The relationship between leaf area index and crop ...

ORIGINAL SCIENTIFIC PAPER

EUROINVENT (Number 1/June 2010/Volume 1/pg. 3-10)

THE RELATIONSHIP BETWEEN LEAF AREA INDEX AND CROP COEFFICIENT FOR TOMATO CROP GROWN IN SOUTHERN ITALY N. Čereković1, M. Todorović2, R. L. Snyder3 1 University of Business Studies, Faculty of Ecology, Banjaluka, Bosnia and Herzegovina 2 CIHEAM – Istituto Agronomico Mediterraneo di Bari, Via Ceglie 9, 70010 Valenzano (BA), Italia 3 Department of Land, Air and Water Resources, University of California, Davis, California, USA

ABSTRACT

A study on tomato crop evapotranspiration was conducted during 2002 in Southern Italy to investigate the influence of weather and management on crop growth and development parameters (e.g. leaf area index) and to evaluate Kc values for this climatic region. The measurements of the main weather parameters and tomato crop data were collected near Policoro (Southern Italy), at experimental station “E. Pantenelli” of Bari University and CNR-Bari. The objective of this research was to make better estimates of crop evapotranspiration (ETc) by improving seasonal crop coefficient (Kc) curves. The relationship between Kc values and leaf area index LAI was investigated using lysimeter-measured ETc data and electronic leaf area meter - leaf area index data from the Bari University and CNR-Bari experimental station located in Policoro (Southern Italy). The results indicated that the s easonal Kc can be modelled satisfactorily for either crop a logarithmic relationship between Kc and LAI. Ključne riječi: Crop coefficient, LAI, Crop evapotranspiration, Mediterranean, Tomato (cv. Dracula) 1. INTRODUCTION

them as evapotranspiration, where evaporation refers to vaporization from all freely wet surfaces, including free-water surfaces (oceans, lakes, and streams), soil and man-made surfaces, and transpiration refers to vaporization inside of plant leaves that then diffuses through leaf pores (stomata) to the atmosphere. Kc values have changed with advances in crops and irrigation systems. Differences in Kc recommendations are often site - and year-specific and depend on local ETo rates, rainfall frequency, and crop management. Coefficient have changed because of changes in orchard architecture, mulching, planting density, varieties, effects of climatic conditions on flower biology and crop growth, etc. (Pruitt et al., 1972; Grattan et al., 1998; Tyagi et al., 2000; Shah and Member, 2000; Kashyap and

Water evaporates from soil and plant surfaces to in response to atmospheric demand, which is primarily controlled by the meteorological factors, whereas the supply of water to the evaporative site is controlled by the soil and plant factors. Thus, evapotranspiration is affected by meteorological, biological, and soil factors(Amayreh i Al Abed, 2005; Hanson i May, 2006; Todorović 2006). The combination of two separate processes whereby water is lost on the one hand from the soil surface by evaporation and on the other hand from the crop by transpiration is referred to as evapotranspiration(Allen et al., 1998). It is difficult to separately estimate the two processes, so we commonly combine 3


Panda, 2001; Medeiros et al., 2001; Liu et al., 2002; Bandyopadhyay and Mallick, 2003; Kang et al., 2003; Lage et al., 2003; Li et al., 2003; Vilallobos et al., 2003; Amayreh and Al-Abed, 2005;Lovelli et al., 2005). Most people using ET-based irrigation scheduling use Kc values from FAO 56 (Allen et al., 1998), so the idea was test if Kc estimates can be improved using data from from the Puglia region of Italy. Since Kc values are developed at a particular location, the ability to transfer those coefficients to other locations is often questionable. Also, variety and management differences can lead to inaccurate Kc values if not properly considered. „Consequently, an analysis of Kc changes and its impact on irrigation scheduling requirements is needed.

at 3.5 m height above the ground. Sunshine duration was measured with a classical Campbell-Stokes sunshine recorder. Precipitation was collected with a mechanical paper-recording rain gage, and evaporation was measured with a “class A” pan. The various sensors were checked for accuracy twice a year, on regular basis. Furthermore, the integrity of Tmin, Tmax, RHmin, RHmax and wind speed data were assessed by comparison with a nearby station through “double mass analysis”, according to Allen (1996). The daily crop evapotranspiration was measured by weighing lysimeter while ETo was estimated using Penman-Monteith equation (Monteith, 1965; Monteith 1973; Monteith and Unsworth, 1990; Smith et al., 1992; Alen et al., 1998; Allen et al., 2005) with input data from the meteorological station. Crop coefficients were determined as the ratio of ETc to ETo. The Kc values measured in this way were related to those proposed by FAO 56. Leaf Area Index (LAI) is defined as the ratio of one-side green leaf area (m2) to ground surface (m2), and it was measured with the electronic leaf area-meter, LI-COR 3100 eight times during the growing season. This method provides indication of the plant growth.

2. MATERIALS AND METHODS The data were collected at the agrometeorological station in the area of Policoro (MT) along the Western Ionian Coast at about 3 km west from the sea, latitude 40˚ 17' N, longitude 4˚ 25' E. This site is located 15 m above sea level and is characterized by sub-humid climate according to the De Martonne classification (Cantore et al., 1987). The Policoro data included information from 356 days recorded over one study year (1999). The data sets included the irrigation dates. Measurements of daily values of maximum and minimum air temperature and relative humidity, solar radiation, wind speed, precipitation and leaf area index were used for the model calculation. The weather station was located above a grass surface at about 30 m from the lysimeter. Air temperature and relative humidity, sensed respectively through a bimetallic thermograph and a hair hydrograph, were measured at 2 m height above the ground within the standard weather shelter and recorded on paper traces. The wind speed was measured with a propeller anemometer

3. RESULTS AND DISCUSSION A study on tomato crop evapotranspiration was conducted to investigate the influence of weather and management on crop growth and development parameter leaf area index and to evaluate Kc values for this climatic region. The area of Policoro has a Mediterranean semi-arid climate, characterized by the following long-term average values of the major climatic parameters: minimum and maximum daily air temperature are 11.0 and 21.4°C respectively; minimum and maximum daily air humidity are 52 and 87% respectively; average wind speed is 2.3 m s-1 and 4


and P 2O5, respectively, utilizing 0.25 t ha -1 of di-ammonium phosphate18-46% and 0.28 t ha-1 of ammonium nitrate 26-27%. Irrigation was applied using polyethylene drip tubing, placing along each row with each pipeline having the emitters with a delivery of 4 L h -1 and 0.5 m spacing. When water lost by evapotranspiration (ET c) reached 40% of maximum available water contained in the soil root-zone, watering water was applied to restore 100% of the water lost. The ETc was measured daily at seven a.m. by two weighing lysimeters having a surface of 2 2 m2 and a depth of 1.3 m, placed in the centre of the two contiguous areas cropped with tomato plants. Each area had a surface of 2500 m2. Irrigation was cut off 15 days before harvest. Twenty irrigation applications were applied, and the seasonal irrigation volume was 4,250 m3 ha-1. Daily values of evaporation from “class A” pan, rainfall and air temperature and humidity, wind speed, solar radiation, at a standard weather station next to the experimental area, were recorded. Every 810 days, the phenological stages of the crop were observed together with the cover index (CI). Destructive analyses were conducted to determine leaf area index (Table 2) LAI (using a LAI-meter from LICOR Inc.). Harvest occurred on September 9. Daily lysimeters data for the crop evapotranspiration were taken from two weighing lysimeters. The resolution of the lysimeter weighing system was 200 g corresponding to 0.05 mm of water that is lost by evapotranspiration or accumulated with precipitation or irrigation. The soil inside of lysimeter and the soil of the surrounding study had the same physical-chemical characteristics. Main soil textural and hydraulic properties are given (Table 3) and the main soil chemical properties are given (Table 4).

average class A pan evaporation is 5.2 mm day-1. Annual precipitation on average amounts to 567 mm, 29% of which is distributed from April to September. The mean annual precipitation of the study year was 616.9 mm and it is distributed mostly during autumn and winter season while summer season was hot and dry. The minimum rainfall occurred in January with 2.4 mm, while the maximum was in December with 231.7 mm. An unexpected dry month occurred in October with only 19.3 mm of precipitation. Mean monthly air temperature varied from 7.1°C in January to 25.4°C in July. Mean maximum monthly air temperature ranged from 12.3°C in January to 29.8°C in July. Mean minimum monthly air temperature varied from 1.8°C in January to 21°C in July. Mean daily air temperature, maximum air temperature, and minimum air temperature by month are presented (Table 1) for the cultivation period. Table 1: The main weather parameters related to tomato cultivation period Months

Tmin

Tmax

Tavg

Rainfall

6

18.4

28.7

23.6

9.2

7

21.0

29.8

25.4

38.3

8

20.1

28.8

24.5

30.2

9

16.5

24.8

20.7

62.3

The seeds of tomato (Lycopersicon esculentum Mill.) for processing cv. Dracula (were seeded in polystyrene trays on May 2 , and seedlings were grown in greenhouse. On June 9, at three true leaves stage, seedlings were transplanted in the field at plant density of 3.33 plants m-2, in double rows spaced as follows: a) 0.60 m between rows of twin; b) 2.0 m between each twin row; 0.30 m between the plants along the row The soil was fertilized before the transplant with 120 and 115 kg ha -1 of N 5


Table 2: Information about leaf area sampling during vegetation period

Data 10/6/02 22/6/02 1/7/02 8/7/02 19/7/02 29/7/02 8/8/02 19/8/02 29/8/02 8/9/02

DOY 161 173 182 189 200 210 220 231 241 251

DAP 1 13 22 29 40 50 60 71 81 91

LAI 0.01 0.06 0.28 0.79 2.33 3.77 5.01 5.02 4.60 3.70

Table 3: Average physical soil characteristics of the Policoro soil site within the 1st meter depth

Depth (cm) Sand (%) (2 > Ø > 0.02 mm) Silt (%) Clay (%) (Ø < 2 µ) Field capacity (%vol.) Wilting point (% vol.) Bulk density (gcm-3, kg dm-3)

100 40 37.1 22.9 31.5 15 1.25

Table 4: Average chemical soil characteristics of the Policoro soil site within the 1st meter depth

Depth (cm) pH Organic matter (%) (Walkley-Black method) Total N (%) (Kjeldahl method) Available P2O5 (ppm) (Olsen method) exchangeable K2O (ppm) (ammonium acetate method) ECe (dSm-1) ESP (%) total limestone (%) active limestone Reference evapotranspiration was estimated by using the daily PenmanMonteith equation (FAO 56 approach). The Penman–Monteith approach is a reliable, physically based method; however, it requires full set of weather data regarding air temperature, solar radiation, relative humidity, and wind speed. Although many areas of the world have few meteorological stations that measure all of these variables, the cost of such stations is decreasing, and it is likely that use of the Penman-Monteith approach will spread even to developing

100 7.7 3.64 1.67 26.7 227 0.95 1.9 1.5 0.5

regions. Crop evapotranspiration was measured by weighing lysimeter and reference evapotranspiration (ETo) was estimated by Penman-Monteith equation using the input data from the Policoro meteorological station. The seasonal ETc values were 489.0 mm while ETo was 467.5 mm. Crop coefficients were determined as the ratio of the measured ETc to ETo. The variation of Kc and leaf area index (LAI) for the tomato crop are plotted as a function of days after planting (DAP) (Fig. 1). 6


2.5

index and crop coefficient observed on the same days when LAI was measured. This was done to exclude days with wet plants and soil because it is assumed that there were not irrigation and precipitation when LAI was sampled. On the basis of these data, a new Kc curve was build for ten days of LAI sampling as illustrated (Fig. 2). This graph shows very strong relationship between LAI and Kc up to an LAI = 2. There was no predictable change in Kc curve after the canopy exceeded LAI = 2.0. Since LAI was nearly constant from DAP 60 through DAP 70, two cases were analysed to determine the relationship between Kc and LAI. The first relation between Kc and LAI has been evaluated from the first day of planting (on June 10 th) until DOP 60 (on August 8 th), taking into consideration first seven out of ten points of the measurements as illustrated (Fig. 3). The following logarithmic relationship between Kc and LAI was developed:

6 Kc LAI

5

2.0

4 1.5 Kc

3 LAI 1.0 2 0.5

1

0.0

0 0

20

40

60

80

100

DAYS AFTER PLANTING

Figur 1: Measured values of daily crop coefficient Kc and LAI of tomato growing at Policoro experimental station in 2002

Total growing season was of 91 days and the daily Kc ranged from 0.23 to 1.95. The average mid-season crop coefficient varied from 0.99 to 1.93. The Kc = 1.93 can be explained due to frequent irrigation and precipitation events that occurred during the season. The Kc end was about 0.60. The effect of the difference in aerodynamic properties between the grass reference surface and agricultural crops is not only crop specific but also varies with the climatic conditions (wind, humidity, etc) and crop height. More arid climates and conditions of greater wind speed will have higher values for Kc mid. More humid climates and conditions of lower wind speed will have lower values for Kc mid.

Kc = 0.2018 Ln(LAI) + 1.0926 .............. (1) The regression coefficient (R 2) of 0.9683 confirmed very good agreement between the Kc and LAI data. A particularly good fitting between Kc and LAI was observed for first 40 days after planting when LAI has reached the value of 2.33. Then after, the Kc is going to stabilize reaching the maximum values in the range between 1.3 and 1.4.

6

1.6 Kc

1.4

5

LAI

1.2

1.6 4

1

1.4

LAI

Kc

3

0.8 0.6

1.2

2

1 Kc

0.4

0.8

1

0.2

y = 0.2018Ln(x) + 1.0926

0.6

R2 = 0.9683

0

0 0

10

20

30 40 50 60 70 DAYS AFTER PLANTING

80

90

0.4

100

0.2

Figure 2: Relation between LAI and Kc during all the growing season

0 0

1

2

LAI

3

4

5

6

Figure 3: The relationship between LAI and Kc for the first 60 days after planting

An analysis of Kc variation was done using the relationship between leaf area 7


A slightly different relationship was observed when 8 out of 10 points have been used considering the period of 70 days between planting and observation of maximum LAI (Fig. 4). The logarithmic function between Kc and LAI was

proposed relationships should be tested at other sites and under different soil and climatic conditions. This approach could contribute to a better estimation of crop water requirements and a more accurate irrigation scheduling on both a whole season and a daily time span.

Kc = 0.1747 Ln(LAI) + 1.0196 .............. (2) The regression coefficient (R =0.8341), however, was much lower than in the previous case. Accordingly, it was observed that a good fitting between the Kc and LAI (Fig. 4) was only for the first 30 days after planting (when LAI has reached the value of 0.79). However, in this case, the maximum Kc was lower, in the range between 1.2 and 1.3 that seems to be more reliable.

REFERENCES

2

[1]

[2]

1.6 1.4 1.2 1

[3]

Kc 0.8 0.6 y = 0.1747Ln(x) + 1.0196

0.4

R2 = 0.8341

0.2 0 0

1

2

3

4

5

6

LAI

[4] Figure 4: The relationship between LAI and Kc for the first 70 days after planting

4. CONCLUSIONS The results of investigations on Policoro data indicate that Kc for tomato can be modelled satisfactorily either through the logarithmic relationship between Kc and LAI as Kc = a * ln (LAI) + b. The logarithmic relationship with leaf area index can be obtain at any location where leaf area index is measured. Nowadays, with the wide extension of computing technologies, use of models is important for modern agricultural production and irrigation management. The presented results and the

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