Influence of Sustainable Land Management Practices on Dairy Farming in Baringo County, Kenya

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Kabaya, George M. Ogendi, Oscar O. Donde This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8627839/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Dairy farming promotes food and nutrition security globally. Despite dairy farming being an important income source for smallholder farmers in Kenya with 80% of milk production, it is commonly faced by challenges such as land degradation and overgrazing which has threatened its sustainability. This study was conducted to assess the influence of SLM practices on dairy production. The study was conducted in Mogotio and Mumberes Wards in Mogotio and Eldama Ravine Sub-Counties respectively in Baringo County, Kenya. A cross-sectional research approach was adopted for the study. Qualitative data was collected from 257 households. Data analysis was conducted using descriptive and inferential statistics. A Multiple linear regression model was used to determine the impacts of SLM practices on milk yields. Results showed that fodder preservation (β = 0.136, p = 0.025) and cover crops (β = 0.141, p = 0.054), significantly increased milk yields by 13.6% and 14.1%, respectively. Agroforestry on the other hand had a negative effect reducing milk yield by 14.1%. Additionally, Friesian cattle (β = 0.293, p < 0.001) and extension support (β = 0.386, p < 0.001) had a positive relationship with milk yield. These results demonstrate that SLM practices have the potential to improve milk yields in arid and semi-arid areas. I recommend that the State Department of Livestock Production and dairy cooperatives enhance capacity building and provide incentives for fodder production and preservation, as well as cover cropping, strengthen access to extension services and breed improvement programs for increased milk production. Sustainable land management Milk yields Arid and Semi-arid lands Multiple-linear regression model Figures Figure 1 Figure 2 Introduction In Kenya, dairy farming is a major source of livelihood, with over 80% of milk produced by smallholder farmers ( 1 ). Additionally, dairy farming promotes food and nutrition security ( 2 ). The sustainable development of many parts of the world, particularly Sub-Saharan Africa, is still significantly impacted by land degradation[1] (4) and overgrazing ( 5 ). Livestock health, growth, and reproductive performance are all adversely impacted by degraded lands because they provide low-quality fodder and less water ( 6 ). Reduced livestock productivity has negative impacts on rural livelihoods, food security, and economic stability, frequently plunging people into more vulnerability ( 7 ). Arid and semi-arid regions (ASAL) of Africa make up around 11% of the world's land area, 27% of all drylands worldwide, and 43% of the continent. An estimated 325 million people depend on dryland resources and ecosystem services for their rural livelihoods ( 8 ). Persistent crop failures and the quick loss of perennial forage grasses in dryland settings have been greatly impacted by frequent extreme weather events, especially droughts and extremely low rainfall ( 9 ). Scarcity and insecurity of forage resources is the most significant factor constraining ASAL areas ( 10 ). Sustainable land management (SLM) practices have emerged as key interventions to mitigate land degradation issues. SLM practices makes use of technologies and natural resources to guarantee that land is used sustainably and productively. It includes a wide range of tasks, from restoration of degraded soil to enhancing soil water retention ( 11 ). In urban, rural, and conservation contexts, land use refers to the kind of activity that is conducted on a unit of land ( 11 ). Previous studies imply that using SLM techniques might greatly increase agricultural production without causing damage to water and soil resources ( 12 ). Low dairy productivity has been caused by small-scale dairy farmers limited adoption of SLM practices, limited access to feed because of the high cost of fodder and improved breeds, and a 5.9% drop in production to 754.3 million Liters in 2022. As a result, Kenya imports dairy products to satisfy growing domestic demand. Seasonal variations in pasture conditions during dry seasons exacerbate low productivity ( 13 ). Despite its potential, adoption of SLM in Kenya remains limited due to various barriers. SLM can double productivity but its success depends heavily on farmer knowledge, economic incentives, land tenure security, and access to extension services ( 12 ). There is limited empirical evidence on how SLM adoption affects dairy farming in Baringo County. This study was therefore conducted to identify the types of SLM practices in the study area, to identify the factors that motivated farmers to adopt SLM practices, to identify the challenges that farmers undergo in the course of adopting SLM practices and to assess the influence of SLM practices on dairy production. This study will contribute to improved livelihoods and food security. Materials and Methods 2.1 Study Area The study was conducted in two regions of Baringo County. Mogotio Ward located in Mogotio Sub County, at latitude and longitude ໿0.02°S, 35.97°E໿ (Fig. 1). The ward has a total of 41,261people, out of which 20,882 are male and 20,378 are female. The total number of households is 8,786 and it covers an area of 564.1 square kilometers ( 14 ). The second study area was Mumberes ward located along the C55 (Makutano-Ravine-Kampi Ya Moto) Route in Eldama Ravine Sub-County in Baringo County, Kenya. It is located at geographical coordinates, latitude − 0.01168 and longitude, 35.60793 (Fig. 1). Mumberes ward has a total of 13,233 people, out of which 6,620 are men and 6,613 are female. It has a total of 3,091 households and a total land area of 306 square kilometers ( 14 ). Figure 1 : Map of Mogotio and Mumberes wards Source: Author modified from Environmental Systems Research Institute (ESRI) website 2.2 Research Design The study used a cross-sectional survey research design. Household questionnaires (ESM 1) were used for data collection. 2.3 Target Population The target population was dairy farmers in Eldama Ravine and Mogotio Sub Counties. 2.4 Sample size determination Sampling technique is the process of choosing a sample that is as representative of the entire population as feasible in order to create a tiny cross-section, also known as a "sample survey" ( 15 ). Target population of dairy farmers in Mumberes is 2300 according to Mumberes Dairy Farmers Cooperative society while target population for Mogotio is 800 according to Mogotio Dairy farmers Cooperative Society. Nassiuma formula ( 16 ) (Eq. 1) was used to determine the sample size. \(\:\varvec{n}=\frac{\:\mathbf{N}\mathbf{C}2}{\varvec{C}2+\left(\varvec{N}-1\right)\varvec{e}2\:}\) …………………………………………………………. (Eq. 1) Where; n = Sample size, N = The population size, C = the coefficient of variation and e = The margin error in the calculation (5%). The majority of social science surveys and studies typically allow for a coefficient of variation of 30% to 70%. 60% is typically used by social science researchers ( 16 ). In order to determine a reasonable and economical sample size, this study chose a coefficient of variation of 60% due to the huge target populations (dairy farming households) in the study areas. The study employed a 95% confidence level and a 5% margin of error. $$\:n\:\left(Mumberes\right)=\frac{\:\:2300\:\left(0.6\:2\right)}{0.6\:2+\left(2300-1\right)\:0.05\:2\:\:\:\:\:\:}$$ \(\:n\) = 135.57 $$\:n\:\left(Mumberes\right)\:=\:135$$ $$\:n\:\left(Mogotio\right)=\frac{\:\:800\:\left(0.6\:2\right)}{0.6\:2+\left(800-1\right)\:0.05\:2\:\:\:\:\:\:}$$ \(\:n\) = 122.16 \(\:n\:\left(Mogotio\right)\:\) = 122 Total dairy farming households = 257 2.5 Sampling Procedure This study used various sampling methods. Baringo County was chosen purposively because it is a semi-arid region with a great potential for dairy farming. The Sub county study areas within Baringo County were also purposively selected, that is, Eldama Ravine and Mogitio ward being the leading and the second leading Sub counties in milk production respectively. Simple random sampling was used to select the dairy farmers households from a list of farmers obtained from Mogotio and Mumberes dairy cooperative societies in the study areas. The eligibility criteria for inclusion in the study was any dairy farmer who had a minimum of one dairy cow. A total of 257 questionnaires (ESM 1) were administered in the study areas, 135 in Mumberes and 122 in Mogotio according to the sample size. 2.6 Data Collection Methods Primary data was collected through household questionnaires (ESM 1) targeting dairy farmers. The tools assessed the socio-economic attributes of the dairy farmers, implementation of SLM practices such as agroforestry, fodder preservation, mulching, rotational grazing, soil conservation techniques, and water harvesting methods, benefits of SLM practices, challenges facing the adoption of the practices and milk yield of farmers per cow per day. 2.7 Validity and Reliability Validity of the questionnaire was evaluated by lecturers from the Department of Environmental Science who offered suggestions that aided in modification of the questionnaire. For reliability, a pilot study was conducted in Menengai West ward, Rongai Sub County, Nakuru County, Kenya, an area with similar agroecological conditions as the study area. Reliability of the questionnaire was tested using Cronbach alpha test which generated a co-efficient of 0.91 (ESM 2) thus the instrument was deemed reliable for data collection. 2.8 Data Analysis Data was analysed using the R version 4.5.0. Descriptive statistics was used to summarize SLM practices. The analysis used a multiple linear regression model to examine how different SLM practices affect milk yield. The response variable (milk yield) was log-transformed to meet statistical assumptions. A multiple linear regression model (Eq. 2) is a statistical analytical test used to simultaneously determine the effect of multiple predictor variables on a single dependent variable ( 17 ). y = β 0 + β 1 X 1 + β 2 X 2 + ... + β p X p + ϵ ……………………………………… (Eq. 2) where; y = Dependent variable (Outcome), β 0 = Constant, X 1 , X 2 …… , X p = Independent variables (Predictors), β 1 = Coefficients of the independent variables ( X 1 , X 2 …. , X p ), representing their impact on Y and ϵ = Error term (accounts for randomness or factors not included in the model). On substitution, y = Average daily milk yield per cow per day in litres, X 1 = Agroforestry, X 2 = Cover crops, X 3 = Fodder preservation, X 4 = Rotational grazing, X 5 = Terraces, X 6 = Water harvesting and X 7 = Mulching Results 3.4 SLM practices adopted Findings in Table 1 indicate that most farmers reported adopting various SLM practices. In Mogotio Ward, 19.7% used agroforestry, 19.7% used cover crops, 62.3% practiced fodder preservation, 16.4% used mulching, 73.8% practiced rotational grazing, 18.9% used terraces, and 68.9% practiced water harvesting. In Mumberes Ward, 18.8% used agroforestry, 24.1% used cover crops, 62.4% practiced fodder preservation, 5.3% used mulching, 69.2% practiced rotational grazing, 13.5% used terraces, and 81.2% practiced water harvesting. Overall, 19.2% of farmers adopted agroforestry, 22% adopted cover crops, 62.4% practiced fodder preservation, 10.6% used mulching, 71.4% practiced rotational grazing, 27.6% practiced soil conservation, 16.1% used terraces, and 75.3% practiced water harvesting. Table 1 SLM practices adopted SLM Practice Mogotio Frequency Mumberes Frequency Total Adopted Total Responses Overall Percent Agroforestry 19.7 18.8 49 255 19.2 Cover crops 19.7 24.1 56 255 22 Fodder preservation 62.3 62.4 159 255 62.4 Mulching 16.4 5.3 27 255 10.6 Rotational grazing 73.8 69.2 182 255 71.4 Terraces 18.9 13.5 41 255 16.1 Water harvesting 68.9 81.2 192 255 75.3 Rotational grazing and water harvesting were the most widely adopted practices, with over 70% of farmers using them. The high frequency of water harvesting and rotational grazing is consistent in semi-arid rangelands such as Mogotio, where decisions about land management are heavily influenced by fodder depletion and water constraint. Similar adoption patterns have been documented in Kenya's arid and semi-arid counties, including Laikipia, Machakos, and Kajiado, where pastoral and agropastoral communities have combined rainwater collection and rotational grazing to improve drought resistance and fodder regeneration ( 18 ). Fodder preservation was also commonly practiced (62.4%). The prevalence of fodder preservation techniques like silage and hay making is consistent with research by ( 19 ) who observed that smallholders have been forced to embrace feed conservation as an adaptive SLM approach due to climate variability and dry-season feed shortages. Less popular practices included mulching, terraces, and agroforestry. Low adoption of agroforestry is mainly due to farmers’ limited awareness that certain tree species can serve as fodder for livestock. Ward-level differences in SLM adoption have been observed elsewhere in Kenya, where micro-climatic variation and local institutional support shape the choice of practices ( 20 ). 3.5 Motivation to adopt SLM practices From Table 2 , in Mogotio Ward, 13.5% of farmers were motivated by advice from extension officers, 20.6% by drought or poor yields, 32.3% by increased milk production, 0.6% by other reasons, 21.5% by soil fertility improvement, and 11.4% by training workshops. In Mumberes Ward, 30.3% were motivated by advice from extension officers, 6.9% by drought or poor yields, 31.5% by increased milk production, none by other reasons, 22.3% by soil fertility improvement, and 9% by training workshops. Overall, 22.2% were motivated by extension advice, 13.6% by drought or poor yields, 31.9% by increased milk production, 0.3% by other reasons, 21.9% by soil fertility improvement, and 10.1% by training workshops. Table 2 Motivation to adopt SLM practices Motivation type Mogotio Frequency Mumberes Frequency Total Mogotio % Mumberes % Total % Advice from extension officers 44 105 149 13.5 30.3 22.2 Drought or poor yields 67 24 91 20.6 6.9 13.6 Increased milk production 105 109 214 32.3 31.5 31.9 Soil fertility improvement 70 77 147 21.5 22.3 21.9 Training workshops 37 31 68 11.4 9 10.1 Other factors 2 0 2 0.6 0 0.3 Total 325 346 671 100 100 100 The findings show that most farmers adopt SLM practices to increase milk production. Farmers in Mumberes Ward were more influenced by advice from extension officers (30.3%) compared to 13.5% in Mogotio, suggesting that access to agricultural extension services plays a significant role in motivating SLM adoption. This agrees with findings by ( 21 ) who reported that effective extension contact and farmer training significantly increase the adoption of soil and water conservation practices among smallholders in central Kenya. Fewer farmers were motivated by drought or poor yields, showing that practical benefits and guidance from experts are the main reasons for adopting SLM practices. 3.6 Challenges in adopting SLM practices From Table 3 , 3.4% of farmers in Mogotio Ward, reported cultural hindrances, 22.1% drought and famine, 30.5% high cost of SLM practices, 11.5% labor shortage, 13.8% lack of knowledge, and 18.7% small land size as challenges. In Mumberes Ward, 0.9% reported cultural hindrances, 4% drought and famine, 34.4% high cost of SLM practices, 17% labor shortage, 13.6% lack of knowledge, and 30% small land size. Overall, 2.2% faced cultural hindrances, 13.4% drought and famine, 32.3% high cost, 14.2% labor shortage, 13.7% lack of knowledge, and 24.1% small land size. Table 3 Challenges in adopting SLM practices Challenge type Mogotio Frequency Mumberes Frequency Total Mogotio % Mumberes % Total (%) Cultural hindrances 12 3 15 3.4 0.9 2.2 Drought and famine 77 13 90 22.1 4 13.4 High cost of SLM practices 106 111 217 30.5 34.4 32.3 Labor shortage 40 55 95 11.5 17 14.2 Lack of knowledge 48 44 92 13.8 13.6 13.7 Land size 65 97 162 18.7 30 24.1 Total 348 323 671 100 100 100 The greatest challenges faced by majority of the farmers is high cost of implementing SLM practices and limited land size. These findings are supported by studies carried out in sub-Saharan Africa which reveal that high implementation costs and lack of finance are the main obstacles to the adoption of SLM ( 22 ). Additionally, it has been demonstrated that small farms with limited staff supply are less likely to use labour and money intensive practices. According to a study in Malawi, concurrent adoption of both short-term and long-term SLM methods was positively correlated with larger plot sizes and more family labour ( 23 ). Fewer farmers reported cultural hindrances or lack of knowledge, suggesting that financial and resource constraints are the main barriers to adopting SLM practices. 3.7 Milk yield in litres per cow per day The results in Table 4 show that the average daily milk yield per cow per day after adoption of SLM practices varied substantially between wards. In Mogotio Ward, the mean milk yield was 5.34 ± 2.39 L cow⁻¹ day⁻¹, with a median of 5 L and an interquartile range (IQR) of 4–6 L. In contrast, Mumberes Ward recorded a markedly higher mean yield of 8.83 ± 3.88 L cow⁻¹ day⁻¹, with a median of 8 L and an IQR of 6–12 L. Overall, across both wards, the mean yield was 7.17 ± 3.69 L cow⁻¹ day⁻¹, ranging between 2 L (minimum) and 16 L (maximum). Table 4 Average Milk yield in litres per cow per day Location Mean yield Median yield Sd yield q1 yield q3 yield Min yield Max yield n Mogotio Ward 5.34 5 2.39 4 6 2 15 122 Mumberes Ward 8.83 8 3.88 6 12 3 16 135 Overall 7.17 6 3.69 4 9 2 16 257 These findings suggest that SLM adoption has been associated with moderate to high milk yields relative to smallholder averages typically reported in Kenya. The higher milk yield in Mumberes Ward may be attributed to better pasture conditions, more consistent water availability, and greater adoption of improved SLM and feeding practices such as fodder cultivation, manure management, and rotational grazing. The area’s relatively higher rainfall and cooler temperatures likely enhance forage productivity and cow comfort, improving lactation performance. This is consistent with previous research conducted in Kenya, which demonstrated that in smallholder systems, better management and feeding techniques lead to noticeably increased milk outputs. ( 24 ) discovered that smallholder farms in Nyeri County with comparatively well-managed practices produced mean yields of about 10.7 L cow⁻¹ day⁻¹. Similarly, ( 25 ), described smallholder dairy typologies in Nakuru and Nyandarua Counties and observed that low resource farms produced low milk yields whereas resource endowed farms produced significantly higher yields ( 25 ). 3.8 Influence of sustainable land management practices on milk yields A multiple linear regression was conducted to examine the influence of various SLM practices on average daily milk yield among dairy farmers. The outcome variable was average daily milk yield per cow per day in litres. The model (ESM 3) included seven SLM practices (predictors): agroforestry, cover crops, fodder preservation, rotational grazing, terraces, water harvesting and mulching. Cow breed, extension support and marital status were included as confounding variables in the model (ESM 3). 3.8.1 Multicollinearity check Before analysis, the multicollinearity of the predictor variables was assessed using the Generalized Variance Inflation Factor (GVIF) as demonstrated in Table 5 to determine the suitability of including them in the model. All adjusted GVIF values ( \(\:GVIF^\frac{1}{2*Df}\) ) ranged from 1.02 to 1.26, which is well below the commonly used threshold of 2. This indicates that there was multicollinearity among the predictors, and the regression coefficients were reliable. Table 5 Multicollinearity Check for categorical variables Variable GVIF Df GVIF^ 1/(2*Df) Agroforestry 1.387063 1 1.177736 Cover crops 1.396588 1 1.181773 Fodder preservation 1.293867 1 1.137483 Rotational grazing 1.272786 1 1.128178 Terraces 1.419281 1 1.191336 Water harvesting 1.167336 1 1.080433 Mulching 1.588193 1 1.260235 Friesian 1.551462 1 1.245577 Crossbreed 1.559435 1 1.248773 Marital status 1.145248 3 1.022861 Extension support 1.11934 1 1.057989 3.8.2 Residual Diagnostics Diagnostic plots were examined to assess model assumptions. Residuals vs Fitted plots indicate that residuals were randomly scattered, supporting linearity and homoscedasticity. For Q-Q Plot, residuals were approximately normally distributed. In the Scale-Location Plot the variance of residuals was relatively constant across fitted values. For Residuals vs Leverage, no extreme outliers or influential points were detected Fig. 2 . The regression analysis results in (Table 6 ) showed that some SLM practices significantly influenced milk yields. Fodder preservation significantly increased milk yield by about 13.6% (β = 0.136, p = 0.025). Cover crops had a marginal positive effect, increasing milk yield by approximately 14.1% (β = 0.141, p = 0.054). In contrast, agroforestry showed a small negative effect, reducing milk yield by around 14.4% (β = -0.144, p = 0.063). Other SLM practices; rotational grazing, terraces, water harvesting, and mulching did not significantly affect milk yield in this study. Cow breed, extension support and marital status were included as confounding variables. The analysis showed that Friesian cows were associated with higher milk yields (β = 0.293, p < 0.001), while crossbreed cows had lower milk yields (β = -0.238, p < 0.001). Extension services was found to increase milk yields by 38.8% (β = 0.385, P = 0.000000441). Table 6 Regression Coefficients for Multiple Linear Regression Model Term Estimate (β) Std error statistic P value Conf low Conf high Signif (Intercept) 1.270592 0.314318 4.042379 7.14E-05 0.651405 1.889779 *** Agroforestry -0.14381 0.076932 -1.86927 0.062808 -0.29536 0.007744 * Cover crops 0.141113 0.072906 1.935548 0.054103 -0.00251 0.284733 * Fodder preservation 0.135723 0.060036 2.260677 0.024679 0.017455 0.253991 ** Rotational grazing 0.044663 0.063517 0.703159 0.482641 -0.08046 0.169788 Terraces 0.051409 0.083632 0.614709 0.539331 -0.11334 0.216158 Water harvesting 0.033234 0.063655 0.522094 0.602088 -0.09216 0.15863 Mulching -0.16159 0.104537 -1.54578 0.123481 -0.36752 0.044341 Friesian 0.293202 0.065486 4.47733 1.17E-05 0.164199 0.422205 *** crossbreed -0.23836 0.064232 -3.71086 0.000257 -0.36489 -0.11182 *** Marital status (Married) 0.117732 0.30167 0.390268 0.696686 -0.47654 0.712004 Marital status (Single) 0.264209 0.314004 0.841419 0.400954 -0.35436 0.882779 Marital Status Widow(er) 0.232527 0.32274 0.720477 0.471936 -0.40325 0.868306 Extension Support 0.385754 0.074271 5.1939 4.41E-07 0.239446 0.532063 *** Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 The findings indicate that farmers who engaged in preserving fodder recorded higher milk yields than those who did not. Similar results were found by ( 26 ), who revealed that one of the main factors influencing increased milk outputs and farmer resistance to climatic variability was the adoption of climate-smart feeding practices, such as feed conservation and storage. Cover crops increased milk yield by approximately 14.1% suggesting that fodder type cover crops improve forage availability thus increasing milk yields. These findings are consistent with those of ( 27 ) who found that improved forage species and legumes increased milk yields by 23–33%. In contrast, agroforestry reduced milk yield by around 14.4% because farmers practicing agroforestry prioritize exotic tree species for timber over immediate forage production. According to research conducted in Machakos County, Kenya, agroforestry systems offer a variety of ecosystem services. However, provisioning services, such as fodder, were ranked third in importance, behind timber/fuelwood and regulatory functions. This suggests that many agroforestry systems are not optimised for the production of fodder ( 28 ). Friesian cows were associated with higher milk yields (β = 0.293, p < 0.001), while crossbreed cows had lower milk yields (β = -0.238, p < 0.001). Extension support also had a positive effect, indicating that education and training on SLM practices increases milk production in the study areas. 3.8.3 Model Fit Statistics The regression model was statistically significant, F₁₃,₂₃₉ = 9.862, p < 0.001. The values for R² = 0.349 and Adjusted R² = 0.314 suggest that approximately 34.9% of the variation in milk yield is explained by the independent variables in the model (Table 7 ). Table 7 :Model Fit Statistics Residual SE R² Adjusted R² F statistic F df1 F df2 F p value 0.413914 0.349138 0.313735 9.861945 13 239 1.85E-16 Conclusion Fodder preservation, cover cropping, improved breeds, and extension services have a positive influence on milk production in the study area. Therefore, county governments, dairy cooperatives and other stakeholders need to support extension services to farmers as well as capacity building on establishment and management of fodder preservation and cover cropping. Additionally, the county government of Baringo should subsidize breed improvement program to ensure equitable access of these services by all farmers in order to increase milk production. Declarations Author Information Authors and affiliations Department of Environmental Science, Egerton University, P.0 BOX 536-20115, Njoro, Kenya Egerton University Nakuru City Campus College, P.0 BOX 13357-20100, Nakuru, Kenya George M. Ogendi Department of Environmental Science, Egerton University, P.0 BOX 536-20115, Njoro, Kenya Oscar O. Donde Department of Environmental Science, Egerton University, P.0 BOX 536-20115, Njoro, Kenya Sarah M. Kabaya Author contribution Sarah M. Kabaya: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization, Project administration, Funding acquisition. George M. Ogendi: Conceptualization, Writing - Review & Editing, Supervision, Project administration. Oscar O. Donde: Conceptualization, Writing - Review & Editing, Supervision, Project administration Corresponding author Sarah M. Kabaya Data availability The research data for this article is published in Mendeley data repository and can be accessed via this link: https://data.mendeley.com/datasets/gj7wd3sryd/1 Funding This research was funded by a grant from the Mastercard Foundation to RUFORUM to implement the TAGDev 2.0 programme at Egerton University. Acknowledgement Not applicable Competing Interest The authors affirm that the work described in this publication was not influenced by any known competing financial interests or personal relationships. Ethics Approval and Accordance The study received ethical approval from the Egerton University's Institutional Scientific and Ethics Review Committee [Approval No: EUISERC/APP/503/2025]. All research procedures were performed in accordance with the ethical guidelines and regulations of Egerton University. A research permit was obtained from the Kenya's National Commission for Science, Technology, and Innovation (NACOSTI) [License No: NACOSTI/P/25/4177546] and the principles outlined in the Declaration of Helsinki. Additional permits were sought from the local authorities in Baringo County and the managers for the dairy co-operatives. Consent to Participate Informed consent was obtained from all participants prior to data collection. The respondents’ identities were kept private and confidential. 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Influence of public agricultural extension services on sustainable land management practice (SLMP) adoption among smallholder farmers in Fetakgomo Tubatse Local Municipality, South Africa. Frontiers in Sustainable Food Systems [Internet]. 2025 [cited 2025 Nov 1];9:1618938. Available from: https://www.frontiersin.org/journals/sustainable-food-systems/articles/ 10.3389/fsufs.2025.1618938/abstract Mangole CD, Maina CM, Mulungu K, Tschopp M, Harari N, Suresh R et al. Adoption of sustainable land and water management practices and their impact on crop productivity among smallholder farmers in sub-Saharan Africa. Land Use Policy [Internet]. 2025 [cited 2025 Oct 31];153:107533. Available from: https://www.sciencedirect.com/science/article/pii/S0264837725000675 Mwaura GG, Kiboi MN, Bett EK, Mugwe JN, Muriuki A, Nicolay G et al. Adoption intensity of selected organic-based soil fertility management technologies in the Central Highlands of Kenya. Frontiers in Sustainable Food Systems [Internet]. 2021 [cited 2025 Oct 31];4:570190. Available from: https://www.frontiersin.org/articles/ 10.3389/fsufs.2020.570190/full Taylor A, Wynants M, Munishi L, Kelly C, Mtei K, Mkilema F et al. Building Climate Change Adaptation and Resilience through Soil Organic Carbon Restoration in Sub-Saharan Rural Communities: Challenges and Opportunities. Sustainability [Internet]. 2021 Jan [cited 2025 Oct 31];13(19):10966. Available from: https://www.mdpi.com/ 2071-1050/13/19/10966. Kansanga MM, Luginaah I, Kerr RB, Dakishoni L, Lupafya E. Determinants of smallholder farmers’ adoption of short-term and long-term sustainable land management practices. Renewable Agriculture and Food Systems [Internet]. 2021 June [cited 2025 Oct 31];36(3):265–77. Available from: https://www.cambridge.org/core/journals/renewable-agriculture-and-food-systems/article/determinants-of-smallholder-farmers-adoption-of-shortterm-and-longterm-sustainable-land-management-practices/7029F70117DA298B0E7EF1A1C68D9621?utm_source=chatgpt.com Ajak PAD, Gachuiri CK, Wanyoike MMM. Evaluation of Dairy Cattle Productivity in Smallholder Farms in Nyeri County, Kenya. EAJSTI [Internet]. 2020 Dec 11 [cited 2025 Oct 31];2(1). Available from: https://www.eajsti.org Otieno GO, Muendo K, Mbeche R. Smallholder Dairy Farming Characterisation, Typologies and Determinants in Nakuru and Nyandarua Counties, Kenya. Journal of Agriculture, Science and Technology [Internet]. 2021 July 6 [cited 2025 Nov 1];20(1):1–23. Available from: https://ojs.jkuat.ac.ke/index.php/JAGST/article/view/177 Chelang’a NC, Mathenge M, Otieno DO, Sassi M. The determinants of greenhouse gas reduction levels among smallholder farmers: insights from the adoption of climate-smart dairy strategies in Central Kenya. Frontiers in Climate [Internet]. 2025 [cited 2025 Oct 31];7:1593584. Available from: https://www.frontiersin.org/journals/climate/articles/ 10.3389/fclim.2025.1593584/abstract Maweu AN, Korir BK, Kuria SG, Ogillo BP, Kisambo BK, Wambulwa LM et al. Promoting improved forages for increased livestock productivity in the Arid and Semi-Arid Lands (ASALs) of Kenya. A case of Kajiado, Narok and Taita Taveta county. International Journal Veterinary Sciences and Animal Husbandry [Internet]. 2023 [cited 2025 Oct 31];8(2):93–101. Available from: https://www.researchgate.net/profile/Annastacia-Maweu/publication/373027334_Promoting_improved_forages_for_increased_livestock_productivity_in_the_Arid_and_Semi-Arid_Lands_ASALs_of_Kenya_A_case_of_Kajiado_Narok_and_Taita_Taveta_county/links/66a787a5de060e4c7e672c39/Promoting-improved-forages-for-increased-livestock-productivity-in-the-Arid-and-Semi-Arid-Lands-ASALs-of-Kenya-A-case-of-Kajiado-Narok-and-Taita-Taveta-county.pdf Kinyili BM, Ndunda E, Kitur E. Trade-Off Between Agroforestry and Ecosystem Services among Smallholder Farmers in Machakos County, Kenya. East African Journal of Forestry and Agroforestry [Internet]. 2019 Oct 2 [cited 2025 Oct 31];1(1):13–23. Available from: https://journals.eanso.org/index.php/eajfa/article/view/14 Footnotes Land degradation is a decrease in the land's present or potential ability to produce ( 3 ). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8627839","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595839295,"identity":"0ffe53c8-1e7a-4429-9928-b0f6abe3bdd1","order_by":0,"name":"Sarah M. Kabaya","email":"data:image/png;base64,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","orcid":"","institution":"Egerton University, Njoro","correspondingAuthor":true,"prefix":"","firstName":"Sarah","middleName":"M.","lastName":"Kabaya","suffix":""},{"id":595839302,"identity":"33a4ca13-eb4b-4564-8ff3-fc1fb4b7500a","order_by":1,"name":"George M. Ogendi","email":"","orcid":"","institution":"Egerton University, Njoro","correspondingAuthor":false,"prefix":"","firstName":"George","middleName":"M.","lastName":"Ogendi","suffix":""},{"id":595839311,"identity":"c268c1c5-0043-4b94-b46c-46c979b68899","order_by":2,"name":"Oscar O. Donde","email":"","orcid":"","institution":"Egerton University, Njoro","correspondingAuthor":false,"prefix":"","firstName":"Oscar","middleName":"O.","lastName":"Donde","suffix":""}],"badges":[],"createdAt":"2026-01-17 18:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8627839/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8627839/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103414222,"identity":"d7346d73-7d12-43a4-86e0-44b1b6d9b5c6","added_by":"auto","created_at":"2026-02-25 11:42:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":449219,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Mogotio and Mumberes wards\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Author modified from Environmental Systems Research Institute (ESRI) website\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8627839/v1/4881471089bcf172958d4479.png"},{"id":103414231,"identity":"21888e27-27ae-4687-8dfd-04a5d94626c9","added_by":"auto","created_at":"2026-02-25 11:42:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":451849,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic Test results\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8627839/v1/1b5f1d2c688907796ffbd273.png"},{"id":105892880,"identity":"2f9b5777-d79c-4823-b08f-98974cef259d","added_by":"auto","created_at":"2026-04-01 08:14:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2174241,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8627839/v1/61361c7a-af1c-408b-9d69-e534ab219073.pdf"},{"id":103414227,"identity":"b24b6094-bb76-4847-885c-7e3bc7e7f438","added_by":"auto","created_at":"2026-02-25 11:42:46","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":26528,"visible":true,"origin":"","legend":"","description":"","filename":"ESM1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8627839/v1/c220a1f46bce2b820fc5ea43.docx"},{"id":103414242,"identity":"7b48f8c6-9e71-41d4-b88a-140f18936e3a","added_by":"auto","created_at":"2026-02-25 11:42:58","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":40751,"visible":true,"origin":"","legend":"","description":"","filename":"ESM2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8627839/v1/3290b388a39c8c5b55aadf2c.docx"},{"id":103414246,"identity":"950bdb81-aded-4c16-b77a-2884ab841123","added_by":"auto","created_at":"2026-02-25 11:42:58","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":75797,"visible":true,"origin":"","legend":"","description":"","filename":"ESM3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8627839/v1/cdf1745f472202a53fffe45f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eInfluence of Sustainable Land Management Practices on Dairy Farming in Baringo County, Kenya\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn Kenya, dairy farming is a major source of livelihood, with over 80% of milk produced by smallholder farmers (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Additionally, dairy farming promotes food and nutrition security (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The sustainable development of many parts of the world, particularly Sub-Saharan Africa, is still significantly impacted by land degradation[1]\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e (4) and overgrazing (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Livestock health, growth, and reproductive performance are all adversely impacted by degraded lands because they provide low-quality fodder and less water (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Reduced livestock productivity has negative impacts on rural livelihoods, food security, and economic stability, frequently plunging people into more vulnerability (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eArid and semi-arid regions (ASAL) of Africa make up around 11% of the world's land area, 27% of all drylands worldwide, and 43% of the continent. An estimated 325\u0026nbsp;million people depend on dryland resources and ecosystem services for their rural livelihoods (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Persistent crop failures and the quick loss of perennial forage grasses in dryland settings have been greatly impacted by frequent extreme weather events, especially droughts and extremely low rainfall (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Scarcity and insecurity of forage resources is the most significant factor constraining ASAL areas (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSustainable land management (SLM) practices have emerged as key interventions to mitigate land degradation issues. SLM practices makes use of technologies and natural resources to guarantee that land is used sustainably and productively. It includes a wide range of tasks, from restoration of degraded soil to enhancing soil water retention (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). In urban, rural, and conservation contexts, land use refers to the kind of activity that is conducted on a unit of land (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Previous studies imply that using SLM techniques might greatly increase agricultural production without causing damage to water and soil resources (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLow dairy productivity has been caused by small-scale dairy farmers limited adoption of SLM practices, limited access to feed because of the high cost of fodder and improved breeds, and a 5.9% drop in production to 754.3\u0026nbsp;million Liters in 2022. As a result, Kenya imports dairy products to satisfy growing domestic demand. Seasonal variations in pasture conditions during dry seasons exacerbate low productivity (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Despite its potential, adoption of SLM in Kenya remains limited due to various barriers. SLM can double productivity but its success depends heavily on farmer knowledge, economic incentives, land tenure security, and access to extension services (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). There is limited empirical evidence on how SLM adoption affects dairy farming in Baringo County. This study was therefore conducted to identify the types of SLM practices in the study area, to identify the factors that motivated farmers to adopt SLM practices, to identify the challenges that farmers undergo in the course of adopting SLM practices and to assess the influence of SLM practices on dairy production. This study will contribute to improved livelihoods and food security.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Area\u003c/h2\u003e \u003cp\u003eThe study was conducted in two regions of Baringo County. Mogotio Ward located in Mogotio Sub County, at latitude and longitude ໿0.02\u0026deg;S, 35.97\u0026deg;E໿ (Fig.\u0026nbsp;1). The ward has a total of 41,261people, out of which 20,882 are male and 20,378 are female. The total number of households is 8,786 and it covers an area of 564.1 square kilometers (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The second study area was Mumberes ward located along the C55 (Makutano-Ravine-Kampi Ya Moto) Route in Eldama Ravine Sub-County in Baringo County, Kenya. It is located at geographical coordinates, latitude\u0026thinsp;\u0026minus;\u0026thinsp;0.01168 and longitude, 35.60793 (Fig.\u0026nbsp;1). Mumberes ward has a total of 13,233 people, out of which 6,620 are men and 6,613 are female. It has a total of 3,091 households and a total land area of 306 square kilometers (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;1\u003c/b\u003e \u003c/p\u003e \u003cp\u003e : Map of Mogotio and Mumberes wards\u003c/p\u003e \u003cp\u003e \u003cb\u003eSource: Author modified from Environmental Systems Research Institute (ESRI) website\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Research Design\u003c/h2\u003e \u003cp\u003eThe study used a cross-sectional survey research design. Household questionnaires (ESM 1) were used for data collection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Target Population\u003c/h2\u003e \u003cp\u003eThe target population was dairy farmers in Eldama Ravine and Mogotio Sub Counties.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Sample size determination\u003c/h2\u003e \u003cp\u003eSampling technique is the process of choosing a sample that is as representative of the entire population as feasible in order to create a tiny cross-section, also known as a \"sample survey\" (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Target population of dairy farmers in Mumberes is 2300 according to Mumberes Dairy Farmers Cooperative society while target population for Mogotio is 800 according to Mogotio Dairy farmers Cooperative Society. Nassiuma formula (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) (Eq.\u0026nbsp;1) was used to determine the sample size.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{n}=\\frac{\\:\\mathbf{N}\\mathbf{C}2}{\\varvec{C}2+\\left(\\varvec{N}-1\\right)\\varvec{e}2\\:}\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;. (Eq.\u0026nbsp;1)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWhere;\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;Sample size, N\u0026thinsp;=\u0026thinsp;The population size, C\u0026thinsp;=\u0026thinsp;the coefficient of variation and \u003cem\u003ee\u0026thinsp;=\u003c/em\u003e\u0026thinsp;The margin error in the calculation (5%).\u003c/p\u003e \u003cp\u003eThe majority of social science surveys and studies typically allow for a coefficient of variation of 30% to 70%. 60% is typically used by social science researchers (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In order to determine a reasonable and economical sample size, this study chose a coefficient of variation of 60% due to the huge target populations (dairy farming households) in the study areas. The study employed a 95% confidence level and a 5% margin of error.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:n\\:\\left(Mumberes\\right)=\\frac{\\:\\:2300\\:\\left(0.6\\:2\\right)}{0.6\\:2+\\left(2300-1\\right)\\:0.05\\:2\\:\\:\\:\\:\\:\\:}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:n\\)\u003c/span\u003e \u003c/span\u003e \u003cem\u003e= 135.57\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Equb\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:n\\:\\left(Mumberes\\right)\\:=\\:135$$\u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Equc\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:n\\:\\left(Mogotio\\right)=\\frac{\\:\\:800\\:\\left(0.6\\:2\\right)}{0.6\\:2+\\left(800-1\\right)\\:0.05\\:2\\:\\:\\:\\:\\:\\:}$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:n\\)\u003c/span\u003e \u003c/span\u003e \u003cem\u003e= 122.16\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:n\\:\\left(Mogotio\\right)\\:\\)\u003c/span\u003e \u003c/span\u003e \u003cem\u003e= 122\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eTotal dairy farming households\u0026thinsp;=\u0026thinsp;257\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Sampling Procedure\u003c/h2\u003e \u003cp\u003eThis study used various sampling methods. Baringo County was chosen purposively because it is a semi-arid region with a great potential for dairy farming. The Sub county study areas within Baringo County were also purposively selected, that is, Eldama Ravine and Mogitio ward being the leading and the second leading Sub counties in milk production respectively. Simple random sampling was used to select the dairy farmers households from a list of farmers obtained from Mogotio and Mumberes dairy cooperative societies in the study areas. The eligibility criteria for inclusion in the study was any dairy farmer who had a minimum of one dairy cow. A total of 257 questionnaires (ESM 1) were administered in the study areas, 135 in Mumberes and 122 in Mogotio according to the sample size.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Data Collection Methods\u003c/h2\u003e \u003cp\u003ePrimary data was collected through household questionnaires (ESM 1) targeting dairy farmers. The tools assessed the socio-economic attributes of the dairy farmers, implementation of SLM practices such as agroforestry, fodder preservation, mulching, rotational grazing, soil conservation techniques, and water harvesting methods, benefits of SLM practices, challenges facing the adoption of the practices and milk yield of farmers per cow per day.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Validity and Reliability\u003c/h2\u003e \u003cp\u003eValidity of the questionnaire was evaluated by lecturers from the Department of Environmental Science who offered suggestions that aided in modification of the questionnaire. For reliability, a pilot study was conducted in Menengai West ward, Rongai Sub County, Nakuru County, Kenya, an area with similar agroecological conditions as the study area. Reliability of the questionnaire was tested using Cronbach alpha test which generated a co-efficient of 0.91 (ESM 2) thus the instrument was deemed reliable for data collection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Data Analysis\u003c/h2\u003e \u003cp\u003eData was analysed using the R version 4.5.0. Descriptive statistics was used to summarize SLM practices. The analysis used a multiple linear regression model to examine how different SLM practices affect milk yield. The response variable (milk yield) was log-transformed to meet statistical assumptions. A multiple linear regression model (Eq.\u0026nbsp;2) is a statistical analytical test used to simultaneously determine the effect of multiple predictor variables on a single dependent variable (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003ey\u0026thinsp;=\u0026thinsp;β\u003c/b\u003e \u003csub\u003e \u003cb\u003e0\u003c/b\u003e \u003c/sub\u003e\u0026thinsp;\u003cb\u003e+\u0026thinsp;β\u003c/b\u003e\u003csub\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sub\u003e\u0026thinsp;\u003cb\u003e+\u0026thinsp;β\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e+ ...\u0026thinsp;+\u0026thinsp;β\u003c/b\u003e\u003csub\u003e\u003cb\u003ep\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003ep\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e+ ϵ \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u003c/b\u003e \u003cb\u003e(Eq.\u0026nbsp;2)\u003c/b\u003e\u003c/p\u003e \u003cp\u003ewhere; \u003cem\u003ey\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Dependent variable (Outcome), \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Constant, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e,\u003c/sub\u003e \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e2 \u0026hellip;\u0026hellip;\u003c/em\u003e,\u003c/sub\u003e \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e = Independent variables (Predictors), \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Coefficients of the independent variables (\u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e,\u003c/sub\u003e \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e2 \u0026hellip;.\u003c/em\u003e,\u003c/sub\u003e \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e), representing their impact on \u003cem\u003eY\u003c/em\u003e and \u003cem\u003eϵ\u003c/em\u003e = Error term (accounts for randomness or factors not included in the model).\u003c/p\u003e \u003cp\u003eOn substitution, \u003cem\u003ey\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Average daily milk yield per cow per day in litres, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Agroforestry, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Cover crops, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Fodder preservation, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Rotational grazing, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e5\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Terraces, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e6\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Water harvesting and \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e7\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Mulching\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 SLM practices adopted\u003c/h2\u003e \u003cp\u003eFindings in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e indicate that most farmers reported adopting various SLM practices. In Mogotio Ward, 19.7% used agroforestry, 19.7% used cover crops, 62.3% practiced fodder preservation, 16.4% used mulching, 73.8% practiced rotational grazing, 18.9% used terraces, and 68.9% practiced water harvesting. In Mumberes Ward, 18.8% used agroforestry, 24.1% used cover crops, 62.4% practiced fodder preservation, 5.3% used mulching, 69.2% practiced rotational grazing, 13.5% used terraces, and 81.2% practiced water harvesting. Overall, 19.2% of farmers adopted agroforestry, 22% adopted cover crops, 62.4% practiced fodder preservation, 10.6% used mulching, 71.4% practiced rotational grazing, 27.6% practiced soil conservation, 16.1% used terraces, and 75.3% practiced water harvesting.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSLM practices adopted\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLM Practice\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMogotio Frequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMumberes Frequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Adopted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal Responses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOverall Percent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgroforestry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCover crops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFodder preservation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMulching\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRotational grazing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerraces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater harvesting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRotational grazing and water harvesting were the most widely adopted practices, with over 70% of farmers using them. The high frequency of water harvesting and rotational grazing is consistent in semi-arid rangelands such as Mogotio, where decisions about land management are heavily influenced by fodder depletion and water constraint. Similar adoption patterns have been documented in Kenya's arid and semi-arid counties, including Laikipia, Machakos, and Kajiado, where pastoral and agropastoral communities have combined rainwater collection and rotational grazing to improve drought resistance and fodder regeneration (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFodder preservation was also commonly practiced (62.4%). The prevalence of fodder preservation techniques like silage and hay making is consistent with research by (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) who observed that smallholders have been forced to embrace feed conservation as an adaptive SLM approach due to climate variability and dry-season feed shortages. Less popular practices included mulching, terraces, and agroforestry. Low adoption of agroforestry is mainly due to farmers\u0026rsquo; limited awareness that certain tree species can serve as fodder for livestock. Ward-level differences in SLM adoption have been observed elsewhere in Kenya, where micro-climatic variation and local institutional support shape the choice of practices (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Motivation to adopt SLM practices\u003c/h2\u003e \u003cp\u003eFrom Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, in Mogotio Ward, 13.5% of farmers were motivated by advice from extension officers, 20.6% by drought or poor yields, 32.3% by increased milk production, 0.6% by other reasons, 21.5% by soil fertility improvement, and 11.4% by training workshops. In Mumberes Ward, 30.3% were motivated by advice from extension officers, 6.9% by drought or poor yields, 31.5% by increased milk production, none by other reasons, 22.3% by soil fertility improvement, and 9% by training workshops. Overall, 22.2% were motivated by extension advice, 13.6% by drought or poor yields, 31.9% by increased milk production, 0.3% by other reasons, 21.9% by soil fertility improvement, and 10.1% by training workshops.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMotivation to adopt SLM practices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMotivation type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMogotio Frequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMumberes Frequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMogotio %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMumberes %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvice from extension officers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrought or poor yields\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncreased milk production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil fertility improvement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraining workshops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e325\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e346\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e671\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe findings show that most farmers adopt SLM practices to increase milk production. Farmers in Mumberes Ward were more influenced by advice from extension officers (30.3%) compared to 13.5% in Mogotio, suggesting that access to agricultural extension services plays a significant role in motivating SLM adoption. This agrees with findings by (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) who reported that effective extension contact and farmer training significantly increase the adoption of soil and water conservation practices among smallholders in central Kenya. Fewer farmers were motivated by drought or poor yields, showing that practical benefits and guidance from experts are the main reasons for adopting SLM practices.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Challenges in adopting SLM practices\u003c/h2\u003e \u003cp\u003eFrom Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, 3.4% of farmers in Mogotio Ward, reported cultural hindrances, 22.1% drought and famine, 30.5% high cost of SLM practices, 11.5% labor shortage, 13.8% lack of knowledge, and 18.7% small land size as challenges. In Mumberes Ward, 0.9% reported cultural hindrances, 4% drought and famine, 34.4% high cost of SLM practices, 17% labor shortage, 13.6% lack of knowledge, and 30% small land size. Overall, 2.2% faced cultural hindrances, 13.4% drought and famine, 32.3% high cost, 14.2% labor shortage, 13.7% lack of knowledge, and 24.1% small land size.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChallenges in adopting SLM practices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChallenge type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMogotio Frequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMumberes Frequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMogotio %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMumberes %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCultural hindrances\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrought and famine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh cost of SLM practices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLabor shortage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e348\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e323\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e671\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe greatest challenges faced by majority of the farmers is high cost of implementing SLM practices and limited land size. These findings are supported by studies carried out in sub-Saharan Africa which reveal that high implementation costs and lack of finance are the main obstacles to the adoption of SLM (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Additionally, it has been demonstrated that small farms with limited staff supply are less likely to use labour and money intensive practices. According to a study in Malawi, concurrent adoption of both short-term and long-term SLM methods was positively correlated with larger plot sizes and more family labour (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Fewer farmers reported cultural hindrances or lack of knowledge, suggesting that financial and resource constraints are the main barriers to adopting SLM practices.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Milk yield in litres per cow per day\u003c/h2\u003e \u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e show that the average daily milk yield per cow per day after adoption of SLM practices varied substantially between wards. In Mogotio Ward, the mean milk yield was 5.34\u0026thinsp;\u0026plusmn;\u0026thinsp;2.39 L cow⁻\u0026sup1; day⁻\u0026sup1;, with a median of 5 L and an interquartile range (IQR) of 4\u0026ndash;6 L. In contrast, Mumberes Ward recorded a markedly higher mean yield of 8.83\u0026thinsp;\u0026plusmn;\u0026thinsp;3.88 L cow⁻\u0026sup1; day⁻\u0026sup1;, with a median of 8 L and an IQR of 6\u0026ndash;12 L. Overall, across both wards, the mean yield was 7.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.69 L cow⁻\u0026sup1; day⁻\u0026sup1;, ranging between 2 L (minimum) and 16 L (maximum).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage Milk yield in litres per cow per day\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean yield\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian yield\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSd yield\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eq1 yield\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eq3 yield\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMin yield\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMax yield\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMogotio Ward\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMumberes Ward\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThese findings suggest that SLM adoption has been associated with moderate to high milk yields relative to smallholder averages typically reported in Kenya. The higher milk yield in Mumberes Ward may be attributed to better pasture conditions, more consistent water availability, and greater adoption of improved SLM and feeding practices such as fodder cultivation, manure management, and rotational grazing. The area\u0026rsquo;s relatively higher rainfall and cooler temperatures likely enhance forage productivity and cow comfort, improving lactation performance. This is consistent with previous research conducted in Kenya, which demonstrated that in smallholder systems, better management and feeding techniques lead to noticeably increased milk outputs. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) discovered that smallholder farms in Nyeri County with comparatively well-managed practices produced mean yields of about 10.7 L cow⁻\u0026sup1; day⁻\u0026sup1;. Similarly, (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), described smallholder dairy typologies in Nakuru and Nyandarua Counties and observed that low resource farms produced low milk yields whereas resource endowed farms produced significantly higher yields (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Influence of sustainable land management practices on milk yields\u003c/h2\u003e \u003cp\u003eA multiple linear regression was conducted to examine the influence of various SLM practices on average daily milk yield among dairy farmers. The outcome variable was average daily milk yield per cow per day in litres. The model (ESM 3) included seven SLM practices (predictors): agroforestry, cover crops, fodder preservation, rotational grazing, terraces, water harvesting and mulching. Cow breed, extension support and marital status were included as confounding variables in the model (ESM 3).\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.8.1 Multicollinearity check\u003c/h2\u003e \u003cp\u003eBefore analysis, the multicollinearity of the predictor variables was assessed using the Generalized Variance Inflation Factor (GVIF) as demonstrated in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e to determine the suitability of including them in the model. All adjusted GVIF values \u003cem\u003e(\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:GVIF^\\frac{1}{2*Df}\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e ranged from 1.02 to 1.26, which is well below the commonly used threshold of 2. This indicates that there was multicollinearity among the predictors, and the regression coefficients were reliable.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMulticollinearity Check for categorical variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGVIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGVIF^\u003csup\u003e1/(2*Df)\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgroforestry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.387063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.177736\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCover crops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.396588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.181773\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFodder preservation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.293867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.137483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRotational grazing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.272786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.128178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerraces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.419281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.191336\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater harvesting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.167336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.080433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMulching\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.588193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.260235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFriesian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.551462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.245577\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrossbreed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.559435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.248773\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.145248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.022861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtension support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.057989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.8.2 Residual Diagnostics\u003c/h2\u003e \u003cp\u003eDiagnostic plots were examined to assess model assumptions. Residuals vs Fitted plots indicate that residuals were randomly scattered, supporting linearity and homoscedasticity. For Q-Q Plot, residuals were approximately normally distributed. In the Scale-Location Plot the variance of residuals was relatively constant across fitted values. For Residuals vs Leverage, no extreme outliers or influential points were detected Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe regression analysis results in (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) showed that some SLM practices significantly influenced milk yields. Fodder preservation significantly increased milk yield by about 13.6% (β\u0026thinsp;=\u0026thinsp;0.136, p\u0026thinsp;=\u0026thinsp;0.025). Cover crops had a marginal positive effect, increasing milk yield by approximately 14.1% (β\u0026thinsp;=\u0026thinsp;0.141, p\u0026thinsp;=\u0026thinsp;0.054). In contrast, agroforestry showed a small negative effect, reducing milk yield by around 14.4% (β = -0.144, p\u0026thinsp;=\u0026thinsp;0.063). Other SLM practices; rotational grazing, terraces, water harvesting, and mulching did not significantly affect milk yield in this study. Cow breed, extension support and marital status were included as confounding variables. The analysis showed that Friesian cows were associated with higher milk yields (β\u0026thinsp;=\u0026thinsp;0.293, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while crossbreed cows had lower milk yields (β = -0.238, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Extension services was found to increase milk yields by 38.8% (β\u0026thinsp;=\u0026thinsp;0.385, P\u0026thinsp;=\u0026thinsp;0.000000441).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression Coefficients for Multiple Linear Regression Model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003estatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eConf low\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eConf high\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSignif\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.270592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.314318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.042379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.14E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.651405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.889779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgroforestry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.14381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.076932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.86927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.062808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.29536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.007744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCover crops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.141113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.072906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.935548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.054103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.00251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.284733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFodder preservation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.135723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.060036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.260677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.024679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.017455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.253991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRotational grazing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.044663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.063517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.703159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.482641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.08046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.169788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerraces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.051409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.083632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.614709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.539331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.11334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.216158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater harvesting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.033234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.063655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.522094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.602088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.09216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.15863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMulching\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.16159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.104537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.54578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.123481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.36752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.044341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFriesian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.293202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.065486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.47733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.164199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.422205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecrossbreed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.23836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.064232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.71086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.36489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.11182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status (Married)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.117732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.30167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.390268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.696686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.47654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.712004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status (Single)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.264209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.314004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.841419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.400954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.35436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.882779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital\u003c/p\u003e \u003cp\u003eStatus Widow(er)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.232527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.32274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.720477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.471936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.40325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.868306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtension\u003c/p\u003e \u003cp\u003eSupport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.385754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.074271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.1939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.41E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.239446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.532063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSignif. codes: 0 \u0026lsquo;***\u0026rsquo; 0.001 \u0026lsquo;**\u0026rsquo; 0.01 \u0026lsquo;*\u0026rsquo; 0.05 \u0026lsquo;.\u0026rsquo; 0.1 \u0026lsquo; \u0026rsquo; 1\u003c/p\u003e \u003cp\u003eThe findings indicate that farmers who engaged in preserving fodder recorded higher milk yields than those who did not. Similar results were found by (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), who revealed that one of the main factors influencing increased milk outputs and farmer resistance to climatic variability was the adoption of climate-smart feeding practices, such as feed conservation and storage. Cover crops increased milk yield by approximately 14.1% suggesting that fodder type cover crops improve forage availability thus increasing milk yields. These findings are consistent with those of (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) who found that improved forage species and legumes increased milk yields by 23\u0026ndash;33%. In contrast, agroforestry reduced milk yield by around 14.4% because farmers practicing agroforestry prioritize exotic tree species for timber over immediate forage production. According to research conducted in Machakos County, Kenya, agroforestry systems offer a variety of ecosystem services. However, provisioning services, such as fodder, were ranked third in importance, behind timber/fuelwood and regulatory functions. This suggests that many agroforestry systems are not optimised for the production of fodder (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Friesian cows were associated with higher milk yields (β\u0026thinsp;=\u0026thinsp;0.293, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while crossbreed cows had lower milk yields (β = -0.238, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Extension support also had a positive effect, indicating that education and training on SLM practices increases milk production in the study areas.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.8.3 Model Fit Statistics\u003c/h2\u003e \u003cp\u003eThe regression model was statistically significant, F₁₃,₂₃₉ = 9.862, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. The values for R\u0026sup2; = 0.349 and Adjusted R\u0026sup2; = 0.314 suggest that approximately 34.9% of the variation in milk yield is explained by the independent variables in the model (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e:Model Fit Statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual SE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted R\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF df1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF df2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF p value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.413914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.349138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.313735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.861945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.85E-16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFodder preservation, cover cropping, improved breeds, and extension services have a positive influence on milk production in the study area. Therefore, county governments, dairy cooperatives and other stakeholders need to support extension services to farmers as well as capacity building on establishment and management of fodder preservation and cover cropping. Additionally, the county government of Baringo should subsidize breed improvement program to ensure equitable access of these services by all farmers in order to increase milk production.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Environmental Science, Egerton University, P.0 BOX 536-20115, Njoro, Kenya\u003c/p\u003e\n\u003cp\u003eEgerton University Nakuru City Campus College, P.0 BOX 13357-20100, Nakuru, Kenya\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGeorge M. Ogendi\u003c/p\u003e\n\u003cp\u003eDepartment of Environmental Science, Egerton University, P.0 BOX 536-20115, Njoro, Kenya\u003c/p\u003e\n\u003cp\u003eOscar O. Donde\u003c/p\u003e\n\u003cp\u003eDepartment of Environmental Science, Egerton University, P.0 BOX 536-20115, Njoro, Kenya\u003c/p\u003e\n\u003cp\u003eSarah M. Kabaya\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSarah M. Kabaya:\u003c/strong\u003e Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Writing - Review \u0026amp; Editing, Visualization, Project administration, Funding acquisition. \u003cstrong\u003eGeorge M. Ogendi:\u003c/strong\u003e Conceptualization, Writing - Review \u0026amp; Editing, Supervision, Project administration. \u003cstrong\u003eOscar O. Donde:\u003c/strong\u003e Conceptualization, Writing - Review \u0026amp; Editing, Supervision, Project administration\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSarah M. Kabaya\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research data for this article is published in Mendeley data repository and can be accessed via this link:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ehttps://data.mendeley.com/datasets/gj7wd3sryd/1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by a grant from the Mastercard Foundation to RUFORUM to implement the TAGDev 2.0 programme at Egerton University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors affirm that the work described in this publication was not influenced by any known competing financial interests or personal relationships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Accordance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study received ethical approval from the Egerton University\u0026apos;s Institutional Scientific and Ethics Review Committee [Approval No: EUISERC/APP/503/2025]. All research procedures were performed in accordance with the ethical guidelines and regulations of Egerton University. A research permit was obtained from the Kenya\u0026apos;s National Commission for Science, Technology, and Innovation (NACOSTI) [License No: NACOSTI/P/25/4177546] and the principles outlined in the Declaration of Helsinki. Additional permits were sought from the local authorities in Baringo County and the managers for the dairy co-operatives.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all participants prior to data collection. The respondents\u0026rsquo; identities were kept private and confidential.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research data for this article is published in Mendeley data repository and can be accessed via this link: https://data.mendeley.com/datasets/gj7wd3sryd/1\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKDB. KDB | Kenya Dairy Board. 2023 [cited 2025 June 25]. Kenya Dairy Board Annual Report and Financial Statements For The Year Ended 30 June 2023. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.researchgate.net/profile/Annastacia-Maweu/publication/373027334_Promoting_improved_forages_for_increased_livestock_productivity_in_the_Arid_and_Semi-Arid_Lands_ASALs_of_Kenya_A_case_of_Kajiado_Narok_and_Taita_Taveta_county/links/66a787a5de060e4c7e672c39/Promoting-improved-forages-for-increased-livestock-productivity-in-the-Arid-and-Semi-Arid-Lands-ASALs-of-Kenya-A-case-of-Kajiado-Narok-and-Taita-Taveta-county.pdf\u003c/span\u003e\u003cspan address=\"https://www.researchgate.net/profile/Annastacia-Maweu/publication/373027334_Promoting_improved_forages_for_increased_livestock_productivity_in_the_Arid_and_Semi-Arid_Lands_ASALs_of_Kenya_A_case_of_Kajiado_Narok_and_Taita_Taveta_county/links/66a787a5de060e4c7e672c39/Promoting-improved-forages-for-increased-livestock-productivity-in-the-Arid-and-Semi-Arid-Lands-ASALs-of-Kenya-A-case-of-Kajiado-Narok-and-Taita-Taveta-county.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKinyili BM, Ndunda E, Kitur E. Trade-Off Between Agroforestry and Ecosystem Services among Smallholder Farmers in Machakos County, Kenya. East African Journal of Forestry and Agroforestry [Internet]. 2019 Oct 2 [cited 2025 Oct 31];1(1):13\u0026ndash;23. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://journals.eanso.org/index.php/eajfa/article/view/14\u003c/span\u003e\u003cspan address=\"https://journals.eanso.org/index.php/eajfa/article/view/14\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Land degradation is a decrease in the land's present or potential ability to produce (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sustainable land management, Milk yields, Arid and Semi-arid lands, Multiple-linear regression model","lastPublishedDoi":"10.21203/rs.3.rs-8627839/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8627839/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDairy farming promotes food and nutrition security globally. Despite dairy farming being an important income source for smallholder farmers in Kenya with 80% of milk production, it is commonly faced by challenges such as land degradation and overgrazing which has threatened its sustainability. This study was conducted to assess the influence of SLM practices on dairy production. The study was conducted in Mogotio and Mumberes Wards in Mogotio and Eldama Ravine Sub-Counties respectively in Baringo County, Kenya. A cross-sectional research approach was adopted for the study. Qualitative data was collected from 257 households. Data analysis was conducted using descriptive and inferential statistics. A Multiple linear regression model was used to determine the impacts of SLM practices on milk yields. Results showed that fodder preservation (β\u0026thinsp;=\u0026thinsp;0.136, p\u0026thinsp;=\u0026thinsp;0.025) and cover crops (β\u0026thinsp;=\u0026thinsp;0.141, p\u0026thinsp;=\u0026thinsp;0.054), significantly increased milk yields by 13.6% and 14.1%, respectively. Agroforestry on the other hand had a negative effect reducing milk yield by 14.1%. Additionally, Friesian cattle (β\u0026thinsp;=\u0026thinsp;0.293, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and extension support (β\u0026thinsp;=\u0026thinsp;0.386, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) had a positive relationship with milk yield. These results demonstrate that SLM practices have the potential to improve milk yields in arid and semi-arid areas. I recommend that the State Department of Livestock Production and dairy cooperatives enhance capacity building and provide incentives for fodder production and preservation, as well as cover cropping, strengthen access to extension services and breed improvement programs for increased milk production.\u003c/p\u003e","manuscriptTitle":"Influence of Sustainable Land Management Practices on Dairy Farming in Baringo County, Kenya","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-25 11:42:24","doi":"10.21203/rs.3.rs-8627839/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"08d5f5b7-2aa6-4291-89f8-16d601d471eb","owner":[],"postedDate":"February 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-01T08:13:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-25 11:42:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8627839","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8627839","identity":"rs-8627839","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}