**7. Importance of thermal modeling in successful implementation of integrated management of** *H. armigera*

**Stage Temperature Lower temperature threshold**

Cumulative degree-day (DD) required for stage development

temperatures, survivorship also decreased very quickly above 28°

**Stage Temperature Lower temperature**

Mironidis and Savopoulou-Soultani [83]).

cle over a much wider range, 10-35°

stant thermal range (15-27.5°

perature. Below 15°

17.5-32.5°

*a*

Egg Constant 11.95 39.68

Larva Constant 10.52 238.09

Pupa Constant 10.17 192.30

Total immature stages Constant 9.57 476.19

**Table 3.** Lower temperature threshold and thermal constant of different life stages of *Helicoverpa armigera* (after

The results obtained by Mironidis and Savopoulou-Soultani [83] revealed that over a wide con‐

thermore, their results showed that *H. armigera,* when reared at constant temperatures, could not develop from egg to adult stage (capable of egg production) out of the temperature range of

> **threshold** (*Tmin***°C**)

Egg Constant 10.58 34.84 39.99

Larva Constant 11.17 34.22 39.11

Pupa Constant 12.31 35.37 40.00

Total immature stages Constant 9.42 34.61 39.81

**Table 4.** Lower temperature threshold, optimal temperature and upper temperature threshold of different life stages of *Helicoverpa armigera* obtained by nonlinear Lactin model (after Mironidis and Savopoulou-Soultani [83]).

C, survivorship decreased rapidly, reached zero at 12.5°

C. Nevertheless, alternating temperatures allowed *H. armigera* to complete its life cy‐

C, compared with constant temperatures.

Alternating 2.33 39.26 40.56

Alternating 1.55 39.35 40.95

Alternating 1.01 41.92 43.54

Alternating 1.85 42.35 42.92

(*Tmin***°C**)

Integrated Management of *Helicoverpa armigera* in Soybean Cropping Systems

Alternating 5.53 57.47

Alternating 2.17 416.16

Alternating 1.06 285.71

Alternating 2.23 769.23

C) total survivorship is stable and apparently not affected by tem‐

**Optimal temperature** (*Topt***°C**)

**Thermal constant a (***K* **DD)**

243

http://dx.doi.org/10.5772/54522

C. At higher

**Upper temperature threshold** (*Tmax***°C**)

C. Fur‐

C and fell to zero at 40°

For decades, models have been an integral part of IPM. For instance, the use of models has helped pest managers decide how the agroecosystem should be changed to favor economy and conservation and not to favor pests. Moreover, models have allowed scien‐ tists to conduct simulated experiments when the conduct of those experiments would not have been possible. Furthermore, models have been used whenever scientists wanted to explore as well as understand the complexities of agroecosystems [26, 73, 74]. Howev‐ er, among the different types of models developed for implication of IPM programmes, forecasting models (especially thermal models) have a highlighted situation. Understand‐ ing the factors governing the pest development and implementing this knowledge into forecast models enable effective timing of interventions and increases efficacy and suc‐ cess of control measures [74]. For a pest manager, being able to predict abundance and distribution of a pest species, and its timing and level, is crucial to both strategic plan‐ ning and tactical decision making. Thermal models, based on insect physiological time‐ scales, have been relatively successful at predicting the timing of population peaks and are useful for timing sampling and control measures.

Temperature is a critical abiotic factor influencing the dynamics of insect pests and their natural enemies [75-77]. Temperature has a direct influence on the key life processes of survivorship, development, reproduction, and movement of poikilotherms and hence their population dynamics [78]. The importance of predicting the seasonal occurrence of insects has led to the formulation of many mathematical models that describe develop‐ mental rates as a function of temperature [79]. Thermal models have been developed for insect pests to predict emergence of adults from the overwintering generation, eclosion of eggs, larval and pupal development, and generation time. These models, all based on a linear relationship between temperature and developmental rate, have been used with varying degrees of success to time pesticide application for pest control [80]. However, linear approximation enables the estimation of lower temperature thresholds (*Tmin*) and thermal constants (*K*) within a limited temperature range and to describe the develop‐ mental rate more realistically and over a wider temperature range, several nonlinear models have been applied [74, 76]. These nonlinear models provide value estimates of lower and upper temperature thresholds and optimal temperature for development of a given stage.

Several studies have been conducted on the effects of temperature on developmental time of *H. armigera* reared on host plant materials or artificial diets [81, 82]. In a recent study by Mir‐ onidis and Savopoulou-Soultani [83], a comprehensive analysis of survivorship and devel‐ opment rates at all life stages of *H. armigera* reared under constant and corresponding alternating temperatures regimes was performed (some of the most important results are listed in Tables 3 and 4).


**7. Importance of thermal modeling in successful implementation of**

For decades, models have been an integral part of IPM. For instance, the use of models has helped pest managers decide how the agroecosystem should be changed to favor economy and conservation and not to favor pests. Moreover, models have allowed scien‐ tists to conduct simulated experiments when the conduct of those experiments would not have been possible. Furthermore, models have been used whenever scientists wanted to explore as well as understand the complexities of agroecosystems [26, 73, 74]. Howev‐ er, among the different types of models developed for implication of IPM programmes, forecasting models (especially thermal models) have a highlighted situation. Understand‐ ing the factors governing the pest development and implementing this knowledge into forecast models enable effective timing of interventions and increases efficacy and suc‐ cess of control measures [74]. For a pest manager, being able to predict abundance and distribution of a pest species, and its timing and level, is crucial to both strategic plan‐ ning and tactical decision making. Thermal models, based on insect physiological time‐ scales, have been relatively successful at predicting the timing of population peaks and

Temperature is a critical abiotic factor influencing the dynamics of insect pests and their natural enemies [75-77]. Temperature has a direct influence on the key life processes of survivorship, development, reproduction, and movement of poikilotherms and hence their population dynamics [78]. The importance of predicting the seasonal occurrence of insects has led to the formulation of many mathematical models that describe develop‐ mental rates as a function of temperature [79]. Thermal models have been developed for insect pests to predict emergence of adults from the overwintering generation, eclosion of eggs, larval and pupal development, and generation time. These models, all based on a linear relationship between temperature and developmental rate, have been used with varying degrees of success to time pesticide application for pest control [80]. However, linear approximation enables the estimation of lower temperature thresholds (*Tmin*) and thermal constants (*K*) within a limited temperature range and to describe the develop‐ mental rate more realistically and over a wider temperature range, several nonlinear models have been applied [74, 76]. These nonlinear models provide value estimates of lower and upper temperature thresholds and optimal temperature for development of a

Several studies have been conducted on the effects of temperature on developmental time of *H. armigera* reared on host plant materials or artificial diets [81, 82]. In a recent study by Mir‐ onidis and Savopoulou-Soultani [83], a comprehensive analysis of survivorship and devel‐ opment rates at all life stages of *H. armigera* reared under constant and corresponding alternating temperatures regimes was performed (some of the most important results are

**integrated management of** *H. armigera*

242 Soybean - Pest Resistance

are useful for timing sampling and control measures.

given stage.

listed in Tables 3 and 4).

**Table 3.** Lower temperature threshold and thermal constant of different life stages of *Helicoverpa armigera* (after Mironidis and Savopoulou-Soultani [83]).

The results obtained by Mironidis and Savopoulou-Soultani [83] revealed that over a wide con‐ stant thermal range (15-27.5° C) total survivorship is stable and apparently not affected by tem‐ perature. Below 15° C, survivorship decreased rapidly, reached zero at 12.5° C. At higher temperatures, survivorship also decreased very quickly above 28° C and fell to zero at 40° C. Fur‐ thermore, their results showed that *H. armigera,* when reared at constant temperatures, could not develop from egg to adult stage (capable of egg production) out of the temperature range of 17.5-32.5° C. Nevertheless, alternating temperatures allowed *H. armigera* to complete its life cy‐ cle over a much wider range, 10-35° C, compared with constant temperatures.


**Table 4.** Lower temperature threshold, optimal temperature and upper temperature threshold of different life stages of *Helicoverpa armigera* obtained by nonlinear Lactin model (after Mironidis and Savopoulou-Soultani [83]).

In another study, temperature-dependent development of *H. armigera* was studied in the laboratory conditions at eight constant temperatures (15, 17.5, 20, 22.5, 25, 30, 32.5 and 35° C) [Adigozali and Fathipour, unpublished data]. In this study, two linear (Ordinary linear and Ikemoto and Takai) and 9 nonlinear (Briere-1, Briere-2, Lactin-1, Lactin-2, Pol‐ ynomial, Kontodimas-16, Analytis-1, Analytis-2 and Analytis-3) models were fitted to de‐ scribe development rate of *H. armigera* as a function of temperature. The lower temperature threshold and thermal constant of different life stages of *H. armigera* estimat‐ ed by linear models are listed in Table 5. The obtained results revealed that both models have acceptable accuracy in prediction of *Tmin* and *K* for different life stages of *H. armi‐ gera* [Adigozali and Fathipour, unpublished data].

ments are often not applicable directly to the field where pests are subjected to diurnal variation of temperature and such information need to be validated under fluctuating tem‐ peratures before using for predictive purpose in the field. Finally, such data provide funda‐ mental information describing development of *H. armigera*, when this information to be used in association with other ecological data may be valuable in integrated management of

Integrated Management of *Helicoverpa armigera* in Soybean Cropping Systems

(*Topt***°C**)

**Table 6.** Lower temperature threshold, optimal temperature and upper temperature threshold of different life stages

Historically pest management on many crops has relied largely on synthetic pesticides and in intensive cropping systems, pesticides are main components of pest management pro‐ grammes that represents a significant part of production costs [84]. However, chemical con‐ trol is still the most reliable and economic way of protecting crops from pests. Beside, over reliance on chemical pesticides without regarding to complexities of the agroecosystem is not sustainable and has resulted in many problems like environment pollution, secondary pest outbreak, pest resurgence, pest resistance to pesticides and hazardous to human health. Furthermore, over dependence on chemical pesticides has also resulted in increased plant

Insecticide treatments, whether or not included in IPM programmes, are currently indis‐ pensable for the control of *H. armigera* in almost all cropping systems around the world [85], so, this pest species has been subjected to heavy selection pressure. Some of the synthetic insecticides currently used for controlling this pest are indoxacarb, methoxyfe‐ nozide, emamectin benzoate, novaluron, chlorfenapyr, imidacloprid, fluvalinate, endosul‐ fan, spinosad, abamectin, deltamethrin, cypermethrin, lambda-cyhalothrin, carbaryl, methomyl, profenofos, thiodicarb and chlorpyrifos [21, 85-87]. Because of indiscriminate use of these chemicals to minimize the damage caused by *H. armigera*, however, it has developed high levels of resistance to conventional insecticides such as synthetic pyreth‐

Egg Lactin-2 33.00 41.98 Larvae Lactin-2 34.50 35.38 Pre-pupa Polynimial 29.00 - Pupa Polynimial 32.50 - Total immature stages Briere-2 34.00 35.00

**8. Strategies for integrated management of** *H. armigera*

**Upper temperature threshold** (*Tmax***°C**)

http://dx.doi.org/10.5772/54522

245

this noctuid pest in soybean cropping systems.

of *Helicoverpa armigera* obtained by nonlinear models.

protection, thus leading to high cost of production.

roids, organophosphates and carbamates [88].

**8.1. Chemical control**

**Stage Model Optimal temperature**


**Table 5.** Lower temperature threshold and thermal constant of different life stages of *Helicoverpa armigera* estimated by Ordinary linear and Ikemoto and Takai models.

According to results obtained by Adigozali and Fathipour [unpublished data], of the nonlin‐ ear models fitted, the Lactin-2, Lactin-2, Polynomial, Polynomial and Briere-2 models were found to be the best for modeling development rate of egg, larva, pre-pupa, pupa and total immature stages of *H. armigera*, respectively (Table 6). However, estimated values for crucial temperatures of different life stages of *H. armigera* by Adigozali and Fathipour [unpublished data] conflict with those reported by Mironidis and Savopoulou-Soultani [83] (Tables 3-6). Some possible reasons for these disagreements are: physiological difference depending on the food quality, genetic difference as a result of laboratory rearing and techniques/equip‐ ment of the experiments. In general, the results obtained from constant temperature experi‐ ments are often not applicable directly to the field where pests are subjected to diurnal variation of temperature and such information need to be validated under fluctuating tem‐ peratures before using for predictive purpose in the field. Finally, such data provide funda‐ mental information describing development of *H. armigera*, when this information to be used in association with other ecological data may be valuable in integrated management of this noctuid pest in soybean cropping systems.


**Table 6.** Lower temperature threshold, optimal temperature and upper temperature threshold of different life stages of *Helicoverpa armigera* obtained by nonlinear models.
