Running Head: Impact of Climate Change on Pink Borer in Rice

Simulation of climate change impact on pink borer, Sesamia inferens population and crop-pest interactions in rice

Running head: Impact of climate change on pink borer in rice

K. Selvaraj1 and Subhash Chander

Division of Entomology, Indian Agricultural Research Institute, New Delhi-110012, India

1Division of Crop Protection, Central Research Institute for Jute and Allied Fibres, Kolkata-700120

Co-author: Dr. Subhash Chander, Principal scientist (Entomology), IARI, New Delhi, Phone: 011-25842482, Fax: 011-25842482, Email:

Address for correspondence.Dr. K. Selvaraj, Scientist (Entomology), Division of Crop Protection, Central Research Institute for Jute and Allied Fibres (CRIJAF), Barrackpore, Kolkata, West Bengal, Pin: 700120, Phone: 033-25356121, Fax: 033-25350415,Email:

Certificate:

We would like to inform you that, the article free from any conflict of interest among authors as well as funding agencies.

With regards

Authors

Introduction section

InfoCrop Model description

The InfoCrop is a generic crop growth model that can simulate the effects of weather, soil, agronomic managements (including planting, nitrogen, residue and irrigation) and major pests on crop growth and yield. The model considers different crop development and growth processes influencingthe yield. The model requires genetic coefficients like thermal time for phonological stages, potential grain weight, specific leaf area, maximum relative growth rate and maximum radiation use efficiency of the crop varieties. The model also requires crop management inputs such as the time of planting, application time, the amount of fertilizer and irrigation and soil data (pH, texture, thickness, bulk density, saturated hydraulic conductivity, organic carbon, water holding capacity and permanent wilting point). Location specificdaily weather data (solar radiation, maximum and minimum temperatures, rainfall, wind speed and vapour pressure) are also required to simulate crop performance. The outputs are based on the source-sink balance of the crop in relation to its environment.

Materials and methods

2.3. Development of population dynamic model

The relational diagram of S. inferens is given in Fig 1.The model comprised of four stages, egg, larva, pupa and adult.

Fig. 1. Relational diagram showing the variables of S. inferens population dynamics simulation model

Values of population model parameters are presented in Table 1. Dynamic section of the model contained various rate and state variable calculations.In this section total number of eggs, larvae, pupae and adults were calculated on daily based on their rate of change. Population in each development stage was a function of its initial numbers and their rate of change.

Table 1: Parameters used in population simulation model of S. inferens

Variable / Description / Value
TTEG / Thermal constant in degree-days (DD) for eggs / 47.6
TTSL / Thermal constant (DD) for small larvae / 500.0
TTLL / Thermal constant (DD) for large larvae / 200.0
TTPU / Thermal constant (DD) for pupae / 166.6
TTAD / Thermal constant (DD) for adults / 50.0
TDEG / Development threshold (oC) for eggs / 13.8
TDL / Development threshold (oC) for larvae / 10.6
TDPU / Development threshold (oC) for pupae / 12.7
TDAD / Development threshold (oC) for adults / 12.0
FECND / Number of eggs laid per day per female / 332
SRAT / Proportion of females in the population / 0.5
EGPAR / Egg parasitism (%) / 62%
INFR / Infertile eggs (%) / 9.2%

Table 2: Development of population simulation model of S. inferens

Model Parameter/formula simulated as / Description / Equation
Lactin 2 Model (Lactin et al. 1995):
1/D = eρ*T - e[ρ*Tmax-(Tmax-T)/ΔT ] + λ / where, D= mean development duration in days, e= exponent, ρ= composite value for critical enzyme catalyzed biochemical reactions, that is an increase in rate of biochemical reactions as temperature increases to optimum temperature, T= temperature at which insect reared, Tmax= upper developmental threshold, ΔT= temperature range above the optimum temperature for development and below Tmax, and λ=directly measurable rate of temperature-dependent physiological process at base temperature. / (4)
EG=IEG+) ∂t / Initial values (IEG, ILV, IPU, and IAD) and rate of change (REG, RLV, RPU and RAD). / (6), (7), (8), (9)
LV=ILV+) ∂t
PU=IPU+) ∂t
AD=IAD+) ∂t
REG=∂ (EG)/ ∂t =EGR-REGLO-LVR / Rate of change in number of different development stages depended on their respective formation rates (EGR, LVR, PUR and ADR), loss rates (REGLO, RLVLO, RPULO, DTHR) and rates of conversion to subsequent stage. / (10), (11), (12), (13)
RLV= ∂ (LV)/ ∂t= LVR-RLVLO-PUR
RPU = ∂ (PU)/ ∂t= PUR-RPULO-ADR
RAD= ∂ (AD)/ ∂t= ADR-DTHR + IMIGR-EMIGR
Rate of oviposition:
EGR= SRAT* AD*FECUND / The rate of oviposition (EGR) depended upon adult number (AD), sex ratio (SRAT) and fecundity (FECND) / (14)
LVR = EG*ETEG/TCEG / Rate of formation of larvae (LVR), pupae (PUR), and adults (ADR) depended upon number of eggs (EG), larvae (LV) and pupae (PU), respectively and ratio of corresponding daily effective temperature (ET) and thermal constant (TT) of different stages. / (15), (16), (17)
PUR= LV*ETLV/TTLV
ADR = PU*ETPU/TTPU
Egg loss rate:
REGLO= EGR*EGMO / Egg loss rate (REGLO) was calculated from egg production rate and egg mortality (EGMO) / (18)
Egg mortality:
EGMO= INFR+EGPAR+EGPRE+ (1-TSUEG) / The egg mortality was comprised of biotic and abiotic mortality factors such as egg infertility, egg parasitism, egg predation, and adverse effect of high or low temperature on egg survival. / (19)
Larval loss rate:
RLVLO= LVR*LVMO / The larval loss rate (RLVLO) depended upon larval formation rate (LVR) and their mortality rate (LVMO) / (20)
Larval mortality:
LVMO=LVPAR + LVPRE + (1-TSURLV) / The larval mortality (LVMO) comprised of larvae parasitism (LVPAR), larvae predation (LVPRE), and adverse effect of temperature on larva survival (TSURLV). / (21)
Pupal loss rate:
RPULO = PUR*PUMO / The pupal loss rate (RPULO) depended upon pupal formation rate (PUR) and their mortality rate (PUMO). / (22)
Pupal mortality:
PUMO = PUPAR+PUPRE + (1-TSURPU) / The pupal mortality (PUMO) comprised of pupal parasitism (PUPAR), pupal predation (PUPRE) and adverse effect of temperature on pupal survival (TSURPU). / (23)
Adult mortality: ADMO = AD*ETAD/TTAD + (ADR*ADPF) / The adult mortality (ADMO) constituted of natural adult death and death due to physical factors (ADPF). / (24)
Effective temperature value temperature on different stages of S. inferens:
ETi = TPAV-TDi*TCORC / Effective temperature values for eggs (ETEG), larvae (ETLV), pupae(ETPU), and adults (ETAD) were determined based on daily average temperature (TPAV) and their respective lower development thresholds.
where i={EG, LV, PU, AD}. Where, TCORC = correction factor for non-linear response in thermal time.
TCORC was introduced to account for non-linear effects of temperature on either side of the favourable temperature range of the pest. For accumulation of heat units, favourable temperature ranges for different developmental stages were maintained as described above for their survival. Departure of average temperature from this range on either side of the favourable temperature range reduced the accumulation of heat units, thus affecting the development rate of the insect. / (25)
TMAX = TMMX + CCTMAX / The effect of global warming was introduced as an increase in both maximum and minimum temperatures.
where TMMX=daily maximum temperature; TMMN=daily minimum temperature; TMAX=daily maximum temperature after global warming; TMIN=daily minimum temperature after global warming; CCTMAX=rise in daily maximum temperature; CCTMIN=rise in daily minimum temperature. / (26)
TMIN = TMMN + CCTMIN / (27)
Effect of the PSB damage:
PBDAMG = LV * LVFDRT / The effect of the PSB damage on crop leaf area and plant organ dryweight was simulated by reducing them in proportion to the ratio of daily PSB damage (SBDAMG) and total stem weight (WST) of the crop: / (28)
Simulation of effect on leaf area:
RLAI=LAII+GLAI-DLAI-LALOSS / Where RLAI= LAI net growth rate, LAII= initial LAI, GLAI=LAI gross growth rate, DLAI= LAI loss rate due to senescence, and LALOSS= LAI loss rate due to the PSB. / (29), (30)
LALOSS=LAI* PBDMG/WST
Effect on leaf weight:
RWLVG = GCROP*FSH*FLV-DLV / Where RWLVG= green leaf growth rate, GCROP= rate of carbohydrate availability for plant growth, FSH= carbohydrate allocation rate to shoot, FLV= carbohydrate allocation rate to leaves, DLV= leaf weight loss rate, DLAI = LAI loss rate, WLVG= green leaf weight. / (31), (32)
DLV=WLVG*(DLAI/LAI+PBDMG/WST)
Effect on stem weight:
RWST = GCROP*FSH*FST –DST / Where, RWST= stem weight growth rate, FST= carbohydrate allocation rate to stem, DST= stem death rate, WST= stem weight, DSAI= stem area death rate due to senescence, and SAI= stem area index. / (33), (34)
DST=WST*(DSAI*0.5/SAI+PBDAMG/WST)
Effect on stem area:
SAIRT= RLAI- (DSAI+ SAI*PBDAMG/WST) / Where, SAIRT= stem area growth rate, DSAI= stem area death rate.
The PSB effects on both LAI and SAI were considered as these contributed to effective LAI of the crop in the model. / (35)
Effect on storage organ: GNODAY=GCROP*GNOCF-(GNLOSS+STRILE) / Where, GNODAY= daily grain formation rate, GNOCF= number of grains produced/kg biomass, GNLOSS= grain loss rate due to stem borer, STRILE= grain sterility rate due to extreme temperature, GNO= total grain number. / (36), (37)
GNLOSS=GNO*PBDAMG/WST
Effect on available nitrogen in the plant, nitrogen loss in plants due to the PSB damage:
Ni = NAi- NLOSi / Where i= {LV, ST, SO}. Ni=nitrogen availability rate to leaves, stems and storage organs,
NAi=nitrogen allocation rate to leaves, stems and storage organs, NLOSi= nitrogen loss rate from leaves, stems and storage organs, ANi=totalavailable nitrogen in leaves, stems and storage organs. / (38), (39)
NLOSi = ANi*PBDAMG/WST
Yield loss assessment: Yield loss (%) was calculated as C = (A-B)/A*100 / Where, C= yield loss (%), A= yield of uninfested crop and B= yield of damaged crop. / -

Temperature dependent development of different stages of S. inferens

Table 3: Effect of temperature on mean percent survival of S. inferens under laboratory conditions

Temperature
(oC) / Egg hatchability
(%) / Larvae reaching fourth instar (%) / Total pupation
(%) / Adult survival
(%) / Overall development
(%)
18±1
21±1
24±1
27±1
30±1
33±1 / 96.66
95.00
93.33
93.33
86.66
85.00 / 93.10
91.22
91.07
89.28
88.46
86.23 / 92.59
94.23
94.11
92.00
91.30
88.88 / 98.00
97.95
95.83
95.65
95.23
92.50 / 84.48
84.21
82.14
78.57
76.92
72.54

Climate change impact on pink borer dynamics and crop-pest interactions in rice

Climate change impact predictions were made for 2020 and 2050. Up to 0.85oC temperature rise has already been recorded in Indian sub-continent. By 2050 up to 2.0-2.5oC rise is expected (Lal et al., 2001).

Table 4: Climate change projections for Indian subcontinent based on CCSR/NIES model predictions (Lal et al., 2001)

Year / Scenario / Temperature change (oc)
A1 / A2 / B1 / B2
2020s / Annual / 1.18 / 1.00 / 1.32 / 1.41
Winter / 1.19 / 1.08 / 1.37 / 1.54
Rainy / 1.04 / 0.87 / 1.12 / 1.17
2050s / Annual / 2.87 / 2.63 / 2.23 / 2.73
Winter / 3.18 / 2.83 / 2.54 / 3.00
Rainy / 2.37 / 2.23 / 1.84 / 2.25

Results

The number of eggs, larvae, and pupae of the PSB, simulated under experimental conditions as in Joshi et al. (2009b), were proximal to the observed number of these development stages (R2=0.99, RMSE= 5.66%; Fig. 1a). Likewise, simulated and observed yield under different larval densities was very close (R2=0.97; RSME=6.89%) (Fig.1b). This coupled InfoCrop model could satisfactorily simulate the PSB dynamics and crop-pest interactions in rice.

Fig. 1 Simulated and observed (A) populations (square root transformed) of S. inferens and (B) rice yield under different damage intensities of S. inferens

Thresholds and thermal constant for different developmental stages of S. inferens

Temperature-dependent development of the PSB over a range of constant temperatures from 18±1oC to 33±1oC followed a linear relationship and facilitated quantification of stage-specific thermal constants and lower development thresholds of the pest (Fig. 2).

Fig. 2. Regression between development rate and temperature to determine development threshold and thermal constant for S. inferens developmental stages