Assessment of a crop growth-water balance model
for predicting maize growth and yield in
a subtropical environment
V.K. Arora
*, P.R. Gajri
Department of Soils, Punjab Agricultural University, Ludhiana 141004, India
Accepted 10 January 2000
Abstract
A combination of a simple and universal crop growth simulator (SUCROS) of van Keulen et al. [van Keulen, H., Penning de Vries, F.W.T., Drees, E.M., 1982. A summary model for crop growth. In: Penning de Vries, F.W.T., Laar van, H.H. (Eds.), Simulation of Plant Growth and Crop Production. Simulation Monographs, Pudoc, Wageningen] with a water balance model (WBM) of Arora et al. [Arora, V.K., Prihar, S.S., Gajri, P.R., 1987. Synthesis of a simpli®ed water use simulation model for predicting wheat yields. Water Resources Res. 23, 903±910] along with some modi®cations was assessed for predicting maize growth and yield in variable climatic and water supply regimes. Model assessment showed that simulated biomass and grain yield of maize were close to the measured data in medium water-retentive sandy loam; while in low retentive loamy sand, biomass was over-predicted for most of water supply regimes. Poor performance of the model in the loamy sand appears due to the reason that the maize grown on such soils during hot and monsoonal rainy season generally suffers more from soil-related constraints other than water stress. The analysis indicates the adequacy of the combination model in medium water-retentive soils.
#2000 Elsevier Science B.V. All rights reserved.
Keywords:Model; Simulation; Water loss; Biomass; Maize
1. Introduction
Crop simulation techniques are increasingly used to support field research focussed toward efficient and sustainable water use in cropping. It involves developing (or adapting) and assessing crop growth and water balance models for analysing the
*Corresponding author.
effects of variation in climatic and water supply regimes on crop yields. Process-based crop growth models may use the concept of radiation use efficiency (RUE) and intercepted solar radiation for computing biomass accumulation (Monteith, 1977), and this approach has been followed in CERES crop models. An alternative approach considers detailed processes of gross photosynthesis and respiration (maintenance and growth) to estimate biomass accumulation. These physiologically-based comprehensive models have been simplified to summary models (e.g. van Keulen et al., 1982) for predicting crop growth in climate-limited environments. Such models can be interfaced with a soil water balance model to account for the effects of variation in water supply on crop growth and development (Dierckx et al., 1988; Xevi et al., 1996).
Soil water dynamics at the field scale have been described through simple empirical and complex process-based models. As empirical models cannot be extrapolated beyond experimental data sets, and input data for complex process-based models are not readily available, attempts have been made to develop and use simplified process-based models (Hanks, 1974; Arora et al., 1987; Saxton, 1989). This paper is an assessment of the performance of a combination of a summary crop growth model (Simple and Universal CROp growth Simulator Ð SUCROS, van Keulen et al., 1982) with a water balance model (WBM of Arora et al., 1987) along with some modifications for predicting maize growth and yield in variable climatic and water supply regimes in a subtropical environment of north-west India.
2. Model details
The SUCROS±WBM combination simulates dry mass accumulation (from emergence) as a function of maximum and minimum air temperature, solar radiation, and soil water
status. Crop aspects of the model include gross CO2 assimilation, maintenance
respiration, assimilate partitioning, dry matter production, green area growth and senescence. Water balance aspects considered are soil evaporation (E), canopy evaporation (I), crop transpiration (T), and drainage (D) at a field scale.
2.1. Crop growth submodel
2.1.1. Canopy gross CO2assimilation
In the SUCROS, the rate of CO2assimilation of the canopy is obtained from the CO2
assimilation±light response curve of individual leaves, total green area of the canopy, spatial arrangement of leaves, and incident irradiation. Daily values are obtained by using
process-based descriptive equations given by Goudriaan and van Laar (1978). Am is
influenced by day-time air temperature, decreasing on either side of the optimal temperature range (Penning de Vries et al., 1989).
2.1.2. Respiration and growth
Maintenance requirements for leaf, stem, root, and storage organs are set to 0.03,
0.015, 0.01, and 0.01 kg CH2O kgÿ1
dry mass per day at 258C; the effect of other
temperatures is taken into account with a Q10 value of 2. In addition, 10% of gross
assimilates is assumed to be consumed in metabolic activity (Penning de Vries et al., 1989), and the remainder is transformed into structural dry mass. The average conver-sion factors (CF) of 0.72, 0.69, 0.72 and 0.73 kg dry mass kgÿ1
CH2O for leaf, stem, root, and storage organs, respectively are weighted with the fractional increment of each organ.
2.1.3. Phenological development
Phenological development is characterized by development stage (DS), a variable with a value of 0 at emergence, 1 at flowering, and 2 at physiological maturity. Intermediate values follow from integrating development rate (DR), which depends on average air
temperature (Ta) and photo-periodic day length (DLP) during the vegetative, and on Ta
alone during the reproductive phase. The development rate constants in vegetative (DRVC) and reproductive (DRRC) phase were estimated using field data. The
coefficients for the effect of Ta and DLP on these constants as given by Warrington
and Kanamasu (1983) were employed.
2.1.4. Partitioning of dry matter
Total dry mass increase is partitioned between root and shoot, and subsequently, aboveground is divided among leaves, stem, and storage organs. Moreover, substantial reserves from vegetative components (15% of stem mass at silking) are also available for cob growth in maize (Simmons and Jones, 1985).
2.1.5. Green area growth
In the SUCROS, the increase in green area of the canopy follows from the growth rate of leaves and stems by considering a constant value of specific leaf (SLA) and stem area (SSA) during the growing season. Leaf senescence is included as linearly increasing DS-dependent relative senescence rate (RSR). In very dense canopies, senescence accelerates due to shading effects. Threshold LAI values and senescence rates due to shading for spring wheat (van Keulen and Seligman, 1987) were assumed to hold good for maize. High air temperatures and water stress increase RSR. The green area of maize stems starts senescing at physiological maturity.
2.2. Water balance submodel
WBM (Arora et al., 1987) is an integration of the model of Hanks (1974) with functional relations to account for effects of rooting depth and distribution onT, and soil water redistribution below field capacity. This submodel was expanded to consider canopy evaporation (I) and transfer of unused soil evaporation energy to the plant canopy using the algorithm of Saxton (1989).
2.2.1. Canopy interception evaporation
2.2.2. Soil evaporation
After accounting forI, the remainder ofETpis partitioned between soil (Em) and plant
(Tm) surfaces depending on the radiation interception factor (FI) based on green area of
leaves and stems. ActualEis limited byEmand decreases as surface soil dries:
EEm
t0:3
The time exponent depends on soil water retentivity and evaporative demand (Jalota and
Prihar, 1990). This water is extracted from various soil layers and remaining Em is
transferred to potential canopy transpiration.
2.2.3. Plant transpiration
Potential T (Tm) is withdrawn from the top soil layer only, regardless of depth and
density of rooting under conditions of abundant water supply (fractional available water,
FAW, exceeding 0.80). Below this threshold, Tm is partitioned in proportion to the
fraction of the root system in each layer using empirical functions fitted to depth and density of rooting (Arora and Gajri, 1996). Extraction of water occurs at the potential rate until FAW reaches 0.25 (Muchow and Sinclair, 1991), below which a linear reduction in extraction is assumed.
2.2.4. Water stress effects on crop growth
Water stress influences crop growth and development through (i) reduction in gross assimilation in direct proportion to the degree of water stress; (ii) assimilate partitioning, i.e. a reduction in shoot fraction (due to reduced expansion growth) causing increased allocation to roots when transpiration deficit exceeds 50% (Penning de Vries et al., 1989); (iii) slowing down of DR in the vegetative phase leading to delayed tasseling and silking (Muchow and Sinclair, 1991); and (iv) acceleration of leaf senescence (Saxton, 1989).
2.3. Data requirements
Weather and location data needed for the model comprises latitude, day number, solar radiation, maximum and minimum air temperature, and Class A pan evaporation (Ep).
Crop-specific physiological information includesAmand its dependence on day-time air
temperature, and EFFE (efficiency of incoming PAR). Cultivar-specific information comprise development rate constants in vegetative (DRVC) and reproductive (DRRC)
phases, factors for the effect of Ta and DLP on DRVC and DRRC, coefficients of
empirical functions fitted to depth to rooting front and density distribution of roots, leaf and root mass at emergence, specific leaf and stem area, partitioning coefficients and relative senescence rate (RSR) of leaves and stems in relation to DS. Soil information defining extractable water and drainage coefficients is needed.
3. Testing data
In order to assess the performance of SUCROS±WBM under variable water supply regimes, data base were obtained from a field study on maize (from 1991 through 1995)
at Punjab Agricultural University Research Farm at Ludhiana (308560
N, 758520
E, 247 m asl). The soils are deep alluvial loamy sand (Typic Ustipsamment) (1991 and 1992 only) and sandy loam (Typic Ustochrept). Information on soil-water retention at the drained upper and lower limits and drainage coefficients for various soil depths is given in Table 1. The field plots were conventionally tilled (one discing, two runs of a cultivator and levelling) after a pre-seeding irrigation. Maize (cv. Partap) was dibbled (seed placed at a
depth of 5 cm and covered with the soil) in 60 cm22.5 cm spacing after drilling 40 kg
N, 60 kg P2O5, 30 kg K2O, and 25 kg ZnSO4haÿ1
in the last week of June. Two splits of 40 kg N haÿ1
each were applied at 20 and 40 days after seeding (DAS). Irrigation amounts were timed in such a way to impose variable regimes of no (I0) or partial (Ip) irrigation, and full irrigation (If). Other agronomic management followed local recommendations and the crop was harvested in the last week of September. Potential
ET(ETp) was assumed equal toEp. Rain was measured at the experimental site, while
data onEpwas obtained from a meteorological station 2 km south-east of the site.
Crop-and cultivar-specific information is given in Table 2.
4. Results and discussion
4.1. Potential production environment
Potential dry mass accumulation (in the assumed absence of water and nutrient stress, and disease and pest damage) in maize was modelled for 1992 growing season. Simulation results showed that harvest-time biomass accumulation in roots, leaves, stems, and storage organs (cobs) was 0.7, 1.7, 3.3 and 6.6 t haÿ1
. Using grain/cob ratio of 0.75, potential grain yield was estimated to be 6.0 t haÿ1
(15% moisture) which is close to best yields obtained in well managed experiments.
Table 1
Extractable water, and drainage coef®cients for various layers of two experimental soils Soil depth,
30±60 20.0 4.0 0±60 11.92 ÿ0.0755
60±90 20.0 4.0 0±90 17.90 ÿ0.0750
90±120 18.0 3.0 0±120 23.17 ÿ0.0773
120±150 16.0 3.0 0±150 27.92 ÿ0.0803
150±180 14.5 3.0 0±180 32.32 ÿ0.0815
Sandy loam
0±30 25.0 5.0 0±30 7.50 ÿ0.0520
30±60 27.0 7.0 0±60 15.60 ÿ0.0510
60±90 29.0 9.0 0±90 24.30 ÿ0.0480
90±120 29.0 9.0 0±120 33.00 ÿ0.0475
120±150 29.0 9.0 0±150 41.70 ÿ0.0470
150±180 29.0 10.5 0±180 50.40 ÿ0.0465
aRegression coef®cients of the empirical equationW
Table 2
Crop- and cultivar-speci®c information for maize required in the SUCROS±WBM combination model Maximum CO2assimilation rate of a single leaf90 kg ha
ÿ1hÿ1
Shoot (SH) as a whole crop fraction 0.7 1.0 1.0
DS 0 0.3 0.8 1.0 1.1 2.5
Leaves as SH fraction 1.0 0.8 0.1 0.0 0.0 0.0
Stem as SH fraction 0.0 0.2 0.9 0.9 0.0 0.0
DS 0 0.25 0.75 1.6 1.9 2.5
RSR (%) (leaves) 0.0 0.0 0.20 1.0 10.0 10.0
Air temperature 10.0 30.0 35.0 40.0
RSR multiplier 1.0 1.0 2.0 3.0
Water stress 0 0.2 0.5 1.0
Accelerated senescence (%) 0.0 0.0 2.0 20.0
DS 2.00
RSR (stem) 0.20
Table 3
Comparison of simulated and measured seasonal water loss (cm) with the combination model in different treatments under maize on the two soils in different years
Year Soil
4.2. Variable-water environments
In variable-water environments, simulation results of seasonal water loss, and time-trends of above-ground biomass, and harvest-time biomass and grain yield were
compared to measured values. Simulated water loss (sum ofI,E,T, andD) was close to
Fig. 1. Time trends of measured (points) and simulated (lines) above ground biomass in (a)Ipregime on loamy
measured values (sum of profile water depletion from seeding till harvest, rain and irrigation) (Table 3). Root mean square of deviations (RMSD) between the two was 3.7 cm for measured loss varying between 42.3 to 106.2 cm. It is also shown that drainage was a substantial component of total water loss.
Fig. 2. Comparison of harvest-time measured and simulated (a) above-ground biomass, and (b) grain yield of maize with the combination model.
Time-trends of biomass accumulation in two widely different treatments viz., Ip on
loamy sand in 1991 andIpon sandy loam in 1992 (Fig. 1) shows that simulated values
were close to measured data throughout the growing season in medium water-retentive sandy loam; while in low retentive loamy sand biomass was over-predicted. Comparison of harvest-time measured and simulated biomass and grain yield for all the treatments on the two soils during different years (Fig. 2) shows that there was a reasonably good
matching in biomass accumulation with a RMSD of 1.6 t haÿ1
for measured values varying between 5.8 to 13.5 t haÿ1
. Simulated grain yield had a greater variance with a
RMSD of 1.2 t haÿ1
for measured yield between 1.6 to 5.5 t haÿ1
.
An analysis of model results indicate that simulation of biomass and grain yield of maize was quite reasonable on medium water-retentive sandy loam; but had a greater variance on the loamy sand soil. Poor performance of the model in the loamy sand appears due to the reason that the maize grown on such soils during hot and monsoonal rainy season generally suffers more from soil-related constraints, other than water stress, which are not accounted in the model. Owing to the high permeability and rapid development of soil strength in coarse textured soils, the maize crop is more prone to stress(es) associated with leaching of nutrients and restricted rooting. This is evidenced by a number of reports on benefits of deep tillage to maize (Chaudhary et al., 1985; Arora et al., 1991; Gajri et al., 1994). Thus, the adequacy of the combination model is restricted to medium water-retentive soils.
5. Conclusions
Extensive assessment of the SUCROS±WBM combination model showed that simulated biomass and grain yield of maize were close to the measured data in medium water-retentive sandy loam; while in low retentive loamy sand, biomass was over-predicted for most of water supply regimes. Poor performance of the model in the loamy sand could be ascribed to the reason that the maize grown on such soils during hot and monsoonal rainy season generally suffers more from soil-related constraints other than water stress.
Acknowledgements
This research was funded by a grant from the United States Department of Agriculture (USDA) under the Cooperative Agricultural Research Programme, US-India Fund.
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