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Is seeing really believing? The effect of video shows and radio listening clubs on

agricultural technology adoption in northern Ghana

Introduction

The adoption of productivity enhancing technologies by smallholder farmers is low in Ghana, as in

other sub-Saharan Africa (SSA) countries. For example, Ghana’s average inorganic fertilizer

consumption is only 15% and 6% of the World and East Asian averages, respectively (World Bank,

2019). The low adoption of modern technologies is due to several factors including low marginal

returns in the presence of poor infrastructure (Michler, Tjernström, Verkaart, & Mausch, 2018; Suri,

2011), liquidity and risk constraints (Karlan, Osei, Osei-Akoto, & Udry, 2014), unavailability of

complementary inputs (Brush, Taylor, & Bellon, 1992; Feder, Just, & Zilberman, 1985), and high

market transactions cost (Aker, 2011; Feder et al., 1985).

But a necessary condition for technology adoption is knowledge and information (Matuschke,

Mishra, & Qaim, 2007; Rogers, 2003). Yet, information about a new technology could be delivered

through several means, including traditional extension or advisory methods (e.g., Training and Visit

by agricultural extension officers, and Farmer Field Schools), modern information and

communication technology approaches such as mobile phone-based applications (Aker & Ksoll,

2016), or through peers or social learning (Conley & Udry, 2010; Maertens & Barrett, 2013).

For information delivery about a new technology to stimulate adoption, extension advisory

services must give attention to the mode of delivery. Information that precipitates behavioral change

depends on both the choice of media used and knowledge of the inherent benefits and disadvantages

of the medium. Although some studies (Aker, 2011; Dercon, Gilligan, Hoddinott, & Woldehanna,

2009; Gautam, 2000; Lambrecht, Vanlauwe, Merckx, & Maertens, 2014; Owens, Hoddinott, &

Kinsey, 2003) have examined the effect of specific approaches of reaching farmers with information

about a new technology on adoption or agricultural outcomes, very few (Genius, Koundouri, Nauges,

& Tzouvelekas, 2014; Krishnan & Patnam, 2014) have evaluated the relative impact of alternative

approaches.

Thus, it is often not clear which communication options are most effective for reaching farmers

with new technology in a manner that boosts adoption. This article contributes to the literature by

evaluating the relative impact of two alternative channels—radio listening club (RLC) and video

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documentary (VDD)—of disseminating knowledge and information about a new technology to

farmers. In the neuroscience literature, or the Science of Learning literature in general, multisensory

input is known to improve memory and recall (Thelen, Cappe, & Murray, 2012). While radio is largely

a unimodal method of learning, the video mode of information delivery of interest to this article is

bimodal (visual and auditory). On this basis alone, one could expect the VDD mode of agricultural

technology dissemination to have greater impact than radio per se. However, a RLC has the potential

advantage of being interactive—being a two-way channel of communication—than the VDD per se.

Thus, although it is reasonable to conjecture that the VDD channel has greater impact on knowledge

and adoption than the radio channel itself, it is not obvious that video would have greater impact than

a RLC—the question “is seeing really better than listening” follows directly from the above.

A few studies have carried out randomized controlled experiments or used quasi-experimental

approaches to compare traditional extension delivery approaches to either radio (Hudson, 2018;

Perkins, Ward, & Leclair, 2011) or video (Abate, Bernard, Makhija, & Spielman, 2019; Hörner,

Bouguen, Frölich, & Wollni, 2019) channels of agricultural extension or advisory services. To date,

we are not aware of previous research that evaluated the impacts of interactive radio and video

channels of agricultural information delivery on new technology uptake and related agricultural

outcomes. We thus set up a ‘real-world’ randomized experiment that enables us to overcome

identification issues associated with selection bias and endogeneity in order to provide robust

estimates of the impact of the two alternative approaches of disseminating information about a new

technology on adoption and other outcomes.

This article can be contextualized within the second Sustainable Development Goal (SDG Goal

#2), which demonstrates the importance of sustainable agriculture. Yet, encouraging agricultural

intensification through increased inorganic nitrogen fertilizer applications, for example, could

compromise long-term sustainability of agricultural production (Zhang et al., 2015). Sustainable

agriculture requires, inter alia, the adoption of environmentally sound technologies that increase

agricultural production such as biological nitrogen fixation.

The biological behavior of grain legumes that enables them to fix atmospheric nitrogen is limited

if the local soil’s rhizobia population is depleted or ineffective (Kermah et al., 2018). This is the case

in most Ghanaian soils, particularly in northern Ghana where grain legumes are important for food

security and income (Fening & Danso, 2002). This limitation can be overcome by introducing

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appropriate rhizobia into soils via inoculation of legume seeds (Ulzen, Abaidoo, Ewusi-Mensah, &

Masso, 2018).

A number of studies have reported significant increases in yields and profits from the application

of the inoculant technology on farmer fields. For example, the use of inoculants more than doubled

legume yields in Rwanda (Rurangwa, Vanlauwe, & Giller, 2018). The application of inoculants did

not only increase yields in Kaduna and Kano (northern Nigeria), but was also economically more

profitable than the application of inorganic fertilizers (Ronner et al., 2016). Similarly in western

Kenya, inoculants increased soybean yields by about 26% with an average gross margin of about

$278/ha (Mutuma, Okello, Karanja, & Woomer, 2014). In northern Ghana, large increases in yields

(between 22% and 200%) and significant economic returns due to inoculation of soybean and cowpea

have been found (Asei, Ewusi-Mensah, & Abaidoo, 2015; Ulzen, Abaidoo, Mensah, Masso, &

AbdelGadir, 2016).

In spite of the yield and economic gains of inoculant use, the unavailability of the product and

lack of information about its usefulness have long been identified as a major hindrances to adoption

(Woomer et al., 1997). Because the inoculant technology is relatively new to smallholder farmers in

Ghana, it is important to disseminate knowledge and information about its use and potential benefits

to farmers using channels that could boost adoption, yields and potential economic gains, all other

factors remaining unchanged.

To evaluate the impact of two alternative approaches to reaching farmers with the inoculant

information, we randomly assigned 113 legume producing villages in northern Ghana to receive

education about inoculants and good agronomic practices (GAPs) through either the VDD (37

villages) or the RLC (37 villages)—the rest of the 39 villages served as the control group or the

counterfactual. After allowing time for the opportunity to apply the information received, we assessed

impact on five outcomes: inoculant adoption, potential demand for inoculants, and improved seed

adoption.

The inoculants intervention

Alliance for a Green Revolution in Africa (AGRA) has an overarching goal of transforming African

agriculture through the implementation of four main programs, one of which is the Soil Health

Program (SHP). The SHP aims to restore degraded soils in Africa in order to boost agricultural

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productivity. To achieve this, the SHP focuses on the rapid dissemination of locally adapted and

environmentally sound integrated soil fertility and water management practices to farmers in SSA.

In Ghana, the Savanna Agricultural Research Institute of the Council Scientific and Industrial

Research (abbreviated CSIR-SARI) proposed rhizobium inoculation of legumes (soybean, cowpea,

and groundnut) as part of the solution to the soil infertility problem in northern Ghana. In 2014, AGRA

provided a grant to help CSIR-SARI produce and distribute inoculants to farmers in 47 districts in

northern Ghana (26 in the Northern Region, 12 in the Upper East Region, and 9 in the Upper West

Region).1 A core component of the grant was inoculant knowledge dissemination to smallholder

farmers through innovative channels that would help boost adoption of the technology. The CSIR-

SARI inoculant project was implemented over a 36-month period from June 2014 to June 2017.

The inoculant project had five main strategies: (1) build a state-of-the-art inoculants production

laboratory, (2) produce and distribute quality inoculants in collaboration with the private sector, (3)

create knowledge and awareness on proper handling, utilization and benefits of the inoculant

technology, (4) create inoculant market access through partnerships with private agro-input dealers,

and (5) build institutional linkages to facilitate the achievement of project objectives. Although all the

five strategies are equally important for the success of the intervention, our impact evaluation focused

mainly on the third strategy—inoculant information communication to increase awareness and

adoption.

A number of activities were carried out under the extension and communication arm of the

inoculants project. On-farm demonstration and farmer learning centres were established in all 47

project districts in the three northern regions to teach farmers and agricultural extension agents

(AEAs) who were expected to serve as Train-of-Trainers (ToT). Agro-input dealers and field officers

of NGOs directly involved in agriculture also benefited from training. Overall, 3,826 farmers, 344

AEAs, 187 agro-input dealers, and 170 Field Officers were trained. The project also embarked on

mass media awareness campaigns using regional and community radio stations. Aside the above

extension communication and advisory channels that covered the entire project districts, CSIR-SARI

also used two innovative approaches (VDD and RLC) to reach farmers with knowledge and

information on the inoculant technology at the village level.

CSIR-SARI produced a VDD that was shown in selected communities across the three regions

using tricycle video vans. The video was in English and the most widely spoken local language in

1 The Northern Region was split up into three regions (Northern, Savannah, and North East) in early 2019.

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each of the regions. The video covered inoculant production, handling and preservation, seed

inoculation, results from famer field demonstrations, and information on how to access the inoculants.

The video also contained information on the need to use improved legume seeds, most of which were

bred by CSIR-SARI researchers. In addition, the video emphasized the adoption of Good

Agronomic/Agricultural Practices (GAPs), particularly those related to integrated soil and water

management activities, and timely weeding.

In addition to the mass media awareness campaigns via radio broadcasts, CSIR-SARI also created

more targeted community level fora through Famer Based Organizations (FBOs) using the RLC to

ensure that legume farmers engage directly in the radio programs about the inoculant technology.

Each RLC was composed of 27 legume farmers, on average. Each club reached a consensus on a time

slot for participation in each radio broadcast over the period of the program. During each broadcast

by inoculant resource persons at the studio of the radio stations, RLC members gathered at a chosen

location and participated in the discussion via mobile phones. The costs of the radio phone-ins were

borne by CSIR-SARI. The content of the broadcast was identical to that of the VDD. Such a

deliberately targeted interactive radio broadcasting approach was expected to be more impactful in

terms of knowledge gained than the mass media awareness campaigns that were not interactive.

The experiment and data collection

Figure 1 provides a timeline of the experiment and data collection activities. CSIR-SARI selected 160

communities for the intervention. From January to February 2015, we visited all the 160 communities

as part of our baseline data collection activities. During these visits, we verified that the list of FBO

members provided by CSIR-SARI as the sample frame for the household survey was accurate. In

some cases, the list was updated in collaboration with the FBOs in the communities; there was one

FBO per community. We took a random draw of 10 FBO members per community, as this was shown

to provide adequate statistical power for detecting impact if indeed present.2 We achieved the 10

households with FBO members in 155 villages and 9 in the remaining 5 communities.

2 At 0.05 level of significance, Intra-Cluster Correlation of 0.20 for new technology adoption, and expected effect size of

0.30, drawing 10 households per community gives approximately 0.84 statistical power, which is slightly above the

conventional minimum of 0.80.

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During our process monitoring visits to the project communities at an early stage of implementing

the VDD and RLC interventions, we discovered that the agreed treatment assignments were being

compromised, mainly due to implementation fidelity. This led to a reduction in the planned number

of experimental arms, from three to two treatment groups because of the associated reduction in

statistical power. We therefore settled on an effective baseline sample size of 1,126 FBO member

households in the 113 communities (10 households in 109 communities, and 9 in the remaining 4).

Figure 1 Intervention and activity timeline

The 113 communities (Figure 2) were randomly assigned to one of three experimental groups:

(i) 39 control communities—exposed to all the project interventions except the VDD and

RLC);

(ii) 37 VDD treatment communities—exposed to all the project interventions except the

RLC); and

(iii) 37 RLC treatment communities—exposed to all the project interventions except the VDD.

Because the plan was to evaluate outcomes related to the 2016 cropping season, the VDD and

RLC treatments needed to be implemented before the 2016 planting season, which for the legumes

was between early June and mid-August. The VDD and RLC interventions were implemented

between November 2015 and May 2016 in the 74 communities. All the 1,126 FBO member

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households were successfully followed up during the endline survey carried out between March and

April 2017.3

We worked with the implementers by organizing monitoring visits to the communities to ensure

compliance with the new experimental assignments. Nonetheless, the experimental assignments were

still slightly compromised: five communities earmarked for the video treatment were not treated at

all, and four assigned to the RLC actually received the video treatment instead. Thus, ex-post, 44

communities were not treated, 36 received the VDD treatment, and 33 received the RLC treatment.

Figure 2 Map of northern Ghana showing treatment and control villages

3 We delayed the follow-up survey to allow some time after the 2016 crop harvest and marketing activities.

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Indicators and baseline orthogonality tests

We evaluate the impact of the VDD and RLC interventions on three outcomes. The main outcome

variable of interest is an indicator variable for whether a farmer used inoculants during the 2016

cropping season.4 Aside actual adoption, we also collected data on potential demand for inoculants

by asking farmers a hypothetical question about the quantity of inoculants they were willing to buy

given that they had received information about the inoculants via the various communication channels

including the mass media campaigns.

As stated in section 3.1, although the main focus of the communication through the various

channels was information about the inoculants, the content also covered information about new

legume seeds that had been bred by CSIR-SARI. Therefore we also assessed the impact of the

interventions on improved legume seed adoption.

Table 1 reports sample mean values of the outcome variables and other relevant characteristics of

the sample at baseline. We find that inoculant use is not entirely new to farmers in the study areas as

about 6% reported using the technology at baseline. This is explained by the activities of the N2Africa

project in the past. What is important is that the use of inoculants at baseline was identical between

the experimental groups. The desired mean quantity of inoculants at baseline was about 60 grams,

which if correctly applied translates to about 0.24 ha of legumes or about 21% of mean area under

the legumes (groundnut, soybean and cowpea) at baseline. At baseline, only about 15% of farmers

reported using any improved legume seeds. Although the intervention’s specific focus was the three

legume crops, the sample of farmers involved have a diversified portfolio of crops—only about a third

of total cultivated land was devoted to the legumes at baseline.

As Table 1 shows, we find insufficient evidence to reject the null hypothesis that the difference-

in-means between the treatment and control groups for all the variables are equal to zero at at the 5%

level, indicating that the randomization was successful at balancing observed baseline characteristics

for our sample.

4 We also collected data on inoculant use at baseline because since 2009, N2Africa (https://www.n2africa.org/) in

collaboration with the International Institute of Tropical Agriculture (IITA) had been carrying out a project aimed at

encouraging nitrogen fixation through inoculation by smallholder legume farmers in Africa including Ghana (Martey,

Ahiabor, Buah, & Kusi, 2016). Therefore, it was possible that some farmers were already using inoculants.

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Table 1. Baseline mean values and orthogonality tests

Mean P-value of difference

Control

n = 390

Video

n = 369

Radio

n = 367

V – C R – C V – R Joint

(1) (2) (3) (4) (5) (6) (7)

Outcome variables

Used inoculants 0.06 0.05 0.07 0.724 0.929 0.663 0.896

Desired quantity of inoculants (grams) 65.2 51.3 64.3 0.349 0.948 0.368 0.566

Used improved legume seeds 0.16 0.14 0.16 0.631 0.927 0.567 0.826

Other characteristics

Female farmer 0.35 0.36 0.43 0.978 0.067 0.084 0.127

Age of household head 55.3 59.3 46.7 0.664 0.154 0.072 0.077

Household size 7.02 7.42 7.17 0.266 0.581 0.435 0.536

English literacy rate 0.19 0.20 0.14 0.807 0.091 0.081 0.138

Legume cultivated area (ha) 1.12 1.47 1.20 0.352 0.768 0.406 0.631

Total cultivated area (ha) 3.35 3.84 3.52 0.421 0.709 0.552 0.721

Share of land cultivated to legumes 0.33 0.38 0.34 0.155 0.643 0.316 0.352

Inorganic fertilizer use on all crops 0.54 0.58 0.46 0.526 0.206 0.055 0.145

Quantity of fertilizer on all crops (kg/ha) 55.9 68.1 46.1 0.395 0.490 0.133 0.321

Inorganic fertilizer use on legumes 0.16 0.18 0.20 0.504 0.256 0.676 0.507

Quantity of fertilizer on legumes (kg/ha) 31.2 42.9 31.7 0.482 0.972 0.473 0.726

Used improved seed 0.24 0.28 0.23 0.581 0.836 0.471 0.757

Number of legumes produced 1.01 0.92 1.04 0.389 0.839 0.329 0.560

Agricultural credit dummy 0.18 0.24 0.23 0.306 0.372 0.877 0.514

Normalized asset index 0.19 0.20 0.17 0.758 0.239 0.155 0.333

Off-farm participation 0.48 0.52 0.56 0.448 0.144 0.454 0.333

Distance to market (km) 6.86 6.71 6.63 0.915 0.857 0.955 0.983

Distance to all-weather road (km) 2.14 1.96 1.91 0.835 0.791 0.949 0.962

Inoculant dealer present at baseline 0.20 0.11 0.19 0.246 0.876 0.323 0.428

Inoculant dealer present at endline 0.64 0.65 0.51 0.932 0.257 0.229 0.408

Lost crops due to drought or flooding 0.39 0.33 0.32 0.314 0.305 0.874 0.520

Note: p-values are based on village-level clustered standard errors after estimating 𝑦𝑖𝑗0 = 𝛼 + 𝛽1𝑉𝐷𝐷𝑖𝑗 + 𝛽2𝑅𝐿𝐶ij + 𝜀𝑖𝑗0

using baseline data. V, C and R in columns (4)–(6) denote VDD, Control and RCL experimental groups, respectively.

Identification strategy

Since the randomized assignment of clusters (the intervention communities) ensured that households

had similar observed mean characteristics at baseline (Table 1), we could estimate the impact of the

interventions using the difference-in-differences (DiD) estimator. Although the DiD estimator also

assumes that changes in the outcomes of interest for the treated and control groups would have

followed a parallel trend in the absence of the intervention, we are unable to provide data driven

evidence that this assumption holds for our sample. This is because we do not have more than one

pre-intervention observations on the outcome variables.

Nonetheless, Abadie (2005) suggests that the parallel trends assumption could be valid if pre-

treatment characteristics that are correlated with changes in the outcome variable of interest are

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balanced between the control and treated groups. In agricultural related interventions such as the one

in the present case, such important pre-treatment characteristics include weather-related shocks, and,

in our particular case, the availability of agro-input dealers who have registered as inoculants sales

agents. We found that while farmer-reported pre-harvest crop loss events and the presence of

inoculant dealers are respectively significantly correlated with legume yields and inoculant adoption,

they are both balanced between the control and treated groups (Table 1).5

Aside the above issues, if the correlation of outcomes between baseline and follow-up is low (i.e.,

less than 0.5), then estimating the impact of the interventions using ANCOVA (Analysis of

Covariance) increases precision and efficiency (McKenzie, 2012). In our sample, the autocorrelations

of the outcome variables between baseline and follow-up were all less than 0.50 (range between 0.01

and 0.27). Therefore, our preferred model is

1 1 2 0 0 1,ij ij ij ij

y VDD RLC y X (1)

where yij1 is the outcome variable of interest for farm household i in cluster j at follow-up; 1 and 2

are the average treatment effects associated with the VDD and RLC treatments, respectively; yij0 is

the value of the outcome variable of interest at baseline included for the purpose of improving

statistical power;0ij

X is the vector of control variables at baseline included to improve precision of

the estimates; and1ijis the random disturbance term. Comparing the difference between the two

average treatment effects (i.e., 1 and 2) is straightforward.

Two of our outcome variables are binary and one is continuous. For the binary outcome variables

we present probit average marginal effects after estimating

1 1 2 0 0

( 1) ( ),ij ij ij ij ij

P y VDD RLC y X (2)

where is the cumulative distribution function of the standard normal distribution, and all other

variables and parameters are as defined above. For the sake of comparison, we also report DiD average

treatment effects after estimating

5 We used ∆𝑦 = 𝛼 + 𝛽∆𝑥 + 𝑒 to evaluate: (a) the correlation between crop losses due to drought/flooding and legume

yield; and (b) the correlation between the availability of an inoculant dealer and the use of inoculants; where ∆y is either

the change in legume yield or change in inoculant use, and ∆x is either the change in drought/flooding related crop losses

or the change in the availability of inoculant dealers in the community.

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1 1 2 1 2

( 1) ( ( ) ( ) ),ijt t ij ij t ij t ij ijt

P y t VDD RLC t VDD t RLC X (3)

where t is the indicator variable that equals 1 for follow-up and 0 for baseline. For the roughly

continuous outcome variable, our alternative specification is based on first-differencing, which weeds

out unobserved heterogeneity:

1 2 1 1 2 2 ,ij ij ij ij ij ijy VDD RLC X X (4)

where Δ represents change in time-varying variables between baseline and follow-up, X1 and X2 are

vectors of time-varying and time-invariant control variables, respectively.

For all the models specified above, we provide both intent-to-treat (ITT) and treatment-on-the-

treated (TOT) estimates. The first is based on the ex-ante treatment assignments while the second is

based on the ex-post experimental arms. We expect both estimates of the average treatment effects to

be similar because of the marginal difference between the ex-ante and ex-post assignments.

Results

We first report and discuss estimates of the average ITT and TOT effects of the intervention on the

five outcome indicators. Further, we explore differential impacts of the intervention on subgroups.

We present heterogeneous effects of the interventions across three key characteristics: farm size,

gender, and distance from the northern regional capital (‘isolation’). Because such heterogeneous

impact analysis was not clearly determined ex-ante, our sampling procedures did not take this into

account by, for example, stratification by predetermined subgroups. This means that our

heterogeneous impact analysis is likely underpowered in some cases.

Overall impact

Table 2 reports ITT and TOT average effects of the VDD and RLC interventions. The ANCOVA

estimates are to be preferred for reasons stated in section 4. First, we note that while the overall use

of inoculants at endline across all groups (9%) and among the ex-ante treatment group (11%) was

much lower than anticipated before the start of the intervention, this was not surprising because of

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delays in inoculant production and reported scarcity of the product according to some farmers and

sales agents.6

Given that the reported scarcity of inoculants were random across the experimental groups (Table

1), the estimated impact of about 9% points (relative to the mean adoption rate of approximately 6%

in the control group) attributable to the VDD treatment (column 2) is large in magnitude and

statistically significant. This finding is consistent with the few studies that have evaluated the impact

of video-mediated agricultural extension services (Abate et al., 2019; Hörner et al., 2019; Vasilaky,

Toyama, Baul, Mangal, & Bhattacharya, 2015). However, we fail to reject the null hypothesis that

the RLC had no effect on the use of the inoculants at conventional levels. Furthermore, the VDD

treatment increased the use of inoculants by approximately 7% points more than the RLC treatment

(p-value = 0.030), suggesting that seeing was indeed more effective for new technology adoption than

listening, even when listening was interactive.

Given the issues with the availability of inoculants, we also evaluated the impact of the

interventions on quantity of inoculants farmers desired to purchase for the 2016 cropping season.

Consistent with the inoculant adoption results, VDD participants reported about 82 grams more

desired quantity of inoculants (std. err. = 40 grams, relative to the control group mean of about 321

grams). Here again, we cannot reject the null of no impact of the RLC at the 5% level, except for the

ToT difference-in-differences result (column 5). Moreover, although average desired quantity of

inoculants was about 29 grams higher for the VDD treatment group than for the RLC group, the null

of no difference cannot be rejected (std. err. = 40 grams).

Together with information about the inoculants, CSIR-SARI also promoted and made available

the Jeguma, Songotra, and Chinesse varieties of soybean, cowpea, and groundnuts, respectively. In

the entire sample, the use of improved legume varieties increased from 15% during the 2014 planting

season to 24% in the 2016 growing season. The question is whether the choice of information

dissemination channel had an effect on the increased adoption rates. The ANCOVA estimates (Table

2, column 1) show that both the video and RLC treatments significantly increased the use of the

modern varieties by about 10% points (std. err. = 4% points) and 12% points (std. err. = 3% points),

respectively, relative to the control group mean of about 16%. These are large impact magnitudes—

6 The inoculant production laboratory was not completed until December 2016, necessitating the importation of

approximately 702 kg of inoculants from Brazil by CSIR-SARI for the 2016 cropping season. This quantity was enough

to supply inoculants to about 2,500 farmers, assuming that all legumes under cultivation were to be inoculated.

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about 59% and 80%, respectively, of the control group mean adoption rate. We fail to reject the null

that the size of the effects are the same between the two treatments.

It is intriguing that the RLC had impact on seed adoption but not the inoculants. From our

qualitative interviews, it seemed that the inoculant technology appeared more complex to farmers

than improved seeds given that new needs have been introduced to them in the past.

Table 2 Impact of the two communication channels

Control mean ITT TOT

at follow-up ANCOVA DD⸷ ANCOVA DD⸷

(1) (2) (3) (4) (5)

Inoculant adoption (1/0) 0.056

Video 0.093*** 0.108** 0.098*** 0.105**

(0.031) (0.047) (0.029) (0.047)

Radio LC 0.023 0.019 0.018 0.017

(0.023) (0.033) (0.025) (0.031)

Video vs. Radio LC 0.069** 0.089** 0.079** 0.089**

(0.032) (0.042) (0.033) (0.044)

Inoculant demand (gram) 321.59 Video 82.450** 92.938** 110.217*** 106.447**

(40.433) (43.309) (38.652) (42.580)

Radio LC 53.199 60.218* 60.014* 71.815**

(32.868) (36.577) (32.932) (35.232)

Video vs. Radio LC 29.251 32.719 50.203 34.632

(40.156) (41.699) (40.541) (42.690)

Used improved seeds (1/0) 0.164 Video 0.097*** 0.118** 0.140*** 0.143***

(0.036) (0.053) (0.034) (0.052)

Radio LC 0.131*** 0.120** 0.129*** 0.100**

(0.026) (0.048) (0.027) (0.048)

Video vs. Radio LC –0.035 –0.002 0.010 0.043

(0.036) (0.055) (0.035) (0.059)

Note: OLS estimates of average treatment effect presented for continuous variables; and probit average partial effects for

binary outcomes. ⸷First-differenced estimates are reported for continuous variables in columns (3) & (5). Clustered

standard errors at the level of randomization (i.e., village) are reported in parentheses. All estimates include control

variables (regional fixed effects, household demographics, farm characteristics and market conditions at baseline).

Statistical significance at the 1%, 5%, and 10% levels are denoted by ***, **, and *, respectively.

Impact heterogeneity

Do the average effects reported above conceal significant variations in response to the VDD and RLC

interventions and their effects on the outcomes evaluated? There could be several subgroups over

which the impact of the interventions could differ. Here, however, we examine heterogeneous effects

of the interventions across three characteristics. The chosen characteristics are based on the

technology adoption literature and our knowledge of the intervention rollout process. For example,

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the impact of the interventions could vary across farm size and gender because new technology

adoption is influenced by these factors (Feder & Slade, 1984; Lambrecht, Schuster, Samwini, &

Pelleriaux, 2018; Theis, Lefore, Meinzen-Dick, & Bryan, 2018). Also, due to the reported scarcity of

inoculants, we conjecture that distance from the study communities to CSIR-SARI (located near the

Northern regional capital, Tamale) could be a source of impact heterogeneity.

We begin the discussion with pre-treatment farm size. The impact of the VDD on inoculant

adoption is about 11% points at the 5th percentile of the farm size distribution (p-value = 0.001), and

9% points at the 95th percentile (p-value = 0.067).7 However, the z-statistic of the difference is only

0.42. So although the impact of the VDD treatment decreases as farm size increases, a pairwise

comparison of the differences between the 5th, 25th, 50th, 75th, and 95th percentiles are not

statistically significant at conventional levels. Table 3 shows the results comparing the outcome for

households at the 25th and 75th percentiles.

While the impact of the interventions on improved seed adoption seems to increase with farm size,

this result is not statistically significant. For example, the impact of the VDD on improved seed

adoption is 11% points at the 5th percentile but 21% points at the 95th percentile, but the z-statistic

of the difference is only 1.64.

Second, we consider heterogeneity of the interventions’ impact by gender of farmer. The mean

effect of the VDD on inoculant adoption is larger among male farmers (approximately 11% points)

than it is among females (about 7% points), but the difference of approximately 4% points is

imprecisely estimated (std. err. = 3.4% points).

We find that the response of the treatments to improved seed adoption in particular differs

significantly by gender, with large differences in impact magnitudes—about 13% points for the VDD

treatment (p-value = 0.035) and 16% points for the RLC treatment (p-value = 0.005). Indeed, both

the VDD and RLC interventions had no significant impact on the use of improved seeds among female

farmers. This finding begs the question: why? While women are key producers of legumes in northern

Ghana, the women FBO members who participated in the program are not always the primary

agricultural decision makers, and yet their husbands were not part of the intervention.8 This calls for

further research to unravel whether providing agricultural information to couples in such patriarchal

societies engenders greater impact than when wives alone are involved.

7 Mean farm size at the 5th and 95th percentiles are 0.44 and 1.51 hectares, respectively. 8 Most of the women live in male-headed households. Only 7.7% of our sample were female-headed households.

15

Table 3 Impact heterogeneity

Scale Gender Isolation

(1) (2) (3)

Inoculant adoption

Video (G=1) 0.106*** 0.076** 0.086***

(0.032) (0.039) (0.026)

Video (G=0) 0.099*** 0.111*** 0.115***

(0.030) (0.036) (0.038)

Video (G=1) – Video (G=0) 0.007 –0.034 –0.029

(0.016) (0.046) (0.030)

Radio (G=1) 0.009 –0.027 0.020

(0.024) (0.033) (0.026)

Radio (G=0) 0.015 0.047 0.011

(0.026) (0.030) (0.027)

Radio (G=1) – Radio (G=0) –0.006 –0.074* 0.008

(0.010) (0.038) (0.024)

Improved seed adoption Video (G=1) 0.117*** 0.058 0.149***

(0.036) (0.055) (0.035)

Video (G=0) 0.146*** 0.186*** 0.141***

(0.033) (0.038) (0.039)

Video (G=1) – Video (G=0) –0.029 –0.128** –0.007

(0.018) (0.061) (0.035)

Radio (G=1) 0.121*** 0.032 0.133***

(0.031) (0.044) (0.030)

Radio (G=0) 0.130*** 0.190*** 0.117***

(0.027) (0.035) (0.032)

Radio (G=1) – Radio (G=0) –0.008 –0.157*** 0.016

(0.025) (0.057) (0.031)

Note: These are ITT estimates based on the ANCOVA model, but the results are qualitatively identical with the TOT

estimates. G = 1 represents the 25th percentile of legume cultivated area, female farmer, the 25th percentile of farmers’

age, 25th percentile of wealth, and 25th percentile of isolation; G = 0 represents the 75th percentile of legume cultivated

area, male farmer, 75th percentile of farmers’ age, 75th percentile of wealth, and 75th percentile distance to Tamale (the

Northern Region capital). Statistical significance at the 1% and 5% levels are denoted by *** and ** respectively.

Finally, we consider distance from the intervention communities to CSIR-SARI in Nyanpkala

(about 17 km from Tamale). Our a priori expectation was that the reported scarcity of inoculants

during the 2016 planting season is associated with diminishing impact as one moves further away

from the project implementers. However, we find insufficient evidence against the null that the impact

of the intervention is distance-neutral.

In sum, while we find indications of differential responses to the VDD and RLC treatments across

some characteristics, with some of the differences being economically meaningful, we to fail to reject

the null hypothesis of no difference in most cases. Yet, we cannot rule out differential effects because

of insufficient statistical power for estimating heterogeneous impact in some cases.

16

Conclusion

Adequate information about an agricultural technology is a necessary condition, even if not sufficient,

for investments. But which method and channel of agricultural technology information dissemination

is effective for boosting adoption and agricultural outcomes under ceteris paribus assumptions? While

a variety of agricultural extension communication methods have been documented in the literature,

surprisingly, there is a paucity of knowledge about the relative impact of alternative approaches,

particularly with respect to the relative effectiveness of audio (radio) versus audiovisual (video)

channels of agricultural information dissemination.

We addressed this gap in knowledge by setting up a randomized controlled experiment in 113

farming communities in northern Ghana where a real world intervention aimed at enhancing soil

nitrogen availability in a cost effective and environmentally sustainable manner was ongoing. By

randomly assigning farming communities to receive information about a new technology

(Bradyrhizobium inoculation) and other good agronomic practices through video documentaries and

radio listening clubs, this article provides lessons for policy makers and practitioners involved in

agricultural information dissemination and advisory services.

Our results provide evidence that, for most of the outcomes evaluated, the ‘hearing and seeing’

option that the video documentaries provided for communicating agricultural technology information

holds more promise than radio, even with the enhanced interactivity that the listening clubs were

expected to provide. This finding needs to be further contextualized within the framework of cost-

effectiveness and operational bottlenecks reported about the radio listening clubs in particular.

Our results were tempered by further implementation challenges, particularly those that delayed

production and distribution of the inoculants. This challenge in itself is an important lesson for policy

and practice, highlighting how project and program impacts could be derailed due to poor

implementation. Our cautious conclusion is that the video documentary intervention in particular has

potential for scaling up. More (qualitative) research is required to unravel contexts within which the

radio listening clubs could be effective.

Also, with such real world impact evaluations that are prone to implementation delays, there is the

need to budget for more than one follow-up survey. This notwithstanding, there is no other compelling

reason to suggest that the video intervention in particular would not succeed elsewhere under similar

conditions.

17

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