Sensitivity analysis of soil hydrologic parameters for two crop growth simulation models

Soil & Tillage Research 50 (1999) 305±318

Sensitivity analysis of soil hydrologic parameters for two crop growth simulation models M. SÏt'astnaÂ*, Z. ZÏalud Department of Bioclimatology, Institute of Landscape Ecology, Mendel University of Agriculture and Forestry, ZemeÏdeÏlska 1, Brno 613 00, Czech Republic Received 11 August 1998; received in revised form 4 December 1998; accepted 22 February 1999

Abstract Soil characteristics are some of the most important inputs in crop simulation models like CERES-Maize and MACROS models under ®eld conditions. A sensitivity analysis of these models was carried out for 1995 and 1996 by incorporating changes (‡2, ‡4, ‡6, ÿ2, ÿ4, and ÿ6%) of the measured values of three basic soil input parameters: wilting point (WP), saturated soil water content (FS) and ®eld capacity (FC). For this purpose, eight soil pro®le layers were chosen for CERESMaize and three for the MACROS model. The MACROS model was found to have a higher degree of sensitivity to changes in the relevant soil parameters than the CERES-Maize model, especially for WP. For the MACROS model, an earlier end of the growing period (75 days) for maize at the ‡6% level for WP was simulated in 1995 as against 152 days for the CERES-Maize model. The yield in 1995 due to 6% increase in WP resulted in a decrease of about 35% yield when CERES-Maize model was used. In 1996 the increase in WP by 6% resulted in decrease of yield by 22.5 and by 0.9% for MACROS and CERES-Maize models, respectively. When WP was decreased by 6% then the yield was found to increase by about 20.4 and 12.0% for MACROS and CERES-Maize models, respectively in 1995. In 1996 the same alteration of WP caused the yield to increase by about 5.7 and 0.7% for MACROS and CERES-Maize models. Alteration of the model's remaining parameters showed a negligible in¯uence on yields for both the models. # 1999 Elsevier Science B.V. All rights reserved. Keywords: Soil water content; Soil hydrologic parameters; Soil parameters; Crop models

1. Introduction Crop simulation models (CSM) may be classi®ed as follows: (1) static and dynamic models, (2) general and particular models, (3) explanatory and descriptive models, (4) validated and non-validated models, (Wit de, 1986). CSM have been based on submodels describing and simulating lower level processes such *Corresponding author. Tel.: +420-5-4513-3083; fax: +420-545212044; e-mail:[email protected]

as photosynthesis, respiration, translocation of the assimilated products into various crop organs, phenology, aging and dying of individual crop organs. These submodels are used, beside others, as implements in general crop models which simulate the development, growth and yield of crops for homogeneous areas of speci®ed soils and at given weather conditions (Goudriaan and Hunt, 1995; Easterling et al., 1992; Semenov et al., 1993). Crop simulation models are proposed as tools for agricultural analysis. In order to evaluate alternative

0167-1987/99/$ ± see front matter # 1999 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 7 - 1 9 8 7 ( 9 9 ) 0 0 0 2 1 - 5

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management strategies for tactical or strategical decisions, it may be necessary to modify or change some of the model parameters based upon sensitivity analysis. The soil water balance is an indispensable component in crop-yield models. The soil water content is important for estimation of actual growth and yield under ®eld conditions. Two well known models have been chosen to evaluate speci®ed soil inputs by a sensitivity analysis: CERES-Maize (Crop-environment resource synthesis, Jones and Kiniry, 1986) and MACROS (Modules for an annual crop simulation, Penning de Vries et al., 1989). For sensitivity analysis, which is generally considered as a kind of model utilization, a de®ned increase or decrease of soil hydrologic parameters were used. The sensitivity analysis is based on an initial experiment and selected treatment. A similar sensitivity test based on available soil, crop and meteorological parameters was carried out for CERES-Maize in Brazil, where three lower limit (WP) levels of plant-extractable soil water were tested (Liu et al., 1989). 2. Materials and methods 2.1. Experimental area The study was performed at the pedotop scale (KutõÂlek and Nielsen, 1994). On this scale we assume to have a single lowest soil taxon. The variability of saturated soil water content at this scale is characterized by coef®cient of variation (CV) up to 10%, while CV of wilting point is between 15 and 50% (KutõÂlek and Nielsen, 1994). Considering these data further possible variation of soil hydrologic characteristics were considered. 2.1.1. Field and crop description The experimental area is located in the south part of Moravia of the Czech Republic. The data on meteorological, soil and plant parameters were measured at the station in ZÏabcÏice (latitude 498010 N, longitude 168370 E and altitude 179 m above mean see level). Maize (Zea maize L. (Dea variety - FAO 300)) was grown for the experiment. The area of the experimental ®eld was 48 m2. The trial had four replications. The crop rotation at the experiment ®eld was as follows: spring barley (Hordeum sativum L.) (1994) ±

maize (1995) ± alfalfa (Medicago sativa, 1995/1996) and maize (1996). The average plant density of maize was in accordance with the plant distribution (0.70 m  0.17 m) in both years. Sowing was done on 4th May in 1995 and on 3rd May in 1996. Adequate amount of fertilizer was applied in order to keep optimum nutrient level. 2.1.2. Climate and weather July represents the warmest month and January the coldest month in this region. The long term average of annual mean temperature is 9.38C, where the mean temperature during the growing period (from April to September) is 15.58C. The maize growing period, which is limited by biological zero from temperature point of view (air temperature has to exceed 88C), starts on an average from 15.3 and ends on 9.11. (the mean duration reaches 239 days) under conditions of the experimental place. Mean daily temperatures above 108C was found to occur for 177 days continuously during the time period from 18.5 to 11.10 (146 days). The mean sum of yearly precipitation (normal value) is 480 mm. This region is considered to be the driest in the Czech Republic. 1995 could be considered as a dry year. Despite the precipitation of 243.7 mm between April and September (only 32 mm below 30 years normal from 1961 to 1990, which is 276 mm, RozÏnovsky and Svoboda, 1995) its non-uniform distribution caused a long duration of dry spells. There were six days dry spells (six times) and also a 10-day dry spell once. Only 8 mm of precipitation was recorded during the period of 186±225 Julian days. The mean daily temperature was 0.88C above the 1961±1990 normal. From April to September the average temperature was higher by 1.48C compared to the normal (1961±1990). These conditions caused a high evapotranspiration and a frequent occurrence of drought stress periods (Fig. 1). On the other hand, the meteorological conditions in 1996 were ideal for maize growth. High amount of precipitation and its good distribution created optimum conditions for canopy development (Fig. 2). The sum of precipitation was 308.3 mm in the growing period (April±August) and the occurrence of 6-day dry spells were observed only three times but never during the critical periods for maize growth and development. The daily mean of the air temperature during the growing season exceeded the temperature (normal) by 0.58C.

Fig. 1. Variation of temperature and precipitation during the growing season 1995 (daily temperature averages and daily precipitation sums).

Fig. 2. Variation of temperature and precipitation during the growing season 1996 (daily temperature averages and daily precipitation sums).

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The different weather conditions during the 2 experimental years were ideal for validation and for sensitivity analysis of the chosen crop models. 2.1.3. Soil description A soil pit was dug near the experimental area. According to our empirical knowledge and experience on the location, the soil pro®le and samples were taken as representative of the soil in the experimental area. The soil of the experimental ®eld falls under the subgroup Oxyaquic Cryo¯uvents (USDA, 1975) with following horizons: (a) 0±28 cm, Ap horizon, coarse subangular blocky, clay loam texture, dark brown color and abrupt boundary, (b) 28±35 cm, Ao horizon, medium granular, silty clay texture, dark brown color and sharp boundary, (c) 35±61 cm, C1 horizon, coarse angular, up to 50 cm silty clay, below 50 cm clay loam texture, brown color and gradual boundary, (d) 61± 80 cm, C2 horizon, no observable aggregation, up to 70 cm loam, below 70 cm clay loam texture, greyish brown color and gradual boundary, (e) 80 cm and more, Cg horizon, coarse angular and gray color. For the sensitivity analysis eight layers of soil for CERES-Maize model and three layers for MACROS model up to the depth of 160 cm (Table 1) were selected. 2.2. Crop models 2.2.1. CERES-Maize model The CERES-Maize model is a part of Decision Support System for Agrotechnology Transfer (DSSAT, Tsuji et al., 1994). It was created in cooperation with the International Benchmark Sites Network for Agrotechnology Transfer (IBSNAT) project Table 1 Chosen soil inputs (soil water content characteristics) used for the sensitivity analysis in the selected crop models Soil layer (m)

FC (cm3/cm3)

WP (cm3/cm3) FS (cm3/cm3)

0±0.05 0.05±0.15 0.15±0.30 0.30±0.45 0.45±0.60 0.60±0.90 0.90±1.20 1.20±1.60

0.3770 0.3860 0.3873 0.3733 0.3520 0.3560 0.3247 0.3340

0.2350 0.2350 0.2417 0.2415 0.2433 0.2307 0.2223 0.2280

0.3980 0.4060 0.3977 0.4083 0.3777 0.3713 0.3788 0.3840

(Jones, 1989; IBSNAT, 1988, 1990a, b) and was coordinated by the University of Georgia, USA. The CERES-Maize is one of the best practical maize models available today. In order to simulate maize growth, development and yield, the model takes into account the following processes: (1) phenological development, as it is affected by genetic characteristics and weather conditions (2) growth of leaves, stems and roots (3) biomass accumulation and partitioning (4) soil water balance and plant water use (5) soil nitrogen balance, its uptake by plant and partitioning among plant parts. There are four groups of input data to the models: meteorological (weather), physiological, soil and management. The weather input data set of the CERES-Maize model includes the daily sum of global radiation (MJ/m2), the daily minimum and maximum air temperatures (8C), and the daily sum of precipitation (mm). Plant parameters and physiological characteristics are given in the form of genotype coef®cients, which describe physiological processes (photosynthesis, respiration, and others) for individual crop varieties. Furthermore, there are physical and chemical characteristics of different soils given in the soil input data set. One set of these data, which was analyzed for the purpose of the study, is the factors in¯uencing the soil water regime. The most important management input data are planting date and depth, organic and mineral fertilizers usage, and the type of soil cultivation and irrigation. The simulation is carried out after all the essential input data are de®ned in the input data ®les. 2.2.2. MACROS model The MACROS model was developed by AB-DLO (Research Institute for Agrobiology and Soil Fertility) and DTPE (Department of Theoretical Production Ecology) University of Wageningen (Penning de Vries et al., 1989) and contains modules which are able to simulate growth processes common for most annual crops. They are speci®ed to them by applying the speci®c plant parameters such as physiological and phenological characteristics (Penning de Vries et al., 1989) for individual plant species and varieties. MACROS is based on the dynamic simulation of the following processes in several steps: (1) photosynthesis and respiration (2) simulation of biomass

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partitioning, growth and leaf area, and (3) phenological development. MACROS is further designed to be used for the simulation of several production levels. The potential yield simulation (named as production level (1) is limited only by temperature, solar radiation and photosynthesis characteristics of the plant. The soil and plant water balance simulation is additionally included in production level 2 (used for ®eld conditions). The third production level takes into account the nitrogen balance also. These levels could be obtained by CERES-Maize model too. Further production levels consider phosphorus and potassium uptake, pests, weeds, diseases and their effects on plant production. Production level 1 (potential) and 2 (water-limited) have been used for the validation and sensitivity analysis in this study. The MACROS model is based on several independent modules. The basic modules L1D (module for computing of the potential production, simulation of transpiration and describing of drought stress in¯uence on growth and development in 24 h time step), and L2SU (simulates water balance and water movement with free drainage) were used in the simulations. The L2SU module (MACROS) simulates the water balance of free-draining soils at production level 2 and is an alternative to the L2SS module for soils with impeded drainage. The choice of the modules depends on the objective of the study, on environmental conditions (e.g. soil) and on the availability of soil data. The WEATHER (meteorological input data), SOIL (soil parameters for soil description and classi®cation), CROP (plant data and functions) and T12 (contents functions and subroutines) modules are used for production level 1. They are called directly from the driving module during the simulation. CERES-Maize and MACROS can be described as dynamic, particular, explanatory and validated multipurpose user-oriented simulation crop models.

the end of the juvenile phase, P2±photoperiod sensitivity coef®cient, P5±growing degree days (base 88C) from silking to physiological maturity, G2±potential kernel number per plant, G3±potential kernel growth rate (in mg/kernel day) and PHINT±phyllochron interval (degree days 8C). MACROS uses parameters and functions related to temperature, development stage and water stress. For example the photosynthesis function is limited by PLMXP (maximum rate of photosynthesis of single leaves for optimal conditions) and PLEI (initial ef®ciency use absorbed light by individual leaves) whereby these parameters are further related to temperature at a speci®ed carbon dioxide concentration. For this study, most of the physiological parameters were not measured, but were selected from literature and other ®eld experiments.

2.3. Field measurements

2.3.4. Parameters for models validation Validation refers to a comparison between ®eld measurements and outputs created by the model. The models simulate crop yields as an output. The grain yield (t/ha) as well as the leaf area index (LAI) (m2/m2) measured during the growing period (from emergence to maturity) have been selected as the validation parameters for both models. The LAI

2.3.1. Physiological parameters Both CERES-Maize and MACROS use physiological characteristics in different forms as inputs. CERES-Maize operates with six genetic coef®cients, which describe growing processes in the plant: P1± growing degree days (base 88C) from emergence to

2.3.2. Meteorological parameters Meteorological data (daily sum of global radiation, the daily minimum and maximum air temperature, daily sum of precipitation, daily wind speed and air humidity) were measured by the Campbell automatic station located at the experimental station. The sample interval was two seconds, which was used to compute ®fteen minute values (averages or sums). The station is located close (30 m) to the experiment. 2.3.3. Soil hydrologic characteristics Soil hydrologic characteristics are important crop model inputs when soil water balance is simulated as water stress normally occurs during the growth period. The basic ones are percentage of sand, clay and silt, bulk density, wilting point (WP), saturated water content (FS) and ®eld capacity (FC). All these parameters were measured to create an experiment ®le which is needed for validation of both of the models. Three of these soil characteristics (WP, FS and FC, estimated in volume percentage) were used in the sensitivity analysis.

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was determined by using the LAI±2000 PLANT CANOPY ANALYZER (Welles and Norman, 1991) and by a transmission method (SÏt'astnaÂ, 1996) using LI-COR pyranometers of the type LI200SZ. After successful validation, the models can be used as a tool, e.g., for the estimation of optimal conditions for individual crops, for determination of crop production potential etc. The LAI value in the MACROS model is computed by the following equation: LAI ˆ INTGRL…LAII; GLA ÿ LLA ‡ GSA†

(1)

where LAI±area of leaves (ha/ha), INTGRL±CSMP function, LAII±area of leaves, initial (ha/ha), GLA± growth rate of leaf area (ha/ha/day), LLA±loss rate of leaf area (ha/ha/day), GSA±growth rate of photosynthetically active stem area (ha/ha/day). The CERES-Maize model uses a different equation to compute LAI LAI ˆ …PLA ÿ SENLA†  PLTPOP  0:0001 (2) where PLA±total plant leaf area (cm2/plant), SENLA± leaf senescence for a given day (cm2/day), PLTPOP± plant population per m2. The following equations are used to compute yield in the MACROS model: YIELD ˆ INTGRL…WSOI; GSO†

(3)

where: WSOI±weight of storage organs, initial (kg/ ha), GSO±growth rate of shielded reserves (kg/ha/ day)For the CERES-Maize model, the following equation was used: YIELD ˆ GRNWT at physiological maturity

(4)

where: GRNWT ˆ GRNWT ‡ GROGRN, GRNWT ˆ grain weight, GROGRN ˆ growth of grain per day. Grain yield with Nitrogen sub-routine: NSDR ˆ

NPOOL NSINK

(5)

where: NPOOL±total N available for grain growth, NSINK±total N demand for growing grain, GROGRN is related to N stress. GROGRN ˆ GROGRN…without N stress†  NSDR (6) Simulated values of grain yield and max. LAI from the output ®le have been compared with the observed

values after inserting all input data sets into the models. The soil water balance submodels of both the models used in this study are described ahead. For MACROS the simple soil water balance module was used for free draining soils L2SU (only unsaturated ¯ow considered). When a soil layer is ®lled beyond ®eld capacity, water percolates into the next lower layer and it is assumed that all drainage occurs within 1 day. If more water enters the deepest layer than can be retained, the excess is lost as deep percolation. Some upward ¯ow is simulated as a contribution of each layer to soil evaporation. The initial soil water content of a soil layer is calculated by: WL1I ˆ WCLI1  TKL1  1:E4

(7)

where: WL1I±volumetric soil water content per compartment (initial, m3/ha), WCLI1±soil water content per layer (initial, m3/m3), TKL1±thickness soil compartment (m), 1.E4±10 000, conversion factor from m2 to ha. The soil water content for each layer is calculated then as follows: WCL1 ˆ

WL1 …TKL1  1:E4†

(8)

WL1 ˆ INTGRL…WL1I; …WLFL1 ÿ WLFL2 ÿEVSW1 ÿ TRWL1†  10:0†

(9)

where: WCL1±relative soil water content per layer (m3/m3), WL1±volumetric soil water content per compartment (m3/ha), WLFL±¯uxes of water into layers (mm/day), EVSW1±evaporation rate from the soil for individual soil compartment (mm/day), TRWL1±transpiration rate canopy, actual value with water stress from individual compartments (mm/day). Computation of the soil water balance in the CERES±Maize model is carried out in a different way. For soil water redistribution during in®ltration, water is moved downward from the top soil layer to lower layers in a cascading approach. Drainage from a layer takes place only when the soil water content (SW(L)) is between ®eld saturation (SAT(L)) and the drained upper limit (DUL(L)). For drainage calculations from each layer the in®ltration PINF is converted from mm to cm and described as a downward ¯ux for each layer calculated

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(FLUX(L)). This water ¯ow rate is also needed for calculating nitrate leaching. When FLUX(L) is not equal to zero, the amount of water that the layer can hold (HOLD) between the current volumetric water content (SW(L)) and saturation (SAT(L)) is calculated. HOLD ˆ …SAT…L† ÿ SW…L†  DLAYR…L† (10) DLAYR(L)±the depth increment of each layer. If FLUX(L) is less than or equal to HOLD, an updated value of SW(L) is calculated prior to drainage. SW…L† ˆ SW…L† ‡ FLUX…L†=DLAYR…L†

(11)

If this new SW(L) is less than the drained upper limit of volumetric soil water in the layer (DUL(L)), no drainage occurs. If this SW(L) is greater than DUL(L), drainage (DRAIN) from the layer is calculated from SW(L), DUL(L), DLAYR(L), and SWCON, the whole pro®le drainage rate constant, DRAIN ˆ …SW…L† ÿ DUL…L††  SWCON DLAYR…L†

(12)

2.3.5. Sensitivity analysis By increasing and or decreasing input values it is possible to detect the impact of these changes on model outputs, especially on yield. For the present study, changes in soil parameters relevant to soil water balance were used, viz. wilting point (WP), saturated soil water content (FS) and ®eld capacity (FC). These parameters were changed from ÿ6 to ‡6% of their values (in steps of ‡2, ‡4, ‡6%, and ÿ2, ÿ4, ÿ6%, respectively), which were measured for each soil level. The simulation was carried out for each of the changed values. For the six different changes of the initial values of the soil parameters for eight soil pro®le layers six curves were obtained after carrying out the simulation in both the years. 3. Results and discussion 3.1. Parametrization and validation of the crop models The ®rst and the most important condition of judging the success of models is a comparison between the sequence of simulated outputs and real observed

311

Table 2 Validation of crop models by comparison of chosen parameters in 1995 (maize, variety DEA)

Yield (t/ha) Maximum LAI (m2/m2) Growth period (day)

Observed

MACROS

CERES

3.6 2.7 129

3.9 2.1 126

4.2 2.4 152

results using the measured inputs. Tables 2 and 3 and Figs. 3±5 represent the successful validation results of the CERES-Maize and MACROS model in 1995 and 1996. From the model outputs the grain yield and the maximum LAI were selected to compare them with observed data. A good agreement was obtained between the measured data simulated outputs for both the years despite their different meteorological conditions (Figs. 1 and 2). The simulated yield by the MACROS model was 7.7% higher then the observed yield in 1995 (10.4% in 1996), and 16.6% higher for CERES-Maize model in 1995 (2% in 1996). It means that the simulated yields were slightly overestimated (Fig. 3). These effects could be attributed to the occurrence of non-simulated impacts like pests, diseases, harvest losses or a weak representative role of some of the input data, e.g. the harvest losses were counted by MaleÂÏr (1989) and for the used harvest machine with a speed of 1.2±1.6 m/s they represent 4% of the grain yield. Estimation of the potential yield is also shown in Fig. 3 for 1995 and 1996. According to SÏt'astna (1998) the potential yields simulated by CERESMaize vary between 9.1 and 15.3 t/ha from 1980 to 1997. The MACROS model showed mostly lower potential yields than the CERES-Maize model in this period (ZÏalud, 1995; ZÏalud and RozÏnovskyÂ, 1998). Differences between measured and computed data on the leaf area index (Figs. 4 and 5) are larger in the CERES-Maize model than in the MACROS model, Table 3 Validation of crop models by comparison of chosen parameters in 1996 (maize, variety DEA)

Yield (t/ha) Maximum LAI (m2/m2) Growth period (day)

Observed

MACROS

CERES

9.6 4.0 130

10.6 3.9 130

9.8 3.9 150

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Fig. 3. Validation of CERES-Maize and MACROS models by comparison between potential, water-limited and observed yields in growing seasons of 1995 and 1996.

Fig. 4. Validation of CERES-Maize and MACROS models by simulated and observed LAI in 1995.

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Fig. 5. Validation of CERES-Maize and MACROS models by simulated and observed LAI in 1996.

when the full length of the vegetation period is considered. However, for the maximum LAI value, the relationship is opposite. The MACROS model underestimated the LAI data in both seasons, while the CERES-Maize model produced a maximum LAI closer to reality. 3.2. Impacts of changed soil hydrologic parameters on yield and LAI Due to the water shortage in 1995, greater differences in yields (Figs. 6 and 7) and max. LAI (Figs. 8 and 9) were found in 1995 with the relevant simulated variations of soil hydrologic characteristics than in 1996. The greatest in¯uence on yield was found due to the wilting point, as expected. The yield values were not changed signi®cantly by altering the FC and FS values. Statistical analysis indicated a quadratic FUNCTION as relationship between yield and WP, FC and FS parameters for both models in 1995 and

1996. The CERES-Maize and the MACROS model showed the same decreasing trend of yield for the sensitivity analysis results of FC in 1995 (Tables 4 and 5). In 1996 the CERES-Maize model showed a reasonable increase in yield while the yield simulated by the MACROS was found to decrease. In 1995 the CERES-Maize model predicted a considerable decrease in yield with an increase in FS values, while the MACROS model predicted a gradual increase. For 1996 both the models predicted increased yields due to increased FS values (CERES-Maize more then MACROS). There is a marked difference between the sensitivity analysis of WP for the CERES-Maize and the MACROS model. The MACROS model is more sensitive to the changes. It is clear from the fact that while WP was increased by 6%, the simulated maize growth was found to complete at about half the normal growing period, while 6% decrease in WP resulted in 20% increase in yield in the year 1995. On the other hand, CERES-Maize model simulated the yield as

314

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Fig. 6. Influence on simulated yield (water-limited) by the selected hydrological parameters (CERES-Maize).

Fig. 7. Influence on simulated yield (water-limited) by the selected hydrological parameters (Macros model).

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Fig. 8. Influence on simulated LAI (water limited) by the selected hydrological parameters (CERES-Maize).

Fig. 9. Influence on simulated LAI (water limited) by the selected hydrological parameters (MACROS model).

315

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Table 4 Statistical analysis of soil hydrologic parameters for the CERES-Maize model LAI 95 (y ˆ ax ‡ b)

WP

Standard error R2 b a

0.0294 0.9215

LAI 96 Standard error R2 b a Yield 95 (yˆax2 ‡ bx ‡ c) Standard error R2 c b a Yield 96 Standard error R2 c b a

0.0033 0.9846

0.0550 0.9940

0.0957 0.9807

Coefficient

2.3186 ÿ0.0213

3.1086 0.0055

3.9695 ÿ0.1325 ÿ0.0073

9.8147 ÿ0.0128 ÿ0.0002

FC 0.0053 0.0250

0.0033 0.625

0.0345 0.9706

0.0208 0.9844

Coefficient

2.3571 ÿ0.0002

3.1071 ÿ0.0009

4.0195 ÿ0.0375 0.0009

9.7948 0.0311 ÿ0.0012

FS 0.0038 0.9888

0.0082 0.7031

0.0900 0.9972

0.0329 0.9866

Coefficient

2.3557 0.0075

3.1029 0.0027

4.0238 ÿ0.0321 ÿ0.0013

9.7885 0.0528 ÿ0.0023

Table 5 Statistical analysis of soil hydrologic parameters for the MACROS model LAI 95 (y ˆ ax ‡ b)

WP

Standard error R2 b a

0.1363 0.9756

LAI 96 Standard error R2 b a Yield 95 (y ˆ ax2‡bx‡c) Standard error R2 c b a Yield 96 Standard error R2 c b a

0.1334 0.9777

0.0310 0.9784

0.0765 0.9956

Coefficient

1.9143 ÿ0.1821

3.7943 ÿ0.1864

3.97 ÿ0.3698 ÿ0.0405

10.7595 ÿ0.2101 ÿ0.0184

FC 0.0314 0.9794

0.0059 0.9989

0.0497 0.9734

0.0224 0.9998

Coefficient

2.0443 ÿ0.0457

3.9414 ÿ0.0371

3.8692 ÿ0.0556 ÿ0.0035

10.7616 ÿ0.0338 ÿ0.0003

FS 0.0005 0.9986

0.0034 0.9412

0.0126 0.9988

0.0309 0.9994

Coefficient

2.0617 0.003

3.9357 0.0029

3.9278 0.0068 ÿ0.0001

10.76 0.0025 0

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35% lower with 6% decrease in WP and 12% more with 6% increase in WP.. For 1996 too the CERESMaize model predicted a lower yield with increasing WP. Same was the case with MACROS model. Due to better climatic conditions (less draught stress) the differences in yields were not evident. The MACROS model showed an increase of 5.7% yield due to decrease of 6% of WP and 22.5% decrease of yield due to increase of 6% of WP. The CERES-Maize model was found to be non sensitive to the change in WP (6% increase caused 0.9% of decreased yield while, 6% decrease caused 0.7% of increased yield) in the year 1996. There are only a few related studies that have been published so far in this area of research. Liu et al. (1989) used the CERES-Maize model to simulate the in¯uence of soil hydrologic parameters on maize yield. A 25% increase in plant-extractable soil water achieved by changing the lower limit from 0.275 to 0.25 resulted in <0.5% increase in grain yield. On the contrary, a decrease of 25%, representing surprisingly a change in the lower limit from 0.275 to 0.30, resulted in 2% increase in yield. The results of simple linear regression and the linearity tests proved the linear dependence of LAI values on FC, WP and FS in 1995 and 1996 for both models. There was no change in LAI values when the FC and FS values were either increased or decreased. The only change due to alteration of WP is in decreased LAI for MACROS model in 1995 and 1996 (Figs. 8 and 9). Changes in the same kind of tested soil water parameters have not been found to reach such a sensitivity for LAI in 1996 (Fig. 8) by the CERES-Maize model. 4. Conclusions The aim of the work was to validate two crop simulation models and make the comparison by their sensitivity to the changes of the selected hydrological input parameters. Both the models, CERES-Maize and MACROS were found to work well in predicting yields and LAI in two seasons with very different meteorological conditions. They were, therefore, well suited to the study on their sensitivity to variation in soil hydrologic characteristics within the de®ned soil.

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The MACROS model was more sensitive to changes of the soil hydrologic parameters, especially to the wilting point, than the CERES-Maize model. This could be attributed to some differences in soil water balance calculations. Acknowledgements The authors are grateful to the Grant Agency of the Czech Republic, which supported this study vide project No. 503/95/1286 and also to M. KutõÂlek for his hints. References Easterling, W.A., Mc.Kenney, M.S., Rosenberg, N.J., Lemon, K.M., 1992. Simulations of crop response to climate change: Effects with present technology and no adjustments (the `dumb farmer' scenario). Agric. For. Meteorol. 59, 53±77. Goudriaan, J., Hunt, L.A., 1995. Reliability of models for prediction of climatic changes and of their possible effects on the yield formation of agricultural plant species. Proceedings, 39. Jahrestagung der Gesellschaft fuÈr Pflanzenbauwissenschaften, ZuÈrich, Wissenschaft. Fachverlag Giessen, pp. 19±26. IBSNAT, 1988. Technical Report 1, Experimental Design and Data Collection Procedures for IBSNAT, 3rd ed. Department of Agronomy and Soil Science, College of Tropical Agriculture and Human Resources, University of Hawaii, Honolulu, Hawaii, p. 348. IBSNAT, 1990a. Technical Report 5, Documentation for IBSNAT crop model input and output files: For the decision support system for agrometeorology transfer (DSSAT v2.1), version 1.1. Department of Agronomy and Soil Science, College of Tropical Agriculture and Human Resources, University of Hawaii, Honolulu, Hawaii. IBSNAT, 1990b. Technical Report 2, Field and laboratory methods for the collection of the IBSNAT minimum data set, 1st ed. Department of Agronomy and Soil Science, College of Tropical Agriculture and Human Resources, University of Hawaii, Honolulu, Hawaii. Jones, C.A., Kiniry, N., 1986. CERES-Maize: A Simulation Model of Maize Growth and Development, Texas A&M University Press, College Station, TX. Jones, J.W., 1989. Integrating models with expert systems and data based for decision making. In: Weiss, A. (Ed.) Proceedings of Climate and Agriculture: Systems Approaches to Decision Making, 5±7 March, Charleston, S. C. Dept. of Agricultural Meteorology, University of Nebraska, Lincoln, pp. 194±211. KutõÂlek, M., Nielsen, D.R., 1994. Soil Hydrology. Catena Verlag. 370. Liu, W.T.H., Botner, D.M., Sakamoto, C.M., 1989. Application of CERES-Maize model to yield prediction of a Brazilian maize hybrid. Agric. For. Meteorol. 45, 299±312.

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