Analyzing wheat productivity responses to climatic,irrigation and fertilizer-nitrogen regimes in a semi-arid sub-tropical environment using the CERES-Wheat model
V.K.Arora *,Harbakhshinder Singh,Bijay Singh
Department of Soils,Punjab Agricultural University,Ludhiana 141004,Punjab,India
1.Introduction
In many irrigated and drylands of the world,crop productivity is increasingly constrained by water limitations.In the intensively-cropped state of Punjab in Indo-Gangetic plains of north-west India,irrigated rice–wheat cropping is practiced since early 1970s.It has led to scarcity of ground water due to fast depletion of aquifers.In the good water quality region of central Punjab,the areas with a water table depth below 10m increased from 3%in 1973to 76%in 2002(Hira et al.,2004).That threatens the sustainability of intensive cropping and calls for enhancing water productivity.It implies that,among other
things,there is a need to re?ne irrigation scheduling to crops by forcing a shift from plentiful-to limited-water.In addition,there is a synergy between fertilizer-nitrogen (N)and water for their effects on crop yields (Prihar et al.,2000)more so in arid and semi-arid regions that generally experience N de?ciency.Over-fertilization with N may lead to loss in crop yields under water limitations (Luebs and Laag,1969).This necessitates optimizing amount of fertilizer N in relation to water availability.
A large volume of research is available on interaction effects of irrigation and fertilizer N on wheat (Prihar et al.,1981;Eck,1988)and empirical analysis for computing
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a r t i c l e i n f o
Article history:
Received 8January 2007Accepted 15July 2007
Published on line 8November 2007Keywords:
Water limitations Simulation modeling Evapotranspiration Water productivity
a b s t r a c t
In order to enhance crop productivity in water-limited environments,there is a need to evaluate and apply water-saving management practices.This study examined the applic-ability of the CERES-Wheat model under variable climatic,irrigation,and fertilizer-nitrogen (N)regimes.The objective was to analyze wheat yield responses to water-and N-application for optimizing crop productivity under water limitations in a semi-arid sub-tropical irrigated environment.Evaluation analysis showed that performance of the model was reasonable as indicated by close correspondence of simulated crop phenology,biomass accumulation,grain yield,and soil water and N use with measured data.The normalized root mean square of deviations ranged between 10and 20%for most of the parameters.Cumulative probability distribution of simulated grain yield and ET showed that for a given irrigation regime,fertilizer N had greater effect on yield than on ET and caused greater water productivity.Scenario analysis also demonstrated that grain yield and water productivity response to irrigation were in?uenced by extractable water capacity of soils.Soil effects on grain yield were more pronounced under I 0regime,and the effect decreased with increase in irrigation.Post-sown irrigation was more effective under conditions of low initial soil water.Initial soil mineral-N status in?uenced the amount of fertilizer N for a given initial soil water and post-sown irrigation scenario.
#2007Elsevier B.V.All rights reserved.
*Corresponding author .Tel.:+911612553502;fax:+911612400945.E-mail address:vkaro58@https://www.doczj.com/doc/d84966244.html, (V.K.Arora).
a v a i l a bl e a t w w w.s c i e nc e d i re c t.c o m
j o u rn a l ho m e pa g e :w w w.e l s e v i er.c om /l o ca t e /a gw a t
combinations of water and N for realizing different yield targets(Gajri et al.,1993).It is,however,physically not possible to study the innumerable soil-and climate-management combinations through costly and time-consuming?eld research.Simulation techniques provide a framework for supporting research focused toward this end.A number of crop-speci?c model packages(CERES,CropSys,InfoCrop),that simulate the dynamics of soil water,N and crop growth and development,are available in literature.The CERES-Wheat model has been widely evaluated in different regions of the world(Timsina and Humphreys,2006)and also employed to optimize irrigation(Yang et al.,2006)and fertilizer N manage-ment(Saseendran et al.,2004;Rinaldi,2004).A few reports on the evaluation of this model under climatic potential-(Pathak et al.,2003)and water limited-environments(Panda et al., 2003)are also available from India.This paper provides an evaluation and application of the CERES-Wheat model under variable climatic,irrigation,and fertilizer N regimes.The objective was to simulate wheat grain yield responses to water-and N-applications for optimizing crop productivity under water limitations in a semi-arid sub-tropical irrigated environment of north-west India.
2.Methods and materials
2.1.The CERES-Wheat Model
The CERES-Wheat,a part of DSSAT-Cropping System Model v 4.0(Hoogenboom et al.,2004),was used in this analysis.The model has been documented extensively since its initial development and evaluation(Ritchie and Otter-Nacke,1985). It simulates the effects of weather,genotype,soil properties, and management on crop growth and development,water and N dynamics.
The crop growth model considers phasic development with nine growth stages,from pre-sowing to harvest,in relation to thermal time.The model calculates biomass accumulation as the product of radiation use ef?ciency and photo-synthetically active intercepted radiation.The number of growing leaves is a function of leaf appearance rate(phyllochron interval, degree-days)and duration of grain?lling(P5).Organ extension depends on potential organ growth,and is limited by sub-optimal temperature and water and N stresses.Partitioning coef?cients of dry biomass in plant parts are in?uenced by phasic development.Grain yield is modeled as the product of grain number(G1),plant population,and grain mass at physiological maturity(G2).
Daily soil water balance is modeled in relation to rainfall/ irrigation,run-off,in?ltration,transpiration,soil evaporation, and drainage from the soil pro?le.The model utilizes the lower and upper limit of plant extractable water to apportion in?ltrated water among different soil layers by a simple cascading approach.Runoff is estimated on the basis of antecedent soil water content,and drainage is controlled by the slowest draining layer of the soil pro?le.Run-off from rainfall is computed using soil conservation service(SCS)–curve number(CN)method,and the excess water in?ltrates saturated water content(SAT).Water?ow among soil layers is based on the assumption that if a layer has water content greater than DUL,saturated downward?ow occurs in proportion to amount of water greater than DUL level.If a layer has water content between LL and DUL,unsaturated upward?ow between two adjacent layers occurs that is computed using soil water diffusivity and water content gradients.In the lowest soil layer,drainage of excess water occurs,and is not available for later extraction.Potential evapotranspiration(ET m)is partitioned between soil and plant surfaces using a leaf area index-based cover factor.Actual soil evaporation(E)is estimated by the two-stage model(Ritchie, 1972).Root distribution and extractable water in a soil layer modi?es potential transpiration for actual water uptake or transpiration(T).Soil water de?cit in?uences the allocation of biomass and growth and death of plant parts.
The N component of the model includes mineralization and immobilization associated with decomposition of organic matter,transformation processes of nitri?cation,de-nitri?ca-tion,and urea hydrolysis,movement through leaching of nitrates,and uptake of N.This model uses the layer-wise soil water balance briefed above.Nitrates and urea movement in the soil pro?le are dependent on water movement.The N uptake is controlled by crop demand for N and soil supply of N and the lesser of the two are used to compute the actual rate. Effects of water and N de?cits on crop growth and develop-ment are taken into account by computing water and N stress factors,and the lesser of the two controlling a given process.
2.2.Experimental datasets
In order to evaluate the performance of the CERES-Wheat model,measured data were obtained from a published study on wheat under variable irrigation regimes(Arora et al.,2006) and an unpublished dataset on wheat response to planting date,irrigation,and fertilizer-N regimes(Singh,2006).Field experiments were conducted at the Punjab Agricultural University Research Farm,Ludhiana,India(308540N,758480E, 247m above mean sea level)for?ve cropping seasons from 2000–2001through2004–2005on an alluvial sandy loam (USDA:Typic Ustochrept)soil developed under hyper-thermic regime.The soil contained740g kgà1sand(2000–20m m)and 110g kgà1clay(less than2m m)in top0.30m layer.The soil was non-saline with EC of0.2dS mà1and had organic carbon content of0.3g100gà1.The depth to ground water at the experimental site was more than10m.Agronomic details of the treatments are given in Table1.Wheat(cv.PBW-343)was planted in0.20m rows using a seed rate of100kg haà1.The net plot size was10–20m2in different cropping seasons.A buffer of0.5m was provided to minimize border effects.Seventy millimeters irrigations were applied with non-saline ground and surface waters.The local agronomic recommendations were followed for weed,disease and pest control.The crop was harvested in the?rst fortnight of April.
The genotypic coef?cients required for the model were derived using iterations till a close match between simulated and measured phenology and biomass and grain yield was obtained under stress-free environments.The?nal values of
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tion),65for P1D (percent reduction in development rate for photoperiod 1h shorter than optimum),780for P5(grain-?lling duration,degree days),80for PHINT (phyllochron interval,degree-days),20for G1(kernel number,g à1),43for G2(kernel mass,mg),and 1.5for G3(non-stressed mass of a single tiller including spike when elongation ceases,g)for spring wheat grown in Punjab.
Soil physical and chemical information (layer-wise DUL,LL and initial soil water and mineral-N content)required for the model is given in Table 2.Soil water was monitored gravimetrically at sowing and at harvest in 0.30m intervals down to 1.5m.Initial soil mineral-N (NO 3àand NH 4+)in different layers was estimated using steam distillation method (Keeney and Nelson,1982).Crop phenology,as durations to anthesis and physiological maturity,were monitored in the ?eld through visual inspections.Biomass accumulation was determined in 0.5m row lengths,and leaf mass was translated into leaf area using speci?c leaf area values.Harvest-time biomass and grain yield was monitored using a net area of 5–10m 2in different cropping seasons.Grain and straw N content was estimated using the steam distilla-tion method after digesting plant material.The ET was determined by water balance technique utilizing soil water content measurements.As most of the treatments involved de?cit-irrigation regimes,drainage loss below the potential
root zone of 1.5m could safely be ignored.The water productivity was estimated as the ratio of grain yield to ET.Weather data on maximum and minimum air temperature,solar radiation,and rainfall from 1991–92through 2004–05was obtained from the meteorological station located 2km south-east of the experimental site.
3.
Results and discussion
3.1.
Model evaluation
The CERES-Wheat model was evaluated for simulations of crop phenology,biomass,leaf area index (LAI)and grain yield,and soil water and N use in variable irrigation and fertilizer N regimes.The model was calibrated using data of I 1.2regime during 2000–2001that recorded greatest yield among the ?ve experimental cropping seasons,and the rest of data were used for model validation.There was a close match between simulated and measured durations (days,d)to anthesis and physiological maturity.The simulated duration to anthesis was between à2to +6d of measured durations varying between 97and 111d under different growing seasons and planting dates.Simulated duration to physiological maturity was also between à5and +4d for measured durations varying
Table 1–Details of treatments used for model evaluation Cropping season
Planting date
Irrigation regime
Amount and timing of fertilizers
2000–01
10November
Common irrigation of 70mm 30days after sowing,thereafter timing at irrigation water to pan evaporation ratio of 1.2(I 1.2)and 0.6(I 0.6)
Basal application of 13kg P,25kg K and 5kg Zn ha à1.120kg N ha à1half at sowing and other half with ?rst irrigation
2001–028November
Four regimes:no post-sown irrigation (I 0),
one irrigation of 70mm 30days after sowing (I 1),two irrigation of 70mm each at 30days and at ?owering (I 2),and timing at irrigation water to pan evaporation ratio of 1.0(I 3)
P,K,Zn as above.120kg N ha à1
half at sowing and other half with ?rst irrigation in I 1,I 2and I 3regimes,and 40kg N ha à1at sowing in I 0regime 2002–0313November
Same as in 2001–02,but only I 0,I 1,and I 3regimes could be imposed due to well distributed rains in the cropping season Same as in 2001–02
2003–0415November Same as in 2002–03Same as in 2001–02
2004–056November
I 0and I 1regimes
P,K,and Zn as in 2000–01,and four N rates:0,60,120,and 180kg ha à1half at sowing and other half with ?rst irrigation in I 1regime,and whole N for a given rate at sowing in I 0regime 3December Same as for ?rst planting date
Same as for ?rst planting date
Table 2–Physical and chemical properties of experimental soil Soil depth (m)
LL (%,v/v)
DUL (%,v/v)
SAT (%,v/v)
Initial soil water (%,v/v)
Initial soil mineral-N
NO 3à(mg kg à1)
NH 4+(mg kg à1)
0–0.15 6.025.035.018.0 3.2–5.2 2.1–4.90.15–0.308.027.036.018.0 2.6–5.0 1.5–4.00.30–0.6010.030.038.019.0 2.1–3.8 1.4–2.10.60–0.9010.030.038.021.0 1.5–2.9 1.0–1.20.90–1.2010.030.038.022.0 1.0–1.5 1.0–1.11.20–1.50
10.0
30.0
38.0
22.0
1.0
1.0
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between 136and 157d.These results endorse earlier reports on simulation of wheat phenology with this model (Hundal and Prabhjyot-Kaur,1997;Pathak et al.,2003).
Comparison of harvest-time simulated and measured biomass for all treatments in the ?ve cropping seasons (Fig.1)showed that matching between the two was reasonable with data scatter close to 1:1line.The root mean square of deviations (RMSD)between the two was 1.2t ha à1and normalized RMSD (100?RMSD/mean)was 14%for measured values ranging between 3.7and 14.0t ha à1under variable irrigation and fertilizer N regimes in different cropping seasons.Grain yield,however,was slightly and consistently over-estimated.The RMSD between the two was 0.8t ha à1and
normalized RMSD was 25%for measured yield ranging between 1.4and 5.1t ha à1under different regimes (Fig.1).The normalized RMSD reduced to 18%when 4data-points of 180N kg ha à1in I 0and I 1regimes for the two planting dates during 2004–05were ignored assuming these treatments to represent extreme stress environment due to imbalance of irrigation and fertilizer N regime.Greater variance in simula-tion of grain yield compared to that of biomass could be attributed to partitioning of simulated biomass into different organs built in the model.In a recent comprehensive review,Timsina and Humphreys (2006)reported that simulation of crop growth with the CERES-Wheat model under non-limiting water and N conditions in the sub-tropical environments of India,Nepal,Bangladesh and Australia had normalized RMSD of 15–17%for biomass,and 7–17%for grain yield.
Temporal changes in LAI accumulation (Table 3)indicate that the correspondence between simulated and measured LAI was poorer compared to that of biomass simulation.The RMSD was 0.1,0.5,0.9and 0.6(for data pooled over irrigation and fertilizer N regimes)and normalized RMSD ranged between 25and 35%for different sampling dates in 6November planted crop during 2004–05.More importantly,the simulated response of LAI to irrigation and fertilizer N showed trends similar to the measured responses,though there were slight differences in their magnitudes.For example,at 82d,measured LAI were 0.8,2.5,1.7and 4.3for I 0N 0,I 0N 120,I 1N 0,and I 1N 120;while simulated values were 0.8,3.1,0.9,and 3.4for the corresponding treatments.One possible reason for generally lower simulated LAI could be the lower values of speci?c leaf area (11.5m 2kg à1)built in the model.
Model simulation of soil water and N use was quite reasonable.The simulated seasonal ET had a close corre-spondence with the measured values for most of the irrigation and fertilizer N regimes in different cropping seasons (Table 4).The RMSD between the two was 25mm and normalized RMSD was 9%for measured values ranging between 187and 380mm.The simulated response of ET to irrigation and N regimes had a trend similar to the measured response.For example,irrigation effects on crop growth,and subsequently on ET are generally greater in years of low rainfall (2000–01and 2001–02),and the effects diminish with increase in rainfall (2002–03and 2004–05).Nitrogen effects on ET were greater under I 1than I 0regime as fertilizer N effects on crop growth are in?uenced by water limitation.Simulated total N uptake (grain N +straw N)had a good correspondence with measured values.The RMSD between the two was 13kg ha à1and normalized RMSD was 18%for measured values ranging between 24
and
Fig.1–Comparison of simulated and measured biomass and grain yield under variable irrigation and fertilizer N regimes in different cropping seasons.
Table 3–Temporal changes in simulated and measured leaf area index under variable irrigation and fertilizer N regimes for November 6planted wheat during 2004–05cropping season Treatments
Leaf area index
32
54
82
124days after planting
Sim.
Meas.
Sim.
Meas.
Sim.
Meas.
Sim.
Meas.
I 0N 00.40.3 1.6 1.10.80.80.50.5I 0N 1200.40.3 1.8 1.4 3.1 2.5 2.3 1.3I 1N 00.40.3 1.7 1.00.9 1.70.50.6a g r i c u l t u r a l w a t e r m a n a g e m e n t 94(2007)22–30
25
138kg ha à1in different treatments.However,there was a greater variance in the simulation of grain N uptake.The RMSD between the two was 26kg ha à1and normalized RMSD was 40%for measured values ranging between 20and 76kg ha à1apparently due to over-estimation of grain yield.
This analysis suggests that simulation of crop phenology and growth,and soil water and N use by the CERES-Wheat model was coherent,barring a few discrepancies,with measured data.Thus,the model could be used to assess the impact of water and fertilizer N options.
3.2.Scenario analyses
The validated CERES-Wheat model was employed to assess climatic potential yield and interactive effects of irrigation and fertilizer N on grain yield,and water productivity for the Ludhiana environment using 14years (1991–92to 2004–05)of weather https://www.doczj.com/doc/d84966244.html,binations of three irrigation regimes irrigations of 70mm each timed as per recommended schedule,and three N rates—0,60,and 120kg ha à1were analyzed for a sandy loam (20cm extractable water (EW)m à1soil depth)and a loamy sand (15cm EW m à1soil depth)soil for wheat planted 1November.These options were examined under three initial soil water conditions (75,50and 25%EW)anticipating water limitations and three initial soil mineral-N (NO 3à+NH 4+)status (60,100,and 140kg ha à1in 1.5m soil depth)re?ecting residual effect of fertilizer N use in preceding irrigated rice.
Simulated potential yield based on 14year-simulations was greatest for wheat planted in the ?rst fortnight of November (mean yield =5.0t ha à1,standard deviation =0.7t ha à1)and had a tendency to decrease with late planting.Grain yield exhibited year-to-year variability due to climatic factors.Temporal changes in potential yield for 1November planted wheat (Fig.2)indicate that there was a gradual decline in potential yield from 5.9t ha à1in 2000–01to 4.7t ha à1in 2004–Table 4–Comparison of simulated and measured water use (ET)and N uptake under variable irrigation and fertilizer N regimes in different cropping seasons Treatment
Irrigation amount (mm)
ET (mm)
Total N Grain N (kg ha à1)Sim.
Meas.
Sim.
Meas.
Sim.
Meas.
2000–01,rainfall—28mm I 1.2280397380I 0.61403142752001–02,rainfall—37mm I 00182187I 170254277I 2140323307I 32103713532002–03,rainfall—190mm I 00297283I 170352350I 31403733772003–04,rainfall—82mm I 00218237I 170283292I 3140
341
352
2004–05,rainfall—145mm Planted 6November I 0N 0024122837353124I 0N 60026924678816556I 0N 12002752481071038966I 0N 180027725512411010268I 1N 07027728040393424I 1N 607032130485757040I 1N 120703293261141239674I 1N 180
7033233113813810676Planted 3December I 0N 0022122732292320I 0N 60024124453574436I 0N 120025124683766943I 0N 1800254252109769140I 1N 07025221030322522I 1N 607029924060665043I 1N 1207031225393847750I 1N 180
70
316
261
119
84
98
44
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1.38C more than that during the corresponding period in 2000–01.This suggests that declining wheat productivity from 4.6t ha à1to 4.2t ha à1during this period in Punjab (Economic and Statistical Organization,2006)may be a consequence of increased air temperature.
Cumulative probability distribution of simulated grain yield and seasonal ET under the three irrigation regimes with 0and 120kg N ha à1at initial soil water of 50%EW,and initial soil-mineral N of 140kg ha à1under a sandy loam soil for wheat planted 1November are given in Fig.3.Grain yield and ET probability curves had a considerable spread indicating yearly variability.Median yield (with 50%probability of exceedance)was 3.2,3.2,and 3.4t ha à1in I 0,I p ,and I f regimes under N 0regime.Under N 120regime,median yields were 3.7,4.2,and 4.7t ha à1in the three irrigation regimes.Similarly,median ET was 201,269and 336mm in I 0,I p ,and I f regimes under N 0,and 203,274,and 346mm in the three irrigation regimes under N 120.These results show that irrigation and fertilizer N effects on ET and grain yield were synergistic.Greater effect of N on yield than on ET,in a given irrigation regime,was due to partitioning a greater fraction of ET to more productive T component.Irrigation and fertilizer N in?uence ET partition-ing due to their effects on crop cover (LAI),thereby affecting T/ET ratios (Arora et al.,1987;Gajri et al.,1993).
The interaction effects of irrigation and N regimes on grain yield and water productivity were in?uenced by
extractable
Fig.3–Cumulative probability distribution of wheat grain yield and seasonal ET in variable irrigation and fertilizer N regimes on a sandy loam soil at initial soil water of 50%EW,and soil mineral-N (NO 3S +NH 4+)of 140kg ha S 1in 1.5m soil depth.The regimes included:no irrigation –no N (I 0N 0),no irrigation –120kg N ha S 1(I 0N 120),partial irrigation –no N (I p N 0),partial irrigation –120kg N ha S 1(I p N 120),full irrigation –no N (I f N 0)and full irrigation –120kg N ha S 1(I f N 120).
Table 5–Simulated influence of extractable water capacity of soil on interaction effects of irrigation and fertilizer N regime on mean *grain yield and water productivity of wheat at initial soil water of 50%EW,and initial soil mineral-N of 100kg ha S 1in 1.5m depth Fertilizer N regime
Sandy loam (20cm m à1soil depth)Loamy sand (15cm m à1soil depth)I 0
I p
I f
I 0
I p
I f
Grain yield (t ha à1)0 2.3 2.4 2.4 2.2 2.3 2.360 3.1 3.5 3.7 2.8 3.4 3.5120
3.5
4.1 4.4 2.9 3.9 4.2Water productivity (kg ha à1mm à1)010.99.37.911.49.58.16014.012.510.914.013.010.71201
5.6
14.5
12.7
14.4
14.8
12.4
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water capacity of soil,initial soil water and mineral-N status.It is interesting to note that grain yields in I p regime under a sandy loam soil were comparable to those realized in I f regime under a loamy sand soil for a given initial soil water and mineral-N status (Table 5).For example,at 120kg ha à1fertilizer N,a grain yield of 4.1t ha à1was achieved in I p regime under sandy loamy against 4.2t ha à1in I f regime under loamy sand.Furthermore,effects of extractable water capa-city of soil on grain yield were more pronounced under I 0regime,and the effects decreased with increase in irrigation.These results endorse ?eld studies on the effect of small irrigation input on wheat grain yields in relation to extractable water capacity of soils (Gajri and Prihar,1983).Generally,water productivity decreases with increase in irrigation as yield gain is less than proportional increase in ET.The water productivity in I p regime under sandy loam soil was greater than that in I f regime in the loamy sand soil.Greater extractable water capacity of soils has a moderating effect on the development of water stress leading to greater and stable yields (Arora and Gajri,1998).
The simulated in?uence of initial soil water (25,50and 75%EW)at 100kg ha à1soil mineral-N on a sandy loam soil on interaction effects of irrigation and fertilizer N regimes on grain yield and water productivity are given in Table 6.It is shown that at a given fertilizer N rate,grain yield obtained in I f regime with 25%EW was comparable to that obtained in I p regime with 50%EW and in I 0regime with 75%EW.For example,at 120kg ha à1fertilizer N,a grain yield of 4.0t ha à1was achieved in I f regime with 25%EW,I p regime with 50%EW and in I 0regime with 75%EW initial soil water.This suggests that effects of post-sown irrigation regimes on grain yield are greater under low initial soil water and corroborate earlier observations (Aggarwal and Kalra,1994).Water productivity decreased with increase in irrigation at high initial soil water (75%EW);while at low initial soil water (25%EW),water productivity increased from I 0to I p regime,and decreased thereafter with more irrigations.In ?eld studies,it has been amply shown that water productivity of wheat in alluvial soils of Punjab was greater when medium-or high-initial soil water conditions were combined with fewer post-sowing irrigations regime (Prihar et al.,1976;Jalota et al.,1980).
The simulated in?uence of initial soil mineral-N status (60,100,and 140kg ha à1in 1.5m deep soil)at 50%EW on interaction effects on grain yield and water productivity is shown in Table 7.It is shown that irrigation response of grain yield with initial mineral-N of 140kg ha à1and fertilizer N of 60kg ha à1was comparable to that with initial mineral-N of 100kg ha à1and fertilizer N of 120kg ha à1.Similarly,irrigation response of grain yield with initial mineral-N of 100kg ha à1and fertilizer N of 60kg ha à1N was comparable to that with initial mineral-N of 60kg ha à1and fertilizer N of 120kg ha à1.It implies that fertilizer N should be adjusted in relation to initial
Table 6–Simulated influence of changes in initial soil water on interaction effects of irrigation and fertilizer N regime on mean *grain yield and water productivity of wheat on a sandy loam soil at 100kg initial soil mineral-N (NO 3S +NH 4+)in 1.5m depth Fertilizer N regime
25%EW
50%EW
75%EW
I 0
I p
I f
I 0
I p
I f
I 0
I p
I f
Grain yield (t ha à1)0 1.6 2.5 2.7 2.3 2.4 2.4 2.4 2.4 2.460 1.7 3.2 3.7 3.1 3.5 3.7 3.5 3.7 3.7120
1.7
3.3
4.0 3.5 4.1 4.4 4.0 4.4 4.4Water productivity (kg ha à1mm à1)010.711.38.710.99.37.99.88.97.76011.314.211.514.012.510.913.011.910.612011.3
14.6
12.3
15.6
14.5
12.7
14.6
13.9
12.4
*
Mean over 14-years (1991–92to 2004–05)simulations.
Table 7–Simulated influence of changes in initial soil mineral-N (NO 3S +NH 4+)on interaction effects of irrigation and fertilizer N regime on mean *grain yield and water productivity of wheat on a sandy loam soil at initial soil water of 50%EW
Fertilizer N regime
60kg 100kg
140kg (NO 3à+NH 4+)-N
I 0
I p
I f
I 0
I p
I f
I 0
I p
I f
Grain yield (t ha à1)0 1.4 1.4 1.5 2.3 2.4 2.4 3.0 3.3 3.460 2.5 2.9 2.9 3.1 3.5 3.7 3.5 4.0 4.3120
3.2
3.7 3.9 3.5
4.1 4.4 3.5 4.2 4.7Water productivity (kg ha à1mm à1)07.6 6.5
5.810.99.37.913.612.010.26011.610.89.014.012.510.915.614.212.412014.4
13.2
11.4
15.6
14.5
12.7
15.6
14.8
13.5
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mineral-N status for a given initial soil water and post-sown irrigation scenario.The water productivity was greater with high initial soil-N against low soil-N for comparable irrigation and fertilizer N regimes.
4.Summary and conclusions
This analysis has demonstrated that performance of the CERES-Wheat model was reasonable as indicated by corre-spondence between simulated crop phenology,biomass accumulation,grain yields,and soil water and N use with measured data.The normalized RMSD ranged between10and 20%for most of the parameters.Cumulative probability distributions of simulated grain yield and ET showed that for a given irrigation regime,fertilizer N had greater effect on yield than on ET,and caused greater water productivity. Scenario analysis showed that grain yield and water produc-tivity were in?uenced by extractable water capacity of soils. Grain yields in I p regime under a sandy loam soil were comparable to those realized in I f regime under a loamy sand soil for a given initial soil water and mineral-N status.Soil effects on grain yield were more pronounced under I0regime, and effects decreased with irrigation.It has also been demonstrated that at a given fertilizer N,grain yield obtained in I f regime with25%EW was comparable to that obtained in I p regime with50%EW and in I0regime with75%EW initial soil water.This signi?es that post-sown supplemental irrigations were more crucial under conditions of low initial soil water represented by dryland areas.Irrigation response of grain yield with high initial mineral-N and60kg haà1fertilizer N was comparable to that with medium initial mineral-N and 120kg haà1fertilizer N.It suggests that initial soil mineral-N status in?uenced the amount of fertilizer N for a given initial soil water and post-sown irrigation regime.This analysis has implications for improving crop water productivity under dryland and limited water scenarios.
Acknowledgements
A part of this study was funded by National Agricultural Technology Project(PSR no.1).
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