Trans. Malaysian Soc. Plant Physiol. 17(2008) · surface warmi and anticipate1 This warming climate...

Trans. Malaysian Soc. Plant Physiol. 17(2008)

TRANSACTIONS OF THE MALAYSIAN SOCIETY OF PLANT PHYSIOLOGY

Volume 17 (2008)

SUSTAINABLE CROP AND PLANT PRODUCTIVITY IN RELATION TO

CLIMATE CHANGE

Proceedings of the 19th Malaysian Society of Plant Physiology Conference held at Copthorn Orchid Hotel, Tg. Bungah, Penang,

Malaysia 18-20 November 2008

Haw a ZE J aafar Umi Kalsom Md Shah

Organized By

~ ~ Malaysian Society of Plant Physiology (MSPP)

Co-organized by Universiti Putra Malaysia (UPM) Universiti Sains Malaysia (USM)

MSPP is a Professional Scientific body dedicated towards promoting research and development in tropical plant biology


First Edition, 2009 Malaysian Society of Plant Physiology (MSPP)

All Rights Reserved No part of this publication may be reproduced,

stored in a retrieval system or transmitted in any form or by any means, electronic, mechanic photocopying, recording or otherwise, without prior

permission of the Copyright owner

Perpustakaan Negara Malaysia Cataloguing-in-Publication Data

Malaysian Society of Plant Physiology Conference ( 19th : 2008 : Pulau Pinang).

11

Sustainable crop and plant productivity in relation to climate change : proceedings of the 19th Malaysian Society of Plant Physiology Conference held at CopthornOrchid Hotel, Tg. Bungah, Penang, Malaysia, 18-20 November 2008 / [editors] Hawa ZE Jaafar, Umi Kalsom Md Shah; organized by Malaysian Society of Plant Physiology (MSPP) ; co- organized by Universiti Putra Malaysia (UPM), Universiti, Yniversiti Sains Malaysia (USM) (Transaction of the Malaysian society of plant physiology) ISBN 978-967-960-258-6 1. Climate changes--Research--Malaysia--Congresses. 2. Sustainable agriculture--Malaysia--Congresses. I. Hawa ZE Jaafar. II. Umi Kalsom Md Shah. III. Malaysian Society of Plant Physiology. IV. Universiti Putra Malaysia. V. Universiti Sains Malaysia. VII. Title. VII. Series. 631.49595

Suggested citation: Trans. Msian. Soc. Plt. Physiol. 1 7(2008):

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Assalamualai

Global food , sustainability agriculture w yields withou practices will of the global f

Evide Climate chani the foremost i: surface warmi and anticipate1 This warming climate pbeno1 glaciers are mi

become very c intense in reef occurred since

Plant J scales, the cii influences phy viability and g greatly increas, change on the l tackling the pre

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SIMULATING THE EFFECTS OF CLIMATE CHANGE ON OIL PALM YIELD IN SERDANG, MALAYSIA

Teh C.B.S. Department of. Land Management, Faculty of Agriculture, Universiti Putra

Malaysia, Serdang, Selangor, Malaysia Tel: 03-89466976; Fax: 03-89434419; Email: [email protected]

ABSTRACT

Analysis of Serdang weather from 1985 to 2007 indicated that Serdang experienced an increasingly warmer and wetter weather. Serdang's minimum and maximum air temperature increased by 0. 6 and 0. 4 °C per decade, respectively, whereas its total annual rainfall increased by 21. 5 mm per year. It was consequently estimated that in 2043, Serdang would be warmer by between 1 to 2 °C and wetter by 770 mm (a 30% increase in total annual rainfall). It was also estimated that Serdang would have an ambient CO2 (CO2) and ground level 0 3 (ozone) concentrations at 450 ppmv and 60 ppbv, respectively. A semi-mechanistic oil palm growth and yield model was developed to determine the effect of climate change on oil palm yield in Serdang. Model simulations revealed that the predicted oil palm yield in Serdang would be reduced by between 0.8 to 31.3% (equivalent to 0.2 to 6 t ha-1 yield reduction). However, if the ground level 0 3 pollution can be kept below 30 ppbv, oil palm yield would instead increase by between 2 to 42% (equivalent to an increase by 0.4 to 8 t ha-1

). Simulations confirmed that oil palm yield would increase with increasing CO2

concentration and total annual rainfall. In contrast, oil palm yield would reduce with higher air temperature and ground level 0 3 concentration. To prevent yield reduction due to the increasing warmth and 0 3 concentration, CO2 concentration must increase to a certain threshold or "critical point" below which oil palm yield will decline. It was shown that, all properties being equal, oil palm yield reduction would be prevented if the critical CO2 level increases by at least: a) 28 ppmv for every 1 °C increase in temperature, and b) 40 ppmv for every 30 ppbv increase in ground level 0 3 concentration. The critical increase in air temperature was also calculated to be 0.6 °C (at 380 ppmv of CO2 and O ppbv of 0 3) . This means that any temperature increases greater than 0.6 °C will reduce yield At 380 ppmv CO2 and 0 °C increase in air temperature, the critical level for 0 3 concentration was 12 ppbv after which further increases in 0 3 concentration would reduce oil palm yield Finally, the importance of CO2, 0 3, air temperature and rainfall on oil palm yield was found to be in decreasing order of CO2 > 0 3 > air temperature > rain.

INTRODUCTION

Since the pre-Industrial period about 250 years ago, world atmospheric concentration of CO2 (CO2) has risen from 280 parts per million by volume (ppmv) to 385 ppmv in 2008. Even by 2005, the then CO2 concentration (380 ppmv) was already 27% higher than at any point in the last 650,000 years (Spahni et al., 2005). Measurements by the Earth System Research Laboratory (ESRL) of the National

48

Oceanic and Atmosp concentration is curr NOAA/ESRL, www.e of human activities a CO2 concentration is concentrations of oth temperature is predict,

Warming ge variability in global r; lesser rainfall in the 1 also predicted that latitudes, while those is because crops in t' near maximum temp effects of global w, atmospheric CO2 con concentration causes (1986), for instance, as high as 50%.

Most studie: increasing temperatu researchers are incre crop yields. Ground volatile organic cor chemically with ox Consequently, the p highest in mid-day a

Ground lev( leaves, which in tuI three decades, 03 cc high latitudes over fuel emissions cont increase by 50% in crop production by Oijen et al. (2004) parts per billion by 26%.

Oil palm (j important to detem Malaysia. Field ex] large and long-tern


Oceanic and Atmospheric Administration (NOAA) reveal that atmospheric CO2

concentration is currently rising by about 2 ppmv per year (Dr. Pieter Tans, NOAA/ESRL, www.esrl.noaa.gov/gmd/ccgg/trends/). Nevertheless, if current trends of human activities and environment degradation continue unabated, atmospheric CO2 concentration is expected to reach 550 ppmv by 2050 (IPCC, 2007). Rising concentrations of other greenhouse gases means that the average global surface temperature is predicted to increase by 1.3 to 1.8 °C by 2050 (IPCC, 2007).

Warming generally increases evaporation, total rainfall and spatial variability in global rainfall. Tt is predicted, for example, that there will be more and lesser rainfall in the higher and lower latitudes, respectively (Ainsworth, 2008). It is also predicted that warming will generally increase crop yields in the higher latitudes, while those in the lower latitudes will see lower yields (Cline, 2007). This is because crops in the lower latitudes like those in tropics are already growing at near maximum temperatures that they can tolerate. Nevertheless, the detrimental effects of global warming on crop yields can be mitigated with the increasing atmospheric CO2 concentration. Plants photosynthesise using CO2, so increasing CO2

concentration causes the corresponding increase in crop yields. Cure and Acock (1986), for instance, found that COi-doubling increased the yield of many crops by as high as 50%.

Most studies in the past, however, have been limited to the effects of increasing temperatures and atmospheric CO2 concentrations on crop yields. Today, researchers are increasingly aware of the additional effect of ground level ozone on crop yields. Ground level ozone (03) is formed when nitrogen oxide gases and volatile organic compounds, by-products when fossil fuels are burned, combine chemically with oxygen. This chemical reaction is aided by sunlight and heat. Consequently, the presence of ground level 0 3 is transient; its concentration is the highest in mid-day and dissipates at night.

Ground level 0 3 is not only toxic to human health, but also damages plant leaves, which in turn reduces photosynthesis and crop yields. Over the last two to three decades, 0 3 concentration has increased by 1 to 2% per year at the middle and high latitudes over Europe (Guicheret, 1989). Recently, it is estimated that if fossil fuel emissions continue their current upward trend, global average 0 3 levels will increase by 50% in 2100. The resulting damage to agriculture could reduce global crop production by at least 10% (Reilly, 2007). Similarly, model simulations by van Oijen et al. (2004) predicted that 0 3, at a mean photoperiod concentration of 100 parts per billion by volume (ppbv), decreased biomass production of spring wheat by 26%.

Oil palm (Elaeis guineensis) is a major crop in Malaysia. Therefore, it is important to determine the impact of predicted climate change on oil palm yield in Malaysia. Field experiments are prohibitive mainly due to the high cost to setup a large and long-term field experiment required to study the effects of climate change

49

Trans. Malaysian Soc. Plant Physiol. I 7(2008)

on the growth and yield of oil palm. Consequently, simulation studies are an essential alternative.

A study by the MOSTE (2000) used the Global Climate Models (GCMs) to predict the yield of several major crops in Malaysia. For oil palm, they found that its yield was generally robust to climate change. Increasing air temperature even by up to 3.4 °C did not affect oil palm yield. Likewise, increasing total mean rainfall by 14% saw little change in oil palm yield. Yield, however, was sensitive to drier weather. A decrease by 14% in total rainfall reduced yield by between 30 to 50%. Tn contrast, annual oil palm yield increased by 1 ton ha·1 for every 100 ppmv increase in CO2 concentration. Their study unfortunately did not include the effect of ground level 0 3 on oil palm yield.

Thus, the objective of this study was to include the effects of ground level 0 3

concentration on oil palm yield. A semi-mechanistic oil palm growth and yield model was developed specifically to simulate the effects of climate change on oil palm yield. The individual and collective effects of air temperature, ambient CO2 and 0 3 concentrations and total rainfall on oil palm yield were simulated. The long term weather and field conditions in Serdang, Malaysia were used as a test case in all simulations.

MATERIALS AND METHODS

Daily weather generation for Serdang

The daily weather of Serdang (3.033° N, 101.7° N, 31 m above sea level), Malaysia from 1985 to 2007 was analysed for its mean monthly and annual properties, and for any trend in climate change. The measured daily weather properties were sunshine hours, minimum and maximum air temperature, relative humidity, wind speed and total rainfall.

To generate the various weather conditions for the expected climate change, the stochastic weather generator called Simmeteo by Geng et al. (1988) was used to generate 20 years of daily weather properties in Serdang. The generated weather was then used by the oil palm model to determine the effect of climate change on the oil palm yield for 20 continuous years.

Oil palm growth and yield model

The model bas seven core components. The first component is the simulation of hourly weather properties based on daily weather data. Weather properties simulated hourly are total solar irradiance (and its diffuse and direct components), air temperature and air vapour pressure according to the methods described in Teh (2006).

50

The second modified form of E when the oil palm Canopy extinction , so-called clumped ; (fully closed canopi (2007).

The third cc al. (1991) which de sink. Leaf pbotosynl method by Norman

The fourth c oil palm pbotosyntb potentially damages and repair the 0 3 dai

costdetox =

where costsdetox is t: ground day-1

); & th level 0 3 concentratic the stomata (taken a as 0.375 g CH2O f 1

is the same as CO2, 0 3 repair is

costrepair =

where costsrepair is th ground day-1

); A the fraction of assimilatt f,epair the fraction o: Consequently, them the leftover of assim

A'=A-C(

The fifth photosynthesis for Kraalingen et al. (I empirical relationshi growth of the oil pal


The second component is the interception of solar radiation that is based on a modified form of Beer's law to account for discontinuous canopies, which occurs when the oil palm canopies have not yet closed during the early growth period. Canopy extinction coefficient for direct and diffuse solar radiation are reduced by a so-called clumped factor which ranges from O (completely opened canopies) to 1 (fully closed canopies). The equations for the clump factor are as described in Teh (2007).

The third component is the mechanistic photosynthesis model by Collatz et al. (1991) which describes that photosynthesis is either limited to Rubisco, light or sink. Leaf photosynthesis is then scaled up to canopy photosynthesis according to the method by Norman (1992).

The fourth component is the estimating the effects of ground level ozone on oil palm photosynthesis. The method by van Oijen et al. (2004) was adapted. Ozone potentially damages the pinnae and it incurs the cost of some assimilates to detoxify and repair the 0 3 damages. The cost of detoxification is

costdetox = gs · 03 a · fdetox · Cdetox '

where costsdetox is the amount of assimilates used for detoxification (g CH2O m-2

ground day-1); & the canopy conductance (g 0 3 m-2 ground day- 1

); O3,a the ground level 0 3 concentration (ppmv); (i010x the detoxified fraction of the ozone flux entering the stomata (taken as 0.9 g i 1

); and cdetox the cost coefficient of detoxification (taken as 0.375 g CH2O g-1 detoxified 0 3). It is assumed that the canopy conductance for 0 3

is the same as CO2, as both molecules have similar molecular weights. The cost of 03 repair is

costrepair = A · f1v · frepair

where costs,epair is the amount of assimilates used for 0 3 damage repair (g CH2O m-2

ground day-1); A the gross assimilates produced (g CH2O m-2 ground day-1

); fiv the fraction of assimilates to pinnae for maintenance and growth (taken as 0.2 g g-1

); and f,epair the fraction of assimilates to pinnae used for repair (taken as 0.05 g i 1

).

Consequently, the new gross assimilates used for tree maintenance and growth, A', is the leftover of assimilates after 0 3 detoxification and damage:

A' = A - c ostdetox - costrepair

The fifth component is the part1t10ning of the assimilates from photosynthesis for maintenance and growth respiration. The method by van Kraalingen et al. (1989) was adapted. The trunk height growth was based on an empirical relationship established from measured data from previous works. The growth of the oil palm canopy (width and height) was also based on measured data.

51


One major deviation from van Kraalingen et al. 's model is this study's model accounts for the factor of water stress on oil palm growth and yield, where reduction in gross photosynthesis was directly in proportion to the degree of water stress.

The sixth core component is the modelling the water balance in the oil palm environment based on the Shuttleworth-Wallace (Shuttleworth and Wallace, 1985) equation. It allows for the interaction of heat fluxes between the tree and soil. The water balance for two soil depths (0-20 mm and 20 mm until rooting depth) were also modelled to determine the critical water amount based on a C3 plant. Thus, the oil palm growth and yield would decrease in proportion to the critical water level.

The seventh component is the modelling the soil water content in the soil. The soil profile was divided into two layers: a thin 20-mm top soil layer, followed by the soil layer until the rooting depth. Water flow was based on Darcy's law. It is assumed that the soil water profile was always uniformly wet so that water always flowed downward, due to gravitational pull only. Equations describing the soil water flow are as described by Teh (2006).

Model simulations

All simulations were based on an oil palm planting density of 148 trees per hectare. The soil type used was a sandy clay soil texture, with an initial volumetric soil water content of 0.3 m3 m-3 (both layers), and bulk density of 1.6 Mg m-3. Simulations begun when the oil palm tree was 1 year old with initial dry weights for rachis, pinnae and roots at 6.12, 2.88 and 1.0 kg, respectively. The initial dry weights for the bunches, flowers and trunk were each set at O kg.

RESULTS AND DISCUSSION

Analysis of Serdang weather 1985-2007

The mean annual and monthly meteorological properties for Serdang are shown in Table 1 and 2, respectively. Both these tables indicate that Serdang from 1985 to 2007 experienced a mean total rainfall of about 2450 mm year-1

, with two heavy rainfall periods in a year: March to April and October to December. For both these periods, their total monthly rainfall exceeded 200 mm month-1

, and on average, more than 15 days per month experienced rainfall. The daylength for Serdang was fairly constant throughout the year at about 12.0 hours day-1

• Although on average, a day experienced 6 hours of sunshine hours (Table 1), Serdang experienced slightly over one hour more sunshine hours per day in the middle of the year from March to July as compared to the other months (August to February) (Table 2). From March to July, the average sunshine hours per day were 6.6 hours, as compared to 5.5 hours for August to February. The annual mean minimum and maximum air temperature were about 23 and 33 °C, with a 10 °C difference between both these extremes. Table 2 also shows that the monthly averages for both temperatures varied little from their respective annual averages. However, April and May had slightly higher mean

52

monthly temperatw winds that do not e~

Figure 1 shows tha total annual rainfall maximum air temp< mean annual total J

upward trend contii warmer by between

Min. temp. (°C)

24

23

22 ~ 0

21

20~---1982 1987 ·

Total rainfall (mm;

4000

3000 (

2000 ~

1000

0+--...--1982 1987


monthly temperatures than the other months. Lastly, Serdang experienced weak winds that do not exceed a monthly average speed of 1 m s-1

•

Predicted climate change in Serdang

Figure 1 shows that Serdang experienced a trend of increasing air temperature and total annual rainfall from 1985 to 2007. For instance, the mean annual minimum and maximum air temperature increased by 0.6 and 0.4 °C per decade, respectively. The mean annual total rainfall in Serdang also increased by 21.5 mm per year. If this upward trend continues, Serdang in the year 2043 (35 years from today) would be warmer by between 1.4 to 2.1 °C and wetter by 770 mm due to heavier rainfall.

Min. temp. (°C)

24

23

22 0

21

a

y = 0.06x - 87.77 R2 = 0.76

20+-----------1982 1987 1992 1997 2002 2007

Total rainfall (mm)

4000

3000

2000

1000

Year

C

y = 21.52x - 40498 R2 = 0.28

0+-----------. 1982 1987 1992 1997 2002 2007

Year

Max. temp. (°C)

34 0

b

0

33

32

31 y = 0.04x - 53.15 R2 = 0.45

30+-----------. 1982 1987 1992 1997 2002 2007

Year

Figure 1. Climate change in Serdang from 1985 to 2007. Charts show the mean annual values of- a) minimum air temperature. b) maximum air temperature, and c) total rainfall.

53


The current ambient CO2 and 0 3 concentrations for Serdang had to be estimated, as they were not measured in this study. Serdang's current ambient CO2

concentration was taken as 380 ppmv, rounded down from the world's latest ambient CO2 concentration at 385 ppmv. The world CO2 concentration currently increases about 2 ppmv per year. Consequently, it is estimated that by 2043 and 2050, the estimated CO2 concentration in Serdang will increase from 380 to 452 and 466 ppmv, respectively.

Ishii et al. (2007) reported that the ground level 0 3 concentration in Serdang from 2000-01 was measured at about 30 ppbv (7-hour average). Similarly, they found that the ground level 0 3 concentration in seven other local towns or cities (Kuala Lumpur, Shah Alam, Klang, Kajang, Gombak, Tanjung Malim and Nilai) ranged between 28.5 to 47.8 ppbv (7-hour average) in 2000. If Serdang sees an increase of 0 3 concentration by 1 to 2% a year (as with countries in the middle and higher latitudes), its 0 3 concentration in 2043 could reach between 46 to 70 ppbv, and in 2050, between 49 to 81 ppbv.

Consequently, the effects of several weather conditions were tested against the yield of oil palm grown in the Serdang area. The following weather conditions were tested: a) both minimum and maximum air temperatures higher by l °C; b) like (a) but both air temperatures higher by 2 °C; c) a wetter weather by increasing total annual rainfall by 30% or by 770 mm; d) a drier weather by reducing total annual rainfall by the same amount as (c) (i.e., by 770 mm); e) increasing atmospheric CO2

concentration from 380 to 420 ppmv, then to 480 and 550 ppmv; and f) increasing ground level 0 3 concentration from Oto 30 ppbv, then to 60 and finally to 120 ppbv. Note that the drier weather condition (less 770 mm annual rainfall) is not expected to occur in Serdang but was included in simulations to determine the effect of a drier weather on oil palm yield. This study assumed that there would be no change in the rainfall pattern or distribution within a year.

Effect of climate change on oil palm yield

At current Serdang weather conditions (see Table 1 and 2) and at CO2 and 0 3

concentrations of 380 ppmv and 0 ppbv, respectively, the simulated mean annual FFB (fresh fruit bunch) yield was 20 t ha·1 (Figure 2). This yield of 20 t ha·1 was used as the baseline or benchmark against which the effect of climate change on oil palm yield was compared (Table 3). It can be summarised from Table 3 that oil palm yield would increase with increasing CO2 concentration and total annual rainfall. In contrast, oil palm yield would reduce with higher air temperature and ground level 0 3 concentration.

Increasing CO2 concentration was beneficial to oil palm yield (Table 3). For instance, in the absence of ground level 0 3 pollution (0 ppbv), oil palm yield could increase by between 50 to 100% should CO2 concentration increase from 380 to 550 ppmv. However, the benefit of CO2 fertilisation would decrease with increasing ground level 0 3 pollution and temperature. If CO2 concentration increases from 380

54

to 550 ppmv, oil pah ppbv of 0 3, respecti insufficient in most< by as much as 26%. ' with high ambient I increase in rainfall).

Table 1. Mean annu1

Weather properties Minimum air temp, Maximum air temp Sunshine hours Relative humidity Total rainfall

Table 2. Mean mont,

Month TMIN Jan. 22.05 Feb. 22.18 Mar. 22.79 Apr. 23.15 May 23 .33 Jun. 22.88 Jul. 22.50 Aug. 22.53 Sep. 22.59 Oct. 22.70 Nov. 22.72 Dec. 22.43

Note: TMIN and j

respectively; SUN} humidity(%); WINl month (number of c total rainfall (mm rr

This showt could reduce oil pa Ishii et al. (2007) f than that without 0 be offset by incre: earlier, 0 3 concentr


to 550 ppmv, oil palm yield could increase by no more than 68 and 45% at 30 and 60 ppbv of 0 3, respectively. At 120 ppbv of 0 3, the benefit of CO2 fertilisation was insufficient in most cases to prevent yield reduction: oil palm yield could be reduced by as much as 26%. The detrimental effect of 120 ppbv of 0 3, however, can be offset with high ambient CO2 concentration (550 ppmv) and total annual rainfall (30% increase in rainfall).

Table 1. Mean annual values for Serdang weather from 1985-2007.

Weather properties Minimum air temperature Maximum air temperature Sunshine hours Relative humidity Total rainfall

Mean annual value 22.7 °C 32.8 °C

6.0 hours day- 1

95.4% 2449 mm year-'

Table 2. Mean monthly values for Serdang weather from 1985-2007.

Month TMIN TMAX SUNHR RH WIND WET Jan. 22.05 32.51 182.87 95.50 0.66 13.43 Feb. 22.18 33.45 179.70 95.00 0.73 12.61 Mar. 22.79 33.63 198.67 95.15 0.78 17.30 Apr. 23.15 33.72 198.03 95.44 0.76 18.52 May 23.33 33.29 210.95 95.53 0.75 14.35 Jun. 22.88 33 .23 196.99 95.31 0.77 11.43 Jul. 22.50 32.59 198.10 95.69 0.83 13.17 Aug. 22.53 32.59 179.40 95.23 0.88 13.65 Sep. 22.59 32.58 158.19 95.47 0.84 14.78 Oct. 22.70 32.40 168.75 95.32 0.81 19.30 Nov. 22.72 32.14 148.68 95.73 0.69 20.96 Dec. 22.43 32.07 155.11 95.64 0.60 17.52

RAIN 156.76 179.77 266.65 269.50 179.59 116.65 172.34 143.65 194.62 232.36 300.11 237.35

Note: TMIN and TMAX are the minimum and maximum air temperature (CC) , respectively; SUNHR is the sunshine hours (hours month-1); RH is the relative humidity(%); WIND is the wind speed (ms-'}; WET is the number of wet days in the month (number of days in the given month that had rained) (days); and RAIN is the total rainfall (mm month-1).

This showed that ground level 0 3 concentration, even at small amounts, could reduce oil palm yield significantly. At 30 ppbv of 0 3 (the level recorded by Ishii et al. (2007) for Serdang), oil palm yield was reduced by 20% (or by 4 t ha-1

)

than that without 0 3 pollution. The detrimental effect of 0 3 at 30 ppbv, however, can be offset by increasing CO2 concentration by 40 ppmv to 420 ppmv. As stated earlier, 0 3 concentration at 120 ppbv was the most detrimental.

55


Yield (t ha.,)

30

25

20

15

10

5

0 2 4 6 8 10 12 14 16 18 20

Age

Figure 2. Annual fresh fruit bunch (FFB) yield for the current weather conditions in Serdang (ambient CO2 and 03 concentrations fixed at 380 ppmv and O ppbv, respectively).

A 30% increase in annual rainfall was beneficial to oil palm because it either increased its yield or reduced the detrimental effects of higher temperature and ground level 0 3 concentration on its yield. In particular, the benefits of having a wetter weather became more evident at higher temperatures. All properties being equal, when the temperature was raised by 2 °C, a 30% increase in annual rainfall increased oil palm yield by an average of 12%. At lower temperatures, the average increase in yield was only about 4%. On the other hand, a 30% decrease in annual rainfall had a detrimental but weaker effect on oil palm yield as compared to the wetter weather. At 2 °C increase in temperature, the drier weather reduced oil palm yield by an average of 4%, but at lower temperatures, the average reduction was only by about2%.

The current climate change trend in Serdang is that Serdang in 2043 will be warmer by between 1 to 2 °C and wetter by 770 mm. Serdang is expected to have an ambient CO2 and 0 3 concentrations of about 450 ppmv and 60 ppbv, respectively. At these levels, the predicted oil palm yield in Serdang will be reduced by between 0.8 to 31.3% (Table 3), or equivalent to a yield reduction by between 0.2 to 6 t ha·'. Table 3 shows that the ground level 0 3 concentration is particularly important. If the 0 3 pollution can be kept below 30 ppbv, oil palm yield will instead increase by between 2 to 42% which is equivalent to an increase by 0.4 to 8 t ha·1

•

The various relationships in Table 3 was summarised using multiple linear regression to relate the effects of the various meteorological properties on oil palm yield. Variable selection was by the stepwise method, and the linear equation found was (standardised coefficients in brackets):

56

YIELD = -157 .908

where YIELD is th atmospheric CO2 (ppbv); TEMP the , rainfall (mm). All significance, and tl ( 1) was not meant conditions than tha information in Tab!

Table 3. Percentag,

Temp. -7 change 03 ([7C) (ppbv) 38

0 0 -( 30 -21 60 -3~ 120 -61

+1 0 -1" 30 -3: 60 -4S 120 -6S

+2 0 -2S 30 -4: 60 -Sf 120 -7(

Note: Value marke mean annual yield concentrations at percentage change denote reduced yie,

The magni have the strongest 1

air temperature, ar coefficients confirr concentration and when ground level


YIELD= -157.908 + 0.424CO2 -0.56803 -12.060TEMP + 0.005RAIN (1) (0.700) (-0.648) (-0.253) (0.075)

where YIELD is the percentage change in oil palm yield from 20 t ha·1 (%); CO2 the atmospheric CO2 concentration (ppmv); 0 3 the ground level 0 3 concentration (ppbv); TEMP the change in temperature {0 C); and RAIN the change in total annual rainfall (mm). All coefficients in Eq. (1) were significant at the 1 % level of significance, and the equation explained 97.8% of the total variance. Note that Eq. (1) was not meant to predict changes to oil palm yield in other weather and field conditions than that described in this paper. Its main purpose was to summarise the information in Table 3 into a simple equation and to help in interpretation.

Table 3. Percentage change in oil palm yield due to climate change in Serdang.

Annual rainfall change Temp. -770 mm (30% less) 0 mm (no change) +770 mm (30% more) change 0 3 CO2 (ppmv) (DC) (ppbv) 380 420 480 550 380 420 480 550 380 420 480 550

0 0 -0.1 22.2 54.2 88.1 *(20) 22.8 54.8 88.2 4.1 28.2 61.6 96.6

+l

+2

30 -21.4 0.4 29.0 60.2 -20.6 0.0 29.5 60.9 -17.4 4.1 35.2 67.7 60 -36.9-18.7 8.8 38.7 -36.9-18.1 9.2 38.7 -33.9-14.8 13.7 44.5 120 -62.1-48.1-26.7 -1.6 -61.1-47.7-26.5 -1.4 -59.7 -45.8 -23.3 2.3

0 -17.1 4.6 34.3 66.1 -13.6 7.9 37.1 68.2 -11.1 11.3 42.1 74.3 30 -35.4-16.0 12.1 41.5 -32.1-12.9 14.6 43.8 -30.3-10.3 18.7 48.7 60 -49.0-32.4 -6.7 21.5 -45.8-29.3 -3.6 23.8 -44.8 -27.4 -0.8 28.2 120 -69.8-58.0-38.9-15.1 -67.3-55.1-35.8-12.7 -66.6 -54.0 -34.2 -10.0

0 -29.2 -9.1 19.5 50.2 -26.2 -6.1 21.9 51.9 -17.8 1.9 31.2 62.6 30 -45.8-27.9 -1.6 26.8 -42.7-24.9 1.3 28.9 -34.2-16.7 9.1 38.0 60 -58.4-42.9-18.7 8.2 -55.2-39.7-15.8 10.7 -46.4 -31.3 -8.0 18.5 120 -76.3 -65.8-48.6 -26.0 -74.5 -63.4 -45.2 -23.1 -68.5 -55.6 -36. 7 -15.3

Note: Value marked * indicates the oil palm yield at 20 t ha·1 which is the simulated mean annual yield for the current Serdang weather conditions and for CO2 and 0 3

concentrations at 380 ppmv and O ppbv, respectively. All other values are the percentage change in oil palm yield due to climate change, where shaded values denote reduced yields.

The magnitude of the standardised coefficients in Eq. (1) reveal that CO2

have the strongest or largest effect on oil palm yield, followed closely by 0 3, then by air temperature, and lastly by total annual rainfall. The signs of the standardised coefficients confirm that oil palm yield can be increased by increasing ambient CO2

concentration and total annual rainfall, but oil palm yield will instead be reduced when ground level 0 3 concentration or air temperature is increased.

57


Eq. (1) was used to estimate the "critical levels" beyond which oil palm yields will start to decline. Tf Serdang experiences a 30% increase in total annual rainfall, for instance, the critical increase in air temperature was calculated to be 0.6 ~C. This critical level was determined for 380 ppmv CO2 and 0 ppbv 0 3• This means that any temperature increase greater than 0.6 °C at these CO2 and 0 3 levels would cause a decline in oil palm yield. Likewise, calculations reveal that at 380 ppmv CO2

and O °C increase in air temperature, the critical level for 0 3 concentration was 12 ppbv after which further increases in 0 3 concentration would reduce oil palm yield.

Finally, Table 4 shows the critical levels for CO2 concentration below which oil palm yields would decline. Increasing 0 3 pollution and warmth would exacerbate yield reduction. To compensate for these yield reductions, increasingly higher CO2

concentration was required. For instance, at 1 °C increase in air temperature and at 30 ppbv of 0 3, the CO2 level must be at least 432 ppmv to prevent yield reduction. But if the temperature was instead increased by 2 °C, the CO2 concentration required must be at least 460 ppmv to compensate for the yield reduction.

Table 4. Critical levels of ambient CO2 concentration (ppmv) below which oil palm yields will be reduced

Temp. change (0 C) 0 3 (ppbv) 0 +1 +2

0 363 392 420 30 404 432 460 60 444 472 501

It follows from Table 4 that, all properties being equal, yield reduction can be prevented if the critical CO2 level increases by at least: a) 28 ppmv for every 1 °C increase in temperature, and b) 40 ppmv for every 30 ppbv increase in ground level 0 3 concentration. Most sophisticated climate models today agree that an increase of about 90 ppmv of CO2 is linked to an increase in temperature by 1 °C (Rahmstorf, 2007). This level of CO2 increase (90 ppmv) is three times higher than the critical CO2 level (28 ppmv) required to prevent yield reduction due to every 1 °C increase in temperature. Subsequently, oil palm, though effected detrimentally by the increase in temperature, will actually have an increase in yield due to CO2 fertilisation, provided that 0 3 pollution do not increase and that total annual rainfall is not reduced significantly.

CONCLUSION

It is expected that Serdang in 2043 will be warmer by between 1 to 2 °C, wetter by 770 mm, and have an ambient CO2 and 0 3 concentrations of about 450 ppmv and 60 ppbv, respectively. At these levels, the predicted oil palm yield in Serdang will be reduced by between 0.8 to 31.3%, which is equivalent to a yield reduction by between 0.2 to 6 t ha-1

• However, if the ground level 0 3 pollution can be kept below

58

30 ppbv, oil palm yi1 increase by 0.4 to 8 t

It was calcu would be prevented every 1 °C increase ground level 03 co; calculated to be 0.6 ' temperature increase °C increase in air te after which further Finally, the importar found to be in deem

Ainsworth EA, Rog future high-C<

Cline WR. 2007. G Center for Gk

Collatz GT, Ball J1 regulation of : that includes 54: 107-136.

Cure JD, Acock B. survey. Agric1

Geng S, Auburn J Meteorologic: Agronomy an

Guicheret R. 1989. Schneider T, Research and

JPCC. 2007. Climat Manning M, Contribution the Intergove Cambridge, l

Ishii S, Bell JNB, pollution on pollution 150

MOSTE. 2000. Ma United Nati< Science, Teel

Norman, JM. 19! Ehleringer J, Academic Pr


30 ppbv, oil palm yield will instead increase by between 2 to 42% (equivalent to an increase by 0.4 to 8 t ha-1

).

It was calculated that, all properties being equal, oil palm yield reduction would be prevented if the critical CO2 level increases by at least: a) 28 ppmv for every 1 °C increase in temperature, and b) 40 ppmv for every 30 ppbv increase in ground level 0 3 concentration. The critical increase in air temperature was also calculated to be 0.6 °C (at 380 ppmv of CO2 and O ppbv ofO3) . This means that any temperature increases greater than 0.6 °C will reduce yield. At 380 ppmv CO2 and 0 °C increase in air temperature, the critical level for 0 3 concentration was 12 ppbv after which further increases in 0 3 concentration would reduce oil palm yield. Finally, the importance of CO2, 0 3, air temperature and rainfall on oil palm yield was found to be in decreasing order of: CO2 > 0 3 > air temperature > rain.

REFERENCES

Ainsworth EA, Rogers A, Leakey DB. 2008. Targets for crop biotechnology in a future high-CO2 and high-O3 world. Plant Physiology 147, 13-19.

Cline WR. 2007. Global Warming and Agriculture: Impact Estimates by Country. Center for Global Development, Washington.

Collatz GT, Ball JT, Grivet C, Berry JA. 1991. Physiological and environmental -regulation of stomata) conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer. Agricultural and Forest Meteorology 54: 107-136.

Cure JD, Acock B. 1986. Crop responses to carbon dioxide doubling: a literature survey. Agricultural and Forest Meteorology 38: 127- 145.

Geng S, Auburn J, Brandstetter E, Baoquan L. 1988. A Program to Simulate Meteorological Variables: Documentation for SIMMETEO: Department of Agronomy and Range Science, University of California, Davis.

Guicheret R. 1989. Concentrations and patterns of ozone in western Europe. In: Schneider T, Lee SD, Wolters GJR, Grant LD (eds.). Atmospheric Ozone Research and its Policy Implications. Elsevier, Amsterdam, pp. 167-176.

lPCC. 2007. Climate change 2007: the physical science basis. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds). Contribution of Working Group I to the Fourth Annual Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK.

Ishii S, Bell JNB, Marshall FM. 2007. Phytotoxic risk assessment of ambient air pollution on agricultural crops in Selangor State, Malaysia. Environmental pollution 150: 267-279.

M0STE. 2000. Malaysia: Initial National Communication. Report submitted to the United Nations Framework Convention on Climate Change. Minisitry of Science, Technology and the Environment, Putrajaya.

Norman, JM. 1992. Scaling processes between leaf and canopy levels. In: Ehleringer J, Field C (eds). Scaling physiological processes: leaf to globe. Academic Press, Inc., N.Y. pp. 41-76.

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Reilly J, Paltsev S, Felzer B, Wang X, Kicklighter D, Melillo J, Prinn R, Sarofim M, Sokolov A, Wang C. 2007. Global economic effects of changes in crops, pasture, and forests due to changing climate, carbon dioxide, and ozone. Energy Policy 35: 5370--5383.

Rahmstorf S. 2007. Anthropogenic Climate Change: Revisiting the Facts. In: Zedillo E (ed). Global Warming: Looking Beyond Kyoto. Brookings Institution Press, Washington, pp. 34-53.

Shuttleworth WJ, Wallace JS. 1985. Evaporation from sparse crops - an energy combination theory. Quarterly Journal of the Royal Meteorological Society 111: 839-855.

Spahni R, Chappellaz J, Stocker TF, Loulergue L, Hausammann G, Kawamura K, Fliickiger J, Schwander J, Raynaud D, Masson-Delmotte V, Jouzel J. 2005. Atmospheric methane and nitrous oxide of the late Pleistocene from Antarctic ice cores, Science 310: 1317-1321.

Teh CBS. 2006. Introduction to Mathematical Modeling of Crop Growth: How the Equations are Derived and Assembled into a Computer Program. Brown Walker Press, Boca Raton, Florida.

Teh CBS. 2007. Equations to estimate the direct and diffuse light interception by discontinuous canopies. In: 18th Malaysian Society of Plant Physiology Conference 2007: Yield and quality enhancement of plant. Kota Kinabalu, Sabah.

van Kraalingen DWG, Breure CJ, Spitters CIT. 1989. Simulation of oil palm growth and yield. Agricultural and Forest Meteorology 46: 227-244.

van Oijen M, Dreccer MF, Firsching KH, Schnieders BJ. 2004. Simple equations for dynamic models of the effects of CO2 and 0 3 on light -use efficiency and growth of crops. Ecological Modelling 179: 39-60.

60

VALIDATION OF GREENH

Diyana J .1, Des 1,2.sFa

Universit 4Malaysi

Climate change due is too high especial~ such as greenhouse Greenhouse in trop system to reduce te solar radiation hec problems for crop profiles of tempera understand microc/1 pads. A method of c study of tropical g, based on heat trans) study was checked resulted compared measured give the vi inside temperature 5

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Future climate seem in some regions, bu1 already exist (Orti: precipitation and su production in green can optimize varim intensity, ventilati01 greenhouse tempen between the gret 1990).Boulard et al. These natural con computational fluid

Trans. Malaysian Soc. Plant Physiol. 17(2008) · surface warmi and anticipate1 This warming climate...

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Transcript of Trans. Malaysian Soc. Plant Physiol. 17(2008) · surface warmi and anticipate1 This warming climate...