Yield features of two soybean varieties under different water supplies and field conditions

Field Crops Research 245 (2020) 107673

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Yield features of two soybean varieties under different water supplies and field conditions

T

Angela Anda*, Gábor Soós, László Menyhárt, Tamás Kucserka, Brigitta Simon University of Pannonia, Georgikon Faculty, P. O. Box 71 Keszthely, H-8361 Hungary

A R T I C LE I N FO

A B S T R A C T

Keywords: Evapotranspiration Soybean Water stress (CWSI) Yield components

The aim of the study was to quantify the impact of water stress, based on crop water stress index on the yield and yield components in two soybean (Glycine max L.) varieties Sinara and Sigalia with distinct water demand. Three water levels were imposed during 2017 and 2018 growing seasons: 1) the unlimited in traditional evapotranspirometers, 2) the halved evapotranspiration during flowering and 3) the rainfed. LAI was recorded at weekly intervals. Total aboveground biomass, seed yield, 1000-grain weight, oil and protein contents were measured at harvest. Irrespective of water supply, the water stress tolerant Sinara had greater seed yield due to increased LAI, dry matter and 1000-grain weight compared to Sigalia that may be advantageous to obtain stable yield under variable weather conditions. The relationship between water stress index and seed yield (R2 = 0.744) was best described by polynomial function; as the evapotranspiration increased, the water stress index in line with crop temperatures, Tc and its SD values decreased. Linear relationship between water stress index and water use efficiency exhibited that each 0.1 increase in water stress index above 0.2 would improve the soybean water use efficiency by 0.49 ± 0.13 kg m−3. This information could particularly be useful for farmers cultivating the soybean on water scarce areas.

1. Introduction Soybean (Glycine max L.) has been gaining attraction in crop growing because of its role in feeding livestock as well as in human nutrition. Although growing area of soybean in Hungary is small (1% of the total growing area, ≈50,000 ha), its size is increasing by about 10% annually. In the same time, the number of varieties seeded in the field is excessively high with uncertain crop-water relation. In the past decades, due to high water and irrigation equipment prices, the soybean watering was not an option for Hungarian farmers. Based on future climate scenarios, dependence on natural precipitation will continue to threat the soybean yield variability among the years and the growing areas (Willamil et al., 2012) even in Hungary. To accelerate in the rate of soybean yield to meet the crop’s water demand, irrigation is required (Cassman et al., 2010). These authors also called attention to an existing “yield gap” between yield potential and achieved yield, probably due to insufficient watering of soybean. The evapotranspiration (ET) is one of the most important outputs of the water budget that could only be compensated by precipitation or irrigation. Wide range of local factors might impact soybean ET and seed yield, among others the practice of management, the climatic and soil conditions of the studied area (Payero et al., 2005) and crop ⁎

properties. Irmak (2017) provided the full particulars of factors impacting soybean water demand that might vary with canopy features, crop surface cover, variety/cultivar group, and pest and disease susceptibilities. All of these details are variety specific parameters. Every member of the above list should be accounted when comparing the ET results of soybean with different origin. Singer et al. (2010) reviewed ET approaches from leaf-scale to field-scale in soybean by summing evaporation and transpiration versus ET using eddy covariance, and scaling leaf transpiration to the canopy level. While a large number of reports accounted the soybean-water relationship, including the ET, the irrigation as well as water-yield interaction (Aydinsakir, 2018; Gajić et al., 2018; Giménez et al., 2017), less attention was paid to water stress timing (Payero et al., 2005). To quantify the risk related to water shortage has of primary importance as the crop growing is one of the most sensitive sectors affected by low and unevenly distributed precipitation. The study site, located in the Carpathian Basin, is considered uncommonly vulnerable to climate variability, mainly to the extremely variable inter- and intra-annual precipitation events. Out of several attempts to detect drought through indices, the theoretical way of Jackson’s (1982) crop water stress index (CWSI) and the empirical one of Idso et al. (1981) are probably the most popular drought indicators. Both of them are based upon the

Corresponding author. E-mail addresses: [email protected], [email protected] (A. Anda).

https://doi.org/10.1016/j.fcr.2019.107673 Received 3 June 2019; Received in revised form 25 October 2019; Accepted 27 October 2019 Available online 13 November 2019 0378-4290/ © 2019 Elsevier B.V. All rights reserved.

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equipment (surface area: 2 m × 2 m; depth: 1 m) six pots of Sin and six pots of Sig were arranged in two separate and complete blocks with three replications. The twelve growing pots were filled with monolith from the surrounding field, layered as its natural state. Half of the containers operated as traditional ones with unlimited watering (WW). In the remaining pots, the water supply was limited to every second day from the reproductive stage of the soybean, to accomplish detectable water stress (RO). In addition to water stress, the precipitation contribution was excluded by means of mobile rainout shelters (2.5 m long, 4.5 m wide, 2–2.5 m height) covered with UV transparent polyethylene film. Detailed description of the technical solution is given in Anda et al. (2019). The two varieties were also planted in separate field plots located behind the evapotranspirometers (50 m wide and 60 m long for each variety) providing rainfed conditions (P). The total seasonal ET for P was estimated by the soil water balance equation expressing the crop water use as the difference in soil water content between May to September including precipitation sum for the same time period. The soil water content profiles were measured gravimetrically in the top 1 m with 0.10 m intervals from May to September, weekly. Three water treatments were investigated in the study: 1) crops in evapotranspirometer with unlimited water supply (WW), 2) exposure to moisture stress at the beginning of the reproductive growth phase, R1 (half of full watering, RO), 3) and rainfed crops (P). Reproductive growth phase was considered to be from R1 (one open flower at any node on the main stem) to R8 (full maturity, that is approximately 95% of the pods have reached their mature pod color). Rainfed crops are grown under natural weather conditions without irrigation. Phenological phases were recorded based on that of Fehr and Caviness (1977). Water use efficiency, WUE for yield was computed as the ratio of seed yield and seasonal total ET expressed in unit of kg m−3.

relationship between canopy temperature (Tc) and air temperature (Ta) difference and vapor pressure deficit (VPD). As long as Jackson (1981) utilized the energy balance of crops, Idso et al. (1981) used the two empirical “baselines”; the upper line stands for non-transpiring crops, whilst the lower one for well-watered crops. As Montoya et al. (2017) reported that the reproductive stage showed the largest sensitivity to soybean’s yield reduction during water deficit, crop’s yield performance exposed to transient water stress (limited to flowering) in a rainout shelter was also included to the investigation. Missing water during pod filling period resulted in low protein and high oil content of soybean seed in Turkey (36 °N 38°E) (Kirnak et al., 2010). Although Rotundo and Westgate (2009) observed somewhat different changes in seed quality coming from drought; water stress reduced both the oil and protein contents in soybean. The dilemma about the direction of variation in the protein and oil contents of water stressed soybean exists even today (Aydinsakir, 2018). Because water deficit is the most important limiting environmental variable contributing to yield reduction in soybean, the objectives of the study were (i) to quantify the soybean water supply using CWSI even under water restricted conditions of uniquely converted evapotranspirometers; (ii) to test the applicability of theoretically based CWSI – canopy temperature, Tc is included – to detect water stress under variable weather conditions; (iii) to analyze yield and its components in two soybean varieties with distinct water demands for better decisionmaking of farmers working under water scarce conditions; (iiii) to reveal the structure of the yield-related variables. To date, evapotranspirometers have only been applicable to detect water losses under unlimited water supply conditions. In the study, this special equipment was reconstructed to measure ET of water deficit soybean during flowering. To our best knowledges, this kind of set up and application have not been performed in any plant experiments yet, including CWSI observations under water stress conditions. Moreover, these observations are remarkable in the context of climate modification, such as increasing drought occurrence, which was projected on-site by Anda and Soós (2016).

2.2. CWSI and its components In both varieties, in the middle of the plots, combined air temperature-humidity probes (type: HP472 AC) were mounted at about 0.5–1 m above the crop surfaces with a log interval of 6 s, at the same time interval as the Tc readings happened. Tc was measured with a hand-held infrared thermometer (RANGER II. RTL, RAYTEK, Santa Cruz, CA, USA) with a 2° field of view, an 8–14 μm spectral band-pass filter and 0.1 °C resolution. The thermometer was kept at an oblique angle of ≈30-40° below the horizontal after canopy closure. Between 20–25 Tc readings (in every 2 s) were taken and repeated 3–5 times in each treatment at high solar angles (about 13.00 LMT) during cloudless weather conditions. The soybean emissivity was 0.96 (Anda et al., 2019). Detected Tc means were used in the computation of theoretical CWSI following the method of Jackson et al. (1981). Sensed Tc values during solar noon were applied in the CWSI computation following the way of Jackson et al. (1981). CWSI equals the ratio of measured ET (ETm) to reference ET (PET):

2. Materials and methods 2.1. Study site, agronomy and treatments with ET measurement A field trial was conducted for two growing seasons (2017–2018) for studying the yield components of two indeterminate soybean varieties with distinct water demand - as communicated by the crop breeding institute of Karintia Ltd. (Karintia, 2019), Sárvár, Hungary (Sinara, Sin: water stress tolerant; Sigalia, Sig: for irrigated conditions) at Keszthely Agrometeorological Research Station, ARS (46°44ʹ N; 17°14ʹ E; elevation: 124 m above sea level), in Hungary (Fig. 1). The soil type was clay loam; Haplic cambisol (FAO WRB 2006) with a mean bulk density of 1.15 Mg m−3 in the top 1 m of the profile. The available water capacity of the soil was 273 mm m-1. Before sowing, 300 kg of N, P, and K ha-1 (1:1:1) was used as a commercial granular fertilizer. Crops were planted on 9th May 2017 (due to the late beginning of spring) and on 24th April 2018 with expected plant density of about 40 plant m-2. Soybean management practice was employed accordingly with the standard best management one as prescribed by the local specialists of the University’s Faculty of Agronomy. Meteorological elements were collected by a QLC-50 climate station (Vaisala, Helsinki, Finland) equipped with a CM-3 pyranometer (Kipp & Zonen Corp., Delft, the Netherlands) established in the ARS. The crops in one of the water treatments received unlimited water supply through the Thornthwaite-type compensation evapotranspirometer. The instrument records soil-water volumetric changes following the complete water balance (precipitation, irrigation, leaking water) under natural conditions and thus the ET of the plant stand can be expressed as a residual term (Anda, 1986). Due to the fixed position of the compensation evapotranspirometer

CWSI = 1 −

γ (1 + rc / ra) − γ ETm = PET Δ + γ (1 + rc / ra)

(1) −1

where rc and ra are canopy and aerodynamic resistances (s m ), γ is a psychrometric constant [hPa K−1], and Δ is the slope of saturated VPD Ta relation [hPa K−1].

γra Rn /(ρcp) − (Tc − Ta )(Δ + γ ) − (es (Tc ) − e ) rc = ra γ [(Tc − Ta) − ra Rn /(ρcp)]

(2)

where es(Tc)˗e is the difference in saturation and actual vapor concentrations of the air [hPa], cp is heat capacity of the air in constant pressure [J kg−1 K−1], ρ is the air density [kg m-3], Rn is the net radiation [W m-2]. The ra was computed by the method of Thom et al. (1981). PET rate [mm day−1] was estimated using the widely used FAO-56 Penman-Monteith formula (Monteith, 1965; Penman, 1948): 2

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Fig. 1. The layout of the field experiment. Legends in the field chambers of the evapotranspirometer were as follows; 1: Sinara with unlimited watering, 2: Sinara with limited watering; 3: Sigalia with unlimited watering; 4: Sigalia with limited watering. Rainfed plots were located above the chambers of the evapotranspirometer with grey (light grey: Sigalia; dark grey: Sinara). In the middle of each plot, just around a measuring tower (□), there were five sub-plots for crop sample taking (X). The lower part of the sketch separated by dashed line contained the meteorological garden with the measuring cellar of the evapotranspirometer (5), the wind mast (6), a QLC50 automatic climate station (7) and tipping bucket rain gauge belonging to QLC-50 (8).

900

PET =

2.3. Weather of the growing seasons

0.408(Rn −G ) + (γ T + 273 ) ∕u (es −e ) Δ + γ (1 + 0.34u)

(3) −2

The long-term (1971–2000) growing season’s average Ta is 16.9 °C at Keszthely, with highly variable and irregular precipitation events totaling of 384.3 mm. The Ta values were 1.3 °C and 2.4 °C (p < 0.01) warmer during 2017 and 2018, respectively, than the long-term average. At the same time, the precipitation sums differed in the two studied seasons; 2017 received 37 mm less precipitation while 2018 had characteristic of a wetter weather (+93.3 mm) than the normal for the area. Disregarding high monthly precipitation sums in September that were not utilized in soybean, 2017 received 44.9% less (p < 0.01) precipitation, while 2018 received 6.4% greater (p < 0.05) precipitation then the long-term average.

where G is soil heat flux density [MJ m day ], Ta is measured at 2 m height [°C], u is wind speed at 10.5 m height [m s-1], es is saturation vapor pressure [kPa], Δ is in [kPa °C-1] and 0.408 is a conversion factor from MJ m−2 day-1 to equivalent evaporation in mm day-1. Rn was estimated from global radiation, daily mean Ta, mean daily e, the site latitude and elevation after Allen et al. (2005). A fixed value of 0.23 was set for albedo (Allen et al., 1998). A CWSI of 0 indicates no water stress and a value of 1 represents maximum water stress. Information about the use of CWSI in corn (Zea mays L.) can be obtained from Anda (2009). -1

3

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2.4. LAI and yield components A histogram based segmentation method was applied using an image processing program to derive the leaf area of each leaflet (Anda et al., 2019), thereafter the weekly LAI was accounted (ratio of leaf area per plant dividing by affected soil surface). Five sample crops were included in the study each treatment. In the experimental field, randomly selecting and measuring five subplot’s (2 m × 2 m) total aboveground biomass (TDM) was harvested in each section at the beginning of Septembers. The biomass was oven dried at 65 °C for at least 48 h to get dry weight. After separating yield components and threshing pods by hand, weights were adjusted to 13% moisture. 1000-grain weight was also weighed. Inframatic 9200 NIR Grain Analyzer (PerkinElmer, US) was applied to get the percent protein and oil content of seeds on a dry matter basis and the protein and oil production per unit soil surface area (g m−2) on a dry matter basis. The harvest index, HI was determined as the ratio between seed yield and TDM obtained at the harvest. The process of yield sample collection was the same in each evapotranspirometer pot. 2.5. Statistical analysis Independent-samples t-tests were applied to compare all the variations of treatments (varieties, water levels, season) for yield components, ET, WUE, CWSI, weather data. Three-way ANOVA was performed using SPSS software, version 17.0 to evaluate the influence of water treatment (W), variety (V) and season (S) on the yield and its components as well as the WUE. The model included all main effects and all two- and three-way interactions. Comparing factor levels within a specific level of another factor was done using Student's t-test or oneway analysis of variance (ANOVA) followed by Tukey's HSD post hoc test. Critical value of significance was 0.05. Fig. 2. Five-day averages of soybean ET in varieties Sinara, Sin and Sigalia, Sig with limited (RO) and unlimited (WW) water supply in the growing seasons of 2017 (a) and 2018 (b).

3. Results and discussions 3.1. Soybean ET and WUE

WW (Table 1). The WUE values (Table 1) ranged from 0.69 ± 0.03 kg m−3 (Sig P, 2018) to 1.16 ± 0.06 kg m−3 (Sin P, 2016). WUE was lower in RO and P due to abundant precipitation during 2018. Irrespective to season and variety, the water stress in reproductive stage improved the WUE (p < 0.001) similarly to previous findings of Montoya et al. (2017) in Uruguay (31 °S), using irrigated soybean, variety Don Mario between 2014 and 2015. WUE was influenced by V (p < 0.001), S (p < 0.001) and W (p < 0.001) and some of their interactions (W × S; W × V; p < 0.001). The insignificant V × S and V × S × W interactions on WUE indicated that the impact of season on the WUE was similar among two varieties in the study. Attributes of aboveground biomass production (length of the growing season, LAI, Tc, CWSI, Ta, RH) In spite of different sowing times, the soybean development was similar in both studied seasons across all treatments regarding the length of phenological phases and vegetation cycles. The lengths of vegetation cycle varied in 114–120 and 115–121 days in 2017 and 2018, respectively. Like a tendency, the crops in WW had a few days longer vegetation cycle than rainfed crops (data is not shown). Considering all observations, LAI mean and ET sum were strongly correlated, as expected. Water stress resulted in lower LAI compared to unlimited watering irrespective to season. The seasonal mean LAI of treatment RO was lower by 24.2% (2018: Sin RO, p < 0.01) to 44.7% (2017: Sig RO, p < 0.001) compared to WW treatments (see Table 1). This observation confirmed the findings of Kirnak et al. (2010). They found LAI decrease of 35–40% due to water deficit, where the water treatments included 0, 25, 50, 75 and 100% of full irrigation in soybean variety, A-3935, at Sanliurfa, Turkey (36 °N) between 2003 and 2004.

The seasonal pentad means (five-day average) of ET ranged from 2.2 ± 1.16 (Sig RO in 2017) to 5.2 ± 2.5 mm (Sin WW in 2017) (Fig. 2) in both seasons. Irrespective to crop watering, the top five-day means of ET were achieved during the reproductive stages (R3-R6), while the lowest values were recorded at the beginning (VE) and at the end (R8) of the soybean’s growing seasons. The maximum five-day means ranged from 3.1 (Sin RO in 2018) to 9.7 mm (Sig WW in 2017). Irrespective to variety, the highest ET five-day averages were observed in WW, while crops of RO subjected to water deficit had significantly lower values in both growing seasons. In both studied time periods, due to water stress during generative stage, declines in seasonal mean ET ranged from 57.7% (p < 0.001; Sig in 2018) to 76.8% (p < 0.001; Sin in 2017). Five-day mean differences in ET between the two studied varieties were negligible in both growing seasons. Surprisingly, despite warmer weather in 2017, the higher precipitation in the season 2018 generated 20.2% lower (p < 0.05) VPD and resulted in smaller crop water demand that declined the ET totals with 12.1% (p < 0.001) and 13.2% (p < 0.001) in Sin WW and Sig WW, respectively, related to ET sums over 2017 (Table 1). The evapotranspiration totals in the present study were comparable to those of 650 mm reported by Candogan et al. (2013) in a fully irrigated soybean variety, Nova, in Turkey (40 °N, 28 °E), between 2011 and 2012. In accordance to the results of the above authors, 50% water stress during flowering approximately halved the ET totals independently of variety in both studied seasons. However, no significant seasonal differences were observed in the ET totals between the two varieties. Anapalli et al. (2018) observed that absolute values of ET in any field study may not compare directly with the results of other locations or different growing seasons elsewhere. The precipitation based estimated water use of P was closer to RO than 4

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Table 1 Cumulative ET, water use efficiency (WUE), seasonal mean LAI, LAImax and crop water stress index (CWSI) in two soybean varieties, Sinara (Sin) and Sigalia (Sig), for the growing seasons of 2017 and 2018. Two microclimate elements, the air temperature (Ta) and relative humidity (RH), in accordance with canopy surface temperature (Tc) were observed around solar noon (close to 13.00 LMT). Abbreviations WW, RO and P denoted unlimited watering, halved water supply (in reproductive stage) and rainfed treatments, respectively. *value is the estimated seasonal water use, based on the change in gravimetric soil water adjusted for rainfall (Anda et al., 2019).

2017 Sin WW Sin RO Sin P Sig WW Sig RO Sig P 2018 Sin WW Sin RO Sin P Sig WW Sig RO Sig P

Total water use [mm]

WUE [kg m−3]

Mean LAI

LAImax

694.9 307.5 330.0* 657.4 291.6 310.0*

0.79 1.00 1.16 0.70 0.94 0.95

± ± ± ± ± ±

0.02 0.04 0.06 0.02 0.02 0.03

5.27 3.67 4.40 5.20 3.33 3.70

± ± ± ± ± ±

0.51 0.52 0.78 0.34 0.64 0.44

9.70 6.76 8.74 9.59 6.16 7.42

± ± ± ± ± ±

0.58 0.46 0.39 0.43 0.72 0.40

28.5 29.0 30.1 29.2 29.7 30.2

± ± ± ± ± ±

0.24 0.23 0.26 0.24 0.23 0.25

28.5 31.2 32.3 28.3 29.7 31.1

± ± ± ± ± ±

2.27 3.14 3.35 2.21 2.29 3.20

48.1 46.0 41.5 46.5 45.4 39.1

± ± ± ± ± ±

1.10 1.15 1.86 1.10 1.92 1.81

0.22 0.59 0.56 0.21 0.51 0.59

± ± ± ± ± ±

0.12 0.19 0.21 0.15 0.21 0.21

615.6 317.2 435.0* 576.1 315.7 405.0*

0.89 0.92 0.91 0.84 0.92 0.69

± ± ± ± ± ±

0.04 0.12 0.09 0.06 0.15 0.03

5.08 4.00 5.10 4.94 3.80 4.80

± ± ± ± ± ±

0.35 0.48 0.57 0.48 0.54 0.61

9.40 6.77 9.13 9.33 7.01 7.98

± ± ± ± ± ±

0.43 0.43 0.52 0.35 1.24 0.49

28.2 28.3 28.9 28.4 28.5 28.8

± ± ± ± ± ±

0.20 0.23 0.23 0.24 0.23 0.22

27.7 29.4 28.3 28.3 29.5 28.3

± ± ± ± ± ±

2.07 3.42 2.66 2.65 3.01 2.79

52.9 52.7 52.2 52.3 50.1 49.4

± ± ± ± ± ±

1.70 1.69 1.73 1.69 1.71 1.75

0.13 0.36 0.20 0.21 0.40 0.21

± ± ± ± ± ±

0.09 0.27 0.17 0.19 0.25 0.21

Ta [°C]

Tc [°C]

RH [%]

CWSI

Kacira et al. (2002) observed a similar pattern regarding SD of Tc-Ta for six New Guinea Impatiens plants. Throughout both growing seasons, the mean CWSI of WW was below 0.22 in agreement with the CWSI = 0.2 of Nielsen (1990) at Akron, USA (40.9 °N), using a well-watered soybean hybrid, Pioneer 9291. The mean CWSI of fully irrigated soybean, cultivar Nova, fluctuated between 0.17 and 0.22 at Bursa, Turkey (40.15 °N), in a 2-year experiment (Candogan et al. 2013). These two field studies on soybean applied the empirical method of Idso et al. (1981) to derive the local (Manhattan, Lincoln and Fargo, USA) CWSI. The required variables to calculate CWSI are presented in Eqs. (2) and (3). Increases in the mean CWSI values during the warm and dry 2017 ranged from 24.2 (Sig RO, p < 0.05) to 94.7% (Sin P, p < 0.001) related to 2018, with the exception of Sig WW, likely as a consequence of increased seasonal precipitation sum in 2018. It is corroborated by the mean CWSI of both P treatments in 2018, being below the threshold of 0.22. In the successive seasons, the deficit water in RO significantly raised the mean CWSI about two and a half times (p < 0.001). These results are comparable to those of Nielsen (1990) ones for rainout-sheltered soybean, hybrid Pioneer 9291 (CWSI = 0.3-0.5) at the USDA Central Great Plains Research Station (40.9 °N), between 1986 and 1987. Seasonal mean CWSI and WUE were linearly related (WUE = 0.493CWSI + 0.715; R2 = 0.616, p < 0.001, RMSE = 0.36), though polynomial provided only slightly better fit (R2 = 0.68) due to high CWSI values of P in the warm season of 2017. A similar polynomial was obtained by Candogan et al. (2017) between soybean CWSI and WUE (WUE=-0.419CWSI 2+ 0.604CWSI + 0.37; R2 = 0.67).

Seasonal mean LAI of P was between those of WW and RO. In case of WW mean LAI was about equal irrespective of season and variety. Growing season’s maximum LAI (LAImax) of different treatments followed the tendency of seasonal means; they peaked at about 65 DAS in 2017 and 75 DAS in 2018. The LAImax values ranged from 7.1 (Sig RO in 2017 and Sin RO in 2018) to 10.5 (Sin WW in 2017) (Table 1). The LAImax of P was 4–5% lower in 2017, likely due to 130 mm less seasonal precipitation than in 2018.In WW the LAImax values between 9 and 10 were close to 9.0 reported by Setiyono et al. (2008) for soybean variety Pioneer P93M11 in Nebraska, USA (40 °N). Kross et al. (2015) observed somewhat lower LAImax values (5.8 in 2012 and 7.7 in 2013) at Ontario, Canada (45 °N) in an irrigated trial. The size of the evapotranspirometer pots, although being affected by daily irrigation of the pot’s edge, was small, so the use of interpretations regarding the impact of water treatments on the elements of microclimate (Ta and RH) are restricted. Mean Ta (limited to solar noon) differences ranging from 0.1 to 0.7 °C did not differ significantly between the two soybean varieties and water supply levels; however, mean Ta of variety Sin in 2017 was 1.6 °C warmer in P than WW treatment (p < 0.05). The Ta measurements taken near solar noon on completely clear-sky conditions were more variable in the warm 2017 than in the cooler and wetter 2018 season. There were no variations in the RH values of the varieties and water treatments in both seasons with the exceptions of 6% (p < 0.05) and 7% (p < 0.01) increments in RH of Sin WW and Sig WW, respectively, as compared to their P values during 2017. Tc and CWSI were used to characterize the crop water stress level (Table 1). Mean Tc recorded around solar noon on the days of cloudless conditions ranged from 27.7 (Sin WW in 2018) to 32.3 °C (Sin P in 2017). We were able to take Tc samples about every third day in both seasons. Tc in the warmer and drier 2017 was higher than in 2018, although significant differences in Tc between the two seasons were only observed in P (Sin P: 4.0 °C, p < 0.001; Sig P: 2.8 °C, p < 0.001). The water stress significantly increased the Tc of soybean between 1.2 °C (Sig in 2018; p < 0.05) and 2.7°C (Sin in 2017; p < 0.001). Due to evenly distributed precipitation in 2018, the Tc values of P were statistically the same as the WW ones, independently on the studied variety. In contrast to 2018, in the warm and dry 2017 about 1 °C increases in Sin P (p < 0.001) and Sig P (p < 0.001) were detected compared to their WW counterparts. Comparing RO and P, there was hardly any difference in Tc in both varieties, confirming the effectiveness of water stress in this study. According to the increasing variability of Tc’s SD due to water scarcity also described by Aston and van Bavel (1972), the highest SD values were detected in the RO, while the lowest ones in the WW treatments (Table 1), irrespective to season and variety.

3.2. Soybean production and quality of seeds The results of the ANOVA suggest that TDM (Table 2) was influenced by the S (p < 0.05) and W (p < 0.001). The insignificant effect of V and all interactions including S × V, S × W, V × W and S × V × W on TDM indicated that the impacts of season and water supply on TDM were similar in the two varieties studied. The substantial amount and evenly distributed precipitation during 2018 increased TDM in rainfed P. The TDM increases were 16.1% (p < 0.05) in Sin P and 4.1% in Sig P related to 2017 (Fig. 3). There was no seasonal difference in the TDM of the water controlled (WW and RO) treatments. During the wet 2018, the rainfed TDM was very close to that of unlimited watering. In the drier 2017, additional water increased TDM by 21.7% (p < 0.001) and 8.9% (p < 0.05) in Sin WW and Sig WW, respectively, compared to the corresponding P. Due to water stress, the significant decreases in TDM of RO ranged from 18.0% (Sig 2017; p < 0.001) to 29.8% (Sin 2018; p < 0.001) compared to 5

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WW, pointing out the importance of proper soybean watering during the reproductive stage (Zang et al. 2018). Ribas-Carbo et al. (2005) concluded that water deficit induced by water deficit caused progressive reduction in net-photosynthesis of soybean leaves due to disturbed soybean respiration. Considering the two years separately, the impact of water stress on TDM differed up to 10% among the varieties; the water stress tolerant Sig performed slightly better in both seasons. The results of the ANOVA suggest that seed yield (Table 2) was influenced by V (p < 0.001) and W (p < 0.001) and their interaction V × W (p < 0.05). The remaining interactions V × S, S × W and S × V × W were non-significant. These non-significant interactions revealed that the response of the seed yield to water supply and variety did not differ between the years. In both growing seasons, the highest seed yields were obtained from WW. The difference compared with rainfed P ranged from 24.4 (Sin WW 2018; p < 0.001) to 46.6% (Sig WW 2017; p < 0.001) (see Fig. 3). As the yield was positively correlated with water supply, slightly lower changes were observed over 2018. The non-irrigated soybean plots (varieties: Armor and Progeny) had 18–79% lower grain yield than all other irrigated ones ranging from one deficit irrigation to fully irrigated crops with seven irrigations in Stuttgart, Germany (47 °N, 19 °E) over a two-season study (Henry et al., 2018). Better seed yield production reported by Hergert et al. (1993) representing 53% (150 mm) of water applied to full irrigation, soybean grain yield of 88% of the fully irrigated crops was obtained at North Platte (41 °N), USA over 1981. In 2017, yield reductions of 78.4 and 67.3% were observed in Sin RO (p < 0.001) and Sig RO (p < 0.001), respectively, compared to the corresponding WW treatments. Hardly any difference was found between RO and WW during the wetter 2018 (Sin: 87.7% (p < 0.001) and Sig: 67.6%, (p < 0.001)). Decreases in seed yield appeared to be slightly higher under RO than in P related to their WW (ranging from 24.2% (Sin 2018) to 46.6% (Sig 2017)). Wijewardana et al. (2018) found yield depression in Mississippi with water stressed soybean (variety Asgrow) due to disruption of carboxylation of photosynthetic products during reproductive stage resulting pod and seed abortion. Significant differences in seed yields between the two varieties were only observed in WW (in 2017: 20.6%, (p < 0.001); in 2018: 13.0% p < 0.001) and in P limited to 2018 (32%; p < 0.001); these yield increments were in favor of Sin. Supporting the previous reports of Gajić et al. (2018), the present study found varied response of TDM and seed yield to growing seasons, mainly due to the amount of precipitation. Rainfed yield results in this study were comparable to those of Paredes et al. (2015) and Wei et al. (2015) ranging from about 3200 to 4200 kg ha−1 for dry land soybean, variety Zhonghuang, grown in Daxing, China (39 °N). Seed of WW in this study was in agreement with a fully irrigated soybean yield of 5400-6500 kg ha−1 reported by Giménez et al. (2017) at western Uruguay (32 °N), between 2009 and 2013. The yield decline ranging from 67.3% (Sig 2017) to 87.7% (Sin 2018) in RO corresponded well to those of about 80% published by the above authors under deficit irrigation from the beginning of flowering. Second order polynomial was fitted to the seasonal mean CWSI and seed yield data (yield = 1.856CWSI2 – 1.9CWSI + 0.765; R2 = 0.744, p < 0.05, RMSE = 0.08). This equation suggests that as the crop water use (ET) increased, the CWSI in line with Tc and its SD values decreased. Candogan et al. (2013) reported an exponential relationship between soybean yield and CWSI in a twoseason investigation (yield = 0.001e-0.68CWSI; R2 = 0.92, RMSE = 13.79). Considering the results of ANOVA assessing the 1000-grain weight (Table 2) only the variety (p < 0.001), the treatment (p < 0.001) and their interactions (p < 0.001) were significant. The non-significant V × S and S × W interactions revealed that the response of 1000-grain weight to water supply and variety did not differ between the two seasons. Irrespective to season and variety, the unlimited water amounts

Table 2 ANOVA for yield and yield components: total aboveground biomass (TDM, kg m−2), seed yield (kg m−2), 1000-grain weight (g), oil content (%), oil mass (g m−2), protein content (%) and protein mass (g m−2). Tests of Between-Subjects Effects Type III Sum of Squares

df

Mean Square

F

Sig.

1 125902.7 1 2852.1 2 1069827.8 1 14768.2 2 3561.1 2 71748.2 2 2856.4 48 23784.9 R Squared = .608)

5.29 0.12 44.98 0.62 0.15 3.02 0.12

0.026 0.731 0.000 0.435 0.861 0.058 0.887

1 4755.6 1 57953.8 2 248892.2 1 321.2 2 2144.1 2 7105.9 2 1957.0 48 2006.6 R Squared = .826)

2.37 28.88 124.04 0.16 1.07 3.54 0.98

0.130 0.000 0.000 0.691 0.352 0.037 0.384

1 482.6 1 6994.7 2 13941.9 1 72.5 2 392.6 2 2768.9 2 620.1 48 192.0 R Squared = .783)

2.51 36.44 72.63 0.38 2.05 14.42 3.23

0.119 0.000 0.000 0.542 0.140 0.000 0.048

1 172.9 1 0.0 2 9.1 1 3.8 2 4.0 2 9.2 2 3.7 24 0.1 R Squared = .989)

2340.31 0.46 122.85 51.46 54.28 123.95 49.41

0.000 0.000 0.000 0.000 0.000 0.000 0.000

1 4561.2 1 1623.0 2 8845.6 1 56.0 2 196.1 2 572.5 2 197.6 48 58.5 R Squared = .880)

78.04 27.77 151.34 0.96 3.36 9.80 3.38

0.000 0.000 0.000 0.333 0.043 0.000 0.042

1 137.7 1 4.3 2 56.4 1 6.9 2 40.7 2 9.5 2 3.7 24 0.1 R Squared = .993)

1842.44 57.16 754.49 92.80 544.31 127.19 49.36

0.000 0.000 0.000 0.000 0.000 0.000 0.000

26.68 62.58 295.92 0.13 7.11 17.12 0.91

0.000 0.000 0.000 0.723 0.002 0.000 0.409

−2

TDM [kg m ] S 125902.7 V 2852.1 W 2139655.6 S×V 14768.2 S×W 7122.1 V×W 143496.5 S×V×W 5712.8 Error 1141673.2 a. R Squared = .681 (Adjusted Seed yield [kg m−2] S 4755.6 V 57953.8 W 497784.3 S×V 321.2 S×W 4288.2 V×W 14211.7 S×V×W 3914.1 Error 96316.5 a. R Squared = .858 (Adjusted 1000-grain weight [g] S 482.6 V 6994.7 W 27883.9 S×V 72.5 S×W 785.3 V×W 5537.7 S×V×W 1240.2 Error 9214.4 a. R Squared = .824 (Adjusted Oil content [%] S 172.9 V 0.0 W 18.2 S×V 3.8 S×W 8.0 V×W 18.3 S×V×W 7.3 Error 1.8 a. R Squared = .992 (Adjusted Oil mass [g m−2] S 4561.2 V 1623.0 W 17691.1 S×V 56.0 S×W 392.2 V×W 1145.0 S×V×W 395.1 Error 2805.6 a. R Squared = .902 (Adjusted Protein contet [%] S 137.7 V 4.3 W 112.8 S×V 6.9 S×W 81.3 V×W 19.0 S×V×W 7.4 Error 1.8 a. R Squared = .995 (Adjusted Protein mass [g m−2] S 3802.0 V 8918.6 W 84344.4 S×V 18.2 S×W 2025.4 V×W 4880.9 S×V×W 259.9 Error 6840.6

1 1 2 1 2 2 2 48

3802.0 8918.6 42172.2 18.2 1012.7 2440.4 130.0 142.5

a. R Squared = .938 (Adjusted R Squared = .924). 6

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400

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Variety Sig Sin

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Fig. 3. Boxplots of yield-related variables by year, variety and water treatment. a) TDM (kg m−2), b) Seed yield (kg m−2), c) 1000-grain weight (g), d) Shoot DM (kg m−2), e) Oil content (%), f) Protein content (%).

p < 0.001) to 24.3% (Sin P; p < 0.001) in comparison to 2018. Continuous watering in WW modified the oil percentage only with a few percent compared to P with the exception of Sin WW in 2017. The oil percentage difference between RO and P ranged from 3.7 (Sin 2017, p < 0.001) to 18.9% (Sig 2017, p < 0.001), which suggested that temporary water stress under flowering was beneficial on oil accumulation. In accordance with the findings of Kirnak et al. (2010) in Sanliurfa, Turkey (36°), with cultivar A-3935, the oil content of RO exceeded the WW ones with the exception of Sig during 2017. The ANOVA results for oil content (Table 2) presented that the influence of W (p < 0.001), S (p < 0.001) and V (p < 0.001) and their interactions S × W (p < 0.05), V × W (p < 0.001) and S × W × V (p < 0.05) with the exception of V × S on the produced oil mass per unit area (g m−2) were significant. The significant W × V and S × W, as well as S × W × V interactions revealed that the response of oil mass to water supply and season differed among varieties in this study. The warmer and drier weather conditions during 2017 were advantageous to oil mass accumulation per a unit of surface area. The total oil production increments ranged from 9.7 (Sin WW, p < 0.01) to 24.4% (Sin P, p < 0.01) over 2017. With respect to the influence of variety Sig P was 24.9% (p < 0.001) and 24.2% (p < 0.001) lower in 2017 (p < 0.001) and 2018, respectively, in comparison to Sin P values. On the contrary to oil content (%), the total seed oil mass (g m−2)

improved the 1000-grain weight ranging from 17.0 (Sig 2017; p < 0.001) to 26.8% (Sin 2017; p < 0.001) related to RO treatments (Fig. 3). Irrespective to seasons, the lowest 1000-grain weight was observed in RO demonstrating the sensitivity of seed size to water shortage during reproductive stage. Water stressed crops produced relatively smaller seeds in accordance with the findings of Wijewardana et al. (2018) in rainfed soybean, variety Asgrow at Mississippi. Water shortage in seed-filling period leaded to decline in 1000-grain weight due to the shortening of seed fill duration (Brevedan and Egli, 2003). The 1000-grain weight of variety Sig was higher in treatment P than in RO in both seasons (2017: 15.3%, p < 0.001; 2018: 16.5%, p < 0.001). However, as for the variety Sin only insignificant differences were observed between the treatments P and RO. The difference 1000-grain weight between the two varieties were mainly observed in treatment WW. The 1000-grain weight of Sin in WW was 16.9% (p < 0.001) and 14.4% (p < 0.01) higher than that of Sig during 2017 and 2018, respectively. The results of the ANOVA showed that all the interactions including V × S (p < 0.001), S × W (p < 0.001), V × W (p < 0.001) and S × W × V (p < 0.001) on the oil content of the seeds (%) were highly significant (Table 2). Lower precipitation in 2017 resulted in higher difference in seed oil percentage in all of the treatments ranging from 10.2 (Sin WW; 7

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consecutive growing seasons (2017 and 2018). Two of the watering levels examined have been in existence for a long time (namely, unlimited water and rainfed conditions) and the third one, the converted evapotranspirometer, created a new way to quantify plant water use in soybean under field conditions. Cloudy conditions around solar noon (12.00–13.00 LMT) occur at least every third day inhibiting the use of IR-thermometer. In this case the detection of CWSI is impossible. As the typical time period of convective cumulus development is usually after 14.00 LMT, Tc sampling is necessary before this time. Seed yield declined when the mean CWSI - after canopy closure – exceeded the limit value of 0.22. This threshold could be considered when scheduling irrigation under the weather conditions close to the study site. Regression equations for the relationships between CWSIseed yield and CWSI-WUE are relevant to crop growers when the proper yield for their growing conditions are derived and predicted. In water scarce areas, to be economically efficient, the CWSI-WUE relationship has of primary importance; each 0.1 increase in CWSI above 0.2 would improve the soybean WUE by 0.49 ± 0.13 kg m−3. Overall seed yield increased as the allowable water shortage got lower in the WW. The conducted experiment demonstrated that an allocation of about 300 mm water each year (which presented half of the water applied to traditional evapotranspirometer) reduced the average seed yield approximately 70% of that water amount applied to unlimited watering. Results in the study confirmed the detrimental impact of water deficit even limiting to soybean reproductive stage. It is also likely that the evenly distributed precipitation in 2018 minimized the effect of the unlimited watering in WW treatments. The seed yield and TDM exposed to water stress during flowering were significantly less than their values either in WW or P, irrespective of variety. Irrespective to weather, Sin has shown greater seed yield quality and quantity performances due to increased LAI, TDM, seed yield, 1000-grain weight, total oil and protein productions (per unit surface area) as compared to Sig. Even the stress tolerant Sin may be advantageous to attain stable and high yield under variable weather conditions of the area, since crop growth characteristic (LAI) affected positively some of its yield components. Although 6–7% protein content growth was only limited to Sin WW (both seasons) and Sin RO (2017) as compared to those of P treatment. Both soybean varieties varied in their response to 50% water deficit during flowering, however, hardly modified by their water requirements. Future investigations are necessary to determine whether the relationships described in the study could be generalizable for other seasonal weather conditions and areas.

was exposed to water deficit. The RO treatment resulted in significantly lower oil mass (from 24% (Sig 2017, p < 0.001) to 39.3% (Sin 2018, p < 0.001)) than the corresponding WW treatments. The results of the ANOVA for protein content (Table 2) showed that all factors (p < 0.001) and their interactions (p < 0.001) were significant. The significant V × S, W × V and S × W interactions revealed that the response of protein content to water supply and season differed among varieties. Different responses of soybean’s protein content to water deficit can be found in the literature. Results in this study strengthened the seed quality dilemma reported by previous authors regarding soybean water supply. Irrespective to treatments, the seed protein contents were higher with the exception of Sin RO during the wetter and cooler growing season of 2018. These increments ranged from 6.1 (Sin WW; p < 0.001) to 26.0% (Sig P; p < 0.001). In 2017, unlimited watering increased the seed protein content related to P. Lobato et al. (2008) reported protein content declines under progressive water stress conditions in Belém, Brazil (1 °S) in soybean, variety Sambaiba. Rotundo and Westgate (2009) discussed that when the water became limiting, the smaller soybean seeds often had higher protein concentration due to less detrimental effect on final protein accumulation relative to other seed yield components. In contrast to 2017, percent protein in 2018 was greater in for plants in P than WW treatment, which agrees with observations of Candogan and Yazgan (2016) in which irrigation decreased seed protein content in soybean, variety Nova, grown in Turkey (40 °N). Summarizing the two-season results for unlimited water supply, Sin produced approximately 8.5% (2017) and 6.2% (2018) greater protein percentage under unlimited watering, compared to Sig WW. In contrast, Sig performed better protein % under rainfed conditions (2017: 25.2%, p < 0.05; 2018: 26.0%) than Sin P. Aydinsakir (2018) also reported that oil content in soybean seed varied in the contrary to protein content of seeds. The results of the ANOVA for protein yield per unit surface (Table 2) suggest that the main effects of V (p < 0.001), S (p < 0.001) and W (p < 0.001) as well as the V × W (p < 0.05) and S × W (p < 0.01) interactions were significant. The non-significant interactions revealed that the response of the protein mass to water supply and season did not differ between the two varieties studied. The difference of the protein production between the two seasons was only significant in treatment P. In the wet season of 2018 the total protein production increased with 25.3% (p < 0.01) in Sin P and 26.0% (p < 0.001) in Sig P. The effect of the season in water controlled treatments (WW and RO) resulted in insignificant changes in protein production. Except of the RO treatments, the increases in total protein production of Sin ranged from 16.8 (WW in 2018, p < 0.001) to 24.5% (P in 2017, p < 0.001) compared to Sig. Similarly to findings of Adeboye et al. (2017) on soybean (variety: TGX) HI in Nigeria (7 °N), the values had irregular variations in different watering levels. On average, WW resulted the highest HI ranging from 0.26 (Sig 2017) to 0.30 (Sin 2017). Out of three treatments, only the water stress reduced the HI to 26.4% (p < 0.01), 28.6% (p < 0.01), 31.1% and 33.3% (p < 0.05) in Sin 2017, 2018, Sig 2017 and 2018, respectively, compared to the corresponding WW ones. These declines compared well with those of Wijewardana et al. (2018) for water stressed soybean in Mississippi. The comparisons of WW and P eventuated that soybean HI was not affected by temporary water deficit similarly to the observations of Demirtas et al. (2010) and Garcia y Garcia et al. (2010) for sub-humid regions of Turkey (40 °N) and Georgia, USA (33.9 °N), respectively. This study also confirmed the conclusion of Campos et al. (2018) that HI values among soybean varieties were remarkably consistent.

Declaration of Competing Interest The authors declare no conflict of interest. Acknowledgements The research leading to these results has received funding from the Hungarian Government and the European Regional Development Fund of the European Union in the frames of the Széchenyi 2020 Programme, under project number GINOP-2.3.2-15-2016-00029. We acknowledge the financial support of Széchenyi 2020 under the EFOP-3.6.1-16-201600015. A special thanks to Karintia Ltd. for their kindness supporting us with good-quality corn-free soybean seed. References Allen, R.G., Clemmens, A.J., Burt, C.M., Solomon, K., O’Halloran, T., 2005. Prediction accuracy for projectwide evapotranspiration using crop coefficients and reference evapotranspiration. J. Irrig. Drain. Eng. ASCE 131 (1), 24–36. Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop Evapotranspiration – Guidelines for Computing Crop Water Requirements. FAO Irrigation and Drainage Paper 56, Rome, Italy.

4. Conclusions Two soybean varieties with distinct water demands, their yield and yield components properties were analyzed in Hungary, during two 8

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