Water availability at sowing and nitrogen management of durumwheat: a seasonal analysis with the CERES-Wheat model

Michele Rinaldi*

Istituto Sperimentale Agronomico, v. C. Ulpiani 5, 70125 Bari, Italy

Received 10 September 2003; received in revised form 27 December 2003; accepted 15 January 2004

Abstract

In water-limited environments soil water content at sowing is important in determining durum wheat germination, emergence

and plant establishment. Soil water content interacts greatly with soil nitrogen content, affecting nitrogen uptake and crop

productivity. Simulation models can be used to confirm the optimal strategy by testing several crop management scenarios.

The CERES-Wheat model, previously calibrated and validated in southern Italy, has been used in a seasonal analysis to

optimise nitrogen fertilisation of durum wheat at different levels of crop available water (CAW) at planting date in southern Italy.

The simulation was carried out for a 48-year period with measured daily climatic data. The 99 simulated scenarios derived from

the combinations of different CAW levels at sowing, nitrogen fertiliser rates and application times.

The results obtained from the simulation indicated that the effect of CAW at sowing was relevant for durum wheat production

at lowest and highest values, while the optimal sowing time to maximise yield and profit can be considered when CAW is 40–

60%. In the case study optimal N fertiliser amount was estimated to be 100 � 20 kg ha�1, from a productive, environmental and

economic point of view. The nitrogen split application—half at sowing and half at stem extension stage—resulted in the best

management practice.

This application of the CERES-Wheat model confirmed the capability of the model to compare several crop management

strategies in a typical durum wheat cropping area.

# 2004 Elsevier B.V. All rights reserved.

Keywords: Durum wheat; Crop available water at sowing; Nitrogen fertilisation; CERES-Wheat; Yield; Economics

1. Introduction

Durum wheat (Triticum durum Desf.) is largely

sown in winter in Italy and its fine flour and pasta,

represent some of Italy’s most valuable export pro-

ducts. Technological quality of grain depends mainly

on grain protein content that is influenced by crop

nitrogen fertilisation, both by the application time and

by the amount of nitrogen (Daniel and Triboi, 2000).

In Mediterranean environments, where water

resources are limited, nitrogen fertilisation is very

important in the crop management because it can

amplify the effect of soil water deficit on cereal crop

growth and grain yield. Durum wheat is very sensitive

to water stress during the germination-seedling emer-

gence and spike formation; this latter phase normally

occurs, in southern Italy, in February when a deep root

system has been already developed and good water

availability has been guaranteed by the winter rainfall.

At the germination phase, on the contrary, the crop can

be more frequently affected by water stress, because

Field Crops Research 89 (2004) 27–37

* Tel.: þ39-080-5475016; fax: þ39-080-5475023.

E-mail address: michele_rinaldi@libero.it (M. Rinaldi).

0378-4290/$ – see front matter # 2004 Elsevier B.V. All rights reserved.

doi:10.1016/j.fcr.2004.01.024

wheat is also sown in dry soil conditions, awaiting

winter rainfall. Moreover, high nitrogen availability

during the vegetative-to-reproductive phase stimulates

crop growth, resulting in greater yield in wet seasons.

In dry seasons, on the contrary, the increased water

uptake by N fertilised crops often causes a water deficit

and a lower yield than unfertilised crops, affecting

harvest index negatively (Dann, 1969; Anderson,

1985; Sherperd et al., 1986; Cooper et al., 1987;

Gonzales Ponce et al., 1993).

Good management of sowing date (dependent on

soil moisture) and nitrogen fertilisation of durum

wheat are essential to obtain high yields and profit.

Furthermore, it is important, from an economic point

of view, to keep the cost of inputs low. At the same

time, a reduction of N application is desirable to

reduce the risk of pollution as a consequence of nitrate

leaching and percolation (Sequi and Vittori Antistari,

1989; Mahler et al., 1994).

To determine the optimal time and amount of

nitrogen application necessitates long and expensive

field experiments. Besides, it is impossible to test all

the interactions between the amount of soil water

content at planting date and rainfall distribution in

spring months in order to assess the yearly variability.

Finally, the soil water content at sowing and winter

rainfall distribution represent important guidelines to

help farmers in nitrogen management.

Simulation models, once calibrated and validated

with experimental data, can help farmers in decision

making about nitrogen application strategies either

before sowing or during crop growth, taking into

account the multiple interactions among soil, climate,

genotype and crop management.

The CERES-Wheat model is one of the most popular

wheat models (Ritchie and Otter, 1985) and it is

embedded in the DSSAT, version 3.5 software program

(Jones et al., 2003). The model has been tested in many

sites across the world and the results proved its cap-

ability to simulate grain yields under various climates

(Savinetal.,1995;Pecetti andHollington,1997;Hundal

and Prabhjyot-Kaur, 1997; Alexandrov and Hoogen-

boom, 2001; Rinaldi, 2001), soil moisture conditions

(Timsina et al., 1995; Travasso and Delecolle, 1995)

and nitrogen fertilisation management (Kovacs et al.,

1995; Timsina et al., 1998; Hasegawa et al., 2000).

This study is an application of the CERES-Wheat

model: its aims are: (i) to analyse the influence of

crop available water at sowing on durum wheat grain

yield, and (ii) to optimise N fertilisation of durum

wheat cropped under the semi-arid climate of the

Mediterranean region, from a productive, N-fertilisa-

tion efficacy and economic points of view.

2. Materials and methods

2.1. The model

The CERES (crop-environment resource synthesis)

family of crop models has been used to simulate the

performance of several cereal crops (Steiner et al.,

1991; Timsina et al., 1995; Thornton et al., 1997;

Hundal and Prabhjyot-Kaur, 1997). The model user

must compile several input files to run the simulation,

in order to provide information about the experiment,

site, soil, climate and genotype.

The Experimental details file contains all the

information needed for the simulation of different

experimental treatments (location, sowing time and

irrigation), conditions (field characteristics, soil analy-

sis data, initial soil conditions, irrigation and fertiliser

management, organic residue application, chemical

application, harvest management) and simulation

control.

The Weather data file contains all the available

daily weather data, organised in standard format.

Air temperature (maximum and minimum), rainfall,

solar radiation are the minimum data required. Dew

point temperature, wind speed and photosynthetic

active radiation are optional input data.

The Soil data file reports for each layer, soil in-

formation, identifier, depth, chemical and physical

properties.

The Genotype data file contains cultivar-specific

genetic parameters (Table 1), related to vernalisa-

tion (P1V), photoperiod (P2D), grain filling duration

(P5), leaf development in term of phyllocron or

duration between successive leaf tip appearances

(PHINT) and three parameters related to grain growth

(G1–G3).

The Output file contains the overview of input

conditions and crop performance, summary of soil

characteristics and cultivar coefficients, crop and soil

status at the main development stages, temporal dis-

tribution of crop variables and soil water content.

28 M. Rinaldi / Field Crops Research 89 (2004) 27–37

The Soil water model is described by Ritchie

(1986). It operates on the basis of a cascading

approach and considers rainfall, infiltration and run-

off, 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 Crop growth model considers a phasic devel-

opment with nine growth stages, from pre-sowing to

harvest. The duration of the phases is related to the

temperature, with cultivar-specific thermal times. The

model calculates biomass accumulation as the product

of radiation use efficiency and photosynthetically

active intercepted radiation. The number of growing

leaves is a function of leaf appearance rate (PHINT

parameter) and duration of grain filling (P5). Organ

extension depends on potential organ growth, while

limitation is imposed by temperature and availability

of water and nitrogen. Partitioning coefficients of dry

matter in the several parts of plant are included in

the model. Yield is modelled as the product of grain

number—calculated from G1 parameter (Table 1) and

plant population (input in experimental file)—and

grain weight at physiological maturity (calculated

from G2 parameter).

The Nitrogen balance model simulates the processes

of organic matter turnover with the associated miner-

alisation and/or immobilisation of nitrogen, nitrifica-

tion, denitrification, 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. The site

The climatic and soil data for the simulation were

collected at the experimental farm of the Istituto

Sperimentale Agronomico, located at Foggia

(418270N, 38040E, 70 m a.s.l.), in southern Italy (Capi-

tanata Plain). The area (about 400,000 ha) is mainly

cultivated with durum wheat and the experimental

farm is representative of soil and climatic conditions

of this plain.

The soil is a vertisol of alluvial origin (2.0 m depth),

fine, termic, Typic Chromoxerert (according to Soil

Taxonomy, USDA), silty-clay with the following char-

acteristics: organic matter 2.1%; total N 0.122%;

NaHCO3-extractable P2O5 41 ppm; NH4OAc-extrac-

table K2O 1598 ppm; pH (water) 8.3; field capacity

0.396 m3 m�3; permanent wilting point 0.195 m3

m�3, available soil water 202 mm m�1.

The climate is ‘‘accentuated thermo-Mediterra-

nean’’ (UNESCO–FAO classification), with tempera-

tures below 0 8C in winter and above 40 8C in summer.

Rainfall is unevenly distributed throughout the year,

mostly concentrated in winter months and class ‘‘A’’

pan evaporation exceeds 10 mm per day in summer.

The main climatic characteristics of the site are sum-

marised in Fig. 1.

The normal planting date of durum wheat in south-

ern Italy is between 1 November and 15 December in

the plains and about one month earlier on the hills and

in the northern regions. November is the rainiest

month in Capitanata Plain, with about 66 mm of

rainfall and a standard deviation of �43 mm (based

Table 1

Genetic coefficients calibrated for the durum wheat, cultivar ‘‘SIMETO’’, required by the CERES-Wheat model

Abbreviation Values

Relative decrease in development rate for each day of unfulfilled vernalisation P1V 2.0

Relative decrease in development rate when plants are grown in a photoperiod 1 h shorter than the optimum P2D 1.5

Relative grain filling duration based on thermal time (degree days calculated with a Tb of 1 8C):

each unit increase above zero adds 20 degree days to an initial value of 430 degree days

P5 6.0

Kernel number unit weight of stem plus spike at anthesis (g�1) G1 2.0

Kernel filling rate under optimum conditions (mg per day) G2 2.7

Non-stressed dry weight of a single stem and spike when elongation ceases (g) G3 1.9

Phyllochron interval (degree days) PHINT 100

M. Rinaldi / Field Crops Research 89 (2004) 27–37 29

on 50 years of climatic data). The farmers, usually,

wait for rainfall to sow, to ensure a good seed germi-

nation. However, in dry years they also sow durum

wheat in low soil water content conditions.

2.3. Simulation

The simulation was performed for the period 1

January 1952–31 December 1999, using a climate

data set collected in the agrometeorological station

of the farm. Measured daily values of temperatures

(minimum and maximum), rainfall and global radia-

tion were used as model input variables. The Priest-

ley–Taylor model was chosen to estimate potential

evapotranspiration.

Eleven levels of soil moisture content at sowing

were simulated, in interaction with four nitrogen

fertiliser amounts, 0, 50, 100 and 150 kg ha�1 and

three fertilisation times: at sowing, at stem extension

(stage 6–7 of Feekes scale, Large, 1954) (dress appli-

cation) and split, half at sowing and half during the

stem extension (only for the two highest applications),

resulting in a total of nine combinations of N fertilisa-

tion rates. Crop available water (CAW) at sowing was

calculated by the model during the editing of experi-

mental details file, modifying ‘‘SW index’’ parameter

from 0 (CAW ¼ 0%) to 1 (CAW ¼ 100%) in the

‘‘initial conditions’’ option.

Crop management dates were implemented in the

experimental data file according to the local practice

(Table 2). Genetic crop parameters of ‘‘SIMETO’’

variety, very widespread in South Italy, were obtained

from a previous calibration and validation study

(Rinaldi, 2001). They were calculated using data sets

of rotation, soil tillage and N fertilisation experiments

(Di Bari et al., 1993; Maiorana, 1998). GENCALC

program utility (Hunt et al., 1993) was used to esti-

mate the best genetic coefficients fitting experimental

data of phenology first, and crop yield then. The

values used in this model application are reported

in Table 1.

A total of 99 input data files (11 CAW levels � 9 N

fertilisation management) were run for the 48-year

period (47 annual harvests), with resetting the soil

initial conditions every year. The DSSAT, version 3.5

was used, with the seasonal simulation and analysis

options (Tsuji et al., 1994).

An extra simulation of the 48-year period was

performed using a nitrogen application of 100 kg ha�1

as split distribution. The ‘‘sequence’’ option of the

model was used, without resetting the soil initial con-

ditions every year, in order to analyse the probability

to exceed a definite value of CAW at sowing date

(25 November).

2.4. Evaluation criteria

CERES-Wheat model was previously calibrated

and positively validated in Italian conditions, in the

same experimental area (Castrignano et al., 1997,

2000; Ferri et al., 2001; Rinaldi, 2001) and in nearby

areas (Pecetti and Hollington, 1997).

The analysis of the consequence of long-term

durum wheat management was evaluated from a

productive, N fertiliser use and economic point of

view, using several model response variables. The

productivity of the cereal was assessed examining

the output values of grain yield (kg ha�1), total

aboveground dry matter (kg ha�1) and protein grain

content (as a percentage, calculated as N grain

content � 5:83).

Nitrogen use efficiency (NUE) (kg of total dry

matter or grain yield/kg of N uptake) and agronomic

efficiency (AE)—yield increase/kg of N applied as

fertiliser (Delogu et al., 1998)—were calculated and

analysed.

The net income of durum wheat in the different

management scenarios was evaluated using the utility

VARAN2 (cropping season analysis tool); it was

expressed in s ha�1, according to the current costs

and the prices in southern Italy (Table 2).

0

5

10

15

20

25

30

Jan

Feb Mar Apr

May Ju

n Jul

Aug Sep OctNov Dec

Months

Dai

ly a

vg. t

emp

. (˚C

) an

d

sola

r ra

d. (

MJ

m-2

d-1

)

0

50

100

150

200

250

300

Mo

nth

ly r

ain

fall

and

ev

apo

rati

on

(m

m)

Rainfall Evap. Avg. Temp. Radiation

Fig. 1. Air temperature, solar radiation, class ‘‘A’’ evaporation and

rainfall: long-term monthly average of the experimental location

(Foggia, southern Italy).

30 M. Rinaldi / Field Crops Research 89 (2004) 27–37

3. Results and discussion

3.1. Yields

3.1.1. Fertiliser amount

In Table 3 the wheat productivity, averages for the

four N fertiliser amounts and the three fertiliser times

as simulated by the model in the 48-year period, are

reported. A maximum N amount for total dry matter

production and grain yield about 125 kg ha�1 was esti-

mated by a second-order equation fitted to the average

simulated data. A range of 100 � 20 kg N ha�1 can be

suggested as the economic optimal nitrogen application

for durum wheat yield.

The grain nitrogen content, and subsequently, the

protein content, resulted related to N doses, showing

a very slow linear increase in the examined range of

0–150 kg N ha�1.

3.1.2. Fertiliser application times

The nitrogen application time had relatively small

effect on the average yield. CERES-Wheat simulated

a grain yield slightly higher in the split application

(half of the mineral nitrogen at sowing and half during

the stem extension) than in the other two modalities

(þ5% in average) (Table 3).

The total dry matter yield resulted the lowest with

the dress application (�8%), but the harvest index was

the highest, for the reduced plant growth in the first

part of crop cycle. About protein content, only a very

slight superiority of split application was observed,

due to a more regular N crop availability during crop

growth cycle.

3.1.3. Crop available water at sowing

Fig. 2 shows total dry matter (a), grain yield (b) and

grain protein content (c) values of a split application of

100 kg N ha�1, in function of CAW at sowing. For

total dry matter (TDM) and grain yield (GY) increas-

ing values until 40% of CAW were observed, with

maximum values of 13.0 and 4.0 t ha�1 for TDM and

GY, respectively. A large data variability was observed

at CAW lower than 40%: in particular, the variability

of GY resulted larger than TDM, especially at low

CAW. The data shown in Fig. 2b indicated as a sowing

with the soil completely dried (CAW ¼ 0%) can

influence wheat productivity with yield nearly halved

respect to the intermediate soil moisture conditions at

sowing (more than 40% of CAW), for both total dry

matter and grain yield (7.7 t ha�1 versus 12.0 t ha�1

for TDM and 1.8 t ha�1 versus 3.9 t ha�1 for GY).

Grain protein content was inversely related to GY. It

decreased from 16 to 12% in function of CAW, reach-

ing a constant value at CAW ¼ 40% (Fig. 2c).

Table 2

Dates and costs of durum wheat agrotechnics

Operations Date Kind of product Cost (s ha�1)

Ploughing 15 October 60

Fertiliser P 20 October Perphosphate mineral (19–21%), 80 kg P2O5 ha�1 48a

Fertiliser N at sowing 20 October Urea (46%), 50–150 kg N ha�1 34 þ 0.36 s kg�1 N

Disk bed 25 October 34

Sowing 25 November Seed 120a

Fertiliser N as top-dressing 20 February Ammonium nitrate (26–27%), 50–150 kg N ha�1 34 þ 0.6 s kg�1 N

Harvest 10 June 60

Current grain yield price is 160 s t�1 (without taking into account government support).a Global cost (operation plus materials).

Table 3

Simulated values (�standard deviation) of the CERES-Wheat

model as a function of nitrogen fertiliser management (47-year

average)

Grain yield

(t ha�1)

Total dry

matter (t ha�1)

Grain protein

contenta (%)

Nitrogen amount (kg N ha�1)

0 1.73 � 0.51 5.65 � 1.43 10.47 � 3.05

50 3.10 � 0.69 9.69 � 1.66 10.93 � 2.88

100 3.58 � 0.84 10.99 � 1.97 11.21 � 3.17

150 3.65 � 0.89 11.21 � 2.10 11.27 � 3.21

Fertilisation time

At sowing 3.47 � 0.79 10.77 � 1.83 11.14 � 2.99

Top-dressing 3.30 � 0.79 10.12 � 1.97 11.08 � 3.01

Split 3.63 � 0.87 11.14 � 2.03 11.25 � 3.05

a Nitrogen content multiplied by 5.83.

M. Rinaldi / Field Crops Research 89 (2004) 27–37 31

The ‘‘0’’ and ‘‘10’’ CAW percentages showed the

greatest variability, for the dependence of final yield

by rainfall of following months, usually erratic and

scarce. Nevertheless, these values of CAW are seldom

reached, for the rainfall occurring during September–

October months. In fact, the results of a cumulative

distribution analysis of the data obtained by the model

without resetting the soil initial conditions at sowing

every year (Fig. 3), indicated that the CAW values at

sowing (25 November) were found in the range of 23–

45% with a probability of 75–25%, respectively, to

exceed this value.

The variability of the simulated values (Fig. 2)

decreased as CAW increased and became constant

with CAW greater than 50%. High CAW values (from

80 to 100%) generally do not allow the sowing

operation for a not trafficability of clay soils in high

moisture conditions. Consequently, the simulation

results indicated that the optimal soil moisture con-

ditions to sow durum wheat and to maximise yield are

in the range of 40–70% of CAW at sowing, for the

three examined parameters. A further confirmation of

this conclusion is shown in Fig. 4, where the results of

a probability analysis are reported for the GYat 5 of 11

CAW levels for a better sight. The GY obtainable with

a probability greater than 50% resulted 2.0, 3.3 and

3.8 t ha�1, respectively at 0, 20% and at the other

levels of CAW (40, 60 and 80%). Moreover, it comes

out a large variability of the simulated values at 0%

(from 0.6 to 5.1 t ha�1) and, on the contrary, a pro-

portionally very low variation at 60 and 80% (from 2.8

to 5.4 t ha�1).

The simulation results of GY for the 9 N fertiliser

managements are reported in Fig. 5. The main indica-

tions obtained were the following: (i) the lowest value

of CAW (0%) resulted very variable in all the treat-

ments, except in the ‘‘no nitrogen’’ thesis; (ii) ferti-

lisation time did not affect GY and its variability as

regard as CAW levels; (iii) the N amount of 100 and

150 kg ha�1 resulted more productive than ‘‘50’’; (iv)

at ‘‘100’’ and ‘‘150’’ doses the probability to exceed a

given GY value resulted very similar from 20 to 80%

of CAW.

These simulation results are coherent with field data,

especially for the low variability in the ‘‘no fertilised’’

0

4

8

12

16

(t h

a-1

)

0

1

2

3

4

5

(t h

a-1

)

0

3

6

9

12

15

18

0 10 20 30 40 50 60 70 80 90 100

Crop available water (%)

(%)

(a)

(b)

(c)

Fig. 2. Total plant dry matter (a), grain yield (b) and grain protein

content (c) of durum wheat, as a function of crop available water at

sowing in a split (50 kg N ha�1 at sowing þ 50 at stem extension

stage) nitrogen fertilisation. The bars are �1 standard error.

0

25

50

75

100

0 10 20 30 40 50 60 70 80 90 100

Crop available water (%)

pro

bab

ility

of e

xcee

den

ce (

%)

Fig. 3. Cumulative probability, using a long-term simulation (47

years), to exceed a value of crop available water at sowing of

durum wheat with a fixed sowing date (25 November).

32 M. Rinaldi / Field Crops Research 89 (2004) 27–37

situation and for the high variability in the soil dry

conditions. This evaluation suggests, therefore, to sow

durum wheat with a CAW at least greater than 20%.

3.2. Nitrogen use

3.2.1. Fertiliser amount

The N uptake was simulated to be greater than N

supplied by the fertiliser in the first three N amounts

(Table 4). The crop absorbed the remaining N from

soil pool derived from mineralisation of organic mat-

ter. On the contrary, with 150 kg ha�1 of N, 92% of

applied N was recovered by the crop.

The highest value of nitrogen use efficiency, both

for total dry matter and grain yield, was observed

in the control; NUE decreased with increasing N

amount.

About the N agronomic efficiency, the ‘‘50 kg N’’

treatment was twice as high as the ‘‘150 kg N’’ treat-

ment, for both total biomass and grain yield. This

parameter can be used in the economic evaluation of N

amount to apply. For example, at the current prices of

fertiliser and durum wheat grain in Italy (Table 2), an

AE greater than 5 and 7 kg kg�1, respectively for urea

and ammonium nitrate, suggests a profitability in

further nitrogen application.

0

25

50

75

100

0 1 2 3 4 5 6

Grain yield (t ha-1)

pro

bab

ility

of

exce

eden

ce (

%)

Fig. 4. Cumulative probability, using a long-term simulation (47 years), to exceed a grain yield value of durum wheat as function of different

crop available water at sowing of durum wheat. Average values of nine N treatments reported in Fig. 5: (&) CAW 0%; (~) CAW 20%; (*)

CAW 40%; (�) CAW 60%; (*) CAW 80%.

Table 4

Simulated nitrogen uptake, nitrogen use efficiency and nitrogen agronomic efficiency as a function of nitrogen fertiliser management

Nitrogen uptake

(kg N ha�1)

Nitrogen use efficiencyb (kg kg�1) Nitrogen agronomic efficiencyc (kg kg�1)

Total dry matter Grain yield Total dry matter Grain yield

Nitrogen amount (kg N ha�1)

0 43.3 130.6 40.0 – –

50 84.5 114.7 36.7 80.8 27.4

100 120.6 91.2 29.7 53.4 18.5

150 136.5 82.1 26.7 37.0 12.8

Fertilisation time

At sowinga 115.0 86.4 28.0 51.2 17.3

Top-dressinga 101.2 86.3 28.4 44.8 15.3

Splita 130.9 85.1 27.7 41.3 14.6

a Considering only the 100 and 150 kg N ha�1 amounts.b The nitrogen use efficiency has been calculated as kg of dry matter/kg of N taken up by the crop.c The nitrogen agronomic efficiency has been calculated as increase respect to no-fertilised treatment (kg of dry matter/kg of applied N).

M. Rinaldi / Field Crops Research 89 (2004) 27–37 33

3.2.2. Fertiliser time

A great N uptake was observed in the split fertiliser

application, for a better N availability for the plant

through the crop season. The lowest value was

observed with the top-dressing N fertilisation, when

crop nitrogen N deficit had already occurred.

No difference was recorded in NUE among

the fertiliser times. On the contrary, for AE the

highest values were recorded with split N applica-

tion compared with the other two N fertilisation

management (þ16% for TDM and þ12% for

GY).

No Nitrogen

0

25

50

75

100

0 2 4 6

prob

abili

ty o

f ex

ceed

ence

(%

)

N50 dress

0

25

50

75

100

0 2 4 6

prob

abili

ty o

f ex

ceed

ence

(%

)

N100 dress

0

25

50

75

100

0 2 4 6

prob

abili

ty o

f ex

ceed

ence

(%

)

N150 dress 0

25

50

75

100

0 2 4 6

prob

abili

ty o

f ex

ceed

ence

(%

)

N100 splitted (sowing + dress)

0

25

50

75

100

0 2 4 6

Grain yield (t ha-1)

prob

abili

ty o

f ex

ceed

ence

(%

)

N150 splitted (sowing + dress)

0

25

50

75

100

0 2 4 6Grain yield (t ha-1)

prob

abili

ty o

f ex

ceed

ence

(%

)N50 sowing

0

25

50

75

100

0 2 4 6

prob

abili

ty o

f ex

ceed

ence

(%

)

N100 sowing

0

25

50

75

100

0 2 4 6

prob

abili

ty o

f ex

ceed

ence

(%

)

N150 sowing

0

25

50

75

100

0 2 4 6

prob

abili

ty o

f ex

ceed

ence

(%

)

Fig. 5. Cumulative probability to exceed a grain yield value of durum wheat, as a function of N application time and amount and crop

available water at sowing: (&) CAW 0%; (~) CAW 20%; (*) CAW 40%; (�) CAW 60%; (*) CAW 80%.

34 M. Rinaldi / Field Crops Research 89 (2004) 27–37

3.2.3. Crop available water at sowing

Fig. 6 shows the relationship between CAW at

sowing and the nitrogen used by the crop in the split

treatment of 100 kg N ha�1. The main results obtained

were the following: (i) N uptake increased slowly, but

linearly with CAW at sowing (Fig. 6a); (ii) NUE was

not influenced by CAW; only a slight increase of NUE

around 40–60% of CAW at sowing was observed

(Fig. 6b); (iii) AE increased significantly from 0 to

40% of CAW (Fig. 6c), with a maximum value about

23 kg of increased GY/kg of applied fertiliser N.

3.3. Economics

3.3.1. Fertiliser amount

The highest net income (NI) in durum wheat culti-

vation was observed in the treatment 100 kg N ha�1,

whereas a negative value resulted in the unfertilised

treatment (Table 5). The parity point (grain yield at

which total expense is equal to the gross return) was

calculated to be equal to 2.1 t ha�1 of grain yield.

Net income increase per N fertilisation unit was

greater than fertiliser unit cost (1.0 s kg�1 of N for the

ammonium nitrate and 0.6 s kg�1 of N for the urea),

for all the N amounts and fertiliser times (Table 5).

3.3.2. Fertiliser time

Minimum differences were observed in relation to

N application time. The application of all mineral

nitrogen during the stem extension stage, showed

the lowest value of NI. The split fertilisation, despite

the highest distribution cost, resulted in the most

profitable N management. Moreover, the split N dis-

tribution allows optimisation of N management during

the crop cycle, since farmers can apply more or less

nitrogen according to crop water availability and crop

growth at the end of winter (February–March).

3.3.3. Crop available water at sowing

The simulation results indicated that the net income

did not change when CAW at sowing exceeded the

threshold value of 50% (Fig. 7). In dry soil conditions

the economic balance was close to zero. Therefore, the

economic evaluation also highlighted the profitability

of sowing durum wheat when soil moisture ranges

between 40 and 50% of CAW. In wet conditions it is

opportune to delay the sowing in order to minimise

damage to the soil structure; in dry situations irrigation

0

40

80

120

160

(kg

ha

-1)

0

30

60

90

120

(kg

kg-1

)

0

5

10

15

20

25

30

0 10 20 30 40 50 60 70 80 90 100

Crop available water (%)

(kg

kg-1

)

(c)

(b)

(a)

Fig. 6. Nitrogen uptake (a), nitrogen use efficiency (b) and nitrogen

agronomic efficiency (c) of durum wheat, as a function of crop

available water at sowing in a split (50 kg N ha�1 at sowing þ 50

at stem extension stage) nitrogen fertilisation. The bars are �1

standard error.

Table 5

Economic results of the CERES-Wheat model simulation as a

function of nitrogen fertiliser management

Net income

(s ha�1)

Net income of applied N

fertiliser (s kg�1 ha�1)

Nitrogen amount (kg N ha�1)

0 �7.7 –

50 219.8 4.2

100 297.2 2.9

150 283.1 1.8

Fertilisation time

At sowing 279.5 2.5a

Top-dressing 252.7 2.2a

Split 291.4 2.3a

a Considering only the 100 and 150 kg N ha�1 amounts.

M. Rinaldi / Field Crops Research 89 (2004) 27–37 35

to wet the top 15–20 cm of soil, where possible, can

greatly help seed germination, seedling emergence

and plant establishment of durum wheat.

4. Conclusions

In the Capitanata Plain, where durum wheat repre-

sents the main crop, either in rainfed or irrigated areas

(more than 35% of about 400,000 ha of cultivated

area), the CERES-Wheat model was used as a research

tool to provide useful estimates of costs and benefits

of agricultural practices in relation to crop available

water at sowing date, nitrogen fertilisation time and

amount.

The results of this application of the model indi-

cated that in a Mediterranean environment, CAW at

sowing is important for winter durum wheat produc-

tivity. Wheat grain yield in rainfed conditions is

affected by the seasonal irregularity of rainfall, quite

common in this area: when irrigation is not available,

the grain yield reached maximum values of 5–6 t ha�1

in wet years, whereas it can be close to zero in drought

conditions. This simulation study confirmed that sow-

ing with CAW over 40% can ensure, with a probability

greater than 50%, a grain yield close to the potential

one for the site and genotype. The irrigation water

availability can increase the probability to obtain pro-

ductive and economic results in very dry soil conditions

at planting. Furthermore, although soil tillage manage-

ment is important in seed-bed preparation to increase

evaporation (when soil moisture is high) or to reduce

evaporation (in low soil moisture conditions), tillage

management does not show great sensitivity on the

productive response in the CERES-Wheat model. The

results suggest that this module would need to be

improved.

The optimal N fertiliser amount for durum wheat

was 100 � 20 kg N ha�1 from both productive and

economic points of view. As regards the fertilisation

time, grain yield was reduced when the entire mineral

N was supplied at the end of winter (top-dressing

treatment). This is in agreement with fertilisation

management usually used by farmers in this area.

The results indicated that a high rate of N fertiliser

is profitable only when CAW at sowing is not limiting.

An annually flexible strategy, that adjusts the fertiliser

rate to CAW, seemed the most economic strategy (split

treatment).

The CERES-Wheat model can be used also as a

decision support system in the ‘‘within-year’’ crop

management strategy, during the period from sowing

to present day. The simulation can give information

about soil water content at deep layers and, in general,

about crop water availability and soil nitrogen content.

From this information the farmer can drive N fertiliser

management, increasing or decreasing the N applied

or choosing the right time of application during the

crop cycle. The simulation of the following period,

from today to harvest, with long-term average daily

climatic data or generated data from forecasting

weather models, can better support the decisions about

crop management, maximising the yield and the

income.

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