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Crop water productivity of cotton (Gossypium hirsutum L.)–wheat (Triticum aestivum L.) system as influenced by deficit irrigation, soil texture and precipitation
agricultural water management 84 (2006) 137–146
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Crop water productivity of cotton (Gossypium hirsutum L.)– wheat (Triticum aestivum L.) system as influenced by deficit irrigation, soil texture and precipitation S.K. Jalota a,*, Anil Sood b, G.B.S. Chahal a, B.U. Choudhury b a b
Department of Soils, Punjab Agricultural University, Ludhiana 141004, India Punjab Remote Sensing Centre, Ludhiana, India
article info
abstract
Article history:
In the agricultural sector there is an urgent need to use dwindling water resources efficiently
Accepted 5 February 2006
and enhancing crop water productivity (CWP). At the farm level, reducing evapotranspira-
Published on line 17 April 2006
tion (ET) through deficit irrigation (lesser number of irrigations) and identification of the most sensitive crop growth stage to water stress has been reported as one of the ways to
Keywords:
enhance CWP. Although information on CWP in relation to irrigation water of some cereal
Crop water productivity
crops based on the field experimental data is available in the literature. However, the
Cotton–wheat system
influence of soil texture, precipitation and deficit irrigation regime and their interactions on
Soil texture
CWP is not well-documented. We explored these components in cotton (Gossypium hirsutum
Irrigation water
L.)–wheat (Triticum aestivum L.) cropping system through simulation analysis, which other-
Precipitation
wise are difficult to be explained through field experimentation. The simulated results showed that by reducing the amount of irrigation water input below economic optima, both the yield and ET of cotton and wheat crops were reduced and consequently CWP to varying magnitudes depending upon soil texture, precipitation and irrigation regimes. With reducing post-sowing irrigation water from 300 to 75 mm, the decrease in CWP in silt loam, sandy loam and loamy sand soils were 15, 4 and 1% for cotton and 8, 36 and 55% for wheat, respectively, indicating higher decrease in CWP for wheat than for cotton, and in coarsetextured than fine-textured soils. Precipitation increased the CWP. The increase was more in wheat crop on coarse-textured soil with less number of irrigations. Averaged over soil texture and irrigation regimes, real CWP (RCWP) (yield/ET) was 47 and 9 and 60% of apparent CWP (ACWP) (yield/irrigation water) in cotton, wheat and cotton–wheat system, respectively. The crop growth stages found to be most sensitive to water stress were from flowering to boll formation in cotton and grain development stage in wheat. # 2006 Elsevier B.V. All rights reserved.
1.
Introduction
Large-scale adoption of rice (Oryza sativa L.)–wheat (Triticum aestivum L.) cropping system in Punjab state of north-west India during the last 30 years has resulted in over-exploitation
of ground water resources. Ground water being the main source of irrigation water, which is declining in 80% area of Punjab at the rate of 0.4 m year1 and in some areas, the decline rate is even more than 0.9 m (Hira et al., 2004). With the shrinking ground water resources and higher energy
* Corresponding author. Tel.: +91 1612402649; fax: +91 1612400945. E-mail address: [email protected] (S.K. Jalota). 0378-3774/$ – see front matter # 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.agwat.2006.02.003
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agricultural water management 84 (2006) 137–146
consumption for pumping, the agriculture sector faces the challenge to sustain the productivity of rice–wheat system. Alternatives need to be explored to use existing water and energy resources efficiently. Cotton (Gossypium hirsutum L.) crop grown under deficit irrigation (lesser number of irrigations) and less evapotranspiration (ET) requirements is a possible alternative to rice to return savings in water and energy. At present farmers grow cotton in rotation with wheat in only 10% of the total cultivated area with four to five irrigations to each crop through border method. The depth of irrigation water is nearly 75 mm per irrigation, which costs Indian rupees (Rs) 250 ha1 for its pumping and application. The recommended dose of nitrogen, phosphorous and potash is 75, 30 and 0 kg ha1 to cotton and 120, 60 and 60 kg ha1 to wheat, respectively. With these irrigation and fertilizer recommendations, the average observed yields of cotton and wheat in Punjab are 1242 and 4221 kg ha1, respectively (Mahindra, 2003). Numerous studies showing the response of water deficit on cotton crop dynamics, leaf expansion and root growth (Ball et al., 1994), abscission pattern (Crozat et al., 1999), flowering, boll formation and its distribution, yield components (Pettigrew, 2004; Gerik et al., 1996; Radin et al., 1992; McMichael and Hesketh, 1982; Grimes et al., 1969; Bruce and Shipp, 1962) are available in literature. For this region Aujla et al. (1991, 2005) have conducted some field trials to study the effects of different methods of irrigation on cotton yield but information about crop water productivity (CWP), which is calculated as marketable yield over actual ET and its potential enhancement for cotton and wheat crops grown in a rotation is lacking. To estimate yield response and CWP in relation to deficit irrigation, adequate observations are required over a wide range of irrigation amounts. Since farmers do not vary their irrigation levels significantly over the years, and often do not use recommended level of water inputs, field data typically may not produce very useful inferences. Research station trials conducted over a broad range of input data and irrigation treatments can also provide data but need to be replicated for a number of years to arrive at accurate management advice. Another source of data information is the pre-validated crop growth models. To enhance CWP through deficit irrigation as suggested by Kijne et al. (2003), quantitative specific information for Punjab on yield and ET of cotton and wheat crops in rotation under different irrigation water regimes, and the most sensitive stages of these crops to water stress needs to be developed. Such information can be obtained either through field experiments or generating data using an appropriate crop model. Recently, Zwart and Bastiaanssen (2004) reviewed the overall CWP of wheat, rice, maize and cotton crops from the
experimental data and ascribed variability of CWP to climate, irrigation water management and nitrogen management. However, in reality CWP is likely to be significantly affected by soil texture and precipitation in addition to amount of irrigation water input. Therefore, a simulation analysis was done to (1) elucidate the effects of different irrigation regimes, soil texture and precipitation on crop yield, ET, CWP and identification of the most sensitive crop growth stage to water stress from the data generated using an existing model and (2) select the optimum irrigation water regime to achieve maximum CWP in cotton–wheat cropping system.
2.
Materials and methods
The crop management and production model CROPMAN was used to simulate the crop yield and CWP response to irrigation water for the cotton–wheat system. The soils used were Randhirpur silt loam, Tulewal sandy loam and Fatehpur loamy sand, which varied in texture, compactness level and hydraulic properties (Table 1). Plant population, harvest index, heat units, plant height and rooting depth used in the model were obtained from experimental data (Praharaj, 1991) and rest were taken as default values (Jalota et al., 2006; Harman et al., 2004). For application of the model, crop budget representing the crop management information on field preparation operations, date of sowing, fertilizer application (amount and time), irrigation scheduling, pesticides/herbicides application (amount and time) and harvesting dates for the cotton–wheat system (Table 2) were prepared using the recommendations given in the package of practices of Punjab Agricultural University, Ludhiana (Mahindra, 2003). The crop yields simulated with the model were compared with the yields observed from experiments by different researchers in this region or those reported in the package of practices by Punjab Agricultural University, Ludhiana. Four irrigation treatments were simulated for 18 years (1982–2000) in cotton–wheat system. These were T1, T2, T3 and T4 referring to 400 mm irrigation water (four post-sowing irrigations of 75 mm each and one pre-sowing irrigation of 100 mm), 325 mm (three post-sowing irrigations of 75 mm each and one pre-sowing irrigation of 100 mm), 250 mm (two postsowing irrigations of 75 mm each and one pre-sowing irrigation of 100 mm) and 175 mm (one post-sowing irrigation of 75 mm and one pre-sowing irrigation of 100 mm), respectively, were analysed. The dates of post-sowing irrigations are presented in Table 2. For all the treatments the maximum amount of irrigation water was limited to 400 mm for each
Table 1 – Characteristics of three soils used in simulation Soil parameter 1
Sand (g kg ) Silt (g kg1) Clay (g kg1) Bulk density (mg m3) Wilting point (m3 m3) Field capacity (m3 m3) Saturated hydraulic conductivity (mm s1)
Randhirpur silt loam 60 690 250 1.40 0.11 0.32 4 103
Tulewal sandy loam 710 120 170 1.50 0.08 0.24 7 103
Fatehpur loamy sand 820 100 80 1.65 0.04 0.13 11 103
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Table 2 – Details of the management file for cotton-wheat cropping system Year 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Month 11 11 11 11 11 11 11 11 11 12 12 12 1 3 3 4 4 5 5 5 5 5 5 5 5 5 6 6 7 7 7 7 7 7 8 8 8 8 8 9 9 9 9 9 9 9 11 11 11
Day 21 22 22 23 23 24 24 24 24 24 27 30 26 5 30 15 15 7 8 10 10 11 11 12 12 12 12 15 5 15 16 18 24 31 9 16 20 26 30 5 13 16 20 25 28 30 14 19 19
Operation
Equipment/method
Plow Disking Disking Plow Plow Plow Sowing Fertilizer Fertilizer Irrigation Herbicide Fertilizer Irrigation Irrigation Irrigation Harvest Kill Plow Irrigation Disking Disking Plow Plow Plow Fertilizer Sowing Irrigation Fertilizer Pesticide Irrigation Pesticide Fertilizer Pesticide Pesticide Pesticide Pesticide Irrigation Pesticide Pesticide Pesticide Pesticide Pesticide Pesticide Pesticide Irrigation Pesticide Irrigation Harvest Kill
Berm maker Offset Offset Cultivator Cultivator Planker Grain drill Hand Hand Flooda Spray hand Hand Flood Flood Flood Combine
Dates of irrigations in different treatments in wheat T1 12/24 1/26 3/5 T2 12/24 3/5 3/25 T3 12/24 3/5 T4 12/24 Dates of irrigation in different treatments in cotton T1 6/12 7/15 8/20 T2 6/12 8/20 9/28 T3 6/12 9/28 T4 6/12 a
Irrigation method is border.
Berm maker Flood Offset Offset cultivator cultivator Planker Hand Hand Flood Hand Hand spray Flood Hand spray Hand Hand spray Hand spray Hand spray Hand spray Flood Hand spray Hand spray Hand spray Hand spray Hand spray Hand spray Hand spray Flood Hand spray Flood
3/25
9/28
Source
18-46-0 Urea (46-0-0) 2,4-D Urea (46-0-0)
Amount/rate
130.0 kg ha1 78.0 kg ha1 75 mm 0.63 kg ha1 130.0 kg ha1 75 mm 75 mm 75 mm
Depth
10 mm 10 mm
10 mm
75 mm
18-46-00
Urea 46-0-0 Thiodone50 WSB Ammo Urea 46-0-0 LORSBAN 30 FL Provado Ammo LORSBAN 30 FL Thiodone 50 WSB Provado Metasystox LORSBAN 30 FL Provado LORSBAN 30 FL CURACON 8E LORSBAN 30 FL
65.00 kg ha1 50000 plants ha1 75 mm 137 kg ha1 4.94 kg ha1 75 mm 0.70 l ha1 163 kg ha1 2.47 l ha1 1.51 l ha1 0.70 l ha 2.47 l ha1 75 mm 4.94 kg ha1 1.51 l ha1 1.36 l ha1 2.47 l ha1 1.51 l ha1 2.47 l ha1 3.37 l ha1 75 mm 2.47 l ha1 100 mm
10 mm 2350 HU
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agricultural water management 84 (2006) 137–146
potential evapotranspiration (mm), i an integer representing the number of crop growth sub-periods, P the multiplication and g the sensitivity factor.
2.1.
Fig. 1 – Time trends of pan evaporation and rain at Ludhiana (18 years average).
crop. This amount was selected from an independent simulation study on economic optima of irrigation water (Jalota, 2004), in which economic water optima was found 408 mm for cotton and 407 mm for wheat. The meteorological data for the study area was collected from the meteorological station located at Punjab Agricultural University, Ludhiana. The average annual rainfall, based on data from 1982 to 2000, at the station was 787 260 mm, out of which 117 59 and 654 262 mm occurred during the wheat and cotton growing seasons, respectively (Fig. 1). The probabilities of rainfall for the range of 0–100, 100–200 and 200–300 mm during wheat crop were 0.33, 0.44 and 0.23, whereas for cotton, these were 0.11, 0.33, 0.28, 0.11 and 0.17 for the range of 200–400, 400–600, 600–800, 800–1000 and 1000–1200 mm, respectively. CWP is generally defined as marketable yield/ET, but economists and farmers are most concerned about the yield per unit of irrigation water applied. The former can be termed as real crop water productivity (RCWP), while the latter as apparent crop water productivity (ACWP). These terms were estimated for individual cotton and wheat crops from the simulated data for cotton–wheat system. The marketable yields of cotton, wheat and cotton–wheat system were cotton seed, wheat grain and cotton seed equivalent, respectively. The cotton seed equivalent yield in cotton–wheat system was calculated as: Cotton seed equivalent yield ¼ wheat grain yield MSP of wheat : MSP cotton seed Here MSP is minimum support price (Rs 6.2 for wheat grain and Rs 17.0 for cotton seed per kilogram). The sensitivity of cotton seed yield and wheat grain yield to ET at different crop stages was determined using the following multiplicative model given by Jensen (1968). Y g n Y ET ¼ Ymax ETmax i¼1 where Y is actual yield (kg ha1), Ymax the maximum yield (kg ha1), ET the actual evapotranspiration (mm), ETmax the
CROPMAN model
A number of simulation models are available for studying the response of individual crops to management practices. However, since the crops growing in a system have influence on each other, there is a need to simulate the cropping system as a whole. CROPMAN is a window-based, multi-year, multicrop, daily time step cropping system simulation model and contains the EPIC crop/environmental simulation model as an engine. In this model, there is a provision to define cropping system as unique combination of the rotation (crop order), as well as the type, timing, rate and method for each operation associated with the rotation. This model has a huge database on tillage equipments, fertilizers, pesticides, and crop and soil parameters. The user can modify the cropping system and management using the management editor module. The equations describing the relationships between rainfall, ET, runoff, erosion, and production are described in a document by Williams et al. (1990). The CROPMAN model can be used to determine the effect of management strategies on agricultural production and soil and water resources. The major components in the model are weather, hydrology, erosion–sedimentation, nutrient cycling, pesticide fate, plant growth, soil temperature, tillage, economics and plant environment control. The input data required for the model are weather, soil, crop and management practices data. The weather data includes solar radiation, maximum and minimum temperatures, mean relative humidity, rain and wind speed. The soil data comprises of soil layer thickness, bulk density of soil layer, wilting point, field capacity, sand, silt, organic nitrogen, pH, sum of bases, organic matter, CaCO3, CEC, initial NO3 concentration, phosphorous, phosphorous sorption rate and saturated hydraulic conductivity The crop data requires biomass energy ratio, biomass energy decline rate, fraction of root weight at emergence, fraction of root weight at maturity, heat units required for germination, LAI decline factor, lower limit of harvest index, maximum crop height, maximum LAI, maximum root depth, maximum stomatal conductance, minimum temperature for plant growth, optimum temperature for plant growth, vapour pressure deficit, threshold vapour pressure deficit, vapour pressure deficit value and harvest index. This model also provides the user with the post simulation analysis for comparing the results obtained under varying soil types and management practices.
3.
Results and discussion
The simulated seasonal ET across soil texture and irrigation regimes in cotton (from 508 to 700 mm) and wheat (from 178 to 381 mm) were within the range reported by some researchers from field experiments in the region (Praharaj, 1991; Jalota et al., 1985; Arora et al., 1987, 1997) and elsewhere (Doss et al., 1964; Howell et al., 1984; Doorenboss and Kassam, 1979; Grimes and El-Zik, 1982). The simulated yields were also within the range as reported by different researchers for this
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agricultural water management 84 (2006) 137–146
Table 3 – Observed and simulated crop yields Simulated (t ha1)
Crop
Observed (t ha1)
Source
Wheat 5.13 5.45 5.25 5.39 3.56 3.48 2.20
5.20 5.40 5.25 5.24 4.67 3.84 3.49
2.22 2.29 0.98 1.49 1.56 1.07
2.30 2.30 1.03 1.49 1.35 1.30
(four irrigations) (two irrigations) (six irrigations) (three irrigations) (two irrigations) (one irrigation)
Mahindra (2003) Jalota et al. (1985) Bajwa (1995)
Cotton
region (Table 3). The statistical analysis of simulated and observed yields showed that the simulated average (over experiments) yield and standard deviation were 1.60 0.55 and 1.62 0.54 t ha1 in cotton, 4.35 1.27 and 4.73 0.77 t ha1 in wheat, respectively. The normalised root mean square difference (RMSD) was 9 and 7% of the measured yields of cotton and wheat, respectively, which indicated that the model can satisfactorily be used for studying yield response to irrigation in cotton–wheat cropping system.
3.1.
Evapotranspiration and cotton seed yield
In general, ET and yield of both the crops (cotton and wheat) decreased with decreasing number of post-sowing irrigations (Table 4). For example, with reducing post-sowing irrigation water from 300 (four irrigations) to 75 mm (one irrigation), ET loss from cotton crop was decreased by 128 (18.3%), 55 (9.3%) and 50 mm (8.9%) in silt loam, sandy loam and loamy sand soils, respectively. The corresponding reductions in yield were 674 (30.8%), 217 (12.5%) and 179 kg ha1 (10.8%), respectively. This showed that with decreasing irrigation water, there was reduction in both the parameters constituting CWP, i.e. yield and ET in all the soils. The magnitude of percent reduction was more in finer textured compared to coarse-textured soils. Amongst the two parameters, the percent reduction was more in the yield than the ET in all the three soils. The cotton seed yield (Yc) was related to ET in silt loam, sandy loam and loamy sand soils following the Eqs. (2a)–(2c), respectively. Yc ¼ 4:8 ET 1081
R2 ¼ 0:84
(2a)
Yc ¼ 4:9 ET 1250
R2 ¼ 0:97
(2b)
Yc ¼ 4:6 ET 875
R2 ¼ 0:97
(2c)
where Yc is yield in kg ha1 and ET in mm. R2 is the coefficient of determination. These relations indicate that to obtain minimum cotton seed yield, water amounting 225 mm from silt loam, 255 mm from sandy loam and 190 mm from loamy sand soils has to be consumed as ET. Sammis (1981) also reported linear relationship between cotton yield and ET. However, from long-term
Mahindra (2003) Praharaj (1991) (M.Sc. thesis) Deol (2001) (Ph.D. thesis) Singh (1997) (Ph.D. thesis) Yadav (2002) (Ph.D. thesis)
studies, Grimes and El-Zik (1982) suggested a slight curvature to the function considering the nature of cotton reproductive development and water relations. An attempt was made to fit that relation (Eq. (3)), but this showed no improvement over the simple linear relation. Yc ¼ 3114:43 þ 0:0125 ET þ 199:42 ET1=2
R2 ¼ 0:84
(3)
Corresponding to ET of 700, 610 and 560 mm for silt loam, sandy loam and loamy sand soils, the simulated cotton seed yields with the CROPMAN model were 2187, 1734 and 1708 kg ha1, respectively. With Eqs. (2a)–(2c), estimated yields were 2279, 1739 and 1701 kg ha1, while with Eq. (3) the respective values were 2165, 1814 and 1607 kg ha1. The lesser differences (4–5%) in the yield with these two relations showed that crop yield and total ET under deficit irrigation were linearly related. It is therefore theorized that curvilinear relationship may hold true in adequately irrigated cotton crops only. Though the variation in yield was explained reasonably well (as evident from higher values of R2, ranging from 0.84 to 0.97) with total ET, some researchers have documented that yields of crops are sensitive to ET differentially in different growth stages. In field studies, Radin et al. (1992) observed in cotton crop, the peak fruiting period was the most sensitive stage to water stress. In this study we apportioned the fruiting period further into two sub-periods, viz. flowering to boll formation (100–120 days) and boll formation to boll opening (120–150 days) as occurring in the region. Using the simulated yield, ET and ETmax, values in these sub-periods along with vegetative (0–80 days) and vegetative to flowering (80–100 days) periods, sensitivity factors were estimated by employing the multiplicative model of Jensen (1968). The sensitivity factor for vegetative stage, vegetative to flowering, flowering to boll formation and boll formation to boll opening stages were 2.286, 1.680, 2.732 and 1.867, respectively. The statistically significant values at 5% probability of the sensitivity factor for flowering to boll formation stage showed that boll formation period was more sensitive to water stress than other stages (Fig. 2). This gives support to the earlier observations by Radin et al. (1989), which indicated that irrigation at this stage increased leaf water potential and boll dry mass per
0.86 0.87 0.87 0.26 0.27 0.30 1513 1517 1529
0.69 0.61 0.62 0.28 0.27 0.30 1720 1530 1540
0.59 0.49 0.50 0.29 0.28 0.31 1918 1600 1630
0.55 0.43 0.25 0.31 0.29 0.31 2187 1734 1708
572 554 508 Silt loam Sandy loam Loamy sand T4
620 564 517 Silt loam Sandy loam Loamy sand T3
651 577 534 Silt loam Sandy loam Loamy sand T2
700 609 558 Silt loam Sandy loam Loamy sand
T1, 1 mm 100 mm pre-sowing and 4 mm 75 mm post-sowing; T2, 1 mm 100 mm pre-sowing and 3 mm 75 mm post-sowing; T3, 1 mm 100 mm pre-sowing and 2 mm 75 mm post-sowing; T4, 1 mm 100 mm pre-sowing and 1 mm 75 mm post-sowing.
0.84 0.63 0.55 0.31 0.27 0.27 2933 2203 1915 942 802 697 2.23 1.08 0.60 3895 1881 1058 340 236 178
1.15 0.80 0.59
0.67 0.58 0.48 0.33 0.33 0.32 3345 2881 2414 1026 872 763 1.78 1.48 0.96 4456 3705 2396 372 297 236
1.20 1.25 1.02
0.56 0.49 0.45 0.34 0.34 0.35 3649 3195 2919 1082 936 830 1.46 1.35 1.09 4747 4374 3536 376 346 285
1.26 1.27 1.24
0.49 0.42 0.40 0.35 0.34 0.37 3913 3375 3193 1120 984 871 1.18 1.13 1.02 4734 4500 4073 381 364 303
1.24 1.24 1.34
RCWP Yield (kg ha1) Yield (kg ha ) ET (mm) RCWP Yield (kg ha )
T1
Soil type
ET (mm)
1
Cotton
ACWP
1
Wheat
RCWP
ACWP
ET (mm)
Cotton–wheat system
ACWP
agricultural water management 84 (2006) 137–146
Treatment
Table 4 – Effect of irrigation regime and soil texture on evapotranspiration (ET), yield (Y), real crop water productivity (RCWP, Y/ET) and apparent crop water productivity (ACWP, Y/irrigation water)
142
Fig. 2 – Relative cotton seed yield and relative evapotranspiration during vegetative growth (0–80days), vegetative to flowering (80–100 days), flowering to boll formation (100–120 days), boll formation to boll opening (120–150 days) stages in cotton and vegetative growth (0–90 days), flowering (90–120 days), grain formation and development (120–150 days) and maturity (150–160 days) in wheat.
plant by 30–50% over the control. They advocated that water is necessary at fruiting stage because there is deterioration of root system during fruiting in the cotton crop on long irrigation cycles. Taylor and Klepper (1974) reported that when the cotton crop becomes water stressed during fruit filling, declines in root length were severe and recovery was much slower than stress during vegetative growth stage. It may also be related to suberization of roots (Cruz et al., 1990) and nutrient transport. The RCWP values for cotton seed under different irrigation treatments and soil textures ranged from 0.26 to 0.31 kg m3 (Table 4). This range was well within the range reported in literature (Bucks et al., 1988) or lower in some cases (Doorenboss and Kassam, 1979; Zwart and Bastiaanssen, 2004). Results of the present study indicated that RCWP declines with decreasing amount of irrigation water. It may be ascribed to relatively more reduction in
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agricultural water management 84 (2006) 137–146
yield parameter compared to ET as described earlier. RCWP was also lesser in medium-textured soils. Higher RCWP in finer and coarser textured soils was due to higher crop yield in the former and lesser ET in the latter. Reduced ET in coarser textured soils is due to lesser evaporation component (Jalota and Arora, 2002) owing to self-mulching effect (Jalota and Prihar, 1986).
3.2.
Yec ¼ 5:4 ET 2211
R2 ¼ 0:99
(5a)
Yec ¼ 6:4 ET 2813
R2 ¼ 0:95
(5b)
Yec ¼ 7:4 ET 3232
R2 ¼ 0:99
(5c)
Evapotranspiration and wheat grain yield
Like cotton, ET under wheat was reduced by 41 (10.8%), 128 (35.2%) and 125 mm (34.3%) in silt loam, sandy loam and loamy sand soils, respectively. The corresponding reductions in yields were 839 (17.7%), 2619 (58.2%) and 3015 kg ha1 (74.0%), respectively (Table 4). Unlike cotton, the magnitude of reduction was lesser in fine- as compared to coarse-textured soils. Yield of wheat grain (Yw) and ET in silt loam, sandy loam and loamy sand soils followed the relations as 4(a)–(c), respectively. Yw ¼ 25:5 ET 4894
R2 ¼ 0:75
(4a)
Yw ¼ 21:9 ET 3152
R2 ¼ 0:89
(4b)
Yw ¼ 25:0 ET 3551
R2 ¼ 0:92
(4c)
These Eqs. (4a)–(4c) indicated that like cotton, in wheat 191, 144 and 142 mm of water in silt loam, sandy loam and loamy sand soils, respectively, has to be used in ET to obtain minimum grain yield. Though the variation in yield with total ET was explained reasonably (R2 = 0.89–0.92) as linear relation, sensitivity factor for different growth stages of wheat crop, viz. vegetative (0–90 days), flowering (90–120 days), grain formation and development (120–150 days) and maturity stages (150–160 days) were 0.468, 0.359, 4.546 and 0.081, respectively. The sensitivity factor for grain formation and development period being statistically significant at 5% probability reflected that this period was relatively more sensitive to water stress than any other stage of crop growth (Fig. 2). This reaffirms the earlier findings of both Jalota et al. (1985) and Arora et al. (1987). The RCWP values for wheat under different irrigation treatments and soil texture indicated that RCWP decreased with reduced irrigation amount in all the three soils studied (Table 4). The RCWP was less in silt loam (1.24 kg m3) under higher irrigation regime and in loamy sand (0.59 kg m3) under lower irrigation regime. This may be ascribed to comparatively higher ET in the former and lower yield in the latter case. With adequate soil moisture in fine-textured soil, more evaporation from bare soil and more ET from cropped soil has been reported by Jalota and Prihar (1998) and Jalota and Arora (2002).
3.3.
yield than the ET in all the three soils. The magnitude of reduction was more in coarse- than fine-textured soil. The equivalent cotton seed yield (Yec) was related to ET in silt loam, sandy loam and loamy sand soils following the Eqs. (5a)–(5c), respectively.
The RCWP values for the cotton–wheat system decreased with reducing the number of irrigations in all the three soils (Table 4). The reduction was 11, 20 and 27% in silt loam, sandy loam and loamy sand soil, respectively.
3.4.
Irrigation water and crop yield
Cotton and wheat yield increased linearly with increasing number of irrigations applied. The values of coefficient of determination ranging from 0.78 to 0.93 with linear relationship were improved further with second order polynomial to 0.98–0.99 in cotton (Eqs. (6a)–(6c)), wheat (Eqs. (7a)–(7c)) and cotton–wheat system (Eqs. (8a)–(8c)) for silt loam, sandy loam and loamy sand soils, respectively. Cotton Yc ¼ 0:0054 IW2 0:4015 IW þ 1479:0
R2 ¼ 0:999
(6a)
Yc ¼ 0:0047 IW2 1:6865 IW þ 1654:9
R2 ¼ 0:998
(6b)
Yc ¼ 0:0032 IW2 0:9849 IW þ 1600:1
R2 ¼ 0:981
(6c)
Wheat Yw ¼ 0:0116 IW2 þ 9:2538 IW þ 2911:2
R2 ¼ 0:989
(7a)
Yw ¼ 0:0355 IW2 þ 28:267 IW 1104:6
R2 ¼ 0:999
(7b)
Yw ¼ 0:0048 IW2 þ 13:832 IW 623:2
R2 ¼ 0990
(7c)
Cotton–wheat system Yec ¼ 0:0016 IW2 þ 4:0454 IW þ 1721:6
R2 ¼ 0:999
(8a)
Yec ¼ 0:0055 IW2 þ 8:9204 IW 229:43
R2 ¼ 0:997
(8b)
Yec ¼ 0:0025 IW2 þ 5:771 IW þ 189:5
R2 ¼ 0:997
(8c)
Unlike RCWP, ACWP increased for cotton crop and cotton– wheat system through reducing the number of post-sowing irrigations from four to one in all the soils. For wheat, it increased till the numbers of post-sowing irrigation were reduced to one in silt loam, two in sandy loam and three in loamy sand soils (Table 4).
Evapotranspiration and cotton–wheat system yield 3.5.
With reducing irrigation water from 800 to 350 mm in cotton– wheat system, the decrease in ET was 16% in silt loam, 19% in sandy loam and 20% in loamy sand. The corresponding reduction in cotton seed equivalent yield was 25, 35 and 40%, respectively. Like cotton and wheat crops individually, in the cotton–wheat system also the percent reduction was more in
Precipitation and crop yield
The response of cotton and wheat yields (averaged over irrigations) to precipitation (PRCP) was quadratic. The optima of PRCP estimated for silt loam, sandy loam and loamy sand soils from the Eqs. (9a)–(9c) in cotton and from Eqs. (10a)–(10c) in wheat were 739, 738 and 758 mm in cotton and 198, 236 and
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agricultural water management 84 (2006) 137–146
253 mm in wheat, respectively. For the cotton–wheat system (Eqs. (11a)–(11c)) the optima of precipitation were 955, 958 and 982 mm, respectively. Cotton R2 ¼ 0:81 (9a) Yc ¼ 0:0052 PRCP2 þ 7:6836 PRCP 659:30 Yc ¼ 0:0038 PRCP2 þ 2:6072 PRCP 230:63
R2 ¼ 0:80
(9b)
Yc ¼ 0:0036 PRCP2 þ 5:4576 PRCP 204:77
R2 ¼ 0:87
(9c)
R2 ¼ 0:99
(10a)
2
Yw ¼ 0:0387 PRCP þ 18:308 PRCP þ 2177:2 R ¼ 0:97
(10b)
Yw ¼ 0:0389 PRCP2 þ 19:655 PRCP þ 1227:6 R2 ¼ 0:99
(10c)
Wheat Yw ¼ 0:046 PRCP2 þ 18:271 PRCP þ 3251:4 2
Cotton–wheat system Yw ¼ 0:0038 PRCP2 þ 7:269 PRCP þ 191:56
R2 ¼ 0:961
(11a)
Yw ¼ 0:0032 PRCP2 þ 6:1374 PRCP þ 293:20 R2 ¼ 0:948
(11b)
2
2
Yw ¼ 0:0026 PRCP þ 5:1086 PRCP þ 350:54 R ¼ 0:847
(11c)
The effect of precipitation on RCWP in cotton was quadratic showing maximum RCWP value within precipitation range of 500–600 mm, irrespective of soil texture (Fig. 3). The variation in RCWP due to soil texture was found only at lower levels of irrigation (175 mm) and precipitation (less than 600 mm). In wheat RCWP increased linearly with precipitation. Precipitation, irrigation regime and soil texture showed an interaction. The RCWP was not affected by soil texture under higher irrigation regime (four irrigations), irrespective of precipitation. However, under lower irrigation regimes, the effects of soil texture became more pronounced in wheat than cotton especially at lesser amounts of precipitation. However, the interactive effects of soil texture, irrigation regime and precipitation on RCWP in cotton–wheat system were negligible.
3.6.
Real and apparent crop water productivity
Comparison of RCWP and ACWP (averaged over soil texture and irrigation regimes values given in Table 4) showed that the former was lesser than the latter in cotton (47%) and wheat (9%). Higher percentage of difference (between RCWP and ACWP) in cotton may be due to the fact that during its growing season, lesser irrigation water is required as most of the ET
Fig. 3 – Real crop water productivity (yield/ET) as influenced by precipitation in cotton, wheat and cotton–wheat system in relation to amount of irrigation water and soil texture.
agricultural water management 84 (2006) 137–146
requirement is met from rain water (654 262 mm, averaged over 18 years), whereas for wheat the major proportion of its ET is met from irrigation water as rainfall during crop growth is relatively low (117 59 mm). In wheat, with decreasing irrigation amount from 400 to 175 mm, ACWP increased upto 175, 250 and 325 mm on silt loam, sandy loam and loamy sand soils, respectively. ACWP increased with decreasing irrigation number, irrespective of soil texture in the cotton and cotton– wheat system. In cotton–wheat system, RCWP was 60% less than ACWP. In general, RCWP decreased with reducing the number of irrigation in the individual crop and in the system irrespective of soil texture.
4.
Conclusions
Water supply lesser than economic optima (400 mm in cotton and 400 mm in wheat) through reduced number of irrigations is of little or no use to enhance RCWP (marketable yield/ET) in cotton and wheat crops individually and of the system as well. Rather with lesser number of irrigations, RCWP is decreased due to relatively more decrease in yield than ET, while ACWP (marketable yield/irrigation water) increases. The enhancement of ACWP is independent of texture in cotton and cotton– wheat system and depends upon the soil texture in wheat. The RCWP increases with precipitation. The increase is of higher magnitude with lesser number of irrigations with coarse-textured soil in wheat crop. Therefore, under limited irrigation water availability only possible way to enhance RCWP in cotton–wheat system is through insured irrigation water application at flowering to boll formation stage of cotton and at grain development stage of wheat crop.
references
Arora, V.K., Prihar, S.S., Gajri, P.R., 1987. Synthesis of a simplified water use simulation model for predicting wheat yields. Water Resour. Res. 23, 903–910. Arora, V.K., Singh, C., Singh, K., 1997. Comparative assessment of soil water balance under wheat in a subtropical environment with simplified models. J. Agric. Sci. Camb. 128, 461–468. Aujla, M.S., Singh, C.J., Vashist, K.K., Sandhu, B.S., 1991. Evaluation of methods for irrigation of cotton (Gossypium hirsutum L.) in a canal irrigated area of southwest Punjab India. Arid Soil Res. Rehabil. 5, 225–234. Aujla, M.S., Thind, H.S., Butter, G.S., 2005. Cotton yield and water use efficiency at various levels of water and N through drip irrigation under two methods of planting. Agric. Water Manage. 71, 167–179. Bajwa, D.S., 1995. Modelling water use-yield relationships in wheat. M.Sc. thesis, Punjab Agricultural University, Ludhiana, India. Ball, R.A., Oosterhuis, D.M., Mauromoustakos, A., 1994. Growth dynamics of the cotton plant during water-deficit stress. Agron. J. 86, 788–795. Bruce, R.R., Shipp, C.D., 1962. Cotton fruiting as affected by soil moisture regimes. Agron. J. 61, 15–18. Bucks, D.A., Allen, S.G., Roth, R.L., Gardner, B.R., 1988. Short staple cotton under micro level basin irrigation methods. Irr. Sci. 9, 161–176.
145
Crozat, Y., Judais, V., Kasemsap, P., 1999. Age-related abscission pattern of cotton fruiting forms: timing of the end of abscission susceptibility in relation to water content and growth of the boll. Field Crops Res. 64, 261–272. Cruz, R.T., Jorden, W.R., Drew, M.C., 1990. Structural Changes and Water Flow in Water Stressed Sorghum Roots, Agronomy Abstracts ASA Madison, WI, p. 120. Deol, J.S., 2001. Growth and yield behaviour of cotton genotype in relation to planting density and growth regulators. Ph.D. dissertation, Punjab Agricultural University, Ludhiana, India. Doorenboss, J., Kassam, A.H., 1979. Yield Response to Water. FAO Irrigation and Drainage Paper No. 33. Food and Agriculture Organization of the United Nations, Rome, Italy. Doss, B.D., Ashley, D.A., Bennett, O.L., 1964. Effect of moisture regime and stage of plant growth on moisture use by cotton. Soil Sci. 98, 156–161. Gerik, T.A., Favour, K.L., Thaxton, P.M., El-Zik, K.M., 1996. Late season water stress in cotton: I. Plant growth, water use, and yield. Crop Sci. 36, 914–921. Grimes, D.W., Yamada, H., Dickens, W.L., 1969. Function for cotton (Gossypium hirsutum L.) production from irrigation and nitrogen variables: I. Yield and evapotranspiration. Agron. J. 61, 769–773. Grimes, D.W., El-Zik, K.M., 1982. Water management of cotton. Univ. California Div. Agric. Sci. Bull. 1904. Harman, W.L., Gerik, T.J., Williams, J.R., 2004. CROPMAN: a crop production/environmental simulation tool to enhance agricultural sustainability. In: Dilawari, V.K., Brar, L.S., Jalota, S.K. (Eds.), Proceedings of sustaining agriculture: problems and prospects, Punjab Agricultural University, Ludhiana, India, November 9–11, 2004, pp. 22–30. Hira, G.S., Jalota, S.K., Arora, V.K., 2004. Efficient management of water resources for sustainable cropping in Punjab. Res. Bull., Department of Soils, Punjab Agricultural University, Ludhiana, India. Howell, T.A., Davis, K.R., McCormick, R.L., Yamada, H., Walhood, V.T., Meek, D.W., 1984. Water use efficiency of narrow row cotton. Irr. Sci 5, 195–214. Jalota, S.K., 2004. Field Water Budgeting and Soil Moisture Profile. Scientific Report Department of Soils. Punjab Agricultural University, Ludhiana, pp. 24–37. Jalota, S.K., Arora, V.K., 2002. Model-based assessment of water balance components under different cropping systems in north west India. Agric. Water Manage. 5, 75–87. Jalota, S.K., Sood, A., Harman, W.L., 2006. Assessing the response of chickpea (Cicer aeritinum L.) yield to irrigation water on two soils in Punjab (India): a simulation analysis using the CROPMAN model. Agric. Water Manage. 79, 312–320. Jalota, S.K., Prihar, S.S., Gajri, P.R., 1985. Drainage losses under different schedules and stage sensitivity of wheat to water stress. Ind. J. Agric. Sci. 55, 574–581. Jalota, S.K., Prihar, S.S., 1986. Effect of atmospheric evaporativity, soil type and redistribution time on evaporation from bare soil. Aust. J. Soil Res. 25, 243–249. Jalota, S.K., Prihar, S.S., 1998. Reducing Soil Water Evaporation with Tillage and Straw Mulching. IOWA State University Press, Ames., USA, pp. 1–31. Jensen, M.E., 1968. Water consumption by agricultural plants. In: Kozlowski, T.T. (Ed.), Water Deficits in Plant Growth, vol. 1. Academic Press, New York, pp. 1–22. Kijne, J.W., Barker, R., Molden, D., 2003. Water Productivity in Agriculture. Limits and Opportunities for Improvement. CAB International, Wallingford, UK. Mahindra, K. (Ed.), 2003. Package of Practices for Crops of Punjab 2001–2002. Punjab Agricultural University, Ludhiana. McMichael, B.L., Hesketh, J.D., 1982. Field investigations of the response of cotton to water deficit. Field Crops Res. 5, 319–333.
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Praharaj, C.S., 1991. Irrigation and nitrogen management of American cotton (Gossypium hirsutum L.). M.Sc. thesis, Punjab Agricultural University, Ludhiana, India. Pettigrew, W.T., 2004. Moisture deficit effects on cotton yield, yield components and boll distribution. Agron. J. 96, 377–383. Radin, J.W., Mauney, J.R., Krerridge, P.C., 1989. Water uptake by cotton roots during fruit filling in relation to irrigation frequency. Crop Sci. 29, 1000–1005. Radin, J.W., Reavees, L.L., Mauney, J.R., French, O.F., 1992. Yield enhancement in cotton by frequent irrigations during fruiting. Agron. J. 84, 551–557. Sammis, T.W., 1981. Yield of alfalfa and cotton as influenced by irrigation. Agron. J. 73, 323–329.
Taylor, H.M., Klepper, B., 1974. Water relations of cotton. 1. Root growth and water use as related to top growth and soil water content. Agron. J. 66, 584–588. Yadav, R.P., 2002. Validation of COTTAM model under different sowing dates, orientation and method of planting and irrigation application. M.Sc. thesis, Punjab Agricultural University, Ludhiana, India. Williams, J.R., Jones, C.A., Dyke, P.T., 1990. The EPIC Model Chap. 2. EPIC-Erosion/-Productivity Impact Calculator: 1. Model Documentation. Tech Bull. No. 1768. U.S. Department of Agriculture, Washington, DC, pp. 3–92. Zwart, S.J., Bastiaanssen, W.G.M., 2004. Review of measured crop water productivity values for irrigated wheat, rice, cotton and maize. Agric. Water Manage. 69, 115–133.
available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/agwat
Crop water productivity of cotton (Gossypium hirsutum L.)– wheat (Triticum aestivum L.) system as influenced by deficit irrigation, soil texture and precipitation S.K. Jalota a,*, Anil Sood b, G.B.S. Chahal a, B.U. Choudhury b a b
Department of Soils, Punjab Agricultural University, Ludhiana 141004, India Punjab Remote Sensing Centre, Ludhiana, India
article info
abstract
Article history:
In the agricultural sector there is an urgent need to use dwindling water resources efficiently
Accepted 5 February 2006
and enhancing crop water productivity (CWP). At the farm level, reducing evapotranspira-
Published on line 17 April 2006
tion (ET) through deficit irrigation (lesser number of irrigations) and identification of the most sensitive crop growth stage to water stress has been reported as one of the ways to
Keywords:
enhance CWP. Although information on CWP in relation to irrigation water of some cereal
Crop water productivity
crops based on the field experimental data is available in the literature. However, the
Cotton–wheat system
influence of soil texture, precipitation and deficit irrigation regime and their interactions on
Soil texture
CWP is not well-documented. We explored these components in cotton (Gossypium hirsutum
Irrigation water
L.)–wheat (Triticum aestivum L.) cropping system through simulation analysis, which other-
Precipitation
wise are difficult to be explained through field experimentation. The simulated results showed that by reducing the amount of irrigation water input below economic optima, both the yield and ET of cotton and wheat crops were reduced and consequently CWP to varying magnitudes depending upon soil texture, precipitation and irrigation regimes. With reducing post-sowing irrigation water from 300 to 75 mm, the decrease in CWP in silt loam, sandy loam and loamy sand soils were 15, 4 and 1% for cotton and 8, 36 and 55% for wheat, respectively, indicating higher decrease in CWP for wheat than for cotton, and in coarsetextured than fine-textured soils. Precipitation increased the CWP. The increase was more in wheat crop on coarse-textured soil with less number of irrigations. Averaged over soil texture and irrigation regimes, real CWP (RCWP) (yield/ET) was 47 and 9 and 60% of apparent CWP (ACWP) (yield/irrigation water) in cotton, wheat and cotton–wheat system, respectively. The crop growth stages found to be most sensitive to water stress were from flowering to boll formation in cotton and grain development stage in wheat. # 2006 Elsevier B.V. All rights reserved.
1.
Introduction
Large-scale adoption of rice (Oryza sativa L.)–wheat (Triticum aestivum L.) cropping system in Punjab state of north-west India during the last 30 years has resulted in over-exploitation
of ground water resources. Ground water being the main source of irrigation water, which is declining in 80% area of Punjab at the rate of 0.4 m year1 and in some areas, the decline rate is even more than 0.9 m (Hira et al., 2004). With the shrinking ground water resources and higher energy
* Corresponding author. Tel.: +91 1612402649; fax: +91 1612400945. E-mail address: [email protected] (S.K. Jalota). 0378-3774/$ – see front matter # 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.agwat.2006.02.003
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agricultural water management 84 (2006) 137–146
consumption for pumping, the agriculture sector faces the challenge to sustain the productivity of rice–wheat system. Alternatives need to be explored to use existing water and energy resources efficiently. Cotton (Gossypium hirsutum L.) crop grown under deficit irrigation (lesser number of irrigations) and less evapotranspiration (ET) requirements is a possible alternative to rice to return savings in water and energy. At present farmers grow cotton in rotation with wheat in only 10% of the total cultivated area with four to five irrigations to each crop through border method. The depth of irrigation water is nearly 75 mm per irrigation, which costs Indian rupees (Rs) 250 ha1 for its pumping and application. The recommended dose of nitrogen, phosphorous and potash is 75, 30 and 0 kg ha1 to cotton and 120, 60 and 60 kg ha1 to wheat, respectively. With these irrigation and fertilizer recommendations, the average observed yields of cotton and wheat in Punjab are 1242 and 4221 kg ha1, respectively (Mahindra, 2003). Numerous studies showing the response of water deficit on cotton crop dynamics, leaf expansion and root growth (Ball et al., 1994), abscission pattern (Crozat et al., 1999), flowering, boll formation and its distribution, yield components (Pettigrew, 2004; Gerik et al., 1996; Radin et al., 1992; McMichael and Hesketh, 1982; Grimes et al., 1969; Bruce and Shipp, 1962) are available in literature. For this region Aujla et al. (1991, 2005) have conducted some field trials to study the effects of different methods of irrigation on cotton yield but information about crop water productivity (CWP), which is calculated as marketable yield over actual ET and its potential enhancement for cotton and wheat crops grown in a rotation is lacking. To estimate yield response and CWP in relation to deficit irrigation, adequate observations are required over a wide range of irrigation amounts. Since farmers do not vary their irrigation levels significantly over the years, and often do not use recommended level of water inputs, field data typically may not produce very useful inferences. Research station trials conducted over a broad range of input data and irrigation treatments can also provide data but need to be replicated for a number of years to arrive at accurate management advice. Another source of data information is the pre-validated crop growth models. To enhance CWP through deficit irrigation as suggested by Kijne et al. (2003), quantitative specific information for Punjab on yield and ET of cotton and wheat crops in rotation under different irrigation water regimes, and the most sensitive stages of these crops to water stress needs to be developed. Such information can be obtained either through field experiments or generating data using an appropriate crop model. Recently, Zwart and Bastiaanssen (2004) reviewed the overall CWP of wheat, rice, maize and cotton crops from the
experimental data and ascribed variability of CWP to climate, irrigation water management and nitrogen management. However, in reality CWP is likely to be significantly affected by soil texture and precipitation in addition to amount of irrigation water input. Therefore, a simulation analysis was done to (1) elucidate the effects of different irrigation regimes, soil texture and precipitation on crop yield, ET, CWP and identification of the most sensitive crop growth stage to water stress from the data generated using an existing model and (2) select the optimum irrigation water regime to achieve maximum CWP in cotton–wheat cropping system.
2.
Materials and methods
The crop management and production model CROPMAN was used to simulate the crop yield and CWP response to irrigation water for the cotton–wheat system. The soils used were Randhirpur silt loam, Tulewal sandy loam and Fatehpur loamy sand, which varied in texture, compactness level and hydraulic properties (Table 1). Plant population, harvest index, heat units, plant height and rooting depth used in the model were obtained from experimental data (Praharaj, 1991) and rest were taken as default values (Jalota et al., 2006; Harman et al., 2004). For application of the model, crop budget representing the crop management information on field preparation operations, date of sowing, fertilizer application (amount and time), irrigation scheduling, pesticides/herbicides application (amount and time) and harvesting dates for the cotton–wheat system (Table 2) were prepared using the recommendations given in the package of practices of Punjab Agricultural University, Ludhiana (Mahindra, 2003). The crop yields simulated with the model were compared with the yields observed from experiments by different researchers in this region or those reported in the package of practices by Punjab Agricultural University, Ludhiana. Four irrigation treatments were simulated for 18 years (1982–2000) in cotton–wheat system. These were T1, T2, T3 and T4 referring to 400 mm irrigation water (four post-sowing irrigations of 75 mm each and one pre-sowing irrigation of 100 mm), 325 mm (three post-sowing irrigations of 75 mm each and one pre-sowing irrigation of 100 mm), 250 mm (two postsowing irrigations of 75 mm each and one pre-sowing irrigation of 100 mm) and 175 mm (one post-sowing irrigation of 75 mm and one pre-sowing irrigation of 100 mm), respectively, were analysed. The dates of post-sowing irrigations are presented in Table 2. For all the treatments the maximum amount of irrigation water was limited to 400 mm for each
Table 1 – Characteristics of three soils used in simulation Soil parameter 1
Sand (g kg ) Silt (g kg1) Clay (g kg1) Bulk density (mg m3) Wilting point (m3 m3) Field capacity (m3 m3) Saturated hydraulic conductivity (mm s1)
Randhirpur silt loam 60 690 250 1.40 0.11 0.32 4 103
Tulewal sandy loam 710 120 170 1.50 0.08 0.24 7 103
Fatehpur loamy sand 820 100 80 1.65 0.04 0.13 11 103
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Table 2 – Details of the management file for cotton-wheat cropping system Year 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Month 11 11 11 11 11 11 11 11 11 12 12 12 1 3 3 4 4 5 5 5 5 5 5 5 5 5 6 6 7 7 7 7 7 7 8 8 8 8 8 9 9 9 9 9 9 9 11 11 11
Day 21 22 22 23 23 24 24 24 24 24 27 30 26 5 30 15 15 7 8 10 10 11 11 12 12 12 12 15 5 15 16 18 24 31 9 16 20 26 30 5 13 16 20 25 28 30 14 19 19
Operation
Equipment/method
Plow Disking Disking Plow Plow Plow Sowing Fertilizer Fertilizer Irrigation Herbicide Fertilizer Irrigation Irrigation Irrigation Harvest Kill Plow Irrigation Disking Disking Plow Plow Plow Fertilizer Sowing Irrigation Fertilizer Pesticide Irrigation Pesticide Fertilizer Pesticide Pesticide Pesticide Pesticide Irrigation Pesticide Pesticide Pesticide Pesticide Pesticide Pesticide Pesticide Irrigation Pesticide Irrigation Harvest Kill
Berm maker Offset Offset Cultivator Cultivator Planker Grain drill Hand Hand Flooda Spray hand Hand Flood Flood Flood Combine
Dates of irrigations in different treatments in wheat T1 12/24 1/26 3/5 T2 12/24 3/5 3/25 T3 12/24 3/5 T4 12/24 Dates of irrigation in different treatments in cotton T1 6/12 7/15 8/20 T2 6/12 8/20 9/28 T3 6/12 9/28 T4 6/12 a
Irrigation method is border.
Berm maker Flood Offset Offset cultivator cultivator Planker Hand Hand Flood Hand Hand spray Flood Hand spray Hand Hand spray Hand spray Hand spray Hand spray Flood Hand spray Hand spray Hand spray Hand spray Hand spray Hand spray Hand spray Flood Hand spray Flood
3/25
9/28
Source
18-46-0 Urea (46-0-0) 2,4-D Urea (46-0-0)
Amount/rate
130.0 kg ha1 78.0 kg ha1 75 mm 0.63 kg ha1 130.0 kg ha1 75 mm 75 mm 75 mm
Depth
10 mm 10 mm
10 mm
75 mm
18-46-00
Urea 46-0-0 Thiodone50 WSB Ammo Urea 46-0-0 LORSBAN 30 FL Provado Ammo LORSBAN 30 FL Thiodone 50 WSB Provado Metasystox LORSBAN 30 FL Provado LORSBAN 30 FL CURACON 8E LORSBAN 30 FL
65.00 kg ha1 50000 plants ha1 75 mm 137 kg ha1 4.94 kg ha1 75 mm 0.70 l ha1 163 kg ha1 2.47 l ha1 1.51 l ha1 0.70 l ha 2.47 l ha1 75 mm 4.94 kg ha1 1.51 l ha1 1.36 l ha1 2.47 l ha1 1.51 l ha1 2.47 l ha1 3.37 l ha1 75 mm 2.47 l ha1 100 mm
10 mm 2350 HU
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agricultural water management 84 (2006) 137–146
potential evapotranspiration (mm), i an integer representing the number of crop growth sub-periods, P the multiplication and g the sensitivity factor.
2.1.
Fig. 1 – Time trends of pan evaporation and rain at Ludhiana (18 years average).
crop. This amount was selected from an independent simulation study on economic optima of irrigation water (Jalota, 2004), in which economic water optima was found 408 mm for cotton and 407 mm for wheat. The meteorological data for the study area was collected from the meteorological station located at Punjab Agricultural University, Ludhiana. The average annual rainfall, based on data from 1982 to 2000, at the station was 787 260 mm, out of which 117 59 and 654 262 mm occurred during the wheat and cotton growing seasons, respectively (Fig. 1). The probabilities of rainfall for the range of 0–100, 100–200 and 200–300 mm during wheat crop were 0.33, 0.44 and 0.23, whereas for cotton, these were 0.11, 0.33, 0.28, 0.11 and 0.17 for the range of 200–400, 400–600, 600–800, 800–1000 and 1000–1200 mm, respectively. CWP is generally defined as marketable yield/ET, but economists and farmers are most concerned about the yield per unit of irrigation water applied. The former can be termed as real crop water productivity (RCWP), while the latter as apparent crop water productivity (ACWP). These terms were estimated for individual cotton and wheat crops from the simulated data for cotton–wheat system. The marketable yields of cotton, wheat and cotton–wheat system were cotton seed, wheat grain and cotton seed equivalent, respectively. The cotton seed equivalent yield in cotton–wheat system was calculated as: Cotton seed equivalent yield ¼ wheat grain yield MSP of wheat : MSP cotton seed Here MSP is minimum support price (Rs 6.2 for wheat grain and Rs 17.0 for cotton seed per kilogram). The sensitivity of cotton seed yield and wheat grain yield to ET at different crop stages was determined using the following multiplicative model given by Jensen (1968). Y g n Y ET ¼ Ymax ETmax i¼1 where Y is actual yield (kg ha1), Ymax the maximum yield (kg ha1), ET the actual evapotranspiration (mm), ETmax the
CROPMAN model
A number of simulation models are available for studying the response of individual crops to management practices. However, since the crops growing in a system have influence on each other, there is a need to simulate the cropping system as a whole. CROPMAN is a window-based, multi-year, multicrop, daily time step cropping system simulation model and contains the EPIC crop/environmental simulation model as an engine. In this model, there is a provision to define cropping system as unique combination of the rotation (crop order), as well as the type, timing, rate and method for each operation associated with the rotation. This model has a huge database on tillage equipments, fertilizers, pesticides, and crop and soil parameters. The user can modify the cropping system and management using the management editor module. The equations describing the relationships between rainfall, ET, runoff, erosion, and production are described in a document by Williams et al. (1990). The CROPMAN model can be used to determine the effect of management strategies on agricultural production and soil and water resources. The major components in the model are weather, hydrology, erosion–sedimentation, nutrient cycling, pesticide fate, plant growth, soil temperature, tillage, economics and plant environment control. The input data required for the model are weather, soil, crop and management practices data. The weather data includes solar radiation, maximum and minimum temperatures, mean relative humidity, rain and wind speed. The soil data comprises of soil layer thickness, bulk density of soil layer, wilting point, field capacity, sand, silt, organic nitrogen, pH, sum of bases, organic matter, CaCO3, CEC, initial NO3 concentration, phosphorous, phosphorous sorption rate and saturated hydraulic conductivity The crop data requires biomass energy ratio, biomass energy decline rate, fraction of root weight at emergence, fraction of root weight at maturity, heat units required for germination, LAI decline factor, lower limit of harvest index, maximum crop height, maximum LAI, maximum root depth, maximum stomatal conductance, minimum temperature for plant growth, optimum temperature for plant growth, vapour pressure deficit, threshold vapour pressure deficit, vapour pressure deficit value and harvest index. This model also provides the user with the post simulation analysis for comparing the results obtained under varying soil types and management practices.
3.
Results and discussion
The simulated seasonal ET across soil texture and irrigation regimes in cotton (from 508 to 700 mm) and wheat (from 178 to 381 mm) were within the range reported by some researchers from field experiments in the region (Praharaj, 1991; Jalota et al., 1985; Arora et al., 1987, 1997) and elsewhere (Doss et al., 1964; Howell et al., 1984; Doorenboss and Kassam, 1979; Grimes and El-Zik, 1982). The simulated yields were also within the range as reported by different researchers for this
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Table 3 – Observed and simulated crop yields Simulated (t ha1)
Crop
Observed (t ha1)
Source
Wheat 5.13 5.45 5.25 5.39 3.56 3.48 2.20
5.20 5.40 5.25 5.24 4.67 3.84 3.49
2.22 2.29 0.98 1.49 1.56 1.07
2.30 2.30 1.03 1.49 1.35 1.30
(four irrigations) (two irrigations) (six irrigations) (three irrigations) (two irrigations) (one irrigation)
Mahindra (2003) Jalota et al. (1985) Bajwa (1995)
Cotton
region (Table 3). The statistical analysis of simulated and observed yields showed that the simulated average (over experiments) yield and standard deviation were 1.60 0.55 and 1.62 0.54 t ha1 in cotton, 4.35 1.27 and 4.73 0.77 t ha1 in wheat, respectively. The normalised root mean square difference (RMSD) was 9 and 7% of the measured yields of cotton and wheat, respectively, which indicated that the model can satisfactorily be used for studying yield response to irrigation in cotton–wheat cropping system.
3.1.
Evapotranspiration and cotton seed yield
In general, ET and yield of both the crops (cotton and wheat) decreased with decreasing number of post-sowing irrigations (Table 4). For example, with reducing post-sowing irrigation water from 300 (four irrigations) to 75 mm (one irrigation), ET loss from cotton crop was decreased by 128 (18.3%), 55 (9.3%) and 50 mm (8.9%) in silt loam, sandy loam and loamy sand soils, respectively. The corresponding reductions in yield were 674 (30.8%), 217 (12.5%) and 179 kg ha1 (10.8%), respectively. This showed that with decreasing irrigation water, there was reduction in both the parameters constituting CWP, i.e. yield and ET in all the soils. The magnitude of percent reduction was more in finer textured compared to coarse-textured soils. Amongst the two parameters, the percent reduction was more in the yield than the ET in all the three soils. The cotton seed yield (Yc) was related to ET in silt loam, sandy loam and loamy sand soils following the Eqs. (2a)–(2c), respectively. Yc ¼ 4:8 ET 1081
R2 ¼ 0:84
(2a)
Yc ¼ 4:9 ET 1250
R2 ¼ 0:97
(2b)
Yc ¼ 4:6 ET 875
R2 ¼ 0:97
(2c)
where Yc is yield in kg ha1 and ET in mm. R2 is the coefficient of determination. These relations indicate that to obtain minimum cotton seed yield, water amounting 225 mm from silt loam, 255 mm from sandy loam and 190 mm from loamy sand soils has to be consumed as ET. Sammis (1981) also reported linear relationship between cotton yield and ET. However, from long-term
Mahindra (2003) Praharaj (1991) (M.Sc. thesis) Deol (2001) (Ph.D. thesis) Singh (1997) (Ph.D. thesis) Yadav (2002) (Ph.D. thesis)
studies, Grimes and El-Zik (1982) suggested a slight curvature to the function considering the nature of cotton reproductive development and water relations. An attempt was made to fit that relation (Eq. (3)), but this showed no improvement over the simple linear relation. Yc ¼ 3114:43 þ 0:0125 ET þ 199:42 ET1=2
R2 ¼ 0:84
(3)
Corresponding to ET of 700, 610 and 560 mm for silt loam, sandy loam and loamy sand soils, the simulated cotton seed yields with the CROPMAN model were 2187, 1734 and 1708 kg ha1, respectively. With Eqs. (2a)–(2c), estimated yields were 2279, 1739 and 1701 kg ha1, while with Eq. (3) the respective values were 2165, 1814 and 1607 kg ha1. The lesser differences (4–5%) in the yield with these two relations showed that crop yield and total ET under deficit irrigation were linearly related. It is therefore theorized that curvilinear relationship may hold true in adequately irrigated cotton crops only. Though the variation in yield was explained reasonably well (as evident from higher values of R2, ranging from 0.84 to 0.97) with total ET, some researchers have documented that yields of crops are sensitive to ET differentially in different growth stages. In field studies, Radin et al. (1992) observed in cotton crop, the peak fruiting period was the most sensitive stage to water stress. In this study we apportioned the fruiting period further into two sub-periods, viz. flowering to boll formation (100–120 days) and boll formation to boll opening (120–150 days) as occurring in the region. Using the simulated yield, ET and ETmax, values in these sub-periods along with vegetative (0–80 days) and vegetative to flowering (80–100 days) periods, sensitivity factors were estimated by employing the multiplicative model of Jensen (1968). The sensitivity factor for vegetative stage, vegetative to flowering, flowering to boll formation and boll formation to boll opening stages were 2.286, 1.680, 2.732 and 1.867, respectively. The statistically significant values at 5% probability of the sensitivity factor for flowering to boll formation stage showed that boll formation period was more sensitive to water stress than other stages (Fig. 2). This gives support to the earlier observations by Radin et al. (1989), which indicated that irrigation at this stage increased leaf water potential and boll dry mass per
0.86 0.87 0.87 0.26 0.27 0.30 1513 1517 1529
0.69 0.61 0.62 0.28 0.27 0.30 1720 1530 1540
0.59 0.49 0.50 0.29 0.28 0.31 1918 1600 1630
0.55 0.43 0.25 0.31 0.29 0.31 2187 1734 1708
572 554 508 Silt loam Sandy loam Loamy sand T4
620 564 517 Silt loam Sandy loam Loamy sand T3
651 577 534 Silt loam Sandy loam Loamy sand T2
700 609 558 Silt loam Sandy loam Loamy sand
T1, 1 mm 100 mm pre-sowing and 4 mm 75 mm post-sowing; T2, 1 mm 100 mm pre-sowing and 3 mm 75 mm post-sowing; T3, 1 mm 100 mm pre-sowing and 2 mm 75 mm post-sowing; T4, 1 mm 100 mm pre-sowing and 1 mm 75 mm post-sowing.
0.84 0.63 0.55 0.31 0.27 0.27 2933 2203 1915 942 802 697 2.23 1.08 0.60 3895 1881 1058 340 236 178
1.15 0.80 0.59
0.67 0.58 0.48 0.33 0.33 0.32 3345 2881 2414 1026 872 763 1.78 1.48 0.96 4456 3705 2396 372 297 236
1.20 1.25 1.02
0.56 0.49 0.45 0.34 0.34 0.35 3649 3195 2919 1082 936 830 1.46 1.35 1.09 4747 4374 3536 376 346 285
1.26 1.27 1.24
0.49 0.42 0.40 0.35 0.34 0.37 3913 3375 3193 1120 984 871 1.18 1.13 1.02 4734 4500 4073 381 364 303
1.24 1.24 1.34
RCWP Yield (kg ha1) Yield (kg ha ) ET (mm) RCWP Yield (kg ha )
T1
Soil type
ET (mm)
1
Cotton
ACWP
1
Wheat
RCWP
ACWP
ET (mm)
Cotton–wheat system
ACWP
agricultural water management 84 (2006) 137–146
Treatment
Table 4 – Effect of irrigation regime and soil texture on evapotranspiration (ET), yield (Y), real crop water productivity (RCWP, Y/ET) and apparent crop water productivity (ACWP, Y/irrigation water)
142
Fig. 2 – Relative cotton seed yield and relative evapotranspiration during vegetative growth (0–80days), vegetative to flowering (80–100 days), flowering to boll formation (100–120 days), boll formation to boll opening (120–150 days) stages in cotton and vegetative growth (0–90 days), flowering (90–120 days), grain formation and development (120–150 days) and maturity (150–160 days) in wheat.
plant by 30–50% over the control. They advocated that water is necessary at fruiting stage because there is deterioration of root system during fruiting in the cotton crop on long irrigation cycles. Taylor and Klepper (1974) reported that when the cotton crop becomes water stressed during fruit filling, declines in root length were severe and recovery was much slower than stress during vegetative growth stage. It may also be related to suberization of roots (Cruz et al., 1990) and nutrient transport. The RCWP values for cotton seed under different irrigation treatments and soil textures ranged from 0.26 to 0.31 kg m3 (Table 4). This range was well within the range reported in literature (Bucks et al., 1988) or lower in some cases (Doorenboss and Kassam, 1979; Zwart and Bastiaanssen, 2004). Results of the present study indicated that RCWP declines with decreasing amount of irrigation water. It may be ascribed to relatively more reduction in
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agricultural water management 84 (2006) 137–146
yield parameter compared to ET as described earlier. RCWP was also lesser in medium-textured soils. Higher RCWP in finer and coarser textured soils was due to higher crop yield in the former and lesser ET in the latter. Reduced ET in coarser textured soils is due to lesser evaporation component (Jalota and Arora, 2002) owing to self-mulching effect (Jalota and Prihar, 1986).
3.2.
Yec ¼ 5:4 ET 2211
R2 ¼ 0:99
(5a)
Yec ¼ 6:4 ET 2813
R2 ¼ 0:95
(5b)
Yec ¼ 7:4 ET 3232
R2 ¼ 0:99
(5c)
Evapotranspiration and wheat grain yield
Like cotton, ET under wheat was reduced by 41 (10.8%), 128 (35.2%) and 125 mm (34.3%) in silt loam, sandy loam and loamy sand soils, respectively. The corresponding reductions in yields were 839 (17.7%), 2619 (58.2%) and 3015 kg ha1 (74.0%), respectively (Table 4). Unlike cotton, the magnitude of reduction was lesser in fine- as compared to coarse-textured soils. Yield of wheat grain (Yw) and ET in silt loam, sandy loam and loamy sand soils followed the relations as 4(a)–(c), respectively. Yw ¼ 25:5 ET 4894
R2 ¼ 0:75
(4a)
Yw ¼ 21:9 ET 3152
R2 ¼ 0:89
(4b)
Yw ¼ 25:0 ET 3551
R2 ¼ 0:92
(4c)
These Eqs. (4a)–(4c) indicated that like cotton, in wheat 191, 144 and 142 mm of water in silt loam, sandy loam and loamy sand soils, respectively, has to be used in ET to obtain minimum grain yield. Though the variation in yield with total ET was explained reasonably (R2 = 0.89–0.92) as linear relation, sensitivity factor for different growth stages of wheat crop, viz. vegetative (0–90 days), flowering (90–120 days), grain formation and development (120–150 days) and maturity stages (150–160 days) were 0.468, 0.359, 4.546 and 0.081, respectively. The sensitivity factor for grain formation and development period being statistically significant at 5% probability reflected that this period was relatively more sensitive to water stress than any other stage of crop growth (Fig. 2). This reaffirms the earlier findings of both Jalota et al. (1985) and Arora et al. (1987). The RCWP values for wheat under different irrigation treatments and soil texture indicated that RCWP decreased with reduced irrigation amount in all the three soils studied (Table 4). The RCWP was less in silt loam (1.24 kg m3) under higher irrigation regime and in loamy sand (0.59 kg m3) under lower irrigation regime. This may be ascribed to comparatively higher ET in the former and lower yield in the latter case. With adequate soil moisture in fine-textured soil, more evaporation from bare soil and more ET from cropped soil has been reported by Jalota and Prihar (1998) and Jalota and Arora (2002).
3.3.
yield than the ET in all the three soils. The magnitude of reduction was more in coarse- than fine-textured soil. The equivalent cotton seed yield (Yec) was related to ET in silt loam, sandy loam and loamy sand soils following the Eqs. (5a)–(5c), respectively.
The RCWP values for the cotton–wheat system decreased with reducing the number of irrigations in all the three soils (Table 4). The reduction was 11, 20 and 27% in silt loam, sandy loam and loamy sand soil, respectively.
3.4.
Irrigation water and crop yield
Cotton and wheat yield increased linearly with increasing number of irrigations applied. The values of coefficient of determination ranging from 0.78 to 0.93 with linear relationship were improved further with second order polynomial to 0.98–0.99 in cotton (Eqs. (6a)–(6c)), wheat (Eqs. (7a)–(7c)) and cotton–wheat system (Eqs. (8a)–(8c)) for silt loam, sandy loam and loamy sand soils, respectively. Cotton Yc ¼ 0:0054 IW2 0:4015 IW þ 1479:0
R2 ¼ 0:999
(6a)
Yc ¼ 0:0047 IW2 1:6865 IW þ 1654:9
R2 ¼ 0:998
(6b)
Yc ¼ 0:0032 IW2 0:9849 IW þ 1600:1
R2 ¼ 0:981
(6c)
Wheat Yw ¼ 0:0116 IW2 þ 9:2538 IW þ 2911:2
R2 ¼ 0:989
(7a)
Yw ¼ 0:0355 IW2 þ 28:267 IW 1104:6
R2 ¼ 0:999
(7b)
Yw ¼ 0:0048 IW2 þ 13:832 IW 623:2
R2 ¼ 0990
(7c)
Cotton–wheat system Yec ¼ 0:0016 IW2 þ 4:0454 IW þ 1721:6
R2 ¼ 0:999
(8a)
Yec ¼ 0:0055 IW2 þ 8:9204 IW 229:43
R2 ¼ 0:997
(8b)
Yec ¼ 0:0025 IW2 þ 5:771 IW þ 189:5
R2 ¼ 0:997
(8c)
Unlike RCWP, ACWP increased for cotton crop and cotton– wheat system through reducing the number of post-sowing irrigations from four to one in all the soils. For wheat, it increased till the numbers of post-sowing irrigation were reduced to one in silt loam, two in sandy loam and three in loamy sand soils (Table 4).
Evapotranspiration and cotton–wheat system yield 3.5.
With reducing irrigation water from 800 to 350 mm in cotton– wheat system, the decrease in ET was 16% in silt loam, 19% in sandy loam and 20% in loamy sand. The corresponding reduction in cotton seed equivalent yield was 25, 35 and 40%, respectively. Like cotton and wheat crops individually, in the cotton–wheat system also the percent reduction was more in
Precipitation and crop yield
The response of cotton and wheat yields (averaged over irrigations) to precipitation (PRCP) was quadratic. The optima of PRCP estimated for silt loam, sandy loam and loamy sand soils from the Eqs. (9a)–(9c) in cotton and from Eqs. (10a)–(10c) in wheat were 739, 738 and 758 mm in cotton and 198, 236 and
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253 mm in wheat, respectively. For the cotton–wheat system (Eqs. (11a)–(11c)) the optima of precipitation were 955, 958 and 982 mm, respectively. Cotton R2 ¼ 0:81 (9a) Yc ¼ 0:0052 PRCP2 þ 7:6836 PRCP 659:30 Yc ¼ 0:0038 PRCP2 þ 2:6072 PRCP 230:63
R2 ¼ 0:80
(9b)
Yc ¼ 0:0036 PRCP2 þ 5:4576 PRCP 204:77
R2 ¼ 0:87
(9c)
R2 ¼ 0:99
(10a)
2
Yw ¼ 0:0387 PRCP þ 18:308 PRCP þ 2177:2 R ¼ 0:97
(10b)
Yw ¼ 0:0389 PRCP2 þ 19:655 PRCP þ 1227:6 R2 ¼ 0:99
(10c)
Wheat Yw ¼ 0:046 PRCP2 þ 18:271 PRCP þ 3251:4 2
Cotton–wheat system Yw ¼ 0:0038 PRCP2 þ 7:269 PRCP þ 191:56
R2 ¼ 0:961
(11a)
Yw ¼ 0:0032 PRCP2 þ 6:1374 PRCP þ 293:20 R2 ¼ 0:948
(11b)
2
2
Yw ¼ 0:0026 PRCP þ 5:1086 PRCP þ 350:54 R ¼ 0:847
(11c)
The effect of precipitation on RCWP in cotton was quadratic showing maximum RCWP value within precipitation range of 500–600 mm, irrespective of soil texture (Fig. 3). The variation in RCWP due to soil texture was found only at lower levels of irrigation (175 mm) and precipitation (less than 600 mm). In wheat RCWP increased linearly with precipitation. Precipitation, irrigation regime and soil texture showed an interaction. The RCWP was not affected by soil texture under higher irrigation regime (four irrigations), irrespective of precipitation. However, under lower irrigation regimes, the effects of soil texture became more pronounced in wheat than cotton especially at lesser amounts of precipitation. However, the interactive effects of soil texture, irrigation regime and precipitation on RCWP in cotton–wheat system were negligible.
3.6.
Real and apparent crop water productivity
Comparison of RCWP and ACWP (averaged over soil texture and irrigation regimes values given in Table 4) showed that the former was lesser than the latter in cotton (47%) and wheat (9%). Higher percentage of difference (between RCWP and ACWP) in cotton may be due to the fact that during its growing season, lesser irrigation water is required as most of the ET
Fig. 3 – Real crop water productivity (yield/ET) as influenced by precipitation in cotton, wheat and cotton–wheat system in relation to amount of irrigation water and soil texture.
agricultural water management 84 (2006) 137–146
requirement is met from rain water (654 262 mm, averaged over 18 years), whereas for wheat the major proportion of its ET is met from irrigation water as rainfall during crop growth is relatively low (117 59 mm). In wheat, with decreasing irrigation amount from 400 to 175 mm, ACWP increased upto 175, 250 and 325 mm on silt loam, sandy loam and loamy sand soils, respectively. ACWP increased with decreasing irrigation number, irrespective of soil texture in the cotton and cotton– wheat system. In cotton–wheat system, RCWP was 60% less than ACWP. In general, RCWP decreased with reducing the number of irrigation in the individual crop and in the system irrespective of soil texture.
4.
Conclusions
Water supply lesser than economic optima (400 mm in cotton and 400 mm in wheat) through reduced number of irrigations is of little or no use to enhance RCWP (marketable yield/ET) in cotton and wheat crops individually and of the system as well. Rather with lesser number of irrigations, RCWP is decreased due to relatively more decrease in yield than ET, while ACWP (marketable yield/irrigation water) increases. The enhancement of ACWP is independent of texture in cotton and cotton– wheat system and depends upon the soil texture in wheat. The RCWP increases with precipitation. The increase is of higher magnitude with lesser number of irrigations with coarse-textured soil in wheat crop. Therefore, under limited irrigation water availability only possible way to enhance RCWP in cotton–wheat system is through insured irrigation water application at flowering to boll formation stage of cotton and at grain development stage of wheat crop.
references
Arora, V.K., Prihar, S.S., Gajri, P.R., 1987. Synthesis of a simplified water use simulation model for predicting wheat yields. Water Resour. Res. 23, 903–910. Arora, V.K., Singh, C., Singh, K., 1997. Comparative assessment of soil water balance under wheat in a subtropical environment with simplified models. J. Agric. Sci. Camb. 128, 461–468. Aujla, M.S., Singh, C.J., Vashist, K.K., Sandhu, B.S., 1991. Evaluation of methods for irrigation of cotton (Gossypium hirsutum L.) in a canal irrigated area of southwest Punjab India. Arid Soil Res. Rehabil. 5, 225–234. Aujla, M.S., Thind, H.S., Butter, G.S., 2005. Cotton yield and water use efficiency at various levels of water and N through drip irrigation under two methods of planting. Agric. Water Manage. 71, 167–179. Bajwa, D.S., 1995. Modelling water use-yield relationships in wheat. M.Sc. thesis, Punjab Agricultural University, Ludhiana, India. Ball, R.A., Oosterhuis, D.M., Mauromoustakos, A., 1994. Growth dynamics of the cotton plant during water-deficit stress. Agron. J. 86, 788–795. Bruce, R.R., Shipp, C.D., 1962. Cotton fruiting as affected by soil moisture regimes. Agron. J. 61, 15–18. Bucks, D.A., Allen, S.G., Roth, R.L., Gardner, B.R., 1988. Short staple cotton under micro level basin irrigation methods. Irr. Sci. 9, 161–176.
145
Crozat, Y., Judais, V., Kasemsap, P., 1999. Age-related abscission pattern of cotton fruiting forms: timing of the end of abscission susceptibility in relation to water content and growth of the boll. Field Crops Res. 64, 261–272. Cruz, R.T., Jorden, W.R., Drew, M.C., 1990. Structural Changes and Water Flow in Water Stressed Sorghum Roots, Agronomy Abstracts ASA Madison, WI, p. 120. Deol, J.S., 2001. Growth and yield behaviour of cotton genotype in relation to planting density and growth regulators. Ph.D. dissertation, Punjab Agricultural University, Ludhiana, India. Doorenboss, J., Kassam, A.H., 1979. Yield Response to Water. FAO Irrigation and Drainage Paper No. 33. Food and Agriculture Organization of the United Nations, Rome, Italy. Doss, B.D., Ashley, D.A., Bennett, O.L., 1964. Effect of moisture regime and stage of plant growth on moisture use by cotton. Soil Sci. 98, 156–161. Gerik, T.A., Favour, K.L., Thaxton, P.M., El-Zik, K.M., 1996. Late season water stress in cotton: I. Plant growth, water use, and yield. Crop Sci. 36, 914–921. Grimes, D.W., Yamada, H., Dickens, W.L., 1969. Function for cotton (Gossypium hirsutum L.) production from irrigation and nitrogen variables: I. Yield and evapotranspiration. Agron. J. 61, 769–773. Grimes, D.W., El-Zik, K.M., 1982. Water management of cotton. Univ. California Div. Agric. Sci. Bull. 1904. Harman, W.L., Gerik, T.J., Williams, J.R., 2004. CROPMAN: a crop production/environmental simulation tool to enhance agricultural sustainability. In: Dilawari, V.K., Brar, L.S., Jalota, S.K. (Eds.), Proceedings of sustaining agriculture: problems and prospects, Punjab Agricultural University, Ludhiana, India, November 9–11, 2004, pp. 22–30. Hira, G.S., Jalota, S.K., Arora, V.K., 2004. Efficient management of water resources for sustainable cropping in Punjab. Res. Bull., Department of Soils, Punjab Agricultural University, Ludhiana, India. Howell, T.A., Davis, K.R., McCormick, R.L., Yamada, H., Walhood, V.T., Meek, D.W., 1984. Water use efficiency of narrow row cotton. Irr. Sci 5, 195–214. Jalota, S.K., 2004. Field Water Budgeting and Soil Moisture Profile. Scientific Report Department of Soils. Punjab Agricultural University, Ludhiana, pp. 24–37. Jalota, S.K., Arora, V.K., 2002. Model-based assessment of water balance components under different cropping systems in north west India. Agric. Water Manage. 5, 75–87. Jalota, S.K., Sood, A., Harman, W.L., 2006. Assessing the response of chickpea (Cicer aeritinum L.) yield to irrigation water on two soils in Punjab (India): a simulation analysis using the CROPMAN model. Agric. Water Manage. 79, 312–320. Jalota, S.K., Prihar, S.S., Gajri, P.R., 1985. Drainage losses under different schedules and stage sensitivity of wheat to water stress. Ind. J. Agric. Sci. 55, 574–581. Jalota, S.K., Prihar, S.S., 1986. Effect of atmospheric evaporativity, soil type and redistribution time on evaporation from bare soil. Aust. J. Soil Res. 25, 243–249. Jalota, S.K., Prihar, S.S., 1998. Reducing Soil Water Evaporation with Tillage and Straw Mulching. IOWA State University Press, Ames., USA, pp. 1–31. Jensen, M.E., 1968. Water consumption by agricultural plants. In: Kozlowski, T.T. (Ed.), Water Deficits in Plant Growth, vol. 1. Academic Press, New York, pp. 1–22. Kijne, J.W., Barker, R., Molden, D., 2003. Water Productivity in Agriculture. Limits and Opportunities for Improvement. CAB International, Wallingford, UK. Mahindra, K. (Ed.), 2003. Package of Practices for Crops of Punjab 2001–2002. Punjab Agricultural University, Ludhiana. McMichael, B.L., Hesketh, J.D., 1982. Field investigations of the response of cotton to water deficit. Field Crops Res. 5, 319–333.
146
agricultural water management 84 (2006) 137–146
Praharaj, C.S., 1991. Irrigation and nitrogen management of American cotton (Gossypium hirsutum L.). M.Sc. thesis, Punjab Agricultural University, Ludhiana, India. Pettigrew, W.T., 2004. Moisture deficit effects on cotton yield, yield components and boll distribution. Agron. J. 96, 377–383. Radin, J.W., Mauney, J.R., Krerridge, P.C., 1989. Water uptake by cotton roots during fruit filling in relation to irrigation frequency. Crop Sci. 29, 1000–1005. Radin, J.W., Reavees, L.L., Mauney, J.R., French, O.F., 1992. Yield enhancement in cotton by frequent irrigations during fruiting. Agron. J. 84, 551–557. Sammis, T.W., 1981. Yield of alfalfa and cotton as influenced by irrigation. Agron. J. 73, 323–329.
Taylor, H.M., Klepper, B., 1974. Water relations of cotton. 1. Root growth and water use as related to top growth and soil water content. Agron. J. 66, 584–588. Yadav, R.P., 2002. Validation of COTTAM model under different sowing dates, orientation and method of planting and irrigation application. M.Sc. thesis, Punjab Agricultural University, Ludhiana, India. Williams, J.R., Jones, C.A., Dyke, P.T., 1990. The EPIC Model Chap. 2. EPIC-Erosion/-Productivity Impact Calculator: 1. Model Documentation. Tech Bull. No. 1768. U.S. Department of Agriculture, Washington, DC, pp. 3–92. Zwart, S.J., Bastiaanssen, W.G.M., 2004. Review of measured crop water productivity values for irrigated wheat, rice, cotton and maize. Agric. Water Manage. 69, 115–133.