Next Article in Journal
Enhancing Red Currant Berry Quality through Fertilization Using Compost from Municipal Sludge and from Vegetal Waste
Previous Article in Journal
Advancing Agricultural Predictions: A Deep Learning Approach to Estimating Bulb Weight Using Neural Prophet Model
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 13
Issue 5
10.3390/agronomy13051364
agronomy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Amendments of Severe Saline-Sodic Paddy Land: Optimal Combination of Phosphogypsum, Farmyard Fertilizer, and Wood Peat

by
Guokang Duan
1,2,3,
Miao Liu
1,3,*,
Zhengwei Liang
1,3,*,
Mingming Wang
1,3,
Haoyu Yang
1,3,
Yang Xu
1,3,
Tianhe Yu
1,3,
Yangyang Jin
1,3,
Jiafeng Hu
1,3 and
Junqing Liu
1,2,3
1
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Jilin Da’an Farmland Ecosystem National Observation and Research Station, Da’an 131317, China
*
Authors to whom correspondence should be addressed.
Agronomy 2023, 13(5), 1364; https://doi.org/10.3390/agronomy13051364
Submission received: 18 April 2023 / Revised: 3 May 2023 / Accepted: 10 May 2023 / Published: 12 May 2023
(This article belongs to the Topic Sustainable Crop Production from Problematic Soils to Ensure Food Security)
Browse Figures
Versions Notes

Abstract

:
We aimed to determine the optimal combination of amendments to increase rice yields in saline-sodic soil. The effects of different proportions of phosphogypsum (P), farmyard fertilizer (F), and wood peat (W) across the main growth period of rice were studied. A total of 14 treatments were designed based on the “3414” fertilizer effect field experiment scheme, with 3 factors (P, F, and W) and 4 application levels per factor. Application of a combination of P, F, and W reduced soil pH and electrical conductance (EC) (p < 0.05), increasing rice yields. The theoretical rice yield after treatment P2F2W2 (P 30, F 50, and W 30 t·ha−1) was 5819.20 kg·ha−1, which was 32.52-fold higher than that after P0F0W0 (P, F, and W, 0 t·ha−1). Panicle weight, number of total filled grains, total grain weight, and seed-setting rate were 9.76, 17.35, 32.11, and 3.96 times higher than those in the control treatment, respectively. Compared with the control P0F0W0 treatment, soil pH in P2F2W2 in 0–5, 5–10, 10–15, and 15–20 cm depth decreased by 12.69, 12.32, 11.18, and 10.70%, respectively, and soil EC was 1.06-fold, and 70.79, 49.30, and 47.76% higher, respectively. Overall, we found that the P2F2W2 treatment, with a combination of P, 29.09–32.38 t·ha−1; F, 40.36–46.97 t·ha−1; and W, 19.57–23.95 t·ha−1 was optimal in this experiment.

1. Introduction

Saline-alkali soils, characterized by high salinity and alkalinity, are widely distributed and found in more than 100 countries globally [1]. Within the context of arable land, this soil type is an important reserve resource that impacts world food security. In China, saline-alkali land covers an area of 0.1 billion hectares (ha), and approximately 37 million hectares of this land are available for use [2]. A portion of this available land is the Northeast saline-sodic land (i.e., 7.65 million hectares), largely concentrated in the Songnen Plain. In terms of physiochemical properties, it is rich in NaCO3 and NaHCO3 [3], which contributes to its highly alkaline state; overall, this stretch of land is not ideal for practicing agriculture [4]. Rice is one of the three major food crops in China, accounting for 27% and 34% of China’s planted area and output, respectively [5,6,7,8]. At present, several studies have shown that efforts to plant rice in saline-alkali land are an effective measure for improving the use of saline-alkali land resources to ensure food security [9,10,11,12]. In terms of measures that have been applied to increase the use of saline-alkali land, amendment application is one of the simplest and most effective methods [13]. Therefore, research delving into the further improvement of this measure is important for increasing the utilization of saline-alkali land. Phosphogypsum (P) is a by-product of the phosphoric acid industry; it mainly consists of calcium phosphate and a small amount of phosphorus. P is available in large quantities across many parts of the world, and more than 22 and 110 million tons of phosphoric acid and P by-products, respectively, are produced annually [14,15]. In terms of production, China is among the largest producers of P, with an annual production of 76 million tons [16]. P is effective at increasing the utilization of saline-alkali land, and the appropriate application amount in severe saline-sodic land is 30 t·ha−1 [17,18,19,20]. Moreover, the application of farmyard fertilizer (F) can improve a variety of soil physiochemical and biological characteristics [21,22,23,24,25]. Regarding rice production, this strategy can significantly up-regulate a variety of growth-linked genes [26], enhance leakage [27], and thus increase crop yield [28]. Additionally, wood peat (W) can be used to optimize saline-alkali soil for agriculture [29]. By reducing pH, total salt content and alkalinity, and bulk density and soil alkalinity, it can increase organic matter [30,31] and promote the absorption of nutrients by plants, thus increasing yield. It has been shown that 30 t·ha−1 is the optimal amount of W that should be added to newly reclaimed land [32]. Numerous studies (theoretical and practical) have been conducted to improve salinization-linked issues in the western Songnen Plain [17,33,34,35]. However, only a few studies have been conducted to determine the optimal manner to combine and apply (i.e., dosage) P, organic fertilizer, and W in this severely saline-sodic land.
The “3414” experimental design refers to a field experiment recording the effect of the fertilizer on plant growth and has been widely used at home and abroad. The experimental design includes 3 factors (here: P, F, and W) and 4 levels of administration, resulting in 14 treatments. It follows a D-optimal design for quadratic regression and has the advantages of reduced required processing and high efficiency [36,37,38]. Thus, this method was used in this experiment to comprehensively analyze and evaluate the effects of different dosage ratios on rice yield and its constituent factors, soil pH, and electrical conductivity (EC). We aimed to elucidate the optimal dosage ratio of amendments to improve rice yield and to provide a theoretical basis to optimize the physiochemical properties of saline-sodic land for the cultivation of rice.

2. Materials and Methods

2.1. Study Site

The study was conducted at the National Field Scientific Observation and Research Station of Farmland Ecosystem (45° 36′ N, 123° 53′ E) in Da’an, Jilin, China, in 2022 (Figure 1). The site has a temperate continental monsoon climate, with an average annual air temperature of 4.3 °C. The average air temperature during the growing season is 13.7 °C, whereas the average air temperature during the non-growing season is −4.4 °C. The average annual rainfall is 431.7 mm, with approximately 56% of rainfall concentrated between July and August, and the annual frost-free period is approximately 137 days. The soil of the experimental site is classified as alkaline salt-affected soil. The salt component in the soil consists mainly of soda (NaHCO3 and Na2CO3).

2.2. Test Materials and Design

2.2.1. Test Materials

The soil tested in this experiment was classified as severe saline-sodic, with the following characteristics: pH 10.1, EC 981.5 µS·cm−1, soluble Ca 1720 mg·kg−1, soluble Na 2450 mg·kg−1, and exchangeable sodium saturation percentage (ESP) 48%. According to the classification by the World Soil Resources Reference Bank (WRB), the soil at the test site is classified as “Solonetz”. The rice cultivar of interest was “Dongdao 122”, independently cultivated by the Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences. Table 1 shows the physicochemical properties of P, F, and W tested. W was provided by the Institute of Soil Science, Chinese Academy of Sciences.

2.2.2. Experimental Design

We designed a pot experiment and adopted the “3414” experimental scheme, which entailed arranging elements in random blocks. Application of the three factors (P, F, and W) was set at four levels (0–3) as follows: no application at level 0; regular application at level 2; and 50% and 150% (i.e., excess application) of level 2 rates at levels 1 and 3, respectively. A total of 14 treatments (Table 2) were randomly arranged, with each treatment repeated five times. Overall, we used 70 pots; a hole was punctured in each pot, and five plants were grown in each hole. The rice was transplanted on May 22, and field management was conducted in accordance with local conventional methods during the whole growth period.

2.3. Determination Index and Method

2.3.1. Agronomic Characteristics of the Main Growth Period

Throughout the rice growth period, the plant height, tiller, and chlorophyll relative content (soil plant analysis development (SPAD)) were evaluated. For SPAD measurements, the most representative plant in each hole was used for SPAD-1 and SPAD-2 measurements by inverting one leaf in each hole. The SPAD values were measured using a SPAD-502 chlorophyll analyzer (Soil-Plant Analyses Development, Minolta Camera Co., Ltd., Osaka, Japan) [39]. Measurements were obtained on 22 July, 1 August, 11 August, 21 August, 1 September, and 11 September. The number of crown roots was calculated as follows:
Number of crown roots = 162.426 + 20.43 × number of crown tillers [40].

2.3.2. Theoretical Yield and Its Components

The rice was harvested on 25 September, and yield components were then determined, allowing for the calculation of theoretical yield, which was performed as follows:
Theoretical yield = Effective panicle per unit area × Number of grains per ear × Seed setting rate × 1000-grain weight × 100.

2.3.3. Soil pH and EC

Soil samples from 0–5, 5–10, 10–15, and 15–20 cm soil depths were collected from the beginning of transplanting on 22 May to the end of yield measurement on 25 September. There were 14 experimental treatments in total, each with 5 replicates. After harvest, soil samples at depths of 0–5, 5–10, 10–15, and 15–20 cm were extracted from each pot, air-dried, and filtered through a 2-mm sieve, mixed with water at a ratio of 1:5, stirred for 30 min, and settled for at least 2 h. A PHS-3C digital pH meter (Shanghai Leici, Shanghai, China) and a DDS-307 EC meter (Shanghai Leici, Shanghai, China) were used to determine pH and EC, respectively.

2.4. Data Processing

SPSS 19.0 statistical software and Excel 2019 were used to collate and analyze the raw data. Origin 2021 was then used to plot the resultant output. p < 0.05 was considered to indicate significance.

3. Results

3.1. Effects of Amendments on Agronomic Characteristics during the Main Growth Period of Rice

As shown in Figure 2, SPAD values, plant height, tillering, and the number of crown roots in rice at the main growth stages were significantly superior after applying amendments than those in the control treatment (P0F0W0).
Plant height, tiller, SPAD-1 and SPAD-2 values, and number of crown roots at the mature stage were significantly improved after the application of amendments compared to those in the control treatment, and there were significant differences between treatments (p < 0.05) (Table 3).

3.2. Effects of Amendments on the Theoretical Yield and Yield Components of Rice

3.2.1. Yield Components

Application of various combinations of P, F, and W affected yield components. Panicle weight after each treatment was significantly higher than that in the control (p < 0.05), with the weight in P2F2W2 showing the highest value (22.65 g); specifically, panicle weight was 45.40, 41.37, and 10.23% and 9.76 times higher than that in the treatments without P, F, or W and in the control treatment, respectively. Panicle weight in P2F2W2 was significantly different from that in the P0F0W0, P0F2W2, P1F2W2, P2F0W2, and P1F1W2 treatments (p < 0.05). The number of total filled grains was significantly higher than that in the control (p < 0.05) and was the highest (1009.00) in the P2F2W2 treatment; it was 50.15, 71.37, 15.84, and 17.35% higher than that in treatments without P, F, or W and in the control treatment, respectively. Additionally, it was significantly different from treatments P0F0W0, P0F2W2, P2F0W2, and P1F1W2 (p < 0.05). The total grain weight was significantly higher than that in the control (p < 0.05), and P2F2W2 resulted in the highest (19.67 g) total grain weight, which was 58.31, 66.26, 18.25, and 32.11% higher than that in the treatments without P, F, or W and the control, respectively. Furthermore, total grain weight was significantly different from treatments P0F0W0, P0F2W2, and P2F0W2 (p < 0.05). The seed setting rate was significantly higher than that in the control (p < 0.05), and P2F2W2 produced the highest (73.70%) seed setting rate, and it was 16.56, 30.37, 26.18, and 3.96% higher than that for no application of P, F, or W and in the control. It was significantly different from treatments P0F0W0, P2F2W0, and P2F0W2 (p < 0.05) (Figure 3).

3.2.2. Effect of Amendments on Rice Theoretical Yield

The theoretical yield after each treatment and application of the amendment was significantly higher than that of the control (p < 0.05), and the P2F2W2 treatment resulted in the highest theoretical yield (5819.20 kg·ha−1). The P2F2W2 treatment increased yield by 62.76, 73.02, 19.05, and 32.52% compared with the treatments without P, F, or W and the control, respectively. The yield after the P2F2W2 treatment was significantly different from that after treatments P0F0W0, P0F2W2, P2F0W2, and P1F1W2 (p < 0.05) (Figure 4).

3.3. Application Effect and Optimum Dosage of the Amendment

3.3.1. Amendment Standardization

Data standardization was adopted to standardize the application of P, F, and W. Thus, the observed value of a variable was subtracted from the minimum value and then divided by the value range for that variable. That is, Xik = [xik − min (xk)] × Rk−1. After standardization, the observed values of various variables range between 0 and 1, and the standardized data are pure quantities without units. The standardized results are presented in Table 2.

3.3.2. Effect of Single Improvement Factor on Rice Yield

The yields were compared when any two of factors P, F, and W were set to level 2; that is, between treatments 2, 3, 6, and 11; 4, 5, 6, and 7; and 8, 9, 6, and 10. The relationship between the application amount of P, F, or W and yield was visualized using a scatter plot (Figure 5).
With the amount of P, F, or W as the independent variable and rice yield as the dependent variable, regression analysis was performed to fit the one-element quadratic regression model. The obtained formulas are presented in Table 4, Equations (1)–(3).
Through the significance test analysis and estimates for R2 and F values, the results showed that the unitary quadratic regression model fits well with the data, indicating that it truly reflects the regression relationship between P, F, and W and yield. The quadratic equations for each variable are open downward parabolas (Figure 5); that is, with the increase of the application amount of P, F, or W, the output initially increases and then decreases. The primary term is positive, while the secondary term is negative, indicating the presence of a maximum value in the equation. By solving Equations (1)–(3), the maximum output values and the optimal amount of P, F, and W, respectively, can be obtained (Table 5).
According to the results of the single factor effect, the optimal coding values of P, F, and W were 0.67, 0.67, and 0.52, respectively, corresponding to 30.33, 50.47, and 23.19 t·ha−1, respectively; the corresponding yields were 5575.92, 5591.69, and 5763.22 kg·ha−1, respectively. It is apparent that the optimal application amount of P is approximately that of level 2, while those of organic F and W are approximately those of levels 2 and 1–2, respectively. During testing, when the dosage of P, F, and W was 30, 50, and 30 t·ha−1, respectively, the maximum yield was 5819.136 kg·ha−1, which theoretically reflects increases by 62.76, 73.01, and 19.05% compared with those without P, F, or W, respectively. Based on the above analysis, the highest rice yield was obtained when the single-factor application level was 2.

3.3.3. Effect of Two Factors on Rice Yield

An interaction effect between P, F, and W can be observed. When W was supplied at level 2 (Figure 6A), and P increased from level 1 to 2, application of F at level 1 led to a yield increase by 555.43 kg·ha−1, while, at level 2, yield increased by 1029.12 kg·ha−1. This shows that F at level 2 influences the effect of P, and P at level 2 increases from level 1 to level 2. Applying W at level 1 increased yield by 687.37 kg·ha−1 and, at level 2, by 1029.12 kg·ha−1, indicating that W influences the effect of P at level 2. When W was at level 2 (Figure 6B), for F increases from level 1 to 2, yield increased by 580.06 kg·ha−1 at level 1 and by 1053.75 kg·ha−1 at level 2, indicating that P was conducive to the effectiveness of F fertilizer effect at level 2. When P was applied at level 2, and F increased from level 1 to level 2, the yield was reduced by 0.84 kg·ha−1 for the application of W at level 1; however, it increased by 1053.75 kg·ha−1 when W was applied at level 2, indicating that W was beneficial to the effectiveness of the application of F at level 2. When F was at level 2 (Figure 6C), and W increased from level 1 to 2, the yield when P was applied at level 1 decreased by 27.67 kg·ha−1, while, at level 2, the yield increased by 314.08 kg·ha−1, indicating that P is conducive to the W effect at level 2. When P was at level 2, and W increased from level 1 to 2, the yield was reduced by 740.51 kg·ha−1 when F was supplied at level 1, while it increased by 314.08 kg·ha−1 for F application at level 2, indicating that F is conducive to the development of the effect of W at level 2.
One factor in the test scheme was fixed at level 2; that is, the yield of P, F, and W application conditions were analyzed according to the results of test treatments 2, 3, 4, 5, 6, 7, 11, and 14; 2, 3, 6, 8, 9, 10, 11, and 13; and 4, 5, 6, 7, 8, 9, 10, and 12. Through regression analysis of the interactions P × F, P × W, and F × W, the binary quadratic regression Equations (1)–(3) (Table 6) was obtained. After the regression equation significance test, where Equation (1) F = 10.277 > F 0.05 = 0.091 and R2 = 0.963; Equation (2) F = 8.504 > F 0.05 = 0.109 and R2 = 0.955; and Equation (3) F = 12.775 > F 0.05 = 0.074 and R2 = 0.970, indicating that there was a significant correlation between the application amount of PF, PW, FW, and the yield. The equation fit was good and in line with the law of decreasing fertilizer returns (i.e., the quadratic term was negative, while the primary term was positive), that is, the typical fertilizer function. The differential partial derivative method was adopted to obtain the highest yield and the best application amount (Table 7).
According to yield analysis, 41.56 t·ha−1 of F combined with 18.71 t·ha−1 of W generated the highest theoretical yield of 5687.37 kg·ha−1. This was followed by applying 31.81 t·ha−1 of P combined with 22.34 t·ha−1 of W, resulting in a theoretical yield of 5640.31 kg·ha−1. Applying 30.62 t·ha−1 of P combined with 51.02 t·ha−1 of F produced the lowest theoretical yield of 5495.18 kg·ha−1.

3.3.4. Three-Factor Improvement Effect Model and Optimal Dosage of Amendments

Based on the “3414” experimental design, with the application amount of P, F, and W as the independent variable and rice yield as the dependent variable, a three-way quadratic regression model was established (Table 8). The significance test showed R2 = 0.992, F = 57.855, and p <0.01; the fit was good and reached an extremely significant level, in which the quadratic term coefficient was negative, and the primary term coefficient was positive. This was in line with the law of decreasing fertilizer returns and could better reflect the relationship between yield and amendment dosage. Since the mathematical model was obtained after non-dimensional treatment and linear substitution, the absolute value of partial regression coefficients of each primary term can directly reflect the degree of influence of P, F, and W on the yield, which is shown as P>F>W. Meanwhile, the partial regression coefficient was positive, indicating that P, F, and W have a positive effect on the yield and growth of rice.
Based on published methods [36,37,38], seven coding levels between P, F, and W and 152 sets of combination schemes exceeding the mean yield of 4449.00 kg·ha−1 were obtained, accounting for 44.32% of the total schemes. Frequency analysis was then used to calculate the frequency of each level of P, F, and W, and a management scheme with a yield greater than the mean yield was obtained (Table 9). The optimal application combinations with 95% confidence intervals were as follows: P = 0.647–0.720; F = 0.538–0.626; and W = 0.435–0.532. The corresponding optimal application dosage is P, 29.09–32.38 t·ha−1; F, 40.36–46.97 t·ha−1; and W, 19.57–23.95 t·ha−1. When the optimal application mean values for P (30.74 t·ha−1), F (43.67 t·ha−1), and W (21.76 t·ha−1) were substituted into the equation, the optimal application yield was 5670.00 kg·ha−1. The yield of 5543.90 kg·ha−1 was the closest to that of P2F2W2 (i.e., P 30, F 50, and W 30 t·ha−1). Therefore, the P2F2W2 treatment was the best combination treatment in this experiment.

3.4. Effects of Different Amendments on pH and EC in Topsoil

As shown in Figure 7, the pH in all samples after the application of the amendments was significantly lower than that in the control (p < 0.05), and EC increased in most samples compared with that in the control. The soil pH after the P2F2W2 treatment in 0–5, 5–10, 10–15, and 15–20 cm layers was 8.86, 8.92, 8.99, and 9.01, respectively, which was 12.69, 12.32, 11.18, and 10.70% lower than that in the control treatment P0F0W0. The soil EC values of 959.20, 814.00, 828.00, and 818.60 µS·cm−1 were 1.06 times and 70.79, 49.30, and 47.76% higher than those in P0F0W0, respectively. The pH in the P2F2W2 treatment in the top (0–5 cm) soil layer was significantly higher than that in P0F0W0-, P2F2W1-, P1F2W2-, P1F1W2-, P0F2W2-, and P1F2W1-treated soil. The EC in P2F2W2-treated soil was significantly higher than that in P0F0W0, P2F2W0, P1F2W2, P1F1W2, P0F2W2, and P1F2W1. The pH in the 5–10 cm layer in P2F2W2-treated soil was significantly higher than that in soil treated with P0F0W0, P2F2W1, P2F1W2, P1F2W2, P0F2W2, P1F1W2, and P1F2W1. The EC was significantly higher than that in P0F0W0-, P1F2W2-, P1F1W2-, and P0F2W2-treated soil layer. In the 10–15 cm soil layer, the pH in the P2F2W2 condition was significantly higher than that in treatments P0F0W0, P3F2W2, P1F1W2, P1F2W2, P1F2W2, and P0F2W2; EC was significantly higher than that in soil treated with P0F0W0, P1F2W2, P1F2W1, P1F1W2, and P0F2W2. Lastly, in the 15–20 cm soil layer, the pH in P2F2W2 was significantly higher than that in P0F0W0, P1F1W2, P1F2W2, P1F2W1, and P0F2W2, and EC was significantly lower than that in P2F3W2 and significantly higher than that in P0F0W0-, P1F2W2-, P1F2W1-, and P0F2W2-treated soil (p < 0.05).

4. Discussion

The patterns that characterize rice growth in saline-alkali soil impact the yield; specifically, saline-alkaline stress reduces plant height and tillering [41,42,43,44,45]. The results of this study show that the combined application of P, F, and W significantly facilitated the optimal development of rice across its growth period.
A rice panicle is a reservoir of organic matter, which embodies the final economic yield [46]. The resultant yield mainly depends on grain growth and development [47]. Salt stress can reduce rice production by directly affecting panicle development [48,49]. Under increasing salt stress, yield, effective panicle number, number of total filled grains, 1000-grain weight, and seed setting rate decrease significantly [50]. Studies show that applying P can significantly increase rice yield, panicle weight, number of total filled grains, total grain weight, and seed setting rate [17,51]. Additionally, applying F in saline-alkali soil can significantly increase crop yield [21,26,27,28,43,52]. Finally, W application can improve crop yield and its constituent factors [53], especially in saline-alkali soil [29]. These results are consistent with our findings. Specifically, we found that the effective panicle number, number of total filled grains, 1000-grain weight, and seed setting rate of rice significantly increased after applying a modifier (P, F, and W), leading to improved rice yield.
Based on these findings, we conclude that P, F, and W interact with each other. Still, the combination of F and W and P and W or F differently affect yield, in the following order: F and W > P and W > P and F. Previous studies have shown that P combined with F can significantly improve crop yield [54] and maintain soil moisture in salt-affected soils [55], which is consistent with our finding. Specifically, we found that the interaction of P with F can improve soil conditions and crop yield. In this experiment, based on a triple regression equation, we concluded that applying P, F, and W positively impacted rice yield, and that P had the greatest effect on yield. Overall, the soda saline-alkali soil is characterized by high pH and contains soluble salts [56], thus becoming the main factor for rice yield reduction. However, P was replaced with a Ca2+–Na+ ion, improving the chemical properties of the soil [57,58].
The main aim of this study was to determine the optimal application of fertilizer in a saline-alkaline environment [59]. Regarding our primary aim, choosing the right model to address the aim is important. The “3414” design to evaluate optimal modern fertilizer rate is a secondary regression model with the advantages of reducing the number of treatments to be applied and improving efficiency; however, its pseudo-synthetic power is low [60]. The ternary quadratic regression model would be ideal in practice. In this study, the application amounts of P, F, and W were considered independent variables, and rice yield was the dependent variable to establish the ternary quadratic regression equation. After the significance test (p < 0.01), the resultant coefficient of the quadratic term was negative, while the coefficient of the primary term was positive. These findings align with the law of decreasing fertilizer returns, and the equation fitting was successful. As the ternary function is a set of points in four-dimensional space, it cannot be calculated using a method similar to that used to solve binary equations. Additionally, even the only stable point cannot be asserted to be the optimal solution [61,62]. Therefore, the frequency analysis method was used to optimize the analysis. The results showed that the optimal application combination of P, F, and W was as follows: P, 29.09–32.38 t·ha−1; F, 40.36–46.97 t·ha−1; and W, 19.57–23.95 t·ha−1. Additionally, a significant reduction in soil pH was observed, which may improve soil salinity.
After rice is planted in saline-alkali soil, the salt content is reduced through capillary movement of the root layer and evaporation from the soil surface, and organic matter is produced due to underground root decay, increasing the partial pressure of carbon dioxide in soil [63,64,65]. Previous studies have shown that applying Ca2+-rich P mainly replaces Na+ in the soil, thus removing Na+ and reducing Na+ absorption by plants. At the same time, the acidity of P neutralizes alkaline soil to a certain extent, and by replacing Na+ with more Ca2+, the stability of soil aggregates can improve, which is conducive to plant growth; however, whether this approach has antagonistic effects on other soil nutrients needs to be further studied [57,58,66,67]. This strategy improves soil structure, increasing water conductivity. Overall, the toxic effect of the saline-alkali soil on rice is greatly reduced [67,68]. Other studies have shown that after applying F, the soil absorbs more Ca2+, Mg2+, and K+, resulting in a decrease in the soil ESP, which reduces soil alkalinity [69]. W is rich in humic acid, which can regulate soil pH to a certain extent [70,71]. These results indicate that there may be a synergistic mechanism between P, F, and C. Additionally, the results of this study show that the combined application of P, F, and C can significantly improve rice yield. We found that the pH and EC of the soil in the root zone in the 14 treatments were largely similar across the four soil layers, indicating that the amendments had a slight effect on the root layer of the soil. This result is consistent with that of a previous study [66]. In that experiment, the pH of soil treated with P3F2W2 was the lowest, while EC was the highest, followed by soil treated with P2F2W2. This may be because, compared with the P2F2W2 treatment, an increased amount of P is required for P3F2W2, leading to more Ca2+ replacing Na+ at the soil binding site, thus reducing Na+ and soil pH. In our experiment, due to the application of P and F, the salt content increased in topsoil, and soil EC became slightly higher than that in the control treatment. In the future, salt content should be reduced by increasing leaching times.

5. Conclusions

We found that rice yields on cultivated saline-sodic land can be increased through the application of different combinations of P, F, and W. Our comprehensive analysis showed that the optimal combination was as follows: P, 29.09–32.38 t·ha−1; F, 40.36–46.97 t·ha−1, and W, 19.57–23.95 t·ha−1. Thus, the P2F2W2 treatment represents an optimal combination of amendments. Overall, this study provides a robust theoretical and technical basis for the worldwide improvement of severe saline-sodic land.

Author Contributions

Project implementation, G.D.; project design, M.L. and Z.L.; investigation, M.W., H.Y., Y.X., T.Y., Y.J., J.H. and J.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (Project No. XDA28110100); the National Key Research and Development Program of China (Project No. 2022YFD1500500); the Technology Cooperation High-Tech Industrialization Project of Jilin Province and the Chinese Academy of Sciences (Project No. 2023SYHZ0045).

Data Availability Statement

Not applicable.

Acknowledgments

Thanks to State Key Laboratory of Black Soils Conservation and Utilization and Institute of Soil Science, Chinese Academy of Sciences for their help and support.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Yang, J.; Yao, R.; Wang, X.; Xie, W.; Zhang, X.; Zhu, W.; Zhang, L.; Sun, R. Prevent soil salinization, improve soil productivity. Science 2021, 73, 30–34 + 2 + 4. [Google Scholar]
  2. Yang, J.; Yao, R. Management and efficient agricultural utilization of salt-affected soil in China. Soil Agric. 2015, 30, 257–265. [Google Scholar]
  3. Zhang, X.; Huang, B.; Liang, Z.; Zhao, Y.; Sun, W.; Hu, W. Study on salinization characteristics of surface soil in western songnen plain. Soils 2013, 45, 332–338. [Google Scholar]
  4. Wang, C. Saline soil resources in Northeast China. In The Salt-Affected Soil in Northeast China; Science Press: Beijing, China, 2004; pp. 23–27. [Google Scholar]
  5. National Bureau of Statistics of the People’s Republic of China. China Statistical Year Book; China Statistics Press: Beijing, China, 2017.
  6. Liu, S.; Li, C.; Fang, F.; Zhang, X.; Mao, Y.; Kong, X.; Zhang, K.; Wu, R. Study on the variation and comparative advantage of regional rice production structure in China. China Rice 2014, 20, 9–13. [Google Scholar]
  7. Wang, X.; Yang, X.; Tao, L.; Zhang, T.; Liu, T.; Xiang, H.; Sun, Y.; Liu, Z. Rice suitability zoning of alternative wetting and drying irrigation mode in three provinces of Northeast China. Trans. Chin. Soc. Agric. Eng. 2018, 34, 111–120. [Google Scholar]
  8. Cao, X.; Sun, B.; Chen, H.; Zhou, J.; Song, X.; Liu, X.; Deng, X.; Li, X.; Zhao, Y.; Zhang, J.; et al. Approaches and research progresses of marginal land productivity expansion and ecological benefit improvement in China. Strategy Policy Decis. Res. 2015, 33, 441–452. [Google Scholar]
  9. Liu, M.; Liang, Z.; Yang, F.; Ma, H.; Huang, L.; Wang, M. Impacts of sand amendment on rice (Oryza sativa L.) growth and yield in saline–sodic soils of Northeast China. Agric. Environ. 2010, 8, 412–418. [Google Scholar]
  10. Singh, R.; Redona, E.; Refuerzo, L. Varietal improvement for abiotic stress tolerance in crop plants: Special reference to salinity in rice. In Abiotic Stress Adaptation in Plants; Dordrecht: Berlin, Germany, 2009; pp. 387–415. [Google Scholar]
  11. Chi, C.; Zhao, C.; Sun, X.; Wang, Z. Reclamation of saline-sodic soil properties and improvement of rice(Oriza sativa L.) growth and yield using desulfurized gypsum in the west of Songnen Plain, northeast China. Geoderma 2012, 187, 24–30. [Google Scholar] [CrossRef]
  12. Huang, L.; Liu, X.; Wang, Z.; Liang, Z.; Wang, M.; Liu, M.; Suarez, D. Interactive effects of pH, EC and nitrogen on yields and nutrient absorption of rice (Oryza sativa L.). Agr. Water Manag. 2017, 194, 48–57. [Google Scholar] [CrossRef]
  13. Oster, J.; Shainberg, I.; Abrol, I. Reclamation of salt affected soils. In Agricultural Drainage; ASA-CSSA-SSSA: Madison, WI, USA, 1999; pp. 659–691. [Google Scholar]
  14. Aagli, A.; Tamer, N.; Atbir, A.; Boukbir, L.; El Hadek, M. Conversion of phosphogypsum to potassium sulfate–Part I. The effect of temperature on the solubility of calcium sulfate in concentrated aqueous chloride solutions. J. Therm. Anal. Calorim. 2005, 82, 395–399. [Google Scholar] [CrossRef]
  15. Zielonka, D.; Szulc, W.; Skowronska, M.; Rutkowska, B.; Russel, S. Hemp-based phytoaccumulation of heavy metals from municipal sewage sludge and phosphogypsum under field conditions. Agronomy 2020, 10, 907. [Google Scholar] [CrossRef]
  16. Wu, H.; Han, C.; Tang, Y. Research progress on reutilization of phosphogypsum in China. Mod. Chem. Ind. 2023, 43, 18–21. [Google Scholar]
  17. Liu, M.; Liang, Z.; Ma, H.; Huang, L.; Wang, M. Responses of rice (Oryza saliva L.) growth and yield to phosphogypsum amendment in saline–sodic soils of North–East China. J. Food Agric. Environ. 2010, 8, 827–833. [Google Scholar]
  18. Bello, S.; Alayafi, A.; AL–Solaimani, S.; Abo–Elyousr, K. Mitigating soil salinity stress with gypsum and bio–organic amendments: A review. Agronomy 2021, 11, 1735. [Google Scholar] [CrossRef]
  19. Xu, L.; Wang, Z.; Zhao, C.; Wang, Y.; Sun, X.; Wei, B.; Cao, L.; Zhu, M. Amelioration of soda saline–alkali soil through equidistant slotting in combining with gyp–sum application. Chin. J. Ecol. 2012, 31, 1179–1185. [Google Scholar]
  20. Komissarov, M.; Gabbasova, I.; Garipov, T.; Suleymanov, R.; Sidorova, L. The Effect of phosphogypsum and turkey litter application on the properties of eroded agrochernozem in the south ural region (Russia). Agronomy 2022, 12, 2594. [Google Scholar] [CrossRef]
  21. Gao, G.; Liu, Y.; Yang, B.; Wang, Y.; Guo, X.; Chen, M.; Zhao, B.; Liu, J. Effects of chemical fertilizer reduction combined with organic fertilizer on soil properties and cadmium forms in saline alkali land. Soil Fertil. Sci. China 2023, 1, 30–38. [Google Scholar]
  22. Zhang, Z.; Lv, F.; Xiao, Y.; Wang, R.; Lin, H.; Yuan, Z.; Wei, L.; Lv, R. Effects of nitrogen fertilizer reduction under organic and inorganic fertilizers combination on yield and quality of peanut in red soil farmland. Soils Crops 2022, 11, 417–427. [Google Scholar]
  23. Guo, Z.; Xue, Z.; Zhao, J.; Wang, Z.; Chen, X. Effects of nitrogen, phosphorus and potassium fertilizer on rice yield and nutrient utilization in a Hapli–Udic Cambisol in Liaoning Province. Soils Crops 2023, 12, 18–24. [Google Scholar]
  24. Sun, N.; Thompson, R.; Xu, J.; Liao, S.; Suo, L.; Peng, Y.; Chen, Q.; Yang, J.; Li, Y.; Zou, G.; et al. Arsenic and cadmium accumulation in soil as affected by continuous organic fertilizer application: Implications for clean production. Agronomy 2021, 11, 2272. [Google Scholar] [CrossRef]
  25. Balik, J.; Kulhanek, M.; Cerny, J.; Sedlar, O.; Suran, P.; Asrade, D. The influence of organic and mineral fertilizers on the quality of soil organic matter and glomalin content. Agronomy 2022, 12, 1375. [Google Scholar] [CrossRef]
  26. Liu, P.; Bi, J.; Li, W.; Hui, Z.; Xiao, G.; Sun, Q.; Wang, J. Transcriptome analysis of effect of bio–organic fertilizer on rice leaves. Acta Ecol. Sin. 2022, 42, 2342–2356. [Google Scholar]
  27. Zheng, G.; Yu, G.; Li, H.; Hu, F.; Li, X.; Ma, Y. Effects of leakage and organic fertilizer on panicle characters and yield of rice in saline–alkali soil. China Rice 2013, 19, 80–82. [Google Scholar]
  28. Li, X. Study on the Influence of Yellow River Sediment Mixed and Application Biological Fertilizer on Water and Salt Transport, Winter Wheat Growth in Saline–Alkali Land. Ph.D. Thesis, Chinese Academy of Agricultural Sciences, Beijing, China, 2020. [Google Scholar]
  29. Zheng, F.; Ma, X.; Cao, H.; Wang, L. Study on mending saline- alkaline soils with peat in fields. Territ. Nat. Resour. Study 2008, 1, 41–42. [Google Scholar]
  30. Zhao, W.; Ma, L.; Xu, J.; Tan, J.; Zhang, J.; Zhao, B. Effect of Application of Straw and Wood Peat for a Short Period on Soil Organic Matter and Microbial Community in Composition and Function in Fluvo–aquic Soil. Acta Pedol. Sin. 2020, 57, 153–164. [Google Scholar]
  31. Zheng, Y.; Zhang, J.; Tan, J.; Zhang, C.; Yu, Z. Chemical Composition and Structure of Humus Relative to Sources. Acta Pedol. Sin. 2019, 56, 386–397. [Google Scholar]
  32. Qu, C.; Chen, X.; Zhang, J.; Fan, S.; Tan, J.; Ruan, Y.; Zhang, Y.; Wu, D.; Han, Z.; Zhang, Z. Techniques and effects of quickly constructing high–quality tillage layers for newly–cultivated arable land in red soil and paddy field based on woody peat and organic materials. J. Soil Water Conserv. 2018, 32, 134–140. [Google Scholar]
  33. Zhao, Y.; Wang, S.; Li, Y.; Liu, J.; Zhuo, Y.; Chen, H.; Wang, J.; Xu, L.; Sun, Z. Extensive reclamation of saline–sodic soils with flue gas desulfurization gypsum on the Songnen Plain, Northeast China. Geoderma 2018, 321, 52–60. [Google Scholar] [CrossRef]
  34. Luo, S.; Wang, S.; Tian, L.; Shi, S.; Xu, S.; Yang, F.; Li, X.; Wang, Z.; Tian, C. Aggregate-related changes in soil microbial communities under different ameliorant applications in saline–sodic soils. Geoderma 2018, 329, 108–117. [Google Scholar] [CrossRef]
  35. Wang, M.; Rengasamy, P.; Wang, Z.; Yang, F.; Ma, H.; Huang, L.; Liu, M.; Yang, H.; Li, J.; An, F.; et al. Identification of the most limiting factor for rice yield using soil data collected before planting and during the reproductive stage. Land Degrad. Dev. 2018, 29, 2310–2320. [Google Scholar] [CrossRef]
  36. Yu, T. Study on the Construction of Alfalfa Fertilizer Model and Physiological Mechanism of Nutrient Efficiency in the Arid Irrigation Area of Northwest China. Ph.D. Thesis, Gansu Agricultural University, Lanzhou, China, 2018. [Google Scholar]
  37. Li, H.; Ye, H.; Li, B.; Li, C.; Shi, W.; Guo, J.; Su, Y.; Li, S. Frequency analysis and fertilization decision data field fertilizer efficiency test. Chin. Agric. Sci. Bull. 2014, 30, 132–138. [Google Scholar]
  38. Liu, D.; Yang, S.; Shi, H.; Zheng, X.; Sun, L.; Chang, C. Effect of combined nitrogen and phosphorus fertilizer application of wheat–maize intercropping system. Chin. J. Eco–Agric. 2014, 22, 262–269. [Google Scholar] [CrossRef]
  39. Zhang, X.; Yu, Z.; Li, H.; Liu, H.; Zhang, Z.; Zhao, M.; Wang, X. Remote sensing estimation model of SPAD for rice leaves in Northeast China. J. China Agric. Niversity 2020, 25, 66–75. [Google Scholar]
  40. Liu, M.; Liang, Z.; Huang, L.; Wang, M.; Yang, H. An effective method for estimation of rice (Oryza sativa L.) crown root numbers at the heading stage in saline-sodic soils of Northeast China. Phyton-Int. J. Exp. Bot. 2016, 85, 162–168. [Google Scholar]
  41. Liang, Z.; Yang, F.; Wang, Z.; Chen, Y. Effect of the main growth characteristics of rice under saline–alkali stress. Ecol. Environ. 2004, 13, 43–46. [Google Scholar]
  42. Suhayda, C.; Yin, L.; Redmann, R.; Li, J. Gypsum amendment improves native grass establishment on saline–alkali soils in northeast China. Soil Use Manag. 1997, 13, 43–47. [Google Scholar] [CrossRef]
  43. Lv, L.; Wu, Y.; Sun, Z.; Bi, Y. Effect of organic fertilizer on growth of castor bean seeding under saline sodic soil. J. China Agric. Univ. 2013, 18, 73–80. [Google Scholar]
  44. Li, B.; Wei, Y.; Xue, Y.; Pan, B.; Liu, J. Effect of basal application of organic fertilizer replacing inorganic N tillering fertilizer retroposition on rice yield and growth. Chin. Agric. Sci. Bull. 2018, 34, 21–26. [Google Scholar]
  45. Hanafi, M.; Azizi, P.; Vijayanathan, J. Phosphogypsum organic, a byproduct from rare-earth metals processing, improves plant and soil. Agronomy 2021, 11, 2561. [Google Scholar] [CrossRef]
  46. Shen, J.; Zheng, H.; Yin, M.; Xie, Z.; He, Z.; Huang, H. Effect of dark–direct seeding on characters of rice panicle and yield. Crops 2014, 4, 84–87. [Google Scholar]
  47. Zhao, Q.; Hao, X.; Ali, I.; Iqbal, A.; Ullah, S.; Huang, M.; Kong, F.; Li, T.; Xuan, Y.; Li, F.; et al. Characterization and grouping of all primary branches at various positions on a rice panicle based on grain growth dynamics. Agronomy 2020, 10, 223. [Google Scholar] [CrossRef]
  48. Zuo, J.; Li, J.; Yang, F. Effects of different soil types on the panicle traits and yield components of northern Japon–ica rice. Chin. J. Ecol. 2013, 32, 59–63. [Google Scholar]
  49. Yang, F.; Liang, Z.; Wang, Z.; Zhang, J.; Chen, Y. Comparison of yield characters between screening test of salinealkali tolerant rice varieties and regional experiment. J. Jilin Agric. Univ. 2007, 29, 596–600. [Google Scholar]
  50. Zhai, C.; Zhang, J.; Cui, S.; Chen, P. Effects of salt stress on the panicle traits and yield components of rice cultivars. Chin. Agric. Sci. Bull. 2022, 38, 1–9. [Google Scholar]
  51. Xiao, F.; Zhou, B.; Wang, H.; Duan, M.; Feng, L. Effects of different soil amendments on physicochemical property of soda saline-alkali soil and crop yield in Northeast China. Int. J. Agr. Biol. Eng. 2022, 15, 192–198. [Google Scholar] [CrossRef]
  52. Su, Q.; Li, W.; Chi, F. Effect of organic fertilizer application on soil salt content and the yield of rice. Chin. Agric. Sci. Bull. 2006, 22, 299–301. [Google Scholar]
  53. Wu, Q.; Luo, J. Discussion on analysis method of ”3414” fertilizer test. Shandong Agric. Sci. 2010, 8, 90–194. [Google Scholar]
  54. Qadir, M.; Schubert, S. Degradation processes and nutrient constraints in sodic soils. Land Degrad. Dev. 2002, 13, 275–294. [Google Scholar] [CrossRef]
  55. Yu, B.; Wu, K.; Huang, Q. Study on the effect of woody peat on the dry matter accumulation and yield of millet. Soil Fertil. China 2018, 5, 102–108. [Google Scholar]
  56. Zhang, P.; Gao, L.; Li, X.; Wang, X.; Liu, J.; Xu, C.; Zhang, X. Phosphogypsum and organic fertilizer: Effects on yield and leaf physiological characteristics of broomcorn millet in saline–alkali soil. Chin. Agric. Sci. Bull. 2018, 34, 26–32. [Google Scholar]
  57. Shu, X.; Peng, B. Effect of combined application of phosphogypsum and organic fertilizer on water environment of saline–alkali soil. Sci. Technol. Innov. 2019, 18, 72–73. [Google Scholar]
  58. Cong, S. Effects of Different Amelioration Techniques on Soil Saline-Alkali Characteristics in Songnen Plain; Northeast Institute of Geography and Agroecology: Changchun, China, 2022. [Google Scholar]
  59. Wang, X.; Chen, X.; Zhang, F.; Mao, D. The application of fertilizer model is recommended in our country. Plant Nutr. Fertil. Sci. 1998, 4, 67–74. [Google Scholar]
  60. Wang, S.; Chen, X.; Gao, X.; Mao, D.; Zhang, F. Study on simulation of “3414” fertilizer experiments. Plant Nutr. Fertil. Sci. 2002, 8, 409–413. [Google Scholar]
  61. Xao, Y.; Ge, G.; Lv, S.; Yin, Q.; Mi, F. The research of “ZhongMu No.2” alfalfa in high yield and fertilizer. J. Arid. Land Resour. Environ. 2016, 30, 183–189. [Google Scholar]
  62. Kamra, S.; Narayana, V.; Rao, K. Water harvesting for reclaiming alkali soils. Agr. Water Manag. 1986, 11, 127–135. [Google Scholar] [CrossRef]
  63. Qadir, M.; Oster, J.; Schubert, S.; Noble, A.; Sahrawat, K. Phytoremediation of sodic and saline–sodic soils. Adv. Agron. 2007, 96, 197–247. [Google Scholar]
  64. Dobermann, A.; Fairhurst, T. Rice Ecosystems. Rice–Nutrient Disorders and Nutrient Management; Potash and Phosphate Institute (PPI) and International Rice Research Institute (IRRI): Los Baños, Philippines, 2000; pp. 2–11. [Google Scholar]
  65. Al–Enazy, A.; Al–Barakah, F.; Al–Oud, S.; Usman, A. Effect of phosphogypsum application and bacteria co-inoculation on biochemical properties and nutrient availability to maize plants in a saline soil. Arch. Agron. Soil Sci. 2018, 64, 1394–1406. [Google Scholar] [CrossRef]
  66. Huang, L.; Liu, Y.; Ferreira, J.; Wang, M.; Na, J.; Huang, J.; Liang, Z. Long-term combined effects of tillage and rice cultivation with phosphogypsum or farmyard manure on the concentration of salts, minerals, and heavy metals of saline–sodic paddy fields in Northeast China. Soil Till Res. 2022, 215, 105222. [Google Scholar] [CrossRef]
  67. Ahmad, S.; Ghafoor, A.; Akhtar, M.; Khan, M. Implication of gypsum rates to optimize hydraulic conductivity for variable–texture saline–sodic soils reclamation. Land Degrad. Dev. 2016, 27, 550–560. [Google Scholar] [CrossRef]
  68. Clark, G.; Dodgshun, N.; Sale, P.; Tang, C. Changes in chemical and biological properties of a sodic clay subsoil with addition of organic amendments. Soil Biol. Biochem. 2007, 39, 2806–2817. [Google Scholar] [CrossRef]
  69. Ranjbar, F.; Jalali, M. Effects of plant residues and calcite amendments on soil sodicity. J. Plant Nutr. Soil Sci. 2011, 174, 874–883. [Google Scholar] [CrossRef]
  70. Chen, S.; Nie, H.; Tan, J.; Xing, W.; Chen, Q. Effect of wood peat on greenhouse tomato growth and soil improvement. China Veg. 2015, 10, 42–46. [Google Scholar]
  71. Takahagi, J.; Open, M.; Saburo, M.; Kazuo, A. Humic fertilizers. Humic. Acid. 1989, 2, 43–61. [Google Scholar]
Agronomy 13 01364 g001 550
Figure 1. Map showing the National Field Scientific Observation and Research Station of Farmland Ecosystem in the Da’an, Jilin, China study site (marked in red), (based on Map data ©2021 Microsoft CorporationGS [2021]1731, ©2021TomTom).
Figure 1. Map showing the National Field Scientific Observation and Research Station of Farmland Ecosystem in the Da’an, Jilin, China study site (marked in red), (based on Map data ©2021 Microsoft CorporationGS [2021]1731, ©2021TomTom).
Agronomy 13 01364 g001
Agronomy 13 01364 g002 550
Figure 2. Effects of amendment application on SPAD-1 (A), SPAD-2 (B), plant height (C), tiller (D), and number of crown roots (E).
Figure 2. Effects of amendment application on SPAD-1 (A), SPAD-2 (B), plant height (C), tiller (D), and number of crown roots (E).
Agronomy 13 01364 g002
Agronomy 13 01364 g003 550
Figure 3. Effects of amendment application on panicle weight (A), number of total filled grains (B), total grain weight (C), and seed setting rate (D). Results were tested by Duncan’s multiple range test. Different letters indicate significant differences among the treatments (p < 0.05).
Figure 3. Effects of amendment application on panicle weight (A), number of total filled grains (B), total grain weight (C), and seed setting rate (D). Results were tested by Duncan’s multiple range test. Different letters indicate significant differences among the treatments (p < 0.05).
Agronomy 13 01364 g003
Agronomy 13 01364 g004 550
Figure 4. Effect of amendment application on theoretical yield. Results were tested by Duncan’s multiple range test. Different letters indicate significant differences among the treatments (p < 0.05).
Figure 4. Effect of amendment application on theoretical yield. Results were tested by Duncan’s multiple range test. Different letters indicate significant differences among the treatments (p < 0.05).
Agronomy 13 01364 g004
Agronomy 13 01364 g005 550
Figure 5. Single factor effect of P (■), F (▲), and W (◆) on yield.
Figure 5. Single factor effect of P (■), F (▲), and W (◆) on yield.
Agronomy 13 01364 g005
Agronomy 13 01364 g006 550
Figure 6. Interactive effects of PF (A), PW (B), and FW (C) on yield.
Figure 6. Interactive effects of PF (A), PW (B), and FW (C) on yield.
Agronomy 13 01364 g006
Agronomy 13 01364 g007 550
Figure 7. Effects of amendment application on pH (A) and EC (B) in soil layers 0–5, 5–10, 10–15, and 15–20 cm. Results were tested by Duncan’s multiple range test. Different letters indicate significant differences among the treatments (p < 0.05).
Figure 7. Effects of amendment application on pH (A) and EC (B) in soil layers 0–5, 5–10, 10–15, and 15–20 cm. Results were tested by Duncan’s multiple range test. Different letters indicate significant differences among the treatments (p < 0.05).
Agronomy 13 01364 g007
Table
Table 1. Test report of P, F, and W.
Table 1. Test report of P, F, and W.
Detection IndexTest Result
PFW
pH4.57.65.41
Cd (%)0.0001<0.10.13
Cr (%)0.000515.43.68
As (%)0.00036.91.19
Pb (%)0.00127.33.67
Hg (%)0.00010.20.04
a(H2O) (%)1628.4
S (%)18.4
N (%) 2.310.685
Organic matter (%) 35.390.98
P2O5 (%) 1.630.007
K2O (%) 1.940.015
Number of fecal coliforms (n·g−1) <3.0
Mortality of ascarid egg (%) 100
GI (%) 80.2
Weed seed activity (%) 0.0
Mechanical impurities (%) 0.0
Cl− (%) 0.75
Dry bulk density (g·cm−4) 0.412
Total amount of humic acid (%) 45.35
CaSO4·2H2O (%)90
a(H2O) represents the mass fraction of free water.
Table
Table 2. Proportion treatment and the amount of P, F, and W.
Table 2. Proportion treatment and the amount of P, F, and W.
NumberingTreatmentCodeApplication Rate (t·ha−1)
PFWPFW
1P0F0W0000000
2P0F2W200.670.6705030
3P1F2W20.330.670.67155030
4P2F0W20.6700.6730030
5P2F1W20.670.330.67302530
6P2F2W20.670.670.67305030
7P2F3W20.6710.67307530
8P2F2W00.670.67030500
9P2F2W10.670.670.33305015
10P2F2W30.670.671305045
11P3F2W210.670.67455030
12P2F1W10.670.330.33302515
13P1F2W10.330.670.33155015
14P1F1W20.330.330.67152530
The numbers in the bottom corner of the second column are the level codes of each factor. P, phosphogypsum; F, farmyard fertilizer; W, wood peat.
Table
Table 3. Effects of amendment application on agronomic characteristics of rice at maturity.
Table 3. Effects of amendment application on agronomic characteristics of rice at maturity.
TreatmentPlant Height (cm)Tiller (n.·hill−1)SP AD-1SP AD-2Number of Crown Root (n.·hill−1)
P0F0W059.08 ± 1.75 e12.60 ± 1.03 e23.22 ± 0.94 e21.94 ± 1.20 d419.84 ± 21.03 e
P0F2W274.50 ± 2.04 d17.40 ± 1.69 cd32.44 ± 1.18 cd34.02 ± 1.15 abc517.91 ± 34.55 cd
P1F2W276.08 ± 3.65 cd18.80 ± 1.07 abcd35.38 ± 1.44 abc37.02 ± 1.10 ab546.51 ± 21.81 abcd
P2F0W282.98 ± 1.59 abc17.00 ± 0.89 d30.02 ± 1.78 d34.16 ± 1.44 abc509.74 ± 18.27 d
P2F1W275.32 ± 2.01 d21.60 ± 1.69 ab36.30 ± 1.75 abc33.34 ± 2.17 bc603.71 ± 34.55 ab
P2F2W279.72 ± 1.45 abcd19.60 ± 0.87 abcd35.90 ± 0.91 abc34.26 ± 1.17 abc562.85 ± 17.81 abcd
P2F3W279.60 ± 2.61 abcd20.80 ± 1.11 abc36.18 ± 0.91 abc35.48 ± 1.13 abc587.37 ± 22.75 abc
P2F2W081.08 ± 3.45 abcd22.20 ± 0.97 a33.38 ± 1.62 bcd36.12 ± 0.87 abc615.97 ± 19.81 a
P2F2W183.38 ± 1.53 ab19.40 ± 0.40 abcd38.08 ± 1.44 a36.90 ± 0.92 ab558.77 ± 8.17 abcd
P2F2W378.50 ± 2.10 abcd20.60 ± 0.98 abc35.04 ± 1.21 abc32.98 ± 0.39 bc583.28 ± 20.02 abc
P3F2W278.62 ± 0.75 abcd20.00 ± 0.84 abcd37.84 ± 1.92 a37.60 ± 1.01 a571.03 ± 17.09 abcd
P2F1W184.64 ± 0.97 a18.40 ± 0.40 bcd37.14 ± 0.49 ab32.18 ± 1.71 c538.34 ± 8.17 bcd
P1F2W175.58 ± 2.15 d18.40 ± 1.03 bcd37.94 ± 0.93 a35.02 ± 1.32 abc538.34 ± 21.03 bcd
P1F1W277.16 ± 1.83 bcd20.80 ± 0.37 abc33.48 ± 0.84 bcd33.50 ± 0.40 bc587.37 ± 7.64 abc
Different letters indicate significant differences between treatments (p < 0.05).
Table
Table 4. Fitting results of single factor equation.
Table 4. Fitting results of single factor equation.
TreatmentsQuadratic Equation with One UnknownSignificant TestNO.
R2FF0.05
PY = −4586.9 P2 + 6182.6 P + 3492.60.9499.30.226(1)
FY = −5090.8 F2 + 6851.8 F + 3286.20.96313.00.193(2)
WY = −3471.6 W2 + 3578.7 W + 4841.00.9336.90.260(3)
In the formula, Y represents rice yield under different treatments of P, F, and W; the application amount of phosphogypsum, farmyard fertilizer, and wood peat, respectively.
Table
Table 5. Optimal single-factor application amount and corresponding yield.
Table 5. Optimal single-factor application amount and corresponding yield.
TreatmentsOptimal Application Amount (t·ha−1)Corresponding Yield (kg·ha−1)
P30.335575.92
F50.475591.69
W23.195763.22
Table
Table 6. Fitting results of bivariate quadratic model.
Table 6. Fitting results of bivariate quadratic model.
TreatmentBinary Quadratic EquationsSignificant TestNO.
R2F0.05F
P, FY = 648.9 + 6825.0 P + 7420.8 F − 4251.6 P2 − 4690.8 F2 − 1526.5 PF0.96310.30.091(1)
P, WY = 2096.3 + 7164.8 P + 4075.3 W − 4502.5 P2 − 2959.3 W2 − 1608.8 PW0.9558.50.109(2)
F, WY = 4730.1 + 3553.4 F − 132.0 W − 4986.4 F2 − 3004.0 W2 + 4746.3 FW0.97012.80.074(3)
Table
Table 7. Optimal application amount and corresponding yield of two factors.
Table 7. Optimal application amount and corresponding yield of two factors.
TreatmentsHighest-Yield Application Amount (t·ha−1)Highest Yield (kg·ha−1)
PFW
P, F30.6251.02 5495.18
P, W31.81 22.345640.31
F, W 41.5618.715687.37
Table
Table 8. Fitting results of ternary quadratic model.
Table 8. Fitting results of ternary quadratic model.
Ternary Quadratic EquationSignificant Test
R2FP
Y = 189.4 + 9435.7 P + 5734.1 F + 1882.3 W − 4533.5 P2−4972.7 F2 − 2990.3 W2 − 2645.7 PF − 2397.3 PW + 4016.8 FW0.99257.90.0007
Table
Table 9. Frequency distribution and application scheme of P, F, and W coding values with higher than average yield.
Table 9. Frequency distribution and application scheme of P, F, and W coding values with higher than average yield.
ItemCodePFW
CountsFrequencyCountsFrequencyCountsFrequency
(%)(%)(%)
00074.61811.8
0.16742.6149.22214.5
0.3331811.82214.52617.1
0.53019.72919.12617.1
0.6673623.73221.12415.8
0.83335233019.72315.1
12919.11811.8138.6
Code the weighted mean0.6830.5820.484
Standard error0.0190.0220.025
95% confidence interval0.647–0.7200.538–0.6260.435–0.532
Optimal fertilizer (t·ha−1)29.09–32.3840.36–46.9719.57–23.95
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Duan, G.; Liu, M.; Liang, Z.; Wang, M.; Yang, H.; Xu, Y.; Yu, T.; Jin, Y.; Hu, J.; Liu, J. Amendments of Severe Saline-Sodic Paddy Land: Optimal Combination of Phosphogypsum, Farmyard Fertilizer, and Wood Peat. Agronomy 2023, 13, 1364. https://doi.org/10.3390/agronomy13051364

AMA Style

Duan G, Liu M, Liang Z, Wang M, Yang H, Xu Y, Yu T, Jin Y, Hu J, Liu J. Amendments of Severe Saline-Sodic Paddy Land: Optimal Combination of Phosphogypsum, Farmyard Fertilizer, and Wood Peat. Agronomy. 2023; 13(5):1364. https://doi.org/10.3390/agronomy13051364

Chicago/Turabian Style

Duan, Guokang, Miao Liu, Zhengwei Liang, Mingming Wang, Haoyu Yang, Yang Xu, Tianhe Yu, Yangyang Jin, Jiafeng Hu, and Junqing Liu. 2023. "Amendments of Severe Saline-Sodic Paddy Land: Optimal Combination of Phosphogypsum, Farmyard Fertilizer, and Wood Peat" Agronomy 13, no. 5: 1364. https://doi.org/10.3390/agronomy13051364

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop