1. Introduction
Soybean (
Glycine max [L.] Merr.) is an important crop for food, protein, and oil. In Japan, soybean is used in traditional foods such as tofu, natto, and miso. However, the soybean yield in Japan is much lower than that in the other major producing countries such as the USA, Brazil, and China, and has stagnated over the past 30 years; the average (1982–2012) soybean yield is only 1.65 t ha
−1 and has increased by less than 0.3 t ha
−1 since 1982 [
1]. Therefore, to increase and stabilize soybean yields, a strategy is needed that includes both practical agronomic management and genetics.
The optimization of sowing dates is the most important and least expensive agronomic practice that affects soybean yield [
2]. Several studies show that recent yield increases in the USA were achieved by optimizing the sowing date with the cultivar maturity group (MG) [
3,
4]. In the Tohoku region of northern Japan, paddy rice monoculture is the dominant crop system, and the optimal transplanting time for paddy rice is mid-May to late May. The currently recommended sowing window for soybeans is late May to early June after the rice transplanting. Recently, the population of farmers in Japan has declined. Consequently, the scale at which farmers must operate has expanded [
1]. Therefore, to avoid the increase in competition associated with rice transplanting and soybean sowing for large-scale production, soybean farmers are using a relatively longer period for sowing in the Tohoku region [
5]. Reductions in soybean yields with delayed sowing are well documented in the Tohoku region, and the simple explanations for the yield reductions are a shorter period from sowing to maturity and cooler temperatures during the reproductive stage [
6].
The effects of sowing date on soybeans depend on the meteorological conditions at the site, particularly the amount and the timing of precipitation [
7,
8]. Waterlogging is a serious problem in soybean production in converted paddy fields, which sometimes have poor drainage and a high water table in Japan [
9]. The rainy season is June to July in the Tohoku region, so waterlogging occurs during the early vegetative stage. Excess soil water during the vegetative stage inhibits root growth of soybean. Plants with poor root systems could not absorb soil water and then seed yield decreased with water deficits in the summer [
10]. Recently, at the Tohoku Agricultural Research Center (TARC) in Akita Prefecture, Japan, we used the FAO 56 evapotranspiration model [
11] in a long-term continuous performance test and analyzed the correlations between soybean yield and soil volumetric moisture content (SMC) [
12]. We found that seed yield over 33 years was significantly negatively correlated with average SMC during August. This result provided evidence that soybean yield decreased in drier years with lower SMC during August.
Yield potential is defined as the maximum yield achieved when a crop is grown in the absence of water and nutrient limitations and of damage by insects and pests [
13]. It is parameterized by different efficiencies in the following equation suggested by Monteith [
14]:
where incident radiation (IR) is the total incident solar radiation during the growing season; radiation interception efficiency (RIE) is determined by the rate of development and the duration of canopy coverage; radiation use efficiency (RUE) is determined by the amount of solar radiation that is transformed into biomass; the harvest index (HI) is determined by the amount of biomass allocated to vegetative versus reproductive organs. This equation can provide insight into how practical agronomic management and genetics determine the yield potential.
In the Tohoku region, late sowing may shorten the growth period from sowing to maturity and thereby may decrease the cumulative solar radiation (CumIR) intercepted. Moreover, it may affect RIE by affecting the development of the canopy and leaf expansion, which depend on climate factors such as temperature, precipitation, and soil water content [
15,
16]. Although RUE is constant for a given crop species in a given environment, large variability in RUE under optimal conditions has been reported in soybean [
17]. The HI also tends to be constant and genotype-dependent; however, some studies show that sowing dates affect biomass partitioning into seed yield [
18,
19]. Despite many previous investigations of the effect of late sowing on soybean yield in the Tohoku region [
6], the importance of radiation interception and use in explaining the lower yields associated with late planting has not been determined. In the USA, yield reductions with late plantings are partially explained by reductions in CumIR around flowering and early pod set [
20,
21].
In this study, we tested two hypotheses: (1) The yield reduction with late sowing date is explained by the reduction in CumIR during reproductive stages in this region; (2) the yield variations among the combinations of years, sowing dates, and cultivars are explained by variations in SMC. To test the hypotheses, we compared the changes in CumIR and RUE from flowering to late reproductive stages and in HI, yield, and yield components among three leading soybean cultivars between normal (early June) and late (late June) sowing in two consecutive years. Furthermore, we analyzed the effects of temperature and SMC on yields, yield components, and growth parameters of the three cultivars. Our results supported the first hypothesis but did not support the second hypothesis.
2. Materials and Methods
The experiments were conducted at the National Agriculture and Food Research Organization (NARO), TARC in Morioka, Iwate Prefecture, Japan (39°44’ N, 141°7’ E), in 2015 and 2016. In each year, different converted paddy fields were used. The soil characteristics of the two fields are summarized in
Table S1. The soil in each field is an Andosol. Each field received 60 g m
−2 fused phosphate fertilizer and 100 g m
−2 magnesium lime a month before sowing in each year. In addition, the fields received 3 g m
−2 N, 12.5 g m
−2 P (P
2O
5 equivalent), and 5 g m
−2 K (K
2O equivalent) in the form of a compound fertilizer one day before sowing each year. Fertilizers were applied and incorporated to a depth of approximately 14 cm by using rotary tillers.
We used three determinate soybean cultivars: Ryuhou, Enrei, and Satonohohoemi. Within the Tohoku region, Ryuhou is widely grown in Akita and Iwate prefectures, and Enrei is widely grown in Yamagata Prefecture [
22,
23]. Satonohohoemi is a new cultivar developed at NARO TARC that was released as a newly recommended cultivar for Yamagata Prefecture in 2009 [
22]. Ryuhou is a mid-season maturing cultivar and Enrei and Satonohohoemi are late-season maturing cultivars according to flowering dates at NARO TARC [
22,
23]. All three cultivars are equivalent to MG IV. Plants were grown to maturity. Seeds were treated with a combined insecticide and fungicide (CruiserMaxx, Syngenta Co., Tokyo, Japan) at the manufacturer’s recommended dose and then sown by hand at three seeds per hill on 5 June (normal sowing) and 25 June (late sowing) in 2015 and on 6 June (normal sowing) and 27 June (late sowing) in 2016. The planting density was 28.5 seeds m
−2 (9.5 hills m
−2) with 0.15 m between hills and 0.70 m between rows. This density corresponded to the recommended local planting densities in the Tohoku region (seven to 15 hills m
−2). Plants were thinned to one per hill after establishment. A pre-emergence herbicide (Ecotop, Maruwa Biochemical Co., Tokyo, Japan), which contained 1.0% dimethenamide-p and 1.4% linuron, was applied immediately after sowing for weed control at the manufacturer’s recommended dose. A month after sowing, the area between the rows was tilled, and ridging was used to control weeds and lodging. In each year, the experimental design was a split-plot arrangement; the main plot was sowing date, and the subplot was cultivar, with three replicates. A subplot consisted of seven rows, each 4.5 m in length.
We surveyed the phenology of 10 plants in the center row of each plot at intervals of two or three days and recorded the dates of growth stages based on the staging system of Fehr and Caviness [
24]: VE, emergence; R2, full flowering; R5, the beginning of seed filling; R7, the beginning of maturity. We determined the dry weight of aboveground parts per m
2 by periodic sampling at R2, R5, and 20 days after R5. The aboveground parts of 0.84 m
2 of the center four rows were harvested on each date. Their dry weights were measured after oven drying at 80 °C for three days. The differences in the dry weights of aboveground parts (increase in aboveground biomass—ΔAGB) between R2 and R5 and between R5 and R5 + 20 days were calculated. We estimated the intercepted solar radiation by using digital imaging techniques (GACS1; Kimura Ouyou-Kougei Co., Ltd., Saitama, Japan) according to Kumagai [
8]: The fractional canopy cover was determined from digital images taken above the canopy at one-week intervals from the early vegetative to late reproductive stages. In each year, total daily solar radiation (MJ m
−2 day
−1) was measured by a pyranometer (CMP11, Campbell Scientific Inc., Logan, UT, USA) and recorded by a data logger (CR1000, Campbell Scientific) at a weather station near the study field, and daily incident radiation above the canopy was computed as the product of daily solar radiation and the fractional canopy cover. The CumIR from R2 to R5 and from R5 to R5 + 20 days was computed by summing the values of daily incident radiation above the canopy. The mean radiation use efficiency (RUE) for biomass production was determined by dividing the ΔAGB by the CumIR.
At maturity, we manually harvested the aboveground parts from 4.62 m2 of the center four rows of each plot. After the plants were completely air-dried, we removed the few remaining leaves and petioles and then weighed the remaining aboveground parts. The aboveground biomass equaled the sum of the weights of the stems, pod shells, and seeds. The seed yield per m2 was adjusted to 15% moisture content, and the harvest index (HI) was determined as the seed yield divided by the aboveground biomass. The yield components (numbers of nodes, pods, seeds per pod, pods per node, and 100-seed weight) were estimated from five representative plants in each plot. The 100-seed weight was also adjusted to 15% moisture.
In addition to solar radiation, daily means of temperature and precipitation during the growing season were recorded at the nearby weather station. The soil volumetric moisture content (SMC, mm3 mm−3) was monitored with time-domain transmission (TDR) sensors (CS625; Campbell Scientific) at approximately seven cm below the soil surface (the midpoint depth of the plow layer) in a plot with the normal sowing date at the two sites. SMC was measured and recorded every 30 min on a data logger (CR200X; Campbell Scientific).
At the end of June (approximately three weeks after sowing in the plot with the normal sowing date), we sampled the soil of the plow layer in 100 mL sampling tubes. The SMC of the samples at field capacity (θ
FC, pF = 1.5) was determined by the sand column method (DIK-3521; Daiki Rika Kogyo Co., Ltd., Saitama, Japan) and that at a permanent wilting point (θ
WP, pF = 4.2) by a dew point potentiometer (WP4C; Meter Group, Inc., Pullman, WA, USA). Because θ
FC and θ
WP differed somewhat between the two years (
Table S1), the fraction of available soil water (FASW) was calculated as (SMC − θ
WP)/(θ
FC − θ
WP) in each year.
We first conducted ANOVA with a split-split-plot design, where treating year as the main factor, sowing date as the split factor, and cultivar as the split-split factor. Year, sowing date, cultivar, and their interaction were each considered as fixed effects, and replicate (block) was considered as a random effect. When the ANOVA produced a significant result (p < 0.05), Fisher’s LSD test was used to detect significant differences between means. We used multiple regression analysis to examine the relative importance of yield components in the two years of yield data. We used the natural logarithm transformation because the relationships between yield and its components were multiplicative. We also examined the relations between yield and yield components and between temperature and FASW during the reproductive stage by simple correlation (Pearson’s) and partial correlation analyses. All procedures were performed in SPSS v. 23.0 software (IBM, Tokyo, Japan).