How to detect water deficit in soybeans

Achieving high productivity in soybean cultivation can be limited by biotic and abiotic stresses, with water deficiency being one of the main obstacles, especially when associated with high temperatures.

04.10.2022 | 16:22 (UTC -3)

A obtaining high productivity in soybean cultivation may be limited by biotic and abiotic stresses, with water deficiency being one of the main obstacles, especially when associated with high temperatures. The damage caused by water stress can be reversible when they occur in a short term, reducing production potential, and even irreversible, in the long term, causing productivity loss of up to 100%.

About 90% of the soybean plant is made up of water, which is essential in the processes physiological and biochemical aspects of the plant. The reduction in the amount of water for plant promotes the induction of stomatal closure to prevent sweating of the plant, which leads to a reduction in photosynthesis due to less CO2 absorption atmosphere, reducing the production of photoassimilates and affecting, consequently, grain productivity.

A water plays a role as a solvent in the plant through the transport of minerals, gases and other solutes, in addition to being fundamental for the root absorption of nutrients from the soil solution. Therefore, water availability is essential to not reduce the productive potential of soybeans, especially in most critical periods, which are from sowing to emergence, during flowering and during grain filling.

At the moment, some phenotyping platforms allow, through sensors micrometeorological associated with multispectral images (e.g. Infrared thermal to check the leaf surface temperature) detect early onset of water stress in plants, even before the appearance of visual symptoms. Thus, these tools allow you to evaluate quickly and efficiently the interaction between the genotype and the environment, allowing greater speed in decision making.

Technological tools to identify the water deficit

O use of tools to identify and evaluate the water status of plants individually or in crops, it is becoming increasingly necessary to assist in management strategies, since water deficit has been occurring in recent years with greater frequency in many soybean producing regions in Brazil. Given this scenario, research needs to look for alternatives to suggest strategies for managing water deficit in different cultivars of soybeans and sowing times, through the use of efficient tools.

To the The main phenotyping tools today are the NDVI (Index Index). Vegetation by Normalized Difference), relative chlorophyll content in leaves, vegetative canopy temperature and thermal reflectance. These tools have different operating principles, which allows for better monitoring water deficit throughout the development cycle of the culture.

Normalized Difference Vegetation Index - NDVI

A Obtaining this index occurs through the use of remote sensing, through of sensors embedded mainly in satellites and/or drones. Furthermore, the use of portable optical sensors (Figure 1) make it possible to evaluate in real time, in the field, the condition of the crop, making it possible to carry out management in situ and in locu. In a simplified way, NDVI can enable the assessment of water condition according to leaf area, total biomass and chlorophyll contents of the plant canopy, with values ​​close to +1 to indication of vegetative vigor and values ​​close to -1 the presence of water or absence of vegetation.

Figure 1 - Optical vegetation sensor that measures the Normalized Difference Vegetation Index (NDVI) in soybean crops. Source: Beneduzzi et al., 2017.
Figure 1 - Optical vegetation sensor that measures the Normalized Difference Vegetation Index (NDVI) in soybean crops. Source: Beneduzzi et al., 2017.

Relative content chlorophyll in leaves

You photosynthetic pigments are of fundamental importance in capturing and absorbing of light in the visible range of the spectrum. The chlorophyll content in the leaves allows the assessment of the nutritional status of nitrogen in the plant canopy and, together with carotenoids, monitors the phenological stage through the number of leaves and evaluates different stresses to which plants can be subjected. The content relative amount of chlorophyll in leaves can be measured using equipment portable device called Chlorophyllometer (Figure 2). Although it requires physical contact with the leaf to evaluate this parameter, this is a method non-destructive method that provides a quick assessment of chlorophyll content.

Figure 2 - Optical vegetation sensor that estimates the relative chlorophyll content in plant tissue, measured with the aid of a SPAD-502 Chlorophyllometer. Source: Authors.
Figure 2 - Optical vegetation sensor that estimates the relative chlorophyll content in plant tissue, measured with the aid of a SPAD-502 Chlorophyllometer. Source: Authors.

Plant Canopy Temperature and Reflectance thermal

A intensity of water stress and stomatal conductance are related directly with the transpiration rate of plants, resulting in the maintenance of ideal temperature for the leaves. An alternative to evaluate the temperature of the vegetative canopy is infrared radiation emitted by plants (Figure 3).

Figure 3 - Infrared sensor that measures the temperature of the plant canopy. Source: Authors.
Figure 3 - Infrared sensor that measures the temperature of the plant canopy. Source: Authors.

A thermal reflectance is a tool for assessing plant temperature and allows you to check the plant's stress level according to the intensity of emission in the long-wave infrared spectral range. The thermal images are related to numerous physiological and biochemical activities of plants, having relation to the water content and evaporation of the leaf or canopy, stomatal conductance, temperature and, consequently, with the process of photosynthesis. Thermal images have a scale from black to red, indicating milder and higher temperatures, respectively (Figure 4).

Figure 4 - RGB and Infrared images of soybean plants in two stages of development (vegetative and reproductive) subjected to water stress. Flir C2 thermal camera model. Source: Authors.
Figure 4 - RGB and Infrared images of soybean plants in two stages of development (vegetative and reproductive) subjected to water stress. Flir C2 thermal camera model. Source: Authors.
Figure 4 - RGB and Infrared images of soybean plants in two stages of development (vegetative and reproductive) subjected to water stress. Flir C2 thermal camera model. Source: Authors.
Figure 4 - RGB and Infrared images of soybean plants in two stages of development (vegetative and reproductive) subjected to water stress. Flir C2 thermal camera model. Source: Authors.

Field Capacity - CC

A Field capacity is the maximum amount of water that a soil can retain in its pores, without losses due to percolation in the soil profile or due to surface runoff. Field capacity decreases when there is consumption gradual increase of water in the soil by plants, this consumption being dependent, mainly, the cultivars used and the phenological stage of the crop. Therefore, monitoring the amount of water available in the soil for Culture is an important parameter for monitoring water deficiency.

Studies carried out to verify efficiency of tools

With In order to verify the efficiency of phenotyping tools, we used the soybean cultivars Vmax RR, TMG 7262 RR, TMG Transgenic (HaHB4) and TMG Conventional, being classified, in relation to the degree of maturation, as 5,9; 6,2; 3,9 and 3,9, respectively.

Tested in a greenhouse, the induction of water stress in the development phases vegetative, between stages V4-V5, and reproductive, in stages R1-R2 (Beginning from flowering to full flowering). After the seven-day period of irrigation, all the primary effects of stress were evaluated with the tools already presented and analyzed using Pearson's Linear Correlation, with level of significance at p=0,01.

Results

You results found with correlation analysis for the tested tools were very promising in identifying early water stress, such as presented in Figure 5. Results with very high correlation: NDVI x SPAD (r=0,93), NDVI x CC (r=0,97) and SPAD x CC (r=0,97). Results with correlation moderate: SPAD x Thermal emission (r=0,64), CC x Thermal emission (r=0,57). Results with low correlation: NDVI x Thermal emission (r=0,47).

Figure 5 - Correlation analysis between the different parameters for the development phases of the vegetative (V4-V5) and reproductive (R1-R2) periods. Source: Authors.
Figure 5 - Correlation analysis between the different parameters for the development phases of the vegetative (V4-V5) and reproductive (R1-R2) periods. Source: Authors.

No significant difference was identified for the cultivars used, thus the Results were analyzed using the average of all. The thermal images showed, from the third day with water deficit, changes in thermal emission in both phases of development.

At vegetative phase (V4-V5), thermal changes were smaller, with an average of 1°C and did not have significant impacts on the canopy, as at this stage there are few leaves and roots, which are less developed and require less water. In the reproductive phase (R1-R2), the deficit water supply was accentuated from the third day of irrigation suppression, as At this stage of development, the plant has a high leaf area index and, consequently, a high daily water demand, being more susceptible to stress.

Conclusion

With these results, it can be indicated the parameters of Normalized Difference Vegetation Index (NDVI) and content relative chlorophyll content in leaves (SPAD) as tools to identify and evaluate water stress in soybean crops during the vegetative and reproductive.

In addition of NDVI can be obtained through images from satellites, drones and others equipment, there are other portable devices that have high efficiency and speed for measuring these two variables.

A thermal imaging proved to be a tool potentially efficient in detecting water stress in soybean crops.

Alexandre Alan Cassinelli, André Luis Vian, Christian Bredemeier, Department of Crop Plants, Faculty of Agronomy, Postgraduate Program in Phytotechnics - Federal University of Rio Grande do Sul; Elizandro Fochesatto, Faculty of Agronomy of the Alto Vale do Rio do Peixe University; João Paulo Vanin, Agricultural Engineer - Executive at SLC Agrícola S.A

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