Computational Fluid Dynamics Modelling of the Microclimate Within the Boundary Layer of Leaves Leading to Improved Pest Control Management and Low-Input Greenhouse

sustainability

Article Computational Fluid Dynamics Modelling of the Microclimate within the Boundary Layer of Leaves Leading to Improved Pest Control Management and Low-Input Greenhouse

Hicham Fatnassi 1,*, Thierry Boulard 2, Christine Poncet 2, Nikolaos Katsoulas 3 , Thomas Bartzanas 4, Murat Kacira 5, Habtamu Giday 1 and In-Bok Lee 6

1 International Center for Biosaline Agriculture, Dubai 14660, United Arab Emirates; [email protected] 2 Université Côte d’Azur, INRAE, CNRS, UMR ISA, 06000 Nice, France; [email protected] (T.B.); [email protected] (C.P.) 3 Department of Agriculture Crop Production and Rural Environment, University of Thessaly, 38446 Volos, Greece; [email protected] 4 Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, 11855 Athens, Greece; [email protected] 5 Department of Biosystems Engineering, College of Agriculture and Life Sciences, University of Arizona, Tucson, AZ 85721, USA; [email protected] 6 Laboratory of Aero-Environmental Engineering, Department of Rural System Engineering College of Agriculture and Life Science, Seoul National University, Seoul 08826, Korea; [email protected] * Correspondence: [email protected] Citation: Fatnassi, H.; Boulard, T.; Poncet, C.; Katsoulas, N.; Bartzanas, T.; Kacira, M.; Giday, H.; Lee, I.-B. Abstract: This work aims at using the Computational Fluid Dynamic (CFD) approach to study Computational Fluid Dynamics the distributed microclimate in the leaf boundary layer of greenhouse crops. Understanding the Modelling of the Microclimate within interactions in this microclimate of this natural habitat of plant pests (i.e., boundary layer of leaves), the Boundary Layer of Leaves is a prerequisite for their control through targeted climate management for sustainable greenhouse Leading to Improved Pest Control production. The temperature and humidity simulations, inside the greenhouse, were performed Management and Low-Input using CFD code which has been adapted to simulate the plant activity within each mesh in the Greenhouse. Sustainability 2021, 13, crop canopy. The air temperature and air humidity profiles within the boundary layer of leaves 8310. https://doi.org/10.3390/ were deduced from the local surrounding climate parameters, based on an analytical approach, su13158310 encapsulated in a Used Defined Function (UDF), and dynamically linked to the CFD solver, a work that forms an innovative and original task. Thus, this model represents a new approach to investigate Academic Editors: Georgios Ntinas the microclimate in the boundary layer of leaves under greenhouses, which resolves the issue of the and Dennis Dannehl inaccessibility of this area by the conventionnel measurement tools. The findings clearly showed

Received: 10 June 2021 that (i) contrarily to what might be expected, the microclimate parameters within the boundary Accepted: 1 July 2021 layer of leaves are different from the surrounding climate in the greenhouse. This is particularly Published: 26 July 2021 visible during photoperiods when the plant’s transpiration activity is at its maximum and that (ii) the climatic parameters in the leaf boundary layer are more coupled with leaf surfaces than with Publisher’s Note: MDPI stays neutral those of greenhouse air. These results can help developing localized intervention strategies on with regard to jurisdictional claims in the microclimate within boundary layer of plant leaves, leading to improved and sustainable pest published maps and institutional afﬁl- control management. The developed climatic strategies will make it possible to optimize resources iations. use efﬁciency.

Keywords: CFD modeling tool; leaf boundary layer microclimate; smart microclimate control; sustainable pest management Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and 1. Introduction conditions of the Creative Commons Knowing which factors contribute to the elaboration of the microclimate in the natural Attribution (CC BY) license (https:// habitats of plants pests, is crucial for the development of non-chemical methods of plant creativecommons.org/licenses/by/ 4.0/). protection [1]. According to Jones et al. [2], insects and mites usually spend most of their

Sustainability 2021, 13, 8310. https://doi.org/10.3390/su13158310 https://www.mdpi.com/journal/sustainability Sustainability 2021, 13, 8310 2 of 13

time within the boundary layer of leaves which, thanks to its physiological activity, has a different microclimate compared to surrounding climate conditions. Several factors are involved in the development of the microclimate in this area related either to plant activity or to surrounding climatic parameters such as plant transpiration, air temperature, relative humidity, airflow, and solar radiation [3]. Thus, the climatic parameter variations inside the greenhouse strongly impact the microclimate within the leaf boundary layer which, in turn, influences all pest activities during their life cycle (oviposition, reproduction, predation, etc.). However, as stressed by Woods and Potter [4]: we know disproportionately a lot about environments around big things but astonishingly little about environments around small things. To fill this gap and to explore this unfamiliar environment, several experimental studies have already been carried out to lift the veil on the microclimate in the boundary layer of leaves. Sheriff [5] is among the first researchers to inspect the microclimate in this inaccessible area and was able to measure the humidity gradient close to the leaf of Nicotiana glauca. In their study, Grace and Wilson [6] measured the air flow closely around a Populus leaf using a hot-wire anemometer, and Boulard et al. [1,7] characterized the gradient of temperature and humidity in the tomato leaf boundary layer at three locations in the greenhouse using a dual approach combing measurement and modelling. Indeed, using an analytical approach, Boulard et al. [1] were able to model the temperature and humidity profiles across the boundary layer of the lower side of greenhouse tomato leaves and validated these simulations using the localized air’s relative humidity and temperature measurements. They later analyzed the relationship between the leaf boundary layer climate and air velocity within the whole greenhouse space [7] and highlighted the strong heterogeneity of the climate parameters at leaf surface, particularly air humidity, mainly under the dependence of the spatial distribution of the convective regime. Katsoulas et al. [8] used artificial rose leaves to evaluate the leaf boundary layer’s conductance in a greenhouse under different ventilation regimes, while more recently, Kimura et al. [9] used artificial tomato leaves to evaluate the leaf boundary layer’s conductance under different regimes of environmental control, and while Nikolaou et al. [10] presented an approach to estimate the leaf boundary layer’s conductance in a Mediter- ranean greenhouse based on instantaneous leaf temperature measurements. Nevertheless, given the complexity of measuring climatic parameters in the boundary layer of leaves, most of researchers have chosen the modeling approach to explore the environment in this natural habitat of pests. Ferro and Southwick [11] presented a model which estimates the relative humidity within the leaf boundary layer that constitutes the microhabitat for leaf-inhabiting arthropods. Roy et al. [12] simulated air temperature and humidity using a CFD model at bean leaf surface for low light levels. The plant model was associated with the CFD model which produced predictions of the environmental variables in good agreement with experimental measurements on Munger cells with a single bean leaf. Despite this extensive research, most studies investigated only considering local boundary layer climate of a single leaf and not considering the boundary layer microclimate of the plant canopy in the greenhouse. To extend this microclimatic characterization to the whole plant canopy in the greenhouse, the present study aims at using a CFD model to explore the distribution of climatic parameters within the leaf boundary layer of all plants in the greenhouse. This model forms an original and innovative work in the field since it allows to give useful information on the microclimate in this inaccessible area and to establish the link between the climate in this leaf boundary layer and the surrounding climate in the greenhouse. Based on the recent advances in the study of pest infestation dependence on local climate parameters [13], this model can later serve as a predictive and decision support tool to prevent pest infestation and offer sustainable pest management practices in the greenhouses. Sustainability 2021, 13, x FOR PEER REVIEW 3 of 13

tool to prevent pest infestation and offer sustainable pest management practices in the greenhouses. Sustainability 2021, 13, 8310 3 of 13 2. Materials and Methods 2.1. The Greenhouse 2. MaterialsThe model and developed Methods in this study was applied to a four-span arched type plastic 2.1.greenhouse The Greenhouse (Figure 1): 4 × 9.6 m wide, 80 m long and 5.90 m ridge height (4 m eave height). The greenhouse is ventilated by convection through 1.5 m wide continuous ridge open- The model developed in this study was applied to a four-span arched type plastic ings (Figure 1). greenhouse (Figure1): 4 × 9.6 m wide, 80 m long and 5.90 m ridge height (4 m eave height). The greenhouseA virtual rose is ventilated crop arranged by convection in a form through of 2 m 1.5 high m wide was continuousused to simulate ridge openingsthe crop (Figureactivity1 inside). the greenhouse.

Figure 1. Summary of the different models used to simulate the microclimate in the greenhouse and inFigure the boundary 1. Summary layer of ofthe plant different leaves. models used to simulate the microclimate in the greenhouse and in the boundary layer of plant leaves. A virtual rose crop arranged in a form of 2 m high was used to simulate the crop activity inside the greenhouse. 2.2. The Numerical Model 2.2. TheThe Numerical developed Model model is composed of different scales of modeling that describe the diverseThe components developed modelthat make is composed up the greenhouse of different and scales crop of environment modeling that and describe their rele- the diversevant heat components and mass exchanges. that make up the greenhouse and crop environment and their relevant heat andThe massmain exchanges.model reproduces airflow, heat and mass transfer inside the greenhouse and itsThe surrounding main model area reproduces is governed airflow, by the heat Navier–Stokes and mass transfer and energy inside equations. the greenhouse In ad- anddition, its a surrounding crop model areahas been is governed associated by with the Navier–Stokesthe main CFD andmodel energy to simulate equations. airflow In addition,and mass aexchanges crop model within has been the crop associated cover withassimilated the main to CFDa porous model medium to simulate considering airflow andtwo major mass exchangesphenomena: within the crop cover assimilated to a porous medium considering two majorThe dynamic phenomena: effect of the crop cover on air flow 𝑆 is expressed by unit volume of the coverThedynamic by the commonly effect of the used crop formula coveron [14]: air flow Sc is expressed by unit volume of the cover by the commonly used formula [14]: 𝑆 =−𝐿𝐴𝐷𝐶𝜌𝑢 (1) S = −LADC ρu2 where u is the air speed within the cropc cover, LADd is the leaf area density, and Cd is(1) a drag coefficient. where u is the air speed within the crop cover, LAD is the leaf area density, and Cd is a drag coefficient. To include the drag effect proportional to the leaf density into our CFD study, the crop cover was considered as a porous medium, and the Darcy–Forsheimer equation was used:

µ C S = − u + F u2 (2) c K K0.5 Sustainability 2021, 13, x FOR PEER REVIEW 4 of 13

To include the drag effect proportional to the leaf density into our CFD study, the crop cover was considered as a porous medium, and the Darcy–Forsheimer equation was used: 𝜇 𝐶 𝑆 =−( )𝑢 + ( )𝑢 𝐾 𝐾. (2) where μ is the dynamic viscosity of the fluid, K the permeability of the porous medium,

and CF is the non-linear momentum loss coefficient. For the low air speed values observed into the crop cover, the first term of Equation (2) can be neglected in front of the Sustainability 2021, 13, 8310 quadratic term. Then, combining Equations (1) and (2) yields: 4 of 13 𝐶 =𝐿𝐴𝐷𝐶 𝐾. (3) wherewhich µisis the the latent dynamic and sensible viscosity heat of the exchanges fluid, K withthe permeability ambient air. of the porous medium, and CTheF is theexchanges non-linear of heat momentum and water loss vapour coefficient. between For the leaves low air and speed air are values described observed by intomeans the of crop the cover, heat and the firstmass term balances of Equation of leaves (2) canwith be the neglected air (Figure in front 2). Each of the mesh quadratic of the term.crop cover Then, is combining assimilated Equations to a “volume (1) and heat (2) source yields: boundary condition” receiving a radiative flux (Gabs). This flux is partitioned into convective sensible (𝑆) and latent (𝑆) heat C fluxes (water vapour) according to the Ffollowing= LADC relations: (3) K0.5 D 𝐺 =𝑆 +𝑆 (4) which is the latent and sensible heat exchanges with ambient air. The exchanges of heat and water vapour between leaves and air are described by 𝑆 =𝐿𝐴𝐷𝜌𝐶 (𝑇 −𝑇 )𝑟⁄ means of the heat and mass balances of leaves with the air (Figure 2). Each mesh of the crop(5) cover is assimilated to a “volume heat source boundary condition” receiving a radiative ∗ flux (Gabs). This flux is partitioned𝑆 =𝛾𝐿𝐴𝐷𝜌𝐶 into convective (𝑞 −𝑞 sensible)(𝑟⁄ +𝑟 (SS))and latent (SL) heat fluxes(6) (water vapour) according to the following relations: where rs and ra are the stomatal and aerodynamic resistances between the virtual solid 𝑇 matrix representing the crop and charGacterizedabs = SS + byS Lits surface temperature ( ) and the(4) air ∗ (Ta); 𝑞 and 𝑞 are the saturation-specific humidity at the leaf temperature and the specific humidity of air, respectively.SS = LADρCP(Tl − Ta)/ra (5) Both couplings have already been amply studied and published in various studies S = γLADρC (q∗ − q )/(r + r ) (6) performed in greenhouse conditionsL for Plettucel cropsa a[15],s rose crops [14], single beans whereleaf [12],rs andtomatora are crops the [16–21], stomatal and and impatiens aerodynamic crops resistances [22], while between the objective the virtual is to explore solid matrixthe microclimate representing inside thecrop the leaf and boundary characterized layer by using its surface an analytical temperature model (T derivedl) and the of airthe ∗ (boundaryTa); ql and layerqa are theory the saturation-specific [23]. humidity at the leaf temperature and the specific humidity of air, respectively.

Figure 2. Schematic drawing of the exchanges of heat and water vapour between leaf and air. Figure 2. Schematic drawing of the exchanges of heat and water vapour between leaf and air. Both couplings have already been amply studied and published in various studies performed in greenhouse conditions for lettuce crops [15], rose crops [14], single beans leaf [12], tomato crops [16–21], and impatiens crops [22], while the objective is to explore the microclimate inside the leaf boundary layer using an analytical model derived of the boundary layer theory [23].

2.3. Numerical Modelling of the Microclimate in the Leaf Boundary Layer Air temperature and humidity proﬁles within the leaf boundary layer can be deduced from surrounding air velocity depending on Prandtl and Schmidt numbers [24]. Since the air velocity inside the greenhouse is lower than 1 m s−1 due to containment conditions, i.e., Reynolds number is smaller than 106 corresponding to the transition to turbulence, the convective transfer can be considered as laminar [1,7,14,16,25]. Consequently, temperature Sustainability 2021, 13, 8310 5 of 13

and speciﬁc humidity proﬁles across the leaf boundary layer can then be deduced from the following expressions [24]:

h 3 2 i T(η) = Ta + 0.0046η + 0.0108η − 0.3621η + 1.0005 /(Tl − Ta) (7)

h 3 2 i ∗ q(η) = qa + 0.0093η + 0.0248η − 0.2787η + 0.9969 /(ql − qa) (8) where η is the non-dimensional variable distance:

r u η = y a (9) νx

y being the distance from the wall; x the distance from the windward end of the leaf surface; ua is the non-disturbed air speed and ν is the dynamic viscosity of air; the subscripts l and a refer to the leaf and the bulk air, respectively. The characteristic thickness of the boundary layer is given by the Blasius relation: s νl δl ≈ 4.92 (10) ua

On an experimental point of view, this boundary layer model was amply validated by Boulard et al. [1,7] with respect to tomato leaves using micro thermo-hygrometers whose protection caps were removed to precisely sense temperature and humidity conditions at distances of 5 and 15 mm from the leaf surface together with omni-directional hot- ball anemometers to sense air speed in leaf vicinity. Model validation was successfully performed as illustrated by the details of the measurements presented in the two previously cited papers. These temperature and air relative humidity distribution of microclimate at leaf surface, based on the boundary layer theory, were input in the Used Deﬁned Functions (UDF) describing the sensible and latent heat exchanges between the air and the crop within each mesh of the crop canopy. This UDF was dynamically linked with the solver Fluent of Ansys software to the parallel solving of the climate parameter equations inside the greenhouse and inside the crop leaves boundary layer (Figure3).

2.4. The Boundary Conditions The boundary conditions deduced from the measurements were imposed at the greenhouse wall level. An inlet logarithmic velocity proﬁle corresponding to the measured wind was imposed outside the greenhouse together with the measured solar radiation, temperature, and humidity values. Using the developed model, we studied the microclimate in the leaf boundary layer of all plants in the greenhouse. A sensitivity study was also carried out to study the effects of varying the environmental factors in the greenhouse on the microclimate in the leaf boundary layer. SustainabilitySustainability2021 2021, 13, 13, 8310, x FOR PEER REVIEW 66 ofof 13 13

FigureFigure 3. 3.Diagram Diagram of of User User Deﬁned Defined Function Function UDF UDF for for crop crop and and boundary boundary layer layer models models linked linked to to the the mainmain solver solver in in the the CFD CFD model. model.

3.2.4. Results The Boundary and Discussion Conditions TheThe results boundary of the conditions effects of thededuced different from case the studies measurements performed were on theimposed distributed at the climate and the boundary layer of the plant leaves of the studied greenhouse and their greenhouse wall level. An inlet logarithmic velocity profile corresponding to the meas- analyses in relation to the surrounding climate parameters are presented in the follow- ured wind was imposed outside the greenhouse together with the measured solar radia- ing sections. tion, temperature, and humidity values. 3.1. LeafUsing Boundary the developed Layer Climate model, Analysis we studied the microclimate in the leaf boundary layer 3.1.1.of all Leafplants Boundary in the greenhouse. Layer Thickness A sensitivity study was also carried out to study the effects of varying the environmental factors in the greenhouse on the microclimate in the leaf As shown in Figure4, which presents the temporal evolution of the boundary layer boundary layer. thickness and air velocity in the crop cover, these two variables are inversely proportional. The leaf boundary layer is thick in the area where the velocity of the surrounding air is low 3. Results and Discussion and vice versa. AccordingThe results to of this the ﬁgure,effects aof difference the different in the case thickness studies performed of this boundary on the layerdistributed was observedclimate and between the boundary day and layer night. of This the differenceplant leaves can of bethe explained studied greenhouse by the containment and their levelanalyses of the in greenhouse. relation to the In surrounding fact, the greenhouse climate parameters is closed at are night, presented which reducesin the following the air velocitysections. in the greenhouse, and conversely, the vent openings during the day increases air circulation. 3.1. Leaf Boundary Layer Climate Analysis 3.1.2.3.1.1. Temperature Leaf Boundary and Layer Humidity Thickness in the Leaf Boundary Layer FiguresAs shown5 and in6 Figuredisplay 4, the which temperature presents and the humiditytemporal patternsevolution in of the the boundary boundary layer layer atthickness various distancesand air velocity from leaves in the (1 crop mm, cover, 5 mm, these 10 mm, two andvariables 15 mm are from inversely under proportional. leaf surface) togetherThe leaf with boundary temperature layer is and thick humidity in the area of ambient where airthe outside velocity the of boundary the surrounding layer and air at is leaflow surface. and vice versa. ConsideringAccording to the this temperatures figure, a difference trends, wein the notice thickness that during of this the boundary daytime layer the ambient was ob- airserved temperature between andday leafand night. temperature This difference are, on average,can be explained generally by close. the containment Consequently, level theof the temperature greenhouse. ﬁeld In infact, the the leaf greenhouse boundary is layer closed is alsoat night, limited which inside reduces this narrowthe air velocity range. However, during nighttime, one can observe a systematically higher temperature gap in the greenhouse, and conversely, the vent openings during the day increases air circu-◦ betweenlation. the ambient air and the leaf (the leaf being slightly warmer) ranging between 1 C (1–6 August) and 2 ◦C (7–13 August). Sustainability 2021, 13, 8310 7 of 13 Sustainability 2021, 13, x FOR PEER REVIEW 7 of 13

Sustainability 2021, 13, x FOR PEER REVIEW 8 of 13 Figure 4. Time evolution ofof leafleaf boundaryboundary layerlayer thickness thickness (brown (brown color) color) as as a a function function of of surrounding surround- airing velocityair velocity (blue (blue color). color).

3.1.2. Temperature and Humidity in the Leaf Boundary Layer Figures 5 and 6 display the temperature and humidity patterns in the boundary layer at various distances from leaves (1 mm, 5 mm, 10 mm, and 15 mm from under leaf surface) together with temperature and humidity of ambient air outside the boundary layer and at leaf surface. Considering the temperatures trends, we notice that during the daytime the ambient air temperature and leaf temperature are, on average, generally close. Consequently, the temperature field in the leaf boundary layer is also limited inside this narrow range. How- ever, during nighttime, one can observe a systematically higher temperature gap between the ambient air and the leaf (the leaf being slightly warmer) ranging between 1 °C (1–6 August) and 2 °C (7–13 August). In the boundary layer of leaves, no large difference in temperature were observed between the different layers along this area during the daytime. During the nighttime, the temperature at 1 mm is close to the temperature of leaf surface while the temperature at 15 mm is close to the ambient air. The temperatures values at 5 and 10 mm are in between. This can be explained by the difference of the thickness of the boundary layer between daytime and nighttime. A thick boundary layer reduces the heat transfer between the leaf and the surrounding environment.

Figure 5. Simulated air humidity within the boundary layer of leaves over time: relative humidity Figureof ambient 5. Simulated air (black air color); humidity relati withinve humidity the boundary at 1 mm layer from of the leaves leaf (brown over time: color); relative relative humidity humidity of ambientat 5 mm air from (black the color);leaf (green relative color); humidity relative at humidi 1 mm fromty at the10 mm leaf from (brown the color); leaf (purple relative color); humidity relative at 5humidity mm from at the 15 leafmm (green from the color); leaf relative(sky blue humidity color). at 10 mm from the leaf (purple color); relative humidity at 15 mm from the leaf (sky blue color). In the boundary layer of leaves, no large difference in temperature were observed between the different layers along this area during the daytime. During the nighttime, the temperature at 1 mm is close to the temperature of leaf surface while the temperature at 15 mm is close to the ambient air. The temperatures values at 5 and 10 mm are in between.

Figure 6. Simulated air temperature within the leaf boundary layer of crop rose over time: temperature of leaf (black color); temperature of ambient air (brown color); temperature at 1 mm from the leaf (green color); temperature at 5 mm from the leaf (purple color); temperature at 10 mm from the leaf (sky blue color); temperature at 15 mm from the leaf (orange color).

A zoom in on three days (Figure 7) on the graph of air temperature at 1 mm from the lower surface of leaves and the ambient air further clarifies this discontinuity between the leaf boundary layer and the surrounding climate. Sustainability 2021, 13, 8310 8 of 13

Figure 6. Simulated air temperature within the leaf boundary layer of crop rose over time: temperature of leaf (black color); temperature of ambient air (brown color); temperature at 1 mm from the leaf (green color); temperature at 5 mm from the leaf (purple color); temperature at 10 mm from the leaf (sky blue color); temperature at 15 mm from the leaf (orange color).

This can be explained by the difference of the thickness of the boundary layer between daytime and nighttime. A thick boundary layer reduces the heat transfer between the leaf and the surrounding environment. A zoom in on three days (Figure7) on the graph of air temperature at 1 mm from the lower surface of leaves and the ambient air further clariﬁes this discontinuity between the leaf boundary layer and the surrounding climate. This layer of one millimeter under the leaf is the area where the body of the insects is included. Indeed, the body circumference of most plant pests is less than one millimeter. This zone protects pest eggs from extreme ambient temperatures [26] and provides a favorable environment for their development. The air humidity course is quite different from the temperature one as a strong discontinuity between the boundary layer and the ambient air can be observed. Due to leaf transpiration, particularly through the lower side of the leaves, air humidity is saturated at the leaf surface and is much higher in the leaf boundary layer. However, this gap is less marked at night than during daytime because the rose’s stomata, as a great variety of plants, are open only during daytime and closed at night. This is not a common rule as an adaptation to arid climatic conditions certain species undergoing CAM photosynthesis, e.g., the pineapple have their stomata opened at night and closed during daytime to reduce transpiration. Thus, the leaf boundary layer’s climate is directly related to the physiological response of crops and especially transpiration. The stomata have a maximum aperture when sunlight is high at noon and from then on closure takes place, slowly at ﬁrst and faster by the end of the day. When stomata are open, an evapotranspiration rate occurs due to the water potential gradient and air humidity increasing up to saturation close to the lower leaf surface and, thanks to the decrease in momentum transfer, contributes to the creation of a climate discontinuity between the boundary layer and the ambient air surrounding the leaf. Sustainability 2021, 13, x FOR PEER REVIEW 9 of 13

This layer of one millimeter under the leaf is the area where the body of the insects is Sustainability 2021, 13, 8310 included. Indeed, the body circumference of most plant pests is less than one millimeter.9 of 13 This zone protects pest eggs from extreme ambient temperatures [26] and provides a favorable environment for their development.

Figure 7. Zoom in on three days of simulated air temperature at 1 mm from the lower part of the Figure 7. Zoom in on three days of simulated air temperature at 1 mm from the lower part of the leaf leaf (green color) and the ambient air (brown color) over time. (green color) and the ambient air (brown color) over time.

TheseThe air results humidity are course in a veryis quite good different agreement from the with temp theerature experimental one as a resultsstrong dis- of Boulardcontinuity et al.between [7] in tomatothe boundary and of layer Sheriff and [5 ]the in Nicotianaambient air glauca can be who observed. highlight Due the to hu-leaf miditytranspiration, gradients particularly close to the through lower surfacethe lower of side the leaf.of the The leaves, main air novelty humidity of our is saturated study is toat showthe leaf that surface the boundary and is much layer higher microclimate in the leaf distribution boundary layer. throughout However, the greenhousethis gap is less is alsomarked directly at night related than to theduring inside daytime climate because heterogeneity, the rose’s as illustratedstomata, as by a Figuresgreat variety8 and9 ,of showingplants, are temperature open only during and humidity daytime distributions and closed at at night. the level This of is thenot leafa common boundary rule layer as an andadaptation ambient to climate arid climatic inside crop conditions rows, respectively. certain species undergoing CAM photosynthesis, e.g.,The the airpineapple temperature have fieldtheir duringstomata daytime opened withinat night the and leaf closed boundary during layer daytime in the to crop re- rowduce (Figure transpiration.8) is marked, Thus, as the in ambientleaf boundary air, by la a four-degreeyer’s climate temperature is directly related gradient to betweenthe phys- theiological top and response base of of the crops canopy and due especially to a higher transpiration. incident radiation The stomata at the have top. a Therefore,maximum boundaryaperture when layer climatesunlight reveals is high no at important noon and discontinuity from then on as closure ambient takes air and place, leaf tempera-slowly at turefirst are and quite faster similar by the and end confirms of the day. the When information stomata given are open, in Figure an 6evapotranspiration and analyzed before rate. occursDuring due daytime,to the water the airpotential humidity gradient field within and air the humidity leaf boundary increasing layer up in theto saturation crop row (Figureclose to9 )the highlights lower leaf gradients surface betweenand, thanks the baseto the and decrease the top in of momentum the crop cover. transfer, Under contrib- low airutes velocities to the creation prevailing of a climate at the base discontinu of the cropity between rows, the the boundary boundary layer layer is and thicker, the ambient and as transpirationair surrounding is still the important, leaf. the air humidity is higher. TheThese observed results discontinuitiesare in a very good between agreement the humidity with the at experimental the boundary results layer andof Boulard in the ambientet al. [7] airin provetomato that and to of improve Sheriff [5] the in crop Nicotiana disease glauca control, who the highlight microclimate the humidity conditions gra- at thisdients level close must to bethe directly lower surface targeted of ratherthe leaf. than The controlling main novelty the of entire our study ambient is to air show climate. that Morethe boundary specifically, layer air microclimate humidity, the distribution crucial parameter throughout controlling the greenhouse pest activity, is mustalso directly not be consideredrelated to the in theinside air atclimate the center heterogeneity, of the greenhouse as illustrated but in by the Figures crop cover 8 and at 9, leafshowing level andtem- techniquesperature and such humidity as localized distributions heating or at ventilation the level of to the control leaf theboundary microclimate layer and at this ambient level shouldclimate be inside implemented. crop rows, respectively. Sustainability 2021, 13, x FOR PEER REVIEW 10 of 13

The air temperature field during daytime within the leaf boundary layer in the crop row (Figure 8) is marked, as in ambient air, by a four-degree temperature gradient be- Sustainability 2021, 13, x FOR PEER REVIEW 10 of 13 tween the top and base of the canopy due to a higher incident radiation at the top. There- fore, boundary layer climate reveals no important discontinuity as ambient air and leaf temperature are quite similar and confirms the information given in Figure 6 and ana- lyzedThe before. air temperature field during daytime within the leaf boundary layer in the crop row (Figure 8) is marked, as in ambient air, by a four-degree temperature gradient between the top and base of the canopy due to a higher incident radiation at the top. There- Sustainability 2021, 13, 8310 fore, boundary layer climate reveals no important discontinuity as ambient air and10 ofleaf 13 temperature are quite similar and confirms the information given in Figure 6 and analyzed before.

Figure 8. CFD simulation of the air temperature during daytime at different locations within the boundary layer of leaves of plants that form the crop row (A cross section along the vertical plane perpendicular to the axis of the crop rows).

During daytime, the air humidity field within the leaf boundary layer in the crop row Figure(Figure 8. 9)CFD highlights simulation gradients of the air between temperature the base during and daytime the top at of different the crop locations cover. Under within thelow Figure 8. CFD simulation of the air temperature during daytime at different locations within the boundaryair velocities layer prevailingof leaves of atplants the basethat formof the the crop crop rows, row (A the cross boundary section alonglayer theis thicker, vertical and plane as perpendiculartranspiration to is the still axis important, of the crop the rows). air humidity is higher.

During daytime, the air humidity field within the leaf boundary layer in the crop row (Figure 9) highlights gradients between the base and the top of the crop cover. Under low air velocities prevailing at the base of the crop rows, the boundary layer is thicker, and as transpiration is still important, the air humidity is higher.

FigureFigure 9.9. CFDCFD simulationsimulation ofof thethe airair humidityhumidity duringduring daytimedaytime atat differentdifferent locations locations within within the the boundaryboundary layer layer of of leaves leaves of of plants plants that that form form the the crop crop row row (A (A cross cross section section along along the the vertical vertical plane plane perpendicularperpendicular to to the the axis axis of of the the crop crop rows). rows).

3.2. EffectsThe observed of Varying discontinuities the Environmental between Factors the in hu themidity Greenhouse at the on boundary the Microclimate layer and in thein the Boundary Layer ambient air prove that to improve the crop disease control, the microclimate conditions at Figure 9. CFD simulation of the air humidity during daytime at different locations within the this Aslevel seen must above, be directly the climate targeted in the rather leaf boundarythan controlling layer directly the entire depends ambient on air the climate. green- boundaryhouse climate layer controlof leaves systems, of plants i.e.,that heating,form the cooling,crop row humidiﬁcation (A cross section andalong dehumidiﬁcation, the vertical plane perpendicularMore specifically, to the axisair humidity,of the crop rows).the crucial parameter controlling pest activity, must not orbe air considered speed via in the the activation air at the of center greenhouse of the greenhouse openings. but in the crop cover at leaf level Using the model described above, we can evaluate the consequences on air temper- The observed discontinuities between the humidity at the boundary layer and in the ature and humidity at 1 mm from the surface of the leaf in response to changes in the ambient air prove that to improve the crop disease control, the microclimate conditions at boundary conditions, i.e., temperature, humidity, and air velocity inside the greenhouse. this level must be directly targeted rather than controlling the entire ambient air climate. Simulations predict that when increasing the temperature of the ambient air of the Moregreenhouse specifically, by 2 ◦ C,air i.e., humidity, by changing the crucial the heat pa settings,rameter thecontrolling temperature pest atactivity, 1 mm undermust not the beleaf considered increases byin 1.08the air◦C. at the center of the greenhouse but in the crop cover at leaf level A decrease in air temperature inside the greenhouse by 2 ◦C when we activate the cooling system involves a reduction of 0.5 ◦C in the temperature at 1mm under the leaf. When increasing the air velocity inside the greenhouse by 0.2 m s−1 and 0.4 m s−1, respectively, either by blowing the surrounding air by means of fans or through air injection systems, i.e., using pipes pierced on the sides and placed in the row crops, we gain 0.5 ◦C Sustainability 2021, 13, 8310 11 of 13

and 0.9 ◦C in temperature, respectively, and we lose 9% and 15% in humidity, respectively, at 1 mm under the leaf compared to the ambient air. These results show that the climatic parameters of the ambient air inside the greenhouse and in the leaf boundary layer do not increase and decrease in the same direction in response to climate control system actions. Understanding this relationship between the climate in these two environments, i.e., the climate in the leaf boundary layer and the greenhouse climate, allows to better manage the climate at the pest ecological niche level to carry out efficient pest control. Knowing that small differences in temperature profiles among leaves can cause large variation in the metabolic rate and development time of insect eggs [26], the results of the present study on the climatic decoupling between the boundary layer of leaves and the ambient air in the greenhouse could be taken advantage of to propose climate control methods against pests and diseases. This method will be based on the modification of the favorable climate of pest installation in the boundary layer of leaves by activating the cooling and heating systems or by playing with the degree of containment of the greenhouse, i.e., by closing or opening the greenhouse vents. The developed model can be also used as a decision support tool by creating alarms based on the favorable climate to the development of pests and disease leading towards to new sustainable strategies of climate control limiting their installation. In fact, this spatiotemporal CFD-model describing the distributed climate within the greenhouse and especially in the boundary layer of plant leaves, could be used as a tool for sensitivity studies to draw up several assumptions to increase control of the microclimate in the level of plant cover to fight against pests. It can be also used for a more effective risk prediction of the onset of epidemic outbreaks in the greenhouse and setting up alarms to activate air conditioning systems available in the greenhouse. This strategy will help fight insect pests in greenhouse plants by limiting the use of chemical products resulting in lower environmental impact. The other application of the developed model is that by cross-referencing the distributed climate data from this model with the data from the spatial sampling of pests or beneficial insects, it is possible to better define the microclimatic conditions favorable to their installation.

4. Conclusions Computational Fluid Dynamics (CFD) modeling tools allowed the exploration of the microclimate inside greenhouses and particularly at the level of the leaf boundary layer. Using this model, the heterogeneity of the microclimate in the boundary layer of leaves, i.e., the ecological niche of pests and diseases, was considered and studied. This made it possible to show the discontinuity of the climatic conditions between the natural habitat of pests (BL) and the surrounding air, particularly during daytime. This distributed climate modeling in the leaf boundary layer has provided crucial information on the climate conditions of the plant pest habitats. Superimposing maps of these climate conditions and those resulting from the spatial sampling of pest population can provide new information about their climate preferences and suggest new sustainable control strategies allowing for a more efﬁcient pest control. These control methods will base on the modiﬁcation of the local climate to make it less favorable to the installation of pests.

Author Contributions: H.F. conceived the idea, developed the model collected and analyzed the data and drafted the paper. T.B. (Thierry Boulard), C.P., N.K., T.B. (Thomas Bartzanas), M.K., H.G. and I.-B.L. helped in analysis of the data, and ﬁnalization of the draft article. All authors commented on previous versions of the manuscript. All authors have read and agreed to the published version of the manuscript. Sustainability 2021, 13, 8310 12 of 13

Funding: This work received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The authors declare that all other data supporting the findings of this study are available within the article. Conflicts of Interest: The authors declare that they have no conflict of interest.

References 1. Boulard, T.; Mermier, M.; Fargues, J.; Smits, N.; Rougier, M.; Roy, J.C. Tomato leaf boundary layer climate: Implication for microbiological control of whiteflies in greenhouse. Agric. For. Meteorol. 2002, 110, 159–176. [CrossRef] 2. Jones, H.G. Plants and Microclimate: A Quantitative Approach to Environmental Plant Physiology, 3rd ed.; Cambridge University Press: Cambridge, UK, 2014; 407p. 3. Pincebourde, S.; Woods, H.A. Climate uncertainty on leaf surfaces: Perspectives on the leaf microclimate from biophysical ecology. Funct. Ecol. 2012, 26, 844–853. [CrossRef] 4. Woods, H.A.; Potter, K.A. Life in leaf boundary layers: How two millimeters of still air affects the performance and ecology of small insects. Integr. Comp. Biol. 2010, 50 (Suppl. 1), E193. 5. Sheriff, D.W. An infra-red psychrometer for detecting changes in the humidity of leaf boundary layers. J. Exp. Bot. 1973, 24, 641–647. [CrossRef] 6. Grace, J.; Wilson, J. Boundary-layer over a populus leaf. J. Exp. Bot. 1976, 27, 231–241. [CrossRef] 7. Boulard, T.; Fatnassi, H.; Roy, J.C.; Lagier, J.; Fargues, J.; Smits, N.; Rougier, M.; Jeannequin, B. Effect of Greenhouse Ventilation on Humidity of inside air and in leaf boundary-layer. Agric. For. Meteorol. 2004, 125, 225–239. [CrossRef] 8. Katsoulas, N.; Baille, A.; Kittas, C. Leaf boundary layer conductance in ventilated greenhouses. An experimental approach. Agric. For. Meteorol. 2007, 144, 180–192. [CrossRef] 9. Kimura, K.; Yasutake, D.; Yamanami, A.; Kitano, M. Spatial examination of leaf-boundary-layer conductance using artificial leaves for assessment of light airflow within a plant canopy under different controlled greenhouse conditions. Agric. For. Meteorol. 2020, 280, 107773. [CrossRef] 10. Nikolaou, G.; Neocleous, D.; Kitta, E.; Katsoulas, N. Estimation of aerodynamic and canopy resistances in a Mediterranean greenhouse based on instantaneous leaf temperature measurements. Agronomy 2020, 10, 1985. [CrossRef] 11. Ferro, D.N.; Southwick, E.E. Microclimates of small arthropods-estimating humidity within the leaf boundary-layer. Environ. Entomol. 1984, 13, 926–929. [CrossRef] 12. Roy, J.-C.; Vidal, C.; Fargues, J.; Boulard, T. CFD based determination of temperature and humidity at leaf surface. Electron. Agric. 2008, 61, 201–212. [CrossRef] 13. Fatnassi, H.; Poncet, C.; Bazzano, M.M.; Brun, R.; Bertin, N. A numerical simulation of the photovoltaic greenhouse microclimate. Sol. Energy 2015, 120, 575–584. [CrossRef] 14. Fatnassi, H.; Boulard, T.; Poncet, C.; Chave, M. Optimisation of greenhouse insect screening with Computational Fluid Dynamics. Biosyst. Eng. 2006, 93, 301–312. [CrossRef] 15. Boulard, T.; Wang, A. Experimental and numerical study on the heterogeneity of crop transpiration in a plastic tunnel. Comput. Electron. Agric. 2002, 34, 173–190. [CrossRef] 16. Fatnassi, H.; Boulard, T.; Bouirden, L. Simulation of climatic conditions in full-scale greenhouse fitted with insect-proof screens. Agric. For. Meteorol. 2003, 118, 97–111. [CrossRef] 17. Kacira, M.; Sase, S. Optimization of vent configuration by evaluating greenhouse and plant canopy ventilation rates under wind induced ventilation. Trans. ASAE 2004, 47, 2059–2067. [CrossRef] 18. Majdoubi, H.; Boulard, T.; Fatnassi, H.; Bouirden, L. Airflow and microclimate patterns in a one-hectare Canary type greenhouse: An experimental and CFD assisted study. Agric. For. Meteorol. 2009, 149, 1050–1062. [CrossRef] 19. Nebbali, R.; Roy, J.; Boulard, T.; Nebbali, R.; Roy, J.; Boulard, T. Dynamic simulation of the distributed radiative and convective climate within a cropped greenhouse. Renew. Energy 2012, 43, 111–129. [CrossRef] 20. Sase, S.; Kacira, M.; Boulard, T.; Okushima, L. Determination of porosity parameters for tomato canopy: An experimental study in a wind tunnel. Trans. ASABE 2012, 55, 1921–1927. 21. Tamimi, E.; Kacira, M.; Choi, C.; An, L. Analysis of climate uniformity in a naturally ventilated greenhouse equipped with high pressure fogging system. Trans. ASABE 2013, 56, 1241–1254. 22. Kichah, A.; Bournet, P.E.; Migeon, C.; Boulard, T. Measurements and CFD simulations of microclimate characteristics and transpiration of an Impatiens pot plant crop in a greenhouse. Biosyst. Eng. 2012, 112, 22–34. [CrossRef] 23. Boulard, T. Recent trends in protected cultivations-microclimate studies: A review. Acta Hortic. 2012, 957, 15–28. [CrossRef] 24. Schlichting, H. Boundary Layer Theory; McGraw-Hill: New York, NY, USA, 1979; 817p. Sustainability 2021, 13, 8310 13 of 13

25. Wang, S.; Boulard, T.; Haxaire, R. Air speed proﬁles in a naturally ventilated greenhouse. Agric. For. Meteorol. 1999, 96, 181–188. [CrossRef] 26. Potter, K.; Davidowitz, G.; Woods, H.A. Insect eggs protected from high temperatures by limited homeothermy of plant Leaves. J. Exp. Biol. 2009, 212, 3448–3454. [CrossRef][PubMed]