Impact of Climate Change on Agricultural Productivity and Food Security among Rice Farmers in Oyo State, Nigeria

Introduction

Agriculture remains central to Nigeria's socioeconomic framework, employing approximately 70% of the rural population and contributing significantly to gross domestic product (GDP), food supply, and national development strategies (FAO, 2021) [11] . Within the agricultural sector, rice plays a particularly vital role. As Nigeria's most consumed staple grain, rice accounts for a growing share of caloric intake, dietary energy, and household food expenditure (IFPRI, 2022; Olawumi et al., 2025) [14,29] . Despite being Africa's leading rice producer, Nigeria has consistently struggled to meet domestic demand, resulting in significant import dependence to bridge supply deficits (Ojo & Akinyemi, 2023) [23] . The productivity of rice farming in Nigeria remains substantially below global averages due to low input use, outdated agronomic practices, poor irrigation infrastructure, and weak extension services (Nwalieji, Igbokwe and Nsoanya, 2022) [21] . Unpredictable rainfall, rising temperatures, and frequent extreme weather events have intensified production risks, creating uncertainty in yields and threatening the sustainability of rice-based livelihoods (Ajayi, Salami and Kodaolu, 2023) [6] . Thus, climate change is emerging as a pivotal constraint on Nigeria's food system-disrupting agricultural productivity, straining food supply chains, and exacerbating the vulnerabilities of farming communities that are already marginalised within the national economy (Olaitan et al., 2025) [27] . Recent evidence demonstrates that climate change is no longer a future concern but an immediate driver of agricultural instability across Nigeria. Over the past two decades, Nigeria has experienced steadily rising temperatures-averaging 1.5 °C above pre-industrial baselines-with annual increases now exceeding global averages in many agro-ecological zones (NiMet, 2022). Concurrently, rainfall patterns have grown more erratic, www.extensionjournal.com with delays in the onset of the rainy season, shortened durations, and increased intensity of rain events, often leading to localized flooding or prolonged dry spells (Ajayi et al., 2023) [6] . These shifts have had pronounced effects on rice production, which is acutely sensitive to changes in water availability, temperature stress, and seasonal timing. For instance, excess heat during the flowering stage can lead to spikelet sterility and reduced grain filling, while erratic rains affect germination, transplanting, and overall crop maturation (Maisule, et al., 2025) [17] . While Nigeria has initiated some adaptive interventions, such as early warning systems and climate-resilient seed varieties, these remain unevenly distributed and poorly institutionalized at the community level (Ayanlade and Oluwatoyin, 2023) [7] . As a result, many smallholder rice farmers remain vulnerable to climate shocks, with few assets or institutional supports to mitigate risk or recover from yield losses. The ramifications of these climate-induced disruptions extend beyond the farm gate to deeply impact food security and rural welfare. In the Nigerian context, food security is not merely about national production totals but the ability of households to access sufficient, safe, and nutritious food on a consistent basis (Joel et al., 2025) [16] . When climate shocks reduce rice yields or destroy harvests, households suffer immediate declines in food availability and income, particularly in communities where rice functions as both a staple food and cash crop (Okonkwo, Okoli and Agwu, 2021; Idu, Sennuga and Owoicho 2025) [24,13] . According to the Cadre Harmonisé food security analysis, more than 17 million Nigerians experienced food insecurity in 2023, with rural farming populations disproportionately affected due to climate-related yield shocks (FAO, 2023; Adeyemi et al., 2025) [12,3] . In areas where floods displaced farming households or prolonged droughts suppressed multiple planting seasons, food insecurity increased in both severity and frequency. These impacts are most acutely felt by vulnerable groups such as women, the elderly, and youthheaded households, who often have limited access to land, credit, extension services, or climate adaptation technologies (Onwualu, Akpan and Dim, 2022; Adeyemi et al., 2025) [32,3] . Despite ongoing efforts to implement climate-smart agriculture and resilience-focused policies at national and sub-national levels, there remains a significant gap in context-specific, data-driven research that links climatic changes to productivity and food security outcomes in a disaggregated manner. This study aims to evaluate the impact of Climate Change on Agricultural Productivity and Food Security among Rice Farmers in Oyo State, Nigeria. To accomplish this, the following objectives are put forward to: 1. describe the socio-economic characteristics of farmers affected by climate change in the study area. 2. assess the impact of climate variability on agricultural production in the study area.

investigate the factors influencing stakeholders'

willingness to adopt climate-smart technologies for climate change mitigation in the study area. 4. examine farmers' adaptation strategies to climate change in the study area.

Literature review Theoretical Framework Sustainable Livelihoods Framework (SLF)

Originally developed by the UK Department for International Development (DFID), the Sustainable Livelihoods Framework (DFID, 1999) [9] conceptualizes how people, especially in rural settings, use various forms of capital-natural, human, social, physical, and financial-to pursue livelihood strategies and achieve improved livelihood outcomes. The framework recognizes the centrality of vulnerability contexts-such as climate variability, economic shocks, and natural disasters-in shaping livelihood trajectories.

In the context of this study, the SLF is used to examine how climate change affects smallholder rice farmers' access to key livelihood assets and how this, in turn, influences their agricultural productivity and food security. The SLF also provides a platform for evaluating the effectiveness of adaptive strategies such as crop diversification, irrigation, and adoption of climate-resilient seed varieties. These adaptations are mediated by institutional and policy factors (e.g., extension services, subsidies, access to markets), which influence how different farmers respond to climate risks. Thus, the SLF not only supports a multi-dimensional analysis of vulnerability but also offers a policy-relevant framework for identifying leverage points to improve farmer resilience and food security.

Conceptual Framework

The conceptual framework for this study, exploring the relationship between the independent variables (climate change indicators) and the dependent variable (agricultural productivity and household food security) being mediated by the intervening variables.

Population of the Study and Research Design

The study targets rice farming households in selected Local Government Areas (LGAs) of Oyo State, Nigeria. The population includes smallholder farmers who have cultivated rice for at least three consecutive years, ensuring reliable experience with climate variability and productivity outcomes.

A mixed-methods cross-sectional design is employed to assess the impact of climate change on productivity and food security. The quantitative component involves structured questionnaires administered to a representative sample of rice farmers. Data collected include socioeconomic characteristics, climate perceptions, and adaptation strategies.

Sample Size and Sampling Techniques

This study adopted a multi-stage sampling technique to systematically select a total of 200 rice farming households across major rice-producing Local Government Areas (LGAs) in Oyo State. The multi-stage method was chosen to ensure balanced representation of the state's ecological and socio-economic diversity in rice production, while also enabling focused analysis of areas particularly exposed to climate change impacts.

In the first stage, five LGAs were purposively selected based on their prominence in rice cultivation, susceptibility to climate variability (e.g., rainfall irregularity, flooding), and availability of both upland and lowland rice ecologies. These LGAs include Iseyin, Atisbo, Saki East, Afijio, and Ogbomoso North.

In the second stage, two rural communities were randomly selected from each of the five LGAs. The selected communities are as follows: from Iseyin LGA-Oke-Ogun and Ajegunle; from Atisbo LGA-Tede and Ofiki; from Saki East LGA-Ago-Amodu and Sepeteri; from Afijio

LGA-Ilora and Fiditi; and from Ogbomoso North LGA-Arowomole and Okelerin. These communities were chosen because they have active rice farming populations and are accessible for field data collection.

In the third stage, 20 rice farming households were randomly selected from each of the ten identified communities using stratified random sampling. Stratification was based on relevant socio-demographic and farming variables such as gender of household head, farm size, and rice production system (upland vs. lowland). This strategy ensured a diverse sample reflective of varying experiences with climate change, levels of adaptive capacity, and food security status. Overall, the selected sample size of 200 respondents is statistically adequate for both descriptive and inferential analysis, while remaining manageable for in-depth field engagement.

Data Collection

The primary data collection instrument for this study was a structured questionnaire designed to gather detailed information from respondents in Oyo State. The questionnaire was administered to a representative sample of 200 smallholder rice farmers. Each session lasted approximately one hour, allowing respondents ample time to provide well-considered responses. To enhance validity and reliability, the questionnaire underwent pre-testing through a pilot study with a small group of participants who were not part of the main study. Based on feedback, necessary adjustments were made to improve its effectiveness. Additionally, trained enumerators facilitated the administration of the questionnaire, ensuring that respondents fully understood each question, thereby promoting accuracy and consistency in data collection for analyzing the impact of climate change on Oyo State's agricultural productivity and food security.

Data Analysis

The data collected in this study were analyzed using a combination of descriptive and inferential statistical techniques to address the study's objectives. Descriptive statistics, including percentages, frequency counts, and mean scores, were utilized to address Objective (i), which seeks to profile the socio-economic characteristics of rice farmers affected by climate change. To address Objective (ii)-which focuses on assessing the impact of climate variability on agricultural productivity-a 5-point Likert scale was used to capture farmers' perceptions and experiences regarding changes in rainfall, temperature, and other climatic variables, as well as their observed effects on yield, input costs, and planting cycles. Objective (iii), which aims to investigate the factors influencing stakeholders' willingness to adopt climate-smart agricultural (CSA) technologies, was analyzed using logit regression analysis. Additionally, Kendall's Coefficient of Concordance was used to examine the adaptation strategies employed by rice farmers to cope with climate change (Objective iv). The analysis was conducted using SPSS (Statistical Package for the Social Sciences), version 24, ensuring accuracy and efficiency in data processing. www.extensionjournal.com

Model specification Model for Likert Scale Rating

The Likert scale was employed to assess perceptions of climate variability and its effects on rice production (Objective ii), as well as the extent of adoption of specific adaptation strategies (Objective iv). The 5-point Likert scale used was structured as follows:

The decision rule for interpreting the mean response was set at 2.5. A mean score ≥ 2.5 was interpreted as agreement (moderate to strong), while a mean < 2.5 indicated disagreement or low perception/impact.

To calculate the mean Likert score ( ), the following formula was used:

Logit Regression Model

To address Objective (iii)-which aims to examine the determinants influencing rice farmers' willingness to adopt climate-smart agricultural (CSA) technologies for mitigating the effects of climate change-a logistic regression model was employed. This econometric approach is appropriate for modelling binary outcome variables, where the dependent variable reflects the probability that a farmer adopts (or does not adopt) CSA technologies. The dependent variable Y is binary, taking a value of 1 if the farmer adopts CSA practices, and 0 otherwise. The implicit functional form of the logistic regression model is expressed as:

The explicit form of the model is given as:

Results and Discussion

Socio-Economic Characteristics of Respondents

The study revealed that 73% of the respondents were male, while 27% were female, indicating male dominance in rice farming in the study area. This aligns with common agricultural gender roles in Nigeria, where land access, decision-making, and financial control are often malecentered (Ayanlade & Oluwatoyin, 2023) [7] . The average age of respondents was 45.6 years, with 36% aged 40-49. This suggests a mature and economically active farming population, though the presence of only 12.5% in the 18 -29 age bracket indicates limited youth involvement. The aging farmer population raises concerns about generational transition and innovation adoption, as younger farmers are typically more responsive to modern agricultural technologies (Ojo & Akinyemi, 2023) [23] . A significant proportion of farmers (78%) were married, followed by 14% single. Marital status often enhances household labour availability and stability in rural farming. Married farmers typically have more access to shared labour and are more likely to invest in long-term farm improvements (Afolami, Obayelu and Agbonlahor, 2020) [4] . Most farmers had formal education: 42% had secondary education, and 19% had tertiary education. Only 15% had no formal schooling. Education is a strong predictor of climate change adaptation, as it improves awareness, decision-making, and the ability to interpret extension information (Ajayi et al., 2023) [6] . The respondents had an average of 16.2 years of farming experience. Most (34%) had 11-20 years, and 31% had over 20 years. This long-term experience suggests familiarity with local climatic conditions and agricultural practices (Eze, Onubuogu and Henri-Ukoha, 2022) [10] . www.extensionjournal.com

The average farm size was 1.95 hectares, with most respondents (38%) cultivating between 1.0-2.0 hectares. Limited landholding can restrict productivity, hinder mechanization, and constrain climate-smart investments (Agwu & Anyanwu, 2020) [5] . About 60.5% of respondents were members of agricultural cooperatives. Cooperatives play a vital role in accessing credit, extension services, and collective resources for climate adaptation (Okpachu, Oladimeji and Ayanwale, 2023) [25] . Approximately 63.5% of farmers had contact with extension agents. Farmers with regular extension contact are more likely to adopt climate-smart practices and technologies (Nnadi, Chikaire and Echetama, 2022) [20] . Only 44.5% of respondents reported access to credit. The low credit access among respondents may limit their resilience to climate shocks and ability to scale up production (Adeola & Oluwatayo, 2021) [2] .

Perceived Impacts of Climate Variability on Agricultural Production

The study found that irregular rainfall patterns significantly disrupted the planting season, as reflected in a mean score of 4.42. Consistent with Ajayi et al. (2023) [6] , erratic rainfall patterns shift the onset of the farming season, delay land preparation, and reduce yield potential by pushing planting beyond optimal agronomic windows. The study found that higher temperatures have contributed to a reduction in rice yields, with a mean score of 4.17. Ajayi et al. (2023) [6] observed that excessive temperatures during flowering and grain-filling stages limit physiological development, increase evapotranspiration, and reduce both grain size and quantity in rainfed rice systems. The study found that erratic weather patterns have increased pest and disease outbreaks, evidenced by a mean score of 4.14. This aligns with Eze et al. (2022) [10] , who reported that changing humidity and temperature levels accelerate pest cycles and disease incidence. The study found that flooding was widely recognized as a major climatic threat, with a mean score of 3.86. Adebayo et al. (2021) [1] affirm that heavy rains often exceed soil infiltration capacity, especially in lowland fields, leading to erosion and crop mortality before establishment or during critical early growth stages. The study found that respondents rejected the notion that www.extensionjournal.com climate variability had improved rice yield, with a mean score of 2.35. Musa and Danladi (2024) [18] reported similar outcomes, noting that yield fluctuations due to heat, drought, or untimely rainfall are predominantly detrimental, especially where adaptive technologies or early warning systems are absent. The study found moderate agreement that warmer temperatures may accelerate rice maturity, as reflected in a mean score of 2.91. Ajayi et al. (2023) [6] explained that heat can reduce the number of days to maturity, allowing earlier harvesting, though sometimes at the cost of reduced grain weight and lower nutritional content due to shortened grainfilling periods.

The study found that climate change was not seen as reducing input costs, with a mean score of 2.22. This rejection suggests that climate variability has instead increased the need for costly inputs (Adeola and Oluwatayo, 2021) [2] . The study found that some farmers experienced total crop failure in certain years due to climate variability, with a mean score of 3.15. Chukwuezi, Anyanwu and Oparaocha (2024) [8] observed that such failures undermine household food security and income stability, particularly in poorly drained fields or where early warning and recovery mechanisms are limited.

Factors Influencing Willingness to Adopt Climate-Smart Technologies

The model statistics in Table 3 suggest that the model's Chi-Square statistic is statistically significant (p < 0.01), indicating that the independent variables collectively contribute to explaining the variation in stakeholders' willingness to adopt climate-smart technologies. The Nagelkerke R² value of 0.287 suggests that about 28.7% of the variance in the dependent variable is explained by the model. Among the nine (9) factors tested, six (6) factors had coefficients that were statistically meaningful: gender, age, educational level, farming experience, cooperative membership, and access to credit, while marital status, farm size, and contact with extension agents were not statistically significant.

The study found that gender was statistically significant at the 10% level, with a positive coefficient, indicating that male farmers were more likely to adopt climate-smart technologies. According to Adebayo, Olaniyi and Akintunde (2021) [1] , men often have greater exposure to extension services and decision-making autonomy, which influences technology uptake.

The study found that age had a significant negative influence (p < 0.05) on the likelihood of adopting climatesmart technologies. This is consistent with Oladele and Fapojuwo (2023) [26] , who found that younger farmers are generally more receptive to innovations, possess higher digital literacy, and are more adaptable to changing agricultural practices.

The study found that educational attainment was highly significant (p < 0.01) and positively associated with willingness to adopt climate-smart technologies. Farmers with higher levels of education were more likely to adopt innovations, likely due to increased understanding and better interpretation of extension messages (Ajayi et al., 2023) [6] .

The study found that farming experience had a negative coefficient and was significant at the 10% level, implying that more experienced farmers were less likely to adopt new climate-smart technologies. This may indicate reliance on traditional methods. Ajayi et al. (2023) [6] suggest that older, more experienced farmers may be risk-averse, preferring familiar practices over unproven innovations.

The study found that cooperative membership was highly significant (p < 0.01) with a strong positive influence on adoption. This supports findings by Irohibe and Agwu (2022) [15] , who noted that cooperatives serve as platforms for information dissemination, collective input acquisition, and shared learning that enhances innovation uptake among smallholder farmers.

The study found that access to credit was statistically significant at the 5% level, positively affecting farmers' willingness to adopt climate-smart technologies. Musa and Danladi (2024) [18] emphasized that credit enables farmers to overcome capital constraints, particularly when adopting practices requiring initial investment, such as improved seed varieties or irrigation.

The study found that marital status was not statistically significant in predicting willingness to adopt climate-smart technologies. Similar findings were reported by Eze et al.

(2022) [10] , who concluded that marital status does not necessarily shape farmers' perception or acceptance of agricultural innovations.

The study found that farm size did not significantly influence willingness to adopt climate-smart technologies.

Olaniyi and Okonkwo (2020) [28] reported similar findings, indicating that both small and large-scale farmers face adoption constraints such as input costs and information gaps, regardless of land size.

The study found that contact with extension agents did not significantly influence adoption in this context. While extension services are expected to support knowledge transfer, their effectiveness may be limited by access frequency or content relevance (Ojo and Akinyemi, 2023) [23] .

Rice Farmers' Adaptation Strategies to Climate Change Using Kendall's Coefficient of Concordance

The Kendall's W value of 0.614 indicates a moderate-tostrong level of agreement among farmers in their ranking of climate change adaptation strategies. The highly significant Chi-square value (p < 0.01) confirms that this agreement is statistically valid. The study found that adjusting planting and harvesting dates had the highest rank (Mean Rank = 5.36), indicating it is the most widely adopted adaptation strategy. This aligns with findings by Ajayi et al. (2023) [6] , who emphasized calendar adjustment as a practical, low-cost method for reducing risks posed by seasonal climate variability in Nigeria. The study found that farmers ranked the use of droughttolerant, early-maturing rice varieties second (Mean Rank = 5.02). This reflects the growing preference for resilient seed technologies to cope with erratic rainfall (Ajayi et al., 2023) [6] . The study found that increased use of organic fertilizers ranked third (Mean Rank = 4.74). This corresponds with Musa and Danladi (2024) [18] , who found that organic amendments enhance crop tolerance to drought and improve long-term productivity, especially under conditions of fluctuating rainfall and rising temperatures in southern Nigerian agro-ecological zones. The study found that crop and livelihood diversification ranked fourth as an adaptive response (Mean Rank = 4.29). Eze et al. (2022) [10] highlighted diversification as a strategic mechanism for building resilience among smallholders facing uncertain weather patterns and increased production volatility in Nigeria's food-producing regions. The study found that irrigation was ranked fifth (Mean Rank = 4.08). While recognized as an important buffer against erratic rainfall, adoption was lower-likely due to cost and infrastructure barriers (Adebayo et al., 2021; Olusanya et al., 2025) [1,30] . The study found that mulching and soil conservation were moderately adopted, ranking sixth (Mean Rank = 3.83). Ayanlade and Oluwatoyin (2023) [7] reported similar results, noting that soil conservation is crucial for rainfed agriculture, though adoption is often influenced by farmers' awareness levels and labour availability for implementing such physical conservation measures. The study found that agroforestry ranked seventh (Mean Rank = 3.51), suggesting limited adoption. Irohibe and Agwu (2022) [15] observed that although agroforestry is climate-resilient, its complexity and capital requirements can discourage uptake, especially among farmers seeking more immediate, short-term coping strategies in vulnerable areas. The study found that engaging in non-farm income activities ranked lowest. Ogundele, Akinola and Alamu (2023) [22] noted that while off-farm diversification supports resilience, barriers such as limited capital, skills, and rural job availability constrain participation in such adaptive livelihood options. www.extensionjournal.com

Conclusion and recommendations

This study examined the impact of climate change on agricultural productivity and food security among rice farmers in Oyo State, Nigeria, through four objectives: profiling socio-economic characteristics, assessing the impact of climate variability on agricultural production, identifying the determinants of willingness to adopt climatesmart technologies, and evaluating farmers' adaptation strategies. The analysis of socio-economic data revealed that the farming population is predominantly male (73%) and middle-aged (mean age = 45.6 years), with a high percentage married (78%) and moderately educated-42% having attained secondary education. Farmers had an average of 16.2 years of farming experience and cultivated an average farm size of 1.95 hectares. Cooperative membership (60.5%), access to extension agents (63.5%), and agricultural credit (44.5%) were notable institutional attributes influencing their adaptive potential. Climate variability was perceived as having both direct and indirect consequences on rice production. Empirical results showed that delayed planting and harvesting (mean = 4.42), reduced yields (mean = 4.17) and pest and disease outbreaks (mean = 4.14) were among the most significant adverse impacts. In contrast, factors such as improved overall rice yield (mean = 2.35) and reduced cost of input use (mean = 2.22) were rated below the decision threshold of 2.5 and thus considered negligible or unfavourable. The logistic regression model identified key socio-economic and institutional variables influencing farmers' willingness to adopt climate-smart technologies. Educational attainment and cooperative membership were significant at the 1% level, demonstrating their strong predictive power. Age and access to credit were significant at the 5% level, while gender and farming experience were statistically significant at the 10% level. Variables such as marital status, farm size and contact with extension agents were not significant in this context. The model's statistical strength was supported by a Log-Likelihood of -89.531, a Pseudo R² of 0.643, and a highly significant Chi-square value (χ² = 122.88, p < 0.001), indicating strong explanatory validity. Farmers' adaptation strategies to climate change revealed a moderate consensus (Kendall's W = 0.614). The most highly ranked strategies were adjusting planting dates (mean rank = 5.36), using drought-tolerant and early-maturing varieties (5.02), and applying organic inputs (4.74). Other adaptive responses such as crop diversification and irrigation were moderately prioritized, while strategies like agroforestry (3.51) and non-farm income generation (3.17) were less favoured. Based on the findings from the study, the following recommendations are proposed to enhance the impact of climate change on agricultural productivity and food security among rice farmers: 1. Given the significant role that contact with extension agents plays in both awareness and adoption of climatesmart technologies, the government and development partners should scale up the number and capacity of extension personnel.

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