*Article* **Effect of Maize (***Zeal mays***) and Soybean (***Glycine max***) Intercropping on Yield and Root Development in Xinjiang, China**

**Wenwen Wei, Tingting Liu, Lei Shen, Xiuyuan Wang, Shuai Zhang and Wei Zhang \***

College of Agriculture, Shihezi University, Shihezi 832003, China; 20212012015@stu.shzu.edu.cn (W.W.); 20202012015@stu.shzu.edu.cn (T.L.); 20192012011@stu.shzu.edu.cn (L.S.); 20192012027@stu.shzu.edu.cn (X.W.); 20212112022@stu.shzu.edu.cn (S.Z.)

**\*** Correspondence: bluesky2002040@shzu.edu.cn

**Abstract:** Intercropping is a breakthrough in land-use optimization. This work aimed to study the effects of intercropping patterns on the growth, yield, root morphological characteristics, and interspecific competition of maize and soybean, as well as provide a reference for the development of intercropping patterns of maize and soybean in Northwest China. Three different cropping patterns were designed: monocropping maize, monocropping soybean, and maize-soybean intercropping. Agronomic traits, intercropping indicators such as land equivalent ratio (LER), aggressivity (A), competition ratio (CR), and actual yield loss (AYL), as well as root morphological characteristics were assessed. The results showed that, compared with monocropping, the intercropping maize plant height increased by 6.07–8.40%, and the intercropping soybean plant height increased by 35.27–38.94%; the root length density (RLD) of intercropping maize was higher than that of monocropping maize, the RLD of intercropping soybean was lower than that of monocropping soybean, in the 0–40 cm soil layer the intercropping increased maize RLD by 1.79–7.44% while the soybean RLD was reduced by 3.06–9.46%; the aggressivity of maize was greater than 0 and the competition ratio was greater than 1, which was the dominant species; the maize/soybean land equivalent ratio was 1.18–1.26, which improved the land utilization rate. Therefore, the effect of increasing yield can be achieved by changing the maize and soybean planting method, which is beneficial to the ecological strategy of sustainable development in the northwest region.

**Keywords:** interspecific competition; land equivalent ratio; planting pattern; root length density; root morphological characteristics

#### **1. Introduction**

Ensuring food security is the foundation of economic development and social stability [1]. In the face of a growing global population, food security and food sovereignty are seriously threatened [2]. China has the largest population and is also the largest agro-based country in the world [3]. Under the enormous pressure of the increasing population, how to ensure food security is an urgent problem needing to be solved. The global spread of COVID-19 has complicated the international equilibrium of grain production and trade, is disrupting China's food security in the short term, while critical quantitative variables such as grain production and grain consumption per capita have declined. Land-saving technological progress will contribute the most to the arable land area per capita of wheat and other grains in the long run [4]. The volume of China's grain imports has increased, and the number of exports has fallen. Therefore, the yield of staple grain, oil, and protein crops must be enhanced to satisfy food demands for daily dietary energy requirements [5,6].

Intercropping is a widely used agricultural system of cultivating two or more crops simultaneously in one field during the same or part of their growing season [7]. About one third of China's arable land adopts the multi-species model and contributes half of the total

**Citation:** Wei, W.; Liu, T.; Shen, L.; Wang, X.; Zhang, S.; Zhang, W. Effect of Maize (*Zeal mays*) and Soybean (*Glycine max*) Intercropping on Yield and Root Development in Xinjiang, China. *Agriculture* **2022**, *12*, 996. https://doi.org/10.3390/ agriculture12070996

Academic Editor: Jochen Mayer

Received: 22 June 2022 Accepted: 8 July 2022 Published: 10 July 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

output of all crops, which is an important part of China's agricultural heritage [8]. Historically, intercropping has contributed greatly to crop production in Chinese agriculture [9]. Compared with monocropping cropping systems, intercropping can increase the total yield per unit of land area and can greatly promote crop production due to the more efficient use of one or more resources in time and space [10,11]. Intercropping has received increased attention in recent years due to its clear agro-ecological advantages.

Maize (*Zea mays* L.) and soybean (*Glycine max* L. Merill) are important grain and oil crops in China. Intercropping can improve the stability of farmland ecosystems while land production efficiency and has become an increasingly popular planting method [12]. Due to the low economic benefits of soybeans, China's soybean planting area has been declining year by year, and the domestic soybean market mainly relies on imports. In the past 10 years, China's soybean consumption has remained more than 80% dependent on the international market, becoming the world's largest soybean importer country [13]. Due to the limited arable land resources in China, it is impossible to significantly increase the area of arable land for soybean cultivation. Therefore, while the maize planting area continues to increase, the development of the maize/soybean compound planting model can achieve a win-win situation for maize and soybean yields. The No.1 Central Document in 2020 clearly pointed out that it is necessary to stabilize grain production and increase support for the promotion of new agronomics for maize and soybean intercropping [14]. The No. 1 Central Document in 2022 also clearly proposes to promote corn and soybean strip compound planting in Huanghuaihai, northwest and southwest regions [15]. According to the report of the Ministry of Agriculture and Rural Affairs of China, the national soybean and maize belt compound planting area reached 467,000 hm2 in 2021 and it will be attempted to increase the soybean and maize belt compound planting area by 1,000,000 hm2 in 2022 [16].

Maize/soybean intercropping has long been widely practiced in China and has played an important role in enhancing crop production and increasing the income of farmers [17,18]. At present, the research on maize/soybean intercropping mode mostly focuses on the research on population yield [19], photosynthetic physiology [20], water use efficiency [21], and nutrient use efficiency [22]. However, there are relatively few studies on the root morphology of the intercropping system under the combination of maize and soybean, especially the response relationship of root morphology under the intercropping condition is still unclear.

The goal of the present study was to simultaneously evaluate root morphology growth characteristics and yield in a maize/soybean intercropping system compared with solecropping. At present, there have been few studies on the dynamic changes and interspecific competition of maize and soybean root systems in different growth stages of intercropping, and most of the previous studies on the underground part of crops used destructive sampling. In the experiment, the root canal method was used to sample the crop roots without damage, so that we could more comprehensively understand the law of dynamic changes of the crop root system and further explore the change law of interspecific competition. The purpose of this study was to understand the following.


We hypothesized that (i) this type of intercropping system would negatively affect the root growth of crops through underground competition. However, we also hypothesized that (ii) this intercropping system would have an overall positive effect on productivity by improving the efficiency of land resource use.

#### **2. Materials and Methods**

#### *2.1. Experimental Site*

The field experiments were conducted from 2019 to 2020 at the Agricultural Research Station of Shihezi University in Xinjiang Uygur Autonomous Region, China (44◦19 N, 86◦03 E). This area has a temperate continental climate. The mean annual temperature is 8.1 ◦C, the annual sunshine duration ranges from 2418 to 2732 h, the annual rainfall

ranges from 180 to 270 mm, and the annual evaporation ranges from 1000 to 1500 mm. The soil has a sandy loam texture with a pH of 7.6, 13.260 g kg−<sup>1</sup> organic matter, 0.890 g kg−<sup>1</sup> total N, 0.023 g kg−<sup>1</sup> quick-acting phosphorus, 0.259 g kg−<sup>1</sup> quick-acting potassium, and 0.058 g kg−<sup>1</sup> alkali nitrogen.

#### *2.2. Plant Materials and Experimental Design*

Field experiments were conducted during the 2019 and 2020 cropping seasons. Maize (*Zea mays*) and soybean (*Glycine max*) were sown on the same days: 28 April 2019, and 30 April 2020. The maize sowing depth was 4 cm, and the soybean sowing depth was 3 cm; a sub-membrane drip irrigation planting method was used. Three treatments (monocropping maize, monocropping soybean, and maize intercropping with soybean) were established. There were a total of 3 plots, 3 repetitions, the test plot was 20 m long, 16 m wide, making a total of 320 m2. The maize variety was kws3654, and the soybean variety was new soybean No. 1.

In monocropping maize, the row spacing and plant spacing were 60 and 30 cm, respectively, and the planting density was 5.5 × 104 plants hm−2, whereas in monocropping soybean, the row and plant spacings were 40 and 30 cm, and the planting density was 8.3 × 104 plants hm<sup>−</sup>2. In maize intercropping with soybean, the row spacing and plant spacing of corn were 60 cm and 30 cm, and the planting density was 2.8 × <sup>10</sup><sup>4</sup> plants hm<sup>−</sup>2. the row and plant spacing of soybean was 30 cm, and the planting density was 5.5 × <sup>10</sup><sup>4</sup> plants hm<sup>−</sup>2. The experiment was carried out under field conditions, while irrigation, fertilization, and crop management were carried out according to local methods, based on fully ensuring the needs of crop growth and development.

#### *2.3. Weather Conditions*

The weather conditions during the study are shown in Figure 1. The annual precipitation was about 211 mm. The highest monthly value was recorded in May (28 mm on average), whereas the lowest values were recorded in January (9 mm) and February (9 mm). The highest average monthly temperature was recorded in July (32 ◦C), whereas the lowest was recorded in January (−21 ◦C).

**Figure 1.** Average monthly precipitation and average temperatures in Shihezi.

#### *2.4. Data Sampling*

The data were collected from 19 May 2019 and from 20 May 2020. The sampling time and the corresponding growth period of sampling are shown in Table 1.


**Table 1.** Sampling time and corresponding crop growth stage.

#### *2.5. Plant Height*

In each plot, five adjacent plants with similar growth and vigor were selected; the height from the base to the top of the plant was measured, and the average value was calculated.

#### *2.6. Chlorophyll Content (SPAD)*

In each plot, five plants with similar growth and vigor were selected, and the SPAD value of the leaves was measured by a hand-held chlorophyll analyzer SPAD-502 (Beijing, China). The 4th leaf was measured at the seedling stage of maize, the 9th leaf was measured at the jointing stage, the three ear leaves were measured after the large bell mouth stage, and the top expanded leaf was measured for soybean. The middle of the leaves was measured at each stage, avoiding the veins, and three points on each leaf were measured and the average value calculated.

#### *2.7. Root Morphological Characteristics*

A CI–600 image acquisition instrument (Shanghai, China) was used to capture root images. The embedded angle of the micro-root canal was 45◦ from the ground, as shown in Figure 2. We collected a soil sample from a depth of 0–20 cm and from a depth of 20–40 cm.

**Figure 2.** Schematic diagram of micro-root canal layout.

#### *2.8. Root Length Density*

The distribution of roots in different soil layers can be indirectly reflected by the root length density, which is given by

$$\text{RLDv} = \frac{\text{RL}}{W \times H \times D} \times \sin 45^{\circ} \tag{1}$$

where RL is the length of the thin root at the observation interface (mm), *W* is the width of the image taken by the instrument (cm), *H* is the length of the image (cm), and *D* (cm) represents the thickness of the soil layer of the observation interface (*D* = 0.2, 0.4 m). The RLD of maize and soybean were obtained according to Equation (1).

#### *2.9. Yield and Competition Index*

During the harvesting period of soybean and maize, three replicated sampling plots (1 m × 1 m area) were randomly selected from each treatment. The number of maize plants, the number of ears per plant, the number of grains per ear were counted and 1000-seed weighed. The number of soybean plants were counted, all pods per soybean harvested, and 1000-seed per pod and the harvested soybeans after drying at 70 ◦C were weighed. The theoretical yield of soybean was calculated by the actual yield of the sample plot and plot area.

The land equivalent ratio (LER) is used as an indicator of land productivity for the intensification of the evaluated alternatives [23]. If the value of LER is greater than one, the intercropping system favors the crop growth and yield of the intercropped species; if the LER value is less than one, the intercropping system reduces the growth and yield of the intercropped species. The LER was obtained as follows:

$$\text{LER} = \frac{\chi\_{mi}}{\chi\_m} + \frac{\chi\_{si}}{\chi\_s} \tag{2}$$

where *Ym* and *Ymi* are the monocropping and intercropping maize yields, respectively. *Ys* and *Ysi* are the monocropping and intercropping soybean yields, respectively. LER > 1 signifies that the intensification alternative is more productive than the sum of the sole crops of the component species.

Actual yield loss is the proportionate yield loss or gain of intercrops in comparison to the respective sole crop [24], where:

$$\text{AYL}\_{\text{m}} = \frac{\chi\_{\text{mi}}/Z\_{\text{mi}}}{\chi\_{\text{m}}/Z\_{\text{m}}} - 1, \text{AYL}\_{\text{s}} = \frac{\chi\_{\text{s}i}/Z\_{\text{s}i}}{\chi\_{\text{s}}/Z\_{\text{s}}} - 1, \text{ AYL} = \text{AYL}\_{\text{m}} + \text{AYL}\_{\text{s}}\tag{3}$$

Here, *Zm* and *Zs* represent the proportion of maize and soybean planting in monocropping, respectively, *Zmi* and *Zsi* represent the planting proportion of maize and soybean in intercropping, respectively, AYL*<sup>m</sup>* and AYL*<sup>s</sup>* represent the actual yield loss of maize and soybean in the intercropping system, respectively, and AYL represents the actual yield losses in intercropping systems. AYL > 0, indicates that the intercropping system has the advantage of intercropping, and AYL < 0, indicates that the intercropping system has no yield advantage.

Aggressivity refers to the degree to which the relative yield increase of a crop in an intercropping system is greater than the yield increase of another crop [25]. The specific calculation of the aggressivity of a crop is as follows:

$$A\_{m} = \frac{\chi\_{\rm mi}}{\chi\_{\rm m} Z\_{\rm mi}} - \frac{\chi\_{\rm si}}{\chi\_{s} Z\_{\rm si}},\ A\_{s} = \frac{\chi\_{\rm si}}{\chi\_{s} Z\_{\rm si}} - \frac{\chi\_{\rm mi}}{\chi\_{m} Z\_{\rm mi}}.\tag{4}$$

Here, *Am* and *As* represent the encroachment power of maize and soybean in the intercropping system, respectively. *Am* = 0, indicates that the two crops have the same competitiveness; *Am* > 0, indicates that the competitiveness of maize is higher than that of soybean; *As* > 0, indicates that the competitiveness of soybean is higher than that of maize.

Competitive ratio is the ability of a crop in an intercropping system to compete relative to another crop [26]. where:

$$\text{CR}\_{\text{m}} = \frac{\text{E}\_{\text{m}}}{\text{E}\_{\text{s}}} \times \frac{\text{Z}\_{\text{si}}}{\text{Z}\_{\text{mi}}}, \quad \text{CR}\_{\text{s}} = \frac{\text{E}\_{\text{s}}}{\text{E}\_{\text{m}}} \times \frac{\text{Z}\_{\text{mi}}}{\text{Z}\_{\text{si}}}. \tag{5}$$

Here, *CRm* and *CRs* represent the competition ratios of maize and soybean in the intercropping system, respectively. *CRm* > 1, indicates that maize is more competitive than soybean; *CRs* > 1, indicates that soybean is more competitive than maize.

#### *2.10. Data Analysis*

An analysis of variance was used to perform data analysis using SPSS 19.0 (SPSS Inc., Chicago, IL, USA). The average values were compared using least significant differences (LSD) at the 0.05 level. Origin 2018 (Northampton, MA, USA) was used to draw the figures.

#### **3. Results**

#### *3.1. Plant Height*

The plant heights of monocropping and intercropping maize and soybean increased with the advancement of the growth period. In the later stage of crop growth, the crop height tended to be stable, showing an overall "S"-shaped growth curve, with a "slow, fast and slow" growth trend (Figure 3).

**Figure 3.** Dynamic changes of plant height of maize and soybean in monoculture and intercropping during 2019 and 2020. Abbreviations: MM—monocropping maize, MS—monocropping soybean, IM—intercropping maize, IS—intercropping soybean. In maize, I–VI mean seedling stage, jointing stage, large bell mouth stage, silking stage, grain filling stage, maturation stage, respectively. In soybean, I–VI mean seedling stage, branching stage, flowering stage, pod setting stage, drumming stage, maturation stage, respectively.

The cropping pattern significantly affected the plant height. The height of monocropping and intercropping maize increased rapidly from the seedling stage to the silking stage and increased significantly from the jointing stage to the large bell mouth stage. Compared with monocropping, intercropping significantly increased the height of maize at the large bell mouth stage, grain filling stage, and mature stage. In 2019, the plant height of intercropping maize increased by 12.44%, 9.40%, and 8.40% at the large flare stage, grain filling stage, and mature stage, respectively; in 2020, it increased by 15.32%, 7.82%, and 6.07%, respectively. The 2-year results showed that the intercropping of maize and soybean increased the height of maize by 6.07–8.40%.

The height of monocropping and intercropping soybean increased rapidly from the emergence stage to the pod setting stage and increased significantly from the seedling stage to the branching stage. Compared with monocropping, intercropping significantly increased soybean height at the flowering, drumming, and maturity stages. In 2019, intercropping increased soybean plant height by 28.09%, 25.15%, and 35.27% at flowering, drumming, and mature stages, respectively; in 2020, it increased by 33.60%, 32.15%, and

38.94%, respectively. The 2-year results showed that the intercropping of maize and soybean increased soybean height by 35.27–38.94%.

#### *3.2. SPAD Values*

The SPAD values of monocropping and intercropping maize and soybean showed a trend of first increasing and then decreasing gradually with the advancement of the growth period (Figure 4). In 2019, the monocropping and intercropping maize increased rapidly from the seedling stage to the large bell mouth stage and reached a peak in the large bell mouth stage, after which the SPAD value gradually decreased. Intercropping significantly increased the SPAD value of the 2019 maize grain filling stage by 15.35% (*p* = 0.01). In 2020, the monocropping and intercropping maize peaked at the jointing stage and then gradually decreased. Intercropping significantly increased the SPAD value of maize at the jointing stage by 8.50% (*p* = 0.045). In 2019, monocropping and intercropping soybeans reached the peak at the branching stage; intercropping significantly reduced the SPAD value of soybeans at the branching stage by 3.01% (*p* = 0.027). In 2020, the SPAD value of mono-crop soybeans peaked at the branching stage; the SPAD value of intercropped soybeans peaked at the flowering stage. Intercropping significantly decreased the SPAD value of soybean by 15.27% at the branching stage (*p* = 0.011).

**Figure 4.** Dynamic changes in the SPAD values of maize and soybean in monoculture and under intercropping during 2019 and 2020. Abbreviations: MM—monocropping maize, MS—monocropping soybean, IM—intercropping maize, IS—intercropping soybean. In maize, I–VI mean seedling stage, jointing stage, large bell mouth stage, silking stage, grain filling stage, maturation stage, respectively. In soybean, I–VI mean seedling stage, branching stage, flowering stage, pod setting stage, drumming stage, maturation stage, respectively.

#### *3.3. Root Morphological Characteristics*

The root system is the main organ for crops to absorb nutrients and water, and the interaction between crops is closely related to the spatial distribution of the root parameters. At the same soil depth, the root length, root surface area, and root volume of intercropped maize were higher than those of monocropping maize, and the root length, root surface area, and root volume of monocropping soybean were higher than those of intercropped soybean (Figure 5). Under the mono intercropping mode, the length, volume, and surface area of crop roots in the 0–20 cm soil layer were greater than those in the 20–40 cm soil layer,

that is, the root parameters showed a downward trend with the increase in soil depth. The root parameters of maize and soybean were more concentrated in the 0–20 cm soil layer.

**Figure 5.** Dynamic changes of root morphological characteristics of maize and soybean in monoculture and intercropping during 2019 and 2020. Abbreviations: MM—monocropping maize, MS—monocropping soybean, IM—intercropping maize, IS—intercropping soybean.

In 2-year, in the 0–20 cm soil layer, compared with monocropping, intercropping increased the root length, root surface area and root volume of maize by 28.79%, 15.48% and 16.67%, respectively; and decreased the root length, root surface area and root volume of soybean by 59.52%, 14.51%, and 15.71%, respectively. In the 20–40 cm soil layer, compared with monocropping, intercropping increased the root length, root surface area and root volume of maize by 50.69%, 19.34%, and 36.66%, respectively; and decreased the root length, root surface area and root volume of soybean by 59.39%, 2.69%, and 4.36%, respectively.

#### *3.4. RLD*

With the advancement of the crop growth period, the RLD value showed an increasing trend. At the same soil depth, the RLD value of intercropped maize was higher than that of monocropping maize, and the RLD value of monocropping soybean was higher than that of intercropped soybean. At different soil depths, the RLD value of crops in the 0–20 cm soil layer was higher than that of the 20–40 cm soil layer. That is, with the increase in the soil depth, the RLD value gradually decreased (Figure 6).

In 2019, in the 0–20 cm soil layer, the RLD of intercropping maize was 1.79% higher than that of monocropping, but the RLD of intercropping soybean was 7.61% lower than that of monocropping. The RLD of soybean decreased by 9.46% compared with that under monocropping. In 2020, in the 0–20 cm soil layer, the RLD of intercropping maize was 7.44%

higher than that under monocropping, but the RLD of intercropping soybean was 3.06% lower than that under monocropping. The RLD of soybean decreased by 8.81% compared with that under monocropping.

**Figure 6.** Dynamic changes of RLD of maize and soybean in monoculture and intercropping during 2019 and 2020. Abbreviations: MM—monocropping maize, MS—monocropping soybean, IM—intercropping maize, IS—intercropping soybean. In maize, I–VI mean seedling stage, jointing stage, large bell mouth stage, silking stage, grain filling stage, maturation stage, respectively. In soybean, I–VI mean seedling stage, branching stage, flowering stage, pod setting stage, drumming stage, maturation stage, respectively.

#### *3.5. Correlation*

Correlation analysis showed that root morphology, root length density, and soil depth were negatively correlated (Table 2). In monocropping maize treatments, root length, root surface area, and root volume were significantly negatively correlated with soil depth. In intercropping maize, root surface area was significantly negatively correlated with soil depth, and root length was significantly negatively correlated with soil depth. In monocropping soybean, root length was significantly negatively correlated with soil depth, and root surface area, root volume, and root length density were significantly negatively correlated. In intercropping soybean treatment, root surface area was significantly negatively correlated with soil depth, and root length density was significantly negatively correlated with soil depth.


**Table 2.** Correlation analysis between soil depth and root morphological characteristics in monocropping maize and soybean and maize/soybean.

Note: \* means *p* < 0.05 significant level, \*\* means *p* < 0.01 extremely significant level. Abbreviations: MM—monocropping maize, MS—monocropping soybean, IM—intercropping maize, IS—intercropping soybean, RL—root length, RSA—root surface area, RV—root volume, RLD—root length density, SD—soil depth.

#### *3.6. Yield Composition*

Compared with monocropping, the yield of intercropping maize was higher (Table 3). The number of grains per panicle and the 1000-grain weight were significantly higher than those in the monocrop planting mode, but the difference in the number of panicles was not significant. In 2019, compared with monocropping maize, the number of ears, kernels per ear and the 1000-grain weight of intercropping maize increased by 30.66%, 4.30%, and 7.67%, respectively. In 2020, compared with monocropping maize, the corresponding values of intercropped maize increased by 34.35%, 8.06%, and 6.96%, respectively. In 2-year, intercropping increased maize yield by 49.39–58.10%.

**Table 3.** Monocropping and intercropping maize yield and yield components in 2019 and 2020.


Note: Means followed by different letters are significantly different at 0.05 levels.

The yield of intercropped soybeans was lower than that of monocropping soybeans (Table 4). The number of pods per plant and the 1000-grain weight of intercropped soybeans were significantly lower than those of monocropping soybeans, and the difference in the number of seeds per pod between the intercropping and monocropping treatments was not significant. The number of pods per plant, the number of grains per pod, and the 1000-grain weight of intercropping soybeans in 2019 were all lower than those under monocropping, by 27.80%, 4.56%, and 4.91%, respectively. In 2020, the number of pods per intercropped soybean plant, the number of grains per pod, and the 1000-grain weight decreased by 22.32%, 5.69%, and 3.41%, respectively, compared with monocropping. In 2-year, intercropping reduced soybean yield by 29.24–34.48%.



Note: Means followed by different letters are significantly different at 0.05 levels.

#### *3.7. Land Equivalent Ratio and Actual Yield Loss*

The LER is used as an indicator to measure the yield advantage, and the LER is calculated from the monocropping and intercropping yields [14]. The land equivalent ratio of the intercropping system was 1.18–1.26, i.e., the monocropping needs to increase the land area by 18–26% to achieve the same yield as the intercropping, showing the obvious intercropping advantage. The actual yield loss of maize in the intercropping system was greater than 0, and the actual yield loss of soybean was less than 0, Y > 0, indicating that the maize/soybean intercropping system has intercropping advantages (Table 4).

#### *3.8. Aggressivity and Competitive Ratio*

Aggressivity measures the intercrop competition using the simple difference between the extents to which crops a and b vary from their respective expected yields. This study showed that *Am* > 0, indicates that the competitiveness of maize is higher than that of soybean, and maize as the dominant species. *CRm* > 1, compared with soybean, maize had a higher competition ratio in the intercropping system, suggesting that maize was more competitive than soybean in the intercropping system (Table 5).



Abbreviations: LER—land L equivalent ratio, AYL—actual yield loss, *Am*—aggressivity of maize, *CRm*—competitive ratio of maize, *CRs*—competitive ratio of soybean.

#### **4. Discussion**

#### *4.1. Agronomic Traits of Crops*

Plant height is one of the basic indicators used in morphological observations and reflects the growth and development of crops and the rate and robustness of plant growth [27]. In the maize–soybean intercropping system, the shading by the taller maize crop modifies the light environment experienced by the lower soybean crop in terms of both light quantity (PAR—photosynthetically active radiation) and quality (R:FR ratio). These changes are affected by the intercropping configuration and crop architecture and cause changes in both plant height and growth of the soybean crop [28]. This study showed that intercropping increased the plant height of maize and soybean. Intercropped maize is a high-level crop that was less affected by soybean in the later growth stage, and the competition for light, water, and nutrients was greater than that of soybean. With the advancement of the growth period, the degree of shading of maize increased, and the plants underwent a series of shading reactions to adapt to shading stress, resulting in the preferential supply of soybean photosynthates to stem elongation, thereby increasing plant height. Liu, et al. [28] also found that the internode length, plant height, and specific leaf area of intercropped soybean

increased due to the reduction of the R: FR ratio of photosynthetically active radiation at the top of the intercropping soybean canopy.

The absorption and utilization of light energy by plants directly affect the growth and development of crops, and the most direct effect of light on crops is photosynthesis. There is a significant positive correlation between the crop SPAD value and the photosynthetic capacity [29]. The increase or decrease of SPAD value affects the content of chlorophyll, and the color of leaves will also change accordingly. The change of leaf color can basically reflect the nitrogen nutritional status of the plant and the nutritional status of nitrogen is also reflected in the change of SPAD value [30]. In intercropped maize and soybean, soybean can supply part of the required nitrogen for maize through its own nitrogen fixation function, improve the efficiency of maize's absorption and utilization of nitrogen, and further increase the chlorophyll content of plant leaves [31]. This study found that the SPAD value of intercropping maize was significantly different from that of monocropping maize, and the SPAD value of intercropping maize was stronger than that of monocropping maize. The SPAD value of intercropped soybean was higher than that of monoculture soybean. Compared with monocropping, intercropping of maize and soybean can maintain a higher level of SPAD, can effectively promote photosynthesis, and is conducive to increasing yield in the later period.

#### *4.2. Root Morphological Characteristics*

The yield advantages of intercropping systems are due to both above- and belowground interactions between the intercropped species [32]. The root system is the main organ for the absorption, transmission, storage, and utilization of underground resources such as water and nutrients in an intercropping compound system. Root systems are key areas of crop resource competition and compensation in intercropping systems and are important contributors to yield formation [33]. Li, et al. [34] and Shinano, et al. [35] showed that root morphologies affect intercrop competition in intercropping systems. The intercropping of broad bean and maize changed the root morphology of crops and increased the effective space for crop water and nutrient absorption [36]. Ren, et al. [37] showed that the intercropping of soybeans expanded the ecological niche of the maize root system in the horizontal and vertical directions, and the root length density and root surface area were positively correlated with nitrogen absorption while promoting the vitality of the maize root system. In legume and Gramineae intercropping, legumes promote Gramineae nitrogen absorption through rhizobia nitrogen fixation and nitrogen transfer, thereby increasing the root growth of Gramineae crops [38]. The results showed that compared with the single cropping mode, the root parameters were improved in the intercropping mode, indicating that the intercropping mode effectively improved the root morphology. Among them, the root length and root surface area increased most obviously, which may be due to the relatively low planting density of the intercropping mode, which gave the root system more growth space and promoted the extension of the root system. Among them, the root length and root volume increased significantly in shallow soil. After the intercropping of maize and soybean, the root morphology of maize (shallow root system) and soybean root system (deep root system) were induced to change, giving full play to the complementarity of the root space niche.

The crop growth and final yield of an intercropping system are closely related to the distribution of roots, which determines the uptake and utilization of water and nutrients. The distribution of roots in different soil layers is reflected by the RLD [39]. Root distribution plays an important role in intercropping dominance. Studies of Gao, et al. [40] have shown that the RLD and root surface area density (RSAD) of peanuts in an intercropping system were lower than those of monocropping peanuts, and the RLD and RSAD of intercropped maize were still higher than those of monocropping maize. This study showed that maize and soybean intercropping had a significant effect on RLD compared with monocropping. The RLD of intercropping maize was higher than that of monocropping maize, intercropping promotes root proliferation of maize crops, and maize has intercropping advantages

in yield due to the increase of root length density. The RLD of intercropping soybean was lower than that of monocropping soybean, and the distribution of soybean roots was inhibited. This result is inconsistent with the first hypothesis that the corn intercropping soybean system has a positive effect on the root distribution of corn and a negative effect on the root distribution of soybean through underground competition.

#### *4.3. Yield and Land Productivity*

Intercropping can optimize the population structure through reasonable crop collocation and appropriate cultivation techniques as well as to give full play to the advantages of the mutual benefits between species to achieve high crop yield and high efficiency [41]. Maize/soybean intercropping has a greater impact on crop yield, but maize and soybean have different performances. The proportion of maize and soybean in intercropping was 1:2, the yield of intercropping maize was equivalent to 49.39–58.10% of the yield of monocropping, and the yield of intercropping soybean was only equivalent to 29.24–34.48% of the yield of monocropping, indicating that intercropping significantly increased the yield of maize, the soybean yield decreased. The nitrogen element supply, fixed by bean plants in the intercropping system, encourages the root system of maize plants to expand their reach therefore it had an impact on raising maize yields [42]. According to Gard and Mckibben [43], an intercropping system has a certain yield reduction effect compared with monocropping. The reduction in seed yield due to intercropping could be the result of interspecific competition and the depressive effect of maize, a C4 species, on soybean, a C3 crop. Crops with the C4 photosynthetic pathway such as maize are known to be dominant when intercropped with C3 crops such as soybean [44]. Further, the reduction in intercropped soybean could be due to shading by the taller maize plants [45]. In intercropping systems, shorter crops experience shading from taller crops, thus increasing plant height, and decreasing yield [46]. Research has shown that the yield of soybean is inhibited by maize, and the appropriate nitrogen application rate cannot alleviate the inhibitory effect on soybean [47]. It may be that soybean is not sensitive to nitrogen fertilizer, and maize responds quickly to nitrogen fertilizer, resulting in soybean being inhibited by maize, while maize shows yield advantage [48].

Yield advantage is usually assessed and quantified by calculating the land equivalent ratio (LER) in intercropping [49]. In this study, the LER values of corn–soybean intercropping were 1.18–1.26. This means that an additional 18–26% of land area was needed for a monoculture cropping system to produce the equal production as an intercropping system. Hafid et al. [50] stated that the increase in land productivity was caused by choosing the right combination of plants and cropping systems and the existence of a relationship or mutualism symbiosis between plants which were planted in an intercropping way. This symbiosis is closely related to the need for nitrogen for the main plant which was fulfilled from the attached plants through its ability to fix nitrogen from the air. On the other hand, plants that are tolerant to shade can live under stands. The combination of cereal crops and legumes was the best combination. This result supports the second hypothesis that such an intercropping system would have an overall positive impact on productivity by increasing the efficiency of land resource use.

#### *4.4. Interspecific Competition*

The competition ratio of maize in the intercropping system was greater than 1, and the aggressivity of maize was greater than 0, indicating that in the symbiotic period of maize and soybean, soybean was at a competitive disadvantage in the intercropping system, and maize was a competitive crop. Previous studies have shown that there is strong interspecific competition among different crops in the intercropping system, and the resource competitiveness of Gramineae crops is higher than that of legumes [51]. According to Banik, et al. [24], the AYL index can give more precise information than the other indices on the inter- and intra-specific competition of the component crops and the behavior of each species involved in the intercropping systems. Quantification of yield loss or gain due to association with other species or the variation of the plant population could not be obtained through partial LERs, whereas partial AYL shows the yield loss or gain by its sign as well as its value. The actual yield loss of maize was greater than 0, indicating that maize has a yield advantage in the intercropping system. The actual reduction of soybean yield is less than 0, indicating that soybean has no yield advantage in the intercropping system, which is consistent with the fact that maize is a competitive crop in the intercropping system, and soybean is a competitive disadvantage crop. The result of interspecific competition was that the actual yield loss of the intercropping system was greater than 0, indicating that the intercropping of maize and soybean had yield advantage because the yield of maize was increased, and the yield of soybean was unchanged or decreased. This is consistent with previous studies on the intercropping of wheat and peas [52], oats and wild peas [26], millet and soybeans [53], which showed that in the legume and Gramineae intercropping system, the yield of grasses increased and the yield of legumes decreased, since the crops in the intercropping system have differences in competitiveness [52]. The intercropping of tall crops (maize) and dwarf crops (soybeans) is caused by the increase of above-ground light interception of maize and the improvement of underground nutrient and water use efficiency [54]. The biological characteristics of soybean are different from those of maize, and it is in a disadvantageous position in the competition for soil water and nutrient absorption and the competition for light interception [55].

#### **5. Conclusions**

Maize/soybean intercropping has effects on crop growth, yield, and root morphology. The growth parameters (plant height, relative chlorophyll content) of maize and soybean in intercropping system were better than with monocropping. The yield components of intercropping maize in terms of the number of spikes per plant, ear grain numbers, and 1000-seed weight were higher than those of monocropping, however, in contrast to soybean, monocropping soybean had higher yield parameters than intercropping. The RLD of intercropping maize increased compared to monocropping, indicating greater root growth. The intercropping of maize and soybean has yield advantages; the land equivalent ratio was between 1.18 and 1.26, the aggressivity of maize was between 0.84 and 0.87, and the competition ratio was between 3.37 and 3.44. The reason for improving the yield of the intercropping population is the increase of maize yield and the higher competitiveness of maize for resources than soybean, and maize is the dominant species. Maize and soybean intercropping can improve land use efficiency and crop yield and should be properly promoted to increase maize/soybean productivity.

**Author Contributions:** Conceptualization, W.W.; Data curation, W.W. and X.W.; Formal analysis, W.W.; Funding acquisition, W.Z.; Methodology, W.W.; Project administration, W.Z.; Resources, W.Z.; Supervision, W.Z.; Visualization, W.W. and T.L.; Writing—original draft, W.W.; Writing—review and editing, T.L., L.S. and S.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was financially supported by the Innovation and Development Project of Shihezi University (CXFZ202008), by the National Natural Science Foundation of China (Project Nos. 31460335 and 31560376).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The datasets used and/or analyzed during the current study are available from the first author upon reasonable request.

**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

#### **References**


## *Article* **Effects of Water-Saving Irrigation on Direct-Seeding Rice Yield and Greenhouse Gas Emissions in North China**

**Xiaoning Hang 1,2, Frederick Danso 3, Jia Luo 1, Dunxiu Liao 1,2, Jian Zhang 1,2,\* and Jun Zhang 3,\***


**Abstract:** Rice cultivation consumes more than half of the planet's 70% freshwater supply used in agricultural production. Competing water uses and climate change globally are putting more pressure on the limited water resources. Therefore, water-saving irrigation (WSI) is recommended for rice production in water scares areas. The impact of WSI techniques on direct-seeding rice production and greenhouse gas emissions in North China is becoming increasingly important in the era of climate change. Therefore, we conducted a two-year field experiment on directly seeded rice to assess the impact of traditional flooding irrigation (CK) and three water saving irrigation (WSI) methods, including drip irrigation with an irrigation amount of 50 mm (DI1) and 35 mm (DI2) at each watering time and furrow wetting irrigation (FWI), on rice yield and greenhouse emissions. Generally, the WSI techniques decreased the number of rice panicles per m−2, spikelet per panicle, 1000-grain weight and rice yield compared to CK. Rice yield and yield components of (DI1) were significantly higher than (DI2). The adoption of either (DI1) or (FWI) showed insignificant variation in terms of rice yield and its yield components measured except for 1000-grain weight. The water productivity was 88.9, 16.4 and 11.4% higher in the FWI plot than the CK, DI1 and DI2 plots, respectively. The WSI decreased cumulative CH4 emission significantly by 73.0, 84.7 and 64.4% in DI1, DI2 and FWI, respectively, in comparison with CK. The usage of DI2 triggered 1.4 and 2.0-fold more cumulative N2O emission compared to DI1 and FWI, respectively. Area-scaled emission among the water-saving irrigation methods showed no significance. The yield-scaled emission in DI1 and DI2 and FWI were 101, 67.5 and 102%, respectively, significantly lower than CK. The adoption of FWI produced an acceptable rice yield with the lowest yield-scaled emission and highest water productivity among the irrigation practices. Our experiment demonstrates that dry direct-seeding with furrow irrigation can impact triple-wins of sustainable rice yield, high water-use efficiency and low GHG emissions in North China.

**Keywords:** rice production; CH4; N2O; water productivity; global warming

#### **1. Introduction**

As the most important staple food of the world, rice represents 19% of human calorific intake [1]. Global population is projected by 2050 to reach 9 billion, and a 50% increase in rice production may be needed for the impending demands [2]. Globally, approximately 70% of the planet's freshwater supply is consumed through agricultural production [3]. In recent times, the sustainability of irrigated rice systems are under threat, owing to agricultural intensification, depleting water reserves and limited water availability across the globe [4]. Rice cultivation, accounting for 40% of the agricultural freshwater usage, worsening climatic conditions, rising population and competing water uses constraints farmers access to adequate and timely supply of water [5]. Therefore, for sustainable

**Citation:** Hang, X.; Danso, F.; Luo, J.; Liao, D.; Zhang, J.; Zhang, J. Effects of Water-Saving Irrigation on Direct-Seeding Rice Yield and Greenhouse Gas Emissions in North China. *Agriculture* **2022**, *12*, 937. https://doi.org/10.3390/ agriculture12070937

Academic Editor: Jose L. Gabriel

Received: 24 April 2022 Accepted: 24 June 2022 Published: 28 June 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

rice cultivation, it is essential that water is managed appropriately. China is among the largest rice producers and the second major user of water for irrigation globally [6]. In recent times, large-scale rice production has moved northward [7]. The cultivated area and total production in North China have increased by 101.1% and 143.2%, respectively since 1990, and account for 18.8% and 20.4% of Chinese total rice sown area and production in 2012, respectively. The expected socioeconomic growth, associated water resource demand and consumption through rice production can be reasonably projected to increase exponentially in North China. From the findings of Jiang et al. [8], the continuous adoption of traditional irrigation practices that use huge volumes of water and accounts for over 60% of water use for producing rice across China may not be sustainable in North China, where water shortage is severe. Additionally regional and seasonal water shortages caused by drought and future climate change scenarios will make water shortage more severe in the region and threaten rice production [9,10]. Although globally, the production of rice contributes only 1.5% of the overall anthropogenic greenhouse gas (GHG), this portion is considerably greater in rice-producing nations [11]. A substantial quantity of greenhouse gas (GHG) is released into the environment with current practices of rice production that consume vast amounts of water [5]. Therefore, target to limit global warming to 1.5 ◦C will be compromised due to insufficient agricultural emission reductions [12]. Accordingly, several water-saving irrigation (WSI) know-hows have been developed and disseminated in China, such as alternate wetting and drying, soil saturated cultivation, drip irrigation, bed-furrow base irrigation and non-flooded mulching cultivation to replace the traditional flood irrigation [13,14]. The choice of these WSI may impact rice growth and greenhouse gas (GHG) emissions. The adoption of WSI can cause a reduction in rice yield [15], maintain or even increase rice yield [16]. Compared with continuous flooding, WSI, which involves one or several drainage methods that minimize CH4 production, demonstrates an important prospect to reduce CH4 emissions [14,17], though it may trigger substantial N2O emissions caused by wet-dry cycles of the soil [18]. In recent times, water-saving irrigation of drip irrigation in combination with plastic film mulch, furrow wetting irrigation and intermittent irrigation has been integrated with dry direct-seeding of rice in North China. Study of the integrated effects of rice planting techniques with water-saving irrigation on the yield of rice and GHG emissions is limited. Therefore, measurement of rice yield and GHG emission could provide additional confirmation to elucidate the integrated impact of dry direct-seeding of rice and WSI measures in North China. Therefore, using a two-year field experiment, three water-saving irrigation methods under the dry direct-seeding system in North China were appraised. Our objectives were to evaluate the effects of the improved planting technique and water management practice on rice yield and yield components— CH4 and N2O emissions.

#### **2. Materials and Methods**

#### *2.1. Experimental Location*

The field experimentation was set up in the Yellow River Irrigation Area at the Ling Wu experimental Farm in 2014 and 2015, Yinchuan City (38◦12 N latitude, 106◦27 E longitude), Ningxia Province, China (Figure 1a). The soil type was an irrigating warped soil with the basic chemical properties: organic matter 12.2 g kg<sup>−</sup>1, total salt 1.2 g kg−1, total N 0.8 g kg−1, available N 57.8 mg kg−1, available P 26.5 mg kg−<sup>1</sup> and available K 141.1 mg kg−1. The experimental site is characterized by a temperate arid climate with mean annual temperature and precipitation of 8.5 ◦C and 200 mm, respectively. The precipitation and air temperatures data obtained from Ling Wu meteorological department during the rice growing seasons in 2015 are shown in Figure 1. Rainfall occurred between June–August and was almost lacking in the course of rice-seed emergence in May. Total rainfall from the seeding stage to maturity stage was 256 mm and 213 mm in 2014 and 2015, respectively. The lowest and the highest daily mean air temperatures were 13.1 ◦C on 5 May and 27.6 ◦C on 12 August in 2014, respectively, and 13.3 ◦C on 14 May and 27.9 ◦C on 10 August

in 2015, respectively. From June until August, air temperature was relatively lower than the optimal temperature required for rice growth.

**Figure 1.** Experimental location (**a**) and daily mean air temperature and daily precipitation (**b**) of rice cropping seasons in 2015.

#### *2.2. Experimental Design*

The field experiment was a randomized block design in three replications and consisted of four irrigation treatments, namely: (1) Traditional flood irrigation (CK); (2) Drip irrigation under plastic film mulching with 50 mm irrigation amount at each watering time when the relative soil water content (RSWC) was less than 100% (DI1); (3) Drip irrigation under plastic film mulching with 35 mm irrigation amount at each watering time at the same time of DI1 (DI2) and (4) Furrow wetting irrigation (FWI). The replicate plot sizes of 15 m × 20 m were separated by 30 cm-wide soil ridges covered with plastic film to inhibit water and nutrient exchange between plots.

#### *2.3. Water and Crop Management*

Land preparation in all the treatments was carried out by ploughing and leveling the soil under dry conditions. The rice variety, Ningjing 31, was directly seeded on 1st May, and harvested between 24–28 September for all the treatments in 2014 and 2015 (Table 1). Based on the local agronomic practices for higher rice yield, similar fertilization rates were adopted for the treatments. The N fertilizer was applied as urea at a rate of 240 kg N ha<sup>−</sup>1, 40% as basal application before seeding, 30% at the tillering stage and 30% at the panicle initiation stage. Basal phosphorus fertilizer of calcium superphosphate was applied at 112.5 kg ha−<sup>1</sup> P2O5, whiles no K fertilizer was added during rice growth (Table 1).

All treatments were flooded with 100 mm of water on 1st May after direct seeding (Figure 2). Subsequently, only the CK followed the traditional continuous flooding. The drip system for DI1 and DI2 consisted of a small pump, a water meter, a control head unit, PVC mainline, polyethylene mains and laterals (Xinjiang Tianye Company, Shihezi, China). DI1 drip irrigated received 50 mm water amount at each irrigating time when the relative soil water content (RSWC) was 0.1 m and below 100%. A similar irrigation schedule was implemented in DI2 except that it received 35 mm of water at each irrigating time. In the furrow wetting irrigation (FWI) treatment, the plots were maintained at moist condition the whole period of rice growth. Each replicate plot of FWI, prior to direct-seeding, was divided into five strips (three meters in width) and separated by furrows (25 cm width and 30 cm in depth). After direct-seeding on the strips, the furrows were filled with water to maintain a constant wet condition on the strips. No obvious water level was retained on

the seedling strips during the entire growth period. The water flow of CK and FWI were measured by separated flume flow meter. TDR100 was used to test the RSWC.

**Table 1.** Mode and timing of experimental field management practices in the four irrigation regimes.


**Figure 2.** Irrigation at each watering period during the rice cropping seasons.

Irrigation times for CK, DI1, DI2 and FMI were 13, 13, 13 and 18 days, respectively (Figure 2). The total irrigation amounts were 1270, 700, 520 and 625 mm in the CK, DI1, DI2 and FMI plots, respectively (Table 1). All treatments were subjected to same pesticide and herbicide applications rates according to the local standards for high yields and pest control.

#### *2.4. Greenhouse Gas Sampling*

The static closed chamber and gas chromatography methods were adopted to sample and measure CH4 and N2O every 10 days in 2015 [19]. Polyvinyl chloride (PVC) chambers in accordance with the rice height and fitted with a battery-operated fan for thorough gas mixture in the head space were used. Collected gases were analyzed to obtain the concentrations of CH4 and N2O using a gas chromatograph (Agilent 7890A, Santa Clara, CA, USA) mounted with a flame ionization detector (FID) and an electron capture detector (ECD) to detect CH4 and N2O, respectively. The CH4 and N2O fluxes were calculated as:

$$\mathbf{G} = (\Delta \mathbf{C} / \Delta \mathbf{t}) \times (\mathbf{V} / \mathbf{A}) \times \boldsymbol{\alpha}$$

where G is the gas flux rate (g N2O-N or CH4-C ha−<sup>1</sup> d<sup>−</sup>1), ΔC/Δt designates the increase of gas concentration in the chamber (g L−<sup>1</sup> d−1), V is the chamber volume (L), A is area enclosed by the chamber (ha), and α is a conversion coefficient for elemental C (α = 0.749) or N (α = 0.636). The slope of the mixing ratio of four sequential samples was used in the determination of both CH4 and N2O fluxes. Cumulative CH4 and N2O emissions were computed using the formula described by Cai et al. [20].

The area-scaled GHG emission was converted to CO2 equivalent (CO2-eq) as follows:

$$\text{Area-scaled GHG emission (kg CO}\_2\text{-eq ha}^{-1}\text{ yr}^{-1}) = 25 \times \text{CH}\_4 + 298 \times \text{N}\_2\text{O}$$

where, CH4 and N2O represent the seasonal cumulative emissions. Yield-scaled GHG emission was computed by dividing area-scaled emission by yield of rice [21].

#### *2.5. Yield and Yield Components Measurement*

A one m2 rice plant at physiological maturity was harvested for yield determination. Grain yield was adjusted to 14% moisture content using the formula:

$$\text{Yield} = \text{GW} \times (100 - \text{GMC})\% / (100 - 14)\%$$

where: GW = Grain weight. GMC = Grain moisture content.

Number of panicles was evaluated by counting the total panicle number per 1 m<sup>2</sup> per plot. Spikelet per panicle was evaluated by counting both the filled and unfilled spikelets per 1 m<sup>2</sup> randomly taken from each plot. Dry weight of 1000 grains from three replicates samples of filled grains per plot were obtained by drying at 70 ◦C in the oven for 72 h to constant dry weight.

#### *2.6. Statistical Analyses*

The data was analyzed using analysis of one-way variance (SPSS 23.0 for windows) to test the differences among the treatments. The least significant difference (LSD) test was used to compared treatment means (*p* < 0.05). Microsoft Excel 2003 was used to compute the standard deviation of the means.

#### **3. Results**

#### *3.1. Rice Plant Growth and Grain Yield*

Differences that were significant at the rice growth stages and biomass production were recorded between irrigation treatments (Table 2). Water-saving irrigation advanced rice heading and maturity stage, resulting in a reduction in the length of the rice growth period. Compared to CK, the primary heading stage was advanced by 2, 1 and 1 day in 2014, and 3, 3 and 2 days in 2015 in the DI1, DI2 and FWI plots, respectively. Consequently, the length of rice growth was shortened by 2, 2 and 1 day(s) in 2014, and 2, 4 and 1 day(s) in 2015 in DI1, DI2 and FWI plots, respectively.


**Table 2.** Impact of irrigation on rice growth stages and aboveground biomass at pre- and post-anthesis phases.

CK (Traditional flood irrigation); DI1 (Drip irrigation under plastic film mulching with 50 mm irrigation); DI2 (Drip irrigation under plastic film mulching with 35 mm irrigation); FWI (Furrow wetting irrigation).

Water-saving irrigation (WSI) practices significantly decreased rice biomass production (Tables 2 and 3). The lowest aboveground biomass production was found in the DI2 plots. As compared to the CK, the pre-anthesis aboveground biomass production over two study years was 11.1%, 23.2% and 11.6% lower in the DI1, DI2 and FWI plots, respectively while the post-anthesis aboveground biomass production was 23.8%, 44.8% and 16.2% lower in the DI1, DI2 and FWI plots, respectively (Table 2). Consequently, the adoption of water-saving irrigation resulted in a reduction of 15.6%, 30.2 and 13.2% relative to the CK in the DI1, DI2 and FWI plots, respectively (Table 3). Rice yields ranging from 5.9 to 8.7 t ha−<sup>1</sup> produced significant differences in the different irrigation treatments (Table 3). The highest yield was found in the CK plot and the lowest existed in the DI2 plot in both years. The choice of DI1, DI2 and FWI produced 10.3%, 32.1% and 8.1% lower rice yield in comparison with CK in 2014, and 11.8%, 34.7% and 10.2% lower in 2015. Non-significant yield differences were noted amid the adoption of CK and FWI in 2014 but were significant in 2015 (Table 3). Water-saving significantly decreased rice panicles per area, with DI2 recording the lowest. The choice of DI1 significantly lowered number of panicles compared to CK. Spikelets per panicle and the 1000-grain weight showed significant variation among the irrigation treatments. Noticeable was the significantly lower spikelets and 1000-grain weight in DI2 plots.

**Table 3.** Rice yield and yield components as impacted by water-saving irrigation.


CK (Traditional flood irrigation); DI1 (Drip irrigation under plastic film mulching with 50 mm irrigation); DI2 (Drip irrigation under plastic film mulching with 35 mm irrigation); FWI (Furrow wetting irrigation). Different letters in the same column shows significant differences at *p* < 0.05.

#### *3.2. CH4 and N2O Emission Fluxes and Seasonal Emission Ratios*

Similar patterns of CH4 fluxes existed in the irrigation methods (Figure 3a). The maximum emission fluxes occurred during rice heading and flowering stages, and the lowest occurred during the seedling and maturity stages amongst the treatments. The variations of CH4 emission fluxes were similar with the seasonal changes of air temperature (Figure 1b). However, differences of significance in the mean peak CH4 emission fluxes between CK and the other irrigation methods were noted (Figure 3a). No variation of significance in the flux peak existed in the three water-saving methods. The peak mean CH4 emission was noted in the CK plots. The mean flux value was 267, 537 and 191% more in the CK plot compared to those of DI1, DI2 and FWI plots, respectively (*p* < 0.05). Seasonal variation patterns of N2O fluxes were variable (Figure 3b). The highest flux peaks were noted in the DI1, DI2 treatments while the lowest occurred in the CK plots. The flux in CK was 55.5, 305.1 and 82.5% lower than those in the DI1, DI2 and FWI treatments, respectively. The flux of the total emission at CO2-eq scale was 147, 140 and 126% lower in the DI1, DI2 and FWI treatments compared to the CK plot (Figure 3c). The adoption of DI1 and DI2 recorded higher emission ratios at the pre-anthesis stage compared to FWI and CK (Figure 3d). At the post-anthesis stage a lower emission ratio was noted in DI1 and DI2 in comparison with FWI and CK.

**Figure 3.** Differences in CH4 (**a**), N2O (**b**), CO2 equivalent of CH4 and N2O (**c**) emission fluxes and emission ratios of pre- and post-anthesis periods (**d**) in irrigation plots.

#### *3.3. Water Productivity and Area and Yield-Scaled Emissions*

The irrigation methods exhibited significantly different water productivity levels (*p* < 0.05) (Table 4). The adoption of DI1, DI2 and FWI showed increased water productivity compared to CK. The highest value of water productivity was noted in the FWI plot, whereas the lowest was detected in the CK plot. The water productivity was 88.9, 16.4 and 11.4% higher in the FWI plot than those in the CK, DI1 and DI2 plots, respectively. Using

the WSI significantly decreased cumulative emission of CH4 by 73.0, 84.7 and 64.4% in DI1, DI2 and FWI, respectively, compared to CK (Table 4). Also, among the water-saving irrigation, significant differences were noted, with DI2 recording 43.6 and 57.2%, lower cumulative CH4 than DI1 and FWI, respectively. Significantly, cumulative N2O emission was 2.8, 4.1 and 2.0-fold more in DI1, DI2 and FWI than CK. The usage of DI2, triggered a 1.4 and 2.0-fold more cumulative N2O emission compared to DI1 and FWI, respectively. The area-scaled emission in the CK was 129, 141 and 116% higher (*p* < 0.05) than those in the DI1 and DI2 and FWI plots, respectively. Area-scaled emission amidst the WSI methods recorded no significant variation, though area-scaled emission between WSI and the CK were significantly different. The yield-scaled emission in DI1 and DI2 and FWI were 101.0, 67.5 and 102.0%, respectively, significantly less than CK (*p* < 0.05). Among the WSI, significant differences in yield-scaled emission were observed, with the lowest yield-scaled emission found in the FWI plot.

**Table 4.** Impact of water-saving irrigation on cumulative CH4, N2O emissions, area and yield-scaled emissions and water productivity.


CK (Traditional flood irrigation); DI1 (Drip irrigation under plastic film mulching with 50 mm irrigation); DI2 (Drip irrigation under plastic film mulching with 35 mm irrigation); FWI (Furrow wetting irrigation). Different letters in the same column shows significant differences at *p* < 0.005.

#### **4. Discussion**

Compared to the traditional continuous flooding, water-saving irrigation (WSI) could increase water productivity [22,23] and maintain or increase rice grain yield [24], although some studies have reported contrary findings [14,25]. The results of this study indicated that the adoption of WSI amplified water-use efficiency but caused a reduction in rice yield (Tables 2 and 3). A substantial decline in water application may adversely impact rice yield due to sensitivity to non-saturated soil environments [26]. This was very prominent in the drip irrigation with 35 mm irrigation (DI2) arising primarily from limited water for rice biomass and panicle per area development and consequently affecting rice yield (Table 3). This also supports the assertion that irrigation volumes impact WSI [14,24]. Although water-saving irrigation caused rice yield reduction, the drop was significant in DI2 water-saving irrigation methods. He et al. [27] established that yield reduction occurs in extreme water-saving irrigation, owing to inadequate tillers and spikes. The lowest reduction in yield was in FWI, which produced the highest water productivity value (Table 4). This arises due to hastened canopy closure and decreased partial stomatal closure for the period of soil drying cycles, helping to minimize evapotranspiration [28,29], and less percolation of water into the soil [27]. Therefore, the choice of FWI may offer an alternative for maintaining yields while minimizing water consumption. Previous studies show that high ground water mitigates the influence of water-saving irrigation on the growth of rice at the post-anthesis stage [30,31]. In our study, lower groundwater table and precipitation during rice growing season in the two study years could have exacerbated water limitation for rice growth, negatively impacted panicles, spikelet numbers and grain filling, and subsequently significantly reduced the 1000-grain weight (Table 3).

No obvious increases in CH4 emission were recorded at the rice tillering stage (Figure 3a). The non-optimal and relatively lower air temperature of 19.2 ◦C during rice tillering may have hindered methanogenic activities that stimulate CH4 production during rice growth [32]. The peak flux of CH4 was noticeable at the heading stage for all the treatments, similar to previous works of Chen et al. [13]. Rising daily mean air temperature of more than 25 ◦C at the rice booting and heading stages, well-developed aerenchyma for CH4 emitting, and increased rice growth that stimulated root-derived exudation for methanogenic activities [15] may explain the peak flux occurring at the heading stage (Figure 3a). Studies show that soil water status affects CH4 formation and emission [17]. In our study, though significant differences were noted in the CH4 emission from the WSI, it did not trigger an exponential increase in CH4 emission in comparison to the CK. Compared to continuous flooding irrigation, WSI irrigation had a superior prospect to decrease CH4 emissions in line with alterations in soil water dynamics [33]. Evidently, a reduction in water use corresponded with a decline in the emission of CH4, especially in DI2. This supports the assertion that WSI shows a significant potential to mitigate CH4 emissions [14]. In comparison with the traditional flooding irrigation, the adoption of WSI substantially triggered N2O emission arising from one or more drainage events and the wet-dry cycles to suppress CH4 production during rice growth [15]. Similar to previous studies [34,35], the adoption of continuous flooding demonstrated higher CH4 emission compared to WSI. The cycle of continuous dry-wet cycles and the smaller amounts of water available in the WSI might have negatively affected CH4 production [36] by inhibiting the formation of soil reductive conditions. A reduction in soil water content via WSI is presumed as a favorable preference for CH4 mitigation. Among the water-saving irrigation practices, the reductions in CH4 emissions in the drip irrigation plots (DI1 and DI2) were significantly higher than that in the furrow wetting irrigation plot (Table 4). This was expounded by the fact that lower soil moisture content, in both DI1 and DI2, stifled the emission of CH4 to a very low-level during rice growth. Our observations support a previous study by Katayanagi et al. [37], who reported a 73% mitigation of CH4 emission via WSI during rice cultivation. Thus, however, these higher reductions in CH4 emissions could not compensate for the higher increases in N2O emissions in the drip irrigation plots. Consequently, the CO2-eq emissions of CH4 and N2O were similar among the three water-saving irrigation methods. Since the reductions in rice yield were higher in the drip irrigation fields compared to that of FWI field, the lowest yield-scaled CO2-eq emission was found in the FWI field.

#### **5. Conclusions**

Sustainable water management in direct-seeded rice highlights the importance of adopting water-saving irrigation to reduce GHG emission, increase water productivity and sustain rice yield. In contrast to continuous flooding, WSI caused a decline in CH4 emissions while essentially triggering N2O emission increases. The highest water productivity and rice yield, lower area and yield-scaled emission among the WSI were observed in the adoption of furrow wetting irrigation (FWI). For sustainable direct-seeded rice production under water-saving irrigation in North China, furrow wetting irrigation (FWI) is recommended to sustain rice yield and minimize greenhouse gas emissions.

**Author Contributions:** Literature search, X.H. and F.D.; figures, X.H. and J.L.; study design, X.H. and J.Z. (Jian Zhang); investigation, X.H. and J.L.; data analysis, D.L. and J.Z. (Jian Zhang); writing review and editing, X.H., J.Z. (Jian Zhang) and J.Z. (Jun Zhang) All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the National Key R&D Program of China (2016YFD0300907).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the authors.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

