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The Derivation of Climate Data for Crop Yield Modelling Purposes

coastline (pers comm. Dr. D.E. Terreblanche, South African Weather Service, Bethlehem).

Zelenka et al. (1998) and Roebeling et al. (1999) established methods of deriving estimates of solar radiation using satellite data. However, these methods are laborious and require high levels of calibration and image refinement and analyses before data can be made available for crop modelling purposes (pers comm. Dr. A. Zelenka, Swiss Meteorological Institute, Zürich, 2001). Roebeling et al. (1999) have also derived a method of estimating evapotranspiration from Meteosat data.

6.3 The Derivation of Climate Data for Crop Yield Modelling

geographical point. All records were complete since infilling techniques had been applied prior to the data extraction (Lynch, 2004; Schulze and Maharaj, 2004).

The Canesim yield model requires reference potential evapotranspiration for sugarcane (ECref in mm.d-1), which is defined as the daily evaporative demand for a three metre high sugarcane canopy under no water stress. This variable could not be calculated by the conventionally used Penman-Monteith method as applied by McGlinchey and Inman-Bamber (1996) since no estimates of solar radiation and wind speed were available in the BEEH climate database. The H&S equation (Eq. 6.1, Hargreaves and Samani, 1985; Allen et al., 1998) and that by Linacre (Eq. 6.2, Linacre, 1991; Schulze, 1995), provide alternative ways of estimating reference evapotranspiration (ETO in mm.d-1). In the H&S equation

(

mean

)(

mx mn

)

a

O T T T R

ET =0.0023 +17.8 − 0.5 (6.1)

where Ra is extraterrestrial solar radiation (in MJ.m-2.d-1) and Tmean, Tmx and Tmn (in

°C) are the daily mean, maximum and minimum temperatures, respectively. In the Linacre (1991) equation

[ ] ( ) ( )

 

 − + −

× + +

× +

= mean mean mean dew

O T z uT T

z T

ET 40 4

84

006 . 0 10 380

10 4 015 .

0 4 6

φ

(6.2)

where z is altitude above sea level (in m), φ is latitude (in °, with positive values indicating the Northern Hemisphere), u is mean wind speed at 2 m (assumed constant at 2 m.s-1) and

( )

0.35 10.9

53 . 0 37

. 0 0023 .

0 + + − + −

=

dew mean mx mn ra

mean T z T T T T

T (6.3)

where Tdew is dew point temperature (in °C) and Tra (in °C) is the range between long- term mean air temperature of the hottest and coldest months in the year.

Allen et al. (1998) noted that the H&S equation may need a linear adjustment at different locations. Likewise, an adjustment was required to convert potential evaporation to reference sugarcane evapotranspiration. It should be acknowledged that non-linear relationships exist when vegetation height changes via the theory of zero plane displacement (Calder, 1992; Shuttleworth, 1992). However, under fixed canopy height specifications, such as assumed for ECref, a linear relationship between potential grass and sugarcane evapotranpiration was assumed. Schulze et al. (1999)

derived adjustments to the Linacre equation in the sugarcane belt of South Africa. For this study data from 15 Automatic Weather Stations (AWS) situated in the sugarcane belt (cf. Table 6.2) were used to relate ETO values derived by the H&S and Linacre equations to ECref values derived by the Penman-Monteith equation (McGlinchey and Inman-Bamber, 1996). A cross validation procedure was performed at each site. This consisted of

• an independent regression fit between the results of the H&S and Linacre equations and ECref using data records from 14 sites and

• subsequently performing a verification at the remaining site using the afore- mentioned regression coefficients.

A RMSE and bias error (in mm.d-1) were calculated for each site (cf. Table 6.2).

6.3.3 The SASRI Climate Dataset

Climate stations managed by SASRI (cf. Table 6.1 and Figure 6.1) generally record daily rainfall, solar radiation (or sunshine hours), relative humidity at 8:00 and 14:00 (alternatively, dry and wet bulb temperatures), wind run and minimum and maximum temperatures. The methods described by Spitters et al. (1986) and Allen et al. (1998) are used to convert measurements of daily sunshine hours and dry and wet bulb temperatures to Photosynthetically Active Radiation (PAR in MJ.m-2.d-1) and relative humidity, respectively. Potential sugarcane reference evapotranspiration was subsequently calculated by the Penman-Monteith equation as used by McGlinchey and Inman-Bamber (1996). In addition, data from several rainfall stations within given HCZs where rainfed sugarcane was cultivated, were also included. A summary of these rainfall stations is supplied in Appendix C.

Data records from both climate stations and rainfall stations were often fragmented owing to one or more of the following reasons:

• Recordings at climate sites were discontinued,

• Theft and technical problems with instrumentation caused certain parameters to be incorrect or missing,

• Manual measurements were taken on weekdays (Monday – Friday) only, and

• Records for an entire month went missing in the mail.

Table 6.1 A summary of climate stations managed by the South African Sugarcane Research Institute at different locations in South Africa. Site numbers coincide with those in Figure 6.1 and provide approximate locations of each station. In some cases (e.g. Site 4), more than one station existed in close proximity to each other

Station name Site Station details (comment) Tenbosch 1 1978 – 1995

Komati (AWS) 2 1996 – 2002 (data problems in 1997) Amaxala (AWS) 2 2001 – 2002

Mhlati 3 1978 – 1998 & 2000 – 2002 (AWS) Kaalrug 4 1978 – 1993 (data often fragmented) Inala (AWS) 4 2000 – 2002

Makatini 5 1978 – 1999 (data often fragmented) Pongola 6 1978 – 2002 & 1997 – 2002 (AWS)

Glenpark 7 1978 – 1996 (few solar radiation data) & 1997 – 2002 (AWS) Riverview 8 1978 – 2002 (solar radiation terminated in 1992)

Monzi (AWS) 9 1997 – 2002 Dangu (AWS) 8 2000 – 2002 Entumeni 10 1978 – 2002 Mtunzini 11 1978 – 1999 Felixton 12 1987 – 2002 Heatonville (AWS) 13 1998 – 2002 Amatikulu 14 1998 – 2002 Doornkop 15 1978 – 1997 Glendale 16 1978 – 2002 Seven Oaks 17 1978 – 2002 Jaagbaan 18 1978 – 2002 Bruyns Hill (AWS) 19 1997 – 2002 Darnall 20 1978 – 2002 Gledhow 21 1996 – 2002 Tongaat 22 1978 – 2002

Mt. Edgecombe 23 1978 – 2002 (manual & AWS) Crammond 24 1978 – 2002 (no wind speed) Powerscourt 25 1978 – 1996

Umbumbulu (AWS) 25 1997 – 2002 (long period with missing data: 1999 – 2001) Eston (AWS) 26 1997 – 2002

Beaumont 26 1984 – 1995 Thornville 27 1984 – 1996 Richmond (AWS) 28 1997 – 2002

Esperanza 29 1978 – 2002 (solar radiation data terminated in 1995) Sezela 30 1978 – 2002

Umzimkulu 31 1990 – 2002

Paddock 32 1983 – 1994 (relative humidity problems) AWS: Automatic Weather Station

A complete climate record for most HCZs from 1978 to 2002 was compiled using the data from different available SASRI climate stations shown in Table 6.1. In most cases, data from more than one climate station were carefully combined according to the procedure explained below. Special attention was given to representivity and notable trends between neighbouring climate stations were first removed before data were combined. For each HCZ a day-to-day procedure was used to assess pre-selected

station records. Stations were ranked and data were selected from the station with the highest rank, i.e. the one containing the most complete record for the day. Rainfall station data were not combined in a similar fashion, but fewer simulations were carried out when rainfall data were unavailable.

Mpumalanga

KwaZulu Natal

2 3 1

4

5 6

7 8

10 9 11

12

14

13

16 17 18 24

15

21 20 19

23 25 26

27 29 33 31 28 30 34 3532 36 37

39 38

41 44 43 42

47 48 46

45

22

Mpumalanga

KwaZulu Natal

2 3 1

4

5 6

7 8

10 9 11

12

14

13

16 17 18 24

15

21 20 19

23 25 26

27 29 33 31 28 30 34 3532 36 37

39 38

41 44 43 42

47 48 46

45

22

40

1

1 2

3

2

4

5 6

3

40

1

1 2

3

2

4

5 6

3

8 4

9 10

5

11 12 6 13

14 7

8 4

9 10

5

11 12 6 13

14 15 7

16 9

8 17

18

19

1011 20

21 15 16

9 8 17

18

19

1011 20

21

22 12

23 24

25 13 26

27

DURBAN 28

22 12

23 24

25 13 26

27

DURBAN 28

29 14 30

31 15

32 SOUTH

AFRICA

1 - 48

1 -32 1 29

14 30

31 15

32 SOUTH

AFRICA

1 - 48

1 -32 1 -15

Homogenous climate zones Meteorological stations

Sugar mills

7

31°E 25.3°S

31°E

30°S

Mpumalanga

KwaZulu-Natal

2 3 1

4

5 6

7 8

10 9 11

12

14

13

16 17 18 24

15

21 20 19

23 25 26

27 29

-15

Homogenous climate zones Meteorological stations

Sugar mills

7

31°E 25.3°S

31°E

30°S

Mpumalanga

KwaZulu-Natal

2 3 1

4

5 6

7 8

10 9 11

12

14

13

16 17 18 24

15

21 20 19

23 25 26

27 29 33 31 28 30 34 3532 36 37

39 38

41 44 43 42

47 48 46

45

22

40

1

1 2

3

2

4

33 31 28 30 34 3532 36 37

39 38

41 44 43 42

47 48 46

45

22

40

1

1 2

3

2

4

5 6

3

8 4

9 10

5

11 12 6

5 6

3

8 4

9 10

5

11 12 6 13

14 15 7

16 9

8 17

18

19

11

13

14 15 7

16 9

8 17

18

19

1011 20

21

22 12

23 24

25 13 26

10 20

21

22 12

23 24

25 13 26

27

DURBAN 28

29 14 30

31 15

32 27

DURBAN 28

29 14 30

31 15

32 SOUTH

AFRICA

1 - 48

1 -32 1-15

Homogenous climate zones Climate stations Sugar mills

7

31°E 25.3°S

31°E

30°S

Figure 6.1 The distribution of homogeneous climate zones, climate stations and sugar mills in the South African sugarcane production areas