Effects of Irrigation and Fertilization Management on Yield and Quality of Rice and the Establishment of a Quality Evaluation System

Abstract

Yield and rice quality indicators of crops are a direct reflection of the rational irrigation and fertilizer strategy. However, the effects of controlled irrigation (CI) combined with the split application of fertilization managements (straw returning, organic fertilizer, and conventional fertilizer) on rice quality are not clear in southeast China. This study aims at exploring the effects of three fertilization managements applied under CI or flooding irrigation on rice yield, quality, enzyme activity, and soluble sugar content including 43 indicators, to determine the optimal comprehensive evaluation model, management, and representative indexes. The results showed that compared with CF (CI + conventional fertilizer), CS (CI + straw returning) significantly increased yield (27.65%), irrigation water use efficiency (6.20%), chalky grain rate (9.67%), chalkiness (1.83%), protein content (4.29%), and amylose content (0.33%), indicating that CS improved yield and milling quality but decreased cooking and appearance quality. This was mainly because CS promoted the activities of alpha-amylase, ADPG (ADP-glucose pyrophosphorylase), and GBSS (granule-bound starch synthase) and reduced the soluble sugar content in rice. Grey relational degree analysis (GRD), the entropy method (ETM), and TOPSIS (the technique for order preference by similarity to an ideal solution) were used to comprehensively evaluate the rice quality and determined that CS treatments could synergistically improve yield and rice quality. The five indexes (adhesive strength, HPV, ADPG, soluble sugar (leaf), yield) and TOPSIS model can be used as the best indexes and model to evaluate the rice quality. These results could provide scientific management and evaluate practices for high-yield and high-quality rice cultivation, which may be promising for a cleaner production strategy.

1. Introduction

Rice is considered one of the main food products around the world other than wheat and corn [1,2]. One-third of the world’s population depends on rice as the staple food to survive, especially in China, Japan, and Southwest Asia. Only by constantly improving the yield and quality of rice can food security be effectively guaranteed [3]. During the 13th Five-Year Plan period, China’s rice high-quality rate increased rapidly from 27.3% in 2015 to 49.2% in 2020 [4]. Adjusting the irrigation and fertilization system is a significant agricultural management method for improving the quality and yield of rice.

Controlled irrigation (CI) is one of the water-saving irrigation techniques [5]. Compared with traditional flooding irrigation (FI), CI reduced greenhouse gas emissions, improved rice milling quality and water utilization, and did not affect grain yield, appearance, cooking, eating, and nutrition quality [6,7]. Rice protein content increased, total soluble sugar and starch content decreased, and cooking quality tended to deteriorate in paddy fields with dry farming or low soil water content [8,9]. CI has little effect on rice yield and can even improve it; there are also reports of yield reduction, but sometimes significantly improved milling quality, appearance quality, and cooking quality [10]. However, the formation of rice quality is closely related to the synthesis of starch in grain and the activity of related enzymes, mainly including alpha-amylase, ADP-glucose pyrophosphorylase (ADPG), and granule-bound starch synthase (GBSS) [11]. Efficient irrigation management of rice should not only focus on a single index such as yield or a certain quality, but also consider multiple indexes such as related enzyme activities and so on.

Fertilizer use plays an important role in crop nutrient metabolism and cycle. Excessive chemical fertilizer application not only causes resource waste, environmental pollution, soil fertility decline, and a series of ecological problems but also reduces the effective tillering rate of rice and affects the grain yield of rice [12,13,14]. Straw returning and organic fertilizer replacing chemical fertilizer improve rice yield, protein content, and rice quality and reduce the amount of chemical fertilizer [15,16], but decrease the appearance quality to a certain extent [17]. Zhang et al. [18] found that many indexes such as the rice root activity, accumulating amount of material, and yield with mulching straw were better than those of soil-returning straw under continuous FI. Previous studies focused on the effect of FI on rice yield. Therefore, a high yields and quality study is particularly important for agricultural products through the improvement of agricultural CI and fertilizer management measures, which might achieve more sustainable production.

The yield and rice quality indicators of crops are a direct reflection of the rational irrigation and fertilizer strategy. At present, hundreds of multi-objective comprehensive mathematical statistical evaluation methods have been used to evaluate rice yield and quality [19]. Shi et al. [20] comprehensively evaluated the rice quality (appearance, milling, cooking, and eating quality) of different nitrogen content management practices by the principal component analysis method and found that low nitrogen conditions were better for rice quality. Fei et al. [21] used TOPSIS with the EW method to identify the optimal N fertilizer management practices leading to high yield, quality, and N use efficiency in rice. Zheng et al. [22] used a TOPSIS assessment of the effects of humic acid on japonica rice production under different irrigation practices. Because of the difference in calculation mechanism and human factors, the evaluation results of these methods vary. To solve the problem of inconsistent conclusions from multiple single evaluation methods and obtain highly objective and accurate evaluation results, a combination evaluation model was put forward [23]. The Fuzzy Borda combination evaluation model considers the evaluation value and the ranking value of a single comprehensive evaluation method, and the evaluation results are more accurate and objective [24]. Therefore, it is very important to determine the indexes and methods of rice quality evaluation under different irrigation and fertilization methods and provide water and fertilization management models to improve rice yield and quality.

The following objectives were derived for this study: (i) to explore the effects of rice yield, quality, enzyme activity, and soluble sugar content by different irrigation and fertilizer treatments; (ii) to identify the high-yield and high-quality optimal management of six treatments by overall evaluation methods (GRA, MFA, TOPSIS, Fuzzy Borda); (iii) to obtain the characteristic index and evaluation methods that can quickly evaluate the comprehensive quality and yield of rice. This study will not only guide fertilizer management to increase rice yield and quality under CI, but also help provide scientific evaluation methods and indexes to reduce environmental pollution and waste of human and material resources.

2. Materials and Methods

2.1. Experimental Design

The study area was located at the Kunshan Experimental Station of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering of Hohai University (34°15′21″ N, 121°05′22″ E) during 2020~2021 (Figure 1). This area belongs to the subtropical monsoon climate zone in southeast China. The local way of farming was to practice rice–wheat rotation. The soil type was yellow-dark-yellow hydromorphic. The soil organic matter was 21.71 g/kg, total nitrogen 1.79 g/kg, total phosphorus 1.4 g/kg, total potassium 20.86 g/kg, pH value 7.4 in the 0–18 cm layer, and the bulk density of 0–30 cm soil was 1.32 g·cm⁻¹ [5].

The tested rice variety was Su Xiang Jing, the plant spacing and rowing were 25 cm, and the emergence quantity per hole was 3~4. We set two factors in the experiment, namely, irrigation mode and fertilization mode. The irrigation mode included controlled irrigation (the paddy field water depth was only kept at 5~25 mm in the re-greening stage, then we avoided the water layer after irrigation in the other stages, namely, CI), and flooding irrigation (except for the late tillering and yellow maturity stages, the paddy field water depth after transplanting was maintained at 30~50 mm, namely, FI). The fertilization mode was the straw returning treatment (in addition to applying the conventional fertilizer, the straw returning 3000 kg/hm² was also applied), organic fertilizer treatment (based on applying the conventional fertilizer, organic fertilizer 5063 kg/hm² was also applied), and conventional fertilizer treatment (the fertilization time and amount were set according to local farmers’ habits in

Table 1. Type, date, and amount of fertilizer application.

Fertilizer	Base Fertilizer (BF)		Tillering Fertilizer (TF)	Panicle Fertilizer (PF)	Total
Type	Compound fertilizer N: P2O5: K2O = 19%: 9%: 17%	Urea: TN ≥ 46.0%	Urea: TN ≥ 46.0%	Urea: TN ≥ 46.0%
Date	3 July		11 July	16 August
N (kg/hm2)	84.00	103.95	69.30	55.44	312.69
P2O5 (kg/hm2)	47.25				47.25
K2O (kg/hm2)	89.25				89.25

). Irrigation and fertilizer management with different treatments in the field were as follows. (i) CS (CI + straw returning); (ii) CM (CI+ organic fertilizer); (iii) CF (CI + conventional fertilizer); (iv) FS (FI + straw returning); (v) FM (FI + organic fertilizer); (vi) FF (FI + conventional fertilizer). The experiment established three replications and a total of 18 plots that were 18 m².

2.2. Growth and Yield Indicators Measurement

The dry matter weight of culms, leaves, panicles, and roots of rice was measured by three plants with uniform growth in each plot at the harvest stage. Five rice plants with uniform growth from each plot were selected, the average number of effective panicles per cave was measured, and the number of effective panicles per square meter was calculated. The total number of grains per panicle and the number of fruit grains borne per panicle were measured to calculate the number of grains per panicle and seed setting rate. From each plot we randomly took 1000 grains of rice and weighed them, which was recorded as a thousand seed weights. Irrigation water use efficiency (IWUE) was estimated as the ratio of grain yield to the amount of irrigation water use (IWU): IWUE = yield/IWU.

2.3. Quality Indicators Measurement

We used imaging technology and long object automatic segmentation technology combined with artificial guidance to identify and measure the chalky grain rate, chalkiness, transparency, grain length, and the length-to-width ratio of rice. Adhesive strength and alkali spreading value were determined according to the NY/T 83-2017 “Determination of Rice Quality” standard. The amylose content was determined by the spectrophotometry method in rice following the NY/T 2639-2014. According to the GB/T 15682-2008 standard, the taste value of the rice was determined through the sensory identification of the personnel, including the smell, appearance, structure, palatability, taste, and other indicators. The Dumas principle was used for protein content according to NY/T 2007-2011.

The grain ground into a fine powder (3.5 g, based on 14% moisture) and distilled water (24.5 g) were mixed in an RVA tank (sample container) to obtain a sample weight of 28 g. Breakdown value (BDV), peak viscosity value (PKV), recooling value (RCV), cool paste viscosity (CPV), hot paste viscosity (HPV), and setback value (SBV) were determined with a Rapid Visco-Analyzer (RVA) model 3D (Newport Scientific-Super3, Warier Wood, Australia) based on NY/T1753-2009 [25].

2.4. Enzyme Activity and Soluble Sugar Content Measurement

The activity of the enzymes related to starch synthesis in grain was varying under different flowering times. Five flowering plants were selected and labeled, and samples were taken 40 days after flowering. From 9:30 to 10:30 in the morning on sunny days, 50 grains were taken and promptly frozen in a liquid nitrogen tank for 3 min, and then refrigerated at −80 °C. The activities of alpha-amylase, ADP-glucose pyrophosphorylase (ADPG), and granule-bound starch synthase (GBSS) amylase were measured [26].

At the harvest stage, three plants with uniform growth potential were selected from each plot, separated by stem, leaf, and grain, and dried in the air-blowing thermostatic oven (kill green at 105 °C for 30 min, dried at 80 °C to constant weight). After crushing and sifting 100 meshes, the content of soluble sugar in each organ was measured by the anthrone colorimetric method [27].

2.5. Construction of Comprehensive Quality Evaluation Model

Three single comprehensive evaluation methods, namely, grey relational degree analysis (GRD), the technique for order preference by similarity to an ideal solution (TOPSIS), and the entropy method (ETM), were used to evaluate rice quality and yield [28,29]. The pre-compatibility test was performed on the ranking results obtained by the three single comprehensive evaluation methods, that is, the Kendall-W consistency test [23]. The Kendall-W under three single evaluation methods was considered to show the divergence degree between the actual coincidence and the maximum coincidence of sample data. The closer the Kendall-W was to 1, the higher the compatibility of the evaluation method. Under the condition that the consistency test passed, the results of the single comprehensive evaluation method were evaluated by Fuzzy Borda comprehensive evaluation methods to construct a comprehensive evaluation system for rice quality and yield [19].

The steps of the Fuzzy Borda combination evaluation method are as follows.

First, the membership goodness of the three evaluation methods score (μ_ij) is calculated:

$$u_{\mathrm{ij}}=\frac{X_{ij}-\min (X_{ij})}{\max (X_{ij})-\min (X_{ij})}\times 0.9+0.1$$

(1)

where $X_{ij}$ is the score of the treatment i evaluated by the j method, and $u_{\mathrm{ij}}$ is the membership degree of the i evaluated by the j method as “good.”

Second, the fuzzy frequency ($p_{ij}$) is calculated:

$$p_{ij}={\displaystyle \sum _{j=1}^m{\delta }_{ih}\times }{u}_{\mathrm{ij}}\begin{array}{ccc}& & h=1,2,\cdots n\end{array}$$

(2)

$${\delta }_{ih}=\left\{\begin{array}{l}1,\mathrm{treatment}i\mathrm{rank}\mathrm{in}h\\ 0,\mathrm{treatment}i\mathrm{does}\mathrm{not}\mathrm{rank}\mathrm{in}h\end{array}\right\}$$

(3)

Normalized fuzzy frequency ($W_{hi}$):

$$W_{hi}=\frac{p_{hi}}{{\displaystyle \sum _{h=1}^n{p}_{hi}}}$$

(4)

$Q_{hi-j}$ represents the score of the treatment i rank in the position h evaluated by the j method.

$$Q_{hi-j}=\frac{(n-h)\times (n-h+1)}{2}$$

(5)

Finally, the Fuzzy Borda (F_i) number is calculated:

$$F_i={\displaystyle \sum _{j=1}^m{W}_{hi}\cdot }{Q}_{hi-j}\begin{array}{ccc}& & (i\end{array}=1,2,\cdots n;j=1,2,\cdots m)$$

(6)

Rank is according to F_i, and the higher the value, the better the comprehensive evaluation of rice yield and quality.

2.6. Statistical Analysis

Microsoft Excel (2019) was used to calculate and process the rice yield, quality, enzyme activity, and soluble sugar content, and the chart was drawn. The rice quality traits were determined through dimensionless processing under the vector normalization method by Microsoft Excel, then we calculated the GRD, ETM, TOPSIS, and Fuzzy Borda. SPSS version 23.0 (SPSS, IBM, Chicago, IL, USA) was used to execute the Pearson correlation analysis. Multi-panel scatter plots and a correlation analysis figure were drawn with Origin 2022 (OriginLab Corporation, Northampton, MA, USA).

3. Results

3.1. Rice Yield

Irrigation and fertilizer management had an extremely significant effect on the accumulation of dry matter (culms, leaves, panicles, roots), as CS > FM > FF > CM > CF > FS (Figure 2). In terms of aboveground dry matter (culms, leaves, panicles), under controlled irrigation (CI), CS increased it by 24.84% and 25.70% compared with CM and CF, indicating that the straw returning was more beneficial to the accumulation of aboveground dry matter than organic fertilizer and conventional fertilizer. CF was slightly lower than FF, but CS was higher than FS, which indicated that CI was not conducive to the accumulation of aboveground dry matter, but straw returning could alleviate the effect of CI. The dry matter of panicles accounted for 56.41~63.57% of the aboveground dry matter. The CS of panicles was higher than FS and CF by 27.91% and 16.54%, respectively. In terms of underground dry matter quality (roots), CS > CM > CF > FM > FF > FS, indicating that CI increased the amount of root dry matter compared with flooding irrigation (FI). In conclusion, the synergistic application of straw returning and CI can promote dry matter accumulation in rice.

The interaction of irrigation level and fertilizer level had no significant effect on the other four indicators (p > 0.05), except for the extremely significant impact on the effective panicle number (EPN) and number of grains per panicle (NGPP) (p < 0.01) or the significant impact on panicle number (PN) and irrigation water use efficiency (IWUE) (p < 0.05) (

Table 2. Rice yield and its component factors under different water and fertilizer management modes.

Treatments	Panicle Number (PN)	Effective Panicle Number (EPN)/m−2	Number of Grains per Panicle (NGPP)	Seed Setting Rate (SR)/%	Thousand Seed Weight (TSW)/g	Yield/kg·hm−2	Irrigation Water Use (IWU)/mm	Irrigation Water Use Efficiency (IWUE)/kg·m−3
CM	13 ± 3 b	480 ± 40.5 a	157 ± 10.75 c	0.79 ± 0.1 ab	24.3 ± 0.86 ab	12247.98 ± 2091.28 ab	652.35 ± 5.26 bc	1.88 ± 0.31 b
CS	15 ± 2.08 ab	444 ± 7.09 ab	160 ± 3.22 c	0.81 ± 0.13 a	24.17 ± 0.46 ab	11787.15 ± 1547.26 ab	487.16 ± 176.09 c	2.42 ± 0.3 a
CF	16 ± 0 ab	448 ± 3.41 ab	174 ± 1.52 b	0.62 ± 0 b	23.29 ± 0.92 b	9594.76 ± 357.18 b	422.38 ± 13.88 c	2.27 ± 0.01 a
FM	18 ± 3.61 a	420 ± 10.5 b	196 ± 1.15 a	0.73 ± 0.05 ab	24.4 ± 0.21 ab	12459.37 ± 817.19 a	966.53 ± 44.88 ab	1.29 ± 0.03 c
FS	13 ± 1.53 b	420 ± 21.22 b	189 ± 9.05 a	0.71 ± 0.04 ab	24.89 ± 0.59 a	11903.38 ± 972.55 ab	935.25 ± 354.72 ab	1.27 ± 0.15 c
FF	17 ± 1 ab	476 ± 0 a	155 ± 1.01 c	0.69 ± 0.07 ab	24.27 ± 0.24 ab	10570.11 ± 1275.13 ab	1157.88 ± 233.05 a	0.91 ± 0.08 c
Significance test
Irrigation level	NS	NS	**	NS	NS	NS	**	**
Fertilizer level	NS	NS	*	NS	NS	*	NS	NS
Irrigation level ×Fertilizer level	*	**	**	NS	NS	NS	NS	*

Note: * means significant difference (p < 0.05). ** means extremely significant difference (p < 0.01), while NS means no significant difference (p > 0.05). Different lowercase letters indicate significant differences at the 0.05 level in the same column.

). Under the application of organic fertilizer and straw returning, CI increased the EPN and setting rate (SR) compare with FI. The thousand seed weight (TSW) of each treatment group varied from 23.29~24.89 g, which showed that the seed size and fullness of rice were little affected by irrigation and fertilization. The yield of FI was higher than CI by 0.99~10.17% under different fertilizer treatments, especially since there was little difference between the CS and CF treatment groups. Compared with CF, the yields of CM and CS increased by 22.85% and 27.65%, respectively. The irrigation water use (IWU) was CF < CS < CM < FS < FM < FF and the IWUE was CS > CF > CM > FM > FS > FF. The IWUE of the CS treatment was higher than the CF and CM treatments by 0.15 and 0.54 kg·m⁻³, showing that straw returning could significantly reduce irrigation water and improve the comprehensive economic value of rice.

There were interrelated correlations among yield indexes (Figure 3). A correlation analysis was performed among culms, leaves, panicles, roots, PN, EPN, NGPP, SR, TSW, yield, IWU, and IWUE. The yield had a positive correlation with SR and TSW (0.85** and 0.52*), because the SR of rice is an important component of yield and improving the SR can directly increase the number of grains per ear, causing an increase in the yield of rice. NGPP had a negative correlation with EPN and dry matter of roots (−0.81** and −0.58*) during the harvest stage. IWU had a positive correlation with TSW (0.53*) and a negative correlation with IWUE (−0.79**).

3.2. Rice Quality

The quality properties of rice under different water and fertilizer management modes are shown in

Table 3. Quality properties of rice under different water and fertilizer management modes.

Treatments		CM	CS	CF	FM	FS	FF
Milling quality	Brown rice rate/%	82.43 ± 0.12 a	82.93 ± 0.45 a	82.5 ± 0.17 a	82.43 ± 0.47 a	82.67 ± 0.23 a	83 ± 1 a
	Polished rice rate/%	54.2 ± 3.05 ab	57.23 ± 2.25 a	51.6 ± 0.36 b	54.97 ± 3.73 ab	57.3 ± 1.39 a	53.4 ± 0.35 ab
	Head milled rice rate/%	68.37 ± 1.06 ab	69.7 ± 1.35 a	67.8 ± 0.4 bc	66.23 ± 1.16 c	67.87 ± 0.38 bc	68.1 ± 0.1 ab
Appearance quality	Chalky grain rate/%	24.67 ± 2.31 a	28.67 ± 6.81 a	19 ± 1.91 a	21.33 ± 4.51 a	25.33 ± 7.64 a	20 ± 0.69 a
	Chalkiness/%	4.43 ± 0.64 a	5.03 ± 2.1 a	3.2 ± 0.36 a	4 ± 0.98 a	4.37 ± 1.63 a	3 ± 0.17 a
	Transparency/level	1 ± 0 a	1 ± 0 a	1 ± 0 a	1.33 ± 0.58 a	1 ± 0 a	1 ± 0 a
	Grain length/mm	5.03 ± 0.06 a	5.07 ± 0.06 a	5 ± 0 a	5.03 ± 0.06 a	5.03 ± 0.06 a	5 ± 0 a
	Length-to-width ratio	1.8 ± 0 a	1.77 ± 0.06 a	1.8 ± 0 a	1.73 ± 0.06 a	1.77 ± 0.06 a	1.8 ± 0 a
Cooking quality	Adhesive strength/mm	78 ± 5.29 ab	69.67 ± 2.52 b	80 ± 5.29 a	70.67 ± 6.43 b	69 ± 7 b	86 ± 5.29 a
	Amylose content/%	9.03 ± 0.06 a	8.93 ± 0.57 a	8.6 ± 0.2 a	8.83 ± 0.9 a	8.87 ± 0.61 a	9 ± 0.17 a
	Alkali spreading value/level	6.5 ± 0.1 a	6.37 ± 0.06 a	6.4 ± 0.1 a	6.43 ± 0.15 a	6.63 ± 0.06 a	6.6 ± 0.26 a
Protein		7.86 ± 0.17 a	8.39 ± 0.82 a	8.03 ± 0.04 a	8.5 ± 0.85 a	8.71 ± 0.33 a	8.3 ± 0.22 a
Taste value		83.67 ± 0.58 a	82.33 ± 1.53 a	82 ± 1 a	81.33 ± 2.52 a	82 ± 1.73 a	84 ± 1.73 a
RVA	Breakdown value/BDV	67 ± 3.87 a	62.63 ± 1.22 ab	63.9 ± 1.11 ab	56.33 ± 7 b	55.7 ± 2.02 b	61.5 ± 5.89 ab
	Peak viscosity value/PKV	209.33 ± 6.51 a	192.33 ± 7.23 b	202 ± 4.36 ab	195 ± 10.15 ab	187.67 ± 6.43 b	197 ± 6.08 ab
	Recooling value/RCV	45.3 ± 1.31 a	44.33 ± 0.81 a	45.8 ± 0.53 a	45.17 ± 2.04 a	45.07 ± 0.74 a	45 ± 1.8 a
	Cool paste viscosity/CPV	187.33 ± 10.41 a	173.67 ± 6.81 b	184 ± 2.65 ab	183.67 ± 4.93 ab	176.67 ± 3.79 ab	180 ± 5 ab
	Hot paste viscosity/HPV	142 ± 10.15 a	129.33 ± 6.43 a	139 ± 2 a	138.67 ± 4.51 a	131.67 ± 4.73 a	135 ± 3.61 a
	Setback value/SBV	−21.7 ± 4.36 b	−18.3 ± 1.59 b	−18.1 ± 0.1 b	−11.17 ± 5.15 a	−10.59 ± 2.67 a	−16.5 ± 0.5 ab

Note: Different lowercase letters indicate significant differences at the 0.05 level in the same line.

. The head milled rice rate of the milling quality was CS > CM > FF > CF > FS > FM, CS was higher than FS by 1.83%. Under CI, the polished rice rate was CS > CM > CF, showing that the combination of CI and straw returning can improve the milling quality. The chalky grain rate and chalkiness under the CF treatment were the lowest, which were 9.67% and 1.83% lower than CS, 5.67% and 1.23% lower than CM, respectively. Compared with FF, CF tended to increase chalkiness but had no effect on transparency, grain length, and length-to-width ratio. In general, the interaction of controlled irrigation, straw returning to the field, and organic fertilizer worsened the appearance quality.

The protein content was CS > CF > CM and FS > FM > FF (Table 3). CS decreased protein content by 3.81% compared with FS and increased it by 4.29% compared with CF, indicating that straw returning integrated the negative effects of CI on protein content. The CF adhesive strength and amylose content decreased by 6.97% and 4.44% compared to FF, indicating CI improved rice cooking quality. CS and CM increased amylose content by 0.33% and 0.43%, respectively, compared with CF content, and reduced adhesive strength content more than CF by 10.33% and 2.00%, respectively. These results indicated that although the addition of straw returning or organic fertilizer could improve the protein of rice, the rice cooking quality was worse and the taste was softer.

Table 3 also shows that the BDV and PKV of paddy rice under CI were significantly higher than those under FI, and the RCV showed no significant difference between irrigation and fertilizer, so CI reduced the hardness of the cooked rice and improved the expansibility, thereby increasing the taste. Compared with FM, FS and FF, the SBV under CM, CS, and CF decreased by 48.53%, 42.13%, and 8.84%, respectively, indicating that the use of organic manure and straw returning to the field under CI more extensively inhibited the reduction value and improved the eating palatability of cooked rice.

3.3. Enzyme Activity and Soluble Sugar Content

The formation of rice quality was closely related to the synthesis and accumulation of starch in the grain and the activity of related enzymes.

Table 4. Effect of enzyme activity under different irrigation and fertilizer management modes.

Treatments	Alpha-Amylase	ADP-Glucose Pyrophosphorylase (ADPG)	Granule-Bound Starch Synthase (GBSS)
CM	2.06 ± 0.12 bc	6.95 ± 0.78 a	0.61 ± 0.01 ab
CS	2.37 ± 0 a	7.39 ± 0.84 a	0.62 ± 0.02 a
CF	2.04 ± 0.07 bc	6.44 ± 0.06 a	0.56 ± 0.01 bc
FM	1.89 ± 0.04 c	6.91 ± 0.54 a	0.56 ± 0.03 bc
FS	2.21 ± 0.19 ab	7.38 ± 0.45 a	0.59 ± 0.04 abc
FF	2.11 ± 0.09 b	7.42 ± 0.81 a	0.54 ± 0.01 c
Significance test
Irrigation level	NS	NS	*
Fertilizer level	**	NS	*
Irrigation level × Fertilizer level	NS	NS	NS

Note: Different lowercase letters indicate significant differences at 0.05 level in the same line. * means significant at p < 0.05; ** means significant at p < 0.01.

shows that irrigation level or fertilizer level had a significant impact on GBSS (p < 0.05), and the fertilizer level had an extremely significant impact on alpha-amylase. Under the CS treatment, alpha-amylase, ADPG, and GBSS were the highest, which were 0.16, 0.01, and 0.03 g higher than FS. The GBSS showed CS > FS, CM > FM, indicating amylose content improved mainly because straw returning and organic fertilizer increased the activity of GBSS. The alpha-amylase, ADPG, and GBSS were CS > CM > CF, and the alpha-amylase and GBSS under FS were higher than FF and FM. These results indicated that straw returning increased the activities of alpha-amylase and GBSS in harvest-stage grains.

The soluble sugar accumulation (stem, leaf, grain) showed a significant correlation under different management modes (Figure 4). The soluble sugar accumulation showed FF > CF, which suggested that under drought conditions of CI, the photosynthesis activity was restricted, leading to the starch degrading the soluble sugar of rice, then to providing energy and carbon. Under organic manure and straw returning, the soluble sugar of stems showed CM > FM and CS > FS. The soluble sugar of the leaf under CM and CS was greater than that under CF by 42.10% and 30.56%, respectively, under CI. The soluble sugar of grain showed FF > CF, FM > CM, FS > CS, indicating that CI was not more conducive to the formation of grains than FI, and the application of non-conventional fertilizer could not change the adverse effect of CI on grains.

Correlation analysis of the rice quality, enzyme activity, and soluble sugar content is indicated in Figure 5. The taste value of the cooked rice was significantly positively correlated with the length-to-width ratio, adhesive strength, amylose content, and BDV The correlation coefficients that positively correlated between the taste value and soluble sugar accumulation (stem, leaf, grain) were 0.77, 0.66, 0.49, respectively; these results indicated that soluble sugars in rice were the main components affecting the taste value of rice. The taste value was significantly negatively correlated with the SBV, the correlation value was 0.63. The protein content of the rice had a significantly positive correlation with the polished rice rate and SBV, the correlation values were 0.69 and 0.9, respectively. The protein content had a significantly negative correlation with taste value, length-to-width ratio, adhesive strength, BDV, PKV, CPV, and HPV.

3.4. Multiple Evaluation Models

The best treatments corresponding to every single indicator (yield, quality, enzyme activity, and soluble sugar content) were different. Therefore, comprehensive evaluation models of rice quality needed to be constructed, and each treatment was ranked to obtain the optimal treatment. The rice quality traits were established through dimensionless processing under the vector normalization method, so that they could be compared under the same standard, and the comprehensive performance of rice quality evaluated more accurately under different irrigation and fertilization treatments. Grey relational degree analysis (GRD), the entropy method (ETM), and the TOPSIS method were used to evaluate rice quality; the best treatment was CS, but the worse treatment differed (

Table 5. The results of the different evaluation models.

Treatments	Grey Relational Degree Analysis		Entropy Method		TOPSIS		Fuzzy Borda
	Values	Rank	Values	Rank	Values	Rank	Values	Rank
CM	0.961	2	0.537	2	0.500	2	10.000	2
CS	0.969	1	0.655	1	0.578	1	15.000	1
CF	0.949	5	0.324	6	0.354	6	0.725	6
FM	0.950	4	0.391	5	0.443	4	2.562	4
FS	0.961	3	0.498	3	0.466	3	6.000	3
FF	0.931	6	0.400	4	0.409	5	1.705	5

). Hence, a Fuzzy Borda combination evaluation model was constructed to determine a highly accurate ranking result because the ranking results were not completely consistent with the various evaluation methods. The Kendall-W coefficient consistency test was performed on the results of every single comprehensive evaluation method. The Kendall-W concord coefficients were 0.750, χ² = m(n − 1) W were 11.25 > χ²_0.05/5 = 11.07, indicating that the three single comprehensive evaluation method results had a certain degree of compatibility and could be combined for Fuzzy Borda comprehensive evaluation. According to the results of the Fuzzy Borda model, the rice quality under the CS treatment was the best (Table 5).

The Kendall correlation coefficient calculations performed on the ranking results of the three methods are shown in

Table 6. Correlation analysis of the evaluation values of the different evaluation models.

Correlation Coefficient	Grey Relational Degree Analysis	Entropy Method	TOPSIS	Average
Grey relational degree analysis		0.690	0.828 *	0.759
Entropy method	0.690		0.867 *	0.779
TOPSIS	0.828 *	0.867 *		0.848

Note: * means significant difference (p < 0.05).

. The average correlation coefficients of each evaluation method and the other methods were within 0.759~0.848, indicating that a certain correlation was obtained between the value results of every single model. The TOPSIS of the average correlation coefficient was 0.848, the highest compared to the GRD and ETM, indicating that TOPSIS might be a suitable model for evaluating the comprehensive rice quality.

Adhesive strength, ADPG, HPV, and soluble sugar (leaf) had a significant positive correlation with the TOPSIS comprehensive quality score (Figure 6), with correlation coefficients of 0.968, 0.866, 0.695, and 0.560, respectively, indicating that they could be used as an ideal index to represent the rice comprehensive quality. Only improving the yield and quality of rice can effectively guarantee food security; thus, they have equal importance in evaluating treatments. Rice quality was composed of 25 indicators in this paper, so we selected the representative quality index (adhesive strength, HPV, ADPG, soluble sugar (leaf)) and the yield to be comprehensively evaluated. The TOPSIS model further used the representative quality index to determine the optimal combination of irrigation and fertilization applied. Based on the TOPSIS model the results showed that the optimal rice quality was obtained under CS; the evaluation value was 0.641 in 2020 and 0.649 in 2021. The FF treatment was the worst; the evaluation value was 0.273 in 2020 and 0.364 in 2021, as seen in

Table 7. Ranking and score of comprehensive evaluations of quality by TOPSIS.

Treatments	2020				2021
	D+	D−	Score	Ranking	D+	D−	Score	Ranking
CM	0.465	0.625	0.574	2	0.288	0.44	0.604	2
CS	0.413	0.738	0.641	1	0.309	0.573	0.649	1
CF	0.754	0.648	0.462	4	0.504	0.324	0.391	5
FM	0.787	0.394	0.334	5	0.344	0.433	0.557	3
FS	0.623	0.633	0.504	3	0.362	0.326	0.474	4
FF	0.748	0.280	0.273	6	0.495	0.284	0.364	6

Note: D⁺ and D⁻ represent positive and negative Euclidean distance, respectively.

.

4. Discussion

4.1. Effects of Irrigation and Fertilization Management on Yield

It is a great challenge to synergistically increase the yield and water saving of rice. In this study, the IWU and yield under CF were decreased by 63.52% and 9.23 compared with FF (Table 1), indicating CI can save a lot of water, but it had a negative impact on yield. This conclusion was consistent with Arif et al. [30], who summarized 31 published studies on CI and concluded that 92% of the CI treatments resulted in reduced yield compared with continuously flooded treatments. Straw returning and organic fertilizers instead of chemical fertilizers were widely used mainly because they improve rice yield and soil quality [31]. CS values of the aboveground dry matter and roots were higher than those of CM and CF because straw returning can increase the content of N fertilizer, significantly reduce the growth inhibition at the early stages of rice growth, and increase dry matter accumulation [32]. CI rice showed greater root activity compared to FI; thus, the rice root dry matter under CI was higher [33]. The SR of rice is an important component of yield, and improving the SR can directly increase the number of grains per ear, causing yield to increase [34]. The SR and IWUE had the highest CS values of 0.81 and 2.42, respectively (Table 2), because straw returning increased the activities of ADPG of grain in middle and late grain filling owing to improving the SR and grain weight, causing the yield to increase (Figure 5); in addition, CI can reduce water. Under the CS treatment, alpha-amylase, ADPG, and GBSS were the highest compared to FS; this was mainly because the α and β amylase activities in the stem and sheath of rice were significantly increased by CI after flowering, and α amylase activity was more significant [26]. These results indicated that the combined application of straw returning and CI can greatly increase rice yield, IWUE, and enzyme activity.

4.2. Effects of Irrigation and Fertilization Management on Quality

Rice quality is a comprehensive characteristic and includes milling quality, appearance quality, eating and cooking quality, and nutritional quality [35]. The milling quality of CF was lower than that of FF because grain quality is very sensitive to soil drought during the rice grain-filling period, and even a moderate soil drought under CI would weaken the grain quality of rice varieties. The CF adhesive strength and amylose content decreased by 6.97% and 4.44% compared to FF. The reason may be that the CI regime can promote material transfer rates and rice grain filling and can ultimately improve rice cooking quality [10]. Chalkiness adversely affects the milling, cooking, and appearance of rice grains [36]. The chalkiness under CF was higher than that under FF by 6.25%, indicating that the milling and cooking quality worsened (Table 3). The results of this study were not consistent with those of Ishfaq et al., who studied the chalkiness of paddy rice under CI [37], which may be related to different varieties, fertilizer operation, soil moisture status, soil texture, and climatic conditions during the experiment [3,17]. High BDV, large PKV, and low SBV could form good taste value for rice RVA flour [38], the BDV and PKV under CF were higher than those under FF, while the SBV under CF was lower than FF. In conclusion, although CI reduced the milling quality of rice, it had no effect on the transparency, grain length, and length-to-width ratio, or increased cooking quality.

Previous studies have shown that straw return and organic fertilizer increase rice yield and grain quality of rice under FI, but few studies have investigated the effect of non-conventional fertilizer on rice quality under CI. In this study, compared with CF, CS increased milling quality and protein, but decreased cooking quality and appearance quality. This was primarily because straw returning provided many nutrients (nitrogen, phosphorus, potassium, and silicon) and organic matter (cellulose, lignin, protein) for rice, but the straw returning also produced toxic gases for the soil environment. The rice roots deteriorated severely, and the nutrient uptake was reduced, thus worsening the appearance quality of the grain [36,39]. Rice protein can improve the nutritional condition of the human diet [40]. Compared with FS, CS decreased protein content by 3.81%, and taste value increased by 0.40%, which was mainly because the protein content had a significantly negative correlation with taste value; the correlation coefficient was positive between the taste value and soluble sugar accumulation (Figure 5). The soluble sugar of grain correlation coefficient was 0.49 with taste value because soluble sugar is a very important osmotic regulating substance, which was the key factor for the development of fresh and sweet flavors in grain [41]. Grain showed FF > CF, FM > CM, FS > CS, indicating that CI was not conducive to the formation of grains and worsened the taste value, and the application of non-conventional fertilizer could not change the adverse effect of CI on grains. The results showed that straw returning could alleviate the negative effect of controlled irrigation on protein reduction, but the appearance quality of rice was worse.

4.3. Multiple Evaluation Models of Rice Quality

Rice quality as a comprehensive index is affected by multiple properties. It is unreasonable to use the pros and cons of one or several traits to characterize the rice quality alone, without considering the relationship among those indicators [28]. Therefore, this study constructed comprehensive evaluation models of rice quality, and each treatment was ranked to obtain the optimal treatment. TOPSIS might be a suitable model for evaluating the comprehensive rice quality compared to GRD, ETM, and Fuzzy Borda; this result was consistent with Fei et al. [21]. First, the TOPSIS of the average correlation coefficient was highest at 0.848 and the rank was consistent with Fuzzy Borda (Table 5). Second, Fuzzy Borda needed to evaluate the results of a variety of computationally heavy evaluation methods. Only improving the yield and quality of rice can effectively guarantee food security; thus, they have equal importance in evaluating treatments [3]. Therefore, TOPSIS was used to evaluate the different irrigation and fertilization regimes by representative indicators of rice (adhesive strength, HPV, ADPG, soluble sugar (leaf) and the yield), which did not compromise the evaluation results and also saved manpower and financial resources, helping consumers assess their willingness to purchase. The larger the evaluation value, the better the evaluation object [28]. The study demonstrated that the comprehensive evaluation score of rice quality was better under CS, which was the highest (Table 7). Therefore, the optimal irrigation and fertilization management (CS) was the most feasible choice for achieving the high yield, high quality, and high efficiency of rice.

5. Conclusions

The present study demonstrated the feasibility of using yield, quality, enzyme activity, and soluble sugar including 43 indicators for the evaluation of rice quality in water-saving irrigation fields in southeast China. The IWUE under the CS treatment was higher than under the CF and CM treatments by 0.15 and 0.54 kg·m⁻³. The head milled rice rate and polished rice rate were CS > CM > CF. The chalky grain rate and chalkiness under the CF treatment were the lowest, which were 9.67% and 1.83% lower than CS, and 5.67% and 1.23% lower than CM, respectively. The CS and CM increased amylose content by 0.33% and 0.43%, respectively, compared with CF, reduced adhesive strength content more than CF by 10.33% and 2.00%. In general, the interaction of CI with straw returning or organic fertilizer improved the milling quality but decreased the appearance and cooking quality. Under CS treatment, alpha-amylase, ADPG, and GBSS were the highest, which were 0.16, 0.01, and 0.03 g higher than FS, thus improving the seed setting rate. CS decreased protein content by 3.81% compared with FS, and the soluble sugar accumulation (stem, leaf, grain) showed FF > CS. The TOPSIS of the average correlation coefficient of different evaluation models was the highest at 0.848, which was the most suitable for the comprehensive quality evaluation of rice. The five indexes (adhesive strength, HPV, ADPG, soluble sugar (leaf), yield) can be used to represent the comprehensive rice quality because it had a significant positive correlation with the TOPSIS model quality score. Based on the TOPSIS model and five indicators, the optimal combination of irrigation and fertilization mode was CS. The present study suggested that the combination of CI and straw returning not only achieved water saving and high yield but also improve the quality of rice.