pesticide level chili Indonesia agricultural systems

Manuscript submitted to Agricultural System journal

1

Factors determinant of pesticides use in chili pepper farming in 1

Central Java, Indonesia 2

Joko Mariyono1 and Madhusudan Bhattarai2 (corresponding author) 3 1 AVRDC-Indonesia Project Office, Tegal, J1 Slamat 51, Kota, Tegal, C-Java, Indonesia. 4 2 AVRDC-The World Vegetable Centre, P O Box 42 , Shanhua, Tainan, 74199, Taiwan 5

E-mail [email protected] 6 Tel No + 886-6-583-7801 ; Fax : +886 6-583-0009 7

8 Abstract 9

This paper analyzes factors affecting chili farmers’ decision to use pesticides 10

in Indonesia, which is based on a comprehensive household survey of 160 chili 11

growing household from three districts of Central Java, in 2008. On average, farmers 12

apply 12 kg of pesticide per hectare of chili in a crop season of four months; thus, 13

farmers’ reliance on pesticides for controlling pests and diseases is very high. Our 14

results suggest that pesticide use can be reduced by training and exposing farmers to 15

improved pest and disease management practices. The response of pesticide prices on 16

pesticide uses was very low, thus, environmental tax policies such as levying an 17

additional tax on hazardous pesticides may not be an effective policy tool. These 18

findings will be useful in making effective agricultural extension and rural 19

development policies for reducing pesticide use in chili farming in Indonesia in 20

particular, and in tropical vegetable farming system, in general. 21

22

Keywords: Chili farming system, pesticide use, subjective economic threshold, 23

vegetable production system, Tobit analysis, Indonesia. 24


2

1. Introduction 25

This paper analyzes factors affecting farmers’ decision to use pesticides on 26

chili farming in Indonesia. Pesticides use on chili and other high value vegetable 27

crops are increasing at alarming rate in many places in tropical Asia. Annually, chili is 28

cultivated on about four million ha worldwide, out of that 2.5 million ha lies in Asia 29

alone (FAOSTAT, 2010; Ali, 2006). In fact, in terms of crop acreage, chili is ranked 30

as the most important vegetable in Asia and the third important vegetable in the 31

world; and thus it is an important component of Asian vegetable farming system. 32

Chili cultivation is also an important source of cash income and employment for 33

millions of smallholder farmers in tropical Asia. Due to high value commodity, given 34

a same unit of crop acreage, 3-4 times more farm employment from chili farming than 35

that of rice farming (Ali, 2006). 36

Vegetables are cultivated on about 1.1 million ha annually in Indonesia, out of 37

that chili alone occupies about 0.2 million ha (Johnson et al., 2008). Chili has the 38

largest crop acreage among fresh vegetables in Indonesia, and it provides about 3-4% 39

of global production (supply) of chili (Ali, 2006; FAO 2010). Chili provides the 40

greatest share in terms of total annual value of vegetables produced in Indonesia (Vos 41

and Duriat, 1995). Annually, over 10 millions of smallholder farmers cultivate chili in 42

Indonesia, thus, it is a very important component of farming system in Indonesia. 43

Chili is an essential component of the daily diet of common Indonesia, with a 44

total population of 230 million in 2008 (Johnson et al., 2008). Most Indonesians 45

consume chili almost daily, usually in a fresh form, which makes demand relatively 46

stable year-round; however, the supply of chili across the seasons is not stable in 47

Indonesia, which largely varies by agro-climatic and agricultural infrastructural 48


3

factors. Because of high importance in farming system and in daily food items of an 49

average consumer, the volatility of chili prices usually becomes an important issue for 50

national public debates in Indonesia. For example, increases or decreases in chili 51

prices and any change in market supply frequently receive coverage in national news 52

media, and becomes a major concern among the policy makers. 53

Despite of increased government emphasizes, the average productivity of chili 54

in Indonesia is only of about 5 ton/ha, which is lower compared to other leading chili 55

growing countries in Asia such as China, India, and Thailand (Ali, 20006). In fact, 56

chili production and acreage in Indonesia fluctuates sharply from year to year, and 57

across the regions of Indonesia. There is a higher fluctuation in prices than production 58

of chili Indonesia (Mustafa et al., 2006). In addition, over the last 20 years, there is no 59

constant upward trend in total national level production and productivity of chili in 60

Indonesia (Mariyono and Bhattarai, 2009). 61

In Indonesia, chili is grown mainly on the islands of Java and Sumatra; they 62

are also the two densely populated and agriculturally important islands in the country. 63

Out of the total national production of chili in 2006-7, more than 50% was produced 64

in three provinces of Java island: East Java, Central Java, and West Java. Central 65

Java is one of the largest chili-producing provinces in the country in terms of chili 66

acreage, with about 24,500 ha in 2007, close to about 20% of the total national level 67

of chili acreage. Central Java supplies chili to several urban centers in Indonesia and 68

also exports the fresh produce in certain months. Chili production in Central Java is 69

largely concentrated in a few districts with suitable agroecological conditions. The top 70

five chili producing districts in Central Java are Brebes, Magelang, Rembang, 71


4

Temanggung, and Wonosobo (Table 1). In 2007, average chili yield was highest in 72

Brebes and lowest in Rembang (BPS, 2008). 73

[TABLE 1 AROUND HERE] 74

Agroecological, socioeconomic, and institutional factors are responsible for 75

the variation in productivity of chili across the districts. Among the agroecological 76

factors, the nature and type of diseases, insect pests, and level of infestation at a place 77

are critical determinants for variation of chili productivity and farmers’ income across 78

the regions (Vos and Duriat, 1995; Vos et al., 1995). Variations in pressure from 79

fungal and viral diseases (largely anthracnose and geminiviruses) are two of the most 80

important factors in the survey sites on Java (Mariyono and Bhattarai, 2009). These 81

two diseases reduce overall crop productivity and the marketable yield, and 82

substantially increase economic risk to farmers due to high levels of crop damage or 83

high probability of crop failure. A majority of farmers rely on pesticides to control 84

diseases and pests. As indicated in Figure 1, pesticide imports in Indonesia have 85

increased more than tenfold over the 15-year period from 1990–2005. 86

[FIGURE 1 HERE] 87

Chemical control measures alone are frequently ineffective and also very 88

expensive for smallholder farmers. The potential for adverse impacts from pesticide 89

misuse on crop health, the environment, and the health of farmers and their 90

communities is significant (Bond, 1996). High levels of pesticides on vegetables and 91

other high value crops have contributed to high levels of chemical residue in the food 92

chain. This has led to a greater awareness of the health and environmental hazards 93

associated with misuse of pesticides on vegetables, from consumers, food exporters, 94


5

and environmental groups (Jipanin et al., 2001). Although some chili farmers in 95

Indonesia use non-chemical control measures, the practice is still not widespread 96

(Mariyono and Bhattarai, 2009). 97

Therefore, it is likely that the level of pesticide use varies across crops, 98

severity of pest and disease infestation, agroecological factors, socioeconomic factors, 99

and knowledge of crop management practices. In general, farmers use more pesticides 100

on vegetables and high value crops than cereals and staple crops (Ali, 2006). 101

A study comparing pesticide use intensity across four vegetable crops in India 102

revealed on an average of about 5.15 kg of active ingredient (ai)/ha of pesticide on 103

chili (Jeyanthi and Kombairaju, 2005). Despite more frequent applications of 104

pesticide on cauliflower, the intensity of application (actual amount applied) was 105

lower than for the other three vegetables examined. Nevertheless, a large number of 106

farmers applied more than 6 kg of ai/ha of different pesticides on chili, cauliflower, 107

and okra. 108

It is not clearly stated in the literature what level of pesticide application 109

should be categorized as low, recommended, and high level (or overuse). For 110

instance, approximately 90% of farmers surveyed in a study with diamondback moth 111

in cabbage in Ghana applied pesticides more than the recommended dose in single 112

applications, but considerably lower than recommended in terms of total amounts 113

applied in a crop season (Horna et al., 2008). Doses that are persistently higher than 114

recommended can contribute to the development of insect resistance to insecticides. 115

In several parts of Asia, pesticides can be considered to be misused if the level is 116

higher than recommended doses (Baral et al., 2006). This is particularly true if the 117

plant protection strategy employed a recommended technology package, and farmers 118


6

spray regularly without taking pests, diseases, and the agronomic condition of crops 119

into account (Horna et al., 2008; Mariyono and Bhattarai, 2009). 120

In general, farmers in many developing countries follow a weekly calendar of 121

spraying with “cocktails” of insecticides specially formulated for high value vegetable 122

crops (Bond, 1996). After the introduction of integrated pest and disease management 123

(IPM) measures in the 1980s, many recent studies have reported that the misuse of 124

pesticides has reduced in Indonesia (van den Berg and Jiggins, 2007). IPM usually 125

suggests taking the local agroecosystem into account and applying pesticides only 126

when substantial attacks from pests and diseases are observed in the field; no 127

pesticides should be applied in the absence of pests or diseases. In Indonesia, the 128

national IPM program and technical support from universities and many local 129

governments are emphasizing use of alternatives to chemical pesticides including 130

many biocontrol products such as Bt (biotechnology) and various NPVs 131

(nucleopolyhedroviruses), and soil amendments such as vesicular arbuscular 132

mycorrhizae (VAM) and Trichoderma that replace synthetic chemicals. Also, an 133

effective insect IPM program must reduce synthetic chemical use to a minimum 134

(ideally zero) to protect populations of natural enemies. 135

Several international environmental protocols and agreements as well as 136

national environmental programs of many developing countries promote wise use of 137

pesticides in agriculture. Nevertheless, for these programs to succeed, not only the 138

high level governmental authority signing the international and national level 139

protocols, but we also need to know factors determining the level and intensity of 140

pesticide use on a particular farming sector (crop or location specific), in order to 141

provide proper policy recommendations for reducing pesticide misuses. In this paper, 142


7

we analyze the factor determinants of pesticide use on chili by doing farmers level 143

analysis in major vegetable production regions in Indonesia. As noted earlier, chili is 144

one of the most widely cultivated and input-intensive vegetables in Indonesia and 145

tropical Asia. 146

The second section summarizes objectives and scope of the paper. The third 147

section provides a brief review of recent literature on the level and intensity of 148

pesticide use in farming in relation to vegetable production in developing countries. 149

The fourth section illustrates research methods and analytical procedures followed by 150

econometric modeling, data, and measurement of variables used in regression 151

analysis. Results and discussions are provided in the fifth section, followed by a 152

summary of findings and policy implications in the last section. 153

2. Objectives and scope 154

The major objective of this paper is to analyze factors affecting farmers’ 155

decision to use pesticide on chili pepper farming in Indonesia. The specific objectives 156

of the study are: (1) to adapt an empirical model to capture farmers’ behavior in using 157

pesticides in chili cultivation; (2) to evaluate and quantify factors determining 158

farmers’ decision to use pesticides on chili farming; and (3) to analyze and 159

recommend policy implications related to effective use of pesticides in chili and 160

related high value vegetables farming. 161

Empirical assessment was carried out in one community in each of three 162

districts in Central Java province. Each community surveyed represent intensive chili 163

cultivation villages and with a distinct agroecological and socioeconomic setting of 164

chili farming. From the three villages, a total of 160 chili-cultivating households were 165


8

individually surveyed with the help of a comprehensive structured questionnaire. The 166

chili farming practices in general differ across the three sites, as did the level of crop 167

intensification and pesticide use. 168

3. Literature review 169

Farmers’ adoption of improved farming practices and technologies such as 170

new cultivation techniques, high-yielding crop varieties, and single-cropping of short 171

duration varieties were the backbone of the successful Green Revolution in Asia 172

(Pingali et al., 1997). Green Revolution varieties that benefit Asia, mainly rice and 173

wheat, were successful primarily because of plant breeding programs that developed 174

high yielding varieties with short, sturdy stalks to support large clusters of grain 175

(Ruttan 1977). These varieties depended heavily on irrigation and large amounts of 176

chemical fertilizers, mainly nitrogen. Chemical pesticides were added to Green 177

Revolution input packages promoted by national governmental agencies even though, 178

for rice in particular, they were unnecessary in many cases and ultimately harmful 179

rather than helpful. The pest complexes that attack vegetables are different than for 180

rice. There are more insect pests and diseases in vegetables, and without any control 181

measure entire crops can be lost. Therefore, in general, vegetable farmers tend to use 182

more chemical pesticides, and they also use excessively and indiscriminately to 183

“protect” the crop, than that of their counter parts growing cereals. The result that 184

intensive vegetable farming is a pesticide treadmill that is not all understood by 185

farmers, and that is largely also the result of persuasive pesticide sales programs 186

sponsored by chemical companies. Alternative control tactics exist and have been 187

demonstrated in many areas, but their use is critically dependent on farmer training, 188


9

which is expensive and difficult to implement on a large enough scale to reach the 189

majority of farmers (Norton et al, 2005; Carlson and Wetzstein, 1993). 190

Along with the pace of Green Revolution, the pesticide use level also has been 191

accelerated in Asia. In fact, many scholars have argued that the Green Revolution in 192

Asia during 1970s and 1980s was possible largely due to timely availability of 193

irrigation, high yielding varieties (HYVs) and related agro-chemicals such as 194

chemical fertilizers and chemical pesticides. Through timely control of devastating 195

pests and diseases, pesticides help minimize crop losses and provide tremendous 196

benefit to farmers. Thus, pesticide use is considered a key part of farming in many 197

Green Revolution-affected regions in Asia. 198

Farmers’ main motives in using pesticides are to ensure a certain level of crop 199

yield and income, and to minimize risk of crop failure (Farrel, 1998). The level of net 200

additional economic gain to farmer is key underlying factor providing incentive and 201

rationality for pesticide use. In Indonesia, the level of pesticide use in a place is 202

strongly affected by nature and scale of the promotion and distribution policies 203

adopted by local agribusiness agencies (Luther et al., 2007). Complete elimination of 204

synthetic chemical pesticides use without appropriate substitution of biocontrol 205

alternatives in intensive farming can substantially reduce vegetable crop yields 206

(Knutson and Smith, 1999), and in many cases, it can lead to zero levels of vegetable 207

production. The loss due to pests and diseases varies by location, nature of farming, 208

history of pest infestation, and many other factors. Many factors affect both the level 209

and intensity of pesticide used for a crop. From an economic point of view, the 210

decision to use pesticides is related to price of pesticides, expected crop prices at the 211

time of harvest (ex-ante price), price of other agricultural inputs, and farmers’ 212


10

expected net returns from selling the produce (Carlson and Wetzstein, 1993). 213

Therefore, the level and intensity of pesticide use is relatively higher for high value 214

crops grown for market (such as vegetables and fruits) than crops grown for home 215

consumption (such as cereals and other staples). A study by Rahman (2003) in 216

Bangladesh suggested that some rice farmers treated pesticides as a substitute for 217

fertilizers; they increased pesticide use on rice as fertilizer prices increased. An 218

increase in the prices of rice and soybean in Indonesia induced farmers to use more 219

pesticides to get more farm income and profit (Mariyono, 2008b). An increase in the 220

price of pesticide is expected to reduce its demand. In a study in Sri Lanka, Selvarajah 221

and Thiruchelvam (2007) reported that high prices for pesticides led to a reduction in 222

the level of pesticides used by farmers; however, households with more family 223

members (labor available for spraying) used more pesticides. Their study did not find 224

any significant relationship between strength of pesticide spray mixtures with 225

farmers’ education or experience in cultivation of the crop. Conceptually, farmers’ 226

decision to use pesticides can be considered the same as buying insurance: it is a 227

preventive mechanism against crop failure due to pests and diseases (Lichtenberg and 228

Zilberman, 1986). In addition, farmers’ decision to use pesticides is likely to be 229

influenced by several factors, such as their past experience with pest infestations, crop 230

types, some level of expectation as per the crop condition at the moment, and 231

expected return from the produce or expected crop productivity (Carlson and 232

Wetzstein, 1993). A study in US by Heimlich et al. (2000) shows a substantial 233

reduction in herbicide use associated with increased adoption of genetically modified 234

soybeans, corn, and cotton. 235


11

The variation of intensity of pesticide use reported in selected recent studies is 236

summarized in Table 2. The level of pesticide use depends on commodity types and 237

agronomical practices followed, socioeconomic factors, and level of awareness of the 238

farmers making the decision. In a study in Bangladesh, farmers applied 12-18 times 239

more pesticide on vegetables than on cereals, but adoption of modern vegetable 240

production technologies led to reduction in number of sprays and the actual quantity 241

of pesticides used; the number of pesticide sprays followed by non-adopters of 242

modern vegetable production technology was double than that of adopters of the 243

modern technologies (Ali and Hau, 2001). The reduction in pesticide use was 244

attributed mainly to training farmers received on the judicious use of pesticides and 245

other agrochemicals in vegetable production. Another study by Selvarajah and 246

Thiruchelvam (2007) in Sri Lanka shows that the estimated average amounts of active 247

ingredients applied were 1.9 kg kg/ha/year and 11.5 kg/ha/year for rice and chili, 248

respectively. That is, farmers applied 6 times more pesticides on chili than on rice. A 249

similar pattern of pesticide use can be seen in other parts of tropical Asia where 250

vegetables are grown intensively for market sale. In a study in Bangladesh, farm size 251

as well as ownership of land were significantly and positively associated with 252

intensity of pesticide use (Rahman, 2003) indicating that large-scale farmers used 253

more pesticides per unit of crop land than smallholder farmers. In the same study, the 254

availability of cash, approximated by the agricultural credit variable, was significantly 255

and positively related with pesticide use, indicating that greater liquidity increases 256

farmers’ pesticide use. 257

Pesticide use is also affected by the kind of knowledge acquired by farmers, 258

and farmers’ access to extension agents and other agricultural experts. In fact, 259


12

farmers’ knowledge on pest management has direct and indirect impacts on pesticide 260

use. Direct impacts arise due to the fact that better knowledge leads to lower levels of 261

pesticide use as the farmers’ substitute pesticide with other alternative methods. 262

Indirect impacts arise as farmers can better predict levels of pest-related damage and 263

yield loss, and subsequently use the pesticides judiciously after such training and 264

contacts with extension (Mariyono, 2008a). A study in Indonesia (Mariyono, 2007) 265

showed that the level of pesticide use in the country declined with the increase in the 266

number of IPM training events hosted in farming communities. Trained farmers will 267

make better predictions on expected yield loss associated with pests and diseases 268

while making pesticide use decisions. It is expected that IPM-trained farmers would 269

be more tolerant of pests, and they would likely delay spraying and wait until the pest 270

attack reaches the economic threshold level of crop damage. Various definitions of 271

economic threshold (ET) are available in the literature. Stern (1973), the pioneer of 272

IPM technologies, defined the economic threshold level as the pest density at which 273

control measures should be initiated to prevent an increasing pest population from 274

reaching economic injury level (Carlson and Wetzstein, 1993). 275

Nature and types of econometric modeling for pesticide use in the literature 276

(Table 2) are mostly based on the underlying input demand function derived from the 277

production function of a commodity. Thus, pesticide is used in those models as an 278

input, similar to fertilizers and other productive inputs. The method of estimation of 279

the input demand function to determine factors affecting farmers’ decision to use 280

pesticides is logical if the input is productive, i.e., higher use of input also leads to 281

higher level of crop output. However, the case of pesticides is different from other 282

farm inputs, such as chemical fertilizers. Higher level of pesticide use does not always 283


13

lead to a higher yield; rather, pesticides are considered protective inputs and usually 284

are used as insurance against the expected yield loss (Lichtenberg and Zilberman, 285

1986). Pesticides will have desirable effects on yield if pest and disease infestations 286

occur; otherwise there is no meaningful use for pesticides. 287

[TABLE 2 HERE] 288

289

4. Methodology 290

4.1 Theoretical framework 291

Lichtenberg and Zilberman (1986) provided a theoretical foundation on why 292

pesticides are protective inputs and not productive inputs as commonly perceived in 293

past studies. Pesticides provide a significant contribution to crop production only if a 294

serious pest attack exists and if the pesticides use is able to control the pest attack 295

effectively. If farmers do not observe the pest attack, then it is likely that they may not 296

apply pesticides at all. In this paper, adapting the concept of economic threshold on 297

use of pesticides, as suggested by Headley (1972) and Mumford and Norton (1984), 298

we have analyzed the factors determining Indonesian chili farmers’ decisions on the 299

level of pesticides. 300

Farmers’ objectives for plant protection are not only to obtain high yield, but 301

also to minimize risk of crop failure and to ensure economic efficiency of farm inputs 302

used, including pesticide chemicals. This is explained by the concept of economic 303

threshold, for both the quantity as well as the intensity of use. The economic threshold 304

is defined as: For any level of pest attack, a maximum acceptable level of pest attack 305

for which the expected value of yield loss associated with the pest is equal to the cost 306


14

of pest control measures using pesticides. The maximum acceptable level of pest 307

attack is called the economic threshold (ET) (Headley 1972; Carlson and Wetzstein, 308

1993; Mariyono, 2007). In simple terms, it refers to the minimum level of pest 309

population below which it is not economical to take pest control measures, and so it is 310

the pest level at which the control measures should be initiated. 311

The concept of ET in plant protection is closely linked with economic 312

efficiency and rationality of the farmer’s decision on the use of farm inputs. Although 313

several concepts and methods to assess economic thresholds in IPM strategies have 314

been introduced, factor determinants of ET, and/or, factors that determine farmers’ 315

behavior on use of pesticide—a particular type of pest control measure, has not been 316

detailed studied in the literature. In this study, using the economic threshold concept, 317

we formulate an econometric model to analyze factors affecting chili farmers’ 318

decision to use pesticides and the intensity of use. 319

A graphical explanation on the basic concept of ET is provided in Figure 2. In 320

the plane of X-axis (pests, or P) and in plane of Y-axis (costs and monetary value of 321

yield loss), value of yield loss function (YL) is a monotonically increasing function of 322

P (level of pest attack); whereas, cost of pesticides use per unit of land, C, is a 323

constant and represented by a horizontal line. The monotonically increasing curve 324

shows the yield loss as a function of the level of pest attack. The horizontal line is the 325

cost of pesticide use per spray per unit of land, which is constant and independent on 326

level of pest infestation. Both curves intersect at P* called the economic threshold 327

(ET) of pest attack, i.e., the level where the value of yield loss caused by a pest equals 328

the costs of pesticide to control the pest. 329

[FIGURE 2 HERE] 330


15

P is the level of pest attack, P* is the economic threshold level, SE are socioeconomic 331

factors, YL is the value of yield losses, and C is the cost of pesticides per spray per unit 332

of land. 333

We test the following proposition: 334

Proposition: The level of pesticide use is dependent on ET value. When the ET is 335

high, the level of effective pesticides use will be low. 336

Proof: The maximum acceptable level of pest infestation represents the ET of pest 337

population. When the ET is high, it is more likely that the observable level of pest 338

infestation is lower than the ET. Consequently, the value of yield loss associated with 339

pest infestation is lower than the cost of controlling pests by applying pesticide. In 340

fact, in this case the use of pesticides may not be required at all. On the other hand, 341

when the ET level of a particular pest is low, it is more likely that the observable level 342

of pest infestation exceeds the level of ET. Consequently, the value of yield loss 343

associated with such pest infestation also would be higher than the cost of controlling 344

the pest by using chemical pesticides. This case suggests prompt and more frequent 345

use of pesticides than when the ET value is high. We formulate this relationship as 346

( )*PfPS = (1) 347

where PS is the level of pesticide use. In this case, 0* <∂∂

PPS , that is, an increase 348

in ET leads to a decrease of pesticide use. 349

The maximum acceptable level of pest attack is not static, however. This 350

depends among others on several socioeconomic and demographic characteristics of 351

the farmer, agroecological, policy, and institutional factors. The price of the produce 352


16

(which determines expected value of yield loss), and the price of pesticides are the 353

two main economic factors. Any change in these two prices will lead to a change in 354

the maximum acceptable level of P (i.e., P *=ET). Furthermore, the maximum 355

acceptable level of pest attack (P*) is a subjective notion, and in practice, it is not 356

observable to a farmer. The subjectivity comes from the fact that the level of ET (or 357

P*) depends upon farmers’ ability to correctly predict the expected value of yield loss 358

associated with a certain level of pest population (P) that one observes at any moment 359

in the field. Each farmer has a different level of prediction of yield loss because of a 360

large variation in socioeconomic characteristics (education farming experience and 361

others) across the farmers. Thus, another formulation of ET can be written, as: 362

),,,,(* ATSEPCPPPfP = (2) 363

where P is the observed level of pest attack, PP is the pesticide price, PC is the 364

expected price of the commodity at the time of harvest, SE is socioeconomic factor, 365

and AT is agronomic and technical factor. 366

When we substitute *P from equation (2) into equation (1), then we have 367

equation (3) as below. 368

),,,,( ATSEPCPPPfPS = (3) 369

Unlike equation (2), all variables in equation (3) are observable to farmers. Equation 370

(3) suggests that the level of pesticide use by farmers is a function of the observed 371

level of pest attack, the pesticide price, crop price, farmers’ socioeconomic 372

characteristics, and agronomical and technical factors of farming. 373

4.2 Model estimation 374


17

From equations 1 to 3, a pesticide use model is formulated, as in equation (4), which 375

can be estimated by a statistical analysis: 376

ε+β+β+β+β+β+β= ATSEPCPPPPS 543210 (4) 377

Where isβ are coefficients to be estimated, and ε is the error term. Socioeconomic 378

factors include the education level of farmers and chili growing experience in the past 379

(years of experience). Both factors could have a similar effect (due to learning 380

experience) on pesticide use, hence the similarity of their coefficients are also tested 381

using a restriction of EXPEREDUC β=β 382

The relative impact of each of the factors on pesticide use can be computed by 383

estimating the mean elasticity (η) of pesticide use with respect to each factor 384

determinant (FDi) as shown in equation (5). 385

PSFD

FDPS

FDPS ∂∂

=η _ (5) 386

Here, FDPS _η represents the percentage of change in pesticide use as a result of 387

percentage change in the factor determinant (FDi). The value of mean elasticity is 388

estimated at the sample average level of pesticide use and sample mean value of all 389

other factor determinants included in the model (equation 4) estimated. 390

Equation (4) could be estimated using an ordinary least squares (OLS) form of 391

regression. However, since the dependent variable is truncated at 0, and the fact that 392

some of the surveyed farmers have also not applied pesticides at all1, we estimated 393

equation (4) using the Tobit form of regression model as suggested by Greene (2003) 394

1 Farmers might have observed adverse impacts of pesticides on human health and the agroecosystem, and consequently might want to reduce their use of pesticides or not use them at all.


18

and Gujarati (1995). The relative strength of each of the factors on pesticide use is 395

estimated by computing elasticity value of pesticide use with respect to each 396

explanatory variable at their sample mean level by multiplying estimated coefficient 397

with the ratio of sample mean of the explanatory variable with the sample mean level 398

of pesticide use, as noted earlier in equation 5 earlier. 399

4.3. Data and variables 400

In March-July 2008, the field survey was conducted in Magelang, Brebes, and 401

Rembang districts, the main chili-producing districts in Central Java. Out of 160 chili-402

growing households surveyed, 49 were from Magelang, 60 from Brebes, and 51 from 403

Rembang2. We surveyed almost all of the chili growing households located in each of 404

the villages/communities. In one village, only around one-third of the households 405

were growing chili regularly, and other farmers grew paddy or other vegetables but 406

not chili. As a part of the project baseline survey, comprehensive data sets on 407

socioeconomic and agronomic characteristics of chili farming were recorded using a 408

one-to-one household survey. Definition, measurement, and summary statistics of 409

these variables used in the study are in Table 3. Detailed findings of the project study 410

and other aspects of chili farming in Central Java are in Mariyono and Bhattarai, 411

2009. 412

[TABLE 3 HERE] 413

The average value of sample means of variables across 160 observations is 414

presented in Table 3. There are large variations in these variables across the three 415

surveyed locations, but relatively much less range of variation on these variables 416 2 We surveyed a total of 222 households; the remaining 62 households were not growing chili for the last 4-5 years. They were growing paddy and other vegetables but not chili for market sale. Only information from chili-growing farmers has been used in this study.


19

within a location. All of these lead to a relatively large standard deviation for overall 417

sample mean of the variables, but smaller standard deviation value with in a location. 418

The variation of variables in Brebes was more obvious than from Magelang and 419

Rembang. Thus, we have included a separate dummy variable for Brebes in the model 420

for specific farming characteristics such as varieties, methods of application, and 421

location. In the case of variable “Variety,” 43% of surveyed farmers grew hybrid 422

chili, and others grew improved open pollinated-high yielding variety seed types and 423

improved local seed types. Likewise, 37% of surveyed farmers applied pesticide 424

“cocktails,” and 38% of farmers surveyed were from Brebes district. In general, 425

farmers in Brebes apply pesticides more frequently than farmers in the other survey 426

sites (Mariyono and Bhattarai, 2009). Among various factors, aggressive promotion 427

of pesticides by local agribusiness dealers in Brebes is also one of the reasons for 428

more use of chemical pesticides in Brebes. 429

5. Results and discussions 430

5.1. Level of pesticide use 431

A summary on the level of pesticide use by methods of application is in Table 432

4. Farmers applied pesticides by both single and mixed method (“cocktail”). All other 433

things remaining constant, on average, the quantity of pesticides applied by the mixed 434

method was four times higher than that by the single method. Quantity of pesticide 435

applied on chili largely varied across the three locations surveyed. It was highest in 436

Brebes (22 kg/ha/crop season), 7.2 kg/ha/crop season in Magelang, and 5.3 kg/ha/crop 437

season in Rembang. Thus, Brebes was markedly different from Magelang and 438

Rembang in terms of intensity of pesticide uses. 439


20

The level of pesticide use by varieties of chili is in Table 5. All other things 440

being equal, pesticide quantities applied were highest for local varieties and lowest 441

was for hybrid varieties. Mostly local varieties of chili were grown in Brebes, where 442

higher quantities of pesticides were applied, in cocktail form. Brebes is known for its 443

heavy pesticide use in vegetable production; massive promotions and advertising 444

campaigns for pesticides are done in this area by pesticide companies and input 445

dealers. 446

[TABLE 4 HERE] 447

[TABLE 5 HERE] 448

5.2. Factor determinants 449

The results from the regression analysis of equation (4) explaining the factor 450

impacts on level of pesticide use are reported in Table 6.3 Both models estimated are 451

significant at 5% level, as shown by high 2χ for log-ratio. All coefficients from both 452

models estimated are significant at 5% of the significant level, except price of 453

pesticides and education that are significant at higher level (10%). As shown by the R 454

square value, 61% of variation in pesticide use can be explained by the models 455

estimated here. 456

[TABLE 6 HERE] 457

Based on the results from Model 1 and 2, we can infer that level of pesticide 458

use would increase when farmers observe more numbers of insect pests in the field. 459

This is reasonable because timely control of pests can minimize the expected yield 460

3 Model 2 is obtained by restricting the assumption that education and experience in chili farming have the same effect in terms of sign and magnitude, and a new variable “Education + Experience” is created by linear summation of the individual variables.


21

loss allowing timely recovery of crop plants. On the other hand, study finding 461

suggests that farmers would reduce level of pesticide use when they observe a greater 462

number of diseases in the field. This appears to be a contradictory to our priori belief 463

and general understanding on the subject, but practically, this result is in fact 464

consistent with farmers’ rational behavior and practical aspects of disease 465

management practices as we have observed in the surveyed sites. Plants severely 466

infected by diseases (or infested by many diseases) are more likely to die without any 467

economic yield, so in practice, farmers would not apply chemical pesticides if the 468

crop is already severely infested by disease. In reality, farmers use pesticides when 469

the level of diseases infestation is at low level, and our empirical result supports this 470

fact (Table 6). Thus, our results suggest that farmers take curative decisions for pest 471

management and preventive decisions for disease management. 472

In models 1 and 2, the sign of pesticide price is negative, which suggests that 473

increases in the price of pesticides would lead to reductions in pesticide use by chili 474

farmers. The sign of chili price, as expected, is positive, which means any increase in 475

price of chili would lead to an increase in intensity of pesticide use. Both results are 476

logical and consistent with the rational behavior of farmers in terms of maximizing 477

profit from crop production by minimizing expected losses due to crop damage by 478

insect pests and diseases. 479

The age of the household head has a positive effect on pesticide use. Older 480

farmers use more pesticides than younger farmers, all things being equal. Because 481

they often lack education and information, older farmers might have less accurate 482

prediction skills to determine the economic threshold level of pests, so they use 483

control measures that are more preventive in nature than that of younger farmers. In 484


22

contrast, the variables of years of experience 4in chili farming and education have 485

negative impact on level of pesticide use. This means that an educated farmer with 486

more experience in growing chili would likely to use less quantity of pesticide than 487

those with lower levels of education and chili-growing experience. Education and 488

chili cultivation experience enable farmers to better predict economic threshold levels, 489

and nature and type of damages likely to be caused by the pest attacks, so that the 490

farmer is less dependent upon preventive measures for pest management. Education 491

and experience might also improve understanding of pest biology, and effectiveness 492

of pesticides on the pest, and also its consequences to the local environment, thus 493

optimizing the use of pesticides. Our results indicate strong positive impacts of 494

improving human capital to enhance efficiency and productivity of chili farming in 495

Indonesia. The results are consistent with the human capital-lead farming sector 496

improvement hypothesis forwarded by several development economists such as 497

Schultz (1964, 1975). This can be achieved either through overall improvement in 498

rural education or crop-specific training to the farmers. 499

Frequency of spray has positive impact on the level of pesticide use. This is an 500

expected result, which suggests that with more frequent spraying, a higher quantity of 501

pesticide is applied. When farmers grow chili on a large area, they apply less quantity 502

of pesticide per unit of crop acreage. This finding is different from that of Rahman 503

(2003) in Bangladesh, who reported that larger farms (and with more crop acreage) in 504

Bangladesh used higher levels of pesticides on vegetables (per unit of land) than 505

small-scale farms. In Indonesia, the scale effect was opposite. Small or medium-size 506

farms operate more intensively than large chili farm, and so a higher level of 507 4 Commonly, age represents experience. However, because farmers take up farming at different ages, age does not necessarily mean more experience in chili farming.


23

pesticides per unit of crop acreage is used by smaller or medium scale of chili 508

farmers. 509

Non-hybrid varieties and the cocktail method of pesticide application have 510

positive effects on level of pesticide use. Non-hybrid varieties and the cocktail 511

method are also strongly related to the locations (or regions) where chili is grown. 512

Many farmers in Brebes region grow local open-pollinated or non-hybrid varieties 513

and apply cocktail spray. The results suggest that farmers who grew open-pollinated 514

and local varieties of chili used more pesticides than those who grew hybrids, which 515

suggests that open pollinated and local chili types need more protection from pests 516

and diseases, all else being equal. The results also suggest that farmers using the 517

cocktail method apply more pesticides than those using the single method. In reality, 518

when farmers observe higher numbers of insect pests and diseases, they are also likely 519

to use a cocktail of various insecticides and fungicides so that the range of pests and 520

diseases would be controlled by single spray. This is also to save labor costs for 521

spraying. As seen in practice, farmers who apply a single pesticide are also more 522

likely to target for one to two pests and would use the appropriate pesticide and dose. 523

The variable “location” is positive and significant, meaning that farmers in Brebes 524

used more pesticides than those in the other two regions. Pesticide application 525

practice in Brebes is deeply rooted in the local farming practices, customs, pace of 526

farm intensification, and historical trends. Because it is located in a peri-urban zone 527

and closer to major market centers, pesticide companies are also more widely and 528

aggressively promoting the use of agrochemicals and pesticides in Brebes than in 529

Magelang and Rembang. Brebes is closer to large vegetable markets in the major 530


24

urban areas of Bandung and Jakarta. All of these factors lead to more intensive, high-531

input vegetable cultivation practices in Brebes. 532

5.3. Relative strength of the factors: elasticity measures 533

We analyze practical policy implications of the results derived in Table 6 by 534

estimating elasticity values of each of the variables at the average of the sample mean. 535

This was done taking the average sample mean value of both dependent and 536

independent variables, as reported in Table 3; and the elasticity 5 measures are 537

reported in Table 7. There are no major differences in sign and value of elasticity 538

measured from the two models; however, to save space, the discussion below is 539

largely based on the results from Model 2. 540

The level of pesticide use is elastic with respect to number of observed pests 541

and diseases. The elasticity value of number of pests and diseases that farmers 542

observe in the field was higher than that of other variables, which suggests higher 543

positive impacts of farmer training and knowledge enhancement activities on properly 544

identifying nature of pests and diseases attack help in proper use of pesticides. But 545

they use less pesticide when disease infection is widespread, as many of them may 546

feel that it is already too late to spray. This finding is contradictory to our expectation, 547

but a well-observed empirical fact in chili farming, and consistent with the nature of 548

average farmers’ risk-averse behavior in Indonesia and in tropical Asia, in general 549

It could be the case that farmers observed wrong objects (between insect-pests 550

and diseases), then they may confuse in responding to those by spraying inappropriate 551

type and level of pesticides. With better understanding and knowledge in pests and 552 5 The elasticity value gives information on unit free measures of the marginal impacts so we can compare the relative marginal effect of each of the variables on relative change of pesticide use, irrespective of the unit of the factors.


25

diseases, farmers would be able to distinguish harmful insect pests from that of other 553

insects, and also better identify the damage on the crop with the corresponding type of 554

insect pest or other pathogens. Not all insects are harmful (Settle et al., 1996); 555

therefore, to minimize the level of pesticide use in chili farming, it is more effective to 556

focus activities on proper observation of pests and diseases, and to enhance farmers’ 557

capacity for proper identification of pest- and disease-related damage. This can be 558

done through training and more crop-specific extension efforts. 559

As expected, the relative impact of the price of chili on pesticide use was 560

positive but in the inelastic range (0.76). This means increase on chili price has 561

positive impact on the level of pesticides used by an average chili farmer, but the 562

degree of impacts is less. It is logical that when the price of chili is high, it is more 563

profitable to the farmer to protect chili from being lost due to pests and diseases even 564

by incurring some additional cost for pesticides. Likewise, relative impact of 565

education, chili-farming experience, and area cultivated to chili on level of pesticides 566

use are negative but moderate in absolute value, with elasticity values for around -567

0.3% to -0.5%. That is, a 1% increase in each of factor would lead to about 0.3-0.5% 568

decrease on the level of pesticide use in chili. As a farmer becomes more experienced 569

in chili cultivation, or more educated, the farmer is expected to reduce the level of 570

pesticide use. Our results also suggest that there is a moderate scale effect on pesticide 571

use. That is, the intensity of pesticide use is less on farms with larger chili acreage. 572

Surprisingly, among the variables selected, the level of pesticide use is most 573

inelastic with respect to price of pesticides. The relative impact of pesticide price was 574

very low in reducing pesticide use. A 10% increase in price of pesticide can only 575

reduce the use of pesticides by 1.2 % estimated at the sample mean level. In other 576


26

words, any environmental tax (pollution tax) on pesticides use may not be an effective 577

policy instrument (tool) in reducing pesticide use in vegetable farming in Indonesia. 578

In fact, same argument may also be extended to pesticides use behavior of 579

smallholder farmers in the tropical Asia, as such. This finding has important 580

implications in designing targeted policies and regulations in restricting excessive use 581

of harmful pesticides in the tropical farming. 582

583

6. Conclusions and policy implications 584

Increasing use of pesticides on vegetables is a growing environmental problem 585

and food safety threat in Indonesia and in several other developing countries where 586

vegetable farming is becoming more intensive and a widespread. A better 587

understanding of farmers’ behavior and factors that affect their decision to use (or 588

abandon) pesticides use, and its level and intensity of uses, on chili (and other 589

vegetables) would provide very valuable information to design effective policy 590

instruments in modifying farmers’ behavior towards reducing pesticide use, and 591

enhancing overall vegetable productivity, and income from the vegetable system. 592

This information is also critically important in enhancing sustainability of chili pepper 593

and other high value vegetable production system in the developing countries. The 594

level of pesticide uses, as well as costs for chemical pesticide on high value 595

vegetables (such as chili and tomato) are substantially higher than that of production 596

of cereal and staple crops. Besides, in Indonesia, the prices of many pesticides in 597

2008-09 increased more than two fold than the price level in 2007, largely due to 598

increased fuel prices. The same trend is there in many other countries in tropical Asia. 599


27

Loss of productivity of chili and other vegetables due to pests and diseases is 600

very high in Indonesia and in many other countries in tropical Asia, where vegetables 601

are intensively cultivated. Pesticide use is thus a core part of chili farming by 602

smallholding farmers, as they seek to mitigate risk of crop failure due to pest and 603

disease by spraying chemical pesticides at regular interval, as a protective measure. 604

Thus pesticide spray decision is like an insurance against crops failure. Despite 605

several attempts of national and local government agencies to curb excessive 606

pesticides use on farming, the use of pesticides is on the increasing trend in Indonesia, 607

and in other countries in tropical Asia, and so in many of the vegetable producing 608

countries in Africa (Kenya, Ghana, etc). In addition, pesticide misuse is widespread in 609

tropical vegetable farming, thus leading to adverse impacts on farm livelihoods, 610

eroding natural enemies and beneficial insect population in agriculture, contamination 611

to groundwater, disruption to local agro-ecosystems, including adverse effects on 612

community health and environment, in general. Assessment on these intersectoral 613

effects of pesticides on overall farming systems, and/or, interaction of pesticides use 614

on a single crop to another subsequent crops are important issues, but analysis of such 615

temporal behavior of pest and residual effects are outside of scope of this paper. 616

Here, we have evaluated relative impacts of selected socioeconomic factors 617

and farmers’ characteristics that directly affect farmers’ decision to use pesticides. 618

This was done using the concept of economic threshold (ET) of pest-related damage 619

and by estimating econometric models. Our empirical results suggest that chili 620

farmers in Indonesia use more pesticides when they observe more insect pests in the 621

field, but this is not the case on level of disease infestation. This means that not only 622

level of pest and disease infestation, but the nature of infestation also equally 623


28

influences farmers’ decision to apply pesticides, and so the level and intensity 624

(frequency) of pesticide uses at a moment. 625

By and large, older farmers were less tolerant of pests and diseases on their 626

chili plot, and they used higher level of pesticide use compared to young farmers, but 627

farmers with higher education level and more experience in chili farming used less 628

pesticide than their counterpart farmers. Farmers using hybrid varieties of chili and 629

applying only one type of pesticide also sprayed fewer times than their counter part 630

farmers who used “cocktail” mixtures of pesticides. Hybrid varieties were more 631

resistant or tolerant to pests and diseases than that of open pollinated varieties. 632

Likewise, farmers applied pesticides more cautiously when applying a single pesticide 633

for a targeted pest than that of using a cocktail method of spray. Training farmers on 634

proper use and application of pesticides will go a long way towards improving their 635

capacity and skill in selecting the most suitable pesticide types, applying the 636

appropriate dose, and at the appropriate time. 637

Based on our results from the elasticity measures, we can conclude that the 638

most effective ways to minimize the level of pesticide use on chili farming in 639

Indonesia are to focus efforts on enhancing farmers’ capacity to observe and 640

accurately diagnose pest- and disease-related symptoms, the nature of damage to 641

crops in the field, and enhance farmers knowledgebase on complex agroecological 642

factors affecting the level of pest infestation at any moment of time. This can be done 643

through proper farmer training and more crop-specific extension efforts. Our results 644

also suggest that price elasticity of pesticide use is very low, this means that 645

environmental tax policies on pesticide use may not be an effective policy instrument 646

in reducing the level of harmful pesticides use in chili farming in Indonesia. 647


29

Information from our analysis can be useful in Indonesia as well as in other 648

developing countries in tropical Asia and Africa for better management of pesticide 649

use in vegetable farming and other high-value crop production activities. The simple 650

form of econometric models used in this paper, and consideration of pesticides as a 651

protective input rather than productive inputs, can be also applied for modeling of 652

farmers’ behavior on use of pesticides on other crops and other locations in the 653

tropical farming system. 654

655

Acknowledgement 656

We thank the farmers who generously gave their time to provide information on crop 657 production practices, and five research assistants and enumerators at Balai Pengkajian 658 Teknologi Pertanian (BPTP)/Assessment Institute for Agricultural Technology 659 (AIAT) Central Java, Indonesia, in collecting the farm survey data. We acknowledge 660 the valuable support of Ms. S. Sisca Piay and Mr. Sutoyo from BPTP-Ungaran, 661 Central Java, Indonesia, and the support of Dr. Paul Gniffke, chili pepper breeder and 662 Dr. Anna Dibiyantoro of AVRDC – The World Vegetable Center, in Taiwan, and the 663 late Mieke Ameriana, economist with the Indonesian Vegetable Research Institution 664 (IVegRI), in Indonesia. We gratefully acknowledge Mike Hammig, professor 665 emeritus (applied economics) of Clemson University, USA, Krishna Paudel, associate 666 professor (agricultural economics) of Louisiana State University, USA, and three 667 reviewers at AVRDC – The World Vegetable Center for their valuable comments and 668 suggestions. We are also grateful to Maureen Mecozzi of AVRDC for editing the 669 paper. Any shortcomings and errors are the responsibility of the authors. We 670 acknowledge the Australian Centre for International Agricultural Research (ACIAR) 671 for the grant support to AVRDC (CP/2004/048) for the project activities in Indonesia. 672


30

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Table 1. Chili area and production in the five top districts of Central Java, 2007 Area Production (t)

District hectare Share (%) t Share (%) Yield (t/ha) Brebes 4302 18 34290 30 7.97 Magelang 4062 17 27425 24 6.75 Rembang 3110 13 5728 5 1.84 Temanggung 2817 12 7505 7 2.66 Wonosobo 2432 10 10072 9 4.14 Central Java (Total) 24,400 100% 114797 100% 4.71

Source: Government statistics (BPS, 2008)


36

Table 2. Selected determinants of pesticide use reported in past studies Author(s) (year) Country Commodity Pesticide use Model Determinant of pesticide use Ali and Hau (2001) Bangladesh Vegetables 3-50 (kg/ha) Direct comparison Modern technology, different crops

Horna et al. (2008) Ghana Vegetables 2.4-6.3 (l/ha) Probit analysis and regression

Education, crop experience, and training in the use of insecticide.

Jeyanthi and Kombairaju (2005) India Vegetables 2.57–5.13 (g ai/ha) Direct comparison Kind of vegetables

Mariyono (2007) Indonesia Soybean 1-1.5 (kg/ha) Economic threshold Prices of pesticides, other inputs, and product; IPM training, pest attack

Mariyono (2008b) Indonesia Soybean 1-1.5 (kg/ha) Simultaneous demand function

Prices of pesticides, other inputs, and product; IPM knowledge , pest attack

Rahman (2003) Bangladesh Various 3-5 times Demand function Farm size ,land ownership, and credit

Selvarajah and Thiruchelvam (2007) Sri Langka Rice and

chili Rice: 1.9 (kg/ha) Chili: 11.5(kg/ha)

Direct comparison Different crops

Sibanda et al. (2000) Zimbabwe Vegetables 57-1000 (g a.i./ha) Direct comparison Tank concentration and volume application rates Wilson and Tisdell (2001)

Asia and Africa

General agriculture N/A Model simulation Subjective discount factors


37

Table 3. Definition and measurement, and its summary statistics of selected variables

Variables Definition Units Mean SD Pesticide use1 Quantity of formulation of pesticide used per ha g/ha 12112 16443 Number of insect pests Number of kinds of insect pests perceived/observed by farmers in chili field number 6.37 2.38 Number of diseases Number of kinds of diseases perceived/observed by farmers in chili field number 4.86 2.04 Chili price Market price of chili received by farmers IDR/kg 6103 2140 Price of pesticides2 Average price of composite pesticide used by farmers IDR/g 288 275 Age of household head Number of years of life year 43.36 10.70 Education level Number of years spent in formal education year 7.87 2.82 Chili growing experience Number of years of chili growing experience year 11.93 10.19 Frequency Number of sprays per crop growing season times 20.16 10.79 Area Area planted to chili in 2007/08 (survey period) m2 2502 1287

Variety Type of chili varieties grown by farmers (dummy variable) 1 = non-hybrid (local and open pollinated high yielding varieties (OPHYV) 0 = hybrid varieties

dummy 0.57 0.50

Methods (Cocktail)

Method of pesticide application (dummy variables) 1= cocktail (mix of several kind of pesticides (insecticides and fungicides) 0 = single pesticide (either insecticide or fungicide)

dummy 0.37 0.48

Location 3 1 = Brebes (locations with a more pesticide use); 0 = Magelang and Rembang, which almost similar level of pesticide use

dummy 0.38 0.49

Note: 1) 1 ml of liquid pesticide is assumed to correspond to 1g, and pesticides consist of both insecticides and fungicides; 2) price of pesticides at farmers’ level = total cost of pesticides/total quantity of pesticides used; SD stands for standard deviation. The standard deviation of pesticide use is high because of high variation across sites. Some farmers applied no pesticide at all. 1 US$=10,000 IDR (Indonesian Rupiah) Source: primary survey done by authors in Central Java, Indonesia in 2008


38

Table 4. Quantity of pesticide use by application methods and by location

Quantity of pesticides (kg/ha)

Particulars Magelang

(N=49) Brebes (N=60)

Rembang (N=51)

All sites (N=160)

N Mean N Mean N Mean N Mean Single 21 6.60 29 6.06 51 5.26 101 5.77

Cocktail 28 7.65 31 3.68 M,S 0 . 59 22.97 S

Overall 49 7.20 60 21.94 MR 51 5.26 160 12.11

Note: It is assumed that a liter of liquid pesticides is equal to a kg. Significant different of mean across sites is indicated by superscript M, B and R, where M = Magelang, B = Brebes, and R = Rembang. Significant different of mean between single and cocktail is indicated by superscript S and C. Mean comparison is tested at 95 % of confidence interval.


39

Table 5. Quantity of pesticide use, by types of variety

Quantity of pesticides (kg/ha)

Particulars Magelang

(N=49) Brebes (N=60) Rembang (N=51)

All sites (N=160)

N Mean N Mean N Mean N Mean Hybrid 49 7.20 0 20 5.33 69 6.66

OP 0 . 38 10.00 0 . 38 10.00

Local 0 . 22 42.58 R,L 31 5.22 53 20.72 HO

Note: The data are compiled based on assumption that a liter of liquid pesticides is equivalent to a kg. Significant difference of the mean value across sites are indicated by superscript M, B and R, where M = Magelang, B = Brebes, and R = Rembang. Significant difference of mean across varieties is indicated by superscript H, O and L. Mean comparison is tested at 95 % of confidence interval.


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Table 6. Factors affecting pesticide use Model 1 Model 2

Variables Coefficient t-value Coefficient t-value Constant -11624.8 -1.64a -13535.3 -2.34a

Number of pests 9185.5 4.33a 9046.4 4.30a

Number of diseases -12153.8 -4.94a -11970.8 -4.93a

Price of pesticides -5.14 -1.57b -5.04 -1.54b

Price of chili 1.51 3.49a 1.50 3.45a

Age of household head 256.7 2.60a 279.9 3.27a

Education -495.3 -1.42n -- -- Chili experience -313.5 -2.54a -- -- Education + experience -- -- -340 -3.09a

Frequency 255.5 2.91a 253.0 2.88a

Chili-sown area -1.54 -2.22a -1.54 -2.22a

Variety of chili 6189.1 2.34a 6214.7 2.35a

Method of application 11826.3 5.58a 11723.2 5.56a

Location 9868.7 2.91a 10404.8 3.25a

Ftest for ercheduc exp_β=β 0.22n

2χ for log-ratio 145.06a 144.84a

Adj. R2 (in OLS) 0.61 0.61Note: Dependent variable is pesticide use per hectare; a: significant at 5% level; b: significant at 10%; n = not significant at 10% level. These models were estimated as equation 4.


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Table 7. Elasticity value of pesticide use with respect to each determinant

Mean Elasticity Independent variables Model 1 Model 2 Number of pests 4.8298 4.7568 Number of diseases -4.8728 -4.7996 Price of pesticides -0.1224 -0.1200 Price of chili 0.7608 0.7558 Age of household head 0.9190 1.0021 Education -0.3218 -- Chili experience -0.3088 -- Education + experience -- -0.5558 Frequency 0.4252 0.4210 Chili-sown area -0.3182 -0.3182

Note: These elasticity values are estimated at sample mean level, as illustrated in equation 5.


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0102030405060708090

100

1990 1995 2000 2005Year

Value

(milli

on U

S$)

Source: FAOSTAT, 2010

Figure 1. Trend on value of pesticide use in Indonesia (in US$).


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Figure 2. Yield loss function and Economic Threshold level

P

YL C

YL=f(P, SE )

C

P* Level of pest attack


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pesticide level chili Indonesia agricultural systems

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