Adoption intensity of KCSAP-promoted TIMPs (Technologies, Innovations, and Management Practices) and their effects on GHG emission indicators of the selected crop value chains of smallholder farms Synthesis Report To Kenyan Climate Smart Agricultural Project By : mazingira.info@cgiar.org : https://mazingira.ilri.org/ : info@cortilescientific.ac.ke : www.cortilescientific.co.ke mailto:mazingira.info@cgiar.org https://mazingira.ilri.org/ mailto:info@cortilescientific.ac.ke http://www.cortilescientific.co.ke/ i 28th Feb 2023 EXECUTIVE SUMMARY Climate change is among the main threats to Kenya's predominant smallholder rain-dependent farmers. Additionally, agriculture is the main contributor to anthropogenic greenhouse gases (GHG; carbon dioxide, methane, and nitrous oxide). To respond to and reduce the adverse effects of climate change experienced by the smallholders, the Kenya Climate Smart Agriculture Project (KCSAP) was designed and developed. The Kenya Climate Smart Agriculture Project (KCSAP) is a Government of Kenya (GoK) project supported by the World Bank under the State Department for Crops Development in the Ministry of Agriculture, Livestock, Fisheries & Irrigation (MoALF&I). The project's development objective (PDO) was "to increase agricultural productivity and build resilience to climate change risks in the targeted smallholder farming and pastoral communities in Kenya, and in the event of an eligible crisis or emergency, to provide an immediate and effective response." As part of the KCSAP project completion evaluation activities, International Livestock Research Institute (ILRI) subcontracted Cortile Scientific Limited to determine the effect of the implementation of Technology, Innovation, and Management Practices (TIMPs) on GHG emission indicators [net GHG emissions, net GHG emissions per unit (GHG emission intensity (EI) of a product), product produced, and percentage reduction in GHG EI] under cassava, sorghum, and finger millet VCs (VC). The research study was conducted in seven counties, i.e., Baringo, Busia, Kisumu, Kericho, Laikipia, Siaya, and West Pokot in Kenya. A cross-sectional household survey was conducted. To select the respondents, multi-stage, purposive, and random sampling procedures were employed. The sampling frame for the respondents included beneficiary and non-beneficiary farmers in the common interest groups (CIG)/vulnerable and marginalized groups (VMG) in the selected seven counties. A sample size of two hundred farms per VC per county was selected (100 from farms with TIMPs and 100 from KCSAP project non-beneficiaries). The primary data was collected from households between October and December 2022. A semi-structured face-to-face interview schedule was used to collect both quantitative and qualitative data. The data were analysed using STATA 15.0 software. Cool Farm Tool (CFT) was used to quantify GHG balance across the different ii cropping systems. The net GHG balance was expressed as CO2 equivalence whereby a positive sign indicated a source, and a negative sign a sink. The study highlights essential findings on finger millet, sorghum, and cassava VCs. There was a varied uptake level of different TIMPs categories in the finger millet VC. Most finger millet smallholder farmers implemented improved varieties (71%), agronomic management practices (100%), integrated pest management (65%), and soil water conservation practices (87%). Nevertheless, value addition (6%), post-harvest management (20%), markets (1%), and mechanization (18%) had low uptake levels. There was high uptake intensity of TIMPs under finger millet farming, with most households in Kericho county implementing four practices. Generally, there was an increase in finger millet yields (kg ha-1), area-scaled GHG emissions (kg CO2e ha-1), GHG EIs (kg CO2e kg-1 grain), and monetary-scaled emissions (kg CO2e USD-1) by 3.51%, 27.79%, 14.55 and 35.56%, respectively, for KCSAP beneficiary's households than the non-KCSAP households. This means GHG emission was at an increasing trend with TIMPs intensities and led to greater GHGs in the atmosphere. For the sorghum VC, the uptake level of different TIMPs categories ranged from low (0%) to high (100%) across the six counties: Busia, Baringo, Kisumu, Laikipia, Siaya, and West Pokot. Across the six counties, improved sorghum varieties (58-91%), agronomic management practices (71- 100%), and soil water management practices (72-91%) had high uptake levels. All counties' uptake levels for post-harvest management (23-54), crop health (0-23%), mechanization (2- 25%), and market strategies (3-19%) were low. Value addition had mixed results, where Busia county (62%) and Siaya county (64%) had a high uptake. The other counties had a low one (<50%). The uptake intensity of the TIMPs ranged from zero to eight. Most sorghum smallholders in Busia (25%), Baringo (36%), and Kisumu (31%) counties implemented four TIMPs, while Laikipia (37%), Siaya (36%), and West Pokot (36%) were implementing three. Across the six counties, smallholders implementing higher TIMPs reduce GHG EIs by at least 94% and monetary scaled emissions by at least 83% compared to those implementing 0 TIMPs. iii Under the cassava VC, there were varied TIMPs uptake levels in Busia and Kisumu. Most cassava smallholder farmers (100 & 96%) in both counties implemented soil and water management practices. Crop health management (27 & 12%), mechanization (27 & 10%), and post-harvest management (40 & 14%) had a low uptake level. Most (29%) smallholder sorghum farmers in Busia implemented three TIMPs, while their counterparts (26%) in Kisumu implemented only two. However, KCSAP project participation by the farmers had no significant influence on cassava yields (kg ha-1), income (USD ha-1), area-scaled emissions (kg CO2 eq. ha-1), GHG EIs (kg CO2 eq. kg-1 grain), and monetary scaled emissions (kg CO2 eq. USD-1). Uptake intensity showed a significant effect. Implementing seven TIMPs reduced area-scaled emissions, GHG EIs, and monetary scaled emissions by 58%, 98%, and 100% in Busia County compared to one TIMP. Implementing six TIMPs reduced area-scaled emissions, GHG EIs, and monetary scaled emissions by 82%, 111%, and 103% in Kisumu County compared to one TIMP. Therefore, implementing more TIMPs led to higher reduction in GHG emissions and Eis. Based on the study findings, there is a need to promote further the uptake of value addition, post-harvest management, markets, and mechanization among smallholder farming households in all the selected VCs. The mixed results of the TIMPs effects on GHG-related indicators may have been due to the short duration the farmers implemented the TIMPs. There is a need for long-term implementation of the TIMPs for significant effects to be felt by smallholder farmers. iv TABLE OF CONTENTS Executive summary ..................................................................................................................... i Table of Contents ...................................................................................................................... iv 1. Introduction ........................................................................................................................ 6 2. Study Objective ................................................................................................................... 7 3. Methodology .......................................................................................................................... 7 3.1 Study sites ......................................................................................................................... 7 3.2 Research design, sampling procedures, and data collection ............................................ 8 3.3 Modelling .......................................................................................................................... 9 4. Findings ................................................................................................................................ 12 4.1 Finger millet Value chain ................................................................................................ 12 4.1.1 Overview .................................................................................................................. 12 4.1.2 Descriptive characteristics of finger millet VC ......................................................... 12 4.1.3 The TIMPs uptake level and intensity ...................................................................... 13 4.1.4 Climate change adaptation and mitigation .............................................................. 14 4.1.5 Crop residue management ...................................................................................... 16 4.1.6 Challenges facing finger millet production .............................................................. 16 4.1.7 Post-harvest management, business and markets, and finger millet value addition .......................................................................................................................................... 16 4.1. 8 Livestock systems .................................................................................................... 18 4.1.9 Finger millet productivity and GHG emissions ......................................................... 18 4.2 Sorghum Value Chain...................................................................................................... 20 4.2.1 Overview .................................................................................................................. 20 4.2.2 Descriptive characteristics of smallholder sorghum farming households ............... 20 4.2.4 Climate change adaptation and mitigation .............................................................. 23 4.2.5 Crop residues management ..................................................................................... 29 4.2.6 Sorghum production challenges .............................................................................. 30 4.2.7 Sorghum post-harvest management, marketing, and value addition ...................... 30 4.2.8 Livestock production ................................................................................................ 33 4.2.9 Sorghum productivity and GHG fluxes ......................................................................... 35 4.3 Cassava value chain ........................................................................................................ 39 v 4.3.1 Overview .................................................................................................................. 39 4.3.2 Descriptive characteristics of cassava-farming households ..................................... 39 4.2.3 Cassava TIMPs uptake .............................................................................................. 39 4.3.4 Climate change adaptation and mitigation .............................................................. 41 4.3.5 Crop residue management ...................................................................................... 46 4.2.6 Challenges facing cassava value chain ..................................................................... 47 4.2.8 Cassava post-harvest management, marketing, and value addition ....................... 48 4.3.9 Cassava livestock integration ................................................................................... 49 4.3.10 Cassava productivity and GHG emissions .............................................................. 51 5. Conclusion and Recommendations ...................................................................................... 55 5.1 Conclusion ...................................................................................................................... 55 5.1.1 Finger millet value chain .......................................................................................... 55 5.1.2 Sorghum value chain ................................................................................................ 55 5.1. 3 Cassava value chain ................................................................................................. 56 5.2 Recommendations .......................................................................................................... 57 1. Acknowledgements ........................................................................................................... 59 APPENDICES ............................................................................................................................. 60 Appendix 1: Questionnaire cassava ...................................................................................... 60 Appendix 3; Questionnaire sorghum .................................................................................... 66 Appendix 3. Questionnaire finger millet ............................................................................... 72 6 1. INTRODUCTION Agricultural ecosystems are the primary sources of greenhouse gases, contributing about 14 to 17%, leading to adverse climate-related changes such as erratic rainfall patterns and amounts and prolonged dry spells and droughts. In Kenya, agriculture is predominantly rain- fed, hence, extremely vulnerable to climate change. The Kenya Climate Smart Agriculture Project (KCSAP) was designed and developed to respond to and reduce the adverse effects of climate change. The Kenya Climate Smart Agriculture Project (KCSAP) is a Government of Kenya (GoK) project supported by the World Bank under the State Department for Crops Development in the Ministry of Agriculture, Livestock, Fisheries & Irrigation (MoALF&I). The project’s development objective (PDO) was “to increase agricultural productivity and build resilience to climate change risks in the targeted smallholder farming and pastoral communities in Kenya, and in the event of an eligible crisis or emergency, to provide an immediate and effective response.” KCSAP Project was to achieve this goal through the triple win strategy: (i) sustainably increase productivity, (ii) enhance the resilience of agricultural systems, and (iii) increase the efficiency of use of resources, among them energy, water, and land while reducing emissions of GHG as a co-benefit. The “triple win” was to be attained through; 1. Improving water/soil management, especially within smallholder systems in the marginal rainfall zones – specifically, in smallholder mixed crop-livestock, crop- livestock-tree (agrosilvopastoral) production systems, and in crop forest (agro-forestry) production systems. 2. Promoting sustainable, community‐driven rangeland management and improved access to quality livestock services in ASALs—specifically, in pastoral/extensive livestock production systems. 3. Supporting the generation and dissemination of improved agricultural Technology Innovations Management Practices (TIMPs) and building sustainable seed systems; and 4. Enhancing access to quality agro-weather, climate, advisory, and market information services among farmers/herders for improved decision-making. The KCSAP project was implemented in 24 counties, namely: 7 • 6 Arid counties: Marsabit, Isiolo, Tana River, Garissa, Wajir, and Mandera • 9 Semi-arid counties: West Pokot, Baringo, Laikipia, Machakos, Nyeri, Tharaka Nithi, Lamu, Taita Taveta and Kajiado • 9 Medium to high rainfall counties: Busia, Siaya, Nyandarua, Bomet, Kericho, Kakamega, Uasin Gishu, Elgeyo Marakwet and Kisumu The KCSAP priority VCs included: cassava, green grams, sorghum, millet, pigeon peas, bananas, tomato, Irish potatoes, beans, apiculture, indigenous poultry (meat and eggs), dairy (cattle, camel and goats), red meat (cattle) and aquaculture. The TIMPs for the selected cropping systems included improved varieties, technology, tillage, farming systems (monocropping, intercropping, or mixed cropping), as well as fertilizer, animal manure, crop residues application, and number of weeding times among others. 2. STUDY OBJECTIVE This study sought to determine the effect of the implemented TIMPs on selected VCs’ (i.e., cassava, sorghum, and finger millet) productivity and greenhouse gas (GHG) emission indicators [net GHG emissions, GHG emissions intensity (GHG EI; GHG emissiions per unit of a product), product produced, and percentage reduction in GHG EI]. 3. METHODOLOGY 3.1 Study sites The study was carried out in seven counties (Baringo, Busia, Kisumu, Kericho, Laikipia, Siaya, and West Pokot) of Kenya, where the KCSAP project has been implemented for the past three years (Table 1). The counties are in Kenya’s humid and semi-arid agro-ecologicalagroecologic zones (AEZ). Arid counties were eliminated from the study due to high insecurity rates, thus hindering study implementation. The selected cropping systems included sorghum (Sorghum bicolor), cassava (Manihot esculenta), and finger millet (Panicum miliaceum) (Table 1). Farmers in the selected counties are predominantly smallholders practicing rain-fed agriculture, non- mechanized, and adversely affected by the vagaries of climate change. 8 Table 1: Study sites (counties), selected cropping systems, and sample size Site Climate zone Cropping system Farms with TIMPs Farms without TIMPs Baringo Semi-arid Sorghum 100 100 Laikipia Semi-arid Sorghum 100 100 West Pokot Semi-arid Sorghum 100 100 Busia Humid/High rainfall Sorghum 100 100 Busia Humid/High rainfall Cassava 100 100 Kericho Humid/High rainfall Finger millet 100 100 Kisumu Humid/High rainfall Sorghum 100 100 Kisumu Humid/High rainfall Cassava 100 100 Siaya Semi-Humid/High rainfall Sorghum 100 100 3.2 Research design, sampling procedures, and data collection A cross-sectional household survey was conducted in seven selected counties (Table 1). Multi- stage, purposive, and random sampling procedures were employed to select the respondents. First, purposive sampling of the KCSAP project sites (counties) was employed. Secondly, the purposive selection of the common interest groups (CIG)/vulnerable and marginalized Groups (VMG) from the selected project counties, i.e., Baringo, Busia, Kericho, Kisumu, Laikipia, Siaya, and West Pokot was done. The CIG/VMG samples comprised the KCSAP project beneficiaries (farmers who adopted the TIMPs) and non-beneficiaries (farmers who did not adopt the TIMPs). The KCSAP team from the Ministry of Agriculture, Livestock, Fisheries & Irrigation provided the sampling frame. Then, beneficiaries and non-beneficiaries were randomly selected within the study area. However, from the provided sampling frame, some farmers who had initially been listed as non-beneficiaries had adopted the TIMPs, thus, they were dropped and replacement done in the field, in consultation with the extension officers during sampling. A sample size of two hundred farms per cropping system per county was selected (100 from farms with TIMPs and 100 from KCSAP project non-beneficiaries). This sample size was based on a 95% (1.96) confidence level and 0.069 % allowable error. Kisumu and Busia had two VCs each, while the other five counties had only one. The study utilized primary data collected from households from October to December 2022. A recall survey approach was applied since the farmers had harvested the crops. A semi- structured face-to-face interview schedule was used to collect quantitative and qualitative data. Pre-testing of the interview schedule was conducted, after which the interview schedule 9 was adjusted accordingly and the survey implemented. The study utilized the Open Data Kit (ODK) mobile App for data collection. The interview schedule had questions on farms’ location, the socioeconomic characteristics of the farming households, TIMPs/crop management practices, farm characteristics, crop residue management practices, climate change awareness and response strategies, harvesting and post-harvest management practices, and livestock systems. 3.3 Modelling Cool Farm Tool (CFT) (Hillier et al., 2011)1 was used to quantify GHG balance across the different cropping systems. The CFT is an excel program that is used to run individual farms. The CFT input variables and output variables are described in Table 2. Table 2: The Cool Farm Tool input and output variables. Category Input variables Description General Location County the study was implemented Year The year the study was implemented Country Kenya (country the study was implemented Default Unit system* metric unit system Product The main product (crop yields) Area The are under the crop in ha Finished product Yields under the production area (kgs) Climate Tropical climatic condition Temperature Default temperature in tropical climate (18oC) Crop management Crop type The crop grown (finger millet, cassava or sorghum) Soil type Texture classification (fine, medium or coarse) SOM Soil organic matter content Soil moisture Moisture status (moist or dry) Drainage Drainage status (good or poor) PH Soil pH Inorganic fertilizer Amount of chemical fertilizer applied (Kg (X)* per ha) Organic fertilizer Amount of organic fertilizer applied (Kg (X)* per ha) Pesticides applications The number of times pesticides applied Crop residues Amount of crop residue (kg per ha) Crop residue management The method of crop residue management Sequestration Land use Change The land use changes Years The number of years since the change was implemented Proportion (%) The percentage area under the change compared to crop area Tillage practices The change in tillage practice Year The number of years since the change was implemented Proportion (%) The percentage area under the change compared to the crop area 1 Teenstra, E. D., de Buisonjé, F. E., Ndambi, A., & Pelster, D. (2015). Manure management in the (Sub-)Tropics: training manual for extension workers (No. 919). Wageningen UR Livestock Research. 10 Cover crop The change in cover crop management Year The number of years since the change was implemented Proportion (%) The percentage area under the change compared to the crop area Compost The change in compost management Year The number of years since the change was implemented Proportion (%) The percentage area under the change compared to crop area Crop residue The change in crop residue management Year The number of years since the change was implemented Proportion (%) The percentage area under the change compared to the crop area Trees biomass Species 1 Name of the tree species This year The number of trees this year Last year The number of trees last year Change The change in the number of trees Species 1 Name of the tree species This year The number of tree this year Last year The number of trees last year Change The change in the number of trees Species n Name of the tree species This year The number of trees this year Last year The number of trees last year Change The change in the number of trees Output variables Area-scaled GHG emissions Greenhouse gas emissions per unit area (kg CO2 eq. per ha) GHG EIs Greenhouse gas emission intensities (kg CO2 eq. per Kg yield) monetary scaled emissions Greenhouse gas emissions per unit income (kg CO2 eq. per USD) *X is the component of interest such as N & P2O5 To estimate the emissions from the crop value chain, the input data in Table 1 keyed in to the CFT excel program per farm. The CFT out generates the area-scaled emissions ((kg CO2 eq. per ha) and GHG EIs ((kg CO2 eq. per Kg yield). In nutshell, the area-scaled emissions show the total emissions from the farm per unit area of production. It is noteworthy, the area-scaled emissions can be a negative or positive value. A positive area-scaled emissions indicated that the specific farm acted as a net source of GHGs. However, the negative area-scaled emissions is a indication of carbon storage in the soil. The crop value chain could act as net sink if the carbon sequestration is higher than emissions. The calculation for GHG EIs and monetary- scaled emissions are shown in equations 1 and 2.2 Area-scaled emissions/ yields = GHG EIs (kg CO2eq kg−1 yield) Equation 1 Area-scaled emissions/ income = MSE (kg CO2eq USD income) Equation 2 2 https://doi.org/10.1016/j.spc.2023.01.010 https://doi.org/10.1016/j.spc.2023.01.010 11 3.4 Statistical analysis The data was cleaned and checked for consistency and completeness in a .csv file. All the variables were coded before analysis. The data were analysed using STATA 15.0 software. TIMPs adoption level, TIMPs adoption intensity, climate-change awareness, indicators, causes, and effects, tropical livestock unit (TLU), post-harvest management, challenges facing the cropping system, market access, value addition. The study employed descriptive statistics, including mean, standard error, frequencies, and percentages. The data subjected to frequencies and percentages were the dummy variable where 1 was yes and 0 otherwise apart from gender. For gender, the coding was 1 for male and 0 for female. Tropical livestock unit (TLU) for smallholder farming households was calculated following Njuki et al., (2011)3. of bull (1.2), oxen (1.24), cattle (1), heifer (0.78) sheep and goat (0.2), pig (0.3), chicken (0.04), and rabbit (0.04). We compared KCSAP members and non-members productivity and yield scaled emissions using an independent variable t-test. The mean between the two groups (members and non-members) were subjected to t-test at p≤0.05. Finally, yields, income, area-scaled emissions, GHG EIs, and monetary-scaled emissions were subjected to one way analysis of variance. The ANOVA was run to determine effects of TIMPs uptake intensities on productivity and GHG emissions. Where productivity and GHG emissions differences existed at p≤0.05, Tukey’s honestly significant difference (HSD) was used to separate the means. 3 Njuki,J.; Poole, J.; Johnson, J.; Baltenweck, I.; Pali, P.N.; Lokman, Z.; and S. Mburu. 2011. Gender, livestock and livelihood indicators. Nairobi, Kenya: ILRI 12 4. FINDINGS This section is organized per VC: finger millet, sorghum, and cassava. The results highlight key areas in the VCs, including descriptive characteristics, uptake level and intensity of TIMPs, climate change awareness, indicators, effects, causes & response, challenges facing the VCs, post-harvest management, business & markets, value addition, productivity, and GHG emissions. 4.1 Finger millet Value chain 4.1.1 Overview This section presents the effect of KCSAP project membership and uptake of TIMPs intensity on finger millet productivity, GHG emissions, and GHG EI in Kericho county. Descriptive characteristics of the sampled finger millet household heads and farm characteristics are presented. Climate change effects on finger millet production are highlighted. The section further highlights the challenges facing finger millet production in Kericho county. 4.1.2 Descriptive characteristics of finger millet VC The descriptive characteristics showed that most smallholder farmers (63%) were male (Table 2). The average age was 47.6%, and the farming experience was 7.7 years. Approximately 96.0% of the smallholders were literate. The findings suggested that the smallholder farmers were experienced in finger millet production and could adopt TIMPs. Table 2: Descriptive characteristics of finger millet VC. Variables Pooled (N=200) KCSAP-members (N=100) KCSAP-non-members (N=100) Mean SE Mean SE Mean SE Gender (male=1) 0.63 0.03 0.49 0.05 0.77 0.04 Age 47.6 0.94 46.8 1.17 48.35 1.5 Experience 7.73 0.46 7.84 0.57 7.61 0.73 Education level 1.53 0.05 1.58 0.07 1.47 0.08 Literacy 0.96 0.01 0.98 0.01 0.93 0.03 The Mean is average, and SE is the standard error of the mean 13 4.1.3 The TIMPs uptake level and intensity The findings showed that uptake of different TIMPs among finger millet farming households ranged from low (1%) for Business and Market to high (100%) for agronomic practices (Table 3). The findings suggested that smallholder farmers have varied uptake levels to different practices. The uptake of the TIMPs had a similar pattern between KCSAP members and non- members. Table 3: Uptake level of TIMPs. Variable Pooled KCSAP- members KCSAP-Non- members Mean SE Mean SE Mean SE Improved-variety 0.71 0.03 0.94 0.02 0.47 0.05 Agronomic practices 1.00 0.00 1.00 0.00 1.00 0.00 Integrated pest & disease management 0.65 0.03 0.65 0.05 0.65 0.05 Soil and water management 0.87 0.02 0.90 0.03 0.83 0.04 Post-harvest management 0.20 0.03 0.30 0.05 0.09 0.03 Value addition 0.06 0.02 0.06 0.02 0.05 0.02 Mechanization 0.18 0.03 0.19 0.04 0.17 0.04 Business and Markets 0.01 0.01 0.02 0.01 0.47 0.00 The Mean is average, and SE is the standard error of the mean Regarding the uptake intensity (number of practices categories) utilized by a smallholder farming household, every farming household utilized at least one practice (Table 4). Most farming households (37%) were implementing four practices. Most KCSAP members and non- members were implementing three TIMPs. Notably, KCSAP members utilized at least 2 TIMPs and members at least one. This indicated that the uptake of TIMPs was higher among members than non-members. This could be attributed to promoting the TIMPs among KCSAP members, thus enhancing uptake. 14 Table 4: Uptake intensity. Intensity Pooled KCSAP-members KCSAP-Non-members Freq % Freq % Freq % 0 0 0 0 0 0 0 1 2 1 0 0 2 2 2 36 18 5 5 31 31 3 42 21 23 23 19 19 4 75 37 39 39 36 36 5 38 19 27 27 11 11 6 7 4 6 6 1 1 Total 200 100 100 100 100 100 4.1.4 Climate change adaptation and mitigation Most smallholders (54%) were aware of climate change (Table 5). The main indicators of climate change highlighted by the respondents were temperature changes, changes in rainfall, unpredictable weather patterns, and crop failure or reduced yields (Table 5). This suggested that the smallholders could track the main indicators of climate change. Table 5: Climate change awareness and indicators. Variable Pooled KCSAP Member KCSAP- Non-member Freq % Freq % Freq % Climate change Awareness 108 54 57 57 51 51 Indicators of climate change Change in rainfall 27 14 17 17 10 10 Change in temperature 43 22 23 23 20 20 Drought 21 11 12 12 9 9 Change rainfall onset 3 2 2 2 1 1 Flooding 14 7 5 5 9 9 Unpredictable weather pattern 35 18 22 22 13 13 Pest and diseases 5 3 3 3 2 2 Soil erosion 3 2 1 1 2 2 Crop failure/ Low yields 25 13 13 13 12 12 Strong winds 15 8 3 3 12 12 The respondents' main causes of climate change were deforestation, GHG emissions, poor farming practices, and unpredictable weather patterns (Table 6). Most KCSAP members perceived deforestation as the main cause of climate change compared to KCSAP non- members, who highlighted unpredictable weather patterns as the major cause. 15 Table 6: Climate change causes and effects. Variable Pooled KCSAP Member KCSAP- Non-member Causes Freq % Freq % Freq % Deforestation 61 31 41 41 20 20 Poor farming methods 30 15 17 17 13 13 Unpredictable weather 31 16 8 8 23 23 Industrialization 6 3 4 4 2 2 GHG emissions 35 18 22 22 13 13 Burning fossil fuel 8 4 3 3 5 5 Overstocking 7 4 6 6 1 1 Effects Reduced crop yields 73 37 36 36 37 37 Pest and diseases 6 3 3 3 3 3 Floods 13 7 7 7 6 6 Soil erosion 3 2 1 1 2 2 Global warming 31 16 20 20 11 11 Drought 23 12 17 17 6 6 Unpredictable weather 36 18 16 16 20 20 Smallholder finger millet farming households utilized various response strategies to address the vagaries of climate change. The uptake of climate response strategies was low, ranging from (1%) for planting cover crops to (27%) for contour bands (Table 7). The uptake pattern was almost similar among KCSAP members and non-members. Table 7: Response to climate change. Variable Pooled Members Non-members Freq % Freq % Freq % Contour bands 53 27 32 32 21 21 Crop rotation 12 6 4 4 8 8 Grass strips 39 20 17 17 22 22 Mulching 25 13 17 17 8 8 Planting cover crops 1 1 1 1 0 0 Rainwater harvesting system 6 3 3 3 3 3 Reduced tillage 21 11 12 12 9 9 Retention ditches 4 2 1 1 3 3 Stone lines 30 15 16 16 14 14 Inter-cropping 18 9 6 6 12 12 16 4.1.5 Crop residue management Crop residue is an important component in the management of finger millets. The main challenge facing crop residue utilization in climate change mitigation is the conflict between farming and livestock. The study revealed that most smallholders (84.5%) used crop residues as animal feed (Table 8). However, 27.5% of the smallholders used crop residues as mulch. Table 8: Crop residue management among finger millet farming households. Variable Pooled KCSAP Member KCSAP- Non-member Freq % Freq % Freq % Burned 1 1 0 0 1 1 Incorporated as mulch 55 28 32 32 23 23 Removed left untreated on a heap 3 2 2 2 1 1 Removed non-forced aeration 2 1 1 1 1 1 Removed used as animal feeds 169 85 84 84 85 85 *Freq = frequency and Perc = percent. 4.1.6 Challenges facing finger millet production Pre-harvest losses, harvesting losses, and pest and disease invasion are finger millet production's main challenges. The findings indicated that smallholders experienced losses before and during harvesting (Table 9). Additionally, pests and diseases affected most (57%) of the smallholders' finger millet. Table 9: Challenges facing finger millet production. Challenges Pooled Members KCSAP- Non-members Freq % Freq % Freq % Lose before harvest 95 48 49 49 46 46 Loss during harvesting 58 29 30 30 28 28 Pests and diseases attack 113 57 57 57 56 56 4.1.7 Post-harvest management, business and markets, and finger millet value addition Post-harvest management is essential for reduced losses and enhanced returns. To minimize the post-harvest losses, smallholder finger millet farmers implemented post-harvest mechanization (41.5%) and improved storage (15%) (Table 10). However, the utilization of post-harvest management strategies was low among smallholders. This suggested that there 17 is still a high risk of post-harvest losses due to limited implementation of management practices. Table 10: Post-harvest management, business and markets, and finger millet value addition in Kericho County. Variable Pooled KCSAP Member KCSAP- Non-member Post-harvest management Freq Perc Freq Perc Freq Perc Improved storage 30 15 23 23 7 7 Mechanization 83 42 34 34 49 49 Business and market school fees 56 28 27 27 29 29 Fellow farmer 52 26 19 19 33 33 Middlemen 19 10 12 12 7 7 Miller 3 2 2 2 1 1 Retailer 23 12 12 12 11 11 Wholesaler 9 5 6 6 3 3 COPMAS memebership# 2 1 2 2 0 0 Value addition Value add (yes) 11 6 6 6 5 5 Blended and flours 10 5 5 5 5 5 Finger millet chapati 1 1 1 1 0 0 Finger millet mandazi 1 1 1 1 0 0 *Freq = frequency and Perc = percent, #The Community Production and Marketing System (COPMAS) sorghum Model The study revealed low (1%) COPMAS membership (Table 10). Only the KCSAP members had joined the COPMAS group. The findings underscored the need to enhance COPMAS membership among finger millet farming households. Most smallholders sold their finger millet through schools and fellow farmers. This highlighted the poor market structure in the finger millet sector. The study showed low (5.5%) value addition among finger millet farming households (Table 10). The main value-added products were blended flour, chapati, and mandazi. The finding highlighted the need for increased awareness among smallholders. 18 4.1. 8 Livestock systems Finger millet smallholder kept livestock for diversification. However, none of the smallholder farming households keep pigs and horses (Table 11). The average TLU was 3.71 units. The KCSAP members had a higher TLU of 4.07 than KCSAP non-members, 3.35. However, we do not know the number of livestock of KCSAP members and non-members from the beginning of the support, this cannot be said for certain because animal numbers might have been different between these 2 groups already at the beginning of the study. Table 11: Livestock systems among smallholder finger millet. Variable Pooled KCSAP Member KCSAP- Non-member Mean SE Mean SE Mean SE Female cows 12 months 1.61 0.09 1.69 0.11 1.53 0.13 Growing cattle 7-11 months 0.62 0.12 0.85 0.23 0.38 0.07 Growing cattle 12 months 0.40 0.07 0.52 0.11 0.28 0.09 Replacement females 7-11 months 0.08 0.02 0.12 0.04 0.04 0.03 Replacement females 12 months 0.09 0.03 0.14 0.05 0.04 0.02 Calves 0-6 months 0.32 0.05 0.30 0.06 0.34 0.06 Bulls 0.22 0.04 0.24 0.06 0.2 0.06 Oxen 0.08 0.04 0.05 0.03 0.11 0.08 Rams 0.13 0.04 0.09 0.05 0.17 0.06 Ewes 0.32 0.07 0.21 0.10 0.42 0.10 Lambs 0.15 0.04 0.08 0.04 0.21 0.06 bucks 0.28 0.04 0.33 0.07 0.22 0.06 Doe 0.76 0.11 0.81 0.13 0.71 0.18 Kids 0.37 0.06 0.34 0.09 0.39 0.09 Poultry 12.7 1.01 14.4 1.75 11.0 0.97 Pigs 0.00 0.00 0.00 0.00 0.00 0.00 Horses, mules, or asses 0.02 0.01 0.03 0.02 0.00 0.00 Rabbits 0.05 0.02 0.04 0.03 0.06 0.03 Tropical Livestock Unit (TLU) 3.71 0.21 4.07 0.30 3.36 0.31 4.1.9 Finger millet productivity and GHG emissions The findings showed that yields (kg ha-1), income (USD ha-1), area-scaled emissions (kg CO2 eq. ha-1), EIs(kg CO2e. kg-1 grain), and monetary scaled emissions (kg CO2 eq. USD-1) were similar between KCSAP members and non-members (Table 12). However, the yield change, area- scaled GHG emissions, GHG EIs, and monetary-scaled emissions were positive. The findings showed that KCSAP membership increased yields, area-scaled GHG emissions, GHG EIs, and 19 monetary-scaled emissions by 3.51%, 27.79%, 14.55 and 35.56%, respectively (Table 13). This suggested that KCSAP members had higher productivity and GHG emission than non-members. Table 12 Effect of KCSAP membership on finger millet productivity and GHG emissions. Variable Pooled KCSAP-Member KCSAP Non-member Diff (A-B) Mean SE Mean SE Mean SE Yields (kg ha-1) 1,182 111 1,205 165 1,163 148 42.3 Income (USD ha-1) 1,818 149 1,762 222 1,881 202 -118 Area scaled emissions (kg CO2 eq. ha-1) 195 81.8 230 112 166 120 64.0 GHG EIs (kg CO2 eq. kg-1 grain) 0.51 0.12 0.55 0.18 0.47 0.17 0.08 Monetary scaled emissions kg CO2 eq. USD-1) 0.37 0.09 0.45 0.15 0.29 0.11 0.16 Table 13 Effect of Intensity on finger millet productivity and GHG emissions. Intensity Yields (kg ha-1) Income (USD ha-1)4 Area scaled emissions (kg CO2 eq. ha-1) GHG intensities (kg CO2 eq. kg-1 grain) Monetary Scaled emissions kg CO2eqUSD-1) 1 346e 377e 554a 2.45a 2.03a 2 669d 1,007d 163a 0.39c 0.28c 3 1,059c 1,645c 381a 0.90b 0.57b 4 928C 1,502c 215a 0.55c 0.41b 5 1,573b 2463b 59.7a 0.10d 0.13d 6 5,402a 7,491a -306a 0.08e 0.03e p-value <0.001 <0.001 0.06 0.02 0.03 Values followed by the same superscript in the same column are not significantly different at p≤0.05 The ANOVA findings showed that uptake intensity significantly (p ≤ 0.05) influenced yields (kg ha-1), Income (USD ha-1), GHG EIs (kg CO2e kg-1 grain), and Monetary scaled emissions (kg CO2e USD-1). The negative area-scaled emissions indicated that the smallholder farmers implementing six practices acted as a sink of GHGs. Smallholders with many practices also had higher yields, income, and low GHGI and Monetary scaled emissions. Smallholders implementing six TIMPs had an increment of yields (1461%), income (1887%), area-scaled emissions (-155%), GHG Eis (-97%), and monetary-scaled emissions (99%) compared to those implementing only 1 TIMPs. This suggested that the implementation of the multiple practices was sustainable. This could be attributed to the synergetic effect of different practices. 4 https://www.exchangerates.org.uk/USD-KES-spot-exchange-rates-history-2022.html https://www.exchangerates.org.uk/USD-KES-spot-exchange-rates-history-2022.html 20 4.2 Sorghum Value Chain 4.2.1 Overview This section presents the effect of KCSAP project membership and uptake of TIMPs intensity on sorghum productivity and GHG emissions across the six counties. The section highlights the key findings under the sorghum VC, including descriptive statics, TIMPs uptake, climate change response, sorghum value addition, and marketing. The section further presents the effect of TIMPs uptake intensity on sorghum productivity and GHG emissions. 4.2.2 Descriptive characteristics of smallholder sorghum farming households Unlike Kisumu, most sorghum farmers were female in all surveyed counties (Table 14). This could be ascribed to sorghum is considered an orphan crop that is considered to be of low value. Men mostly control high-value crops leaving the women to operate the low-value crops. The average age ranged from 36.0 years in West Pokot to 53.0 years in Laikipia. The lowest experience was observed in West Pokot (7.1 years) and the highest in Laikipia (22. 9). Laikipia had more experience. Across the six counties, the literacy level was high. The lowest literacy level was 58% in West Pokot, and the highest in 98% in Baringo and Busia counties. Further, the findings showed moderate family size. Most smallholders in Kisumu heightened more failed seasons over the last six cropping seasons. 21 Table 14: Descriptive characteristics of smallholder sorghum farming households in the six counties. County Category Gender (male=1) Age years Experience years Literacy Family size N Failed seasons Baringo Pooled 0.45 43.75 8.01 0.98 5.83 1.47 Member 0.46 44.60 7.91 1.00 6.08 1.35 N-Member 0.44 42.90 8.12 0.96 5.58 1.58 Busia Pooled 0.45 43.75 8.01 0.98 5.83 1.47 Member 0.46 44.60 7.91 1.00 6.08 1.35 N-Member 0.44 42.90 8.12 0.96 5.58 1.58 Kisumu Pooled 0.66 48.10 13.04 0.96 5.91 2.90 Member 0.75 48.74 12.63 0.95 5.97 3.04 N-Member 0.56 47.45 13.45 0.96 5.85 2.76 Laikipia Pooled 0.44 53.01 22.89 0.79 4.92 3.75 Member 0.51 53.96 22.90 0.78 4.94 3.41 N-Member 0.36 52.07 22.87 0.79 4.89 4.10 Siaya Pooled 0.42 51.09 18.42 0.97 6.13 0.68 Member 0.48 54.30 19.86 0.97 6.29 0.88 N-Member 0.35 47.95 17.00 0.97 5.98 0.49 West Pokot Pooled 0.46 36.09 7.06 0.58 6.34 1.64 Member 0.45 35.82 7.05 0.54 6.34 1.58 N-Member 0.46 36.35 7.07 0.61 6.35 1.70 The values presented are mean The study revealed a high uptake level of improved varieties, agronomic management practices, soil & water management, and sorghum crop health (Table 15). However, post- harvest management, mechanization, and marketing uptake level were low. 22 Table 15: Uptake level of TIMPs among Sorghum farmers in the six counties. County Category Uptake level Im p ro ve d s o rg h u m va ri et ie s A gr o n o m ic m an ag em en t p ra ct ic es So il an d w at er m an ag em en t So rg h u m C ro p H ea lt h P o st -h ar ve st m an ag em en t V al u e ad d it io n M ec h an iz at io n B u si n es s an d M ar ke t Baringo Pooled 0.82 0.99 0.86 0.12 0.54 0.49 0.12 0.03 Member 0.94 1.00 0.86 0.17 0.61 0.50 0.24 0.06 N-Member 0.70 0.98 0.86 0.08 0.47 0.47 0.01 0.00 Busia Pooled 0.69 1.00 0.72 0.00 0.31 0.62 0.05 0.18 Member 1.00 0.99 0.76 0.00 0.36 0.62 0.07 0.27 N-Member 0.26 1.00 0.65 0.00 0.24 0.62 0.02 0.06 Kisumu Pooled 0.69 1.00 0.91 0.23 0.29 0.30 0.17 0.19 Member 0.70 1.00 0.86 0.29 0.24 0.39 0.19 0.14 N-Member 0.67 1.00 0.96 0.17 0.35 0.22 0.15 0.25 Laikipia Pooled 0.87 0.97 0.89 0.01 0.23 0.45 0.10 0.13 Member 0.94 0.98 0.93 0.03 0.30 0.47 0.17 0.22 N-Member 0.80 0.96 0.84 0.00 0.17 0.43 0.03 0.04 Siaya Pooled 0.58 1.00 0.79 0.02 0.28 0.64 0.25 0.07 Member 0.76 1.00 0.93 0.02 0.16 0.74 0.37 0.12 N-Member 0.41 0.99 0.65 0.03 0.39 0.55 0.14 0.02 West Pokot Pooled 0.91 0.71 0.86 0.17 0.19 0.12 0.02 0.11 Member 0.99 0.81 0.88 0.23 0.22 0.17 0.02 0.14 N-Member 0.83 0.60 0.84 0.11 0.15 0.06 0.02 0.08 The values presented are means The uptake intensity ranged from zero to eight (Table 16). The finding highlighted that some smallholder farmers did not implement the TIMPs categories. Additionally, the eight suggested that some smallholder farmers implemented all the TIMPs categories. The results showed that smallholders implemented multiple practices to benefit from their complementariness. 23 Table 16: The TIMPs uptake intensity among sorghum smallholder farming households. County Category Intensity of uptake 0 1 2 3 4 5 6 7 8 Baringo Pooled 0 0 10 27 36 16 6 5 0 Member 0 0 4 22 39 14 11 11 0 N-Member 0 0 16 32 34 18 1 0 0 Busia Pooled 0 2 28 19 25 16 9 2 0 Member 0 0 13 21 31 21 13 2 0 N-Member 0 4 50 17 17 9 2 1 0 Kisumu Pooled 0 2 16 23 31 19 9 0 0 Member 0 3 11 23 36 22 6 0 0 N-Member 0 1 21 23 26 17 12 1 0 Laikipia Pooled 1 0 13 37 25 17 4 2 0 Member 0 0 7 35 29 21 7 4 1 N-Member 2 1 19 42 22 13 2 0 0 Siaya Pooled 0 3 7 36 33 18 2 0 0 Member 0 0 3 25 38 29 4 1 1 N-Member 0 7 11 47 28 7 0 0 0 West Pokot Pooled 1 12 24 36 10 13 6 1 0 Member 0 6 17 37 14 18 7 1 0 N-Member 1 17 30 34 5 8 5 0 0 The values presented are percentages 4.2.4 Climate change adaptation and mitigation Most smallholder farmers were aware of climate change (Table 17). The awareness ranged from 76% in West Pokot to 95% in Busia. The main indicators of climate change were unpredictable rainfall patterns, changes in rainfall amount, increased drought, and temperature changes. Smallholder sorghum farmers perceived deforestation, GHG emissions, weather changes, and poor farming methods as the main causes of climate change (Table 18). The findings suggested that smallholder farmers were able to highlight the reasons for climate change. Given that smallholders could correctly highlight the causes of climate change; they could embrace mechanisms for curbing its vagaries. 24 Table 17: Awareness and indicators of climate change in the sorghum value chain in the six counties. Climate change Baringo Busia Kisumu Laikipia Siaya West Pokot Climate change awareness Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Awareness 86 86 86 95 74 32 90 85 94 85 90 79 79 94 65 76 74 77 Indicators Not aware 0 0 1 0 0 0 0 0 0 0 0 0 2 1 3 2 3 1 Unpredictable weather patterns 0 0 0 0 0 0 11 8 15 7 10 5 12 16 8 0 0 0 Unpredictable rainfall pattern 16 12 20 4 4 0 27 26 28 0 0 0 46 59 33 3 0 6 Change in rainfall amount 20 21 19 44 34 15 16 14 19 41 49 34 0 0 0 18 10 25 Pest and diseases 0 0 1 1 1 0 9 9 10 10 11 10 2 2 3 0 0 0 Increased drought 21 29 14 15 15 1 29 25 33 24 27 20 2 3 2 5 6 3 Change in temperature 33 35 31 37 26 16 21 19 23 27 32 21 11 12 11 33 39 27 Soil erosion 0 1 0 0 0 0 3 4 3 0 1 0 0 0 1 0 0 0 Change in cropping calendar 3 4 2 15 13 4 13 11 15 14 14 15 11 16 6 0 0 0 Low yields/ crop failure 2 2 2 7 5 3 8 7 10 13 13 13 5 5 5 0 0 0 Strong winds 6 4 8 3 3 0 15 21 10 5 7 3 4 6 2 12 11 13 Increased sun intensity 9 6 12 0 0 0 4 6 3 8 9 8 3 4 3 0 0 0 Increased floods 0 0 1 0 0 0 7 6 8 2 4 0 0 0 0 3 3 2 Increased clouds 0 1 0 0 0 0 12 16 9 0 0 0 0 0 0 3 4 2 Loss of livestock 1 0 2 0 0 0 2 0 4 0 0 0 0 0 0 0 0 0 Drying of water sources 4 4 4 3 2 2 0 0 0 3 5 2 0 0 0 5 7 3 The values presented are percentages 25 Table 18: The causes of climate change in the sorghum value chain in the six counties. Causes Baringo Busia Kisumu Laikipia Siaya West Pokot Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Calamities 1 1 1 0 0 0 2 1 4 0 1 0 25 24 26 0 0 0 Not aware 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 Increased drought 4 5 4 0 0 0 0 0 0 2 4 1 0 0 0 3 3 3 Low rains 0 1 0 0 0 0 0 0 0 1 2 0 0 0 0 3 4 2 Increased floods 0 0 1 0 0 0 0 0 0 0 0 0 1 2 0 1 1 0 Weather changes 0 0 1 6 5 2 2 4 1 0 0 0 34 47 22 0 0 0 Deforestation 66 67 64 28 23 8 70 73 67 63 71 54 9 13 5 52 52 51 Air pollution 7 10 4 5 3 2 19 19 20 18 13 23 3 1 5 0 0 0 Burning charcoal 9 8 11 0 0 0 8 5 11 2 2 2 2 4 1 0 0 0 GHG emissions 11 13 9 5 4 2 6 6 7 16 25 8 0 1 0 1 1 1 Poor farming methods 2 3 2 0 0 0 8 7 10 13 13 13 10 13 8 1 1 0 Soil erosion 0 1 0 2 1 1 9 9 9 1 2 1 0 0 0 0 0 0 Overgrazing 7 8 7 0 0 0 4 2 7 1 2 0 3 5 1 0 0 0 Industrialization 0 0 0 4 2 3 12 11 13 4 8 1 0 0 0 3 2 4 Fossil fuel 0 0 0 4 2 3 0 0 0 0 0 0 0 0 0 9 8 9 Population increase 0 0 0 1 1 0 16 11 21 0 0 0 1 2 0 6 5 6 Ozone layer depletion 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 Burning Bricks 0 0 0 0 0 0 4 2 6 0 0 0 3 3 3 0 0 0 Global warming 0 0 0 0 0 0 1 1 2 3 4 3 3 4 3 5 5 4 Gods/ Spiritual 0 0 0 0 0 0 1 0 3 0 0 0 0 0 0 0 0 0 The values presented are percentages 26 Table 19: The effects of climate change on the sorghum value chain in the six counties. Variable Baringo Busia Kisumu Laikipia Siaya West Pokot Effects of climate change Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Pest and diseases 2 2 3 5 3 3 11 6 16 5 5 6 9 4 14 4 1 6 Change in cropping calendar 2 1 3 1 1 0 3 4 3 4 5 4 6 5 7 0 0 0 Calamity 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 High temp 3 2 4 4 2 3 11 7 16 1 3 0 2 2 2 1 1 1 Lack of rains 4 4 5 1 1 0 11 6 16 15 23 8 14 17 11 8 9 6 Deforestation 8 12 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Flooding 3 1 5 16 16 0 11 13 10 2 4 0 2 2 2 2 3 1 Loss of livestock 11 13 9 0 0 0 7 6 8 21 25 18 2 3 1 0 0 0 Low yields and crop failure 49 47 50 48 38 16 65 62 67 61 61 61 60 77 42 44 42 46 Drought 35 34 37 22 17 9 28 26 31 31 34 27 13 17 10 13 15 10 Soil erosion 1 1 2 1 1 0 5 8 2 0 0 1 0 0 0 0 0 0 Conflicts 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 3 0 5 Change in the weather pattern 0 0 0 28 24 7 0 1 0 0 0 0 0 0 0 0 0 0 Water logging 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 acid rain 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 Unaware 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 2 2 1 Strong winds 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 3 4 GHG emissions 0 0 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 0 Drying water sources 0 0 0 0 0 0 0 0 0 3 4 2 0 0 0 0 0 0 The values presented are percentages 27 Smallholders' perceptions of climate change's effects are central to implementing adaptation and mitigation strategies. The smallholder sorghum farmers identified low productivity, drought, change in weather patterns, pests and diseases, livestock losses, and decline in rainfall as the key effects of climate change (Table 19). Since climate change has tremendous effects on smallholder farmers, especially crop and livestock productivity, they could adopt response strategies. Smallholder farmers adopt multiple adaptation and mitigation practices to cope with its shocks. The study revealed low uptake of climate change response practices. The main practices were crop rotation, intercropping, mulching, cover crop, rainwater harvesting, retention ditches, and use of organic sources (Table 20). The findings suggested that implementing specific climate response practiced practices was low. 28 Table 20: Response to Climate change among sorghum farming households in the six counties. Response Baringo Busia Kisumu Laikipia Siaya West Pokot Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Contour bands 5 8 3 3 2 1 3 1 1 5 5 20 10 14 6 12 15 9 Farmers practicing crop rotation 27 25 30 29 22 11 13 11 11 16 16 29 14 20 9 11 11 10 Grass strips 4 4 4 38 35 6 3 2 2 5 5 2 1 1 1 20 17 22 Intercropping sorghum with other crops 22 24 20 14 8 9 31 29 29 33 33 7 3 6 1 5 2 7 Mulching 20 22 18 27 19 13 16 12 12 20 20 57 28 37 19 3 4 2 Planting cover crops within sorghum fields 26 28 25 26 17 13 5 6 6 5 5 5 2 3 2 1 0 1 Rainwater harvesting system 28 28 28 11 7 6 14 16 16 12 12 29 14 18 11 2 2 2 Reduced tillage 10 10 10 8 7 2 6 2 2 10 10 32 16 20 12 1 0 1 Retention ditches 6 9 4 2 1 2 20 9 9 31 31 36 18 20 16 27 28 25 Stone lines 6 7 6 1 1 0 2 2 2 2 2 1 0 1 0 31 33 29 Tied ridges 4 5 4 1 1 1 1 1 1 1 1 0 0 0 0 2 1 3 Use of organic manure 13 8 18 11 7 6 8 8 8 8 8 34 17 20 14 4 5 2 Zai pits 0 0 0 3 1 2 3 2 2 4 4 22 11 12 10 13 7 18 Agroforestry 1 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Trees 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 Planting time 0 0 0 0 0 0 10 18 18 3 3 3 1 2 1 0 0 0 Trenching 0 0 0 0 0 0 14 20 20 8 8 0 0 0 0 0 0 0 Drought resistance varieties 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 Terracing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16 14 17 The values presented are percentages 29 4.2.5 Crop residues management Crop residues have multiple uses in the farm. Most smallholders used crop residue to feed livestock (Table 21). Therefore, the results underscored the conflict between soil management and livestock feeding. However, some farmers practiced unhealthy management practices such as burning. Additionally, the crop residue was used to generate income and energy. Table 21: Crop residue management in the sorghum value chain in the six counties. County Category B u rn ed Le ft o n th e fi el d in co rp o ra te d a s m u lc h R em o ve d fo rc ed ae ra ti o n c o m p o st R em o ve d l ef t u n tr ea te d o n h ea p o r p it s R em o ve d no n -f o rc ed ae ra ti o n c o m p o st R em o ve d u se d as a n im al fe ed s Fi re w o o d So ld Baringo Pooled 0 33 0 3 1 65 0 0 Member 0 35 0 3 0 62 0 0 N-Member 1 31 0 3 2 67 0 0 Busia Pooled 49 85 13 12 5 50 0 0 Member 26 53 12 1 4 32 0 0 N-Member 34 47 2 16 2 26 0 0 Kisumu Pooled 16 55 0 3 1 33 2 3 Member 8 61 0 6 0 35 1 3 N-Member 25 49 0 1 3 31 3 3 Laikipia Pooled 10 27 2 17 4 60 62 2 Member 8 33 2 18 5 59 62 4 N-Member 13 21 3 17 4 61 62 0 Siaya Pooled 22 42 15 16 5 13 0 0 Member 21 39 5 20 1 18 0 0 N-Member 24 45 25 13 10 8 0 0 West Pokot Pooled 14 34 2 0 1 68 0 0 Member 19 37 3 0 2 64 0 0 N-Member 9 31 1 0 0 72 0 0 The values presented are percentages. 30 4.2.6 Sorghum production challenges The study found four main challenges facing sorghum production: pre-harvest, during-harvest, and post-harvest losses coupled with pest and disease infestation (Table 22). Implementation of good management practices is essential for enhanced yields and returns. Table 22: Challenges facing sorghum production in the six counties. C o u n ty C at eg o ry Lo se b ef o re h ar ve st in g Lo se d u ri n g h ar ve st in g A ff ec te d b y p es ts a n d d is ea se s Lo se p o st - h ar ve st Baringo Pooled 26 13 31 8 Member 26 11 34 8 N-Member 27 15 28 9 Busia Pooled 120 94 78 67 Member 76 44 44 36 N-Member 66 73 49 45 Kisumu Pooled 69 100 75 30 Member 64 99 73 24 N-Member 73 100 76 37 Laikipia Pooled 48 46 62 35 Member 52 53 66 34 N-Member 45 40 57 37 Siaya Pooled 53 9 43 6 Member 61 6 47 4 N-Member 44 12 39 9 West Pokot Pooled 32 8 48 4 Member 38 10 55 7 N-Member 25 5 41 1 The values presented are percentages 4.2.7 Sorghum post-harvest management, marketing, and value addition The majority of the smallholder in Baringo (53.9%) and Busia (52.3%) practiced improved storage (Table 23). However, the implementation of improved storage in Kisumu, Laikipia, Siaya, and West Pokot was low, ranging from 19% to 35%. The mechanization uptake was low and ranged from 8% in Busia to 25% in Laikipia. The findings suggested low implementation of mechanization practices in sorghum production. 31 Most smallholder farmers in Siaya, Busia, Laikipia, and West Pokot sold part of their sorghum (Table 23). However, the selling rate in Baringo and Kisumu counties was below 50%. The main buyers were fellow farmers, middlemen, and retailers. The COPMAS membership was low across the six counties. None of the farmers in West Pokot had joined the COPMAS groups. 32 Table 23: Post-harvest management and marketing of sorghum in the six counties. Variable Baringo Busia Kisumu Laikipia Siaya West Pokot Post-harvest management Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Improved storage 54 61 47 52 36 24 29 24 35 23 29 17 28 16 39 19 22 15 Mechanization 12 24 1 8 7 2 17 19 15 10 17 3 25 37 14 2 2 2 Market and business Selling (yes) 35 41 29 90 65 38 33 34 32 57 72 42 41 51 32 77 66 88 Breweries 10 18 2 0 0 0 1 0 2 4 6 2 4 8 0 1 1 1 Brokers 3 2 5 22 22 2 3 1 5 13 12 14 1 2 0 43 35 50 Fellow farmer 11 14 9 27 17 16 16 19 14 17 20 14 13 15 12 11 9 13 Middlemen 3 5 1 29 22 10 1 0 2 11 12 11 2 0 4 23 22 24 Miller 1 2 0 15 12 5 2 4 0 3 6 0 4 3 5 2 1 2 Retailer 18 17 20 22 13 14 19 19 19 14 22 6 14 21 8 22 17 27 Wholesaler 2 4 1 16 12 7 1 1 1 17 24 10 9 8 10 0 1 0 COPMAS membership 3 6 0 31 27 6 19 14 25 13 22 4 7 12 2 0 0 0 The values presented are percentages 33 Table 24: Value addition in the sorghum value chain in the six counties. Value addition C ak e C h ap at i Pi la u U ga li Po rr id ge B re ad Po p co rn Fl o u r Te a Baringo Pooled 0 14 0 47 14 0 0 0 0 Member 0 18 0 50 16 0 0 0 0 N-Member 0 10 1 45 13 0 0 0 0 Busia Pooled 16 10 1 10 0 5 16 0 0 Member 9 8 1 62 0 5 14 0 0 N-Member 11 2 1 62 0 1 3 0 0 Kisumu Pooled 0 0 0 30 1 0 0 0 0 Member 0 0 0 39 1 0 0 0 0 N-Member 0 0 0 21 1 1 0 0 0 Laikipia Pooled 9 12 0 14 20 3 1 8 0 Member 18 20 0 21 22 4 2 2 0 N-Member 1 5 0 8 19 2 0 15 0 Siaya Pooled 3 2 0 63 10 1 0 0 1 Member 6 4 0 71 17 3 0 0 2 N-Member 0 0 0 54 3 0 1 0 1 West Pokot Pooled 0 0 1 11 0 0 0 0 0 Member 0 0 1 16 0 0 0 0 0 N-Member 0 0 0 6 0 0 0 0 0 The values presented are percentages The value addition in the sorghum VC ranged from low (0%) to high (71%) across the counties (Table 24). Most of the smallholder farming households used sorghum for ugali. Other value- added products were chapati, flour, pilau, cake, and popcorn. 4.2.8 Livestock production Most of the smallholders integrated sorghum farming with livestock. The main livestock were cattle, goats, and poultry (Table 25). However, none of the smallholder farmers kept pigs. The tropical livestock unit ranged from 0.3 in Busia to 1.6 in West Pokot. The finding showed that west Pokot mainly kept livestock. 34 Table 25: Livestock integration in sorghum farming in the six counties. Livestock Baringo Busia Kisumu Laikipia Siaya West Pokot Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Po o le d M em b er N -M em b er Female cows 12 months 0.2 0.4 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.3 0.1 0.2 0.1 0.5 0.9 0.6 Growing cattle 7-11 months 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.3 0.1 0.1 0.1 0.1 0.1 0.1 0.4 0.4 0.7 Growing cattle 12 months 0.1 0.1 0.1 0.0 0.1 0.0 0.1 0.1 0.2 0.0 0.1 0.0 0.0 0.1 0.1 0.3 0.4 0.4 Replacement females 7-11 months 0.1 0.1 0.1 0.0 0.1 0.1 0.1 0.1 0.1 0.0 0.0 0.0 0.0 0.1 0.0 0.4 0.4 0.7 Replacement females 12 months 0.1 0.1 0.1 0.0 0.1 0.0 0.1 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.1 0.4 0.5 0.5 Calves 0-6 months 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.0 0.1 0.1 0.0 0.1 0.0 0.3 0.4 0.5 Bulls 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.0 0.1 0.1 0.1 0.1 0.2 0.2 Oxen 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.1 0.1 0.1 0.0 0.1 0.1 0.2 Rams 0.1 0.3 0.1 0.0 0.0 0.1 0.2 0.3 0.1 0.1 0.2 0.2 0.1 0.1 0.1 0.3 0.3 0.5 Ewes 0.3 0.5 0.3 0.1 0.1 0.1 0.3 0.3 0.4 0.4 0.6 0.4 0.1 0.2 0.1 0.4 0.4 0.6 Lambs 0.1 0.2 0.2 0.0 0.1 0.1 0.1 0.2 0.1 0.2 0.2 0.2 0.0 0.1 0.0 0.3 0.4 0.5 bucks 0.2 0.2 0.2 0.0 0.1 0.0 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.4 0.6 0.5 Doe 0.4 0.4 0.6 0.1 0.1 0.1 0.2 0.2 0.2 0.2 0.3 0.3 0.1 0.2 0.1 0.7 0.8 1.1 Kids 0.2 0.2 0.2 0.0 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.1 0.0 0.4 0.4 0.6 Poultry 1.8 3.5 0.8 1.1 1.6 1.2 2.5 4.8 1.0 5.9 5.2 10.6 4.1 8.1 1.7 0.7 1.0 1.0 Pigs 0.0 0.0 0.0 0.1 0.2 0.1 0.0 0.1 0.1 0.0 0.0 0.0 0.1 0.0 0.2 0.0 0.0 0.1 Horses, mules, or asses 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.0 0.1 0.0 0.0 0.0 0.0 Rabbits 0.0 0.1 0.0 0.1 0.1 0.0 0.0 0.0 0.1 0.2 0.2 0.3 0.0 0.0 0.0 0.0 0.0 0.1 Tropical Livestock Unit (TLU) 0.5 0.8 0.5 0.3 0.4 0.4 0.4 0.6 0.6 0.4 0.5 0.7 0.4 0.6 0.3 1.6 2.1 2.4 35 4.2.9 Sorghum productivity and GHG fluxes The findings showed that yields (kg ha-1), income (USD ha-1), area-scaled emissions (kg CO2 eq. ha-1), GHG EIs (kg CO2 eq. kg-1 grain), and monetary scaled emissions (kg CO2 eq. USD-1) were similar between KCSAP members and non-members (Tabel 25). In Baringo, KCSAP members had 10.6%, 4.3%, 45.2%8.8%, and 68.5% higher yields, income, area-scaled GHG emissions, GHG Eis, and monetary scaled emissions than non-members. The positive change in yields and income indicated KCSAP members had higher yields than non-members. In Busia, KCSAP membership increased yields, income, area-scaled GHG emissions, GHG EIs, and monetary scaled emissions by 49.5%, 372%, 69.8%, -8.5%, and 62.5% compared to non-members. The negative area-scaled emissions indicated that the sorghum cropping system was a sink of GHGs. The negative fluxes could be attributed to using organic inputs that stored carbon in the soil. The change was yielded (14.6%), income (16.8%), area-scaled emissions (15.2%), GHG EIs (25.6%), and monetary-scaled emissions (44.1) in Kisumu County. This indicated that though members had higher productivity, they also contributed highly to GHG production. The binary comparison in Laikipia County showed that KCSAP membership increased yields, income, area- scaled GHG emissions, GHG EIs, and monetary scaled emissions by 12.7%, 9.8%, 14.8%, -8.4%, and 25.4%. In Kisumu, the change in area-scaled emissions was negative, indicating that non- members contributed more to emissions than members. This suggested that the implementation of TIMPs by members could have enhanced sustainable production. In West Pokot, KCSAP participation increased yields, income, area-scaled GHG emissions, GHG EIs, and monetary scaled emissions by 12.9%, 11.0%, 59.0% -34.1%, and 113.4%, respectively. The positive areas scaled emissions suggested that the soil showed that the sorghum cropping system was a source of GHGs. This could be due to the increased use of inorganic fertilizers. 36 Table 26: Productivity and GHG emissions by the KCSAP TIMPS implementing farmers. Pooled Member N-Member (B-C) B ar in go Yields (kg ha-1) 759 802 717 85.35 Income (USD ha-1) 331 338 324 14.66 Area scaled emissions (kg CO2 eq. ha-1) 5,135 6,636 3,635 3,001 GHG EIs (kg CO2 eq. kg-1 grain) 0.64 0.68 0.61 0.06 Monetary scaled emissions kg CO2 eq. USD-1) 25.5 38.7 12.2 26.5 B u si a Yields (kg ha-1) 1,474 1,858 937 9225 Income (USD ha-1) 760 900 565 3356 Area scaled emissions (kg CO2 eq. ha-1) 1,231 1,737 524 1,213 GHG EIs (kg CO2 eq. kg-1 grain) 2.20 2.12 2.30 -0.18 Monetary scaled emissions (kg CO2 eq. USD-1) 10.5 14.2 5.36 8.81 Ki su m u Yields (kg ha-1) 994 1,072 916 156 Income (USD ha-1) 868 947 788 159 Area scaled emissions (kg CO2 eq. ha-1) 428 464 393 70.5 GHG EIs (kg CO2 eq. kg-1 grain) 1.57 1.80 1.34 0.46 Monetary scaled emissions kg CO2 eq. USD-1) 1.52 1.95 1.08 0.86 La ik ip ia Yields (kg ha-1) 1,634 1,745 1,524 221 Income (USD ha-1) 1,069 1,124 1,015 110 Area scaled emissions (kg CO2 eq. ha-1) 3,121 3,371 2,871 500 GHG EIs (kg CO2 eq. kg-1 grain) 4.37 4.19 4.54 -0.35 Monetary scaled emissions kg CO2 eq. USD-1) 7.57 8.67 6.47 2.20 Si ay a Yields (kg ha-1) 1,106 1,171 1,043 128 Income (USD ha-1) 511 543 479 64.0 Area scaled emissions (kg CO2 eq. ha-1) 1,357 965 1,740 -775 GHG EIs (kg CO2 eq. kg-1 grain) 0.53 -0.03 1.08 -1.11 Monetary scaled emissions kg CO2 eq. USD-1) 3.40 3.06 3.73 -0.67 W es t P o ko t Yields (kg ha-1) 917 981 854 127 Income (USD ha-1) 653 692 615 76.3 Area scaled emissions (kg CO2 eq. ha-1) -1,605 -2,277 -934 -1,343 GHG EIs (kg CO2 eq. kg-1 grain) 1.51 1.29 1.73 -0.44 Monetary scaled emissions kg CO2 eq. USD-1) -2.43 -5.61 0.75 -6.36 5 https://www.exchangerates.org.uk/USD-KES-spot-exchange-rates-history-2022.html https://www.exchangerates.org.uk/USD-KES-spot-exchange-rates-history-2022.html 37 Table 27: Effects of Uptake intensity on productivity and GHG emissions. Variable Uptake Intensity p-value B ar in go 0 1 2 3 4 5 6 7 8 Yields 433.4d 620.4c 697.6c 773.8b 1478.9a 1337.9a 0.001 Income 196.0d 288.8c 401.4a 271.6c 452.6a 357.4b 0.04 Area scaled emissions 4300.4c 1266.3d 9738.1a 6286.3b -893.2e -1669.4e 0.03 GHG EIs 1.4b 0.1d 0.7c 1.3b 1.8a -1.3e 0.03 Monetary scaled emissions 2.7c 4.0c 51.9a 32.5b 6.2c -1.7d 0.01 B u si a Yields 267e 302e 1,072d 1,563c 1,521c 4,809b 8,694a 0.001 Income 101e 171e 473d 752c 869c 2595b 4,628a 0.001 Area scaled emissions 1,613a 1,603a 1,417b 1,312b 376e 954c 677d 0.01 GHG EIs 3.6b 4.4a 1.6c 1.7c 0.5d 0.8d 0.1e 0.02 Monetary scaled emissions(USD6) 52.5a 23.5b 6.5c 6.0c 0.7d 1.6d 0.1d 0.001 Ki su m u Yields 358d 650c 785c 648c 954b 3,549a 1,112b 0.003 Income 244e 504d 482d 439d 755c 4330a 1413b 0.001 Area scaled emissions 761a 565b 485b 450b 450b -144d 183c 0.004 GHG EIs 2.9a 2.8a 1.8a 0.7a 2.2a 0.3a 1.5a 0.6 Monetary scaled emissions 4.9a 3.1a 2.1a 0.8a 1.1a 0.0a 0.1a 0.08 La ik ip ia Yields 481e 1,482c 1,087c 1,687b 1,624b 2,185a 1,305c 936d 1,112c 0.001 Income 204e 628d 596d 1,180b 1,034b 1,460a 829c 328e 283e 0.03 Area scaled emissions 173c 817c 3,327b 3,311b 2,038b 2,819b 7,100a 7,923a -2493 0.04 GHG EIs 0.0c 2.4c 1.0c 5.7b 4.5b 0.3c 18.4a 4.8b -2.2d 0.05 Monetary scaled emissions 0.4d 1.3d 9.8c 8.7c 3.1d 2.5d 26.5b 35.1a -8.8e 0.001 Si ay a Yields 480c 962b 1,077b 1,111b 1,227b 1,291b 1,556b 2,979a 0.001 Income 274b 401b 496b 526b 554b 689b 528b 1,162a 0.003 Area scaled emissions 614c -1,987e 680c 2,428b 2,092b -277d 8,559a 2,024b 0.04 GHG EIs 2.0b -2.2d 0.8c 0.7c 0.3c 0.6c 5.5a 0.7c 0.5 Monetary scaled emissions 5.5b -5.4c 3.6b 1.1b 10.1a 1.2b 16.2a 1.7b 0.02 W es t P o ko t Yields 24.7c 133c 361c 1,229b 809bc 1,438b 1,848a 1,235b 0.001 Income 20.9e 163d 317d 720c 771c 975c 1567b 2094a 0.001 Area scaled emissions 589a 200b 358b -4,435e -174c -1,113d 311b 371b 0.04 GHG EIs 3.0a 2.8a 2.6a -0.8b 3.4a 2.6a 3.0a 3.0a 0.001 Monetary scaled emissions 28.1a 4.7b 0.9b -7.4c 3.1b -7.3c -0.7c 0.2b 0.04 6 https://www.exchangerates.org.uk/USD-KES-spot-exchange-rates-history-2022.html https://www.exchangerates.org.uk/USD-KES-spot-exchange-rates-history-2022.html 38 The study showed mixed findings on the effects of uptake intensity on yields (kg ha-1), income (USD ha-1), area-scaled emissions (kg CO2 eq. ha-1), GHG EIs (kg CO2 eq. kg-1 grain), and monetary scaled emissions (kg CO2 eq. USD-1) in the six counties (Table 27). Sorghum yields, income, area-scaled emissions, and monetary-scaled emissions significantly (p = 05) differed across the uptake intensity in all six counties. The GHG EIs were significant (p = 0.05) in Baringo, Busia, Laikipia, and West Pokot counties but not in Kisumu and Siaya counties. The findings showed that smallholder sorghum farms in Baringo, Busia, Kisumu, and Laikipia were sources of GHGs. They emitted, on average, 428 to 5,135. However, smallholders with a high number of TIMPs uptake acted as sinks of GHG emissions in West Pokot and Siaya counties. Using seven TIMPs increased yield and income by 1,237% and 257%, respectively. Smallholders implementing seven practices had lower area-scaled emissions (-1769.4%), GHG EIs (-101.3%), and monetary scaled emissions (-101.7%) compared to those implementing only two TIMPs. Across the six counties, smallholders implementing higher TIMPs reduce GHG EIs by at least 94% and monetary scaled emissions by at least 83% compared to those implementing the lowest number of TIMPs. The findings suggested that implementing more TIMPs increased crop productivity while reducing GHG EIs and monetary-scaled emissions. The multiple agriculture practice could enhance crop yields while reducing or without significantly influencing area-scaled GHG emissions. 39 4.3 Cassava value chain 4.3.1 Overview This section presents the effect of KCSAP project membership and uptake of TIMPs intensity on cassava productivity and GHG emissions in Busia and Kisumu counties. highlights the key findings under the cassava VC, including descriptive statics, TIMPs uptake, climate change response, cassava value addition, and marketing. The section further presents the effect of TIMPs uptake intensity on productivity and GHG emissions in the cassava VC. 4.3.2 Descriptive characteristics of cassava-farming households The study revealed that most smallholder cassava farmers were female (Tabel 2728). The findings could be ascribed to cassava being a low-value crop. The average age is 50 years in Busia and Kisumu. The cassava farming experience was 11 years in Busia and 10 years in Kisumu. The literacy level was 93% in Busia and 94% in Kisumu. Finally, the study reveals a moderate family size and a low number of failed seasons. Figure 27Table 28: Descriptive characteristics of cassava-farming households. Variables Busia Kisumu Pooled Member Non-member Pooled Member Non-member Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE Gender (male=1) 0.26 0.03 0.23 0.04 0.29 0.04 0.53 0.04 0.60 0.05 0.47 0.05 Age 50.07 0.78 50.30 1.00 49.83 1.21 49.93 0.92 51.28 1.34 48.57 1.25 Experience 11.27 0.79 11.77 1.12 10.77 1.13 10.45 0.79 11.93 1.29 8.97 0.90 Family size 6.85 0.18 7.17 0.25 6.53 0.26 5.70 0.16 5.68 0.21 5.72 0.24 Failed seasons 1.08 0.09 1.14 0.13 1.03 0.11 2.29 0.99 2.32 0.17 2.19 1.98 Literacy 0.93 0.02 0.97 0.02 0.89 0.03 0.94 0.02 0.94 0.02 0.93 0.03 4.2.3 Cassava TIMPs uptake The uptake level of TIMPs ranged from low (12%) to high (85%) (Table 29). Mechanization had the lowest uptake level in Busia (27%) and Kisumu (12%). Most smallholders implemented improved varieties (85%)and soil water management practices (100%). 40 Table 29: Cassava TIMPs uptake level in Busia and Kisumu counties. Variables Busia Kisumu Pooled Member Non-member Pooled Member Non-member Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE Improved varieties 0.85 0.02 0.98 0.01 0.72 0.04 0.31 0.03 0.38 0.05 0.24 0.04 Agronomic Practices 0.50 0.03 0.58 0.05 0.41 0.05 0.71 0.03 0.70 0.05 0.72 0.04 Soil and water management 1.00 0.00 1.00 0.00 0.99 0.01 0.96 0.01 0.95 0.02 0.96 0.02 Crop health management 0.27 0.03 0.34 0.05 0.19 0.04 0.12 0.02 0.10 0.03 0.14 0.03 Value addition 0.51 0.03 0.62 0.05 0.41 0.05 0.13 0.02 0.22 0.04 0.04 0.02 Mechanization 0.27 0.03 0.40 0.05 0.14 0.03 0.10 0.02 0.06 0.02 0.14 0.03 Business and Market 0.51 0.03 0.71 0.04 0.32 0.05 0.42 0.03 0.47 0.05 0.36 0.05 Post-harvest management 0.40 0.03 0.53 0.05 0.27 0.04 0.14 0.02 0.11 0.03 0.18 0.04 41 The adoption intensity ranged from zero to eight TIMPs (Table 30). Most of the smallholders implemented 3 TIMPs. Notably, none of the KCSAP members in Busia implemented zero TIMPs. The smallholder implements more than one practice to benefit from the complementary. Table 30: Cassava TIMPs uptake intensity in Busia and Kisumu Counties. Variables Busia Kisumu Pooled Member Non-member Pooled Member Non-member Freq % Freq % Freq % Freq % Freq % Freq % 0 0 0 0 0 0 0 3 1 2 2 1 1 1 7 3 0 7 7 32 16 13 13 19 19 2 20 10 2 2 18 17 54 26 23 23 31 30 3 60 29 22 21 38 37 49 24 28 27 21 21 4 33 16 15 15 18 17 41 20 23 23 18 18 5 34 17 21 20 13 13 15 7 9 9 6 6 6 13 6 9 9 4 4 10 5 4 4 6 6 7 32 16 29 28 3 3 0 0 0 0 0 0 8 7 3 5 5 2 2 0 0 0 0 0 0 Total 206 100 103 100 103 100 204 100 102 100 102 100 4.3.4 Climate change adaptation and mitigation Most smallholder cassava farmers were aware of climate change (Table 31). The main indicators of climate change were drought, rainfall patterns, and a decline in crop yields. The findings indicated that smallholder farmers knew about climate change and could enumerate its indicators. The main causes of climate change were deforestation, industrialization, poor farming methods, and GHG emissions (Table 32). Additionally, some farmers perceived climate change as a punishment from God. The findings illustrated that smallholders were aware of the causes of climate change. The main effects of climate in the cassava VC were low yields, unpredictable weather patterns, livestock losses, pests, and diseases (Table 33). The findings highlighted that cassava production was immensely affected by climate change. Therefore, climate change adaptation and mitigation could lower the shocks. 42 Smallholder farmers implemented various climate response strategies (Table 34). The main strategy was the use of organic manure, retention ditches, rainwater harvesting, mulching, contour bands, and crop rotation. The findings highlighted smallholder farmers' dedication to implementing TIMPs that could reduce the vagaries of climate change. 43 Table 31: Climate change awareness and indicators in Busia and Kisumu counties. Variables Busia Kisumu Pooled Member Non-member Pooled Member Non-member Awareness of climate change Freq Perc Freq Perc Freq Perc Freq Perc Freq Perc Freq Perc Aware of climate change 180 87 89 86 91 88 199 98 99 97 100 98 Indicators of climate change 0 0 0 0 0 0 0 0 0 0 0 0 Drought 63 31 35 34 28 27 100 49 49 48 51 50 Water logging 0 0 0 0 0 0 1 0 1 1 0 0 Cloud 0 0 0 0 0 0 10 5 3 3 7 7 Floods 21 10 16 16 5 5 46 23 15 15 31 30 Unpredictable weather patterns 8 4 4 4 4 4 84 41 48 47 36 35 High Temp 17 8 5 5 12 12 108 53 58 57 50 49 Calendar 14 7 9 9 5 5 9 4 5 5 4 4 Rainfall patterns 108 52 49 48 59 57 44 22 22 22 22 22 Low Rains 0 0 0 0 0 0 20 10 9 9 11 11 Sunshine 9 4 8 8 1 1 9 4 3 3 6 6 Pets and Diseases 3 1 2 2 1 1 13 6 8 8 5 5 Water level rise 0 0 0 0 0 0 2 1 0 0 2 2 Low yields/ Crop failure 25 12 6 6 19 18 28 14 15 15 13 13 Winds 1 0 1 1 0 0 14 7 8 8 6 6 Death of animals 0 0 0 0 0 0 2 1 1 1 1 1 Rivers drying 0 0 0 0 0 0 6 3 5 5 1 1 Environmental pollution 2 1 1 1 1 1 0 0 0 0 0 0 44 Table 32: Causes and effects of climate change in Busia and Kisumu counties. Variables Busia Kisumu Pooled Member Non-member Pooled Member Non-member Causes Freq Perc Freq Perc Freq Perc Freq Perc Freq Perc Freq Perc Global warming 3 1 3 3 0 0 13 6 8 8 5 5 Population increase 10 5 9 9 1 1 8 4 5 5 3 3 Change in Rainfall 3 1 1 1 2 2 0 0 0 0 0 0 GHG emissions 3 1 1 1 2 2 58 28 33 32 25 25 Industrialization 13 6 2 2 11 11 24 12 13 13 11 11 Poor farming methods 5 2 2 2 3 3 30 15 16 16 14 14 Deforestation 141 68 73 71 68 66 165 81 85 83 80 78 Pollution 8 4 5 5 3 3 47 23 25 25 22 22 Change in cropping calendar 3 1 1 1 2 2 0 0 0 0 0 0 Normal/ gods 21 10 8 8 13 13 0 0 0 0 0 0 Charcoal burning 1 0 0 0 1 1 15 7 6 6 9 9 Not aware 0 0 0 0 0 0 8 4 3 3 5 5 Crop failure 1 0 0 0 1 1 0 0 0 0 0 0 Increased drought 6 3 4 4 2 2 0 0 0 0 0 0 Increased floods 1 0 1 1 0 0 0 0 0 0 0 0 Overstocking 0 0 0 0 0 0 12 6 3 3 9 9 Fossil fuel 0 0 0 0 0 0 53 26 27 26 26 25 45 Table 33: Effects of climate change in Busia and Kisumu counties. Variable Busia Kisumu Pooled Member Non- member Pooled Member Non- member Fre q % Fre q % Freq % Fre q % Fre q % Freq % Low yields/ crop failure 133 6 5 71 6 9 62 60 172 8 4 87 8 5 85 83 Frequent floods 23 1 1 13 1 3 10 10 0 0 0 0 0 0 Unpredictable weather 3 1 1 1 2 2 71 3 5 33 3 2 38 37 Frequent drought 25 1 2 14 1 4 11 11 0 0 0 0 0 0 Low rains 11 5 5 5 6 6 0 0 0 0 0 0 Invasive weeds 4 2 0 0 4 4 4 2 1 1 3 3 Pests and diseases 12 6 6 6 6 6 62 3 0 34 3 3 28 27 Pollution 1 0 0 0 1 1 0 0 0 0 0 0 Change in cropping calendar 4 2 2 2 2 2 0 0 0 0 0 0 Livestock death 1 0 0 0 1 1 103 5 0 39 3 8 64 63 Human risks 2 1 1 1 1 1 0 0 0 0 0 0 Increased temperature 1 0 0 0 1 1 0 0 0 0 0 0 None or Unaware 10 5 3 3 7 7 0 0 0 0 0 0 Drying of water sources 0 0 0 0 0 0 17 8 7 7 10 10 46 Table 34: Response to climate change in Busia and Kisumu counties. Variable Busia Kisumu Pooled Member Non member Pooled Member Non member Freq % Freq % Freq % Freq % Freq % Freq % Contour bands 39 19 27 26 12 12 34 17 15 15 19 19 Farmers practicing crop rotation 13 55 63 61 50 49 69 34 28 27 41 40 Grass strips 34 17 24 23 10 10 32 16 12 12 20 20 Intercropping cassava with other crops 41 20 21 20 20 19 79 39 31 30 48 47 Mulching 40 19 20 19 20 19 75 37 45 44 30 29 Planting cover crops within cassava fields 17 8 7 7 10 10 35 17 11 11 24 24 Rainwater harvesting system 24 12 20 19 4 4 30 15 7 7 23 23 Reduced tillage 9 4 7 7 2 2 4 2 2 2 2 2 Retention ditches 34 17 12 12 22 21 19 9 6 6 13 13 Stone lines 1 0 0 0 1 1 5 2 4 4 1 1 Tied ridges 2 1 1 1 1 1 7 3 1 1 6 6 Use of organic manure 11 5 8 8 3 3 19 9 8 8 11 11 Zai pits 2 1 0 0 2 2 52 25 25 25 27 26 Tress 0 0 0 0 0 0 8 4 4 4 4 4 Terraces 0 0 0 0 0 0 4 2 4 4 0 0 4.3.5 Crop residue management Most of the farmers used crop residues as mulch (Table 35). This is contrary to most cropping systems, where crop residues are used to feed livestock. This suggested that cassava cropping systems incorporate crop residues to manage the soil. However, some of the smallholders burned their crop residue. 47 Table 35: Crop residue management in Busia and Kisumu counties. Variable Busia Kisumu Pooled Member Non member Pooled Member Non member Freq % Freq % Freq % Freq % Freq % Freq % Burned 29 14 18 17 11 11 19 9 7 7 12 12 Left on the field incorporated as mulch 13 63 55 53 75 73 93 46 45 44 48 47 Removed forced aeration compost 4 2 4 4 0 0 4 2 3 3 1 1 Removed left untreated on the heap 36 17 22 21 14 14 55 27 19 19 36 35 Removed non-forced aeration compost 8 4 5 5 3 3 43 21 10 10 33 32 Removed used as animal feeds 14 7 14 14 0 0 9 4 5 5 4 4 4.2.6 Challenges facing cassava value chain The cassava VC's challenges are losses before, during, and after harvesting (Table 36). Additionally, 46% of the respondents in Busia and 48% in Kisumu reported that cassava was infected by pests and diseases (Table 37). Therefore, integrated pest and disease management is essential in cassava production. Prevention and control of losses through effective management are critical in ensuring optimal cassava VC returns. Table 36: Challenges facing cassava production. Variables Busia Kisumu Pooled Memb er Non- memb er Pooled Memb er Non- memb er Freq % Fr eq % Fr eq % Fr eq % Fr eq % Fr eq % Lose any cassava before harvesting 104 50 54 52 50 49 77 38 41 40 36 35 Lose and cassava during harvesting 72 35 36 35 36 35 68 33 32 31 36 35 Infected by pests and diseases 95 46 49 48 46 45 97 48 48 47 49 48 Lose any cassava post-harvest 81 39 38 37 43 42 60 29 27 26 33 32 48 4.2.8 Cassava post-harvest management, marketing, and value addition Implementing mechanization in Busia and Kisumu was modest (Table 37). The implementation rate was 27% and 12%, respectively. Only a few farmers (30%) in Busia and (10%) in Kisumu implemented improved storage. The findings underscored the need to promote post-harvest management practices to enhance yields and income. Most of the farmers in Busia (84%) and Kisumu (67%) were selling cassava (Table 36). Most smallholders sold cassava through brokers, middlemen, retailers, and wholesalers. The study revealed high SWOT analysis, 5 Ps, COPMAS, and organization membership. However, the contracting farming approach was low, 5% in Busia and 4% in Kisumu. Table 37: post-harvest management and marketing in Busia and Kisumu counties. Variables Busia Kisumu Pooled Member Non-member Pooled Member Non-member Post-harvest management Freq % Freq % Freq % Freq % Freq % Freq % Mechanization 55 27 41 40 14 14 25 12 7 7 18 18 Improved storage 61 30 42 41 19 18 20 10 6 6 14 14 Market and business Sell (yes) 173 84 92 89 81 79 136 67 62 61 74 73 Breweries 7 3 7 7 0 0 0 0 0 0 0 0 Brokers 37 18 26 25 11 11 26 13 7 7 19 19 Fellow farmer 72 35 44 43 28 27 58 28 33 32 25 25 Middlemen 33 16 26 25 7 7 40 20 9 9 31 30 Miller 8 4 4 4 4 4 19 9 9 9 10 10 Retailer 100 49 46 45 54 52 79 39 33 32 46 45 Wholesaler 48 23 33 32 15 15 48 24 23 23 25 25 SWOT 96 47 69 67 27 26 66 32 35 34 31 30 The 5 Ps 76 37 59 57 17 17 71 35 36 35 35 34 COPMAS member 71 34 55 53 16 16 4 2 3 3 1 1 Contract farming 11 5 6 6 5 5 9 4 6 6 3 3 Organization member 72 35 60 58 12 12 17 8 16 16 1 1 The value addition level was 51% in Busia and 13% in Kisumu (Table 38). Most of the smallholders made chips from cassava. However, none of the farmers made porridge, gari, and millet ugali. 49 Table 39: Cassava value addition in Busia and Kisumu counties. Variables Busia Kisumu Pooled Member Non-member Pooled Member Non-member Freq % Freq % Freq % Freq % Freq % Freq % Value addition 106 51 64 62 42 41 27 13 22 22 5 5 Dried cassava chips 72 35 39 38 33 32 12 6 10 10 2 2 Cassava/wheat chapati 29 14 20 19 9 9 5 2 5 5 0 0 Cassava/maize Ugali 22 11 19 18 3 3 9 4 9 9 0 0 Cassava/ Sorghum Ugali 0 0 0 0 0 0 4 2 2 2 2 2 Cassava/millet Ugali 0 0 0 0 0 0 0 0 0 0 0 0 Cassava/ Pigeon-pea porridge 0 0 0 0 0 0 0 0 0 0 0 0 Gari (Witabix Mtaani) 0 0 0 0 0 0 0 0 0 0 0 0 Cassava Crisps 1 0 1 1 0 0 0 0 0 0 0 0 4.3.9 Cassava livestock integration Smallholder cassava farming households integrated livestock to increase returns and cope with climate change. The main livestock type kept were cattle, sheep, poultry, and goats (Table 39). The average TLU was 3.4 and 5.0 units in Busia and Kisumu, respectively. This showed that Kisumu cassava farmers kept more livestock than their counterparts in Busia. 50 Table 39 The cassava livestock systems in Busia and Kisumu counties. Livestock Busia Kisumu Pooled Member Non-member Pooled Member Non-member Freq % Freq % Freq % Freq % Freq % Freq % Female cows 12 months 1.4 0.1 1.6 0.2 1.1 0.1 1.7 0.1 1.5 0.2 1.8 0.2 Growing cattle 7-11 months 0.3 0.1 0.5 0.1 0.2 0.1 0.6 0.1 0.5 0.1 0.6 0.1 Growing cattle 12 months 0.1 0.0 0.1 0.0 0.1 0.1 0.3 0.1 0.3 0.1 0.4 0.1 Replacement females 7-11 months 0.1 0.0 0.1 0.0 0.0 0.0 0.2 0.0 0.1 0.1 0.2 0.1 Replacement females 12 months 0.2 0.0 0.2 0.1 0.2 0.0 0.3 0.0 0.2 0.1 0.4 0.1 Calves 0-6 months 0.2 0.0 0.2 0.0 0.1 0.0 0.5 0.1 0.5 0.1 0.5 0.1 Bulls 0.3 0.1 0.4 0.1 0.2 0.1 0.5 0.1 0.5 0.1 0.5 0.1 Oxen 0.1 0.0 0.2 0.1 0.1 0.0 0.3 0.2 0.4 0.3 0.2 0.1 Rams 0.1 0.0 0.0 0.0 0.1 0.0 0.5 0.1 0.5 0.1 0.5 0.1 Ewes 0.1 0.0 0.2 0.1 0.1 0.0 1.2 0.2 1.2 0.2 1.3 0.2 Lambs 0.1 0.0 0.1 0.0 0.0 0.0 0.5 0.1 0.4 0.1 0.5 0.1 bucks 0.2 0.0 0.2 0.0 0.3 0.1 0.3 0.1 0.2 0.1 0.4 0.1 Doe 0.5 0.1 0.6 0.1 0.5 0.1 0.9 0.1 1.0 0.2 0.9 0.1 Kids 0.2 0.0 0.2 0.1 0.2 0.1 0.5 0.1 0.4 0.1 0.5 0.1 Poultry 10.7 1.2 13.3 2.2 8.1 1.1 11.3 0.9 11.1 1.3 11.5 1.1 Pigs 1.1 0.1 1.3 0.2 0.9 0.2 0.0 0.0 0.0 0.0 0.0 0.0 Horses, mules, or asses 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Rabbits 0.1 0.0 0.1 0.0 0.2 0.1 0.1 0.0 0.1 0.1 0.1 0.1 Tropical Livestock Unit (TLU) 3.4 0.2 4.0 0.3 2.7 0.3 5.0 0.4 4.8 0.6 5.2 0.4 51 4.3.10 Cassava productivity and GHG emissions The findings showed that yields (kg ha-1), income (USD ha-1), area-scaled emissions (kg CO2 eq. ha-1), GHG EIs (kg CO2 eq. kg-1 grain), and monetary scaled emissions (kg CO2 eq. USD-1) were similar between KCSAP members and non-members (Table 39). There were no significant differences. Member had more yields, 61.45 & 10.1%, and income 97% & 24% in Busia and Kisumu, respectively. In Busia, non-members had higher area-scaled emissions (92%), GHG EIs (52%), and monetary-scaled emissions (82%) than members. In Kisumu, members had higher area-scaled emissions (7.5%) and lower GHG EIs (31%) and monetary-scaled emissions (41%) than non-members. 52 Table 40: The effect of KSCAP membership and TIMPS on cassava productivity and GHG emissions. Variable Busia Kisumu Pooled Member Non-member B-C Pooled Member Non-member B-C Yields (kg ha-1) 1,474 1,752 1,085 667 3,324 3,485 3,164 330 Income (USD ha-1)7 760 957 485 472 1,470 1,632 1,310 322 Area scaled emissions (kg CO2 eq. ha-1) 1,231 197 2,677 -2,480 -1,944 -2,015 -1,874 -141 GHG EIs (kg CO2 eq. kg-1 grain) 2.20 1.51 3.16 -1.65 -0.44 -0.36 -0.51 0.16 Monetary scaled emissions kg CO2 eq. USD-1) 10.5 3.58 20.2 -16.6 -13.5 -9.95 -16.9 6.97 7 https://www.exchangerates.org.uk/USD-KES-spot-exchange-rates-history-2022.html https://www.exchangerates.org.uk/USD-KES-spot-exchange-rates-history-2022.html 53 The ANOVA findings showed that uptake intensity significantly (p≤0.05) influenced yields (kg ha-1), Income (USD ha-1), area-scaled emissions (kg CO2 eq. ha-1), GHG EIs (kg CO2 eq. kg-1 grain) and Monetary scaled emissions (kg CO2 eq. USD-1) in Busia and Kisumu counties (Table 41). In Busia County, smallholders implementing seven technologies had 3159% and 4485 higher yields and income than those implementing one TIMP. Implementing seven TIMPs reduced area-scaled emissions, GHG EIs, and monetary scaled emissions by 58%, 98%, and 100% in Busia County compared to one TIMP. In Kisumu, smallholders implementing six technologies had 492.4% and 170.2% higher yields and income than those implementing none of the TIMPs. Implementing six TIMPs reduced area-scaled emissions, GHG EIs, and monetary scaled emissions by 82%, 111%, and 103%, respectively, in Kisumu County compared to none of the TIMPs. The GHG emissions in Busia were positive, indicating that smallholder cassava farms acted as a source of GHGs. However, in Kisumu, the soil acted as a sink on GHGs among smallholders with 3 to 6 TIMPs. The findings showed that smallholders implementing more TIMPs increased yields while lowering GHG emissions. 54 Table 41; The effect of TIMPS uptake intensity on cassava productivity and GHG emissions. Intensity Yields (kg ha-1) Income (USD ha-1) Area scaled emissions (kg CO2 eq. ha-1) GHG EIs (kg CO2 eq. kg-1 grain) Monetary scaled emissions kg CO2 eq. USD-1) 8 Busia 1 267d 101e 1612.95a 3.61a 52.54a 2 302d 171e 1602.82a 4.44a 23.51b 3 1,072c 473d 1417.40b 1.63b 6.46c 4 1,563c 752c 1311.68b 1.70b 6.02c 5 1,521c 869c 376.42d 0.50c 0.65e 6 4,809b 2,595b 953.79c 0.76c 1.60d 7 8,694a 4,628a 677.44d 0.08d 0.14e P value 0.002 0.01 0.04 0.03 0.001 Kisumu 0 412d 248d 371a 2.44a 3.70a 1 3062b 1,314b -1,757c 0.80b -19.1d 2 3,153b 1,381b -2,086d 0.07c -21.8d 3 3,343b 1,552b -3,077e -1.39d -5.27c 4 3,058b 1,372b -1,682c -1.02d -15.7d 5 6,114a 2,801a -4,989b -0.02c 0.08b 6 2,439c 672c 67.5a -0.26e -0.13b p value 0.04 0.03 0.001 0.02 0.01 Values followed by the same superscript in the same column are not significantly different at p≤0.05 8 https://www.exchangerates.org.uk/USD-KES-spot-exchange-rates-history-2022.html https://www.exchangerates.org.uk/USD-KES-spot-exchange-rates-history-2022.html 55 5. CONCLUSION AND RECOMMENDATIONS 5.1 Conclusion This section presents the conclusions from the effects of TIMPs uptake level and intensity on productivity and GHG emissions. The conclusion highlights important findings on finger millet sorghum and cassava VCs. The key components captured are TIMPs uptake level and intensity, effects of TIMPs uptake intensity on productivity, and GHG emissions. 5.1.1 Finger millet value chain Majority of finger millet smallholder farmers implemented improved varieties (71%), agronomic management practices (100%), integrated pest management (65%), and soil water conservation practices (87%). Nevertheless, value addition (6%), post-harvest management (20%), markets (1%), and mechanization (18%) had low uptake levels. There was high uptake intensity of TIMPs under finger millet farming, with most households in Kericho county implementing four practices. However, most farms participating in the KCSAP project utilized at least 2 TIMPs. High uptake intensity of the TIMPs significantly influenced finger millet yields (kg ha-1), income (USD ha-1), area-scaled emissions (kg CO2e ha-1), GHG EIs (kg CO2e kg-1 grain), and monetary scaled emissions (kg CO2e USD-1). Generally, KCSAP membership increased yields, area-scaled GHG emissions, GHG EIs, and monetary-scaled emissions by 3.5%, 27.8%, 14.6% and 35.6%, respectively, than non-members. The areas scaled emissions, GHG EIs, and monetary scaled emissions were positive, indicating that the finger millet production led to GHGs in the atmosphere. Smallholders implementing six TIMPs had an increment of yields (1461%), income (1887), area-scared emissions (-155%), GHG EIs (-97%), and monetary-scaled emissions (99%) compared to those implementing only 1 TIMPs. The implementation of more TIMPs increased yields while reducing GHG emissions. 5.1.2 Sorghum value chain The uptake level of different TIMPs categories ranged from low (0%) to high (100%) across the six counties: Busia, Baringo, Kisumu, Laikipia, Siaya, and West Pokot. Across the six counties, improved sorghum varieties (58-91%), agronomic management practices (71-100%), and soil water management practices (72-91%) had high uptake levels. All counties' uptake levels for 56 post-harvest management (23-54), crop health (0-23%), mechanization (2-25%), and market strategies (3-19%) were low. Value addition had mixed results, where Busia County (62%) and Siaya county (64%) had a high uptake. The other counties had a low one (<50%). The uptake intensity of the TIMPs ranged from zero to eight. Most sorghum smallholders in Busia (25%), Baringo (36%), and Kisumu (31%) counties implemented four TIMPs, while Laikipia (37%), Siaya (36%), and West Pokot (36%) were implementing three. The findings showed that Uptake intensity had significant effects on sorghum yields (kg ha-1), income (USD ha-1), area-scaled emissions (kg CO2e ha-1), GHG EIs (kg CO2e kg-1 grain), and monetary scaled emissions (kg CO2e. USD-1). Across the six counties, smallholders implementing higher TIMPs reduce GHG EIs by at least 94% and monetary scaled emissions by at least 83% compared to those implementing the lowest number of TIMPs. 5.1. 3 Cassava value chain There were varied TIMPs uptake levels in Busia and Kisumu. Most cassava smallholder farmers (100 & 96%) in both counties implemented soil and water management practices. Crop health management (27 & 12%), mechanization (27 & 10%), and post-harvest management (40 & 14%) had a low uptake level. Most (29%) smallholder sorghum farmers in Busia implemented three TIMPs, while their counterparts (26%) in Kisumu implemented only two. Though KCSAP project participation by the farmers had no significant influence on cassava yields (kg ha-1), income (USD ha-1), area-scaled emissions (kg CO2 eq. ha-1), GHG EIs (kg CO2 eq. kg-1 grain), and monetary scaled emissions (kg CO2 eq. USD-1), uptake intensity showed a significant effect. Cassava production in Busia contributed to GHG emissions, while Kisumu sequestered the fluxes. Implementing seven TIMPs reduced area-scaled emissions, GHG EIs, and monetary scaled emissions by 58%, 98%, and 100% in Busia County compared to one TIMP. Implementing six TIMPs reduced area-scaled emissions, GHG EIs, and monetary scaled emissions by 82%, 111%, and 103%, in Kisumu County compared to one TIMP. Therefore, the uptake of more TIMPs led to GHG emissions sinking. 57 5.2 Recommendations Based on the study findings, the following recommendations are made:follow ing recommendations were made. There is a need to promote uptake of value addition, post- harvest management, markets, and mechanization among smallholder finger millet farming households. Value addition i.e. processing of finger millet by the farmers could be promoted through intensive training workshops and linkage to business development service providers e.g. processors, markets. The enhanced adoption of value addition will improve smallholder economic gains through sale of the surplus produce. Proper post-harvest management is vital to improving quality and reducing losses and contamination and market availability. Further, mechanisation can be promoted through linking farmers with the service and financial providers. This will be critical in promoting efficiency and sustainability in the finger millet value chain. Connecting smallholders to ready markets enhances market-led production. (i) Promote uptake of value addition, post-harvest management, markets, and mechanization among smallholder finger millet farming households. (ii) Promote uptake of more TIMPs for enhanced finger millet yields and income while reducing area-scaled emissions, GHG EIs, and monetary-scaled emissions. Promote the uptake Uptake of post-harvest management, crop health, mechanization, and market strategies across sorghum-growing counties should be promoted. Good post-harvest management e.g. drying to the recommended moisture content, is essential in reducing losses and diseases e.g. aflatoxin. Integrated pest and disease management is important for ensuring healthy, productive crops and increasing sorghum yield. Notably, having effective and ready markets such as through contract farming and farmers' groups helps farmers gain fair prices and ready markets. Therefore, promoting post-harvest management, crop health, and marketing TIMPs is best-fit for enhancing sorghum value chain sustainability. (iii) Promote uptake of more TIMPs for enhanced sorghum yields and income while reducing area-scaled emissions, GHG EIs, and monetary-scaled emissions. Promote uptake Uptake of crop health management, mechanization, and post-harvest management among cassava smallholder farming households should be promoted. Cassava crop is highly prone to pest and disease infestation, the stakeholders, therefore, need to promote the uptake of pest 58 and disease management practices, such as integrated pest and disease management practices, to curb the losses due to the infestation. Additionally, low uptake of mechanization limits the actualization of the potential yields. Promoting cassava mechanization is necessary for increased yields and income returns. Finally, poor storage leads to losses. Promoting better post-harvest management practices, such as storage using hermetic bags, could lower losses and enhance overall returns. Promote uptake Uptake of more TIMPs for enhanced finger millet, sorghum, and cassava yields and income while reducing area-scaled emissions, GHG EIs, and monetary-scaled emissions. should be promoted. Uptake of multiple and appropriate TIMPs enhances crop yields and revenue while lowering the area-scaled emissions, GHg EIs, and monetary scaled emissions compared to using none or a few technologies. Therefore, outscaling the uptake of multiple TIMPs is vital in enhancing economic gains (Yields and Income) while lowering or sinking the GHGs. 59 1. ACKNOWLEDGEMENTS Main funds for this project were provided through Kenya Climate Smart Agriculture Project (KCSAP) is a Government of Kenya project jointly supported by the World Bank. Additional, support for this study was provided through Mitigate+: Research for Low Emissions Food Systems and Livestock and Climate. We would like to thank all funders who supported this research through their contributions to the CGIAR Trust Fund. Funded by: https://www.cgiar.org/initiative/32-mitigate-plus-research-for-low-emission-food-systems/ https://www.cgiar.org/initiative/32-mitigate-plus-research-for-low-emission-food-systems/ https://www.cgiar.org/initiative/34-livestock-climate-and-system-resilience/ https://www.cgiar.org/funders/ 60 APPENDICES Appendix 1: Questionnaire cassava Geographic location 1. Geopoint 2. County 3. Sub-county 4. Ward 5. Village Farmer demographic profile 6. Name of the respondent 7. Phone number 8. Gender 9. Age 10. Farming experience 11. Education level 12. Annual income 13. Family size 14. Land tenure 15. Were you involved in the implementation of KCSAP project? Cassava crop management practices The farmer will be notified that all the questions are regarding the last successful cropping season 16. Did you grow cassava in the last cropping season? 17. When was your last successful cropping season? 18. What was the main soil type under cassava during the last successful season? 19. Did you plant improved varieties in the last successful cropping