Emergy-Based Evaluation of Changes in Agrochemical Residues on the Qinghai–Tibet Plateau, China

Abstract

Study of changes in agrochemical residues on the Qinghai–Tibet Plateau is necessary for the agricultural green development of the fragile plateau and its downstream regions. The total agrochemical residue (TR) caused by main agrochemical inputs was estimated in the study area of Qinghai province and the Tibet Autonomous Region over 1995–2017 by using the emergy synthesis method. The total agrochemical residue was decomposed into the intensity factor, the structure factor, the productivity factor, and the labour factor by using the Logarithmic Mean Divisia Index (LMDI) decomposition method. The change in TR could be divided into four time periods, i.e., a rapidly increasing period during 1995–1998, a stable period during 1999–2004, a slowly increasing period during 2005–2011, and a fluctuant period during 2012–2017. The study area had a mean TR intensity in area (TRA) of 3.31 × 10¹⁴ sej/ha, which was only 38.21% of that in China; however, the annual growth rate of TRA in the study area was 2.93%, higher than the rate of 1.91% in China over 1995–2017. The study area had a mean TR intensity in production (TRP) of 4.06 × 10¹⁰ sej/CNY (Chinese Yuan), which was 71.05% of that in China; however, the annual decreasing rate of TRP in the study area was 0.95%, lower than the rate of 1.98% in China over 1995–2017. All the LMDI decomposed factors contributed to the TR increase during 1995–1998; the intensity factor, the structure factor, and the labour factor contributed to the TR decrease during 1999–2004; the structure factor and the productivity factor contributed to the TR increase during 2005–2011; and only the productivity factor contributed to the TR increase during 2012–2017. Compared with the whole country, the study area has more potential to reduce TR by improving agrochemical use efficiency, strengthening the recovery of plastic film residue, increasing organic agricultural materials, raising the efficiency of agricultural production, and accelerating the transfer of rural labours to secondary and tertiary industries.

1. Introduction

Nonpoint source pollution is one of the rising environmental problems that may directly and indirectly threaten public health and the ecological conditions [1,2,3,4], which commonly includes an urban nonpoint source and an agricultural nonpoint source. Agricultural nonpoint source pollution has become a major reason for eutrophication, water and food quality degradation, and soil pollution in many regions of the world [2,5,6,7]. Mainly resulted from the development of intensive agriculture based on excessive applications of agrochemicals, agricultural nonpoint source is difficult to quantify [8,9] because it is concealed, dispersed, and difficult to detect [10]. Rational assessment of the economic benefits and environmental problems of agricultural chemical inputs is crucial to regional sustainable development.

Quantitative assessments of agricultural nonpoint source pollution affecting environmental quality, food safety, and human health were mainly carried out in the intensive agricultural development countries or regions. For example, it was reported that agriculture activities contributed to approximately 48% and 55% of the total nonpoint source flux of surface waters in Germany and the entire European Union (EU) [11]. Total nitrogen and total phosphorus contributions in the agriculture sector were approximately 56.5% and 14.3%, respectively, in Taihu Lake in China [12]. Of all the foodstuffs tested for pesticides in Nepal, vegetables were found to have the highest levels [13]. Pesticide residues in 4% of the eggplant, 44% of the tomato, and 19% of the chili samples exceeded the EU maximum residue limits in Southern Nepal [7]. A quantitative comparison of the effects of agricultural pesticide uses on peripheral nerve conduction in Guangdong, Jiangxi, and Hebei provinces in China demonstrated the importance of developing health-friendly pesticides to replace organophosphorus and organonitrogen insecticides and fungicides [14]. The macro- and microplastic contamination on an agricultural farmland in southeast Germany was studied by using Fourier transform infrared analysis, and 206 macroplastic pieces per hectare and 0.34 ± 0.36 microplastic particles per kilogram dry weight of soil were quantitatively identified [15]. Cropland had 64.89% of the plastic film residue mainly concentrated in the topsoil of 0–10 cm, and more film residue was found in the deeper soil with the increase of mulching years in the Hetao Irrigation District in China [16]. Compared with the intensive agricultural development countries or regions, less attention has been paid to the agricultural nonpoint source pollution in relatively extensive agricultural regions. In addition, so far, there is no method to evaluate the comprehensive status of agrochemical residues from different agrochemical inputs.

The theory of emergy, initially developed by the American ecologist H.T. Odum in the 1980s [17,18], has an epoch-making concept, which is defined as the total solar equivalent energy/exergy of one kind that was used up (directly or indirectly) in making a product or a service [19]. Emergy synthesis represents a practical way to convert all inputs and outputs in the same type of energy. Based on the theory of emergy, any natural and man-managed system has embodied solar energy, which can be measured on a common scale for addition or subtraction through the emergy synthesis method [19]. Based on the emergy inputs and outputs, a number of emergy indices can be used to analyze the intensification of cropland land use, the resource use efficiency, the environmental load, the coupling of farming and stockbreeding, and the sustainability of the agricultural system in the study area [20,21,22,23,24,25,26]. Different scholars have different opinions on various operational indexes. However, it is widely accepted that the theory of emergy provides a common scale for direct addition or subtraction of emergy inputs or outputs through the emergy synthesis method. Thus, the emergy synthesis method can be used to evaluate the total agrochemical residue resulted from different agrochemical inputs.

Proposed by Ang, the Logarithmic Mean Divisia Index (LMDI) decomposition analysis is an ideal method to identify the most important factors that determine changes of the main analytic objectives. Particularly, multiplicative decompositions decompose the ratio change of the factors and additive decompositions decompose the amount of change in the factors [27,28,29,30,31]. Thus, the LMDI decomposition method can be used to decompose the total agrochemical residue in the study area, as it has the advantage of being able to be used for the decomposition of incomplete data sets and benefits from the lack of an unexplainable residual in the results [29]. By using the LMDI method, the driving factors of changes in the total agrochemical residue in the study area can be identified during the different periods, which are associated with a series of ecological and supporting agricultural policies since 1995.

Located in western China, the Qinghai–Tibet Plateau is commonly regarded as a relatively extensive agricultural, less agricultural nonpoint source pollution affected, and ecologically fragile region. We took Qinghai Province and the Tibet Autonomous Region as a study area because these two provincial-level administrative regions constituted the main body of the Qinghai–Tibet Plateau. We used emergy synthesis as an evaluation method to examine the agrochemical residues of crop production systems, and adopted the Logarithmic Mean Divisia Index (LMDI) model to analyze the contributions of various factors affecting the total agrochemical residue. The objectives of this paper were: (1) to evaluate the changing trend in total agrochemical residue (TR) of crop production systems in the study area; (2) to clarify the main components affecting TR associated with the structure of agrochemical inputs; and (3) to assess the contributions of various factors affecting TR associated with the agrochemical residue intensity in production, the proportion of crop production value in the total agriculture production value, the agricultural labour productivity, and the agricultural labour input. The paper emphasizes the significance of the balance between higher crop production efficiency and lower agrochemical residues in the study area.

2. Data and Methodology

2.1. Study Area

Qinghai Province is located in the northeast region of the Qinghai–Tibet Plateau, with a geographical position of north latitude 31°39′–39°19′ and east longitude 89°35′–103°04′. It covers an area of 7.18 × 10⁵ km² [32]. The Tibet Autonomous Region is located in the southwest region of the Qinghai–Tibet Plateau, with a geographical position of north latitude 26°52′–36°32′ and east longitude 78°24′–99°06′ [33]. The study area covers an area of 1.90 × 10⁶ km², which is 74.74% of the total area (2.54 × 10⁶ km²) of the plateau [34] (Figure 1).

The agricultural land mainly includes cropland, grassland, and forest land. The study area had a proportion of grassland much higher than that of cropland [35]. Both crop production value and agricultural production value increased over 1995–2017 [36,37,38]. The cropland area was much lower than the grassland area; yet, it had a production value basically equivalent to that from stock raising, and contributing about 48% of the total agricultural production value (Figure 2).

2.2. Data Sources

The input data for agrichemicals of chemical fertilizers, pesticides and plastic film, crop production value and agricultural production value, and employment labours for the agricultural industry were available in the National Data, and Qinghai and Tibet Statistical Yearbooks [36,37,38]. Crop production value and agricultural production value were recalculated based on their annual increasing index in order to keep the values comparable over 1995–2017. Utilization coefficients of agrichemicals were based on the research by Chen et al. [39] and the “China green agriculture development report 2018” [40].

2.3. Methodology

Each agrochemical residue in the agricultural system was estimated based on its utilization coefficient. The emergy synthesis was used to holistically evaluate the total agrochemical residues (TR) caused by main inputs of chemical fertilizers, pesticides, plastic film in crop production systems over 1995–2017.

The total agrochemical residue of agrochemical inputs for crop production was estimated based on Equation (1):

$$TR={\displaystyle \sum }_{i=1}^{6}AIi \left(1-UCi\right) STi$$

(1)

where TR is the total amount of agrochemical residues caused by agrochemical inputs in sej; AIi is the amount of agrochemical input i in g; UCi is the utilization coefficient of agrochemical inputi; ST is solar transformity for agrochemical i in sej/g; and i means the agrochemical type, including nitrogen fertilizer, phosphorus fertilizer, potassium fertilizer, compound fertilizer, pesticides, and plastic film.

Table 1. Agrochemical input and its utilization coefficient and solar transformity in crop production systems in the study area.

Agrochemicals	Utilization Coefficient (%)	References	Solar Transformity (109sej/g)	References
Nitrogen fertilizer	35.0	[39]	3.80	[26]
Phosphorus fertilizer	19.5	[39]	3.90	[26]
Potassium fertilizer	47.5	[39]	1.10	[26]
Compound fertilizer	38.8	[40]	1.60	[26]
Pesticides	60.0	[40]	0.380	[26]
Plastic film	35.0	[40]	3.80	[26]

lists the individual agrochemical input and its utilization coefficient and solar transformity in crop production systems in the study area.

Considering the advantage of Logarithmic Mean Divisia Index (LMDI) decomposition analysis, we used the LMDI model based on Kaya to identify the factors affecting TR change over 1995–2017. The TR could be decomposed into four factors, namely: (1) the intensity factor, which was the total agrochemical residue per unit of crop production value (TR/CV), reflecting the agrochemical residue intensity in production; (2) the structure factor, which was the crop production value per unit of agricultural production value (CV/AV), reflecting the relative contribution of crop production value to total agricultural production value; (3) the productivity factor, which was the agricultural production value per agricultural labour (AV/AL), reflecting the agricultural labour productivity; and, (4) the labour factor, which was the quantity of labour force in the primary industry (AL).

The equation to reflect the relationship among factors contributing to TR can be expressed as:

$$TR=\frac{TR}{CV}\times \frac{CV}{AV}\times \frac{AV}{AL}\times AL=Ri\times Ci\times Pi\times AL$$

where factors Ri, Ci, Pi, and AL represent contributors to the total agrochemical residues in crop production systems.

Based on the LMDI method, if TR₀ and TR_t denote the total agrochemical residue in the base period and period t, respectively, the change of TR (ΔTR) from the base period to period t can be decomposed into the four factors, including ΔRi, ΔCi, ΔPi, and ΔAL:

$$\Delta TR =T{R}_t-T{R}_0= \Delta Ri + \Delta Ci + \Delta Pi + \Delta AL$$

$$\mathsf{\Delta }Ri=wtln\frac{R{i}_t}{R{i}_0}$$

$$\mathsf{\Delta }Ci=wtln\frac{C{i}_t}{C{i}_0}$$

$$\mathsf{\Delta }Pi=wtln\frac{P{i}_t}{P{i}_0}$$

$$\mathsf{\Delta }AL=wtlnln\frac{A{L}_t}{A{L}_0}$$

$$wt=(T{R}_t - T{R}_0)ln\frac{T{R}_t}{T{R}_0}$$

3. Results and Analysis

3.1. Change in TR for Crop Production

The TR induced by agrochemical inputs roughly increased from 1.86 × 10²⁰ to 3.19 × 10²⁰ sej over 1995–2017, with a mean value of 2.74 × 10²⁰ sej (Figure 3,

Table 2. Growth rates for TR, TR intensity in area (TRA) and TR intensity in production (TRP) in the study area and China for five periods.

Period	TR-Qinghai–Tibet Mean (1020 sej)	TR-Qinghai–Tibet Growth Rate (%)	TR-China Growth Rate (%)	TRA-Qinghai–Tibet Growth Rate (%)	TRA-China Growth Rate (%)	TRP-Qinghai–Tibet Growth Rate (%)	TRP-China Growth Rate (%)
1995–1998	2.25	12.09	3.98	11.52	3.74	4.79	−1.76
1999–2004	2.34	0.42	1.81	1.69	2.99	−1.11	−1.68
2005–2011	2.82	1.40	2.76	0.25	3.22	−4.04	−1.75
2012–2017	3.35	0.38	−0.23	0.10	−2.17	−1.53	−4.30
1995–2017	2.74	3.26	2.16	2.93	1.91	−0.95	−1.98

). The changing trend for TR could be divided into four time periods, i.e., a rapidly increasing period during 1995–1998, a stable period during 1999–2004, a slowly increasing period during 2005–2011, and a fluctuant period during 2012–2017 (Figure 3). The stable period during 1999–2004 with a lower mean TR value coincided with the initial implementation period of the Grain for Green Policy (GFGP), which converted the marginal cropland with higher risk of degradation to grassland or forest land with higher environmental value.

Reflecting the total agrochemical residue per cropland area, the TR intensity in area (TRA) had a changing trend similar to that of the TR over 1995–2017. The TRA growth rate was slightly lower than the TR growth rate, as the cropland growth rate was lower than the TR growth rate over 1995–2017; however, the TRA growth rate was higher than the TR growth rate during 1999–2004, as the cropland rapidly decreased in the initial implementation period of GFGP. The TRA had a mean value of 3.31 × 10¹⁴ sej/ha and an annual growth rate of 2.93%. Reflecting the total agrochemical residue per unit of crop production value, the TR intensity in production (TRP) had a mean value of 4.06 × 10¹⁰ sej/CNY and an annual rate of decrease of 0.95% over 1995–2017, which indicated that the production efficiency of agrochemicals was improved with increasing TRA.

3.2. Contribution of Individual Agrochemical Residue to TR

The ranking order of relative contribution of individual agrochemical residue in unit of sej to TR over 1995–2017 was N fertilizer (46.55%) > P fertilizer (25.90%) > compound fertilizer (25.16%) > K fertilizer (1.25%) > pesticides (0.96%) > plastic film (0.18%). It is clear that most of the TR was contributed by N, P, and compound fertilizer. The TR contributed by plastic film was only 0.18%; however, it had the highest increasing rate among all agrochemicals over 1997–2017, and had significantly increased after 2008. The TR contributed by plastic film in 2017 was 21.75 times that in 1995; while the TR contributed by N in 2017 was only 1.34 times that in 1995.

The relative contribution of individual agrochemical residue for four periods was displayed in Figure 4. The relative TR contribution of N fertilizer became lower, while that of compound fertilizer became higher from the first period to the fourth period. The highest relative TR contribution of P fertilizer, K fertilizer, pesticides, and plastic film appeared in the periods 1999–2004, 1995–1998, 1999–2004, and 2012–2017, respectively. Thus, relative more N and K fertilizer residue contributed to TR before the GFGP, relative more P fertilizer and pesticides residue contributed to TR during the GFGP, and relative more compound fertilizer and plastic film residue contributed to TR after the GFGP.

3.3. LMDI Decomposition of Total Agrochemical Residue

Based on LMDI model analysis, the contributions of the four factors to total TR variation were shown in a bar graph, while the change in TR was displayed as a line over the study period in Figure 5. LMDI model analysis indicated that the productivity factor cumulatively increased the TR by 2.92 × 10²⁰ sej from 1996 to 2017, with higher contributions occurring during 2003–2004, 2006–2007, 2011–2012, and 2015–2016; the intensity factor, the structure factor, and the labour factor cumulatively reduced the TR by 1.58 × 10²⁰ sej from 1996 to 2017. The intensity factor was the largest contributor to the reduction of TR. Compared with the initial year of 1995, the intensity factor cumulatively reduced 7.50 × 10¹⁹ sej of TR over 1996–2017, with higher TR reduction proportion occurring during 1998–1999, 2013–2014, and 2016–2017. The structure factor was the moderate contributor to the reduction of TR. Compared with the initial year of 1995, the structure factor cumulatively reduced 5.55 × 10¹⁹ sej of TR over 1996–2017, with higher TR reduction proportion occurring during 2008–2009 and 2015–2016. The labour factor was the smallest contributor to the reduction of TR. Compared with the initial year of 1995, the labour factor cumulatively reduced 2.77 × 10¹⁹ sej of TR over 1996–2017, with higher TR reduction proportion occurring during 2002–2003 and 2011–2012 (Figure 5).

Based on the four time periods, the TR increased by 6.75 × 10¹⁹ sej during the first period, decreased by 1.81 × 10¹⁹ sej during the second period, increased by 5.67 × 10¹⁹ sej during the third period, and increased by 2.73 × 10¹⁹ sej during the fourth period, respectively (Figure 6). Vertically indicated in Figure 6, all factors contributed to the TR increase during the first period; the intensity factor, the structure factor, and the labour factor contributed to the TR decrease during the second period; the structure factor and the productivity factor contributed to the TR increase during the third period; and only the productivity factor contributed to the TR increase during the fourth period. Transversely indicated in Figure 6, the intensity factor and the labour factor mainly contributed to the TR increase during 1995–1998, but contributed to the TR reduction during the other three periods; the productivity factor mainly increased the TR during four periods; the structure factor increased the TR during 1995–1998 and 2005–2011, but reduced the TR during 1999–2004 and 2012–2017.

4. Discussion

4.1. Changes in TR and TR Intensities in the Study Area and China

The study area had a mean TR of 2.74 × 10²⁰ sej, which was only 0.25% of that in China; however, the annual TR growth rate in the study area was 3.26%, higher than the rate of 2.16% in China over 1995–2017. This is because the TR in the study area kept growing during 1995–2017, while a negative annual TR growth rate appeared in China during 2012–2017. The study area had a mean TRA of 3.31 × 10¹⁴ sej/ha, which was only 38.21% of that in China; however, the annual TRA growth rate in the study area was 2.93%, higher than the rate of 1.91% in China over 1995–2017. Similar to the changing trend of TR, a negative annual TRA growth rate also appeared in China during 2012–2017 (Figure 7, Table 2 and

Table 3. The means and annual growth rates of agrochemical residues in the study area and China.

Residue	Qinghai–Tibet Mean (1014 sej/ha)	Qinghai–Tibet Growth Rate (%)	China Mean (1014 sej/ha)	China Growth Rate (%)
N fertilizer	1.52	1.27	4.42	0.27
P fertilizer	0.85	2.71	1.88	0.98
K fertilizer	0.04	3.15	0.23	5.57
Compound fertilizer	0.85	7.55	1.99	9.95
Pesticides	0.03	2.61	0.12	2.14
Plastic film	0.01	90.19	0.02	7.55
TRA	3.31	2.93	8.66	1.91

).

As indicated in Table 3, the mean of individual agrochemical residue in the study area was lower than that in China, especially the mean of K fertilizer residue in the study area, which was only 18.58% of that in China; yet, the annual growth rate of individual agrochemical residue in the study area was higher than that in China, especially the annual growth rate of plastic film residue in the study area, which was 11.95 times that in China.

The study area had a mean TRP of 4.06 × 10¹⁰ sej/CNY, which was 71.05% of that in China; however, the annual TRP decreasing rate in the study area was 0.95%, lower than the rate of 1.98% in China over 1995–2017. This is because a positive annual TRP growth rate appeared in the study area during 1995–1998 (Figure 7 and Table 2).

Sensitive to larger agricultural policies, such as the Grain for Green Policy after 1999 and China’s abolishment of the agricultural tax after 2006 [27,41,42], the TRA and TRP in the study area changed in a more fluctuating manner than those in China. Thus, the increasing TRA warrants more attention, and safe limit of TRA and TRP needs to be studied in the fragile study area.

4.2. Contribution of Individual Agrochemical Residue to TR in the Study Area and China

The relative contributions of P and compound fertilizer to TR or TRA in the study area were higher than those in China; yet, relative contributions of others to TR or TRA in the study area were lower than those in China over 1995–2017. The relative growth rates of residues and residues intensities in area for N fertilizer, P fertilizer, pesticides, and plastic film in the study area were higher than those in China. Particularly, the relative growth rate of plastic film in the study area fluctuated between 2.39 and 7.88 times that in China during 2008–2017 (Figure 8). Plastic film can improve the hydrothermal conditions of cultivated soil on the plateau. However, accumulated plastic film residue can largely destruct production capacity and sustainability of cropland on the plateau, and has a significant negative impact on its downstream areas.

Obvious regional differences in agrochemical residues intensity in area exist as a result of obvious regional differences in agrochemical input intensity in the study area. More agrochemical residues were usually left in intensive agricultural areas, where most of the modern facility agriculture, rivers, and populations were concentrated, because more agrochemicals were used for cash crops planted in these areas [43]. For example, in 2016, Lhasa, the capital city of the Xizang autonomous region, had an input intensity of chemical fertilizers of 437.90 kg/ha, which was greater than the intensity of 173.26 kg/ha in the study area and the intensity of 153.13 kg/ha in India, and close to the intensity of 443.60 kg/ha in China; it had an input intensity of plastic film of 28.45 kg/ha, which was greater than the value of 11.48 kg/ha in the study area and the value of 19.30 kg/ha in China; and it had an input intensity of pesticides of 4.31 kg/ha, which was greater than the value of 3.58 kg/ha in the study area and the value of 0.30 kg/ha in India, but less than the value of 12.90 kg/ha in China [44,45,46]. Thus, an increase in agrochemical inputs may contribute to the greening [47]; however, regional higher agrochemical input intensities cannot be ignored.

4.3. Changes in Factors Affecting TR in the Study Area and China

The means of intensity factor (Ri), structure factor (Ci) and structure factor (AL) in the study area were 4.06 × 10¹⁰ sej/CNY, 47.79%, and 2.29 × 10⁶ persons, respectively, which were lower than those (5.62 × 10¹⁰ sej/CNY, 52.82%, 3.06 × 10⁸ persons) in the whole country over 1995–2017; however, the relative Ri, Ci, and AL in the study area had more undulant and slower decreasing rates than those in the whole country, which indicated that the intensity factor, the structure factor, and the labour factor in the study area generally had lower contributions to the TR reduction than those in China (Figure 9). The productivity factor (Pi) in the study area had a mean of 6.51 × 10³ CNY/person, which was much lower than the mean of 1.70 × 10⁶ CNY/person in the whole country over 1995–2017. The relative Pi in the study area also had a lower increasing rate than that in the whole country, which indicated that the productivity factor contributed less to the increase of TR in the study area than that in the whole country.

Thus, compared with the whole country, the study area has more potential to reduce TR by improving agrochemical use efficiency, strengthening the recovery of plastic film residues, increasing organic agricultural materials, raising the efficiency of agricultural production, and accelerating the transfer of rural labor to secondary and tertiary industries, which can be realized by related agricultural policy and tax policy reforms.

5. Conclusions

The total agrochemical residue (TR) caused by main agrochemical inputs was estimated in the study area of Qinghai Province and the Tibet Autonomous Region over 1995–2017 by using the emergy synthesis method. The total agrochemical residue was decomposed into the intensity factor, the structure factor, the productivity factor, and the labour factor by using the LMDI decomposition method.

The TR roughly increased from 1.86 × 10²⁰ to 3.19 × 10²⁰ sej, with a mean of 2.74 × 10²⁰ sej, which was only 0.25% of that in China; however, the annual TR growth rate in the study area was 3.26%, higher than the rate of 2.16% in China over 1995–2017. The changing trend for TR could be divided into four time periods, i.e., a rapidly increasing period during 1995–1998, a stable period during 1999–2004, a slowly increasing period during 2005–2011, and a fluctuant period during 2012–2017. The stable period with a lower mean TR value coincided with the initial implementation period of the Grain for Green Policy.

Relative more N and K fertilizer residue contributed to TR before the GFGP, relative more P fertilizer and pesticides residue contributed to TR during the GFGP, and relative more compound fertilizer and plastic film residue contributed to TR after the GFGP. The relative growth rates of N fertilizer, P fertilizer, pesticides, and plastic film in the study area were higher than those in China. Particularly, the relative growth rate of plastic film in the study area fluctuated between 2.39 and 7.88 times that in China during 2008–2017.

All the LMDI decomposed factors contributed to the TR increase during 1995–1998; the intensity factor, the structure factor, and the labour factor contributed to the TR decrease during 1999–2004; the structure factor and the productivity factor contributed to the TR increase during 2005–2011; and only the productivity factor contributed to the TR increase during 2012–2017. Compared with the whole country, the study area has more potential to reduce TR by improving agrochemical use efficiency, strengthening the recovery of plastic film residues, increasing organic agricultural materials, raising the efficiency of agricultural production, and accelerating the transfer of rural labour to secondary and tertiary industries, which can be realized by related agricultural policy and tax policy reforms.