This high-throughput, telemetric, whole-plant water relations gravimetric phenotyping method enables direct and simultaneous real-time measurements, as well as the analysis of multiple yield-related physiological traits involved in dynamic plant–environment interactions.
Food security for the growing global population is a major concern. The data provided by genomic tools far exceeds the supply of phenotypic data, creating a knowledge gap. To meet the challenge of improving crops to feed the growing global population, this gap must be bridged.
Physiological traits are considered key functional traits in the context of responsiveness or sensitivity to environmental conditions. Many recently introduced high-throughput (HTP) phenotyping techniques are based on remote sensing or imaging and are capable of directly measuring morphological traits, but measure physiological parameters mainly indirectly.
This paper describes a method for direct physiological phenotyping that has several advantages for the functional phenotyping of plant–environment interactions. It helps users overcome the many challenges encountered in the use of load-cell gravimetric systems and pot experiments. The suggested techniques will enable users to distinguish between soil weight, plant weight and soil water content, providing a method for the continuous and simultaneous measurement of dynamic soil, plant and atmosphere conditions, alongside the measurement of key physiological traits. This method allows researchers to closely mimic field stress scenarios while taking into consideration the environment’s effects on the plants’ physiology. This method also minimizes pot effects, which are one of the major problems in pre-field phenotyping. It includes a feed-back fertigation system that enables a truly randomized experimental design at a field-like plant density. This system detects the soil-water-content limiting threshold (θ) and allows for the translation of data into knowledge through the use of a real-time analytic tool and an online statistical resource. This method for the rapid and direct measurement of the physiological responses of multiple plants to a dynamic environment has great potential for use in screening for beneficial traits associated with responses to abiotic stress, in the context of pre-field breeding and crop improvement.
Ensuring food security for a growing global population under deteriorating environmental conditions is currently one of the major goals of agriculture research1,2,3. The availability of new molecular tools has greatly enhanced crop-improvement programs. However, while genomic tools provide a massive amount of data, the limited understanding of actual phenotypic traits creates a significant knowledge gap. Bridging this gap is one of the greatest challenges facing modern plant science4,5,6. To meet the challenges that arise in the process of crop improvement and minimize the genotype–phenotype knowledge gap, we must balance the genotypic approach with a phenocentric one7,8.
Recently, various high-throughput phenotyping (HTP) platforms have made possible the nondestructive phenotyping of large plant populations over time and these platforms may help us to reduce the genotype–phenotype knowledge gap6,8,9,10. HTP screening techniques allow the measurement of traits in massive numbers of plants within a relatively short period of time, thanks to robotics and conveyor belts or gantries used to move the plants or sensors (respectively), as opposed to hand-operated techniques based on gas exchange or photography. Nevertheless, the massive amounts of data produced by HTP systems present additional data-handling and analytical challenges11,12.
Most of these HTP platforms involve the assessment of phenotypic traits through electronic sensors or automated image acquisition13,14. Advanced field phenomics involve the deployment of proximal sensors and imaging technologies in the field, as well as a high-resolution, precise and large-population scale of measurement15. Sensor and image data need to be integrated with other multi-omics data to create a holistic, second-generation phenomic approach16. However, methodological advances in data acquisition, handling and processing are becoming increasingly important, as the challenges of translating sensor information into knowledge have been grossly underestimated during the first years of plant phenomics research13. However, the reliability and accuracy of currently available imaging techniques for in depth phenotyping of dynamic genotype–environment interactions and plant stress responses are questionable17,18. Moreover, the results from controlled environments are often very different than those observed in the field, especially when it comes to drought-stress phenotyping. This is due to differences in the situation the plants experience in terms of soil volume, soil environment and mechanical impedance due to declining soil moisture during drought stress. Therefore, results from controlled environments are difficult to extrapolate to the field19. Finally, the entry price of image-based HTP systems is very high, not only due to the price of sensors, but also due to the robotics, conveyor belts and gantries, which also require higher standards of growth-facility infrastructure and significant maintenance (many moving parts working in a greenhouse environment).
In this paper, we present an HTP-telemetric phenotyping platform designed to solve many of the problems mentioned above. Telemetry technology enables the automatic measurement and transmission of data from remote source(s) to a receiving station for recording and analysis. Here, we demonstrate a nondestructive HTP-telemetric platform that includes multiple weighing lysimeters (a gravimetric system) and environmental sensors. This system can be used for the collection and immediate calculation (image-analysis is not needed) of a wide range of data, such as whole-plant biomass gain, transpiration rates, stomatal conductance, root fluxes and water-use efficiency (WUE). The real-time analysis of the big data that is directly fed to the software from the controller in the system represents an important step in the translation of data into knowledge14 that has great value for practical decision-making, substantially extending the knowledge that can be acquired from controlled environment phenotyping experiments, in general, and greenhouse studies of drought stress, in particular.
Other advantages of the telemetry platform are its scalability and ease of installation and its minimal growth-facility infrastructure requirements (i.e., it can be easily installed in most growth facilities). Moreover, as this sensor-based system has no moving parts, maintenance costs are relatively low, including both the entry price and long-term maintenance costs. For example, the price of a 20-unit gravimetric system, including the feedback fertigation system for each plant, meteorological station and software, will be similar to the price of one portable gas-exchange system of a leading brand.
Rice (Oryza sativa L.) was used as a model crop and drought was the examined treatment. Rice was chosen as it is a major cereal crop with wide genetic diversity and it is the staple food for over half of the world's population20. Drought is a major environmental abiotic stress factor that can impair plant growth and development, leading to reduced crop yields21. This crop–treatment combination was used to demonstrate the platform’s capabilities and the amount and quality of data that it can produce. For more information regarding the theoretical background for this method, please see 22.
In this protocol, we referred to 4 L pots loaded on 20 cm x 20 cm scales, with each pot containing one plant. The same protocol is easily scalable and can be used with much bigger pots (up to 25 L loaded on 40 cm x 40 cm scales, with only a linear adaptation to the protocol measures) and several plants per pot. Thus the protocol can be easily adapted for plants of many types and sizes. Please refer to Figure 1 and Figure 2 for the system components.
1. Prepare the pots for the experiment
2. Grow the plants
3. Improve the signal-to-noise level
NOTE: The following steps improve the quality of the measurements and reduce the noise levels.
4. Setting up the experiment
NOTE: The process of setting up the experiment is designed to take into account the weight of all the parts of the system, namely, the weight of the potting medium (including the soil-water weight at pot capacity) and the initial weight of the seedlings. Follow the steps below:
5. Starting the experiment
NOTE: The data collected at this stage will be used as reference values for the rest of the experiment. Therefore, it is important to follow the next steps carefully.
6. Change the Plants Table
7. Run the experiment
8. Analyze the data using data analysis software
The duration of the experiment was 29 days. The experiment was conducted in August, when the local weather is warm and stable and the days are long. Two different irrigation scenarios were used to demonstrate the capability of the phenotyping platform for comparing the physiological behavior of three different varieties of rice (i.e., Indica, Karla, and Risotto) in the presence of drought stress. There were two drought-stress treatments: (i) optimal irrigation [until each pot reached its pot capacity at night after irrigation (control)] and (ii) a drought that started 5 days after the experiment started, lasted for 14 days, and was followed by a 10-day recovery period (optimal irrigation, Days 19–29). For the sake of simplicity, not all of the varieties and groups are shown in the figures presented here. The results showed that the HTP-telemetric system can efficiently measure changes in atmospheric conditions, the soil and the physiology of the plants.
Environmental conditions
Environmental conditions [photosynthetically active radiation (PAR) and vapor pressure deficit (VPD)] were monitored throughout the experiment by an atmospheric probe. The collected data indicate that PAR and VPD remained similar over the different days and over the course of the day (Figure 4).
The VWC of the drought-treated pots was measured by soil probes throughout the experimental period. The VWC data collected from one drought-treated cv. Indica plant is plotted in Figure 5.
Physiological parameters
The daily transpiration gradually increased in all four treatments (Karla-control, Karla-drought, Risotto-control and Risotto-drought) during the first stage of the experiment, during which all of the plants were well-irrigated. Later, there was a reduction in transpiration that was associated with the drought period (Day 5 to Day 18) in the two water-deprived treatments. Subsequently, during the recovery period (from Day 18 onward), the daily transpiration increased again in the two water-deprived groups, but to a much lower level than that observed before the drought treatment (Supplementary Figure 9B).
The mean calculated plant weight (i.e., rate of plant weight gain) increased consistently among both the Karla–control and the Karla-drought treatments during the first stage of the experiment, when all of the plants received similar irrigation (Days 1–5). When the drought treatment was applied to the cv. Karla plants (Days 5–18), those plants stopped gaining weight and did not resume gaining weight until the recovery stage. At that point, there was an increase in weight that proceeded more slowly than what was observed for the control. In contrast, the weights of the Karla–control plants increased continuously throughout the experimental period (Figure 6).
Figure 1: Components and setup of the gravimetric phenotyping system.
(A) Weighing lysimeter. The lysimeter includes the load cell, which converts the mechanical load of an object into an electrical charge, and a metal platform that covers the upper and lower parts of the load cell, so that the object’s weight can be properly measured. (B) The lysimeter is covered with a polystyrene block and a plastic cover for heat insulation. (C) Scale parts. A water reservoir (green container) is placed on the lysimeter cover to collect the liquid that drains from the pot. The green container is coupled to a green cover, which has a large round opening through which the pot is inserted. A black rubber gasket ring is attached to one side of the green cover and the pot is attached to the other side, to minimize water loss via evaporation from the container. The green cover has two sampling holes (small and big) above the drainage extension, which are sealed with rubber plugs. (D) Plugs. The container has a drainage extension with four holes (with plugs) at different heights, which can be used to adjust the water level in the container after the drainage through a particular hole stops (the reserve water volume). The desired water volume will depend on the plant species, the type of potting medium being used and the water requirements of the plants (i.e., estimated daily transpiration volume). (E) The control unit consists of a green rectangular box that contains the electronic controller and solenoid valves. There are holes through which fertigation solution can enter and exit the pots, as well as sockets for connecting the load cell and different sensors. Different treatments, such as different levels of salinity or different mineral compositions, can be applied via the fertigation solution. A metal stand is connected to the controller, to hold the pipes and cables and prevent them from touching the pots and adding weight. The other components required are (F) soil probes (e.g., moisture, temperature and EC sensors – 5TE), optional (G) multi-outlet drippers (for fertigation and/or treatment applications) and (H) atmospheric probes [for measuring vapor pressure deficit (VPD) and radiation]. (I) Fully equipped single array. (J) Fully equipped array in the greenhouse, yellow arrows pointing the atmospheric probes which enables the stomatal conductance normalization based on the local atmospheric conditions. Please click here to view a larger version of this figure.
Figure 2: Parts required for a single pot set-up.
(A–C) The following components are needed: one 4 L pot, one 4 L pot with no bottom to serve as a net holder, one circular piece of nylon mesh (pore size = 60 mesh) with a diameter double that of the bottom of the pot, one cover with designated holes for plant and irrigation drippers, one 60 cm, white fiberglass stick (pole) and one black gasket ring. (D) Example of a table plan in which the pots have been randomized. In the greenhouse, each table had 1–18 columns and four rows, here we used 24 positions. However, the array structure can be easily adjusted to any shape based on the size of the own greenhouse. Please click here to view a larger version of this figure.
Figure 3: Pot set-up.
(A) Plants growing in cavity trays. (The tomato seedlings shown here are only an example; many other plant species could be grown in the same way). (B) Casts of molds for (C) creating cavities in the potting medium that will (D) closely fit the root-soil plugs of the seedlings, to ensure the successful transplanting of (E) the seedlings into the pots. Please click here to view a larger version of this figure.
Figure 4: Atmospheric conditions over the course of the experiment.
The y-axis on the right shows the daily vapor pressure deficit (VPD) and the y-axis on the left shows the photosynthetically active radiation (PAR) over the 29 consecutive days of the experiment. This graph was produced by the Data Analysis software. Please click here to view a larger version of this figure.
Figure 5: Volumetric water content (VWC) measured by a soil probe over the course of the experiment.
The data represent the VWC values for one cv. Indica plant that was subjected to the drought treatment for the entire experiment period, including recovery. This graph was produced by the Data Analysis software. Please click here to view a larger version of this figure.
Figure 6: Whole-plant weights (means ± SE) over the entire experimental period for cv. Karla under well-irrigated (control) and drought conditions.
Groups were compared using ANOVA (Tukey’s HSD; p < 0.05). Each mean ± SE represents at least four plants. The graph and the statistical analysis were produced by the Data Analysis software. Please click here to view a larger version of this figure.
Supplementary Figure 1: Operating software windows for setting up an experiment. Please click here to download this figure.
Supplementary Figure 2: ‘Plants’ table as a spreadsheet; Operating software. Please click here to download this figure.
Supplementary Figure 3: Software window for calculating the soil dry weight; Operating software. Please click here to download this figure.
Supplementary Figure 4: Software window for setting up an irrigation treatment; Operating software. Please click here to download this figure.
Supplementary Figure 5: Data Analysis Graph Viewer window. In our experiment, we used three cultivars of rice (i.e., Indica, Karla, and Risotto) and two different irrigation scenarios, well-irrigated (control) and drought. The raw data revealed variation in the weight of the plants over the course of the experiment. Each line represents one plant/pot. During the day, the plants transpired, so the system lost weight, as can be seen in the slopes of the daily curves. The pots were irrigated every night to full capacity, as represented as the peaks in the curves. The irrigation event was followed by drainage of any excess water after the potting medium had been saturated. Initially, all plants were well irrigated (control). From 7 August 2018, half of the plants were subjected to a drought treatment. At the same time, the rest of the plants continued to receive optimal irrigation. Differential recovery was achieved by restoring the irrigation to the drought-treated plants, beginning on 20 August 2018 (allowing each plant to experience a similar degree of stress) and continuing through the experiment end. Please click here to download this figure.
The system’s feedback-irrigation tool enables the user to design irrigation programs for each individual pot based on time, pot weight, data from a soil sensor (e.g., VWC) or plant transpiration over the previous day. Each plant can be irrigated individually in a customized manner based on its own performance. This differential irrigation minimizes the differences between the plants’ soil water contents, so that all of the plants are exposed to a controlled drought treatment regardless of their individual water demands.
Supplementary Figure 6: Data Analysis window for the data analysis. Please click here to download this figure.
Supplementary Figure 7: Data Analysis histogram window. This figure shows a graphical representation of the distribution of daily-transpiration values in the three different rice cultivars (i.e., Indica, Karla, and Risotto) under well-irrigated (control) conditions. The bottom diagram represents a heat-map visualization of the plants daily transpiration based on the physical location of the pots on the table. Please click here to download this figure.
Supplementary Figure 8: Data Analysis T-test window. Lines represent the differences in daily transpiration (a fundamental and important physiological trait) between two rice cultivars (i.e., Karla and Risotto) under well-irrigated (control) conditions. The window shows the daily transpiration of the individual plants (top right) and a comparison of the means ± SE of each group conducted using Student’s t-test (bottom right). The statistical analysis was performed automatically by the software. The red dots represent significant differences between treatments according to the Student’s t-tests; p < 0.05. Please click here to download this figure.
Supplementary Figure 9: Data Analysis ANOVA window. (A) Graphical representation of the differences in daily transpiration between two rice varieties (i.e., Karla and Risotto) under well-irrigated (control) and drought conditions over the entire experimental period. The drought treatment was started 5 days after the experiment started. Clicking on any day will present the (B) Groups comparison using ANOVA (Tukey’s HSD; p < 0.05), here on AUG the 12th. Each mean ± SE represents at least four plants. The same groups could be also presented as a (C) Continuous whole-plant transpiration-rate (Means ± SE) over the entire experimental period. The graphs and the statistical analysis were produced by the Data Analysis software. Please click here to download this figure.
Supplementary Figure 10: Data Analysis piece-wise linear curve window. This window shows the piece-wise linear curves of three rice cultivars (i.e., Indica, Karla and Risotto) under drought conditions. The software can perform a piece-wise linear fit analysis of the relationship between any physiological parameter (here, daily transpiration) and the calculated volumetric water content (VWC) of the plants subjected to the drought treatment. Please click here to download this figure.
Supplementary Materials. Please click here to download these materials.
Medium | Description | |
Coarse sand | Silica sand 20-30 (upper and lower mesh screens through which the sand was passed: 0.841 and 0.595 mm, respectively) | |
Fine sand | Silica sand 75-90 (upper and lower mesh screens through which the sand was passed: 0.291 and 0.163 mm, respectively) | |
Peat-based soil | Klasmann 686 | |
Loamy soil (natural soil) | Sandy loam soil taken from the top layer of a plot at the experimental farm of the Faculty of Agriculture, Food and Environment, Rehovot, Israel | |
Vermiculite | Vermiculite 3G | |
Perlite | Perlite 212 (Size range: 0.5-2.5 mm) | |
Compost | Bental 11 Potting soil | |
Porous, ceramic, small-sized medium | Profile Porous Ceramic 20-50 (upper and lower mesh screens through which the ground ceramic was passed: 0.841 and 0.297 mm, respectively) | |
Porous, ceramic, mixed-sized medium | Profile Porous Ceramic 50% 20-50 mesh and 50% 20-6 mesh, 0.841– 3.36 mm |
Table 1: Potting media.
Soil media type / Parameters | Coarse sand | Fine sand | Loamy soil | Perlite | Vermiculite | Porous ceramic mixed-sized | Porous ceramic small-sized | Peat-based soil | Compost |
Total water (TW, ml) | 860 ± 7.2 (F) | 883.1 ± 24 (F) | 1076.3 ± 35.9 (E) | 1119.9 ± 8.5 (E) | 1286 ± 22.4 (D) | 1503.6 ± 15.4 (C) | 1713 ± 25.9 (B) | 1744.3 ± 8.2 (B) | 2089.6 ± 61.6 (A) |
Volumetric water content (VWC, ml3/ml3) | 0.26 (F) | 0.27 (F) | 0.33 (E) | 0.35 (E) | 0.4 (D) | 0.46 (C) | 0.53 (B) | 0.54 (B) | 0.65 (A) |
Bulk density (BD, g/cm3) | 1.7 (A) | 1.6 (B) | 1.5(C) | 0.1 (H) | 0.2 (F) | 0.8 (D) | 0.7 (E) | 0.2 (G) | 0.1 (G) |
Soil weight stability (SWS, g/d) | ±2.3 ± 0.3 (B) | ±4.3 ± 0.3 (B) | ±2.9 ± 0.9 (B) | ±14.9 ± 0.7 (A) | ±7.6 ± 2.8 (B) | ±1.3 ± 0.1 (B) | ±1.9 ± 0.4 (B) | ±6.7 ± 0.8 (B) | ±4.3 ± 1.2 (B) |
Soil weight stability with reserved water in the bath (g/day; please see Section 6.14) | 3 ± 0.4 (B) | 3.3 ± 0.4 (B) | 3.2 ± 1.2 (B) | 6.3 ± 0.5 (A) | 2.7 ± 0.8 (B) | 1.6 ± 0.3 (B) | 1.9 ± 0.3 (B) | 10.6 ± 3 (A) | 1.5 ± 0.3 (B) |
Pot capacity gravimetric moisture content (SWC; please see Section 8.2) | 0.18 (G) | 0.23 (G) | 0.23 (G) | 3.79 (C) | 3.0 (D) | 0.74 (F) | 0.99 (E) | 4.25 (B) | 6.13 (A) |
Relative drainage capability | Excellent | Medium | Medium-low | Excellent | Excellent | Excellent | Excellent | Low | Medium |
Relative time to reach pot capacity | Fast | Fast | Fast | Slow | Slow | Fast | Fast | Slow | Slow |
Relative cation exchange capacity (CEC) | Low | Low | Low | Low | High | High | High | High | High |
Compatibility with: | |||||||||
Root washing (at the end of the experiment) | ++ | ++ | + | ++ | + | ++ | ++ | – | – |
Nutrient/biostimulant treatment | ++ | ++ | – | ++ | + | + | + | – | – |
Salinity treatments | ++ | ++ | + | ++ | + | ++ | ++ | + | – |
Accurate measurement of growth rates | ++ | ++ | + | -,+ | + | ++ | +++ | + | + |
Physical soil structure recovery after drought | +++ | +++ | ++ | + | – | +++ | +++ | -,+ | – |
* Total water (TW, ml) = soil wet weight (at pot capacity) – soil dry weight. Volumetric water content (VWC) = TW/soil volume. | |||||||||
Bulk density (BD) = soil dry weight/soil volume. Soil weight stability (SWS) = Average change in soil wet weight over 4 consecutive days (medium at pot capacity with no plant after the last irrigation). | |||||||||
Pot capacity gravimetric moisture content (SWC); for the calculation, please see Section 7.2. |
Table 2: General characteristics of 9 different potting media and their compatibility with the gravimetric platform. The measurements were taken using 4-L pots filled with 3.2 L of medium at field capacity (pot capacity). Data are shown as means ± SE. Different letters in the columns indicate significant differences between the media, according to Tukey's HSD test (P < 0.05; 3 ≤ n ≤ 5).
Fertigation components | Final concentration (ppm) | Final concentration (mM) |
NaNO3 | 195.8 | 2.3 |
H3PO4 | 209 | 0.000969 |
KNO3 | 271.4 | 2.685 |
MgSO4 | 75 | 0.623 |
ZnSO4 | 0.748 | 0.0025 |
CuSO4 | 0.496 | 0.00198 |
MoO3 | 0.131 | 0.00081 |
MnSO4 | 3.441 | 0.0154 |
Borax | 0.3 | 0.00078 |
C10H12N2NaFeO8 (Fe) | 8.66 | 0.0204 |
The pH of the final irrigation solution from the dripper (after dilution with tap water) varied between 6.5 and 7. |
Table 3: Fertigation components.
The genotype–phenotype knowledge gap reflects the complexity of genotype x environment interactions (reviewed by18,24). It might be possible to bridge this gap through the use of high-resolution, HTP-telemetric diagnostic and phenotypic screening platforms that can be used to study whole-plant physiological performance and water-relation kinetics8,9. The complexity of genotype x environment interactions makes phenotyping a challenge, particularly in light of how rapidly plants respond to their changing environments. Although various phenotyping systems are currently available, most of those systems are based on remote sensing and advanced imaging techniques. Although those systems provide simultaneous measurements, to a certain extent, their measurements are limited to morphological and indirect physiological traits25. Physiological traits are very important in the context of responsiveness or sensitivity to environmental conditions26. Therefore, direct measurements taken continuously and simultaneously at a very high resolution (e.g., 3 min intervals) can provide a very accurate description of a plant’s physiological behavior. Despite those substantial advantages of the gravimetric system, the fact that this system has some potential disadvantages must also be taken into account. The main disadvantages result from the need to work with pots and in greenhouse conditions, which can present major challenges for treatment-regulation (particularly the regulation of drought treatments) and experimental-repeatability.
In order to address these issues, one should standardize the applied stresses, create a truly randomized experimental structure, minimize pot effects and compare multiple dynamic behaviors of plants under changing environmental conditions within a short period of time. The HTP-telemetric functional phenotyping approach described in this paper addresses those issues as noted below.
In order to correlate the plant’s dynamic response with its dynamic environment and capture a complete, big picture of complex plant–environment interactions, both environmental conditions (Figure 4) and plant responses (Supplementary Figure 9B) must be measured continuously. This method enables the measurement of physical changes in the potting medium and atmosphere continuously and simultaneously, alongside plant traits (soil–plant–atmosphere continuum, SPAC).
To best predict how plants will behave in the field, it is important to perform the phenotyping process under conditions that are as similar as possible to those found in the field18. We conduct the experiments in a greenhouse under semi-controlled conditions to mimic field conditions as much as possible. One of the most important conditions is the growing or potting medium. Selecting the most suitable potting medium for the gravimetric-system experiment is crucial. It is advisable to choose a soil medium that drains quickly, allows for the rapid achievement of pot capacity and has a highly stable pot capacity, as those features allow for more accurate measurements by the gravimetric system. In addition, the different treatments to be applied in the experiment must also be considered. For example, treatments involving salts, fertilizers or chemicals call for the use of an inert potting medium, preferably one with a low cation-exchange capacity. Drought treatments applied to low-transpiring plant species would work best with potting media with relatively low VWC levels. In contrast, slow drought treatments applied to high-transpiring plants would work best with potting media with relatively high VWC levels. If the roots are required for post-experiment analysis (e.g., root morphology, dry weight, etc.), the use of a medium with relatively low organic matter content (i.e., sand, porous ceramic or perlite) will make it easier to wash the roots without damaging them. For experiments that will continue for longer periods, it is advisable to avoid media that are rich in organic matter, as that organic matter may decompose with time. Please see Table 1 and Table 2 for more detailed information on this topic.
Field phenotyping and greenhouse phenotyping (pre-field) have their own objectives and require different experimental set-ups. Pre-field phenotyping assists the selection of promising candidate genotypes that have a high probability of doing well in the field, to help make field trials more focused and cost-effective. However, pre-field phenotyping involves a number of limitations (e.g., pot effects) that can cause plants to perform differently than they would under field conditions18,27. Small pot size, water loss by evaporation and heating of the lysimeter scales are examples of factors in greenhouse experiments that may lead to pot effects18. The method described here is designed to minimize those potential effects in the following manner:
(a) The pot size is chosen based on the genotype to be examined. The system is capable of supporting various pot sizes (up to 25 L) and irrigation treatments, which enables the examination of any type of crop plant.
(b) The pots and the lysimeter scales are insulated to prevent heat from being transferred and any warming of the pots.
(c) This system involves a carefully designed irrigation and drainage system.
(d) There is a separate controller for each pot, to enable true randomization with self-irrigating and self-monitored treatments.
(e) The software takes into account the plants’ local VPD in calculating the canopy stomatal conductance. Please see the multiple VPD stations localization in Figure 1J.
This system involves direct physiological measurements at field-like plant densities, which eliminates the need for either large spaces between the plants or moving the plants for image-based phenotyping. This system includes real-time data analysis, as well as the ability to accurately detect the physiological stress point (θ) of each plant. This enables the researcher to monitor the plants and make decisions regarding how the experiment is to be conducted and how any samples are to be collected over the course of the experiment. The system’s easy and simple weight calibration facilitates efficient calibration. High-throughput systems generate massive amounts of data, which present additional data-handling and analytical challenges11,12. The real-time analysis of the big data that is directly fed to the software from the controller is an important step in the translation of data into knowledge14 that has great value for practical decision-making.
This HTP-telemetric physiological phenotyping method might be helpful for conducting greenhouse experiments under close-to-field conditions. The system is able to measure and directly calculate water-related physiological responses of plants to their dynamic environment, while efficiently overcoming most of the problems associated with the pot effect. This system’s abilities are extremely important in the pre-field phenotyping stage, as they offer the possibility to predict yield penalties during early stages of plant growth.
The authors have nothing to disclose.
This work was supported by the ISF-NSFC joint research program (grant No. 2436/18) and was also partially supported by the Israel Ministry of Agriculture and Rural Development (Eugene Kandel Knowledge Centers) as part of the Root of the Matter – The Root Zone Knowledge Center for Leveraging Modern Agriculture.
Atmospheric Probes | SpectrumTech/Meter group | 3686WD | Watchdog 2475 |
40027 | VP4 | ||
Array Randomizer | None | The software “Array Randomizer” can be used for creating an experimental design of a randomized block design, or fully random design. It was developed to have better control over the random distribution of the experimental samples (plants) in order to normalize the atmospheric microvariation inside the greenhouse. | |
Free download and more information, please click on the following link: https://drive.google.com/open?id=1y4QbTpxRK5Lx430xzu1RFdrlcL8pz_1q | |||
Cavity trays | Danish size with curved rim for nursery | 30162 | 4X4X7 Cell, 84 cell per tray https://desch.nl/en/products/seed_propagation_trays/danish-size-with-curved-rim-for-nursery~p92 |
Coarse sand | Negev Industrial Minerals Ltd., Israel | ||
Compost | Tuff Marom Golan, Israel | ||
Data Analysis software | Plant-Ditech Ltd., Israel | SPAC Analytics | |
Drippers | Netafim | 21500-001520 | PCJ 8L/h |
Fine sand | Negev Industrial Minerals Ltd., Israel | ||
Loamy soil (natural soil) | |||
Nylon mesh | Not relevant (generic products) | ||
Operating software | Plant-Ditech Ltd., Israel | Plantarray Feedback Control (PFC) | |
Peat-based soil | Klasmann-Deilmann GmbH, Germany | ||
Perlite | Agrekal , Israel | ||
Plantarray 3.0 system | Plant-Ditech Ltd., Israel | SCA400s | Weighing lysimeters |
PLA300S | Planter unit container | ||
CON100 | Control unit | ||
part of the planter set | Fiberglass stick | ||
part of the planter set | Gasket ring | ||
Operating software | |||
SPAC Analytics software | |||
Porous, ceramic, mixed-sized medium | Greens Grade, PROFILE Products LLC., USA | ||
Porous, ceramic, small-sized medium | Greens Grade, PROFILE Products LLC., USA | ||
Pots | Not relevant (generic products) | ||
Soil | Bental 11 by Tuff Marom Golan | ||
Soil Probes | Meter group | 40567 | 5TE |
40636 | 5TM | ||
40478 | GS3 | ||
Vermiculite | Agrekal , Israel |