Forecasting of Lipaphis erysimi on oilseed Brassicas in India—a case study

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Crop Protection 24 (2005) 1042–1053 www.elsevier.com/locate/cropro

Forecasting of Lipaphis erysimi on oilseed Brassicas in India—a case study C. Chattopadhyaya,, R. Agrawalb, Amrender Kumarb, Y.P. Singha, S.K. Royc, S.A. Khand, L.M. Bharb, N.V.K. Chakravarthye, A. Srivastavaf, B.S. Patelg, B. Srivastavah, C.P. Singhi, S.C. Mehtab a

National Research Centre on Rapeseed-Mustard (ICAR), Sewar, Bharatpur 321303, India b Indian Agricultural Statistics Research Institute (ICAR), New Delhi 110012, India c Pulses and Oilseeds Research Station, Ranibagan, Berhampur 742101, WB, India d Bidhan Chandra Krishi Viswavidyalaya, Mohanpur 741252, WB, India e Indian Agricultural Research Institute (ICAR), New Delhi 110012, India f Oilseeds Research Station, C.S.K. Himachal Pradesh Krishi Viswavidyalaya, Kangra 176001, HP, India g Gujarat Agricultural University, S.K. Nagar 385506, Gujarat, India h Agricultural Research Station, Rajasthan Agricultural University, Sriganganagar 335001, Rajasthan, India i G.B. Pant University of Agriculture and Technology, Pantnagar 263145, Uttaranchal, India Received 9 August 2004; received in revised form 23 February 2005; accepted 25 February 2005

Abstract Experiments at Bharatpur, Pantnagar, Berhampur, Mohanpur, New Delhi, S.K. Nagar, Kangra and Sriganganagar, India were sown with oilseed Brassica cultivars Varuna and an important cultivar for the area on 10 dates at weekly intervals. Data of experiments conducted previously at Hisar and Ludhiana as available in reports were also used for the study. Mustard aphid (Lipaphis erysimi) appearance on inflorescences of the plants was positively correlated to a maximum temperature between 20–29 1C in the preceding week and also to a morning relative humidity (RH) 492% and daily mean RH of 475%. Long hours of leaf wetness and minimum temperature 45 1C also favoured aphid infestation. Regional and cultivar specific models were developed to predict the crop age at which the mustard aphid first appears on the crop, the peak number of aphids and the crop age at peak number at least 1 week ahead of first appearance of the pest on the crop. These will allow growers to apply insecticides in a more timely and effective manner. Here we report only the models that were found effective based on validation in the 2 years. r 2005 Elsevier Ltd. All rights reserved. Keywords: Lipaphis erysimi; Brassica; Weather; Prediction models; Forecast; Crop age; India

1. Introduction Oilseed Brassicas are among the major oilseed crops cultivated in India and around the world. India produces about 11.3% of the world’s rapeseed-mustard (Damodaram and Hegde, 2002). Lipaphis erysimi (Kalt.), the mustard aphid, is an important constraint Corresponding author. Tel.: +91 5644 260379; fax: +91 5644 260419. E-mail address: [email protected] (C. Chattopadhyay).

0261-2194/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.cropro.2005.02.010

in husbandry of oilseed Brassicas in India and causes up to 83% losses in mustard (Mandal et al., 1994). The timing and severity of infestation of L. erysimi on oilseed Brassicas differ between seasons and regions. In the absence of stable, desirable and diverse source of resistance to the mustard aphid with a broad genetic base, systemic insecticides remain the only effective means to manage L. erysimi. Although consumption of systemic insecticides on rapeseed-mustard crops in India is high (IASRI, 2002), timing their application has been erratic. Crops requiring treatment have been left

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unsprayed and others sprayed unnecessarily. In India, oilseed Brassicas are sown from late August to November, depending on the crop, prevailing temperature and availability of soil moisture for seed germination. Harvest occurs from February to May. Off-season crops are grown in non-traditional areas from May to September and this, coupled with harbouring of the mustard aphid by vegetable Brassica crops and alternative hosts, could be reasons for carryover of the aphid from one crop to another. Knowledge of the aphid-plant relationships in relation to environmental conditions is limited. Efficient, economical and environmentally friendly control of the aphid may be obtained through knowledge of its timing of attack in relation to weather factors, which may enable prediction of its occurrence so as to allow growers to take timely action in an efficient manner for crop management. Weather is an important factor in the population dynamics of L. erysimi. Empirical models have been developed to relate L. erysimi infestation on oilseed Brassicas to temperatures, relative humidity (RH) and bright sunshine hours (Bakhetia and Sidhu, 1983; Prasad et al., 1984; Singh et al., 1986, 1990; Bakhetia and Ghorbandi, 1989; Ahuja, 1990; Bishnoi et al., 1992; Rana et al., 1993; Kumar et al., 1993; Samdur et al., 1997; Kar and Chakravarthy, 2000; Roy and Baral, 2002). However, they provide no insight into quantitative prediction of the aphid on the oilseed Brassica crops in different parts of India. Hence, the present study was undertaken to develop forecasts for crop age at time of attack by L. erysimi, peak number of aphids on the crop in the season and crop age at peak population of the aphid.

2. Materials and methods 2.1. Field trials Selection of centres for the study was based on the area of crop and importance of L erysimi as a pest problem in the region. All experiments relied entirely on natural infestations of L. erysimi. Field trials were sown on 10 dates at weekly intervals (01, 08, 15, 22, 29 October, 05, 12, 19, 26 November and 03 December) at each of the locations (Fig. 1) viz., Bharatpur (271120 N; 771270 E), Pantnagar (291N; 79130 E), Berhampur (24160 N; 881190 E), Mohanpur (221570 N; 881200 E), New Delhi (281390 N; 771130 E), S.K. Nagar (24150 N; 721E), Kangra (32140 N; 761160 E) and Sriganganagar (291550 N; 751530 E) in 2001–2002, 2002–2003 and 2003–2004 postrainy (rabi) crop seasons with cultivar Varuna (Brassica juncea) and an important cultivar for the area (cultivars of B. juncea at all centres except cv. YSB-9 of Brassica rapa var. yellow sarson at Berhampur and Mohanpur). Each plot measured 1.5 m  5 m with plants spaced at

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10 cm intervals in rows 30 cm apart. Recommended doses of N and P fertilizers (NRCRM, 1999) were applied. No K fertilizer was applied. Disease protection practices comprised seed treatment with carbendazim (Bavistin 50 WP at 2 g/kg)+Apron (metalaxyl) 35 SD 6 g/kg and spray of mancozeb at 3 g/l at 15-day intervals. No insecticide was applied. The build-up of L. erysimi populations on the main shoots of oilseed Brassica crops was monitored in each location. The initial date of appearance of the mustard aphid in each plot was recorded. Numbers of aphids on 10 cm long terminal shoots were chosen as basis for estimating populations as these have been shown to be best correlated with abiotic factors (Singh et al., 1989). Numbers of aphids on 10 cm long terminal shoots were recorded twice a week (on Tuesday and Friday) until harvest from 10 randomly tagged plants to monitor L. erysimi densities in the crop. Weather data for maximum and minimum daily temperatures, morning (0700 h Local Apparent Time or LAT) calculated on the basis of longitude of a location as per standard norms of the World Meteorological Organisation or WMO (Doorenbos, 1976; Ghadekar, 2002) and afternoon (1400 h LAT) RH, bright sunshine hours and wind speed were recorded from standard meteorological observatories at all locations. Meteorological observatories at all locations were 100–170 m from the experimental site and the data recording instruments were installed as per standard specifications of the WMO (Doorenbos, 1976; Ghadekar, 2002). Weather data were also recorded at Bharatpur using automatic weather stations during 2001–2002 and 2002–2003, located 35 m from the experimental site, where apart from recording the weather variables mentioned earlier, data for leaf wetness were also recorded. The sensors were installed as per standard specifications (Doorenbos, 1976; Ghadekar, 2002). Data for seed yield was recorded at harvest of the crop. 2.2. Use of data from published reports Data on aphid counts on 10 cm of terminal shoot from 10 unsprayed crop plots of size and spacing as above recorded at weekly intervals from 10 randomly tagged plants at Bharatpur (seven crop seasons: 1993–1994, 1994–1995, 1996–1997 through 2000–2001), Ludhiana (301540 N; 751480 E; 13 crop seasons: 1984–1985, 1986–1987 through 1988–1989, 1990–1991 through 1996–1997, 1998–1999, 2000–2001), Pantnagar (10 crop seasons: 1988–1989, 1990–1991, 1991–1992, 1993–1994 through 1997–1998, 1999–2000, 2000–2001), Hisar (291100 N; 751160 E; nine crop seasons: 1986–1987, 1989–1990, 1992–1993 through 1997–1998, 1999–2000) and Berhampur (five crop seasons: 1996–1997 through 2000–2001) and provided in Annual Reports of corresponding years of the All India Coordinated Research

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C. Chattopadhyay et al. / Crop Protection 24 (2005) 1042–1053

KANGRA LUDHIANA SRI GANGANAGAR

HISAR

PANTNAGAR

NEW DELHI BHATARPUR

BERHAMPUR S.K. NAGAR

MOHANPUR

N W

E S

Fig. 1. Map of India indicating different experimental sites.

Project on Rapeseed-Mustard were utilized for the investigation (Fig. 1). Data from published reports were combined with data from experiments conducted during 2001–2002 as related to Bharatpur, Berhampur and Pantnagar for statistical analyses. Models for Ludhiana and Hisar were fitted using data only from published reports. The crop at all locations was B. juncea except at Berhampur (cv. YSB-9), where it was B. rapa var. yellow sarson. Weather data for the respective periods of the locations were also collected from the records of meteorological observatories of the centres. Models devised using published data for Bharatpur, Berhampur, Pantnagar (combined with unpublished data of 2001–2002), Ludhiana and Hisar were different from those for other locations; the difference being, composite weather indices were based on data from 45th to 50th standard meteorological week or SMW (Ghadekar, 2002). Data of initial year(s) were used for devising models while the observations of 1999–2000 for Hisar, 2000–2001 for Ludhiana and of 2002–2003 and

2003–2004 for other locations were matched with the corresponding predicted values for validation of the devised models. 2.3. Data analysis and model fitting For each assessment date, aphid numbers on 10 randomly tagged plants from each plot were averaged to give a single aphid count. Correlations of timing (days after sowing or d.a.s.) of attack of L. erysimi, peak number of aphids on the crop and crop age (d.a.s.) at peak aphid population with weather variables were determined. Linear prediction models based on the weather parameters as independent variables and crop age (d.a.s.) at time of first appearance of aphid on crop, peak number of aphids on the crop in the season and crop age at peak population of the pest at each week starting from week of sowing as dependent variables were fitted by multiple stepwise regression (Draper and Smith, 1981). The stepwise regression took care of

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autocorrelation. Based on correlation coefficients between dependent variables under study with the respective weather parameter (i) in different weeks, a composite weather variable (zi ) was developed as the weighted sum of the weather variable in different weeks starting from pre-sowing week up to week of the prediction (Agrawal et al., 1986; Desai et al., 2004). Similarly, interaction terms (zij ) were developed as weighted sums of product between two weather variables, weightings being correlation coefficients of dependent variable under study with products of weather variables in respective weeks. The important weather indices were selected through stepwise regression. Models were fitted for prediction of the dependent variables viz., highest aphid population or crop age at peak aphid population or crop age at time of first appearance of aphid on weekly basis starting from the time of sowing, 2nd week after sowing and so on (f ¼ 1, 2,y). The interactions of weather parameters were also found to be significant. The models were developed in the following format: Y ¼ a0 þ

p X

a i zi þ

i¼1

p X

bij zij þ e,

(1a)

iaj

¼ a0 þ amaxtmp zmaxtmp þ amintmp zmintmp þ . . . þ aws zws þ bmaxtmpmintmp zmaxtmpmintmp þ . . . þ bbsshws zbsshws þ e;

ð1bÞ

where zi ¼

f X

riw xiw ,

(2a)

w¼1

zij ¼

f X

rijw xiw xjw

(2b)

w¼1

with Y being the dependent variable, xiw the value of ith weather parameter in wth week, riw the value of correlation coefficient between Y and ith weather parameter in wth week, rijw the correlation coefficient between Y and product of xi and xj in wth week, p the number of weather variables, f the week after sowing when predicted and e the error term (maxtmp: maximum daily temperature; mintmp: minimum daily temperature; bssh: bright sunshine hours; ws: wind speed). Weather indices based on summation of weightings of different meteorological factors as per correlation coefficients in different weeks after sowing until the forecast was provided, were taken into account. The fourth (2002–2003) and fifth (2003–2004) crop seasons were used to validate the models for forecasting the targeted parameters at different locations based on the models developed for each of the parameter, viz., crop age at first appearance of the aphid on main shoot (Y 1 ),

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crop age at highest population of aphid on main shoot (Y 2 ) and highest number of aphid on main shoot (Y 3 ); paired t-test was used to assess the difference among predicted and observed values.

3. Results 3.1. Field trials Correlation study of the data from different locations revealed that aphid appearance on traps and that on inflorescence of the plants was influenced by a maximum temperature in the preceding week between 20–29 1C (r: 0.9), more so at a narrower maximum temperature range of 22–25 1C (r: 0.99) as also by a morning RH492% (r: 0.88), favoured further when 498% (r: 0.94) and daily mean RH of 475% (r: 0.92). Empirically, a look at weather data available from the automatic weather station indicated, long hours of leaf wetness and minimum temperature 45 1C during the preceding 3 days also favoured the aphid infestation. At all the centres, aphid infestation was higher on later sown plots (Table 1). Appearance of the pest on the crop was by first SMW and there was a period of abrupt build-up in the aphid population just before it reached the peak. The vulnerable growth stage of plants having tender shoots coincided with hot (22–25 1C) and humid (mean RH475%) weather in case of later sown crops. Mustard aphids colonized the late sown crop at an earlier growth stage. Models for the five locations Mohanpur, New Delhi, S.K. Nagar, Kangra and Sriganganagar were devised to predict crop age at first appearance of aphid on main shoot (Y 1 ), crop age at highest aphid number (Y 2 ) and highest aphid number on main shoot (Y 3 ) on cv. Varuna and respective locally important cultivar for each location (Table 2–4). At Mohanpur, first appearance of aphid occurred 6 weeks after sowing, while at New Delhi, Kangra and Sriganganagar that happened more lately in first SMW and at S.K. Nagar in third SMW. However, at Mohanpur, New Delhi, S.K. Nagar and Kangra, prediction of first appearance of aphid on inflorescence of cv. Varuna and locally important cultivar was possible by the third week after sowing. At Sriganganagar in cv. Varuna, the prediction was possible at the fourth week after sowing. Thus the growers could arrange and apply the insecticides in time and avoid unnecessary sprays. The model for S.K. Nagar could not be devised to forecast crop age at first appearance of aphid as the first occurrence of the insect pest took place on all plots on the same date. The model to forecast the highest number of aphid on cv. Rohini at Bharatpur was excluded from Table 3 as it failed in validation done in 2002–2003 and hence was not considered thereafter.

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Table 1 Effect of date of sowing on highest aphid count per inflorescence (10 cm) of oilseed Brassicas in 2001–2002 in different locations and cultivars Date of Number of aphids per 10 cm inflorescence of oilseed Brassica cultivar at different locations sowing Bharatpur Mohanpur New Delhi Berhampur Pantnagar Sriganganagar Kangra

S.K. Nagar

Varuna Rohini Varuna YSB-9 Varuna Pusa Jaikisan Varuna YSB-9 Varuna Krishna Varuna Laxmi Varuna RCC-4 Varuna GM-2 01 08 15 22 29 05 12 19 26 03

Oct Oct Oct Oct Oct Nov Nov Nov Nov Dec

0.0 0.0 21.0 95.0 457.0 463.0 471.0 480.0 491.0 650.0

17.0 20.0 30.8 42.4 225.0 329.0 330.0 333.0 365.0 495.0

118.6 198.5 237.6 326.9 354.4 415.5 486.4 505.5 537.9 802.5

46.0 242.0 269.0 480.0 598.0 574.8 609.7 655.0 690.0 702.0

0.0 17.0 1.9 47.0 3.2 43.9 11.9 86.0 45.8 106.5 187.5 449.7 291.5 852.8 310.5 1009.0 513.5 1289.0 1154.3 1431.5

4.7 7.6 8.7 9.2 9.2 18.9 72.4 106.5 106.5 132.6

1.0 2.7 3.7 4.0 7.3 8.4 23.1 70.4 69.7 125.5

0.4 4.8 19.0 20.6 34.6 187.4 188.0 202.6 359.4 559.6

4.6 34.0 34.6 41.0 128.0 140.0 161.2 176.0 359.4 404.0

7.9 8.4 8.5 10.0 10.4 11.0 17.1 18.9 23.7 30.9

7.2 8.5 12.7 11.7 16.3 18.0 23.7 25.5 25.6 28.2

2.2 2.5 4.3 6 6.5 8.1 8.5 8.6 9.5 13.3

1.1 1.4 2.7 5.5 6.5 10.2 10.5 10.6 11.1 11.8

0.6 3.6 6.1 6.6 7.8 10.2 10.4 10.6 10.9 12.2

0.4 3.6 6.6 7.0 7.0 10.3 11.1 11.4 11.8 12.7

Table 2 Models to forecast crop age at first appearance of aphid on main shoot of cv. Varuna and locally important cultivar (Y 1 ) R2

Location

Cultivar

Crop age (week) Model of prediction

Mohanpur

Varuna, YSB-9

3

Y 1 ¼ 45:9 þ 0:54Zmaxtmp

0.87

New Delhi

Varuna Pusa Jaikisan

3 3

Y 1 ¼ 5:18 þ 0:58Zws þ 0:002ZmornRHmeanRH Y 1 ¼ 5:16 þ 0:03Zmintmpws

0.95 0.84

Kangra

Varuna RCC-4

3 3

Y 1 ¼ 77:48 þ 0:02Zmaxtmp  0:023ZmintmpaftRH Y 1 ¼ 85:63 þ 0:003Zmaxtmpmintmp  0:065ZmaxtmpaftRH

0.94 0.91

Sriganganagar

Varuna Laxmi

4 3

Y 1 ¼ 131:73 þ 0:01ZmintmpaftRH þ 0:0064ZmornRHaftRH  2:27Zmaxtmp Y 1 ¼ 117:25 þ 0:038ZmaxtmpmornRH

0.98 0.91

Bharatpur

Rohini

8

Y 1 ¼ 194:43 þ 0:36ZmornRH

0.93

maxtmp: maximum daily temperature; mintmp: minimum daily temperature; morn: morning; aft: afternoon; RH: relative humidity; ws: wind speed.

Table 3 Models to forecast crop age at highest population of aphid on main shoot of cv. Varuna and locally important cultivar (Y 2 ) R2

Location

Cultivar

Crop age (week) Model of prediction

Mohanpur

Varuna YSB-9

6 5

Y 2 ¼ 26:51 þ 0:021ZaftRHbssh þ 0:0040Z mornRHbssh Y 2 ¼ 3:98 þ 0:019Zmintmpbssh þ 0:007ZmornRHbssh

0.98 0.96

New Delhi

Varuna Pusa Jaikisan

3 4

Y 2 ¼ 14:62 þ 0:04ZmornRHws þ 0:03Z aftRHbssh Y 2 ¼ 2:51 þ 0:02ZmornRHbssh

0.82 0.92

S.K. Nagar

Varuna GM-2

4 4

Y 2 ¼ 16:67 þ 0:13ZmornRHbssh Y 2 ¼ 128:02 þ 1:39Zmaxtmp

0.94 0.99

Kangra

Varuna RCC-4

4 4

Y 2 ¼ 237:05  0:02Zmaxtmpmintmp þ 0:06ZmintmpmornRH þ 1:469ZaftRH Y 2 ¼ 131:84 þ 0:07ZmintmpmornRH

0.98 0.66

Sriganganagar

Varuna Laxmi

3 3

Y 2 ¼ 166:23 þ 0:06ZmaxtmpmornRH þ 10:01Zbssh Y 2 ¼ 86:22 þ 0:02Zmaxtmpmintmp

0.93 0.99

Bharatpur

Rohini

3

Y 2 ¼ 4:94 þ 0:01ZmaxtmpmornRH

0.96

maxtmp: maximum daily temperature; mintmp: minimum daily temperature; morn: morning; aft: afternoon; RH: relative humidity; bssh: bright sunshine hours; ws: wind speed.

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Table 4 Models to forecast highest count of aphid on main shoot of cv. Varuna and locally important cultivar (Y 3 ) R2

Location

Cultivar

Crop age (week) Model of prediction

Mohanpur

Varuna YSB-9

4 4

Y 3 ¼ 426:68 þ 3:41Zmaxtmp Y 3 ¼ 8:97 þ 0:91Zbssh

0.78 0.78

New Delhi

Varuna Pusa Jaikisan

6 6

Y 3 ¼ 778:62 þ 9:8ZaftRH  0:1ZmornRHmeanRH þ 0:26ZmornRHbssh Y 3 ¼ 344:32 þ 0:26ZmaxtmpaftRH  0:12ZmeantmpmornRH þ 1:41Zmaxtmpws

0.98 0.98

S.K. Nagar

Varuna GM-2

4 3

Y 3 ¼ 21:04 þ 0:03ZmornRHbssh Y 3 ¼ 23:86 þ 0:03Zmintmpbssh

0.91 0.96

Kangra

Varuna RCC-4

3 4

Y 3 ¼ 13:74 þ 0:004Zmaxtmpmintmp Y 3 ¼ 336:8 þ 0:029ZmaxtmpmornRH

0.69 0.84

Sriganganagar

Varuna Laxmi

3 4

Y 3 ¼ 144:63 þ 0:02ZmaxtmpmornRH  0:06ZaftRHbssh þ 5:2Zbssh Y 3 ¼ 44:59 þ 0:011ZmintmpaftRH

0.97 0.95

maxtmp: maximum daily temperature; mintmp: minimum daily temperature; aft: afternoon; RH: relative humidity; bssh: bright sunshine hours; ws: wind speed. Table 5 Models to forecast crop age at first appearance of aphid (Y1) on main shoot of oilseed Brassicas Location

Model

R2

Bharatpur Pantnagar Berhampur Ludhiana Hisar

Y1 Y1 Y1 Y1 Y1

0.71 0.70 0.97 0.87 0.60

¼ 112:91 þ 0:02Z maxtmpmornRH þ 4:98Zmaxtmp ¼ 798:06 þ 0:56Zmaxtmpbssh  8:32ZmornRH ¼ 56:83 þ 0:07Zmaxtmpmintmp ¼ 735:69 þ 0:47Zmintmpbssh  3:82ZmornRH ¼ 56:72 þ 0:029ZmaxtmpaftRH þ 2:74maxtmp

maxtmp: maximum daily temperature; mintmp: minimum daily temperature; morn: morning; aft: afternoon; RH: relative humidity; bssh: bright sunshine hours.

3.2. Use of data from published reports The models predicted crop age (Y 1 ) at first appearance of aphid on main shoot of plant (Table 5; Fig. 2), crop age (Y 2 ) at highest aphid population on main shoot of plant (Table 6; Fig. 3) and highest L. erysimi number (Y 3 ) of the crop season on main shoot of plant (Table 7; Fig. 4) at Bharatpur, Pantnagar, Berhampur, Ludhiana and Hisar at 50th SMW. The models devised using both short (1 year 2001–2002) and long (multiple years as detailed in Section 2.2) duration data for different locations and cultivars were validated in 2002–2003 and 2003–2004 crop seasons, when residual values or difference between predicted and observed values in different cases were not significant (Po0:1) or zero (Table 8). Common models for the targeted three parameters or dependent variables (Y 1 , Y 2 , Y 3 ) with the common cultivar Varuna of B. juncea used for experimentation at all the locations were attempted. But they had low R2 values, hence were not considered for validation and are not shown here. These models could help in prediction of time of first appearance of mustard aphid on the crop and the risk

involved on the crop as related to the insect pest. The prediction was possible at least 1 week ahead of first appearance of the aphid on crop as the first appearance of L. erysimi at these five locations never took place before 51st SMW during the period under study. Most of the models saw entry of variables maximum temperature and morning RH with minimum temperature, afternoon RH and sunshine hours also getting entered in a few cases. Predictions from the models devised in this study were closer to observed values in years of higher infestation as compared to others (Figs. 2–4, Table 8). Further, the later sown plots provided lower seed yield compared to earlier sown ones (Table 9).

4. Discussion 4.1. Field trials Our finding here were fairly close in agreement with earlier findings (Bakhetia and Sidhu, 1983; Singh et al., 1990; Bishnoi et al., 1992), where 22.6–25.6 1C of maximum temperature, 7–13.5 1C minimum temperature

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Pantnagar

80 60 40 20

year

20 03 -0 4 4 03

-0

7 20

96

-9

5 -9 94

19

3 19

92

-9

1

year

19

90

-9

8 19

19

87

-8

5 -8 84

03 20

19

crop age (days after sowing)

4

Fitted/ predicted

90 80 70 60 50 40 30 20 10 0

-0

3

2 20

20

02

01

-0

-0

1

0

-0

00

20

-2 99 19

00

9

8

-9

-9

98 19

97 19

-9 96 19

Ludhiana Observed

Observed Fitted/ predicted

7

crop age (days after sowing)

year

Berhampur

90 80 70 60 50 40 30 20 10 0

20 01 -0 2

19 99 -2 00 0

19 96 -9 7

19 94 -9 5

19 91 -9 2

0 19 88 -8 9

sowing)

100 80 60 40 20 0

Observed Fitted/ predicted

100

crop age (days after sowing)

Observed Fitted/ predicted

19 93 -9 19 4 94 -9 19 5 97 -9 19 8 98 19 -9 99 9 -2 00 20 0 00 -0 20 1 01 -0 20 2 02 -0 20 3 03 -0 4

crop age (days after

Bharatpur

year

crop age (days after sowing)

Hisar 100 80 60 40

Observed Fitted/ predicted

20

4 03

20

-2 19

99

-0

0

8

00

-9

7 19

97

6

-9

19

96

5

-9

-9 19

94 19

95

4

19

93

-9

3 -9 92

19

19

89

-9

0

0

year Fig. 2. Fitted (initial years) or predicted crop age at first appearance of Lipaphis erysimi compared to observed values at Bharatpur, Pantnagar, Berhampur (predicted for 2002–2003, 2003–2004), Ludhiana (predicted for 2000–2001, 2003–2004) and Hisar (predicted for 1999–2000, 2003–2004).

Table 6 Models to forecast crop age at highest population of aphid (Y2) on main shoot of oilseed Brassicas Location

Model

R2

Bharatpur Pantnagar Berhampur Ludhiana Hisar

Y2 Y2 Y2 Y2 Y2

0.92 0.72 0.91 0.67 0.82

¼ 77:1 þ 0:03Zmaxtmpmintmp ¼ 735:69 þ 0:47Zmintmpbssh 23:82ZmornRH ¼ 52:48 þ 0:02Zmaxtmpmintmp þ 1:58Zbssh ¼ 133:56 þ 0:09ZmintmpaftRH ¼ 152:98 þ 0:22Zmaxtmpmintmp þ 0:0049ZmornRHaftRH

maxtmp: maximum daily temperature; mintmp: minimum daily temperature; morn: morning; aft: afternoon; RH: relative humidity; bssh: bright sunshine hours.

and 53–90% mean RH were noted to favour aphid infestation. Higher aphid infestation on later sown crops have been reported (Singh et al., 1984). However, appearance of the pest on the crop was earlier (first SMW) than that reported previously (Yadav and Kalra, 1990), who found the appearance to occur in fifth SMW. The period

of abrupt build-up in the aphid population also matched with the observation of Singh et al. (1990). A delayed sowing results in coincidence of the growth stage of plants having tender shoots with hot (22–25 1C) and humid (mean RH475%) weather. Thus mustard aphids colonize the crop at an earlier growth stage of late sown crop. The sustenance of such favourable conditions

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100 95 90 85

-9 9 -2 00 20 0 00 -0 20 1 01 -0 20 2 02 -0 20 3 03 -0 4

19

99

Berhampur

20

00

-9 7 96

-9 5 94

-9 3 92

-9 1 19

87

19

1996- 1997- 1998- 1999- 2000- 2001- 2002- 200397 98 99 2000 01 02 03 04 year

19

84

0

-0 1

Fitted/ predicted

-8 5

20

Observed

90

Observed Fitted/ predicted

19

60

-8 8

80

140 120 100 80 60 40 20 0

19

crop age (days after sowing)

Ludhiana

19

-9 8

19

crop age (days after sowing)

year

year

100

40

Fitted/ predicted

120 100 80 60 40 20 0

98 19

-9 7

97 19

94

96 19

19

19

93

-9 4

-9 5

80

120

Observed

88 19 -89 90 19 -91 91 19 -92 93 19 -94 94 19 -95 95 19 -96 96 19 -97 19 97-9 99 8 -2 0 20 00 00 20 -01 01 20 -02 02 -0 3

105

crop age (days after sowing)

crop age (days after sowing)

Pantnagar

Bharatpur Observed Fitted/ predicted

110

1049

year

140 120 100 80 60

Observed

40

Fitted/ predicted

20

-9 7 96

-9 6 19

94

95

-9 5 19

-9 4 19

92

93

-9 3 19

-9 0 19

89 19

86 19

19 97 19 -98 99 -2 00 0

0

-8 7

crop age (days after sowing)

Hisar

year Fig. 3. Fitted (initial years) crop age at highest population of Lipaphis erysimi on main shoot compared to observed values at Bharatpur, Pantnagar, Berhampur (predicted for 2002–2003, 2003–2004), Ludhiana (predicted for 2000–2001, 2003–2004) and Hisar (predicted for 1999–2000, 2003–2004). Table 7 Models to forecast highest count of aphid (Y3) on main shoot of oilseed Brassicas Location

Model

R2

Bharatpur Pantnagar Berhampur Ludhiana Hisar

Y3 Y3 Y3 Y3 Y3

0.89 0.82 0.97 0.87 0.98

¼ 512:87 þ 3:45Zmaxtmpmintmp þ 0:16Z maxtmpaftRH ¼ 168:81 þ 3:4ZmintmpaftRH þ 101:22Zmintmp ¼ 60:14 þ 0:09Z maxtmpaftRH ¼ 259:5 þ 2:84Z maxtmpaftRH þ 5:11ZaftRHbssh ¼ 230:21 þ 4:29Zmaxtmpmintmp

maxtmp: maximum daily temperature; mintmp: minimum daily temperature; aft: afternoon; RH: relative humidity; bssh: bright sunshine hours.

influence the longevity of the period of aphid infestation on the crop, which consequently affects yield. Thus the damage caused to a crop by mustard aphids is likely to

be related to sowing date i.e. late sowing results in higher aphid densities at flowering (McVean et al., 1999). Thus it would be appropriate to sow the crop at

ARTICLE IN PRESS C. Chattopadhyay et al. / Crop Protection 24 (2005) 1042–1053

1050

Pantnagar

Bharatpur

600

700 600 500 400 300 200 100 0

Observed

400

Fitted/ predicted

300 200

-0 4

-0 2

03

01

20

99 19

20

96 19

19

year

year

Ludhiana

Berhampur

Observed

Observed

20 00 -0 1

95 19 96 -9 7

93 2-

19 94 -

91

year

Hisar Observed

900 800 700 600 500 400 300 200 100 0

-2 00 20 0 03 -0 4

-9 8

19

99 19

year

97

-9 7

19

95

96

-9 6

-9 5 19

94

-9 4 19

93

-9 3 19

-9 0

92 19

89

-8 7

Fitted/ predicted

19

86

19 9

19

1996- 1997- 1998- 1999- 2000- 2001- 2002- 200397 98 99 2000 01 02 03 04 year

19

19 8

84

0

7-

-8 5

50

0-

100

Fitted/ predicted

19 9

150

1400 1200 1000 800 600 400 200 0 88

no. of aphid

Fitted/ predicted

no. of aphid

-2 00 0

-9 7

-9 5 94

-9 2 19

19

99

19

91

-8 9 88

-9 9 -2 00 20 0 00 -0 20 1 01 -0 20 2 02 -0 20 3 03 -0 4

-9 8

98 19

-9 7 96

97 19

19

94

-9 5

0

200 no. of aphid

500

100

19

93 19 250

no. of aphid

Fitted/ predicted

-9 4

no. of aphid

Observed

Fig. 4. Fitted (initial years) highest count of Lipaphis erysimi on main shoot compared to observed values at Bharatpur, Pantnagar, Berhampur (predicted for 2002–2003, 2003–2004), Ludhiana (predicted for 2000–2001, 2003–2004) and Hisar (predicted for 1999–2000, 2003–2004), Ludhiana and Hisar.

the earliest possible time to enable escape or noncoincidence of the tender shoot stage with favourable temperature and humidity factors leading to build-up of aphid population on the plant. 4.2. Use of data from published reports The prediction was possible atleast 1 week ahead of the first appearance of the aphid on a crop as the first appearance of L. erysimi at these five locations never took place before 51st SMW during the period under study, which could allow growers to arrange and apply timely insecticides and avoid unnecessary sprays. Many researchers have developed forecast systems based on accumulated air temperatures (Coaker and Wright,

1963) or soil temperatures (Finch and Collier, 1986) for forecasting of other insect pests on other crops. The ‘day-degree’ concept of forecasting has been used to predict the timings of occurrence of insect pests (Chakravarthy and Gautam, 2003). These forecasts have limitations as they are based on the assumption that the relationship between the rate of insect development and temperature is linear (Baker, 1980). Moreover, the ‘day-degree’ system can be used only to predict the mean activity of a univoltine insect population whereas L. erysimi population includes multivoltine groups of nymphs, alates and apterae ones (Baker, 1980). Hence, suitability of degree-day accumulation-based systems for forewarning timing of attacks, particularly in the absence of holistic information regarding bio-ecology of

Table 8 Validation of models for different dependent variables, locations and cultivars in 2002–2003 and 2003–2004 Location/Cultivar

2002–2003

2002–2003

2003–2004

2003–2004

2002–2003

2003–2004

Predicted

Observed

Predicted

Observed

Predicted

Observed

Predicted

Observed

Predicted

Observed

Predicted

Observed

82 85 51 49 59 57 53 51 66 66 79 81

91 91 52 52 63 59 53 57 70 73 77 78

72 68 56 67 67 65 73 62 67 71 56 49

77 70 56 70 70 63 77 63 69 69 51 51

a

a

a

a

51 49

55 53

0 96 21 0 241 232 18 17 12 13 81 88 8 8 0b 559d

32 33 33 16 232 237 14 25 10 16 87 93 8 7 0b 599d

a

a

98 112 91 84 115 124 91 84 125 127 118 118 89 82

42

a

96 118 93 83 111 121 89 78 124 128 115 120 98 98 126 133

a

a

105 126 99 91 94 94 91 84 140 140 114 117 98 98 105b 105d

33

a

100 125 100 93 89 87 91 77 137 139 115 120 85 87 102b 106d

10 30 7 5 39 0 7 14 22 25 4 4 0 0

3 36 12 9 31 3 10 13 27 31 1 1 0 0

b

56 66d

b

56 77d

c c

Models could not be devised or not considered for validation. 2000–2001. c Since aphid infestation was nil, specific age for highest count could not be measured. d 1999–2000. b

ARTICLE IN PRESS

a

Crop age (days after sowing) at highest population of Highest count of aphid on main shoot of oilseed aphid on main shoot of oilseed Brassicas (Y 2 ) Brassicas (Y 3 )

C. Chattopadhyay et al. / Crop Protection 24 (2005) 1042–1053

Bharatpur/Varuna Bharatpur/Rohini Mohanpur/Varuna Mohanpur/YSB-9 New Delhi/Varuna New Delhi/Pusa Jaikisan Berhampur/YSB-9 Pantnagar/Varuna Sriganganagar/Varuna Sriganganagar/Laxmi Kangra/Varuna Kangra/RCC-4 S.K. Nagar/Varuna S.K. Nagar/GM-2 Ludhiana/Varuna Hisar/Varuna

Crop age (days after sowing) at first appearance of aphid (Y 1 )

1051

ARTICLE IN PRESS C. Chattopadhyay et al. / Crop Protection 24 (2005) 1042–1053

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Table 9 Effect of date of sowing on seed yield of oilseed Brassicas in 2001–2002 in different locations and cultivars Date of Seed yield of oilseed Brassica cultivar at different locations (kg/ha) sowing Bharatpur Mohanpur New Delhi Berhampur

Pantnagar

Sriganganagar

Kangra

S.K. Nagar

Varuna Rohini Varuna YSB-9 Aruna Pusa Jaikisan Varuna YSB-9 Varuna Krishna Varuna Laxmi Varuna RCC-4 Varuna GM-2 01 08 15 22 29 05 12 19 26 03

Oct Oct Oct Oct Oct Nov Nov Nov Nov Dec

2979 2375 1800 1383 1354 1341 1187 896 617 467

2917 2367 2008 1937 1792 1467 1104 833 675 646

1567 1532 1248 1017 852 596 434 403 344 255

990 973 928 771 640 602 424 329 280 197

2620 2542 2471 2423 2255 1936 1558 1171 752 554

2975 2750 2580 2576 2361 1870 1359 1168 801 653

1440 1320 1240 1180 1093 946 567 260 240 127

L. erysimi can be debated through further research in future. Conditions for crop culture vary widely along with the specific conditions that favour the insect pest at different locations. There also could be variation in biotype of L. erysimi between locations, which also could be one of the reasons for the different weather parameters getting entered in models for different locations. Unfortunately, available literature does not throw any light on variability aspect of L. erysimi. Further investigation can pinpoint the importance of the different weather factors favouring mustard aphid build-up in order of their priority. Most of the systems of forecasting insect pests are based on temperature (Collier et al., 1991). Though in the present study, relationship of behaviour of the mustard aphid with weather factors were not considered, the models based on weather data and population dynamics of L. erysimi on rapeseed-mustard crop could provide effective prediction about the crop age as related to their time of first appearance, peak number on the crop and crop age at peak population of the pest. Further, in all the cases, the models invariably included temperature among the weather factors. Weather indices based on summation of weightings of different meteorological parameters as per correlation coefficients in different weeks after sowing until the forecast was provided, were taken into account. Proper monitoring of insect pest population could provide accurate forecasts of time, size of pest appearance and infestation to within 1 week (Collier and Phelps, 1994), which was made possible in the investigation. This study fortunately is spread over several locations in the oilseed Brassica cropping regions of the country facing the mustard aphid problem. Using these models in combination with crop planting dates and standard meteorological data, it would be possible to provide necessary forecasts for the time being centrally from the National Research Centre on Rapeseed-Mustard at Bharatpur. While on one hand, the models could be improved with

1360 1246 1186 1100 1060 1040 367 200 140 67

1849 1549 1333 1282 1233 1216 660 600 580 525

1749 1449 1433 1433 1416 1416 1266 820 680 575

2508 2501 2150 2005 1470 1060 970 910 860 609

2912 2850 2751 2423 1750 1248 1241 1100 907 601

1888 1665 1545 1524 1444 1440 1288 1110 626 604

445 440 434 425 20 355 303 301 297 255

2367 2305 2265 2123 2095 1907 1891 1865 1507 1241

2407 2393 2361 2243 2205 2069 2057 1897 1680 1228

further detailed study, there would be need to provide a simple computer package to enable any user to get the forecast on the internet. This is expected to result in making insecticidal sprays more effective. The forecasters need to take into consideration the other findings reported here along with the output of location and cultivar-specific models. While the brighter side of the models was the low or no residual values between observed and predicted values, particularly in years of higher infestation as compared to others, this could be due to the activity of natural enemies, which are important is some years and not in others, thus affecting the pattern of aphid population development (Carter et al., 1982). The models presented here did not take into account the activity of natural enemies. The economic threshold level (ETL) for mustard aphid worked out for different regions of India (e.g. 23–25 per plant for Rajasthan state) represented by the locations considered in this study (NRCRM, 1999) mostly do not hold well. This is because, the number of aphids per plant, as indicated in the ETLs, is mostly crossed every year, thereby denying an opportunity to provide a ‘no-spray’ forecast. Hence the ETLs for different oilseed Brassica crops and regions of India may require a review and revision. The available literature indicates association of several weather factors with mustard aphid infestation in India (Bakhetia and Sidhu, 1983; Prasad et al., 1984; Singh et al., 1986, 1990; Bakhetia and Ghorbandi, 1989; Ahuja, 1990; Bishnoi et al., 1992; Rana et al., 1993; Kumar et al., 1993; Samdur et al., 1997; Kar and Chakravarthy, 2000; Roy and Baral, 2002) but is silent about providing forecast of the insect pest. Hence, as per available literature, this seems to be the first report of devising prediction models for forecast of L. erysimi for the important oilseed crop in India. In years of appearance of aphid on crop before the decision week, growers may be advised about the risk of damage threshold expected. Further, the forecasts need to account for the margin of

ARTICLE IN PRESS C. Chattopadhyay et al. / Crop Protection 24 (2005) 1042–1053

error in order to maintain the confidence of resource poor oilseed Brassica growers of India in the forecast system. More study in this direction to improve the models for real-time forewarning of outbreaks of the pest based on data on climatic variables is in progress.

Acknowledgements The facilities provided by the Directors of research units where the investigation was carried out, the funding received for the investigation from the Indian Council of Agricultural Research under the World Bank Funded National Agricultural Technology Project and the All Indian Coordinated Research Project on Rapeseed-Mustard are gratefully acknowledged. Help received from Sh Praveen Kumar in analysis of data is hereby acknowledged. References Agrawal, R., Jain, R.C., Jha, M.P., 1986. Models for studying rice crop-weather relationship. Mausam 37, 67–70. Ahuja, D.B., 1990. Population dynamics of mustard aphid, Lipaphis erysimi (Kalt.) on Indian mustard, Brassica juncea (sub sp. juncea). Indian J. Plant Prot. 18, 233–235. Baker, C.R.B., 1980. Some problems in using meteorological data to forecast the timing of insect life cycles. EPPO Bull. 10, 83–91. Bakhetia, D.R.C., Ghorbandi, A.W., 1989. Relationship between the parameters of aphid population per plant and percentage of plant infested by Lipaphis erysimi (Kaltenbach) in Indian mustard crop. J. Aphidol. 3, 119–124. Bakhetia, D.R.C., Sidhu, S.S., 1983. Effect of rainfall and temperature on the mustard aphid Lipaphis erysimi Kalt. Indian J. Entomol. 45, 203–205. Bishnoi, O.P., Singh, H., Singh, R., 1992. Incidence and multiplication of mustard aphid (Lipaphis erysimi) in relation to meteorological variables. Indian J. Agric. Sci. 62, 710–712. Carter, N., Gardner, S.M., Fraser, A., Adams, T.H.L., 1982. The role of natural enemies in cereal aphid population dynamics. Ann. Appl. Biol. 101, 190–195. Coaker, T.H., Wright, D.W., 1963. The influence of temperature on the emergence of the cabbage root fly (Erioischia brassicae) from overwintering pupae. Ann. Appl. Biol. 52, 337–343. Collier, R.H., Phelps, K., 1994. Carrot fly monitoring as an effective tool for pest management: how many flies have to be trapped? Aspects Appl. Biol. 37, 259–263. Collier, R.H., Finch, S., Phelps, K., 1991. A simulation model for forecasting the timing of attacks of Delia radicum on cruciferous crops. EPPO Bull. 21, 419–424. Chakrabarthy, N.V.K., Gautam, R.D., 2003. Weather based forewarning system for mustard aphid. In: Prasad, R.D., Sudhakara Babu, S.N.S., Hegde, D.M., Sujatha, M., Dinesh Kumar, V., Ramanjaneyulu, G.V. (Eds.), Extended Summaries, National Seminar on Stress Management in Oilseeds for Attaining SelfReliance in Vegetable Oils. 28–30 Jan 2003, Hyderabad. Indian Society for Oilseeds Research, Hyderabad 500030, India, pp. 31–35. Damodaram, T., Hegde, D.M., 2002. Oilseeds Situation: A Statistical Compendium 2002. Directorate of Oilseeds Research, Hyderabad 500030, India 471 pp.

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