International Rice Congress 2002
Economical impact of IPM
Bong Hoon Lee
UNDP Developing and Promoting Support System and International Cooperation on
Environment-friendly Agriculture Programme
Rural Development Administration
250 Seodundong, Suwon 441-707, Republic of Korea
Abstract
An assessment of Integrated Pest Management (IPM) training was conducted through
comparison trials of IPM and Farmers’ Practice on rice from 1996 to 1998. The results of
IPM validation studies show that the number of pesticide sprayed can be reduced by 34 to
54% without significant changes in yield. The number of application and the amount of active
ingredient of pesticides were also reduced. The comparison of returns, receipts and operation
cost showed that there were no significant differences because of the savings in pesticides, a
result from not mixing of unnecessary pesticides at some crop stage. According to the
production efficiency analyzed by nonparametric efficiency analysis model, the efficiency
scores of IPM plots were higher than those of Farmers’ Practice plots: 0.5824 vs. 0.4192,
respectively, in 1996; 0.5558 vs. 0.5195, respectively, in 1997; and 0.6961 vs. 0.6281,
respectively, in 1998. While IPM implementation by field-oriented approach was very
effective in improving plant protection practices and production efficiency, there were also a
number of constraints which must be addressed.
Introduction
Since the concept and practice of IPM was first initiated during the 1950s and 1960s,
IPM has been acknowledged worldwide as the best practice for management of crop pests
and diseases to reduce input costs and increase yields and have been expanded to all the plant
protection principles and now is established as the main focus of pest management (Barfield
and Swisher, 1994; Kogan, 1998). Present IPM programs are designed to improve economic,
social, and environmental conditions and replace pesticides, fertilizers, and other off-farm
inputs with improved management skills.
Economic evaluations of IPM programs have been in progressed for over 30 years,
and the methods of analysis and issues have expanded from emphasis on simple budget
analyses of IPM and non-IPM to assessment of pesticide risk reduction, and to aggregate
evaluations of social impacts of IPM programs on producers and consumers including
environmental benefits (Norton and Mullen, 1994). Various methods to analyze the costs and
benefits of pest management and pesticide use were attempted to support pesticide risk
reduction. However, relatively little empirical work regarding on-farm training that attempts
to estimate the economic benefits of IPM program has been completed.
The purpose of this paper is to illustrate the effects of IPM on pesticide use on rice
and to quantify the economic impact of IPM practice. The constraints and possible solutions
shall also be discussed in this paper.
IPM Programme : Extension and Farmer Education
From 1993 to February 2000, IPM Program in Korea has been developed in
collaboration with the United Nations Development Programme (UNDP), Ministry of
Science and Technology (MOST), and the Rural Development Administration (RDA). The
aim of the project was to transform existing pest control system by using field extension staff
responsible for the plant protection at the City/County Agricultural Development and
Technology Centers (ADTCs) disseminate relevant IPM methods during farmers’ training.
This attempt was agreed to build an IPM program based on Korean and regional research
using the new field training methods emerging from the FAO Intercountry Programme for
Integrated Pest Control in Rice in South and South East Asia (Gallagher, 1995).
To achieve an effective orientation program, field staff were required to participate in
extensive field training, the Training of Trainers (TOT). The training included the following:
a. agronomic practices
b. main disease symptoms and development
c. detritivore, herbivore, and natural enemy recognition and population dynamics
d. pest control methods (cultural, biological, and chemical)
e. pesticide impact on ecosystem and health
f. weed management
g. decision-making using agro-ecosystem analysis methods
h. training methods for farmers
i. group dynamic exercise
Usually all topics were explored during each crop stage through field experiments,
field observation and analysis, discussion, and presentation by resource persons.
Graduates of TOT set up season-long Farmer Field Meetings (FFMs) based on a
highly modified version of Farmer Field Schools (FFS) implemented in other FAO
Intercountry Programme countries. The FFM consisted of approximately 25 farmers, a trainer,
and training fields. The chosen training fields were the same throughout the season and
allowed sub-set of the TOT activities and included defoliation and detillering experiments,
agro-ecosystem analysis, and experiential learning activities on predation, pesticide impact,
and pest population dynamics. The decision-making during each critical crop stage (roughly
monthly) was followed by the discussion of FFMs based on intensive field practice. IPM
fields were observed regularly to determine plant production and protection needs. No
economic thresholds were used, rather, the potential for insect pest development was
determined by assessing current field levels in relationship to weather and natural enemies
that were present in the field. Disease assessment in IPM field considered weather and crop
development stage. Herbicides were used in IPM based on conventional practices. In
conventional treatments, the local recommendations used were based on crop stage and
weather forecasting.
Effects of IPM implementation
In order to validate IPM methods, an assessment was carried out in 79 cities/counties
in 1996, 66 cities/counties in 1997, and 25 cities/counties in 1998. Each site consisted of one
IPM plot and one Farmers’ Conventional plot. Size of each plot was 0.1ha. The trials have
been carried out in farmers’ fields. The trials were supervised by the extension staff.
Effects of pesticide use. Typical results indicated that the pesticide use (including the number
of pesticides sprayed and the number of application) was reduced with no significant
difference in rice yield. The number of pesticides sprayed was reduced by 54.4% in 1996
(58.0% of fungicide and 62.1% of insecticide), 44.8% in 1997 (57.1% of fungicide and
43.3% of insecticide), and 33.7% in 1998 (46.2% of fungicide and 45.9% of insecticide).
Reduction of application increased from 35 to 49% (Figure 1).
The active ingredient (a. i.) of pesticides was also reduced by 40.4% in 1996 (45.5%
of fungicide and 52.1% of insecticide), 36.5% in 1997 (46.8% of fungicide and 45.1% of
insecticide), and 27.5% in 1998 (21.0% of fungicide and 39.6% of insecticide). Herbicide use
was slightly reduced by 5.7 to 19.3% (Figure 2). The total amount of pesticides and the total
number of sprays were reduced because IPM methods do not allow for mixing of pesticides
during application, unlike conventional recommendation which suggest mixing during each
spray for ‘preventive’ control.
Economic evaluation. The comparison of receipt, production cost, and net returns between
IPM plots and Farmers’ Conventional plots are verified in Table 1. The net returns have no
significant differences between IPM plots and Farmers’ Conventional plots. However, the
cost of pesticides decreased in IPM plots, while the cost of fertilizer and other inputs were
increased. It seems that the anxiety of income loss allowed for IPM trained farmers to use
more fertilizers (Lee, 1999). However, Kim et al. (1985) showed that increasing nitrogen
input increases the levels of diseases in the rice field given that weather conditions are
conducive to epidemics. Nitrogen is also known to slightly promote growth rates of some
planthoppers (Uhm et al., 1985). Furthermore, studies on fungicides have shown that some
common fungicides and insecticides are active against natural enemies (Yoo et al., 1984).
Thus, nitrogen causing higher pathogen development can lead to natural enemy mortality due
to increased fungicide applications.
Production efficiency. In order to compare the production efficiency of IPM Plots with
Farmers’ Conventional Plots, a nonparametric efficiency analysis model belonging to the data
envelopment analysis (DEA) model, which was developed by Banker et al. (1984) and Färe
et al. (1985), was employed. A nonparametric model used for this study has the advantage of
analyzing the production technology without imposing any functional specifications on the
production frontier (Kwon, 1998; An, 1998).
Nonparametric Efficiency Analysis Model
Define x  RN as the input vector used by a farmer to produce rice yield y  R .
The production technology can be represented by the following input requirement set V ( y ) .
V ( y )  {x  R
N

: y can be produced with x} (1)
V ( y ) , which is a subset of RN , is a set of inputs that are required to produce a
certain level of rice output y. V(y) does not include 0 for any positive y, and is a closed set. A
nonparametric production model constructs the set V(y) with an observed data set of input
and output and analyzes the characteristics of the farmer's production technology based on
the constructed set. In an actual analysis, some assumptions on technology need to be
employed, and the constructed set of V(y) depends on those assumptions. First, free
disposability of inputs and outputs are assumed. That is, if a certain level of output can be
produced with a given input, then it is possible to produce any level of output lower than that
with the same input. In addition, if a certain level of output can be produced with a given
input, then this level of output can always be produced when more input is used.
Suppose there areⅠfarmers in the economy, under the assumption of the variable
returns to scale (VRS), no restrictions on returns to scale are imposed. The input requirement

set V(y) can be approximated by the following set V ( y) v which is constructed with each
farmer's input and output data:
I
I
I
i 0
i 1
i 1
V ( y ) v  {x : x   i xi , y   i y i , i  R ,  i  1}
(2)
xi is production input of the ith farmer while yi is his rice output. i is a

nonnegative scalar. { i } is an intensity vector which allows for V ( y) v to approximate V(y)
by increasing or decreasing the input and output of each farmer. Suppose that a convex
combination of some farmers' output levels is greater than or equal to y. Then, the definition

in (2) implies that the input requirement set V ( y) v is the set of inputs that are greater than or

equal to the convex combination of those farmers' inputs. The set V ( y) v contains all the
properties of V ( y ) .
The input requirement sets, which correspond to the aforementioned production
technologies, are generated by solving the following linear or mixed-integer programming
problem for each farmer.
S ( yi, xi)V =
min

0,{i}
{ : xi  V ( y )V }
(3)
By solving the problem, it can be figured out where each farmer is located in the
input requirement set. S ( yi, xi)V is the ratio of the ith farmer’s minimum input required to
produce yi to his actual input usage under the assumption of VRS. Hence, S ( yi, xi)V lies

between 0 and 1. The ith farmer is located on the frontier of V ( y )V if the value of
S ( yi, xi)V is equal to 1 while he is located inside of the set when value of S ( yi, xi)V is less
than l. The ith farmer is regarded efficient (inefficient), when the value of S ( yi, Ci)V is
equal to (less than) 1. Therefore, both technical and scale in efficiency of each farmer can be
investigated by calculating his efficiency scores. The size of the efficiency score calculated
by model (3) represents the degree of technical inefficiency of each farmer. However, no

restrictions on returns to scale are imposed by V ( y) v may exhibit constant, increasing, or
decreasing returns to scale.
Define another set of V ( y ) C which is constructed by removing the constraint
I
 i  1

from V ( y) v .
i 1
I
I
i 0
i 1
V ( y ) C  {x : x   i xi , y   i y i , i  R }
(4)
The production technology represented by V ( y ) C exhibits constant returns to scale
(CRS). Then, the input requirement sets which correspond to the CRS production
technologies are generated by solving the following linear or mixed-integer programming
problem for each farmer.
S ( yi, xi) C =
min

0,{i}
{ : xi  V ( y ) C }
(5)
The solution to this problem i lies between 0 and 1 like as in the case of VRS. The
same interpretations are given to the S ( y i , xi ) C calculated by (5) as well.
Since the input and output data of rice production from IPM validation plots and
Farmers’ Conventional plots were measured into 0.1ha instead of total cultivated area, the
production technology should be assumed as CRS. Thus, in this study, the efficiency score
was measured by (5). The production technology was analyzed using only the data of three
kinds of production cost (pesticide cost, fertilizer cost, and the other cost) and output of 340
plots which consisted of 170 IPM plots (79 in 1996, 66 in 1997, and 25 in 1998) and 170
Farmers’ Conventional plots (79 in 1996, 66 in 1997, and 25 in 1998).
Comparison of efficiency scores. According to production efficiency analysis, IPM plots
scored 0.5824, while Farmers’ Conventional plots scored 0.5008 in 1996. The differences of
scores showed similar tendency in 1997 and 1998. This meant that the IPM method is more
efficient than Farmers’ Conventional method.
Discussion
The results of validation trials showed that IPM method can reduce pesticide use
without significant differences in yield and net returns. However, all data collected for rice
cultivation in Korea indicates that the current conventional level of pesticide use, and
government recommendations for pesticide use, are about twice what is necessary to maintain
yields. Pesticide use presently amounts to ca. 9,103 M/T active ingredients per year, and the
treated area with some type of pesticide reaches 9.9 times of total planted area . On a national
scale, rice yield has reached a production maximum since the 1980s, but pesticide cost and
use have continued to grow. In other words, production with respect to pesticides has become
less efficient and more polluting in recent years. Pesticide use and costs continued to increase
from 1980s to the present even though average yield levels remained relatively steady (Figure
3). Therefore, the verification trials show that the IPM method play an important role in
reducing the reliance on chemical pesticides and in preventing pest problems through better
crop management.
The figures derived from the economic evaluation of IPM benefits would not be
useful to farmers to adopt IPM. This is because of the cost of pesticides is relatively small in
proportion to production cost. Immediate economic effects to the farmers will not be
sufficient to capture the true impact of IPM.
In most countries, the results of research are transferred to the farmers by extension
or advisory service. In Korea, IPM research has been undertaken since the 1970s to
understand the basic ecological relationships of weed, insect pests, and diseases in most crops.
At the same time, the national forecasting system and other extension activities were
developed to lessen the likelihood of crop failure and large-scale pest outbreaks. While these
programs have been effective, in many cases, an over-dependence on pesticides has been
continuously practiced and has resulted in an overuse of pesticides on a calendar basis by
farmers (Hyun, 1978).
The results also indicated that the training for farmer and extension staffs was an
important strategy to implement IPM in the field. The FFM usually meet four times in a
season, with each meeting conducted at a critical point in the crop cycle. By the end of the
farmers’ training, most farmers were able to make an appropriate management decision based
on their field’s situation by merely observing their field in a few minutes. This is a major
move, away from conventional calendar sprays that often mix several pesticide compounds in
one application.
Since IPM requires higher quality training for both extension officers and farmers
than most existing extension systems (Kenmore et al, 1987), IPM training should continue to
reach a greater number of farmers and applied to more crops. There is strong interest in doing
IPM and in developing farmer field-based training to improve plant production and protection
skills of farmers. Therefore, it could be very possible to reduce pesticide use by 30% by 2005,
which was announced by Ministry of Agriculture and Forestry (MAF) as “Sustainable
Agriculture Promotion Act” in 1997. However, farmers who received IPM training indicated
a reluctance to adopt IPM because of the anxiety of income loss, and difficulty in shifting the
way of thinking and in selecting safe pesticides (Lee, 1999). Thus, a re-evaluation of the role
of extension agencies and the broadcasting system, and stressing the importance of pesticide
management have been strongly recommended. When farmers implement IPM, negative
environmental and health impact will also be reduced while profits will be increased. The key
to better IPM is better farming by farmers who are empowered with the knowledge of
managing their own ecosystem in ways that benefit themselves and society as a whole
(Matteson et al. 1994). Public concern intensified over the environmental effects and human
health of pesticide use and the return on public investments as well in general, and attempts
to quantify and qualify the economic consequences of IPM practices expanded to include
environmental and social benefits (Norton and Jeffrey, 1994; Wabel et al., 1999). Such an
evaluation will help to determine the effectiveness and long term viability of IPM
implementation.
References
An, D. H. 1998. An Analysis of Productivity Change in Korean Rice Farms: Decomposition
of Productivity Change into Efficiency and Technical Changes. Ph. D. Dissertation. Seoul
National University. 151 p. [In Korean with English abstract]
Banker, R. D., A. Charners, and W. W. Cooper. 1984. Some models for estimating technical
and scale inefficiencies in data envelopment analysis. Management Science. 30(9):1078-1092.
Barfield, C. S. and M. E. Swisher. 1994. Integrated pest management: ready for export?
Historical context and internationalization of IPM. Food Rev. International. 10(2):215-267.
Färe, R. S., S. Grosskopf, and C. A. K. Lovell. 1985. The Measurement of Efficiency of
Production. Boston. Kluwer-Nijhoff Publishing.
Gallagher, K. D. 1995. Lessons learned and new concepts for farmer IPM training from the
FAO inter-country IPM program. Korean J. Appl. Entomol. 34:15-19.
Hyun, J. S. 1978. An urgent problems and prospect of plant protection. Korean J. Pl. Prot.
17(4):201-215. [In Korean]
Kenmore, P. E., J. A. Litsinger, J. P. Bandong, A. C. Santiago, and M. M. Salac. 1987.
Philippine Rice Farmers and Insecticides: Thirty Years of Growing Dependency and New
Options for Change. In: Management of Pests and Pesticides: Farmer’s Perception and
Practices. Tait, J. and B. Napompeth. (eds.). Boulder, Colorado, Westview Press. p 98-108.
Kim, C. K., Ra, D. S., and Min H. S. 1985. Ecological studies on rice sheath blight caused by
Rhizoctonia solani Ⅲ. Cultural method and disease development. Korean J. Plant Protection.
24(1):7-10,
Kogan M. 1998. Integrated pest management: Historical perspectives and contemporary
developments. Annu. Rev. Entomol. 43:243-270
Kwon, O. S. 1998. The rice production technology of Korean farmers: a nonparametric
analysis of technical and scale efficiencies. Korean Soc. Sci. J. 25(1):179-196.
Lee, B. H. 1999. A Study on the Effects of Integrated Pest Management Implementation on
Rice in Korea. Ph. D. Dissertation, Seoul National University, Korea. 105 p.
Matterson, P. C., K. D. Gallagher, and P. K. Kenmore. 1994. Extension of Integrated Pest
Management for Planthopers in Asian Irrigated Rice: Empowering the User. In: Planthoppers,
Their Ecology and Management. Denno R. F. and T. J. Perfect (eds.). Chapman and Hall.
New York. p 656-685.
Norton G. W. and J. Mullen. 1994. Economic Evaluation of Integrated Pest Management
Programs: A Literature Review. Virginia Cooperative Extension. Virginia State University.
112 p.
Uhm, K. B., Hyun, J. S., and Choi, K. M. 1985. Effects of the different levels of nitrogen
fertilizer and planting space on the population growth of the brown planthopper (Nilaparvata
lugens Stal). Research Report RDA (P.M&U). 27(2):79-85.
Hermann W., G. Fleischer, P.E. Kenmore, and G. Feder. 1999. Evaluation of IPM Programs –
Concepts and Methodologies. Papers presented at the First Workshop on Evaluation of IPM
Programs, 16 March-18 March 1998. Hannover. 67 p.
Yoo, J. K., Kwon, K. W., Park, H. M., and Lee, H. R. 1984. Studies on the selective toxicity
of insecticides for rice insect pests between some dominant rice insect pests and a predacious
spider, Pirato subpiraticus. Korean J. Applied Entomol. 23(3):166-171.
Figures and Tables
Figure 1. Number of pesticide sprayed and application in 1996-1998 (**<0.01, *<0.05)
Figure 2. Pesticide use (a.i. g/0.1ha) in 1996-1998 (**<0.01, *<0.05)
Figure 3. Total pesticide cost and the average rice yield in Korea from 1982 to 2001.
Table 1. Descriptive Statistics
Table 2. Production Efficiency
Figure 1. Number of pesticide sprayed and application in 1996-1998 (** <0.01, * <0.05)
6
1996
5
4
**
**
**
3
2
**
1
0
6
1997
Number
5
4
**
**
**
3
2
*
1
0
7
1998
6
5
**
4
**
**
3
2
1
0
Fungicide
Insecticide
IP M
H erbicide
FP
A pplication
Figure 2. Pesticide use (a. i. g/0.1ha) in 1996-1998 (** <0.01, * <0.05)
300
**
250
**
1996
200
**
150
100
50
0
300
**
g(a.i.)/0.1ha
250
**
1997
200
150
100
50
0
300
*
250
1998
200
150
100
50
0
F u n g ic id e
In s e c tic id e
IP M
H e rb ic id e
FP
Table 1. Descriptive Statistics
Year
Variable
1996
Receipt
1997
IPM
824,718 ± 142,195
Min.
Max.
562,470
1,355,750
586,470
1,150,000
FC
830,056 ± 107,000
Pesticide
Cost
IPM
12,026 ± 8,080
0
40,000
FC
23,783 ± 15,969
4,100
75,000
Fertilizer
Cost
IPM
37,740 ± 29,063
8,500
180,000
FC
33,025 ± 21,281
1,150
128,200
Other Cost
IPM
103,174 ± 63,451
19,500
292,000
FC
114,607 ± 95,128
20,045
767,500
IPM
875,949 ± 127,127
660,956
1,247,100
FC
888,395 ± 113,850
683,922
1,260,800
IPM
15,059 ± 7,023
2,800
39,000
FC
24,166 ± 10,178
4,500
54,000
Fertilizer
Cost
IPM
38,869 ± 42,113
9,800
260,000
FC
33,001 ± 34,599
9,290
222,000
Other Cost
IPM
125,161 ± 61,071
23,820
341,466
FC
131,639 ± 62,450
30,120
371,376
IPM
FC
IPM
FC
921,148 ± 100,279
942,148 ± 96,532
19,584 ± 8,751
30,121 ± 15,636
652,500
702,500
6,500
14,000
1,194,500
1,194,500
36,000
93,000
IPM
FC
IPM
FC
47,864 ± 44,795
37,735 ± 35,542
158,559 ± 78,689
158,981 ± 83,901
11,825
12,300
8,154
8,154
169,430
169,430
411,070
413,290
Receipt
Pesticide
Cost
1998
Mean ± SD
Receipt
Pesticide
Cost
Fertilizer
Cost
Other Cost
Table 2. Production Efficiency
Mean of Efficiency Score
Year
1996
1997
1998
* : p<0.05, ** : p<0.01
IPM
0.5824 **
FC
0.4192
Total
0.5008
IPM
0.5558 *
FC
0.5195
Total
0.5377
IPM
0.6961 *
FC
0.6281
Total
0.6621
Figure 3. Total pesticide cost and the average rice yield in Korea from 1982 to 2001.
kg/0.1ha
Million Won
400000
600.0
350000
500.0
300000
400.0
250000
200000
300.0
150000
200.0
100000
50000
Total pesticide cost
Average rice yield
0.0
19
82
19
83
19
84
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
0
100.0
Year