Through learnings and reflections from a water-use efficiency (WUE) pilot study, this paper examines the use of co-innovation. Led by hydrologists, this paper tracks the cycle of trust building among stakeholders, co-learning of WUE problem, co-developing of possible solutions and practices, identifying the need for capability development to overcome constraints, and finally enabling confidence among stakeholders in adapting new practices. The hydrologists built the trust among stakeholders by matching and validating stakeholders’ experiential knowledge through on-farm biophysical observations of water use (irrigation) practices. This trust allowed the stakeholder group to identify constraints to improving WUE and helped the hydrologists to devise biophysical solutions and practices that support farmers in better managing their irrigations. Observations also indicated that the process of capability development needed to take into account of farmers’ experiential knowledge and integrate a learning–practice–confirmation cycle that would boost their (farmers’) confidence in using newly acquired capability.

KEY MESSAGE

  • An understanding of co-innovation process in practice.

  • An understanding of how trust among stakeholders could be used in communicating science outputs for better outcomes.

  • An understanding of how co-innovation could lead to co-learning and capability development and improved uptake of science outputs.

INTRODUCTION AND BACKGROUND

In New Zealand (NZ), the use of technologies such as soil moisture sensors to assess demand and schedule irrigation has remained stagnant over the last three decades, even though the area under irrigation has been doubling every 12 years since 1970 [1]. It was reasoned that the use of a tech-transfer approach to disseminate efficient irrigation scheduling practices has failed to include wider water management issues, such as limits on water allocation, competing demands for water, multiple water users, and farmers’ aspirations to achieve greater water-use efficiency (WUE) [2]. Irrigation management in NZ was identified as a wicked problem [2, 3], and when addressing a wicked problem, a linear tech-transfer approach, or a linear sequence of production and diffusion of knowledge, has been found ineffective [4]. It was highlighted that on-farm irrigation management is complex as it is “influenced by several layers of technical, hydrological, climatic, societal, environmental, economic, regulatory and cultural factors, which individually and collectively impose controls and constraints on farmers’ ability and desire to adopt efficient irrigation practices” [2], pg 139.

A pilot study was initiated in a river-based irrigation scheme, the Waimakariri Irrigation Scheme (WIS) in 2012, to examine the barriers to the uptake of irrigation scheduling practices among the farmers and the usefulness of a co-innovation approach in improving the uptake. Co-innovation is described as a multi-directional, multi-level, and multi-stakeholder approach, where knowledge and input from every stakeholder are valued in every phase of the project, from defining the problem to evolving solutions. The co-innovation principles used in the pilot study were (1) take time to understand the problem from multiple (stakeholder) perspectives; (2) be inclusive of all perspectives; (3) value all sources of knowledge; (4) strive to learn from each other by actively listening and understanding; (5) keep sight of the shared vision; (6) be honest, open, and constructive while interacting with stakeholders; (7) be aware of the wider context of the problem; (8) be flexible and adaptable; and (9) stick with the co-innovation process despite its frustrations [5, 6].

Through a selection of examples from the 5-year pilot study, we reflect on the cycle of trust building, co-learning, capability development, and confidence building as guided by a co-innovation approach.

CASE EXAMINATION

Pilot Study Description

The WIS is a farmer cooperative with ~240 shareholders, irrigating a total area of 18,000 ha. The irrigation scheme is located in the South Island of NZ. The scheme abstracts water from the Waimakariri River, which undergoes significant flow fluctuations in summer, resulting in poor supply reliability. An earlier study, which examined the irrigation practices in WIS, concluded that poor supply reliability had been an impediment to efficient irrigation practices and that farmers tend to manage their irrigations based on supply rather than on demand (soil/crop needs) [7]. Based on this, the hydrologists in the present pilot study hypothesised that irrigation scheduling could be improved by combining current soil moisture (a proxy for soil and/or crop water demand), supply (river flow), and future supplies (next 2-, 6-, and 15-day rainfall).

The role of hydrologists in the pilot study was to establish a co-innovation process that would be inclusive of all stakeholders—pilot study funders, hydrologists, farmers, industry professionals, regulators, and irrigation scheme managers. The pilot study originally included five farms (Figure 1). At each pilot farm, a rain gauge and soil moisture sensor were installed under one spray irrigator, and the data were relayed back to farmers electronically every hour. Pilot farmers were given 24/7 web access to farm-specific, near real-time rainfall, irrigation, soil moisture and soil temperature data, and 2-, 6-, and 15-day weather forecasts that were updated every 6 h. The forecast data included an estimate of timing and amount of rainfall expected. A daily email was also sent to all pilot farmers, displaying the rainfall, irrigation, soil moisture, and soil temperature data of the last 7 days and the 2-, 6-, and 15-day weather forecasts. The hydrologists did not provide any irrigation recommendation.

FIGURE 1.

Rainfall and soil gradient across the WIS area as defined by the farmers at the start of the pilot. The five pilot farms (four dairy and one crop) are also identified in the scheme area.

FIGURE 1.

Rainfall and soil gradient across the WIS area as defined by the farmers at the start of the pilot. The five pilot farms (four dairy and one crop) are also identified in the scheme area.

Throughout the season, several one-on-one conversations via email, phone, and in-person about the information ensued between the pilot farmers and hydrologists. At the end of each irrigation season (typically in May), all stakeholders were invited to come together for a workshop (referred to as “farmer workshop” hereafter). The co-innovation approach used in the pilot study was aimed at creating and enhancing co-learning opportunities that would lead to an agreement on problem description, identification of barriers to change, and opportunities to adopt improved irrigation practices. The farmer workshops would start with a hydrological appraisal of the just-concluded irrigation season, discussing how much irrigation was applied, when, and why, and the differences in irrigation practices between the pilot farms (see an example in Figure 2). The hydrologists also presented modelled information on drainage based on measured rainfall and irrigation and published soil hydraulic properties. The stakeholder group collectively reflected on the current irrigation practices and explored opportunities to improve WUE through improved irrigation scheduling. The discussions were unmoderated, open, and transparent, and provided a window into the diverse perspectives and knowledge existing within the stakeholder group. Detailed description of the workshop processes and the outputs and outcomes from the co-innovation processes used in the pilot study are available elsewhere [2, 8]. Here, we have drawn examples from the pilot study to describe the genesis of trust among the stakeholders and how the trust enabled co-learning and capability development. We also present an example highlighting the need to extend the cycle of trust building, co-learning, and capability development into confidence building in using new technologies and practices.

FIGURE 2.

Rainfall, irrigation, and drainage gradient across the pilot farms resulting in gradients in rainfall, soil type, and irrigation management. Both rainfall and irrigation were measured and drainage estimated based on soil water holding capacity. The gradients matched that of farmers shown in Figure 1.

FIGURE 2.

Rainfall, irrigation, and drainage gradient across the pilot farms resulting in gradients in rainfall, soil type, and irrigation management. Both rainfall and irrigation were measured and drainage estimated based on soil water holding capacity. The gradients matched that of farmers shown in Figure 1.

Building Trust

Building trust between the pilot farmers and hydrologists was identified as a key task for the project team to achieve its goal of improved WUE. On-farm biophysical observations of irrigation practices and observations of local weather and soil conditions that could influence irrigation scheduling (e.g., differences in rainfall and soil water holding capacities across WIS area) were used as tools, or boundary objects, to initiate a conversation between the pilot farmers and hydrologists. When considering the selection of pilot farms, the hydrologists met with the prospective farmers and the irrigation scheme manager to gain an understanding of local soil and weather conditions and how they were incorporated currently (then) incorporated into irrigation management decisions. Even though the farmers lacked scientific skills or tools for monitoring of weather, they possessed an enormous amount of experiential knowledge on local weather gradients. Farmers understood that the prevailing weather brought more rainfall to one end of the scheme than the other, although they (farmers) did not know the strength of this gradient, and they did not use it in their irrigation management decisions. Similarly, the farmers possessed a good knowledge of variability in soil water holding capacities, though they rarely applied that knowledge in practice. Based on these preliminary conversations, the hydrologists deliberately chose pilot farms distributed across the length and breadth of the scheme to quantify the gradient, specifically the rainfall differences (see Figure 1).

Measurement of rainfall at the pilot farms helped to quantify the rainfall gradient across the scheme. At the 2013 farmer workshop, when the hydrologists presented the gradient information (Figure 2), it matched farmers’ experiential knowledge (Figure 1). This match between scientific observations and experiential knowledge enabled a trust in the co-innovation process. The hydrologists took this opportunity to further the use of this knowledge by highlighting the differences in irrigation applied between farms, as influenced by weather and soil water holding capacity gradients. This also allowed the irrigation scheme manager to understand the demand (water ordering) gradient across the scheme.

The co-innovation-based pilot study was built on the premise that stakeholders could co-learn and co-identify barriers, opportunities, and constraints to achieve a better WUE. To enhance this experience, each pilot farmer, in addition to their farm, was provided with access to irrigation data from other pilot farms. This open data access helped in building trust among the farmers. When the pilot farmers arrived at the farmer workshop, since everyone had prior knowledge of others’ irrigation practices, a free and transparent discussion ensued. The concept of matching current and forecast supplies against current demand was a new concept, so the pilot farmers commented on the need for a confirmation of their practice. This need was intensified as hydrologists deliberately did not provide any irrigation recommendation. The hydrologists indicated that farmers need to upskill their decision-making capability, taking their own constraints and capacities into consideration, instead of relying on external agents (e.g., the hydrologists in the pilot study) who may lack on-farm knowledge. The ability to see others’ irrigation data was helpful with this capability building. When irrigation scheduling between pilot farms coincided, it provided those pilot farmers a positive confirmation that they are on the right track. However, when their scheduling did not match, it gave them a moment to reflect on their practice and that of the other pilot farmer. Without such a reference, these moments of pause-and-reflect would not have been possible in the pilot study. Here, the role of hydrologists was limited to enabling such co-learning and capability development opportunities.

Trust Leading to Co-Learning

Once a trust in the co-innovation process was established between the pilot farmers and hydrologists, the latter delved into understanding farmers’ perception to the use of weather forecasts for irrigation scheduling. A discussion at the 2014 farmer workshop indicated that farmers generally use weather forecast at the start and end of irrigation seasons (termed as “shoulder” season, September, October, March, and April) and rarely during peak seasons (November through February). Farmers perceived that any interruption to irrigation during the peak season would aggravate the dry conditions, thereby stressing the crop and affecting the yield. While farmers acknowledged that rainfall during the peak season would add to soil moisture, they generally discounted these additions, as they do not use rainfall forecasts for irrigation scheduling. This provided an insight into farmer decision-making and an opportunity for the hydrologists to analyse irrigation applications and drainage during the peak season.

At the following farmer workshop in 2015, based on drainage data collected at one of the pilot farms, the hydrologists presented evidence for significant drainage from irrigation during the peak season of 2014 (Figure 3). The hydrologists highlighted that the drainage during peak season resulted from poor irrigation scheduling, either irrigating more than the soil could hold (exceeding soil water holding capacities), irrigating when soil was wet from previous irrigation and/or rainfall events, or irrigating before a significant rainfall event. Unlike the earlier example, where the farmers’ experiential knowledge and the hydrologists’ science data agreed, here they contradicted. However, the trust built in the co-innovation process helped in effectively communicating the results. This also led to further discussion among the stakeholders, which revealed that farmers lacked capability to manage irrigation and drainage together, an important step to better WUE.

FIGURE 3.

Rainfall, irrigation, and estimated drainage at one of the pilot farms during an irrigation season. During the shoulder (irrigation) seasons, farmers use weather forecast but rarely during the peak season. The evidence presented here shows that use of weather forecast during the peak season could have reduced the number of instances of irrigation-induced drainage.

FIGURE 3.

Rainfall, irrigation, and estimated drainage at one of the pilot farms during an irrigation season. During the shoulder (irrigation) seasons, farmers use weather forecast but rarely during the peak season. The evidence presented here shows that use of weather forecast during the peak season could have reduced the number of instances of irrigation-induced drainage.

Co-Learning Leading to Capability Building

At the start of the pilot study (2012), soil moisture sensors were installed at 20 cm (depth to root zone, 40 cm), and soil moisture was used as a proxy to trigger irrigations. In 2014, the local regulatory authority, Environment Canterbury (ECan), published agricultural good management guidelines [9] that emphasised the need to reduce drainage to control nutrient leaching, as leaching below root zone in irrigated farms was identified as a key environmental issue in NZ [10]. Since ECan was a stakeholder at the farmer workshop, the emergence of drainage and leaching management issues was conveyed directly to the group, allowing them sufficient time to look for solutions.

At the 2015 workshop, the hydrologists, based on a water balance model developed from observed drainage data from one of the pilot farms, presented estimations of drainage at all five pilot farms (Figure 2). These data provided an indication of the extent of drainage during the irrigation seasons, but these data were not available to farmers in real time. This meant that the farmers could not effect a change to their irrigation practice in real time. The pilot study farmers urged the hydrologists for a solution that would allow them (farmers) to assess the impact of their irrigation in real time. The hydrologists sensed that real-time knowledge of drainage would provide an opportunity to enhance the uptake of irrigation scheduling practices and weather forecast.

Following the 2015 farmer workshop, at each pilot farm, the hydrologists installed a profile soil moisture sensor that measured soil moisture within (0–40 cm) and below (40–80 cm) the root zone. The soil moisture measurements within the root zone were used as a proxy for irrigation demand and below the root zone as an indicator of drainage. Whenever irrigation (or rainfall) drains below the root zone, this was recorded as an increase in soil moisture in that zone, and thus the farmers were able to see the impact of the last irrigation (or rainfall). Also, combining the management of irrigation and drainage using one sensor greatly increased the capability of farmers and helped them to provide evidence to the regulator of their irrigation practices. However, these changes demanded that the hydrologists remain open and flexible to alter their biophysical observations in response to emerging signals.

Co-Development of Solutions

Co-development of solutions is central to a co-innovation process. At WIS, farmers are required to place their irrevocable water orders 48-hour ahead of time. However, the pilot farmers found that the 2-day rainfall forecast to be the most reliable, which meant that there was very little time available for them to process the forecast information and decide on water ordering. This quandary was discussed in one of the farmer workshops. One of the pilot farmers questioned the usefulness of 2-day rainfall forecast for their farm where no storage was available to store the ordered water in case of a significant rainfall forecast. The neighbouring farmer (not a pilot study farmer), who was present at the workshop, offered their pond for storage if such a situation arises. Neither the constraint nor the solution emerged from the hydrologists. The workshop offered the stakeholders a forum to discuss such constraints and co-evolve solutions. Here is a case where the farmers, because of their proximity to each other and being a part of the same irrigation scheme, were able to devise a solution within constraints. The role of the hydrologists was limited to providing an enabling environment, such as the farmer workshop, where such constraints to change could be discussed and resolved, where possible.

Transiting from Capability Building to Confidence Building

Finally, the cycle of learning, practice, and confirmation was found to be very important in sustaining the uptake of new technology or practice, such as the use of profile soil moisture sensor. Such a cycle needs to be repeated more than once to ensure the learning is well integrated into practice. When the profile soil moisture sensors were installed in 2015, the hydrologists worked with the pilot farmers to ensure that they understood its operation, i.e., soil moisture within root zone was proxy to irrigation demand and below root zone to drainage. The pilot farmers were told to keep the soil moisture increases in the below root-zone layer to be minimal during irrigation events, which would give them the confirmation that all applied irrigation had been held within the root zone. This was a case of self-referencing and confirming their practices based on real time observations. Following a few one-on-one training sessions, one of the pilot farmers reported significant changes to their irrigation practice. Over the month following the training, none of the irrigations in that farm resulted in increases in soil moisture below root zone, confirming an irrigation practice that resulted in no drainage (see Figure 4, section A). The pilot farmer even skipped a few irrigation events based on root zone soil moisture measured during this period and weather forecast, thereby saving on time, labour, money, and water. However, the same pilot farmer, 2 months later scheduled irrigations that had resulted in frequent increases in soil moisture in the below root-zone layer (Figure 4, section B). On enquiry, the farmer indicated that they scheduled those later irrigations as they perceived that the grass growth was low, which they concluded due to dry soil conditions. Even though they could access soil moisture data from within the root zone, in the absence of any explicit reason for the lack of grass growth, they reverted back to their original irrigation practice. Further investigation proved that the poor grass growth might have resulted from poor nutrient availability. The drainage and leaching resulting from poor irrigation practices further depleted nutrient availability, eventually impacting the grass growth. While the capability development allowed a change early on, a lack of confidence in that newly acquired capability resulted in the pilot farmer falling back to their original practice. This emphasised the need to take into account the farmer’s inherent knowledge and integrate an optimal learning–practice–confirmation cycle when such new technologies and practices are introduced.

FIGURE 4.

The cycle of learning–confirmation–practice is critical in enabling and establishing new practices. The farmers trained in managing irrigation and drainage simultaneously were able to eliminate irrigation-induced drainage in December and January (see section A), but practiced irrigation that led to drainage in February and March (see section B).

FIGURE 4.

The cycle of learning–confirmation–practice is critical in enabling and establishing new practices. The farmers trained in managing irrigation and drainage simultaneously were able to eliminate irrigation-induced drainage in December and January (see section A), but practiced irrigation that led to drainage in February and March (see section B).

CONCLUSION

A co-innovation approach provides opportunity to all stakeholders to explore and learn about a problem from multiple perspectives. The role of researchers in a co-innovation setting varies with context, which itself could vary during the span of a project. Trust among stakeholders and in the co-innovation process is seldom recognised as an outcome due to its non-tangible nature. However, in a multi-stakeholder, building trust among participants is a key enabler to sharing perspectives, co-learning and co-definition of problem space, and co-development of solutions. Biophysical observations, specifically observations of stakeholders’ (on-farm) practices, provide a good platform in establishing stakeholders’ trust in a co-innovation process.

Even when on-farm observations contradicted stakeholder perceptions, the trust in co-innovation process could enable moments of reflection and (co-)learning. In the irrigation pilot study described here, the co-learning was noticeable among all stakeholders and not just among pilot farmers (end-users). For instance, the hydrologists learned to present information that takes into account of stakeholders’ perceptions and knowledge gaps. One of the key learnings for all stakeholders was the need to spend more time in practising the lessons learned. In the absence of any confirmation of their practice, the confidence in using new practices could decline over time, resulting in old habits to creep up.

CASE STUDY QUESTIONS

  1. What are the advantages and limitations of a co-innovation process in a multi-stakeholder setting?

  2. Apart from biophysical observations of stakeholders’ irrigation practices, how else could trust be established in a multi-stakeholder setting?

  3. While transferring specific on-farm observations of irrigation practices from one stakeholder group to another, how could the experiences and lessons be transformed across the groups?

AUTHOR CONTRIBUTIONS

The lead author MS is the programme manager for the research programme “Justified Irrigation” and the lead researcher of “Primary Innovation” programme. GE is an irrigation scientist who contributed to the designing and implementation of Justified Irrigation and Primary Innovation programmes. Both authors contributed equally to idea conceptualisation, funding acquisition, and investigation.

We thank the pilot study farmers and the Waimakariri Irrigation Scheme for their continued support and cooperation in undertaking this study.

FUNDING

We acknowledge the New Zealand Ministry of Business, Innovation and Employment for funding the programmes and Primary Innovation (CONT-30071-BITR-AGR) and Justified Irrigation (CO1X1617) that supported the work presented here.

COMPETING INTERESTS

The authors declare that no competing interests exist.

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