The study was conducted in a controlled environment inside a greenhouse (Fig. 1A) comprising 3 interconnected bays with a total area of approximately 400 m2 and a height of 5.2 m, featuring a transparent plastic roof diffusing radiation and black shade screens covering the sides, intercepting 50% of global radiation. The greenhouse was located in Piracicaba – SP (Fig. 1B), which has an Aw tropical climate according to the Köppen scale, characterized by summer rainfall and dry winters (Dias et al. 2017).
The experimental unit consisted of 396 independent plots (Fig. 1C), each containing 330 liters of Red-Yellow Latosol soil, irrigated via a surface drip system with an average flow rate of 3.6 liters per hour. The irrigation was individually operated through manual valves to control water application.
For this experiment, the beetroot crop (Beta vulgaris L.), cultivar "Ferry Morse - Early Wonder Tall Top," belonging to the Chenopodiaceae family, was selected. This tuber develops through the swelling of the hypocotyl, exhibiting characteristics of large, erect foliage, smooth roots, and intense red coloration
(Tivelli et al. 2011).
In the southeastern region of Brazil, its planting window extends throughout the year, with a preference for periods of milder temperatures. It has an average cycle of 75 days after sowing, with spacing of 10 x 18 cm and a density of up to 350,000 plants per hectare, resulting in an average yield of 20 to 35 tons per hectare (Tivelli et al. 2011; Sousa et al. 2020).
The irrigation management control techniques were initially divided into two groups: the first based on climatological data and the second on soil data, both compared with a commercial management system, as presented in Table 1. It is noteworthy that in all employed methodologies, a variable irrigation schedule was utilized.
Table 1
Presentation of the methodologies employed in irrigation management.
| Treatment | Methodology | Equipment | Description |
| 1 | Soil | Tensiometer* | Manual reading; installed a 15 cm Ψcc = -5 kPa; Ψlimit = -15 kPa |
| 2 | SoilWatch Capacitive sensor | Manual reading; installed a 15 cm Ψcc = -5 kPa; Ψlimit = -15 kPa |
| 3 | VCS Capacitive sensor | Manual reading; installed a 15 cm Ψcc = -5 kPa; Ψlimit = -15 kPa |
| 4 | Weater | Standard station | Daily ETo by Penman-Monteith kc Embrapa, f = 0,6 |
| 5 | Daily ETo by Hargreaves and Samani kc Embrapa, f = 0,6 |
| 6 | Azevedo Station Do Yourself Movement (DYM) | Daily ETo by Penman Monteith kc Embrapa, f = 0,6 |
| 7 | Daily ETo by Hargreaves and Samani kc Embrapa, f = 0,6 |
| 8 | Class A pan | ETo Pan x kp kc Embrapa, f = 0,6 |
| 9 | Commercial irrigation management system |
| *Reference technique. |
For the management based on climatological methodology, two weather stations were installed inside the greenhouse, positioned at a height of two meters above the ground, with sensors accommodated on a specific bar for this purpose. Additionally, the Class A evaporation pan was placed on a wooden platform, isolating it directly from the surrounding soil.
Thus, the study comprises 9 treatments with 6 replications each, forming a randomized complete block design (RCBD 9x6), aiming to reduce experimental variability and increase the precision of comparisons between treatments. Each experimental unit consisted of 18 plants, with a planting layout depicted in Fig. 2. Planting was conducted on July 27th, with harvest on October 10th, encompassing a period of 75 days.
For the tensiometry technique, a sensor element with a ceramic porous capsule was selected, with a diameter of 21.5 mm and a length of 60 mm, connected to a PVC tube with a length of 220 mm. The PVC tube had an acrylic tube attached to its upper part, measuring 100 mm in length and 15 mm in diameter, which was sealed with the aid of a cork for puncture tensiometers.
For reading the sensor element, a digital tensiometer from the brand Tensimeter was used, which displays the water retention tension in the soil in units of mbar or kPa, as shown in Fig. 3A. It is noteworthy that the center of the ceramic porous capsule was installed at a depth of 15 cm (thus in contact with the layers of 12–18 cm), estimating the average moisture of the 0–20 cm layer through its reading, as schematically presented in Fig. 3B.
Nonetheless, the SoilWatch (Pino Tech) and VCS (Tinovi) soil sensors (Fig. 4) have dimensions of 19 x 84 mm and 19 x 53 mm, respectively. These dimensions represent the width and length of the sensor element. The center of the sensor element is installed at a depth of 15 cm, vertically fixed within the soil, in contact with the soil layer ranging from 10.8 to 19.2 cm for SoilWatch and 12.35 to 17.65 cm for VCS, as illustrated in the schematic of Fig. 4B.
It is noteworthy that for the soil sensors, a depth of installation of 15 cm was considered. Although there is no limitation on the installation depth for capacitive sensors, the same proposal was followed for tensiometry. This decision was made because installation in a very shallow zone may result in the tipping over of the tensiometer, rendering it unusable (Jiao et al. 2021).
To obtain the actual soil moisture through the soil water retention curve (Fig. 5), previously generated for the soil in question, adjustment parameters (Table 2) for Eq. 1, the van Genuchten model, were extracted for moisture estimation (van Genuchten 1980).
Table 2
Adjustment parameters for the red-yellow latosol for the van Genuchten equation.
| Parameter | Value |
| θr | 0,1549 |
| θs | 0,4629 |
| α | 0,6352 |
| m | 0,4550 |
| n | 1,8347 |
$$\:{\theta\:}_{a}={\theta\:}_{r}+\frac{{\theta\:}_{s}-{\theta\:}_{r}}{{\left[1+{\left(\alpha\:\bullet\:\left|{\psi\:}_{m}\right|\right)}^{n}\right]}^{m}}$$
1
Where:
θa - current soil moisture (cm3 cm− 3);
θr - residual moisture (cm3 cm− 3);
θs - saturation moisture (cm3 cm− 3);
Ψ - retention tension (kPa);
α, m, n - adjustment coefficients.
Similarly, the capacitive soil moisture sensors were calibrated for the specific soil in question, where their responses to variations in volumetric moisture can be observed in Fig. 6 below.
Furthermore, the equations for estimating moisture retention tension are highlighted, with Equations 2 and 3 for the VCS sensor and Equations 4 and 5 for the SoilWatch sensor.
| \(\:=-\text{0,32181}\bullet\:{P}^{3}+\text{90,5035}\bullet\:{P}^{2}-\text{8477,79}\bullet\:P+264553\) | (2) |
| \(\:=\:-\text{1,65409}\bullet\:{P}^{3}+\text{476,56}\bullet\:{P}^{2}-\text{45766,4}\bullet\:P+1465080\) | (3) |
| \(\:=\text{0,0000575486}\bullet\:{V}^{2}-\text{0,245001}\bullet\:V+\text{287,383}\) | (4) |
| \(\:=\text{0,000461047}\bullet\:{V}^{2}-\text{2,53516}\bullet\:P+\text{3531,09}\) | (5) |
| Where: |
| θ - volumetric moisture (%); |
| Ψ - retention tension (kPa); |
P - dielectric permittivity measured by the sensor (F m− 1); and
V - voltage in millivolts at the sensor output (mV).
In order to reduce the coefficient of variability of soil sensor readings, readings are taken at 60-second intervals, followed by averaging to carry out irrigation management. This process homogenizes the readings and creates an estimate of the real trend in soil water behavior (Cardenas-Lailhacar and Dukes, 2010; Domínguez-Niño et al., 2020).
For climate-based management, the weather station models used included a complete professional station, commercially available from Campbell, equipped with a CR1000 datalogger, silicon photodiode LI200x, anemometer 03002, air temperature and relative humidity sensor model HMP45, and a Vaisala CS 106 barometer, as shown in Fig. 7. Additionally, a second model developed by the author (Azevedo 2021) was employed, as depicted in Fig. 8.
This model offers the same functionalities as the previous one, with the added capability to calculate evapotranspiration using the Penman-Monteith methodology (Eq. 6) and Hargreaves and Samani method (Eq. 7). It provides real-time data to the user through an online database and a bot on the Telegram application, utilizing a central controller model ESP8266 (Hargreaves and Samani 1985; Allen et al. 2006).
$$\:ETo=\:\frac{0.408\bullet\:\:\varDelta\:\bullet\:\left({R}_{n}-G\right)+\:\gamma\:\bullet\:\frac{900}{T+273}\bullet\:{u}_{2}\bullet\:\left({e}_{s}-{e}_{a}\right)}{\varDelta\:+\:\gamma\:\bullet\:\left(1+0.34\bullet\:{u}_{2}\right)}$$
6
Where:
ETo - Reference Evapotranspiration (mm day− 1);
∆ - Slope of the saturation vapor pressure curve (kPa ºC− 1);
γ - Psychrometric coefficient (kPa ºC− 1);
T - Air temperature at 2 meters height (ºC);
u2 - Wind speed at 2 meters height (m s− 1);
Rn - Net radiation (MJ m− 2 day− 1);
G - Soil heat flux density (MJ m− 2 day− 1);
es - Saturation vapor pressure (kPa); and
ea - Partial vapor pressure (kPa).
$$\:ETo=a\bullet\:\left(\frac{{R}_{a}}{\text{2,45}}\right)\bullet\:{\left({T}_{máx}-{T}_{mín}\right)}^{b}\bullet\:\left({T}_{méd}+c\right)$$
7
Where:
ETo - Reference Evapotranspiration (mm day− 1);
a, b, and c - Adjustment parameters;
Ra - Extraterrestrial solar radiation (MJ m2 day− 1);
Tmax - Maximum temperature in the period (ºC);
Tmin - Minimum temperature in the period (ºC); and
Tméd - Average temperature in the period (ºC).
Still, evapotranspiration is also calculated using a Class A pan, which had its readings taken every day around 8:00 AM, with evapotranspiration calculated using Eq. 8 (Doorenbos and Pruitt 1977).
$$\:ETo=ECA\bullet\:kp$$
8
Where:
ETo - Reference Evapotranspiration (mm day− 1);
ECA - Pan evaporation (mm day− 1);
Kp - Pan coefficient.
It is worth noting that for the estimation of crop evapotranspiration, the reference evapotranspiration was multiplied by the crop coefficient (Kc), derived from calibration performed for the southeastern region for cultivation without mulch cover. Therefore, the initial Kc was 1.02, the average Kc was 1.18, and the final Kc was 0.84 for periods of 30, 24, and 21 days, respectively (Carvalho et al. 2011b).
Additionally, it was considered for both methodologies a daily average root growth of 0.5 cm, reaching a total depth of 20 cm, which altered the actual water availability (AWA) daily until the 40th day (Guerra and Machado 2022).
It is important to highlight that for each of the methodologies, a calculation spreadsheet was created, where the depletion of actual water availability in the soil was performed daily, irrigating only when the AWA fell below zero (θ < 23.84%), restoring the moisture to field capacity (θ = 33.62%).
Lastly, the commercial system employed is called FieldNET (Fig. 9) from the company Lindsay. This is a telemetry solution for real-time online irrigation control and management, configured for the local condition with sandy loam soil with an available water capacity of 79 mm/m, classified as hydrological group C, with maximum crop growth and root depth of 40 and 20 cm, using rainfall-free meteorological data and the same Kc values (0.5; 1.05; and 0.95), practicing conventional cultivation and using 1100 degree days for crop maturity.
The chlorophyll a, b, and total (ICF) indices were determined using an electronic chlorophyll meter (Chlorolog CFL1030, Falker), taking three readings in each plot. Leaves from the middle third of the plant were selected to obtain an average corresponding to the respective treatment. Leaf temperature was also evaluated using an infrared thermometer to calculate the Crop Water Stress Index (CWSI), as follows in Eq. 9, complemented with leaf water content (LWC [%]) in Eq. 10 (Turner 1981; Martínez et al. 2017).
$$\:CWSI=\frac{\left({T}_{c}-{T}_{a}\right)-{\left({T}_{c}-{T}_{a}\right)}_{LBI}}{{\left({T}_{c}-{T}_{a}\right)}_{LBS}-{\left({T}_{c}-{T}_{a}\right)}_{LBI}}$$
9
Where:
CWSI - Crop Water Stress Index, dimensionless;
Tc - Crop temperature (°C);
Ta - Air temperature (°C);
(Tc - Ta)LBI - Lower base temperature line, which corresponds to the difference in temperature between the environment and the surface of a leaf without water restrictions in °C. It represents the smallest difference between air and leaf temperature among all evaluated measurements;
(Tc - Ta)LBS - Upper base temperature line, which corresponds to the difference in temperature between the ambient air and a leaf surface with water deficit in °C. It represents the largest difference between air and leaf temperature among the evaluated measurements.
$$\:LWC=\frac{MU-MS}{MU}\bullet\:100$$
10
Where:
LWC - Leaf Water Content (%);
MU - Leaf Moisture Mass (g);
MS - Leaf Dry Mass (g).
In addition, gas exchange analysis and carbon assimilation by leaf surface were conducted at 50 days after sowing (DAS) using a portable photosynthesis analyzer - IRGA (LI-COR model LI-6400XT). The analysis utilized a CO2 concentration of 400 ppm, an air flow rate of 300 mL min-1, and a light source coupled with 1200 µmol m2 s-1. The evaluation included measurements of net CO2 assimilation rate, intercellular CO2 concentration, stomatal conductance, transpiration rate, and stomatal resistance, with readings taken in the morning (Gondim et al. 2015; Gonçalves and Dias 2021).
For the description of leaf geometry, a benchtop leaf area meter, LI-3100C model from Li-Cor, was used to estimate measurements of width, length, and unit leaf area, with the estimation of leaf area index (LAI) through Eq. 11.
$$\:LAI=\frac{\sum\:ULA}{Cs}$$
11
Where:
LAI - Leaf Area Index;
ULA - Unit Leaf Area (m2);
Cs - Crop spacing (m2).
Still, at the end of the cycle, the estimation of productivity for each plot was carried out, complemented by qualitative analyses, including average root diameter, fresh and dry shoot mass, fresh and dry root mass, leaf number, and leaf area index. This was followed by root juice extraction to obtain soluble solids measurement expressed in degrees Brix using a portable refractometer.
Interpolating productivity data with management metrics allows for the estimation of crop water efficiency indices such as Water Use Efficiency (WUE) and Water Footprint (Equations 12 and 13), as well as the average irrigation depth and average irrigation interval used (Equations 14 and 15).
| \(\:WUE=\frac{\left(\frac{Productivity}{Depth}\right)}{10}\) | (12) |
| \(\:PGH=\frac{Depth}{Productivity}\bullet\:10\) | (13) |
| \(\:{D}_{m}=\frac{Depth}{{N}_{i}}\) | (14) |
| \(\:{II}_{m}=\frac{Cycle}{{N}_{i}}\) | (15) |
| Where: |
| Productivity (kg ha− 1); |
| WUE - Water Use Efficiency (kg m− 3); |
| WF - Water Footprint (L kg− 1); |
| Depth - Volume of water applied (mm); |
| Dm - Average irrigation depth (mm); |
| Ni - Number of irrigations; |
| IIm - Average irrigation interval (days); and |
Cycle - Crop cycle duration (days).
To provide a comprehensive analysis combining qualitative and quantitative parameters, a ranking system is proposed for indices segmented into three aspects: quality of the final product (represented by equatorial tuber diameter), sustainability of the activity (represented by water use efficiency), and productive potential (represented by total productivity). In the individual evaluation, the first-ranked treatment will receive 9 points, with a reduction of 1 point for each subsequent position, thus composing an evaluation system that will sum up the final score and rank the treatments accordingly.
Additionally, it is evident from the analysis of climatological norms of the locality that there is low variability in monthly average temperature (Fig. 10 - A), as well as incident radiation (Fig. 10 - B). This, combined with cultivation in a protected environment, suggests that the cultivation may be influenced solely by the management practices employed. Therefore, a single cultivation cycle is implemented as an experimental strategy.
Data analysis was performed using R software in conjunction with the ExpDes.pt package, applying Tukey's test (p < 0.05) for mean comparison among treatments. Additionally, Shapiro-Wilk and Bartlett's tests were conducted to verify the assumptions of normality and homogeneity of variances, respectively. Exploratory data analysis, such as scatter plots and boxplots, complemented the interpretation of results.