Based on a comprehensive dataset covering major producing areas and a large number of crops and pathogens, results show that Decision Support Systems can play an important role in reducing the use of fungicides while maintaining a high level of crop protection. Across the 80 selected experiments, the median number of fungicide sprays applied with DSSs was 43% lower compared to standard calendar-based strategies. Moreover, for a given number of sprays, DSS-based fungicide programs were equally and even more effective (by up to 5.5%) for disease control.
More specifically, a higher efficacy was observed for DSSs when the number of spray applications was relatively low (<4). When the number of sprays increased, both DSS-based and calendar-based strategies showed similar disease control efficacy. The good performances of DSSs can be explained by the fact that, with DSS, spray timing is based on the observed or predicted risk of disease, allowing farmers to apply fungicides when they are most effective during the growing season.
In contrast, with calendar-based strategies, spray timing is preset without considering the changes in disease dynamics, leading to suboptimal treatments. In the case of low numbers of sprays, DSSs can target the optimal application periods better to halt disease progress, while some risk periods may be missed with calendar-based programs. When the number of sprays increases, calendar-based programs may then also cover all the risk periods, but at a cost of applying unnecessary high numbers of sprays.
Study shows that the goal of a 50% reduction in the number of fungicides (as envisioned by the ‘from farm to fork’ strategy of the European Green Deal) is not a utopia. When the number of sprays was reduced by 50% with DSSs compared to recommended calendar strategies, the increase in disease incidence never exceeded +5%. Considering the major economic savings and reduced environmental impacts obtained by halving the number of sprays, this increase in disease incidence can be considered bearable.
With the calendar-based programs, a 50% reduction in the number of sprays resulted in a higher increase in disease incidence of about +10%. Although twice as high as the level obtained with DSS, this effect also remains relatively small, i.e., much smaller than the level of 50% that would have been reached in the case of a one-to-one relationship between spray number and disease incidence. This result can be explained by the fact that the recommended calendar programs are probably overdosing fungicides.
Importantly, the found results robust to several important factors, such as the geographic location of the experiments, the taxonomic groups (necrotrophic or biotrophic lifestyles, dispersed by wind and water, causing monocyclic, polycyclic, and polyetic diseases with rather different epidemiological traits), the type of host (woody and non-woody hosts) and the types of fungicide.
Despite substantial progress in spray application methods with dose adjustment and reduced spray volumes to maximize coverage while minimizing drift, the number of fungicides sold annually in the EU increased by up to 11% over the last decade. Substantial reductions in the number of fungicides applied can therefore only be achieved by constraining the number of applications, which also results in greater economic savings than by just reducing spray volumes.
Study clearly shows that this is a realistic strategy. The use of fungicides in agriculture may be further reduced if DSSs are integrated with other disease management methods, now facilitated by the advent of precision farming. For instance, agronomic practices such as canopy management, crop sequences, and timing can reduce disease pressure and thus the need for fungicide sprays.
In the short term, the dependency of fungicides can be greatly reduced with the use of resistant cultivars. Nevertheless, those cultivars are typically bred for monoculture systems where pathogens are under intense selection pressure. Host resistance can be also exploited to design diversified farming systems with cultivar mixtures and intercrops, resulting in more durable plant resistance and fungicide efficacy.
The reduction in the use of fungicides is not only an issue in conventional agriculture but also in organic production. Control of airborne diseases by means of fungicides can be even more demanding in organic farming because the plant protection products allowed are often less effective. Of the 164,345 tonnes of fungicides sold in 2018 in the EU, 86,231 tonnes (52%) were inorganic fungicides1 (i.e., copper and sulphur), which are allowed in organic production.
The ‘from farm to fork’ strategy of the European Green Deal11 aims to boost the amount of agricultural land under organic farming in the EU from 7.5–25% by 2030. Under this scenario, DSSs will become even more important for optimizing treatments against fungal diseases as applications of fungicides need to be timed as precisely as possible on organic farms due to the relatively low efficacy of the products available.
Disease prediction models and action thresholds are essential components of DSSs for plant disease control. Empirical and mechanistic models are often evaluated (i.e., validated) by comparing predictions against independent disease observations. Model evaluation can be performed for instance by monitoring disease progress or exposing trap plants. Proper evaluation is an essential step to assess the reliability and generalization of the disease model under different situations.
However, the evaluation of DSSs should not be restricted to the evaluation of disease models and should also consider the assessment of the action thresholds determining appropriate deployment of disease management measures. The evaluation of DSSs also integrates factors related to data availability and communication, fungicide, and spray performance, among others, which in certain situations may be more important than the disease prediction model itself.
DSSs may sometimes be released onto the market without proper evaluation, resulting in inefficient disease management actions undermining their trustworthiness and rate of adoption. Proper evaluation of DSSs is typically performed by comparing disease intensity (i.e., incidence or severity) of a DSS-driven fungicide spray schedule with that of a routine calendar program and an untreated control21, as was the case in all the experiments included in meta-analysis.
The fungicides and modes of action included in meta-analysis represented all FRAC categories in relation to the risk of developing resistance. The database included mainly programs involving non-systemic fungicides (n = 42) and combinations of non-systemic and systemic products (n = 31). Only n = 7 experiments included programs with systemic fungicides alone.
These fungicides generally act against single biochemical targets and are thus considered of medium or high risk for the development of resistance. The application of fungicides with more than one mode of action, either in mixtures or in alternation, is recommended for resistance management. Typically, single-site systemic fungicides were combined with multi-site non-systemic ones with a low risk of resistance. However, due to their associated non-target effects, multi-site fungicides often present higher ecotoxicity than single-site compounds and so they are being progressively withdrawn.
With the increasing use of single-site fungicides, fungal resistance development and the subsequent loss of efficacy of fungicides are of increasing concern. In addition to reduced application dose and the combination of different modes of action, a limitation in the number of applications is also essential for the effective management of fungicide resistance.
In fact, for some groups of fungicides, the maximum number of applications per season is already strictly limited in order to slow down the build-up of resistance. The reduction in the number of sprays minimizes the exposure time and the overall selection for fungicide resistance. Therefore, in addition to lessening the environmental and economic costs of disease control, a reduction in the number of sprays based on DSSs could substantially diminish the risk of developing resistance, thereby prolonging the effective life of the fungicides, with a limited increase in disease risk.
A number of the selected publications with experiments comparing an untreated control with the calendar and DSSs fungicide programs were not included in meta-analysis because the sample size was not reported (n = 45), thus precluding the weighting of the individual studies. Sample sizes in the experiments included in meta-analysis were relatively large, ranging from 80–1500 with a median of 500.
The database covered practically the full range of disease incidence values (from 1.5–100%), representing different disease pressure scenarios. Studies reporting disease severity or derived metrics, such as the area under the disease progress curve (AUDPC), were not included (n = 28). While disease incidence is the number of diseased plant organs in relation to the total number evaluated, disease severity is the proportion of the actual host area affected. Disease severity is typically evaluated using standard area diagrams, disease scales, or ordinal rating scales.
However, severity measurements are not standardized. Depending on the study, the intervals, ranges, and ratings used differ greatly among diseases and even for the same disease. Moreover, they seldom represent equal gradations of the underlying continuous disease severity scale. This leads to serious statistical constraints when, as in meta-analysis, experiments using different disease severity assessment methods should be combined.
In contrast, analyzing disease incidence is relatively straightforward, and robust statistical methods can be applied based on generalized linear mixed models. Moreover, in the case of fruit and vegetables, disease incidence is more informative than severity in relation to loss in marketable yield, since the presence of just a few lesions makes this produce out of grade.
After more than a decade with Directive 2009/128/EC in force, official reports noted the limited implementation of the measures to achieve more sustainable use of pesticides in the EU. Action thresholds and reduced application frequencies were among the measures with a relatively low level of adoption. Growers’ aversion to risks has been pointed out as one of the main reasons for the limited implementation of DSSs.
Adoption of DSSs is even more restricted in intensive high-input crops, because the consequences of a disease outbreak by missing a spray (i.e., false-negative case) sometimes exceed the economic benefits of reducing the number of fungicides applications13,27. Indeed, the use of fungicides is highly dependent on the crop, with dosages ranging from 0.2 kg ha−1 in arable crops to 11.3 kg ha−1 in fruit and vegetables in the EU.
Consequently, different degrees of adoption of DSSs depending on the crop may have a considerable effect on the overall reduction in fungicide use. Nevertheless, study indicated that crop or pathogen types did not have a substantial impact on the risk of disease when halving the number of sprays, suggesting that perceived rather than actual risks are likely driving growers’ cautiousness regarding the adoption of DSSs.
Those perceived uncertainties in the timing of fungicide applications can be narrowed down by increasing the amount of timely and spatially explicit data on the weather and the onset of disease outbreaks available to growers16. Furthermore, it is essential to deploy DSSs with a high degree of credibility, after proper calibration and evaluation. The involvement of growers in the process of DSS development through a participatory approach is also an interesting and promising avenue.
Finally, the new European Green Deal, through its major role in reshaping the EU common agricultural policy, will certainly set the scene for much wider adoption of DSSs and more sustainable disease management.
Source: Lázaro, E., Makowski, D. & Vicent, A. Decision support systems halve fungicide use compared to calendar-based strategies without increasing disease risk. Commun Earth Environ 2, 224 (2021). Doi: 10.1038/s43247-021-00291-8