Climate change impacts on insect pests for high value specialty crops in California

Full text

Science of the Total Environment 906 (2024) 167605
Available online 5 October 2023
0048-9697/© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Climate change impacts on insect pests for high value specialty crops
in California
Prakash Kumar Jha
a
, Ning Zhang
c
, Jhalendra P. Rijal
a
, Lauren E. Parker
b
,
c
, Steven Ostoja
b
,
c
,
d
,
Tapan B. Pathak
a
,
e
,
*
a
Division of Agriculture and Natural Resources, University of California, 2801 2
nd
St., Davis, CA 95618, United States of America
b
Institute of the Environment, University of California Davis, One Shields Ave., Davis, CA 95616, United States of America
c
USDA California Climate Hub, Davis, CA 95616, United States of America
d
Sustainable Agricultural Water Systems Research Unit, Agricultural Research Service, United States Department of Agriculture, Davis, CA 95616, United States of
America
e
Department of Civil and Environmental Engineering, University of California Merced, 5200 N. Lake Rd., Merced, CA 95343, United States of America
HIGHLIGHTS GRAPHICAL ABSTRACT
•Climate change will increase pest pres-
sure in high value agriculture in
California.
•Earlier onset and rapid completion of
insect lifecycle under climate change
•Results can serve as a guide for man-
aging pest risks under future climate.
•Results are relevant globally and
methods scalable to other regions and
pests.
ARTICLE INFO
Editor: Jacopo Bacenetti
Keywords:
Climate change
Walnuts
Almonds
Peaches
Codling moth
Peach twig borer
Oriental fruit moth
Biox
Generations
ABSTRACT
California is a global leader in production and supply of walnuts and almonds, and the state is the largest
producer of peaches in the U.S. These crops have an important contribution to the California’s agricultural
economy. Damages to these crops from lepidopteran pests, mainly from Codling moth (Cydia pomonella) (family:
Tortricidae), Peach twig borer (Anarsia lineatella) (family: Gelechiidae) and Oriental fruit moth (Grapholita
molesta) (family: Tortricidae), are still high, despite the improvement in pest management activities. Given that
temperature increase can directly impact the rate of growth and development of these pests, it is important to
understand to what extent dynamics of these pests will change in future in California. The objective of this study
was to quantify changes in the biox, lifecycle length, and number of generations for these pests for the entire
Central Valley of California. Using a well-established growing-degree days (GDD) model calibrated and validated
using observations from orchards of California, and climate change projections from the Coupled Model Inter-
comparison Project phases 5 and 6 (CMIP5 and CMIP6) General Circulation Models, we found that biox dates of
these pests are expected to shift earlier by up to 28 days, and length of generations is expected to be shortened by
* Corresponding author at: Division of Agriculture and Natural Resources, University of California, 2801 2
nd
St., Davis, CA 95618, United States of America.
E-mail address: tpathak@ucmerced.edu (T.B. Pathak).
Contents lists available at ScienceDirect
Science of the Total Environment
journal homepage: www.elsevier.com/locate/scitotenv
https://doi.org/10.1016/j.scitotenv.2023.167605
Received 16 June 2023; Received in revised form 14 September 2023; Accepted 3 October 2023

Science of the Total Environment 906 (2024) 167605
2
up to 19 days, and up to 1.4 extra generations of these pests can be added by the end of the century depending on
the scenario. Results from this work would enable industries to prioritize development of practices that are more
effective in the long run, such as developing better cultural and biological pest solutions and insect tolerant
varieties. Growers and researchers can take proactive actions to minimize future risks associated with these
damaging pests. This work can be scalable to other pests and regions to understand regional dynamics of
damaging agricultural pests under climate change.
1. Introduction
California is the largest and the most diverse agricultural economy in
the nation with revenue from farms and ranches exceeding $50 billion,
making up >13 % of total U.S. agricultural receipts (USDA, 2023). Due
to the favorable Mediterranean climate and unique microclimatic zones,
California produces >400 commodities including just over one third of
the country’s vegetables and two-thirds of its fruits and nuts (CDFA,
2021; USDA, 2023). This includes walnuts (Juglans regia), almonds
(Prunus dulcis), and peaches (Prunus persica), which are highly valued
commodities with a combined value of over $6 billion in cash receipts
(CDFA, 2021). California supplies two-thirds of the world’s walnut trade
and produces >99 % of the total commercial supply of walnuts in the U.
S. (Walnut Board of California, 2023). Similarly, the state is the largest
producer of almonds in the world, accounting for about 80 % of the
world’s supply of almonds, and almost all of the U.S. commercial supply
(Almond Board of California, 2021). Also, California is the top producer
of peaches in the U.S., and in 2020 alone 468,000 tons of peaches were
produced in the state (CDFA, 2021).
As with any crop, walnuts, almonds, and peaches are susceptible to
damages from lepidopteran insect pests. Some of the most damaging
pests for these crops in California include Codling Moth (Cydia pomo-
nella) (CM, hereafter), Peach Twig Borer (Anarsia lineatella) (PTB,
hereafter), and Oriental Fruit Moth (Grapholita molesta) (OFM, here-
after). Almost all walnut plantations are susceptible to damage from CM
(UC IPM, 2023). This pest overwinters as a full-grown larva, whose 1st
ight begins around late March to early April and is able to complete 2
or 3 generations in most places with 4th generations found in some
warmer areas (UC IPM, 2023). Each overwintered female lays about 30
eggs and this number doubles in later generations. The feeding of the 1st
generation larvae causes nutlets to drop from the tree, and the later
generations feed on the kernels rendering them unsellable. Moreover,
these damaged nuts serve as a breeding ground for navel orangeworm
(Amyelois transitella), which is the most damaging pest across three nut
crops – almonds, walnuts and pistachios (Grant et al., 2020; Haviland
et al., 2021). PTB (Anarsia lineatella) is a major pest of almonds and
peaches in California. Female PTB lay eggs on twigs, fruits and leaves.
PTB is able to complete 3 to 4 generations per year (UC IPM, 2023). In
almonds, larvae feed on both shoots and nuts causing damage to twigs
and reducing the quality of kernels. PTB can attack shoots and fruits of
peach as well. It starts attacking peach fruits from the color break stage
until harvest and therefore multiple insecticide sprays are needed to
minimize economic loss (Hasey et al., 2015). OFM (Grapholita molesta)
larvae bore deeper into the shoots of peaches and almonds than the PTB.
These pests are typically able to complete 5 generations per year, even 6
generations in years with warm early springs and late falls (UC IPM,
2023). The early generations (1st and 2nd) larvae attack peach shoots
but as the fruits begin to develop and mature, they prefer to feed on fruit,
which makes the fruit unmarketable. The early larval instar enters inside
the fruit, feed on and grow inside until advanced larval stage and exit the
fruit to pupate.
Despite being the leader or top producer of these specialty crops in
the U.S., productions of these crops are highly vulnerable to climate
change due to high chill requirements, and low tolerance to heat among
others (Pathak et al., 2018; USDA, 2023). According to California’s
Fourth Climate Change Assessment Report, temperatures have increased
by between 1 ◦F (0.5 ◦C) and 2 ◦F (1.1 ◦C) since the start of the 20th
century (Bedsworth et al., 2018). Future conditions depend on future
greenhouse gas (GHG) emissions, but temperatures may rise by >3 ◦C by
the mid-21st century and by >5 ◦C by the end of the 21st century (Pierce
et al., 2018). Such temperature increases are expected to modify pest
pressure in agriculture as the distribution and rates of survival, growth,
development, and reproduction of insects are highly dependent on
temperature (Dukes et al., 2009; Kocm´
ankov´
a et al., 2009). Although
other factors such as photoperiod might modify the impact of temper-
ature on pest phenology (Bale et al., 2002), under warmer conditions,
insects can be more damaging because of a hastening of their develop-
ment rate, resulting in their early presence in spring, and an increase in
the number of generations in a season resulting from a compressed life
cycle (Luedeling et al., 2011). Additionally, high temperature combined
with elevated CO
2
concentrations can affect the population dynamics of
insects resulting in more crop damages (Cauleld and Bunce, 1994; Fand
and Kamble, 2012; Roth and Lindroth, 1995). Despite careful planning
and implementation of various types of integrated pest management
(IPM) activities such as mating disruption, biological control, cultural
control and responsible use of insecticides based on pest monitoring data
(UCIPM, 2023; Kadoi´
c Balaˇ
sko et al., 2020), the economic losses from
these pests are high. Considering the importance of walnut, almond and
peach in California’s agriculture, it is important to assess how pressure
of these pests will change in future in the context of climate change.
To our knowledge, there has been no published study that quantied
the changes in biox and generations of CM, PTB, and OFM across
California using currently available downscaled global climate model
projections. The objective of this study is to quantify changes in popu-
lation dynamics of these three pests, measured in terms of change in
biox and number and length of generations under climate change in
walnuts, almonds and peaches growing areas of the Central Valley of
California. Improved understanding of these pests’ population dynamics
in the future can help prepare Integrated Pest Management (IPM) pro-
grams and improve farm prot margins.
2. Methodology and data
2.1. Overview
Our aim is to understand the temperature-driven change in the
number of generations of the three pests (CM, PTB and OFM) under
projected future climatic conditions as compared to 2016–2021, here-
after referred as the ‘recent period,’ over the state of California. Using
life cycle data of these pests observed in the recent period (Sect. 2.2),
and temperature data of the recent period from the Gridded Surface
Meteorological (gridMet) dataset (Sect. 2.4.1), we computed the
growing degree-days (GDD) thresholds required to reach biox and
complete the life cycle of each of these three pests using a GDD model
(Sect. 2.3). We used these GDD thresholds to calculate number of days
required to reach biox and complete the life cycle, allowing us to es-
timate the total number of generations possible under future (2030 to
2099) temperature projections (Sect. 2.4.2), for the entire study area
(Sect. 2.5). We calculated the difference in number of generations be-
tween the recent period and the future period, its statistical signicance
and the trend over the years.
P.K. Jha et al.

Science of the Total Environment 906 (2024) 167605
3
2.2. Biox observation
Biox is the date from which thermal days are accounted for pest
management purposes (Murray, 2020), and can be represented by a
biological event such as rst moth ight, or a calendar date. The tradi-
tional way to determine biox is by checking the pheromone traps in an
orchard until the rst moths are captured consistently for at least few
days (Alston et al., 2018). In this study, the dates of the rst moth
captured during the spring ight were considered as biox dates for
each of these three pests (Hasey et al., 2015; Grant et al., 2020). The
eld-based biox observations were obtained by placing traps in walnut
(for CM) and almond and peach (for PTB and OFM) orchards within a
10-mile radius of the University of California Cooperative Extension
ofce at Modesto, California (indicated by a red dot in Fig. 1(b)(c)(d)). It
is a common IPM practice among the Pest Control Advisers and growers
throughout the Central Valley of California to use pheromone traps to
record the biox of these three moths as a part of management of these
pests (Hogmire et al., 1995; Hasey et al., 2015; Grant et al., 2017). For
each insect, three Pherocon VI delta-style red traps, baited with the
species-specic lures (Tr´
ec´
e, Inc., Adair, OK), were placed in each of the
three orchards. All trap types were placed in three rows (i.e., replica-
tions) that were separated by a 5-row distance (=30.5 m). OFM traps
were installed in mid-February, while CM and PTB traps were installed
in mid-March, and the traps were checked on a weekly basis throughout
the growing season, unless there were some restrictions in the orchard in
which case it was deferred for a day or two. The lures were changed at
every four weeks interval. Since the presence of other host crops can
confound capture of insects in the traps, these traps were purposively
placed in orchards in both Modesto and Clarksburg, where the other
potential host crops of OFM and CM, such as apples were not present
Fig. 1. Study area and focused specialty crops based on 2020 cropland data layer.
P.K. Jha et al.

Science of the Total Environment 906 (2024) 167605
4
within a reasonable distance. In Modesto, almond and peach, two host
crops of OFM and PTB, were present in the area. However, we do not
consider a signicant interference between these two host crops as the
phenology of OFM and PTB are similar between two crops, and in fact,
pest control practitioners regularly install traps in almond orchard to
predict the phenology of these pests in peach or vice versa. To minimize
the inuence of pesticides on emergence of these pests (Biondi et al.,
2013), we made sure that the orchards where pheromone traps were
placed were free from insecticide applications before the spring biox
that we used for this study. None of the orchards included in this study
had insect mating disruption implemented during the years when these
data were collected. Table 1 summarizes the pest information used in
this study obtained from the UCIPM’s webpage (https://ipm.ucanr.edu/
WEATHER/ddretrievetext.html), including the upper and lower tem-
perature thresholds and the end dates for the GDD model, the observed
generation length of the three pests, and the corresponding crops of
focus. To get the GDD needed to complete 1st, 2nd and 3rd generations
for a particular insect, we need to run the model by selecting the insect
and crop associated with that insect, followed by specifying the weather
station (in this case county Stanislaus and weather station CIMIS # 71),
and time period from 2016 to 2021.
Because the observational data of biox and number of generations
were limited to few locations and because future changes in a location’s
climate cannot be known with certainty, we used a GDD model to
compute GDD thresholds required to reach biox and complete gener-
ations based on the observational data. The GDD model has been suc-
cessfully applied to determine the biox and number of generations of
navel orangeworm (Pathak et al., 2021). Given that the GDD model uses
thermal thresholds for insects to reach biox and complete generations,
if these thresholds differ signicantly among locations, a serious bias
could incur in estimating biox and number of generations for all lo-
cations using the thresholds from one location. Therefore, we compared
the GDD thresholds corresponding to biox dates of CM and OFM from
Modesto with the same from another location (Clarksburg California –
Sacramento River Delta). Since, we did not have biox dates of PTB from
another location, we compared the GDD thresholds corresponding to
PTB’s biox dates from Modesto for the periods 2016–2021 and
2002–2012. Additionally, we compared GDD thresholds to reach biox
of these three moths from Modesto with other locations in California
(Merced, San Joaquin, and Stanislaus counties) for verication purpose,
which were available from the Tr´
ec´
e, Inc. (https://www.trece.com/
eld-reporter/).
2.3. Growing degree-days (GDD) model
Given that most pests require a certain amount of heat to develop
from one stage of their life cycle to another, the temperature-based GDD
model has been widely used to predict the timing of key pest growth
stages (Clark and Kowalsick, 1992; Jones et al., 2013; Murray, 2020;
Zalom et al., 1998). The most common way to calculate degree-days is to
use the mean air temperature computed as the average of maximum and
minimum daily temperatures (Herms, 2004; McMaster and Wilhelm,
1997; Murray, 2020). For example, if the daily mean temperature is one
degree higher than the lower temperature threshold for a certain pest at
one day, one degree-day is accumulated for that pest at that day. One
modication to this calculation involves incorporating a horizontal
cutoff, which assumes that temperatures above a dened upper
threshold do not count towards degree-day accumulation, i.e. the insect
development occurs at a constant rate when daily temperature exceeds
the upper threshold (Beasley and Adams, 1996). In this study, we used
January 1st as the starting date for GDD accumulation, in each year, to
make it easier to track GDD. However, we used the spring biox to
calculate the number of generations, similar to what pest control prac-
titioners do. The end dates for calculating GDD and generations were
October 31 for CM and September 15 for PTB and OFM (Table 1). These
start and end dates were based on experiences from eld observations.
We adopted the simplest GDD model used by Pathak et al. (2021) which
considers both upper and lower thresholds as shown in Eq. 1.
GDD =
⎧
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎩
Tmax +Tmin
2−Tlower,Tlower <Tmax +Tmin
2<Tupper
Tupper −Tlower,Tmax +Tmin
2≥Tupper
0,Tmax +Tmin
2≤Tlower
(1)
T
max
is daily maximum temperature, and T
min
is daily minimum tem-
perature. T
lower
is the temperature threshold at which pest development
begins in the spring. T
upper
is the temperature threshold above which the
rate of pest growth begins to decrease or stop depending on cut-off
methods. The horizontal cutoff is recommended by UCIPM
(http://www.ipm.ucdavis.edu/WEATHER/ddconcepts.html), and it has
been the most widely accepted method for degree day estimation
globally including California (Allen, 1976; Pruess, 1983; Zalom et al.,
1983). When daily mean temperature is between the upper and lower
thresholds, the pest growth rate is presumed to be linearly related to
temperature. The temperature thresholds vary with respect to different
insects. The lower and upper temperature thresholds for CM, PTB and
OFM in California are listed in Table 1.
2.4. Temperature data
2.4.1. Recent observation (2016–2021)
We obtained daily T
max
and T
min
from the gridMet gridded dataset
(Abatzoglou, 2013) available in NetCDF format for different locations
where traps were placed. First location was in 3800 Cornucopia Way,
Modesto, Ca 95,358 (37.58188◦N, 120.99176◦W) and data were from
2016 to 2021, which were used to estimate GDD thresholds to reach
biox in the present and future periods. Also, gridMet temperature data
of the same location but from 2002 to 2012 were used to validate biox
of PTB. Additionally, we used gridMet daily T
max
and T
min
of Clarksburg
California – Sacramento River Delta (38.42◦N, 121.53◦W), and other
three locations (Merced, San Joaquin and Stanislaus counties) for vali-
dating biox of CM and OFM. gridMet data combine attributes of the
high spatial resolution (800 m) Parameter-elevation Regressions on In-
dependent Slopes Model (PRISM, Daly et al., 2008) data available at
monthly time steps with temporally rich data from the North American
Land Data Assimilation System Phase 2 (NLDAS-2, Mitchell et al., 2004)
with a spatial resolution of 1/8◦(approximately 12 km) at hourly time
Table 1
Summary of pest biology information, degree days information, and generation
lengths of codling moth (Pitcairn et al., 1992), peach twig borer (Brunner and
Rice, 1984), and Oriental fruit moth (Rice et al., 1984) used in this study.
Pests Lower
Thld.
(◦C)
Upper
Thld.
(◦C)
GDD
end
date
Generation Length Focused
crops
(degree-days in ◦C)
1st 2nd 3rd
and
later
Codling
Moth
(CM) 10 31.1
Oct.
31 571 593 649 Walnut
Peach twig
borer
(PTB) 10 31.1
Sep.
15 554 554 554
Almond,
Peach
Oriental
fruit
moth
(OFM) 7.2 32.2
Sep.
15 518 518 518
Almond,
Peach
P.K. Jha et al.

Science of the Total Environment 906 (2024) 167605
5
steps. PRISM data was derived using local regression of station data with
physiographic elements using an extensive information on spatial
climate factors (Daly et al., 2008). Similarly, the NLDAS-2 dataset was
primarily derived from the North American Regional Reanalysis (NARR,
Mesinger et al., 2006), interpolated from the NARR grid at 32 km spatial
resolution to 1/8◦NLDAS-2 grid, by adjusting for elevation differences
(e.g., standard lapse rates), while the time steps were disaggregated
from three-hourly to hourly. These two datasets were merged from the
NLDAS-2 grid (approximately 12 km) to a 4-km grid using a bilinear
interpolation. The resulting T
max
and T
min
data were bias corrected using
climatologically aided interpolation (CAI). The CAI was performed by
superimposing interpolated daily departures of monthly averages from
NLDAS-2 with monthly data from PRISM. Further information is avail-
able in Abatzoglou (2013).
2.4.2. Future (2030–2099)
Future climate was divided into two periods: 2030–2059 (‘mid-
century’, hereafter) and 2060–2099 (‘late-century’, hereafter). Future
projections of temperature, from 2030 to 2099, were obtained from an
ensemble of three global climate models (GCMs) — HadGEM3-GC31-LL,
CNRM-ESM2–1, MIROC6 — which participated in the Coupled Model
Intercomparison Project phase 6 (CMIP6). These GCMs were chosen
because high-resolution (3 km) daily T
max
and T
min
data from these
GCMs, downscaled using the Localized Constructed Analogues statistical
downscaling version 2 (LOCA2), were available for the California
domain (Pierce et al., 2023). Another reason for choosing these GCMs
was the same institutions participated in CMIP5 as well and we were
interested in checking consistency in our results using projections from
CMIP6 and CMIP5. For comparison, we used daily T
max
and T
min
from
four CMIP5 GCMs — HadGEM2-ES, CNRM-CM5, CanESM2, MIROC5 —
which have been prioritized by Cal-Adapt based on their ability to
represent California’s observed climate (Pierce et al., 2018). Data from
these CMIP5 GCMs were downscaled at a spatial resolution of 1/16
◦
(approximately 6-km) using LOCA method (Pierce et al., 2014).
The gridded downscaled data of these CMIP6 and CMIP5 GCMs for
the future (2030–2099) are available from Cal-Adapt (https://cal-adapt.
org/data/download/) in NetCDF format. These downscaled data are
better in terms of representation of extremes, orography, and spatially
coherent variables than the original outputs from GCMs (Pierce et al.,
2014; Pierce et al., 2023), and various previous studies have used the
data for climate change impact assessment (Engstr¨
om and Keellings,
2018; Guirguis et al., 2018; Pathak and Stoddard, 2018; Pierce and
Cayan, 2016).
We used two shared socioeconomic pathways (SSP) from CMIP6 (SSP
2–4.5 and SSP 5–8.5) to understand the range of potential outcomes
under moderate and business as usual emissions scenarios respectively.
Also, we used two Representative Concentration Pathways (RCPs) from
CMIP5 (RCP 4.5 and RCP 8.5) as SSP 2–4.5 and SSP 5–8.5 are compa-
rable with RCP 4.5 and RCP 8.5 respectively (Meinshausen et al., 2020).
We understand two different GCMs, even from the same institution,
can be different in terms of representation of various physical processes
(Wyser et al., 2019), however the GCMs from same institutions in CMIP5
and CMIP6 have relatively similar patterns than the GCMs from other
institutions (Try et al., 2022). Also, the purpose of our comparison is to
check the general consistency; a detailed investigation of the difference
between CMIP5 and CMIP6 GCMs is beyond the scope of this study.
2.5. Study area
The study was conducted in 20 counties in the Central Valley of
California, a topographically homogenous region of the state that trends
north-south (Fig. 1a). In the Central Valley, we focused exclusively on
the locations where walnuts, almonds and peaches are cultivated
(Figs. 1b, c, d). To extract the locations where these three crops are
cultivated, we used 30-m crop location data from the US Department of
Agriculture National Agriculture Statistics Service (USDA-NASS)
Cropland Data Layer (CDL) 2020 (https://nassgeodata.gmu.edu/C
ropScape/). Crop masks for each of these three crops were created by
aggregating the 30-m CDL data to the 3-km resolution of the CMIP6-
LOCA2 grid and 6-km resolution of the CMIP5-LOCA grid, classifying
crop locations as those 3-km and 6-km grid cells where there was at least
one 30-m grid from the CDL of that particular crop.
2.6. Step by step calculation
Step 1: Clip the temperature data, both observed and future pro-
jections, to regions where these 3 crops are cultivated (Fig. 1b, c, d; Sect.
2.5).
Step 2: Get the rst ight biox dates of the three pests for the recent
period from eld observations (Sect. 2.2).
Step 3: For each pest, compute the GDD required to reach biox
during the recent period by averaging the GDD accumulated in each year
from January 1st until the biox date using Eq. (1). Also, compute the
number of days needed to reach biox by counting the calendar days in
the recent period starting from January 1st of each year to the biox
date.
Step 4: For each pest, start accumulating GDD from the date when
biox was observed until GDD required to complete 1st generation, as
listed in Table 1, is reached and then count the number of days passed
for each year in the recent period. Use the average number of days to
complete life cycle of these pests in the recent period as the length of 1st
generation. Repeat this step for 2nd and 3rd generations.
Step 5: For both present and future periods in the entire study area,
start accumulating GDD from the end of previous generation until the
date when GDD required to complete next generation, listed in Table 1,
is reached. Count number of days passed and repeat this step until the
end of season mentioned in Table 1. This process gives the maximum
number of generations possible in each year, and number of days
required to complete each generation in each year for these 3 pests.
Step 6: Compute the trend of lengths of each generation and number
of generations.
2.7. Statistical analysis
We used the Mann Kendall (MK) statistical test (Kendall, 1948;
Mann, 1945) to conrm whether there were monotonic upward or
downward trends present in the biox, number of generations and
length of generations over the 2030–2099 period at a signicance level
of 5 % with a null hypothesis of no trend and an alternative hypothesis of
trends in number and length of generations. Given that the MK test is a
non-parametric test, there is no requirement that the data be normally
distributed. There is a requirement of independence and the 70-year
time series in this study is a sufciently large time series to full that
requirement. Also, we computed the statistical signicance of the dif-
ference in GDD to reach biox of these pests from Modesto with the same
from other locations using the t-test (Student, 1908). As the t-test is a
parametric test, we checked the data for the normality assumptions
using Shapiro-Wilk normality test function and Q-Q plots of these data
before running the t-test. Additionally, we analyzed whether there was
statistically signicant difference between the daily T
min
(T
max
), average
of all GCMs, from CMIP6 compared with the same variable from CMIP5
using the t-test.
3. Results
3.1. Baseline information based on observation
Using the GDD model and observed data from 2016 to 2021, we
found that the total GDD, starting from the 1st of January, needed to
reach biox for the CM, PTB and OFM were 240 ◦C, 226 ◦C and 221 ◦C
respectively during the recent period (Fig. 2). Similarly, the number of
calendar days needed to reach biox of CM, PTB and OFM were 97, 94
P.K. Jha et al.

Science of the Total Environment 906 (2024) 167605
6
and 61 respectively from January 1st. The number of calendar days
required to complete 1st generation of CM, PTB and OFM were 61, 62
and 63 respectively. These calendar days were estimated based on
thermal thresholds of these three pests using observed temperature data.
We did not nd any signicant difference (p <0.05) in the GDD
thresholds corresponding to biox of the CM and OFM between Modesto
and Clarksburg in the recent period (Table S1). Neither did we nd any
signicant difference (p <0.05) between the GDD threshold of PTB in
Modesto between recent period and 2002–2012. Furthermore, we did
not nd any statistically signicant difference (p <0.05) between the
GDD thresholds to reach biox of these pests at Modesto and the same
from the other three locations (Merced, San Joaquin, and Stanislaus
counties). The Shapiro-Wilk normality test of these data from different
locations revealed that these data were not signicantly different from a
normal distribution (p >0.05). Also, by plotting Q-Q plots of these data,
we found these data were normally distributed. Additionally, we
compared GDD corresponding to biox in Modesto with some interna-
tional studies. The average GDD required to reach biox of CM in our
study, 240 ◦C, was close to Damos et al. (2018) who found biox of CM
between 250 and 300 ◦C in Greece. In another study, Damos et al. (2022)
found that PTB requires 150.6 ◦C to reach biox, which is 75.4 ◦C lower
than the same found in our study. The reason for lower GDD in Damos
et al. (2018) is because they used lower temperature thresholds of
11.4 ◦C, compared to 10 ◦C in our study. By having lower temperature
thresholds (LTT) higher than the LTTs used in our study, more GDD is
accumulated in our study (Fig. 2).
3.2. Change in pest generation length under climate change
The number of days needed to complete a generation (total duration
between egg laying to adult emergence) of these three pests depends on
the ambient temperature, as they are poikilothermic. Under warmer
future conditions, the number of days needed to reach biox and the
length of life cycle are gradually decreasing at a statistically signicant
(p <0.05) trend for these three pests, with some exceptions (Table 2,
Figs. 3, 4, S1, S2).
The CM reached biox in 97 days based on the observations, and
until mid-century the biox could even further delay by 3 days but in
late-century, depending on SSP, biox is expected to be earlier by 8–17
days earlier than the recent climate. CM is expected to nish its 1st, 2nd
and 3rd generations around 2–6 days earlier than the recent climate
during both mid and late-century. Compared to the earlier generation,
CM is expected to complete 4th generation very rapidly in future — up to
11 days earlier during mid-century and 13–18 days earlier during late-
century than the recent climate (Table 2, Figs. 3, 4). The PTB reached
biox in 97 based on the observations. Under future climate, biox is
expected to be 1–5 days earlier in mid-century and 12–21 days earlier in
late-century. All generations are expected to be completed 1–6 days
earlier in future compared to now. The OFM needed 59 days to reach
biox in recent observations, and until mid-century the biox could
even further delay by 4–5 days but in late-century biox is expected to
be 1–9 days earlier than now. The length of all generations of OFM is
expected to be shortened by 0 to 5 days in future compared to the recent
climate.
We repeated these computations using CMIP5 projections and found
that our results regarding the reduction in length of generations and
days needed to reach biox in future are consistent, and the reductions
are even greater, in some cases, when CMIP5 projections are used
considering both low (RCP 4.5/SSP 2–4.5) and high emission scenarios
(RCP 8.5/SSP 5–8.5) (Table 2, Figs. S1, S2). For example, unlike CMIP6,
in CMIP5 projections there is no chance that the bioxes of CM can be
delayed in mid-century; rather its biox is expected to be earlier by 2–7
days, and in late-century biox is expected to be 13–27 days earlier
(Table 2). Similarly, CM is expected to complete its 4th generation more
rapidly in CMIP5 than CMIP6. The length of 4th generation of CM is
shortened by 11–15 days during mid-century and 16–19 days during
late-century than the recent climate (Table 2). Likewise, compared to
CMIP6, bioxes of PTB and OFM are expected to be earlier in CMIP5 by
2–3 more days during mid-century and 3–5 more days during late-
century than the recent climate.
The reason for earlier biox and greater reduction in generation
lengths in CMIP5 compared to the same in CMIP6 is because tempera-
tures in CMIP5, mainly T
min
, in our areas of interest are relatively higher
than the same in CMIP6. We compared the T
min
/T
max
from RCP 4.5 (RCP
8.5) of CMIP5 with the same from SSP 2–4.5 (SSP 5–8.5) of CMIP6, and
the differences in temperatures were statistically signicant (p <0.05)
(Figs. S5, S6).
Unlike late generations, the lengths of 1st and 2nd generations,
computed using CMIP5 projections, did not decrease over time in future
(Figs. S7, S8). The reason for this is with the earlier biox in future
climate in CMIP5, the 1st generation starts earlier when the GDD
accumulation is low, and consequently it requires longer time to meet
the required GDD threshold to complete the life-cycle than the time it
would have taken if biox had occurred later.
3.3. Change in number of generations under climate change
As a result of the shortening generation length (i.e., calendar days)
due to higher thermal accumulation in a given period, these insects are
expected to add extra generations during a growing season. We found
that different locations within the valley differ in terms of the addition of
extra generation of these three pests. Figs. 5 and 6 show spatial distri-
bution of change in number of generations of these three pests in future
under the SSP 2–4.5 and SSP 5–8.5 respectively.
According to recent observations, CMs were able to complete an
average of 3.8 generations per growing season in walnuts. By 2040,
these pests are expected to complete 4–4.5 generations per season in less
than half of the walnut growing regions according to SSP 2–4.5 from the
Fig. 2. Statistics of a) accumulated GDD from January 1 through the rst biox date, b) calendar days from January 1 through the rst biox date and c) length
(calendar days) of 1st generation of three pests based on observed data of recent six years (2016–2021).
P.K. Jha et al.

Science of the Total Environment 906 (2024) 167605
7
Table 2
Average biox, number and length of generations of CM, PTB and OFM during the Recent period, mid-century (2030–2059) and late-century (2060–2099) along with
their trends in California. The future climate was projected using 4 GCMs from CMIP5 and 3 GCMs from CMIP6 for RCP4.5/SSP 2–4.5 and RCP8.5/SSP 5–8.5.
Pest Pest development
indicator
Recent
(median)
Projection SSP 2–4.5/RCP4.5 SSP 5–8.5/RCP8.5
Trend 2030–2059
(median)
2060–2099
(median)
Trend 2030–2059
(median)
2060–2099
(median)
Codling Moth
Biox 97 CMIP6 −0.26 100 89 −0.44 96 80
97 CMIP5 −0.23 95 84 −0.46 90 70
Number of
generations 3.8 CMIP6 0.01 4 4 0.03 4 5
3.8 CMIP5 0.01 4 5 0.02 4 5
Length of 1st
generation 62 CMIP6 0.04 56 60 0 60 58
62 CMIP5 0.05 60 63 0.04 62 65
Length of 2nd
generation 40 CMIP6 −0.01 38 38 −0.04 37 36
40 CMIP5 −0.01 37 37 0 37 36
Length of 3rd
generation 40 CMIP6 −0.06 38 36 −0.09 37 34
40 CMIP5 −0.03 36 35 −0.05 35 34
Length of 4th
generation 52 CMIP6 −0.12 43 39 −0.16 41 34
52 CMIP5 −0.1 41 36 −0.13 37 33
Length of 5th
generation NaN CMIP6 −0.09 44 42 −0.18 43 38
NaN CMIP5 −0.05 44 43 −0.18 42 35
Length of 6th
generation NaN CMIP6 0.04 40 41 −0.12 42 38
NaN CMIP5 −0.05 42 41 −0.05 40 38
Peach Twig
Borer
Biox 97 CMIP6 −0.26 96 85 −0.44 92 76
97 CMIP5 −0.23 94 83 −0.46 90 69
Number of
generations 3.4 CMIP6 0.01 3.5 4.0 0.02 3.7 4.5
3.4 CMIP5 0.01 3.7 4.0 0.02 3.9 4.6
Length of 1st
generation 62 CMIP6 0.04 56 60 0 60 59
62 CMIP5 0.05 60 63 0.04 62 64
Length of 2nd
generation 37 CMIP6 −0.01 36 35 −0.03 36 34
37 CMIP5 0 35 35 0.01 35 35
Length of 3rd
generation 33 CMIP6 −0.03 32 30 −0.06 31 29
33 CMIP5 −0.02 30 30 −0.03 30 29
Length of 4th
generation 34 CMIP6 −0.03 31 30 −0.07 31 28
34 CMIP5 −0.03 31 29 −0.05 30 28
Length of 5th
generation NaN CMIP6 0 28 29 −0.03 29 28
NaN CMIP5 −0.01 29 29 −0.03 28 28
Length of 6th
generation NaN CMIP6 NaN NaN 27 −0.01 27 27
NaN CMIP5 NaN 28 28 −0.01 27 27
Oriental Fruit
Moth
Biox 59 CMIP6 −0.22 64 58 −0.33 63 50
59 CMIP5 −0.15 61 55 −0.32 60 45
Number of
generations 5.0 CMIP6 0.01 5.1 5.6 0.03 5.4 6.2
5.0 CMIP5 0.01 5.3 5.7 0.02 5.5 6.4
Length of 1st
generation 61 CMIP6 0.01 60 61 −0.06 60 59
61 CMIP5 −0.02 62 61 −0.09 62 60
Length of 2nd
generation 38 CMIP6 0.01 35 36 −0.02 35 34
38 CMIP5 0.01 35 35 0.03 35 37
Length of 3rd
generation 29 CMIP6 −0.01 27 28 −0.03 27 26
29 CMIP5 −0.01 27 27 −0.01 27 27
Length of 4th
generation 27 CMIP6 −0.02 25 25 −0.04 25 24
27 CMIP5 −0.01 25 24 −0.03 25 24
Length of 5th
generation 28 CMIP6 −0.04 26 24 −0.06 25 23
28 CMIP5 −0.03 25 23 −0.04 24 23
Length of 6th
generation NaN CMIP6 −0.02 25 24 −0.05 25 23
NaN CMIP5 −0.02 25 24 −0.05 24 22
Length of 7th
generation NaN CMIP6 0 23 23 −0.02 23 22
(continued on next page)
P.K. Jha et al.

Science of the Total Environment 906 (2024) 167605
8
CMIP6 (Fig. 5). However, if RCP 4.5 from CMIP5 is considered, most
areas would be exposed to 4–4.5 generations of this pest by 2040
(Figs. 5, S3). By 2060, under SSP 2–4.5, however, the pest is expected to
complete 4–4.5 generations in most of the northern regions and 4.5–5
generations in some areas in south. From 2080 onwards, regions with
4.5–5 generations of these pests increase gradually and by late-century
the pest is able to complete 4.5–5 generations in most of the regions
and even 5–5.5 generations in a small area in the far south (Fig. 5). The
addition of extra generations of these pests will be more rapid under SSP
5–8.5 than SSP 2–4.5 —by the end of the century, the pest is expected to
complete 5.5–6 generations in around half of the regions (Fig. 6). On an
average, depending on the projections and scenarios, 0.2 to 0.6 and 0.5
to 1.3 generations of CM will be added by mid-century and late-century
periods respectively (Table 2).
In the case of the PTB, recent observations showed that they were
able to nish 3.4 generations on an average during a growing season. By
2040, these Borers will be able to nish 3.5–4 generations in most areas,
and even 4–4.5 generations in some southern areas under both SSPs.
After that, areas exposed to 4.5–5 generations of these pests in south,
and 4–4.5 generations in the remaining parts will gradually increase. By
the end of the century, most areas will have 4–4.5 generations of PTB
while some areas in south, PTB will have 4.5–5 generations. More areas
in south will be under 4.5–5 generations by late-century period under
RCP 4.5 from CMIP5 compared to the SSP 2–4.5 from CMIP6. Under SSP
5–8.5, the insects will complete more generations even 40 years earlier
than the situation in SSP 2–4.5. By the end of the century, under SSP
5–8.5, the insects will be able to complete 5–5.5 generations in southern
parts and 4.5–5 generations in northern parts, and even 5.5–6 genera-
tions in a small area in far south. The areas under 5.5–6 generations will
be relatively bigger under SSP5–8.5 than the RCP 8.5(Figs. S3, S4). On
average, depending on projections and scenarios, 0.1–0.5 and 0.6–1.2
generations of PTB will be added during mid-century and late-century
periods respectively (Table 2).
In the case of OFM, recent observations showed that they can com-
plete 5 generations on an average during a growing season. By 2040 the
insect will be able to complete 5–5.5 generations in northern areas and
5.5–6 generations in southern area under both SSPs. By the end of the
century a signicant part of south will be exposed to 6–7 generations of
this pest, and most of the remaining areas is expected to be under 5.5–6
generations of this pest. Under SSP 5–8.5, situation like 2100 under SSP
2–4.5 will happen 40 years earlier, in 2060. Under SSP 5–8.5, by then
end of the century most areas will be exposed 6.5–7 generations and in
some areas in far south the insect will be able to complete 7–7.5 gen-
erations. These areas where the insect is expected to nish 7–7.5 gen-
erations would be relatively larger if RCP 8.5 of CMIP5 was considered
(Figs. S3, S4). Overall, depending on projections and scenarios, 0.1–0.5
and 0.6–1.4 generations of OFM will be added during mid-century and
late-century periods respectively under CMIP6 (Table 2).
Table 2 (continued )
Pest Pest development
indicator
Recent
(median)
Projection SSP 2–4.5/RCP4.5 SSP 5–8.5/RCP8.5
Trend 2030–2059
(median)
2060–2099
(median)
Trend 2030–2059
(median)
2060–2099
(median)
NaN CMIP5 −0.01 24 23 −0.02 23 22
Length of 8th
generation NaN CMIP6 NaN NaN 21 0 NaN 21
NaN CMIP5 NaN NaN 22 −0.01 22 22
Fig. 3. Comparison of the number of days between January 1 to the rst biox date (left column) and pest generation numbers (right column) for three pests
between current (2016–2021) and future (2030–2099) climate projections using CMIP6.
P.K. Jha et al.

Science of the Total Environment 906 (2024) 167605
9
4. Discussion
Results from this study suggest that codling moth, peach twig borer
and oriental fruit moth are expected to complete more number of gen-
erations under future climate due to the higher rate of heat accumula-
tion. Previous studies (Luedeling et al., 2011; Ziter et al., 2012) using
degree-days models also supported our nding that these three insects
will be able to complete more generations in future in the Central Valley
of California due to climate change. In many cases, the economic dam-
age from late generations pests is expected to be more serious (Higbee
and Siegel, 2009), because their abundance and thereby reproductive
potential tend to be greater due to the compounding effect and also their
tendency to feed on the in-season fruits and nuts unlike the early gen-
eration pests, which feed on non-economic parts such as twigs (UCIPM,
2023). Our study used a well-established GDD model, which was vali-
dated using data from eld observations, and it used an ensemble of
latest generation of climate model projections using GCMs from CMIP6,
downscaled using LOCA2 technique, to assess potential changes in pest
phenology under climate change. Our study also compared the results
from CMIP6 with CMIP5 to check consistency. Despite these strengths,
there are several factors which could modify our ndings but have not
been considered in this study. For example, the elevated levels of at-
mospheric CO
2
in future could increase (Cauleld and Bunce, 1994) or
decrease survival of pests (Tocco et al., 2021) depending on pest species
and other factors. Also, in order to compensate for the decrease in
nutritional quality of plant tissues at a higher CO
2
concentration, pests
need to feed more which can increase cumulative damage to crops
(Jayawardena et al., 2021). Similarly, at high temperature the biological
control of pests could be less effective in cases where temperature-
increase may favor pests over their natural enemies (Furlong and
Zalucki, 2017); or could be more effective if it weakens hosts’ resistance
(Iltis et al., 2018). Pest pressures could also unexpectedly surge in case of
extreme precipitation events (Salih et al., 2020). Temperature increase
can also decrease the effectiveness of chemical control of pests due to
higher risk of building pesticide resistance (Kadoi´
c Balaˇ
sko et al., 2020;
Musser and Shelton, 2005). Likewise, high temperature and dryer con-
ditions can signicantly reduce the efcacy of the applied pesticide
products (Delcour et al., 2015). Temperature increase combined with
water shortage can make crops more vulnerable to pests’ attack as
stressed plants attract more pests in general (Popov et al., 2006; Rose-
nzweig et al., 2001; Showler, 2012). In order to properly estimate the
expected damages from these pests, it is necessary to understand which
stages of these crops are exposed to the critical phases of life-cycle of
these pests, and coupling the impact of pests and pathogen in crop
models (Gregory et al., 2009). The impacts of warming due to climate
change on insects’ population could be different when the uctuations
in daily temperatures are considered compared to the daily mean tem-
perature because the more climate extremes avoid any benecial effects
of warmer temperature (Vasseur et al., 2014). Using a eld experiment
with cotton aphid (Acyrthosiphon gossypii M.) in China, Gao et al. (2018)
found that population dynamics of insects, including reproduction,
survival, and intrinsic rate of increase, is negatively impacted by
frequent exposure to extreme temperature. The extent of such a
decrease, however, depends on the thermal safety margin of insects
(Kellermann et al., 2012), and accurate representation of natural tem-
perature extremes in eld experiments. In nature, extreme temperature
does not occur constantly for twenty-four hours a day (Ma et al., 2015;
Zhao et al., 2014), and after certain number of days, there are periods of
normal temperature (Ma et al., 2018; Zhu et al., 2019). These normal
periods provide opportunities for insects to recover from injuries related
to heat (Bai et al., 2019; Ma et al., 2018). Also, impacts of extreme
temperatures differ according to seasons for different insects (Bokhorst
Fig. 4. Generation length (in calendar days) of three pests in current period (2016–2021) and future (2030–2099) climate projections using CMIP6.
P.K. Jha et al.

Science of the Total Environment 906 (2024) 167605
10
et al., 2012; Kudo and Ida, 2013; Radchuk et al., 2013; Stuhldreher et al.,
2014). Realistic representations of such statistics (intensity, frequency,
duration, and diurnal cycle) of extreme temperatures in eld experi-
ments is crucial to correctly understand impact of extreme heat (Ma
et al., 2021).
Insects may cope with heat damages by various physiological and
molecular changes — such as production of certain molecules to prevent
protein denaturation from excessive heating (Hendrix and Salvucci,
1998; Lopez-Martinez and Denlinger, 2008; Nguyen et al., 2009) —
morphological changes such as by decreasing size (Atkinson, 1994;
Tseng et al., 2018) and with the help of their symbionts (Montllor et al.,
2002; Zhang et al., 2019). Furthermore, insects are able to partly or fully
adapt to extreme temperature by migrating to relatively cooler micro-
climates, a mechanism called behavioral thermoregulation (Caillon
et al., 2014; May, 1979), their mix age structure in a community and
heat tolerance ability specic to development stages (Bowler and Ter-
blanche, 2008; Zhang et al., 2019), their plasticity to tolerate heat by
means of rapid hardening (van Heerwaarden et al., 2016), acclimation
and acclimatization (Jensen et al., 2019; Kellermann et al., 2017) and
evolution of thermal tolerance (Buckley and Huey, 2016; Hoffmann
et al., 2013). However, these adaptation strategies have very limited
potential, and have negative implications as well for survival, develop-
ment, reproduction, and other traits (Ma et al., 2021). Although, to some
extent, our model does account for negative impact of extreme tem-
perature by not accumulating GDD in proportion to temperature beyond
the upper threshold of T
max
, it does not account for the other complex-
ities. Future work should update this study to understand these
uncertainties in pest pressures projections.
Results from this study indicates a potential serious problem in
future pest management resulting from a rapid increase in future tem-
perature, which necessitates nding ways to mitigate such an increase in
temperature in future. These results can assist growers to prepare for
pest management by reviewing their existing strategies, allocating
proper resources, coordinating with stakeholders and service providers,
and evaluating different options proactively. To deal with possible in-
crease in pest pressures due to climate change, development of a holistic
climate smart pest management approach is needed to upgrade the
standard Integrated Pest Management (IPM) practices (Heeb et al.,
2019). The IPM approach not only considers pest control methods such
as insecticides but also combines pest prevention and reduction — by
implementing cultural practices such as using resistant varieties, sani-
tizing the pest infested orchards during the winter, harvesting early to
avoid later generation infestation (Haviland et al., 2021; Grant et al.,
2020), and biological control such as using natural enemies, mating
disruption techniques that ultimately support a balanced ecosystem
(Stern et al., 1959; Stenberg, 2017), reduce GHG emissions and build
resilient agriculture (Barzman et al., 2015). Pest monitoring is an inte-
gral part of IPM (Stern et al., 1959), and the future IPM-based model
should adopt pest forecasting component including the long-term pre-
diction and short-term potential outbreak, pest-scouting, and early
detection and warning to better understand the interactions of climate
change, pest pressures and pest management strategies.
Since various studies have reported that climate change is expected
to exacerbate the pest issues on agricultural crops globally (Bebber et al.,
Fig. 5. Spatial distribution of generation numbers of three pests under SSP 2–4.5 of CMIP6.
P.K. Jha et al.

Science of the Total Environment 906 (2024) 167605
11
2013; IPPC, 2021; Skendˇ
zi´
c et al., 2021), the methodology used in this
study is scalable to other regions globally to quantify the risks of
exposure of various crops to different pests. Assessing such a risk would
be crucial to plan and implement effective IPM and other risk manage-
ment measures.
5. Conclusions
Findings from our study suggested that climate change is expected to
signicantly impact the population dynamics and abundance of three
major pests of fruit and nut crops – codling moth, peach twig borer and
oriental fruit moth in the future. Specically, biox of these three pests
is expected to shift earlier by up to 28 days, and number of days required
to complete different generations is expected to be shortened by up to
19 days. Consequently, by the end of the 21st Century, 0.5–0.6 and
1.2–1.4 additional generations of these pests are projected to occur
respectively under RCP 4.5/SSP 2–4.5 and RCP 8.5/SSP 5–8.5. Based on
these results, it is suggested that fruits-and-nuts-based industries, and
other research and educational organizations educate public about its
potential consequences into future production, help develop pest man-
agement strategy, and encourage implementation of adaptive measures
to address this issue on a timely manner.
CRediT authorship contribution statement
PJ: First draft preparation, TP and JR: Conceptualization, Method-
ology, PJ and NZ: Data analysis PJ TP JR: Visualization, Investigation,
TP: Supervision, PJ, TP, JR, LP, SO: manuscript reviewing and editing,
all authors discussed the results and contributed to the nal manuscript.
Declaration of competing interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Data availability
Data will be made available on request.
Acknowledgements
This research received support from the United States Department of
Agriculture award numbers 58-2032-1-066 and 2022-68017-36358. We
thank Randy Hansen for providing biox data of CM and OFM from
Clarksburg California for 2014–2021. We thank Roger Duncan for
providing biox data of PTB from Modesto for 2002–2012.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.scitotenv.2023.167605.
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