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Article

Development of an Algorithm to Indicate the Right Moment of Plant Watering Using the Analysis of Plant Biomasses Based on Dahlia × hybrida

by
Agata Jędrzejuk
1,*,
Marcin Bator
2,*,
Adrian Werno
1,
Lukasz Karkoszka
1,
Natalia Kuźma
1,
Ewa Zaraś
1 and
Robert Budzynski
2
1
Institute of Horticultural Sciences, Warsaw University of Life Sciences, Nowoursynowska 159 Str., 02-787 Warsaw, Poland
2
Institute of Information Technology, Warsaw University of Life Sciences, Nowoursynowska 159 Str., 02-787 Warsaw, Poland
*
Authors to whom correspondence should be addressed.
Sustainability 2022, 14(9), 5165; https://doi.org/10.3390/su14095165
Submission received: 6 February 2022 / Revised: 15 April 2022 / Accepted: 22 April 2022 / Published: 25 April 2022
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Abstract

:
Water management in agriculture and horticulture has a strong ecological importance related to the necessity of optimizing the use of water resources. To achieve sustainable water use, it is necessary to optimize the time, frequency and the methods of water application. In this study, we hypothesized that the right moment for watering may be established on the grounds of the observation of the physiological state of the plant (if the plant is still in turgor) and the biomass of plant and the substrate. The proper irrigation scheduling, that is, just before the plant loses turgor, which appears at ca. 73% of LRWC in dahlias, determined with the use of the proposed measurement and computing system, makes it possible to save ca. 30% of irrigation water, in comparison to standard watering. Controlled watering also affected plant growth parameters, such as the content of chlorophyll a and b and carotenoid, as well as total and reducing sugar content (ca. 7%, 9% and 23% more than in plants watered in a standard way, respectively). Plants watered in a controlled way were 12% more compact when compared with the ones watered in a standard way. The results clearly proved that the computing system connected to scales made it possible to save water used for irrigation without a negative impact on the parameters of plant growth.

1. Introduction

All over the world, the decreasing availability of water resources is a key challenge for horticulture [1]. It makes plant production less profitable for producers and less attractive for consumers. The ability to grow plants in plastic containers caused a major shift from field to aboveground production of several crops. Aboveground plant production has necessitated the application of intensive irrigation compared with field production. A number of areas in the EU, Australia as well as arid regions in the USA have approved regulations restricting agricultural water use or runoff [2,3]. Inefficient water management and the lack of a proper schedule of watering are among the major factors contributing to water scarcity. In greenhouse production, the use of precise irrigation reduces the water consumption per unit area in comparison to that in open-air containers [4]. However, water consumption also depends on the technology of irrigation (e.g., drip emitters, micro-emitters or spray stakes). Unoptimized production practices, such as overwatering, are wasteful and harmful for the environment and for plant quality [2,3]. Therefore, water mismanagement in horticulture should be minimized. Suitable cultivation (i.e., optimized fertilization and irrigation according to the requirements of the species) allows to optimize the crop water use efficiency and to minimize the risk of water runoff [4,5]. Sensor-based control systems can optimize irrigation scheduling [6,7] and minimize excessive leaching [8,9], thus improving flower yield and quality [10,11,12,13,14,15,16,17,18,19,20]. Therefore, to optimize the irrigation scheduling, it is essential to study the plant yield and the physiological responses to substrate moisture deficit [7,8,9,10,11,12,13,14].
There are two ways of monitoring water deficit and its effect on a plant: monitoring plant physiology, based, e.g., on chlorophyll content and chlorophyll a fluorescence, or measuring the substrate moisture. In container production, the water that can be held in the rhizosphere is restricted because of the limited container volume, so effective irrigation is required [15,16,17]. Currently, to monitor the loss of the water from the substrate in real time, substrate moisture sensors coupled with wireless node networks may be used. This technology in the horticultural production of plants growing in containers may help in making a precise irrigation decision and, at the same time, in saving water, labor and fertilization costs [18,19,20,21,22,23,24,25]. The disadvantage of this method is that only the moisture of the substrate is monitored, not the state of the whole plant. On the other hand, the connection of the two methods, the monitoring of the plant biomass and substrate together with a pot, may offer a complete answer to the question of when to water the plants.
The aim of the study is also to develop an algorithm to indicate the right moment of plant watering using the analysis of plant biomasses. We attempted to keep the substrate as dry as possible, but without losing plant turgor and yield (flowering in this case). Dahlia variabilis was chosen as a model plant, because of its quick reaction to water deficit stress, such as turgor loss, decrease in chlorophyll and increase in carotenoids in leaves, as well as carbohydrate content [26]. We decided to indicate precisely the optimum moment of watering in an algorithmic way by using the analysis of plant and substrate biomasses (whole plant with a pot and substrate volume). The tested hypothesis was that the right moment of watering may be appointed by the observation of plant physiological status (if the plant is still in turgor) and the biomasses of plant with the substrate. The novelty of the current research is not only to use the percentage of water content of the substrate as the data, but to monitor also the plant status (if it is still in turgor), which may allow the producer of the plant to optimize the watering frequency to each species.

2. Material and Methods

2.1. Plant Material

The experiments were carried out in 2019 and 2020. The plant material was Dahlia variabilis, a compact yellow-flowering cultivar ‘Lubega® Power Yellow’. The plants were obtained from the Volmary Polska Company in Gawartowa Wola (52°14′28″ N 20°30′04″ E) as 4-week-old rooted cuttings in the beginning of April. The plants were transferred to production pots P11 (11 cm diameter) into a substrate composed of high peat (50%), wood fibers Ecofibrex® (20%) and bark (30%), with pH 5.5 (Lasland, Poland) and placed in a greenhouse at the Warsaw University of Life Sciences–SGGW, Poland, on a greenhouse table with an ebb and a flow bench (95 × 480 cm), at a density of 1 plant per pot. The average night/day temperatures were 18 °C and 22 °C, respectively, and the relative air humidity was 75%, controlled by a HortiMax climate computer with a Synopta–HortiMax software.

2.2. Fertilization

During the experiments, the plants were regularly fertilized with 0.2% liquid fertilizer CristalonTM Gena (EC 1.2 mS cm−1), dedicated to bedding plants fertigated with water rich in Ca and Mg ions, provided by the company Yara, Poland. To avoid iron deficiency in apical parts of plants, 0.02% Rexolin × 60 and Calcinit (Yara, Poland), according to manufacturer recommendations, were applied bi-weekly as foliar application.

2.3. Water Monitoring

Water available for plants in the substrate together with the biomass of plant were monitored. Remote sensing was applied, which means that after the chosen plants were put on scales, no more human attention to the measurement process was needed. Scales automatically reported plant biomasses (above and underground part of the plant together with substrate and a pot) every 30 s and the information was directly sent to the external server (location Warsaw University of Life Sciences: 52°16′04″ N, 21°04′83″ E). The experiment was divided into two parts. Half of the plants (n = 30) were watered in a standard way (average every 2 days, when the top layer of the substrate dried), and the second half of the plants (n = 30) were watered at the moment when the substrate was dry (it was checked palpatically). Three of thirty plants in each treatment were standing on the scales and their biomasses were permanently measured (Figure 1). In the first season (2019), the feasibility study was carried out to confirm that the limited plant irrigation did not affect the plant quality, and even improved it additionally to provide precision data to analysis. In the first season, the watering frequency was controlled by human decision, basing on the visual condition of the plant (if it was still in turgor) and on the substrate humidity (palpatic evaluation). After the experiment, the collected data (plant biomass together with substrate and pot biomass) were analyzed to find patterns and variants that could be used to design an algorithm during plant watering. In the second season (2020), for plants watered in a standard way, the data of plant biomass were only collected, while for plants watered in a controlled way, the data were collected permanently, and the values of biomass sent to the server were analyzed each hour. If, from the analysis of the data, it resulted that the watering should take place (according to the developed algorithm), the signal was sent by the application to the mobile phone of the person responsible for the experiment. A process in which the designed algorithm was implemented used only the remotely acquired data that were required to calculate a decision about watering. All plants were watered by surface irrigation in the first season and by the drip irrigation system in the second season. The whole experiment lasted 61 days. A specialized scale was designed. It is worth underlining that remote sensing, without any human intervention after the installation, was used. The algorithm was designed with the assumption made that a plant biomass grows during its vegetation growth. Directly after watering, the biomass of a plant with the substrate increased by the mass of the used water. After this, a loop in the algorithm began. When the biomass of the plant with the substrate started to decrease, the algorithm compared the actual biomass of the plant with the biomass expected to start the watering. As long as the actual biomass of the plant was higher than the expected one, the loop was repeated. The expected biomass was calculated in a simple model in which an assumption was made that the biomass of the plant should increase by 0.06% every hour. Due to this condition, the natural growth of the plant biomass and the biomass of roots was automatically taken into the account. To avoid errors occurring during the calculation of the biomass, the first three watering cycles were used to teach the algorithm the expected biomass of the plant.

2.4. Biometric Measurements

Biometric measurements were made three times (D1–20 days after experiment started, D2–40 days after experiment started, D3–60 days after experiment started–made in both seasons). Plant height, number of branchings, leaf area (leaf area scanner ADC AM350, Geomor Technik, Poland), stomatal conductance (leaf porometer SC–1, Geomor Technik, Poland), fresh biomass of the plant and fresh biomass of the root ball were measured. Biometric parameters were measured on 30 plants randomized for 3 blocks, 10 plants each. Fresh biomass of plant and the root ball were measured on three plants from each treatment and on each date.

2.5. Evaluation of Substrate and Leaf Relative Water Content

The substrate relative water content (SRWC) was measured in plants watered in a standard as well as in a controlled way as follows. The fresh biomass of the substrate (5 g) was oven-dried at 105 °C for 24 h to determine its dry weight (DW). SRWC was measured from three randomly chosen plants in each treatment and on each date. The total soil water content (SWC) was calculated according to the formula:
SWC = ((FW − DW))/DW × 100%
The leaf relative water content (LRWC) was also measured in plants watered in a standard as well as in a controlled way as follows: A total of 5 leaves from each treatment and on each date were collected at morning hours, weighed immediately (FW) then floated on distilled water for 24 h, and weighed again having attained a turgid weight (TW). Leaves were dried in the laboratory incubator (CPLRKI-BIO-043, Adverti, Poland) at 105 °C for 24 h [21] and their dry weight (DW) was measured. The LRWC was calculated using Barrs’s formula.
LRWC = ((FW − DW)/(TW − DW)) × 100%
where FW = Fresh weight, DW = dry weight, and TW = turgor weight.
Samples for SRWC and LRWC were collected on the dates as described above. In plants watered in a controlled way, substrate and leaf samples were collected just before the planned watering.

2.6. Biochemical Analyses

For biochemical analyses made on dates as described above (see biometric measurements), fresh young leaves (the third node from the apex) from three randomly chosen plants in each treatment were collected on three dates as described above, three replications per analysis from each treatment and date. Total and reducing sugar and starch content as well as chlorophyll a, b a + b and carotenoid contents were measured. Total sugars were measured as described by Dubois et al. [19] and expressed as mg glucose per g of a dry weight basis.
The reducing sugar content was measured by the Somogyi method as modified by Nelson [20] and expressed as mg glucose per g on dry weight basis. Starch content was determined by the anthrone method [22]. Total chlorophyll and carotenoids were extracted according to Lichtenhaler [23].

2.7. Statistical Analysis

Data were analyzed by the General Linear Model program of the IBM SPSS Statistics Data Editor (Softonic, Poland). For main effects, a two-way ANOVA was performed. For the detailed effects, a one-way ANOVA was performed for each treatment (standard or controlled watering) and each term of analysis (20, 40 or 60 days after experiment started). Means were compared by the LSD or the Tukey–Kramer multiple range test.

3. Results

3.1. Average Use of Water during Plant Irrigation

The volume of water used for watering was calculated for each hour and each scale, for which the mean change of biomass was calculated. The explanation is as follows. The volume of used water was not measured directly, but it was calculated by the analysis of the measured biomasses: Every hour for each scale, an average change of mass was calculated. If the calculated change represented an increase in the biomass larger than 10 g, the calculated biomass increase was added to the sum of used water (the threshold of 10 g was chosen because of natural plant biomass fluctuations during the day, i.e., air humidity absorption, while the biomass increase during irrigation was always larger than 10 g).
The daily values for each scale were calculated. The mean values of daily watering of ABC scales (controlled watering) and DEF scales (standard watering) were calculated separately. The mean usage of water during the whole experiment in the first season for the plants watered in the standard way (scales DEF) was 5259 g and 3834 g in the plants watered in the controlled way (scales ABC), according to the algorithm. This means that there was 27% of water savings during plant watering (43 vs. 45 watering cycles, see Figure 2). In the second season, for the plant watering in the standard way (scales DEF), the value was 4351 g and 2952 g in the plants watered in the controlled way (scales ABC), according to the algorithm. This means that there was 30% of water savings during plant watering (22 vs. 41 watering cycles, see Figure 3).

3.2. Biometric Measurement

On average, the plants subjected to controlled watering were more compact (13.65%) and had less branchings (7.7%) and a smaller leaf area (22.0%) than plants watered in the standard way (Table 1, Figure 4). The fresh biomass of the green part of the plant (above the peat substrate level) in the plants subjected to controlled watering was 19.3% lower than in the plants watered by the standard method (Table 1). The fresh biomass of the root ball was higher in the plants watered in the controlled way, while in the plants watered in the standard way, it was lower, at 16.3% (Table 1). Stomatal conductance was also higher in the plants subjected to controlled irrigation (20.1% higher than in the standard-watered plants), with the exception of measurements made on D2, when the plants watered in the standard way had 39.0 U, and the plants subjected to controlled watering had 37.2 U (Table 1). On average, the leaf relative water content was 19.2% lower in the plants subjected to controlled watering than in the plants watered in the standard way in all dates of observations (Table 2). The substrate relative water content was also lower in the plants subjected to controlled watering. The difference between the substrate content in the standard and controlled watering was ca. 27.2% (Table 2).

3.3. Biochemical Analyses

In the dahlia plants subjected to controlled as well as standard watering, the chlorophyll a content was similar on D1 and D2, while on D3, it was significantly lower in the plants watered in the standard way (44.8%, see Table 3). In all plants, irrespective of the watering mode, the chlorophyll a content was the lowest on D3. The chlorophyll b content was similar in all plants, irrespective of the watering mode (Table 3). On D3, the chlorophyll b content was significantly lower than on D1 and D2 in the plants subjected to the standard watering and on D1 in the plants subjected to controlled watering. The carotenoid content was lower in the plants subjected to standard watering, irrespective of the date of measurements (Table 3). On average, the total and reducing sugar content was higher in the plants subjected to the controlled watering (Table 4). On the first two dates of measurements, there was no significant difference in the content of both sugars. On D3, in the plants subjected to controlled watering, the content of both sugars significantly increased in relation to their content in the plants watered in the standard way (Table 4). Starch content was higher in the plants subjected to standard watering, irrespective of the date of measurements (Table 4). In all plants, the lowest starch content was observed on the first date of observations, and it increased in the further dates (Table 4).

4. Discussion

The aim of our research was to monitor two parameters, water deficiency in the substrate as well as in the plant. A system of scales that monitored plant condition as well as substrate moisture by weighing the whole plant biomasses (together with substrate and pot) was connected to a computer program with a suitable algorithm, designed in the first season of the experiment, which determined the proper moment of watering. This method made it possible to monitor both the water deficiency in the substrate and in the plant. What is important is that this method is not harmful for the plant, which was confirmed with the measurements of several plant physiological parameters and the water capacity of the substrate.
According to the literature [27,28], abiotic stresses, such as drought, can reduce average plant productivity by 65–87%, depending on the crop. To adapt to drought conditions, plants have evolved several physiological and morphological strategies to manage with drought stress, i.e., the decrease in leaf area, the height of the plant and the number of leaves and flowers, the increase in the biomass of roots, the decrease in chlorophyll and starch, while increasing the content of carotenoids and reducing sugars [28,29,30,31,32].
In dahlias, the mean leaf RWC during standard watering was ca. 93.7% and 75.8% in plants watered in a controlled way. According to Auge et al. [33], the lethal leaf RWC for dahlias is 64%, so the leaf RWC achieved in the plants watered in the controlled way was over 10% higher than the lethal RWC, with a mean soil water content of 56.6%. According to the literature data [34,35], growth reduction in plants and, hence, a lower plant biomass represent the final expression of a number of processes analyzed to determine water stress tolerance. In the study made by Tribulato et al. [36] on Viburnum, severe drought stress led, in particular, to a decrease in the shoot dry biomass. Even if shrubs reduced their biomass, they managed to maintain their ornamental value. In dahlias, the average fresh biomass of the plants watered in the controlled way was 144.9 g (16.2% lower than the fresh biomass of the plants watered in the standard way), but, at the same time, the fresh biomass of the root ball in the plants watered in the controlled way was 16.3% higher than in the plants watered in the standard way. A higher root/shoot ratio made it possible to obtain hardy plants that are able to rapidly overcome environmental crises [37] and better adapt to subsequent water shortages, which is necessary in urban environment.
According to Giordano et al. [38], deficit irrigation reduces plant growth in several ornamental species, although the reaction to drought stress varies among species, being a typical species-specific response. Lower shoot growth is common, caused by a reduction in new leaf emission and reduced leaf growth. The leaf area reduction allows the plants to reduce water loss by transpiration. This is a typical stress-avoidance mechanism adopted by plants to overcome water stress condition [4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55]. In the dahlias watered in a controlled way, the visual symptoms were related to plant adaptation to water deficiency, but stomatal conductance was higher than in the plants watered in the standard way. The most significant difference in the stomatal conductance between the plants watered in the standard and controlled ways occurred 20 days after the experiment started. Drought stress, even a moderate one, usually decreases stomata conductivity and equally affects photosynthesis [56,57,58,59,60]. In the current experiment, nigh/day temperatures were not higher than 18/22 °C. According to Cornic [61], the stomatal conductance of plants subjected to moderate drought may be manipulated by the set temperature. In this case, it may be possible that the temperature between 18 and 22 °C caused a higher stomatal conductance. A high water deficit can also modify the concentrations of chlorophyll and carotenoids [62,63]. A reduced chlorophyll content was found in several ornamental species, such as Catharanthus roseus, Helianthus annuus or Vaccinium myrtillus, grown in severe water stress conditions, but an increase in chlorophyll content was observed in plants under moderate water stress conditions [29,64,65]. Chlorophylls and carotenoids are important pigments that contribute to the visual appearance and ornamental quality of plants, especially bedding plants, such as dahlias. Therefore, ornamental drought-tolerant plants should not show significant variations under the available water regimes. In the current study, chlorophyll and carotenoid contents were higher in the plants watered in the controlled way, which may significantly show that a moderate drought does not cause chlorophyll damages in bedding plants such as dahlias. Drought conditions are also thought to change the allocation of assimilates from leaves to roots or seeds, which increase plant survival in adverse environments [66,67,68]. Drought stress definitely lowers the rate of photosynthesis and alters the distribution and metabolism of carbon in plant, which leads to the depletion of energy and decreases the yield [55]. In soybean leaves, drought significantly decreased the photosynthetic capacity and sucrose content and negatively affected the growth and metabolism of shoot and root tissues [62,63,69]. In the present study, the total and reducing sugar content was higher in the leaves of dahlias subjected to controlled watering. The analysis of the starch content revealed that dahlias watered in the standard way produced more starch than the other plants. Soluble sugars are the main components of energetic metabolism, but they may also act as compatible osmolytes, reestablishing the osmotic balance, and protect macromolecules against reactive oxygen species [38,55,65,66,70]. The accumulation of soluble sugars (total and reducing) and proline takes part in the plant drought-response system [55,65]. Taking this into consideration, under stress conditions, plants must produce osmolytes to protect the photosynthetic apparatus, maintain cell turgor and avoid a hydraulic failure, so this may explain the higher soluble sugar accumulation in the dahlias subjected to controlled watering (185.8; 178.8 mg glucose·g−1 DW) than in the dahlias watered in the standard way (124.5; 136.8 mg glucose·g−1 DW), as well as the same lower starch content in the former. The obtained results may suggest that the controlled watering activated the defense system in the plants, but it did not decrease plant quality (Figure 4).
The current experiment definitely showed that the algorithm designed on the basis of plant biomass analysis significantly decreased water use up to 30%, but it did not decrease plant decorativeness during its production. In the aboveground production of ornamental plants growing in the containers, on a greenhouse table with an ebb and a flow bench, the use of water is significant and its loss may be quite vast, while water recovery is not popular. In that case, we claim that the use of the algorithm for calculating the frequency of watering causes less consumption as well as less water loss. According to our expectations, the algorithmic method can be applied even when the water is monitored by the measurement of the whole biomass of a plant, its substrate and the pot together. It is also worth emphasizing the practical aspect of the presented method. The measurement was made for only a few plants, and the result of its analysis was generalized and applied to all plants.

5. Conclusions

To conclude, the obtained results of our study clearly showed that the algorithmic optimization of the proper moment of plant watering, using the analysis of plant biomasses, not only saves water used for watering, by ca. 30%, but also does not significantly affect the morphological features of the plants. Plants watered in the controlled way had smaller fresh biomass, smaller leaf area and were shorter than the plants watered in the standard way, but not more than by 25%. Additionally, the plants watered in the controlled way had a higher content of chlorophyll and carotenoids than the plants watered in the standard way. Based on the above results, we may confirm that the method based on the optimization of the proper moment of plant watering may be used in the production of containerized plants as well as of those growing under cover, without losing the esthetical features of the products. The practical aspect of the presented method is also worth emphasizing. The measurement was made only for a few plants and the result of its analysis was generalized and applied to all plants.

Author Contributions

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. A.J. designed the experiment and wrote the manuscript. M.B. designed and implemented the algorithm, described the IT and water usage. E.Z. conducted the experiments. N.K., A.W. and L.K. were responsible for technical assistance with laboratory analyses. R.B. designed and implemented most of IT and all the electronics (scales, database and communication). All authors have contributed to the writing of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

The research was partially funded by Ministry of Science and Higher Education in Poland.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The view of scales used in the experiment on D2 (40 DAES). Three randomly chosen plants from each treatment were located on the scales measuring the biomass of the whole plant (above and underground part). Plants were irrigated according to the indications of plant biomass. DAES–days after the experiment started.
Figure 1. The view of scales used in the experiment on D2 (40 DAES). Three randomly chosen plants from each treatment were located on the scales measuring the biomass of the whole plant (above and underground part). Plants were irrigated according to the indications of plant biomass. DAES–days after the experiment started.
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Figure 2. An average use of water (g) by the plants watered in the standard and the controlled way during the first season of experiment (2019). Red line—mass of water used during watering in the standard way (g) (scales DEF). Blue line—mass of water used during watering in the controlled way (g) (scales ABC).
Figure 2. An average use of water (g) by the plants watered in the standard and the controlled way during the first season of experiment (2019). Red line—mass of water used during watering in the standard way (g) (scales DEF). Blue line—mass of water used during watering in the controlled way (g) (scales ABC).
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Figure 3. An average use of water (g) by the plants watered in the standard and the controlled way during the second season of experiment (2020). Red line—mass of water used during watering in the standard way (g) (scales DEF). Blue line—mass of water used during watering in the controlled way (g) (scales ABC).
Figure 3. An average use of water (g) by the plants watered in the standard and the controlled way during the second season of experiment (2020). Red line—mass of water used during watering in the standard way (g) (scales DEF). Blue line—mass of water used during watering in the controlled way (g) (scales ABC).
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Figure 4. Differences in the heights of the plants subjected to different water regimes (D 2, 40 DAES): (A) Plants watered in a standard way; (B) Plants watered in a controlled way. Average height of the plants watered in the standard way—24.7 cm; average height of the plants watered in the controlled way—19.6 cm. Plants were measured from the substrate level to the highest leaf level. DAES—days after the experiment started.
Figure 4. Differences in the heights of the plants subjected to different water regimes (D 2, 40 DAES): (A) Plants watered in a standard way; (B) Plants watered in a controlled way. Average height of the plants watered in the standard way—24.7 cm; average height of the plants watered in the controlled way—19.6 cm. Plants were measured from the substrate level to the highest leaf level. DAES—days after the experiment started.
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Table
Table 1. The effect of different methods of watering on the chosen morphological features of Dahlia variabilis.
Table 1. The effect of different methods of watering on the chosen morphological features of Dahlia variabilis.
Plant Height (cm)
D1(20 DAES)D2(40 DAES)D3(60 DAES)mean
Standard watering19.6 Ac * ± 0.0924.7 Ab ± 0.3130.87 Aa ± 0.3625.05 A
Controlled watering18.2 Bc ± 0.1019.6 Bb ± 0.1728.28 Ba ± 0.2222.04 B
mean18.93 c22.15 b29.58 a
Number of branchings
D1(20 DAES)D2(40 DAES)D3(60 DAES)mean
Standard watering5.8 Ab ± 0.096.7 Aab ± 0.197.1 Aa ± 0.236.5 A
Controlled watering5.6 Bb ± 0.236.0 Bab ± 0.216.6 Ba ± 0.216.1 B
mean5.7 b6.3 ab6.85 a
Leaf area (mm2)
D1(20 DAES)D2(40 DAES)D3(60 DAES)mean
Standard watering2045.8 Ca ± 0.112160.8 Ba ± 0.062350.5 Aa ± 0.092185.7 A
Controlled watering1756.5 Bb ± 0.081759.6 Bb ±0.121856.4 Ba ± 0.081790.3 B
mean1901.15 b1960.2 ab2103.45 a
Stomatal conductance (mmol·m−2·s−1)
D1(20 DAES)D2(40 DAES)D3(60 DAES)mean
Standard watering30.8 Bc ± 0.1639.03 Ab ± 0.1652.4 Ba ± 0.0840.7 B
Controlled watering51.8 Ab ± 0.1837.2 Bc ±0.2157.8 Aa ± 0.1048.9 A
mean41.3 b38.1 c55.1 a
Fresh biomass of the plant (g)
D1(20 DAES)D2(40 DAES)D3(60 DAES)mean
Standard watering45.8 Ac ± 0.10183.2 Ab ± 0.22289.9 Aa ± 0.04172.9 A
Controlled watering38.9 Bc ± 0.11149.7 Bb ± 0.23246.2 Ba ± 0.09144.9 B
mean42.3 c166.5 b268.0 a
Fresh biomass of the root ball (g)
D1(20 DAES)D2(40 DAES)D3(60 DAES)mean
Standard watering259.5 Bb ± 0.11260.8 Bb ± 0.39276.0 Aa ± 0.02265.4 B
Controlled watering274.9 Ac ± 0.11321.3 Ab ± 0.31329.9 Aa ± 0.30308.7 A
mean267.2 c291.0 b302.9 a
Small letters—date of measurements. Capital letters—treatment. * Data marked with the same small or capital letter do not differ significantly, with p > 0.05. DAES—days after the experiment started.
Table
Table 2. The effect of different modes of watering on the LRWC and SWC in Dahlia variabilis.
Table 2. The effect of different modes of watering on the LRWC and SWC in Dahlia variabilis.
Leaf Relative Water Content %
D1(20 DAES)D2(40 DAES)D3(60 DAES)mean
Standard watering97.7 Aa * ± 0.0293.2 Ab ± 0.0690.3 Ac ± 0.0793.7 A
Controlled watering82.3 Bb ± 0,0857.8 Bc ± 0.0687.3 Ba ± 0.1075.8 B
mean90 a75.5 c88.8 b
Substrate relative water content %
D1(20 DAES)D2(40 DAES)D3(60 DAES)mean
Standard watering95.1 Aa ± 0.0270.6 Ab ± 0.1267.7 Bc ± 0.0977.8 A
Controlled watering57.6 Bb ± 0.0237.2 Bc ± 0.1875.1 Aa ± 0.0956.6 B
mean76.4 a53.9 c71.4 b
Small letters—date of measurements. Capital letters—treatment. * Data marked with the same small or capital letter do not differ significantly at p > 0.05. DAES—days after the experiment started.
Table
Table 3. The effect of different modes of watering on chlorophyll and carotenoid content in the leaves of Dahlia variabilis.
Table 3. The effect of different modes of watering on chlorophyll and carotenoid content in the leaves of Dahlia variabilis.
Chorophyll a (mg·g−1 DW)
D1(20 DAES)D2(40 DAES)D3(60 DAES)mean
Standard watering15.4 Aa *14.9 Ab6.7 Bc12.3 B
Controlled watering15.5 Aa15.0 Aa9.7 Ab13.4 A
mean15.5 a14.9 b8.3 c
Chorophyll b (mg·g−1 DW)
D1(20 DAES)D2(40 DAES)D3(60 DAES)mean
Standard watering5.9 Aa5.5 Aa2.2 Ab4.6 A
Controlled watering5.6 Aa5.4 Aab3.4 Ab4.8 A
mean5.8 a5.5 b2.8 c
Chorophyll a + b (mg·g−1 DW)
D1(20 DAES)D2(40 DAES)D3(60 DAES)mean
Standard watering21.4 Aa20.4 Ab8.9 Bc16.9 B
Controlled watering21.1 Aa20.5 Ab13.1 Ac18.2 A
mean21.2 a20.4 b11.1 c
Carotenoids (µg·g−1 DW)
D1(20 DAES)D2(40 DAES)D3(60 DAES)mean
Standard watering163.1 Bb234.6 Ba147.1 Bc181.6 B
Controlled watering170.1 Ac239.6 Aa190.2 Ab199.9 A
mean166.6 c237.1 a169.0 b
Small letters—date of measurements. Capital letters—treatment. * Data marked with the same small or capital letter do not differ significantly at p > 0.05. DAES—days after the experiment started.
Table
Table 4. The effect of different modes of watering on total and reducing sugar and starch content in the leaves of Dahlia variabilis.
Table 4. The effect of different modes of watering on total and reducing sugar and starch content in the leaves of Dahlia variabilis.
Total Sugars Content (mg glucose·g−1 DW)
D1(20 DAES)D2(40 DAES)D3(60 DAES)mean
Standard watering70.5 Ac *154.5 Aa148.5 Bb124.5 B
Controlled watering70.5 Ac150.5 Ab335.7 Aa185.6 A
mean70.5 c152.5 b242.1 a
Reducing sugars (mg glucose·g−1 DW)
D1(20 DAES)D2(40 DAES)D3(60 DAES)mean
Standard watering124.3 Ac147.7 Aa138.5 Bb136.8 B
Controlled watering122.8 Ab76.1 Bc337.5 Aa178.8 A
mean123.5 b111.9 c237.9 a
Starch (mg·g−1 DW)
D1(20 DAES)D2(40 DAES)D3(60 DAES)mean
Standard watering65.2 Ac87.1 Ab119.7 Aa90.67 A
Controlled watering59.1 Bb84.8 Ba77.0 Ba73.64 B
mean62.1 c85.9 b98.4 a
Small letters—date of measurements. Capital letters—treatment. * Data marked with the same small or capital letter do not differ significantly at p > 0.05. DAES—days after the experiment started.
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Jędrzejuk, A.; Bator, M.; Werno, A.; Karkoszka, L.; Kuźma, N.; Zaraś, E.; Budzynski, R. Development of an Algorithm to Indicate the Right Moment of Plant Watering Using the Analysis of Plant Biomasses Based on Dahlia × hybrida. Sustainability 2022, 14, 5165. https://doi.org/10.3390/su14095165

AMA Style

Jędrzejuk A, Bator M, Werno A, Karkoszka L, Kuźma N, Zaraś E, Budzynski R. Development of an Algorithm to Indicate the Right Moment of Plant Watering Using the Analysis of Plant Biomasses Based on Dahlia × hybrida. Sustainability. 2022; 14(9):5165. https://doi.org/10.3390/su14095165

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Jędrzejuk, Agata, Marcin Bator, Adrian Werno, Lukasz Karkoszka, Natalia Kuźma, Ewa Zaraś, and Robert Budzynski. 2022. "Development of an Algorithm to Indicate the Right Moment of Plant Watering Using the Analysis of Plant Biomasses Based on Dahlia × hybrida" Sustainability 14, no. 9: 5165. https://doi.org/10.3390/su14095165

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