Impact of Delaying Irrigation on Wilting, Seed Yield, and Other Agronomic Traits of Determinate MG5 Soybean

Abstract

Soybean production in the U.S. Mid-South relies heavily on irrigation with 85% of soybean surfaces irrigated in Arkansas. Reduction in irrigation due to water quantity restrictions will significantly affect soybean seed yield, making variety selection increasingly important. The objective of the study was to assess if irrigation onsets at different reproductive stages affect wilting, seed yield, and key agronomic traits on determinate maturity group 5 (MG 5) soybean. One-hundred sixty-five F₄-derived populations of recombinant inbred lines with determinate growth habit, similar maturity, and contrasting wilting potential were planted in an augmented strip-plot design in four environments as a single replicate. Four irrigation onsets were applied at R1 (initiation flower), R2 (full bloom), R3 (initiation pod), and R4 (full pod) using an atmometer. Results indicated significant differences in wilting and yield but no significant differences in maturity, protein, oil concentration, and 100-seed weight across different irrigation onsets. There was no significant difference between the fast and slow wilting genotypes across different irrigation onsets for each trait. Allowable depletions measured in this study indicated that both fast and slow wilting soybean genotype determinate MG5 can tolerate high allowable depletion with no significant yield penalty at R3 growth stage in silt loam soil.

1. Introduction

Soybean (Glycine max (L.) Merr.)) is one of the most important worldwide crops, with a cultivated area of 126 million hectares (Mha) and a total production of 353 million tons in 2021, with 113 million tons produced by the United States (U.S.) [1]. Soybean yield increased dramatically as a result of market development, breeding advances, and improved management during the Green Revolution [2]. It is one of the largest row crops in the U.S Mid-South accounting for 5.6 Mha for Missouri (MO), 3.6 Mha for Texas (TX), 3.1 Mha for Arkansas (AR), 2.1 Mha for Mississippi (MS), 1.5 Mha for Tennessee (TN), and 1 Mha for Louisiana (LA) [3].

Soybean production in the U.S. Mid-South relies heavily on irrigation [4], with 85% of soybean surfaces currently supplemented with water in Arkansas [5]. An average of 575 mm of water is needed during a soybean crop cycle [6]. Water is usually the main limiting factor for soybean productivity [7,8,9,10,11]. Indeed, in a study of two soybean varieties under three irrigation levels, Anda et al. [9] observed an impact of water availability on seed yield when a crop water stress index, proportional to the observed versus potential evaporation, was greater than a given threshold at reproductive soybean stages. Moreover, in a study of soybean water productivity under irrigated and dryland conditions, Mekonnen et al. [10] observed large variations across the state of Nebraska, due to differences in climate, soil, water management, planting date, soybean maturity group/genetics, and duration of the growing period.

The National Centers for Environmental Information reported a water shortage in eastern Arkansas between May and July 2018 [12], causing a reduction in water levels at irrigation reservoirs and generating concern on water availability for crop irrigation during reproductive stages. Additionally, some areas in AR, including key soybean growing counties like Lonoke, Prairie, and Arkansas, have seen alluvial aquifer depth to water greater than 30 m, increasing irrigation costs and reducing well output [13]. A reduction in groundwater availability could result in farmers having to skip or delay irrigation at a certain reproductive stage. Reduction in irrigation due to water restrictions will significantly affect soybean output and state revenue. As crop water availability becomes hard to predict at planting, soybean variety availability and selection become increasingly important for farmers. Yet, advances in genetic improvement for drought resistance in soybean is still limited [14,15,16].

Crops are subject to different abiotic and biotic stress during their growing season. Among abiotic stress, drought has been claimed to be the most devastating, having a drastic effect on productivity in rain-fed areas as it reduces plant growth and seed yield [17]. According to Clement et al. [18], as part of the Fabaceae family, soybean is one of the most drought-sensitive legumes. One typical feature of legume plants is the presence of nodules resulting from the relationship between plants and Bradyrhizobium spp. for biological N₂ fixation. However, this relation is particularly sensitive to drought [19,20,21]. Hence, soybean seed yield might be reduced by 40% under water stress [6,22,23]. Flowering (R1 and R2 stages [24]) and subsequent periods (pod setting: R3 and R4, and seed filling: R5 and R6 [24]) were found to be the most critical for water stress in soybeans [25,26].

Plants have different mechanisms to adapt to climatic variations by employing biochemical, molecular, physiological, and morphological changes [27]. In soybean, drought tolerance mechanisms include: increased rooting depth, reduced stomatal conductance, leaf rolling/folding, reduced evaporation surface, increased leaf-surface wax accumulation, and enhanced water-storage abilities in specific organs [28,29,30,31]. Canopy wilting is the first visible symptom of water stress, and a number of genotypes have been identified as slow wilting under field conditions [32,33]. Slow wilting genotype could maintain cell turgor under drought condition [34,35]. In soybean, slow canopy wilting and sustained nitrogen fixation under drought have resulted in maximizing yield under water-limited environments [36].

In the U.S Great Plains, the effect of reduced irrigation on soybean has been well characterized [37]. However, no information is available on the impact of delaying irrigation in soybean in the Mid-South, and on relative performance of slow wilting versus non-slow wilting genotypes under delayed irrigation practices. Therefore, the research objective of this study was to assess if different irrigation onsets at different reproductive stages affect soybean seed yield and other key agronomic traits, including wilting, maturity, protein, oil concentration, and 100-seed weight, of determinate maturity group 5 (MG 5) soybean genotypes using contrasting wilting-potential populations.

2. Materials and Methods

2.1. Plant Materials and Experimental Design

Progeny from two populations were used in this study to magnify the presence of different genotypes with slow and fast wilting responses. The choice of maturity for this experiment was based on the availability of genetic material with slow wilting at the University of Arkansas, System-Division of Agriculture soybean breeding program. A total of 165 F_4:7 breeding lines (73 derived from the cross N07-14753/R11-1057 and 92 derived from R11-2933/R11-1057) were used, all of which had a determinate grow habit and similar maturity group 5 (MG 5), along with two parental checks (PC) (R11-2933 and R11-1057) (Supplementary Materials, Figure S1) and two commercial checks (CC) based on seed availability (AG55X7, AG56X8, P53AG7X, P55A49X). Trials were grown in four environments (location-year combination) using an augmented strip-plot design under four furrow-irrigation onset treatments as a single replicate. Environments included Stuttgart, AR (silt loam soil) in 2019 and 2020 (19STU and 20STU), and Rohwer, AR (silt loam soil) in 2019 and 2020 (19ROH and 20ROH). The four irrigation onsets were: (1) full irrigation (irrigation initiated at flowering—R1 stage), (2) irrigation initiated at full flowering (R2 stage), (3) irrigation initiated at beginning of pod development (R3 stage), and (4) irrigation initiated when pods were 2 cm at one of the four uppermost nodes (R4 stage).

The irrigation at each designated growth stage was triggered using the decision table developed by Henry et al. [38] for atmometer (water-filled device measuring the actual evaporation of water) measurements based on 50% of the plots reaching the desired stage. The atmometer consists of a green canvas cover with a ceramic plate on the top that simulates the transpiration of the leave surface (Supplementary Materials, Figure S2). Each strip of irrigation onset (R1, R2, R3, and R4) was composed of ten blocks. One block was composed of 4 checks (2 parental checks and 2 commercial cultivars) and 16 randomly assigned genotypes, including 7 and 9 genotypes from the first and second population, respectively, where individual lines were a random factor within populations (Figure 1 and Figure S3).

The plots were 4.6 m long with 1.5 m alley, and consisted of two rows 0.97 m apart in 19ROH and 20ROH, and 0.91 m apart in 19STU and 20STU. The planting date was 30 May 2019, 28 May 2019, 21 May 2020, and 19 May 2020 for 19STU, 19ROH, 20STU, and 20ROH, respectively. Standard agronomic practices were used at each location, including fertilization to recommended levels as defined by Slaton et al. [39].

2.2. Weather Conditions, Soil Properties, Soil Water Content, and Irrigation Estimation

Weather data (air temperature and rainfall) and soil samples were collected to characterize the growing conditions. The average minimum temperature, maximum temperature, and rainfall for 19ROH, 19STU, 20ROH, and 20STU were accessed from the Southern Regional Climate Center (www.srcc.lsu.edu/station_search accessed on 4 February 2022), by searching for the respective weather stations where the study was conducted. The estimated cumulative potential evapotranspiration (PET) for each environment was calculated based on the sum of the daily potential evapotranspiration (ET₀). The ET₀ was estimated by the FAO–Penman–Monteith method [40], using the following equation:

$$E{T}_0=\frac{0.408 \left(R_n-G\right)+\gamma \frac{900}{T+273}{u}_2\left(e_s-e_a \right)}{∆+\gamma \left(1+0.34u_2\right)}$$

(1)

where $E{T}_0$ is the daily potential evapotranspiration (mm·day⁻¹), $R_n$ the net radiation at the crop surface (MJ·m⁻²·day⁻¹), $G$ the soil heat flux density (MJ·m⁻²·day⁻¹), $T$ the daily mean air temperature (°C), $u_2$ the wind speed at 2 m height (m·s⁻¹), $e_s$ the saturation vapor pressure (kPa), $e_a$ the actual vapor pressure (kPa), $\left(e_s-e_a\right)$ the saturation vapor pressure deficit (kPa), $∆$ the slope vapor pressure curve (kPa· °C⁻¹), and $\gamma$ the psychrometric constant (kPa·°C⁻¹).

Soil samples were collected at 15 cm depth before planting and before harvest, for each environment (19ROH, 19STU, 20ROH, and 20STU). At each sampling date, composite samples from five random subsamples were collected on the sides of the planting bed (on each front half and back half of the field) to account for field heterogeneity. Samples were sent to the Agriculture Diagnostic Laboratory (University of Arkansas, Fayetteville, NC, USA) for analysis. Soil pH and electrical conductivity (EC) were determined using a potentiometer at a ratio of 1:2 (w/v). Nutrients in the soil (P, K, Ca, Mg, S, Na, Fe, Mn, Zn, B, and Cu) were extracted with Mehlich-3 at a ratio of 1:10 (v/v) and their concentrations analyzed by inductively coupled argon-plasma spectrometry (ICAP, Spectro Analytical Instruments, Spectro Arcos ICP, Kleve, Germany). The sand, clay, and silt contents were expressed in percentage to determine the soil texture, based on the USDA soil texture triangle.

A total of 64 sensors (WATERMARK Soil Moisture Sensors-200 SS, Irrometer, Riverside, CA, USA) were installed in 2020 in Stuttgart (20STU) and Rohwer (20ROH) to measure the soil matric potential. For both 20STU and 20ROH, sensors were installed at 15, 30, 46, and 76 cm depths, on the side of the bed at the ¾ of the field for each irrigation onset (R1, R2, R3, and R4). Two sets of 15 and 30 cm depth sensors were placed randomly in each irrigation onset. To quantify the water stress intensity, a manual reading of the watermark sensors was done at the time of the canopy wilting rating using a hand-held meter (30 KTCD-NL, Irrometer, Riverside, CA, USA). The volumetric water content (VWC) of the soil (%) at the time of canopy wilting rating was calculated using the average of the soil matric potential (SMP) at 15 cm and 30 cm depths (cbar), converted to VWC using soil water retention curve reported by Henry et al. [41] for a silt loam soil in DeWitt (AR), a location close to the study site. The field capacity (FC) was 35.6% and the wilting point (WP) 8.9%. The available water content (AWC in %) was calculated to be 26.7% (FC-WP) [41]. The allowable depletion (AD) at the time of canopy wilting rating and prior to each irrigation onset, which indicates the maximum amount of plant available water allowed to be removed from the soil before irrigation refill occurs, was then calculated using the following equation:

$$\mathrm{AD}=\left(1-\frac{\left(\mathrm{VWC}-\mathrm{WP}\right)}{\mathrm{AWC}}\right)\ast 100$$

(2)

In addition to the watermark sensors, a time-domain reflectometer sensor (TDR-315L, Acclima) was installed at 10 cm depth on the side of the bed, in the middle of the field for 20ROH and 20STU to monitor the soil moisture in each irrigation onset (R1, R2, R3, and R4). The TDR sensors were coupled with AquaTrac (AgSense, Huron, SD, USA) telemetry units for logging and transmitting the soil moisture data. TDR sensors were installed on 20 July 2022 in Stuttgart (20STU) and later in the season (6 August 2020) in Rohwer (20ROH), due to the unavailability of the equipment. The VWC was measured from the TDR sensors before the irrigation was used to compute AD since it has a recording of a 30 min interval.

A flowmeter (McCrometer, Hemet, CA, USA) was used in 20STU to measure the flow rate of irrigation. Readings were performed at the beginning and the end of each irrigation. Based on the flow rate and the surface irrigated, the amount of irrigation was calculated as follows:

$$Irr=\frac{\left(F_R-I_R\right)\ast 0.01}{S}\ast 25.4$$

(3)

where Irr is the irrigation amount (mm), $F_R$ the flow rate at the end of irrigation (acre-inch), $I_R$ the flow rate at the beginning of the irrigation (acre-inch), and $S$ the surface irrigated (acres).

Since the flowmeter was not available for 19STU, an approximated equal surface was used for 20STU for each irrigation onset. The amount of irrigation for 19STU was estimated using the average acre-inch of the flow rate for 20STU. For 19ROH and 20ROH, the amount of irrigation was also an estimation using an average flow rate of 1000 gallons per minute and the duration of irrigation of each irrigation onset; the calculation was based on the following equation:

$$Irr=\frac{Flow rate\ast duration}{27514}\ast S\ast 25.4$$

(4)

where Irr is the irrigation amount (mm) and $S$ the surface irrigated (acres).

2.3. Soybean Phenotyping

Visual rating for canopy wilting was taken using a 10-point scale, from 0 to 9, where (0) represents no wilting and (9) a dead plant. The canopy wilting was rated one time prior to the onset of irrigation at the designated reproductive stage. Rating evaluations were performed at least six days in dry conditions (no precipitation), and to reduce the impact of diurnal variation in evaporative demand, data were collected between 11:00 a.m. and 3:00 p.m. Maturity was recorded when 95% of the pods in a plot had reached mature pod color [24], and was expressed as the number of days after 31 August. Total yield (t·ha⁻¹) was calculated from seed moisture, weight, and plot dimensions. Seed protein and oil estimation concentration in percentage of dry matter was performed for each line using subsamples of 50 seeds via Near-Infrared Spectroscopy DA 7250 NIR analyzer (Perten, Sweden). Lastly, 100-seed weights were evaluated in grams (g).

2.4. Data Analysis

For the soil properties analysis, a matched paired t-test was performed in JMP Pro 16.0 (SAS Institute, Cary, NC, USA) to analyze the difference between pre-planting and pre-harvest samplings within each irrigation onset in each environment. The canopy wilting for the slow-wilting R11-2933 and the fast-wilting parent R11-1057 at the R4 stage were compared using a t-test in JMP Pro 16.0 (SAS Institute, Cary, NC, USA). Prior to analysis of each trait, lines were classified as fast-or slow-wilting based on the mean of the canopy wilting of R11-2933 ± σ, where σ is the sample standard deviation. Mean and standard deviation were calculated from a dataset of the four location-year environments under non-irrigated conditions at the R4 stage. Entry values greater than the mean plus the standard deviation (>2.59) were considered a fast-wilting genotype, while entries with values equal to or less than the mean plus the standard deviation (≤2.59) were considered slow wilting.

Data, including canopy wilting, maturity, yield, protein, oil, and 100 seed-weight, were analyzed as an augmented strip-plot design using PROC GLIMMIX in SAS (SAS Institute, Cary, NC, USA). The statistical model for the analysis was the following:

$$y_{ijkl}=\mu +t_i+g_j+s_k+b_{k\left(l\right)}+g{t}_{ij}+t{s}_{ik}+g{s}_{jk}+e_{ijkl}$$

(5)

where i is the number of irrigation onsets: R1, R2, R3, and R4 (1, 2, 3, 4), j the number of wilting classes: fast wilting and slow wilting classes (1,2), k the number of the environments: 19ROH, 19STU, 20ROH, and 20STU (1,2,3,4), and l the number of the blocks (1…10). $y_{ijkl}$ is the mean response of the ijklth observation (canopy wilting, maturity, yield, protein, oil concentration, and 100-seed weight), and μ the overall mean response. Fixed effects were the irrigation onset $t_i$, the wilting class $g_j$, and the interaction between the wilting class and the irrigation onset $t{g}_{ij}$. The random effects were the environment $s_k$, the block nested within the environment $b_{k\left(l\right)}$, the interaction between the irrigation onset and the environment $t{s}_{ik}$, the interaction between the wilting class and the environment $g{s}_{jk}$, and the experimental error $e_{ijkl}$.

3. Results

3.1. Environment Characteristics

The four environments presented a silt loam soil texture (Supplementary Materials, Table S2). Results in the difference between pre- and post-samplings showed an increase of 0.3 in pH and an average decrease of 134.3 µmhos·cm⁻¹ (p-value < 0.05) in electrical conductivity (EC) for each irrigation onset (Supplementary Materials, Figures S6 and S7). We also measured a general decrease in macronutrients including P, K, S, Ca, and Mg, and micronutrients (Fe, Mn, Zn) between pre-planting and pre-harvest samplings (Supplementary Materials, Table S3). However, an increase in Na was observed in the 19ROH environment with an increase of 14.42 mg·kg⁻¹ soil DW in R1 stage and 36 mg·kg⁻¹ soil DW in the R4 stage (Supplementary Materials, Table S3). Likewise, a general increase in B was found in each environment except for 20STU in R2, R3, and R4 (Supplementary Materials, Table S3).

The monthly average temperature from planting to harvest in each environment is given in Supplementary Materials (Table S1). The period of July–August corresponds to the pod initiation stage, where the average maximum monthly temperature ranged from 30 to 33 °C in each environment. On the day of the canopy wilting rating, the air temperature ranged between (20–30), (22–31), (23–36), and (23–33) °C at R1, R2, R3, and R4, respectively, for 19STU, and between (20–30), (22–31), (23–36), and (23–34) °C at R1, R2, R3, and R4, respectively, for 19ROH. For 20STU, temperature ranged between (24–33), (23–33), (21–30), and (24–33) °C at R1, R2, R3, and R4 canopy wilting rating, respectively. Similar trend of temperature was recorded for 20ROH: (24–33), (23–31), (20–30), and (22–31) °C at R1, R2, R3, and R4 canopy wilting rating, respectively. The cumulative potential evapotranspiration (PET) indicated a high evaporative demand between June and July (Supplementary Materials, Figure S5), where the highest cumulative PET was calculated in 20STU (400 mm). Pod initiation (R4 stage) was recorded at the end of July when the highest evaporative demand was recorded in each environment.

The cumulative rainfall from planting to harvest for each environment (19ROH, 19STU, 20ROH, 20STU) is given in Supplementary Materials (Figure S4). Rainfall was more important in Rohwer (ROH) than in Stuttgart (STU) with, respectively, 587 mm and 541 mm in 2020 and, respectively, 471 mm and 417 mm in 2019, in Rohwer (ROH) and Stuttgart (STU). The distribution of rainfall and irrigation during the growing season in each environment, and the time of canopy wilting rating is presented in Figure 2 and Figure 3.

Prior to canopy wilting rating at R1, there had been no rainfall for 9 to 11 days in each environment (19ROH, 19STU, 20ROH, and 20STU). Rating at R2 was carried out when there had been no rain for 15 to 17 days for 19ROH, 20ROH, and 20STU. In 19STU, 4 days prior to the canopy wilting at R2, there had been 1.5 mm precipitation (Figure 3). Canopy wilting rating at R3 was recorded when there had been no rainfall for 23 days, 8 days, and 7 days in 19ROH, 19STU, and 20ROH, respectively. Five days prior to canopy wilting at R3, we recorded 2.79 mm of rainfall in 20STU. There had been no rainfall for 9 to 14 days prior to canopy wilting rating at R4 in 19ROH, 19STU, and 20ROH while 3 mm of rainfall was recorded prior to the canopy wilting rating at R4 in 20STU (Figure 3).

The hydraulic properties of the soil at the time of canopy wilting rating are given in

Table 1. Average soil matric potential (SMP), soil volumetric water content (VWC), and allowable depletion (AD) at the time of canopy wilting rating for different irrigation onsets in two environments 20STU and 20ROH (15–30 cm depth).

Environment	Irrigation Onset	SMP (Mean ± Se, Cbar)	VWC (%)	AD (%)
20STU	R1	−128.2 ± 36.5	25.5	38
	R2	−104.6 ± 46.7	27.9	29
	R3	−199 ± 0	21	55
	R4	−199 ± 0	21	55
20ROH	R1	−55.2 ± 16.4	33.5	8
	R2	−90 ± 10.1	28.8	25
	R3	−108 ± 21.1	26.9	33
	R4	−124.5 ± 17.9	25.5	38

.

At the time of the canopy wilting rating, the soil matric potentials (SMP) in 20STU at 46-cm depth were −101, −58, −199, and −199 cbar at R1, R2, R3, and R4, respectively. At 76 cm depth, the SMP were −52, −77, −199, and −199 cbar for R1, R2, R3, and R4, respectively. R3 and R4 showed the lowest value of the average SMP at 15–30 cm depth (−199 ± 0 cbar) in 20STU. Similarly, in 20ROH, R3 and R4 presented the lowest SMP (−108 ± 21.1 and −124.5 ± 17.9 cbar) among the irrigation treatment. At R1 and R2, AD was relatively low in 20ROH compared to 20STU. The AD at R3 and R4 reached 55% in 20STU while in 20ROH, AD ranged from 33 to 38%. The lowest VWC was recorded at R3 and R4 for both environments, however, 20ROH displayed relatively higher VWC compared to 20STU at R3 and R4 (Table 1).

The irrigation was applied based on the atmometer, but the soil moisture content as well as the subsequent allowable depletion (AD) was determined before each irrigation onset (

Table 2. Average percent of soil volume water content (VWC) and allowable depletion (AD) before irrigation from different irrigation onsets in two environments 20STU and 20ROH.

Environment	Irrigation Onset	VWC before Irrigation (%)	AD before Irrigation (%)
20STU	R1	11.1	92
	R2	12	88
	R3	15.7	75
	R4	19.5	60
20ROH	R1	11.4	91
	R2	18.5	64
	R3	16.7	71
	R4	12.4	87

).

In 20STU, irrigation onset at R4 stage presented the highest average of VWC before the onset of irrigation (19.5%). For both environments, the lowest values of VWC before irrigation were recorded at R1 with 11.1% and 11.4% for 20STU and 20ROH, respectively. The average allowable depletion was 92, 88, 75, and 60% when irrigation was onset at R1, R2, R3, and R4, respectively, in 20STU. In 20ROH, the average depletion in each different irrigation onset was 91% at R1, 64% at R2, 71% at R3, and 87% at R4. Both environments had experienced water stress since allowable depletion exceeded 50% (assumed stress).

3.2. Canopy Wilting

The t-test across four environments at R4 stage results showed a highly significant difference for canopy wilting (p-value < 0.0001) between the slow wilting parental check R11-2933 (1.59 ± 0.62) and the fast-wilting parental check R11-1057 (3.34 ± 0.87). Furthermore, a highly significant wilting class (fast-wilting (FW) vs. slow-wilting (SW)) effect on the canopy wilting (p-value < 0.001) was observed; results somewhat expected because of the nature wilting classes were constructed, nonetheless demonstrating that the group means were statistically different and not just one standard deviation away. Moreover, there was a significant treatment effect (irrigation onset at different reproductive stages) on canopy wilting (p-value < 0.001), with a highly significant interaction effect between wilting class and irrigation treatment (p-value < 0.0001). As irrigation was further delayed, higher canopy wilting was observed. The FW group mean increased from 1.67 ± 0.32 when irrigation was triggered at R1, to 3.17 ± 0.32 when irrigation was triggered at R4. A significant difference in wilting severity between FW and SW occurred when the irrigation was triggered at R3 and R4 (Figure 4).

3.3. Maturity

There was no significant wilting-class-by-treatment interaction on maturity (p-value = 0.53). Moreover, no significant difference was shown for treatment and maturity effect (p-value = 0.68), and for wilting class and maturity (p-value = 0.13). Delaying irrigation did not affect maturity under our experimental conditions (Figure 5).

3.4. Seed Yield

There was a significant treatment effect on seed yield (p-value < 0.05). However, there was no significant wilting class effect on yield (p-value = 0.05), and no interaction between the wilting class and the irrigation treatment (p-value = 0.33). Results showed that when irrigation was triggered at R4 stage, there was a significant yield reduction (23%) for determinate MG 5 soybean. Nevertheless, no significant yield difference was reported by triggering irrigation at R1, R2, and R3 stages under our environmental conditions (Figure 4). In addition, no yield differences between FW and SW genotypes were found under the delayed irrigation methods (Figure 6).

3.5. Seed Protein and Oil Concentration, and 100-Seed Weight

There was no statistical difference for wilting-class-by-irrigation-treatment interaction in terms of seed protein (p-value = 0.6433) or oil concentration (p-value = 0.2603). Moreover, results showed that there was no effect of irrigation treatment on seed protein (p-value = 0.7939) and oil concentration (p-value = 0.8571). Likewise, there was no significant difference in terms of wilting class for protein (p-value = 0.3711) and oil concentration (p-value = 0.4423). Reduced irrigation did not affect protein and oil concentration for FW and SW determinate MG 5 even if irrigation was triggered at R4 stage (

Table 3. Average soybean seed protein, oil concentration, and 100-seed weight of each wilting class under different onset irrigations (R1, R2, R3, and R4) evaluated in four environments (location-year combination 19ROH, 19STU, 20ROH, and 20STU). Values with same letters are not significantly different (Tukey’s; p-value < 0.05).

Wilting Class	Irrigation Onset	Protein (%)		Oil (%)		100-Seed Weight (g)
Fast wilting	R1	39.33	a	21.39	a	14.99	a
Slow wilting		39.34	a	21.40	a	15.07	a
Fast wilting	R2	40.08	a	21.28	a	15.16	a
Slow wilting		39.94	a	21.44	a	15.16	a
Fast wilting	R3	39.85	a	21.48	a	15.23	a
Slow wilting		39.57	a	21.60	a	15.25	a
Fast wilting	R4	39.62	a	21.52	a	15.31	a
Slow wilting		39.36	a	21.52	a	15.43	a

). The average protein concentration ranged from 39.33% to 40.08%; while the oil concentration ranged from 21.28% to 21% (Table 3).

No significant interaction effect between wilting class and irrigation treatment (p-value = 0.8127) was found for 100-seed weight. Similarly, there was no statistical difference of irrigation treatment (p-value = 0.9885) or wilting class (p-value = 0.5200) on 100-seed weight. Seed size was not affected if irrigation was delayed at R4 stage under our experimental conditions (Table 3). The average 100-seed weight was 14.99 g to 15.43 g (Table 3).

4. Discussion

Drought is one of the greatest threats to crop profitability. Thus, circumventing this problem is a priority for farmers [42]. When facing precipitation or groundwater shortages, Mid-South soybean famers might skip or delay irrigation at critical stages of soybean. The present investigation aimed to appraise if different irrigation onsets at different reproductive stages affect soybean wilting, seed yield, and other key agronomic traits, including maturity, protein, oil, and 100-seed weight for determinate maturity group 5 (MG 5) soybean genotypes using contrasting wilting potential populations.

We observed an increase in soil pH with irrigation, in agreement with Bouaroudj et al. [43]. The increase in pH could be hypothesized by the result of high content of basic cations such as Na⁺, Ca²⁺, and Mg²⁺ in the irrigation water, which increase the alkaline reserve of the soil and enhances the rate of denitrification thereby producing hydroxyl ions. Unlike Bouaroudj et al. [43], we observed a decrease in EC with irrigation. In our experiments, pre-harvest soil samplings were done in October. The cumulative precipitation (mm) displayed a steep increase before harvest (September to October) (Supplementary Materials, Figure S4), indicating high precipitation right before harvest time, concomitantly decreasing EC as salts move with water. The general decrease in nutrients across environments in our results could be explained by the uptake of nutrients of soybean plants during their growth.

Our results showed an increase in canopy wilting when irrigation was delayed to R3 and R4 stages, and that the fast wilting (FW) group had a significantly higher canopy wilting than slow wilting (SW) group. The severity of canopy wilting in response to drought varies among soybean genotypes and the onset time [32,44,45]. Higher canopy wilting at R1 and R2 stages compared to R3 and R4 stages could be explained by the greater soil water content at R1 (25.5% and 33.5%) and R2 stage (27.9% and 28.8%) (Table 1). Soybean genotype experienced more water stress at R3 stage and R4 stage in 20STU since the allowable depletion exceeded 50%. Moreover, the greater magnitude of canopy wilting response at R4 compared to earlier growth stages was because the evaporative demand was greater. In fact, the canopy was probably more fully closed (greater transpiration per unit area), and the soil moisture was more depleted at R4 stage. According to Valliyodan et al. [45] and Charlson et al. [46], as the soil dries, soybean with SW have delayed leaf wilting compared with FW, which agrees with the results of the current study. Under full soil moisture, plants will absorb water through their roots. This water will be used by the plant or released through transpiration by opening the stomata in the leaves. Photosynthesis will also occur normally with CO₂ and O₂ being absorbed and released through the open stomata. Once soil moisture becomes limited, water loss through transpiration still occurs; therefore, water loss leads to wilting. The first visible symptom is wilting under water stress [32,33]. SW genotypes maintain cell turgor under drought condition [34,35]. SW mechanism, a basis for drought tolerance, has been studied by several researchers. Pantalone et al. [47] stated that SW appeared to be involved as a better water resource exploration by a larger root system, while Tanaka et al. [48] reported that the SW trait was due to a lower stomatal conductance. Bellaloui et al. [49] speculated that the mechanisms were related to the accumulation of minerals (such as K, Ca, B, Na) or organic compounds (such as sucrose, raffinose, stachyose, and oleic acid) under drought stress in SW. Therefore, plants could maintain cell turgor, conserve water, and achieve osmoregulation. The higher leaf water potentials detected in SW genotypes suggested their ability to retain more water through water conservation and nutrient homeostasis [49,50]. Recently, Ye et al. [51] confirmed the SW mechanism was linked to the water conservation strategy of limited maximum transpiration rates.

Delaying irrigation until R4 stage did not affect maturity of FW and SW in the current study. Determinate MG 5 soybean genotypes could sustain their development under mild drought. This phenomenon is valuable since it enables delaying irrigation without shortening the cycle of soybean. Yield reductions in this study (23%) when irrigation was delayed at R4 stage were higher than previously reported by Karam et al. [52] but lower than the studies carried out by Dogan et al. [6] under non-irrigated conditions. In our study, yield results indicated that soybean was more sensitive to water stress at R4. At an early reproductive stage R1, R2, and R3, both fast and slow wilting genotypes were under water stress as AD was higher than 50% (Table 2). However, soybean genotypes can recover from any effect of moisture stress until R3 stage in silt loam soil as rainfall or irrigation were triggered; thus, it could compensate the deficit of water during R1, R2 and R3 stages. However, when irrigation was withheld at R4 in silt loam soil, both fast and slow wilting soybean genotypes experienced water stress (AD greater than 50%) that led to a decrease in seed yield. Indeed, the R4 stage has also been identified as the most critical drought-sensitive stage by Karam et al. [52] and Smith et al. [53]. At R4, the plant reaches the full-pod stage in which the pod grows rapidly, and seed development begins. As a result of water stress, lower water potential in the leaves reduces the water potential gradient between leaves and pods, reducing the flow of metabolites to the expanding cells [54]. Water stress imposed on soybean throughout the growth stages reduces growth and affects seed yield [55,56,57], similar to our finding. In contrast, Sweeney and Granade [58] and Marais and Bufé [59] reported that water stress during flowering followed by full irrigations increased yield. This study is also in agreement with Foroud et al. [60] and Huck et al. [61], who reported that soybean yield components can recover from any effect of moisture stress at the R2 stage.

The SW phenotype has been used as one of the indicators to screen drought tolerance in the field [46]. This trait was predicted using a simulation model to improve yield under drought by >80% of the growing seasons in most regions of the U.S [62]. However, our investigation under reduced irrigation showed no statistical difference in seed yield of SW versus the FW genotypes. Similar to the current study, Ye et al. [51] stated that under non-stress (irrigated) conditions, the FW recombinant inbred lines showed no statistically significant seed yield over the SW recombinant lines, but 12.8% to 13.7% yield advantage over the FW lines under non-irrigated conditions. The disagreement of our research results and Ye et al. [51] could result by the fact that we did not have a non-irrigated treatment and our environment received sufficient rainfall for a successful soybean crop development. In addition, Ye et al. [51] indicate that when the yield was evaluated separately for each recombinant population, there was no significant difference between FW and SW under either condition (irrigated and rain-fed), which agrees with our results.

The effect of water deficit on soybean protein and oil concentration was evaluated in several studies, and different responses have been observed. Foroud et al. [60] and Ghassemi-Golezani and Lotfi [63] detected an increase in protein concentration under well-watered conditions; contrarily, Specht et al. [64], Rotundo and Westgate [65], and Navabpour et al. [66] found that water stress during soybean seed filling (R5 and R6) increased protein concentration and concluded that the increase in protein concentration could be due to the stimulation of protein synthesis rather from a concentration effect due to lower biomass production under the stress condition. In our studies, since irrigation was triggered before or at R4 stage (full pod development), we did not see a significant impact of irrigation treatment on seed protein concentration, as expected due to the timing of seed protein accumulation [67].

Oil has considerable importance to the soybean industry because of its high economic value as a source of edible oil and a major renewable feedstock for biodiesel production [68]. Previous studies showed that drought stress reduced the oil concentration of seeds at later stages of grain filling in soybean [63,69]. Indeed, Dornbos and Mullen [70] found that serious water shortages during seed filling (R5 and R6) reduced seed oil concentration by 12.4%. On the contrary, Bellaloui et al. [71] documented that severe drought can increase soybean oil seed concentration. In the present investigation, there was no significant difference in oil concentration regardless of the irrigation onsets (R1, R2, R3, and R4) and the wilting class. Under different irrigation onsets applied for the current study, the oil concentration was an average of 21%, which is above the minimum value of 20% required by the industry [72].

Dogan et al. [6] stated that water stress along with severe climatic conditions during R3 stage in soybeans increased pod numbers, resulting in lower yield and 1000-seed weights. In contrast, McWillimas et al. [73], Desclaux et al. [74], Clemente et al. [75], and Xiong et al. [11] reported that if soybeans are under severe temperature and soil water stress conditions, seed size will decrease. In our study, we noted a trend towards the reduction in size as irrigation was delayed, but it was not of statistical significance at level of 5%.

5. Conclusions

Overall, no yield differences between FW and SW determinate MG 5 soybean genotypes under delayed irrigation were observed in the current study. As irrigation was further delayed, higher wilting severity occurred as soil water content is lower. Moreover, delaying irrigation until the R4 stage led to a reduction in seed yield. However, delaying irrigation did not affect maturity, protein, oil concentration, and 100-weight under our experimental conditions. Allowable depletions measured in this study indicated that both fast and slow wilting soybean genotype determinate MG5 can tolerate high allowable depletion up to 90% with no significant yield penalty at R3 stage in silt loam soil. The study suggests that even if high water deficits are experienced at early stages from delayed or inadequate irrigation that yields will likely not be significantly reduced in a furrow irrigation production system for soybean in silt loam. A deficit irrigation which is a water-saving irrigation strategy without compromising seed yield, could be implemented for farmers in the Mid-South as a result of a groundwater shortage.