Next Article in Journal
Agave schidigera Transcriptome Reveals Stress-Responsive Phenylalanine ammonia-lyase Genes in Agave
Previous Article in Journal
Effects of Habitat Loss on Tenebrionidae in Gravel–Sand Mulching Areas of Desert Steppe in Ningxia, China
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 14
Issue 11
10.3390/agronomy14112519
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

A New Approach to Differentiate the Causes of Excessive Cadmium in Rice: Soil Cadmium Extractability or Rice Variety

by
Erdange Li
1,2,
Kun Li
3,4,
Jumei Li
3,
Yang Wu
1,2 and
Yibing Ma
1,2,*
1
National Observation and Research Station of Coastal Ecological Environments in Macao, Macau University of Science and Technology, Macao SAR 999078, China
2
Macao Environmental Research Institute, Macau University of Science and Technology, Taipa, Macao SAR 999078, China
3
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
4
Sichuan Academy of Forestry, Chengdu 610081, China
*
Author to whom correspondence should be addressed.
Agronomy 2024, 14(11), 2519; https://doi.org/10.3390/agronomy14112519
Submission received: 12 September 2024 / Revised: 9 October 2024 / Accepted: 18 October 2024 / Published: 26 October 2024
(This article belongs to the Topic Effect of Heavy Metals on Plants, 2nd Volume)
Browse Figures
Versions Notes

Abstract

:
In order to effectively decrease cadmium (Cd) in rice grains in contaminated paddy soil and maintain the safe production of rice, identifying excessive Cd in rice caused by rice varieties or soil Cd is critical, but it is currently lacking. In the present study, the soil ethylenediaminetetraacetic acid (EDTA)-extractable Cd (EDTA-Cd) and the bioaccumulation factors of rice based on EDTA-Cd (BCFEDTA-Cd) were used to develop an approach to identify excessive Cd in rice caused by rice varieties or soil Cd. Based on an empirical soil–plant transfer model and species sensitivity distribution (SSD), BCFEDTA-Cd and EDTA-Cd were divided into five grades. The results showed that the five grades of the EDTA-Cd (minimum value less than 0.11 mg/kg and maximum value greater than 2.93 mg/kg) and BCFEDTA-Cd (minimum value less than 0.09 and maximum value greater than 1.40) were classified in the normal soil pH range. Further, the conversion equation between EDTA-Cd and diethylene triamine pentaacetic acid (DTPA)-Cd was obtained through linear regression analysis using 67 sets of soil data from the literature. In addition, the four selected rounding thresholds for the percentage of EDTA-Cd to total soil Cd (EDTA-Cd) (%) were 52.5, 67.5, 82.5, and 97.5%. A selected soil EDTA-Cd (%) (about 75%) can be used to identify the status of soil bioavailability, especially in soil with high background Cd. Finally, a set of 1084 pairs of rice and soil data for Cd-contaminated soils was used to investigate the respective contributions of rice varieties and soil Cd when Cd in rice exceeds the limit (0.2 mg/kg). Based on field experiment data, a systematic identification approach for the causes of rice Cd exceeding the limit, soil Cd or rice variety, was established and applied. In conclusion, under Cd exposure conditions, the importance of the causes of Cd in soil and rice varieties can be identified, and their contributions can be distinguished, thus helping to identify the causes of Cd contamination in rice.

1. Introduction

With rapid industrialization and urbanization in China, 16% of soil samples and 19% of agricultural soils are contaminated mainly with heavy metals and metalloids, based on China’s soil environmental quality limits and nationwide surveys. The soil cadmium (Cd) exceeds the standard for cultivated land in China at the highest rate, as high as 7.0%, among the heavy metals [1]. The average Cd concentration is 0.27 mg/kg in Chinese arable soil [2]. About 45.1% of the total soil Cd content in China exceeds the risk screening values for agricultural soil (GB 15618-2018) [3,4]. Furthermore, the highly toxic Cd is easily accumulated in soil and absorbed by crops, and its contamination has occurred, significantly exceeding the contamination grades of the other heavy metals [5]. The Chinese Ministry of Agriculture Rice Product Quality Inspection and Supervision Center noted that the Cd concentration in 10% of rice grains exceeded the upper limit (0.2 mg/kg) [6]. In China, a major source of human Cd intake is rice grains; the bioaccumulation factor (BCF) of rice (0.4) is higher than the reported Cd enrichment factors in wheat (0.25) and maize (0.16) [7]. Therefore, Cd contamination in rice, especially in paddy soil, is a crucial and serious issue to be settled [8]. For the agro-environmental sustainability of world rice production and food safety, effective remediation and control strategies are necessary to reduce Cd contamination in paddy soils [9]. Scientists from different fields have proposed different strategies for the management and remediation of Cd-contaminated paddy soils [10,11]. Although it is more critical to differentiate the causes of excessive Cd in rice from soil contamination or from rice varieties, this is currently lacking.
Reducing Cd accumulation in rice grains and controlling Cd transportation in rice is particularly important for the normal growth of crops and better human health [12]. Bioavailable Cd refers to the Cd in the soil that can be absorbed by rice, which is the main concern related to rice absorption and accumulation of Cd. Soil available Cd concentration serves as a precise indicator for evaluating the risk associated with Cd absorption and accumulation in rice [13]. The results have shown that the Cd content in the soil is the most significant factor influencing Cd accumulation in soil–rice systems; in particular, bioavailable Cd in soil contributed to 54.78% of the Cd in rice [7]. Compared with the total amount, the available amount of soil heavy metals obtained by the chemical extraction method can better analyze the law of contaminant migration, reflect the ecological risk, and predict contaminant accumulation between soil and crops [14]. Chelating agents (such as ethylenediaminetetraacetic acid (EDTA)) as a single extraction reagent comprise a common and effective method for determining the bioavailable state of soil metal elements. Some experiments have shown that the best chemical extract for heavy metal extraction is EDTA [15,16,17]. Previous studies have indicated that the EDTA-Cd could be used as an indicator to derive the threshold concentrations of Cd for rice food safety and better explain the accumulation of Cd in rice grains [18,19,20].
The bioavailability of Cd to rice mainly depends on the physical and chemical properties of the soil and the physiological and ecological characteristics of the rice. Among them, the physical and chemical properties that affect the bioavailability of Cd include soil redox potential, soil pH, organic matter content, etc. These factors affect the solubility of Cd and thus the absorption of Cd by rice [21]. Some studies have indicated that soil pH and soil organic matter (SOM) are significantly correlated with the accumulation of heavy metals in rice grains [22,23,24,25]. Strategies to improve soil physicochemical properties and fix Cd in contaminated soils are mainly some agronomic measures, including the application of soil amendments and fertilizers, and water and tillage management.
In addition, the uptake and transport of Cd may be significantly different among rice varieties. Bioaccumulation factors refer to the ratio of the experimentally measured Cd concentration in plants to the total concentration in soil, which has been used as an indicator to describe the characteristics of Cd transfer and to predict the concentrations of Cd in plant tissues [26]. Therefore, the bioaccumulation factors of rice based on the EDTA-Cd (BCFEDTA-Cd) refer to the ratio of the experimentally measured Cd concentration in plants to the soil EDTA-extractable Cd (EDTA-Cd). Screening, breeding, and popularizing rice varieties with low Cd accumulation is one of the effective technical means to reduce the risk of Cd pollution in rice [27].
Bioaccumulation factor prediction models have been utilized by many countries and institutions to develop environmental quality standards for contaminants and to determine the grade of heavy metal contamination in soil. According to recent studies, from 2016 to 2020, the development of soil–crop transfer prediction models for the derivation of thresholds in contaminated farmland gradually became one of the research hotspots [28,29]. An empirical model was developed for setting soil Cd criteria [30]. Furthermore, recent research developed reliable soil–plant transfer and species sensitivity distribution (SSD) models that can be used to derive soil probabilistic criteria for Cd [31]. A study summarized five prediction models for Cd content in rice grains [32]. It was evident that pH, soil Cd, and SOM are major factors influencing Cd accumulation in rice grains. Two types of rice Cd prediction models (low-accumulation varieties and high-accumulation varieties) were established based on soil properties [33]. Previous studies have shown that by combining an empirical soil–plant transfer model with SSDs which were derived with the Burr type III distribution model, the bioaccumulation factor of Cd in rice grains could be divided into five grades [26].
The current approaches to manage and control excessive Cd in rice are mostly the arbitrary selection or combination of agricultural measures and rice varieties, and there is no systematic approach to separately identify the two factors affecting Cd content in rice and take targeted control. As previously stated, the aim of the present research was to differentiate the causes of excessive Cd in rice soil Cd extractability or rice cultivars for the safe production of rice. A specific approach was established to identify the contribution of the EDTA-Cd and BCFEDTA-Cd when rice Cd exceeds the limit. The overall steps are described briefly as follows: (1) develop the evaluation method of the EDTA-Cd and BCFEDTA-Cd; (2) classify the BCFEDTA-Cd, the EDTA-Cd (%), and the EDTA-Cd into the following categories: ultra-low risk, low risk, moderate risk, high risk, and ultra-high risk; and (3) apply the systematic identification method for the soil Cd extractability and rice variety causes when rice Cd exceeds the limit.

2. Materials and Methods

2.1. Data Collection

Data used in this research were collected from two sources. Firstly, 67 data sets were obtained from the published literature and the Chinese Core Journal databases (CNKI databases), Springer and ScienceDirect, by using the keywords “Cd”, “DTPA”, “EDTA”, and “soil” (note: DTPA denotes diethylene triamine pentaacetic acid). The data were selected according to the following criteria: (1) soil was used as the medium (excluding hydroponics experiments), and (2) the total Cd concentration and pH value of the soil were obtained, and the total Cd concentration of the soil did not exceed 3 mg/kg in order to eliminate the soils contaminated with slag in mining areas. Then, 1084 data sets were collected from real data of rice fields in Hunan in 2016. The measured data for each sample include rice Cd concentration, soil total Cd concentration, EDTA-Cd concentration, DTPA-Cd concentration, and pH value. The data selection criterion was that the total Cd value in the soil should be greater than the EDTA-Cd value in the soil.
Among the collected data, the soil pH was measured using a pH meter and a water ratio of 1:2.5 (g/mL) [34]. Soil samples were dissolved with aqua regia. The rice was husked according to the Chinese Ministry of Agriculture’s “The Testing Methods of Rice Qualities” (NY 147-1988) [35] and then digested with HNO3-HClO4 or HNO3-H2O2 mixed solution according to the Chinese standard “Determination of Cadmium in Food” (GB 5009.15-2014) [36]. The available Cd concentrations in soil samples were extracted using DTPA and EDTA extraction solutions. The concentrations of total Cd in soil and Cd in rice grains were determined by flame atomic absorption spectrometry or by inductively coupled plasma mass spectrometry [37].

2.2. Data Analyses

2.2.1. Soil Factors

The EDTA-Cd (%) was calculated using a linear regression model established by using the relation between the total Cd and EDTA-Cd [38]. The EDTA-Cd (%) was calculated as follows:
E D T A – C d   % = E D T A − C d S o i l − C d × 100
where EDTA-Cd (%) is the ratio of EDTA-Cd to total Cd, EDTA-Cd (mg/kg) is the concentration of soil EDTA-extractable Cd, and Soil-Cd (mg/kg) is the total Cd concentration in the soil.
In previous research, a conversion model for total Cd and EDTA-Cd concentrations in soil was obtained (coefficient of determination, R2 = 0.77) [38]. In the present study, refitting data from the previous study while eliminating the values with large differences, Table 1 lists the descriptive summary statistics of the data on Cd concentration in soil and the EDTA-Cd (mg/kg) across the sample period. The concentration of the EDTA-Cd was calculated as previously described. Moreover, because of better accuracy, the average of the foregoing values was applied to the following equation:
E D T A – C d = 0.578 S o i l – C d  ( R 2 =0.989,  n =36,  p  < 0.001)
where EDTA-Cd (mg/kg) is the concentration of soil EDTA-extractable Cd, n is the number of samples used to derive the equation, and p is the level of marginal significance within a statistical hypothesis test.

2.2.2. Bioaccumulation Factors

Bioaccumulation factors (BCFs) were calculated as follows:
B C F = G r a i n − C d S o i l − C d
where Grain-Cd (mg/kg) is the Cd concentration of rice grains.
As a consequence, the bioaccumulation factors of rice based on the EDTA-Cd (BCFEDTA-Cd) were computed as follows:
B C F E D T A − C d = G r a i n − C d E D T A − C d

2.2.3. Transfer Factors

Transfer factors (TFs) were calculated as follows:
T F = G r a i n − C d S t r a w − C d
where Straw-Cd (mg/kg) is the Cd concentration of straw.

2.2.4. Normalization of the BCF Data

The soil–plant transfer model used in the present study is as follows:
log 10 ⁡ B C F t o t a l = 0.943 − 0.205   p H − 0.312   log ⁡ ( O C )
where B C F t o t a l is the bioaccumulation factor, pH is the value of pH, OC (g/kg) is the soil organic carbon content, and 0.943 is the intrinsic sensitivity for evaluating the ability of rice to accumulate soil Cd [39]. The process of normalization for the BCFEDTA-Cd values was similar to that of the BCFtotal values. The relation between the total Cd and EDTA-Cd was applied to convert the empirical soil–plant transfer model based on the total Cd concentration into the model with the EDTA-Cd concentration. Then, combining Equations (2) and (6), the empirical soil–plant transfer model based on EDTA-Cd is as follows [38]:
log 10 ⁡ B C F E D T A − C d = 1.181 − 0.205 p H − 0.312 log ⁡ ( O C )

2.2.5. SSD Curve Construction

Using probability distribution functions, the species sensitivity distribution (SSD) method can not only extrapolate the toxicological level between different species but also realize the risk assessment of pollutants at biological community and ecosystem levels. The SSD method is considered to be more accurate for the baseline values used to assess the quality of the soil environment. For modeling empirical SSD, a Burr type III distribution is used for data sets of 8 species or more [40]. The Burr type III model often presents the best fit SSD curves of toxicity data [41]. The parametric equation of the Burr type III function, F(x), is as follows:
F x = 1 1 + b x c k
H C q = b 1 q 1 k − 1 1 c
where HC(q) is the soil concentration of Cd that can secure the food safety for 95% of the crop species in the SSD analysis when q is 0.05 mg/kg; x is a fitted parameter; and b, c, and k are the three parameters of the Burr type III distribution. As a product of Equation (9), HC5 is the cumulative concentration corresponding to the proportion of hazardous species on the SSD curve when it reaches 5%; a value smaller than HC5 means that it represents a more toxic heavy metal.

2.2.6. The Conversion of EDTA-Cd and DTPA-Cd

Among the 67 soil samples, the total Cd concentration in soil ranged from 0.18 to 2.98 mg/kg, the EDTA-Cd concentration in soil ranged from 0.063 to 1.678 mg/kg, and the DTPA-Cd concentration in soil ranged from 0.051 to 1.3 mg/kg. Therefore, through linear regression analysis, taking into account the variables (the total soil Cd and soil pH), the conversion equation (R2 = 0.9707) of EDTA-Cd to DTPA-Cd was obtained as the follows:
E D T A – C d = 1.3177 ( D T P A – C d ) + 0.01114 p H + 0.04779 ( S o i l – C d ) − 0.083
where DTPA-Cd (mg/kg) is the concentration of soil DTPA-extractable Cd.

2.2.7. Analysis of Paddy Field Data

A set of data containing the pH value, grain Cd concentrations, the DTPA-Cd, and the total soil Cd at 1084 sampling points was surveyed on agricultural land contaminated with Cd from rice. According to Equation (10), the DTPA-Cd values were converted to the EDTA-Cd values, applying Equation (4) to calculate the BCFEDTA-Cd for each of these sampling points and applying Equation (1) to calculate the EDTA-Cd (%). Then, regarding data processing, the first step was to identify outliers, which refer to extreme values that are abnormally beyond the overall pattern of the variable distribution. Outliers can significantly affect the estimation process of statistical data (such as the mean and standard deviation of the sample), causing the values to be overestimated or underestimated [42]. Values outside the range of three standard deviations are usually marked as outliers [43]. Therefore, values outside the range of three standard deviations (SDs or σ) were eliminated (3σ = 3.71). After that, the number of samples was reduced to 1003, and the statistics in Table 2 were computed.

2.2.8. Determining the Respective Contributions of the Causes of Rice Cd Contamination

Comparison of the risk grade values of BCFEDTA-Cd with EDTA-Cd can trace the respective contributions of the different causes of Cd contamination in rice. The two most critical factors affecting Cd concentration in rice were obtained by comparison. The flow chart for the determination of the contributions of the causes is shown in Figure 1.

2.2.9. Statistics

The statistical package SPSS 26.0 was utilized for the statistical analysis and frequency distribution plot. Spreadsheets and figures were developed using Excel 2019. The difference at the level of p < 0.05 was considered significant.

3. Results

3.1. Soil Factor of the EDTA-Cd

3.1.1. Risk Classification of Soil-Cd

The causes of Cd contamination of rice were distinguished for the purpose of rice safety, so the risk classification of Cd in rice was the core concern, and the risk classification of the EDTA-Cd and BCFEDTA-Cd was conducted. Using the rice Cd contamination index as the evaluation standard, the rice Cd content limit value (0.2 mg/kg) was taken as the ultra-low risk value for the rice Cd content index, and two times the rice Cd content limit value was taken as the moderate risk critical value. Five risk grades of rice Cd and the corresponding rice content were obtained [44], as listed in Table 3.
Based on the Cd content of rice at each risk grade in Table 3, the total soil Cd concentration was calculated according to the SSD model (Equation (6)) based on soil properties (pH = 4, 5, 6, 7, and 8 and OC = 20). The total Cd values were divided into the five grades, from ultra-low to ultra-high risk. The derived ultra-low risk level critical value for total soil cadmium was slightly higher than the current soil quality standard, which was consistent with other research [45]. Table 4 lists the relation between the risk classes, soil pH, and Soil-Cd content.

3.1.2. Risk Classification of EDTA-Cd

According to Equation (7), the EDTA-Cd values were calculated based on soil properties (pH and OC). The EDTA-Cd values were divided into five risk grades, with a minimum value of less than 0.11 mg/kg and a maximum value greater than 2.93 mg/kg. Similar to the present results, other research found that the threshold value for Cd extractable by DTPA for safe rice production was 0.13 mg/kg when the pH in the soil was below 5.5 [20]. The risk classification of EDTA-Cd contamination is listed in Table 5.

3.1.3. Risk Classification of EDTA-Cd (%)

Regarding the EDTA-Cd (%), the statistics and frequency distribution table determined by frequency statistics are listed in Table 6. Figure 2 shows the cumulative distribution curve, normal distribution curve, and frequency histogram of the EDTA-Cd (%). Based on previous experience, the EDTA-Cd (%) ranged from 30 to 90% approximately. For convenience, the four selected rounding thresholds were 52.5, 67.5, 82.5, and 92.5%, corresponding to the 5th, 25th, 75th, and 95th percentile quantiles of the four cumulative frequency curves, respectively. The contamination risk grades were divided into five categories, in the following order: ultra-low risk was less than 52.5%, low risk was 52.5% to 67.5%, moderate risk was 67.5–82.5%, high risk was 82.5–92.5%, and ultra-high risk was greater than 92.5%. Thus, as shown in Figure 2, the median EDTA-Cd (%) of about 75% can be used to identify the status of soil bioavailability in practical applications, especially in soil with high background Cd. Other studies have found that the extraction rate of EDTA was the highest in acidic, neutral, and alkaline soils, up to 93.31% [15].

3.2. Bioaccumulation Factor of Rice Grains

Classification of Bioaccumulation Factors of Rice Based on EDTA-Cd

Referring to a previous study [26], combining the empirical soil–plant transfer model and species sensitivity distribution, the bioaccumulation factor of Cd in rice grains can be divided into five grades. Therefore, similarly, considering the relation between total soil Cd and EDTA-Cd, the BCF threshold under soil extractable Cd conditions was calculated. The normalized BCF decreased with increasing soil pH, and the critical value of BCFEDTA at a pH greater than 6.5 was 47.8–62.8% lower than that at a pH less than or equal to 5.5. Under high-pH conditions, the variation range of BCF decreased. The risk classification of BCFEDTA-Cd is listed in Table 7.

3.3. Comparison of Soil Factors and Extractable Bioaccumulation Factors at Measured Rice Concentrations

The risk grade values of EDTA-Cd and BCFEDTA-Cd for 1003 sample points are plotted in Figure 3. When the BCFEDTA-Cd grade value is higher than the EDTA-Cd grade value, the Cd content in rice grains is strongly affected by the extractable bioaccumulation factor of the rice grains. In this state, which occurred 97% of the time, selecting and cultivating low-Cd-accumulating rice cultivars might be a better option for the reduction in Cd accumulation in rice [9].
When the EDTA-Cd grade value exceeded the BCFEDTA-Cd grade value, which occurred 1% of the time, it suggests that decreasing the EDTA-extractable fractions of Cd is a more urgent method to control Cd contamination in rice. If the soil is acidic, increasing soil pH is the first priority. For example, the application of organic manure at a proper rate can reduce the accumulation and availability of Cd in the surface soil [46]. The addition of amendments of calcareous materials, such as lime, calcium carbonate, and calcium silicate, which can significantly raise soil pH and cause the immobilization of the labile or exchangeable Cd in soils, can help immobilize Cd2+ in the soils and reduce its availability [47,48]. A study indicated that the application of three different passivators could increase the soil pH by 0.62 to 0.86 units and reduce the Cd content in brown rice by 68% to 70% [49].
When the EDTA-Cd grade value and the BCFEDTA-Cd grade value were similar, which occurred 2% of the time, it meant that both rice varieties and soil properties were responsible for the excessive Cd in rice. Besides the foregoing approaches, irrigation management can also provide economically viable and environmentally friendly options for the remediation of Cd-contaminated soil. Water management also is a promising, controllable, and environmentally friendly approach to reduce Cd bioavailability and uptake [50]. A properly designed rice-based intercropping mode can provide an attractive alternative for Cd remediation in paddy fields [51]. Because the severity of Cd contamination in rice varies among different geographical locations, the primary causes and conditions of Cd contamination are different. For example, in northeast, central, southwest, and south China, the contents of Cd increase gradually from the northwest to southeast and from the northeast to southwest. In some provinces, mineral exploitation and industrial production are the main causes of Cd contamination, while in other provinces, these are irrigation with sewage and chemical fertilizer application [52]. Hence, it is necessary to adjust measures to better adapt to different conditions and choose the most effective and economical method to control excessive Cd in rice.

3.4. Applying Model by Using Experimental Field Data

The model for identifying cause contributions was applied to the experimental field data to test its feasibility and applicability. The data were derived from the previous literature. As shown in Table 8, in Scenario 1, the EDTA-Cd risk grade was low, and the BCFEDTA-Cd risk grade was ultra-low, while the risk of Cd contamination in rice was still ultra-low, so there was no need to take management measures. The only concern was to monitor the Cd content of rice. In Scenario 6, the Cd contamination risk grade of rice was ultra-high, so in theory, these fields should be fallow or should apply crop reform, but the respective contributions of the two factors, EDTA-Cd and BCFEDTA-Cd, can still provide references for subsequent risk management and control.
In Scenario 4, the risk of Cd contamination in rice was high, and the contribution of soil Cd extractability was greater than that of rice variety in relation to excessive Cd content in rice. Hence, the cause of soil Cd extractability in the Cd contamination of rice was important. Research has demonstrated a significant inverse relation between the effective content of soil Cd and the pH value. Specifically, an increase in soil pH will lead to a reduction in the effective content of soil Cd, thereby decreasing its toxicity to rice [53]. Therefore, the risk grade of EDTA-Cd can be brought down by increasing the pH, especially in Scenario 4, where the pH value is 5.00. Research results also showed that adding zeolite increases the soil pH and the soil adsorption capacity for Cd, thereby significantly reducing soil Cd availability. Soil amendments such as lime are effective in immobilizing Cd in acidic soil but have limited effects in alkaline and neutral soil [54]. The Cd content in rice can also be effectively reduced by applying organic fertilizers and water management.
In Scenario 3, the risk grade of EDTA-Cd was lower than the risk grade of BCFEDTA-Cd; thus, the cause of rice variety is of greater concern and had a greater contribution to Cd risk. The primary management of rice varieties can more quickly and specifically reduce the Cd content of rice, such as selecting low-Cd-accumulating rice cultivars. When the risk grade of rice variety factors is higher, rice with low-Cd-accumulating traits is more beneficial for reducing Cd concentration in rice grains.
Finally, it is worth mentioning that in Scenario 2, the risk grade of EDTA-Cd was equal to that of BCFEDTA-Cd, which was moderate. Therefore, the two factors, EDTA-Cd and BCFEDTA-Cd, had the same contribution to the excess of Cd in rice. In order to effectively reduce the Cd content of rice, the use of soil amendments and low-Cd rice varieties are both essential measures.
Table 8. Soil and rice data under different Cd contamination scenarios and corresponding management measures.
Table 8. Soil and rice data under different Cd contamination scenarios and corresponding management measures.
Scenarioa Cdgrain
(mg/kg)		pHb EDTA-Cd
(mg/kg)		c BCFEDTA-Cdd Measures
(Primacy)		Reference
10.04
Ultra-low		5.260.26
Low		Ultra-low [55]
20.30
Moderate		5.600.77
Moderate		ModerateA = B[56]
30.38
Moderate		8.171.48
Moderate		HighA < B[57]
40.40
High		5.000.86
Ultra-high		ModerateA > B[58]
50.81
Ultra-high		6.570.16
Ultra-low		Ultra-high [59]
a Cdgrain is the Cd concentration of rice grains and the corresponding grade. b EDTA-Cd is the concentration of soil EDTA-extractable Cd and its corresponding grade. c BCFEDTA-Cd is the grade of bioaccumulation factors of rice based on EDTA-Cd. d Measures are management measures to reduce Cd content in rice, A is to add soil conditioners, B is to change the rice variety, and the primacy of the two measures is compared (>, =, <).

4. Discussion

4.1. Risk Assessment of EDTA-Cd in Soil

Different studies have different standards for the risk assessment of soil Cd contamination. Soil Cd contamination levels were classified as low, moderate, and high [60]. A recent study divided the Cd contamination status into four categories, which represent noncontaminated conditions and low, moderate, and high contamination grades [61]. The present research followed previous studies and classified soil Cd contamination into five grades, which are ultra-low risk, low risk, moderate risk, high risk, and ultra-high risk [44]. Some studies were focused on the risk assessment of total Cd in soil [62,63]. The threshold standard for EDTA-Cd was calculated to be 0.07 mg/kg when the pH value is less than 6.5 [18]. In the present study, the risk grade of EDTA-Cd contamination was divided into ultra-low, low, moderate, high, and ultra-high according to the degree of contamination from light to heavy. Meanwhile, the present research provides a reference for the Cd content standard under different extraction conditions and is conducive to the formulation of the standard of available Cd in the future.

4.2. Risk Assessment of BCFEDTA-Cd

Soil risk assessment should consider not only soil characteristics but also differences between rice varieties [45]. Previous studies classified rice varieties into five grades based on the bioaccumulation of Cd in rice grains [26]. EDTA-Cd is more suitable to be used as a soil Cd threshold criterion for the food safety of rice crops [18]. If the extracted available Cd concentration is less than 0.19 mg/kg, 95% of the agricultural products grown in the soil could be below levels of regulatory concern [64]. Different from previous studies, the present research considered the importance of EDTA-Cd and proposed risk classification standards for BCFEDTA-Cd. The evaluation of BCFEDTA-Cd can improve the accuracy of Cd bioaccumulation identification in rice grains.

4.3. Differentiating the Causes of Excessive Cadmium in Rice: Soil Cadmium Extractability or Rice Varieties

In the soil–rice system, the important factors affecting the absorption and accumulation of Cd by rice are the bioavailability of Cd in the soil and the capacity of Cd bioaccumulation in rice grains. The present study assessed the risk of soil Cd extractability and rice varieties. This was the first time that the two factors were distinguished, and the risk grade of the two factors can determine which factor needs to be the primary concern so as to more efficiently and accurately control Cd contamination in rice. However, the present research classifies the risk of Cd in the EDTA extraction state, and different Cd extraction methods can be considered in the future. The accuracy of this new approach still needs to be evaluated and improved.

5. Conclusions

In summary, unlike the total bioaccumulation factor and total soil Cd, the present research used the risk classification of soil Cd extractability and rice varieties to differentiate the causes of excessive Cd in rice. The EDTA-Cd, as well as the BCFEDTA-Cd, could be divided into five grades. The present study derived a new approach which combines the EDTA-Cd with the BCFEDTA-Cd to identify the respective contributions of both causes in rice Cd contamination under Cd exposure. This work confirmed that the model can effectively demonstrate the contribution of the soil property factor and rice cultivar factor based on soil and rice data when rice Cd exceeds the standard. The difference between this method and the traditional method is that the two factors are analyzed separately. Then, by showing specific cases of Cd contamination in rice, it is recommended that managers primarily apply soil management or change rice varieties or both according to different situations. Overall, the present research provides support for the risk assessment of soil Cd contamination in rice production to ensure food safety. This present research contributes to appropriate land use management and accurate rice Cd contamination risk control in the future.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy14112519/s1.

Author Contributions

Conceptualization, Y.M.; Methodology, J.L.; Resources, Y.W.; Data curation, K.L.; Writing—original draft, E.L.; Writing—review & editing, Y.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Science and Technology Development Fund, Macau SAR (0008/2022/AFJ and 001/2022/NIF).

Data Availability Statement

One part of the data is included in this article or Supplementary Material; another part of the data presented in this study is available on request from the corresponding author due to privacy.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Guan, Q.Y.; Liu, Z.; Shao, W.Y.; Tian, J.; Luo, H.P.; Ni, F.; Shan, Y.X. Probabilistic risk assessment of heavy metals in urban farmland soils of a typical oasis city in northwest China. Sci. Total Environ. 2022, 833, 155096. [Google Scholar] [CrossRef] [PubMed]
  2. Zhang, X.Y.; Chen, D.M.; Zhong, T.Y.; Zhang, X.M.; Cheng, M.; Li, X.H. Assessment of cadmium (Cd) concentration in arable soil in China. Environ. Sci. Pollut. Res. 2015, 22, 4932–4941. [Google Scholar] [CrossRef] [PubMed]
  3. Wang, C.C.; Zhang, Q.C.; Yan, C.A.; Tang, G.Y.; Zhang, M.Y.; Ma, L.Q.; Gu, R.H.; Xiang, P. Heavy metal (loid) s in agriculture soils, rice, and wheat across China: Status assessment and spatiotemporal analysis. Sci. Total Environ. 2023, 882, 163361. [Google Scholar] [CrossRef]
  4. GB 15618-2018; Soil Environmental Quality Risk Control Standard for Soil Contamination of Agricultural Land. Ministry of Ecology and Environment of China, and State Administration for Market Regulation of China: Beijing, China, 2018. (In Chinese)
  5. Teng, Y.G.; Wu, J.; Lu, S.J.; Wang, Y.Y.; Jiao, X.D.; Song, L.T. Soil and soil environmental quality monitoring in China: A review. Environ. Int. 2014, 69, 177–199. [Google Scholar] [CrossRef]
  6. Duan, M.M.; Wang, S.; Huang, D.Y.; Zhu, Q.H.; Liu, S.L.; Zhang, Q.; Zhu, H.H.; Xu, C. Effectiveness of simultaneous applications of lime and zinc/iron foliar sprays to minimize cadmium accumulation in rice. Ecotoxicol. Environ. Saf. 2018, 165, 510–515. [Google Scholar] [CrossRef]
  7. Mu, Y.; Cui, J.X.; Liu, A.D.; Wang, S.; Shi, Q.J.; Wang, J.; Wei, S.Q.; Zhang, J.Z. Interactions and quantification of multiple influencing factors on cadmium accumulation in soil-rice systems at a large region. Sci. Total Environ. 2023, 881, 163392. [Google Scholar] [CrossRef]
  8. Huang, L.K.; Wang, Q.; Zhou, Q.Y.; Ma, L.Y.; Wu, Y.J.; Liu, Q.Z.; Wang, S.; Feng, Y. Cadmium uptake from soil and transport by leafy vegetables: A meta-analysis. Environ. Pollut. 2020, 264, 114677. [Google Scholar] [CrossRef]
  9. Rizwan, M.; Ali, S.; Adrees, M.; Rizvi, H.; Zia-ur-Rehman, M.; Hannan, F.; Qayyum, M.F.; Hafeez, F.; Ok, Y.S. Cadmium stress in rice: Toxic effects, tolerance mechanisms, and management: A critical review. Environ. Sci. Pollut. Res. 2016, 23, 17859–17879. [Google Scholar] [CrossRef] [PubMed]
  10. Chen, L.M.; Wu, W.G.; Han, F.X.; Li, J.X.; Ye, W.L.; Fu, H.H.; Yan, Y.H.; Ma, Y.H.; Wang, Q. Agronomic management and rice varieties controlling Cd bioaccumulation in rice. Int. J. Environ. Res. Public Health 2019, 16, 2376. [Google Scholar] [CrossRef]
  11. NaziaTahir; Ullah, A.; Tahir, A.; Rashid, H.U.; Rehman, T.U.; Danish, S.; Hussain, B.; Akca, H. Strategies for reducing Cd concentration in paddy soil for rice safety. J. Clean. Prod. 2021, 316, 128116. [Google Scholar] [CrossRef]
  12. Uraguchi, S.; Fujiwara, T. Cadmium transport and tolerance in rice: Perspectives for reducing grain cadmium accumulation. Rice 2012, 5, 5. [Google Scholar] [CrossRef] [PubMed]
  13. Huang, H.; Mao, J.; Tan, J.; Zhong, K.; Chen, J.X.; Huang, D.; Gu, X.Y.; Zhang, C.L. Heavy metal contamination, accumulation, and risk assessment in a paddy field near Pb-Zn mine, in Guangxi Province, China. J. Soils Sediments 2023, 23, 1345–1355. [Google Scholar] [CrossRef]
  14. Li, J.H.; Nie, D.T.; Liu, M.N.; Mao, X.Y.; Liao, Z.W.; Chen, X. Comparison of Cd bioavailability determination methods and the risk control value of Cd for typical Cd-contaminated paddy soils in Guangdong. J. Agric. Resour. Environ. 2021, 38, 1094–1101. (In Chinese) [Google Scholar]
  15. Li, F.S.; Han, M.; Xiong, D.Q.; Lu, G.L.; Liu, F. Efficiency of some extractants for available heavy metals from several typical soils. J. Agro-Environ. Sci. 2003, 22, 704–706. (In Chinese) [Google Scholar]
  16. Yi, L.; Zhang, Z.Q.; Shen, F.; Liu, H. Impact of different extraction conditions and different extracts on heavy metals from several typical soils. J. Northwest Agric. Sci. 2012, 21, 156–160. (In Chinese) [Google Scholar]
  17. Qian, E.; Zhao, Y.J.; Liu, X.W.; Li, Z.T.; Zhang, C.C.; Sun, Y.; Zhou, Q.W.; Liang, X.F.; Wang, H.H. Screening and evaluation of soil cadmium extraction methods for predicting cadmium accumulation in rice. J. Agro-Environ. Sci. 2020, 39, 1000–1009. (In Chinese) [Google Scholar]
  18. Wang, Y.F.; Su, Y.; Lu, S. Predicting accumulation of Cd in rice (Oryza sativa L.) and soil threshold concentration of Cd for rice safe production. Sci. Total Environ. 2020, 738, 139805. [Google Scholar] [CrossRef]
  19. Kim, R.Y.; Yoon, J.K.; Kim, T.S.; Yang, J.E.; Owens, G.; Kim, K.R. Bioavailability of heavy metals in soils: Definitions and practical implementation—A critical review. Environ. Geochem. Health 2015, 37, 1041–1061. [Google Scholar] [CrossRef]
  20. Kong, F.Y.; Lu, S.G. Prediction model for Cd accumulation of rice (Oryza sativa L.) based on extractable Cd in soils and prediction for high Cd-risk regions of southern Zhejiang Province, China. Environ. Sci. Pollut. Res. 2023, 30, 15964–15974. [Google Scholar] [CrossRef]
  21. Li, H.; Luo, N.; Li, Y.W.; Cai, Q.Y.; Li, H.Y.; Mo, C.H.; Wong, M.H. Cadmium in rice: Transport mechanisms, influencing factors, and minimizing measures. Environ. Pollut. 2017, 224, 622–630. [Google Scholar] [CrossRef]
  22. Soubasakou, G.; Cavoura, O.; Damikouka, I. Phytoremediation of cadmium-contaminated soils: A review of new cadmium hyperaccumulators and factors affecting their efficiency. Bull. Environ. Contam. Toxicol. 2022, 109, 783–787. [Google Scholar] [CrossRef] [PubMed]
  23. Kong, L.L.; Guo, Z.H.; Peng, C.; Xiao, X.Y.; He, Y.L. Factors influencing the effectiveness of liming on cadmium reduction in rice: A meta-analysis and decision tree analysis. Sci. Total Environ. 2021, 779, 146477. [Google Scholar] [CrossRef] [PubMed]
  24. Li, D.Q.; Wang, L.L.; Wang, Y.H.; Li, H.S.; Chen, G.K. Soil properties and cultivars determine heavy metal accumulation in rice grain and cultivars respond differently to Cd stress. Environ. Sci. Pollut. Res. 2019, 26, 14638–14648. [Google Scholar] [CrossRef] [PubMed]
  25. Huang, J.W.; Fan, G.P.; Liu, C.; Zhou, D.M. Predicting soil available cadmium by machine learning based on soil properties. J. Hazard. Mater. 2023, 460, 132327. [Google Scholar] [CrossRef]
  26. Li, K.; Cao, C.L.; Ma, Y.B.; Su, D.C.; Li, J.M. Identification of cadmium bioaccumulation in rice (Oryza sativa L.) by the soil-plant transfer model and species sensitivity distribution. Sci. Total Environ. 2019, 692, 1022–1028. [Google Scholar] [CrossRef] [PubMed]
  27. Jiang, N.; Yan, X.; Zhou, Y.B.; Zhou, Q.F.; Wang, K.; Yang, Y.Z. Factors affecting cadmium accumulation in rice and strategies for minimization. Chin. J. Rice Sci. 2021, 35, 342. (In Chinese) [Google Scholar]
  28. Du, Z.L.; Lin, D.S.; Li, H.F.; Li, Y.; Chen, H.A.; Dou, W.Q.; Qin, L.; An, Y. Bibliometric analysis of the influencing factors, derivation, and application of heavy metal thresholds in soil. Int. J. Environ. Res. Public Health 2022, 19, 6561. [Google Scholar] [CrossRef]
  29. Gao, J.T.; Ye, X.X.; Wang, X.Y.; Jiang, Y.J.; Li, D.C.; Ma, Y.B.; Sun, B. Derivation and validation of thresholds of cadmium, chromium, lead, mercury and arsenic for safe rice production in paddy soil. Ecotoxicol. Environ. Saf. 2021, 220, 112404. [Google Scholar] [CrossRef]
  30. Mu, D.M.; Zheng, S.N.; Lin, D.S.; Xu, Y.M.; Dong, R.Y.; Pei, P.G.; Sun, Y.B. Derivation and validation of soil cadmium thresholds for the safe farmland production of vegetables in high geological background area. Sci. Total Environ. 2023, 873, 162171. [Google Scholar] [CrossRef]
  31. Li, X.Z.; Du, J.Y.; Sun, L.; Zhang, Y.; Feng, Y.H.; Zheng, L.P.; Wang, G.Q.; Huang, X.H. Derivation of soil criteria of cadmium for safe rice production applying soil–plant transfer model and species sensitivity distribution. Int. J. Environ. Res. Public Health 2022, 19, 8854. [Google Scholar] [CrossRef]
  32. Zou, M.M.; Zhou, S.L.; Zhou, Y.J.; Jia, Z.Y.; Guo, T.W.; Wang, J.X. Cadmium pollution of soil-rice ecosystems in rice cultivation dominated regions in China: A review. Environ. Pollut. 2021, 280, 116965. [Google Scholar] [CrossRef] [PubMed]
  33. Mu, T.T.; Zhou, T.; Li, Z.; Hu, P.J.; Luo, Y.M.; Christie, P.; Wu, L.H. Prediction models for rice cadmium accumulation in Chinese paddy fields and the implications in deducing soil thresholds based on food safety standards. Environ. Pollut. 2020, 258, 113879. [Google Scholar] [CrossRef] [PubMed]
  34. Yin, H.Q.; Lu, X.Z.; Sun, R.; Huang, C.L.; Kang, Z.J.; Xu, M.X.; Wei, Y.C.; Cai, Z.H. Spatio-temporal variation prediction on Cd content in the rice grains from Northern Zhejiang Plain during 2014–2019 based on high-precision soil geochemical data. J. Geogr. Sci. 2023, 33, 413–426. [Google Scholar] [CrossRef]
  35. NY 147-1988; Good Quality and Edible Rice Grains. Ministry of Agriculture, the People’s Republic of China: Beijing, China, 1988. (In Chinese)
  36. GB 5009.15-2014; National Food Safety Standard-Determination of Cadmium in Food. National Health and Family Planning Commission: Beijing, China, 2015. (In Chinese)
  37. Luo, Q.H.; Bai, B.; Xie, Y.H.; Yao, D.P.; Zhang, D.M.; Chen, Z.; Zhuang, W.; Deng, Q.Y.; Xiao, Y.H.; Wu, J. Effects of Cd uptake, translocation and redistribution in different hybrid rice varieties on grain Cd concentration. Ecotoxicol. Environ. Saf. 2022, 240, 113683. [Google Scholar] [CrossRef] [PubMed]
  38. Li, L.J.; Li, K.; Jiang, B.; Li, J.M.; Ma, Y.B. Derivation and validation of soil total and extractable cadmium criteria for safe vegetable production. J. Integr. Agric. 2023, 22, 3792–3803. [Google Scholar] [CrossRef]
  39. Ye, X.X.; Li, H.Y.; Ma, Y.B.; Wu, L.; Sun, B. The bioaccumulation of Cd in rice grains in paddy soils as affected and predicted by soil properties. J. Soils Sediments 2014, 14, 1407–1416. [Google Scholar] [CrossRef]
  40. Fox, D.R.; van Dam, R.A.; Fisher, R.; Batley, G.E.; Tillmanns, A.R.; Thorley, J.; Schwarz, C.J.; Spry, D.J.; McTavish, K. Recent developments in species sensitivity distribution modeling. Environ. Toxicol. Chem. 2021, 40, 293–308. [Google Scholar] [CrossRef]
  41. Xu, F.L.; Li, Y.L.; Wang, Y.; He, W.; Kong, X.Z.; Qin, N.; Liu, W.X.; Wu, W.J.; Jorgensen, S.E. Key issues for the development and application of the species sensitivity distribution (SSD) model for ecological risk assessment. Ecol. Indic. 2015, 54, 227–237. [Google Scholar] [CrossRef]
  42. Kwak, S.K.; Kim, J.H. Statistical data preparation: Management of missing values and outliers. Korean J. Anesthesiol. 2017, 70, 407–411. [Google Scholar] [CrossRef]
  43. Zhang, L.X.; Ren, Z.H.; Chen, B.; Gong, P.; Xu, B.; Fu, H.H. A prolonged artificial nighttime-light dataset of China (1984–2020). Sci. Data 2024, 11, 414. [Google Scholar] [CrossRef]
  44. Liu, Z.W. Risk Control and Management Index System of Cadmium Contaminated Paddy Field. Master’s Thesis, Macau University of Science and Technology, Macau, China, 2022. (In Chinese). [Google Scholar]
  45. Li, L.J.; Jiang, B.; Wan, Y.N.; Li, J.M.; Ma, Y.B. Integrating bioavailability and aging in the criteria derivation of cadmium for the safe production of rice in paddy soils. Ecotoxicol. Environ. Saf. 2021, 219, 112356. [Google Scholar] [CrossRef] [PubMed]
  46. Duan, H.Q.; Qin, Q.; Lu, W.G.; Xue, Y.; Sun, L.J.; Song, K. Effects of long-term application of organic manure on contents of total and available cadmium in greenhouse soil. Acta Pedol. Sin. 2021, 58, 1486–1495. [Google Scholar]
  47. Chen, H.P.; Zhang, W.W.; Yang, X.P.; Wang, P.; McGrath, S.P.; Zhao, F.J. Effective methods to reduce cadmium accumulation in rice grain. Chemosphere 2018, 207, 699–707. [Google Scholar] [CrossRef]
  48. Hu, Y.A.; Cheng, H.F.; Tao, S. The challenges and solutions for cadmium-contaminated rice in China: A critical review. Environ. Int. 2016, 92, 515–532. [Google Scholar] [CrossRef]
  49. Zhang, J.; Kong, F.Y.; Lu, S.G. Remediation effect and mechanism of inorganic passivators on cadmium contaminated acidic paddy soil. Chin. J. Environ. Sci. 2022, 43, 4679–4686. (In Chinese) [Google Scholar]
  50. Mubeen, S.; Ni, W.J.; He, C.T.; Yang, Z.Y. Agricultural strategies to reduce cadmium accumulation in crops for food safety. Agriculture 2023, 13, 471. [Google Scholar] [CrossRef]
  51. Xue, T.; Liao, X.Y.; Li, H.Y.; Xie, Y.H.; Wei, W.; Chen, J.; Liu, Z.B.; Ji, X.H. Remediation of Cd contaminated paddy fields by intercropping of the high-and low-Cd-accumulating rice cultivars. Sci. Total Environ. 2023, 878, 163133. [Google Scholar] [CrossRef]
  52. Wu, Y.F.; Li, X.; Yu, L.; Wang, T.Q.; Wang, J.N.; Liu, T.T. Review of soil heavy metal pollution in China: Spatial distribution, primary sources, and remediation alternatives. Resour. Conserv. Recycl. 2022, 181, 106261. [Google Scholar] [CrossRef]
  53. Li, C. Effects of Soil Conditioners on Remediation of Heavy Metal Cadmium Pollution Meta-Analysis and Empirical Research. Master’s Thesis, Northeast Agricultural University, Harbin, China, 2022. (In Chinese). [Google Scholar]
  54. Wan, Y.N.; Liu, J.; Zhuang, Z.; Wang, Q.; Li, H.F. Heavy metals in agricultural soils: Sources, influencing factors, and remediation strategies. Toxics 2024, 12, 63. [Google Scholar] [CrossRef]
  55. Chen, L.; Guo, L.; Liao, P.; Xiong, Q.Q.; Deng, X.Y.; Gao, H.; Wei, H.Y.; Dai, Q.G.; Pan, X.H.; Zeng, Y.J.; et al. Effects of biochar on the dynamic immobilization of Cd and Cu and rice accumulation in soils with different acidity levels. J. Clean. Prod. 2022, 372, 133730. [Google Scholar] [CrossRef]
  56. Sheng, H.; Gu, Y.; Yin, Z.R.; Xue, Y.; Zhou, P.; Thompson, M.L. Consistent inter–annual reduction of rice cadmium in 5-year biannual organic amendment. Sci. Total Environ. 2022, 807, 151026. [Google Scholar] [CrossRef] [PubMed]
  57. Singh, R.P.; Agrawal, M. Variations in heavy metal accumulation, growth and yield of rice plants grown at different sewage sludge amendment rates. Ecotoxicol. Environ. Saf. 2010, 73, 632–641. [Google Scholar] [CrossRef] [PubMed]
  58. Zhu, Z.L.; Xu, Y.X.; Chen, D.; Xiao, W.D.; Ye, X.Z. Effects of different passivators on Cd absorption in rice. J. Zhejiang Agric. Sci. 2023, 64, 282–285. (In Chinese) [Google Scholar]
  59. Zhang, Y.S.; Jiang, C.C.; Xiao, H.; Wu, Y.J.; Yang, X.F.; Wang, Z.Q.; Zheng, H.P.; Ao, H.J. Preliminary study on the effects of soil modifier on available Cd in soil and cadmium content in brown rice. China Rice 2019, 25, 53–57. [Google Scholar]
  60. Zhuang, S.K.; Lu, X.W. Environmental risk evaluation and source identification of heavy metal(loid)s in agricultural soil of Shangdan Valley, Northwest China. Sustainability 2020, 12, 5806. [Google Scholar] [CrossRef]
  61. Fei, X.F.; Lou, Z.H.; Xiao, R.; Ren, Z.Q.; Lv, X.N. Source analysis and source-oriented risk assessment of heavy metal pollution in agricultural soils of different cultivated land qualities. J. Clean. Prod. 2022, 341, 130942. [Google Scholar] [CrossRef]
  62. Wan, M.X.; Hu, W.Y.; Wang, H.F.; Tian, K.; Huang, B. Comprehensive assessment of heavy metal risk in soil-crop systems along the Yangtze River in Nanjing, Southeast China. Sci. Total Environ. 2021, 780, 146567. [Google Scholar] [CrossRef]
  63. Liu, H.W.; Zhang, Y.; Yang, J.S.; Wang, H.Y.; Li, Y.L.; Shi, Y.; Li, D.C.; Holm, P.E.; Ou, Q.; Hu, W.Y. Quantitative source apportionment, risk assessment and distribution of heavy metals in agricultural soils from southern Shandong Peninsula of China. Sci. Total Environ. 2021, 767, 144879. [Google Scholar] [CrossRef]
  64. Qin, L.Y.; Yu, L.; Wang, M.; Sun, X.Y.; Wang, J.; Liu, J.X.; Wang, Y.; White, J.C.; Chen, S.B. The environmental risk threshold (HC5) for Cd remediation in Chinese agricultural soils. J. Environ. Manag. 2024, 362, 121316. [Google Scholar] [CrossRef]
Agronomy 14 02519 g001
Figure 1. Flow chart for determining the respective contributions of the causes of excessive Cd in rice.
Figure 1. Flow chart for determining the respective contributions of the causes of excessive Cd in rice.
Agronomy 14 02519 g001
Agronomy 14 02519 g002
Figure 2. Cumulative distribution curve, normal distribution curve, and frequency histogram of the EDTA-Cd (%).
Figure 2. Cumulative distribution curve, normal distribution curve, and frequency histogram of the EDTA-Cd (%).
Agronomy 14 02519 g002
Agronomy 14 02519 g003
Figure 3. The Cd concentration of rice in all sample points and its corresponding risk grade of BCFEDTA-Cd and EDTA-Cd.
Figure 3. The Cd concentration of rice in all sample points and its corresponding risk grade of BCFEDTA-Cd and EDTA-Cd.
Agronomy 14 02519 g003
Table 1. Mean, standard error, maximum, and minimum for the EDTA-Cd (mg/kg).
Table 1. Mean, standard error, maximum, and minimum for the EDTA-Cd (mg/kg).
Sample SizeEDTA-Cd (mg/kg)
MeanStandard ErrorMaximumMinimum
360.41930.02710.72510.2286
Table 2. Descriptive statistics on soil properties and Cd concentrations in soils and grains.
Table 2. Descriptive statistics on soil properties and Cd concentrations in soils and grains.
VariablesMinimumMaximumMeanMedianSD
Soil–riceSoil pH4.527.865.565.440.51
(n = 1003)Cdsoil (mg/kg)0.1051.1950.3680.3330.163
CdEDTA (mg/kg)0.0630.9980.2780.2550.135
Cdgrain (mg/kg)0.0112.0960.4290.3790.257
BCFEDTA-Cd0.0573.6941.6601.5690.811
Cdsoil, CdEDTA, and Cdgrain represent the Cd concentrations in soil, EDTA-extractable state, and grains, respectively; BCFEDTA-Cd represents the bioaccumulation factors of rice based on EDTA-Cd; SD denotes the standard deviation.
Table 3. Risk classification of rice Cd.
Table 3. Risk classification of rice Cd.
RiskUltra-LowLowModerateHighUltra-High
Rice Cd content (mg/kg)<0.20.2–0.30.3–0.40.4–0.5>0.5
Table 4. Risk classification of total Cd in soil.
Table 4. Risk classification of total Cd in soil.
GradeRiskSoil-Cd Content (mg/kg)
pH ≤ 4.54.5 < pH ≤ 5.55.5 < pH ≤ 6.56.5 < pH ≤ 7.5pH > 7.5
1Ultra-low<0.19<0.31<0.49<0.79<1.26
2Low0.19–0.380.31–0.610.49–0.990.79–1.581.26–2.53
3Moderate0.38–0.580.61–0.920.99–1.481.58–2.382.53–3.81
4High0.58–0.770.92–1.231.48–1.972.38–3.173.81–5.07
5Ultra-high>0.77>1.23>1.97>3.17>5.07
Soil pH values were classified according to the soil quality standard of China (GB 15618-2018) [4]. The rounded thresholds at soil pH values of 4.0, 5.0, 6.0, 7.0, and 8.0 were used for scenarios of soil pH ≤ 4.5, 4.5–5.5, 5.5–6.5, 6.5–7.5, and >7.5, respectively. The scenario with soil OC was 20 g/kg.
Table 5. Risk classification of EDTA-Cd.
Table 5. Risk classification of EDTA-Cd.
GradeRiskEDTA-Cd Content (mg/kg)
pH ≤ 4.54.5 < pH ≤ 5.55.5 < pH ≤ 6.56.5 < pH ≤ 7.5pH > 7.5
1Ultra-low<0.11<0.18<0.29<0.46<0.73
2Low0.11–0.220.18–0.360.29–0.570.46–0.920.73–1.47
3Moderate0.22–0.330.36–0.530.57–0.860.92–1.371.47–2.20
4High0.33–0.440.53–0.710.86–1.141.37–1.832.20–2.93
5Ultra-high>0.44>0.71>1.14>1.83>2.93
Soil pH values were classified according to the soil quality standard of China (GB 15618-2018) [4]. The rounded thresholds at soil pH values of 4.0, 5.0, 6.0, 7.0, and 8.0 were used for scenarios of soil pH ≤ 4.5, 4.5–5.5, 5.5–6.5, 6.5–7.5, and >7.5, respectively. The scenario with soil OC was 20 g/kg.
Table 6. Minimum (Min), maximum (Max), mean, and median of EDTA-Cd (%) and the percentile of the cumulative frequency curve.
Table 6. Minimum (Min), maximum (Max), mean, and median of EDTA-Cd (%) and the percentile of the cumulative frequency curve.
EDTA-Cd (%)
Sample SizeMin
(%)		Max
(%)		Mean
(%)		Median
(%)		Percentile (%)
Percentage (%)
1003329874765257595
51678393
Table 7. Bioaccumulation factors of rice based on EDTA-Cd (BCFEDTA-Cd) under different risk grades.
Table 7. Bioaccumulation factors of rice based on EDTA-Cd (BCFEDTA-Cd) under different risk grades.
GradeRiskpH ≤ 5.55.5 < pH ≤ 6.56.5 < pH ≤ 7.5pH > 7.5
1Ultra-low<0.24<0.17<0.12<0.09
2Low0.24–0.380.17–0.280.12–0.210.09–0.14
3Moderate0.38–0.570.28–0.420.21–0.290.14–0.21
4High0.57–1.400.42–1.000.29–0.730.21–0.52
5Ultra-high>1.40>1.00>0.73>0.52
Soil pH values were classified according to the soil quality standard of China (GB 15618-2018) [4].
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

Li, E.; Li, K.; Li, J.; Wu, Y.; Ma, Y. A New Approach to Differentiate the Causes of Excessive Cadmium in Rice: Soil Cadmium Extractability or Rice Variety. Agronomy 2024, 14, 2519. https://doi.org/10.3390/agronomy14112519

AMA Style

Li E, Li K, Li J, Wu Y, Ma Y. A New Approach to Differentiate the Causes of Excessive Cadmium in Rice: Soil Cadmium Extractability or Rice Variety. Agronomy. 2024; 14(11):2519. https://doi.org/10.3390/agronomy14112519

Chicago/Turabian Style

Li, Erdange, Kun Li, Jumei Li, Yang Wu, and Yibing Ma. 2024. "A New Approach to Differentiate the Causes of Excessive Cadmium in Rice: Soil Cadmium Extractability or Rice Variety" Agronomy 14, no. 11: 2519. https://doi.org/10.3390/agronomy14112519

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