modeling the impact of climate change on peanut production...

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Modeling the Impact of climate change on Peanut Productionon the Basis of Increasing 2oc temperature in FutureEnvironmental conditions of guilan Province, Iran

Seyyed Ali Noorhosseini 1*, Afshin Soltani 2 and Hossein Ajamnoroozi 3

Keywords: ArcGIS zoning, climatechange, peanut production,temperature rise

Received: 14 August 2017,Accepted: 18 September 2017 To evaluate the effect of climate change on peanut production

in Northern Iran on the basis of 2oC rise in temperature, astudy was conducted using the SSM-Peanut. The simulationwas done based on the long-term data obtained from synopticstations in Guilan including Anzali, Astara, Kiashahr (AstanehAshrafieh), Lahijan, Rasht (Agriculture station), Rasht (Airportstation), Roudsar and Talesh. When model was run for eachyear and each scenario, the following parameters were recordedin the outputs: days to beginning bloom, days to beginningpod, days to beginning seed, days to harvest maturity, maximumleaf area index, accumulated crop dry matter, seed yield, andpod yield. Data analysis: data analysis was done using SPSS18. Furthermore, from ArcGIS was used for zoning of Guilanin terms of peanut production in the current condition and afterthe climate change. To compare the difference between peanutgrowth and yield in the current condition and when the climatechange happens, t-test and discriminant analysis were used.The results showed that there is a statistically significantdifference in terms of all parameters between the currentcondition and after climate change 2oC rise in temperature) inGuilan Province. With the rise temperature, average peanutgrowth period in Guilan decreased from 142 days to 123 days.Generally, the average peanut yield changes in Guilan with 2-degree rise in temperature is 8.73 percent more than that in thecurrent condition.

Abstract

International Journal of Agricultural Management and Development (IJAMAD)Available online on: www.ijamad.iaurasht.ac.irISSN: 2159-5852 (Print)ISSN:2159-5860 (Online)

1 Department of Agronomy, Gorgan Branch, Islamic Azad University, Gorgan, Iran2 Agronomy Group, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran3 Department of Agronomy, Gorgan Branch, Islamic Azad University, Gorgan, Iran* Corresponding author’s email: [email protected],

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IntroDuctIonToday, population growth and human activities

have led to an increase in greenhouse gas emissions(especially CO2, CH4 and N2O) which is themain cause of global warming and climate change(Intergovernmental Panel on Climate Change, 2013).An increase in the concentration of CO2 with arate of 2.4 percent a year (Goudriann, 1995)has resulted in a rise in temperature and achange in rain pattern in different parts of theworld and the changes are still taking place. Ithas been predicted that the average temperatureof the world in year 2100 will be two degreeshigher than that of year 1990 (Saunders, 1999).According to the latest report of Intergovern-mental Panel on Climate Change (IPCC), theaverage temperature of the world will increaseby 0.6-2.5 degrees in 50 years, and by the endof this century, it will have increased by 1.1-6.4degrees, and the scope of these changes will sovast in the regional scale (IPCC, 2007). Climatechange processes will directly affect the pro-duction of crops (Bannayan, 2009). Climatechanges in some part of the world has positiveeffects on the production of crops; however, thenegative effects of these changes will be severein hot and dry areas in developing countries(Gregory et al., 2005), a rise in temperature anda decrease in rainfall intensify natural disasters(drought, heatwave, flood, frost) (IPCC, 2007).Temperature rise also affects the crops production byincreasing their development rate (Rawlins, 1991).So evaluating the effect of climate changes oncrop production requires consideration of a risein temperature that per se results from the in-creasing greenhouse gas emissions especiallyin the sensitive stages of growth.

Barzegar and Soltani (2007) have reportedthat the 26.17% increase in average yield ofrain-fed pea in the Northwest of Iran is due tothe decrease in plant growth period and prematureripening of it, and consequently, better adaptationof plant growth in rain-fed condition with waterreservoir and a decrease in stress period at theend of growth season. Mirsaneh et al. (2010)concluded that with the temperature rise resultingfrom climate change in the future, the growthperiod of sugar beet will decrease and the irri-

gation requirement will increase considerablyas a result of temperature rise. The result ofstudy by Hajarpour et al. (2013) showed thatthe climate change will increase pea yield by89% in rain-fed cultivation and 33% decreasein water cultivation. Meghdadi et al. (2015)simulated climate change effect on pea yield inZanjan and reported that the future climatechange on average increases the pea productionby 33% comparing to the present condition.This is while yield sustainability in differentyears will decrease. Fengmei et al. (2007) eval-uated the effects of climate change on rice from2070 to 2090 and showed that by consideringthe direct effect of CO2, rice yield will increase inall synoptic. At the same time, Roy et al. (2009)showed that rice and potato yield will have de-creased in 2075 by 4 and 7.8 percent respectivelycompared to 1990. Ababaei et al. (2010) alsoshowed that the average wheat yield decreasedby 4.19 and 17.9 in Isfahan as a result of climatechange. Lashkari et al. (2011) reported that inall their case study regions in the next followingyears corn yield will decrease by 1 to 39 percentcomparing to the base period.

Peanut is one of the most important and eco-nomic oil seed in tropical and semi-tropical re-gions which is cultivated to produce oil andprotein (Maiti & Ebeling, 2002). The area underpeanut cultivation in the world is 24.07 millionhectares, of which 11.45 million hectares are inAsia. The global production of peanut pod is37.64 million tons annually (FAO, 2010). Thearea under cultivation of this plant in Iran isabout 3500 hectares of which the highest amountis produced in Guilan (North of Iran) with about2,800 ha. The average pod harvest in NorthernIran is about 3500 to 4000 Kg/ha (Safarzadeh,2008; Noorhosseini et al., 2016). In addition, inregard to peanut, despite the fact that it hasbeen a century since peanut was cultivated andproduced in Iran and in spite of the fluctuationin the area under cultivation in different regionsdue to climate condition no effective predictionhas been made for the future.

Although in recent years experiments performedin controlled environments have provided a lotof information about the effect of CO2 or tem-

Modeling the Impact of Climate Change on Peanut Production ... / Noorhosseini et al.

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perature rise on growth processes, they dependon the presence of exact tools. Development ofmodelling methods is a good alternative andcheap for these studies which has been consideredby many researchers (Mattews et al., 1994). Toevaluate the effect of climate change on pro-duction of crops, plant models can be used. TheSSM model is one of these models that cansimulate many crops in a vast range of climates(Soltani & Sinclair, 2012). On this account, thecurrent studies attempts to evaluate the effectof climate change on peanut production in north-ern Iran on the basis of 2oC rise in temperatureusing the SSM-Peanut.

MAtErIALS AnD MEtHoD Study site and observed climate data

The case study site is in Guilan province, andthe meteorological data of this region are fromAnzali, Astara, Kiashahr (Astaneh Ashrafieh),Lahijan, Rasht (Agriculture station, AG), Rasht(Airport station, AP), Roudsar and Talesh synopticstations that are presented in Table 1.

used model structureThe SSM model was used to simulate growth,

development and yield of peanut. The SSMmodel predicts phenological stages as a functionof temperature and day length. Leaf area devel-opment and senescence is a function of temper-ature, nitrogen for leaf growth, plant densityand nitrogen remobilization. The SSM-modelsimulates the process of plant growth and de-velopment in response to environmental solar

radiation, temperature, nitrogen, and water avail-ability. The daily amount of dry matter producedcan be readily calculated based on the amountof received global radiation (SRAD), the fractionof the incident radiation intercepted by the leaves(FINT) and radiation use efficiency of the crop(RUE). In this study, simulation of seed growthrate and formation of yield calculated based onlinear increase harvest index. The SSM-modelperforms simulation on a daily basis and uses soiland the weather data (Soltani & Sinclair, 2012).It should be noted that this model does not con-sider the effects of pests, diseases and weeds onthe plant, therefore, in this study, the effect ofthese factors on physiological characteristicsand plant traits are not discussed. Long-termdaily weather data, including minimum andmaximum temperature, rainfall, and daily sun-shine hours for all the available years, werecollected from synoptic stations. Sunshine hourswere converted to global radiation using theAngstrom equation. For this purpose, the Sradcale (Soltani & Maddah, 2010) program wasused. In this study, data from different field ex-periments conducted in Astaneh Ashrafieh,northern Iran were used for coefficient estimationand model evaluation. Then, the model wasused to simulate the effect of climate change onpeanut growth and yield. In order to parameterizethe SSM-peanut model for peanut (variety NorthCarolina 2, NC2), first approximate values ofparameters were extracted from previous refer-ences and inserted in the model. With regard toparameters that are approximately fixed in dif-


Station Latitude(°N)

Longitude(°E)

Altitude(m)

Baselineperiod

Mean temperature(oC) Rainfall

in year(mm)

Sunnyhours

Minimum Maximum

AnzaliAstaraKiashahr LahijanRasht (AP)Rasht (AG)RoudsarTalesh

37°29'38°21'37°23'37°12'37°19'37°12'37°07'37°50'

49°27'48°51'49°53'50°01'49°37'49°38'50°19'48°52'

-23.6-21.1-2234.2-8.624.9-22.07

1992-20151992-20152007-20152005-20151992-20152000-20152007-20152006-2015

14.2912.0013.2312.0212.4212.1612.9612.77

19.1519.1920.4721.0620.8621.1520.4019.81

1718.201359.271302.561383.061305.681317.211287.491054.51

5.255.094.775.044.764.915.264.39

Table 1List of Synoptic Stations of Investigated Locations with Latitude, Longitude and Altitude

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ferent varieties, there was no report of anychanges to the parameters and thus were regardedfixed for NC2. However, with regard to otherparameters, where differences were possibleconsidering the variety of the region, results ofthe conducted studies in the region were usedand Soltani and Sinclair's parameterizationmethod was adopted (Soltani & Sinclair, 2012).Evaluation of the model was done separatelyusing data of experiments in the parameterizationstage and data of independent experiments.First, observed phenological stages (includingdays to beginning bloom (dtR1), beginning pod(dtR3), beginning seed (dtR5) and harvest ma-turity (dtR8)), maximum leaf area index (MXLAI,m2 m-2), accumulated crop dry matter at harvestmaturity (WTOP, g m-2), seed yield (WGRN, gm-2) and pod yield (WPOD, g m-2) were comparedwith model simulation values using data of ex-periments in the parameterization stage. Then,an independent model evaluation was performedusing extracted data from other experiments,including days to harvest maturity (dtR1), ac-cumulated crop dry matter in harvest at harvestmaturity (WTOP, g m-2), seed yield (WGRN, gm-2) and pod yield (WPOD, g m-2). So, Noorhos-seini et al., (2018) reported that there was nosignificant difference between the values simu-lated by SSM-Peanut model and the observedvalues in the field.climate change scenarios

Evaluating reaction to temperature rise: toevaluate the reaction of peanut growth and yieldto temperature rise in Guilan, the current conditionis compared to 2oC increase in temperature.CO2 concentration for both conditions is 350ppm (According to current climate conditions).

First the temperature changes in long-term me-teorological data from synoptic stations wereimplemented based on two degrees increase intemperature. Then peanut growth and yield weresimulated based on these changes.

SimulationsAs was mentioned, SSM-Peanut model was

used to simulate the growth and yield of peanutunder existing climatic conditions of GuilanProvince and different scenarios. To this end,the plant density of peanut was set at 6.25plants m-2. The planting date was May 14 for allscenarios and the parameters of locally prevailingcultivar ‘North Carolina 2’ (NC2) were used.Soil net N content which was absorbable at thebeginning of the season was estimated at 2.76 gm-2. Since peanut is locally grown without irri-gation because of high phreatic zone in theregion, after a look at the phreatic zone, 0.5was considered for fraction of soil availablewater (FTSW). Table 2 summarizes soil datawith respect to geographical point and soiltexture in the studied region derived from globaldatabase of Batjes (2000).

After the model was run for each year andeach scenario, the following parameters wererecorded in the outputs: days to beginning bloom(R1), days to beginning pod (R3), days to be-ginning seed (R5), days to harvest maturity(R8), maximum leaf area index (m2 m-2), accu-mulated crop dry matter (g m-2), seed yield (gm-2), and pod yield (g m-2).

Data analysisData analysis was done by SPSS. In the section

of descriptive statistics, standard error of the


Parameters Value References

Volumetric water content when the soil is fully saturated with water (SAT, m3 m−3)Volumetric water content at drained upper limit (DUL, m3 m−3)Volumetric water content extractable by the crops (EXTR, m3 m−3)Volumetric water content at lower limit (LL, m3 m−3)Soil inputs of depth (SOLDEP, mm)Soil albedo (SALB)Drainage factor (DRAINF)Curve number (CN)

0.4580.4050.1720.23312000.0500.20085.00

Batjes, 2000Batjes, 2000Batjes, 2000Batjes, 2000Batjes, 2000Batjes, 2000Batjes, 2000Batjes, 2000

Table 2Important Parameters in Soil Water Balance for the Study Area

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mean (S.E. Mean) and coefficient of variation(CV) were used. To compare the difference be-tween peanut growth and yield in the currentcondition and when the climate change happens,t-test and discriminant analysis were used. Fur-thermore, zoning of Guilan in terms of peanutproduction in the current condition and afterthe climate change, ArcGIS was used.

rESuLtS Days to beginning bloom

Table 3 shows the days to beginning bloom ofpeanut in current condition and 2oC rise in tem-perature with standard error and coefficient ofvariation for the synoptic stations in Guilan.Predicting the days to beginning bloom of peanutwith the SSM model showed that the minimumdays to beginning bloom of peanut was in Ki-ashahr synoptic station in current condition and2oC rise in temperature was 43 and 38 days re-spectively. The results showed that in all synopticstations with 2oC rise in temperature, days tobeginning bloom of peanut will decrease too(Table 3). The result of t-test showed that thereis a significant difference (p< 0.01) betweenthe average days to beginning bloom predictedfor the current condition (46 days) and the 2oC

rise in temperature (40 days) in Guilan province(Table 3). Generally, the average changes ofpeanut days to beginning bloom in Guilan with2oC rise in temperature are 6 days less than thecurrent condition (Table 3).

Days to beginning podTable 4 shows the days to beginning pod of

peanut in current condition and 2oC rise in tem-perature with standard error and coefficient ofvariation for the synoptic stations in Guilan.Predicting the days to beginning pod of peanutwith the SSM model showed that the minimumdays to beginning pod of peanut was in Kiashahrsynoptic station in current condition and 2oCrise in temperature was 59 and 53 days respec-tively. The results showed that in all synopticstations with 2oC rise in temperature, days tobeginning pod of peanut will decrease too (Table4). The result of t-test showed that there is a sig-nificant difference (p<0.01) between the averagedays to beginning pod predicted for the currentcondition (62 days) and the 2oC rise in temperature(55 days) in Guilan province (Table 4). Generally,the average changes of peanut days to beginningpod in Guilan with 2oC rise in temperature are 7days less than the current condition (Table 4).


Table 3Comparison of Peanut Days to Beginning Bloom (R1) in Current Conditions and 2oC Rise inTemperature in Guilan, Iran

Parameter

Station

Replications (years)

Current conditions

After climatechange

(2oC rise in temperature)

Change Days to R1

(days)

t-test

Sig.

Days to R1

S.E. Mean

CV (%)

Days to R1

S.E. Mean

CV (%)

AnzaliAstaraKiashahrLahijanRasht (AG)Rasht (AP)RoudsarTaleshGuilan

24249121624910128

464843474545444646

0.860.861.081.000.940.670.970.980.35

9.218.657.547.488.277.356.636.798.53

404238414039394040

0.660.660.920.740.720.510.670.770.27

8.107.687.286.367.206.395.216.087.60

-6-6-5-6-6-6-5-6-6

5.0975.6563.6034.8034.8146.6914.6154.582

13.033**

0.0000.0000.0020.0000.0000.0000.0000.0000.000

** p<0.01

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Days to beginning seedTable 5 shows the days to beginning seed of

peanut in current condition and 2oC rise in tem-perature with standard error and coefficient ofvariation for the synoptic stations in Guilan.Predicting the days to beginning seed of peanut

with the SSM model showed that the minimumdays to beginning seed of peanut was in Kiashahrsynoptic station in current condition and 2oCrise in temperature was 76 and 69 days respec-tively. The results showed that in all synopticstations with 2oC rise in temperature, days to

Modeling the Impact of Climate Change on Peanut Production ... / Noorhosseini et al.Table 4Comparison of Peanut Days to Beginning Pod (R3) in Current Conditions and 2oC Rise inTemperature in Guilan, Iran

Parameter

Station


Current conditions

After climatechange


Change Days to R3

(days)

t-test

Sig.

Days to R3

S.E. Mean

CV (%)

Days to R3

S.E. Mean

CV (%)


24249121624910128

626559636261606262

0.990.971.361.251.000.811.051.140.40

7.847.316.946.846.456.495.275.777.25

555853565555545555

0.730.780.960.990.780.590.770.830.30

6.456.615.456.125.675.274.294.756.22

-7-8-6-8-7-7-6-7-7

5.4486.0603.8004.7025.3836.8924.9464.975

13.896**

0.0000.0000.0020.0000.0000.0000.0000.0000.000

** p<0.01

Table 5Comparison of Peanut Days to Beginning Seed (R5) in Current Conditions and 2oC Rise inTemperature in Guilan, Iran

Parameter

Station


Current conditions

After climatechange


Change Days to R5

(days)

t-test

Sig.

Days to R5

S.E. Mean

CV (%)

Days to R5

S.E. Mean

CV (%)


24249121624910128

808376828079788180

1.111.101.561.371.080.861.201.250.44

6.826.496.105.815.445.304.654.906.25

727469737271707272

0.760.811.080.960.800.630.810.860.32

5.215.364.674.554.484.313.453.794.96

-8-9-7-9-8-8-7-9-8

5.7006.6243.7555.1926.0327.5625.1395.732

15.044**

0.0000.0000.0020.0000.0000.0000.0000.0000.000

** p<0.01

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beginning seed of peanut will decrease too (Table5). The result of t-test showed that there is a sig-nificant difference (p<0.01) between the averagedays to beginning seed predicted for the currentcondition (80 days) and the 2oC rise in temperature(72 days) in Guilan province (Table 5). Generally,the average changes of peanut days to beginningseed in Guilan with 2oC rise in temperature are8 days less than the current condition (Table 5).

Days to harvest maturityTable 6 shows the days to harvest maturity of

peanut in current condition and 2oC rise in tem-perature with standard error and coefficient ofvariation for the synoptic stations in Guilan.Predicting the days to harvest maturity of peanutwith the SSM model showed that the minimumdays to harvest maturity of peanut was inKiashahr synoptic station in current conditionand 2oC rise in temperature was 135 and 119days respectively. The results showed that in allsynoptic stations with 2oC rise in temperature,days to harvest maturity of peanut will decreasetoo (Table 6). The result of t-test showed thatthere is a significant difference (p <0.01) betweenthe average days to harvest maturity predictedfor the current condition (142 days) and the 2oC

rise in temperature (123 days) in Guilan Province(Table 6). The maximum changes of days toharvest maturity are related to Astara Synopticstation with 23 days and the minimum changesof days to harvest maturity is related to Kiashahrand Roudsar with 16 days. Generally, the averagechanges of peanut days to harvest maturity inGuilan with 2oC rise in temperature are 19 daysless than the current condition (Table 6).

Maximum leaf area index Table 7 shows the maximum leaf area index

(MXLAI) of peanut in current condition and2oC rise in temperature with standard error andcoefficient of variation for the synoptic stationsin Guilan. Predicting the maximum leaf areaindex of peanut with the SSM model showedthat the highest MXLAI of peanut was inKiashahr synoptic station in current conditionand 2oC rise in temperature was 4.22 and 4.46m2.m-2 respectively. However, after increasingby 2oC temperature, the MXLAI in Talesh was4.48 m2.m-2. The results showed that in all syn-optic stations with 2oC rise in temperature,MXLAI of peanut will increase too (Table 7).The result of t-test showed that there is a signif-icant difference (p<0.01) between the average

Modeling the Impact of Climate Change on Peanut Production ... / Noorhosseini et al.Table 6Comparison of Peanut Days to Harvest Maturity (R8) in Current Conditions and 2oC Rise inTemperature in Guilan, Iran

Parameter

Station


Current conditions

After climatechange


Change Days to R8

(days)

t-test

Sig.

Days to R8

S.E. Mean

CV (%)

Days to R8

S.E. Mean

CV (%)


24249121624910128

141151135145140141136143142

2.662.423.323.382.322.212.443.121.04

9.257.867.418.056.617.685.416.898.29

123128119124122122120123123

1.231.531.531.571.120.951.301.510.52

4.885.893.844.393.653.793.253.884.80

-18-23-16-21-18-19-16-20-19

6.0358.0384.1605.6836.9247.6535.4555.859

16.323**

0.0000.0000.0020.0000.0000.0000.0000.0000.000

** p<0.01

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MXLAI predicted for the current condition(4.06 m2.m-2) and the 2oC rise in temperature(4.29 m2.m-2) in Guilan province (Table 7). Themaximum MXLAI changes are related to TaleshSynoptic station with 10.01 percent and the minimumMXLAI changes is related to Roudsar with 0.92.

Generally, the average peanut MXLAI changes inGuilan with 2oC rise in temperature are 5.57percent more than the current condition (Table 7).

Accumulated crop dry matter Table 8 shows the value of peanut accumulated


Table 7Comparison of Peanut Maximum Leaf Area Index (MXLAI, m2 m-2) in Current Conditions and2oC Rise in Temperature in Guilan, Iran

Parameter

Station


Current conditions

After climatechange


Change MXLAI (%)

t-test

Sig.

MXLAI

(m2m-2)

S.E. Mean

CV (%)

MXLAI

(m2m-2)

S.E. Mean

CV (%)


24249121624910128

4.143.904.223.994.014.114.204.084.06

0.110.100.190.130.130.100.200.120.04

12.6112.6813.4011.6913.0412.0114.158.9412.34

4.274.244.464.274.254.274.234.484.29

0.070.070.180.120.110.080.180.130.04

7.738.1912.3210.1010.008.7312.649.129.31

3.118.725.857.176.053.800.9210.015.57

1.0212.75709391.5601.4411.2380.1462.3553.995**

0.3120.0080.3620.1330.1600.2220.8860.0300.000

** p<0.01

Table 8Comparison of Peanut Accumulated Crop Dry Matter (WTOP, g m-2) in Current Conditions and2oC Rise in Temperature in Guilan, Iran

Parameter

Station


Current conditions

After climatechange


Change WTOP (%)

t-test

Sig.

WTOP (g.m

-2)

S.E. Mean

CV (%)

WTOP (g.m

-2)

S.E. Mean

CV (%)


24249121624910128

846810831795801801827793814

20.9818.7436.1431.6122.4018.2140.6631.668.66

12.1611.3313.0513.7811.1911.1414.7512.6312.04

865855856833825814843835841

18.5514.7628.6827.5720.5415.9929.8528.447.41

10.518.4510.0611.469.969.6210.6310.779.97

2.275.532.984.803.001.651.915.303.34

0.6861.8800.5370.9100.7900.5450.3130.9872.386**

0.4960.0660.5990.3730.4360.5880.7580.3370.018

** p<0.01

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crop dry matter in current condition and 2oCrise in temperature with standard error and co-efficient of variation for the synoptic stations inGuilan. Predicting the value of peanut accumu-lated crop dry matter with the SSM modelshowed that the maximum accumulated cropdry matter of peanut was in Anzali and Kiashahrsynoptic station for current condition with 846and 931g.m-2 respectively and for 2oC rise intemperature with 865 and 856g.m-2 respectively.The results showed that in all synoptic stationswith 2oC rise in temperature value of accumulatedcrop dry matter will increase too (Table 8). Theresult of t-test showed that there is a significantdifference (p<0.01) between the average ac-cumulated crop dry matter predicted for thecurrent condition (814 g.m-2) and the 2oC risein temperature (841 g.m-2) in Guilan province(Table 8). The maximum accumulated cropdry matter changes are related to Astara Synopticstation with 5.53 percent and the minimum ac-cumulated crop dry matter changes is relatedto Rasht (Airport) with 1.65. Generally, theaverage peanut accumulated crop dry matterchanges in Guilan with 2oC rise in temperatureare 3.34 percent more than the current condition(Table 8).

Seed yield Table 9 shows the value of peanut seed yield

in current condition and 2oC rise in temperaturewith standard error and coefficient of variationfor the synoptic stations in Guilan. Predictingthe value of peanut seed yield with the SSMmodel (with the use of long-term water, air, andsoil data) showed that the highest yield of peanutwas in Kiashahr synoptic station in current con-dition and 2oC rise in temperature was 320 and348 g.m-2 respectively. The results showed thatin all synoptic stations with 2oC rise in temperaturevalue of peanut seed yield will increase too(Table 9 and Figure 1). The result of t-testshowed that there is a significant difference(p<0.01) between the average peanut seed yieldpredicted for the current condition (279 g.m-2)and the 2oC rise in temperature (322 g.m-2) inGuilan province (Table 9). Figure 2-A and 2-Bshow respectively zoning of peanut seed yieldin current condition and the 2oC rise in temper-ature with the use of GIS. As can be seen,changes of yield increase with 2oC rise in tem-perature in all parts of Guilan are noticeable.The maximum seed yield changes are related toAstara Synoptic station with 13.32 percent andthe minimum seed yield changes is related to


Table 9Comparison of Peanut Seed Yield (g.m-2) in Current Conditions and 2oC Rise in Temperature inGuilan, Iran

Parameter

Station


Current conditions

After climatechange


Change yield (%)

t-test

Sig.

Seed yield

(g.m

-2)

S.E. Mean

CV (%)

Seed yield

(g.m

-2)

S.E. Mean

CV (%)


24249121624910128

310282320290288295305296297

11.3610.9919.4518.3012.3110.5021.2616.684.84

17.9319.0618.2421.8417.1217.4620.9217.8318.46

326320348324314310327332322

9.167.829.1612.3310.638.6711.7113.183.82

14.0412.218.3313.7113.9513.9611.3313.1613.41

5.1813.328.7311.719.225.387.1512.268.73

1.0842.7491.2691.4991.5881.1460.8721.6394.197**

0.2840.0090.2230.1480.1230.2580.3960.1190.000

** p<0.01

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Anzali with 5.18. Generally, the average peanutseed yield changes in Guilan with 2oC rise intemperature are 8.73 percent more than thecurrent condition (Table 9).

Pod yield Table 10 shows the value of peanut pod yield

in current condition and 2oC rise in temperaturewith standard error and coefficient of variationfor the synoptic stations in Guilan. Predicting

the value of peanut pod yield with the SSMmodel showed that the highest pod yield ofpeanut was in Kiashahr synoptic station incurrent condition and 2oC rise in temperaturewas 415 and 451g.m-2 respectively. The resultsshowed that in all synoptic stations with 2oCrise in temperature value of peanut pod yieldwill increase too (Table 10 and Figure 3). Theresult of t-test showed that there is a significantdifference (p<0.01) between the average peanut


Figure 1. Comparison of peanut seed yield (g.m-2) in current conditions and afterclimate change (2oC rise in temperature) in Guilan, Iran

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pod yield predicted for the current condition(385 g.m-2) and the 2oC rise in temperature (419g.m-2) in Guilan province (Table 10). Figure 4-A and 4-B show respectively zoning of peanutpod yield in current condition and the 2oC risein temperature with the use of GIS. As can beseen, changes of pod yield increase with 2oCrise in temperature in all parts of Guilan are no-ticeable. The maximum pod yield changes arerelated to Astara Synoptic station with 13.36percent and the minimum pod yield changes isrelated to Anzali with 5.17. Generally, theaverage peanut pod yield changes in Guilanwith 2oC rise in temperature are 8.73 percentmore than the current condition (Table 10).

Discriminant analysisWe used discriminant analysis on the basis of

the observed characteristics to make predictionsfor future and distinguish the effects of climatechange vs. the current conditions. The significanceand fit of the function were examined by Wilks’Lambda. The canonical coefficient was used toverify the function fit and its repeatability. In

the present study, the discriminant analysiscould identify a canonical discriminant func-tion in which the eigenvalue was 1.792. Also,the square of canonical correlation was 0.8.The function could account for 64.2 percentof the total variance between the two groups(current climate and climate change) (Wilks’Lambda = 0.358). Results showed that themeans of the two groups (current climateconditions versus post-climate change con-ditions) were different in the presence of allgrowth and yield variables (χ2 = 258.23, P <0.01). Results revealed that all studied vari-ables were present in discriminating thecurrent conditions and post-climate changeconditions, so that significant differenceswere observed between current climate con-ditions and post-climate change conditionsin terms of various phenological phases (daysto flowering, days to pod formation, days toseed formation, and days to maturity), leafarea index, dry matter accumulation, andgrain and yield. Since there are variableswith different units and scales in discriminant


Figure 2. Zoning of seed yield of peanut in the current conditions (A) andafter climate change (B) in Guilan (North of Iran)

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analysis, they should be converted to stan-dardized coefficients to lend themselves tothe determination of their relative contribution.It was found that the variables included inthe model have a very high capability to dis-criminate the two groups of current conditionsand post-climate change conditions. The cor-relation coefficient between each independentvariable with the discriminant function wasshown by structure matrix. These values areequivalent to factor loads in factor analysis.

The closer these number are to 1, the moreeffective they are in the discriminant function.In this study, the variable dtR8 were foundto be effective in discriminating the groupswith structure matrix of and 0.765, respectively(Table 11).

Finally, Table 12 shows how successfulthe discriminant function was in the soundclassification of the observations. The clas-sification process of this function indicatedits high prediction potential so that it could


Table 10Comparison of Peanut Pod Yield (g.m-2) in Current Conditions and 2oC Rise in Temperature inGuilan, Iran

Parameter

Station


Current conditions

After climatechange


Change yield (%)

t-test

Sig.

Pod yield

(g.m

-2)

S.E. Mean

CV (%)

Pod yield

(g.m

-2)

S.E. Mean

CV (%)


24249121624910128

403367415377374383396384385

14.7514.3025.3123.7516.0113.6327.6521.596.29

17.9319.1018.2821.8417.1417.4520.9417.7718.47

424416451421408403424431419

12.4110.5913.2617.4314.6911.7416.9618.944.96

14.3412.478.8214.3314.4014.2711.9913.8913.41

5.1713.368.6411.819.255.377.0912.268.73

1.0812.7541.2561.5111.5911.1420.8671.6404.197**

0.2850.0080.2270.1450.1220.2600.3990.1180.000

** p<0.01

Parameters Wilks' Lambda F Sig. StandardizedCoefficients

Structure Matrix 1

dtR1dtR3dtR5dtR8MXLAIWTOPWGRNWPOD

0.5990.5680.5290.4880.9410.9780.9350.935

169.867193.088226.307266.44215.9595.69217.61517.611

0.0000.0000.0000.0000.0000.0180.0000.000

-0.135-0.8810.8811.3620.1871.558-1.029

0.6110.6510.7050.765-0.187-0.112-0.197-0.196 a

Table 11Tests of Equality of Group Means and Canonical Discriminant Functions

a This variable not used in the analysis.

1 Pooled within-groups correlations between discriminating variables and standardized canonical discriminant functionsVariables ordered by absolute size of correlation within function.

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classify 93.4 percent of the cases correctly.The results of the classification revealedthat only 10.2 percent of replications pre-dicted by this function in current conditionswere erroneously placed in post-climatechange group and that 3.1 percent that wererelated to the climate change conditions were

placed in a wrong current conditions group.The results indicate that this function hadhigh discrimination potential for classifyingthe groups. The coefficients of all studiedvariables for predictions by this function arepresented in Table 11 in which structurematrix is included too.


GroupsPredicted Group Membership

TotalCurrent Climate Climate Change

Count

Percent

Current ClimateClimate ChangeCurrent ClimateClimate Change

1154

89.83.1

1312410.296.9

128128100.0100.0

Table 12Classification Results

93.4% of original grouped cases correctly classified.

Figure 3. Comparison of peanut pod yield (g.m-2) in current conditions and after climate change(2oC rise in temperature) in Guilan, Iran

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DIScuSSIonPhenology responses to climate change may alter

the ability of plants to acquire soil resources (waterand nutrients) by altering the timing and durationof the deployment of roots and leaves, which driveresource acquisition (Nord & Lynch, 2009). Thisreduced period happens in a significantly wetterpart of the year, sufficient to outweigh the lowerradiation levels before and during grain filling(Ittersum et al., 2003).

increase in temperature resulted in a temperatureshift from sub-optimal temperatures to theoptimal range which may favour crop mass ac-cumulation, but in spring-sown crops, the resultwas a shift in temperatures from optimal rangeto super-optimal range which had negative effecton yield (Hajarpoor et al., 2014).

In a study carried out by Gholipoor and Soltani(2009), positive differential grain yield of rainfedchickpea was discerned between the sites theyexamined. They represented that the increase ingrain yield was not proportional to increase inbiomass as the differential harvest index tended

to be mainly negative. Due to lack of rainfallduring flowering, podding and seed filling, terminaldrought stress is a major abiotic stress that reduceschickpea productivity in drylands (Sabaghpour etal., 2006). It seems that the main reason for theincreased grain yield as a result of increased tem-peratures might be attributed to reduced risk oflate season drought stress due to earliness. Leportet al. (2006) in a study of the effect of terminaldrought on chickpea expressed that in water-limited Mediterranean and subtropical environ-ments, the plants are usually subjected to terminaldrought unless irrigated. Soltani and Sinclair(2012) indicated that higher yields of chickpeawere obtained with a 4°C increase in temperatureand they concluded that this was mainly due toaccelerated development rate and earlier maturityunder higher temperature and resultant droughtescape. Simulation results by Koocheki andNassiri (2008) showed that wheat yield reductionat the target year could be prevented considerablywith increasing temperature threshold of wheatcultivars at flowering by 2-4 °C.


Figure 4. Zoning of pod yield of peanut in the current conditions (A) and afterclimate change (B) in Guilan (North of Iran)

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concLuSIonSIn general, the results showed that there is a

statistically significant difference in terms ofall parameters between the current conditionand after climate change (2oC rise in temperature)in Guilan province. With the rising temperature,the average peanut growth period in Guilan de-creased from 142 days to 123 days. Generally,the average peanut yield changes in Guilan witha 2-degree rise in temperature is 8.73 percentmore than the current condition.

All in all, climate changes influence the con-centration of greenhouse gases and other at-mospheric pollutants. The rising concentrationof these gases varies with the emissions fromnatural and man-made resources. The data onthe emission of greenhouse gases and other at-mospheric pollutants as well as the data on theland use and vegetation cover are of importanceas the inputs to the climate model. The climatechange scenarios whose development is basedon assumptions pertaining to the effective factors,such as economic patterns, population growth,and technology development, can provide moreeffective predictions of future crop production.Accordingly, the scenarios pertaining to economicand environmental factors can effectively analyzethe consequences of climate change by climaticmodeling and assessment of impacts, adaptation,and adjustment on crops. Each of these scenarioscan be based on various assumptions aboutpopulation growth, economic development, tech-nology change, living standards, and energyproduction alternatives, which are referred toas emission scenarios. Although future greenhousegases emission rate is a key variable for predictingpeanut growth and yield, other factors shouldalso be considered including technology devel-opment, variations of energy production andland use, global and local economic conditions,and population growth. Therefore, numerousfactors should be taken into account when pre-dicting how future global warming contributesto climate change. In order for the studies in thefield of climate change to be complementaryand comparable among different groups, a stan-dard set of scenarios should be used to ensurethat initiation conditions, past data, and future

manifestation are uniformly used among differentdisciplines of the climate change. Thus, in additionto direct impacts of temperature rise on peanutproduction and yield, it is recommended to explorethe effect of climate change on peanut growthand yield in future research by considering green-house gasses emission scenarios on the basis ofthe economic and environmental aspects.

AcknowLEDgEMEntSThis paper is extracted from the PhD thesis of

S.A. Noorhosseini by Supervisor A. Soltani andAdvisor H. Ajamnourozi. The authors acknowl-edge all those who improved the quality of thisresearch.

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How to cite this article:Noorhosseini, A., Soltani, A., & Ajamnoroozi , H. (2018). Modeling the impact of climate changeon Peanut production on the basis of increasing 2oC temperature in future environmental conditionsof Guilan Province, Iran. International Journal of Agricultural Management and Development, 8(2),257-273.urL: http://ijamad.iaurasht.ac.ir/article_541234_3eef716e187e51c09f7a99e567d4e501.pdf

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