Simulating Impact of Climate Change in Crop Productivity

Using Future Climate Projections and DSSAT Crop

Simulation Models

GUIDE MODULE

Arnold R. Salvacion

School of Science and Management

University of the Philippines Los Baños

College 4031, Laguna, Philippines

INTRODUCTION

Climate change is one of the major issues nowadays (Mendelsohn, 2007) because of the

threat it poses to environment and on biological existence (Chipanshi et al., 2003; Salvacion and

Lansigan, 2007). According to Fischer et al. (2002), the issue of climate change is global, long-

term, and involves complex interactions between different factors and processes such as climate,

environment, economic, political, institutional, social, and technological processes. Hence,

national governments, international institutions, and research agencies are putting their efforts on

studying and addressing the possible impacts of climate change (UNFCC, 2008).

Based on different literatures, climate change can be defined as a statistically significant

variation in either the mean state of the climate or in its, variability, persisting for an extended

period (typically decades or longer) which may be due to natural internal processes or external

forcing, or to persistent anthropogenic changes in the composition of atmosphere (Peter and Hay,

2002; Giorgi, 2005; Chalinor et al., 2007) or in land use (IPCC, 2001). According to Bogardi et

al. (2005) rise in air and sea temperatures and changes in precipitations patterns were some of the

manifestations of this phenomenon which was supported by the reports published by the

Intergovernmental Panel on Climate Change (IPCC).

Changes in air temperature and rainfall pattern brought about by climate change are

likely to affect agricultural production system directly and indirectly. Besides its direct influence

on crop growth and development, weather also affects other process that may impact agricultural

production system as a whole. Different farm practices such as land preparation, sowing,

fertilizer application, and harvesting are also highly weather dependent. Conversely, occurrence

of crop pest and diseases are also climate driven. Therefore, changes on natural state and pattern

of weather will also translate to changes on the above mentioned process involved in agricultural

production system.

Assessing the impact of the future climate change on agricultural production system is an

imperative for the development of different adaptation measures to counteract its possible

negative effect. In developed countries like the United States, researchers were able to assess

impact of different changes in weather parameters (i.e. temperature and CO2 levels) under

controlled environment on crop growth and development (e.g. Leaky et al., 2004; Long et al.

2004; Leaky et al., 2006 ). However, such facilities were limited and most of the times not

available in developing countries. Hence, an alternative system to assess impact of climate

change on crops is to use crop simulation models.

Crop Simulation Models

Crop simulation models are defined as computer programs that simulate of crop growth

by numerical integration of constituent processes with (Sinclair and Seigman, 1996; Matthews et

al, 2002). More specifically, it is a computer program describing the dynamics of the growth of a

crop (e.g. rice, wheat, maize, groundnut, tea, etc.) in relation to the environment, operating on a

time-step an order of magnitude below the length of growing season, and with the capacity to

output variables describing the state of crop at different points in time (e.g. biomass per unit area,

stage of development, yield, canopy nitrogen content, etc.) (Matthews, and Stephens, 2002).

These crop models mimic crop growth and developments for a given set of inputs or information

of soil, weather, and crop specific model parameters.

In agricultural production system, crop simulation models are normally used to assess

impact of projected climate change. This has been proven by several scientists (Jones et al.,

1995; Carbone et al., 2003; Chipansi et al., 2003; Tsvetsinskaya et al., 2003; Chalinor et al.,

2007). Crop simulation models have been an effective and extensive tool in studying plant and

climate relationship or climate impact studies (Matthews and Stephens, 2002, Jones et al., 2003;

Carbone et al., 2003; Easterling et al., 2003; Timsina and Humpreys, 2006). Using ORYZA 1

and SIMRIW, Matthews et al. (1995a; 1995b) simulated the impact of climate change in Asia.

Saseendran et al. (2000) used CERES-Rice to assess the effect of climate change on rice

production in tropical humid area of Kerala, India. In 2007, Yao et al. also used CERES-Rice to

assess the impact of climate change on rice yield on main rice growing areas of China. On the

other hand, Tsvetsinskaya et al. (2003) used DSSAT crop models to determine the effect of

spatial scale of climate change scenarios on the crop production in the Southeastern United

States. Xiong et al. (2007) modeled the potential corn production in China under two climate

change scenarios using CERES-Maize. Iminguez et al. (2001) coupled CERES models with

climate models to assess the climate change and agriculture in Spain. A long list on the

application of crop simulation models can be seen in Reddy and Hudges (2000) and Wallach et

al. (2006), and papers of Jones et al. (2003) and Timsina and Humpreys (2006).

DSSAT

Decision Support System for Agrotechnology Transfer (DSSAT) is a software package

that integrates the effect of crop phenotype, soil, weather, and crop management system through

a database system and allows users to simulate experiments on desktop computers in a minute,

which would take significant quantity of time to conduct (ICASA, 2005). According to Jones et

al (2003), DSSAT enables the user to study the “what if” results of different management option

and strategies through its different independent programs that operate together. These programs

include crop simulation models and databases that describe weather, soil, experiment condition

and measurements, and genotype information (Fig. 1). The software also enables users to

prepare inputs for each of the programs and compare simulation results with observation, giving

users confidence in the models or determine possible modification to achieve improve accuracy.

In addition, DSSAT programs allow users to assess risk associated with different crop production

strategies through its multi-year simulation option. Conversely, DSSAT also has a built-in

function to specify changes in weather variables without directly modifying the original weather

file which suits it for climate change impact studies. In recent updates of the software it can also

directly reads historical atmospheric carbon dioxide data from Mauna Loa, Hawaii

(Hoogenboom et al., 2010)

DSSAT was developed through collaboration between scientist and researchers of

different institution such as University of Florida, the University of Georgia, University of

Hawaii, University of Guelph, the International Center for Soil Fertility and Agricultural

Development, Iowa State University, and the International Consortium for Agricultural Systems

Application (Tsuji et al., 1994; Hoogenboom, et al., 2003; Jones et al., 2003; ICASA, 2005).

DSSAT is consist of different crop models (Jone et al., 2003) such as CERES-Maize (Jones and

Kiniry, 1986) for corn, CERES-Rice (Hoogenboom et al., 1994) for rice, CANEGRO

(Hoogenboom et al., 1994) for sugarcane, PNUTGRO (Boote et al., 1986) for peanut, and a

model for tomato (Hoogenboom et al., 1994).

Recently, DSSAT crop models have been cited by UNFCC (UNFCC, 2008) as a tool

which can be combined or integrated with other tools or methods to evaluate impacts,

vulnerability, and adaptation to climate change.

Figure 1. Different components and software applications of DSSAT (Source: Jones et al., 2003)

OBJECTIVE

The objective of this paper is to serve as step-by-step guide on how to assess impact of

projected climate change using DSSAT software. This module is divided into five (5) numbers

of exercises that will give users knowledge and skill to use the DSSAT software in assessing

impact of climate change on crop productivity. The first exercise is on the input data preparation.

Although, this may not involve working directly on the DSSAT software, proper input data

preparation (especially weather data) is very important and first thing needed to proceed with

crop model simulation. The second part of the module introduces the user to weather database

management system of DSSAT, the Weatherman. The third part of the module guide the user on

how to step by step input set of crop management schemes (e.g. planting date, amount and

timing of fertilization, amount and timing of irrigation, etc.) that are normally employed to grow

crops in the field. The fourth part of the module will direct user on how to extract simulation

output from DSSAT and manipulate it using a spreadsheet program for analysis. The fifth part of

the module is on the computation of changes in crop productivity as result of climate change.

The last chapter discusses some limitations and assumptions of using crop simulation model for

climate change impact assessment. In addition, it will give some insights on how the models can

be used to assess possible climate change mitigation measures.

MODULE 1

Importing Weather Data to WeatherMan

Normally records of weather data come in spreadsheet file, which makes it easy to be

manipulated for use in crop model simulation. However, in order to utilize records of weather

data in DSSAT in should be formatted to fit the system. Weatherman is the program for

importing, analyzing, and exporting climate data for use in crop simulation in DSSAT (Wilkens

et al., 2004). In addition, the program has a built-in function that can read weather data with

different system of measurement (i.e. English and SI system).

In order to easily import weather data into WeatherMan, weather data should be opened

or checked using a spreadsheet program.Using the example weather data (Butuan.xls),

preparation and checking errors in weather data will be demonstrated in the following

procedures.

1. Open the Butuan.xls file. The file should contain 8 columns of data which include YEAR,

MONTH, DAY, RR, Tmean, Tmax, Tmin, RH (Fig. 2). Make a note on the arrangement

of column arrangement of variables (i.e. YEAR, MONTH, DAY, RR, Tmean, Tmax,

Tmin, RH) and then delete the header and save the file.

Figure 2. Screen capture of Butuan.xls file, (A) with header and (B) without header.

(A)

(B)

2. Open WeatherMan directly windows Start-Up environment (Fig. 3A) or from DSSAT

workspace (Fig. 3B). In the Weather Man window, select New Station (Fig. 4A) then

select Input or import raw weather data then save as a new station (Fig. 4B), and then hit

Go.

Figure 3. WeatherMan accessed from windows Start-Up environment (A) and from

DSSAT workspace (B).

Figure 4. Menu used to create New Station (A) and Import raw weather data (B) in

WeatherMan.

(A) (B)

(A) (B)

3. After hitting the button Go, a Data Import window will pop-up in the screen (Fig. 5A).

On the File menu, select Open (Fig. 5B) or click on the icon in the menu tab.

Select the Butuan.xls by navigating to the folder where it was located (Fig. 5C). After

selecting the Butuan.xls, notice that the records it contain were transferred to the Data

Import window (Fig. 5D)

Figure 5. Screen captures of procedures used to import spreadsheet file into WeatherMan

Data Import window.

(A)

(B)

(C)

(D)

4. The next step is to label each of the column variables in order for Weatherman to

recognize what type of variable it is. In order to does this, right-click on the icon

above the first column then select the appropriate variable description and units. In the

case of the first column (YEAR) select Date for Variable and Year (yyyy) for Units (Fig.

6A). Repeat the same procedure for the other columns until finish (Fig. 6B). Note: since

the CERES-Model does not require average temperature (Tmean), it was not included for

import.

Figure 6. Screen captures of procedures to assign variable description and units to column

data in the Data Import window of WeatherMan.

(A)

(B)

5. When all the data columns have been properly described with corresponding variable

name and unit, it can now be imported to WeatherMan by selecting Import data into WM

in the File menu (Fig. 7A) or clicking the icon on the menu tab. When a pop-up

message “Construct new date column combining column 1,2, and 3?”, click Yes to

proceed. Then, when in the File Option window (Fig. 7B) appears, maintain the default

“Create a new climate station database and merge data” in the Import Options and

“Discard raw data after importing into a WM database and exit” in the Temporary File

Options, and click OK. Enter 4 characters (e.g. BUTN) to serve as station name (Fig. 7C).

Then click OK and SAVE on the next window (Fig. 7D). For the station parameter, input

the necessary information and the hit OK again (Fig. 7E). Wait until data import was

done. When asked to “save the current data into external file before exiting”, select No.

Save the imported data by selecting File then Save Station (Fig. 7F). Click on the Write

Files on the next window (Fig. 7G) to make the imported weather data into DSSAT

readable format. Exit WeatherMan.

(A)

(B) (C)

Figure 7. Screen captures of procedures for importing and saving weather station data in

WeatherMan.

(D)

(E)

(F)

(G)

6. To check if the data is successfully imported to WeatherMan, re-launch it from DSSAT

window or from Program Files. Upon opening WeatherMan, the latest created weather

station will show up (in this case Butuan) (Fig. 8). Click on the

on the left side of WeatherMan window to view the imported daily weather (observed).

This data can be edited by double-clicking on the cell that contains each data points.

Figure 8. WeatherMan window showing station information of the latest imported

weather data.

Note: In order to run any crop simulation model in DSSAT, weather variables such as

rainfall, maximum temperature, minimum temperature, and solar radiation is needed. In

the absence of solar radiation data observe solar radiation data, solar radiation data

estimate can be derived from other weather variables such as rainfall, maximum

temperature, and minimum temperature. However, such procedure will not be covered

here. The papers of Allen (1995), Allen (1997), Donatelli and Bellochi (2001), and

Donatelli et al. (2002) describe different methods and approach to implement such

procedure.

MODULE 2

Creating Crop Management File

Crop management information is an essential part of crop simulation models. Planting

date, planting density, and crop variety are the most necessary data that a crop model needs.

Information on fertilization (type of fertilizer, rate, and timing) and irrigation (method, amount,

and timing) can also be used (if available) to provide a more realistic simulation.

In this module, encoding of crop management information for crop model simulation will

be demonstrated using the crop data management program within DSSAT, the XBuild. In

DSSAT, the file that contains crop management information is referred as experimental file since

it does not only contain data on crop management but as well as information on different

simulation options and management combinations (treatments) the user wants to perform.

1. Open XBuild directly from program files (Fig. 9A) or from DSSAT environment window

(Fig. 9B).

Figure 9. Xbuild accessed from windows Start-Up environment (A) and from DSSAT

workspace (B).

(A) (B)

2. Once XBuild window opens (Fig. 10A), go to File then select New (Fig. 10B).

Figure 10. Xbuild window environment (A) and File menu (B).

3. Then, put the following information on the General Information section of Xbuild:

Institute Code: BT, Site Code: AG, Year: 2011, Experiment Number: 1, Crop: Maize

(Fig.11). These are the five (5) important information that has to be supplied on the

General Information section of XBuild. The experimental file name is directly created out

of these information as well as the DSSAT directory where the file will be saved. In this

case, the experimental file was named BTAG1101.MZX and saved under the

C:\DSSAT45\MAIZE. The Institute Code (BT) was used as the first character in the file

name followed by the Site Code (AG) then by the last two digit of the Year (11) and

lastly by the Experiment Number (01). The information on Crop (Maize) was used to

determine the file extension (.MZX) and the file directory. Although optional, you can

put any description on Experiment Name for documentation and easy identification. In

this case, Butuan Climate Change was set as the experiments name. Once the done with

these information, ignore the other boxes and hit the button on the lower

right portion of the window.

(A) (B)

Figure 11. General Information section of XBuild.

4. The next window that will show-up is the Cultivar section (Fig.12). Here specific corn

variety for simulation can be selected. In the case of the Philippines, only Lansigan et al

(2002) were be able to generate corn genetic coefficient that is required to run the

CERES-Maize model of DSSAT. For the purpose of demonstrations, the Medium Season

variety available in DSSAT can be selected using the drop-down menu under the Cultivar

option. Again, hit the button on the once done in selecting cultivar.

Figure 12. Cultivar section of XBuild.

5. After the Cultivar section, the Fields section (Fig. 13) follows. In this section, the user

specifies which weather stations and soil data the model will use in the simulation. In this

case, the weather station (Butuan, PH) created earlier in WeatherMan will be selected as

Weather Station and the Default Deep Silty Loam will be selected as the Soil for the area.

Put 00000001 as Field ID and Butuan 2000 on the Level 1 Description. This will help the

user to identify later which Field level contains observed weather data (base line) and

climate change scenario data set. Hit when done.

Note: Default Silt Clay Loam was selected as an approximation of the soil types in the

area which are mostly describe as loam in nature. Actual characteristic of the soil in the

area may vary in terms profile depth and other characteristics.

Figure 13. Fields section of XBuild.

6. The Planting section (Fig. 14) follows after the Fields section. Planting information such

as planting date, planting method, planting distribution, planting density, plant spacing,

and planting depth were specified in this section. In the case of the demo simulation in

this module, the planting date of in Butuan is assumed to be on the first week of June

(06/01/2000), the plating method is Dry seed, planting distribution is Row, with plant

population of 7.2 plants/m2 both on planting and emergence, row spacing of 75 cm, and

planting depth of 3 cm. Again, use the BUTUAN 2000 on the Description box. Click

when done.

Figure 14. Planting section of XBuild.

7. In this step, additional weather stations. This will enable simulation of additional set of

experiment with different weather station and soil type. In order to assess impact of

climate change, additional weather station (with climate change scenario weather data

set) will be added in the Fields section. To go back to the Fields section, go to the menu

then select Environment then Fields (Fig. 15A). When backed at the Fields section, click

button. Notice that a new level was added (Fig. 15B). Replace Butuan, PH

weather station to Butuan Climate Change (Fig. 15C). Also, rename the level description

of Level 1 to Butuan Baseline and Level 2 to Butuan CC Scenario (Fig. 15D). Click

when done.

Note: Butuan Climate Change station was imported to WeatherMan following the

procedure on the first module.

Figure 15A. Screen capture showing Fields option on XBuild main menu.

Figure 15B. Additional field level (Level 2) added to Fields section.

Figure 15C. Drop-down menu showing Butuan Climate Change station.

Figure 15D. Renamed Fields level description.

8. Additional planting date can be added to evaluate the effect of climate change on

different corn planting dates. To do this, go to Main Menu, Management, and then

Planting (Fig. 16A). Same as the previous step, additional planting date can be added by

clicking on the button. Add three (3) more planting date with 1 week

interval from the first one (06/01/2000) up to 4th

week (06/22/2000) then put a

description such as 1st week, base line (1WBS) to 4

th week, baseline (4WBS) (Fig. 16B).

After the four (4) baseline planting date, add the same set of weekly planting dates for the

years 2025 and 2050. Use the same naming convention such as 1W25 for the 1st week

2025, 1W50 for the 1st week 2050, and so on (Fig. 16C).

Figure 16A. Screen capture showing Planting option on XBuild main menu.

Figure 16B. Screen capture showing Planting section with 4 levels of planting date.

Figure 16C. Screen capture showing Planting section with 4 levels of planting date for

year 2025.

9. After setting-up the weather stations and planting date for simulation, the next part is to

specify simulation options for the crop model. Start of simulation date, number of

simulations, simulation methods, and simulation outputs are controlled or specified on

the Simulation Options section of DSSAT. For this simulation, three (3) simulation

options should be formulated. One for the baseline (2000) simulation, one for the 2025

scenario, and one for the 2050 scenario. Starting with the baseline simulation put

01/01/2000 as the start of simulation date, the click on the Option tab then select No for

most of the options except for Water (Fig. 17). Under this option, the crop model

simulates growth under non-limited nitrogen condition and will enable user to only

evaluate the effect of changing weather condition between weather scenarios (baseline,

2025, 2050). Create simulation option for the two scenarios (2025 and 2050) by adding

two more levels on the simulation option and adjusting appropriate start of simulation

date for 2025 (01/01/2025) and 2050 (01/01/2050). Click when done.

Figure 17. Screen capture showing Planting section with 4 levels of planting date.

10. Specification of the experimental treatments is the last part on the creation of crop

management file in DSSAT. In this case, the experimental treatments for simaltion are the

different planting dates (1st week to 4

th week of June) by different weather year scenarios

(baseline, 2025, and 2050). In order to set-up these treatment combinations in DSSAT, go

the XBuild main menu the select Treatments (Fig. 18A). On the next window, rename

Treatments Level 1 description to 1WBS which has the same meaning as before (1st week

planting date, baseline scenario). Add the second treatment (2WBS) by clicking on the

button and then selecting the Level 2 -2WBS on the drop-down menu

under Plant. Column (Fig. 18B). Do the same with the remaining treatments by selecting

appropriate Plant. level and Sim. Contr. To end up with the screen the same as Figure

18C. Click when done and save the crop management file by selecting

File on the main menu and then Save (Fig. 18D). Close XBuild window when done.

Figure 18A. Treatments option on Xbuild main menu.

Figure 18B. Screenshot capture of Treatments section selecting another level on Plant.

column.

Figure 18C. Screen capture of Treatments section with all the experimental treatments.

Figure 18D. Screen capture of XBuild main menu showing Save option.

MODULE 3

Running Model and Extracting Model Output

In the previous module encoding of crop management information and creation of

experimental treatment file was discussed. Once all of those information have been encoded and

created in DSSAT XBuild, the next step is to run the crop model (CERES-Maize). If there are no

mistakes or missing input data, the simulation run smoothly and will took few minutes to finish.

However, if there are mistakes on crop management specifications or missing input (e.g. weather

or soil data) the model will output a Warning.OUT file which contains the information about the

error.

In this module, running the crop model with the created experimental treatment file and

how to extract model output will be discussed.

1. Go back to the DSSAT window (Fig. 19A) or if close open it. Under the Selector section,

click on Cereals then Maize (Fig. 19B). On the Data section of the window, select the

BTAG1101.MZX file (Fig. 19C). Remember this is the file that was created earlier which

contains the crop management information and the experimental treatments for the

climate impact studies.

Figure 19A. DSSAT window.

Figure 19B. Selector section of DSSAT window.

Figure 19C. Screen capture of DSSAT window Data section showing BTAG1101.MZX

file

2. Check the square box beside the BTAG1101.MZX (Fig. 20A). Then, click on the

button on the main menu tab of DSSAT window (Fig. 20B).

Figure 20A. Screen capture of DSSAT window Data section showing selected

BTAG1101.MZX file

Figure 20B. DSSAT main menu tab.

3. After clicking on the button, the DSSAT simulation window will show up (Fig.

21A). Then, on the left side of the window click on button to finally

run the model. A DOS-window (Fig. 21B) will appear on the screen which shows the

simulation process. When the simulation is over the DOS-window will disappear.

Figure 21A. DSSAT simulation window.

Figure 21B. DOS-simulation window.

4. Once the simulation the simulation is done, click on the Analysis tab of DSSAT

Simulation window (Fig. 22A). The model simulation output can be viewed on by

selecting the OVERVIEW.OUT and Summary.OUT. In order to do these checks the box

beside either OVERVIEW.OUT or Summary.OUT (Fig. 22B), then click the

button. Figure 22C and 22D show the content of the OVERVIEW.OUT and

Summary.OUT, respectively.

Figure 22A. Analysis tab of DSSAT Simulation window.

Figure 22B. Analysis tab of DSSAT Simulation window showing selected

OVERVIEW.OUT and Summary.OUT.

Figure 22C. Screen capture of OVERVIEW.OUT.

Figure 22D. Screen capture of Summary.OUT.

5. In order to easily extract simulation output copy the data inside the Summary.OUT by

selecting the entire output using mouse or pressing CTRL+A (Fig. 23A) then CTRL+C.

Open a blank spreadsheet file then press CTRL+V. Notice that the information on

Summary.OUT file were all transferred on the spreadsheet file. Also, a clipboard icon was

present on the lower side of first column with tip box message “Paste Options” (Fig.

23B). Click on that clipboard icon the select “Use Import Text Wizard” (Fig. 23C). In the

Text Import Wizard window (Fig. 23D), select the Delimited option and then click

On the next window, select Space as the delimeter (Fig. 23E) then click

Notice that the data were separated in each column of the spreadsheet (Fig.

23F). Clean the data by removing columns and rows to come up with the spreadsheet

containing only TNAM and HWAM (Fig. 23G). TNAM column contain the treatment

names and HWAM column contains the corresponding yield of each of the treatments.

Figure 23A. Screen capture of selected Summary.OUT data.

Figure 23B. Screen capture of spreadsheet file with pasted dataset and “Paste Option”.

Figure 23C. Screen capture of spreadsheet file showing “Use Text Import Wizard” paste

option.

Figure 23D. Screen capture of Step 1 of 3 of Text Import Wizard.

Figure 23E. Screen capture of Step 2 of 3 of Text Import Wizard.

Figure 23F. Screen capture of Step 2 of 3 of Text Import Wizard.

Figure 23G. Screen capture of spread sheet file containing only TNAM and HWAM

column.

6. The graphing function of the spread program can be used to visualize the effect of

climate change on corn productivity. Figure 24 shows the yield trend on different

planting date on different climate change scenarios (baseline, 2025,2050).

Figure 24. Bar graph of simulated yield of four different planting dates under different

climate change scenarios.

CONCLUDING REMARKS

This guide module successfully showed how crop simulation models can be used to

simulate impact of climate change using DSSAT crop simulation model (CERES-Maize). Based

on result (Fig. 24), it can be observed that lower yield can be observed under projected climate

change scenarios for the year 2025 and 2050 compared to the current (baseline) condition. Also,

it can be noted that under the 2025 condition, a much higher decrease in yield can be observed.

This suggest that with other factors held constants the weather condition in Butuan, Philippines

in 2025 is more devastating than the possible weather condition in 2050.

On the other hand, the simulation result on this module only approximate possible impact

of climate change with only changes in weather parameters (rainfall and temperatures) as factors

affecting changes in crop yield. Other crop management factors such as irrigation, nutrient

management, improvement in crop variety and other technologies were not considered.

Furthermore, other input information was estimated from secondary information and other

available data sources. Simulation result may vary with actual field collected soil and crop model

parameter in the specific area.

The approach in this module can be adapted to simulate and evaluate impact of climate

change on other crops. Though, the availability of specific crop model and required input data

still determines the applicability and validity of the approach on other crops. The book of Lobell

and Burke (2010), on the other hand provide different approach in estimating impact of climate

change on crops which ranges from the use of crop simulation models and application of time-

series analysis.

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