The ornamental horticulture industry is a diverse agriculture sector that includes growers, wholesalers, landscapers, and retailers who produce and sell, install and maintain plants for beautification, property improvement, and ecological goods and services (e.g., commercial and residential landscapes, interiorscapes, urban parks, etc.). Hall et al. [
1] estimated that the U.S. ornamental horticulture industry had an economic contribution of
$159.57 billion in 2018 and employed 217,574 people. Between 2007 and 2018, the number of employees in the industry increased 2.75%. This increase was primarily due to landscape services (a gain of 15.6%), whereas employees for the nursery and floriculture production sector decreased by 18.9%. This loss of labor has not been isolated to horticulture production, but rather, has been felt across all agriculture sectors [
2,
3]. However, unlike other agricultural sectors that can be automated due to the uniformity in space and time of monoculture crops and crop tolerance of mechanical handling (e.g., precision farming, combining), many of the production tasks related to ornamental plant production are still performed by hand, making this a very labor-intensive agricultural sector. For instance, several studies have estimated that labor is responsible for 40 to 44 percent of production costs in the ornamental plant industry [
4,
5]. Consequently, actions to reduce labor needs or improve labor efficiency could aid the industry’s economic sustainability. Automation of production tasks is one means of improving labor efficiency given the potential benefits of reduced resources, labor, costs, and time [
6,
7,
8,
9].
1.1. Theoretical Framework
The Theory of Planned Behavior states that three constructs influence behavioral intention, including attitudes toward the behavior, subjective norms, and perceived behavioral control [
19]. Subjects’ attitudes include their positive and negative perceptions of the behavior, or the “degree to which a person has a favorable or unfavorable evaluation or appraisal of the behavior” [
19] (p. 188). For example, an individual may perceive or evaluate ANTs as being efficient and cost-saving and therefore positive or as complicated and costly and therefore negative. The Theory of Planned Behavior states that the attitude of the grower has an influence on their adoption behavior, and many studies support this claim. A study conducted to observe the effect of different variables on the adoption of native plants in their garden reported that individuals’ attitudes had a significant relationship with their intentions of using native plants [
10]. A significant positive relationship was reported between attitude and the intention to install rain garden technologies [
11]. Similarly, a significant and positive relationship was reported for attitude and students’ intention to practice Green Information Technology [
12], intention to adopt sustainable technology in greenhouse horticulture [
13], intention to adopt sustainable floriculture practices [
14], and intention to conserve water [
15]. However, there are a few conflicting studies which highlights the value in investigating these relationships in specific contexts and environments. Kumar Chaudhary et al. [
16] and Warner [
17] reported that attitude was not a significant predictor of behavioral intention for water conservation. Similarly, Hattam [
18] reported that attitude had a negative and insignificant influence on farmers’ conversion to organic practices.
Subjective norms are actual and perceived social pressure from peer or professional groups or other individuals who are perceived as having influence or expertise related to the behavior. Ajzen [
19] described subjective norms as “perceived social pressure to perform or not to perform the behavior” (p. 188). To provide clarity to adoption of ANTs, in this study, two additional nuances of subjective norms are used. The first is the segregation of subjective norms into descriptive and injunctive norms. Descriptive norms refer to what others actually do or what the norm is, and injunctive norms refer to what others approve of or what ought to be done [
20]. Injunctive and descriptive norms are often not distinguished from one another, which can lead to missed opportunities in understanding influences on behaviors [
21]. The second nuance is the concept of referent groups. While subjective norms as described in the Theory of Planned Behavior typically refer to the people that are important to a decision-maker [
19], individuals belong to many different referent networks that influence their behavior unequally [
22]. Referent groups are considered to be important factors affecting an individual’s behavior and social orientation, as well as of people’s behavior in multi-group contexts [
23]. In the case of nursery growers, important referent groups may include peer growers, customers, family, and the broader nursery industry. Therefore, rather than referring to growers’ subjective norms in general, it is possible to consider descriptive and injunctive norms from multiple referent groups. For example, a grower may perceive the growers they know would approve of them adopting ANTs (a strong injunctive norm) and also perceive the growers they know do not personally use ANTs themselves (a low descriptive norm).
A strong positive descriptive norm from close peers [
17,
24], a significant positive descriptive norm from other state residents, and a significant negative descriptive norm from the neighborhood were reported for the intention to adopt water conservation practices [
17]. Similarly, neighborhood gardening, a celebrity living in the neighborhood, and celebrity endorsement in the media have all been cited as sources of descriptive norms promoting the rise of native gardens and ecological gardening techniques in a community [
25]. However, in another study, a strong descriptive norm explained only the current adoption of water-saving practices and did not predict future behavioral intentions for water conservation [
26].
A strong positive injunctive norm from the neighborhood was reported to be more important than those from close peers in predicting the intention to adopt outdoor household water conservation practices [
17]. Uren et al. [
25] reported a strong injunctive norm for adopting native gardening, in which community members felt guilty for not adopting environmentally friendly and native gardening practices because the community and neighborhood placed a high value on environmental care and conservation.
Lastly, the third predictor of behavioral intention is perceived behavioral control, which is defined as “the perceived ease or difficulty of performing the behavior, and it is assumed to reflect past experience as well as anticipated impediments and obstacles” [
19] (p. 188). Past experiences [
16], skills, and resources are some of the factors impacting perceived behavioral control and the probability of the behavior occurring. Clark and Finley [
15] and Kumar Chaudhary et al. [
16] reported a positive and significant correlation between perceived behavioral control and the intention to conserve water. Similarly, a significant and positive relationship was reported between perceived behavior control and the intention to practice Green Information Technology [
12], and the intention to convert to organic production [
18,
27]. However, other studies reported no significant relationship between perceived behavioral control and the intention to conserve water [
11,
17]. These differences in findings again point to the importance of evaluating these relationships in specific contexts and environments.
Each of the factors described above (i.e., attitudes, descriptive and injunctive norms, perceived behavioral control) can impact the probability of a behavior occurring. For instance, if a grower perceives an ANT positively, observes another grower succeeding with that technology, perceives there would be approval for adopting, and has the resources to install the technology, s/he may be more receptive of adopting that technology compared to a grower lacking these qualities. In turn, behavioral intent can serve as a proxy to adoption and is predicted by attitudes, subjective norms and behavioral controls.
The Theory of Planned Behavior has been used successfully to explain grower adoption of water-saving technologies by strawberry farmers [
28] and nursery [
29], conservation agricultural practices by farmers from drought-prone areas [
30], green pesticides by pea farmers [
31], sustainable agriculture practices among pepper farmers [
32], organic practices for small-scale avocado farmers [
18], plastic recycling in strawberry [
33], landscape water conservation behavior among Florida residents [
16], and sustainable technology in the greenhouse horticulture industry [
13], among others. Here, we use these methods to address adoption of specific ANTs among U.S. nurseries.
When behavioral adoption is the end goal, interventions will be most impactful if they are informed by audience- and innovation-specific research [
34]. Very little is known about the processes leading to adoption of ANTs. In 2022, Rihn et al. [
35] reported several factors correlated with the propensity to adopt ANTs. Furthermore, in 2022, Warner et al. [
36] used the Diffusion of Innovations [
37] to identify perceptions of ANTs and factors predicting current and future adoption of ANTs. However, they noted their findings may have been diluted since they considered overall current adoption and likelihood of adoption of 27 ANTs collectively. The present study was undertaken to increase the precision in understanding influences on adoption by examining closely related ANTs. We applied the concept of technology clusters, or a grouping of connected ideas [
37] and the four clusters were: irrigation application, plant transport, plant handling, and agrochemical application. The value in assessing behaviors using technology clusters is the adoption of one innovation within these groups of innovations can spur the adoption of others [
37].
1.2. Overview of Nursery Automation
Nurseries have slowly increased automation adoption to ease their reliance on insufficient work force availability [
35]. Overall adoption rates remain low at near 33% [
38]. Automation potential varies by task [
9,
38]. While automation is perceived as advantageous and recognized by producers as having the potential to improve both crop quality and consistency, these technologies can be expensive to purchase and install because, in part, installation may necessitate changing nursery infrastructure [
35,
36]. For example, a potting machine can cost
$100,000 USD or more with additional expenses incurred for installation and infrastructure required for operation. Anecdotally, the conventional practice has been to hire and lay off employees in response to fluctuating production demands; neither capital intensive automation adoption nor current low labor availability affords that flexibility. Growers weigh these benefits and barriers among other characteristics as they determine whether to adopt ANTs. In this study, we evaluate growers’ intent to adopt ANTs to improve their labor efficiencies. ANTs are grouped into four technology clusters which are described below: irrigation application, agrochemical application, plant transport, and plant handling. Examples of a technology from each cluster is presented in
Figure 1.
1.2.1. Irrigation Application
Irrigation application in container nursery production must occur at least daily during the growing season. Historically, nursery employees would manually operate valves to control irrigation throughout the day until every zone had been irrigated. Often this is a dedicated position, and the irrigation technician spends every day all day opening and closing valves. While deciding how much, at what time, and at what interval to irrigate is complex, the physical task of opening and closing a valve is repetitive, time consuming, and time sensitive, and thus, lends itself to automation [
39].
Timers that can be programmed to open a valve at a given time for a given duration are inexpensive and have been commercially available for decades [
Figure 1a]. While unable to provide decision-making support, these timers can replicate the static irrigation operation often performed by low-level laborers. These automated systems generally perform as intended and greatly reduce person-hours spent on irrigation by eliminating the need to manually operate each valve and replacing it with less labor-intensive monitoring the system’s operation-verifying that it is running and troubleshooting as needed. Belayneh et al. [
40] calculated that the overall return on investment (ROI) for a sensor-based, automated irrigation system was 37.5%, largely due to a reduction in irrigation employee time. They attributed a
$12,150 annual savings to reducing irrigation management time that included physically monitoring irrigation zones. In another study comparing a nursery’s standard once every 48-h irrigation application to an automated daily irrigation system, a grower anecdotally reported labor savings as the most significant benefit [
41], despite the automated daily system reducing water use by 60%, due to the time-consuming, inefficient, and disruptive nature of manually operating their irrigation. While both Belayneh et al. [
40] and Cypher et al. [
41] compare two automated control systems: a timer-based and a sensor-based, their findings underscore the labor and, thereby, economic potential that reducing irrigation labor can have.
1.2.2. Plant Transport
According to Fang et al. [
42], the potting and transport of plants to both production and shipping areas require maximum labor inputs due to extensive materials handling operations. For example, labor is required to gather and stage raw materials in the queue that occurs between storage of raw materials (containers, substrates, fertilizer, plants, tags) and the potting area. Then, labor is required to pot plants, load them onto transport vehicles, transport plants to growing areas and unload them. The travel distance after potting affects labor availability during potting if potters also unload plants. If there are enough workers to stage and reload the queue, pot plants, load them, transport and unload them, then the post-potting travel distance can affect potting speed since the carts necessary to stage potted plants can become a bottleneck, particularly if there are not enough carts.
For transport after potting, a tractor or vehicle attached to one or more wagons or carts is used extensively. These mechanisms are propelled by either employees, electric motors, or combustion engines in the forms of rugged forklifts, tractors, or vehicles. The Trike™ Horticultural Forklift (AgriNomix, Oberlin, OH, USA), or self-engineered alternatives, can lift a block of plants simultaneously into a specially sized cart or wagon (
Figure 1b). Subsequently, a Trike™ can be stationed at the final growing destination to unload the plants in the production area if the ground and growing area can handle such articulated weight. The ability to integrate this type of technology depends on production system evolution as many nursery production beds, or growing areas, were not designed to be traversed by heavy equipment.
Upon harvest, plants will generally be transported to the sales yard for display and pickup or shipping area for delivery. Generally, this is done manually or by use of conveyors to bring plants to the front of the row and loaded onto similar wagons or carts. Because of the variable nature of sales and nonuniform orders by end use consumers, there is usually not enough plant material of any one cultivar to use the Trike™ to harvest plants for sales. Plants can be left in the field, lifted using a skid steer loader with articulated attachments (Nursery Jaws®, DPM, Inc., Davenport, NE, USA or Tree Boss® Tree Equipment Design, Inc., New Ringold, PA, USA), or a rugged forklift onto wagons for transport to the storage and shipping areas, or loaded directly onto shipping trailers in the field immediately.
1.2.3. Plant Handling
Nursery producers may pot container grown plants in substrates adjacent to the growing area to simply pot, move, and space plants directly in their final growing area (
Figure 1c). Depending on species and container size, plants may be grown using tight spacing (i.e., no space between pots) for a period of time or spaced at an interim or final spacing distance between pots to accommodate canopy expansion and growth habit for quality standards. The mobile potting apparatus, which may be no more than a cart full of substrate with hand trowels and input materials is moved to the next designated growing area. When inclement weather occurs or the nursery expands, a more concentrated, covered potting area is preferred to maximize labor utilization and availability, as well as raw materials delivery and handling.
The queue for planting field-grown plants can be quite different because soil preparation and subsequent raw materials handling can occur months previously (e.g., lime amendment), or during planting (e.g., nutrient application), or weeks later (e.g., both nutrient amendments and agrichemicals applied as a drench). Plant material handling consists of covering roots of bare-root plants to prevent desiccation, storing plants in a cold room, or both. It may be preferable to plant by hand because mechanical liner setters or pull behind tractor planters can place plants too deeply, which can affect transplant survival. However, with careful attention to root depth, mechanical planters with labor supervision can efficiently plant bare root or container liners at the proper depth. Manufacturers produce variously shaped tree spades for digging field grown tree root balls and placing them into wire baskets lined with burlap (i.e., balled and burlapped or B&B). The burlap can be tied around the rootball with string or fastened together with a pneumatic c-ring fastener.
During production of both container- and field-grown plant material, labor is used to space plants to manipulate their canopy growth to achieve final market quality. Robotic spacing uses an onsite, calibrated gridded system with guidelines to move individual plants short distances within current growing areas. Field grown trees and shrubs can be simultaneously lifted and shaken to remove soil and either planted again onsite for further growth at greater spacing or sold as bare root liner plants. Once lifted and soil is shaken loose from roots, plants can be mechanically bundled and tied for storage, and later shipped. If plants are to be planted again for further market size at wider spacings, canopy manipulation can be achieved by either pruning, staking and tying trunks or stems, or a combination of both. Stakes can be installed by hand, which occurs mostly in container production) or driven into ground with a mechanical stake driver that can be purchased or fabricated. Tying machines (e.g., Max Tapener®, MAX USA, Plainview, NY, USA) increase efficiency and accuracy after staking.
1.2.4. Agrochemical Application
Nursery producers routinely apply pesticides, plant growth regulators, and fertilizers to crops during production. These inputs may be solid or liquid. Liquid pesticides are typically applied to the crop canopy using air-assisted sprayers that are on trailers pulled by a tractor or attached to the tractor and operated using the tractor power take off. These mechanized air-assisted sprayers provide consistent constant-rate applications. The application rate (i.e., gallons of pesticide solution applied per acre) is often based on grower experience, an approximation of tree row volume, or a combination thereof. Smart farming technology was recently developed to partially automate this application rate decision-making by sensing the crop and calculating the crop volume and plant density based on crop characteristics (
Figure 1d). Additionally, the technology controls the nozzle actuation so that the sprayer only applies pesticide to the crop (i.e., does not spray between trees), as opposed to a conventional sprayer that sprays continuously [
43].
Numerous studies have shown that pest control was equivalent if not better utilizing the automated technology. For example, Chen et al. [
44] found equivalent or better control of five insects and six diseases on eight woody crops in Ohio. Fessler et al. [
45] found equivalent and commercially acceptable levels of powdery mildew control. Moreover, this technology reduced foliarly applied fertilizer and pesticide volume by 30–65%. Manandhar et al. [
46] examined the costs of this technology on two sizes of apple orchards, which have similar crop sizes and spray frequencies as nurseries. They calculated the payback time at 1.1 to 3.8 years depending on acreage in production and determined that the pesticide application time was reduced 27–32%, which led to a reduction in labor and fuel of nearly 30%. In spite of unbiased and seemingly compelling efficacy and economic data, and a relatively low capital investment (<
$35,000), this technology was not immediately widely adopted upon commercialization in 2020 (Smart Apply, Smart Guided, LLC, Indianapolis, IN, USA), underscoring the challenges to nursery technology adoption and the need for further behavioral science research.
Fertilizer is normally applied at the time a crop is potted into a container by either incorporating fertilizer into the substrate or by top dressing it on the surface of the substrate after planting. Depending on the length of the production cycle, fertilizer may be applied again during production. In its most typical form, top dressing entails stooping over and manually spooning fertilizer granules on container-grown crops by hand. It is an uncomfortable, repetitive task. Some producers use a “belly grinder” applicator to broadcast fertilizer but that can waste product due to its imprecise nature. Producers of field-grown nursery crops often apply fertilizer using implements that band or broadcast the fertilizer although more expensive controlled release fertilizers are hand applied. Low-cost (<$500 USD) mechanical fertilizer dispensers are commercially available and offer a labor savings. For example, a comparison between an automated fertilizer dispenser that allows workers to remain in a standing position reduced application time by 43% (Fulcher, unpublished data). Additionally, applicators in this study reported no decrease in energy level after using this dispenser but experienced a 0.25 point on a 1 to 5-point scale energy decrease after manually spooning on fertilizer. Similarly, workers estimated their mobility was reduced 0.25 points versus 1 point after applying fertilizer with the dispenser versus manually applying it.
Data for on-farm adoption of many ANTs and their subsequent effect on economic outcomes is lacking as mechanical advantage proof of concept in addition to reduced labor needs for tasks where some form of advantage was adopted are self-evident. Currently, there are few autonomous or even human-guided robots for use in nursery production of either container or field grown crops. A review of 18 economic analysis publications between 1990 and 2018 investigated the effect of autonomous or automated technologies on field production of various non-ornamental horticulture crops [
47]. Most of the publications reviewed reported positive results with automation adoption for returning investment and reducing labor needs. The authors noted that data are lacking for many automations across several disciplines and firm sizes.