Determinants of the level of compliance with recommended production practices among rice farmers in Osun state, Nigeria

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Despite the expansion of rice production in Nigeria over the past decade, a marked discrepancy in yields between farmers’ fields and demonstration sites threatens food security and economic growth by limiting domestic supply. This suggests that rice farmers are not fully implementing recommended practices. This study therefore employed the Fractional Response Probit Model (FRM), suited for analyzing bounded dependent variables, to examine the factors influencing the level of compliance with recommended practices among rice farmers in Osun State. The study utilized primary data gathered through interviews using a structured questionnaire administered to 180 rice farmers. The study revealed that most rice farmers demonstrated only moderate adherence to recommended practices, with a mean compliance level of 0.48, representing a substantial 52% shortfall from optimal yield. Several factors were identified as positively and statistically significantly influencing compliance, including age, sex, household size, years of education, and frequency of visits from extension agents. High input costs and limited access to credit were among the key obstacles to compliance encountered by the farmers. This study concludes that rice farmers in the study area are on average, 52% below maximum compliance with recommended practices, underscoring the need for improved adoption. It is recommended that rice sector stakeholders such as government agencies, extension agents, non-governmental organisations, and farmer associations support farmers through fertilizer subsidies and by creating platforms to reduce the cost and difficulty of accessing essential production inputs.

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Akinola et al. Discover Agriculture (2025) 3:132
https://doi.org/10.1007/s44279-025-00317-1
*Correspondence:
Olutosin Ademola Otekunrin
[email protected]
1
Department of Agricultural
Economics, Obafemi Awolowo
University, Ile- Ife, Nigeria
2
Innovation Lab for Policy
Leadership in Agriculture and
Food Security (PiLAF), University of
Ibadan, Ibadan, Nigeria
3
Disaster Management Training
and Education Centre for Africa,
University of the Free State,
Bloemfontein, South Africa
4
Department of Business & Social
Sciences, Faculty of Agriculture,
Dalhousie University, Truro, Canada
Determinants of the level of compliance
with recommended production practices
among rice farmers in Osun state, Nigeria
Oluwaseyi Hannah Akinola
1
, Taiwo Alimi
1
, Temitope Oluwaseun Ojo
1,3,4
and Olutosin Ademola Otekunrin
2,3*
1 Introduction
The agricultural sector still remains one of the most important sectors of developed
and developing economies which contributes to Gross Domestic Product (GDP), food
security and provides employment [1, 2]. For instance, in Asia, 25% of the region’s GDP
and 60% of employment comes from agriculture [3]. This is even higher in the African
regions; especially West Africa where agriculture contributes about 35% to GDP and
Discover Agriculture
Abstract
Despite the expansion of rice production in Nigeria over the past decade, a marked
discrepancy in yields between farmers’ fields and demonstration sites threatens food
security and economic growth by limiting domestic supply. This suggests that rice
farmers are not fully implementing recommended practices. This study therefore
employed the Fractional Response Probit Model (FRM), suited for analyzing bounded
dependent variables, to examine the factors influencing the level of compliance with
recommended practices among rice farmers in Osun State. The study utilized primary
data gathered through interviews using a structured questionnaire administered
to 180 rice farmers. The study revealed that most rice farmers demonstrated only
moderate adherence to recommended practices, with a mean compliance level
of 0.48, representing a substantial 52% shortfall from optimal yield. Several factors
were identified as positively and statistically significantly influencing compliance,
including age, sex, household size, years of education, and frequency of visits from
extension agents. High input costs and limited access to credit were among the
key obstacles to compliance encountered by the farmers. This study concludes that
rice farmers in the study area are on average, 52% below maximum compliance
with recommended practices, underscoring the need for improved adoption. It is
recommended that rice sector stakeholders such as government agencies, extension
agents, non-governmental organisations, and farmer associations support farmers
through fertilizer subsidies and by creating platforms to reduce the cost and difficulty
of accessing essential production inputs.
Keywords Compliance, Production practices, Rice farmers, Fractional response probit
model (FRM), Nigeria

2. Page 2 of 19Akinola et al. Discover Agriculture (2025) 3:132
offers its populace more than 60% employment [4]. In Nigeria in particular, agricul-
ture contributes about 29.31% of total GDP [5], securing 88% of non-oil earnings, and
offering employment and income through agricultural and related activities to approxi-
mately half of the population [5]. This notwithstanding, agricultural production has been
declining and domestic production has not been able to meet demand [2]. The rice sec-
tor provides a clear example of this.
Globally, total rice trade rose to $32.1 billion in 2022 [6] and rice production reached
799.9 million tonnes in 2023 [7]. According to data from the Observatory of Economic
Complexity (OEC), India was the leading rice producer at 206.7 million tonnes and has
also emerged as the top exporter of rice with a trade value of $11.1 billion, accounting
for 34.7% of global exports [6]. In 2022 however, China was the largest rice importer,
with its total imports valued at $2.4 billion. On the other hand, Nigeria occupied a very
minor position, ranked 117th among rice exporters and 162nd as an importer of rice in
2022 [6]. In terms of production, Nigeria however ranked 14th globally (among 116 pro-
ducers), recording 8.9 million tonnes in 2023 [7].
Rice is cultivated in virtually all of Nigeria’s agro-ecological zones in both rainfed
and irrigated systems and can be cultivated effectively in Benue, Kogi, Kano, Kaduna,
Sokoto, Niger, Ebonyi, Enugu, Cross River, Ogun, Ekiti, Lagos, Osun [8] and Anambra
States. Rainfed rice which is by far the most prominent, accounts for over 80% of total
rice produced in Nigeria [9, 10]. Additionally, upland and lowland cultivation systems
are employed in Nigeria. In the lowland system, rice is cultivated on fields which are
flooded all through the growing season or at some periods. On the other hand, level and
natural terrain fields with no flooding are used for rice cultivation in upland cultivation
systems [10, 11]. The upland system offers advantages which is not limited to its adapt-
ability to a various climatic condition and soil types, its minimal water requirements in
comparison with lowland rice and minimal susceptibility to disease [9]. For lowland sys-
tem, it offers advantages like higher maintenance of soil fertility, better weed and pest
control and higher build-up of soil organic matter [11]. Also, there are many varieties of
rice being cultivated in Nigeria; some of which are considered traditional varieties while
others are improved varieties introduced by research institutes over the past 20 years
[12] one of which is the Federal Agriculture Research Oryza (FARO) 44.
Although Nigeria doubles as the largest producer and importer of rice in West Africa
[13], local rice production in the country which rose from 229,000 metric tonnes in 2015
to 8.9 million metric tonnes in 2023 is still largely unable to meet the yearly domestic
demand for rice [11]. The increased demand has been tied to rapid population growth
and changes in preference of consumers from other traditional staples to rice [6, 13]and
[14]. This demand-supply gap is not surprising as Nigeria’s rice yield has varied over two
decades, peaking at 2.60 tons per hectare in 2014, but declining to an estimated 1.97
tons per hectare in 2023 [7, 11]. This yield is significantly lower than what is obtainable
in some other African countries like Côte d’Ivoire and Senegal with an average yield of
3.0 metric tonnes per hectare from upland and lowland cultivation systems and up to
7.0 metric tonnes per hectare from irrigation systems [15]. Nigeria’s agricultural sector
faces challenges beyond rice, as yields for key crops like cassava and cocoa lag behind
other major producers. While Nigeria leads global cassava production in quantity, its
yield has declined significantly, from 11.2 tonnes per hectare (t/ha) in 2011 to 6.9 t/ha
in 2021. In contrast, Cambodia, despite ranking ninth in production quantity, achieved

3. Page 3 of 19Akinola et al. Discover Agriculture (2025) 3:132
a cassava yield of 27.4 t/ha in 2021. Similarly, though Nigeria ranked among the top ten
cocoa bean producers globally in 2023, its yield of 2.71 t/ha ranked 14th in Africa and
43rd globally [7, 16–19].
Factors responsible for the low yield include problems like low adoption of improved
technologies thereby mitigating large-scale production, an aging farming population
with little or no technological know-how, policy inconsistencies and poor compliance
with recommended production practices, thus creating a wide gap between potential
and actual yield [11, 14].
In a bid to address this stagnating yield situation and boost production, policies such
as those under the Agricultural Transformation Agenda (ATA) have been put in place
to improve domestic production by increasing the intensity of production in all rice-
producing States across the nation, one of which is Osun State. The Nigerian govern-
ment has also facilitated the encouragement of the participation of private investors,
implemented the total removal of import duty on agricultural machinery and tax holi-
days for agricultural investments [20], introduced improved varieties such as FARO 44,
ensured better access to mechanization as well as the recommendation of practices (see
Table 7 in the appendix) needed to achieve the maximum yield possible. This is because
a proper combination of these practices such as the cultivation of improved varieties
at the appropriate planting dates, spacing, fertilizer application, weed, and pest control
among others will bring about the desired boost in rice yield [21, 22]. Hence it is essen-
tial that farmers comply strictly with the use of these practices.
Studies have examined the issue of the adoption of technologies and production prac-
tices [23–28]. Abubakar et al. [24] and [26] investigated the factors influencing farm-
er’s decision to adopt production practices and argued that economic incentives like
increased profit, higher turnover, lesser production costs and minimal risks were the
most important influencing factors. Additionally, On-farm and on-station trials con-
ducted by [8], and [29] revealed that the adoption of technologies and compliance with
improved production practices is expected to raise yield from rice production substan-
tially. Also, Abdulmumini et al. [8] and [26] opined that the decision of a farmer to com-
ply with recommended production practices can be influenced by several independent
factors such as socio-economic, institutional, organisational, psychological and personal
factors of the farmers in question. It is therefore worthy of note as revealed by [30] that
achieving optimum yield in rice production is not just a function of the farmer’s level
of efficiency; be it technical or allocative, good policies, favourable weather conditions
among others but a combination of these factors as well as how much they are able to
adhere to the package of practices recommended for rice production.
While other studies focused on rice production and consumption in Nigeria [11, 31],
farmers’ adoption wiliness to adopt recommended practices [32], impact of technology
adoption on productivity [26, 29], the issue of compliance with recommended produc-
tion practices has hitherto not been given deserving attention as a contributing factor
to low yield on farmer’s farms [30]. To further attest to this, it has been observed that
several policies and programs aimed at boosting rice production in Nigeria such as the
National Accelerated Food Production Project (NAFPP), Abakaliki Rice Project (ARP),
and the Presidential Initiative on Rice (PIR), among others, have focused on the intro-
duction of better yielding and improved varieties, and large scale production through
the opening up of virgin lands, rather than monitoring how effectively farmers use or are

4. Page 4 of 19Akinola et al. Discover Agriculture (2025) 3:132
compliant with the practices recommended to them for optimum rice production [11,
20].
Meanwhile, studies like [33, 34], and [35] have argued that the mere adoption of one
agricultural practice may not yield the desired result if the all the recommended prac-
tices are not complied with and used simultaneously. While this suggest the existence
of a subtle link between adoption of agricultural practices and compliance, compliance
goes beyond the mere uptake of a practice, it is the ability of a farmer to use the practice
exactly as it has been specified [27, 34]. Furthermore, while studies examining the adop-
tion of production practices abound [8, 29, 30], those providing empirical evidences on
the level of rice farmer’s compliance with recommended practices in Nigeria and the fac-
tors responsible for the compliance decision is scarce. As its main objective, this study
thus seeks to bridge this critical knowledge gap by investigating the determinants of
the level of compliance with recommended production practices among rice farmers in
Osun State, Nigeria. In specific terms, the paper examined the farmers’ level of compli-
ance with recommended rice production practices, determined the factors influencing
the rice farmers’ compliance with recommended practices and assessed the constraints
to compliance with the recommended practices.
This study contributes to knowledge as it provides insight as to how well farmers are
carrying out their production activities, in which practice they are lagging and what fac-
tors are to be boosted for better performances. Exposing the level of compliance of the
farmers with the recommended production practices also helps to shift the focus of pol-
icy makers from mere scale expansion to a currently subtle but pertinent issue such as
compliance. This is necessary because attaining optimum yield is a result of a combina-
tion of several factors which include the use of quality seeds, favourable weather con-
ditions, improved planting implements and other production practices as well as strict
adherence to the recommended production practices [9].
2 Research methodology
2.1 Study area
The study was conducted in Osun State, situated in the southwestern region of Nigeria.
It is a landlocked entity with its capital at Osogbo. Its geographical coordinates place it
between Longitude 2.75
0
and 6.75
0
North of the Greenwich meridian and Latitude 7
0
and
9
0
East of the equator. The state’s territorial boundaries are shared with Kwara State
to the north, Ekiti and Ondo States to the east (partially), Ogun State to the south and
Oyo State to the west. Covering 9,251 square kilometres, Osun is home to an estimated
3.4  million people [36]. State’s climate is characterized by an average annual rainfall
ranging from 1,105 mm in the derived savannah to 1,475 mm in the rainforest, alongside
mean annual temperatures that range between 27.3 °C in June and 39 °C in December.
Osun is further segmented into three agricultural development zones (ADPs): Ife/Ijesha,
Iwo, and Osogbo, which are all conducive to rice production, with Ife/Ijesha being the
most significant rice cultivation area. The majority of the population engages in small-
holder farming, focusing on arable crops such as cassava, yam, maize, and rice, as well as
permanent crops like cocoa and oil palm, benefiting from favourable climatic conditions.

5. Page 5 of 19Akinola et al. Discover Agriculture (2025) 3:132
2.2 Quantitative methodology and survey design
The study employed a quantitative methodology which entails the collection of numeri-
cal data to the end that discernible relationships among variables are unveiled [37]. For
the purpose of this inquiry, quantitative method was employed because of its effective-
ness in providing objective and impartial analysis of a given research issue [38]. Further-
more, the survey research design which is hinged on the assumptions of the deductive
approach and quantitative method was employed. Using this design allows for the effec-
tive collection of data from a large population via questionnaires.
2.3 Population and sample size determination
The study’s target population includes all members of the Rice Farmers Association of
Nigeria (RFAN), Osun State Chapter who grow the FARO-44 rice variety. Hence, the
total population of the rice farmers is 328 in number. To determine the representa-
tive sample size for the study, the formula proposed by Yamane [39] was employed as
expressed below in Eq. 1:
n=
N
1+N(e
2
)
(1)
Where n  = the needed sample size, N  = population (328 rice farmers) and e  = error mar-
gin (taken as 0.05).
n=
328
1 + 328
(
0.05
2
)
(2)
Slotting these values into Eq. 1, the sample size arrived at is 180 rice farmers.
2.4 Sampling technique
To select the 180 rice farmers who participated in this study, multi-stage sampling tech-
nique was utilized. This is a probability sampling technique that facilitates the collection
of a representative sample from a large population in different stages [40]. The first stage
involved choosing the Iwo and Osogbo Agricultural Development Programme (ADP)
zones, as these areas have a significant number of farmers growing FARO-44 rice. In the
second stage, three Local Government Areas (LGAs) were purposefully selected within
each ADP zone, choosing LGAs known for their intensity of rice production. In the third
stage, three villages were then randomly chosen from each of these selected LGAs. The
final stage involved randomly selecting ten rice farmers from each village using lists
obtained from the local chapter of the RFAN, resulting in a total sample of 180.
2.5 Data collection and data collection instrument
Primary data was then collected through interviews using a well-structured question-
naire. This data was collected first hand by the researchers between the months of July
to September, 2019. The questionnaire was sectionalised with the first section contain-
ing information about the rice farmers’ socio-economic and farm characteristics. In
the second section, questions on the various production practices complied with was
asked. Each farmer was awarded a score of 1 if they complied with a particular practice
as specified and 0 if not. The third section assessed the constraints faced by rice farmers
in complying with the various recommended practices in the study area.

6. Page 6 of 19Akinola et al. Discover Agriculture (2025) 3:132
2.6 Conceptual framework
Building on the traditional model of technology or management adoption [41], propose
that farmers make rational decisions about adopting new technologies or practices by
assessing the expected costs and benefits, aiming to maximize their profit. They essen-
tially conduct a cost-benefit analysis of various alternatives. Consequently, elements that
influence profitability play a crucial role in farmers’ decisions to embrace new produc-
tion methods for commercial purposes. A farmer’s decision to comply with a recom-
mendation is driven by the expectation that the resulting net benefits will surpass those
of alternative choices, including not complying [26]. The conceptual framework in Fig. 1
shows that different socio-economic, institutional, farm and social factors are expected
to influence rice farmers’ level of compliance with the recommended rice production
practices.
The framework underscores the interconnectedness of recommended farming prac-
tices, farmers’ compliance decisions, and their resulting livelihood outcomes. Farmers
are expected to implement certain recommended practices, but compliance can vary
in degree. These varying levels of compliance affect their yields, profitability, and ulti-
mately, their well-being. Following the approach of [24], a compliance index was gener-
ated to measure farmers’ adherence to these practices. To identify what influences the
level of compliance, a fractional response probit model was employed.
Source: Adapted from Olayemi et al. [26]
2.7 Analytical framework
2.7.1  Compliance index
To determine the level of compliance with recommended production practices, a com-
pliance index was calculated for the farmers which was generated from the formula used
Fig. 1 Diagrammatic representation of the study’s conceptual framework

7. Page 7 of 19Akinola et al. Discover Agriculture (2025) 3:132
in calculating the adoption index [24]. In this study, the compliance level of a rice farmer
was determined using the following formula in Eq. 3:
CIi=
(
CPi
NPi
)
(3)
Where,
CI
i
is the compliance index of a farmer,
CP
i is the number of practices complied with by a farmer,
NP
i
is the number of practices recommended.
A maximum compliance index obtainable is fixed at 1. The mean compliance index
was calculated. To determine the mean compliance level, this study adopted the formula:
Cl=
∑
fx
N
(4)
Where Cl = mean compliance index; f  = frequency of each value of compliance observed;
N = number of observations of the variable x.
Using the mean compliance score for the farmers plus or minus one standard devia-
tion as the reference point, compliance levels were formed. Farmers with scores higher
than the mean compliance index value plus one standard deviation, were said to have
high compliance with the practices, those with scores that lie within the middle range of
the mean compliance index  ± one standard deviation was said to have moderately com-
plied while those with scores lesser than the mean compliance index minus one standard
deviation have low compliance.
2.7.2  Fractional response probit model
Traditional empirical analyses of dichotomous and continuous dependent variables fre-
quently utilize Ordinary Least Squares (OLS) regression, probit models, or one-stage
Tobit models [26, 28]and [42]. However, these models prove inadequate for our study’s
objectives given the specific properties of our explanatory variable: the level of farm-
ers’ compliance. We therefore opt for a fractional response model (FRM), specifically, a
fractional response probit model, a generalized linear model extension suitable for out-
comes constrained between zero and one [43]. This methodology is specifically appli-
cable to fractional, proportional, rate, index, and probabilistic outcomes [44, 45]. The
FRM, particularly the fractional response probit, addresses both the bounded nature of
our dependent variable and provides robust standard errors, making it a more appropri-
ate and reliable choice [46].
Fractional response estimators, including probit, logit, heteroskedastic probit, and beta
regression, are used to model continuous data bounded between zero and one. However,
beta regression is not appropriate when the data contains values at the extreme ends,
i.e. zero or one. In contrast, fractional response probit can accommodate both zero and
one, making it a more suitable approach when such extreme values are present [46]. The
difficulty in justifying beta distribution with datasets having zero or one values makes
fractional response probit preferable. Further, fractional response regression, which uti-
lizes quasi-likelihood methods, offers consistent parameter estimates and robust stan-
dard errors, regardless of the true distribution. This method also ensures that predicted
values fall within the 0 to 1 range. Furthermore, the calculation of marginal effects is

8. Page 8 of 19Akinola et al. Discover Agriculture (2025) 3:132
essential for the proper interpretation of the results, as the original coefficients are not
easily interpreted [46].
The extent to which rice farmers comply with recommended agricultural practices
is seen as a result of numerous interacting factors. These factors include the social and
institutional contexts that shape farmers’ decisions about whether to adopt these recom-
mendations. In this study, the dependent variable is farmers’ compliance level, which is
operationalized as an index, calculated as the proportion of recommended practices a
farmer follows out of the total available. This index generates continuous values that are
bounded within the range of zero to one. The vector of independent variables X used
are age in years, sex, marital status, size of household, literacy level in years, years of
experience, rice farm size, revenue, labour used and frequency of extension agent visits
(Table 1), with the aim to fit a regression for Y’s mean conditional on X such that E(Y/X).
Because Y is in (0, 1), the fractional response model makes it possible that E(Y/X) is also
in (0, 1). The model is thus expressed as:
E(Yi/XiYi>0) =β(X iΨ)(5)
Where Yithe observed response on the compliance level, E the expectation operator; Xi
is a vector of the household characteristics; Ψ is a vector of parameters to be estimated.
Fractional probit regression is of an advantage here as it avoids model misspecifica-
tion and dubious statistical validity and can capture particular non-linear relationships,
particularly when the outcome variable is near zero or one facilitating its extensive use
in literature [45–47].
2.7.3  Importance indices
To determine the relative importance of constraints to compliance with recommended
rice production practices, importance indices as employed by [49] were constructed.
Rice farmers were asked to identify constraints to compliance with recommended rice
production practices and rank them on an ordinal scale; assigning the value 1 to the
most important constraint, 2 to the next and sequentially down the line. For analysis,
the percentage of respondents identifying each constraint as the most important was
Table 1 Measurements of the dependent and independent variables
Variables Measurements/Description of variables Signs expectedSourc-
es
Compliance (Dependent
Variable)
Individual farmer’s compliance scores [24]
Independent Variables
 Age Age of rice farmer (years) ± [32, 26]
 Sex 1 if rice farmer is male, 0 if female ± [30, 27]
 Marital status 1 if rice farmer is married, 0 if others/single/
divorced
± [32, 28]
 Years of education Years of education of rice farmer + [32, 48]
 Farm size Total land owned by farmer, in hectares± [26, 27]
 Experience Years of farmer’s experience in rice production± [8, 28]
 Revenue Amount realized from sales of rice, in naira+ [27, 26]
 Household size Number of household members ± [8, 29]
 Labour used 1 if labour is hired, 0 if otherwise + [26]
 Extension agent visitsNumber of times + [8, 26]

9. Page 9 of 19Akinola et al. Discover Agriculture (2025) 3:132
multiplied by the average score computed for each identified problem to arrive at the
importance index.
2.8 Validity and reliability analysis
A number of steps were taken in line with [50] to ensure the validity and reliability of the
data collected and analysed in this study. Firstly, a clear definition of the study’s research
objective was provided and the data collection instrument was carefully constructed to
address each of the objective. In addition, the researchers ensured that the question-
naire used was written in very simple and unambiguous words so that the farmers easily
understand the questions being asked. Likewise, a representative sample was selected
using a proven formula which fosters the generalisability of the study’s findings [51]. To
further enhance the validity of the research instrument, a pilot test was done prior to the
main data collection. Executive members of the RFAN Osun State chapter were asked to
fill the questionnaire and the questions were refined based on their responses.
2.9 Ethical considerations
The research followed standard ethics of research in all phases. Firstly, voluntary partici-
pation of the rice farmers was ensured and only those who consented took part in the
survey. Likewise, the farmers were made to understand that they could withdraw their
participation if need be. Furthermore, to guide against undesirable consequences and to
protect the farmers’ images, findings were anonymously reported. Proper in-text refer-
encing was also ensured and a detailed reference list was attached to avoid plagiarism.
3 Results
3.1 Socioeconomic characteristics of rice farmers
Table 2 offers a summary of the socio-economic characteristics of the participating rice
farmers. The farmers have an average age of 44 years, the majority at 83% were male,
married (88%) and have an average household size of five members. The farmers’ average
years of education was 10 years, indicating that most had attained a basic level of lit-
eracy. The average years of experience were 10 years which could assert an influence on
farmers’ readiness to comply with the different recommended practices [26]. Regarding
Table 2 Distribution of respondents by their socio-economic characteristics
Variables Mean SD
Age 43.57 11.23
Gender 0.83 0.38
Marital Status 0.88 0.33
Household Size 5.44 1.79
Literacy level 10.39 4.00
Years of Experience 10.39 5.44
Rice Farm Size 2.18 1.42
Output 2115.56 1267.64
Revenue 239444.44 159620.54
Land Sources 0.31 0.46
Farm Labour Used 0.87 0.33
Source of Finance 0.78 0.41
Extension Agent Visit 0.56 0.50
Awareness of Practices 1.00 0.00
Source: Field Survey, 2019

10. Page 10 of 19Akinola et al. Discover Agriculture (2025) 3:132
the farm enterprise characteristics as presented in Table 3, it was found that rice produc-
tion in the State was mostly practiced on a relatively small scale as the average farm size
was two hectares, the mean output was 2115.56 kg with an average revenue of ₦239,444.
This is in line with the findings of [29] who revealed that rice farming is still done on a
small scale in Nigeria with farmers cultivating on about 2 hectares of land and recording
annual revenue of less than ₦250,000. Only 31% of the farmers owned the land used for
their productive activities indicating that the majority sourced for land through other
means such as lease and rent. Also, 87% of the rice farmers used hired labour most likely
due to the rigorous activities involved in producing rice and the majority (78%) financed
their production from their savings. This may be a result of the complex conditions and
high interest rates that loans attract [26]. Additionally, 56% of the farmers have had con-
tact with extension agents within the last production season and all the farmers were
aware of all the recommended practices needed for rice production (Table 3).
3.2 Results on the level of compliance of farmers with recommended production practices
The compliance score was calculated for each farmer and the average compliance score
was found to be 0.48 (Table 3). This result implies that the average level of compliance
of the rice farmers with recommended production practices falls 52% short of the maxi-
mum possible level. Each of the rice farmer’s compliance index score was calculated as
described in the methodology and the mean compliance index score was calculated.
Using the mean score (0.48) plus or minus one standard deviation (0.19) as a benchmark
for compliance, the farmers’ index score was grouped into three levels: high compliance
(> 0.67), moderate compliance (0.29–0.67) and low compliance (<  0.29). The result on
Table 3 further shows that the majority (59.4%) of the farmers moderately complied with
the recommended production practices for rice while only 17.8% had high compliance
and 22.8% had low compliance.
3.3 Results on the factors influencing compliance with recommended practices among
farmers
Test for multicollinearity was conducted to enhance the credibility of the study’s findings
and the results are presented in Table 4. Since the mean variance inflation factor (VIF) of
1.113 is significantly lower than 10, it is revealed that there is no multicollinearity among
the independent variables.
Table 5 presents the results of the Fractional Response Probit model with Quasi
Maximum Likelihood estimates of factors influencing the level of compliance with rec-
ommended production practices among rice farmers in the study area. The FRM esti-
mates of the regression of the data revealed that the model was properly estimated with
pseudo-R
2
of 0.0422 and Wald chi-square value of 179.97. This is statistically significant
at the 1% level indicating high goodness of fit of the model used for the analysis. From
Table 5, the coefficient of age was positive and statistically significant at a 1% level. In
Table 3 Level of compliance of rice farmers with recommended production practices
Mean (SD) Compliance index range Level Frequency Percentage
0.48 (0.19)> 0.67 High compliance 32 17.78
0.29–0.67 Moderate compliance 107 59.44
< 0.29 Low compliance 41 22.78
Source: Field Survey, 2019.

11. Page 11 of 19Akinola et al. Discover Agriculture (2025) 3:132
line with [26], the marginal effects from the FRM model showed that increasing the age
of rice farmers by 1 year increases their level of compliance by 0.5%. Also, the model
revealed a positive and statistically significant (at the 1% level) coefficient for sex. The
marginal effect analysis demonstrated that male farmers’ compliance levels were 8.8%
higher than female farmers’ levels. In a similar manner, a positive and statistically signifi-
cant (at the 5% level) relationship was revealed between household size and compliance
with recommended production practices. Specifically, a one-member increase in house-
hold size was associated with a 1.7% increase in compliance.
Table 5 also shows that the coefficient of farmer’s literacy level was positively signif-
icant at 1%, meaning that compliance increases with literacy level. In agreement with
[30], the marginal effect from the FRM showed that increasing the years of education
by one will increase the level of compliance by 0.9%. Finally, a statistically significant
positive relationship (at the 1% level) was found between the frequency of extension vis-
its and compliance levels. This indicates that farmers who received more frequent vis-
its from extension agents were significantly more likely to adhere to the recommended
Table 4 Multicollinearity test result
Independent Variables VIF 1/VIF
Age 1.250 0.800
Sex 1.078 0.927
Marital status 1.079 0.927
Household size 1.045 0.957
Years of education 1.096 0.913
Years of experience 1.258 0.795
Farm size 1.057 0.946
Labour used 1.052 0.951
Revenue 1.087 0.920
Extension agent visits 1.128 0.887
Mean VIF 1.113
Source: Data Analysis, 2019
Table 5 Factors influencing farmers’ level of compliance with recommended rice production
practices using FRM
FRM Marginal Effect
Variables Coeff. Std. Err.P-value Coeff. Std. Err.P-value
Constant – 1.478***0.290 0.000***
Age 0.012 0.003 0.000*** 0.005 0.001 0.000***
Sex 0.222 0.083 0.008*** 0.088 0.032 0.007**
Marital status 0.068 0.108 0.529 0.027 0.043 0.529
Household size 0.043 0.017 0.014** 0.017 0.007 0.014**
Years of education 0.023 0.005 0.000*** 0.009 0.002 0.000***
Years of experience – 0.009 0.006 0.124 – 0.0030.002 0.124
Farm size 0.008 0.020 0.682 – 0.0030.008 0.682
Labour used 0.055 0.071 0.447 0.021 0.028 0.447
Revenue – 0.000 0.000 0.443 – 0.0000.000 0.443
Extension agent visits0.123 0.023 0.000*** 0.05 0.009 0.000***
Wald Chi
2
179.97
Pseudo R
2
0.0422
Prob > chi
2
0.0000
Log pseudolikelihood- 119.358
*** and ** denote 1% and 5% level of significance, respectively
Source: Data Analysis, 2019.

12. Page 12 of 19Akinola et al. Discover Agriculture (2025) 3:132
agricultural practices. Specifically, each additional extension visit was associated with a
0.5% increase in farmer compliance.
3.4 Results on constraints to compliance with recommended production practices among
rice farmers
According to the result in Table 6, the high price of inputs was ranked as the most
important. Next in order of importance was poor access to institutional credit, followed
by pest and diseases affecting output. In the fourth position was heavy rain, followed
by unavailability of farm implements and poor access to extension agents was the least
important constraint to the rice farmers’ compliance with the practices recommended
to them. Other studies like [8, 29] and [30] also found high input price, poor access to
credit and susceptibility to pest and diseases as key constraints to the adoption of rec-
ommended agricultural practices.
4 Discussion
4.1 Socioeconomics characteristics
The average age of 44 years suggests the majority were within their prime working years
(18–55 years) [52], which should make them more receptive to adopting improved
practices necessary for achieving higher yields. This finding is supported by the earlier
research of [30] and [53] who reported the majority of rice farmers to be in their active
and productive years. The wide gap observed between the number of male and female
farmers can be attributed to the physically demanding nature of rice farming, which may
limit the participation of women, as documented by [30]. The ability of most of the farm-
ers to read and write is likely to facilitate their compliance with different recommended
production practices, as they are better positioned to access information about those
practices through both print and electronic media. This aligns with the findings of [29]
and [8] who highlighted the positive relationship between education and the adoption of
recommended agricultural techniques. In terms of production scale, it can be deduced
that subsistence agriculture is still prevalent among rice farmers in the State. It has been
reported in several studies also that subsistent agriculture is very common in Nigeria [7,
25, 52]and [53]. However [8], argued that for rice production, there has been a substan-
tial increase in hectarage especially in the northern parts of Nigeria.
Table 6 Constraints to compliance with recommended rice production practices
Constraint Importance rating Importance
index
Mean Most important constraintIndexRank
High price of inputs 3.12 50.00 156.001st
Poor access to institutional credit2.75 21.90 60.232nd
Pest and disease still affecting output2.48 21.21 52.603rd
Heavy rain 3.69 7.69 28.384th
Unavailability of farm implements2.57 10.89 27.995th
Poor access to extension agents2.80 6.38 17.866th
Source: Data Analysis, 2019.

13. Page 13 of 19Akinola et al. Discover Agriculture (2025) 3:132
4.2 Discussion on the level of compliance of farmers with recommended production
practices
The average compliance index showed that the rice farmers complied only up to 48%
with the recommended practices and can still improve their level of compliance by 52%.
This shows that the level of compliance with the recommended production practices is
not high enough among rice farmers in the State. None or partial compliance with the
recommended production practices may be a factor in the low yield of rice which may
also significantly reduce the farmers’ profits [24]. The majority (59.44%) of the rice farm-
ers were found to have moderate compliance with the practices recommended. In line
with [28] and [30], the uptake of recommended practices is facilitated by proper aware-
ness and contacts with extension agents. Since the rice farmers in the study area were
aware of all the recommended practices and the majority have had at least on contact
with extension services, the moderate compliance level observed is justified. Contrary to
this study where many of the farmers had moderate compliance with the recommended
practices for rice cultivation [26], reported an abundance of low adopters among the
farmers surveyed in their study. Apart from the fact that [26] focused on maize farmers,
the study looked into climate smart agricultural practices which is a more recent area of
study. The low level of adoption observed in the study was tied to ignorance on the part
of the farmers and poor access to extension services. As such, farmers’ compliance level
is expected to increase when contact with extension agents and training is increased
[28–30].
4.3 Discussion on factors influencing compliance with recommended practices among
farmers
This study reveals that out of the ten independent variables, only the coefficients of five
variables viz. age, sex, household size, literacy level in years and frequency of exten-
sion visits were statistically significant. It was established that older rice farmers were
more likely to comply better with the recommended production practices when com-
pared with younger rice farmers. This finding agrees with [26] and [29] who found that
a positive relationship existed between the age of farmers and the adoption of improved
technologies. It however disagrees with [25] and [27] who reported that a negative
relationship was found between the adoption of innovation or improved practices and
age; such that the probability of adoption of improved production practices decreases
as farmers’ age increases. While this study was conducted in the Southwestern part of
Nigeria where western education is more common [25], was conducted in the North-
ern part where many farmers are illiterate. This differences in location and educational
attainment may be responsible for the disparity observed in the results. Furthermore
[27], argued that farmers become more risk averse and conservative with age and may be
less willing to take up newly introduced practices. However [29], opined that risk aver-
sion is not necessarily a function of age but a function of an individual’s personality.
Furthermore, male rice farmers exhibited significantly higher levels of compliance
with recommended rice production practices compared to their female counterparts.
This finding is consistent with [26], who also reported that adoption rates increased
among male rice farmers. These results might stem from the fact that male rice farmers
are more numerous, and they often hold greater decision-making power regarding inno-
vation adoption [30, 52]. Likewise, this study reveals that farmers with larger households

14. Page 14 of 19Akinola et al. Discover Agriculture (2025) 3:132
were more likely to adopt these practices. This finding is likely due to the fact that larger
households often have a greater available labour force, which is a critical factor in tra-
ditional agriculture. Although household size significantly influences the adoption of
improved practices in Osun, this reliance on family labour represents a barrier to a more
sustainable, modern farming system [28]. The long-term development of the region’s
agriculture requires a shift towards mechanized farming practices and adherence to pre-
scribed protocols, as demonstrated in global commercial agriculture. This move towards
individual farm ownership will drive increased efficiency. This conclusion aligns with the
work of [27] and [30], who also found that farmers with larger households were more
likely to adopt new technologies and practices.
Educational attainment is a critical influencing factor as it was revealed that farm-
ers who spent more years attaining formal education were more likely to fully comply
with the recommended practices than those with fewer years of education. As stated by
[27], education enhances the farmer’s ability to take up agricultural innovation thereby
improving their productivity and efficiency. This might be ascribed to the fact that edu-
cated farmers may know and understand better the values inherent in complying with
improved practices. Additionally, the positive influence of extension visits on compli-
ance is not surprising due to the established link between frequent interactions with
extension agents and the rate of knowledge acquisition regarding new technologies [8,
28]. Several studies not limited to [25, 27, 29] and [30] have demonstrated that expo-
sure to on-farm demonstrations significantly increases farmers’ adoption of improved
management practices. Abubakar et al. [25] reported that not only does contact with
extension agents increase farmers’ likelihood of adoption, multiple and frequent con-
tacts increase the chances of full compliance with recommended practices.
4.4 Discussion on constraints to compliance with recommended production practices
among rice farmers
Regarding the constraints to farmers’ compliance ability, the high price of inputs was
ranked as the most important. This could be because the success of the FARO 44 rice
variety which is the dominant improved variety planted by farmers in the study area is
largely dependent on the use of inputs including certified FARO 44 seeds, NPK and urea
fertilizers, pesticides and herbicides [8, 30]. As such, the high input costs can discour-
age the farmers from procuring the right quantity of inputs recommended to them as at
when due. This is in line with [53] who found that high input costs such as fertilizer are
the most severe constraint rice farmers face in the area studied. Likewise, poor access
to institutional credit is a major challenge to farmers because obtaining credit from
financial institutions is bedevilled by several factors such as high interest rates, collateral
requirements as well as bureaucratic problems [27, 29]and [52]. Therefore, even when
the farmers were willing to comply with some of the recommended practices, especially
capital-intensive ones, they were debarred by inadequate and untimely access to credit.
This is seen as most of the rice farmers financed their rice production activities through
personal savings, further attesting to the problems they face in accessing funds from
financial institutions. Riliwanu et al. [30] posited that poor access to credit limits the
farmer’s ability to purchase inputs, clear land, pay for the use of machinery, and intro-
duce irrigation.

15. Page 15 of 19Akinola et al. Discover Agriculture (2025) 3:132
Furthermore, pest and diseases still affecting outputs constitute an important con-
straint for rice farmers. This is not surprising as rice production has been known to be
heavily affected by bird attacks resulting in sleepless nights for rice farmers particularly
at the rice flowering stage until maturity [8, 53]. This reduces the quality and quantity
of rice output from farmer’s fields. As a result, a number of farmers in the study area
have reduced the level at which they comply with some practices. Additionally, the huge
losses in output that results from heavy rainfall can make farmers quite reluctant to
adhere strictly to the recommended rice production practices. Lastly, access to extension
services has been known to enhance the adoption of production technologies as they
provide information on agricultural production [23]. Inadequate contacts with extension
agents deprive farmers of new information about improved production practices needed
for maximum yield.
5 Conclusion and recommendations
Rice farmers in Osun State demonstrate an understanding of recommended rice produc-
tion methods. However, their average level of adherence to these practices is notably low,
falling 52% short of the maximum, which is likely a major factor behind the low yields
experienced in their fields. Their level of compliance was also significantly influenced
by some of their socio-economic characteristics such as age, gender, household size, lit-
eracy level and frequency of visits received from extension agents. The most important
constraint faced by rice farmers in complying with the recommended practices was the
high price of the recommended inputs which are needed for rice production. The impli-
cation of the findings presented in this study is that if significant improvement in rice
production is to be pursued in the State, measures should be put in place to encourage
better compliance decisions among the farmers. Following the findings of this study, it is
recommended that stakeholders in the rice sector like government agencies and farmers
associations should provide the necessary support to rice farmers by providing subsidies
and creating platforms that will ease the cost and stress of accessing inputs needed for
production. In the same vein, extension agents should not only be made more acces-
sible to the rice farmers but the frequency of their visits to the farmer’s farms must be
increased. Furthermore, it is recommended that cooperative societies and agricultural
financial institutions should relax the requirements for loans and make credit more
accessible to the rice farmers. Lastly, since literacy level boosts compliance, a scheme
targeting educated farmers should be implemented by government agencies. Imple-
menting these recommendations is expected to boost compliance which will in turn
raise rice yields.
6 Limitations
Having used a cross sectional data in this study, changes in the rice farmers’ compliance
over time cannot be measured. Hence, future studies can employ a longitudinal data to
properly capture the reoccurring determinants over time.

16. Page 16 of 19Akinola et al. Discover Agriculture (2025) 3:132
S/NRecommended
practices
Specification Explanation of practices
1 Land preparationPloughing
Harrowing
Bund Preparation
Ploughing is the first step in the preparation of land
for rice cultivation which has to do with the tilling of
the soil using a plough for proper soil aeration and the
removal of weeds and stubbles.
Harrowing is the process by which the ploughed soil
is levelled and further broken down to attain optimum
soil structure, texture and improve water retention
Bunds are raised embankments on the rice field mea-
suring 50 cm by 30 cm. This prevents excess water loss
and facilitates proper water management on the rice
field [9].
2 Planting method1. Direct sowing
2. Transplanting
3. Broadcasting
Direct sowing involves the direct planting of rice
seeds in the prepared field. It requires lesser labour
and ensures better root growth.
In transplanting method, rice seeds are first grown in
a nursery and then the seedlings are transplanted to
field 15 to 40 days after. This method minimises weed
infestation and ensures a uniform rice stands.
In broadcasting, rice seeds are scattered on the
prepared field by hand or by machine. This method is
labour efficient and the most cost effective.
3 Seed separationSoaking seeds in water for
24 h to remove floating
seeds (WARDA, 2008)
This done to ensure that only viable seeds are sown
to improve the chances of germination. Seeds that
float in water after 24 h are not viable and should not
be used.
4.Seed treatmentApplication of insecticide
and fungicide such as Apron
star or Seed plus
This minimises the exposure of the planted rice seed
to insect and other microorganisms. This should be
done prior to planting.
5 Number of Im-
proved varieties
to be planted
per hole
4–5 seeds per hole Holes are made in the ground and about 4 to 5 rice
seeds should be planted in each hole during direct
sowing or in the nursery.
6 Improved line
spacing
1. Direct sowing – 20 cm by
20 cm
2. Transplanting – 20 cm by
20 cm
3. Broadcasting – none
Line spacing refers to the distance that should be
between each hole made. This should be 20 cm by
20 cm in both direct sowing and in transplanting.
7 Planting depth3–4 cm The seeds should be placed no more than 3 to 4 cm
deep in the soil.
8 Optimum seed
rate per hectare
1. Direct sowing – 50 kg/ha
2. Transplanting – 30 kg/ha
3. Broadcasting – 60–80 kg/
ha (WARDA, 2008)
The seed rate is the quantity of rice seed that is used
to cultivate a hectare of land. Broadcasting method of
planting usually requires more seed due to high level
of seed wastage and lack of precision involved [9].
9 Herbicide usage
1.
Pre-emergence
2.
Post-emergence
Apply Glyphosate one or
two weeks before dibbling
or transplanting at 6 L/ha
Apply propanil plus 2,4-D
amine (e.g. ORYZO plus) 3 to
4 weeks after dibbling at the
rate of 4 L/ha
This is done to eliminate weeds prior planting and
after transplanting. Weeding can be done manu-
ally or by herbicides as specified here. While manual
weeding requires more labour and time, the use of
herbicides is faster and more effective [9].
10Pesticide usageApplication of insecticides
like Dithane
TM
M-45 at 1.0 kg
ai/ha; or Benlate
TM
at 1.5 kg
a.i/ha in 500 L of water)
Pesticides should be used as rice is often affected by
pests like weevils, thrips, and rice seed midge among
others
11Weeding First weeding: done 2–3
weeks after establishment
Second weeding: 2–3 weeks
after first weeding
NB: Weeding should come
before fertilizer application
This involves the removal of weed to allow for opti-
mum rice growth.
Table 7 Recommended practices for optimum rice production

17. Page 17 of 19Akinola et al. Discover Agriculture (2025) 3:132
Appendix
See Table 7.
Author contributions
The conceptualization, methodology by O.H.A., T.A., and T.O.O. Writing – original draft preparation, and editing by O.H.A.,
T.A., T.O.O. and O.A.O. The author has read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Data availability
The dataset used and analysed in this study is available upon reasonable request.
Declarations
Ethics approval and consent to participate
The protocol for this survey-based research presented in this manuscript was approved by the Postgraduate Committee
of Obafemi Awolowo University, Ile-Ife, Nigeria in accordance with relevant guidelines and regulations of the Declaration
of Helsinki.
Consent for publication
was obtained from all the participants involved in the study.
Competing interests
The authors declare no competing interests.
Received: 8 January 2025 / Accepted: 13 August 2025
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