Using the CERES-Maize model in a semi-arid
Mediterranean environment. Evaluation of model
performance
Be´chir Ben Nouna
a, Nader Katerji
b, Marcello Mastrorilli
c,*
aCIHEAM Istituto Agronomico Mediterraneo,70010Valenzano,Italy
bINRA,Unite´ de Recherche En6ironnement et Grandes Cultures,78850Thi6er6al-Grignon,France cIstituto Sperimentale Agronomico,6ia Celso Ulpiani 5,I-70125Bari,Italy
Received 20 July 1999; received in revised form 1 February 2000; accepted 10 May 2000
Abstract
The CERES-Maize model was tested in a semi-arid Mediterranean environment during a period of 2 years under three different soil moisture conditions (well-watered and two limited irrigation regimes). In well-watered plots, growth and yield were adequately simulated by the model (differences between simulated values and observations were less than 10%). Results suggest that the absence of air humidity among the model inputs does not limit the CERES-Maize performance, even under dry-air conditions. On the contrary, under mild soil water shortage, CERES-Maize underestimated the leaf area index (LAI) (up to 26% for maximum LAI), above-ground biomass (up to 23%) and grain yield (up to 15%). Mismatches between observations and predictions increased with water stress level (by up to 46, 29 and 23% for maximum LAI, biomass and grain yield, respectively). It is suggested that the functions describing leaf growth and senescence and those calculating the soil water deficit functions should be modified to adapt CERES-Maize to Mediterranean environments. © 2000 Elsevier Science B.V. All rights reserved.
Keywords:Maize; CERES-Maize model; Water stress; Mediterranean environment
www.elsevier.com/locate/eja
1. Introduction
In the Mediterranean region, soil water availability is the major factor determining the length of the growing period and final yield.
Rain-fall varies from season to season, both in inter-seasonal distribution and inter-seasonal total, causing the total quantity of plant-available soil water, as well as, its distribution over the growing season to vary from year-to-year. Management (mainly irri-gation) can affect soil water content in order to match crop water requirements with seasonal wa-ter supply. To do so, a betwa-ter understanding of the use of available soil water by a crop is neces-sary and this can be obtained by an integrated * Corresponding author. Tel.:+39-080-5475014; fax:+
39-080-5475023.
E-mail address:[email protected] (M. Mastrorilli).
approach using a dynamic model of crop growth. The model must be conceptually appropriate for the research in hand and it must have input requirements which can be met and must give reasonable predictions. Models are presently available for many crops, but these have generally been developed in temperate locations.
We chose the CERES-Maize model (Jones and Kiniry, 1986), because we thought it appropriate for fulfilling these criteria. It was tested under temperate conditions characterised by the absence of water stress in North America (Hodges et al., 1987; Piper and Weiss, 1990) and in Europe, precisely in Belgium (Lahrouni et al., 1993), in France (Lorgeou, 1991; Plantureux et al., 1991) and in Germany (Entenmann et al., 1989). To our knowledge, the model has not been tested under Mediterranean conditions (Ruget and Bon-homme, 1991), characterised by both atmospheric and soil drought (Hamdy and Lacirignola, 1999).
Results of these tests show some limitations in the use of CERES-Maize. Carberry et al. (1989) tested the model in semi-arid regions of tropical Australia and proposed new modules for phenol-ogy, leaf development and increase of biomass. Phenological module is not precise in the case of water excess in the soil (Hodges et al., 1987; Carberry et al., 1989) or in the case of cold seasons (Liu et al., 1989). Finally, Carberry (1991) underlines that water stress does not have any effect on phenology and proposes a correction. Lahrouni and Ledent (1991) show the conse-quences of biased estimations of phenological stage duration on leaf area and dry matter parti-tioning simulated by the CERES-Maize. Finally, grain yield and its components can be overesti-mated or underestioveresti-mated by the model in rainy or dry seasons, respectively (Wu et al., 1989).
The objective of this study was to compare the predictions of CERES-Maize with observed data from field experiments, in which the moisture regime varied, but nitrogen nutrition was always adequate. The results of this study allow us to evaluate model performance under contrasting soil water conditions in a Mediterranean environ-ment. Moreover, this study allows us to identify model subroutines that should be modified to improve to fit better in the observed Mediter-ranean conditions.
2. Materials and methods
2.1. The field experiment
2.1.1. Site and climate
Field studies examining the response of maize (cv Maltus) to water deficit were made at Rutigliano on the experimental farm of the Isti-tuto Sperimentale Agronomico (latitude 41°01%N,
longitude 17°17%24¦E and altitude 122 m a.s.l.) during 1996 and 1997.
The Rutigliano climate is characterised by warm dry summers, with maximum air tempera-ture sometimes higher than 40°C, and minimum relative air humidity often less than 20%. Mean annual rainfall is 600 mm, almost exclusively con-centrated in spring and autumn. Fig. 1 shows the Fig. 1. Maximum and minimum air temperature; solar
time course of maximum and minimum tempera-ture, solar radiation, and precipitation measured during the cropping seasons 1996 and 1997 at the agrometeorological station located about 50 m from the experimental site. The year 1997 was sunnier and warmer than 1996, especially early in the crop season. For both the years the central period of growth was characterised by an ex-tended drought (except for a short rain in 1997). Precipitation was more frequent and heavier at the end of the 1996 crop season.
The soil is silty clay loam, a well-drained red earth or ‘Alfic Xerarent Mixed Thermic Fine’ (USDA Soil Taxonomy). The soil profile is shal-low (up to 0.6 m in depth), and consequently water availability is low (about 110 mm, calcu-lated between the field capacity and the wilting point), so irrigation is necessary during the maize crop season.
2.1.2. Crop management and growth analysis Crops were sown on 24th May 1996 and on 27th May 1997 (144 and 147th day of the year, respectively) in rows 60 cm apart and at a rate of 10 seeds m−2
. The final density was 5 plants m−2
. All the crops were grown under high-input condi-tions (120 kg P2O5 ha
−1) before sowing and 100
kg ha−1 of nitrogen as ammonium nitrate (26%
N), in two split applications, the first one early in the crop season and the second one at the jointing stage.
The experimental design was a randomised block replicated three times (nine plots in total). Each plot was 9 m long and 9.6 m wide.
Dates of the main phenological stages were collected during the 2 years of trial and included: (1) seedling emergence; (2) fourth leaf (collar visi-ble); (3) tenth leaf (collar visivisi-ble); (4) 50% tassel-ing; (5) milky maturity; (6) dough maturity; (7) maturity. Leaf area and above-ground dry biomass were determined at each phenological stage by sampling the plants over a 1.5 m2 area
per plot. Leaf area was measured with a LiCor 1300 leaf metre and dry biomass, separated into different plant parts, was measured after drying (at 80°C for 48 h in an oven).
2.1.3. Irrigation scheduling and experimental design
Irrigation scheduling tried to produce three dif-ferent soil water conditions. The adopted method-ology is based on plant water relationships. It was observed that in maize, stomatal conductance does not limit gas exchange when the pre-dawn leaf water potential (C) is higher than −0.3 MPa, but if C becomes more negative than this threshold value, maize stomata tend to close (Katerji and Bethenod, 1997). Moreover if C
decreases to −1.5 MPa, stomatal conductance values do not change. Therefore, −0.3 MPa rep-resents a threshold value, we chose to separate no water stress from stress conditions.
During each trial year, plants were well-watered until leaf area index (LAI) was just higher than 1. Thereafter, three water treatments were imposed: full irrigation (IRR), moderate water stress (STR1) and severe stress (STR2). Irrigation was scheduled with a low-pressure system (drip irriga-tion), whenever C, measured daily, equalled − 0.3 MPa (IRR), −0.6 MPa (STRI) and −1.2 MPa (STR2), respectively. The pre-dawn leaf wa-ter potential (C) was measured on the last devel-oped leaves before sunrise. Five leaves per plot were harvested from the three treatments and the water potential was measured with a pressure chamber (Scholander et al., 1965). C is not quired for testing CERES-Maize, but it is re-quired for providing the protocol for irrigation scheduling, and C is necessary for calibrating the new versions of the model.
2.1.4. E6apotranspiration estimation
Evapotranspiration was estimated by using a different simplified soil water balance approach. At our site, runoff and capillary rise can be neglected, owing to the flat ground and to the presence of a cracked rocky layer which limits soil depth and ascending water. The equation for soil water balance can be expressed as
ET=P+DSw−Dr (1)
where; ET, is crop evapotranspiration (mm); P, precipitation or irrigation (mm); DSw, the
Table 1
Input data required by CERES-Maize model
Acronym
Parameter or variable Units
Location data
Plant population PLANTS Plant m−2 Climatic data
Day JDATE Day of the
year Maximum temperature TEMPMAX °C
TEMPMN
Culti6ar data
Thermal time from P1 °C emergence to end of
juvenile stage
P2
Photoperiod sensitivity Day h−1
coefficient
Thermal time from silking P5 °C to physiological
maturity
Potential kernel number G2 Kernel per plant Potential kernel growth G3 mg per kernel
per day
Whole-profile drainage SWCON cm per day rate coefficient
Runoff curve number CN2 – LL cm3cm−3
Lower limit of soil water content
Root growth factor –
showed that ET estimates were similar to those provided by the Bowen ratio method (differences between the two methods were less than 10%). The Bowen ratio method has been thoroughly tested and its validity as a reference evapotranspi-ration measurement has been well established (Fuchs and Tanner, 1970; Sinclair et al., 1975), even for high canopies (Rana and Katerji, 1998).
2.2. CERES-Maize model
CERES-Maize is a deterministic simulation model, designed to simulate the effects of cultivar, planting density, weather, soil water and, in one of the versions, nitrogen on crop growth, develop-ment and yield (Jones and Kiniry, 1986). To simulate accurately maize growth, development and yield, the model takes into account the fol-lowing processes.
Phenological development, especially as it is
affected by genetics, temperature, and photoperiod.
Leaf area development and growth of stems and roots.
Biomass accumulation and partitioning to grains and other organs.
Soil water balance and water used by the crop.
Soil nitrogen uptake and its partitioning
among plant organs.
CERES standard version (V3.0), used in our study, assumes that nitrogen supply is not limit-ing. The input parameters needed to run CERES-Maize are listed in Table 1.
2.2.1. Model calibration
Data from treatment IRR, collected during the 1996 season, were used to calibrate CERES-Maize. The remainder of the data was used for validation of the model (IRR in 1997; STR1 and STR2 in both the years). This procedure allowed us to validate the model using experimental data that are effectively independent from those used for its calibration.
Thermal time from seedling emergence to the end of juvenile stage (P1) and the photoperiod sensitivity coefficient (P2) were obtained by cali-bration based on observed silting date, according to an approach presented by Ritchie and Aloga-1502 C) in two layers, 0 – 30 and 30 – 60 cm, in
only one block and with two replicates per plot. For the calculation of volumetric soil water con-tent a local calibration curve was used. The drainage (Dr) was estimated as the amount of
raswamy (1989). Potential kernel number (G2) and growth rate (G3) were found by calibration of kernel weight and kernel number. The duration of grain-filling (P5) was the value observed in the data-set used for calibration (IRR 96). Values for the lower, drained upper and saturated limits (LL, DUL, SAT) were based on those measured in soil samples collected in an independent trial (Castri-gnano` et al., 1994).
2.3. Model 6alidation
The accuracy of model predictions of crop de-velopment and growth (LAI and above-ground biomass) and grain yield was estimated using two statistical procedures. The first consisted of a linear regression between measured and predicted values on all the observation dates for each of the
three variables under study. Two Student’s t-tests were then applied to verify the following two ‘null’ hypotheses: intercept=0 and slope=1.
The second procedure followed the methodol-ogy proposed by Addiscott and Whitmore (1987) and Whitmore (1991) and consisted in partition-ing the sum of the squares of the residuals into pure error (i.e. random variation) and lack of fit (i.e. systematic variation).
The accuracy of maize evapotranspiration sim-ulations was tested by linear regression between ET values provided by the model and ET esti-mates from the simplified soil water balance, pre-sented above, and based on soil water measurements with the TDR. Also for the ET validation, estimates from IRR 96 were not considered.
3. Results and discussion
3.1. Experimental obser6ations
Fig. 2 shows the temporal variation in pre-dawn leaf water potential for each irrigation treat-ment during the two cropping seasons. Differentiation between treatments began 49 and 40 days after sowing, in 1996 and 1997, respec-tively. Owing to the warmer climate in 1997, development was speeded up and crop cycle dura-tion reduced, whereas pre-dawn leaf water poten-tial remained almost constant, ranging from −0.3 to −0.1 MPa for the well-watered treat-ment. Since, in maize the optimum for photosyn-thesis and stomatal conductance coincides with pre-dawn leaf water potential values less negative than −0.3 MPa (Katerji and Bethenod, 1997), IRR treatments may be considered as controls. For the other water regimes the plant experienced several water-shortage periods of intensity, which increased with the imposed stress level. Rainfall distribution in the 2 years differed in intensity and timing. The situation was usually different late in the crop season, when the extended rainless pe-riod in 1997 caused an increased water stress in the maize plants.
The time variations in soil water availability for each treatment during 1996 and 1997 are shown Fig. 2. Time evolution of observed pre-dawn leaf water
Fig. 3. Time evolution of water stored (mm) into soil profile calculated on the basis of TDR measurements.
precipitation in 1996 partially compensated for water depletion in the soil. In addition, treatments were more differentiated towards the end of the crop season 1997. Plant water consumption was affected by water stress level, which caused a reduction in both the daily evapotranspiration (Fig. 4) and seasonal evapotranspiration.
The effects of water regime on the development of leaf area and the accumulation of above-ground dry biomass during the crop cycle were examined. Fig. 5 shows the green LAI evolution for all the treatments during 1996 and 1997. There were no significant differences among the treat-ments until the 56th and 42nd day after sowing in 1996 and 1997, respectively. Thereafter, LAI dif-fered significantly among water regimes; the lower irrigation volume gave the smaller LAI. In 1996 maximum LAI values were 16 and 33% lower
Fig. 4. Time evolution of ET estimated independently from the CERES-Maize model.
in Fig. 3. Soil water changes were similar to those in pre-dawn leaf water potential for all irrigation treatments and during both the years. This confi-rms the accuracy of pre-dawn leaf water potential for diagnosing soil water status (Mastrorilli et al., 1999).
Daily crop evapotranspiration, estimated from the soil water balance for all treatments and dur-ing 1996 and 1997, is shown in Fig. 4. Close agreement exists between the evapotranspiration and pre-dawn leaf water potential through time. During the first 30 days after sowing, most of the ET consisted of soil evaporation, controlled mainly by soil hydraulic properties and solar radi-ation. This period is characterised by a mean value of daily ET of about 3 mm. As the crop canopy grew, ET increased and reached its highest values (about 9 mm per day) at flowering after irrigation.
Fig. 5. Evolution of measured LAI; vertical bars represent
S.D. Fig. 7. The evolution in time of observed grain yield growth (kg ha−1).
Evolution of cumulative above-ground dry biomass (Fig. 6) shows that the above-given considerations for LAI also hold for cumulative biomass. The accumulation of biomass differed in 1996 and 1997. This was due to a different rainfall pattern between the 2 years. Water stress intensity was higher in 1997 during the waxy maturity stage, mainly for the STR2 treat-ment. In the stressed treatments, the extent of reduction in the total biomass depended on the degree of water deficit; losses in percent of con-trol (fully irrigated treatment) were 19 and 34%
in 1996, and 16 and 38% in 1997 for STRI and STR2, respectively.
Grain growth for all the treatments and both the seasons is shown in Fig. 7. Grain yield was highest in the controls, approaching 9200 kg ha−1 at 15% moisture content in the first year.
However, the greater growth in leaf area and biomass that year caused a higher sensitivity to water stress; consequently the reduction in grain yield (relatively to IRR treatments) was greater in 1996 (26 and 45% for STRI and STR2, re-spectively) than in 1997 (17 and 40%).
3.2. Calibration
Observed values for the well-watered treat-ment in the year 1996 (IRR96) were used to approach by trial and error (Godwin et al., 1989) the ‘genetic coefficients’ that gave the most realistic estimates of phenology and pro-duction. These coefficients were used in all the subsequent simulation runs (validation) over a range of meteorological and water stress condi-tions. The genetic coefficient values estimated for Maltus cultivar are listed in Table 2. In the same table there are also the genetic coefficients for the cultivar A632×WI 17 which were re-ported in the user’s guide to CERES model. The latter cultivar has been chosen because it belongs to the same maturity class as Maltus. Fig. 6. Evolution in time of cumulative measured
above-ground dry biomass (kg ha−1). Vertical bars represent S.D. of
Table 2
Genetic coefficients estimated for the cultivar Maltus and those reported for the cultivar A632×W117, and soil data of Rutigliano (Italy)
Genetic Cultivar P1 P2 P5 G2 G3
220 0 780
Maltus 781.5 6.18
187 0 685
A632×W117 825.4 10
U SWCON
Soil SALB CN2
8.5 0.6 81
0.13
DUL SAT
Layer (cm) LL WR
0.330 0.470 1
0–20 0.155
0.345 0.470 0.7
0.159 20–40
0.349 0.476 0.1
40–60 0.159
3.3. Validation
Validation was based on all the data set except IRR96, which was used for calibration. We focused on two kinds of variables given as output by the CERES-Maize model; (a) final simulations of biomass, grain yield, yield components, maximum LAI and seasonal evapotranspiration at maturity, and (b) daily simulations of LAI, above-ground biomass, grain yield, evapotranspiration and plant available soil water content during the crop cycle. Table 3 shows the comparison between observa-tions and simulaobserva-tions. In the case of treatment IRR in 1997, the simulated values and observations generally matched well and differences were less than 10%, with the exception of simulated kernel weight, which was higher.
As regards the stress treatments (STR1 and STR2) in both the years, simulated and observed maturity dates coincide. On the contrary, a delay of 1 – 2 days (STR1) or 5 – 6 days (STR2) was observed between the recorded and simulated an-thesis dates.
The final simulations were also satisfactory for actual cumulative evapotranspiration, whereas grain yield, above-ground biomass and maximum LAI were generally underestimated. The differ-ences between observations and simulations were more severe for the STR2 treatment than for STR1.
3.3.1. Daily simulations
Fig. 8 shows the time trend of available soil water content. In the case of the IRR treatment (in the 1997 season), the overall simulation is satisfactory,
but during the early stages of crop growth, the model overestimated the changes in soil water content. Regarding the stress treatments, the model overestimated the observations in two cases.
B
Comparison between predicted and observed data for each treatment and percent difference
D (%)
Units IRR D (%) STR1 STR2 D (%)
Variable
Predicted Observed Predicted Observed
Predicted Observed
Maturity date (d.o.y) 270 –
6836 −7.27 4441 5030 −11.71
Anthesis date (d.o.y)a 213
262 – 262
Maturity date (d.o.y) 262 262 – 262 262 –
7212 −15.42 3975 5180 −23.26
6100 (kg ha−1)
Grain yield 8046 8676 −7.26
0.1938
(g) 0.2059 0.2400 −14.20 0.2100 −7.71 0.1561 0.2000 −21.95
Kernel weight
Grains ear – 781.5
2.95
Seasonal ET (mm) 498 0
Fig. 9. Time variation of evapotranspiration (mm per day) estimated by TDR measurements and simulated by CERES-Maize.
Fig. 11 shows the change in LAI through time. For the IRR treatment in 1997, a slight overesti-mation early in the season and an underestima-tion from flowering to maturity was observed, so the model seems to overestimate leaf senescence. These results apply also to the treatments STR1 and STR2. The underestimation from flowering to physiological maturity appears to increase with the intensity of water stress.
The model underestimates above-ground dry biomass for the treatment (Fig. 12) before flower-ing, but later on the matching with observations becomes better. For STR1 and STR2 in both the seasons, the simulated values of biomass are
sys-Fig. 10. Cumulative evapotranspiration (SET in mm per season) estimated by TDR measurements (fine lines) and simulated by CERES model (bold lines).
Early in the crop cycle when the crop is vegeta-tive, for both STRI and STR2 treatments dur-ing the season 1997.
When the total soil water reserve became less
than 110 mm for the STR2 treatment.
Fig. 11. Time variation of LAI simulated by the model (con-tinuous lines). Dots are the measured data and the vertical bars represent their S.D.
different from 0). Also dry biomass was systemat-ically underestimated and ET overestimated, as indicated by the slope significantly greater and smaller than 1, respectively. For the LAI, grain yield, available water, the correspondence be-tween observation and simulation was 1:1, once the bias was taken into account, as indicated by a slope not significantly different from 1.
Lack of fit (Table 5) was significant for all the studied variables and for both the years (PB 0.05). This implies that the model could be im-proved, mainly for dry above ground biomass and LAI, under soil water shortage.
4. Conclusions
Under well-watered conditions the CERES-Maize model’s performance, evaluated on the ba-sis of both the daily and final simulations, was reasonably good in terms of above-ground biomass, LAI, grain yield, actual
evapotranspira-Fig. 12. Measured and simulated above-ground dry biomass, (kg ha−1). Dots and vertical bars represent the measurements
and their S.D., respectively. tematically smaller than the observed values and
again the differences were more acute in the case of higher stress (STR2 treatments). Senescence measurements demonstrate that CERES-Maize overestimates leaf senescence in stressed plants.
Fig. 13 shows a good match between simula-tions and observasimula-tions of grain yield for IRR treatment in 1997 season. In the other treatments we find underestimation as was the case for biomass.
3.4. Statistical analysis
The results of the linear regression between observations and predictions of CERES-Maize for all the data-sets used for validation are shown in Table 4. The fitting was generally quite good, as indicated by the high values of the determina-tion coefficients (R2). The only exception was for
Fig. 13. Grain growth: simulation (continuous line) and mea-surement (dots) with their S.D. (vertical bars).
The results reported here for the hybrid used, seem to suggest that the absence of air humidity among the model inputs does not limit the CERES-Maize performance, even under dry-air conditions.
On the contrary, under soil water shortage, simulated values of LAI, above-ground biomass, and grain yield were systematically underesti-mated, the mismatching increasing with the de-gree of water shortage. So CERES-Maize cannot be used without adaptation to soil water shortage conditions.
Leaf area simulation is not satisfactory, mainly under soil water shortage. Even under well-watered conditions other authors have al-ready observed a leaf area underestimation by the model, consequently resulting in an underes-timation of biomass (Hodges et al., 1987; Car-berry et al., 1989; Liu et al., 1989; Wu et al., 1989; Birch et al., 1990).
The model takes into account and simulates the effect of water stress, through the estimation of soil water deficit factors affecting leaf area, leaf senescence, photosynthesis and grain filling, and calculated as a function of soil water bal-ance. However, the simulated stress appears to be more severe than the stress actually experi-enced by the plant under field conditions, there-fore, a new definition of the stress function is required.
Most probably, the model performance is the result of both the effects acting simultaneously. In the light of the above considerations, we in-tend to introduce two modifications into the original version of the CERES-Maize model, one about growth of leaf area including senes-cence and the other concerning the water stress function definition. The comparison between the performances of the original model and those of the modified version, will be presented in a fur-ther paper.
Acknowledgements
The authors wish to thank Dr Annamaria Castrignano` for her useful help and suggestions in statistical analysis.
tion and available soil water content. In spite of LAI underestimations observed during the earli-est growth stages and from flowering to harvearli-est time, it seems that the CERES-Maize model could be successfully used also in the semi-arid conditions of a Mediterranean climate to crops fully supplied with irrigation water.
Table 4
Linear regression between observations and predictions of CERES-Maize for all the data-sets used for validation
Variables Intercept9S.D. Slope9S.D. R2
803.239219.44a
Dry biomass 1.1590.3b 0.94
Table 5
Whitmore statistical test,Ftable values are also reported MSLOFIT
Variables MSSE MSLOFIT/MSSE F
PB0.1 PB0.05
1996
11 744 354 38.31
306 547 Dry biomass
LAI 0.063 1.588 25.08 1.32 1.43
1 161 333 5.16
224 951 Grain yield
1997
15 305 945 17.83
858 537 Dry biomass
0.088
LAI 2.911 32.96 1.44 1.59
2 351 033 5.59
420 433 Grain yield
References
Addiscott, T.D., Whitmore, A.P., 1987. Computer simulation of changes in soil mineral nitrogen and crop nitrogen during autumn, winter and spring. J. Agric. Sci. 109, 141 – 157.
Birch, C.J., Carberry, P.S., Muchow, R.C., McCown, R.L., Hargreaves, J.N.G., 1990. Development and evaluation of a sorghum model based on CERES-Maize in a semi-arid tropical environment. Field Crops Res. 24, 87 – 104. Carberry, P.S., 1991. Test of leaf-area development in
CERES-Maize: a correction. Field Crops Res. 27, 159 – 167.
Carberry, P.S., Muchow, R.C., McCown, R.L., 1989. Testing the CERES-Maize simulation model in a semi-arid tropical environment. Field Crops Res. 20, 297 – 315.
Castrignano`, A., Di Bari V., Colucci, R., Marrone, G., 1994. Scale factors in modelling spatial variability of soil water characteristic cure. In: Acts of the International Confer-ence on Land and Water and Water Resource Manage-ment in the Mediterranean Region, CIHEAM-IAM Valenzano (Ba), Vol. II. 4 – 8 September 1994, pp. 507 – 518.
Entenmann, T.A., Allison, B.E., Van der Ploeg, R.R., 1989. Abschatzung der evapotranspiration eines maisbestandes. Mitt. Dtsch. Bodenkd. Ges. 59 (1), 129 – 132.
Fuchs, M., Tanner, C.B., 1970. Error analysis of Bowen ratio measured by psychrometry. Agric. Meteorol. 7, 329 – 334. Godwin, D., Ritchie, J.T., Singh, U., Hunt, L., 1989. A Users
Guide to CERES Wheat-V2.1.0. International Fertilizer Develop Centre, Muscle Shoals, AL.
Hamdy, A., Lacirignola, C., 1999. Mediterranean Water Re-sources: Major Challenges Towards The 21st Century. CIHEAM-IAM, Bari, p. 570.
Hodges, T., Botner, D., Sakamoto, C., Hays Haug, J., 1987. Using the CERES-Maize model to estimate production for the US corn belt. Agric. For. Meterol. 45, 293 – 303. Jones, C.A., Kiniry, J.R., 1986. CERES-Maize A Simulation
Model of Maize Growth and Development. Texas A & M University Press, College Station, p. 194.
Katerji, N., Bethenod, O., 1997. Comparison du comporte-ment hydrique et de la capacite´ photosynthe´tique du mais et du tournesol en condition de contrainte hydrique. Con-clusions sur l’efficience de l’eau. Agronomie 17, 17 – 24. Lahrouni, A., Ledent, J.F., 1991. Test des fonctions de´crivant
la mise en place du feuillage dans mode`le CERES-Maize. In: Picard, D. (Ed.), Physiologie et Production Du Maı¨s. AGPM, INRA, Paris, pp. 437 – 443.
Lahrouni, A., Ledent, J.F., Cazanga Solar, R., Mouraux, D., 1993. Testing the CERES-Maize model in Belgian condi-tions. Eur. J. Agron. 2, 193 – 203.
Liu, W.T.H., Botner, D.M., Sakamoto, C.M., 1989. Applica-tion of CERES-Maize to yield predicApplica-tion of a Brazilian maize hybrid. Agric. For. Meteorol. 45, 299 – 312. Lorgeou, J., 1991. Simulation du nombre de grains par
CERES-Maize. In: Picard, D. (Ed.), Physiologie et Produc-tion Du Maı¨s. AGPM, INRA, Paris, pp. 455 – 468. Mastrorilli, M., Katerji, N., Rana, G., Ben Nouna, B., 1998.
Daily actual evapotranspiration measured with T.D.R. technique in Mediterranean conditions. Agric. For. Meteo-rol. 90, 81 – 89.
Mastrorilli, M., Katerji, N., Rana, G., 1999. Productivity and water use efficiency of sweet sorghum as affected by soil water use deficit occurring at different vegetative growth stages. Eur. J. Agron, 11, 3 – 4, 206 – 216
Piper, E.L., Weiss, A., 1990. Evaluating CERES-Maize for reduction in plant population or leaf area during the growing season. Agric. Syst. 33, 199 – 213.
Plantureux, S., Girardin, P., Fouquet, D., Chapot, J.Y., 1991. Evaluation et analyse de sensibilite´ du mode`le CERES-Maize en conditions alsaciennes. Agronomie 11, 1 – 18. Rana, G., Katerji, N., 1998. A measured based sensitivity
analysis of the Penman – Monteith actual evapotranspira-tion model for crops of different height and in contrasting water status. Theor. Appl. Climatol. 60, 141 – 149. Ritchie, J.T., Alogaraswamy, G., 1989. Genetic coefficient for
12. International Crop Research Institute for the Semi-arid Tropics, Patancheru, India, pp. 27 – 29.
Ruget, F., Bonhomme, R., 1991. Applications et utilisations du CERES-Maize dans diffe´rentes re´gions du monde. In: Picard, D. (Ed.), Physiologie et Production Du Maı¨s. AGPM, INRA, Paris, pp. 413 – 418.
Scholander, P.F., Hammel, H.T., Bradsteet, E.D., Hem-mingser, E.A., 1965. Sap pressure in the atmosphere and in the soil. Ann. Rev. Plant Physiol. 37, 247 – 270.
Sinclair, T.R., Allen, L.H., Lemon, E.R., 1975. An analysis of errors in the calculation of energy above vegetation by Bowen ratio profile method. Boundary Level Meterol. 8, 129 – 139.
Whitmore, A.P., 1991. A method for assessing the goodness of computer simulation of soil processes. J. Soil Sci. 42, 289 – 299.
Wu, Y.A., Sakamoto, C.M., Botner, D.M., 1989. On the application of the CERES-Maize model to the North China plain. Agric. For. Meteorol. 49, 9 – 22.
