Introduction

Droughts emerge as the most important effects of global climate change on terrestrial ecosystems. It is also an important environmental abiotic stress factor that has reduced crop yields worldwide in recent years [1]. Agricultural drought not only indicates soil water status but also can be used to reflect the degree of soil water deficiency, plant growth and morphology, as well as plant water needs [2]. Therefore, accurate monitoring and assessment of crop water stress is not only the key to scientific countermeasures to reduce adverse effects but also fundamental research in terms of monitoring, warning and evaluation of agricultural drought [3].

Traditional methods for monitoring crop water stress are largely dependent on soil moisture measurements or the determination of matric potential in the effective root zone [4]. However, it is often difficult to account for soil heterogeneity in the field and in addition, soil water status is an indirect way of determining true crop water status [5]. Remote sensing methods such as spectral vegetation indices and infrared thermometers have been actively used in the monitoring of plant water stress in recent years due to their reliability, data collection speed and no damage to the plant [6].

Generally, field-based spectro-radiometry techniques and remote monitoring and evaluation studies cover the structural and biochemical contents of the plant such as chlorophyll, nitrogen content and water content. Among these, it makes important contributions to the remote estimation of vegetation water content, determination of the physiological state of the vegetation, decision of irrigation time and drought assessment [7,8,9].

Canopy temperature is used as the most practical way to learn about plant water status. Plant stomata close up, transpiration and photosynthesis decrease and plant canopy temperature rises in response to soil moisture depletion. Therefore, plants under water stress will generally have less transpiration and higher leaf temperatures than plants that are not stressed [10]. Crop water stress index (CWSI), calculated with the use of the difference in plant canopy temperature and air temperature and the vapor pressure deficit, has gained importance in recent years [11,12,13].

Black cumin (Nigella sativa L.) is an annual herb native to Egypt and the Eastern Mediterranean, mostly grown in arid and semi-arid areas. Nigella sativa is highly popular as a spice in culinary and foodstuffs. It is also commonly used in traditional medicine and industrial pharmacology. It is now widely used in traditional medicine for asthma, bronchitis, rheumatism, headaches and backaches, and hypertension, as well as in the treatment of skin and eczema diseases [14]. Black cumin essential oil is used in cosmetics and pharmacy.

As it was in other parts of the world, most of the medicinal and aromatic plants are collected from their natural habitats in Türkiye and they are not cultivated [15]. It is expected that the cultivation of black cumin, which has a high export potential, and increasing the amount of production with irrigated agriculture will meet the demands of both domestic and foreign markets and bring significant income to the farmers [16].

This study was conducted to (1) investigate the effects of different irrigation water levels on yield and quality parameters of black cumin, (2) determine the potential use of CWSI value in irrigation scheduling and threshold value in irrigation, (3) determine the potential use of vegetation indices in the estimation of plant characteristics through the reflections at different irrigation water levels.

Material and method

Experimental area

This study was carried out over the experimental fields of Erciyes University Agricultural Research Center (ERUTAM) in 2022. Experimental fields are located in Kayseri province between 35º 30' east longitudes and 38º41′ north latitudes and have an altitude of 1084 m. The experimental area has a dominant terrestrial climate with hot-dry summers and cold-snowy winters. According to long-term meteorological data, Kayseri province has an average annual temperature of 10.7 °C, the average temperature of the coldest month (January) is − 1.7 °C and the temperature difference (Tmax−Tmin) is 28.9 °C. The hottest month is July with an average temperature of 30.6 °C and a temperature difference of 18.7 °C. Climate data for the year 2022 are provided in Table 1.

Table 1 Climate data for the year 2022
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Soil samples were taken from 0 to 30, 30 to 60, 60 to 90 and 90 to 120 cm depths of 3 different locations representing the entire trial area. Soil texture, electrical conductivity (EC), field capacity (FC), permanent wilting point (PWP), bulk density, organic matter, lime, nitrogen, phosphorus and potassium contents were determined. Analysis results are provided in Table 2. The soil texture was generally loamy and there was no salinity problem. EC values varied between 0.173 and 0.258 dS m−1 and pH values varied between 8.13 and 8.17.

Table 2 Soil physical and chemical properties
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Infiltration tests were conducted at 3 different locations with a double-ring infiltrometer to determine the soil infiltration rates. Infiltration tests revealed the stable infiltration rate of the soil as 23.3 mm/h.

Irrigation water quality parameters (electrical conductivity, anion, and cation contents) were determined with the use of the methods specified in Tüzüner et al. [17]. Irrigation water samples were taken from the well and analysis results are given in Table 3. Irrigation water quality class was identified as C1S1 without and pH and EC problems.

Table 3 Irrigation water quality parameters
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Experimental design and agronomic practices

Çameli black cumin cultivar registered by Eskişehir Transition Zone Research Institute was used as the plant material of the experiments. Experiments were conducted in randomized block design with 3 replications in the 2022 growing season. Sowing was performed manually to have 25 cm row spacing, 2 cm sowing depth and 2 kg da−1 sowing density. Plots were 5 m long and each plot had 6 plant rows. A spacing of 2 m was provided between the plots and 2.5 m between the blocks. Side rows and 0.5 m from the top and bottom of the plots were considered as side effects, observations and measurements were made on middle rows. Based on soil analysis results, N, P and K fertilizers were applied at the dose of 10, 6 and 3 kg da−1, respectively. While phosphorus and potassium fertilizers were applied at once before sowing; half of the nitrogen fertilizer was applied at sowing and the remaining half was applied at the flowering stage of the plants.

Sowing was performed on April 4, 2022. Following the sowing, manual weed control was practiced twice. Due to the differences in irrigation water levels, vegetation periods have changed, so the treatments were not harvested at the same time. Harvest was practiced when the capsules reached sufficient maturity. The earliest harvests were made from I0 treatments with a water stress and the latest from I100 treatments without water stress. The I0 treatments were harvested on the 5th of August, I25 treatments on the 19th of August, I50 treatments on the 2nd of September, I75 treatments on the 8th of September and I100 treatments on the 16th of September 2022.

Irrigation system and irrigation treatments

The drip irrigation method was used for the irrigation of black cumin plants. Dripper spacing was 0.33 m, dripper discharge was 4 lt/h, the lateral diameter was 20 mm, the manifold pipe diameter was 40 mm and the main pipe diameter was 63 mm. Irrigation schedules were made based on the principle of supplying certain percentages of depleted moisture within the plant root zone. Scheduled irrigations were initiated when 35–40% of available moisture with the effective root zone was depleted. Plant effective root depth was taken as 6 cm [18]. Irrigation treatments used in the present experiments are provided in Table 4.

Table 4 Irrigation treatments
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In full irrigation treatments (I100), irrigations were initiated when 35–40% of the available moisture within the effective root zone was depleted and soil moisture was brough to field capacity each time. In deficit irrigations (I75, I50 and I25), a certain percentage of full irrigation was practiced. The amount of irrigation water to be applied (mm) was determined with the use of the following equation:

$$d=\frac{({P}_{vFC}-{P}_{vm})}{100}\times D$$

where; d: Amount of irrigation water to be applied, mm; PvFC: Field capacity in percent by volume,%; Pvm: Soil moisture before irrigation in percent by volume,%; D: Soil depth to be wetted, mm. The resultant value (mm) was multiplied by the area to be irrigated (m2) to convert the amount of water to be applied into liters (L). The amount of irrigation water calculated for each treatment was separately applied through water meters.

Soil moisture was monitored gravimetrically. Soil samples were taken throughout the effective root zone twice a week and moisture contents were determined accordingly.

Plant water consumptions were determined with the use of water budget equation given in by James [19].

$$\begin{gathered} {\text{ET}} = {\text{ I}} + {\text{R}} + {\text{Cr}} - {\text{Dp}} - {\text{Rf}} \pm \Delta s \hfill \\ \hfill \\ \end{gathered}$$

where; ET; Plant water consumption (mm), I; Irrigation water quantity (mm), R; Effective precipitation (mm), Cr; Capillary rise (mm), Dp; Deep percolation (mm), Rf; Runoff (mm), Δs; Change in soil moisture (mm).

Since the water was applied to bring the depleted moisture to field capacity, deep percolations were assumed to be zero. Since there were buffer zones between the plots and a drip irrigation system was used, runoff was also assumed to be zero. Since there was no groundwater up to 5–6 m depth, capillary rise was not expected. Change in soil moisture was monitored at 60–90 cm depth to see if there were any deep percolations.

Water use efficiencies (water productivity, WP; irrigation water productivity, IWP) were calculated with the following equations given in [20]:

$$WP=\frac{{E}_{Y}}{ET}; IWP=\frac{(Ey-Eyo)}{I}$$

where; WP = Water productivity (kg/m3); Ey = Economical yield (kg/ha); Ey0 = Yield of rainfed treatment (kg/ha); ET = Plant water consumption (mm), IWP = Irrigation water productivity (kg/m3); I = Irrigation water (mm).

Stewart model [21] was used to assess the relationship between the proportional ET deficit and the proportional yield reduction and the yield-response factor (ky).

$$\left( {1 - \frac{{Y_{a} }}{{Y_{m} }}} \right) = k_{y} \left( {1 - \frac{{ET_{a} }}{{ET_{m} }}} \right)$$

where; Ya = Actual yield (kg/ha); Ym = Maximum yield (kg/ha); ky = Yield-response factor; ETa = Actual plant water consumption (mm); ETm = Maximum water consumption (mm).

Crop water stress index (CWSI)

Everest 100 L model infrared thermometer (IRT) with an 8–14 μm spectral band range and 4 degrees viewing angle was used to determine the vegetation temperature. Black objects, whose surface temperature can be determined, were used in the calibration of the IRT device [22].

Canopy temperature measurements were made between 13:00 and 14:00 when the weather was completely clear or the clouds did not block the sun. Canopy temperature measurements were made at least 3 days a week, before and after irrigation, when the above-mentioned conditions were met. The average canopy temperature of a plot was found by taking the average of 12 measurements, 3 replicates in each plot, in the direction of diagonals of the plot. Plant canopy temperatures were measured with a portable infrared thermometer (IRT). To keep the soil surface out of the field of view of the IRT, canopy temperature was determined by directing the IRT to the plant surface at an angle of 30-40º with the horizontal. During the measurement of plant canopy temperatures, air temperature and relative humidity values were also recorded.

Crop water stress index (CWSI) was calculated with the use of the following equation given in Idso et al. [23]:

$$CWSI = \frac{{\left[ {\left( {T_{c} - T{}_{a}} \right) - LL} \right]}}{UL - LL}$$

where: Tc, vegetation cover temperature; Ta, air temperature; LL, lower limit of water stress and UL, upper limit of water stress.

Spectral vegetation indices

ASD Hand-Held 2 brand portable handheld spectroradiometer was used to measure spectral reflectance ratios. With the ASD Hand-Held 2 device, measurements can be taken in the wavelength range of 325–1075 nm with 1 nm steps. Before each radiance measurement with a spectroradiometer, irradiance measurements were made with the device using a spectralon panel. By taking the ratio of radiance to irradiance, spectral reflectance values were calculated for each wavelength. Measurements were made twice a week when the weather was cloudless and the sun angle was 45 degrees. In all treatments, readings were taken from 5 plants selected from the plots and each plant in 3 replications. Accordingly, 15 readings were taken from each plot in total, with 5 measurements in each replication. Vegetation indices were calculated with the data obtained through these readings. In the present study, 9 different vegetation indices were used (Table 5).

Table 5 Spectral vegetation indices
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Morphological observations and measurements

Plant Height, Stem Diameter, First Capsule Height: Measurements were made on 10 randomly selected plants of each harvest plot. Plant height was measured as the distance from the soil surface to the tip of the plant. The stem diameter was measured from the first internode with a digital caliper. The first capsule height was determined as the height of the lowest capsule from the ground.

Number of Branches per Plant, Number of Capsules per Plant, Number of Seeds per Capsule: Ten plants were randomly selected from each plot. A number of branches of each plant was counted and an average of 10 measurements was taken. The same procedures were repeated for many capsules and several seeds.

Thousand-Seed Weight: From each plot, 100 seeds were weighed in replicates. The average of these 4 weights was then multiplied by 10 to get 1000-seed weight.

Statistical analyses

Experimental data were subjected to variance analysis with the use of SAS [32] software. Significant means were compared with the use of Least Square Difference (LSD) test.

Results and discussion

Irrigation water quantity, plant water consumption (ET), water productivity and yield

Irrigation water quantity (I), plant water consumption (ET), water productivity (WP), Irrigation water productivity (IWP) and yield values of irrigation treatments are provided in Table 6.

Table 6 Effects of different irrigation levels on I, ET, WP, IWUE and yield of black cumin
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Sowing was performed on April 4, 2022 and 20 mm emergence irrigation was practiced to ensure homogeneous emergence. Throughout the growing season, a total of 102.5 mm precipitation was received. Scheduled irrigations were initiated on the 17th of June and the last irrigation was practiced on the 2nd of August. The amount of irrigation water applied to black cumin plants varied between 20 and 276 mm (I0 = 20 mm, I25 = 84 mm, I50 = 148 mm, I75 = 212 mm and I100 = 276 mm). Ozer et al. [33] conducted a study in Erzurum for irrigation of black cumin plants based on evaporations from Class-A pan and reported the highest irrigation water applications as 230.2 mm in 2014 and 270.3 mm in 2015. Senyıgıt and Arslan [16] reported that the amount of irrigation water applied to black cumin plants varied between 122.5 and 255.6 mm in 2013 and between 97 and 209.2 mm in 2014.

Plant water consumption (ET) values of different irrigation treatments varied between 182 and 425 mm. ET values increased with increasing irrigation water levels. As compared to I100 treatments, 12.9, 36.7, 43.5 and 57.2% decreases were seen in I75, I50, I25 and I0 treatments, respectively. Senyıgıt and Arslan [16] conducted a study in Afyonkarahisar in 2014 with the use of a Class-A pan and reported water consumption values of black cumin plants as between 166.9 and 350.5 mm and indicated that ET values decreased with increasing water deficits.

The highest WP (1.73 kg/m3) was obtained from I50 treatments and the lowest (1.48 kg/m3) from I25 treatments. IWP values varied between 2.42 and 3.94 kg/m3 with the lowest value from I100 and the highest from I25 treatments (Table 6). Ghamarnia et al. [34] reported the WP value as 1.39 kg/m3 for 50% water deficit and as 1.10 kg/m3 for full irrigation treatments. Ghamarnia and Jalili [35] reported WP values as between 0.12 and 1.87 kg/m3. Ozer et al. [33] reported IWP values of black cumin as between 0.56 and 0.93 kg/m3 in 2014 and between 0.57 and 0.87 kg/m3 in 2015. Present differences could be attributed to differences in climate parameters and irrigation programs.

Different irrigation water levels had significant effects on yields (p < 0.01). The lowest seed yield (25 kg/ha) was obtained from I0 treatments and the highest (692 kg/ha) from I100 treatments. I75 and I100 treatments were placed into the same statistical group (Table 6). There was a significant increase in yield with increasing irrigation water levels [35]. Reported average seed yields of black cumin as between 23 and 905 kg/ha in the first year and between 28.5 and 1131 kg/ha in the second year. In other studies, it was reported that black cumin yields increased with increasing irrigation water levels [33, 36, 37].

Effects of irrigation on morphological parameters

Effects of different irrigation water levels on biological yield, plant height, stem diameter, first capsule height, number of capsules per plant, number of branches per plant, number of seeds per capsule and 1000-seed weight of black cumin are provided in Table 7.

Table 7 Effects of irrigations on morphological parameters of black cumin
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Different irrigation water levels had significant effects on the morphological parameters of black cumin (p < 0.01) (Table 7). Significant decreases were observed in morphological parameters with increasing water deficits.

Biological yield was significantly affected by irrigation treatments (p < 0.01). Increasing values were seen with increasing irrigation water levels. The lowest biological yield (116.7 kg ha−1) was obtained from rain-fed (I0) treatments and the highest (1680 kg ha−1) from full-irrigation (I100) treatments. As compared to full-irrigation, a 93.1% yield reduction was seen in rain-fed treatments. Ghamarnia et al. [34] reported biological yield as 1050.5 kg ha−1 for full-irrigation and 827.4 kg ha−1 for 50% water deficit. In another study, it was reported that water stressed reduced the biological yield of black cumin [36]. Sufficient moisture levels maintain a high photosynthetic rate, which can increase cell elongation and proliferation much faster. Drought stress significantly alters photosynthetic pigments and components. Such a case reduces the activities of Calvin cycle enzymes, which in turn results in yield reduction [38, 39].

Irrigation treatments resulted in significant differences in plant heights of black cumin (Table 7). The lowest plant height and stem diameter (26.3 cm and 1.5 mm) were obtained from I0 treatments and the highest from I100 treatments (47.3 cm and 2.3 mm). Both plant height and stem diameter decreased with increasing water stress. Drought stress encountered during vegetative growth reduces cell division and cell elongation [40]. Similar findings were also reported in previous studies for black cumin [16, 33, 41].

Irrigation treatments had highly significant effects on number of branches and number of capsules per plant (Table 7). The highest number of branches was observed in the I100 and I75 treatments (4.27 and 4.0) and the lowest number of branches was seen in I0 and I25 treatments (1.93 and 2.13). The highest number of capsules per plant was obtained from I100 and I75 treatments (4 capsules) and the lowest from I0 treatments (1.93 capsules). Previous studies also reported highly significant effects of irrigations on number of branches and capsules per plant [37, 42, 43]. A high number of branches is desired in black cumin farming since higher number of branches means higher seed yields [44].

The number of seeds per capsule is directly related to seed yield and thus is the most important yield component of black cumin. Drought stress can cause a significant yield loss by affecting the number of seeds. Deficit irrigations had highly significant effects on number of seeds per capsule (Table 7). Significant decreases were seen in some seeds per capsule with increasing water stress. The highest number of seeds per capsule (70 seeds) was obtained from full-irrigation and the lowest (25 seeds) from rain-fed treatments. [33], and [37] indicated that plant water requirements should fully be met to achieve the highest number of seeds per capsule. Negative effects of water stress during the flowering and seed-filling stages are well known and possible drought stress in these stages reduces the number of capsules and seeds, thus generating negative effects on yield [45].

The effect of deficit irrigations on 1000-seed weights was found to be significant (p < 0.01). The lowest 1000-seed weight was observed in I0 (1.66 g) and the highest from I75 (2.55 g) treatments. Gutierrez-Prieto and Kirici [46] reported the highest 1000-seed weight for 30% water deficit.

Yield-response factor

The relative yield loss that occurs when the irrigation water requirement of the plant is not met fully is a measure of the response of that plant to water stress. Yield-response factor (ky) is obtained from the relationship established between the relative loss in yield versus the decrease in relative water consumption. If the ky value is greater than 1, the plant is sensitive to water stress and if the ky value is less than 1, the plant is tolerant to water stress [21]. Since the yield-response factor is a parameter that is frequently used in water-yield studies, it is important for sustainable agriculture to determine the value of the decrease in yield in response to water deficits on a seasonal basis and to plan irrigation programs accordingly [47].

The ky value of the black cumin plant was identified as 1.33 (Fig. 1). Such a value indicated that black cumin was sensitive to water stress. It can be stated that with the appropriate irrigation programs, there will be significant yield increases in black seed plants.

Fig. 1
figure 1

ky graph of the black cumin plant

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Crop water stress index (CWSI)

Canopy temperature measurements were started on July 6 and the last measurement was made on July 29. The Tc−Ta graph of irrigation treatments is presented in Fig. 2.

Fig. 2
figure 2

Tc-Ta graph for irrigation treatments

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The Tc−Ta value increased with increasing water stress levels (Fig. 2). The Tc−Ta values became negative with increasing irrigation water levels. The highest Tc−Ta values were seen in the I0 treatments with the greatest water stress and the lowest from the I100 treatments without any water stresses. While the plant canopy temperature was above the air temperature in unirrigated conditions, canopy temperature approached the air temperature with the applied irrigation water and fell below the air temperature in treatments with water deficits below 50%. Although it changed based on air humidity under full irrigation conditions, black cumin kept the canopy temperature − 1.7 to − 6.6 ºC below the air temperature. Under non-irrigated conditions, plant canopy temperature increased by 6.2 to 15.9 ºC as compared to full irrigation conditions.

The graph, which is the basis for the calculation of CWSI value, was obtained using the empirical equation specified in [23] using Tc−Ta and vapor pressure deficit (VPD) values and is shown in Fig. 3.

Fig. 3
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CWSI base graph

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The upper limit (UL) value was obtained from the I0 treatment with the greatest water stress and the lower limit (LL) value was obtained from the I100 treatment without water stress. The LL equation was determined as Tc−Ta = − 1.7524 × VPD + 0.7698 (R2 = 0.54) and UL as 10.9 ºC.

Treatment-based change in CWSI prepared based on the principles specified in [23] is presented in Fig. 4.

Fig. 4
figure 4

Treatment-based change in CWSI

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It was seen that different irrigation treatments affected CWSI values (Fig. 4). Transpiration decreased with increasing water stress, Tc−Ta values turned into positive and CWSI value increased above 1. On the other hand, Tc−Ta value was negative and CWSI value was low in plants that were sufficiently irrigated and potentially transpired. Seasonal CWSI values for I0, I25, I50, I75 and I100 treatments were found to be 0.80, 0.35, 0.24, 0.12 and 0.08, respectively.

CWSI—yield relationships

The relationship between seasonal CWSI values and black cumin seed yield is presented in Fig. 5. There were significant correlations (R2 = 0.97) between CWSI and yield. For yield estimation, the equation of Yield = − 903.37 × CWSI + 722.85 was obtained. Yields decreased with increasing CWSI and vice versa. Kirnak et al. [11] reported significant relationships between pumpkin seed yield and CWSI. Bozkurt Colak et al. [12] reported similar relationships for quinoa, [48] for mung bean and [49] for maize. It was indicated that CWSI could reliably be used for yield estimations.

Fig. 5
figure 5

CWSI—Yield relationships

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Spectral reflectance values

Treatment-based change in spectral reflectance ratios is presented in Fig. 6.

Fig. 6
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Treatment-based spectral reflection ratios

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In terms of spectral reflection ratios, there are significant differences between water-stressed and unstressed treatments. Water-stressed treatments had high reflection ratios at the visible wavelength (400–700 nm), whereas unstressed treatments had low reflection ratios. Such a case revealed that healthy plants absorbed more at the visible wavelength for photosynthesis, thus yielding lower reflection ratios. While there were high reflection ratios at the NIR wavelength (700–1400 nm) in treatments without water stress, the reflection ratios at this wavelength decreased in treatments under water stress. Stressed plants had higher reflection ratios in blue and red regions. Such a case could be attributed to a decrease in leaf water and the resultant decrease in photosynthetic pigment concentration. In addition, as the harvest approached, the difference in reflection ratios became quite close to each other. Spectral reflections are largely affected by photosynthetic pigments [9]. Also, spectral reflectance is less affected by chlorophyll absorption properties beyond 730 nm in NIR and only changes if leaf morphology or water content changes in response to stress. If the stress is well developed and beyond the shortwave infrared, the effect of water absorption is better perceived near 970 nm [50,51,52].

Relationships between vegetation indices and yield

Spectral reflectance ratios were used to assess the relationships between vegetation indices and yields (Fig. 7). In the present experiments, 10 vegetation indices were examined. Since there is no study on vegetation indices of black cumin, comparisons were made with the other plants.

Fig. 7
figure 7

Relationships between vegetation indices and yields. **Significant at p < 0.01

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There were significant linear relationships between yield and vegetation indices (excluding GNDVI). The yield had the greatest correlation with EVI (R2 = 0.97) and the lowest with WI (R2 = 0.42). There was no significant relationship between GNDVI and yield. Irik and Kirnak [9] reported significant correlations of pumpkin seed yields with SR, NDVI, SAVI, EVI, GNDVI and OSAVI. In a study on tomatoes, it was reported that yield estimations could be made quickly and non-destructively with spectral data [53]. Coelho et al. [54] reported a significant relationship between NDVI and oat grain yield (R2 = 0.91). Gopinath et al. [55] reported that vegetation indices can be used successfully in estimating rice yield. Saravia et al. [56] reported in their study on beans that the EVI was successful in yield prediction (R2 = 0.94). Colovic et al. [51] emphasized the selection of appropriate indices to monitor the physiological response of plants to water stress.

Relationships between vegetation indices and morphological parameters

Relationships between vegetation indices and morphological parameters (plant height, number of seeds per capsule and 1000-seed weight) are provided in Table 8.

Table 8 Relationships between vegetation indices and morphological parameters
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There were highly significant correlations between vegetation indices and morphological parameters (Table 8). Plant height had significant linear relationships with all vegetation indices. The highest correlation was obtained with MCARI (R2 = 0.99) and the lowest with NDVI (R2 = 0.89). Marino and Alvino [57] reported the relationships of wheat plant height with NDVI as between R2 = 0.52–0.83 and with OSAVI as between R2 = 0.52–0.82. Ryu et al. [58] reported the relationship of paddy plant height with NDVI as R2 = 0.93.

The number of seeds per capsule had highly significant correlations with all vegetation indices. The highest correlations were encountered with SAVI, EVI and VI index (R2 = 0.97). It can be said that the number of seeds per capsule, an important yield parameter, could accurately be estimated with the use of spectral vegetation indices.

The 1000-seed weight also had significant correlations with vegetation indices p < 0.01. The lowest correlation was obtained with SAVI (R2 = 0.83) and the highest with NPCI (R2 = 0.97). Kaur et al. [59] reported significant correlations of wheat 1000-grain weight with NDVI and LCI indices.

Conclusions

In this study, changes in black cumin plants because of different irrigation water applications were tried to be revealed by using a spectroradiometer. Present findings revealed that irrigation played an important role in black cumin farming in arid and semi-arid regions.

The amount of irrigation water applied to black cumin plants varied between 20 and 276 mm and plant water consumption (ET) values varied between 182 and 425 mm. The lowest seed yield was obtained from I0 treatments (25 kg/ha) and the highest from I100 treatments (692 kg/ha). The productivity increase achieved by irrigation was 27.7 times.

CWSI lower limit equation to be used in irrigation scheduling was identified Tc–Ta = − 1.7524 × VPD + 0.7698 (R2 = 0.54) and the upper limit 10.9 ºC. For black cumin plants, irrigation is recommended when the CWSI value is between 0.08 and 0.12. There was a significant relationship between CWSI and seed yield (R2 = 0.97). It was concluded that CWSI could reliably be used in irrigation scheduling and yield estimation.

Increasing stress levels were encountered with increasing water deficits and plant response differed accordingly. As a result of the examinations made between 10 different vegetation indices and productivity, it was found that there were significant relationships between all indices except WI and GNDVI indices. The vegetation indices obtained by determining the changes in the visible and NIR regions, had significant correlations with morphological parameters such as yield, plant height, number of seeds per capsule and 1000-seed weight. It was concluded based on present findings that vegetation indices could reliably be used in irrigation programming and yield estimation of black cumin.