Experimental characterization
The experiment was carried out in a greenhouse, at the Federal Institute of Education, Science and Technology of Bahia - Campus of Governador Mangabeira (12º36’30.53” South latitude and 39º01’51.71” West longitude).
The greenhouse used is an arched roof type (oriented in the east/west direction), with length of 27 m, width of 6 m and ceiling height of 3 m. The structure of the greenhouse consisted of anti-aphid screen on the sides and a 150-nm-thick anti-UV plastic film on the top. In total, 245 polyethylene pots, with individual volumetric capacity of 8 L, were placed inside the greenhouse. The pots were distributed in 7 cultivation rows, using the two rows closest to the sides of the greenhouse as borders (Fig. 1). The planting spacing was 1 m between rows and 0.5 m between plants. The polyethylene pots were filled with commercial substrate composed of limestone, sphagnum peat, coconut fiber and rice husk, with dry matter density of 160 kg m− 3 and pH of 6.5.
Each pot with plant represented one experimental plot. Irrigation was applied by a drip system, with management via SWS, keeping the lower critical limit (moment of starting the motor pump) fixed at -40 kPa and varying the upper critical limit (moment of turning off the motor pump): -6 kPa and − 14 kPa. Additionally, the effect of using the value of -14 kPa in only one stage of crop development (I; II; or III) and in two stages of crop development (I and II; I and III; II and III) was evaluated. Therefore, the experiment consisted of 8 treatments, according to Table 1. The treatments subjected to one of the two potential ranges (-6 to -40 kPa or -14 to -40 kPa) throughout the crop cycle and those subjected to combinations of these ranges according to the crop cycle had 21 and 6 replicates, respectively, thus totaling 78 experimental plots.
Table 1
Variations in the application of the ranges of matric potential of water in the substrate by phenological stages of grape tomato
Matric potential range |Ym| (kPa)
|
Phenological stage of application
|
|Ym| 6 to 40 kPa
|
Entire cycle
|
|Ym| 14 to 40 kPa
|
Entire cycle
|
|Ym| 14 to 40 kPa
|
Stage I
|
|Ym| 14 to 40 kPa
|
Stage II
|
|Ym| 14 to 40 kPa
|
Stage III
|
|Ym| 14 to 40 kPa
|
Stages I and II
|
|Ym| 14 to 40 kPa
|
Stages I and III
|
|Ym| 14 to 40 kPa
|
Stages II and III
|
Nutrition, Fertilization And Cultural Practices
Fertilization was performed according to the recommendations of Nick et al. (2018) and Martinez (2017). The seedlings of tomato plants were obtained by sexual propagation, through sowing in trays with 126-mL cells filled with substrate. The transplanting of the seedlings to the experimental plots was performed 25 days after germination, according to Abaurre (2010), who recommends that the seedlings be transplanted when they have four to six true leaves (usually between 20 and 30 days after germination), without etiolation, are well developed and in optimal sanitary conditions.
At 10 days after transplanting (DAT), tomato seedlings were vertically supported by stakes and, as the plants grew, they were tied to the support using plastic twine, following the technical recommendations indicated by Abaurre (2010).
In addition, the electrical conductivity of the substrate (ECsub) was monitored throughout the crop cycle. For this, porous-cup extractors were installed in the growing pots at 0.1 m depth, and readings were performed every 3 days. The electrical conductivity of the substrate solution extracted varied between 1.9 and 2.9 dS m− 1, throughout the tomato crop cycle. The nutrient solution used for fertigating the plants ranged between 0.90 and 1.80 dS m− 1, in order to maintain the electrical conductivity range of the substrate solution between 1.8 (electrical conductivity of the nutrient solution using water of 0.25 dS m− 1 and the nutrient ions of the nutrient solution adopted/recommended for tomato cultivation) and 4.9 dS m− 1. The maximum electrical conductivity of the substrate solution of 4.9 dS m− 1 was adopted based on the study conducted by Andriolo et al. (2003), who observed a non-significant effect on the yield of tomato when it was cultivated below this electrical conductivity level in substrate.
Substrate Water Sensors And Weighing Lysimeters
The soil water sensors were calibrated, generating curves and equations (Figs. 2A and 2B) that correlate values of substrate volumetric water content (θ) with those of electrical signal (mV V− 1) for the use of GS1 sensors (Decagon Devices, Inc., Hopkins CT, Pullman, USA) and dielectric constant (Ka) for the use of TDR (Campbell Scientific, INC., Logan, Utah, USA).
The TDR used was the TDR-100 model, connected to a set of multiplexers and a CR800 Campbell Scientific datalogger to obtain and store the values of substrate volumetric water content, at time intervals of 30 min.
The GS1 sensors were used in the automation of irrigation management, monitoring two growing pots to control irrigation management with the Ψ range from − 6 to -40 kPa and two more growing pots for the Ψ range from − 14 to -40 kPa. In turn, TDR sensors were used for additional monitoring of θ variation in two weighing lysimeters (one lysimeter for the range from − 6 to -40 kPa, and another for the range from − 14 to -40 kPa) and in two pots of each irrigation management condition used in the experiment, totaling 18 pots monitored by TDR.
The weighing lysimeters were installed in the center of the greenhouse, one lysimeter to determine the evapotranspiration of a plant cultivated under the potential range between − 6 and − 40 kPa, and another for a plant cultivated under the range between − 14 and − 40 kPa. Each lysimeter consisted of an 8 L polyethylene pot filled with substrate and a weighing platform with capacity of 60 kg and weighing accuracy of 0.006 kg.
The weighing lysimeters were used to quantify the evapotranspiration of the crop between two irrigation events. For this, the hourly mass variation was automatically recorded on the weighing platforms. Thus, it became possible to determine the volume of water consumed, for the conditions of cultivation under both the potential range between − 6 and − 40 kPa and the potential range between − 14 and − 40 kPa, according to Eq. 1.
$${Volume}_{water}= {Mass}_{t}-{Mass}_{t+1}$$
1
Where,
Volumewater = volume of water consumed by the crop at a specific time (L);
Masst = mass (kg) of the weighing lysimeter immediately after the end of an irrigation;
Masst+1 = mass (kg) of the weighing lysimeter immediately before starting an irrigation.
The weighing platforms were calibrated by applying and removing known masses, thus obtaining fitting equations that correlate mass (kg) with electrical signal (mV V− 1) (Equations 2 and 3).
\(Mass=46.707\times Electrical signal-14.002\) (R2 = 0.9998) (2)
\(Mass=88.895\times Electrical signal-13.357\) (R2 = 1.0000) (3)
Where,
Mass = weighing lysimeter mass (kg);
Electrical signal = Electrical signal emitted by the load cell (mV V− 1).
Obtaining Substrate Hydraulic Properties (Shp) By Inverse Modeling
SHP were determined through an inverse modeling experiment, using HYDRUS − 1D software, version 4.16.0110 (Simunek et al., 2013). For this, two GS1 substrate moisture sensors (Decagon) were installed at depths of 0.05 and 0.12 m in each of the two weighing lysimeters.
The weighing lysimeters were saturated with the drain closed. The lysimeter substrate was allowed to dry, so water outflow occurred only through the evaporation process. Variations in the water content in the substrate and the mass variation that occurred on the weighing platform (water loss by evaporation) were measured and stored in a Campbell Scientific CR800 datalogger at 15-minute intervals, for a period of 52 days.
The data obtained from the variation of water content in the substrate and evaporation were entered in HYDRUS − 1D software, to solve Eq. 4 of Richards (Richards, 1931), which estimates the flow of water in the substrate.
$$\frac{\partial \theta }{\partial t}= \frac{\partial }{\partial z}\left[K\left(h\right)\left(\frac{\partial h}{\partial z}+1\right)\right]$$
4
Where,
h = water pressure in the substrate (m H2O);
θ = water content in the substrate (m3.m− 3);
t = time (h);
z = vertical coordinate (m);
K(θ) = represents the substrate hydraulic conductivity function (m h− 1).
SWRC (Eq. 5) and the substrate water conductivity curve (SWCC) (Eq. 6) were described using the Mualem-van Genuchten model (Mualem, 1976; Van Genuchten, 1980).
\(\left\{\begin{array}{c}\theta \left(h\right)={\theta }_{s}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}h\ge 0\\ \theta \left(h\right)={\theta }_{r}+\left({\theta }_{s}-{\theta }_{r}\right){\left[\frac{1}{1+{\left|\alpha h\right|}^{n}}\right]}^{\left(1-\frac{1}{n}\right)}\hspace{0.33em}\hspace{1em}h<0\end{array}\right.\)
(5)
\(K\left(\theta \right)={K}_{S}{{S}_{e}}^{\lambda }{\left[1-{\left(1-{{S}_{e}}^{\frac{n}{n-1}}\right)}^{1-\frac{1}{n}}\right]}^{2}\)
(6)
$${S}_{e}=\frac{\left(\theta -{\theta }_{r}\right)}{\left({\theta }_{s}-{\theta }_{r}\right)}$$
7
Where,
θr = residual water content (m3 m− 3);
θs = saturated water content (m3 m− 3);
h = matric potential (m);
K (θ) = unsaturated hydraulic conductivity of the substrate (m h− 1);
Ks = saturated hydraulic conductivity of the substrate (m h− 1);
Se = effective saturation;
α, n and 𝛌 = empirical parameters.
In HYDRUS − 1D software, the hydraulic parameters of the substrate (θr, θs, α, n, λ and Ks) were determined by minimization between observed and simulated θ in space and time. For this, the total differences obtained between the observed and simulated values of θ were used, which can be expressed from an objective function (Φ) (Eq. 8).
\(\varPhi \left(\theta ,\beta \right)={\sum }_{j=1}^{m}{\sum }_{i=1}^{nj}{\left[{\theta }_{TDR,j}\left({z}_{i},{t}_{i}\right)-{\theta }_{EST,j}\left({z}_{i},{t}_{i},\beta \right)\right]}^{2}\)
(8)
Where,
Φ = objective function;
θTDR = water content in the substrate;
θEST = soil water content estimated using hydraulic parameters of the soil optimized in β (Өr, Өs, α, n, λ and Ks);
ti = time of reading;
zi = position of the moisture sensor;
j = number of readings performed at the same point;
m = number of different sites of moisture measurements;
n = number of measurements performed.
It can be noted in Eq. 8 that the right side of the equation refers to the residual between the sums of the values of water content observed with the GS1 sensor at the time ti for j measurements at zi and the corresponding values of water content estimated using the substrate parameters optimized in β. The objective function (Φ) was minimized using the nonlinear Levenberg-Marquardt method.
With the hydraulic properties of the substrate, the pairs of values (θ x Ψ) that represented the critical limits for irrigation management were determined (Fig. 3).
Irrigation Management And Substrate Moisture Monitoring
Irrigation management was carried out in an automated manner, using an Arduino board, GS1 sensors, solenoid valves and a motor pump set.
The decision-making to start and end an irrigation event was based on the lower and upper limits of substrate moisture (Table 1). Two values of matric potential were used as upper limit: -6 kPa (0.52 cm3 cm− 3) and − 14 kPa (0.34 cm3 cm− 3). The lower limit was equal to -40 kPa (0.19 cm3 cm− 3), fixed for both conditions of irrigation depth replacement adopted, a value derived from the studies of Zheng et al. (2013), Coolong et al. (2011) and Wang et al. (2007). Thus, it became possible to determine the irrigation time required to raise the substrate moisture from the lower limit to the upper limit, according to Eq. 9.
$${T}_{irrigation}=\frac{\left({\theta }_{UL}-{\theta }_{LL}\right) \times {Volume}_{Pot }\times \frac{1}{Ef}}{{Q}_{E}}$$
9
Where,
Tirrigation = irrigation time required to raise the substrate moisture from the lower limit to the upper limit (h);
θUL = volumetric water content of the substrate at the upper limit (cm3 cm− 3);
θLL = volumetric water content of the substrate at the lower limit (cm3 cm− 3);
VolumePot = volume of the growing pot (L);
Ef = water application efficiency of the irrigation system (decimal) equal to 90%;
QE = emitter flow rate (L h− 1).
The loss of water by deep percolation after completion of irrigation events was not identified after 10 days after transplantation (DAT).
Meteorological Monitoring
A weather station, composed of pyranometer, thermometer and hygrometer to measure solar radiation, temperature and relative air humidity, respectively, was set up inside the greenhouse (Fig. 4A and 4B). The average temperature in the period was 23.8°C. The minimum and maximum daily temperatures in the period ranged from 15.99 to 22.89°C and from 25.83 to 41.50°C, respectively. The average relative air humidity in the period was 79.19%, with the daily minimum and maximum values ranging from 32.63 to 74.76% and from 90.40 to 96.20%.
Analysis Of Tomato Growth And Yield
For the conditions of replacement of the irrigation depth required to return the substrate moisture to the potentials of -6 and − 14 kPa, destructive analyses of three plants (for each irrigation management condition) were performed at 25-day intervals, determining the following variables: stem diameter (SD), plant height (PH), leaf fresh mass (LFM), stem fresh mass (SFM), number of leaves (NL), leaf dry mass (LDM), stem dry mass (SDM) and leaf area (LA).
SD and PH were determined using a digital caliper and a measuring tape, respectively, whereas the variables LFM, SFM, LDM and SDM were determined using a precision scale (± 0.01 g) and NL was determined by counting.
For determining the LA of tomato plants, fitting equations were initially obtained through the correlation between the actual values of leaf area (obtained by scanning the leaves and using ImageJ software) and leaf length (L) and/or leaf width (W). The correlation between actual values and (L x W) was adopted to obtain the fitting equations because it showed the highest R2 values. The fitting equations were obtained using two plants (for each collection and/or destructive analysis), for the collections carried out at 25 (Eqs. 10) and 49 days after transplantation (DAT) (Eq. 11). These fitting equations were used to estimate the leaf area of the other plants collected for destructive analysis.
\(LA=0.2117\times L\times W+18.440\) (R2 = 0.9135) (10)
\(LA=0.3120\times L\times W-21.677\) (R2 = 0.9306) (11)
Where,
LA = leaf area of a single leaf (cm2);
L = leaf length (cm);
W = maximum leaf width (cm).
Ripe tomatoes were harvested weekly in all plants and for both irrigation management conditions investigated, in the period between 56 and 111 DAT. In the laboratory, the diameter, length, number and mass of the fruits were determined.
Irrigation Water Productivity
The irrigation water productivity (Prodwater) for the different irrigation management conditions was quantified based on the relation between production and water consumption of the crop after transplantation, according to Eq. 12.
$${Prod}_{water}= \frac{FrFM}{{Total volume}_{water}}$$
12
Where,
Prodwater = irrigation water productivity (Kg m− 3);
FrFM = fruit fresh mass (Kg);
Total volumewater = total volume of water applied via irrigation after transplanting (m3).
Statistical analysis
The variables related to the growth and yield of tomato crop and irrigation water productivity were subjected to analysis of variance and F test at 5% probability level. Subsequently, the means were compared using the Tukey test at 5% probability level.