a g r i c u l t u r a l w a t e r m a n a g e m e n t 8 7 ( 2 0 0 7 ) 9 1 – 1 0 5

avai lab le at www.sc iencedi rec t .com

journal homepage: www.e lsev ier .com/ locate /agwat

Comparison of nitrogen and irrigation strategies in tomatousing CROPGRO model. A case study from Southern Italy

Michele Rinaldi a,*, Domenico Ventrella a, Caterina Gagliano b

aC.R.A., Istituto Sperimentale Agronomico, Via Celso Ulpiani 5, 70125 Bari, ItalybC.R.A., Istituto Sperimentale per l’Assestamento Forestale, Piazza Nicolini 6, 38050 Loc. Villazzano, Trento, Italy

a r t i c l e i n f o a b s t r a c t

Article history:

Accepted 9 June 2006

Published on line 18 September 2006

Keywords:

Lycopersicon esculentum Mill.

DSSAT software

CROPGRO model

Model calibration

Model validation

Water use efficiency

Nitrogen use efficiency

Environmental risk

Economic return

The CROPGRO simulation model was calibrated for processing tomato in Southern Italy with

a 2002 data set and validated with three independent data sets with acceptable results.

Subsequently this model was combined with 53 years of local historical weather data and it

was used as a research tool to evaluate the benefits, risks and costs of 23 different interactive

irrigation and/or N-management scenarios. Irrigation water was applied (i) on reported

dates with 3 and 5 days intervals and application rates of 15 and 25 mm or (ii) with automatic

irrigation initiated at residual soil moisture levels in the upper 30 cm of the soil profile of 25,

50, or 75%. Three amount levels of N application (100, 200 and 300 kg ha�1 as ammonium

nitrate) were considered. A simple economic analysis, including tomato marketable yield

and price, irrigation and nitrogen cost and other fixed production costs, was used to

estimate expected net return for each management scenario.

Based on simulation results it is concluded that irrigation scenario with low amount but

with frequent applications (‘‘3-day 15 mm’’ scenario) resulted in high value of irrigation

water use efficiency; seasonal irrigation application between 600 and 800 mm, that are

ordinary in Southern Italy, resulted in low irrigation water and N use efficiencies; a great

sensitivity of tomato to N fertilization rates was observed; frequent irrigation applications

combined with low N rates reduced crop stress and represented the best scenario

from both a production and environmental point of view (low N leaching); the optimal

average seasonal irrigation amount to reduce excessive drainage and N leaching should be

approximately about 400 mm; the economic evaluation suggested the possibility to reduce

irrigation water (no differences between irrigation at 75 and 50% of crop available water in

the soil), but not N application, at not less than of 200 kg of N ha�1.

The model was confirmed to be a useful decision support system to help the farmers to

verify the optimal crop management strategy from several points of view.

# 2006 Elsevier B.V. All rights reserved.

1. Introduction

Italy is one of the major producer and supplier of processing

tomato (Lycopersicon esculentum Mill.) in the world (18% of total

* Corresponding author. Tel.: +39 080 5475016; fax: +39 080 5475023.E-mail addresses: michele.rinaldi@entecra.it (M. Rinaldi), domenic

(C. Gagliano).

0378-3774/$ – see front matter # 2006 Elsevier B.V. All rights reservedoi:10.1016/j.agwat.2006.06.006

production in 2005, World Processing Tomato Council, 2006).

According to FAO trade data, Italy dominates the global

processed tomato products market. Tomato production

represents one of the most intensive agricultural land use

o.ventrella@entecra.it (D. Ventrella), caterina.gagliano@entecra.it

d.

a g r i c u l t u r a l w a t e r m a n a g e m e n t 8 7 ( 2 0 0 7 ) 9 1 – 1 0 592

in terms of water use and chemical inputs (Rinaldi et al., 2003).

In Southern Italy this crop can produce up to 200 and 12 t ha�1

of fresh and dry total biomass, respectively, with an average

water use of 800 mm, and depending on the climate, with

irrigation water requirement between 400 and 600 mm (Rana

et al., 2000).

To improve nitrogen use efficiency of a specific production

system, it is helpful to study the effects of different N rates on

dry matter production and fruit yield via a growth analysis

(Scholberg et al., 2000). During the crop cycle, tomato can

uptake 150–300 kg ha�1 of nitrogen and, during rapid growth

removal rates may exceed 4.3 kg ha�1 day�1 (Dumas, 1990).

Nitrogen requirements of tomato are greatest during the

vegetative phase and are in the range of 100–400 kg of mineral

N ha�1 (Warner et al., 2004). Doorenbos and Kassam (1986)

argue that the lower fertilizer requirement for high-yielding

tomato varieties can be as low as 100–150 kg ha�1. According

to Larouche et al. (1989), the growth response to added N

typically is most pronounced between 0 and 100 kg ha�1, with

similar findings reported for greenhouse experiments.

Sustainable tomato crop production requires optimal

fertilizer and water management to attain high yields and

to maximize profits. In general, prolonged severe water

deficit and N stress condition limits growth and reduces

yields (Scholberg, 1996; Scholberg et al., 2000). Because of the

high value of commercial tomato crops and the relatively low

cost of chemical fertilizers, farmers tend to over-fertilize in

order to minimize any risk of yield reduction (Locascio et al.,

1992).

In Mediterranean environments, where water resources

are limited and risk of groundwater contamination with

nitrate is high, it is critical to optimize irrigation and

fertilization use efficiencies via use of sound water and

nitrogen management practices.

To achieve better control and management of water in

tomato production, irrigation schedules should be based on

crop water requirement (crop evapotranspiration, ETc),

according to FAO guidelines (Doorenbos and Pruitt, 1977;

Allen et al., 1998): potential evapotranspiration (ETo) multi-

plied a crop coefficient (Kc). Another approach is development

of a daily water balance to calculate ETc and to schedule

irrigation events according to effective soil water storage

capacity and estimated crop water removal. This method for

irrigation scheduling can be very efficient, but it is difficult and

expensive to implement at a farm level.

Tomato requires a constant and adequate water supply

during the growing season because it is sensitive to water

stress, especially during the reproductive stage (Waister and

Hudson, 1970). Drought reduces fruit growth and size and

excessive fluctuations in soil moisture content may induce to

physiological disorders such as blossom end rot (Pill and

Lambeth, 1980).

However, excess water at any time during growth may

increase N leaching and after fruit set it may increase the

fruit’s susceptibility to cracking, which can reduce fruit

quality and yield (Peet and Willits, 1995). In tomato, cutting

off irrigation supply 2–4 weeks before harvesting maximized

the soluble solids content of the fruit, facilitated harvesting,

and minimize soil compaction from mechanical harvest

operations (Colla et al., 1999; Lopez et al., 2001). However,

use recently developed determinate processing tomato

hybrids with more robust fruits and suitable for mechanical

harvesting, allow farmers to continue irrigation until

complete crop maturity (May and Gonzales, 1999). Usually,

farmers plan irrigation scheduling according to local water

availability, using fixed intervals (from 2 to 6 days) between

irrigation applications to reduce crop water stress.

Total crop N requirements to attain target fruit yield

depend on the water management practices. The interactive

effects of both N and water stress on water relations and crop

nutrition are well-documented (Bennet et al., 1986; Morgan,

1986). Water stress can reduce the ability of plant roots to

absorb maximum nutrients while on the other hand, nitrogen

shortage can reduce the water use efficiency.

The main environmental problem associated with tomato

cultivation is nitrate leaching. Potential N leaching below

beyond the rootzone was considerably affected by amounts of

nitrogen fertilizer, the amounts of irrigation water and time of

its applications, and amounts of annual precipitation (Ersahin

and Karaman, 2001).

Properly timed and more frequent application of small

doses of N is the key to minimize nitrate pollution. Stark

et al. (1983) suggested that when small amounts of N are

applied to tomatoes with irrigation, leaching and denitrifica-

tion N will be reduced. Similarly, Kafkafi et al. (1978) argued

that leaching could be almost eliminated by combining

drip irrigation with the exact amount of crop nutrient

requirements.

Development and certification of site-specific guidelines

for optimal timing and water and nitrogen requirements

requires extensive and expensive field experiments. Since it is

impossible to test all the interactions between the amount of

water and nitrogen during the seasons, use of simulation

models can greatly facilitate the evaluation of different

production practices and/or environments and thereby

streamline the decision-making process.

Few models have been used for the simulation of tomato in

greenhouse and in field conditions. TOMSIM (Heuvelink and

Bertin, 1994; Heuvelink, 1996), EPIC (Cavero et al., 1998, 1999;

Rinaldi et al., 2001) and CROPGRO-Tomato, also indicated as

TOMGRO (Jones et al., 1991; Bertin and Gary, 1993; Scholberg

et al., 1996, 1997; Pien et al., 1999; Messina et al., 2001; Koo,

2002; Sauviller et al., 2002; Cooman and Schrevens, 2004a,b;

Ramırez et al., 2004) are the most cited in literature. This last

model has been used mainly in field conditions and success-

fully calibrated and validated in North America (Florida and

California), Central and Southern America, Spain, and

Portugal. For these reasons we selected CROPGRO model in

this study, to compare and analyze irrigation and nitrogen

fertilization scenarios in processing tomato in field condi-

tions.

The objectives of the current study were to:

a. e

valuate the capability of CROPGRO model to simulate yield

production of field-grown tomato under Mediterranean

conditions;

b. c

ompare several management scenarios (irrigation sche-

dules and different nitrogen application levels) from

a production, environmental and economic points of

view.

a g r i c u l t u r a l w a t e r m a n a g e m e n t 8 7 ( 2 0 0 7 ) 9 1 – 1 0 5 93

2. Materials and methods

2.1. Model description

The crop model used for this research is embedded in the

Decision Support System for Agro-technology Transfer

(DSSAT) software application (Jones et al., 2003), version 4.

To run the model several input files must be compiled that

contain information pertaining to the experiment site, soil,

climate and genotype (Tsuji et al., 1994).

Crop growth is based on CROPGRO, a generic crop growth

model that was originally developed for legumes (Boote et al.,

1998; Hoogenboom et al., 1994; Jones et al., 2000; Porter and

Jones, 1998) and adapted to other crops including tomato

(Scholberg et al., 1996, 1997).

The model assumes a phasic development with seven

growth stages, from emergence to harvest. Development

processes (rate of emergence, leaf appearance, and progress

toward flowering and maturity) and growth processes (photo-

synthesis, leaf expansion, fruit and seed growth, N mobiliza-

tion, etc.) depend on the base and optimum temperatures. The

model calculates biomass accumulation as the product of

radiation use efficiency and photosynthetically active inter-

cepted radiation. Growth of new tissues depends on daily

available carbohydrate, partitioning to different tissues, and

respiration costs of tissue synthesis. The CROPGRO model

uses partitioning coefficients to allocate new assimilates to

vegetative (leaves and stems), reproductive (fruits) and root

organs. Leaf area expansion depends on leaf weight growth

and specific leaf area, where the latter depends on tempera-

ture, light, and water deficit. Leaf expansion during repro-

ductive growth is reduced by decreased assimilate allocation

to leaf growth followed by a phase that ultimately terminates

further leaf expansion.

The model requires three crop-specific genotype parameter

files being the species, ecotype, and cultivar files. Key

parameters include crop phenology, growth rates, light

interception, radiation use efficiency, canopy and leaves

characteristics, nitrogen contents, and dry matter partition-

ing. Cultivar parameters are those that are expected to vary

the most and these need to be estimated for cultivars that are

not included in the current file version. The parameters in the

ecotype file are intended for use by many cultivars that have

similar growth habits, and users typically should not have to

modify these under normal circumstances (Jones et al., 2000).

The Soil water model was described by Ritchie (1986). It

operates on the basis of a ‘‘tipping bucket’’ approach and

includes rainfall, infiltration and runoff, drainage, soil

evaporation, plant transpiration, root absorption or flow to

an adjacent layer. Upper and lower limits of water availability

are required as input in the soil data file.

The Nitrogen balance model simulates the processes of

organic matter turnover with the associated mineralization

and/or immobilization of nitrogen, nitrification, denitrifica-

tion, hydrolysis of urea, ammonia volatilisation, N plant

uptake and translocation to the different organs during crop

cycle. Transport of nitrate occurs at the same rate as the flow

of water (Booltink et al., 1996).

Water and nitrogen submodels calculate feedback effects

on plant growth and development. A detailed description of

the model can be found in Ritchie (1986) and Godwin and Jones

(1991).

2.2. Experimental data sets

The data sets used for model evaluation were derived from a

field experiment carried out at the experimental farm of the

Agronomic Research Institute in Foggia (418270 latitude N,

158040 longitude E) in Southern Italy at an elevation of 90 m

a.s.l. The area (about 400,000 ha) is mainly cultivated with

durum wheat which is grown in a 3–4 years rotation with

tomato or sugar beet. The experimental farm is representative

of soil and climate conditions of this plain which is the most

productive area of tomato field crop in Italy.

Daily weather data used for simulations (maximum and

minimum temperatures, solar radiation and rainfall) were

collected from an on-site meteorological station.

The soil was a silty-clay vertisol of alluvial origin (1.20 m

depth) and is classified as a fine, termic, Typic Chromoxerert

(according to USDA Soil Taxonomy) with 24.9% sand, 39.7% silt

and 35.4% clay. The soil had the following characteristics:

organic matter 2.1%; total N (Kjehldahl) 0.122%; NaHCO3-

extractable P2O5 41 ppm; NH4OAc-extractable K2O 1598 ppm;

pH (2:1 water extraction) 8.3. The field capacity of the upper

50 cm soil layer was (�0.03 MPa, Richards plates) 0.48 m3 m�3

and the permanent wilting point (�1.5 MPa) 0.24 m3 m�3.

Overall total available soil water was 225 mm up to a depth of

0–120 cm.

The climate is classified as an ‘‘accentuated thermomedi-

terranean’’ (Unesco-FAO classification), with temperatures

that may fall below 0 8C in winter and rise above 40 8C in

summer. Rainfall is unevenly distributed throughout the year,

mostly concentrated in the winter months with a long-term

yearly average of 550 mm. Long-term seasonal (28 April–28

September) potential evapotranspiration (ETo) and rainfall of

processing tomato amount to 484 and 178 mm, respectively.

The experimental data were collected from a 2002–2004

fertilizer trial and only one inorganic N treatment was

considered which was applied at a rate of 100 kg ha�1 as

ammonium nitrate (34.5% of N). Half of the fertilizer was

broadcasted half prior to transplanting with the remainder

being applied at fruit formation of first trust. The experimental

design was a completely randomised block with four replica-

tions and with a plot size of 50 m2 (5 m � 10 m). The tomato

plants were transplanted on 29 April 2002, on 5 May 2003 and

on 18 May 2004. Tomatoes were planted in twin rows (0.4 and

1.7 m, respectively between rows and between twins), spaced

0.4 apart resulting in a plant population of 30.000 plants ha�1.

Irrigation was applied via drip tube (Agrifim, in line emitters,

2 L h�1, spaced 0.4 m). Overall irrigation applications were 330,

540 and 560 mm during 2002, 2003, and 2004, respectively; total

rainfall during tomato growing season was 141, 68 and

226 mm, respectively in the 3 years. Standard weed and pest

control practices were followed. In 2003 an additional data set

that was managed similarly, was collected and used for

simulations.

Destructive plant tissue samples were collected at 2-week

interval throughout the growth cycle. Two representative

plants per plot were sampled and separated in leaves, stems

and fruits. Samples were dried at 65 8C per 48 h. Leaf area of

a g r i c u l t u r a l w a t e r m a n a g e m e n t 8 7 ( 2 0 0 7 ) 9 1 – 1 0 594

green leaf blades was measured with LICOR-3000 leaf area

meter and values were used for LAI calculations. At harvest (19

August 2002, 21 August 2003 and 7 September 2004) tomato

fruits were counted and weighed. Total fresh and dry biomass

(fruit and plant residues) were also determined at this time.

Gravimetric soil water content at soil depths of 5–15, 15–30 and

30–45 cm was determined during plant sampling and was

multiplied by soil bulk density (1.20 kg dm�3) to calculate

volumetric water content. For further details about the

experiments see Rinaldi et al. (2003, 2004) and Elia et al. (2006).

2.3. Model calibration and validation

Genetic parameters for the experimental tomato variety

(hybrid PS 1296, a determinate, processing variety with

globe-shaped fruits) were calculated and used for the

genotype files. Parameters were either derived from measure-

ment (crop phases length, leaf size and specific leaf area) or

calculated through continuous approximations using an

iterative method, until simulated values of phenology and

productive variables became similar to correspondent

observed field values (Castrignano et al., 1997) using experi-

mental data collected during 2002. Flowering and maturity

dates, yield and fruit (number and size) at harvest, dry matter

weight of leaf, stem and fruit, leaf area index and soil water

content during crop cycle, were also used during model

calibration.

The validation was carried out using experimental data

collected in 2003 (two data sets) and 2004. Root mean square

error (RMSE) (1) and modelling efficiency (ME) (2) indexes,

according to Loague and Green (1991) were used:

root mean square error ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPni¼1 ðPi � OiÞ2

n

s100

O(1)

and

modeling efficiency ¼Pn

i¼1 Oi � O� �2 �

Pni¼1 ðPi � OiÞ2

h iPn

i¼1 Oi � O� �2

(2)

where Pi andOi are predicted and observed values, respectively

and O is the observed mean value. RMSE provides a measure

(%) of the relative difference between simulated and observed

data. Simulation results are considered to be excellent when

RMSE < 10%, good if 10–20%, fair if 20–30%, and poor if values

are >30% (Jamieson et al., 1991). The ME range is <1 (optimal

value) and it compares modelling variability with experimen-

tal variability. A negative value of ME indicates that the mod-

elling variability is greater than the experimental variability

and, therefore, the simulation is not satisfactory. The Pear-

son’s correlation coefficient (3) also was used to measure the

degree of association between the two variables:

r ¼P

Pi � P� �

Oi � O� �

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPPi � P� �2P

Oi � O� �2q (3)

where Pi and Oi are predicted and observed values, P and O are

simulated and observed mean values. The correlation coeffi-

cient may take any value between �1 and +1. Positive correla-

tion indicates that both variables increase or decrease

together, whereas negative correlation indicates that as one

variable increases, so the other decreases, and vice versa.

2.4. Seasonal analysis

The seasonal analysis option of the DSSAT software was used

to simulate the long-term effects of crop management

scenarios using historic weather data. Twenty-two different

scenarios featuring different ‘‘irrigation and N application

practices’’ were simulated (Table 1). An additional scenario,

potential yield, was calculated by disengaging the water and

nitrogen modules of the CROPGRO model. Under these

conditions crop development and yield are controlled by

temperature and radiation only under non-limiting conditions

for water and nitrogen. A very important application of these

potential production models is the determination of the yield

that can be achieved with current varieties in a specific

environment (Kropff et al., 2001).

The irrigation water was applied (i) with automatic

irrigation of 40 mm being initiated when 25, 50 or 75% of

the crop available residual moisture (CAW) in the upper

30 cm of the soil profile is being depleted (in this case the N

module was disabled); (ii) with automatic irrigation at 50% of

CAW and 100, 200 and 300 kg of N ha�1; (iii) with fixed

irrigation of 15 and 25 mm at 3 and 5 days interval with N

module disabled; (iv) with irrigation on reported dates at 3

and 5 days interval, varying the amount of water supply (15

and 25 mm) in interaction with 3 N rates. The nitrogen

fertilization was scheduled in three applications, before

sowing (day of the year, DOY 115), at first flowering (DOY

140) and at bud formation (DOY 165) with seasonal N

amount of 100, 200 and 300 kg of N ha�1, applied as

ammonium nitrate.

Simulations were performed for the period 1 January 1952–

31 December 2004 (53 years), using a climatic data set collected

in the weather station of the experimental farm. Measured

daily values of temperatures (minimum and maximum),

rainfall and global radiation were used as model input

variables. The Priestley-Tailor model was chosen to estimate

potential evapotranspiration, because was the best model

with the daily available climatic variables. Fixed date of

tomato transplant (28 April, DOY 119) and automatic harvest

date at fruit maturity (before 28 September, DOY 271) options

were selected in the experimental file.

A simple economic analysis VARAN2 (Cropping Season

Analysis Tool) that included final yield, tomato price, irrigation

and nitrogen application cost and fixed production costs, was

used to estimate expected net return for each management

scenario. The net return was calculated (in s ha�1) as

difference between yield value and crop production cost,

according to the current (2005) costs and prices of the

processing tomato crop in Southern Italy (Table 2).

The comparison of nitrogen and irrigation strategies of

field-grown tomato, from production, environmental and

economic points of view, was performed using several model

output variables being: total plant (TDM, t ha�1) and fruit dry

matter (FDM, t ha�1) at harvest; irrigation water use efficiency

(IWUE, FDM/irrigation water amount, kg ha�1 mm�1) and

nitrogen use efficiency (NUE, FDM/N uptake, kg kg�1) (Delogu

a g r i c u l t u r a l w a t e r m a n a g e m e n t 8 7 ( 2 0 0 7 ) 9 1 – 1 0 5 95

Table 1 – Nitrogen and irrigation management scenarios of tomato simulated with CROPGRO model in a seasonal analysisfor 53 years

Scenarios Watermodule

Nitrogenmodule

CAWthreshold (%)

Irrigationinterval (days)

Irrigationamount (mm)

Nitrogenamount (kg ha�1)

Referencenumber

Description

1 Potentiala N N – – – –

2 noN CAW 25%b Y N 25 Changeable Variable –

3 noN CAW 50%b Y N 50 Changeable Variable –

4 noN CAW 75%b Y N 75 Changeable Variable –

5 N100 CAW 50%b Y Y 50 Changeable Variable 100

6 N200 CAW 50%b Y Y 50 Changeable Variable 200

7 N300 CAW 50%b Y Y 50 Changeable Variable 300

8 noN-3-day 15 mmc Y N – 3 15 –

9 N100-3-day 15 mmc Y Y – 3 15 100

10 N200-3-day 15 mmc Y Y – 3 15 200

11 N300-3-day 15 mmc Y Y – 3 15 300

12 noN-5-day 15 mmc Y N – 5 15 –

13 N100-5-day 15 mmc Y Y – 5 15 100

14 N200-5-day 15 mmc Y Y – 5 15 200

15 N300-5-day 15 mmc Y Y – 5 15 300

16 noN-3-day 25 mmc Y N – 3 25 –

17 N100-3-day 25 mmc Y Y – 3 25 100

18 N200-3-day 25 mmc Y Y – 3 25 200

19 N300-3-day 25 mmc Y Y – 3 25 300

20 noN-5-day 25 mmc Y N – 5 25 –

21 N100-5-day 25 mmc Y Y – 5 25 100

22 N200-5-day 25 mmc Y Y – 5 25 200

23 N300-5-day 25 mmc Y Y – 5 25 300

a With water, nitrogen and other modules disabled, with crop growth depending only by temperature and radiation.b The CAW is crop available water (in % of total) in the 0–30 cm soil depth; irrigation was simulated with application of 40 mm.c The irrigation was applied at fixed dates.

et al., 1998); drainage water amount (mm) and the nitrogen

leached (kg ha�1); production cost and net return (s ha�1).

Frequency distributions and cumulated probability func-

tions were also developed for the simulation results.

3. Results and discussion

3.1. Model calibration

Table 3 outlines genetic coefficients as well and for the species

file both original and calibrated values are shown.

To improve the crop development simulation for the

used cultivar, we calculated crop phase duration and

Table 2 – Prices and costs of tomato field crop inSouthern Italy (2005 season), used in the economicevaluation with seasonal analysis of DSSAT software

Costs and prices Values

Base production cost (s ha�1) 4900

Price of harvest product (s t�1 of fruit dry matter) 1200

Nitrogen fertilizer cost (s kg�1 of N) 1

Cost per N fertilizer application (s ha�1) 10

Irrigation cost (s mm�1) 1

Cost per irrigation application (s ha�1) 15

modified the EM-FL parameter (photothermal days between

plant emergence and flower appearance) to better predict

anthesis date; a new value of FL-SH parameter (the timing

from flowering to first fruit) resulted in a better fit of timing

of fruit appearance and growth; a new value of PODUR

parameter (duration of fruit addition) that influenced the

initial rise in fruit and seed mass was also required.

Plant growth simulation showed a strong sensitivity to leaf

characteristics. To take this into account, estimated para-

meters of leaf growth from experimental data were used and

changes were made in the species file parameters. In particular,

specific leaf area (SLAMAX, SLAMIN, SLAPAR) and leaf size

(SIZREF) parameters were modified.

Finally, other parameters influencing leaf growth in ecotype

and cultivar file were modified, in order to improve the fit

between simulated and measured LAI and leaf weight data.

LFMAX (maximum rate of leaf photosynthesis), SLAVR

(specific leaf area of cultivar under standard growth condi-

tion), TRIFL (rate of appearance of leaves on the main stem)

and SIZLF (maximum size of full leaf) were also modified until

simulated LAI and leaf weight closely matched measured

ones.

We also modified the vegetative partitioning parameters

in the species file to better balance dry matter allocation

between vegetative and reproductive organs to improve

model predictions.

a g r i c u l t u r a l w a t e r m a n a g e m e n t 8 7 ( 2 0 0 7 ) 9 1 – 1 0 596

Table 3 – Genetic coefficients of tomato generated in the calibration phase

File Measured Values

Cultivar EM-FL 21

FL-SH 10

FL-SD 117

PODUR 21

File Estimated by available data Values

Species SLAMAX 500

SLAMIN 250

SLAPAR 0.5

SIZREF 200

File Calibrated Values

Ecotype TRIFL 0.6

LFMAX 3.5

Cultivar SLAVR 280

SIZLF 180

Vegetative partitioning parameters (species file)

Default values Calibrated values

(1) 0.4–0.4–0.58–0.55–0.52–0.48–0.45–0.42–0.41–0.37 0.4–0.45–0.65–0.7–0.7–0.7–0.7–0.7–0.6–0.6

(2) 0.3–0.3–0.27–0.31–0.35–0.39–0.44–0.47–0.49–0.54 0.3–0.25–0.2–0.2–0.2–0.2–0.2–0.2–0.3–0.3

EM-FL: time (photothermal days) between plant emergence and flower appearance; FL-SH: time between first flower and first pod; FL-SD: time

between first flower and first seed; PODUR: time required for cultivar to reach final pod load under optimal conditions; SLAMAX: maximum

specific leaf area (cm2 g�1); SLAMIN: minimum specific leaf area (cm2 g�1); SLAPAR: daily photosynthetically active radiation or photon flux

density (mol[quanta] m�2 d�1); SIZREF: leaf dimension of the crop species standard (cm2); TRIFL: rate of appearance of leaves on the main stem

(leaves per thermal day); LFMAX: maximum leaf photosynthesis rate at 30 8C, 350 ppm CO2, and high light (mg CO2 m�2 s�1); SLAVR: specific

leaf area of cultivar under standard growth conditions (cm2 g�1); SIZLF: maximum size of full leaf (three leaflets) (cm2). At 10 equally crop cycle

times, the parameters represents the partitioning of total plant dry matter to leaves (1) and stems (2).

Table 4 – Statistical indexes of comparison of simulated vs. observed values of tomato in the validation phase, 2003–2004

Variable Numberof data

Observed Std. ofobserved

Simulated Std. ofsimulated

Difference(%)

RMSEa

(%)MEb rc

Maturity date (dap) 3 106.0 3.7 107.0 5.0 0.9 6.6 �2.55 �0.27

At harvest

Fruit dry matter (t ha�1) 3 6.81 1.61 8.54 1.65 25.5 26.0 �0.20 0.98

Fruit number (n m�2) 3 162 36 129 46 �20.2 22.8 0.96 0.94

TDMd (t ha�1) 3 10.82 2.79 12.19 1.56 12.7 17.1 0.56 0.99

During crop cycle

LAI (cm2 cm�2) 21 1.40 1.00 1.10 0.70 �19.6 50.1 0.50 0.76

Leaf weight (t ha�1) 24 1.38 1.15 1.01 0.76 �26.6 49.6 0.65 0.90

Stem weight (t ha�1) 24 0.84 0.76 1.11 1.03 32.8 63.3 0.51 0.92

Fruit weight (t ha�1) 14 3.98 2.57 5.09 2.61 27.9 46.0 0.49 0.84

Tops weight (t ha�1) 24 4.48 4.52 5.09 4.81 13.8 38.9 0.85 0.94

SWC 5–15 cme (cm3 cm�3) 29 0.34 0.06 0.39 0.05 14.6 22.6 �0.30 0.37

SCW 15–30 cm (cm3 cm�3) 29 0.35 0.06 0.40 0.05 15.8 25.0 �0.87 0.14

SWC 30–45 cm (cm3 cm�3) 29 0.36 0.06 0.40 0.03 10.2 19.8 �0.81 0.20

32.3 �0.02 0.64

a Root mean square error.b Modelling efficiency.c Pearson’s correlation coefficient.d TDM: total dry matter.e SWC: soil water content.

a g r i c u l t u r a l w a t e r m a n a g e m e n t 8 7 ( 2 0 0 7 ) 9 1 – 1 0 5 97

3.2. Model validation

In Table 4 the main statistical indexes used to evaluate the

accuracy of the model are reported. The average RMSE index

was 32% (fair) and the average ME showed an acceptable

underestimation (�0.02).

Based onthe four statistical indicesused, the bestsimulation

results were obtained in for maturity date, tops yield at harvest

and soil water content in the deeper layer. Also fruit yield at

harvest was predicted relatively well by the model, with

acceptable values of RMSE (26%) and a high ‘‘r’’ value (0.98).

Simulated LAI fitted the measured data during initial

growth as shown by a slow increasing of LAI due to the

transplant shock coupled with the crop being source limited

due to incomplete light interception (Fig. 1). However, there

after the fit was less perfect possibly due to large variability in

the observed data. Simulated LAI increased slower than

measured ones probably because the model does not take

into account the twin rows plant distribution and over-

estimates the competition for light among plants. The

simulation curve followed the classic course of LAI that in

tomato decreases very slowly at the end of cycle, when leaf

senescence occurred.

Total dry matter during crop cycle was predicted very well,

with ME close to the optimal value of 1 (0.85) and correlation

coefficient close to 1 (0.94) (Table 4). The behaviours of

simulated and measured values through tomato crop cycle

were excellent for both years (Fig. 1). Besides the average

difference, the measured and simulated values were lower

than the variation range (standard deviation).

Fig. 1 – CROPGRO simulated (lines) and measured (points) toma

(left) and 2004 (right) during the growth season. Bars indicate s

The modelling efficiency decreased for the simulation of

leaf, stem and root partitioning parameters. Simulations of

leaf, stem and fruit weight during the growth cycle were

relatively inaccurate with RMSE being greater than 40%

(Table 4). In particular, stem weight during the cycle was

poorly simulated by the model and weights were always

overestimated.

The changes in the soil water content (SWC) during the

crop cycle were well described by the model in the three soil

layers and in both years (Fig. 2). In the 30–45 cm depth, the

most relevant layer for plant water uptake, the model

simulated SWC better than in the other two layers with

favourable RMSE values (19.8%) and an overestimation of just

10% (Table 4). On the whole, the SWC simulated values in the

three soil layers were in agreement with observed data, also in

consideration of large standard deviation ranges.

The validation activity confirmed that CROPGRO model is

able to simulate tomato maturity date, fruit number, tops and

fruit yield at harvest, LAI, and SWC in the most pertinent part

of the rhizosphere for drip irrigated tomatoes. In particular,

fruit yield, TDM and SWC were accurately predicted and

simulation results are satisfactory for our objectives.

3.3. Seasonal analysis

3.3.1. Yield

In Table 5 total dry matter (TDM) and fruit dry matter (FDM) at

harvest for different irrigation alternatives versus N fertiliza-

tion levels are reported, derived from output of the seasonal

analysis option of the DSSAT program.

to leaf area index (LAI) and total dry matter (TDM) in 2003

tandard deviation of measured data.

a g r i c u l t u r a l w a t e r m a n a g e m e n t 8 7 ( 2 0 0 7 ) 9 1 – 1 0 598

Fig. 2 – CROPGRO simulated (lines) and measured (points) volumetric soil water content values in 2003 (left) and 2004 (right),

in days after transplanting. Bars indicate standard deviation of measured data.

Under non-limiting conditions (scenario 1) TDM exceeded

15 t ha�1 and FDM was greater than 10 t ha�1 of FDM

translating to 200 t ha�1 of fresh fruit yield. Radiation and

temperature are conducive to high production during tomato

crop cycle (April–August). Consequently, tomato crop yield

could be, in the test area, improved up to 30–40% than the

actual (6–7 t ha�1 of FDM) by partially eliminating limiting

factors including water and/or, nutrient stress and growth

reductions associated with pest and weeds.

Use of the automatic irrigation modelling option (scenarios

2, 3 and 4) showed that changing the initiation threshold for

irrigation from 50 to 75% of crop available water (CAW)

(scenario 3 versus 4) did not significantly increase total dry

matter nor fruit yield (+2.3%). On the contrary, starting

irrigation at 25% of CAW compared to 50% (scenario 2 versus

3) showed a much greater improvement in both TDM and FDM

(+11.2%). Cumulative probability functions showed similar

results (Fig. 3a), with comparable curves of CAW50 and

CAW75, while the CAW25 one lightly more distant, confirming

a quadratic response to additional irrigation supply water of

tomato explained by the leaching of nutrients (Locascio and

Smajstrla, 1996) and the detrimental effects of excessive soil

moisture on plant growth.

Nitrogen application levels (scenarios 5, 6 and 7) signifi-

cantly increased tomato yield (Table 5). With minimum N

fertilizer amount (100 kg ha�1) nitrogen became yield limiting

factor, as shown by low variability of N100 treatments curves

(Fig. 3b–f). The rising of N application level increased

significantly tomato yield, particularly doubling the N amount

(N200) and more for fruit than for total dry matter (+21% with

N200 and +37% with N300 for TDM, and +33% and +52% for

FDM, compared with N100). This confirms the results reported

in literature that suggest how high N rates promote vegetative

development (Doss et al., 1975; Huett and Dettmann, 1988;

Larouche et al., 1989).

Pertaining to irrigation, using irrigation scheduling with

fixed application times (3 and 5 days, scenarios 8 and 16, 12

and 20), the seasonal irrigation water applied was greater in

a g r i c u l t u r a l w a t e r m a n a g e m e n t 8 7 ( 2 0 0 7 ) 9 1 – 1 0 5 99

Table 5 – Yield and economic results of simulation of field-tomato with CROPGRO model

Reference number Scenarios Total dry matter(t ha�1)

Fruit dry matter(t ha�1)

Production cost(s ha�1)

Net return(s ha�1)

Potential

1 15.1 � 2.0 10.9 � 1.6 4900 � 851 8106 � 1897

Automatic irrigation

2 noN CAW 25% 12.2 � 2.0 8.9 � 1.6 5144 � 954 5501 � 1853

3 noN CAW 50% 13.6 � 2.0 9.9 � 1.6 5240 � 959 6604 � 1896

4 noN CAW 75% 13.9 � 2.0 10.1 � 1.6 5398 � 960 6706 � 1899

Nitrogen application rates

5 N100 CAW 50% 8.1 � 2.1 5.2 � 1.1 5301 � 957 947 � 1320

6 N200 CAW 50% 9.8 � 2.7 6.9 � 1.9 5430 � 953 2867 � 2274

7 N300 CAW 50% 11.1 � 3.1 7.9 � 2.4 5539 � 971 3983 � 2831

Fixed irrigation

8 noN-3-day 15 mm 14.3 � 2.0 10.3 � 1.6 5884 � 1071 6521 � 1913

12 noN-5-day 15 mm 13.0 � 1.9 9.6 � 1.6 5489 � 1002 6008 � 1923

16 noN-3-day 25 mm 14.3 � 2.0 10.4 � 1.6 6212 � 1179 6241 � 1942

20 noN-5-day 25 mm 14.0 � 2.0 10.1 � 1.6 5693 � 964 6453 � 1914

Interaction ‘‘water � nitrogen’’

9 N100-3-day 15 mm 10.0 � 1.5 5.7 � 0.6 6014 � 1004 881 � 701

10 N200-3-day 15 mm 12.6 � 2.6 8.7 � 1.6 6114 � 1006 4278 � 1863

11 N300-3-day 15 mm 13.6 � 3.1 9.7 � 2.3 6214 � 1007 5408 � 2723

13 N100-5-day 15 mm 9.5 � 1.1 5.4 � 0.6 5608 � 945 885 � 664

14 N200-5-day 15 mm 12.4 � 2.2 8.4 � 1.3 5716 � 966 4318 � 1580

15 N300-5-day 15 mm 13.0 � 2.8 9.2 � 2.0 5819 � 971 5252 � 2438

17 N100-3-day 25 mm 8.8 � 1.2 4.8 � 0.4 6342 � 1077 �580 � 497

18 N200-3-day 25 mm 11.8 � 2.5 7.7 � 1.3 6442 � 1078 2815 � 1562

19 N300-3-day 25 mm 12.9 � 3.0 9.0 � 2.0 6542 � 1093 4290 � 2353

21 N100-5-day 25 mm 10.0 � 1.4 5.7 � 0.5 5823 � 944 1064 � 643

22 N200-5-day 25 mm 12.5 � 2.4 8.6 � 1.5 5923 � 956 4398 � 1748

23 N300-5-day 25 mm 13.4 � 2.9 9.6 � 2.1 6023 � 998 5480 � 2579

Each scenario average derives from 53 annual data (seasonal analysis).

the 3-day scenario than in 5-day one (on average, 656 versus

396 mm) with 33 versus 20 applications. Despite this great

difference of irrigation water, TDM and FDM tomato yield only

improved by 5.9 and 5.1%, respectively, in the 3-day irrigation

scheduling scenario. The two irrigation supply amounts (15 and

25 mm, scenarios 8 and 12, 16 and 20) did not influenced tomato

yield at all, with very low increments in the higher amounts

(+0.5 t ha�1 for TDM and +0.3 t ha�1 for FDM). The most

productive irrigation scheduling, without considering N effect,

seemed to be the 3-day interval with 15 and 25 mm (scenarios 8

and 16),while the5-day with 15 mm(scenario 12) appearedtobe

the worst scenario, as confirmed in literature about the

advantage of a short irrigation interval on tomato fruit yield

(Al-Ghawas and Al-Mazidi, 2004; Marouelli and Silva, 2005). A

further consideration can be made comparing ‘‘5-day 25 mm’’

versus ‘‘3-day 25 mm’’ scenarios, showing the first one a large

water saving (�36%) but very similar fruit yield (�2%).

The interactive ‘‘water � nitrogen’’ approaches (scenarios

9–11, 13–15, 17–19, 21–23) indicated how the effect of N

fertilizer amount in increasing yield is more evident in the ‘‘3-

day 25 mm’’ scenario (+47% and +88%, in TDM and FDM,

respectively, comparing N300 versus N100), less evident, but

always significant in the ‘‘5-day 25 mm’’ scenario (+34% and

68%), intermediate effect in the other two scenarios (Table 5

and Fig. 3c–f). The ‘‘3-day 25 mm’’ scenario seems to be the

condition when the crop suffered more water shortage though

more amount of water applied and fruit yield was lower than

the other scenarios, especially at N100 rate (Fig. 3e versus c, d,

and f): this because of water lodging affected crop water and

nitrogen uptake.

3.3.2. Environmental risk

Changing the CAW threshold (scenarios 2, 3 and 4), and

assuming no nitrogen stress, scenarios influenced seasonal

irrigation requirements (331 mm versus 203 mm with

CAW = 75% and 25%, respectively), and improved irrigation

water use efficiency for fruit yield from 30.5 to

43.8 kg ha�1 mm�1 for CAW values of 75% and 25%, respectively

(Table 6). The negligible drainage values suggested that the

water applied during growing season using automatic irrigation

criteria (based on soil moisture measurement) matched well

tomato water requirements. On the contrary, using fixed-date

and fixed-amount irrigation scheduling, the water lost by

drainage becomes considerable. In Fig. 4 the four irrigation

scenarios at intermediate (N200) N fertilization rate (no

difference was observed between N rates) are shown: the most

irrigated scenario ‘‘3-day 25 mm’’ displays the highest values

(up to 700 mm) and a probability of 50% to exceed a seasonal

drainage of 400 mm (about 47% of total irrigation application).

Thelowestvalueswereobservedinthe‘‘5-day15 mm’’scenario,

while the remaining two scenarios was very similar (Fig. 4).

In the scenarios 5, 6 and 7 nitrogen fertilizer levels affected

irrigation water use efficiency, increasing with N rates,

simulating an effect of N limitations in plant water use

a g r i c u l t u r a l w a t e r m a n a g e m e n t 8 7 ( 2 0 0 7 ) 9 1 – 1 0 5100

Fig. 3 – Cumulative probability density function based on historic weather data (53 years) to do not exceed a value of tomato

fruit dry matter yield for the following scenarios: (a) potential vs. automatic irrigation levels (CAW = 25, 50 and 75%); (b)

automatic irrigation at CAW = 50% vs. N fertilization amount (100, 200 and 300 kg haS1); (c) fixed irrigation every 3 days with

15 mm vs. N fertilization amount (100, 200 and 300 kg haS1); (d) fixed irrigation every 5 days with 15 mm vs. N fertilization

amount (100, 200 and 300 kg haS1); (e) fixed irrigation every 3 days with 25 mm vs. N fertilization amount (100, 200 and

300 kg haS1); (f) fixed irrigation every 5 days with 25 mm vs. N fertilization amount (100, 200 and 300 kg haS1).

capability (Table 6). The greatest absolute values of WUE, both

for TDM and FDM transformation, was observed in the less

irrigated scenario (5-day 15 mm), on the contrary, the lowest

values in the best irrigated one (3-day 25 mm). Between

irrigation scenarios using 25 mm (17–19 versus 20–22) the

same consideration before said can be made: the 5-day

interval resulted more efficient in irrigation water use, water

saving and fruit yielding than the 3-day interval scenario.

Nitrogen application amount predisposed positively N crop

uptake (Table 6), while the interaction with irrigation demon-

strated that with ‘‘3-day 25 mm’’ irrigation scenario the crop

uptook lowest N amount, explained by the high simulated of

drained water and, consequently, of N leached, no more

available for tomato crop. The efficiency of N use (NUE) was

higher at N100, lower at N300 for both total plant (TDM) and fruit

dry matter (FDM), but the differences were more marked for

TDM than FDM; finally, minimum differences among the

irrigation scenarios were observed for NUE.

When irrigation water was applied at fixed dates and the N

management utility was disabled (scenarios 8, 12, 16 and 20),

irrigation applications increased until 820 mm (scenario 16)

and, at the same time, the irrigation WUE decreased

dramatically because of excessive water displacement below

the rootzone: in fact, about 47% of irrigation water applied was

lost as drainage in the ‘‘3-day 25 mm’’ irrigation scheduling

scenario.

a g r i c u l t u r a l w a t e r m a n a g e m e n t 8 7 ( 2 0 0 7 ) 9 1 – 1 0 5 101

Table 6 – Water and nitrogen components in the field-tomato seasonal analysis with CROPGRO model

Scenarios Waterirrigation

amount (mm)

Irrigation WUE(kg ha�1 mm�1)

Drainage(mm)

N applied(kg ha�1)

N uptake(kg ha�1)

NUEb

(kg kg�1)N leached(kg ha�1)

TDMa FDM TDM FDM

Automatic irrigation

2 noN CAW 25% 203 60.3 43.8 8

3 noN CAW 50% 264 51.4 37.3 8

4 noN CAW 75% 331 42.0 30.5 13

Nitrogen application rates

5 N100 CAW 50% 210 38.2 24.8 9 100 149 53.7 34.9 0.1

6 N200 CAW 50% 233 42.1 29.7 9 200 243 40.3 28.5 0.1

7 N300 CAW 50% 240 46.3 33.1 9 300 331 33.5 23.9 0.1

Fixed irrigation

8 noN-3-day 15mm 492 29.0 21.0 82

12 noN-5-day 15mm 295 44.2 32.5 25

16 noN-3-day 25mm 820 17.4 12.7 384

20 noN-5-day 25mm 496 28.1 20.4 110

Interaction ‘‘water � nitrogen’’

9 N100-3-day 15 mm 492 20.4 11.7 82 100 149 67.4 38.6 3.4

10 N200-3-day 15 mm 492 25.7 17.6 82 200 248 51.0 34.9 3.5

11 N300-3-day 15 mm 492 27.6 19.7 82 300 338 40.2 28.7 3.8

13 N100-5-day 15 mm 289 32.8 18.7 26 100 143 66.4 37.9 0.6

14 N200-5-day 15 mm 293 42.4 28.5 25 200 241 51.5 34.7 0.6

15 N300-5-day 15 mm 294 44.1 31.3 25 300 316 41.0 29.2 0.6

17 N100-3-day 25 mm 820 10.8 5.9 383 100 127 69.7 37.8 23.6

18 N200-3-day 25 mm 820 14.3 9.4 383 200 204 57.7 37.8 35.7

19 N300-3-day 25 mm 820 15.7 11.0 383 300 272 47.2 33.2 50.4

21 N100-5-day 25 mm 496 20.1 11.6 112 100 148 67.4 38.8 4.4

22 N200-5-day 25 mm 496 25.2 17.3 112 200 246 50.7 35.0 4.7

23 N300-5-day 25 mm 496 27.0 19.3 113 300 337 39.8 28.5 5.2

a TDM: total plant dry matter; FDM: fruit dry matter.b NUE: TDM or FDM/nitrogen uptake.

The nitrogen and irrigation WUE were significantly influ-

enced by the interactive water and nitrogen management

components. Irrigation WUE for both total and fruit dry matter

yield, reached the highest values in the ‘‘5-day 15 mm’’

Fig. 4 – Cumulative probability density function based on

historic weather data (53 years), to do not exceed a value of

seasonal water drainage amount for the following

scenarios: N fertilization amount of 200 kg haS1 vs. fixed

irrigation every ‘‘3 days with 15 mm’’, ‘‘5 days with

15 mm’’, ‘‘3 days with 25 mm’’ and ‘‘5 days with 25 mm’’.

scenario, characterized by the lowest irrigation water applica-

tion (294 mm),and under condition of high N application (N300).

Nitrogen use efficiency were similar for most scenarios

(average values of 52 and 33 kg of total and fruit dry matter per

Fig. 5 – Cumulative probability density function based on

historic weather data (53 years), to do not exceed a value of

seasonal N leaching amount for the following scenarios: N

fertilization amount of 200 kg haS1 vs. fixed irrigation ‘‘3-

day 15 mm’’, ‘‘5-day 15 mm’’, ‘‘3-day 25 mm’’ and ‘‘5-day

25 mm’’. For the ‘‘3-day 25 mm’’ scenario the N300

fertilization rate is also shown.

a g r i c u l t u r a l w a t e r m a n a g e m e n t 8 7 ( 2 0 0 7 ) 9 1 – 1 0 5102

Fig. 6 – Cumulative probability density function based on historic weather data (53 years) to do not exceed a value of net

return for the following scenarios: (a) potential vs. automatic irrigation levels (CAW = 25, 50 and 75%); (b) automatic

irrigation at CAW = 50% vs. N fertilization amount (100, 200 and 300 kg haS1); (c) fixed irrigation every 3 days with 15 mm

vs. N fertilization amount (100, 200 and 300 kg haS1); (d) fixed irrigation every 5 days with 15 mm vs. N fertilization amount

(100, 200 and 300 kg haS1); (e) fixed irrigation every 3 days with 25 mm vs. N fertilization amount (100, 200 and 300 kg haS1);

(f) fixed irrigation every 5 days with 25 mm vs. N fertilization amount (100, 200 and 300 kg haS1).

kg of N uptake), but NUE was higher in the ‘‘3-day 25 mm’’

scenario, characterized by about 35 kg of N ha�1 y�1 leached,

and increased with the reduction of N application. Due to

frequent application of water with drip irrigation method,

nitrogen was effectively utilized, as there was direct contact

with the root system with negligible N loss through leaching,

as applied irrigation water did not move beyond 30 cm soil

depth (Singandhupe et al., 2003). On the other hand, when

irrigation supply exceeds crop water requirement excessive

drainage and N leaching may occur. In fact, when drainage

was large (383 mm in the ‘‘3-day 25 mm’’ scenario) N leaching

resulted greatly influenced by N application levels: in fact, the

probability to exceed 50 kg of leached N ha�1 y�1 was about

20% in the N200, about 50% in the N300 scenarios (Fig. 5). N

leaching in the remaining scenarios, as a consequence of a

modest water drainage, was very low (Fig. 5), possibly due to a

deep effective rootzone and the high N demand and uptake

capacity of tomato.

Automatic irrigation, based on sound knowledge of soil (and

crop) available water in a fixed soil layer, ensured an acceptable

environmental risk in terms of water drainage and N leached,

compared to when fixed application times and irrigation rates

a g r i c u l t u r a l w a t e r m a n a g e m e n t 8 7 ( 2 0 0 7 ) 9 1 – 1 0 5 103

were used, which hampered effective control of water and

nitrogen losses. For this reason, cheap, simple and reliable

technologies to monitoring soil water status should be searched

andapplied,toachieveawatersavingandenvironmentalsafety.

3.3.3. Economic returnA crop economic evaluation is highly dependent by product

price and production cost and this latter is dependent by the

labour, farm size, equipment, investment, and so on. For this

reason the aim of this comment is to compare only the crop

management scenarios, simulated taking into account the

market situation as reported in Table 2, and not to give an

overall profitability of tomato in Southern Italy.

The results of simulation (Table 5 and Fig. 6a) indicated that

themaximumprofitability fortomatoproductionsystemsunder

optimal (non-limiting) conditions may exceed an average of

8000 s ha�1 (with a range of 4000–12,000 s ha�1 in the 53 years).

For the automatic irrigation scenarios, net returns (NR)

reached high values due to the absence of nitrogen manage-

ment costs and reduced nitrogen stress. A significant

difference (�21%) was observed with the reduced irrigation

scheduling (CAW = 25% versus average of 50 and 75%), while

the same return was observed with the two higher CAW

threshold values (Fig. 6a).

Similar results characterized all irrigation scenarios at fixed

time and with N module disabled (scenarios 8, 12, 16 and 20)

with an average NR of 6250 s ha�1 (Table 5).

On the contrary, simulating N fertilization rates effects and

applying automatic irrigation at CAW = 50%, the model pre-

dicted that NR increased on average from 950 s ha�1 (N100) to

4000 s ha�1 (N300), highlighting a great sensitivity of tomato to

nitrogen application and the low incidence of N application and

fertilizer cost on the global crop management cost (Fig. 6b). In

general, the irrigation scenarios fertilized with 100 kg of N ha�1,

showed the lowest values of NR, also in probabilistic terms, and

always very close to zero value (Fig. 6c–f).

When we consider the interaction between water and

nitrogen, the variation range of NR arrayed from a minimum

of negative value �580 s ha�1 to a maximum of about

5500 s ha�1. This latter value was registered for ‘‘5-day

25 mm’’ and ‘‘3-day 15 mm’’ at the same N rate (N300)

scenario that received about the same water irrigation amount

(496 and 492 mm). The minimum NR was registered in the

‘‘3-day 25 mm’’ at N100 scenario, where the N availability was

further reduced for the N leaching (24 kg ha�1 y�1) due to the

large irrigation water application (820 mm): this carried out

a low simulated dry matter fruit yield (4.8 t ha�1). In this case

the production cost was high, but N crop availability was

reduced (large number of days with plant N stress) and,

consequently, also fruit yield level decreased. For the same

reason is possible to point out a greater difference between

N200 and N300 in the ‘‘3-day 25 mm’’ scenario (Fig. 6e), where

the N application compensated the N leached and differ-

entiated more markedly the 3 N doses.

4. Conclusions

The CROPGRO model was tested as a research tool to provide

useful estimates of benefits, risks and costs of different

agricultural practices in field processing tomato production in

Southern Italy.

The scenarios tested through this simulation showed how

to design and evaluate appropriate water management

practices for drip irrigated production systems based on crop

water requirements. In addition, simulation results may be

used to develop water conservation practices that can prevent

excessive N leaching below the rootzone.

Use of the seasonal analysis option of the DSSAT model

combined with long-term historic weather data clearly

demonstrated the importance of water and nitrogen supply

for the tomato productivity. Soil nitrogen availability therefore

must be controlled to ensure TDM and FDM close to the

potential one and that the optimal water management

condition took place with frequent irrigation supplies, but

with low amount (‘‘3-day 15 mm’’ scenario).

But the same factors could cause environmental risks,

when the water and N application rates exceed actual crop

requirements. Based on simulation it is concluded that

irrigation applications of 600–800 mm which are represen-

tative of the typical application rates used in Southern Italy

will results in low WUE and NUE values. Optimal irrigation

rates to minimize crop water stress and excessive drainage/

N leaching should be approximately about 400 mm as

confirmed also in field experimental researches carried

out in the same environment (Rana et al., 2000; Rinaldi and

Rana, 2004).

The economic evaluation is an important and additional

tool to choose the appropriate crop management strategy, also

considering the quick variations in cost and product prices.

In this case study, the economic evaluation corroborates

the results obtained with agronomic and environmental

approaches (200–300 kg of N ha�1 application and ‘‘3-day

15 mm’’ or ‘‘5-day 25 mm’’ irrigation scenarios).

However, the presented results are based on certain

assumptions and model predictions may be affected by a

degree of uncertainty of the validity of these assumptions and

the accuracy by which input parameters can be established.

This may affect results and may also bias conclusions in terms

of production, environmental risks and economic return.

Further, the interactions between weather, soil characteris-

tics, plant growth dynamics, management alternatives may

affect simulation results and currently the CROPGRO models

does not include effects of weed and pests which also can

affect predictions.

In conclusion, the CROPGRO model may be a useful

decision support system to assist farmers to make more

efficient use of resources and to develop appropriate

agricultural management practices for processing tomato

and can provide them with general information how

irrigation and fertilizer management practices may affect

production, water saving, N leaching and the potential net

returns.

Acknowledgement

This work has been funded by the Italian Ministry of

Agricultural and Forestry Policies (AQUATER Project, contract

no. 209/7303/05).

a g r i c u l t u r a l w a t e r m a n a g e m e n t 8 7 ( 2 0 0 7 ) 9 1 – 1 0 5104

r e f e r e n c e s

Al-Ghawas, S.A., Al-Mazidi, A.K., 2004. Influence of fertigationfrequency on the yield of some vegetables cultivated insand culture. Acta Horticulturae 644, 485–492.

Allen, R.B., Pereira, L.S., Raes, D., Smith, M., 1998. Cropevapotranspiration. Irrigation and Drainage, Paper 56, Foodand Agriculture Organization of the United Nations, Rome,Italy, pp. 300.

Bennet, J.M., Jones, J.W., Zur, B., Hammond, L.C., 1986.Interactive effects of nitrogen and water stress on waterrelations on field-grow corn leaves. Agron. J. 78, 273–280.

Bertin, N., Gary, C., 1993. Tomato fruit-set: a case study forvalidation of the model TOMGRO. Acta Horticulturae 328,185–193.

Booltink, H.W.G., Thornton, P.K., Verhagen, J., Bouma, J., 1996.Application of simulation models and weather generatorsto optimize farm management strategies. In: Robert, P.C.,Rust, R.H., Larson, W. (Eds.), Proceedings of the ThirdConference on Precision Agriculture. Minneapolis, USA,June 23–26 (SSSA Special Publication).

Boote, K.J., Jones, J.W., Hoogenboom, G., 1998. Simulation ofcrop growth: CROPGRO model. In: Peart, R.M., Curry, R.B.(Eds.), Agricultural Systems Modeling and Simulation.Marcel Dekker, Inc., New York (Chapter 18).

Castrignano, A., Di Bari, V., Stelluti, M., 1997. Evapotranspirationpredictions of CERES-Sorghum model in Southern Italy. Eur.J. Agron. 6, 265–274.

Cavero, J., Plant, R.E., Shennan, C., Friedman, D.B., Williams, J.R.,Kiniry, J.R., Benson, V.W., 1998. Application of EPIC model tonitrogen cycling in irrigated processing tomatoes underdifferent management systems. Agr. Syst. 56 (4), 391–414.

Cavero, J., Plant, R.E., Shennan, C., Friedman, D.B., Williams, J.R.,Kiniry, J.R., Benson, V.W., 1999. Modeling nitrogen cycling intomato-safflower and tomato-wheat rotations. Agr. Syst. 60,123–135.

Colla, G., Casa, R., Locascio, B., Saccardo, F., Temperini, O.,Leoni, C., 1999. Responses of processing tomato to waterregime and fertilization in Central Italy. Acta Horticulturae487, 531–535.

Cooman, A., Schrevens, E., 2004a. The uncertainty on TOMGROpredictions caused by variations on model parameters. ActaHorticulturae 654, 155–162.

Cooman, A., Schrevens, E., 2004b. Sensitivity analyses ofTOMGRO output variables to variations in climateconditions. Acta Horticulturae 654, 317–324.

Delogu, G., Cattivelli, L., Pecchioni, N., De Falcis, D., Maggiore, T.,Stanca, A.M., 1998. Uptake and agronomic efficiency ofnitrogen in winter barley and winter wheat. Eur. J. Agron. 9,11–20.

Doorenbos, J., Kassam, A.H., 1986. Yield response to water.Irrigation and Drainage, Paper 33, Food and AgricultureOrganization of the United Nations, Rome, Italy.

Doorenbos, J., Pruitt, W.O., 1977. Crop water requirements.Irrigation and Drainage, Paper 24, Food and AgricultureOrganization of the United Nations, Rome, Italy.

Doss, B.D., Evans, C.E., Johnson, W.A., 1975. Rates of nitrogenand irrigation for tomatoes. J. Am. Soc. Hortic. Sci. 100, 435–437.

Dumas, Y., 1990. Tomatoes for processing in 90s: nutrition andcrop fertilization. Acta Horticulturae 277, 155–166.

Elia, A., Trotta, G., Convertini, G., Vonella, A.V., Rinaldi, M., 2006.Alternative fertilization for processing tomato in SouthernItaly. Acta Horticulturae 700, 261–265.

Ersahin, S., Karaman, M.R., 2001. Estimating potential nitrateleaching in nitrogen fertilized and irrigated tomato usingthe computer model NLEAP. Agr. Water Manage. 51, 1–12.

Godwin, D.C., Jones, C.A., 1991. Nitrogen dynamics in soil-plantsystems. In: Hanks, R.J., Ritchie, J.T. (Eds.), Modelling Plantand Soil Systems, Monograph No. 31. ASA, CSSA, SSSAPublication, Madison, WI, USA.

Heuvelink, E., 1996. Dry matter partitioning in tomato:validation of a dynamic simulation model. Ann. Bot. 77,71–80.

Heuvelink, E., Bertin, N., 1994. Dry matter partitioning in atomato crop: comparison of two simulation models. J.Hortic. Sci. 69, 885–903.

Hoogenboom, G., Jones, J.W., Wilkens, P.W., Batchelor, W.D.,Bowen, W.T., Hunt, L.A., Pickering, N.B., Singh, U., Godwin,D.C., Baer, B., Boote, K.J., Ritchie, J.T., White, J.W., 1994. Cropmodels. In: Tsuji, G.Y., Uehara, G., Balas, S. (Eds.), DSSATVersion 3, vol. 2. University of Hawaii, Honolulu, HI, pp. 95–244.

Huett, D.O., Dettmann, E.B., 1988. Effect of nitrogen on growth,fruit quality and nutrient uptake of tomatoes grown in sandculture. Aust. J. Exp. Agr. 28, 391–399.

Jamieson, P.D., Porte, J.R., Wilson, D.R., 1991. A test of thecomputer simulation model ARC-WHEAT1 on wheat cropsgrown in New Zealand. Fields Crop Res. 27, 337–350.

Jones, J.W., Dayan, E., Allen, L.H., Van Keulen, H., Challa, H.,1991. A dynamic tomato growth and yield model (TOMGRO).Trans. ASAE 34, 663–672.

Jones, J.W., Hoogenboom, G., Porter, C.H., Boote, K.J., Batchelor,W.D., Hunt, L.A., Wilkens, P.W., Singh, U., Gijsman, A.J.,Ritchie, J.T., 2003. The DSSAT cropping system model. Eur. J.Agron. 18, 235–265.

Jones, J.W., White, J., Boote, K., Hoogenboom, G., Porter, C.H.,2000. Phenology Module in DSSAT V.4.0 Documentation andSource Code Listing. Research Report Number 2000-102.

Kafkafi, U., Bar-Yosef, B., Hadas, A., 1978. Fertilisation decisionmodel. A synthesis of soil and plant parameters in acomputerised program. Soil Sci. 125, 261–268.

Kropff, M.J., Bouma, J., Jones, J.W., 2001. Systems approaches forthe design of sustainable agro-ecosystems. Agr. Syst. 70,369–393.

Koo, J., 2002. Modeling the impacts of climate variability ontomato disease management and production. Ph.D. Thesis.University of Florida, USA, 220 pp.

Larouche, R., Gosselin, A., Vezina, L., 1989. Nitrogenconcentration and photosynthetic photon flux ingreenhouse tomato production. I. Growth and development.J. Am. Soc. Hortic. Sci. 114, 458–461.

Loague, K., Green, R.E., 1991. Statistical and graphical methodsfor evaluating solute transport models: overview andapplication. J. Contam. Hydrol. 7, 51–73.

Locascio, S.J., Clark, G.A., Czizinszky, A.A., Stanley, C.D., Olson,S.M., Rhoads, F.M., Smajstrla, A.G., Vellidis, G., Edling, R.J.,Hanna, H.Y., Goyal, M.R., Crossmann, S., Navarro, A.A.,1992. Water and nutrient requirements for drip-irrigatedvegetables in humid regions. Florida Agri. Exp. Stn.Southern Coop. Series Bull. 363. University of Florida,Gainesville.

Locascio, S.J., Smajstrla, A.G., 1996. Water applicationscheduling by pan evaporation for drip-irrigated tomato. J.Am. Soc. Hortic. Sci. 121, 63–68.

Lopez, J., Ballesteros, R., Ruiz, R., Ciruelos, A., 2001. Influence ontomato yield and brix of an irrigation cut-off fifteen daysbefore the predicted harvest date in southwestern Spain.Acta Horticulturae 542, 117–125.

Marouelli, W.A., Silva, W.L.D.E., 2005. Drip irrigation frequencyfor processing tomatoes during vegetative growth stage dripirrigation frequency for processing tomatoes duringvegetative growth stage. Pesquisa Agropecuaria Brasileira40 (7), 661–666.

May, D.M., Gonzales, L., 1999. Major California processingtomato cultivars respond differently in yield and fruit

a g r i c u l t u r a l w a t e r m a n a g e m e n t 8 7 ( 2 0 0 7 ) 9 1 – 1 0 5 105

quality to various levels of moisture stress. ActaHorticulturae 487, 525–529.

Messina, C.D., Jones, J.W., Hansen, J.W., 2001. UnderstandingENSO effects on tomato yields in Florida: a modelingapproach. In: Proceedings of the Second InternationalSymposium Modelling Cropping Systems, Florence, Italy,July 16–18, pp. 155–156.

Morgan, J.A., 1986. The effects of N nutrition on the waterrelations and gas exchange characteristics of wheat(Triticum aestivum L.). Plant Physiol. 80, 52–58.

Peet, M.M., Willits, D.H., 1995. Role of excess water in tomatofruit cracking. Hortic. Sci. 30, 65–68.

Pien, H., Lemeur, R., De Cordt, W., Baets, W., 1999. The use ofTOMGRO as a simplified diagnostic tool for growers. ActaHorticulturae 507, 285–292.

Pill, W.G., Lambeth, V.N., 1980. Effects of soil water regime andnitrogen form on blossom-end rot, yield, water relations,and elemental composition of tomato. J. Am. Soc. Hortic.Sci. 105 (5), 730–734.

Porter, C.H., Jones, J.W., 1998. Modular Structure in CROPGRODocumentation and Source Code Listing. Agricultural andBiological Engineering Department. Research Report No. 98-601. University of Florida, Gainesville, Florida.

Ramırez, A., Rodrıguez, F., Berenguel, M., Heuvelink, E, 2004.Calibration and validation of complex and simplifiedtomato growth models for control purposes in thesoutheast of Spain. Acta Horticulturae 654, 147–154.

Rana, G., Rinaldi, M., Introna, M., Ciciretti, L., 2000.Determinazione sperimentale dei consumi idrici delpomodoro da industria in Capitanata. Atti Convegno POMB19. Gutenberg, Salerno, pp. 99–106.

Rinaldi, M., Di Paolo, E., Colucci, R., Di Lena, B., 2001. Validationof EPIC model in simulating tomato field crop in Italianenvironments. In: Proceedings of the Second InternationalSymposium Modelling Cropping Systems, Florence, Italy,July 16–18, pp. 167–168.

Rinaldi, M., Rana, G., 2004. I fabbisogni idrici del pomodoro daindustria in Capitanata. Riv. Italiana di Agrometeorologia 1,31–35.

Rinaldi, M., Trotta, G., Convertini, G., Vonella, A.V., Elia, A., 2003.Impiego su pomodoro da industria di fertilizzanti azotatialternativi. L’Informatore Agrario 11, 75–78.

Rinaldi, M., Trotta, G., Convertini, G., Vonella, A.V., Elia, A., 2004.Fertilizzanti azotati alternativi su pomodoro da industria.L’Informatore Agrario 11, 65–68.

Ritchie, J.T., 1986. A user-oriented model of the soil waterbalance in wheat. In: Day, W., Atkins, R.K. (Eds.), WheatGrowth and Modelling. Plenum Press, New York, USA, pp.203–305.

Sauviller, C., Baets, W., Pien, H., Lemeur, R., 2002. SIMULTOM: adiagnostic tool for greenhouse tomato production. ActaHorticulturae 593, 219–223.

Scholberg, J.M.S., 1996. Adaptive use of crop growth modelsto simulate the growth of field-grown tomato. Doctoraldissertation. University of Florida, Gainesville,Florida.

Scholberg, J.M.S., Boote, K.J., Jones, J.W., McNeal, B.L., 1997.Adaptation of the CROPGRO model to simulate field-growntomato. In: Kropff, M.J., Teng, P.S., Aggarwal, P.K., Bouma,J., Bouman, B.A.M., Jones, J.W., van Laar, H.H. (Eds.),Application of Systems Approaches at the Field Level.Systems Approaches for Sustainable AgriculturalDevelopment. Kluwer Academic Publishers,Dordrecht, The Netherlands, pp.135–151.

Scholberg, J.M.S., McNeal, B.L., Hoogenboom, G., Jones, J.W.,Boote, K.J., Olson, S.R., 1996. Application of the CROPGROmodel to predict fruit yield of freshmarket tomato.Agronomy Abstracts 14 .

Scholberg, J.M.S., McNeal, B.L., Jones, J.W., Boote, K.J., Stanley,C.D., Obreza, T.A., 2000. Nitrogen stress effects on growthand nitrogen accumulation by field-grown tomato. Agron. J.92, 159–167.

Singandhupe, R.B., Rao, G.G.S.N., Patil, N.G., Brahmanand, P.S.,2003. Fertigation studies and irrigation scheduling in dripirrigation system in tomato crop (Lycopersicon esculentum L.).Eur. J. Agron. 19, 327–334.

Stark, J.C., Jarrell, W.M., Letey, J., Valoras, N., 1983. Nitrogen useefficiency of trickle-irrigated tomatoes receiving continuousinjection of N. Agron. J. 75, 672–676.

Tsuji, G.Y., Jones, J.W., Balas, S. (Eds.), 1994. DSSAT v3, vols. 1–3.University of Hawaii, Honolulu, Hawai.

Waister, P.D., Hudson, J.P., 1970. Effects of soil moisture regimeson leaf water deficit, transplantation and yield of tomatoes.Hortic. Sci. 45, 359–370.

Warner, J., Zhang, T.Q., Hao, X., 2004. Effects of nitrogenfertilization on fruit yield and quality of processingtomatoes. Can. J. Plant Sci. 84, 865–871.

World Processing Tomato Council, 2006. http://www.wptc.to/releases/releases10.pdf.