Figure 1 Conceptual framework illustrating the relationship between irrigation farming adoption and livelihood outcomes among smallholder farmers in Uganda. Irrigation farming adoption is hypothesized to be associated with improved livelihood outcomes, while socioeconomic and institutional factors may influence both adoption decisions and livelihood status.

This study employed a cross-sectional design to investigate the relationship between irrigation farming adoption and livelihood outcomes among smallholder farmers in Uganda. The sample size of 422 respondents was determined using the Krejcie and Morgan (1970) formula, ensuring statistical adequacy for generalization. A 100 percent response rate was obtained, strengthening the reliability of the findings.

A non-probability sampling technique was applied, with purposive selection of districts, sub-counties, and farmers based on the extent of irrigation farming adoption. District Agricultural Officers (DAOs), extension workers, and farmer association representatives assisted in identifying households actively engaged in irrigation farming. To ensure proportional representation, the proportion-to-size method was used, giving districts with larger farming populations higher inclusion probability.

Quantitative data were processed using SPSS and STATA 18. Descriptive statistics (means, frequencies, percentages) summarized demographic characteristics and irrigation adoption patterns. Pearson’s Chi-square test assessed associations between irrigation adoption and livelihood outcomes such as income, food security, and asset ownership. At the bivariate level, regression analysis were conducted to identify significant strength of association between irrigation adoption and livelihood outcomes. Multiple logistic regression was employed to evaluate the effect of irrigation farming adoption on household welfare indicators.

Linearity was tested using Pearson correlation coefficients, while multicollinearity diagnostics ensured reliability of regression models. Poisson regression and linear mixed models were additionally applied to strengthen the robustness of findings.

This study adhered to the highest ethical standards in conducting research with smallholder farmers in Uganda. Ethical approval was obtained from the Clarke International University Research Ethics Committee (Approval No. CLARKE-2025-1685) and the Uganda National Council for Science and Technology (Reference No. SS4673ES), with permissions secured from relevant district and community authorities. Informed consent was obtained from all participants, emphasizing voluntary participation, the right to withdraw, and confidentiality of responses. Data were anonymized and securely stored, while risks were minimized by focusing on non-sensitive topics and benefits maximized through dissemination of findings to communities and stakeholders. Researcher integrity was maintained through transparency, respect for cultural norms, and fair representation of participants’ perspectives.

The study had an initial sample size of 422, however, only 387 participants participated, giving a response rate of 91.7%. Table 1. Shows that, the highest rate for the respondents were 219 (56.6%), majority were male 286 (56.6%), with 367 (94.8%) being married, and majority 189 (48.8%) had obtained primary education.

Table 2 presents descriptive statistics on respondents' adoption of irrigation technologies. The table shows the extent to which various irrigation methods, including sprinkler, drip, surface, pump, hose pipe, rainwater harvesting, small-scale, and solar-powered systems, are utilized by farmers. It includes the distribution of responses across a five-point Likert scale (Strongly Disagree to Strongly Agree), along with mean scores, standard deviations, and an interpretation of adoption levels.

For sprinkler irrigation systems, adoption is very low. More than half of respondents (52.20%) strongly disagreed, and 28.94% disagreed with using sprinklers for crop watering. Only a small proportion, 7.75%, reported using this method. The Likert mean of 1.788 (SD = 1.059) confirms very low adoption, indicating that sprinklers are largely inaccessible or impractical for most farmers.

Drip irrigation systems show the lowest adoption among all technologies. A majority of farmers, 55.04%, strongly disagreed, and 26.87% disagreed with using drip systems. Only 8.01% reported using drip irrigation. The Likert mean of 1.734 (SD = 1.010) emphasizes very low adoption, likely due to high costs or technical complexity.

The use of surface irrigation methods is similarly low, with 51.94% strongly disagreeing and 29.72% disagreeing. The Likert Mean of 1.744 (SD = 0.973) indicates minimal usage, suggesting that even traditional irrigation methods are not widely applied, possibly due to limited water availability. Rainwater harvesting techniques have slightly higher adoption, with 11.89% of respondents strongly agreeing and 5.17% agreeing. Over 80% of respondents either disagreed or were not sure about using rainwater harvesting. The Likert Mean is 1.959 (SD = 1.342), indicating low adoption, suggesting potential constraints in infrastructure or knowledge.

Pump irrigation systems are underutilized, with 45.99% strongly disagreeing and 30.75% disagreeing. Only 12.66% reported using pumps. The Likert mean of 1.984 (SD = 1.226) shows low adoption, likely due to limited access to pumps or operational costs. Hose pipe irrigation systems also show low adoption. A majority, 41.86% strongly disagreed and 35.66% disagreed with using hoses. The Likert mean of 1.995 (SD = 1.170) confirms low usage, reflecting preference for simpler water distribution methods. The highest adoption is observed for small-scale irrigation systems such as buckets or manual pumps. Here, 18.86% agreed, and 17.31% strongly agreed to using these methods. The Likert mean of 2.695 (SD = 1.498) indicates moderate adoption, showing that farmers prefer low-cost, easy-to-use irrigation solutions.

Solar-powered irrigation systems have low adoption, with 48.84% strongly disagreeing and 30.23% disagreeing. Only 9.04% reported using solar systems. The Likert mean of 1.886 (SD = 1.155) confirms limited uptake, likely due to high cost and technical requirements. Overall, the adoption of irrigation technologies among respondents is low, with a mean of 1.973 (SD = 0.897). Farmers primarily rely on basic, affordable irrigation methods rather than modern systems. To improve adoption, interventions should focus on raising awareness, providing technical training, and expanding access to affordable irrigation technologies.

The findings reveal actual adoption of irrigation technologies among smallholder farmers in the Teso Sub-Region. While farmers demonstrate a solid understanding of various irrigation methods, including drip and sprinkler systems, and express strong interest in adopting these technologies, adoption rates remain exceedingly low, as evidenced by the quantitative data (e.g., low Likert mean scores for drip and sprinkler systems). Despite these challenges, the significant motivation and willingness of farmers to adopt modern irrigation technologies, if given the opportunity, reinforces the need for targeted policy interventions. Expanding subsidized irrigation programs, strengthening extension services, and improving access to affordable financing are crucial to overcome these barriers. In conclusion, while there is widespread awareness and interest in adopting irrigation technologies among smallholder farmers in the Teso Sub-Region, adoption rates remain low due to structural and systemic barriers. The consistent findings from both qualitative and quantitative analyses highlight issues such as high initial costs, limited access to credit, and uneven distribution of government support as key challenges. Nevertheless, farmers’ enthusiasm for adopting irrigation technologies emphasizes the potential for significant improvements in agricultural productivity and livelihood outcomes if these barriers are addressed. Therefore, targeted interventions are essential to promote greater uptake of irrigation technologies, ultimately improving the resilience and livelihoods of smallholder farmers in the region.

Table 3 presents the descriptive statistics on the livelihood outcomes of smallholder farmers in selected districts of the Teso Sub-Region. The table summarizes farmers’ perceptions across multiple dimensions of livelihoods, including agricultural productivity, income growth, food security, access to social services, and asset accumulation. Responses were measured using a five-point Likert scale ranging from “Strongly Disagree” to “Strongly Agree,” with higher mean scores indicating better outcomes. The statistics provide insights into the extent to which smallholder farmers have experienced improvements in crop yields, household income, food availability, social welfare, and asset acquisition as a result of agricultural interventions, technologies, and support systems in the region

Respondents reported high crop yields, with 53.49% agreeing and 40.57% strongly agreeing that they achieve high productivity. The Likert mean of 4.297 (SD = 0.732) indicates very high productivity as indicated in Table 4. Similarly, 50.90% agreed and 39.02% strongly agreed that crop production has consistently increased (mean = 4.214, SD = 0.819), confirming very high positive trends. The input-to-output ratio is efficient for most, with a mean of 3.881 (SD = 1.049), reflecting high profitability. Minimal post-harvest losses were also reported (mean = 3.708, SD = 1.103). However, adaptation to varying climate conditions is moderate, with only 30.23% agreeing and 11.63% strongly agreeing, resulting in a mean of 2.907 (SD = 1.241). Overall, agricultural output productivity is high (mean = 3.802, SD = 0.688), highlighting generally strong farm performance but with some vulnerability to climate variability (refer to Table 4).

Household income growth is mostly high, with 53.75% agreeing and 27.65% strongly agreeing that income has increased due to improved agricultural productivity (mean = 3.920, SD = 0.993). Investments in other income-generating activities and savings from agricultural earnings were also high (means = 3.770 and 3.866). Improvement in farm profitability due to market access and government support is moderate (mean = 3.134, SD = 1.346), showing some constraints in external support. Overall, income growth is high (mean = 3.672, SD = 0.894), suggesting that agricultural productivity translates into tangible financial benefits for farmers.

Food security outcomes are very high, with most households reporting sufficient food. For instance, 58.40% agreed and 36.69% strongly agreed that their families have enough food (mean = 4.302, SD = 0.614). The ability to produce diverse crops for nutrition is also high (mean = 4.401, SD = 0.757), as is food storage (mean = 4.313, SD = 0.696). Contributions to community food security are slightly lower (mean = 3.884, SD = 1.101), but overall food security remains very high (mean = 4.228, SD = 0.609).

Respondents reported high access to social services. Education and healthcare expenses are manageable, with means = 4.364 and 4.339. Access to clean water (mean = 4.455, SD = 0.529) is among the highest, while investing in household energy solutions is slightly lower (mean = 3.775, SD = 1.232). Participation in savings and credit associations is also very high (mean = 4.292, SD = 0.864). Overall, social services outcomes are very high (mean = 4.240, SD = 0.551).

Farmers reported high acquisition of assets, including land (mean = 3.504, SD = 1.370), farming equipment (mean = 3.561, SD = 1.214), livestock (mean = 4.003, SD = 1.047), and home improvements (mean = 4.013, SD = 0.903). Purchase of smaller assets, such as bicycles and motorcycles, is moderately high (mean = 3.718, SD = 1.326). Overall, asset growth is high (mean = 3.760, SD = 0.795), reflecting increased wealth and productive capacity (refer to Table 4).

The overall livelihood outcomes of smallholder farmers are high (mean = 3.963, SD = 0.577). Farmers demonstrate strong agricultural productivity, income growth, food security, access to social services, and asset accumulation. The highest outcomes are observed in food security and social services, while climate adaptation within agricultural productivity shows relatively lower performance, indicating areas for targeted interventions.

The correlational analysis shows a weak but positive and statistically significant relationship (r = 0.208, p = 0.000) between the use of irrigation and the livelihood outcomes of smallholder farmers in the Teso Sub-Region. This means that farmers who adopt irrigation tend to experience slightly improved livelihoods, such as better food security, higher incomes, and increased asset acquisition compared to those who do not. Although the correlation is modest, its significance indicates that irrigation contributes meaningfully to livelihood improvement (refer to Table 5).

The regression analysis shows a positive and statistically significant relationship between the use of irrigation and the livelihood outcomes of smallholder farmers in the Teso Sub-Region. The coefficient for irrigation technologies is 0.134 (p = 0.000), indicating that for each unit increase in irrigation adoption, livelihood outcomes improve by approximately 0.13 units, holding other factors constant. The model is statistically significant overall (F(1, 385) = 17.42, p = 0.000), and the adjusted R-squared of 0.0408 indicates that about 4.1% of the variation in livelihood outcomes is explained by irrigation adoption.