An eco-engineering assessment index for chemical pesticide pollution management strategies to complex agro-ecosystems

Ecological Engineering 52 (2013) 203–210

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An eco-engineering assessment index for chemical pesticide pollution management strategies to complex agro-ecosystems Nian-Feng Wan a,b,1 , Xiang-Yun Ji a , Jie-Xian Jiang a,∗ , Xing Deng a , Kai-Hua Huang a , Bo Li b,∗∗ a

Eco-environment Protection Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai Key Laboratory of Protected Horticultural Technology, Shanghai 201403, China Coastal Ecosystems Research Station of the Yangtze River Estuary, Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, Institute of Biodiversity Science, Fudan University, Shanghai 200433, China b

a r t i c l e

i n f o

Article history: Received 4 September 2012 Received in revised form 3 December 2012 Accepted 3 December 2012 Available online 9 February 2013 Keywords: Analytic Hierarchy Process Chemical pesticide management Complex ecosystem Eco-engineering assessment Generalized Function Theory Ratio of comprehensive cost to comprehensive profit

a b s t r a c t Human inputs into complex agro-ecosystems, such as those involving rice production, have ecological, economic, and social effects that can be positive or negative. We here described positive effects using the term comprehensive profit (CP), which included ecological, economic and social profits. The negative effects are termed comprehensive cost (CC), including ecological, economic and social losses. To develop an eco-engineering assessment system based on these parameters, we used a matrix of the ratios of comprehensive cost to comprehensive profit (RCC/CP ), where the RCC/CP index matrix WCC/CP is defined as the index optimization matrix of CC divided by the index optimization matrix of CP. We applied the basic principles and methods of Analytic Hierarchy Processes (AHP) to determine the comparative weight of indices derived in this manner as the basis for identifying one or more pesticide pollution management strategies for use in rice production systems in Shanghai, China. We used a generalized function to nondimensionalize the value of the index, and then to calculate the systematic evaluation index. The evaluating result range for CP used for this purpose was classified into 5 levels: excellent, better, general, bad and worst, as was the range for CC (very high, much high, common, much low and very low). Applying our assessment system (RCC/CP ) for five different pest control strategies in rice fields gave the order of value of the strategies to be: “applying environment-friendly pesticides 6 times and frequency vibration lamps with rice–milk vetch rotation mode” (0.744) < “applying environment-friendly pesticides 5 times and frequency vibration lamps with rice–milk vetch rotation mode” (0.747) < “applying environment-friendly pesticides 5 times and postponing transplanting date with rice–milk vetch rotation mode” (0.753) < “applying environment-friendly pesticides 6 times and postponing transplanting date with rice–milk vetch rotation mode” (0.760) < “applying common pesticides 9 times and following conventional transplanting date with rice–wheat rotation” (0.930). The treatment “applying environment-friendly pesticides 6 times and frequency vibration lamps with rice–milk vetch rotation mode” was determined to be the optimal chemical pollution management strategy for use in rice fields in Shanghai, China. Crown Copyright © 2012 Published by Elsevier B.V. All rights reserved.

1. Introduction Chemical pesticide pollution is a major threat to agroecosystems, leading to higher pesticide resistance, environmental contamination, issues of food safety and social stability. To reduce chemical pesticide pressure, America, Sweden, Denmark, Norway, France, Spain, and Holland have already achieved a 50% reduction in the annual application rate of chemical pesticides (Pimentel et al.,

∗ Corresponding author. Tel.: +86 021 62205462; fax: +86 021 62205462. ∗∗ Corresponding author. Tel.: +86 021 65642178; fax: +86 021 65642178. E-mail addresses: [email protected] (N.-F. Wan), [email protected] (J.-X. Jiang), [email protected] (B. Li). 1 Tel.: +86 021 62205462; fax: +86 021 62205462.

1992; Jansma et al., 1993; Sathre et al., 1999; Smet et al., 2005). In Indonesia, pesticide applications to rice have been reduced by 65% (Oka, 1991). In order to realize the goal of chemical pesticide pollution control, some chemical reduction engineerings have been reported (Schnoor et al., 1995; Susarla et al., 2002; Syversen, 2005; Rogers and Stringfellow, 2009). Official records indicate that chemical pesticide application in Shanghai of China is 5.76 kg hm−2 per year, which is far higher than the national average in China, which arouses wide attention of the public, decision-makers and scientists. In order to protect the environment, maintain food safety and advocate low-carbon agriculture, new eco-engineering techniques are needed to reduce chemical pesticide use in field crops and to curb chemical pesticide pollution at the source in Shanghai suburbs.

0925-8574/$ – see front matter. Crown Copyright © 2012 Published by Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.ecoleng.2012.12.028

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The development of feasible and rational eco-engineering technologies and approaches for reducing chemical pesticide use is very important for agro-ecosystem development itself. Over the recent decade, we screened out biological, agricultural, and physical control methods, as well as environmentally friendly chemical control methods that are potential eco-engineering ways to reduce chemical pesticide pollution at the source. Our studies in practice suggest that using pest-resistant rice varieties, rotating different classes of pesticides, using green manure–rice rotation schemes, installing insect-killing lamps, and postponing of the conventional rice transplanting dates are the main pollution-curbing techniques presently used in rice fields in the Shanghai suburbs of China. However, little is known about which of the eco-engineering techniques is the best, how to evaluate the superiority of the various techniques, or how to maximize the efficacy of combinations of the different techniques. The agricultural ecosystems, consisting of economic, ecological and social subsystems (Ma and Wang, 1984; Holling, 2001; Wang et al., 2011) are contaminated by chemicals, which acquire a characteristic we term “morbidity” (Wan et al., 2009; Jiang and Wan, 2009). The technologies used to lessen or repair this morbidity can produce both positive and negative effects. The positive effect of any eco-engineering technologies entering the agro-ecosystems is termed comprehensive profit (CP) and has economic, ecological and social benefits. The negative effect is termed comprehensive cost (CC) and includes economic, ecological and social costs. Here, we developed as the index, expressed as the ratio of comprehensive cost to comprehensive profit (RCC/CP ), which can be used as a tool to evaluate the superiority of different eco-engineering technologies for reducing pesticide usage. This tool comprises an RCC/CP model based on the Analytic Hierarchy Process (AHP) (Saaty, 1977, 1980, 1988) and Generalized Function Theory (GFT), where the RCC/CP matrix (WCC /WCP ) is defined as the index optimization matrix of cc divided by the index optimization matrix of cp. We also provided examples of using these indices to optimize the best chemical pesticide pollution management technology for practical eco-engineering application in rice field in Shanghai suburbs of China. 2. Materials and methods 2.1. Experimented materials Conventional japonica rice: “Xiushui09” was provided by Crop Research Institute, Shanghai Academy of Agriculture Sciences. Frequency vibration lamps were provided by Jiaduo Company Limited of Henan province of China, PS-15II type. The insecticides we used included 15% hexaflumuron and triazophos EC, 25% buprofezin WP, 90% monosultap WP, 18% bisultap AS, 10%, and imidacloprid WP. Fungicides included 20% validamycin WP, 20% tricyclazole WP, and 20% fentin-acetate WP. Herbicides we used included 20% bensulfuron and butachlor WP. 2.2. Experiment design and method Experiments were conducted in rice fields in Modern Agriculture Park of Fengxian District, Shanghai of China (121.65◦ E, 30.92◦ N). Four treatments were included in the experimental design. In treatments 1 and 2, frequency vibration lamps with 1.5 m height were installed after the 30th day of seeding with conventional transplanting day (20th June). In treatments 3 and 4, lamps were not applied in rice fields, but transplanting date was postponed for 4 days (24th June). Four of the treatments were the entire rice–milk vetch rotation mode. Treatments 1 and 3 received environment-friendly pesticides 5 times, while

treatments 2 and 4 received environment-friendly pesticides 6 times. The control areas: (1) common control and common resistant rice areas without lamps; (2) applying common pesticides 9 times and (3) following conventional transplanting date with rice–wheat rotation mode. Environment-friendly insecticides (hexaflumuron and triazophos, buprofezin) in four treatments were used while conventional insecticides (monosultap, bisultap and imidacloprid) were used in control areas. Arthropods were scouted and sampled every 7 d in all experiment fields. “Z” style sampling with 5 dots of about 100 plants was employed at random from seedling stage to harvesting stage (Wan et al., 2009). All insects and spiders were carefully counted. We routinely recorded the service condition of the agricultural resources in the different treatment regions, the planting and growing management procedures that were used, cost accounting factors, and the sales status of crops and products. All data presented in this paper were analyzed with SPSS16.0 software. 2.3. Development of assessment system 2.3.1. Determination of evaluation indicators To obtain the target set that best estimates the functional quality of the technologies for curbing chemical pesticide pollution, we focused on whether the economic subsystem is beneficial, whether the natural subsystem is stable, and whether the social subsystem is rational. According to the three goals, a specific indicator system was identified that maximized the comprehensive profit (CP) term while minimizing the comprehensive cost (CC) term. Development of assessment indicators for curbing chemical pesticide pollution should adhere to the following principles (Zhang et al., 2006; Jiang and Wan, 2009; Wan et al., 2009, 2013): (1) all assessment indicators should reflect social, economic and natural aspects, economic cost and profit, resources and time consumption, user acceptance, as well as the impact of candidate technologies on society and on eco-environment; (2) the short-term and long-term profits, reflecting both partial and total costs; (3) to adopt quantitative indicators as far as possible, the judgments of qualitative indicators should refer to the original archive experiment data; (4) comprehensive indicators should be adopted as more as possible, and every indicator should keep independent without direct interactive relationship; the indicator should complement each other, and the structure produced by the indicators should reflect fully and even completely its various functional characteristics and be constituted completely; (5) indices should reflect the structure characteristics and stability of arthropods. Using the above-cited requirements as a guide, relevant experts and academics engaged in plant protection, agronomy, ecological economics, ecosystem and environmentology were consulted for information regarding conventional chemical pesticide pollution management practices in rice fields. The indicators are summarized and listed in Table 1. 2.3.2. AHP-based method Pair-wise comparison matrices are established by adopting expert investigation method. The relative importance of all elements at the same level is given certain scale judgment, and then forms the pair-wise comparison matrices. After a hierarchy structure is established, the subordinate relationships between each element at the current level and a certain element at the previous level are constructed. The relative scale measurement is defined in Table 2 according to Satty 1–9 scale for pair-wise comparison. A pair-wise comparison matrix A is established using the relative importance (Eq. (1)): A = [aij ],

aij =

wi , wj

aji =

1 , aij

aii = 1

(1)

N.-F. Wan et al. / Ecological Engineering 52 (2013) 203–210

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Table 1 Comprehensive profit and comprehensive cost indices for chemical pesticide pollution management in rice fields. Sub-object layer

Economic profit (A1 )

Ecological profit (A2 )

Social profit (A3 )

Indicator layer

Sub-object layer

Rice income (B1 ) Saving time (B2 ) Benefits to chemical industry (B3 ) Technique extension profit (B4 )

Economic cost (D1 )

Percentage of the number of natural enemies to the one of pests (B5 ) Percentage of the number of neutral arthropods to one of the pests (B6 ) Arthropod diversity index (B7 ) Resistance variety longevity (B8 ) Pesticide safety (B9 ) Impact on human lives and production (B10 ) Stimulation of market and economy (B11 ) Technology period (B12 ) Percentage of farmer acceptance (B13 )

where wi is the weight of criterion i, and wi is the weight of criterion j. The weights from the above matrix can easily be determined by solving the following Eq. (2): AW = nW,

W = (w1 , w2 , . . . , wn )T

(2)

where n is the natural number or number of decision elements. From Eq. (2), n is determined as eigenvalue (max ) of the matrix, that is (Eq. (3)): AW = max W

(3)

And wi satisfies the following Eq. (4): i=n 

wi = 1

(4)

i

The judgment for the satisfaction of max is made by determining the consistency index and consistency ratio, given by CI and CRI from Eqs. (5) and (6): CI =

max − n n−1

CRI =

(5)

CI RI

(6)

max is the principal eigenvalue of the judgment matrix and RI is the average random consistency index for matrices of order n, Table 2 Saaty’s 1–9 scale for pair-wise comparison. Intensity of weight

Definition

1

Equal importance

3

5

7

9

2, 4, 6, 8

Reciprocals of above non-zero numbers

Explanation

Two activities contribute equally to the objective Moderate importance Experience and judgment slightly favor one over another Strong importance Experience and judgment strongly favor one over another Very strong importance An activity is strongly favored and its dominance is demonstrated in practice Absolute importance The importance of one over another affirmed on the highest possible order Intermediate values Used to represent compromise between the priorities listed above If activity i has one of the above non-zero numbers assigned to it when compared to activity j, then j has the reciprocal value when compared with i

Ecological cost (D2 )

Indicator layer Pesticide cost (E1 ) Lamp and electric cost (E2 ) Labor cost (E3 ) Instrument depreciation cost (E4 ) Seed cost (E5 ) Killing natural enemy (E6 ) Pesticide residues in rice (E7 ) Active pesticide components dosage (E8 ) Pesticide residue in soil (E9 ) Water resource consumption (E10 ) Impact on industry chain (E11 ) Supply and demand of pesticides (E12 ) –

Social cost (D3 )

calculated as follows: RI = 0(n = 1, 2), 0.58(n = 3), 0.90(n = 4), 1.12(n = 5), 1.24(n = 6), 1.32(n = 7), 1.41(n = 8) When CRI is greater than 0.10 (10%), max is not satisfied, and inconsistent judgments must be readjusted in order to improve the consistency. If the sequencing weight vector of the (k−1)th layer factor (k−1)

(k−1)

(k−1) T

toward the total goal is W (k−1) = (W1 , W2 , . . . , Wn ) , the entire factors of the kth layer toward the synthetic sequence vector W(k) of the total goal are given by the following Eq. (7): W (k) = (P1 , P2 , . . . , Pn )W k−1 = P (k) W k−1 (k)

(k)

(k)

(7)

where P(k) (nk × nk–1 matrix) is the sequencing weight vector of the kth layer factor toward the (k−1)th layer factor. 2.3.3. Determination of evaluation criteria based on Generalized Function Theory The calculated value of each indicator is not an objective and accurate estimate for all candidate technologies, especially when the system under consideration contains a number of indicators whose ranges of values are not identical. To obtain an accurate and comprehensive result, it is necessary to convert each of indicator value into evaluation index with the same range of values. To unify the evaluation result with the grade standard of hundred-mark system, the value range [0,100] was selected in this paper. Specific calculation steps for this purpose are: Step 1: according to the practical agricultural production, especially pesticide application under existing situations in rice fields, the evaluation criteria were confirmed. Step 2: according to the results of grading calibration, a corresponding generalized function was selected. Each indicator value is then converted into a nondimensionalized index () and then the index this value used to represent the contribution of each indicator to the whole level. Step 3: calculate the total index of evaluated object.  is calculated by Eq. (8): =



wi i

(8)

where wi is the weight of criterion i, i is the weight of index i. For the evaluation of pesticide pollution management in rice fields, each indicator has five levels {excellent, better, general, bad, worst}; and the range for comprehensive cost is similarly divided into 5 categories {very high, much high, common, much low, very low}. The relationship between indicator value (Z) index value is shown in Table 3.

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N.-F. Wan et al. / Ecological Engineering 52 (2013) 203–210 Table 3 Value of indicator grade classification and value of evaluation index classification.

Indicator value

Z0

Levels

Index value

Z1

Z2

Z3

Z4

Z5

Excellent or

Better or

General or

Bad or much

Worst or

very high

much high

common

low

very low

100

f1

f2

f3

f4

0

Table 4 Indicator evaluation criteria of chemical pesticide pollution management in rice fields. Evaluation indicators

Excellent or very high

Better or much high

General or common

Bad or much low

Worst or very low

Pesticide cost (RMB hm−2 ) Active pesticide component dosage (kg hm−2 ) Lamp and electric cost (RMB hm−2 ) Labor cost (RMB hm−2 ) Seed cost (RMB hm−2 ) Instrument depreciation cost (RMB hm−2 ) Rice yield (kg hm−2 ) Technique extension profit (%) Technology period (a) Percentage of the number of natural enemies to the one of pests (%) Percentage of the number of neutral arthropods to the one of pests (%) Arthropod diversity index Resistance variety longevity (a) Percentage of farmer acceptance (%) Save time (8 h hm−2 ) Pesticide commodity dosage (kg hm−2 ) Water resource consumption (m3 hm−2 ) Pesticide residue in rice (mg kg1 ) Pesticide residue in soil (mg kg1 ) Species of killing natural enemies

>1650 >13.5

[1350, 1650] [10.5, 13.5]

[1050, 1350] [7.5, 10.5]

[750, 1050] [4.5, 7.5]

<750 <4.5

>300 >600 >450 >160

[225, 300] [450, 600] [375, 450] [110, 160]

[150, 225] [300, 450] [300, 375] [70, 110]

[75, 150] [150, 300] [225, 300] [30, 70]

<75 <150 <225 <30

>8250 >70 >10 >100

[6750, 8250] [50, 70] [8, 10] [50, 100]

[6000, 6750] [30, 50] [6, 8] [20, 50]

[5250, 6000] [10, 30] [4, 6] [5, 20]

<5250 <10 <4 <5

>100

[50, 100]

[20, 50]

[5, 20]

<5

>5 >10 >90 >135 >36 >15,000

[4, 5] [8, 10] [70, 90] [105, 135] [29, 36] [12,000, 15,000]

[3, 4] [6, 8] [50, 70] [75, 105] [22, 29] [9000, 12,000]

[2, 3] [4, 6] [30, 50] [45, 75] [15, 22] [6000, 9000]

<2 <4 <30 <45 <15 <6000

>0.20 >0.20 >20

[0.15, 0.20] [0.15, 0.20] [15, 20]

[0.10, 0.15] [0.10, 0.15] [10, 15]

[0.05, 0.10] [0.05, 0.10] [5, 10]

<0.05 <0.05 <5

The function relationship between indicator value and evaluation index was established with the above classification method, and then the continuous subsection function was got in Eq. (9):

f =

⎧ (100 − f1 ) × (z − z1 ) ⎪ f1 + (z1 ≤ z ≤ z0 or z0 ≤ z ≤ z1 ) ⎪ ⎪ z0 − z1 ⎪ ⎪ ⎪ ⎪ (f1 − f2 ) × (z − z2 ) ⎪ ⎪ (z2 ≤ z ≤ z1 or z1 ≤ z ≤ z2 ) f2 + ⎪ ⎪ z1 − z2 ⎪ ⎪ ⎨ f3 +

(f2 − f3 ) × (z − z3 )

z2 − z3 ⎪ ⎪ ⎪ ⎪ (f − f ⎪ 3 4 ) × (z − z4 ) ⎪ f4 + ⎪ ⎪ z ⎪ 3 − z4 ⎪ ⎪ ⎪ × (z − z ) f ⎪ 5 4 ⎩ z4 − z5

(z3 ≤ z ≤ z2 or z2 ≤ z ≤ z3 )

(9)

(z4 ≤ z ≤ z3 or z3 ≤ z ≤ z4 ) (z5 ≤ z ≤ z4 or z4 ≤ z ≤ z5 )

Using recommendations for standard agricultural practices and results from the expert investigation and analysis, the set-value statistical method was used to determine the interval value of evaluation criteria for each indicator (seen in Table 4). 3. Results and analysis 3.1. Pair-wise comparison matrices From Table 5, we derived CRI = 0 < 0.1, so the comparison result was satisfied. Meanwhile, the vector W = (0.6667, 0.2222, 0.1111)T . On this basis, economic profit (A1 ) is moderately superior to

Table 5 Judging matrix from economic profit (A1 ), ecological profit (A2 ), social profit (A3 ), economic cost (D1 ), ecological cost (D2 ) and social cost (D3 ). Criteria

A1

A2

A1 A2 A3

1 1/3 1/6

3 1 1/2

A3 6 2 1

Weigh

Criterion

D1

D2

D3

Weigh

0.667 0.222 0.111

D1 D2 D3

1 1/3 1/6

3 1 1/2

6 2 1

0.667 0.222 0.111

ecological profit (A2 ) and strongly to very strongly superior to social profit (A3 ), and economic profit predominated in comprehensive profit. Meanwhile, the relative importance among economic cost (D1 ), ecological cost (D2 ), and social cost (D3 ) indicated the same relationships. From Table 6, we derived CRI = 0.0078 < 0.1, so the comparison result was satisfied. Meanwhile, we could clearly see that rice income (B1 ) was superior to time saving (B2 ), moderately to strongly superior to flourish of benefits to the chemical industry (B3 ), and equally to moderately superior to technique extension Table 6 Judging matrix A1 − B. A1

B1

B2

B3

B4

Weight

B1 B2 B3 B4

1 1/5 1/4 1/2

5 1 2 3

4 1/2 1 2

2 1/3 1/2 1

0.5068 0.0863 0.1428 0.2641

N.-F. Wan et al. / Ecological Engineering 52 (2013) 203–210 Table 7 Judging matrix D1 − E.

207

Table 10 Judging matrix A3 − B.

D1

E1

E2

E3

E4

E5

Weigh

A3

B10

B11

B12

B13

Weigh

E1 E2 E3 E4 E5

1 1/2 1/2 1/2 1/3

2 1 1 1 1/2

2 1 1 1 1/2

2 1 1 1 1/2

3 2 2 2 1

0.3491 0.1843 0.1843 0.1843 0.0980

B10 B11 B12 B13

1 1/2 2 3

2 1 4 5

1/2 1/4 1 2

1/3 1/5 1/2 1

0.1539 0.0809 0.2880 0.4773

Table 8 Judging matrix A2 − B. A2

B5

B6

B7

B8

B9

Weigh

B5 B6 B7 B8 B9

1 1/2 2 3 4

2 1 4 5 6

1/2 1/4 1 2 3

1/3 1/5 1/2 1 2

1/4 1/6 1/3 1/2 1

0.0938 0.0521 0.1650 0.2666 0.4224

profit (B4 ); B3 was equally to moderately superior to B2 ; B4 was moderately superior to B2 and equally to moderately superior to B3 . Thus, rice income (B1 ) predominates in economic profit (A1 ). From Table 7, we derived CRI = 0.0022 < 0.1, so the comparison result was also satisfied. Meanwhile, we could clearly see that pesticide cost (E1 ) was equally to moderately superior to lamp and electric cost (E2 ), labor cost (E3 ), instrument depreciation cost (E4 ), and moderately superior to seed cost (E5 ); E2 was equally to moderately superior to E5 . Thus, pesticide cost (E1 ) predominates in economic cost (D1 ). From Table 8, we derived CRI = 0.0141 < 0.1, so the comparison result was satisfied. Meanwhile, we could clearly see that percentage of the number of natural enemies to one of pests (B5 ) was equally to moderately superior to percentage of the number of neutral arthropods to one of pests (B6 ); Arthropod diversity index (B7 ) was equally to moderately superior to B5 and moderately to strongly superior to B6 ; resistance variety longevity (B8 ) was moderately superior to B5 , strongly superior to B6 , and equally to moderately superior to B7 ; pesticide safety (B9 ) was moderately to strongly superior to B5 , strongly to very strongly superior to B6 , moderately superior to B7 , and equally to moderately superior to B8 . Thus, pesticide safety (B9 ) predominates in economic profit (A2 ). From Table 9, we got CRI = 0 < 0.1, so the comparison result was satisfied. Meanwhile, we could clearly see that pesticide residue in rice (E7 ) was moderately to strongly superior to killing natural enemy (E6 ), and equal superior to active pesticide components dosage (E8 ) and pesticide residue in soil (E9 ); E8 and E9 were both moderately to strongly superior to E6 . Thus, E7 , E8 and E9 predominate in ecological cost (D2 ). From Table 10, we got CRI = 0.0078 < 0.1, so the comparison result was satisfied. Meanwhile, we could clearly see that impact on people’s lives and production (B10 ) was equally to moderately superior to stimulate market and boom economy (B11 ); technology period (B12 ) was equally to moderately superior to B10 and moderately to strongly superior to B11 ; percentage of farmer acceptance (B13 ) was moderately superior to B10 , strongly superior to B11 and equally to moderately superior to B12 . Thus, percentage of farmer acceptance (B13 ) predominates in social profit (A3 ). Table 9 Judging matrix D2 − E. D2

E6

E7

E8

E9

Weigh

E6 E7 E8 E9

1 4 4 4

1/4 1 1 1

1/4 1 1 1

1/4 1 1 1

0.0769 0.3077 0.3077 0.3077

From Table 11, we got CRI = 0.0079 < 0.1, so the comparison result was satisfied. Meanwhile, we could clearly see that water resource consumption (E10 ) was moderately superior to impact on industry chain (E11 ) and equally to moderately superior to supply and demand of pesticides (E12 ) which was equally to moderately superior to (E11 ). Thus, water resource consumption (E10 ) predominates in s social cost (D3 ). 3.2. Evaluation value 3.2.1. Results analysis and evaluation Table 12 shows that the decreasing order of pesticide cost, active pesticide components dosage, or pesticide commodity dosage was control (no lamps, conventional transplanting date + applying pesticides 9 times + rice–wheat rotation) > treatment 4 (postponing transplanting date + applying pesticides 6 times + rice–milk vetch rotation) > treatment 3 (postponing transplanting date + applying pesticides 5 times + rice–milk vetch rotation) > treatment 2 (lamps + applying pesticides 6 times + rice–milk vetch rotation) > treatment 1 (lamps + applying pesticides 5 times + rice–milk vetch rotation); the pesticide residue in rice or in soil was significantly higher in the control than in treatments which had no mutually significant difference; the order of labor cost and instrument depreciation cost were both control > treatment 2 or 4 > treatment 1 or 3; the order of time saving was treatment 3 > treatment 4 > treatment 1 > treatment 2. Four treatments saved 18.18% more water compared to the control; treatments 1 and 2 had significant yield-enhancing effects, while treatments 3 or 4 had no significant effect; technique extension profit in treatment 1 or 2 was 25% higher than in treatment 3 or 4, but was 20% lower than in CK. Meanwhile, 80% of farmers accepted treatment 1 or 2; 70% of farmers treatment 3 or 4, and 95% of farmers control, indicating that the concept centering on traditional chemical control was deeply rooted in farmer’s mind. In treatment 1 or 2, the technology period is seven years, which is three years longer than in treatment 3 or 4, and five years longer than in the control. The index values of percentage of the number of natural enemies to the one of pests, percentage of the number of neutral arthropods to the one of pests, and arthropod diversity in four treatments was significantly higher than in Control, the effects of four treatments on killing natural enemy species were all less than that of the control, indicating that four treatments could significantly enhance the arthropod stability. 3.2.2. Matrix of evaluation index In terms of the above comprehensive evaluation result given in Table 12, matrix of incidence degree Wcc of five candidate technologies (four treatments and Control) for comprehensive cost indices was Wcc = (64.0662, 64.6895, 60.0292, 61.6027, 76.3711)T ; Wcp for Table 11 Judging matrix D3 − E. D3

E10

E11

E12

W

E10 E11 E12

1 1/3 1/2

3 1 2

2 1/2 1

0.5396 0.1634 0.2970

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N.-F. Wan et al. / Ecological Engineering 52 (2013) 203–210

Table 12 Index values for different management regimes of chemical pesticide pollution in rice fields. Indicator −2

Pesticide cost (RMB hm ) Active pesticide components dosage (kg hm−2 ) Lamp and electric cost (RMB hm−2 ) Labor cost (RMB hm−2 ) Instrument depreciation cost (RMB hm−2 ) Seed cost (RMB hm−2 ) Rice yield (kg hm−2 ) Technique extension profit (%) Technology period (a) Resistance variety longevity (a) Percentage of the number of natural enemies to the one of pests (%) Percentage of the number of neutral arthropods to the one of pests (%) Arthropod diversity index Percentage of farmer acceptance (%) Save time (8 h hm−2 ) Pesticide commodity dosage (kg hm−2 ) Water resource consumption (m3 hm−2 ) Pesticide residue in rice (mg kg1 ) Pesticide residue in soil (mg kg1 ) Species of killing natural enemies Pesticide safety Vivify Stimulation of market and economy Impact on human lives and production Impact on industry chain Supply and demand of pesticides

Treatment 1 (scores)

Treatment 2 (scores)

Treatment 3 (scores)

Treatment 4 (scores)

CK (scores)

825 (62.5) 3.64 (54.3)

937.5 (65.2) 3.76 (55.0)

1020 (67.7) 4.38 (59.0)

1125 (73.2) 4.55 (61.5)

2179.5 (93.0) 14.03 (91.5)

45 (50.5) 187.5 (62.5) 75 (72.5)

45 (50.5) 225 (65.0) 90 (75.0)

0 (0) 187.5 (62.5) 75 (72.5)

0 (0) 225 (65.0) 90 (75.0)

0 (0) 525 (85.0) 135 (85.0)

360 (78.5) 8542.4 ± 99.48a (91.0) 65.0 (88.0) 7.0 (75.0) 11.0 (91.0) 36.3 ± 2.14a (74.5)

360 (78.5) 8730 ± 80.29a (93.8) 65.0 (88.0) 7.0 (75.0) 11.0 (91.0) 35.9 ± 1.08ab (74.10)

360 (78.5) 7657.5 ± 79.43c (82.1) 40.0 (75.0) 4.0 (60.0) 11.0 (91.0) 31.7 ± 1.21b (69.0)

360 (78.5) 7950 ± 89.76b (85.7) 40.0 (75.0) 4.0 (60.0) 11.0 (91.0) 33.1 ± 1.27ab (70.5)

360 (78.5) 8100 ± 121.27b (87.5) 85.0 (90.0) 2.0 (32.0) 11.0 (91.0) 15.2 ±± 0.91c (65.0)

42.1 ± 1.71a (77.5)

44.3 ± 1.26a (78.1)

40.1 ± 1.78a (76.5)

41.1 ± 1.36a (77.1)

18.3 ± 1.71b (68.0)

3.45 ± 0.10a (74.5) 80.0 (85.0) 94.5 (76.0) 14.79 (56.5) 13,500 (85.0)

3.39 ± 0.09a (74.2) 80.0 (85.0) 87 (74.5) 15.80 (60.5) 13,500 (85.0)

3.50 ± 0.08a (75.0) 70.0 (80.0) 103.5 (79.0) 16.36 (61.5) 13,500 (85.0)

3.47 ± 0.07a (74.6) 70.0 (80.0) 100.5 (77.5) 17.86 (63.5) 13,500 (85.0)

2.18 ± 0.06b (60.5) 95.0 (95.0) 48 (61.5) 33.3 (86.5) 16,500 (92.0)

0.050 ± 0.002b (60.0) 0.060 ± 0.004b (61.4) 4 (50.5) – (90.0) – (80.0)

0.053 ± 0.003b (61.0) 0.062 ± 0.005b (62.0) 4 (50.5) – (90.0) – (83.5)

0.055 ± 0.004b (61.5) 0.067 ± 0.004b (63.0) 5 (60.0) – (90.0) – (79.0)

0.057 ± 0.004b (62.0) 0.068 ± 0.004b (63.5) 5 (60.0) – (90.0) – (80.0)

0.166 ± 0.006a (86.5) 0.20 ± 0.018a (90.2) 19 (88.5) – (75.0) – (85.0)

– (80.0) – (69.0) – (72.5)

– (81.5) – (64.0) – (68.5)

– (75.5) – (71.0) – (72.2)

– (76.0) – (72.5) – (74.5)

– (65.0) – (78.5) – (60.5)

Note: Data outside brackets were measured values, while data inside brackets were score values; Treatment 1: lamps + applying pesticides 5 times + rice–milk vetch rotation; Treatment 2: lamps + applying pesticides 6 times + rice–milk vetch rotation; Treatment 3: postponing transplanting date + applying pesticides 5 times + rice–milk vetch rotation; Treatment 4: postponing transplanting date + applying pesticides 6 times + rice–milk vetch rotation; Control: no lamps, conventional transplanting date + applying pesticides 9 times + rice–wheat rotation.

comprehensive profit Wcp = (85.7691, 86.9122, 79.7148, 81.0674, 82.1539)T . Thus, matrix of RCC/CP was WCC/CP = (0.7470, 0.7443, 0.7530, 0.7599, 0.9296)T . Thus, the increasing order of the value for treatments and control was treatment 2 (lamps + applying pesticides 6 times + rice–milk vetch rotation) < treatment 2 (lamps + applying pesticides 5 times + rice–milk vetch rotation) < treatment 3 (postpone transplanting date + applying pesticides 5 times + rice–milk vetch rotation) < treatment 4 (postpone transplanting date + applying pesticides 6 times + rice–milk vetch rotation) < control (no lamps, conventional transplanting date + applying pesticides 9 times + rice–wheat rotation). Theoretically, the lower the value of RCC/CP , the more superior the corresponding technology is, so treatment 2 was recommended and adopted into management practices. 4. Discussion Mitsch (1998, 2012) maintains that the goal of ecological engineering integrating human society with its natural environment for the benefit of both included the restoration of ecosystems that have been substantially disturbed by human activities and the development of new sustainable ecosystems that have both human and ecological values. The principles of ecological engineering have been applied to many different aspects, such as ecosystem health (Costanza, 1992, 2012), ecosystem services (Costanza et al., 1997; Costanza, 2012), ecological restoration (Teal and Weinstein, 2002; Peterson et al., 2005; Day et al., 2009). Up to now, many case studies of ecological engineering for pesticide reduction or pollution control have been reported (Xi and Qin, 2009; Elsaessera et al., 2011; Rasmussen et al., 2011; Tournebize et al., in press), but the idea combining with complex engineering ecosystem consisting of

ecological, economic and social subsystems, has rarely been considered for rice fields. Non-point source pollution by chemical pesticides is widely acknowledged as one of the most important anthropogenic stressors in ecosystems (Baker, 1992; Dzikiewicz, 2000; Rasmussen et al., 2011), and is also a threat to low-carbon agriculture. In order to restore the morbid agro-ecosystems contaminated by chemical pesticides, eco-engineering approaches have been explored and applied such as phytoremediation (Schnoor et al., 1995; Susarla et al., 2002) and vegetated buffer zones (Syversen and Bechmann, 2004; Syversen, 2005; Rogers and Stringfellow, 2009). In this study we adopted the ecological engineering technologies of optimizing pesticide variety structure (hexaflumuron and triazophos EC substituting for monosultap WP and bisultap AS to control rice borers; buprofezin WP substituting for imidacloprid WP to control rice planthoppers), changing crop planting mode (rice–milk vetch rotation substituting for rice–wheat rotation), and utilizing ecological management technologies (installing insect-killing lamps to trap and kill pests; and postponing the conventional transplanting date to avoid the potentially serious harm caused by Laodelphgax striatellus). These ecological engineering methods controlled the outbreaks of rice pests to a certain extent, and thus provided an effective base for chemical pesticide management. The difference of technique input in treatments and Control, may lead to the difference in the degree of insect damage, which may greatly influence rice yields. Different technique inputs in treatments and Control may affect the growth and development of arthropods in rice fields, which further leads to the difference in insect damage. This study indicated that after adopting the technologies of installing insect-killing lamps or postponing the conventional transplanting date in the rice–milk vetch

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rotation, the active ingredients of chemical pesticides were reduced by 73.20–74.05%, 67.57–68.78%, respectively, compared to the control. Rice yields increased by 5.46–7.78% in treatments with lamps, while treatments with postponing the conventional transplanting date did not have the yield-increasing effect. Compared to the control, the species of natural enemies increased by 14–15 species, the number increased by more than 45.2%, and the diversity and stability of arthropod were both significantly strengthened. Models for eco-engineering assessment have been widely reported (Ulgiati et al., 1995; Chou et al., 2007; Williams and Adamsen, 2008; Cheng et al., 2012); meanwhile, AHP-based ecoengineering assessment has been applied in land preservation (Duke and Aull-Hyde, 2002), forest management (Stirn, 2006), wetland management (Herath, 2004), environmental performance (Pineda-Henson et al., 2002). In addition, models for chemical pesticide contamination management in vegetable fields (Jiang and Wan, 2009; Wan et al., 2009, 2013) and rice fields (Xi and Qin, 2009) have been reported, but eco-engineering assessment combined with AHP and GFT has not been reported in rice fields. The value of RCC/CP in four treatments were close to each other, which indicates a relatively consistent pest-control function, while the control had the maximum index of 0.9296, which was far higher than those of four treatments and indicates that the control was the most irrational or unacceptable chemical pesticide pollution management system. Strategic choice of a method for curbing chemical pesticide pollution in rice fields is a complex multi-objective decision-making process that covers a wide range of factors. Our conclusions here are drawn from analyses based on AHP and GFT. Additional useful information in this regard may be obtained via the use of other analytical methods and techniques and by considering other kinds of rice production methods or theories. In reference to choice of an optimal ecological engineering for chemical pesticide pollution management in Shanghai Agriculture Demonstration Park, we viewed the issue as complicated and the compound ecosystem as complex (Wu and Danielle, 2002; Wu and David, 2002). We used the RCC/CP model based on AHP and GFT to establish a range of possible alternative strategies. Our conclusion from this study is similar to that of the expert group. However, it is necessary to continue to determine if a scientific and reasonable evaluation system has been identified and to validate that system in practice. In future research, we need to gradually perfect both our chemical pesticide reduction engineering and eco-engineering assessment methods to optimize the identification of low-carbon technology for the production of rice and other agricultural commodities. In this study the RCC/CP model was to explore different eco-engineering strategies for chemical pesticide pollution management in rice fields in Shanghai. In this regard, RCC/CP can be used as an eco-engineering assessment index and to identify ecoengineering strategies for evaluation, and application in the future. The RCC/CP model can be analyzed using fuzzy mathematics, gray theory, game theory, Markov chain, and genetic algorithm techniques so as to address problems of fuzziness, complexity and uncertainty in the eco-engineering systems.

Acknowledgements This study was funded by the Natural Science Foundation of China (31171904), Shanghai Municipal Science and Technology Commission (08dz1900401, 12ZR1449100 and 10DZ1960100). We would like to thank Professor Donald R. Barnard of United State Department of Agriculture who helped us to revise and polish this paper, and thank Professor Murugan of Bharathiar University as

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well as anonymous reviewers for their constructive criticism and suggestions.

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