Experimenting for self and for others: farmer experimentation as a public good

Our experiment involves distribution of maize seed trial packs and setting up of experimental plots by the randomly selected farmers. We vary the proportion of farmers in a village to whom seed trial packs are offered, splitting the low concentration treatment arm to an incentive and non-incentive group as below:
1) low concentration treatment: 10% of villagers receiving trial packs.
2) low concentration plus incentives treatment in which hosts will be compensated for participation, to evaluate the role of incentivizing host farmers to share information actively and intentionally with their fellow villagers.
3) high concentration treatment: 35% of villagers receiving trial packs.

Randomly selected households from the treatment villages were presented with trial packs of two preselected experimental maize varieties for them to endogenously select one. Below are the criteria used for selection of the experimental varieties:
1) they are relatively new varieties – at most 5-years and below since they were commercially released
2) they are preferred varieties for that locale, as recommended by breeders and agronomists working in that geographic area,
3) they are available in local markets BUT
4) are not yet widely used by local farmers (purchased by <10% of buyers?).

Randomly selected households from treatment villages were presented with 0.5kg maize seed packs. Seed distribution was conducted at the same time as baseline survey. this was accompanied by brief explanation on the trial protocol, which was also handed out to the participating farmers. All seed pack distributions were also accompanied by signs/varietal posters where the participating farmers were encouraged to display in the experimental plots.

Our study design will inform decisions concerning the proportion of farmers in a given area/region seed companies and other stakeholders should be distributing seed trial packs to and whether incentives are necessary to promote experimentation. These are important decisions given that scale directly affects marketing budgets for new varieties. Our work will measure the impacts of alternative promotion models on outcomes of interest at the farmer- and village-level, including awareness of new varieties, perceptions and beliefs about new varieties, demand and willingness to pay for new varieties.

2023-01-30

2023-08-30

• Willingness to experiment with new varieties
• Varietal switching – from old well known to newer varieties. Measured as 1) Proportion of planted varieties that are new, 2) Proportion of maize area cultivated with new varieties, and 3) Weighted average age of varieties planted by a household.
• Awareness and exposure to the specific varieties used in the study (experimental varieties)

• Adherence to experimental recommendations (for trial hosts in treated villages)
• trial hosts dissemination of experimental process and results
• Perceptions about new varieties (vis-à-vis old known ones) on performance, quality and production costs
• Expected yield distributions for new varieties vis-à-vis established ones (status quo)

Our treatments vary at cluster/village level. Treatments are defined at the village-level in order to evaluate village-level effects on awareness, perception and demand for experimental varieties. Our experimental design includes a control group and three treatment arms as defined below:
1) T1: Light treatment – this involves low saturation in terms of the proportion of farmers hosting the trials in a village. We endorsed 10% saturation level for this treatment arm. The randomly selected hosts were given 0.5kg of seeds to experiment with.
2) T2: Light treatment + incentives – we layer incentives to host farmers on top of T1. The hypothesis here is that incentivizing host farmers could encourage them to share information among their counterparts, which could then improve the effectiveness of trial packs model, even with low concentration. For the incentives, we are sending Ksh 1500 (approx. USD 11) to host farmers under this treatment arm in three instalments of equal amount.
3) T3: Heavy treatment – this involves high saturation of trial hosts in a village where 35% of households in a village host the trials of 0.5kg of seeds.
4) T0: Control – this is a comparison group where no intervention will be implemented.

Not available

Randomization was done in the office by the researchers using Stata.

We employed cluster level randomization where treatments were assigned at village level

Yes

208 villages

4,160 households

52 clusters control, 52 clusters treatment1 (low saturation), 52 clusters treatment2 (low saturation plus incentives), and 52 clusters treatment3 (high saturation)

Since we do not have data on outcomes of interest in our target population, our power calculations require assumptions. If we want to be able to compare each treatment against the control, as well as compare treatments against one another, then we maximize power by making each group the same size. This means that with a total of 208 candidate villages, we can work with 4 groups (1 control and 3 treatment arms) of 52 villages each. If we want to use the likelihood of purchasing a new variety in any given year as the basis for power analysis, we have no data on this, so we must assume some distributional characteristics. To begin with, let us assume that 10% of HHs are switchers, or do purchase a new maize variety in any given year. With a fixed number of clusters, we then examine the number of households required for data collection in each village to detect a treatment-induced change from that 10%, assuming that the intra-cluster correlation is 0.2 and standard levels of acceptable type I and II errors (i.e., alpha = 0.05 and power of 80%, using a one-sided test). We first consider an analysis of spillover effect among non-hosts that compares T1 (the lightest treatment) to the control group. Assuming baseline/control outcome of 10% switchers and MDE of 0.086 or 8.6 percentage points increase in uptake of new varieties among the treatment villages, we need 16 households per village. For the analysis of trial-hosts outcomes, one can assume a higher MDE since they receive the treatment directly (heavier treatment). With 52 clusters and MDE of 0.112 or 11.2 percentage points increase in uptake of new varieties among the hosts in treatment villages, we need 4 host households interviewed per village. These results are shown in Figure 1 below. For an analysis that compares treatment groups among each other, we assume T1 as the comparison group for T2 and T3, and assume that, after the intervention, 20% of T1 will be switchers. Assuming equal sample size across experimental groups, we shall be able to detect an MDE of 0.106 (10.6 percentage points) with 16 non-host households surveyed and 0.137 (13.7 percentage points) with 4 hosts households surveyed.

Experimenting for self and for others: farmer experimentation as a public good