Leaf Wetness Research Articles

Can machine learning classification methods improve the prediction of leaf wetness in North-Western Europe compared to established empirical methods?

Leaf wetness is an important input parameter into disease prediction models. The use of machine learning algorithms for the classification of leaf wetness measurements from 30 meteorological stations in North Western Europe during the period of January 2014 to October 2018 was assessed in this study. The accuracy of the empirical models utilised within in this study was enhanced by increasing the relative humidity threshold from 90% to 92%. Increasing the relative humidity threshold led to an average increase in the classification accuracy of 1.12%. The use of machine learning classification algorithms consistently provided more accurate results for the prediction of leaf wetness when compared to the empirical models that were studied with an average increase in the classification accuracy of 4.85%. The sub-division of the data into regional subsets had a greater effect on the accuracy of the models than the temporal sub-division of the data. Machine learning classification techniques performed well compared with previously established empirical models for the prediction of leaf wetness. Further improvements in the algorithms are possible, making the techniques studied here a viable research tool.

Approaches for the Prediction of Leaf Wetness Duration with Machine Learning.

The prediction of leaf wetness duration (LWD) is an issue of interest for disease prevention in coffee plantations, forests, and other crops. This study analyzed different LWD prediction approaches using machine learning and meteorological and temporal variables as the models’ input. The information was collected through meteorological stations placed in coffee plantations in six different regions of Costa Rica, and the leaf wetness duration was measured by sensors installed in the same regions. The best prediction models had a mean absolute error of around 60 min per day. Our results demonstrate that for LWD modeling, it is not convenient to aggregate records at a daily level. The model performance was better when the records were collected at intervals of 15 min instead of 30 min.

Epidemiology and prediction models for themanagement of rapeseed–mustard diseases: current status and future needs

The productivity of rapeseed-mustard in India is quite low as compared to the world scenario. It is mainly due to important diseases, Alternaria blight, white rust, downy mildew, powdery mildew, and white or Sclerotinia rot. Knowledge of epidemiologyand forecasting provide the basic information to developefficient and workable plant disease control models. The various weather variableslike temperature (T), relative humidity (RH), rainfall, wind velocity, and direction, leaf wetness duration, and solar radiation influence differentparameters of infection process, and disease development. Interaction between these weather variables and disease development pave the way for the development of the prediction models. Prediction models developed for the management of important diseases of rapeseed-mustard revealed that Alternaria blight is favoured by Tmax of 20–25 °C, Tmin of 15 °C, RHmor > 90% and RHeve > 50% where as white rust influencedby > 15 °C and RH > 65% with intermittent rains. Similarly, for downy mildew, Temprange of 15–20 °C with high RH was considered optimal for its progress. Leaf wetness duration of 4–6 h at 20 °C and 6–8 h at 15 °C is essential to initiate the downy mildew infection. Stag-head due to mixed infection of downy mildew and white rust is favoured by a Temp 20 °C with high RH and reduced period of sunshine (2–6 h/day) with rainfall up to 161 mm. Powdery mildew development is favoured by Temprange of 16–28 °C, mean RH 80%), Tmax up to 25 °C and Tmin of 5–12 °C. Often prediction models developed at one location may not fit atother locations. It indicates that data needs to be generated for a longer period and the model be tested atMultilocation. The disease-forecasting models must be developed by taking into account the crop variety, the prevalence of a particular pathotype and the microclimatic factors.

Measurement and modelling of the dynamics of NH<sub>3</sub> surface–atmosphere exchange over the Amazonian rainforest

Abstract. Local and regional modelling of NH3 surface exchange is required to quantify nitrogen deposition to, and emissions from, the biosphere. However, measurements and model parameterisations for many remote ecosystems – such as tropical rainforest – remain sparse. Using 1 month of hourly measurements of NH3 fluxes and meteorological parameters over a remote Amazon rainforest site (Amazon Tall Tower Observatory, ATTO), six model parameterisations based on a bidirectional, single-layer canopy compensation point resistance model were developed to simulate observations of NH3 surface exchange. Canopy resistance was linked to either relative humidity at the canopy level (RHz0′), vapour pressure deficit, or a parameter value based on leaf wetness measurements. The ratio of apoplastic NH4+ to H+ concentration, Γs, during this campaign was inferred to be 38.5 ± 15.8. The parameterisation that reproduced the observed net exchange of NH3 most accurately was the model that used a cuticular resistance (Rw) parameterisation based on leaf wetness measurements and a value of Γs=50 (Pearson correlation r=0.71). Conversely, the model that performed the worst at replicating measured NH3 fluxes used an Rw value modelled using RHz0′ and the inferred value of Γs=38.5 (r=0.45). The results indicate that a single-layer canopy compensation point model is appropriate for simulating NH3 fluxes from tropical rainforest during the Amazonian dry season and confirmed that a direct measurement of (a non-binary) leaf wetness parameter improves the ability to estimate Rw. Current inferential methods for determining Γs were noted as having difficulties in the humid conditions present at a rainforest site.

Modeling Profiles of Micrometeorological Variables in a Tropical Premontane Rainforest Using Multi‐Layered CLM (CLM‐ML)

AbstractThis study updates the multi‐layered Community Land Model (CLM‐ml) for hillslopes and compares predictions from against observations collected in tropical montane rainforest, Costa Rica. Modifications are made in order to capture a wider array of vertical leaf area distributions, predict CO2 profiles, account for soil respiration, and adjust wind forcings for difficult topographic settings. Test results indicate that the modified multi‐layer CLM model can successfully replicate the shape of various micrometeorological profiles (humidity, CO2, temperature, and wind speed) under the canopy. In the single‐layer models (CLM4.5 and CLM5), excessive day‐to‐night differences in leaf temperature and leaf wetness were originally noted, but CLM‐ml significantly improved these issues, decreasing the amplitudes of diurnal cycles by 67% and 47%. Sub‐canopy considerations, such as canopy shapes and turbulent transfer parameters, also played a significant role in model performance. More importantly, unlike single layer models, the results that CLM‐ml produces can be compared to variables measured within the canopy to provide far more detailed diagnostic information. Further observations and model developments, aimed at reflecting surface heterogeneity, will be necessary to adequately capture the complexity and the features of the tropical montane rainforest.

A process-based model to forecast risk of potato late blight in Norway (The Nærstad model): model development, sensitivity analysis and Bayesian calibration

Late blight caused by Phytophthora infestans is a serious, worldwide disease on potato (Solanum tuberosum). Phytophthora infestans normally reproduces in a clonal manner, but in some areas, as the Nordic Countries, sexual reproduction has become the major determinant of the population structure. To improve the late blight forecasting in Norway, the process-based Nærstad model was developed. The model includes the structure of the underlying processes in the disease development, including spore production, spore release, spore survival and infection of P. infestans. It needs hourly weather records of air temperature, precipitation, relative humidity, leaf wetness and global radiation. The model contained 19 uncertain parameters, and from a sensitivity analysis, 12 were detected as weakly sensitive to model outputs and fixed to a nominal value within their prior boundaries. The remaining seven parameters were detected as more sensitive to model outputs and were parameterized using maximum a'posteriori (MAP) estimates, calculated through Bayesian calibration. The model was developed based on literature combined with field data of daily observed number of lesions on trap plants of the Bintje cultivar (late blight susceptible) at Ås during the seasons 2006-2008 and 2010-2011. It was further tested on daily observed number of lesions on trap plants of the cultivars Bintje, Saturna (medium susceptible) and Peik (medium resistant) at Ås during the seasons 2012-2015. For all three cultivars, the Nærstad model improved with a higher model accuracy compared to the existing HOSPO-model and the Førsund rules that both have shown relatively good correlation with blight development in field evaluations in Norway. The best accuracy was found for Bintje (0.83) closely followed by Saturna (0.79), whereas a much lower accuracy was detected for Peik (0.66).

Vein-to-blade ratio is an allometric indicator of leaf size and plasticity.

PremiseAs a leaf expands, its shape dynamically changes. Previously, we documented an allometric relationship between vein and blade area in grapevine leaves. Larger leaves have a smaller ratio of primary and secondary vein area relative to blade area compared to smaller leaves. We sought to use allometry as an indicator of leaf size and plasticity.MethodsWe measured the ratio of vein‐to‐blade area from the same 208 vines across four growing seasons (2013, 2015, 2016, and 2017). Matching leaves by vine and node, we analyzed the correlation between the size and shape of grapevine leaves as repeated measures with climate variables across years.ResultsThe proportion of leaf area occupied by vein and blade exponentially decreased and increased, respectively, during leaf expansion making their ratio a stronger indicator of leaf size than area itself. Total precipitation and leaf wetness hours of the previous year but not the current showed strong negative correlations with vein‐to‐blade ratio, whereas maximum air temperature from the previous year was positively correlated.ConclusionsOur results demonstrate that vein‐to‐blade ratio is a strong allometric indicator of leaf size and plasticity in grapevines measured across years. Grapevine leaf primordia are initiated in buds the year before they emerge, and we found that total precipitation and maximum air temperature of the previous growing season exerted the largest statistically significant effects on leaf morphology. Vein‐to‐blade ratio is a promising allometric indicator of relationships between leaf morphology and climate, the robustness of which should be explored further.

Perennial ryegrass phytotoxicity increases with mesotrione rate and growth‐promoting environmental conditions

AbstractBleaching of young leaf tissue is a common response to the herbicide mesotrione and varies from one application to another. To better elucidate factors that may contribute to perennial ryegrass (Lolium perenne L.) phytotoxicity from mesotrione, studies were conducted at Blacksburg, VA. Continuous field monitoring of temperature, humidity, solar radiation, soil moisture, and leaf wetness was used to elucidate environmental conditions that led to perennial ryegrass injury by mesotrione applied at 0.14 kg a.i. ha−1. The study found that 2 of 17 weekly treatments caused unacceptable perennial ryegrass injury at two field sites. Ten‐day temporal trends prior to mesotrione treatment showed that temperature, for one injury event, and volumetric soil water content, for the other injury event, were exclusively correlated to visually and digitally assessed perennial ryegrass injury. Both instances suggest that alleviation of suboptimal temperature or moisture with more optimal growing conditions influenced the turf injury response. Additional studies in growth chambers showed that carotenoid levels and turf phytotoxicity were strongly temperature dependent and followed a quadratic response with peak injury occurring at perennial ryegrass growth optima. Carotenoid levels decreased and perennial ryegrass injury increased in response to increased mesotrione rate. These data suggest that mesotrione is more likely to injure perennial ryegrass turf when used at higher rates and under conditions of rapid turfgrass growth.

Effects of Temperature and Moisture on Conidia Germination, Infection, and Acervulus Formation of the Apple Marssonina Leaf Blotch Pathogen (Diplocarpon mali) in China.

Apple Marssonina leaf blotch (AMLB; Diplocarpon mali) is a severe disease of apple that mainly causes premature leaf defoliation in many apple growing areas worldwide. AMLB epidemic development is closely related to temperature and rainfall. In this study, the effects of temperature and moisture on conidium germination, infection on leaves, and acervulus production were investigated under controlled environments. The temperature required for conidium germination and infection ranged from 5 to 30°C, with the optimum at approximately 23°C. The temperature required for acervulus formation was slightly higher, with the optimum at 24.6°C. Wetness was needed in order for conidia to germinate and infect; only a few conidia germinated at 100% RH. However, lesions can produce acervuli in dry conditions. The minimum duration of leaf wetness required for conidia to complete the entire infection process was 14, 8, 4, and 6 h at 10, 15, 20, and 25°C, respectively. A model describing the effect of temperature and leaf wetness duration was built. The model estimated that the optimum temperature for conidial infection was 22.6°C and the minimum wetness duration required was 4.8 h. This model can be used to forecast D. mali conidial infection to assist in disease management in commercial apple production.

Miniature Head Lettuce Yield and Anthocyanin Concentration under High Tunnels and the Field in Georgia

This study evaluated the yield of eight miniature lettuce (Lactuca sativa) cultivars (i.e., mini-lettuce) grown under organically managed high tunnels compared with a field system during two spring seasons in Georgia. Mini-lettuce required an average of 36 to 40 days to harvest in both systems with a 86% to 97% marketability rate. The high tunnels provided a heat gain on the coldest days, decreased leaf wetness, and resulted in a lower daily light integral compared with the field. In 2015, mini-lettuce yields were similar between the high tunnel and field, but in 2016, yields were greater under the high tunnels. In 2016 only, there was a significant system by cultivar interaction for yield, suggesting that the high tunnels provided a yield increase for ‘Baby Green Oakleaf’ and ‘Spretnak’ mini-lettuce. Differences in the daily light integral between the high tunnels and field appeared to affect the accumulation of anthocyanins in red-pigmented mini-lettuce. Anthocyanin concentrations were 26% to 194% greater in mini-lettuce grown in the field compared with under high tunnels. The cultivar Rhazes had the greatest anthocyanin concentrations of all red-pigmented mini-lettuce evaluated but also lower yields.

Emulators of a Physical Model for Estimating Leaf Wetness Duration

Leaf wetness duration (LWD) has rarely been measured due to lack of standard protocol. Thus, empirical and physical models have been proposed to resolve this gap. Although the physical model provides robust performance in diverse conditions, it requires many variables. The empirical model requires fewer variables; nevertheless, its performance is specific to a given condition. A universal LWD estimation model using fewer variables is thus needed to improve LWD estimation. The objective of this study was to develop emulators of the LWD estimation physical model for use as universal empirical models. It is assumed that the Penman–Monteith (PM) model determines LWD and can be employed as a physical model. In this study, a simulation was designed and conducted to investigate the characteristics of the PM model and to build the emulators. The performances of the built emulators were evaluated based on a case study of LWD data obtained in South Korea. It was determined that a machine learning algorithm can properly emulate the PM model in LWD estimations based on the simulation. Moreover, the poor performances of some emulators that use wind speed may have been due to the limitation of wind speed measurement. The accuracy of the anemometer is thus critical to estimating LWD using physical models. A deep neural network using relative humidity and air temperature was found to be the most appropriate emulator of those tested for LWD estimation.

Ramularia leaf spot disease of barley is highly host genotype-dependent and suppressed by continuous drought stress in the field

Since the 1980s, Ramularia leaf spot (RLS) is an emerging barley disease worldwide. The control of RLS is increasingly aggravated by a recent decline in fungicide efficacy and a lack of RLS-resistant cultivars. Furthermore, climate change increases drought periods in Europe, enhances variable weather conditions, and thus will have an impact on severity of plant diseases. Hence, identification of RLS-resistant cultivars and understanding of disease progression under abiotic stress are important aims in integrated disease management under climate change. In the present study, we evaluated quantitative RLS resistance of 15 spring barley genotypes under drought, controlled irrigation and field conditions between 2016 and 2019 and monitored microclimatic conditions within the canopy. We identified genotypes that show robust quantitative resistance to RLS in different field environments. Our findings suggest that long-lasting drought periods create unfavourable conditions for the disease and supports that the extent and duration of leaf wetness is a key factor for RLS epidemics.

Measuring the vertical profile of leaf wetness in a forest canopy

Plant canopies are wet for substantial amounts of time and this influences physiological performance and fluxes of energy, carbon and water at the ecosystem level. Leaf wetness sensors enable us to quantify the duration of leaf wetness and spatially map this to canopy structure. However, manually analysing leaf wetness data from plot-level experiments can be time-consuming, and requires a degree of subjective judgement in delineating wetness events which can lead to inconsistencies in the analysis. Here we:•Describe how to set up an array of leaf wetness sensors (Phytos 31, Meter) enabling the measurement of leaf wetness duration through the profile of a forest canopy,•Present a method and R script to objectively identify and distinguish periods of rain and dew from the output of leaf wetness sensors,•Provide a criteria for separating the leaf wetness sensor output into dew and rain events which may form a reference standard, or be modified for use, in future studies.

Development of a Wireless Sensor Network for Field Level Strawberry Disease Alert Systems

HighlightsFungal diseases are considered a major challenge for strawberry farmers.Disease risk monitoring systems can be improved with increased spatial coverage of environmental conditions.Wireless Sensor Networks (WSN) with customized nodes can efficiently collect site-specific data inputs for disease risk models.WSN providing disease risk levels with increased spatial and temporal resolution opens the opportunity for site-specific control actions.Abstract. The United States is the world’s largest producer of strawberries, harvesting over 680 million metric tons in 2017, valued at approximately $3.2 billion. Fungal diseases are considered a major challenge for strawberry farmers. Even in well-managed fields, losses from fungal diseases can exceed 50% when environmental conditions favor disease development. Anthracnose fruit rot (AFR), caused by Colletotrichum acutatum and Botrytis fruit rot (BFR), caused by Botrytis cinerea, are the most significant diseases monitored (or present) during strawberry production in Central Florida and worldwide. The Strawberry Advisory System (SAS) was developed by researchers at the University of Florida to alert strawberry growers of infection risk of AFR and BFR. SAS also recommends control actions when necessary. In Florida, the SAS uses leaf wetness duration and temperature observed at Florida Automated Weather Network (FAWN) stations and private weather stations to estimate strawberry disease risk. A good representation of local conditions is crucial, but unfortunately uncommon in modern fungal disease warning systems (FDWS), such as the SAS. In this study, we developed and deployed an in-field Wireless Sensor Network (WSN) with customized nodes (WetBerry). WetBerry is a distributed mesh network of wireless mini weather stations equipped with leaf wetness, temperature, and relative humidity sensors developed to monitor in-field environmental conditions related to the risk of fungal diseases in strawberry production. The WSN mini weather stations were installed and tested in a strawberry field during the 2017/2018 strawberry season, and the results were validated against a standard agrometeorological weather station. The WSN provided high spatial and temporal density of weather data for agricultural applications, thereby has the potential to improve the capabilities of site-specific fungal disease warning tools and control actions. Keywords: Decision making, Fungal disease, Leaf wetness, Precision agriculture, Site-specific, WSN.

An Automated Optimize Utilization of Water and Crop Monitoring in Agriculture Using IoT

Agriculture is the primary occupation in our country for ages. But now due to migration of people from rural to urban there is hindrance in agriculture. So, to overcome this problem we go for smart agriculture techniques using IoT. This paper includes various features like GPS based remote controlled monitoring, moisture & temperature sensing, intruders scaring, security, leaf wetness and proper irrigation facilities. It makes use of wireless sensor networks for noting the soil properties and environmental factors continuously. Various sensor nodes are deployed at different locations in the farm. Controlling these parameters are through any remote device or internet services and the operations are performed by interfacing sensors, Wi-Fi, camera with microcontroller. This concept is created as a product and given to the farmer’s welfare. AI Solution for Farmers perform soil analysis, climate analysis, and productivity analysis using linear regression. It helps farmers to understand about the crop to be sown as well as the factors affecting their productivity with the help of different types of graphs and tables. Farmers need not to do anything on the application as it is highly interactive as by using speech API.

How to cope with the vulnerability of site specific fungicides on the control of Asian soybean rust

Asian soybean rust (ASR) caused by Phakopsora pachyrhizi is the most important disease of the crop. The pathogen is highly aggressive under field conditions when the environmental conditions favor the disease development. The disease was first described in Japan, in 1902 and in 1914, it had already spread to several countries in Southeast Asia.). The first report in South America was made in Brazil in 1979 in wild soybeans and later in 2001 in the soythern area of the country in comercial plantations. P. pachyrhizi can naturally infect a wide range of plant species, including 41 species in 17 genera of the Fabaceae family. The symptoms of the disease can be seen on the abaxial and eventually on the adaxial surface of the lesions. In susceptible varieties the number of uredia per lesion varies from four to eight and the latent period around seven days. P. pachyrhizi requires more than 8 hours a day of continuous leaf wetness, and the optimal temperature for maximum germination around 22oC. There is no varieties with complete resistance to the disease available to planting. The main method of control of the disease is the application of fungicides. The main groups of fungicides to control the disease belongs to the demetilation inhibitores (DMI’s), quinone outside inhibitor (QoI’s) and carboxamides (SDHI). But with the continuous spraying with DMI’s and QoI’s alone to control ASR resistant mutants of P. pac HIhyrhizi multiplied in the population over the country. The use of mixture of DMI’S with QoI’s in the beginning proportioned resonable controle of the disease. But few years latter P. pachyrhizi acquired resistance to the mixture of triazol with stobolurins. Then it was introduced the carboxamide group (SDHI) to use in mixture with triazol with stobolurins. The triple mixture was then recommended for more a few years giving good results. Finally researchers decided to incorporate multisite fungicides in a mixture with triazol, strobilurin and or carboxamide to minimize the probability to build up resistant mutants in the population of P. pachyrhizi. The addition of multisite fungicides in a mixture with site specific is very important to reinforce the fight against fungal resistance. In conclusion to cope with the vulnerability of P. pachyrhizi to site specific fungicides the strategy has to involve integration of measures such as sanitary vacuum, rotation of a mixture of different biochemical mechanisms of action plus multisite, avoid sequential and curative applications, use of early cultivars and sowing at the beginning of the recommended season, eliminate voluntary soybean plants from the field, use of fungicides in the onset of preventively at least once before the planting lines closed, sowing at the beginning of the recommended season, and use of cultivars with resistance gene (s) to reduce the number of spraying. The present overview discuss how difficult is to cope with the resistance of P. pachyrhizi to site specific fungicides.

Effect of microclimate modification on Karnal bunt of wheat under ludhiana conditions

A field experiment was conducted to study the effect of microclimate on Karnal Bunt (KB) in wheat under Ludhiana district, Punjab. The microclimate effect was studied using three dates of sowing (20th October, 05th November and 20th November), two methods of cultivation (recommended cultivation and additional leaf wetness) and three wheat varieties (Unnat PBW 343, PBW 725 and HD 2967). The maximum disease incidence (22.15%) was observed in crop sown on 5th November followed by 20th November (19.64%) and 20th October (16.03%). Maximum KB incidence was observed in variety PBW 725 (26.52%) followed by HD 2967(19.77%) and Unnat PBW 343(11.54%). Additional leaf wetness resulted in higher disease incidence (26.99%) as compared to recommended cultivation (11.56%). Relative humidity inside the crop canopy and canopy temperature showed 71 and 65 per cent variability in disease incidence, respectively. The study indicated an important role of microclimatic parameters viz. canopy temperature and relative humidity within canopy in KB development.

Research Article Severity of rust infection in soybean genotypes with partial resistance as a function of temperature and leaf wetness duration

Research Article Severity of rust infection in soybean genotypes with partial resistance as a function of temperature and leaf wetness duration

BEHAVIOR OF SOME WALNUT VARIETIES UNDER JUVENILE PHASE TO BACTERIOSE AND ANTRACNOSE ATTACK

Walnut (Juglans regia L.) is an important species for the European and Romanian fruit growing, but, despite its resilience, it can be attacked by specific diseases such as bacteriose (Xanthomonas arboricola pv. juglandis (Pierce) Dye) and anthracnose (Gnomonia leptostyla D.C. Trav) which due to the climatic changes became more active than in the past decades. This paper presents results on the behavior of 48 walnut varieties preserved into the new collection established at RIFG Pitesti Romania to the field infections with these pathogens, their resilience in juvenile phase and their overall classification. The data collected reveal that the microclimate conditions during years of the study (2018-2020) were very favorable for the walnut diseases attack. The correlations monthly average temperatures (*C) -- average leaf wetness hours sum (h/month) and monthly average precipitation sum (mm) -- average leaf wetness hours sum (h/month), contributed to the infections speed and diseases development on the studied biologic material. For these the coefficients R2=0.6086*** (r=0.7801***, n=35) and R2=0.5584*** (r=0.7472***, n=35) were calculated. Under such circumstances, the best behavior on the walnut blight had the varieties: 'Făleşti', 'Geoagiu 453', 'Jupâneşti', 'Miroslava', 'Novaci', 'Timval', 'Valcor', 'Valrex', 'Velniţa', 'Victoria' and 'Vlădeşti' with damages degree DD% on leaves no higher than 0,03. Also, The most resilient on anthracnose attack on leaves were the varieties: 'Argeşean', 'Gemenea 20', 'Pestişani', 'Pecianski', 'Schinoasa', (DD%=0.13), 'Valcris' (DD%=0.11), 'Codrene', 'Secular RM', 'Sibişel 265' (DD%=0.10) and 'Valrex' (DD%=0.05).

Dew formation reduction in global warming experiments and the potential consequences

Dew, as an important contribution of non-rainfall water (NRW), plays a vital role in ecosystem processes in arid and semi-arid regions and is expected to be affected by climate warming. Infrared heater warming systems have been widely used to simulate climate warming effects on ecosystems. However, how this warming system affects dew formation has been long ignored and rarely addressed. In a typical alpine grassland ecosystem on the northeast of the Tibetan Plateau, we measured dew amount and duration using three independent methods: artificial condensing surfaces, leaf wetness sensors and in situ dew formation on plants from 2012 to 2017. We also measured plant traits related to dew conditions. The results showed that (1) warming reduced the dew amount by 41.6%-91.1% depending on the measurement method, and reduced dew duration by 32.1 days compared to the ambient condition. (2) Different plant functional groups differed in dew formation. (3) Under the infrared warming treatment, the dew amount decreased with plant height, while under the ambient conditions, the dew amount showed the opposite trend. We concluded that warming with an infrared heater system greatly reduces dew formation, and if ignored, it may lead to overestimation of the effects of climate warming on ecosystem processes in climate change simulation studies.