Connecting consumers to available crops is challenging in areas with choice or competition. Let’s explore ways to address matchmaking demand with availability on the farm.

Communication by Subscription

flowchart TD
  F["Farms"]
  C["Crops"]
  B["Consumers"]
  M["Messages"]
  subgraph S["Subscribe"]
    S1["sign up"] --> S2["listen"] --> S3["filter"]
  end
  subgraph P["Purchase"]
    P1["desire"] --> P2["find"] --> P3["visit"]
  end
  subgraph G["Grow"]
    G1["plant"] --> G2["monitor"]
  end
  F -->G --> C
  F -->|send| M
  B --> S --> M
  B --> P --> C
  classDef cluster fill:white,stroke:grey,stroke-dasharray:5,font-weight:bold

Direct Subscription

Listening to announcements from known farms is a common pattern. Consumers have direct knowledge of farms from past experiences and subscribe to their communications by social media or email. Let’s consider how this scales to multiple crops and farms.

This model works when consumers want to support a certain farm. They just wait for announcements on crops being available for purchase. Perhaps the customer follows a few complementary farms for various crops.

This model struggles to keep customers informed. Constant announcements risk being tuned out. Consumers can make a phone call or check a website for updates, but it becomes a chore for multiple farms.

Aggregated Subscription

Promoting a collective of local farms to its community lends itself to a newsletter. This marketing approach is useful for a community that finds convenience in supporting local farms. This is popular with farmers markets but also works for clusters of farms with their own community.

This model removes the burden of multiple sources of information. A single communication answers availability questions for many options in the community.

A newsletter takes effort to put together so releases can happen less frequently than directly from farms. Consequently, they don’t replace direct subscriptions when more current information is required.

Consolidating direct feeds into a single stream achieves similar benefits as a newsletter, but the burden of parsing many messages with varying formats is high for the consumer.

In the end, neither form of subscription delivers current information and low cognitive load for the consumer. Having all the information is not helpful without a method to filter and summarize it.

Realtime Search

flowchart TD
  F["Farms"]
  C["Crops"]
  B["Consumers"]
  A["Availability"]
  subgraph S["Search"]
    S1["filter"] --> S2["verify"]
  end
  subgraph P["Purchase"]
    P1["desire"] --> P2["find"] --> P3["visit"]
  end
  subgraph G["Grow"]
    G1["plant"] --> G2["monitor"]
  end
  F -->G --> C
  F -->|update| A
  B --> S --> A
  B --> P --> C
  classDef cluster fill:white,stroke:grey,stroke-dasharray:5,font-weight:bold

Aggregation

Timeliness of information can be improved by moving from a push model to a pull model. Instead of farms advertising directly to consumers, they send updates to a centralized service. This service offers the consumers a way to identify options by filtering farmer updates.

The centralization of information reduces effort by both the farmer and consumer. The service becomes a common interface for finding any crop.

The consumer does not need to remember which farm has what crops. The options are presented based on current availability. Memory of past experiences is still a factor in selection but not a requirement for discovery.

Likewise, the farmer does not rely on name recognition for consumer discovery in this model. Competition becomes a matter of crop availability and consumer convenience.

Staleness and reliability of information is a concern in this model. Trust can be lost without timely updates, and confidence is not built with lack of confirmation from other consumers.

Collaborative Mapping

flowchart TD
  F["Farms"]
  C["Crops"]
  B["Consumers"]
  M["Map"]
  subgraph R["Report"]
    R1["visit"] --> R2["observe"] --> R3["submit"]
  end
  subgraph L["Locate"]
    E1["filter"] --> E2["verify"]
  end
  subgraph P["Purchase"]
    P1["desire"] --> P2["find"] --> P3["visit"]
  end
  subgraph G["Grow"]
    G1["plant"] --> G2["monitor"]
  end
  F --> G --> C
  F --> R
  R --> M
  B --> L --> M
  B --> P --> C
  B --> R

  classDef cluster fill:white,stroke:grey,stroke-dasharray:5,font-weight:bold

Crowdsourcing

Staleness of information can be addressed by allowing consumers to assist the farmers in updates. Confidence can be built here when other consumers are giving feedback on availability.

A map is a good form factor for delivering this information. Distance is an important criteria when determining convenience. A map allows consumers to choose their definition of convenience based on starting location.

The issue with allowing anyone to give status updates is trust. The farmer is the authority on the crop with consumers, so their crop updates are implicitly trusted. Other unknown sources don’t get the same courtesy. Who are they? Where are they?

Where they are is important! U-pick crop information is only valuable when timely from the farm. Only accepting reports that originate on the farm builds confidence. Those updates have the potential of being informed and accurate. Disinformation is still possible, so verifying with the farm is always a good idea before visiting.

Cropvine is this solution! Seasonal u-pick crops can be reported on by anyone at the farm. The farm staff and customers have the opportunity to keep the information discoverable on the map. This activity informs others looking for a farm to visit. The map stays current by expiring reports that could be outdated. This encourages participation and limits the lifespan of false reports.