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Development in Practice
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/cdip20
Gender gaps in sorghum productivity: evidence
from male- and female-managed plots in Uganda
Philip Miriti, David Jakinda Otieno, Evans Chimoita, Edward Bikketi, Esther
Njuguna & Chris O. Ojiewo
To cite this article: Philip Miriti, David Jakinda Otieno, Evans Chimoita, Edward Bikketi, Esther
Njuguna & Chris O. Ojiewo (2022): Gender gaps in sorghum productivity: evidence from male- and
female-managed plots in Uganda, Development in Practice, DOI: 10.1080/09614524.2022.2069228
To link to this article: https://doi.org/10.1080/09614524.2022.2069228
Published online: 01 Jun 2022.
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Gender gaps in sorghum productivity: evidence from male- and
female-managed plots in Uganda
Philip Miriti, David Jakinda Otieno, Evans Chimoita, Edward Bikketi, Esther Njuguna, and
Chris O. Ojiewo
ABSTRACT
Gender gaps in crop productivity can negatively affect rural socio-
economic development of agrarian economies with serious implications
for food and nutrition security. We assess productivity in male- and
female-managed sorghum plots in Lira, Kumi, and Serere districts in
Uganda. We find male-managed plots have higher productivity of 850.6
kgs/ha compared to female-managed plots (832.6 kgs/ha). An observed
mean gender gap of 18% due to structural advantages of male plots
(57%), structural disadvantages of female plots (33%), and endowment
(10%) exists. Therefore, interventions aimed at promoting equitable
access to institutional support services are paramount in reducing
structural disadvantages against female farmers.
ARTICLE HISTORY
Received 15 July 2021
Accepted 19 April 2022
KEYWORDS
Gender yield gaps; plot
manager; sorghum;
agricultural productivity;
Oaxaca–Blinder
decomposition
1. Introduction
Agricultural productivity plays an important role in promoting welfare and stimulating growth in on-
farm and non-farm activities of rural households. Specifically, sorghum (Sorghum bicolor) contributes
approximately half of the micronutrient requirements of small-holder farmers living within the semi-
arid tropics of Africa and Asia; it is therefore a key crop in promoting food and nutrition security
(Kumar et al. 2018). Sorghum is a drought-tolerant cereal crop that is mainly grown in the semi-
arid tropics. In Africa, it is the second staple grain crop after maize (Mundia et al. 2019) and is
mainly cultivated for food and fodder, especially by smallholder farmers in drylands that are charac-
terised by low soil fertility, inadequate extension services, and erratic rainfall (Assefa et al. 2020).
In Uganda, sorghum is an important dryland cereal after maize and wheat in terms of total pro-
duction (Wang et al. 2015). Uganda supplies approximately 0.5% of the world’s sorghum output
(USDA 2018). It is the staple crop for a majority of people in the Northern and Eastern parts
where it is primarily grown. Farmers in these areas exhibit low levels of input use; hence, productivity
over the years has remained relatively low and the sorghum value chain greatly remains under-
developed (Nangobi and Mugonola 2018; Tenywa et al. 2018).
Various studies have documented the existence of gender disparities in agricultural production in
sub-Saharan Africa (SSA) due to the patriarchal kinship structures where men tend to have stronger
rights to assets, especially land ownership, thus skewing power relations between spouses (Sell and
Minot 2018; Danso-Abbeam, Baiyegunhi, and Ojo 2020; Gebre et al. 2021). This leads to men having a
higher power in household decision-making in access, utilisation of assets and control of income
generated from economic activities, hence lowering women’s bargaining power (Osanya et al. 2020).
Differences in productivity across plot managers, which refers to the person involved in making
day-to-day farming decisions on a plot have been attributed to gender-specific constraints in access
to farm inputs leading to lower and unequal levels of fertiliser, labour, and land use (Palacios-Lopez
© 2022 Informa UK Limited, trading as Taylor & Francis Group
CONTACT Philip Miriti miritiphilip@gmail.com
DEVELOPMENT IN PRACTICE
https://doi.org/10.1080/09614524.2022.2069228
and Lopez 2015; Khan et al. 2021). Access to institutional support services, such as information and
credit facilities, by female plot managers is limited compared to male-managed plots (Sell et al.
2018). In addition, female plot managers in SSA have more limited access and weak tenure security
over land than male farmers (Chigbu, Paradza, and Dachaga 2019). However, where there is joint plot
management, these constraints have been addressed to some extent due to the complementary role
played by men (Miriti et al. 2021).
The majority of studies on gender gaps in productivity use the household head as a proxy to esti-
mate plot-level outcomes rather than the plot manager who is directly involved in the plot activities
(Kilic, Palacios-López, and Goldstein 2015; Mukasa and Salami 2016; Mugisha et al. 2019). There is a
likelihood that the household head is not the person responsible for making farm-level decisions.
Therefore, associating the household head with farm productivity may lead to inaccurate pro-
ductivity measurements (Kilic, Palacios-López, and Goldstein 2015). The present study attempts to
address this knowledge gap by using plot manager to reduce the gender gap.
We provide rigorous evidence on gender gaps in productivity between male- and female-
managed sorghum plots using the Oaxaca–Blinder decomposition approach by taking into
account observable and unobservable factors, examining productivity differences and their determi-
nants in Uganda. The rest of the paper is organised as follows. Section 2 comprises the methodology
highlighting the study area, sampling, and the empirical analysis. Section 3 presents the results and
discussions while Section 4 provides the conclusion and policy implications.
2. Methodology
2.1. Description of the study area
The study was undertaken in the Lira, Serere, and Kumi districts of Uganda, which are the predomi-
nant sorghum producing areas (Figure 1).
Lira district lies in the Northern part of Uganda at a latitude of 2.2316° N and a longitude of
32.9438° E. The district receives about 1300 mm of rainfall annually and approximately 71% of its
residents depend on subsistence farming for their livelihood (UBOS 2017a). Serere district lies at a
latitude of 1.4994° N and a longitude of 33.5490° E in Eastern Uganda. In the 2012 national popu-
lation census, the district had a population of approximately 294,100. The district receives an
average of 1250 mm rainfall annually, and has an annual mean temperature of 25° C (UBOS
2017b). Kumi district is located in the Eastern region of Uganda at a latitude of 1.4877° N and a longi-
tude of 33.9304° E and has a population of about 255,500. The district receives an average of about
900 mm rainfall, and has a mean annual temperature of 24° C (UBOS 2017c).
2.2. Data sources and sampling
The study used plot-level gender-disaggregated data collected by the International Crops Research
Institute for the Semi-Arid Tropics (ICRISAT). A multi-stage sampling procedure was used to identify
study areas and respondents. In the first stage, the Kumi, Lira, and Serere districts were selected pur-
posively because they predominantly produce sorghum for food security and commercial purposes.
In the second stage, four sub-counties within the districts with the highest sorghum production were
selected: Amach and Agali sub-counties in Lira, Katete in Kumi, and Mukongoro in Serere. In the third
stage, a systematic random sampling procedure was used to select respondents within the four sub-
counties who were then interviewed using structured questionnaires. A total sample of 362 (182
male plot managers and 180 female plot managers) were interviewed –47% from Lira, 31% from
Serere, and the rest from Kumi district –which were allocated proportionately to their population
sizes. The necessary ethical approvals were obtained before the study commenced from the Insti-
tutional Ethics Committee (IEC) at ICRISAT and informed consent from all farmers was sought
before the interview process.
2P. MIRITI ET AL.
2.3. Empirical analysis
The data collected was downloaded from the Open Data Kit (ODK) and analysed using Stata 15 to
obtain frequencies, mean, standard deviations of socio-economic characteristics, and estimation
of determinants of sorghum farmers. Productivity estimates for male and female managed plots
were estimated following the partial factor productivity (PFP) equation as follows:
PFPai =Ya
Xai
(1)
where PFPai denotes the partial factor productivity of plot a under plot manager a,Yais the output
(sorghum yield in kilograms) from plot a, while Xai is the primary input (land in hectares) used for plot
aby the plot manager i. Since the dependent variable is continuous in nature, the Ordinary least
square (OLS) regression was fitted to evaluate determinants of sorghum productivity following
Figure 1. A map of Uganda showing the study sites. Source: Adapted from UBOS (2016).
DEVELOPMENT IN PRACTICE 3
Udry et al. (1995), who expressed a simple productivity function as:
Yai =
a
Xai +vGai +
m
ai (2)
where Yai = sorghum productivity (kgs/ha) of plot aunder manager i;Xai denotes variables used,
such as age, years of formal schooling completed, years of sorghum farming experience, household
size, distance to plot from homestead, amount of seed used, hired labour, family labour, access to
credit, access to extension services and farmer group membership; Gai is a dummy variable denoting
the gender of the plot a; and
m
ai is the error term.
The approach of estimating determinants discussed in Equation 2 does not give insights on rela-
tive contribution of the vectors of covariates to the gender productivity gap (Joe-Nkamuke et al.
2019). In order to account for this, we adopt the Oaxaca–Blinder decomposition approach
(Oaxaca 1973; Djurdjevic and Radyakin 2007). The decomposition technique is increasingly being
applied in gender and productivity studies (see for example, Kilic, Palacios-López, and Goldstein
2015; Bello et al. 2021; Nchanji et al. 2021; Singbo et al. 2021). The approach starts by estimating
the expected yield value per ha for the male (m) and female (f) plot managers as presented in
Equation 3:
E(Yg)=ag+E(Xg)′
b
g′(3)
where gdenotes the gender of the plot manager,
a
is the intercept,
b
is the slope coefficient, and X
denotes independent variables. The gender gap in mean productivity between the male- and
female-managed plots is expressed as follows:
Gender gap =E(ym)−E(yf)=
s
m+E(Xm)′
b
m−
s
f−E(Xf)′
b
f(4)
The gap in the equation above can further be decomposed into two parts, where the first part is
composed of the gender gap that is explained by the male and female differences, and the
second part consists of the unexplained part (Oaxaca 2007). Therefore, it can be expressed as:
Gender gap =Q+U(5)
where Qdenotes the gender gap due to the differences between the male and female plot managers
and can be expressed as:
Q=[E(Xm)′
−E(Xf)′]
b
∗(6)
where
b
∗represents the non-discriminatory coefficients. The second part of Equation 5 is referred to
as the unexplained Uor the structural effect and it accounts for the differences in returns or the dis-
crimination between the male and female plot managers. Following Fortin, Lemieux, and Firpo
(2010), it can be expressed as follows.
U=(
a
m−
a
)+[E(Xm)′(
b
m−
b
∗)] +(
a
−
a
f)+[E(Xf)′(
b
∗−
b
f)] (7)
Equation 7 can be subdivided further into two components:
Um=(
a
m−
a
)+[E(Xm)′(
b
m−
b
∗)] (8a)
and
Uf=(
a
−
a
f)+[E(Xf)′(
b
∗−
b
f)] (8b)
where Equation 8a represents the discrimination in favour of the structural advantage of the male
managed plots and Equation 8b denotes the discrimination or the structural disadvantage of the
female managed plots (Oaxaca 2007; Fortin, Lemieux, and Firpo 2010).
4P. MIRITI ET AL.
3. Results and discussion
3.1. Socio-economic characteristics of the respondents
Table 1 shows the summary statistics of the interviewed plot managers. The majority of sorghum
plot managers were adults with less than seven years of formal education although male plot man-
agers had a relatively higher number of years spent in school. This shows that rural sorghum farmers
in Uganda have little formal human capital.
The average years of sorghum production for the plot managers were more than ten years for the
two groups, showing that the majority of the farmers have been growing sorghum for quite a long
time. The World Bank (2018) similarly noted that in Uganda, about 70% of households rely on agri-
culture as a source of livelihood. The mean household size for the male- and female-managed plots is
slightly above the national mean household size of 4.7 persons, which has remained relatively stable
in the last decade (UBOS 2016). This variance from the national household means can be explained
by the polygamous nature of rural households where the study sites are located.
The mean plot size of sorghum farmers is less than two hectares, implying that they are small-
holder farmers. This is consistent with Cervantes-Godoy (2015) who defined smallholder farmers
as those owning less than two hectares of land. However, the average plot size in the three study
areas is below the national average farm size of 0.97 ha (Priegnitz et al. 2019). This can be attributed
to the subdivision of agricultural land due to rapid population growth and its attendant pressure on
productive resources, such as arable land.
Distance to the plot from the homestead was larger for male plot managers; this could be attrib-
uted to the likelihood of farmers leasing land from various areas to increase profit margins. For farm
inputs such as hired labour, family labour, and the amount of seed and fertiliser used, male plot man-
agers had higher quantities compared to female plot managers. This corroborates Palacios-Lopez,
Christiaensen, and Kilic (2017) who observed that female plot managers have lower access to and
utilisation of farm inputs. We observe a similar pattern for access to credit facilities, access to exten-
sion services, and farmer group membership. Lastly, the age of the plot manager, years completed in
school, household size, plot size, farmer group membership, and access to credit were significantly
different between the two groups.
3.4. Measurement of sorghum productivity
Sorghum productivity was computed using plot-level data on the male-managed plots, the female-
managed plots, and the pooled sample. Male plot managers had the highest productivity (850.6 kgs/
Table 1. Summary characteristics by gender of the plot managers and statistical difference.
Male plot managers
Female plot
managers Statistical differences
Variable Mean SD Mean SD (p-value)*
Age of the plot manager 46.40 15.64 43.82 14.05 0.056
Years of formal schooling 6.19 5.20 4.24 3.26 0.000
Household size 5.93 3.22 7.17 2.95 0.000
Years of sorghum farming 13.20 13.31 12.63 10.23 0.300
Average distance to the plot (metres) 583.25 1016.83 505.40 987.64 0.346
Plot size (mean ha) 0.86 0.25 0.27 0.25 0.055
Family labour (mean person hours) 32.95 13.36 30.69 11.90 0.281
Hired labour (mean person hours) 6.29 8.55 4.41 8.65 0.862
Average quantity of seed used (Kgs) 11.72 19.71 10.25 12.24 0.866
Average amount of fertiliser used (Kgs) 0.04 0.57 0.00 0.00 0.179
Farmer group membership (% yes) 55.13 40.31 0.080
Access to credit (% yes) 47.42 30.10 0.055
Access to agricultural extension (% yes) 48.60 44.90 0.266
Note: SD is standard deviation, *pairwise comparisons done using Bonferroni test
Source: Survey Data (2017).
DEVELOPMENT IN PRACTICE 5
ha) compared to the female managed plots (832.6 kgs/ha) and the pooled sample (841.6 kgs/ha).
This finding is similar to Djurfeldt et al. (2019); (Miriti et al. 2021), and Singbo et al. (2021), who attrib-
uted it to male plot managers’superior access to productive resources and institutional support ser-
vices. The productivity of the male-managed plots, the female-managed plots, and the pooled
sample is presented through a kernel density plot in Figure 2.
Further, Peterman et al. (2011) noted that female farmers have lower human capital, resource
bases, access to, and utilisation of farm inputs than male farmers, hence they have low ability to
respond to subsidy programmes and agricultural incentives, thus leading to low productivity. More-
over, female plot managers spend more time on domestic chores than male plot managers, as well
as taking disproportionate responsibility of care for their children, thus limiting the time they spend
on their plots (Donald et al. 2018). Consequently, this impacts negatively on the productivity of their
sorghum plots.
3.5. Determinants of sorghum productivity
Results show that years of formal learning positively influences sorghum productivity for female
managed sorghum plots (Table 2). Formal education plays a crucial role in decision making, especially
in the use of better inputs. This corroborates findings by Wouterse and Badiane (2019) that invest-
ment in human capital, such as education, stimulates productivity. Similarly, the age of the plot
manager is positive and significant for the female plot manager. Therefore, a one unit increase in
the age of a female plot manager would lead to a 9% increase in productivity, which can be attributed
to the possibility that older female plot managers may have a higher access to productive resources
and institutional support services than younger women (Ingutia and Sumelius 2022).
However, household size has a negative and significant effect on sorghum productivity for the
female-managed plots. Rural sorghum farmers are resource-poor and tend to have large household
sizes. This corroborates the socio-demographic characteristics in Table 1, where the mean household
Figure 2. Gendered distribution of sorghum productivity in Uganda at plot level. Source: Survey data (2017).
6P. MIRITI ET AL.
size was higher than the national average. This increases expenses on food and health services for
the household, thus limiting resources available to invest in farm inputs. As noted by Mango et al.
(2015), this results in low technical efficiency, thus lowering productivity.
Interestingly, the coefficients for labour (both family and hired) were positive and significant for
the male-managed plots, female-managed plots, and the pooled sample. However, the amount of
seed used was positive and significant for the male-managed plots and the pooled sample.
Similar results were observed by Kloss and Petrick (2018) and Anang et al. (2022), who argued
that proper utilisation of farm inputs enhances crop productivity.
There was a significant inverse relationship between plot size and sorghum productivity for all
categories of plot managers; the largest decline is in male-managed plots (−2.42). Empirical evidence
has shown that small farms are more manageable, thus increasing efficiency in utilisation of farm
inputs as well as close monitoring of farm activities. These results are consistent with Muyanga
and Jayne (2019) and Sheng, Ding, and Huang (2019), who found that productivity tends to
decline as the farm size increases.
Access to extension had a negative and significant effect on sorghum productivity for female-
managed sorghum plots. This shows that male plot managers who had extension services were
less productive than those who did not. This could be explained by three possibilities: improper
application of the services by the plot managers, the extension service provided was not specific
to sorghum growing, or the services were not suitable for the local agro-ecological condition. This
finding corroborates Abate, Dessie, and Mekie (2019). On the other hand, Mwololo et al. (2019)
and Rajkhowa and Qaim (2021) argue that access to extension plays a critical role in farmers’
ability to diversify farming enterprises, which leads to increased productivity.
3.6. Oaxaca–Blinder decomposition of the gender agricultural productivity gap
As noted in Section 2.3, the application of OLS in gender and productivity studies does not account for
the contribution of the explanatory variables to the gender gap. Table 3 presents the decomposition
estimates on the differential in the log of the productivity value between male and female sorghum
plot managers. The Oaxaca–Blinder decomposition in terms of endowment and structural effects
offers detailed insights into the magnitude of the contribution of observable factors to the gender gap.
The estimates are grouped in three parts; the first part (mean gender differential) presents the
observed mean gender gap in productivity of 18.3%. This shows that, on average, sorghum plot
Table 2. Determinants of sorghum productivity by gender of the plot manager.
Male Managed plots Female managed plots Pooled sample
Variables Coefficient
Standard
error Coefficient
Standard
error Coefficient
Standard
error
Age of the plot manager 0.019 0.063 0.091* 0.047 0.071* 0.041
Years completed in school 0.046 0.071 0.127** 0.056 0.080* 0.047
Household size −0.083 0.109 −0.215*** 0.078 −0.139** 0.069
Years of sorghum farming −0.030 0.064 −0.086 0.056 −0.057 0.044
Average distance to the plot (metres) 0.010 0.027 0.0003 0.022 0.005 0.018
Family labour (person hours) 0.211*** 0.051 0.104** 0.041 0.168*** 0.034
Hired labour (person hours) 0.092*** 0.033 0.150*** 0.025 0.131*** 0.021
Amount of seed (kgs) 0.017*** 0.004 0.0002 0.003 0.008*** 0.002
Plot size (mean ha) −2.423*** 0.508 −0.874*** 0.296 −1.443*** 0.283
Farmer group membership (1 = Yes) −0.002 0.121 0.096 0.097 0.003 0.081
Access to credit (1 = Yes) 0.016 0.137 −0.010 0.100 −0.007 0.087
Access to agricultural extension
(1 = Yes)
−0.253** 0.119 0.155* 0.089 −0.076 0.076
Constant 6.53*** 0.637 5.893*** 0.452 5.936*** 0.393
Adjusted R-squared 0.3487 0.2579 0.2251
***, ** and * denote statistical significance at 1%, 5%, and 10% levels, respectively.
Source: Survey Data (2017).
DEVELOPMENT IN PRACTICE 7
managers are 18.3% significantly more productive compared to female-managed plots. The second
part consists of the aggregate decomposition in terms of the endowment effect which accounts for
the gender gap in terms of socio-demographic differences between male and female plot managers
and also includes the structural advantages and disadvantages. The aggregate endowment signifi-
cantly accounts for 4.7% of the observed gender gap between the male and female plot managers
and it explains 10% of the mean gender productivity differential. Likewise, the male structural advan-
tage and female structural disadvantage significantly explain the mean gender productivity differen-
tials of 57% and 33%, respectively. Similar findings were reported by Kilic, Palacios-López, and
Goldstein (2015); Joe-Nkamuke et al. (2019); and Singbo et al. (2021).
The third part (detailed decomposition) captures the magnitude of contribution or reduction of
the gender gap in productivity where a positive coefficient increases the gender gap while a nega-
tive coefficient reduces the disparity (Nchanji et al. 2021). The amount of seed, hired labour, access to
credit, and farmer group membership contributes positively to the endowment effect, thereby
exacerbating the gender gap in sorghum productivity between the male- and female-managed
plots. On the other hand, years of farming sorghum and use of family labour reduce the endowment
effect; therefore, it reduces the gender gap.
The age of male plot managers, hired labour, and membership of farmer groups contributes posi-
tively to the male structural advantages, which, in turn, act as positive contributors to the gender
gap, while the household size and access to credit play a role in reducing the gender productivity
gap. Similar findings were reported in Malawi by Nsanja, Kaluwa, and Masanjala (2021). On other
hand, the amount of seed and use of family labour reduce the structural disadvantage of female
plot managers, thus reducing the gender gap, while we find that access to extension services and
the plot size increase the gender gap.
Generally, the main driver of the gender gap in sorghum production is the structural advantage of
men (57%) which corroborates Addai, Ng’ombe, and Temoso (2022), who reported that gender gaps
in Ghana are significantly attributed to men’s structural advantages. However, our results differ from
those of Joe-Nkamuke et al. (2019), who found that a 76% gender gap in Malawi was due to the
endowment effect. Singbo et al. (2021) reported that the main driver of the gender gap remained
Table 3. Oaxaca decomposition of log value of sorghum productivity per hectare.
Mean Gender differential Coefficient Standard error
Mean male-managed plot productivity 6.538*** 0.048
Mean female-managed plot productivity 6.354*** 0.065
Mean gender differential in productivity 0.183** 0.081
Aggregate decomposition Endowment effect Male structural advantage Female structural
disadvantage
Coefficient Standard Error Coefficient Standard Error
Total 0.047** 0.020 0.259*** 0.042 0.148** 0.075
Share of the gender differential (%) 10 57 33
Detailed decomposition Endowment effect Male structural advantage Female structural
disadvantage
Age of the plot manager 0.002 0.006 0.017* 0.009 0.475 0.478
Years completed in school −0.003 0.006 −0.005 0.008 0.124 0.133
Household size 0.001 0.005 −0.028*** 0.011 −0.23 0.233
Years of sorghum farming −0.034* 0.019 −0.007 0.008 −0.118 0.159
Average distance to the plot (metres) 0.0004 0.003 0.0003 0.002 −0.048 0.176
Family labour (person hours) −0.040** 0.018 0.03 0.021 −0.594* 0.340
Hired labour (person hours) 0.039* 0.022 0.054** 0.025 0.100 0.062
Amount of seed (kgs) 0.059** 0.030 0.001 0.014 −0.184*** 0.049
Plot size (mean ha) −0.051 0.036 −0.031 0.022 1.031*** 0.382
Farmer group membership (1 = Yes) 0.041* 0.022 0.022** 0.011 0.037 0.056
Access to credit (1 = Yes) 0.098*** 0.017 −0.020*** 0.011 −0.005 0.052
Access to agricultural extension (1 = Yes) −0.032 0.020 −0.012 0.002 0.195*** 0.066
***, ** and * denote statistical significance at 1%, 5%, and 10% levels, respectively.
Source: Survey Data (2017)
8P. MIRITI ET AL.
unexplained (56%) by the structural disadvantage of women in Mali. Therefore, drivers of gender
gaps in productivity vary from one context to another.
4. Conclusions and policy implications
This study assessed productivity differentials between male- and female-managed sorghum plots
in Uganda. An Oaxaca –Blinder decomposition technique was applied to quantify the magnitude
of the gender productivity differentials. Overall, the male-managed plots were found to be more
productive than the female-managed plots. The amount of seed, family, and hired labour had
significant positive effects on the productivity of male-managed sorghum plots. Age, access to
extension, family, and hired labour positively influenced the productivity of female-managed
plots. On average, male-managed plots are 18% more productive than female-managed plots.
About 57% of the gender differential in productivity is accounted for by the male structural
advantages. Access to extension services, access to credit, and the amount of hired labour are
the most important factors that constitute the endowment effect. These findings offer novel
insights for enhancing equity and progressive development through the empowerment of
female plot managers. Due to the relative contribution of access to extension services to the
gender gap, the government should put in place systems that promote efficient dissemination
of extension services, especially to female plot managers. Private agricultural companies and
non-governmental organisations that are involved in the provision of extension services should
offer gender-inclusive and tailor-made services to farmers. For example, women farmers require
a specialised agricultural extension that can help ease the double role they play as household
caregivers and as farmers to improve the participation of female farmers and increase access
and utilisation of farm inputs.
Access to agricultural credit by female plot managers can be facilitated by the legislature through
the formulation of banking rules that favour female farmers. For instance, offering flexible repayment
terms, lower interest rates, and credit facilities that are demand-driven and use of other forms of col-
lateral besides land, such as women’s group guarantees, can help increase uptake of financial pro-
ducts by female plot managers.
Female structural disadvantages, such as the size of plot owned and access to extension services
by female plot managers, are greatly influenced by cultural and social norms; these calls for contin-
ued behavioural research on gender dynamics to promote the dissemination of information and
interventions aimed at showcasing the importance of enhancing women’s land rights and access
to institutional support services, such as agricultural extension.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This work was supported by the Bill and Melinda Gates Foundation [Grant No OPP1198373].
Notes on contributors
Philip Miriti is an Agricultural Economist and a research officer in the Sustainable Livestock Systems program (SLS),
International Livestock Research Institute (ILRI), Nairobi, Kenya. He previously worked at the International Crops
Research Institute for the Semi-Arid Tropics (ICRISAT) on several projects such as Harnessing Opportunities for Pro-
ductivity Enhancement (HOPE II) project and The Accelerated Varietal Improvement and Seed Delivery of Legumes
and Cereals in Africa (AVISA) among other projects in Kenya, Ethiopia, Tanzania, and Uganda. Miriti holds a Collaborative
Master of Science in Agricultural and Applied Economics (CMAAE) and a bachelor’s degree in Agribusiness Management
from the University of Nairobi, Kenya.
DEVELOPMENT IN PRACTICE 9
Dr. David Jakinda Otieno is an Agricultural Economist and Senior Lecturer in the Department of Agricultural Economics,
University of Nairobi. His research interests include Consumer Preference Analysis, Agricultural Marketing/Commercia-
lization, Analysis of Land Leasing, Economics of Climate Change, Livelihood Diversification, Value Chain Analysis, Agri-
business Development, Participatory Research Methods and Policy Analysis.
Dr. Evans Chimoita is an Agricultural Extension and community development expert and a Lecturer in the Department
of Agricultural Economics, University of Nairobi, Kenya. Evans is an Alumni of University of Nairobi where he earned
PhD in Agricultural Information and Communication Management. He also holds Bsc. and Msc. degrees in Agricul-
tural Education and Extension. He has published and supervised both undergraduates and postgraduate students. He
has vast knowledge and consultancy experience in several community development and extension projects including
world bank-based Kenya Climate Smart Agriculture Project (KCSAP), Agricultural Sector Development and Support
Programme II, European Union Horizon FoodLAND project, TMEA-UNES cross border food safety manuals and guide-
lines development project among other consultancies. Chimoita has been mentored by ILRI-AgriFoSe 2030 and Kenya
Policy Briefs Vision 2030 programmes on research and development of policy briefs. He has passion for innovative
software development on applications supporting socio-economic research and output, artificial intelligence inno-
vations promoting transformation of agricultural value chains, new approaches in agricultural Extension enhancing
community development such as participatory research methods, farmer research networks (FRN) and farmer field
schools (FFS).
Dr. Edward Bikketi is a Research Scientist & Consultant with over 17 years’experience gender and social research with
comprehensive experience in designing and implementing social science and inclusivity research projects for sustain-
able development, using Mixed methods (Q-squared studies), participatory and social learning approaches that facili-
tate gender - responsive and transformative approaches for empowerment of youth and women in agricultural
development. Currently work with Microsave Consulting, Leading Private Sector Development in Digitizing Agriculture.
Esther Njuguna is a Senior Social Scientist working as a Gender Specialist at the International Livestock Research
Institute, Nairobi, Kenya. Her research interests include gender dynamics in livestock feeds and forages, women’s
participation in agricultural food systems, and the interface between gender and technology use that leads to
the transformation of norms and empowerment of women and the youth. She has been working with male
and female smallholder farmers since 1996. She is a graduate of Wageningen University and Research Centre (Agri-
cultural Development - MSc) and the University of Nairobi-(Agricultural Development and Economics) –PhD.
Dr. Chris O. Ojiewo is a Strategic Partnerships and Seed Systems Lead at CIMMYT and Co-PI of a multidisciplinary
multi-stakeholder, multidoor project on developing and delivering high yielding, nutrient dense, climate-smart,
market-preferred varieties of grain legumes and dryland cereals. His research aims to enhance varietal turnover to
mitigate losses from evolving climate patterns, especially in dry areas with the poorest of the poor farmers, while
addressing pest and disease complexes, and enabling public-private partnerships for enhanced seed delivery. Core
to his sense of purpose is: improving productivity and profitability for smallholder farmers; gender equity; youth
empowerment; nutrition security; knowledge sharing; and solving the perpetual problem of food, nutrition and
income insecurity of the less privileged in developing countries. He works to establish a robust system that
ensures sustainable, timely availability of and access to quality seed of dryland cereals and grain legumes at afford-
able prices through the participation of multiple stakeholders along the seed value chain. He is committed to gender
equity as a guiding principle, considering the critical role women play in choosing legume and cereal varieties and
seed sources.
ORCID
Philip Miriti http://orcid.org/0000-0002-3756-0736
Chris O. Ojiewo http://orcid.org/0000-0002-2885-9381
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