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Determinants of Climate Smart Agricultural Practice Adoption among Rice Farmers in Lafia Local Government Area Nasarawa State Nigeria

This study of rice farmers in Lafia, Nigeria, reveals that while the adoption of climate-smart agricultural practices is moderate and widespread, it is driven more by institutional and agronomic constraints like poor market access and high labor costs than by household socioeconomic characteristics, suggesting a need for policy focused on strengthening markets and information systems rather than targeting specific farmer demographics.

Original authors: Abdulrafiu Tayo Yusuf, Anamayi Sokogaye Ezra, Salami Abubakar Onoja¹, Bojuwon Fridaous Gbolahan

Published 2026-07-03

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Abdulrafiu Tayo Yusuf, Anamayi Sokogaye Ezra, Salami Abubakar Onoja¹, Bojuwon Fridaous Gbolahan

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

The Big Picture: A Farming Dilemma

Imagine a group of rice farmers in Lafia, Nigeria, trying to grow their crops in a world where the weather is becoming as unpredictable as a toddler with a mood swing. Sometimes it rains too much, sometimes not enough. To survive, they need to switch to "Climate-Smart Agriculture" (CSA). Think of CSA as a survival toolkit containing seven specific tools:

Better seeds (like upgrading from a bicycle to a motorcycle).
Mulching (putting a blanket of straw on the soil to keep moisture in).
Organic manure (natural fertilizer).
Crop rotation (changing what you plant so the soil doesn't get tired).
Agroforestry (planting trees with crops).
Minimum tillage (disturbing the soil less).
Water management (controlling how water flows).

The researchers wanted to know: Are the farmers using these tools? Who is using them? And what is stopping them from using more?

The Cast of Characters

The study looked at 96 rice farmers. Here is the profile of the typical farmer in this group:

The Gender Mix: Surprisingly, the majority (62.5%) are women. While many studies in Northern Nigeria focus on men, here, women are the main drivers of the rice fields.
The Experience: They are seasoned veterans, averaging 45 years old with nearly 19 years of farming experience. They know the land well.
The Team: Almost everyone (100%) belongs to a farmer cooperative, and nearly everyone (94%) has talked to an agricultural extension officer recently. They are well-connected.
The Size: They farm on small plots, roughly the size of two and a half basketball courts (about 2 hectares).

The Results: How Much Are They Using?

The researchers found that the farmers are using the tools, but only halfway.

The Score: If the farmers used all seven tools, they would get a score of 1.0. The average score here was 0.49. It's like a student who passed the test but barely got a C.
The Favorites: The most popular tools were mulching (the blanket), improved seeds, and organic manure. These are cheap, easy to understand, and give quick results.
The Struggler: The least popular tool was water management. This is like the "advanced level" of the game—it requires more money, more knowledge, and more effort, so fewer people are trying it.

The Verdict: Adoption is "widespread but shallow." Most farmers have picked up a few tools, but very few have the whole kit.

The Mystery: What Drives Adoption?

Usually, researchers expect to find that certain types of people adopt new methods more than others. For example, they might guess that:

More educated farmers adopt more.
Farmers with more money (credit) adopt more.
Younger farmers are more adventurous.

The Twist: In this specific study, none of these guesses were statistically proven.
The data showed that whether a farmer was rich or poor, educated or not, young or old, didn't significantly change their likelihood of adopting these practices.

Why? Because the playing field was too level. Since everyone in the study belonged to a cooperative and almost everyone had talked to an extension officer, there was no "unconnected" group to compare against. It's like trying to see if wearing a specific brand of shoes makes you run faster when everyone in the race is already wearing the exact same brand of shoes. The "shoe factor" disappears because there's no variation.

The Roadblocks: Why Not More?

If the farmers are connected and experienced, why aren't they using all the tools? The researchers asked the farmers what was holding them back. The answers were a bit of a "tug-of-war" where no single problem was the clear winner, but three stood out slightly:

Poor Market Access: It's hard to sell the rice or buy the tools because the roads are bad or the markets are far away.
Lack of Information: They don't know enough about how to use the harder tools (like water management).
High Cost of Labor: It's too expensive to hire people to help with the work.

The researchers noted that these problems were all roughly equal in severity. It wasn't that one giant wall stopped them; it was a barricade made of many small bricks.

The Conclusion & Recommendations

The paper concludes that in this specific area, the farmers' personal traits (like age or education) matter less than the environment they are farming in. Because everyone is similarly connected, the difference between a "high adopter" and a "low adopter" isn't about who they are, but about the system around them.

What should be done?
Instead of trying to target specific types of people (like "only help the educated"), the researchers suggest fixing the system:

Fix the Roads and Markets: Make it easier to sell rice and buy seeds.
Peer-to-Peer Learning: Since everyone is in a cooperative, use the farmers to teach each other, especially about the tricky water management techniques.
Save Labor: Introduce tools that require less human muscle, since labor is expensive.
Focus on Women: Since women are the majority of the farmers, ensure all training and credit programs are designed specifically for them.

In a nutshell: The farmers in Lafia are ready and willing, but they are stuck in a "middle ground." They need the government and support groups to clear the path (roads, markets, info) rather than just trying to change the farmers themselves.

Technical Summary: Determinants of Climate Smart Agricultural Practice Adoption among Rice Farmers in Lafia Local Government Area, Nasarawa State, Nigeria

Problem Statement
Climate variability poses a severe threat to rainfed rice systems in Nigeria's North-Central zone, yet empirical evidence regarding how smallholder farmers in Nasarawa State are responding through Climate-Smart Agriculture (CSA) remains scarce. While policy attention and extension messaging regarding CSA have increased, the specific level of adoption, the relative uptake of individual practices, and the drivers behind these decisions in Lafia have not been quantified. Existing literature on CSA adoption in Nigeria is heavily concentrated in the South-East, South-West, and far-Northern states, leaving the North-Central rice belt understudied. Furthermore, many local studies rely solely on descriptive profiling or treat practices in isolation, lacking a combined analytical approach that integrates adoption intensity, probabilistic determinants, and structured constraint ranking. This study addresses these knowledge, empirical, methodological, and geographic gaps to provide evidence-based insights for adaptation support.

Methodology
The study employed a cross-sectional design in Lafia Local Government Area (LGA), the capital of Nasarawa State. Data were collected from 96 rice farmers selected via a multistage sampling procedure: Lafia LGA was purposively selected for its rice production prominence; specific rice-producing communities were chosen; and registered farmers were drawn using simple random sampling.

The analysis utilized four primary techniques aligned with the study's objectives:

Descriptive Statistics: To profile the socioeconomic characteristics of the farmers.
Adoption Index: A composite index ( $AI = \sum A_i / 7$ ) was calculated for each farmer based on seven CSA practices (improved seed, crop rotation, mulching, organic manure, agroforestry, minimum tillage, and water management). Farmers were categorized into low, medium, and high adoption groups.
Binary Logit Regression: A probabilistic model was used to examine the determinants of adoption. Farmers were classified as adopters ( $Y=1$ ) if they adopted at least the mean number of practices, and non-adopters ( $Y=0$ ) otherwise. The model tested the significance of socioeconomic variables (gender, age, education, household size, farming experience, farm size, extension contacts, credit access, and distance to market). Cooperative membership was excluded from the regression due to lack of variation (100% membership).
Constraint Analysis: A four-point Likert-scale was used to rank constraints, with mean severity scores computed and the degree of agreement among farmers assessed using Kendall's coefficient of concordance ( $W$ ).

Key Results

Socioeconomic Profile: The farming population is distinctively feminized, with 62.5% of respondents being female. The average age was 45.3 years, with a mean farming experience of 18.9 years. Educational attainment was moderate, with 24% having no formal education and the remainder distributed across primary, secondary, and tertiary levels. Notably, the sample exhibited institutional homogeneity: 100% were cooperative members, 93.8% had recent extension contact, and 52.1% had access to credit.
Adoption Levels: Overall adoption was moderate, with a mean adoption index of 0.49 (approximately 3.5 practices per farmer). The majority of farmers (54.2%) fell into the medium-adoption category.
- Most Adopted: Mulching (55.2%), improved seed (52.1%), and organic manure (52.1%).
- Least Adopted: Water management (43.8%).
- Pattern: Adoption is widespread but shallow; few farmers have embraced the full CSA portfolio.
Determinants of Adoption: The binary logit model revealed that none of the household socioeconomic variables significantly predicted adoption status at the 5% significance level. The model's explanatory power was modest (McFadden pseudo- $R^2$ $R^{2}$ = 0.04).
- Education showed the strongest positive association (marginal effect of ~8 percentage points per educational category) but was only marginally significant ( $p \approx 0.11$ ).
- Credit access also showed a positive, though insignificant, coefficient.
- Variables such as age, farm size, and extension contact showed no significant relationship.
- The null hypothesis ( $H_1$ ) that socioeconomic characteristics have no significant effect on adoption was not rejected.
Constraints: Farmers face a broad, overlapping set of barriers rather than a single dominant constraint.
- Top Constraints: Poor market access (mean score 2.68), lack of information (2.65), and high cost of labour (2.55).
- Agreement: Kendall's $W$ was 0.011 (not significant), indicating low consensus among farmers regarding the ranking of constraints.
- The null hypothesis ( $H_2$ ) that there is no significant difference in the severity of constraints was not rejected.

Significance and Contributions
The paper claims its significance lies in three main areas:

Geographic Expansion: It provides primary, disaggregated evidence on CSA adoption in Lafia, extending research coverage to the under-represented North-Central rice belt of Nigeria.
Methodological Integration: It demonstrates a replicable analytical template that combines an adoption index, a probabilistic determinant model, and a concordance-tested constraint ranking within a single study design.
Theoretical and Policy Insight: By transparently reporting non-significant socioeconomic findings, the study challenges the assumption that household characteristics are always the primary drivers of adoption. It suggests that in institutionally homogeneous settings (where access to cooperatives and extension is universal), adoption may be governed more by shared market and agronomic conditions than by individual farmer traits. This shifts the policy focus from narrowly targeted, characteristic-based interventions to broader institutional and market-level support.

Conclusion and Recommendations
The study concludes that CSA adoption in Lafia is shaped more by institutional and market conditions than by household characteristics. Consequently, the authors recommend:

Strengthening rice marketing infrastructure (aggregation, storage, roads) to ensure returns on CSA investment.
Leveraging the existing cooperative network for farmer-to-farmer information systems, specifically targeting complex practices like water management.
Promoting affordable, labour-saving technologies to address high labour costs.
Expanding functional, field-based farmer education (e.g., demonstration plots).
Designing extension and credit programs specifically to reach the feminized farming population.
Implementing integrated support packages that address markets, information, labour, and finance simultaneously, rather than isolated interventions.