Front-Line demonstration approaches for showcasing climate-resilient agricultural technologies to farming communities

Introduction

How do you persuade a farmer that a technology works when the evidence sits in a controlled research plot 200 kilometres from their paddock? This is the translation gap that front-line demonstrations attempt to close. Climate change is reshaping agricultural risk profiles worldwide, and New Zealand is no exception: Canterbury has experienced three of its five driest years on record since 2015, while Otago faces increasing frost variability and summer moisture deficits [1] . Adaptation requires that farmers adopt climate-resilient technologies, but adoption consistently lags behind the pace at which new options become available [2] . Front-line demonstrations (FLDs) differ from conventional research station trials in a fundamental way: they are conducted on farmers' own land, under their management, with their resources [3] . This shift from controlled to realworld conditions changes the demonstration from an abstract proof of concept into a tangible, locally credible showcase. When a neighbouring farmer's paddock produces 18% more under drought-tolerant wheat than the conventional variety planted alongside it, the message carries a weight that no extension pamphlet can replicate [4] . International evidence from India's national FLD programme shows adoption rates of 35 to 60% following well-managed demonstrations, compared to 10 to 20% from information-only approaches [5] . New Zealand's extension system, largely privatised and advisory-fee based since the 1990s, has been slower to adopt systematic FLD approaches than countries with publicly funded extension infrastructure [6] . Climate-resilient technologies face additional adoption barriers because their benefits often become visible only under stress conditions that may not occur during a single demonstration season [7] . This research evaluated FLD approaches for three climateresilient technology categories across Canterbury and Otago, tracking exposure-adoption relationships over three growing seasons and identifying which demonstration components farmers found most valuable [8,9] .

Research Area Description

The research was conducted across Canterbury and Otago regions on New Zealand's South Island. Canterbury, centred on the Canterbury Plains, is New Zealand's largest agricultural region, covering approximately 45,346 square kilometres and supporting intensive cropping, dairy, and dryland sheep farming [10] . Otago, to the south, spans 31,186 square kilometres with a more varied landscape ranging from coastal farmland to semi-arid inland basins. Research sites were selected from 24 farming properties distributed across both regions, chosen to represent a range of soil types, rainfall zones, and farming systems. Lincoln University's research farm network provided logistical support for demonstration establishment and monitoring.

Climatic and Environmental Conditions

Canterbury receives mean annual rainfall of 550 to 750 mm on the plains, with pronounced summer moisture deficits that have intensified over the past decade. The 2022-2023 season recorded only 389 mm at some inland stations, placing it among the three driest on record [1] . Otago's inland basins receive 350 to 500 mm annually, while coastal areas average 600 to 800 mm. Mean temperatures during the research ranged from 11.2 to 12.8 degrees Celsius annually, with an increasing frequency of late-spring frosts affecting establishment of summer crops [11] . These conditions made the research area particularly suitable for evaluating climateresilient technologies, as environmental stress occurred naturally during two of the three monitoring seasons.

Materials and Methods

Materials

Three categories of climate-resilient technologies were demonstrated: drought-tolerant crop varieties (four wheat and two barley cultivars), water harvesting structures (contour bunding and micro-catchment basins), and soil conservation practices (cover cropping and minimum tillage). Demonstration plots were established as paired comparisons on 24 host farms, with the climate-resilient option planted alongside the farmer's conventional practice. A structured assessment instrument measured demonstration exposure (attendance frequency, active participation, and peer discussion engagement) on an 8-point composite scale, and adoption decisions using binary and continuous measures. Pre-testing with 16 farmers from mid-Canterbury (August 2021) yielded Cronbach's alpha of 0.81 [12] . Ethical approval was obtained from the Lincoln University Human Ethics Committee (Ref: LU/HEC/2021-037, dated 14 July 2021).

Methods

A longitudinal mixed-methods design tracked 193 farmers (118 male, 75 female) across three consecutive growing seasons from September 2021 to June 2024. Farmers were recruited through catchment group networks and DairyNZ extension linkages. Quantitative data were collected at three time points: baseline, end of season two, and end of season three. Adoption was measured as both a binary variable (adopted/not adopted) and a continuous intensity scale. Exposure-adoption relationships were assessed using Pearson correlation and logistic regression, controlling for farm size, farmer age, and risk attitude [13] . Thirty semistructured interviews explored decision-making processes and demonstration preferences, analysed thematically [14] . Seasonal crop yield data from demonstration plots were recorded by trained field officers. < 0.001. Adoption rates increased sharply with exposure level across all three technology categories. High-exposure farmers adopted at 3.7 times the rate of low-exposure farmers for drought-tolerant varieties and 4.3 times for water harvesting. Drought-tolerant varieties had the highest overall adoption (38.9%), likely because they required no structural investment and could be tested alongside existing practices. The model correctly classified 76.4% of adoption decisions (Nagelkerke R-squared = 0.52). Experience of seasonal stress was the strongest predictor (OR = 1.88), followed by FLD exposure (OR = 1.62) and peer adopter contact (OR = 1.51). Each unit increase in FLD exposure increased adoption odds by 62%. extensive FLD programme has shown at scale: more exposure translates to more adoption, and the relationship is not simply about information transfer but about confidence building through repeated observation [5] . The finding that high-exposure farmers adopted at 3.7 times the rate of lowexposure farmers for drought-tolerant varieties closely matches the 3.5-fold difference reported by Singh and colleagues (2019) [15] for wheat FLDs in Rajasthan, despite the very different agricultural contexts. The emergence of seasonal stress experience as the strongest adoption predictor (OR = 1.88) is particularly relevant for climate-resilient technologies. Farmers who had personally experienced crop loss from drought or frost were nearly twice as likely to adopt, independent of demonstration exposure. This suggests that FLDs are most effective when timed to coincide with or closely follow stress events, a finding that supports the concept of teachable moments in extension pedagogy [16] . The 2022-2023 drought season, which delivered visible yield differences between demonstrated and conventional practices, coincided with the sharpest uptick in adoption intentions across the sample [17] . Peer adopter contact (OR = 1.51) reinforces the value of the farmer-to-farmer relay component built into the FLD design. Qualitative interviews revealed that farmers trusted yield data from neighbours' paddocks more than identical data from research stations or extension reports. As one participant noted, seeing results on land you drive past daily removes the uncertainty that printed material cannot address [18] . This observation aligns with Rogers' (2003) [19] concept of trialability and observability as key innovation attributes driving adoption.

Results

Practical Applications

These findings support several recommendations for extension organisations seeking to promote climate-resilient technologies. FLD programs should plan for multi-season engagement rather than single-event demonstrations, as adoption rates rise sharply with repeated exposure. Demonstration sites should be selected to maximise neighbourhood visibility, enabling the observability that qualitative data identified as a key trust-builder. Climate data should be integrated into demonstration reporting so farmers can connect yield differences to specific weather conditions. Extension services should invest in training and supporting host farmers as peer demonstrators, as farmer-tofarmer relay extends FLD reach at marginal cost. Finally, demonstration scheduling should anticipate and respond to climatic stress events, capitalising on the heightened receptivity that follows direct experience of production loss [20] .

Conclusion

This research demonstrates that front-line demonstrations are an effective approach for accelerating the adoption of climate-resilient agricultural technologies among New Zealand farming communities. The dose-response relationship between demonstration exposure and adoption was consistent across three technology categories and three growing seasons. Drought-tolerant varieties showed the highest adoption rates, reflecting their low entry cost and compatibility with existing farming systems. Experience of climatic stress amplified the effect of demonstrations, suggesting that extension timing matters as much as extension content. Peer adopter contact provided an additional adoption pathway that operated independently of formal demonstration events, confirming the value of embedding farmer-to-farmer relay within FLD designs. These findings argue for sustained, multi-season demonstration programs that combine on-farm evidence with peer learning, climate data integration, and strategic timing relative to environmental stress events. For New Zealand's largely privatised advisory system, the results point toward a public-good case for investing in structured FLD programs as a climate adaptation tool [21] .

References

1. National Institute of Water and Atmospheric Research (NIWA). Annual climate summary 2023: Drought conditions in Canterbury and Otago. Wellington: NIWA; 2024. p. 1-28.
2. Reisinger A, Kitching RL, Chiew F. Australasia: Climate change impacts and adaptation options. In: IPCC Working Group II Sixth Assessment Report. 2022. Chapter 11, p. 1581-1688.
3. Kishore K, Gupta AK. Front line demonstration methodology: Principles and guidelines for agricultural extension. Indian J Ext Educ. 2018;54(3):1-12.
4. Choudhary AK, Yadav DS, Singh A. Technological and extension yield gaps in oilseeds in Mandi district of Himachal Pradesh. Indian J Soil Conserv. 2009;37(3):224-229.
5. Meena ML, Singh D. Impact of front-line demonstrations on yield improvement of wheat in arid zone of Rajasthan. Indian Res J Ext Educ. 2017;17(3):30-33.
6. Payne T, White T. The future of agricultural extension in New Zealand: A post-reform perspective. J Agric Educ Ext. 2006;12(3):183-193.
7. Vermeulen SJ, Aggarwal PK, Ainslie A. Options for support to agriculture and food security under climate change. Environ Sci Policy. 2012;15(1):136-144.
8. Brown B, Llewellyn R, Nuberg I. Global learnings to inform the local adaptation of conservation agriculture in eastern and southern Africa. Glob Food Sec. 2018;17:213-220.
9. Pannell DJ, Marshall GR, Barr N. Understanding and promoting adoption of conservation practices by rural landholders. Aust J Exp Agric. 2006;46(11):1407-1424.
10. Statistics New Zealand. Agricultural production statistics: Canterbury and Otago regions. Wellington: Stats NZ; 2023. p. 1-67.
11. Salinger MJ, Renwick J, Mullan AB. The climate of New Zealand and recent trends. NIWA Sci Technol Ser. 2019;75:1-22.
12. Tavakol M, Dennick R. Making sense of Cronbach's alpha. Int J Med Educ. 2011;2:53-55.
13. Hosmer DW, Lemeshow S, Sturdivant RX. Applied logistic regression. 3rd ed. Hoboken: John Wiley & Sons; 2013. p. 1-528.
14. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77-101.
15. Singh AK, Singh RP, Kumar M. Front line demonstration impact on wheat productivity in Rajasthan: A multi-district analysis. Indian J Agric Sci. 2019;89(7):1148-1152.
16. Rickards L, Howden SM. Transformational adaptation: When incremental adaptations to climate change are not enough. Mitig Adapt Strateg Glob Change. 2012;17(2):261-282.
17. Fleming A, Vanclay F. Farmer responses to climate change and sustainable agriculture: A review. Agron Sustain Dev. 2010;30(1):11-19.
18. Klerkx L, Proctor A. Beyond fragmentation and disconnect: Networks for knowledge exchange in the English agri-food sector. Land Use Policy. 2013;30(1):13-24.
19. Rogers EM. Diffusion of innovations. 5th ed. New York: Free Press; 2003. p. 1-551.
20. Howden SM, Soussana JF, Tubiello FN. Adapting agriculture to climate change. Proc Natl Acad Sci U S A. 2007;104(50):19691-19696.
21. Greer G, Kaye-Blake W. Public and private roles in agricultural extension in New Zealand: A policy analysis. N Z J Agric Res. 2019;62(4):423-438.