INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025

An Approach for Enhancing the Adoption Rate of Pfumvudza

Farming on Maize Production in Gwanda District

Mercy Bita^1*, Benjamine Hanyani Mlambo² and Obert Kasirai³

¹MSc student, Department of Agricultural Economics &Extension University of Zimbabwe

²M&E, Agribusiness, Livelihoods & Climate Change Expert Department of Agricultural Economics &

Extension University of Zimbabwe

³Lecturer, Department of education, Joshua Mqabuko Nkomo polytechnic, Gwanda., Zimbabwe

*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.91100301

Received: 01 December 2025; Accepted: 05 December 2025; Published: 08 December 2025

ABSTRACT

This study assessed approaches to enhance the adoption rate of Pfumvudza farming among smallholder farmers

in Gwanda district. Descriptive statistics were used to analyze the adoption rate. The sample consisted of 538

smallholder communal farmers selected through stratified random sampling across all 24 wards in Gwanda

district. Results showed 126 adopters and 412 non-adopters, giving an adoption rate of 23.42% and a non-

adoption rate of 76.58%. Data were analyzed using STATA version 13. Multicollinearity was tested using the

Variance Inflation Factor (VIF), with results below 5 indicating its absence. A logit regression identified the

number of plots and household size as factors determining adoption. Propensity Score Matching was used to

assess the impact of Pfumvudza farming on household food security. The Average Treatment Effect results

indicated that adopters achieved 48% higher food security than non-adopters. Additionally, adopters obtained

888 kg per hectare more maize yield than non-adopters. Capacity building, training, and government support

were identified as key strategies to enhance adoption. The study recommends the adoption of Pfumvudza farming

by smallholder farmers.

Key words: Adoption rate, communal farmers, food security, Pfumvudza farming, smallholder farmers

INTRODUCTION

Conservation Agriculture (CA) has been increasingly promoted as a strategy to address food insecurity,

particularly in Sub-Saharan Africa [21]. CA is defined by three core principles: minimum mechanical soil

disturbance, permanent organic soil cover, and diversified crop rotations or associations, including legumes [21].

It was designed to mitigate the impacts of climate change on agriculture, with the goal of boosting productivity

and increasing resilience to climate-induced challenges [23]. Many studies have explored factors influencing

farmers' adoption of new technologies [4], though adoption can occur in various forms—partial, discontinued,

or resumed [9]—and may happen suddenly due to changing circumstances rather than through gradual diffusion

[12]. There has also been growing interest in social networks and farmer-to-farmer extension as alternatives to

traditional public-sector approaches [58].

However, the labor-intensive nature of CA often discourages farmers from continuing its practice [63]. In

response, Foundations for Farming developed Pfumvudza, an intervention aimed at reducing labor demands in

field preparation and mulch collection while providing necessary inputs. As highlighted by [20], the primary

goal is for a family to feed itself. This approach simplifies CA principles, making it accessible and sustainable

for smallholders.

The Government of Zimbabwe introduced the Pfumvudza program during the 2020/21 cropping season to

improve maize production and food security [46]. It is based on three core principles: minimum soil disturbance,

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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025

mulching, and specific spacing (60 cm × 75 cm) [47]. Despite its potential, the adoption rate of agricultural

technologies remains low in general [3]. This study therefore investigates approaches to enhance the adoption

rate of Pfumvudza farming among smallholder maize farmers in Gwanda district.

Adoption refers to the decision to use a new practice, while adoption rate measures its prevalence among a target

group. Although Pfumvudza is widely practiced in Zimbabwe, its adoption rate is not well documented. This

study aims to provide data on the adoption rate of Pfumvudza for maize production in Gwanda district and

identify strategies to enhance it.

Research objectives

General objective

To design an approach for enhancing adoption rate of Pfumvudza farming by smallholder farmers on maize

production in Gwanda district.

B. Specific objectives

i)To analyze the adoption rate of Pfumvudza farming on maize production in Gwanda district.

ii)To assess factors that determine the adoption of Pfumvudza farming on maize production in Gwanda district

iii) To assess the impact of Pfumvudza farming adoption on household food security.

iv)To identify strategies for enhancing the adoption of Pfumvudza farming by smallholder farmers.

MATERIALAND METHODS

The study is based on a cross-sectional survey of the 2023/2024 farming season of Gwanda rural district in

Zimbabwe. The researcher uses, formular postulated by Yamane [62] to calculate sample size. The study has

four hypotheses which make use of different methods of analysis within a certain framework of analysis. The

study seeks to assess how the government led Pfumvudza Program has impacted household’s food security and

at the same time looking into the determines that influences household’s participation into the program and the

rate of participation. As well finding strategies that enhance the adopt of Pfumvudza adoption on maize

production by small holder farmers.

The first specific hypothesis raised was that the adoption rate of Pfumvudza farming on maize production in

Gwanda district. To test this hypothesis so as to address the question of what is the adoption rate of Pfumvudza

farming on maize production in Gwanda district. Descriptive statistics was used to calculate adoption rate.

The second objective of the study is to assess factors that determine the adoption of Pfumvudza farm the study

uses a binomial logit model to analyses factors that determine the adoption of Pfumvudza farming on maize

production in Gwanda district. This method has been used by several authors to study household decision to

adopt to new technologies [56], [5], [25], [2], [39]; Researchers who looked into household's decisions to adopt

or dis-adopt conservation agriculture and in climate change adaption studies, [52].

The dependent variable in this study is a dichotomous, that is, households' decision to participate or not to

participate in the Pfumvudza Program, this makes the Binary Logit more appropriate as it considers the

relationships between a binary dependent variable and a set of explanatory variables, [49] The binary logit model

in this case is appropriate because it considers the relationship between a binary dependent variable and a set of

independent variables.

Binary Logistics regression models are used during the prediction of categorical dependent variables with an

explanatory variable or a set of explanatory variables, [33]. The model uses the Maximum Likelihood (ML)

program during the selection of the coefficient estimates that maximizes the log of the probability of observing

the particular set of values of the dependent variable in the sample for a given set of explanatory values, [33].

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The Logit model is specified as:

P (Y_i=1|X_i) =F (X_iβ_i)

[1]

Where:P is the probability that Y = (0, 1); Y_i=dependent variable; Xi =independent variables; β_i= the regression

coefficient; F (.) is the density function for logistic distribution of the model

By transforming the probability (Y) in (1) into log odds the model takes the following expression:

Logit (P) = β₀+ β₁X₁+ β₂X₂+…. +β_nX_n+u_i

[2]

The empirical binary logit to be estimated consist of 5 covariates that will be regressed against the dependent

variable. The study specific model is expressed in its implicit form as follows:

Y=f (X₁, X₂, X₃, X₄, X₅,)

The model variables and their justification for use in the study were guided by previous studies on adoption and

dis-adoption of CA technologies, [34]; [52]; [49].

Where Y is the adoption status (1= farmers who adopted, 0= farmers who did not adopt;

X1 is yield;

X2 is cattle ownership (number of cattle)

X3 is household size8

X4); household head gender (1=male,0=female)

X5 is number of plots

The third objective of the study was to assess the impact of Pfumvudza farming adoption on house hold food

security. The study uses propensity score matching to assess the impact of Pfumvudza farming adoption on house

hold food security. This method was first proposed in [55]. The participants in a project are matched with non-

participants from a larger survey. In this case, the counterfactual is the matched comparison group. Each program

participant is paired with one or more non-participants that are similar based on observable characteristics. It

assumes that, conditional on the set of observables, there is no selection bias based on unobserved heterogeneity.

However, steps of running a propensity score matching were done as follows: Firstly, logit regression, secondly

P-score summary and lastly average treatment effect [6]; [30]. Prior to non- parametrically estimating the impact

of scores required specification justifying that a household had been included in the adoption. It was noted in

[10] that the idea of matching is to compare a beneficiary with one or more beneficiary who are similar in terms

of a set of observed characteristics. This requires predicting the propensity scores for each individual using logit

or probit model. In this study, the researcher used a logit model to predict the probability that a household

participates in the food security program, in this model, household characteristics are included as regressors.

Before presenting the Average Treatment Effect estimates, the researcher shows the results of the first stage logit

model and check for the quality of matching. The logit regression is used to compute the propensity score used

for matching method. The objective of this selection model is not to explain the of Pfumvudza farming as exactly

as possible but to form a basis for eliminating the observed and non-observed differences between treated and

non-treated in the matching procedure [27].

RESULTS AND DISCUSSION

Results for objective one

The adoption rate results, was calculated using number of adopters divided by the sample population or number

of possible adopters. The results are presented with respect to gender in percent. Below is table shows the

adoption rate of Pfumvudza farming in Gwanda district.

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Table 1. Contingency table showing adoption rate of Pfumvudza farming on maize production in Gwanda

district, Zimbabwe 2023/2024 season

Adoption

status

Percentage of female

adoption rate

Percentage of male

adoption rate

Total percentage of P-value

Pearson

chi2(1)

adoption rate

(Pr)

Adopt

24.26%

75.74%

100%

22.77%

77.23%

100%

23.42%

0.687

0.1623

Non adopt

Total

76.58%

100%

Source: survey data

Table 1. above shows that chi-square (◻2) value is (0.1623). It measures the difference between observed and

expected frequencies. A small (◻2) value indicates that frequences are close to expected frequences. Also, p-

value (Pr) is (0.687), it is the probability of observing the test statistics (◻2) under the null hypothesis. P> 0.05

indicates failure to reject the null hypothesis. Where the null hypothesis state that there is no significant

association between household head sex and adoption.

Therefore, the Pearson chi-square test (◻2 = 0.1623, p= 0.687) suggest that no significant association between

household sex and adoption. The adoption rates are largely similar across female headed (57/235= 24.3%) and

male-headed households (69/303 =22.8%). The difference in adoption is not statistically significant. This

analysis indicates that household head sex does not significantly influence adoption decisions. No significant

differences rates of fertilizer use by male- and female- farmer in Ghana in [17]. Similarly, in [8] and [26] found

that the gender of the household head has no significant effect on the adoption and intensity of use of chemical

fertilizer in Zimbabwe and Kenya, respectively.

The adoption rate of Pfumvudza farming was calculated as percentage of sampled smallholder farmers practicing

Pfumvudza farming on maize production over the total number of smallholder farmers used in the study. The

study established that 23.42% of the smallholder farmers in Gwanda district adopted Pfumvudza farming. This

was also hypothesized that the adoption rate is too low in Gwanda district. This was also supported in [29] on

their study determinants of adoption and dis-adoption of minimum tillage by cotton farmers in eastern Zambia.

Their adoption rate was 24%. Their results came out after combining data from survey and semi structured

interviews, they examine farmers motivations for adopting CA and determinants of adopting and dis adoption of

hand hoe and oxen drawn minimum tillage. On their study the adoption rates were relatively low that is 12% of

cotton area and 20% of maize area. They argued that farmers are interested in adopting minimum tillage but the

available minimum tillage technologies do not match their resource endowments. Labor constrains limits use of

hand hoe basins while equipment costs limit oxen ripping machines. In [18], on their study, Agricultural

technology adoption for smallholder small grain farmers in Zimbabwe, their results showed that 56% of the

sample were non-adopters while 44% were adopters. In this study, most of the adopters were from Hwange

(26.4%) followed by Chiredzi and Binga with 24.8% each and Matobo had the least (24%). On Conservation

Agriculture few farmers (30%) in Binga are adopting CA. Respondents through focus group discussion

highlighted that it is difficult to practice CA as it requires a lot of labor.

Apart from that, the rate of adoption theory state that adoption takes place over time with innovation going

through a slow gradual period followed by dramatic and rapid growth and then a gradual stabilization and finally

decline [1]. Therefore, the researcher is highly expecting that in Gwanda District the Pfumvudza adoption rate

will increase with time. Also, in [11] on their study factors influencing the adoption of conservation agriculture

practices among smallholder farmers in Mozambique, find out that 44.6% of smallholder farmers adopted one

or more of the Conservation Agriculture (CA) practices, and 55.4% did not. It was also clear that most farmers

did not adopt all components CA. These results signify a low adoption rate on new farming method (CA).

In [53] on their study the intensity of conservation agriculture by smallholder farmers in Zimbabwe also

postulated that across the four years of the panel, smallholder farmers commonly used between four and seven

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CA components with a mean of five and median of six components. On average, only 7.4% of farmers used all

eight techniques in any given year, whereas 16.6% did not apply any of the components in any given year, as the

farmers reverted to the plough. The proportion of farmers using the full package declined after 2009 just as donor

support declined. Indications of a high-intensity of adoption are that more than half of the farmers used more

than five components in any given year. Few of the farmers (23%) used fewer than three techniques, which is

indicative of low levels of adoption intensity.

Conclusively, the researcher’s results indicate that there is a lower significate level of adoption rate of Pfumvudza

farming on maize production in Gwanda district, therefore there is need for interested parties together with

government to find working solutions to address the barriers.

Results for objective two; To assess factors that determine the adoption of Pfumvudza farming on maize

production in Gwanda district.

Firstly, the researcher presents binary model diagnostic test results which are multicollinearity test using VIF. In

order to test if the model was a good model for the empirical analysis diagnostic test should be conducted by

[31].

Multicollinearity test

The researcher test for model assumption, run multiple linear regression followed by VIF test for

multicollinearity. This is because one should run multiple linear regression first before VIF. Table 2 below shows

the VIF test results for multicollinearity.

Table 2. VIF test for multicollinearity

Mean VIF

1.34

Source: survey data

The results on table 2 above shows that there is no presence of multicollinearity. After conducting the test, the

results did not indicate any presence of multicollinearity within the model. Multicollinearity occur when two or

more predictor variables in regression model are highly corelated, making it difficult to determine the individual

effect of each variable on the outcome. Mean VIF is 1.34, if there is presence of multicollinearity VIF will be

greater than 5 hence if its less than 5, it means no multicollinearity. Therefore, the results tested for model

diagnostic test allows the researcher proceeds to run the binary logit regression test.

Binary logistic results

Factors which determine the adoption of Pfumvudza farming are number of plots, number of cattle, household

size, household head sex, and maize yield. These factors were analyzed using the binary logit regression on

STATA 13 software. Below is table 3 showing the binary logit regression results.

Table 3 binary logistic results

Variables (Adopt / non adopt)

Number of cattle

Coefficient

.0044717

7.216128

-.1415281

-1.27441

.0842635

Significant level (p<0.1*, 0.05** & 0.01***)

0.919

number of plots

0.000***

0.820

Household head gender

Household size

0.019**

0.828

Maize yield (th)

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NB, Number of observations = 538, LR chi2(5) = 494.12, Prob > chi2 = 0.0000, Log likelihood = -45.77301&

Pseudo R2 = 0.8437.

Source: survey data

From the logit regression results shown on table 3 above, adjusted R-squared is 84%, which means the model is

explained by 84% of the variation in the dependent variable. R- squared of 84% shows that the model was

correctly specified. An adjusted R- squared of 84% indicates that the model explains approximately 84% of the

variation in the dependent variable (adopt/non adopt) after accounting for the number of predictors and sample

size. The model has a high explanatory power, capturing about 84% of the variation in the outcome variable.

The remaining 16% of the variation is unexplained by the model, possibly due to omitted variables, measurement

error and fluctuations.

The model’s signification probability chi-square is 0.0000, this means that the data model is a good fit to the

data and the probability of observing the data (or more extreme) assuming that the model is true. Log likelihood

is ( -45.77301) is a measure of how well the logit regression model fits the data, a lower likelihood indicates

better fit. Log likelihood with larger negative values indicates poor fit while smaller negative values indicate

good fit but values close to zero indicate excellent fit. According to [33] log likelihood helps during the selection

of the coefficient estimates it maximizes the log of probability of observing the particular set of values of the

dependent variables in the samples for a given set of explanatory values. Therefore, a log likelihood of ( -

45.77301) means the logit regression model fits the data.

Apart from that, factors which determine the adoption of Pfumvudza which are significant are as follows, number

of plots is significant at 1% confidence interval, its p- value is (0.000), it is highly statistically significant. This

suggest that number of plots is also one of the factors that determine the adoption of Pfumvudza farming by

smallholder farmers in Gwanda district.

Number of plots has a positive coefficient of (7.216128) and is statistically significant at 1 %, therefore rate of

adoption increases or is greater as the number of plots increase. If a farmer has more plots, it means a farmer is

likely going to adopt Pfumvudza farming on maize production. Farmers with more plots are more likely to adopt

Pfumvudza farming possibly due to economies of scale or greater resources. All in all, it indicates an increase in

number of plots is associated with a higher likelihood of adoption. This was also supported in [43], on their study

factors influencing adoption of conservation agriculture done in Swaziland. Their research findings were that

adopters had more land than non-adopters.

Household size, is also another factor which determine the adoption of Pfumvudza farming and it is significant

at 5 % with a p- value of (0.019) and a negative coefficient of (-1. 27441). This was also supported, in [34] on

factors affecting adoption and intensity of conservation agriculture techniques applied by smallholders in

Masvingo district, Zimbabwe; in [49] on their study sustainable agriculture and food security in Africa,

postulated that the consumption pressure as a result of a large household size may result in diversion to off-farm

activities to generate more income therefore limiting chances of adopting. In [53] on their study the intensity of

adoption of conservation agriculture by smallholder farmers in Zimbabwe find out that household size as a

measure of family labor has a negative impact on adoption intensity, contrary to expectation. In [53] used a man

day equivalents rather than total household size to measure access to labor, but this failed to reverse the sign of

the estimated coefficient This means that household size is one of the factors which determine the adoption of

Pfumvudza farming by smallholder farmers in Gwanda district. Despite Pfumvudza farming being a high labor

requirement, family with a bigger size see it not worth to spend more of their time digging hole on a small plot

rather to work on large area using moldboard plough. Therefore, an increase in household size is less likely lead

to the adoption of Pfumvudza farming by smallholder farmers in Gwanda district. Also, increase in household

size, will lead to other members engaging on off farm activities to bring food on the table. Especially in Gwanda

district, other family members are into gold mining and vending.

Conclusively, the researcher finds out that, number of plots and household size are the most factors which

determine the adoption of Pfumvudza farming. Also, number of plots are positively influencing adoption of

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Pfumvudza while household size is negatively influencing the adoption of Pfumvudza farming. These factors

were also supported by different researchers.

Results for objective 3; to access the impact of Pfumvudza adoption on household food security.

The study uses propensity score matching to assess the impact of Pfumvudza farming adoption on household

food security. Steps of running a propensity score matching were done as follows: firstly, logit regression,

secondly P -score summary and lastly average treatment effect [6]; [30]. Yield per kg was calculated using

descriptive statistics. Below is the table showing the p -score matching results.

Table 4. Propensity score matching

Observation

536

23

Mean p score

Source: survey data

Number of observations were 536, p score variable represents the propensity score, which is a measure of the

probability of being treated (adopting Pfumvudza farming) based on observed characteristics. The mean p score

is approximately 0.23, indicating that, on average, the probability of adoption is around 23%. In the context of

propensity score matching, the p score is used to match treated and control units based on their observed

characteristics, to estimate the treatment effect. Below are the average treatment effect results

Table 5. Treatment effect estimation of impact of adopting Pfumvudza on selected outcomes

Non adopters

412

Adopters

126

Average treatment effect (ATE)

.4813433

.3387097

888.54

0.000

Average treatment effect treated (ATET)

Maize yield

Z

Source: survey data

The Average treatment effect on table 5 above represents the difference in food security outcomes between

adopters and non-adopters, accounting for the observed covariates. The results show Pfumvudza had a positive

and significant impact of maize yield and food security. The impact on food security was significant. The

magnitude of the impact on maize yield was quite marked, at 888 kg per ha. This means that, on average a farmer

who practiced Pfumvudza achieved maize yield per ha that were 888kg higher than a comparable farmer who

did not practice Pfumvudza. In [57] carried out a research study on the Impact of CA on productivity and food

security, 488 farmers were used as the sample size. The propensity score-matching approach CA was used and

had a positive impact on maize grain yield. In [57] also find out that CA have a positive impact on maize grain

yield (ATT= 473 kg per ha). This means that, on average adopters tend to have better food security outcome than

non-adopters after controlling for the observed covariates. The 48% food security was acquired by adopters

which is above food security acquired by non-adopters.

A research study on the potential impact of the adoption of soil and water conservation technologies on household

food security in the Chinyanja Triangle using 312 households was done by [37]. Propensity Score Matching

results shows CAhas a significant impact on cereal consumption. Impact of CSAon household income and asset

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accumulation among smallholder farmers in Kenya was done by [51]. A sample size of 433 households was used

and the Propensity Score Matching (PSM), adoption results show that significantly enhances household income

which, in turn, improves household asset accumulation.

Table 6. Frequence distribution for food security status

Access to food

Food insecure

Food secure

Non adopter

Adopters

Total

397

350

62

47

79

141

Source: survey data

The table 6 above shows that of the total sample of 538 farmers, 350 who are non-adopters were food insecure

while 47 of the adopters were also food insecure. However ,62 where food secure for the non-adopters and 79

adopters were food secure. Therefore, this shows that even if the overall numbers of adopters were very low but

there where food secure most of them than non-adopters. The is strong association between adoption and food

security status. Adopters are more likely to be food secure compared to non-adopters.it was also postulated in

[43] that if farmers had inadequate food, that did not influence the adoption of conservation agriculture.

Results for objective four: Strategies for enhancing the adoption of Pfumvudza farming by smallholder

farmers.

See figure 1 below showing the mean distribution summary of strategies which enhance the adoption of

Pfumvudza farming by smallholder farmers.

Figure 1: mean distribution of strategies for enhancin gpfumvudza adoption

Source; survey data

These results indicates that training among other strategies is essential for the adoption of Pfumvudza farming.

According to [47], also postulated that it is possible to feeding a family from such a small area of land

(Pfumvudza plot) but it requires training to ensure success. Training programs, led by extension agents and

experts, educates farmers on the benefits and implementation of Pfumvudza farming techniques such as

mulching, crop rotation and minimum tillage [44]. From the mean distribution graph above, it shows that most

of the smallholder farmers who did not adopt, are food insecure and they did not receive training, government

support and capacity building.

The government of Zimbabwe has established a policy framework that supports the adoption of Pfumvudza

farming. Capacity building initiatives are aligned with the framework, ensuring that farmers receive training and

support that is consistent with national policies [28]. Also, government support comes through provision of

extension services, which is critical for capacity building. Extension agents work closely with farmers, providing

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training, guidance and support to ensure the successful adoption of Pfumvudza practices [44]. Government of

Zimbabwe offers input support such as seeds, fertilizer and lime to ensure that benefits

Strategies which enhance the adoption of Pfumvudza farming at different levels.

Figure 2; strategies which enhance the adoption of Pfumvudza at different levels.

Source; survey data

See figure 2 above showing the strategies which enhance the adoption of Pfumvudza at different levels. Policy

strategies include regulatory support, awareness campaign and offering of grants and subsidies. These policy

strategies enhance the adoption rate of Pfumvudza concept hence increase maize yield leading to an improve on

farmers food security status.

Conclusively capacity building plays a vital role in enabling the adoption of Pfumvudza farming and government

support. Through the provision of training, extension service and inputs support, capacity building initiatives

empower farmers to usefully implement Pfumvudza farming thereby improve their food security status.

CONCLUSION

This study analyzed approaches to enhance the adoption of Pfumvudza farming among smallholder maize

farmers in Gwanda district. The adoption rate was low (23.42%), influenced positively by the number of plots

and negatively by household size. Pfumvudza adoption significantly improved food security and maize yield.

Key strategies to enhance adoption include training, capacity building, and government support. These findings

emphasize the need for coordinated efforts to promote Pfumvudza as a viable approach to improving food

security and agricultural productivity.

In [47], also postulated that it is possible to feeding a family from such a small area of land (Pfumvudza plot)

but it requires training to ensure success. Researcher results shows that adoption rate of Pfumvudza farming is

very low about 23 % in Gwanda district. Factors such number of plots and household size determine the adoption

of Pfumvudza farming.

There is a strong association between adoption status and food security status. Adopters are more likely to be

food secure (64.0% of adopters are food secure) compared to non-adopters (26.5% of the non-adopters are food

secure). Also, nonadopters are more likely to be food insecure (76.6% of the non-adopters are food insecure)

compared to adopters (35.7% of the adopters are food insecure). Being food secure increases the likelihood of

adoption, while being mildly food insecure decreases it. Smallholder farmers, who adopt Pfumvudza farming

are less likely to experience food insecurity but more likely to experience food secure. In [43] also postulated

that if farmers had inadequate food, that did not influence the adoption of conservation agriculture.

Conclusively, the results suggest that adoption is associated with improved food security and that adopters are

more likely to be food secure compared to non-adopters. Also, Pfumvudza farming on maize production in

Gwanda district have a positive impact on food security.

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ACKNOWLEDGEMENT

Sincere gratitude goes to my co-writer Obert Kasirai his remarkable support and motivation, and this played a

significant role to the completion of this research paper. I would like also to express my profound gratitude to

my other co-writer B. H. Mlambo for his extraordinary guidance, in support of this paper.

Ethical Approval

This study did not obtain any ethical approval from any institution but ethical approval was obtained from

participants before data collection

Data availability

Data used for this research is available, below.

Conflict of interest

The researchers do not have any conflict of interest.

Funding

No funding was done for the research to be carried out.

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