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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: [email protected] (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
[email protected] (D. Ventrella), [email protected]
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 yieldproduction 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).
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