grace and the entire implementation team for their active participation, coordination, and real- time supervision during data collection. We also appreciate the farmers who voluntarily participated in the survey and provided valuable insights. Their contributions forms the cornerstone of agriGHALA’s evidence-based approach to improving post-harvest management and climate resilience in Kenya’s grain systems. We further acknowledge the leadership of Felix Ngetich and the support of the entire Research Centre for Smallholder Farmers (RCFSF) team for their technical guidance, coordination, and commitment throughout the survey process. Photo by: Neil Palmer / CIAT TABLE OF CONTENTS EXECUTIVE SUMMARY 3 ACKNOWLEDGMENTS 3 1. INTRODUCTION 6 2. METHODOLOGY 7 2.1. Study Area 7 2.2. Research Design 7 2.3. Population and Sampling 7 2.4. Data Collection 7 2.5. Data Management and Analysis 8 3. STUDY FINDINGS 9 3.1. Participant Demographics 9 3.2. Post-Harvest Management Practices 10 3.3. Market Access and Financial Inclusion 12 3.4. Climate Awareness and Practices 15 3.5. Agricultural Inputs and Sustainability Practices 17 3.6. Financial Behavior 20 3.7. Digital Access and Technology 23 4. CONCLUSION AND RECOMMENDATIONS 24 4.1. Conclusion 24 4.2. Recommendations 24 6 • Baseline Report 1. INTRODUCTION Food security is a pressing issue in many developing countries, particularly in sub-Saharan Africa, where agriculture is the primary source of livelihood for a large portion of the population. In Kenya, smallholder farmers play a critical role in ensuring food production, but they often face significant challenges that undermine their ability to achieve sustainable productivity. These challenges include limited access to markets, poor storage facilities, climate change, and financial barriers, all of which contribute to food insecurity and economic instability. The situation is particularly dire in regions like Uasin Gishu and Bungoma counties, where the majority of farmers rely on traditional farming methods and face limited opportunities for financial support and market access. Addressing these challenges is crucial to improving food security and agricultural sustainability in the country. Climate change has emerged as one of the most significant threats to agriculture in Kenya, with changing rainfall patterns, prolonged droughts, and rising temperatures reducing crop yields and increasing vulnerability to food shortages. Farmers in both Uasin Gishu and Bungoma have reported negative impacts on their agricultural productivity due to these climatic shifts (Mungai & Ndung’u, 2016). To mitigate these effects, adopting climate-smart agricultural (CSA) practices has become imperative. CSA includes techniques that enhance agricultural resilience, increase productivity, and reduce environmental degradation. Practices such as soil conservation, efficient water use, and integrated pest management are key components of CSA, which can help farmers adapt to the changing climate and improve their livelihoods (Wambugu & Mbugua, 2019). However, despite the potential benefits of CSA, there is still a need for greater adoption and integration of these practices, particularly among smallholder farmers in regions that are highly vulnerable to climate change. In addition to climate-smart practices, the Warehouse Receipt System (WRS) has been identified as an important tool for improving food security and enhancing farmers’ access to markets and financial services. The WRS allows farmers to store their produce in certified warehouses and use the receipts as collateral to access credit, providing financial security and mitigating the impact of price fluctuations in agricultural markets (KARI, 2020). This system has the potential to transform the financial landscape for smallholder farmers, particularly in counties like Uasin Gishu and Bungoma, where access to formal financial services is limited. However, despite the benefits of WRS, the implementation and awareness of the system remain low among farmers in these regions, suggesting the need for greater education and infrastructure development to facilitate its adoption. The WRS plays a pivotal role in improving agricultural productivity and market efficiency, while also contributing to environmental sustainability. By allowing farmers to store their produce in certified warehouses and use the receipts as collateral for loans, the WRS helps reduce the need for immediate sales, which often leads to low returns due to poor pricing or even exploitation by middlemen. This system can indirectly help mitigate greenhouse gas (GHG) emissions by promoting better storage practices, which minimise spoilage and the associated emissions from decaying crops. In areas where storage facilities are inadequate, farmers are forced to sell immediately, often resulting in inefficient transport and energy use, which increases carbon emissions. Moreover, the reduction of post-harvest losses through improved storage can significantly lower the need for additional agricultural inputs such as fertilisers and pesticides. This is because when farmers are able to safely store a larger proportion of their harvest, overall productivity is preserved, and the pressure to compensate for previous losses through increased input use in subsequent seasons is reduced. In many cases, farmers experiencing substantial post-harvest losses tend to re-intensify their production by applying higher fertiliser doses or increasing pesticide use in the following season in an attempt to make up for lost income or food stocks. By minimising losses through effective storage systems like WRS, farmers retain more of their output, maintain household food security and marketable surplus, and are less likely to resort to input-intensive strategies. This not only reduces production costs but also lowers the greenhouse gas (GHG) emissions associated with the manufacture, transport, and field application of synthetic inputs, thereby supporting more sustainable and climate-resilient farming systems. Therefore, the successful implementation of WRS not only improves farmers’ economic resilience but also offers a pathway for reducing agriculture’s carbon footprint and promoting more sustainable farming practices. The main objective of this study is to assess the baseline status of the implementation of the Warehouse Receipt System (WRS) in Uasin Gishu and Bungoma counties. Specifically, the study aims to evaluate the extent to which farmers in these areas have adopted the WRS, identify the barriers to its implementation, understand and quantify the extent to which the WRS can help reduce the carbon footprint of the small-scale farmers Agricultural Sustainability and Financial Inclusion through Warehouse Receipts: A Baseline Study from Uasin Gishu and Bungoma • 7 it serves, and assess its potential for scaling up to enhance market access, reduce post-harvest losses, and improve financial inclusion. By examining the current level of WRS implementation, the study will provide critical insights into the challenges and opportunities for strengthening agricultural storage and financial systems in these counties. 2. METHODOLOGY 2.1. Study Area The study was conducted in Uasin Gishu and Bungoma counties, located in the Rift Valley and Western regions of Kenya, respectively. These counties were selected due to their agricultural significance, focusing on maize and soybean farming, which are this study’s primary crops of interest. In addition, the two counties were selected based on their contrasting farming systems and climatic conditions, offering a representative sample of both commercial and smallholder farming practices in Kenya. Uasin Gishu county, located in the Rift Valley region of Kenya, is characterised by a temperate climate with varying agro-ecological zones, making it suitable for diverse agricultural activities. The county spans an altitude of 2,100 meters to 2,300 meters above sea level, benefiting from an annual rainfall of 900–1,200 mm. Uasin Gishu is renowned for its fertile volcanic soils, ideal for large-scale farming. The study focused on two sub-counties within the county: Ainobkoi and Kesses. Ainobkoi experiences favourable conditions for maize and soybean farming, and the area is known for its commercial farming activities. Kesses supports both maize and soybean cultivation, though the sub-county also has smallholder farmers who face challenges related to infrastructure, financial access, and climate change. The dominant crops in Uasin Gishu include maize, soybean, wheat, and potatoes, with maize being the primary crop. While the region enjoys high agricultural productivity, it also faces challenges such as post-harvest losses and market price fluctuations, particularly for smallholder farmers. Bungoma County, located in western Kenya, has a range of agro-ecological zones, from semi-arid to humid conditions, making it suitable for crop and livestock farming. The county lies at an altitude of 1,000 to 2,000 meters above sea level and experiences an average annual rainfall of 1,200 mm to 1,800 mm, with most of the rainfall occurring during the long rains (March to May) and the short rains (October to December). The fertile volcanic soils in the higher altitudes of the county support a variety of crops, including maize, beans, and soybeans. The study was conducted in four sub-counties: Kimilili, Sirisia, Kaptama, and Bumula. Kimilili and Sirisia are well-known for their maize farming, with Kimilili having a higher proportion of commercial maize production. Kaptama and Bumula are predominantly smallholder areas where maize farming is often intercropped with beans and other crops. The introduction of soybeans in these areas has increased due to market demand. Despite the favourable climatic conditions, Bungoma farmers face significant challenges, such as soil degradation, limited access to modern farming inputs, and vulnerability to climate change. These factors hinder agricultural productivity and limit the adoption of improved farming practices. 2.2. Research Design The agriGHALA Baseline Assessment is a cross- sectional study designed to collect data from farmers in two counties in Kenya, Uasin Gishu and Bungoma, focusing on two primary crops: maize and soybean. The study aims to assess key aspects of agricultural practices, post-harvest management, financial inclusion, climate-smart practices, access to digital tools and to determinine to what extend the WRS can contribute to carbon footprint reduction. 2.3. Population and Sampling The target population for this study consisted of farmers from Uasin Gishu and Bungoma counties who are engaged in the cultivation of maize and soybeans. A total of approximately 200 farmers participated in the baseline survey. The farmers were selected using a stratified random sampling technique, ensuring representation from both counties and the two primary crops. The sampling frame was created using agricultural data, categorising each farm by crop type, maize or soybean. Key inclusion criteria for participants included farmers residing in Uasin Gishu or Bungoma counties, actively cultivating maize or soybeans, and being willing to participate in the survey and provide informed consent. 2.4. Data Collection The data for this study were collected using the Open Data Kit (ODK) (Hartung et al., 2010), an open-source platform that offers a cost-effective and customisable solution for mobile data collection, enabling real-time, 8 • Baseline Report offline-capable survey administration in remote and resource-limited settings. This mobile-based data collection tool allows for efficient and accurate data capture in the field. Also, its choice was due to its ability to streamline the data collection process, reduce errors, and improve data management. Trained enumerators used ODK on mobile devices to administer structured questionnaires through face-to-face interviews with farmers in Uasin Gishu and Bungoma counties. The ODK platform facilitated the collection of quantitative and qualitative data, including farm demographics, crop production practices, post-harvest management, financial behaviours, market access, and climate-smart agriculture adoption. Each participant’s responses were recorded electronically in real time, ensuring prompt data validation and minimising the need for manual data entry. The survey was designed to capture detailed information, with sections dedicated to farm profile, post-harvest losses, cooperative membership, mobile money usage, access to credit, and climate change awareness. Enumerators, working in collaboration with local agricultural extension officers, ensured that participants fully understood the survey questions. Where the respondents were not proficient in Swahili or English, the questions were asked using local language. Informed consent was obtained from all farmers before starting the interviews1. ODK also allowed for easy synchronisation of the collected data with a central server, where it was validated for completeness and consistency. This data collection method improved the overall accuracy and timeliness of the survey, enabling efficient analysis using tools like SPSS for further statistical processing. 2.5. Data Management and Analysis The data collected through the ODK platform was first synchronised with a central server, ensuring real-time availability and security of the responses. Once the data was collected, it underwent a thorough cleaning process in Microsoft Excel, where inconsistencies, missing responses, and duplicates were addressed. The data was then standardised, ensuring all variables were uniform and ready for statistical analysis. This data management process enabled accurate and consistent datasets, which were crucial for the next analysis phase. Using the cleaned dataset, statistical analysis was performed using the Statistical Package for the Social Sciences (SPSS), ensuring the data were structured correctly for meaningful analysis. Descriptive statistical measures, including mean, frequency, percentage, minimum, and maximum values, were employed. Descriptive statistics such as frequencies and percentages were used to summarise categorical variables. The mean was calculated for continuous variables such as yield and farm size to understand typical values, while the minimum and maximum values helped identify the range of responses for each variable. The data was categorised by county first, then further analysed by crop type within each county, allowing for a comparison of farming systems and challenges faced by farmers in Uasin Gishu and Bungoma counties, as well as between maize and soybean growers. This approach facilitated a nuanced understanding of the data, providing valuable insights for targeted interventions. 1 You are invited to participate in a research survey as part of the agriGHALA project, which aims to improve the livelihoods of farmers in Kenya through a climate-smart Warehouse Receipt System (WRS). This survey is being conducted by agriBORA and the Alliance Bioversity & CIAT, with funding from P4G (Partnering for Green Growth and the Global Goals 2030). The purpose of this survey is to collect information on your farming practices, storage and marketing challenges, access to credit, and awareness of climate-smart agriculture. The information you provide will help improve services such as storage facilities, credit access, market linkages, and training for farmers. Your participation is expected to take about 30 to 45 minutes. Participation is voluntary. You may choose not to answer any question, and you can stop the interview at any time without penalty or loss of benefits. There are no known risks from participating, although some questions may be personal. Your responses will be confidential, and your name or personal details will not be included in any reports. Only authorized project staff will have access to the information you provide. If you have any questions about the survey, you may contact Dr. Benson Kenduiywo at b.kenduiywo@cgiar.org. Approximately 200 farmers from Uasin Gishu and Kisumu counties will participate in this baseline survey. mailto:b.kenduiywo%40cgiar.org?subject= Agricultural Sustainability and Financial Inclusion through Warehouse Receipts: A Baseline Study from Uasin Gishu and Bungoma • 9 3. STUDY FINDINGS 3.1. Participant Demographics The gender distribution reveals that Uasin Gishu has a predominantly male farming population, with 76% of maize farmers being male, compared to Bungoma, where 57.7% of maize farmers and 56.8% of soybean farmers are male (Table 1). The remaining farmers in Bungoma are female, with 42.3% of maize farmers and 43.2% of soybean farmers being female. This indicates that Bungoma has a more gender-balanced farming population, particularly in soybean farming. In contrast, Uasin Gishu’s male-dominated farming population may be influenced by cultural or socio- economic factors that tend to favour male participation in agricultural activities, possibly limiting opportunities for female farmers in the region. CATEGORY DESCRIPTION UASIN GISHU BUNGOMA Maize (n=100) Maize (n=26) Soybean (n=74) Gender Male 76 (76%) 15 (57.7%) 42 (56.8%) Female 24 (24%) 11 (42.3%) 34 (45.9%) Age group 18-35 years 44 (44%) 6 (23.1%) 29 (39.2%) 36-65 years 47 (47%) 16 (61.5%) 35 (47.3%) Above 65 years 9 (9%) 4 (15.4%) 10 (13.5%) Educational level None 1 (1%) - 1 (1.4%) Primary 32 (32%) 14 (53.8%) 14 (18.9%) Secondary 50 (50%) 10 (38.5%) 42 (56.8%) College/University 17 (17%) 2 (7.7%) 17 (23.0%) Decision maker Female 17 (17%) 2 (7.7%) 12 (16.2%) Male 70 (70%) 12 (46.2%) 29 (39.2%) Joint 13 (13%) 12 (46.2%) 33 (44.6%) Farm size Acres 92.5 (30-120) 5 (5-5) - Cooperative membership Yes 22 (22.0) 4 (19.2) 41 (55.4) Household size Number 6 (2-12) 5 (3-12) 5 (3-12) Crop acreage Acres 2.55 (0.5-20) 0.98(0.3-2.0) 1.32(0.3 – 6.0) * Numbers in parentheses indicate min–max for continuous variables and frequencies for categorical variables. TABLE 1. Participant Demographics Profile in Uasin Gishu and Bungoma Counties* The age group distribution indicates that Uasin Gishu county has a higher proportion of younger farmers, with 44% of maize farmers in the 18-35 age group (Table 1). In contrast, Bungoma county has a larger proportion of farmers in the 36-65 age group, with 61.5% of maize farmers and 47.3% of soybean farmers falling within this category. This suggests that Uasin Gishu may have a more dynamic, younger farming population that could be more receptive to innovative farming practices and technologies. However, Bungoma’s older farming population may indicate more experience but also possibly lower levels of adoption for new technologies. In terms of educational level, Uasin Gishu has a higher proportion of farmers with secondary education (50%) compared to Bungoma (38.5% for maize farmers), as shown in Table 1. In Bungoma, primary education is more common, with 53.8% of maize farmers reporting this level of education. Interestingly, soybean farmers in Bungoma 10 • Baseline Report show a higher secondary education level (56.8%), suggesting that they may be better equipped to adopt climate-smart agricultural practices and other innovations. Uasin Gishu’s higher proportion of secondary and college/university-educated farmers may indicate better access to information and technologies. Regarding decision-making, Uasin Gishu has a strong male-dominant structure, with 70% of maize farmers making decisions related to crop sales and storage (Table 1). In contrast, Bungoma shows more joint decision-making, among both maize and soybean farmers, where 46.2% and 44.6%, respectively, of decisions are made jointly. This indicates that Bungoma may have a more inclusive decision-making structure, with both male and female farmers actively contributing to agricultural decisions. This could provide an opportunity for agriGHALA to promote gender-inclusive practices and empower female farmers in Uasin Gishu, where male dominance in decision-making is more pronounced. Regarding cooperative membership, Uasin Gishu has a relatively low proportion of farmers engaged in cooperatives, with only 22% of maize farmers participating in a cooperative (Table 1). In contrast, Bungoma has a significantly higher proportion, especially among soybean farmers, with 55.4% of soybean farmers involved in cooperatives. This suggests that Bungoma farmers are more integrated into cooperative networks, which could enhance their access to market linkages, credit, and agricultural inputs. Uasin Gishu, on the other hand, may benefit from initiatives that encourage cooperative participation, which could help farmers gain access to these vital resources. The average household size is similar across both counties, with Uasin Gishu farmers reporting an average of 6 people in their households, and Bungoma farmers reporting an average of 5 people (Table 1). Household size is an important factor when considering the labour available for farming activities, as well as the social dynamics influencing decision-making within families. In both counties, household size indicates a reasonable availability of labour, but it also suggests that family members, especially female household members, may play key roles in supporting farming activities, particularly in Bungoma, where more joint decision- making was reported. The crop acreage varies significantly between Uasin Gishu and Bungoma counties. Uasin Gishu farmers report an average crop acreage of 2.55 acres for maize, ranging from 0.5 to 20 acres, indicating a more commercial farming setup with larger farm sizes (Table 1). In contrast, Bungoma farmers have smaller plots, with maize farmers averaging 0.98 acres (ranging from 0.3 to 2.0 acres) and soybean farmers averaging 1.32 acres (ranging from 0.3 to 6.0 acres). This suggests that farming in Bungoma is generally on a smaller scale, often focused on subsistence production. The larger farm sizes in Uasin Gishu may allow for greater crop diversification and higher production levels. At the same time, the smaller acreages in Bungoma may limit farming practices and require more intensive management. The difference in crop acreage across the two counties highlights varying farming systems and the potential need for different agricultural interventions to maximise productivity. 3.2. Post-Harvest Management Practices The average yield per acre in Uasin Gishu is significantly higher at 22.76 bags, compared to Bungoma, where maize farmers report 9.42 bags and soybean farmers have an average yield of only 4.04 bags (Table 2). This suggests that Uasin Gishu benefits from more favourable farming conditions or better access to resources, which may be linked to soil quality, farming practices, or technology use. The lower yields in Bungoma highlight potential areas for intervention to improve agricultural productivity, particularly in soybean farming. Addressing challenges such as soil fertility, farming techniques, and access to modern agricultural technologies could help increase yields in Bungoma. Uasin Gishu farmers rely entirely on traditional storage methods, with 100% of them using this approach, while Bungoma maize farmers predominantly use traditional methods, though also 26.9% use private storage (Table 2). Soybean farmers in Bungoma mostly use private storage (48.6%). The absence of agriGHALA usage in Bungoma presents an opportunity to introduce better storage and handling technologies. Post-harvest losses are prevalent in both counties, with Uasin Gishu farmers facing losses mainly due to pests (46%) and mould/ spoilage (33%). In Bungoma, maize farmers report significant losses from poor drying (69.2%), while soybean farmers suffer mainly from pests (71.6%). These losses highlight the challenges of inadequate drying methods and pest management, suggesting that addressing these issues could significantly reduce post- harvest losses and improve overall productivity. Agricultural Sustainability and Financial Inclusion through Warehouse Receipts: A Baseline Study from Uasin Gishu and Bungoma • 11 CATEGORY DESCRIPTION UASIN GISHU BUNGOMA Maize (n=100) Maize (n=26) Soybean (n=74) Yield per acre Bags/acre 22.76 (4-50) 9.42 (3-20) 4.04 (2-10) Storage methods Traditional 100 (100) 73.1 (19) 54.1 (40) Private 1 (1) 26.9 (7) 48.6 (36) AgriGHALA 3 (3) 0 0 Cooperative 0 0 6.8 (5) Post-harvest losses Pest 46 (46) 11.5 (3) 71.6 (53) Mould/spoilage 33 (33) 19.2 (5) 6.8 (5) Poor drying 19 (19) 69.2 (18) 6.8 (5) Theft 1 (1) 0 5.4 (4) Lack of containers 1 (1) 0 9.5 (7) WRS awareness Yes 37 (37) 3.8 (1) 8.1 (6) AgriGHALA use Yes 4 (4) 0 1.4 (1) Bags stored last season 50 kg bags 92.5 (30-120) 0 5 (5-5) Reason for not using agriGHALA Not aware 82 (82) 100 (26) 0 Prefer own storage 10 (10) 0 0 Storage cost 4 (4) 0 0 Distance to facility 3 (3) 0 0 Trust Issues 1 (1) 0 0 Market price per 90 kg Bag Price 3,246 (2,000-4,200) 2,480.77 (1,800-4,000) 5,164.86 (1,500-14,000) TABLE 2. Productivity and Post-harvest Management in Uasin Gishu and Bungoma Counties* * Numbers in parentheses indicate min–max for continuous variables and frequencies for categorical variables. Uasin Gishu has higher WRS awareness (37%) compared to Bungoma, where awareness is low among both maize (3.8%) and soybean (8.1%) farmers (Table 2). This suggests that Uasin Gishu farmers are more informed about WRS, which could help improve market access and secure better prices for their produce. Increasing WRS awareness in Bungoma through training and outreach could facilitate better market participation for farmers. AgriGHALA is limited across both counties, with only 4% of Uasin Gishu farmers and 1.4% of Bungoma soybean farmers utilising it. The low adoption rates point to a lack of awareness or trust in the system and issues related to cost and accessibility. Promoting agriGHALA’s benefits, such as easier access to storage and market opportunities, could encourage greater usage. In Uasin Gishu, farmers stored an average of 92.5 bags last season, reflecting larger-scale farming operations, while Bungoma Soybean farmers stored an average of 5.5 bags. The difference in storage capacity may be influenced by farm size or infrastructure. Encouraging Bungoma farmers to increase their storage capacity could reduce post-harvest losses and improve market timing. The primary reason for not using agriGHALA in Uasin Gishu was lack of awareness (82%), followed by a preference for own storage (10%), while Bungoma maize farmers reported a complete lack of awareness. These findings highlight the need for more educational efforts to increase awareness of agriGHALA’s benefits and address barriers such as distance and storage costs. Uasin Gishu farmers received an average price of 3,246 Kenyan shilling for a 90kg bag of maize. In contrast, 12 • Baseline Report Bungoma maize farmers received a lower price of KSh$2,480, and Bungoma soybean farmers experienced much higher price variability at KSh$5,164 (per 90 kg bag). The lower prices in Bungoma maize may indicate challenges in market access or the quality of produce. These differences highlight the need for better price stabilisation mechanisms and improved market linkages to ensure farmers receive fair and consistent prices for their produce. Despite storing an average of 92.5 bags, only 4% of Uasin Gishu maize farmers used agriGHALA, pointing to a major opportunity to optimize utilization through better access and trust-building. 3.3. Market Access and Financial Inclusion In Uasin Gishu, 46.2% of maize farmers sell their harvest immediately due to the need for urgent cash, while 26.9% cite favourable prices as the reason for immediate sales, and another 26.9% sell because they have no storage options (Table 3). In contrast, all of the Bungoma maize farmers (100%) report selling immediately due to the need for urgent cash. For Bungoma soybean farmers, 39.1% also sell immediately due to a lack of storage options, while 39.1% cite the need for urgent cash. These findings suggest that financial pressures, particularly for school fees, drive the immediate sale of crops across both counties, especially in Bungoma. CATEGORY DESCRIPTION UASIN GISHU BUNGOMA Maize (n=100) Maize (n=26) Soybean (n=74) Main reason for the immediate sell Price usually favorable 7 (26.9) - 5 (21.7) No storage options 7 (26.9) - 9 (39.1) Need for urgent cash 12 (46.2) 7 (100) 9 (39.1) Purpose of urgent cash School fees 7 (70) 6 (85.7) 6 (75) Purchase inputs 2 (20) - - Finance business 1 (10) - - Pay loan - - 1 (12.5) Medical treatment - 1 (14.3) 1 (12.5) * Numbers in parentheses indicate percentaje. TABLE 3. Market Access and Financial Inclusion* When examining the purposes for urgent cash, the majority of Uasin Gishu maize farmers (70%) use the proceeds for school fees, followed by 20.0% who use it to purchase inputs, and a small proportion (10%) for business financing (Table 3). In Bungoma, the majority of maize farmers (85.7%) also use the immediate sale proceeds for school fees, with smaller proportions using it for medical treatment (14.3%) or loan repayment (12.5%). Similarly, 75% of Bungoma soybean farmers sell to pay for school fees, with smaller proportions using the funds for medical treatment (12.5%) or other expenses. This indicates that school fees remain a major financial burden for farming households in both counties, underlining the importance of developing financial solutions to support agricultural families, such as education-focused loans or savings programs. Market access and digital integration behaviour of farmers in Uasin Gishu and Bungoma counties is shown in Table 4. Credit access is limited across all regions, with only 1% of Uasin Gishu maize farmers accessing credit, compared to 11.5% of Bungoma maize farmers and 4.1% of Bungoma soybean farmers (Table 4). The loan amounts vary significantly, with Uasin Gishu farmers receiving a much higher average loan of KSh$1,300,000, while Bungoma maize farmers receive KSh$150,000 and Bungoma soybean farmers receive KSh$21,667. The repayment periods are consistent across all regions, with farmers typically having a 24-month repayment term. Interest rates also vary, with Uasin Gishu farmers facing a lower rate of 2%, compared to Bungoma farmers, where maize farmers pay between 10% and 48%, and soybean farmers face rates ranging from 12% Agricultural Sustainability and Financial Inclusion through Warehouse Receipts: A Baseline Study from Uasin Gishu and Bungoma • 13 to 13%. This disparity in credit access and loan terms reflects the differences in financial infrastructure and accessibility between the counties. The primary buyers for Uasin Gishu maize farmers are local traders (100%), while in Bungoma, 96.2% of maize farmers also sell to local traders, with a smaller portion (3.8%) selling to exporters (Table 4). Bungoma soybean farmers have a more diversified customer base, with 40.5% selling directly to consumers, 18.9% selling to cooperatives, and 87.8% selling to local traders. Additionally, 5.0% of Uasin Gishu maize farmers and 6.8% of Bungoma soybean farmers sell their produce to processors. These findings highlight the dependence on local markets in both counties and the limited access to formal buyers like cooperatives and exporters, especially for Uasin Gishu farmers. Access to premium prices for quality or sustainable practices is reported by 22% of Uasin Gishu maize farmers, 50% of Bungoma maize farmers, and 25.7% of Bungoma soybean farmers (Table 4). The premium prices vary, with Uasin Gishu maize farmers receiving an average of KSh$1,572.73 per bag, Bungoma maize farmers receiving KSh$1,690.91 per bag, and Bungoma soybean farmers receiving KSh$998.42 per bag. The differences in access to premium prices suggest that Bungoma maize farmers may be able to capitalise more on quality or sustainable practices, potentially due to stronger market linkages or buyer demand in the area. The use of mobile technology in agriculture is widespread across all regions, with 70% of Uasin Gishu maize farmers, 65.4% of Bungoma maize farmers, and 91.9% of Bungoma soybean farmers using mobile services for agricultural purposes (Table 4). Common uses of mobile technology include receiving payments for crops (62% in Uasin Gishu, 65.4% in Bungoma maize, and 82.4% in Bungoma soybean), paying suppliers (56% in Uasin Gishu, 46.2% in Bungoma maize, and 64.9% in Bungoma soybean), and agricultural savings (13% in Uasin Gishu, 15.4% in Bungoma maize, and 45.9% in Bungoma soybean). These findings demonstrate the increasing reliance on mobile technology to facilitate agricultural transactions, especially in Bungoma soybean farming, where mobile money is widely used for a range of purposes, enhancing financial inclusion and convenience. The cooperative structure among Bungoma soybean farmers offers an efficient channel for collective storage, bulk marketing, and accessing WRS-linked finance. Photo by: Neil Palmer / CIAT 14 • Baseline Report CATEGORY DESCRIPTION UASIN GISHU BUNGOMA Maize (n=100) Maize (n=26) Soybean (n=74) Credit acesss Yes 1 (1%) 3 (11.5%) 3 (4.1%) Loan amount (KSh) Loan amount 1,300,000 (1,300,000- 1,300,000) 150,000 (150,000-150,000) 21,667 (5,000-50,000) Repayment period (Months) Repayment period 24 (24-24) 24 (24-24) 24 (24-24) Interest rate Interest rate 2%: 1 (100%) 10%: 1 (50%), 48%: 1 (50%) 12%: 2 (66.7%), 13%: 1 (33.3%) Main buyers Consumers 2 (2%) - 30 (40.5%) Cooperative - - 14 (18.9%) Exporters - 1 (3.8%) - Local 100 (100%) 25 (96.2%) 65 (87.8%) Processors 5 (5%) - 5 (6.8%) Access premium prices Yes 22 (22%) 13 (50%) 19 (25.7%) Higher Premium price KSh per bag 1,572.73 (200-4,500) 1,690.91 (400-5,000) 998.42 (200- 4,600) Mobile use in agriculture Yes 70 (70%) 17 (65.4%) 68 (91.9%) Purposes mobile Use Agricultural savings 13 (13%) 4 (15.4%) 34 (45.9%) Paying Suppliers 56 (56%) 12 (46.2%) 48 (64.9%) Obtaining loan 3 (3%) 1 (3.8%) 4 (5.4%) Receiving payments for crops 62 (62%) 17 (65.4%) 61 (82.4%) Sources of information about current market prices Buyers/traders 76 (76.0%) 3 (11.5%) 34 (45.9%) Extension officers 35 (35.0%) - 2 (2.7%) Mobile alert SMS 4 (4.0%) 13 (50.0%) 38 (51.4%) None 4 (4.0%) - - Other farmers 53 (53.0%) 5 (19.2%) 41 (55.4%) Radio/TV 13 (13.0%) 22 (84.6%) 19 (25.7%) * Numbers in parentheses indicate percentaje for categorical variables and minimum to maximum for contentious. TABLE 4. Smallholder Market Access and Digital Integration in Uasin Gishu and Bungoma Counties* In Uasin Gishu, the primary source of market price information is buyers and traders (76%), followed by other farmers (53%) and extension officers (35%) (Table 4). In contrast, Bungoma maize farmers primarily rely on radio/TV (84.6%) and mobile alert SMS (50%) for market price information, while Bungoma soybean farmers depend on a mix of other farmers (55.4%), mobile alerts (51.4%), and buyers/traders (45.9%). These findings indicate that while Uasin Gishu farmers have more direct and immediate access to price information through buyer relationships, Bungoma farmers rely more on indirect sources, such as media and mobile alerts. This could impact their ability to make timely and informed market decisions, highlighting the need for improved access to real-time price data in Bungoma to enhance market participation and decision-making. Agricultural Sustainability and Financial Inclusion through Warehouse Receipts: A Baseline Study from Uasin Gishu and Bungoma • 15 3.4. Climate Awareness and Practices The majority of farmers in all regions are aware of climate change and its impact on farming. In Uasin Gishu, 63% of maize farmers acknowledge the effects of climate change, while awareness is slightly higher in Bungoma, with 73.1% of maize farmers and 64.9% of soybean farmers recognising its impact (Table 5). This suggests a general understanding among farmers in both counties about the importance of addressing climate-related challenges in agriculture. However, the slightly higher awareness in Bungoma maize farmers may indicate a stronger recognition of climate issues in areas with more exposure to climate variability. Access to climate and weather information is another critical factor. In Uasin Gishu, 63% of maize farmers report receiving climate or weather information, while this is higher in Bungoma maize (73.1%) but lower in Bungoma soybean farmers (58.1%) (Table 5). The availability of climate information plays a significant role in helping farmers make informed decisions about their farming practices. The data suggests that while Uasin Gishu farmers are somewhat well-informed, Bungoma farmers, particularly soybean farmers, may have less access to relevant weather information, which could affect their ability to plan and adapt to changing weather patterns. The usefulness of the climate/weather information received varies across the counties. In Uasin Gishu, 82.5% of maize farmers find the information very useful, while in Bungoma, only 26.3% of maize farmers rate it as very useful, with a larger portion (46.2%) finding it somewhat useful. Among Bungoma soybean farmers, 51.2% find the information somewhat useful, indicating that while some farmers appreciate the climate data, its relevance or quality may vary. This disparity in the perceived usefulness of weather information suggests that improving the quality and applicability of climate data could enhance farmers’ decision-making, especially in Bungoma. The adoption of CSA is higher in Uasin Gishu, where 52% of maize farmers have adopted CSA practices. In contrast, only 23.1% of Bungoma maize farmers and 51.4% of Bungoma soybean farmers have done so (Table 5). While interest in CSA is high, uptake is constrained by access and capacity. In Bungoma, only 6 farmers had adopted CSA despite 72 expressing interest. The higher adoption rate in Uasin Gishu suggests that farmers in this region are more proactive in incorporating sustainable farming techniques, likely due to better access to information and resources. Common CSA practices adopted include minimum tillage, crop rotation, intercropping, agroforestry, use of organic manure and compost, cover cropping, and the application of drought-tolerant seed varieties. This also indicates that there is potential for expanding CSA adoption in Bungoma, particularly for maize farmers who may need further support in transitioning to more resilient farming practices. Photo by: AgriBORA 16 • Baseline Report VARIABLE DESCRIPTION UASIN GISHU BUNGOMA Maize (n=100) Maize (n=26) Soybean (n=74) Aware of climate change Yes 63 (63%) 19 (73.1%) 48 (64.9%) Receive climate weather information Yes 63 (63%) 19 (73.1%) 43 (58.1%) Weather information rating Very useful 52 (82.5%) 5 (26.3%) 22 (51.2%) Somewhat useful 10(10%) 12(46.2%) 20(46.5%) Not useful 1(1%) 2(77.7%) 1(2.3%) CSA adoption Yes 52 (52%) 6 (23.1%) 38 (51.4%) Willing to adopt agriGHALA & CSA Yes 77 (77%) 12 (46.2%) 72 (97.3%) Change storage or drying Yes 33 (33%) 4 (15.4%) 30 (40.5%) Reason for storage change Enhance quality 12 (12%) 3 (11.5%) 27 (36.5%) Get better prices 7 (7.4%) - 13 (17.6%) Reduce emissions 8 (8.3%) - 4 (6.2%) Reduce post-harvest losses 20 (20.2%) 1 (3.8%) 1 (1.6%) Climate factors affecting yields Increased pest diseases 53 (53%) 15 (57.7%) 19 (25.7%) Rainfall patterns 75 (75%) 26 (100%) 59 (79.7%) Soil degradation 16 (16%) 13 (50%) 3 (4.1%) Temperature changes 13 (13%) 12 (46.2%) 41 (55.4%) * Numbers in parentheses indicate percentaje for categorical variables and minimum to maximum for contentious. TABLE 5. Climate Awareness and Practices in Uasin Gishu and Bungoma Counties* There is significant interest in adopting agriGHALA services combined with climate-smart agriculture. In Uasin Gishu, 77% of maize farmers express willingness to adopt agriGHALA and CSA services, while 46.2% of Bungoma maize farmers and 97.3% of Bungoma soybean farmers show similar interest (Table 5). The high willingness in Bungoma soybean farmers suggests that they are particularly open to integrating new services that combine climate advisories with farming support. This interest presents an opportunity for agriGHALA to tailor its services to meet the needs of farmers in both counties, particularly in enhancing the adoption of climate-smart practices. Changes in storage or drying methods are being adopted by farmers, with 33% of Uasin Gishu maize farmers making modifications, compared to only 15.4% in Bungoma maize farmers and 40.5% in Bungoma soybean farmers (Table 5). These changes are often driven by the desire to enhance quality, with 12% of Uasin Gishu maize farmers making changes to improve product quality, and 36.5% of Bungoma soybean farmers sharing the same goal. The move toward improved drying and storage practices reflects the growing awareness among farmers of the importance of maintaining quality to fetch better prices and reduce post-harvest losses. Farmers in both counties identify various climate factors that affect their yields. In Uasin Gishu, 75% of maize farmers cite rainfall patterns as a significant factor, while in Bungoma, 100% of maize farmers identify rainfall patterns as the key factor affecting their yields (Table 5). Soil degradation and temperature changes also impact yields, with Uasin Gishu farmers highlighting increased pest diseases (53%) and temperature changes (13%). Bungoma maize farmers report similar concerns, with 57.7% citing pest diseases and 46.2% highlighting temperature changes as challenges. The high impact of rainfall patterns on both counties suggests that addressing water management and improving irrigation techniques could be crucial for improving agricultural productivity, especially in areas vulnerable to changing weather patterns. Agricultural Sustainability and Financial Inclusion through Warehouse Receipts: A Baseline Study from Uasin Gishu and Bungoma • 17 3.5. Agricultural Inputs and Sustainability Practices Fertiliser use is common across all regions, with 70% of Uasin Gishu maize farmers, 88.5% of Bungoma maize farmers, and 59.5% of Bungoma soybean farmers applying fertilisers (Table 6). Uasin Gishu maize farmers use an average of 3.02 bags per acre, which is significantly higher than Bungoma maize farmers who apply 1.09 bags, and Bungoma soybean farmers who use 2.50 bags per acre. The frequency of fertiliser applications also differs, with Uasin Gishu farmers applying fertiliser 3.3 times per season, while Bungoma maize farmers apply it 2 times and Bungoma soybean farmers apply it 2.05 times. This indicates that fertiliser usage is more intensive in Uasin Gishu, reflecting potentially better access to fertilisers or more extensive farming practices. Manure use is less common in Uasin Gishu, with only 6.0% of maize farmers using manure compared to 23.1% of Bungoma maize farmers and 63.5% of Bungoma soybean farmers (Table 6). Among manure users, Uasin Gishu farmers predominantly use poultry manure (6%), while Bungoma maize farmers also use poultry manure (100%). Interestingly, soybean farmers in Bungoma rely heavily on poultry manure (85.1%), indicating its preference as a key input for organic farming. The quantity of manure applied is highest in Bungoma maize farming, with an average of 7,833 kg per acre, compared to 1,518 kg per acre in Uasin Gishu. These differences highlight the varying reliance on manure as an input across the counties, with Bungoma farmers applying significantly more manure than those in Uasin Gishu. VARIABLE DESCRIPTION UASIN GISHU BUNGOMA Maize (n=100) Maize (n=26) Soybean (n=74) Fertiliser use Yes 70 (70%) 23 (88.5%) 44 (59.5%) Fertiliser quantity Bags 3.02 (1-8) 1.09 (2-2) 2.50 (0.5-5) Number of applications Number 3.30 (1-6) 2 (2-2) 2.05 (1-3) Manure use Yes 6 (6%) 6 (23.1%) 47 (63.5%) Manure type Cow - - 7 (14.9%) Poultry 6 (6%) 6 (100%) 40 (85.1%) Manure quantity Kgs 1,518 (4-5,000) 7,833 (200-36,000) 460 (20-3,000) Compost use Yes 8 (8%) 1 (3.8%) 28 (37.8%) Compost quantity Kg 781.38 (2-3,000) 1,800 (1,800-1,800) 680.93 (100-5,000) Crop residue Yes 25 (25%) 2 (7.7%) 54 (73%) * Numbers in parentheses indicate percentaje for categorical variables and minimum to maximum for contentious. TABLE 6. Agricultural Inputs in Uasin Gishu and Bungoma Counties* The use of compost is relatively low across all regions, with only 8% of Uasin Gishu maize farmers, 3.8% of Bungoma maize farmers, and 37.8% of Bungoma soybean farmers using compost (Table 6). Among those using compost, the average quantity applied is highest in Bungoma soybean farming, where 680.93 kg per acre is applied, followed by Uasin Gishu at 781.38 kg per acre. Bungoma maize farmers, however, use a fixed amount of 1,800 kg per acre, indicating the consistency in compost application within this group. The higher rate of compost use in Bungoma soybean farming suggests that it may be a more commonly adopted practice in areas with a greater emphasis on organic farming methods. Crop residue management varies significantly across the counties. In Uasin Gishu, 25% of maize farmers incorporate crop residues back into the soil, compared to only 7.7% in Bungoma maize farmers. However, crop residue management is much higher among Bungoma soybean farmers, with 73% of them incorporating residues back into the soil (Table 6). This suggests that soybean farming in Bungoma is more likely to benefit from sustainable practices such as residue incorporation, which helps improve soil fertility and reduce the need for synthetic fertilisers. In contrast, Uasin Gishu maize farmers show a relatively lower adoption of crop residue management, potentially indicating a greater reliance on synthetic inputs rather than organic practices. 18 • Baseline Report Fuel use is relatively low across all regions, with 13% of Uasin Gishu maize farmers using fuel for their farming activities, compared to 8.1% of Bungoma soybean farmers, and no fuel use reported by Bungoma maize farmers (Table 7). Uasin Gishu farmers consume an average of 20.77 liters of fuel, ranging from 5 to 50 liters, indicating that they may rely more on fuel for various farming operations such as irrigation or machinery. In comparison, Bungoma soybean farmers use an average of 8.67 liters of fuel, ranging from 2 to 20 liters, suggesting that their fuel consumption is lower, potentially due to smaller-scale farming or more manual practices. The variation in fuel use between Uasin Gishu and Bungoma highlights the need for region-specific solutions to address energy requirements. The higher fuel consumption in Uasin Gishu may reflect larger-scale farming operations, while the lower usage in Bungoma points to an opportunity for more efficient and renewable energy solutions, such as solar-powered irrigation systems, which could reduce dependency on fuel and support both large and small-scale farmers. VARIABLE DESCRIPTION UASIN GISHU BUNGOMA Maize (n=100) Maize (n=26) Soybean (n=74) Fuel use Yes 13 (13%) - 6 (8.1%) Fuel quantity Litres 20.77 (5-50) - 8.67 (2-20) Electricity use Yes 15 (15%) - 2 (2.7%) Electricity quantity kWh/month 18.80 (5-50) - 32.50 (20-45) * Numbers in parentheses indicate percentaje for categorical variables and minimum to maximum for contentious. TABLE 7. Energy Use in Uasin Gishu and Bungoma Counties* Electricity use varies significantly between Uasin Gishu and Bungoma counties. In Uasin Gishu, 15% of maize farmers use electricity, suggesting its importance for energy-intensive activities such as irrigation and processing (Table 7). In contrast, only 2.7% of Bungoma soybean and no Bungoma maize farmers report using electricity. The quantity of electricity used is also higher in Uasin Gishu, where maize farmers consume an average of 18.8 kWh per month, compared to Bungoma soybean farmers who use an average of 32.5 kWh. This discrepancy highlights that while Uasin Gishu farmers are more likely to have access to electricity for farm-related activities, Bungoma farmers, particularly maize farmers, have limited access to this resource. The overall low electricity usage in both counties suggests the need for better energy infrastructure and access, with renewable energy solutions, such as solar-powered systems, potentially offering an affordable alternative to support farming productivity, especially in more remote areas. The mode of transport for farm produce varies significantly between Uasin Gishu and Bungoma counties. In Uasin Gishu, 89% of maize farmers rely on tractors for transporting their produce, highlighting the larger scale of farming and access to mechanised transport (Table 8). In contrast, only 26.9% of Bungoma maize farmers use tractors, with a more significant proportion (73.1%) relying on carts for transport. Bungoma soybean farmers also use carts (35.1%), but a substantial portion (24.3%) relies on pick-up trucks for transporting their crops. Additionally, 28.4% of Bungoma soybean farmers use bicycles or motorcycles, showing a more diverse approach to transportation, which may be influenced by farm size and infrastructure availability. The reliance on carts and manual labour in Bungoma reflects the more subsistence-oriented farming system in the region. Fumigant use is reported by 24% of Uasin Gishu maize farmers, with all of them using Accetile as the fumigant (Table 8). In comparison, only 23.1% of Bungoma maize farmers use fumigants, and 6.8% of Bungoma soybean farmers report using them. The primary fumigant used in both Uasin Gishu and Bungoma is Accetile, although Bungoma soybean farmers also use pesticides (60%) and fungicides (20%). The use of fumigants is a common practice in Uasin Gishu, suggesting a greater focus on pest and disease management, likely due to the larger-scale farming operations and the need to preserve crop quality. The more varied use of pest management products in Bungoma soybean farming could point to a more diverse set of challenges faced by farmers, such as pest infestations and fungal diseases. Crop residue management practices show notable differences between Uasin Gishu and Bungoma counties. In Uasin Gishu, 85% of maize farmers feed crop residues to animals, while only 3% sell them (Table 8). This indicates that crop residues are primarily seen as valuable animal feed. Similarly, in Bungoma, 88.5% of maize farmers use residues for animal feed, and 12% Agricultural Sustainability and Financial Inclusion through Warehouse Receipts: A Baseline Study from Uasin Gishu and Bungoma • 19 incorporate them into the soil, reflecting sustainable practices in managing organic waste. However, 16% of Uasin Gishu farmers burn crop residues, a practice that is also seen in 3.8% of Bungoma maize farmers and 4.1% of soybean farmers. The practice of burning residues, while more common in Uasin Gishu, can contribute to environmental degradation, and alternative practices like composting or incorporation into the soil could be encouraged to reduce its negative impact. VARIABLE DESCRIPTION UASIN GISHU BUNGOMA Maize (n=100) Maize (n=26) Soybean (n=74) Mode of Transport Tractor 89 (89%) 7 (26.9%) 7 (9.5%) Pick-up truck 1 (1%) - 18 (24.3%) Cart 2 (2%) 19 (73.1%) 26 (35.1%) Bicycle or motorcycle - 21 (28.4%) Man power 8 (8%) 2 (2.7%) - Fumigant use Yes 24 (24%) 6 (23.1%) 5 (6.8%) Fumigant type Accetile 24 (100) 6 (100) 1 (20.6) Pesticides - - 3 (60) Fungicides - - 1 (20) Crop residue management Burn in the field 16 (16%) 1 (3.8%) 3 (4.1%) Compost 5 (5%) 1 (3.8%) 12 (16.2%) Animal Feed 85 (85%) 23 (88.5%) 60 (81.1%) Incorporate in soil 12 (12%) 3 (11.5%) 33 (44.6%) Sell 3 (3%) - 2 (2.7%) Fuel 37 (37%) 5 (19.2%) - Renewable energy used None 91 (91) 26 (100) 72 (97.3) Solar 9 (9) - - Biogas - - 1 (1.4) Wind - - 1 (1.4) Irrigation energy source Diesel pump 9 (9%) 2 (7.7%) 1 (1.4%) Manual irrigation 33 (33%) 1 (3.8%) 3 (4.1%) Solar pump 3 (3%) - 1 (1.4%) * Numbers in parentheses indicate percentaje for categorical variables and minimum to maximum for contentious. TABLE 8. Post-harvest Sustainable Management Practices Implementation in Uasin Gishu and Bungoma Counties* Renewable energy use is quite limited across all regions. In Uasin Gishu, 9% of farmers use solar power, while 91% report no use of renewable energy sources (Table 8). Similarly, in Bungoma, 100% of maize farmers do not use renewable energy, and only 1.4% of soybean farmers use biogas and wind energy. These figures suggest that access to renewable energy is limited, particularly in farming areas where resources for renewable technologies may be scarce. The minimal use of solar and biogas indicates an opportunity to introduce more affordable and sustainable energy solutions that could reduce dependence on fossil fuels and enhance farming efficiency. The energy sources for irrigation also differ across regions. In Uasin Gishu, 9% of maize farmers use diesel pumps, while 33% rely on manual irrigation, and 3% use solar pumps (Table 8). In Bungoma, 7.7% of maize farmers use diesel pumps, while 3.8% use manual irrigation. Bungoma soybean farmers rely mainly on manual irrigation (4.1%) and a small proportion (1.4%) use solar pumps. These differences indicate that Uasin Gishu, with its more mechanised farming 20 • Baseline Report systems, utilises a wider range of irrigation methods, including solar-powered pumps. In contrast, Bungoma’s more manual approach to irrigation suggests that many farmers in this region may not have access to modern irrigation technologies, which could limit productivity during dry periods. Encouraging the adoption of solar irrigation systems could help mitigate this challenge. In general, the survey reveal that the widespread use of synthetic fertilisers, pesticides, and energy sources, particularly diesel and electricity for irrigation, storage, and transport, across farms in Uasin Gishu and Bungoma contributes significantly to the agricultural carbon footprint. For instance, Uasin Gishu maize farmers applied an average of 3.02 bags of fertiliser per acre with 3.3 application events per season, and 13% reported using fuel-powered equipment, indicating intensive input use. Additionally, post-harvest residue burning (reported by 16% of Uasin Gishu maize farmers) releases avoidable GHGs. These carbon-emitting practices, driven partly by the need to recover from post-harvest losses, can be mitigated through the adoption of the Warehouse Receipt System (WRS). By reducing losses and enabling delayed sale, WRS minimises the pressure to overproduce, reduces unnecessary reapplication of inputs, and encourages more sustainable residue use and storage energy efficiency. While precise quantification of GHG reduction would require modeling, the study provides clear evidence that integrating WRS into farm systems reduces emissions intensity per unit of output. For P4G, this positions WRS not just as a financial service, but as a scalable sustainability model that contributes to low-emission, climate-smart grain value chains in Kenya. 3.6. Financial Behavior Delayed sale of crops is a common financial behaviour among farmers in both Uasin Gishu and Bungoma counties. In Uasin Gishu, 50% of maize farmers reported delaying the sale of their harvest, while in Bungoma, a higher proportion of maize farmers (76.9%) and 55.4% of soybean farmers also engage in delayed sales (Table 9). The reasons for delaying sales largely revolve around seeking better prices, as farmers wait for more favourable market conditions. The duration of the delay varies, with Uasin Gishu maize farmers delaying sales for an average of 3.66 months, while Bungoma maize farmers delay for 3.7 months. Interestingly, Bungoma soybean farmers report the longest delay, with an average of 6.09 months, reflecting their strategy of holding onto crops for price fluctuations. This behaviour suggests that many farmers in these regions are strategically waiting for better prices, indicating the potential demand for reliable storage and price forecasting solutions like agriGHALA to minimise losses during price delays. Willingness to pay for agriGHALA storage services varies across regions, with 34% of Uasin Gishu maize farmers, 50% of Bungoma maize farmers, and 25.7% of Bungoma soybean farmers willing to pay, depending on the price (Table 9). In contrast, a significant proportion of Uasin Gishu farmers (49%) are not willing to pay for storage, with 15.4% of Bungoma maize farmers and just 1.3% of Bungoma soybean farmers also indicating they would not pay for agriGHALA services. The remaining farmers in all regions express varying degrees of willingness to pay, depending on the price, with Bungoma soybean farmers (73%) showing the most flexibility in their payment preferences. The varying willingness to pay underscores the need for agriGHALA to consider affordable and flexible pricing models that can cater to the financial capacities of farmers across different regions. The maximum amount that farmers are willing to pay for storage per bag varies across counties. Uasin Gishu maize farmers report being willing to pay an average of KSh$81.21 per 50 kg bag, with a range from KSh$2 to KSh$517 (Table 9). Bungoma maize farmers are willing to pay slightly more, with an average of KSh$87.69 per bag, ranging from KSh$30 to KSh$200. However, Bungoma soybean farmers have a much higher average willingness to pay, with a range of KSh$50 to KSh$2000 and an average of KSh$485.14. This significant difference suggests that soybean farmers in Bungoma may have greater financial flexibility or are willing to pay more for storage solutions due to higher crop value or income, while Uasin Gishu and Bungoma maize farmers are more cautious about storage costs. Awareness of the WRS as a means of securing loans is relatively low across both regions. Only 15% of Uasin Gishu maize farmers, 19.2% of Bungoma maize farmers, and 39.2% of Bungoma soybean farmers are aware that they can use stored grain as collateral for loans (Table 9). This indicates a knowledge gap that could limit access to credit for many farmers, particularly in Uasin Gishu and Bungoma maize farming. Increasing awareness and education around the use of WRS could help farmers access more financial products, improve financial inclusion, and enhance market participation. The willingness to take loans using WRS as collateral is relatively low in Uasin Gishu, with only 15% of maize farmers willing to use stored commodities for loans, while 34% are not interested (Table 9). In contrast, a higher proportion of Bungoma maize farmers (23.1%) and 91.9% of Bungoma soybean farmers are willing to take loans using stored commodities as collateral. The significant difference in willingness between Uasin Gishu and Bungoma farmers, particularly soybean farmers, reflects varying levels of trust and knowledge of the system, with Bungoma farmers showing more Agricultural Sustainability and Financial Inclusion through Warehouse Receipts: A Baseline Study from Uasin Gishu and Bungoma • 21 VARIABLE DESCRIPTION UASIN GISHU BUNGOMA Maize (n=100) Maize (n=26) Soybean (n=74) Delayed sale Yes 50 (50%) 20 (76.9%) 41 (55.4%) Months sales delayed - 3.66 (1.0-6.0) 3.7 (2-6) 6.09 (1-24) Willing to pay for agriGHALA storage Depend on price 34 (34%) 13 (50%) 19 (25.7%) No 49 (49%) 4 (15.4%) 1 (1.3%) Depend on price 17 (17%) 9 (34.6%) 54 (73%) Maximum storage fee per bag Ksh 81.21 (2-517) 87.69 (30-200) 485.14 (50-2000) Aware WRS as collateral Yes 15 (15%) 5 (19.2%) 29 (39.2%) Willing to take a loan using WRS No 34 (34%) - 4 (5.4%) Yes 15 (15%) 6 (23.1%) 68 (91.9%) Not sure 51 (51%) 20 (76.9%) 2 (2.7%) Reason for not willing take loan using WRS Availability of other sources 2 (5.9%) - - High risk - - 2 (50%) Interest rate 2 (5.9%) - 2 (50%) Not interested 30 (88.2%) - - WRS: Warehouse Receipt System * Numbers in parentheses indicate percentaje for categorical variables and minimum to maximum for contentious. TABLE 9. Financial Behaviour Among Farmers in Uasin Gishu and Bungoma Counties* openness to collateralised loans. However, many farmers are still hesitant, with Uasin Gishu maize farmers citing other sources of finance, while Bungoma farmers are concerned about high-interest rates and the perceived risk of using their produce as collateral. The primary reasons for not taking loans using WRS as collateral differ between regions. In Uasin Gishu, 88.2% of farmers who are not willing to use WRS as collateral cite availability of other financing sources, while 5.9% mention concerns over high interest rates (Table 9). In Bungoma soybean farmers, 50% are deterred by the perceived high risk of taking loans against stored commodities, while another 50% cite interest rates as a major barrier. These findings indicate that while some farmers are willing to engage with the WRS system, others have reservations due to financial risks and unfavorable loan conditions, highlighting the need for more accessible and farmer-friendly credit systems. Awareness of crop insurance varies across Uasin Gishu and Bungoma counties, with 34% of Uasin Gishu maize farmers, 46.2% of Bungoma maize farmers, and 52.7% of Bungoma soybean farmers being aware of crop insurance (Table 10). The relatively higher awareness in Bungoma, especially among soybean farmers, suggests that insurance awareness is more prevalent in areas where farming practices are more diverse or where agricultural risks are more pronounced. This awareness could serve as a foundation for promoting crop insurance to help farmers mitigate financial risks, especially in regions vulnerable to climate variability. The willingness to purchase crop insurance differs significantly across the counties. In Uasin Gishu, 32% of maize farmers are uncertain about taking insurance, while 46% are not willing to purchase it, and 22% are willing (Table 10). Bungoma maize farmers show a higher willingness, with 50% open to taking insurance, while 26.9% are not interested, and 23.1% are uncertain. Notably, 93.2% of Bungoma soybean farmers express a strong willingness to take insurance, reflecting a proactive approach to risk management in this group. These findings suggest that while awareness of crop insurance exists, there is a significant need for education and financial products that address the specific concerns and barriers farmers face, particularly in Uasin Gishu and Bungoma maize farming. 22 • Baseline Report VARIABLE DESCRIPTION UASIN GISHU BUNGOMA Maize (n=100) Maize (n=26) Soybean (n=74) Aware crop insurance Yes 34 (34%) 12 (46.2%) 39 (52.7%) Willing to take insurance Maybe 32 (32%) 13 (50%) 3 (4.1%) No 46 (46%) 7 (26.9%) 2 (2.7%) Yes 22 (22%) 6 (23.1%) 69 (93.2%) Does insurance increase agriGHALA use No 39 (39%) - 2 (2.7%) Not sure 29 (29%) 22 (84.6%) 7 (9.5%) Yes 32 (32%) 4 (15.4%) 65 (87.8%) The biggest financial risk in farming Crop failure 40 (40%) - 15 (20.3%) Price fluctuation 53 (53%) 23 (88.5%) 52 (70.3%) Storage losses 7 (7%) 3 (11.5%) 7 (9.5%) Keep records Yes 41 (41%) 4 (15.4%) 50 (67.6%) Preferred source of financial information Farmer group 47 (47%) 5 (19.2%) 32 (43.2%) Radio 11 (11%) 22 (84.6%) 20 (27%) SMS 77 (77%) 13 (50%) 45 (60.8%) Voice call 6 (6%) - 3 (4.1%) In person meeting 18 (18%) 2 (7.7%) 30 (40.5%) * Numbers in parentheses indicate percentaje. TABLE 10. Risk Aversion and Financial Literacy Among Farmers in Uasin Gishu and Bungoma Counties* The impact of crop insurance on the use of agriGHALA services varies across the counties. In Uasin Gishu, 39% of maize farmers believe insurance would not increase their use of agriGHALA, while 29% are uncertain, and 32% think it would (Table 10). In Bungoma, the majority of soybean farmers (87.8%) feel that insurance would increase their use of agriGHALA, highlighting the potential for bundling insurance with agriGHALA services to enhance adoption. However, only a small portion of Bungoma maize farmers (15.4%) share this view, indicating that additional factors may influence their decision to use agriGHALA such as cost or awareness of the platform’s benefits. Price fluctuation is identified as the biggest financial risk for most farmers, particularly in Uasin Gishu (53%) and Bungoma maize farmers (88.5%) (Table 10). Crop failure is the second-largest concern for Uasin Gishu maize farmers (40%) and Bungoma soybean farmers (20.3%), while storage losses are less significant, though still noted by some farmers (7% in Uasin Gishu, 11.5% in Bungoma maize, and 9.5% in Bungoma soybean). These findings highlight the vulnerability of farmers to price volatility, particularly for maize, and indicate a potential opportunity for agriGHALA to provide financial products that help stabilise prices and reduce the risks associated with market fluctuations. The practice of keeping financial records varies widely across the counties, with 41% of Uasin Gishu maize farmers, 15.4% of Bungoma maize farmers, and 67.6% of Bungoma soybean farmers reporting that they keep financial records (Table 10). The higher rate of record- keeping among Bungoma soybean farmers indicates a more structured approach to managing farm finances, which could aid in accessing loans or insurance. In Uasin Gishu, record-keeping is less common, which may hinder farmers’ ability to track expenses, revenues, and profits, potentially limiting their access to financial services. This suggests that financial literacy programs could be beneficial in improving farm management practices and enhancing access to credit and insurance products. Farmers’ preferred sources of financial information vary across counties. In Uasin Gishu, 77% of maize farmers prefer receiving financial information via SMS, while 47% rely on farmer groups and 18% prefer in-person meetings (Table 10). In Bungoma, maize farmers largely depend on radio (84.6%) for financial information, Agricultural Sustainability and Financial Inclusion through Warehouse Receipts: A Baseline Study from Uasin Gishu and Bungoma • 23 while 50% rely on SMS and 19.2% on farmer groups. Bungoma soybean farmers also prefer SMS (60.8%) and in- person meetings (40.5%) for financial information, with fewer using radio (27%). These differences in information preferences suggest that communication strategies should be tailored to each region, with a mix of digital, community-based, and media channels to ensure that farmers receive timely and relevant financial advice and information. 3.7. Digital Access and Technology Smartphone ownership is relatively high among Uasin Gishu maize farmers, with 73% owning smartphones (Table 11). In contrast, only 38.5% of Bungoma maize farmers report owning smartphones, while 67.6% of Bungoma soybean farmers have access to a smartphone. This disparity in ownership suggests that Uasin Gishu farmers are more likely to use digital tools for agricultural purposes, possibly due to better access to mobile technology and infrastructure. Bungoma maize farmers, with lower smartphone ownership, may face challenges in adopting digital tools, which could hinder their access to information, market prices, and financial services. CATEGORY DESCRIPTION UASIN GISHU BUNGOMA Maize (n=100) Maize (n=26) Soybean (n=74) Smartphone ownership Yes 73 (73%) 10 (38.5%) 50 (67.6%) Cost of devices/data 69 (69%) 23 (88.5%) 34 (45.9%) Main barrier in using digital tools Difficult to use/ understand 4 (4%) 13 (50%) 3 (4.1%) Content not relevant to my need 5 (5%) 7 (26.9%) 2 (2.7%) Poor network coverage 17 (17%) 3 (11.5%) 17 (23%) No barriers 5 (5%) - 6 (8.1%) * Numbers in parentheses indicate percentaje. TABLE 11. Digital Access and Barriers in Uasin Gishu and Bungoma Counties The primary barrier to using digital tools in both Uasin Gishu and Bungoma counties is the cost of devices and data, with 69% of Uasin Gishu maize farmers and 88.5% of Bungoma maize farmers, along with 45.9% of Bungoma soybean farmers, citing it as the main obstacle (Table 11). This high cost of access to digital tools limits the widespread use of mobile apps, weather forecasts, and financial services, especially in Bungoma. Additionally, some farmers in both regions, particularly 21.6% of Bungoma soybean farmers, report difficulties in using digital tools, suggesting that digital literacy is also a significant barrier. Content relevance and poor network coverage further exacerbate the challenges, with 5.0% of Uasin Gishu farmers and 17% of Bungoma farmers noting that digital tools do not meet their needs, and 17% and 23% of farmers, respectively, citing network issues as obstacles (Table 11). Despite these barriers, a small proportion of farmers in each region (5% in Uasin Gishu, 6% in Bungoma soybean) report no challenges in using digital tools, indicating that while some farmers successfully integrate digital solutions, most still face barriers related to cost, digital literacy, and connectivity. These findings highlight the need for affordable data and devices, improved network infrastructure, and digital literacy programs to support greater digital integration in agriculture. NB: The younger age profile and high smartphone penetration in Uasin Gishu make it ideal for piloting digital agriGHALA modules, such as SMS alerts, mobile record-keeping, and WRS-linked financial apps. 24 • Baseline Report 4. CONCLUSION AND RECOMMENDATIONS 4.1. Conclusion This study highlights the significant agricultural, financial, and technological challenges faced by smallholder farmers in Uasin Gishu and Bungoma counties, while emphasising the transformative potential of the Warehouse Receipt System (WRS) to address these issues. The findings reveal that Uasin Gishu farmers, with their relatively larger-scale operations, demonstrate higher awareness and limited use of WRS (4%), whereas Bungoma farmers, particularly maize farmers, face compounded barriers related to storage infrastructure, digital access, and financial inclusion, with WRS awareness as low as 3.8%. Despite these gaps, Bungoma soybean farmers show a strong willingness (97.3%) to adopt both WRS and climate- smart agriculture (CSA) practices, signalling readiness for innovation uptake. Uasin Gishu farmers benefit from better digital access, with 73% owning smartphones, while Bungoma farmers remain constrained by device and data costs. The study identifies price fluctuations as the greatest financial risk, especially among Bungoma maize farmers, underscoring the importance of improved market linkages and structured trading systems. Additionally, poor record-keeping and low crop insurance uptake further weaken farmers’ resilience, particularly in Bungoma. Importantly, the study underscores the link between post-harvest inefficiencies and environmental sustainability: widespread post- harvest losses, due to pests, mould, and inadequate drying, lead to compensatory input intensification in subsequent seasons, thereby increasing the carbon footprint. The WRS, by enabling safe storage and reducing avoidable losses, not only stabilises incomes but also lowers greenhouse gas emissions associated with replanting, over-fertilisation, and spoilage-related transport inefficiencies. Thus, scaling up WRS adoption serves a dual purpose: improving household-level food security and contributing to low-emission, climate- resilient agriculture. The study concludes that to enhance sustainability and resilience in these regions, urgent investment is needed in WRS infrastructure, CSA promotion, digital inclusion, financial literacy, and carbon-aware post-harvest systems that unlock the full environmental and economic benefits for Kenya’s smallholder farmers. Farmer awareness remains the single biggest bottleneck to agriGHALA uptake. Peer- led sensitization and participatory engagement with trusted actors can unlock rapid diffusion, especially in Bungoma where uptake is currently at 0% among maize farmer. Reducing losses (up to 71.6% pest- related) through wider agriGHALA adoption offers immediate gains for food security, income stability, and carbon footprint reduction. Finally, consider expanding aggregation pilots in Bungoma soybean zones using agriGHALA–cooperative partnerships. Support cooperatives to serve as storage access points and digital intermediaries. 4.2. Recommendations Based on the findings from the study, several recommendations can be made to enhance the agricultural productivity, financial inclusion, and climate resilience of smallholder farmers in Uasin Gishu and Bungoma counties. The results show that while there are significant opportunities for improvement in storage systems, financial services, and climate adaptation, addressing barriers such as limited knowledge of the Warehouse Receipt System (WRS) and digital tool access is crucial. The following recommendations aim to create a more sustainable and inclusive agricultural environment for farmers in these regions. • Increase Awareness and Adoption of WRS: Efforts should be made to enhance farmers’ knowledge and Photo by: Neil Palmer / CIAT Agricultural Sustainability and Financial Inclusion through Warehouse Receipts: A Baseline Study from Uasin Gishu and Bungoma • 25 understanding of the Warehouse Receipt System (WRS) through targeted education and outreach programs, particularly in Bungoma. Providing accessible information on how WRS can be used as collateral for loans could improve financial inclusion and reduce post-harvest losses. • Promote Climate-Smart Agriculture (CSA): Expanding training on CSA practices and providing resources to farmers can help improve their resilience to climate change. Encouraging crop diversification, soil health improvement, and water- efficient practices will enable farmers to adapt to changing weather patterns and ensure long-term sustainability. • Enhance Digital Literacy and Access: To bridge the digital divide, farmers should be provided with affordable access to smartphones and mobile data, along with training in using digital platforms for market information, financial services, and agricultural advice. Partnerships with telecom companies and agricultural organisations could facilitate this access. • Improve Financial Literacy and Support: Financial literacy programs should be implemented to teach farmers about budgeting, record-keeping, and using financial products like crop insurance and credit. Additionally, introducing affordable credit facilities and insurance options will help farmers mitigate risks such as price fluctuations and crop failure. • Develop Storage Infrastructure: Investment in modern, climate-resilient storage facilities is critical for reducing post-harvest losses and ensuring that farmers can store their produce until market conditions are favorable. This would also enable farmers to access better prices through the WRS and maintain the quality of their crops. • Consider launching a CSA-agriGHALA bundled extension model—where farmers accessing storage also receive training on CSA. Incentivize cessation of residue burning through compost training and carbon-benefit education. • Leverage social learning by engaging early adopters and peer influencers in structured agriGHALA sensitization efforts. Work closely with CIAT and local cooperatives to design village-level demos, peer farmer days, and audio-visual case stories that enhance visibility and trust in agriGHALA services. • Tailor agriGHALA onboarding materials and digital interfaces to appeal to the younger, digitally literate demographic. Youth-led farmer field schools and mobile-based Q&A support lines should be explored. BIBLIOGRAPHY GOK (Government of Kenya). (2018). Agriculture Sector Transformation and Growth Strategy (2019–2029). Ministry of Agriculture, Nairobi. Hartung C; Lerer A; Anokwa Y; Tseng C; Brunette W; Borriello G. (2010). Open Data Kit: Tools to build information services for developing regions. Proceedings of the 4th ACM/IEEE International Conference on Information and Communication Technologies and Development. https://doi.org/10.1145/2369220.2369236 Jaetzold R; Schmidt H; Hornetz B; Shisanya C. (2006). Farm Management Handbook of Kenya. Ministry of Agriculture, Nairobi. KARI (Kenya Agricultural Research Institute). (2020). The Role of Climate-Smart Agriculture in Enhancing Agricultural Productivity in Sub-Saharan Africa. KARI Publications, Nairobi. Laderchi C; Bamberger M. (2018). Digital Inclusion for Farmers: Case Studies from Rural Kenya. Journal of Digital Development 25(4): 225-235. Makau C; Waweru N. (2020). Use of Mobile Phones and ICTs for Agricultural Development in Kenya. Information and Communication Technology for Rural Development 12(1): 77-95. Mungai N; Ndung’u J. (2016). The Role of Smallholder Farmers in Achieving Food Security in Kenya. Journal of Agricultural Economics 47(3): 375-386. Nambiro B. (2020). Understanding the Financial Challenges of Smallholder Farmers in Rural Kenya. African Economic Review 42(2): 109-123. Otieno R. (2021). Climate Change and Agriculture in Kenya: An Assessment of Adaptation Strategies in Uasin Gishu and Bungoma. Climate Action Journal, 12(2): 110-125. Wambua J; Kimuyu P. (2018). Financial Inclusion and Agricultural Productivity: The Case of Smallholder Farmers in Kenya. Kenya Financial Review 30(1): 90- 102. Wambugu R; Mbugua M. (2019). Post-Harvest Losses and Storage Technologies in Kenya: A Case Study of Maize and Soybean Farmers in Uasin Gishu and Bungoma Counties. Agriculture and Food Security, 8(1): 55-67. https://doi.org/10.1145/2369220.2369236 p4gpartnerships.org agribora.comalliancebioversityciat.org cgiar.org p4gpartnerships.org agribora.com alliancebioversityciat.org cgiar.org EXECUTIVE SUMMARY ACKNOWLEDGMENTS 1. INTRODUCTION 2. METHODOLOGY 2.1. Study Area 2.2. Research Design 2.3. Population and Sampling 2.4. Data Collection 2.5. Data Management and Analysis 3. STUDY FINDINGS 3.1. Participant Demographics 3.2. Post-Harvest Management Practices 3.3. Market Access and Financial Inclusion 3.4. Climate Awareness and Practices 3.5. Agricultural Inputs and Sustainability Practices 3.6. Financial Behavior 3.7. Digital Access and Technology 4. CONCLUSION AND RECOMMENDATIONS 4.1. Conclusion 4.2. Recommendations