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Article

Preliminary Mapping of the Spatial Variability in the Microclimate in Tropical Greenhouses: A Pepper Crop Perspective

by
Angel Triana
1,
Alfonso Llanderal
1,2,
Pedro García-Caparrós
1,
Manuel Donoso
2,
Rafael Jiménez-Lao
2,
John Eloy Franco Rodríguez
1 and
María Teresa Lao
2,*
1
Faculty of Technical Education for Development, Catholic University of Santiago of Guayaquil, Av. C. J. Arosemena Km. 1.5, Guayaquil 09014671, Ecuador
2
Department of Agronomy, University of Almeria, 04120 Almeria, Spain
*
Author to whom correspondence should be addressed.
Agriculture 2024, 14(11), 1972; https://doi.org/10.3390/agriculture14111972
Submission received: 27 August 2024 / Revised: 12 October 2024 / Accepted: 29 October 2024 / Published: 3 November 2024
(This article belongs to the Section Agricultural Technology)
Browse Figures
Versions Notes

Abstract

:
The objectives of this experiment were to (1) discern the spatial variability in climatic parameters within a greenhouse throughout different phenological stages of pepper cultivation and (2) develop an empirical model aimed at establishing predictive equations for temperature, relative humidity, vapor pressure deficit, and crop evapotranspiration (ETc) within the greenhouse considering the climatic parameters recorded on the outside. The experiment was conducted in the coastal area of Ecuador within a bamboo-constructed greenhouse facility. Pepper plants were cultivated in plastic bags using a specific cultivation medium common in Ecuador and a fertigation system. Climatic parameters were monitored within the greenhouse using data loggers, and the external conditions were recorded using an external meteorological station throughout the duration of the pepper cultivation. Statistical analyses revealed correlations between internal climatic parameters and plant growth stages, as well as external climatic conditions. The spatial distribution analysis of climatic parameters within the greenhouse revealed that the lowest values for temperature (27 °C) and vapor pressure deficit (VPD) (1.25 kPa) and the highest values for relative humidity (RH) (68%) were observed on the northwest corner of the greenhouse. This observed pattern was linked to the prevailing wind direction (south–east (SE)) outside the greenhouse. Stepwise regression analyses identified significant outdoor climate variables (RH, temperature, VPD, and instantaneous wind speed (WS) Inst) in the climatic conditions recorded within the greenhouse.

1. Introduction

The cultivation of crops in tropical conditions faces challenges posed by elevated temperatures and humidity, particularly prevalent during the winter months [1]. In the case of the Ecuadorian coastal region, over the summer season, precipitation levels range between 1400 and 2200 L m−2, accompanied by temperatures ranging from 23 to 36 °C [2]. Consequently, greenhouses are increasingly being adopted as a viable production system in tropical and subtropical climates, offering protection mainly against pests and excessive rainfall [3].
The spatial distribution of climatic parameters within a greenhouse is influenced by several factors. These factors encompass the greenhouse’s structural configuration, including aspects such as its geometry, size, height, and orientation. Additionally, external factors, such as solar radiation intensity, wind speed, temperature, and relative humidity, contribute to the complexity of greenhouse climate dynamics [4,5]. Besides these factors, an additional challenge arises from inherent inaccuracies within the monitoring sensors themselves. Inaccurate monitoring of growth environments may result in the development of suboptimal control strategies for regulating growth conditions, which can, in turn, compromise energy efficiency in greenhouse systems [6].
When assessing crop water requirements, it is essential to determine the values of evapotranspiration (ET) and the corresponding crop coefficients (Kc) under varying environmental conditions. While extensive research has focused on evapotranspiration and crop coefficients in open-field environments, there is also a growing body of work addressing these parameters in greenhouse settings, particularly in relation to pepper cultivation [7,8,9].
The microenvironment within greenhouses plays a pivotal role in influencing key aspects of crop cultivation, including growth dynamics, yield output, and the overall quality of the harvested fruit [10,11]. Excessive temperatures within the greenhouse environment can induce detrimental effects on pepper plants, such as pollen degradation and foliar wilting. Conversely, excessively low temperatures can disrupt enzymatic processes essential for metabolic pathways, thereby affecting overall plant growth and development [12]. In pepper cultivation, humidity levels exceeding the threshold of 70% are associated with adverse consequences, including decreased plant transpiration rates, susceptibility to fruit cracking, and heightened proliferation of fungal and bacterial pathogens [13,14].
Considering the preceding information, it is crucial to underscore the necessity of accurately forecasting and controlling both the air temperature and relative humidity within greenhouse settings. These parameters are mutually linked and should be simultaneously controlled because of their high impact on crop growth and development [15]. Furthermore, it is noteworthy to emphasize the crucial role of air temperature in determining water consumption via evapotranspiration processes [16]. Several studies have reported the spatial variability in the temperature and relative humidity within agriculturally protected conditions. For instance, Reyes-Rosas et al. [17] evaluated the spatial variability in these climatic parameters for tomato crops cultivated in multi-span greenhouses, whereas Singh et al. [18] focused on similar endeavors for cucumber cultivation in soil-less media within the same greenhouse conditions, emphasizing the crucial role of the internal temperature in the greenhouse. Ting-ting et al. [19] extended this research line, encompassing both tomato and pepper crops cultivated within mono-span greenhouses, whereas Dewi et al. [20] focused on a Chinese broccoli crop cultivated under analogous conditions, where the structural design clearly affected the internal climate of the greenhouse. However, it is worth noting that the spatial variability in climatic conditions inside a bamboo greenhouse in tropical conditions has been rarely studied in the previous literature. Moreover, this information will be very valuable and innovative in enhancing the implementation of bamboo as a sustainable building material well adapted to the challenges of tropical climates. Hence, the aims of this study are to (1) discern the spatial variability in climatic parameters within a greenhouse throughout the different phenological stages of pepper cultivation and (2) develop an empirical model aimed at establishing predictive equations for temperature, relative humidity, vapor pressure deficit, and crop evapotranspiration (ETc) within the greenhouse considering the climatic parameters recorded on the outside.

2. Materials and Methods

2.1. Plant Material and Experimental Conditions

The experiment was conducted within a bamboo-constructed greenhouse facility totaling a surface area of 350 m2 (Figure 1). The greenhouse was naturally ventilated without any forced ventilation based on the permanent opening of the windows (side and zenithal). The anti-aphid insect screen of the ventilation openings remained open on the lateral side and on the roof without any type of management during the experimental period. The greenhouse was located at the International Centre for Protected Agriculture, affiliated with the Catholic University of Santiago de Guayaquil, Ecuador. The pepper (Capsicum annuum cv. “Inka”) was cultivated at a planting density of 1.42 plants per m2 (10 June 2023 to 2 October 2023). The cultivation medium consisted of 10 L volume plastic bags containing a substrate blend comprising river sand, rice chaff, and planting soil in the proportions of 50:25:25 v/v/v, respectively. Nutrient applications were administered through fertigation, using a trickle-feed distribution irrigation system with each emitter set to a flow rate of 4.7 L h−1. The amount of water provided to each plant during the experimental period ranged from 0.5 to 2.0 L according to the crop water requirements. The nutrient solution comprised the following concentrations (in mmol L−1): NO3− 15.00, H2PO4− 1, SO42− 2, K+ 6, Ca2+ 4, and Mg2+ 2, with an electrical conductivity (EC) of 2 dS m−1 and a pH value of 6 [21]. The yield of the crop during the experimental period was 2.3 Kg m−2.

2.2. Experimental Framework and Assessed Parameters

The experimental design consisted of the distribution of 5 data loggers within the greenhouse, as depicted in Figure 1. Temperature and relative humidity readings within the different modules were recorded continuously and averaged every 15 min with the data logger model Elitech RC-51H (Elitech, London, UK). These data loggers were placed at a standardized elevation of 1.5 m above ground level.
External climatic parameters were acquired from a NIPON meteorological station (model HG4050, Nagoya, Japan). The following variables were systematically recorded continuously and averaged every 15 min: average and instantaneous wind speed (WS Ave. and WS Inst., respectively, expressed in m s−1), average and instantaneous wind direction (WD Ave. and WD Inst., respectively, denoted in degrees), temperature (°C), relative humidity (RH, expressed as a percentage), and global radiation (GR, measured in MJ m−2 h−1 and MJ m−2 day−1).
The transmission coefficient of the material covering the greenhouse was estimated by calculating the ratio between the PAR radiation received inside and outside the greenhouse using a luxometer model Q201 PAR radiometer Irradian (Irradian, East Lothian, United Kingdom). The value of the greenhouse transmission coefficient was 0.72.

Climatic Parameters

The vapor pressure deficit (VPD, kPa) both externally and internally within the modules of the greenhouse was estimated using the following equation proposed by Idso et al. [22]:
VPD = es − ea
where es denotes the saturation pressure at a given air temperature and ea denotes the actual vapor pressure within the atmosphere.
The saturation vapor pressure (es) was estimated using the following equation:
es (T°) = 0.611 exp ((17.27 * T)/(T° + 237.3))
where T denotes the air temperature expressed in °C.
The saturation pressure (ea) was defined as follows:
ea = (RH * es)/100
where RH is the relative humidity expressed in kPa.
The reference evapotranspiration (ET0) was estimated using the equation of Hargreaves [23,24]
ET0 = 0.0023 * Ra * (Tmed + 17.78) * (Tmax − Tmin)0.5
where Tmed, Tmax, and Tmin denote the average daily temperature, mean daily maximum temperature, and mean daily minimum temperature, respectively, and Ra denotes the extraterrestrial radiation expressed in mm day−1.
Crop evapotranspiration (ETc) was estimated using the equation proposed by Allen et al. [25]
ETC = ET0 * Kc
where ET0 denotes the reference evapotranspiration (expressed in mm), and Kc denotes the crop coefficient calculated based on the crop’s water consumption. The following values of Kc in the greenhouse conditions were obtained from the methodology reported by Fernandez et al. [26]: 0.40 (vegetative), 0.60 (flowering), 1.4 (fruit development), and 0.90 (harvesting) in the different phenological stages of a pepper crop.

2.3. Statistical Analysis

Aiming to capture basic spatial effects, this experiment considered the use of spatial coordinates as covariates as an initial practice in spatial modeling since this methodology might be a valid initial step in exploring spatial patterns. This approach can be particularly useful when the goal is to assess the overall influence of location without assuming a complex spatial structure from the outset, as reported by Cressie [27]. The acquired data were analyzed by calculating the average, minimum, and maximum values of all climatic variables both within and outside the greenhouse environment. The values of days after transplanting (DAT) were also included in the analysis in order to consider the effect of the phenological stage of the crop in the resulting model. The statistical analyses of the climatic parameters were conducted using Statgraphics Centurion XVI© software version 16.1 (Statpoint Technologies, Inc., Warrenton, VA, USA) with different confidence levels (p < 0.05 and p < 0.01). To determine the relationship between climatic factors (spatial distribution) and the position of sensors within the greenhouse, a multiple linear regression analysis was used. The collected data from the sensors, positioned throughout the length (X-axis) and width (Y-axis) of the greenhouse (X-axis), included measurements of temperature, relative humidity, and vapor pressure deficit (Z-axis). A linear multiple regression model was developed using the ordinary least squares (OLS) method in Statistica 7.0 software (TIBCO Software, Inc., Palo Alto, CA, USA) based on these data. Subsequently, a three-dimensional surface plot was generated to determine how the positions of the sensors influenced the measured climatic variables. In order to streamline the model and identify the independent variables that have the greatest influence on the climatic parameters within the greenhouses, a stepwise regression analysis was conducted. The stepwise regression technique iteratively includes or excludes variables from the regression model in order to obtain the most parsimonious model that includes only statistically significant predictors while still retaining potentially valuable variables.

3. Results

3.1. Experimental Growth Conditions

The environmental conditions within the greenhouse and on the outside were recorded throughout the experimental period over the different phenological stages of the crop (Table 1). The values of temperature and VPD recorded in the greenhouse were higher than those in the outside conditions. This increase in these climatic parameters showed a clear trend to be higher over the different phenological stages. Conversely, the values of relative humidity in the greenhouse were lower than those in the outside conditions. Regarding global radiation, as can be expected, the values in the greenhouse were lower than those in the outside conditions.
In terms of climate in greenhouses, the wind direction and speed outside of the greenhouses are critical factors. The data recorded revealed that the prevailing winds originated from three cardinal directions: the north–northwest (NNW) sector (31.64%), the north–west (NW) sector (35.34%), and the west–northwest (WNW) sector (9.56%), representing 76.54% of the observed wind occurrences. Concerning the speed characteristics of the wind from these three cardinal directions, the values of the average and maximum instantaneous wind speed were 1.97, 2.91, and 2.77 m s−1 and 8.50, 9.1, and 6.7 m s−1, respectively. Additionally, the average and maximum wind speeds were 0.65, 0.99, and 0.76 m s−1 and 3.40, 3.80, and 2.30 m s−1, respectively (Figure 2).

3.2. Spatial Distribution of Climatic Parameters in the Greenhouse over the Different Crop Phenological Stages

The temperature, RH, and VPD data were related to the data logger positioning in the greenhouse during the vegetative phase of the pepper crop (Figure 3). The comparative analysis through linear regressions revealed that the temperature and VPD profiles within the greenhouse showed a lower coefficient of determination (0.22 and 0.18, respectively), with the lowest values on the northwest corner of the greenhouse (Figure 3a,c). In contrast, the RH profile showed a different pattern characterized by maximal values on the northwest corner (Figure 3b).
The spatial distribution of temperature, RH, and VPD in the greenhouse throughout the flowering stage is shown in Figure 4. In the greenhouse, temperature, RH, and VPD fluctuated within the ranges of 28 to 30 °C, 56 to 66%, and −1.40 to −1.90 kPa, respectively. The spatial distribution analyses revealed the same pattern observed during the vegetative growth stage (Figure 4a–c).
The spatial distribution of temperature, RH, and VPD in the greenhouse throughout the fruit development stage is shown in Figure 5. The comparative analysis revealed that the climatic parameters in this stage were similar to those in the flowering stage. In the greenhouse, temperature, RH, and VPD fluctuated within the ranges of 27 to 29 °C, 60 to 68%, and −1.25 to −1.75 kPa, respectively. When examining the climatic parameters within the greenhouse, it was observed that their coefficient of determination showed lower values, ranging from 0.16 to 0.23. Furthermore, the spatial distribution analyses revealed that temperature and VPD showed the lowest values on the northwest corner of the greenhouse (Figure 5a,c). Conversely, relative humidity (RH) showed an opposing trend, with elevated values on the northwest corner of the greenhouse (Figure 5b).
The spatial distribution of temperature, RH, and VPD in the greenhouse throughout the harvesting stage is shown in Figure 6. The comparative analysis revealed that the climatic parameters in this stage were similar to those in the flowering and fruiting stages. In the greenhouse, temperature, RH, and VPD fluctuated within the ranges of 27 to 30 °C, 58 to 66%, and −1.50 to −1.90 kPa, respectively. When examining the climatic parameters within the greenhouse, it was observed that the coefficient of determination showed lower values, ranging from 0.14 to 0.23. Furthermore, the spatial distribution analyses revealed that temperature and VPD showed the lowest values on the northwest corner of the greenhouse (Figure 6a,c). Conversely, relative humidity (RH) showed an opposing trend, with elevated values observed on the northwest corner of the greenhouse (Figure 6b).
Figure 7 shows the spatial distribution of ETc (mm day−1) within the greenhouse. Notably, no significant coefficient of determination was found, with values around 1.45 mm day−1.

3.3. Correlations Among Climatic Parameters and ETc Inside and Outside of the Greenhouse

The climatic conditions within the greenhouse, as well as ETc, were influenced by external climatic conditions and the phenological stage of the plants. To elucidate these associations, correlation coefficients were computed between external climatic conditions, days after transplanting (DAT), and variables such as temperature, RH, VPD, and ETc, in the greenhouse (Table 2).
The internal temperature within the greenhouse model with plants showed discernible correlations with external climatic variables. These correlations were characterized as follows: notably high ((RH, 0.92), (GR, 0.92), (T, 0.81)), moderate ((VPD, 0.69), (WS Inst., 0.54), (WS Ave., 0.40)), low (WD Ave., −0.33), and notably low (WD Inst (−0.17), DAT (−0.14)). Nonetheless, it is imperative to point out that internal relative humidity (RH) exhibited an inverse relationship with internal temperature. This signifies that a parameter exerting a positive influence on temperature concurrently exerts a negative influence on RH. Regarding internal VPD, it is noteworthy to emphasize its significant correlation with eight of the nine external climatic variables considered. These correlations were characterized as follows: temperature (0.89), RH (0.79), GR (0.79), VPD (0.72), WS Inst. (0.51), WS Ave. (0.36), WD Ave. (−0.28), and WD Ins (−0.17). In the case of ETc, only seven of the nine climatic variables showed a correlation between them. These correlations were characterized as follows: high ((VPD, 0.82), (GR, 0.74), (RH, 0.74)), moderate ((DAT, 0.52), (WS Inst, 0.51), (WS Ave., 0.41)), and notably low (temperature, 0.17). Notably, the absence of any significant correlation was detected between temperature, crop reference evapotranspiration (ETc), and days after transplanting (DAT).
A stepwise regression analysis was conducted for each climatic parameter within the greenhouse to determine the most suitable models characterized by the fewest significant predictor variables (Table 3). At each stage of the stepwise procedure, the inclusion or exclusion of variables in the model was determined based on the outcomes of the partial F-test. Specifically, a variable was added to the model if its associated F-value surpassed the threshold of 8, while it was removed if its F-value fell below this threshold. Table 3 presents the regression coefficients along with the selected independent variables identified through the stepwise regression method. Furthermore, it provides the values of various goodness-of-fit measures. A coefficient of determination (R2) exceeding 88.47 indicated that all adjusted models had a high goodness of fit. This indicates that the adjusted models account for a significant proportion of the variation in climatic parameters within both modules in the greenhouse. Furthermore, at a 95% confidence level, the p-values associated with each assessed test were below 0.05, indicating statistically significant correlations among the variables included in each model. Notably, variables such as temperature, RH, VPD, and WS Inst. exhibited significance across all models, further highlighting their consistent importance in explaining variations in climatic parameters.

4. Discussion

4.1. Experimental Growth Conditions

Considering the external climatic variables, our results align closely with the findings reported by Arredondo et al. [28] in their empirical study conducted along the coastal regions of Ecuador. Specifically, temperatures, relative humidity levels, global radiation flux, and wind speed were observed within a comparable range, encompassing from 8 to 36 °C for temperatures, 59 to 97% for relative humidity, 18.36 to 22.32 MJ m−2 day−1 for global radiation, and 2.6 to 10 m s−1 for wind speeds. During the experimental period, the temperatures in the greenhouse exceeded the values recorded outside, which can be attributed to the accumulation of near-infrared radiation and the properties of the greenhouse cover material, as outlined by Villagran et al. [29]. These findings underscore the critical need to regulate excessive temperatures, particularly in greenhouses situated in tropical regions, through the use of cover materials possessing varied physical properties.
The maintenance of optimal temperatures and relative humidity is crucial for ensuring the proper growth and development of plants. According to Reche-Marmol [14], the optimal biological temperature range for pepper cultivation spans from 10 to 35 °C. Considering this range, it is notable that throughout the four different physiological stages of the experimental period (vegetative, flowering, fruit development, and harvesting), the recorded temperatures never fell below the lower threshold of 10 °C. Nevertheless, in terms of the upper threshold of 35 °C, our results revealed that during these stages, the pepper crop experienced a proportion of hours exceeding this temperature threshold: specifically, 3.16%, 5.75%, 4.57%, and 9.25% of the time during the vegetative, flowering, fruit development, and harvesting stages, respectively. These elevated temperatures may affect the overall growth performance of pepper plants, resulting in a clear yield decrease. These outcomes highlight the significance of utilizing evaporative cooling systems, particularly during the most critical stages of crop development, because natural ventilation, as observed in our experiment, is sometimes insufficient to meet the necessary cooling requirements.
In terms of relative humidity, as depicted by Reche-Marmol [14], the optimal ranges for pepper crop cultivation are from 50 to 70%. Nevertheless, our recordings during the experimental period within the greenhouse indicated that 20.95%, 20.69%, 18.65%, and 23.24% of the hours, corresponding to the vegetative, flowering, fruit development, and harvest stages, respectively, were below the minimum recommended relative humidity threshold throughout the cultivation cycle. Notably, the recordings of reduced RH were mainly observed during hours of heightened solar radiation within the greenhouses, aligning with the findings reported by Huertas [30]. Concerning the maximum threshold, our results revealed that 15.29%, 28.71%, 33.18%, and 31.75% of the hours, corresponding to the vegetative, flowering, fruit development, and harvest stages, respectively, exceeded the established maximum RH. Notably, the recordings of exceeded RH were predominantly observed during the nocturnal and early morning hours, consistent with the findings reported by Dimitrijević et al. [31]. In our experiment, the fluctuations in relative humidity during crop cultivation indicate the need to improve natural ventilation within the greenhouse. Future research should focus on modifying the design of lateral and zenithal openings to address this issue effectively.
Adequate development of pepper plants within greenhouse environments requires VPD values within the specified range of 0.5 to 1.7, as outlined by Piñero et al. [32]. Nevertheless, our recordings during the experimental period within the greenhouse with plants indicated that 0%, 2.54%, 3.82%, and 2.53% of the hours, corresponding to the vegetative, flowering, fruit development, and harvest stages, respectively, were below the minimum recommended VPD threshold throughout the cultivation cycle. Reduced VPD values can limit plant transpiration, thereby inducing physiological changes linked to hormonal imbalances and hindering ion transport to the plant’s reproductive organs [33]. Concerning the maximum threshold, our results revealed that 40.37%, 37.06%, 35.45%, and 36.68% of the hours, corresponding to the vegetative, flowering, fruit development, and harvest stages, respectively, exceeded the established maximum VPD. There is clear evidence of the correlation between environmental parameters, such as low humidity and elevated temperatures, and their effects on plant physiology. Under such climatic conditions, plants suffer an increase in the transpiration rate, thereby culminating in consequential dehydration. Consequently, the rate of net photosynthesis is reduced, triggering physiological disorders such as apical necrosis, particularly prevalent in the fruits of pepper plants [34]. Regarding the values of global radiation recorded within the greenhouse environment, our results revealed no limitations since all the values exceeded the threshold of 9.00 MJ m−2 day−1, which is the minimum required for optimal greenhouse pepper production, as reported by Montero-Torres [35]. The elevated values of temperature, relative humidity, and vapor pressure deficit (VPD) observed during various phases of the experimental period significantly impacted the overall growth performance. These unfavorable conditions, likely associated with the tropical climate, differ markedly from the typical environmental conditions of greenhouses located in temperate zones. Such discrepancies underscore the importance of conducting studies under these specific climatic conditions to better understand their effects on plant growth dynamics.

4.2. Spatial Distribution of Climatic Parameters and Modeling

The comprehension of the spatial variations in climatic parameters within a greenhouse is of paramount importance to ensure an optimized yield. In our trial, the northwest corner of the greenhouse modules showed the lowest temperatures. This fact can be ascribed to the predominant wind direction, as evidenced by our radar plot analysis of wind speed and direction during the experiment, with the prevalent winds coming from the northwest corner. Similar observations were documented by Villagrán et al. [36] in their study of a multi-tunnel greenhouse employing natural ventilation, wherein the regions experiencing the lowest temperatures coincided with those directly exposed to predominant winds. This observed pattern can be ascribed to the mechanism of air exchange facilitated by the lateral windows, as elucidated by Flores Ortega et al. [37], whereby the ingress of cooler external air displaces warmer interior air. Additionally, a negative correlation exists between external wind speed and internal greenhouse temperatures, as heightened wind speeds result in reduced internal thermal values [38]. Regarding the spatial distribution of relative humidity, our results revealed an inverse correlation with temperature. Consistent with our observations, Ramon et al. [39] reported similar results, noting that increasing RH within the pepper greenhouse led to a decrease in temperature. This cooling effect is likely attributable to the exchange of air between the greenhouse’s exterior and interior environments. The spatial distribution of the VPD throughout the experimental period mirrored the temperature pattern, notably showing lower values along the northwest corner of the greenhouse. This observed distribution may be attributed to temperature dynamics, wherein elevated temperatures within the greenhouses result in reduced relative humidity levels, thereby increasing water vapor pressure deficits (VPDs) and consequent thermo-water stress on cultivated crops [38]. The spatial distribution of crop evapotranspiration (ETc) remained consistent throughout the greenhouse, likely because of the uniform growth rate of the pepper plants across different areas of the greenhouse.
Our established models, delineating the relationship between internal climatic parameters and external climatic variables, showed a robust correlation, as evidenced by R2 values exceeding 89. Nonetheless, it is noteworthy that several researchers have considered the effects of air renovations within the greenhouse environment, aiming to enhance model adjustments in terms of climatic parameters [17,40].

5. Conclusions

This experiment considered the use of spatial coordinates as covariates as an initial practice in spatial modeling since this methodology might be a valid initial step in exploring spatial patterns. Nevertheless, it would be appropriate to work with more advanced methods, such as kriging, in subsequent studies. The current investigation revealed significant insights into the dynamics of greenhouse climatic parameters and their consequential effects on pepper cultivation. The spatial distribution analysis of climatic parameters within the greenhouse revealed that the lowest values for temperature (27 °C) and vapor pressure deficit (VPD) (1.25 kPa) and the highest values for relative humidity (RH) (68%) were observed on the northwest corner of the greenhouse. Our results, aligning closely with previous studies, evidencing the importance of the interplay between external climatic variables and internal greenhouse conditions. Notably, the meticulous examination of temperature, relative humidity, VPD, and transpiration revealed subtle patterns influencing plant development and productivity. Furthermore, the development of robust models elucidating these relationships highlights the potential for predictive modeling in optimizing the microclimate within greenhouse conditions. Nevertheless, continued research efforts are imperative, aiming to refine the models to encompass air exchange dynamics. These efforts are essential for advancing the understanding and management of greenhouse environmental conditions among greenhouse designers. Furthermore, the improved knowledge of greenhouse climate control will facilitate the optimization of crop yield and quality, thereby enhancing the overall profitability for growers. All the information previously stated highlights the wide range of potential applications derived from the information compiled in this experiment.

Author Contributions

M.T.L.; conceptualization, M.T.L. and A.L.; methodology, A.L. and P.G.-C.; formal analysis, A.T., M.D., R.J.-L. and A.L.; investigation, A.T., R.J.-L. and J.E.F.R.; resources, A.L., A.T., M.D. and J.E.F.R.; writing—original draft preparation, A.L. and P.G.-C.; writing—review and editing, M.T.L., A.L. and P.G.-C.; supervision. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by AACID, grant number 2018UI002, within the framework of the cooperation project “Creation of a model of greenhouse adapted tropical dry floor Ecuadorian coastline, as a basis for a sustainable horticultural development, based on the experience of the Almeriense horticulture model”.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Cuce, P.M.; Riffat, S. A state of the art review of evaporative cooling systems for building applications. Renew. Sustain. Energy Rev. 2016, 54, 1240–1249. [Google Scholar] [CrossRef]
  2. Anda López, M.R.; Aranda, M.; Plata, M.R.A.; Castillo, M.H.J.; Campuzano, M.F.B.; Camazama, E.J. Sistema inteligente de fertirrigación y control ambiental para invernaderos de zonas tropicales, un detonante económico para horticultores y floricultores de la zona sur del Estado de México CAMAZAMA, Intelligent system of irrigation and environmental control for greenhouses of tropical zones, an economic detonator for horticulturists and flower grower of the south zone of the State of Mexico. In Proceedings of the XXXII Congreso Internacional y II Congreso Iberoamericano en Administración de Empresas Agropecuarias (sesión carteles), Guanajuato, Mexico, 26–29 May 2019; p. 45. [Google Scholar]
  3. Salcedo, G.A.; Reca, J.; Pérez-Sáiz, M.; Lao, M.T. Irrigation water consumption modelling of a soilless cucumber crop under specific greenhouse conditions in a humid tropical climate. Ciência Rural 2017, 47, 20151538. [Google Scholar] [CrossRef]
  4. Ahmed, H.A.; Tong, Y.X.; Yang, Q.C.; Al-Faraj, A.A.; Abdel-Ghany, A.M. Spatial distribution of air temperature and relative humidity in the greenhouse as affected by external shading in arid climates. J. Int. Agric. 2019, 18, 2869–2882. [Google Scholar] [CrossRef]
  5. Villagrán, E.A.; Bojacá, C.R. Simulacion del microclima en un invernadero usado para la producción de rosas bajo condiciones de clima intertropical. Chil. J. Agric. Anim. Sci. 2019, 35, 137–150. [Google Scholar] [CrossRef]
  6. Van Mourik, S.; Van Beveren, P.J.; López-Cruz, I.L.; Van Henten, E.J. Improving climate monitoring in greenhouse cultivation via model-based filtering. Biosyst. Eng. 2019, 181, 40–51. [Google Scholar] [CrossRef]
  7. Qiu, R.; Kang, S.; Li, F.; Du, T.; Tong, L.; Wang, F.; Chen, R.; Liu, J.; Li, S. Energy partitioning and evapotranspiration of hot pepper grown in greenhouse with furrow and drip irrigation methods. Sci. Hortic. 2011, 129, 790–797. [Google Scholar] [CrossRef]
  8. Sharma, H.; Shukla, M.K.; Bosland, P.W.; Steiner, R. Soil moisture sensor calibration, actual evapotranspiration, and crop coefficients for drip irrigated greenhouse chile peppers. Agric. Water Manag. 2017, 179, 81–91. [Google Scholar] [CrossRef]
  9. Hadad, D.; Lukyanov, V.; Cohen, S.; Zipilevitz, E.; Gilad, Z.; Silverman, D.; Tanny, J. Measuring and modelling crop water use of sweet pepper crops grown in screenhouses and greenhouses in an arid region. Biosyst. Eng. 2020, 200, 246–258. [Google Scholar] [CrossRef]
  10. Teitel, M.; Atias, M.; Barak, M. Gradients of temperature, humidity, and CO2 along a fan-ventilated greenhouse. Biosyst. Eng. 2010, 106, 166–174. [Google Scholar] [CrossRef]
  11. Singh, M.C.; Singh, K.G.; Singh, J.P. Performance of soilless cucumbers under partially controlled greenhouse environment in relation to deficit fertigation. Indian J. Hortic. 2018, 75, 259–264. [Google Scholar] [CrossRef]
  12. Bakker, J.C. The effects of temperature on flowering, fruit set and fruit development of glasshouse sweet pepper (Capsicum annuum L.). J. Hortic. Sci. 1989, 64, 313–320. [Google Scholar] [CrossRef]
  13. Bakker, J.C. The effects of air humidity on flowering, fruit set, seed set and fruit growth of glasshouse sweet pepper (Capsicum annuum L.). Sci. Hortic. 1989, 40, 1–8. [Google Scholar] [CrossRef]
  14. Reche Mármol, J. Cultivo Del Pimiento Dulce en Invernadero; Agricultura. Estudios e Informes Técnicos-Junta de Andalucía (España): Seville, Spain, 2010; p. 294. [Google Scholar]
  15. Soussi, M.; Chaibi, M.T.; Buchholz, M.; Saghrouni, Z. Comprehensive review on climate control and cooling systems in greenhouses under hot and arid conditions. Agronomy 2022, 12, 626. [Google Scholar] [CrossRef]
  16. Muharomah, R.; Setiawan, B.I.; Purwanto, M.Y.J.; Liyantono. Temporal crop coefficients and water productivity of lettuce (Lactuca sativa L.) hydroponics in planthouse. Agric. Eng. Int. CIGR J. 2020, 22, 22–29. [Google Scholar]
  17. Reyes-Rosas, A.; Rodríguez-García, R.; Zermeño-González, A.; Jasso-Cantú, D.; Cadena-Zapata, M.; Burgueño-Camacho, H. Evaluation of a model for estimating the temperature and relative humidity Inside greenhouses with natural ventilation. Rev. Chap. Ser. Hortic. 2012, 18, 125–140. [Google Scholar] [CrossRef]
  18. Singh, M.C.; Singh, J.P.; Singh, K.G. Development of a microclimate model for prediction of temperatures inside a naturally ventilated greenhouse under cucumber crop in soilless media. Comput. Electron. Agric. 2018, 154, 227–238. [Google Scholar] [CrossRef]
  19. Ting-ting, W.; Zai-qiang, Y.; Lin, W.; He-li, Z.; Jia-shuai, L. Simulation model of hourly air temperature inside glass greenhouse and plastic greenhouse. Chin. J. Agrometeorol. 2018, 39, 644. [Google Scholar]
  20. Dewi, V.A.K.; Setiawan, B.I.; Minasny, B.; Liyantono, L.; Waspodo, R.S.B. Modeling air temperature inside an organic vegetable greenhouse. AGRIVITA J. Agric. Sci. 2020, 42, 295–305. [Google Scholar] [CrossRef]
  21. Hoagland, D.R.; Arnon, D.I. Nutrient solutions for hydroponic culture. Univ. Calif. Agric. Exp. Stn. Circ. 1938, 347. [Google Scholar]
  22. Idso, S.B.; Clawson, K.L.; Anderson, M.G. Foliage temperature: Effects of environmental factors with implications for plant water stress assesment and the CO2/climate connection. Water Resour. Res. 1986, 22, 1702–1716. [Google Scholar]
  23. Hargreaves, G.H.; Sumani, Z.A. Reference crop evapotranspiration from temperature. Appl. Eng. Agric. 1985, 8, 96–99. [Google Scholar] [CrossRef]
  24. Hargreaves, G.H.; Allen, R.G. History and evaluation of Hargreaves equation. J. Irrig. Drain. Engi.-ASCE 2003, 129, 53–63. [Google Scholar] [CrossRef]
  25. Allen, R.G.; Pereira, L.S.; Raes, D.; Smith, M. Crop Evapotranspiration-Guidelines for Computing Crop Water Requirements-FAO Irrigation and Drainage Paper 56; FAO: Rome, Italy, 1998; Volume 300, p. D05109. [Google Scholar]
  26. Fernández, M.D.; Orgaz, F.; Fereres, E.; López, J.C.; Céspedes, A.; Pérez, J.; Gallardo, M. Programación Del Riego de Cultivos Hortícolas Bajo Invernadero en El Sudeste Español; Caja Rural: Almería, Spain, 2001. [Google Scholar]
  27. Cressie, N.A.C. Statistics for Spatial Data; John Wiley & Sons: Hoboken, NJ, USA, 1993; p. 867. [Google Scholar]
  28. Arredondo Navarro, E.; Franco Rodríguez, J.; Lao Arenas, M.; Llanderal, A. Estudio de Las Condiciones Agroclimáticas en la Costa Del Ecuador, 1st ed.; Universidad Católica de Santiago de Guayaquil: Guayaquil, Ecuador, 2022; Volume 1, p. 144. ISBN 978-9942-825-56-8. [Google Scholar]
  29. Villagrán Munar, E.A.; Gómez Arias, L.Y.; Gómez Latorre, D.A.; Rodríguez Giraldo, Y.; Chacón Garzón, I.E.; Numa Vergel, S.J.; Bonilla Buitrago, R.R. Estrategias de Adaptación y Mitigación al Cambio Climático en Sistemas de Producción Agrícola: Un Enfoque Desde la Agricultura Protegida y Técnicas de Biotecnología Para el Manejo del Cultivo; AGROSAVIA: Cundinamarca, Colombia, 2023; p. 260. [Google Scholar]
  30. Huertas, L. El control ambiental en invernaderos: Humedad relativa. Hortícola 2008, 205, 52–54. [Google Scholar]
  31. Dimitrijević, A.; Bajkin, A.; Blažin, S.; Blažin, D. Uniformity of air temperature and relative humidity inside and outside the different types of greenhouses. J. Proc. Energy Agric. 2014, 18, 107–110. [Google Scholar]
  32. Piñero, M.C.; Lorenzo, P.; Sánchez-Guerrero, M.C.; Medrano, E.; López-Marín, J.; Del Amor, F.M. Reducing extreme weather impacts in greenhouses: The effect of a new passive climate control system on nutritional quality of pepper fruits. J. Sci. Food Agric. 2022, 102, 2723–2730. [Google Scholar] [CrossRef]
  33. Erickson, A.N.; Markhart, A.H. Flower production, fruit set, and physiology of bell pepper during elevated temperature and vapor pressure deficit. J. Am. Soc. Hortic. Sci. 2001, 126, 697–702. [Google Scholar] [CrossRef]
  34. Mayorga-Gómez, A.; Nambeesan, S.U.; Coolong, T.; Díaz-Pérez, J.C. Temporal relationship between calcium and fruit growth and development in bell pepper (Capsicum annuum L.). HortScience 2020, 55, 906–913. [Google Scholar] [CrossRef]
  35. Montero Torres, J. Relación de la radiación solar con la producción de plantas: Agroproductivas. Rev. Investig. Inn. Agrop. Rec. Nat. 2022, 9, 48–62. [Google Scholar] [CrossRef]
  36. Villagrán, E.; Flores-Velazquez, J.; Akrami, M.; Bojacá, C. Influence of the height in a colombian multi-tunnel greenhouse on natural ventilation and thermal behavior: Modeling approach. Sustainability 2021, 13, 13631. [Google Scholar] [CrossRef]
  37. Flores Ortega, A.; Gutiérrez Vaca, C.; Saldaña Robles, N.; Saldaña Robles, A.; Saldaña Robles, A. Estudio de las estrategias para la gestión del clima en invernaderos de baja tecnología en climas cálidos. Rev. Mex. Cienc. Agríc. 2018, 9, 4383–4394. [Google Scholar] [CrossRef]
  38. Villagrán, E.A.; Jaramillo, J.E.; León-Pacheco, R.I. Ventilación natural en invernadero con mallas anti-insecto evaluadas con un modelo computacional de fluidos: Uso de pantallas anti-insectos en un invernadero colombiano. Agron. Mesoam. 2020, 709–728. [Google Scholar] [CrossRef]
  39. Ramón, E.; Da-Silva, R.; Allende, J.; Rada, F.; Figueiral, R. Variaciones microclimáticas en invernadero: Efecto sobre las relaciones hídricas e intercambio de gases en pimentón (Capsicum annuum). Agrociencia 2005, 39, 41–50. [Google Scholar]
  40. Leal, J. Efecto de la variación de la densidad del aire en la temperatura bajo condiciones de invernadero. Ciencia UANL 2006, 9, 290–297. [Google Scholar]
Agriculture 14 01972 g001
Figure 1. The structural layout of the greenhouse and the spatial distribution of climatic sensors within the greenhouse. (A) Frontal perspective, (B) lateral perspectives, and (C) data logger (blue marks) positioning within the greenhouse.
Figure 1. The structural layout of the greenhouse and the spatial distribution of climatic sensors within the greenhouse. (A) Frontal perspective, (B) lateral perspectives, and (C) data logger (blue marks) positioning within the greenhouse.
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Figure 2. Radar plot analysis for (a) wind direction frequency and (b) instantaneous wind speed average and maximum over the experimental period in the outdoor conditions.
Figure 2. Radar plot analysis for (a) wind direction frequency and (b) instantaneous wind speed average and maximum over the experimental period in the outdoor conditions.
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Figure 3. Relationship between data logger positioning and environmental parameters in the greenhouse (x = length, y = width, and z = parameter): (a) temperature (°C), (b) relative humidity (%), and (c) vapor pressure deficit (VPD) expressed in absolute value (kPa) during the vegetative stage.
Figure 3. Relationship between data logger positioning and environmental parameters in the greenhouse (x = length, y = width, and z = parameter): (a) temperature (°C), (b) relative humidity (%), and (c) vapor pressure deficit (VPD) expressed in absolute value (kPa) during the vegetative stage.
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Figure 4. Relationship between data logger positioning and environmental parameters in the greenhouse (x = length, y = width, and z = parameter): (a) temperature (°C), (b) relative humidity (%), and (c) vapor pressure deficit expressed in absolute value (kPa) during the flowering stage.
Figure 4. Relationship between data logger positioning and environmental parameters in the greenhouse (x = length, y = width, and z = parameter): (a) temperature (°C), (b) relative humidity (%), and (c) vapor pressure deficit expressed in absolute value (kPa) during the flowering stage.
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Figure 5. Relationship between data logger positioning and environmental parameters in the greenhouse (x = length, y = width, and z = parameter): (a) temperature (°C), (b) relative humidity (%), and (c) vapor pressure deficit expressed in absolute value (kPa) during the fruit development stage.
Figure 5. Relationship between data logger positioning and environmental parameters in the greenhouse (x = length, y = width, and z = parameter): (a) temperature (°C), (b) relative humidity (%), and (c) vapor pressure deficit expressed in absolute value (kPa) during the fruit development stage.
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Figure 6. Relationship between data logger positioning and environmental parameters in the greenhouse (x = length, y = width, and z = parameter): (a) temperature (°C), (b) relative humidity (%), and (c) vapor pressure deficit expressed in absolute value (kPa) during the harvesting stage.
Figure 6. Relationship between data logger positioning and environmental parameters in the greenhouse (x = length, y = width, and z = parameter): (a) temperature (°C), (b) relative humidity (%), and (c) vapor pressure deficit expressed in absolute value (kPa) during the harvesting stage.
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Figure 7. Relationship between data logger positioning and environmental parameters in the greenhouse (x = length, y = width, and z = parameter): ETc (mm day−1).
Figure 7. Relationship between data logger positioning and environmental parameters in the greenhouse (x = length, y = width, and z = parameter): ETc (mm day−1).
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Table 1. Climatic parameters outside and inside the greenhouse over the experimental period.
Table 1. Climatic parameters outside and inside the greenhouse over the experimental period.
ParameterPeriodOutsideInside
Ave.Max.Min.Ave.Max.Min.
T (°C)09/06/23 to 13/07/23 (1)25.3333.9820.8028.6939.1122.71
14/07/23 to 06/08/23 (2)24.1932.9020.0028.5238.0221.83
07/08/23 to 03/09/23 (3)24.4734.4018.3027.7944.9519.97
04/09/23 to 02/10/23 (4)24.9635.9019.0028.2940.2120.74
RH (%)09/06/23 to 13/07/23 (1)87.6799.9650.7159.8780.3133.42
14/07/23 to 06/08/23 (2)87.3499.4654.8360.5183.9135.59
07/08/23 to 03/09/23 (3)85.6199.3548.4163.1292.0223.45
04/09/23 to 02/10/23 (4)82.0698.5548.3161.5988.6131.36
VPD (kPa)09/06/23 to 13/07/23 (1)0.472.520.021.684.680.54
14/07/23 to 06/08/23 (2)0.482.250.011.654.270.42
07/08/23 to 03/09/23 (3)0.542.760.011.537.320.18
04/09/23 to 02/10/23 (4)0.682.730.031.645.110.27
GR (MJ m−2 day−1)09/06/23 to 13/07/23 (1)13.1318.237.089.4513.135.10
14/07/23 to 06/08/23 (2)13.5817.859.089.8514.956.53
07/08/23 to 03/09/23 (3)14.6324.717.8310.5617.795.64
04/09/23 to 02/10/23 (4)15.9724.086.1311.5017.344.41
(1) Vegetative, (2) flowering, (3) fruit development, and (4) harvesting periods.
Table 2. Relationship between the climatic parameters inside and outside of the greenhouse. Climatic parameters inside the greenhouse were temperature (Temp.), relativity humidity (RH), vapor pressure deficit (VPD), and ETc. Climatic parameters outside of the greenhouse were average (WS Ave.) and instantaneous (WS Inst.) wind speed, average and instantaneous (WD Inst) wind direction, temperature (Temp.), relative humidity (RH), global radiation (GR) and vapor pressure deficit (VPD), and days after transplanting (DAT).
Table 2. Relationship between the climatic parameters inside and outside of the greenhouse. Climatic parameters inside the greenhouse were temperature (Temp.), relativity humidity (RH), vapor pressure deficit (VPD), and ETc. Climatic parameters outside of the greenhouse were average (WS Ave.) and instantaneous (WS Inst.) wind speed, average and instantaneous (WD Inst) wind direction, temperature (Temp.), relative humidity (RH), global radiation (GR) and vapor pressure deficit (VPD), and days after transplanting (DAT).
Climatic ParameterDATClimatic Parameters Outside the Greenhouse
WS Ave.WS Inst.WD Ave.WD InstTemp.RHVPDGR
Inside greenhouseTemp−0.14 *0.40 *0.54 *−0.33 *−0.17 *0.81 *0.92 *0.69 *0.92 *
RH0.16 *−0.42 *−0.56 *0.29 *0.18 *−0.86 *−0.91 *−0.76 *−0.91 *
VPD−0.010.36 *0.51 *−0.28 *−0.17 *0.89 *0.79 *0.72 *0.79 *
ETc0.52 *0.41 *0.51 *0.010.050.17 *0.74 *0.82 *0.74 *
* Indicates significant differences at p < 0.05. The readings of WS Ave., WS Inst., WD Ave., WD Inst., Temp., RH, GR., and VPD were recorded continuously and averaged daily. The calculation of ETc was performed using average daily data WS Ave., WS Inst., WD Ave., WD Inst., Temp., RH, and VPD; in the case of GR, the daily accumulated data were used.
Table 3. Parameters of the linear multiple regression between climatic parameters inside and outside of the greenhouse. The climatic parameters inside the greenhouse were temperature (°C), relative humidity (%), vapor pressure deficit (VPD) (kPa)), and crop evapotranspiration (ETc (mm day−1). The climatic parameters outside the greenhouse were average and instantaneous wind speed (WS Ave. and WS Inst., respectively (m s−1)), average and instantaneous wind direction (WD Ave. and Inst., respectively (°), temperature (Temp. (°C)), relative humidity (RH (%)), global radiation (GR: MJ m−2 h−1 for ETc), vapor pressure deficit (VPD in kPa), and days after transplanting (DAT). Coefficient of determination (R2), adjusted determination coefficient (Adj R2), F-ratio, and p-value of the stepwise regression for each parameter.
Table 3. Parameters of the linear multiple regression between climatic parameters inside and outside of the greenhouse. The climatic parameters inside the greenhouse were temperature (°C), relative humidity (%), vapor pressure deficit (VPD) (kPa)), and crop evapotranspiration (ETc (mm day−1). The climatic parameters outside the greenhouse were average and instantaneous wind speed (WS Ave. and WS Inst., respectively (m s−1)), average and instantaneous wind direction (WD Ave. and Inst., respectively (°), temperature (Temp. (°C)), relative humidity (RH (%)), global radiation (GR: MJ m−2 h−1 for ETc), vapor pressure deficit (VPD in kPa), and days after transplanting (DAT). Coefficient of determination (R2), adjusted determination coefficient (Adj R2), F-ratio, and p-value of the stepwise regression for each parameter.
Regressions CoefficientStatistics and Test
ParameterDATWS Ave.WS Inst.WD Ave.WD InstTemp.RHGRVPDCR2
(Adj R2)		F-Ratiop-Value
Temp--0.020 (0.007)--1.082 (±0.008)0.023 (±0.004)-0.605 (±0.100)−1.144 (±0.521)0.9431 (0.9430)50,067.710.05
RH--−0.073 (±0.023)--−3.804 (±0.026)0.211 (±0.014)0.140 (±0.053)7.477 (±0.324)134.186 (±1.682)0.9384 (0.9018)36,826.370.05
VPD--0.007 (±0.001)--0.194 (±0.001)0.040 (±0.001)-1.121 (±0.024)−7.333 (±0.125)0.9370 (0.9363)44,889.100.05
ETc0.006 (±0.0006)−1.407 (±0.187)0.631 (±0.092)--−0.403 (±0.026)0.183 (±0.024)0.183 (±0.009)4.924 (±0.602)−9.795 (±2.207)0.8847 (0.8837)494.210.05
The readings of WS Ave., WS Inst., WD Ave., WD Inst., Temp., RH, GR., and VPD were recorded continuously and averaged daily. The calculation of ETc was performed using average daily data WS Ave., WS Inst., WD Ave., WD Inst., Temp., RH, and VPD; in the case of GR, the daily accumulated data were used. (-) indicates no significant correlation between parameters.
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Triana, A.; Llanderal, A.; García-Caparrós, P.; Donoso, M.; Jiménez-Lao, R.; Franco Rodríguez, J.E.; Lao, M.T. Preliminary Mapping of the Spatial Variability in the Microclimate in Tropical Greenhouses: A Pepper Crop Perspective. Agriculture 2024, 14, 1972. https://doi.org/10.3390/agriculture14111972

AMA Style

Triana A, Llanderal A, García-Caparrós P, Donoso M, Jiménez-Lao R, Franco Rodríguez JE, Lao MT. Preliminary Mapping of the Spatial Variability in the Microclimate in Tropical Greenhouses: A Pepper Crop Perspective. Agriculture. 2024; 14(11):1972. https://doi.org/10.3390/agriculture14111972

Chicago/Turabian Style

Triana, Angel, Alfonso Llanderal, Pedro García-Caparrós, Manuel Donoso, Rafael Jiménez-Lao, John Eloy Franco Rodríguez, and María Teresa Lao. 2024. "Preliminary Mapping of the Spatial Variability in the Microclimate in Tropical Greenhouses: A Pepper Crop Perspective" Agriculture 14, no. 11: 1972. https://doi.org/10.3390/agriculture14111972

APA Style

Triana, A., Llanderal, A., García-Caparrós, P., Donoso, M., Jiménez-Lao, R., Franco Rodríguez, J. E., & Lao, M. T. (2024). Preliminary Mapping of the Spatial Variability in the Microclimate in Tropical Greenhouses: A Pepper Crop Perspective. Agriculture, 14(11), 1972. https://doi.org/10.3390/agriculture14111972

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