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Many farmers hesitate to adopt new management strategies with actual or perceived risks and uncertainties. Especially in ornamental plant production, farmers often stick to current production strategies to avoid the risk of economically harmful plant losses, even though they may recognize the need to optimize farm management. This work focused on the economically important and little-researched production system of ornamental heather (Calluna vulgaris) to help farmers find appropriate measures to sustainably improve resource use, plant quality, and profitability despite existing risks. Probabilistic cost-benefit analysis was applied to simulate alternative disease monitoring strategies. The outcomes for more intensive visual monitoring, as well as sensor-based monitoring using hyperspectral imaging were simulated. Based on the results of the probabilistic cost-benefit analysis, the expected utility of the alternative strategies was assessed as a function of the farmer’s level of risk aversion. The analysis of expected utility indicated that heather production is generally risky. Concerning the alternative strategies, more intensive visual monitoring provides the highest utility for farmers for almost all levels of risk aversion compared to all other strategies. Results of the probabilistic cost-benefit analysis indicated that more intensive visual monitoring increases net benefits in 68% of the simulated cases. The application of sensor-based monitoring leads to negative economic outcomes in 85% of the simulated cases. This research approach is widely applicable to predict the impacts of new management strategies in precision agriculture. The methodology can be used to provide farmers in other data-scarce production systems with concrete recommendations that account for uncertainties and risks.

Ornamental heather (Calluna vulgaris) production is characterized by high risks such as occurrence of fungal diseases and plant losses. Given the general absence of formal research on this economically important production system, farmers depend on their own approaches to assess plant vitality. We provide a reproducible, affordable and transparent workflow for assessing ornamental plant vitality with spectroscopy data. We use hyperspectral imaging as a non-invasive alternative for monitoring plant performance by combining the long-term experience of experts with hyperspectral images taken with a portable hyperspectral camera. We tested a custom-made setup deployed in a horticultural production facility and screened thousands of heather plants over a period of 14 weeks during their development from cuttings to young plants under production conditions. The vitality of shoots and roots was classified by experts for comparison with spectral signatures of shoot tips of healthy and stressed plants. To identify wavelengths that allow distinguishing between healthy and stressed heather plants, we evaluated the datasets using Partial Least Squares regression. Reflectance in the green (519–575 nm) and red-edge (712–718 nm) region of the spectrum was identified as most important for classifying plants as healthy or stressed. We transferred the trained Partial Least Squares regression model to independent test data obtained on a different date, correctly classifying 98.1% of the heather plants. The setup we describe here is adjustable and can be used to measure different plant species. We identify challenges in data evaluation, point out promising evaluation approaches, and make our dataset available to facilitate further studies on plant vitality in horticultural production systems.

Agricultural management decisions are usually made without perfect knowledge. Decision Analysis (DA) approaches translate available uncertain information on costs, benefits and risks involved in decisions into actionable management recommendations. We illustrate the use of DA procedures to inform decisions on disease management strategies in ornamental plant production. We worked with heather growers and other stakeholders in North Rhine-Westphalia, Germany, to model the impacts of changing disease management practices and to generate comprehensive forecasts of net returns. Through sensitivity analysis and Value of Information assessment we identified critical uncertainties regarding the feasibility of improved practices. Partial Farm Budgets for decision options ranged from a loss of more than 200,000 € to a gain of nearly 70,000 € per hectare and year. Findings suggest that reducing pesticide applications without additional monitoring may substantially increase production risks (chance of loss of 76%) and that intensified plant monitoring is likely to increase net benefits (chance of gain of 68%) by allowing earlier detection and more focused fungicide application. Our Decision Analysis approach facilitated ex-ante evaluation of innovative management strategies in heather production, and it holds promise for similar evaluations in other agricultural production systems.