Field experiments were carried out for 3 years to assess the effect of using drip irrigation on the growth and yield of two Japanese soybean cultivars in Kagawa Prefecture, which has the second-lowest rainfall in Japan. The treatments were drip irrigation (Drip) and rainfed (Rainfed) from the blooming stage to the full-seed stage. The largest seed yield was achieved in 2017, followed by 2018 and 2016. This order corresponded to the total water input (TWI, the sum of effective rainfall and irrigation) throughout cultivation. TWI was the main factor affecting the variation of yield and its components among years. Similarly, the higher TWI in Drip than in Rainfed contributed to the higher yield in Drip than in Rainfed within each year. ANOVA detected a significant effect of drip irrigation on total seed yield, above-ground dry matter (AGDM) at maturity, and numbers of branches, nodes, and fertile pods. AGDM had a significant correlation with the mean crop growth rate (CGR) during the treatment, and CGR was closely correlated with the mean net assimilation rate (NAR). Significant positive correlation among NAR, radiation use efficiency, and leaf water potential suggested that drip irrigation prevented the decrease of plant water status that contributed to maintain dry matter production. The advantage of using drip irrigation for soybean cultivation at the experiment site would be suppressing the decrease in yield in years with low rainfall rather than achieving higher yield than standard in years with normal or high rainfall. Abbreviations: AGDM, aboveground dry matter; CGR, crop growth rate; CISR, cumulative intercepted solar radiation; DAS, days after sowing; ETa, actual evapotranspiration; ETr, reference evapotranspiration; FIPAR, fraction of intercepted photosynthetically active radiation; LAI, leaf area index; ΨL, leaf water potential; mLAI, mean leaf area index; NAR, net assimilation rate; PAR, photosynthetically active radiation; PCC, percent of canopy coverage; RUE, radiation use efficiency; TWI, total water input
Keywords: Aboveground dry matter; drip irrigation; evapotranspiration; leaf area index; radiation use efficiency; seed yield; soybean
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Recent soybean yield in Japan has been 1636 kg ha
Among various irrigation methods, drip irrigation enables a more effective water supply to plants compared with other irrigation systems because of the ease of controlling watering (Dasberg & Or, [
The standard spatial arrangement of soybean plants in Japan is 10 to 20 plants m
Irrigation in areas where water is deficient has been reported to significantly increase seed yield in soybean (Garcia Y Garcia et al., [
Consequently, this study aimed to assess whether using drip irrigation on soybean cultivation in Kagawa Prefecture increases the growth and yield of the Japanese determinate soybean cultivars Hatsusayaka and Sachiyutaka. Because drought stress during the reproductive stages severely decreases the yield of soybean (Çigdem et al., [
Three field experiments were conducted at the Faculty of Agriculture, Kagawa University (34°16ʹN, 134°7ʹE), in 2016, 2017, and 2018. The experimental field had a well-drained loam soil. Four ridges (1.8 m by 20 m) in 2016 and three ridges (1.8 m by 21 m) in 2017 and 2018 were established with 1 m distance between ridges. The experiment was arranged in a split-plot design with irrigation treatments as the main plot and cultivars as the subplot with four replications in 2016 and three replications in 2017 and 2018. The plot area was 1.8 × 5.0 m (2016) and 1.8 × 5.5 m (2017 and 2018) with six rows separated by 0.3 m running from north to south. Determinate type soybean cultivars [Glycine max (L.) Merr., cv. Hatsusayaka and Sachiyutaka] were sown on 12 July 2016, 11 July 2017, and 10 July 2018 at 0.09 m between seeds using a hand-powered sowing machine (HS-120; Mukai Kogyo, Inc., Osaka, Japan). Seedlings were thinned at the first-node stage (V1 stage) to a spacing of 0.18 m between plants (18.5 plants m
Treatments were drip irrigation (hereafter, Drip) and rainfed (hereafter, Rainfed) during stages R1 (14 August for all years) to R6 (27 September 2016, 19 September 2017 and 2018). Three drip tubes with emitters every 0.3 m (Dripnet PC 12, Netafim Japan Ltd, Tokyo, Japan) were installed between the rows at 0.6 m intervals for each ridge. The daily irrigation amount was determined based on the daily reference evapotranspiration (ETr) and rainfall over the past few days. Irrigation was suspended on rainy days and for several days after heavy rain. Actual evapotranspiration (ETa) was estimated as the product of ETr and a crop coefficient (Kc). ETr was measured using an atmometer (ETgage, model A, ETgage Company, Colorado, USA) installed in the experimental field. The ETgage with No. 54 canvas cover estimates the evapotranspiration of green well-irrigated alfalfa with 75% canopy coverage. We determined Kc from emergence to R1 stage based on the percent of canopy coverage (PCC) according to the ETgage product manual (Complete Model A manual, www.etgage.com), i.e. Kc = 0.3 if PCC<10%; Kc = 0.5 if 10%≤PCC<50%; Kc = 0.8 if 50%≤PCC<75%, Kc = 1.0 if 75%≤PCC. The closed linear relationship between PCC and the fraction of photosynthetically active radiation intercepted by the plant canopy (FIPAR) was used to convert FIPAR to PCC. The measurement of FIPAR will be described later. The linear regression equation (PCC = −7.36 + 1.08× FIPAR) was derived from our previous data set of soybean Sachiyutaka canopy coverage estimated by digital image as Purcell ([
Daily maximum and minimum air temperature, daily total solar radiation and daily precipitation were measured at the meteorological station of the Faculty of Agriculture, Kagawa University, which is located adjacent to the experimental field. We assumed effective rainfall (effective rainfall ≥ 5 mm d
Leaf water potential (Ψ
Above-ground material from four plants (before the start of irrigation treatment) or three plants (after the start of irrigation treatment) per plot were collected at 2-week intervals: at 15, 29, 44, 58, 72, and 86 days after sowing (DAS) in 2016, at 15, 29, 43, 57, 72, and 86 DAS in 2017 and at 15, 29, 48, 63, 77 and 91 DAS in 2018 (Supplemental Table 1). The event of plant correction was named as '1st sampling', '3rd sampling' according to the chronological order in each year. We did not correct plants in the border rows to avoid edge effects. We counted the number of branches [including sub-branches (second- and third-order racemes with compound leaves)], nodes and pods per plant. After that, all leaf blades were removed and spread in a single layer on a large copy stand, except for dead and yellowed parts, and images were taken with a digital camera. The leaf area per plot was measured from the digital images using image analysis software (LIA32; http://www.agr.nago yau.ac.jp/~shinkan/LIA32/). The dry weight of the plant parts was determined after oven-drying at 80°C for more than 72 h. The above-ground dry matter (AGDM, g m
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The fraction of photosynthetically active radiation (PAR) intercepted by the plant canopy (FIPAR) in each plot was measured weekly using the SunScan Canopy Analysis System (Delta-T Devices, Cambridge, UK). PAR above the canopy was measured using a sunshine sensor placed at the center of the experimental field at a point higher than the canopy, and PAR below the canopy was measured using a 1-m probe inserted into the canopy at ground level, perpendicular to the row direction. The mean of five measurements per plot was used to calculate FIPAR according to Purcell et al. ([
FIPAR = [1 − (PAR below the canopy) × (PAR above the canopy)
Daily total intercepted solar radiation was calculated based on the daily total solar radiation measured at the meteorological station multiplied by the FIPAR measured with the SunScan instrument. FIPAR for days on which no measurements were taken was estimated through linear interpolation between the two closest measurements. Cumulative intercepted solar radiation (CISR, MJ m
At maturity, 20 plants from each plot were sampled on 26 November (137 DAS) in 2016, 2 November (114 DAS) in 2017, and 31 October (114 DAS) in 2018. Yield ('fine' seed weight, adjusted to a 15% moisture content) and the yield components were obtained from the number and dry weight of the main stems, branches (including sub-branches), nodes, pods (total and fertile), and seeds. Seeds were classified into fine and nonconforming (which includes seeds that were damaged by pests and diseases, that split, or that showed other obvious defects) using a 7.3-mm sieve, and weighed. The number of fine seeds was calculated from the fine seed weight divided by the 100-seed weight. The 100-seed weight doesn't include the nonconforming seeds.
Data were analyzed by means of three-way ANOVA using the model for a split-plot design with four (2016) or three (2017 and 2018) replications to evaluate the effects of irrigation, cultivars, years, and their interaction on all measured variables except Ψ
Table 1 shows the mean monthly maximum and minimum temperature, solar radiation, and rainfall. In all years, the maximum and minimum temperatures and solar radiation in July and August were higher than the 15-year mean. The maximum temperatures and solar radiation in September of all years and October in 2016 and 2017 were lower than the 15-year mean. Rainfall in July in 2016 was lower than the 15-year mean, and those in 2017 and 2018 were higher than the 15-year mean. But the rainfall in July after sowing was 18, 60, and 28 mm for 2016, 2017 and 2018, respectively. After August, rainfall was lower than the 15-year mean in all months except September in 2016 and 2018, and higher than the 15-year mean in all months except November in 2017.
Table 1. Mean monthly maximum and minimum temperature, solar radiation, and rainfall measured at the meteorological station of the Faculty of Agriculture, Kagawa University
Year July August September October November Temp. Max 2016 31.8 (0.5)† 34.3 (1.8) *28.8 (−0.2) *24.1 (−0.7) *17.5 (−0.1) (°C) 2017 32.4 (1.1) 32.8 (0.4) 27.5 (−1.1) 20.9 (−2.1) 16.3 (−0.8) 2018 32.9 (1.6) 33.7 (1.2) 27.0 (−1.5) 23.5 (0.5) 18.5 (1.4) Temp. Min 2016 23.4 (0.3) 24.5 (0.6) *22.4 (3.7) *17.1 (1.1) *9.8 (1.9) (°C) 2017 24.3 (0.4) 24.6 (0.8) 19.0 (−0.8) 14.0 (0.4) 6.2 (−1.3) 2018 24.4 (1.3) 24.4 (0.5) 20.2 (0.4) 13.5 (−0.1) 7.3 (−0.2) Radiation 2016 20.7 (1.3) 22.3 (2.3) 12.2 (−3.7) 11.0 (−1.8) 8.7 (−0.5) (MJ m−2 d−1) 2017 19.8 (0.4) 20.4 (0.3) 13.3 (−2.6) 8.4 (−4.4) 9.4 (0.2) 2018 21.3 (1.9) 22.0 (1.9) 11.6 (−4.4) 12.8 (0.0) 10.2 (1.0) Rainfall 2016 62 (−78) 117 (−22) 244 (109) 40 (−61) 53 (−3) (mm) 2017 203 (63) 200 (61) 254 (119) 381 (281) 35 (−22) 2018 283 (143) 98 (−41) 540 (405) 40 (−61) 14 (−43)
1
2 * Maximum and minimum temperature of Takamatsu meteorological observatory were substituted for the Faculty data from September to November in 2016 due to mechanical trouble.
Table 2 shows total rainfall, amount of irrigation, ETr, ETa, and days of rainfall and irrigation during and before or after the irrigation treatment period. Before treatment, there was no irrigation event in 2017 due to high effective rainfall. Conversely, the amount of irrigation before treatment was 180 mm (2016) and 177 mm (2018) due to low effective rainfall. Effective rainfall during treatment in 2016 and 2017 was 23% and 34% lower than that in 2018. The amount of irrigation during treatment was the highest in 2016 (115 mm), followed by 2017 (99 mm) and 2018 (42 mm). After the treatment, little effective rainfall continued in 2016, whereas there was much effective rainfall in 2017 and 2018. The ETr and ETa before the treatment were the highest in 2018, followed by 2016 and 2017. However, the difference in those values between years was small. Since the PCC during the treatment period was over 75%, ETr and ETa values during treatment are the same, and there was no clear difference between ETr and ETa after the treatment. ETr and ETa after treatment was the highest in 2016, followed by 2018 and 2017. The total effective rainfall throughout cultivation was the highest in 2017 (875 mm), followed by 2018 (679 mm) and 2016 (395 mm). The rank order of the total ETa and irrigation throughout cultivation was the opposite of the order of total rainfall. Total water input throughout cultivation (TWI, the sum of effective rainfall and irrigation) in Drip were 690, 974, and 897 mm, and those in Rainfed were 575, 875, and 856 mm for 2016, 2017, 2018, respectively.
Table 2. Mean, total values and days of rainfall and irrigation, reference evapotranspiration (ETr) and estimated actual evapotranspiration (ETa) during and before or after the irrigation treatment period
Period Year Rainfall (mm)a) Irrigation (mm)b) ETr (mm) ETa (mm) No. days Total No. days Daily meanc) Total Daily mean Total Total Before 2016 4 (3) 36 (36) 19 9.5 180 6.1 200 122 2017 5 (3) 244 (239) 0 0 0 5.6 183 112 2018 3 (2) 32 (32) 22 8.0 177 7.0 246 134 During 2016 20 (11) 318 (302) 16 7.2 115 4.3 188 188 2017 8 (6) 262 (259) 17 5.8 99 4.6 170 170 2018 16 (10) 399 (391) 6 7.0 42 4.6 165 165 After 2016 24 (6) 87 (57) 0 0 0 2.3 136 121 2017 21 (13) 390 (377) 0 0 0 2.2 80 73 2018 15 (7) 274 (257) 0 0 0 3.0 112 95 Mean or Totald) 2016 48 (20) 441 (395) 35 (19) 8.4 (9.5) 295 (180) 4.2 524 431 2017 34 (22) 895 (875) 17 (0) 5.8 (0) 99 (0) 4.1 433 355 2018 34 (19) 705 (679) 28 (22) 7.8 (8.0) 218 (177) 4.8 523 394
- 3
a Numbers within parenthesis in rainfall are values of effective rainfall (rainfall ≥ 5 mm d−1 ) - 4
b Numbers within parenthesis in irrigation are values of the rainfed treatment. - 5
c Daily means of irrigation did not include days with no case. - 6
d Mean or total throughout the cultivation period.
Figure 1 shows accumulated ETa, irrigation, and rainfall after sowing. Accumulated rainfall in 2016 was low until late August (47 DAS), and it increased higher than the accumulated ETa in late September (70 DAS). The increase in accumulated rainfall after late September was small, so the total effective rainfall throughout cultivation in 2016 was the lowest among the 3 years. The change in accumulated rainfall in 2018 was similar to that in 2016, but the accumulated rainfall in 2018 exceeded accumulated ETa after early September (56 DAS). Accumulated rainfall at the beginning of treatment in 2017 was higher than accumulated ETa. Although the gaps between accumulated rainfall and ETa in 2017 after R1 gradually narrowed due to low rainfall during the treatment period, the accumulated rainfall increased again about a week before R6 and eventually reached the maximum total rainfall in 3 years.
PHOTO (COLOR): Figure 1. Accumulated actual evapotranspiration (ETa), irrigation, effective rainfall and daily effective rainfall after sowing in 2016 (a), 2017 (b), and 2018 (c). There was no irrigation event throughout cultivation for Rainfed in 2017. The gray bar on X axis indicates the period of irrigation treatment
Table 3 shows the mean date and DAS of cultivars and treatments of reproductive stages for each year. ANOVA detected significant effects of year and cultivar in all stages except the effect of cultivar at R8, but the effect of irrigation was not significant in any stage (Supplemental Table 2). The mean DAS of cultivars and treatments up to R5 in 2016 slightly advanced than in 2017 and 2018. But the mean DAS after R6 in 2016 was delayed up to 6 days (R6) and 24 days (R8) than in 2017 and 2018.The date to reach each stage in Sachiyutaka advanced approximately one day than in Hatsusayaka.
Table 3. Mean date and days after sowing (DAS) of R1, R3, R5, R6, and R8 stages of soybean grown under drip irrigation and a rainfed condition in 2016, 2017, and 2018
Year R1 R3 R5 R6 R8 Date DAS Date DAS Date DAS Date DAS Date DAS 2016 8/14 33 8/24 43 8/30 49 9/26 76 11/26 137 2017 8/13 33 8/25 44 8/31 51 9/19 70 11/2 114 2018 8/13 34 8/23 44 9/1 53 9/19 71 10/31 113
Table 4 shows fine and total seed yield, 100-seed weight, AGDM, and harvest index. Fine seed yield ranged from 217 to 518 g m
Table 4. Fine seed yield, total seed yield, 100-seed weight, aboveground dry matter (AGDM), and harvest index (total seed yield/AGDM) of soybean grown under drip irrigation and a rainfed condition during the reproductive stages (from R1 to R6) in 2016, 2017, and 2018
Year Cultivara) Fine seed yield (g m−2) Total seed yield (g m−2) 100-seed weight (g) AGDM (g m−2) Harvest indexb) (%) Drip Rainfed Drip Rainfed Drip Rainfed Drip Rainfed Drip Rainfed 2016 Hats 354 217 445 327 37.5 37.7 868 754 37.5 25.9 Sach 457 382 538 463 41.8 41.8 976 846 42.5 41.3 2017 Hats 484 433 715 548 33.9 34.7 1190 909 37.4 44.1 Sach 505 518 655 640 35.5 36.3 1062 1032 43.5 46.0 2018 Hats 314 360 466 476 34.5 32.7 879 784 33.7 43.8 Sach 499 477 585 553 36.9 38.4 897 845 51.1 50.5 Mean 2016 353 443 39.7 861 36.8 2017 485 639 35.1 1049 42.8 2018 412 520 35.6 851 44.8 Hats 353 485 35.4 889 36.5 Sach 468 565 38.8 940 45.4 Drip 436 560 37.0 973 40.9 Rainfed 388 491 37.2 856 41.1 ANOVA Year (Y) ** *** *** *** ** Cult. (C) *** * *** ns *** Irrig. (I) ns * ns ** ns Y × C ns ns *** ns ns Y × I ns ns ns ns * C × I ns ns * ns ns Y × C × I ns ns ** ns ns
- 7 Fine seed yield and 100-seed weight were adjusted 15% w/w moisture.
- 8 Values are means of four replication plots (2016) and three replication plots (2017, 2018).
- 9 *p < 0.05, ** p < 0.01, *** p < 0.001, ns: not significant.
- 10
a Hats: Hatsusayaka, Sach: Sachiyutaka,b Harvest index = total seed yield/AGDM.
Table 5 shows number of fine seeds and its components associated with the numbers (i.e. numbers of seeds, pods, nodes, and branches). The number of fine seeds ranged from 573 to 1427 seed m
Table 5. Number of branches, nodes, total pods, fertile pods, seeds per square meter, and number of seeds per pod at maturity of soybean grown under drip irrigation and a rainfed condition during the reproductive stages (from R1 to R6) in 2016, 2017, and 2018
Year Cultivara) Branch no. (m−2) Node no. (m−2) Total pod no. (m−2) Fertile pods no. (m−2) Fine seed no. (m−2) Seed no. per pod Drip Rainfed Drip Rainfed Drip Rainfed Drip Rainfed Drip Rainfed Drip Rainfed 2016 Hats 173 163 758 734 1275 1299 941 828 944 573 1.00 0.68 Sach 129 126 726 657 1393 1324 969 879 1092 914 1.11 1.04 2017 Hats 189 165 771 669 1971 1714 1678 1387 1427 1248 0.85 0.91 Sach 159 154 687 648 1628 1514 1402 1306 1424 1426 1.01 1.09 2018 Hats 171 164 752 677 1455 1245 1105 1048 911 1099 0.82 1.05 Sach 154 145 640 596 1190 1177 1019 944 1349 1239 1.33 1.29 Mean 2016 148 719 1323 904 881 0.96 2017 167 694 1707 1443 1381 0.97 2018 159 666 1267 1029 1150 1.12 Hats 171 729 1472 1137 1006 0.88 Sach 143 662 1370 1070 1217 1.14 Drip 161 724 1470 1163 1174 1.02 Rainfed 152 667 1372 1044 1049 0.99 ANOVA Year (Y) *** ** * *** *** * Cult. (C) *** *** ns ns * *** Irrig. (I) ** *** ns * ns ns Y × C ns ns ns ns ns ns Y × I ns ns ns ns ns * C × I ns ns ns ns ns ns Y × C × I ns ns ns ns ns ns
- 11 Values are means of four replication plots (2016) and three replication plots (2017, 2018).
- 12 *p < 0.05, ** p < 0.01, *** p < 0.001, ns: not significant.
- 13
a Hats: Hatsusayaka, Sach: Sachiyutaka.
Figure 2 shows the changes in the number of nodes, branches, and pods. ANOVA detected a significant effect of irrigation on all variables, in some sampling mainly in the last half of growth (Supplemental Table 3). The number of those variables in Drip tended to be larger than in Rainfed.
Graph: Figure 2. Changes in the number of pods (a, b, c), the number of nodes (d, e, f), and the number of branches (g, h, i) of the two soybean cultivars Hatsusayaka (H) and Sachiyutaka (S) grown under drip irrigation (D) and a rainfed (R) condition in 2016, 2017, and 2018. Values are means ± S.E. (n = 4, 2016; n = 3, 2017 and 2018). The gray bar on X axis indicates the period of irrigation treatment
Figure 3 shows the changes of AGDM and LAI. AGDM and LAI in Drip tended to be higher than in Rainfed throughout cultivation for both cultivars for all years. ANOVA detected a significant effect of irrigation on AGDM and LAI from 3rd to 6th sampling (Supplemental Table 4). Significant effect of cultivar and year were detected more in LAI compared to those in AGDM. The significant year × irrigation interaction at 4th sampling in both AGDM and LAI was owing to that AGDM and LAI in Drip were obviously higher than in Rainfed in 2016 and 2017, while those difference between irrigation was not clear in 2018.
Graph: Figure 3. Changes in AGDM (a, b, c) and LAI (d, e, f) of the two soybean cultivars Hatsusayaka (H) and Sachiyutaka (S) grown under drip irrigation (D) and a rainfed (R) condition in 2016, 2017, and 2018. Values are means ± S.E. (n = 4, 2016; n = 3, 2017 and 2018). The gray bar on X axis indicates the period of irrigation treatment
Figure 4 shows the changes in CGR, NAR, and mLAI. The maximum CGR across cultivars, irrigations, and years ranged from 24.9 to 45.5 g m
Graph: Figure 4. Changes in CGR (a, b, c), NAR (d, e, f), and mLAI (g, h, i) of the two soybean cultivars Hatsusayaka (H) and Sachiyutaka (S) grown under drip irrigation (D) and a rainfed (R) condition in 2016, 2017, and 2018. Values are means ± S.E. (n = 4, 2016; n = 3, 2017 and 2018). The gray bar on X axis indicates the period of irrigation treatment
There were highly significant correlation between CGR and NAR in any period, whereas correlation between CGR and mLAI were significant in 1st and 4th period (Supplemental Table 6).
Table 6 shows the RUE and CISR calculated from the dataset during the 1st sampling to the sampling on 86 DAS in 2016 and 2017, and 91 DAS in 2018. RUE across cultivars, irrigations, and years ranged from 1.01 to 1.96 g MJ
Table 6. Radiation use efficiency (RUE) and cumulative intercepted solar radiation (CISR) of soybean grown under drip irrigation and a rainfed condition during the reproductive stages (from R1 to R6) in 2016, 2017, and 2018. RUE and CISR were calculated from the dataset during the 1st sampling to the sampling on 86 DAS in 2016 and 2017, and 91 DAS in 2018
Year Cultivar RUE (g MJ −1) CISR (MJ m−2) Drip Rainfed Drip Rainfed 2016 Hatsusayaka 1.39 1.01 1083 1067 Sachiyutaka 1.34 1.13 1102 1087 2017 Hatsusayaka 1.96 1.35 1124 1116 Sachiyutaka 1.56 1.18 1120 1122 2018 Hatsusayaka 1.42 1.10 1145 1098 Sachiyutaka 1.19 1.13 1114 1116 Mean 2016 1.22 1085 2017 1.51 1120 2018 1.21 1118 Hatsusayaka 1.35 1103 Sachiyutaka 1.25 1108 Drip 1.47 1112 Rainfed 1.14 1098 ANOVA Year (Y) *** *** Cult. (C) ns ns Irrig. (I) *** ns Y × C ns ns Y × I ns ns C × I ns ns Y × C × I ns ns
- 14 Values are means of four replication plots (2016) and three replication plots (2017, 2018).
- 15 *** p < 0.001, ns: not significant.
Figure 5 shows the changes of Ψ
Graph: Figure 5. Changes in ΨL (a, b, c), and SPAD values (d, e, f) of the two soybean cultivars Hatsusayaka (H) and Sachiyutaka (S) grown under drip irrigation (D) and a rainfed (R) condition in 2016, 2017, and 2018. 'C' and 'I': Significant effect of cultivar and irrigation, respectively by two-way ANOVA. Values are means ± S.E. (n = 4, 2016; n = 3, 2017 and 2018). The gray bar on X axis indicates the period of irrigation treatment
The change of SPAD values was similar in the first half of growth in all 3 years, whereas SPAD values at the last sampling in 2016 were about 23 and 20% higher than in 2017 and 2018, respectively. The higher SPAD value in 2016 may be due to the occurrence of delayed leaf senescence. Two-way ANOVA detected significant effect of cultivars on SPAD values in many measurement, whereas the effect of irrigation was only significant at 4th measurement in 2017. Hatsusayaka had higher SPAD values than in Sachiyutaka.
We performed correlation analysis to identify the factors responsible for the variation in yield and its components among years, cultivars and irrigation treatments. Total water input had significant positive correlations with total seed yield, AGDM, harvest index, numbers of fine seeds, fertile pods, and branch pod ratio (Figure 6).
PHOTO (COLOR): Figure 6. Relationships between total water input and (a) total seed yield, (b) AGDM, (c) harvest index, (d) number of fertile seed, (e) number of fertile pods, and (f) branch pod ratio of the two soybean cultivars Hatsusayaka and Sachiyutaka grown under drip irrigation and a rainfed condition in 2016, 2017, and 2018. Values are means ± S.E. (n = 4, 2016; n = 3, 2017 and 2018). r: correlation coefficient, *p < 0.05, **p < 0.01
Table 7 shows correlation coefficients among total seed yield, its components, RUE, CISR, the mean of growth parameters (CGR, NAR, and mLAI) during treatment, and the mean of Ψ
Table 7. Correlation coefficients among total seed yield and its components, growth parameters and physiological variables associated with dry matter production of soybean grown under drip irrigation and a rainfed condition during the reproductive stages (from R1 to R6) in 2016, 2017, and 2018. Correlation analysis was performed for pooled data of two cultivars
Variables AGDM HI CGRa) NARb) mLAIa) ΨLb) SPADb) RUEc) CISRc) Total seed yield 0.888*** 0.768** 0.745** 0.687* 0.616* 0.806** 0.236 0.649* 0.659* AGDM 0.392 0.916*** 0.824*** 0.725** 0.776** 0.209 0.836*** 0.542 HI 0.228 0.233 0.251 0.523 0.171 0.163 0.593* CGRa) 0.958*** 0.779** 0.809** −0.030 0.868*** 0.477 NARa) 0.644* 0.818** −0.038 0.711** 0.385 mLAIa) 0.736** −0.423 0.907*** 0.696* ΨLb) −0.217 0.694* 0.595* SPADb) −0.210 −0.081 RUEc) 0.494
- 16 *p < 0.05, ** p < 0.01, *** p < 0.001.
- 17
a parameter means of period 2 to 4 (29 − 72 DAS in 2016; 29 − 72 DAS in 2017, and 29 − 77 DAS in 2018). - 18
b means of the measurements performed during 38 − 80 DAS in 2016, 37 − 80 DAS in 2017, and 43 − 84 DAS in 2018. - 19
c calculated from the dataset during the 1st sampling to the sampling on 86 DAS in 2016 and 2017, and 91 DAS in 2018.
Next, we performed correlation analysis among fine seed yield and its components (Table 8). Find seed yield was closely correlated with number of seeds but not with 100-seed weight. Number of fine seed had significant correlations with number of fertile pod and branch pod ratio, but not with numbers of branches and pods.
Table 8. Correlation coefficients among fine seed yield and its components of soybean grown under drip irrigation and a rainfed condition during the reproductive stages (from R1 to R6) in 2016, 2017, and 2018. Correlation analysis was performed for pooled data of two cultivars
Variables Fine seed no. 100 seed weight Branch no. Node no. Pod no. Fertile pod no. Branch pod ratioa) Fine seed yield 0.947*** 0.002 −0.162 −0.454 0.321 0.546 0.861*** Fine seed no. −0.317 0.119 −0.360 0.462 0.728** 0.877*** 100 seed weight −0.830*** −0.178 −0.411 −0.599* −0.205 Branch no. 0.551 0.494 0.558 0.064 Node no. 0.410 0.192 −0.103 Pod no. 0.919*** 0.453 Fertile pod no. 0.645*
- 20 *p < 0.05, ** p < 0.01, *** p < 0.001.
- 21
a Branch pod ratio = number of pod in branch (m−2 )/total pod number (m−2 ).
Accumulated ETa of soybean in Drip in this study ranged 355 to 431 mm, which is lower than the values under various irrigation regimes (452 to 601 mm) in Nebraska (Irmak et al., [
Because seasonal rainfall deficits and their occurrence times during the growing season account for much of the annual variation in soybean yield (Heatherly, [
ANOVA revealed a significant effect of drip irrigation on total seed yield, AGDM at maturity, RUE, and numbers of branches, nodes, and fertile pods (Table 4, 5, 6). Because seed yield is the product of AGDM at maturity and harvest index, the more closed linear relationship between total seed yield and AGDM rather than between total seed yield and harvest index indicated the greater contribution of AGDM to seed yield (Table 7). AGDM has a significant correlation with the means of growth parameters (CGR, NAR, and mLAI) during the treatment period, and CGR correlated with NAR more closely than with mLAI. Highly significant correlation coefficients among NAR, RUE, and Ψ
On the other hand, the variation of fine seed yield were analyzed by correlation among yield components associated with the numbers (i.e. numbers of seeds, pods, nodes, and branches) (Table 8). The results revealed a significant correlation among fine seed yield, number of fine seeds, fertile pods and branch pod ratio. Although ANOVA detected a significant effect of drip irrigation on the number of branches, nodes, and fertile pods, only the number of fertile pods had a significant correlation with the number of fine seed. The significant increase of the number of branch and node by drip irrigation would affect the number of fine seed indirectly through the increase in branch pod ratio. Namely, the significant correlations between branch pod ratio and numbers of fine seeds and fertile pods suggested that an increase of branch growth by drip irrigation contributed to an increase in the number of pod in branch, which increased number of fine seed in Drip. This result consistent with Frederick et al. ([
AGDM and LAI in Drip tended to be higher than in Rainfed throughout cultivation for both cultivars for all years. Although considerably high AGDM and LAI were observed in Drip in 2017, AGDM and LAI in 2016 and 2018, and those in Rainfed in 2017 were within the range of our previous study (Maitree & Toyota, [
ANOVA showed the significant effect of irrigation on CGR and NAR in the 2nd period (Supplemental Table 5). The highly significant correlation between CGR and NAR in the 2nd period (Supplemental Table 6) suggested that large contribution of NAR to CGR. Although ANOVA showed significant effect of irrigation on mLAI from 2nd to 5th period (Supplemental Table 5), correlation between CGR and mLAI were only significant in 1st and 4th period. The significant correlation between Ψ
Previous studies have reported that a decrease in RUE during drought was caused by a decline in canopy photosynthesis as a consequence of senescence due to water stress (Jefferies & Mackerron, [
Garcia Y Garcia et al. ([
Total rainfall during the same growth season with this study (sown on July 10 and harvested on November 10) at the Faculty of Agriculture, Kagawa University from 2001 to 2019 were varied between 112 mm (2012) and 903 mm (2017), with a mean of 507 mm. The 95% confidence interval for the 19-year mean was 393 mm (lower) and 622 mm (upper). Total rainfall in 2017 was the highest among 19-year mean and that in 2018 was higher than the upper 95% confidence interval. Total rainfall in 2016 was lower than 19-year mean but within the 95% confidence interval. There were 6 years during 19 years when total rainfall during the soybean cultivation season fell below the 95% confidence interval of 19-year mean.
The results of this study indicated that the use of drip irrigation suppresses the decrease of yield in years with rainfall as low as 2016. Therefore, the probability of avoiding a decrease in soybean yield in a drought year by using drip irrigation will be about once every 3 years. The probability will increase about once every 1.7 years (≒ 2 years) if we expect a significant effect of using drip irrigation in a year when rainfall is lower than 19-year mean. More information is needed to estimate the threshold rainfall, which is expected to have a significant effect of using drip irrigation in this region.
Total water input was the main factor affecting the variation of yield and its components among years. Similarly, the higher total water input in Drip than in Rainfed contributed to the higher yield in Drip than in Rainfed within each year. ANOVA revealed a significant effect of drip irrigation on total seed yield, AGDM at maturity, RUE, and numbers of branches, nodes, and fertile pods. AGDM has a significant correlation with the means of growth parameters (CGR, NAR, and mLAI) during treatment period, and CGR correlated with NAR more closely than with mLAI. A highly significant correlation among NAR, RUE, and Ψ
From the results, the advantage of using drip irrigation for soybean cultivation at the experiment site would be suppressing the decrease in yield in years with low rainfall rather than achieving higher yield than standard in years with normal or high rainfall. The probability of avoiding a decrease in soybean yield in a drought year by using drip irrigation will be about once every 3 years, or about once every 1.7 years, depending on the level of rainfall judged to be effective. More information is needed to estimate the threshold rainfall, which is expected to have a significant effect on using drip irrigation in this region.
No potential conflict of interest was reported by the authors.
Supplemental data for this article can be accessed https://doi.org/10.1080/1343943X.2021.1893607.
Correction Statement
This article has been republished with minor changes. These changes do not impact the academic content of the article.
By Kanchana Chomsang; Masahiro Morokuma; Sakae Agarie and Masanori Toyota
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