Evapotranspiration and water use of full and deficit irrigated cotton in the Mediterranean environment in northern Syria

Agricultural Water Management 98 (2011) 1239–1248

Contents lists available at ScienceDirect

Agricultural Water Management journal homepage: www.elsevier.com/locate/agwat

Evapotranspiration and water use of full and deficit irrigated cotton in the Mediterranean environment in northern Syria T.Y. Oweis a,∗ , H.J. Farahani b , A.Y. Hachum c a b c

Integrated Water and Land Management Program, ICARDA, Aleppo, Syria Biosystems Engineering Department, Clemson University, SC, USA Irrigation and Water Resources Engineering, Mosul University, Mosul, Iraq

a r t i c l e

i n f o

Article history: Received 2 July 2010 Accepted 18 February 2011 Available online 15 April 2011 Keywords: Drip irrigation Production functions Water productivity Nitrogen application

a b s t r a c t Cotton (Gossypium hirsutum L.) is the most important industrial and summer cash crop in Syria and many other countries in the arid areas but there are concerns about future production levels, given the high water requirements and the decline in water availability. Most farmers in Syria aim to maximize yield per unit of land regardless of the quantity of water applied. Water losses can be reduced and water productivity (yield per unit of water consumed) improved by applying deficit irrigation, but this requires a better understanding of crop response to various levels of water stress. This paper presents results from a 3-year study (2004–2006) conducted in northern Syria to quantify cotton yield response to different levels of water and fertilizer. The experiment included four irrigation levels and three levels of nitrogen (N) fertilizer under drip irrigation. The overall mean cotton (lint plus seed, or lintseed) yield was 2502 kg ha−1 , ranging from 1520 kg ha−1 under 40% irrigation to 3460 kg ha−1 under 100% irrigation. Mean water productivity (WPET ) was 0.36 kg lintseed per m3 of crop actual evapotranspiration (ETc ), ranging from 0.32 kg m−3 under 40% irrigation to 0.39 kg m−3 under the 100% treatment. Results suggest that deficit irrigation does not improve biological water productivity of drip-irrigated cotton. Water and fertilizer levels (especially the former) have significant effects on yield, crop growth and WPET . Water, but not N level, has a highly significant effect on crop ETc . The study provides production functions relating cotton yield to ETc as well as soil water content at planting. These functions are useful for irrigation optimization and for forecasting the impact of water rationing and drought on regional water budgets and agricultural economies. The WPET values obtained in this study compare well with those reported from the southwestern USA, Argentina and other developed cotton producing regions. Most importantly, these WPET values are double the current values in Syria, suggesting that improved irrigation water and system management can improve WPET , and thus enhance conservation and sustainability in this water-scarce region. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Cotton (Gossypium hirsutum L.) is grown in many countries, under both rainfed and irrigated conditions. In the semi-arid Mediterranean region, in places like southern Spain, northern Syria and western Turkey, the crop is entirely irrigated (Janat and Somi, 2001) with very limited rainfall during the summer growing season. Cotton requires large quantities of water, while water supplies are declining. Clearly, there is an urgent need for technical options as well as policy measures to encourage environmentally sustain-

∗ Corresponding author at: International center for agricultural research in dry areas (ICARDA), Integrated Water and Land Management Program (IWLMP), P. O. Box 5466, ICARDA, Aleppo, Syrian Arab Republic. Tel.: +963 21 26912538; fax: +963 21 2213490. E-mail address: [email protected] (T.Y. Oweis). 0378-3774/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.agwat.2011.02.009

able, yet economically viable practices. This study focuses on Syria, where cotton is vital to the national economy, while simultaneously, shallow aquifers are being depleted at an alarming rate. Water is increasingly pumped from deeper groundwater and the two most important production inputs – irrigation and fertilizers – are becoming the two largest production costs (Janat and Somi, 2001).

1.1. Cotton production in Syria Cotton production in Syria dates back to ancient times. Cotton is economically more competitive than any other summer crop. It is a major source of income for one-fifth of the country’s economically active population. Most fields range from 2 to 25 ha. Cotton is grown in a two year rotation with wheat, sugar beet, potato, legumes, and vegetables. Typically, wheat is harvested in June; land

1240

T.Y. Oweis et al. / Agricultural Water Management 98 (2011) 1239–1248

is plowed twice and left fallow. A third plowing is done in February or March of the following year, and cotton planted in April and May. Planting is mostly by hand, and quality seeds are available from the government. The harvest starts in September and lasts through December; the entire crop is handpicked in two or more pickings. Water is conveyed to the fields mainly through earthen ditches. The most common practice (seen on 75% of all cotton fields) is flooding of small basins, followed by furrow irrigation (24% of the fields). Irrigation applications are double (or even higher) the crop requirements, with a national average of about 1500 mm per season. Cotton accounts for an estimated 25% of Syria’s total agricultural water use. 1.2. Cotton water use and productivity The national average for seed cotton yield is around 4 t ha−1 . Productivity per unit area is at acceptable levels, but yield per unit of applied water is extremely low because of inefficient irrigation systems and improper water management. On a global level, acceptable yield of irrigated cotton is 4–5 t ha−1 seed cotton with water productivity (WPET ) values of 0.4–0.6 kg per m3 of depleted water. This range is also inferred from the recent reviews of Grismer (2002) and Zwart and Bastiaanssen (2004), for data from multiple countries. Average WPET was reported as 0.65 and 0.23 kg m−3 for seed cotton and lint cotton, respectively, with a large variability ranging from 0.41 to 0.95 kg m−3 for seed cotton and 0.14–0.33 kg m−3 for lint cotton. The large variability is due to many factors, including differences in climate, soil, and irrigation and nutrient management, but suggests opportunities for maintaining or increasing production with less water. In Syria, WPET in the traditional surface irrigated cotton is only 0.2–0.25 kg m−3 of seed cotton and 0.07–0.09 kg m−3 for lint cotton. These values are only half to one-third of those achieved in most major cotton producers such as Argentina, Turkey, and USA (Hunsaker et al., 1998; Ayars et al., 1999; Howell et al., 2004; Dagdelen et al., 2006). They are also lower than the minimum values reported in global reviews (Grismer, 2002; Zwart and Bastiaanssen 2004). More than 60% of irrigated lands in Syria use groundwater (Salman, 2004) and are not part of government irrigation schemes. These irrigators enjoy on-demand irrigation water, and thus potentially have the flexibility and control to implement an effective on-farm water management through scheduling, reallocation and other means. Currently, most farmers aim to maximize yield (and presumably profit) by maximizing irrigation. This is both unwise and unsustainable in basins where water is being withdrawn faster than it is being replenished. A better alternative is a targeted demand management strategy that may include water rationing or deficit irrigation (Farahani et al., 2006). Deficit irrigation, either voluntarily or regulated, is an option that may increase WPET (Kijne et al., 2002), and would most certainly improve resource sustainability. Cotton is an indeterminate perennial shrub that is suitable for conditions of limited water and tolerant to salinity. Past research, dating back to at least the 1930s (DeTar, 2008), documents various aspects: physiological and morphological responses to water, deficit irrigation and its economics (English et al., 1990), and water use and yield relationship (Wanjura et al., 2002; Howell et al., 2004; Dagdelen et al., 2006). Drip irrigated cotton data from Wanjura et al. (2002) show reduced yields for deficit as well as for over-irrigation. Falkenberg et al. (2007) reported that irrigation at 75% of ETc did not reduce cotton yield. Data from Howell et al. (2004) and DeTar (2008) do not show any gains in WPET due to deficit irrigation. For a range of irrigation regimes starting at about 50% of the optimum application of 654 mm, DeTar (2008) observed reduced yields due to deficit irrigation. At the optimum application, WPET averaged 0.219 kg m−3 for lint cotton, which was reduced in deficit irrigated

plots, as well as in over-irrigated plots where application exceeded requirements by 30%. However, past research clearly shows cotton yield reductions due to excess water (Wanjura et al., 2002 and Karam et al., 2006). The literature reports mixed results on the impact of water rationing on WPET of cotton, but a reduction in WPET due to over-watering is most certain. An increase in WPET due to deficit irrigation is neither obvious nor universally observed as it is a complex interaction of many factors including timing and duration of the stress in addition to variations in application methods and efficiencies. Literature from Syria includes comparison of irrigation methods and fertilizers on yield, but no study of deficit irrigation is available. Local production functions are needed, especially since results from literature are mixed and difficult to transfer with certainty. To determine yield response to water, precise implementation of irrigation regimes and accurate measures of crop water use (ETc ) and yield are needed. This was the objective of this study that was implemented in drip-irrigated cotton in northern Syria under four levels of water rationing regimes and three levels of nitrogen fertilizer during the period 2004–2006. Results quantify yield response to water and are useful for potential development of water demand management strategies and economic analysis.

2. Materials and methods 2.1. Site description and field practices The field study examined the effects of varying soil water and fertility regimes on production and water productivity of drip-irrigated cotton (Gossypium hirsutum L.) in the typical Syrian practice of two-year cotton-wheat rotation. This study was conducted at Tel Hadya research station (36◦ 01 N, 36◦ 56 E, and 284 m above mean sea level), the headquarters of the International Center for Agricultural Research in the Dry Areas (ICARDA), located 35 km south of Aleppo in northern Syria. The study was done over three growing season from 2004 to 2006. Soil at the site is typical for the area and generally deep (1.5–2.0 m), classified as fine clay (montmorillontic, thermic, Chromic Calcixerert), with average clay and silt of 65 and 27%, respectively (Ryan et al., 1997). Volumetric water contents at field capacity and wilting point were measured at 38 and 22%, respectively. Field capacity was measured in-site by flooding a 2 m by 2 m area outside the experimental plots and measuring the soil profile water content (0.15 m increments) in an access tube in the center of the square plot using a neutron probe, 48 h after wetting. The wilting point value is the average water content of soil samples drained in the pressure chambers to 1500 kPa tension. There is 160 mm of plant-available water within the top 1 m of soil profile. Field bulk density measurements at the site do not indicate soil compaction problems and it ranges from 1.1 g cm−3 for the top 0.15 m to about 1.35 g cm−3 at one meter depth, with the increase mainly due to increased clay and decreased organic matter. Northern Syria has an eastern Mediterranean climate with a single rainy season starting in the fall and extending through the spring, during which winter-grown cereals and food legumes dominate. Long-term mean annual rainfall at the site is 351 mm, but temporal variability is high. There is limited rainfall in May and June and almost none in summer when irrigation is needed. The short season cotton variety ‘Aleppo-118’ was used, with uniform management over the three-year study. The experimental plots (10 m wide by 13.3 m long) were sown by hand the first week in May at a depth of 50 mm and a rate of 9 (2004 and 2005) and 8 seeds m−2 (2006) in 0.7 m flat rows (Table 1). Each year, there were 12 plots per replication, for a total of 36 plots per year for the three replications. Each replication comprised four levels of irrigation, each under three levels of nitrogen (N) treatments. The three

T.Y. Oweis et al. / Agricultural Water Management 98 (2011) 1239–1248

1241

Table 1 Summary of planting and harvesting dates and measured crop evapotranspiration (ETc ) for the 2004–2006 growing seasons at Tel Hadya, northern Syria.

a

First irrigation (planting) Last irrigation Seasonal Irrigation for the 100% water level treatment (mm) Number of irrigation First hand-pick harvest Second hand-pick harvest Length of growing season (first irrigation after sowing to first harvest) Length of growing season (first irrigation after sowing to second harvest) Measured ETc from first irrigation to first harvest (mm) Measured ETc from first irrigation to second harvest (mm)

2004

2005

2006

May 4 August 29 800 9 September 12 October 3 130 151 835 895

May 3 September 1 810 10 September 15 September 25 133 143 852 927

May 4 August 24 760 9 September 13 September 23 131 141 791 813

Because of seed inactivity in dry soil, the effective planting date is assumed the date of first irrigation. a Sowing occurred in very dry soils a few days before the first irrigation.

N rates were 100, 150, and 200 kg N ha−1 . Nitrogen (46% N urea) was applied in 4 doses, 20% at planting, and the remaining three doses (40, 20, and 20%) fertigated over the growing season. Adequate phosphorus (P2 O5 ) was broadcast prior to sowing based on pre-season soil testing Adequate phosphorus (P2 O5 ) was broadcast prior to sowing based on pre-season soil testing. The field was monitored for insects and diseases and chemicals were applied as needed. Weeds were controlled using manual and chemical methods. Seed cotton was harvested by hand on two occasions every year (in order to duplicate local farm practices) from an area 10 m long and seven rows wide in the center of each plot (Table 1). The picking dates varied depending on the irrigation regimes, but the first picking was mostly in the second week of September and produced more than 70% of the total yield, and the second picking in late September. For analysis, the period from 6 May (one day after emergence, DAE) to 24 September (142 DAE, marking the end of second picking) was selected as the best representation of the growing season in the three years, providing a fixed duration for comparison.

2.2. Irrigation management and treatments The drip-irrigated field experiments aimed to analyze the effect of water and N stress on cotton growth, water use, and yield. The experimental design was a randomized split plot with four levels of irrigation and three levels of N treatments and was replicated three times each year. For each nitrogen treatment, the experiment included four irrigation regimes, namely 40, 60, 80, and 100% of full irrigation (meeting 100% of crop water needs). All 36 plots were irrigated on the same day, but with application durations reduced to 40, 60, and 80% of the time period required to irrigate the 100% level plots. In the 100% irrigation treatment, water is applied when available soil water, in the active root zone, approaches 50% of total available soil water (the difference between field capacity and wilting point). Immediately after each soil moisture measurement, the variation in the water content of each soil layer was noted. Due to the absence of significant rain during the cotton growing season, the top (surface) layer always exhibits the lowest water content; even below wilting point (usually prior to irrigation). The variation in water content at the lower layers decreases with depth until there is a layer with no change in water content. This is evident by comparing current water content with a previous one. The depth to this layer is assumed equal to the depth of active root zone and referred to as “top soil layer”. At each application, the soil water profile was refilled to field capacity. The average application depth in the full irrigation treatment was 85 mm water for the three years. Irrigation scheduling is summarized in Table 2, indicating an application range of 60–120 mm per event, excluding the first irrigation at sowing. Irrigation scheduling (amounts and timing) was consis-

tent between years, with cotton requiring nine (2004 and 2006) and ten (2005) irrigations per season. A first irrigation of 60 mm in 2004, 50 mm in 2005, and 43 mm in 2006 was applied immediately after sowing. Following expert recommendations in Syria, there was a gap of 30–35 days between the first irrigation at sowing and the second irrigation. During this period, soil water content was monitored, but irrigation was not needed (Table 2). The irrigation season ended by late August (Table 2), allowing late-season soil water drawdown to expedite boll opening. Average irrigation frequency for the season was 10, 11, and 10 days for 2004, 2005, and 2006, respectively (excluding the initial 30–35 days between the first and second irrigations), implying non-frequent wetting events comparable to surface and sprinkler systems. The replicated plots were irrigated to meet full crop water requirements using groundwater applied through a drip irrigation system. The irrigation system was designed and managed to ensure uniform application. Polyethylene drip laterals (16 mm inside diameter) were installed after sowing in every plot (except in 2004 where the drip system was installed after the first irrigation), laid along every crop row with emitters (rated at 4 L h−1 discharge) spaced every 0.4 m on the laterals. Observations of emitter wetting patterns after irrigations showed complete closure between adjacent emitters on the same lateral and more than 85% closure between the rows in the full irrigation plots. The applied water per plot was measured using mechanical flow meters installed at plot-inlets, occasionally verified against measurements of multiple emitter flow rates. Soil water content was monitored using an on-site calibrated neutron probe (Type IH-II, Didcot Instruments, Co, Ltd., Abington, UK) at a maximum of weekly intervals, for 30–35 readings per season. Soil water measurements were always taken the day before and two days after each irrigation event (minimizing compaction of the wet soil). Prior to sowing, an aluminum access tube was installed in the center of each plot, and always along a crop row, to a depth of 1.80 m. Measurements were made for each 0.15 m layer in the soil profile to the bottom of access tube, except the top 0.15 m which was measured gravimetrically. An average bulk density of 1.1 g cm−3 was used for surface samples to convert measurements to volumetric soil water content values. Crop height was measured on a weekly basis in all plots to detect the effects of water and N treatments on plant growth. High-quality groundwater (EC = 0.62 dS m−1 ; SAR = 1.1 −1 0.5 (mmol L ) ] was used for irrigation. Cation concentrations in this water were Ca2+ 1.7, Mg2+ 0.9, Na+ 1.7, K+ 0.06 mmol L−1 . The corresponding anion concentrations were: CO3 2− trace, HCO3 − 2.6, SO4 2− 0.8, and NO3 − 1.32 mmol L−1 . Cotton was grown in rotation with wheat, which is the usual practice in the area. The experimental area was divided into two halves, cotton on one half and unfertilized rainfed wheat as a cover on the other half. The wheat cover crop aims at eliminating, to a

1242

T.Y. Oweis et al. / Agricultural Water Management 98 (2011) 1239–1248

Table 2 Drip irrigation scheduling (date and amounts in mm) for the 100% water level (no deficit) treatment for the three growing seasons at Tel Hadya, northern Syria. Year

2004 Date Depth 2005 Date Depth 2006 Date Depth

Irrigation sequence during the season

Total (mm)

1

2

3

4

5

6

7

8

9

10

May 4 45

June 12 115

June 28 80

July 12 100

July 21 100

August 2 120

August 10 80

August 18 80

August 29 80

– –

800

May 3 50

June 6 70

June 30 80

July 12 95

July 19 65

July 28 100

August 4 80

August 14 100

August 22 90

September 2 80

810

May 4 60

June 12 80

July 3 80

July 11 60

July 20 120

July 31 100

August 8 100

August 17 100

August 24 60

– –

760

large extent, the variability in N and soil moisture created by the previous cotton treatments. The same treatment layout or combinations were used in the same plots when moving from one half of the experimental area to the other.

Volumetric soil water content (%) 10

15

20

25

30

35

40

0 12-Jul

4-May

30

2.3. Irrigation and soil moisture monitoring

12-Jun

ETc = P + I − D − R − S

(1)

where ETc denotes estimated crop evapotranspiration, P is precipitation, I is irrigation, D is deep percolation below the root zone (or an upward flow, if negative, into the root zone in case of a shallow water table), R is runoff (or run-on, if negative, in case of surface flow into the area under consideration), and S is the change in soil profile water storage (end-of-period value minus beginningof-period value), with all variables in mm and determined for each period between two consecutive soil water measurement days. The reliability of ETc estimates from the soil water budget method depends on the measurement or estimation accuracy of the variables in the right-hand side of the equation. Deep percolation (or upward flow) is the most difficult variable to quantify and accounts for most mass balance errors in estimating ETc . No shallow water tables existed at the study site, thus only percolation below the root zone was of concern. One source of mass balance errors is when the depth of the profile measured by the neutron probe is less than the wetting front by irrigation, with deep percolation, if any, undetected through the bottom of the profile (Wright, 1990). In this study, the access tubes were installed up to 1.80 m, to provide sufficient depth for detection of potential deep percolation. An analysis of the soil profile water content measurements revealed negligible changes in soil water in layers below 1.20–1.35 m depth as compared to their water content at and/or before sowing, suggesting limited percolation. Fig. 1 clearly demonstrates that most changes in soil water content during the season occurred in the soil layers above 1.2 m depth, with negligible changes in the deeper layers. Profile data also show that over 95% of root water extraction was from soil layers above 1.20 m depth. The bottom of the access tubes were well below these layers, so that deep percolation could not have occurred undetected. For analysis, ETc for cotton was calculated from Eq. (1) using the entire 1.80 m profile for each period between two consecutive soil water measurements and assuming that deep percolation was negligible compared to the volume of water applied. Measured precipitation was less than 5 mm during summer in each of the three years, and thus no appraisal of its effective portion was made. The dripirrigation system produced no runoff, with R, in Eq. (1), equaling

Soil depth (cm)

In the absence of lysimetry, soil water budget method is a sound alternative for determining ETc . This method is most accurate in areas with limited or no rainfall as was the case in this study. The soil water budget method involved measuring the components of the water balance equation for a control volume defined by the soil profile of a given root zone depth and is written as:

60

90

120

2-Aug

150

180

Fig. 1. Soil moisture distribution in the cotton root zone before and after selected irrigations in 2004 (dates indicating measurements after irrigation) under 100% irrigation, 150 kg N ha−1 at Tel Hadya, northern Syria.

zero for analysis. The three replications yielded similar ETc values for a given level of water and N, and were therefore averaged to obtain a single seasonal ETc data set for each year for further analysis. 2.4. Meteorological data and reference evapotranspiration A weather station at the experimental site recorded air temperature and relative humidity, wind speed, solar radiation, class-A pan evaporation, and rainfall. Table 3 shows the distribution of measured monthly rainfall and class-A pan evaporation. Using the on-site climate data, daily and seasonal ET0 were computed with the Penman–Monteith equation as described in Allen et al. (1998). Seasonal computed values of ET0 are also provided in Table 4 for the three years, with seasonal total values of 1204, 1224, 1336 mm for ET0 in 2004, 2005, and 2006, respectively, which compare well with the long-term (1979–2005) mean of 1216 mm. 2.5. Evaluation of water productivity Water productivity was determined as kg of lint + seed (lintseed) of cotton per m3 of consumed (evapotranspired) water (called WPET ) as well as lintseed per m3 of irrigation water (called WPiw ). Statistical analysis of the data included analysis of variance (ANOVA), using the GENSTAT 5 program, to test the effects of year, irrigation amount, and nitrogen level on yield and WPET . The yield and ETc data were regressed to evaluate their relationship. Analysis of variance (ANOVA) was conducted to evaluate treatment effects on yield and ETc , with differences considered significant at P < 0.05.

T.Y. Oweis et al. / Agricultural Water Management 98 (2011) 1239–1248

1243

Table 3 Mean max and min monthly air temperature (Tmax , Tmin ), mean max and min relative humidity (Hmax , Hmin ), monthly rainfall (R), mean monthly wind speed (W) and class-A pan evaporation (E) over the three cotton growing seasons at Tel Hadya, northern Syria. Season 2004 Tmax (◦ C) Tmin (◦ C) Hmax (%) Hmin (%) Rain (mm) Wind (m/s) Evap. (mm) 2005 Tmax (◦ C) Tmin (◦ C) Hmax (%) Hmin (%) Rain (mm) Wind (m/s) Evap. (mm) 2006 Tmax (◦ C) Tmin (◦ C) Hmax (%) Hmin (%) Rain (mm) Wind (m/s) Evap. (mm)

May

June

July

August

Sept.

Total/mean

28.4 12.4 88.5 31.3 10.5 3.5 268

34.4 17.9 74.1 24.9 0 4.5 406

38.3 21.6 70.2 27.3 0 4.7 478

36.6 21.8 79.9 29.4 0 5.3 415

35.1 16.5 68.8 22 3.7 2.9 285

34.6 18.0 76.3 27.0 14.2 4.18 1852

30.1 11.9 92.3 31.4 4.1 3.1 285

33.7 17.5 74.2 30.2 0.5 4.3 383

37.5 22.3 69.2 28.3 0 5.6 469

37.1 22.2 71 28.8 0 5.5 414

33.3 16.9 74 28.2 0.6 3.8 280

34.3 18.2 76.1 29.4 5.2 4.46 1831

31.2 12.8 74.2 19.8 0.9 3 296

35.7 18.1 67.9 20.6 0 4.5 412

38.9 21.9 68.2 24.2 0 6.3 452

36.8 22 69.1 24.8 0 5 405

34.2 16.7 74.4 21.9 13 3.4 274

35.2 18.3 70.8 22.3 13.9 4.4 1840

Table 4 Seasonal applied irrigation water for the 100% water level treatment and estimated cotton evapotranspiration (ETc ) from soil moisture measurements using Eq. (1), along with the calculated Penman-Monteith reference evapotranspiration (ETo ) and seasonal crop coefficient in northern Syria. Season

Irrigation (mm)

2004 2005 2006 Mean

800 810 760 790

a

ETc a Mean (mm)

Std dev (mm)

895 927 813 878

14 14 17 –

ET0 (mm)

Seasonal crop coefficient ETc /ET0

1204 1224 1336 1244

0.74 0.76 0.61 0.71

ETc-Estim values represent mean of 9 plots per year (3 replications of each of three N rate applications). Growing season length is 142 days (6 May–24 September).

3. Results and discussion 3.1. Rainfall and temperature The climate during the three growing seasons was very similar (Table 3). There was practically no rainfall during the three months of June, July and August, and only a few millimeters of rain in May and September. The monthly Class-A pan evaporation and total for the three seasons show little variations with an average value of 1841 mm over the three seasons. These variations are typical for the Mediterranean climate that is characterized by a long hot and dry summer, during which full irrigation is essential for growing crops. Wind speed is notably high during the months of July and August, exceeding 5 m s−1 . 3.2. Lint and seed yield Mean lint plus seed (lintseed) yield for the various treatments are summarized in Table 5; the lint portion of the yield is about 35% of the lint plus seed. The highest mean yield obtained was 4585 kg ha−1 in the 2005 season for the 100% water with 200 kg N ha−1 . As expected, the lowest yields were at the lowest water and N rates (40% irrigation, 100 kg N ha−1 ). This yield was 1006 kg ha−1 in 2006. The data clearly indicate that the 2005 season had the highest yields (Table 5) with an overall mean of 2894 kg ha−1 . Nevertheless, the relatively drier treatments of 60% and 40% gave better yields in 2004 than in 2005. This may be due to the effect of the initial water stored in the root zone, especially

in the lower soil layers, which was relatively higher in the first year of the experiment. The data also show that yields for all treatments were lowest in the 2006 season, possibly because of higher temperatures and lower humidity in this season (Table 3). Furthermore, Table 4 indicates that the reference evapotranspiration, ET0 , calculated by the FAO Penman–Monteith method, is the highest among the three growing seasons, while irrigation applied in the 2006 season was the lowest. For instance, irrigation amount at the 100% treatment was 760 mm in 2006 compared to 810 mm in 2005 and 800 mm in 2004. The lower irrigation in 2006 may be due to the lower seeding rate, and thus lower ETc , in 2006 (8 seeds m−2 ) than in 2004 and 2005 (9 seeds m−2 ). In all years, cotton yield steadily increased with higher levels of water and N (Table 5). Table 6 gives a summary of the year-byyear analysis of variance performed on lintseed yield and shows the effects of the two primary factors of irrigation and N. The effect of irrigation level on yield is highly significant (P < 0.001) in all three years. The effect of nitrogen is highly significant (P < 0.001) in 2004 and 2006, and significant (5%) in 2005. It is not known whether this lesser impact of N on yield in 2005 was due to natural variability or possible over-application of N or greater pre-season nitrogen availability. In 2006, however, N effects and N-irrigation interaction are both highly significant, indicating the possibility of some water shortage in some treatments, with the negative effect being compensated by differences in N dosage among the treatments. The hot dry conditions in 2006 also lend weight to this conclusion. The combined analysis of variance for all seasons (Table 7) indicated that the primary factors (year, irrigation, N) had a very significant effect

1244

T.Y. Oweis et al. / Agricultural Water Management 98 (2011) 1239–1248

Table 5 Mean lint plus seed (lintseed) yield (t/ha) of cotton for all water and nitrogen levels in the three cropping seasons at Tel Hadya, northern Syria. Water level (% of full irrigation)

Nitrogen kg/ha

2004

2005

2006

100

200 150 100

3846 3271 2945

4585 4127 3768

3444 2855 2285

Mean

3354

4160

2861

200 150 100

3037 2788 2634

3411 3372 3244

2597 2327 2179

Mean

2820

3342

2368

200 150 100

2600 2385 2485

2546 2276 2387

1787 1628 1589

80

60

40

Year

Mean

2490

2403

1668

200 150 100

1993 1732 1708

1836 1702 1471

1164 1065 1006

Mean Overall mean

b

1811

1670

1078

2619

2894

1994

a

3958 3418 2999

3015 2829 2686

2311 2096 2154

1664 1500 1395

2502

b

SE = 135

SE a

Mean of all years

SE = 120

When comparing two water-level means with the same nitrogen level and year. When comparing two nitrogen-level means with the same water level and year.

Table 6 Summary of year-by-year results of analysis of variance on lintseed yield and seasonal evapotranspiration of cotton for different irrigation levels (100%, 80%, 60%, and 40% of full irrigation) and different nitrogen doses (100, 150, and 200 kg/ha), at Tel Hadya, northern Syria. Source of variation

Degree of freedom

Variance ratio Lintseed

Irrigation (I) Nitrogen (N) I×N * ***

3 2 6

ET

2004

2005

2006

2004

2005

2006

219.6*** 18.8*** 2.8*

57.1*** 5.0* 1.0

176.7*** 64.5*** 14.9***

1077*** 1.6 0.3

1366*** 1.2 0.4

901.7*** 0.2 0.1

Significant at the 5% probability level. ** Significant at the 1% probability level. Significant at the 0.1% probability level.

(P < 0.001) on yield. Interactions between irrigation and nitrogen and between irrigation and year are also highly significant. Table 8 shows that the yields under the four irrigation levels are significantly different from each other (LSD test, 5% probability). Yields under the three N levels are also significantly different from each other. Mean yields in 2004 and 2005 are not significantly different, whereas mean yield in 2006 season is significantly different from the other two seasons. As explained previously, the lower seeding rate in 2006 may have contributed to the lower yields. Another fac-

tor is that soil water content at planting time was lower in 2006 than in the other years (Table 9). As demonstrated in Fig. 2, the effect of irrigation and N was also evident on crop height, with water having a relatively larger impact than nitrogen. Mean maximum crop heights were 73, 75, and 80 cm for 100, 150, and 200 kg N ha−1 , respectively; and 61, 72, 80, and 92 cm for 40%, 60%, 80%, and 100% irrigation, respectively. Thus, maximizing irrigation level increased crop height about three times as much as maximizing N. Maximum plant heights were 110, 104,

Table 7 Summary of results of combined analysis of variance on lintseed yield, evapotranspiration (ET), and water productivity (WP) of cotton under different levels of irrigation (100, 80, 60, and 40% of full irrigation) and different nitrogen dose (100, 150, and 200 kg/ha), at Tel Hadya, northern Syria. Source of variation

Degree of freedom

Variance ratio Lintseed yield

Year Irrig Year. Irrig Nitr Year. Nitr Irrig. Nitr Year. Irrig. Nitr NS = non-significant. * Significant at 5% probability. ** Significant at 1% probability. *** Significant at 0.1% probability level.

2 3 6 2 4 6 12

***

31.6 241.4*** 6.6*** 39.3*** NS 6.9*** NS

ET

WP ***

1278.3 3265.5*** NS NS NS NS NS

12.2** 11.7*** 3.5* 33.4*** NS 4.0** NS

T.Y. Oweis et al. / Agricultural Water Management 98 (2011) 1239–1248

1245

Table 8 Comparing the treatment means of the primary factors (year, water level, and nitrogen) by the least significance test (LSD) at 5% probability. Variant

Year

LSD (5%)

2004

2005

2006

Lintseed ETc WPET

2619 aa 702 a 0.37 a

2894 a 706 a 0.40 a

1994 b 627 b 0.31 b

Lintseed ETc WPET

Water level (% of full irrigation) 40 60 1520 a 2187 b 474 a 616 b 0.32 a 0.35 ab

80 2843 c 749 c 0.38 bc

Lintseed ETc WPET

Nitrogen level (kg/ha) 100 2308 a 674 0.34 a

200 2737 c 681 0.39 b

150 2461 b 680 0.35 a

284 4.3 0.046 100 3459 d 874 d 0.39 c

160 9 0.03

99 NS 0.014

NS = non-significant. a Means having a letter in common are not significantly different at 5% probability level Table 9 Change in the water content (mm) of the entire 1.80 m soil profile (between sowing and harvesting) for years 2004–2006 with initial total water of 501, 472 and 418 mm, respectively and at various levels of irrigation for cotton in northern Syria. Irrigation level 40% 60% 80% 100%

2004

2005

2006

151 144 125 104

139 142 124 90

91 82 74 61

and 84 cm in 2004, 2005, and 2006, respectively and was always observed at the highest irrigation and N levels. The larger impact of irrigation on yield and plant height was equally evident in the measured green leaf area per plant.

1.0 Crop height (m)

100 N

150 N

200 N

0.8 0.6

As summarized in Table 4, ETc values calculated by Eq. (1) are consistently greater than the applied depth of irrigation, implying that the cotton crop is mining the soil profile for water, with greater extent of mining in the drier treatment as shown in Table 9. In Table 4, the seasonal crop coefficient is calculated using the seasonal ETc and ET0 values. Another key factor is soil water content at planting time. This depends on the land use in the previous season. In the experiment, cotton was sown in May; therefore, part of the rainwater falling on the fallow plots in fall, winter and spring prior to cotton planting is assumed to be stored in the soil profile. Rainfall during the seasons 2003/04, 2004/05, and 2005/06 was 398, 304, and 306 mm, respectively. This resulted in initial water stored in the soil profile of 501 mm, 472 mm, and 418 mm at sowing time. Table 7 indicates the significant effect (P < 0.001) of year and irrigation on ETc ; year represents the climate which has a direct impact on ETc . The analysis indicates that nitrogen does not significantly affect ETc even though yield was reduced at lower N levels. One explanation for the observed constancy of ETc under varying N is an increase in soil evaporation at the expense of reduced transpiration. 3.4. Water productivity

0.4 0.2 0.0 0

20

40

60

80

100

120

140

1.0 40%

60%

80%

100%

0.8 Crop height (m)

3.3. Evapotranspiration and irrigation

0.6 0.4

Water productivity, WP, is expressed here in terms of yield per unit of irrigation water (WPiw ) and yield per unit of evapotranspired water (WPET ). Table 10 presents mean WPET for all years and irrigation and N levels. Values of WPET range from 0.23 kg m−3 at water-stress treatments (40% irrigation) to 0.51 kg m−3 under 100% irrigation. The combined ANOVA (Table 7) shows that irrigation and N have a highly significant effect on WPET . There is no clear trend for the effect of irrigation on WPET (Table 10). However, the effect of N on WPET is quite evident in that WPET increases with an increase in N. For WPiw mean values were 0.48, 0.46, 0.45, and 0.43 kg m−3 when averaged over N and years for 40, 60, 80, and 100% irrigation. Although the differences among these values are not highly significant (LSDP=0.05 equals 0.039), they confirm that deficit irrigation can slightly improve irrigation water productivity.

0.2

3.5. Production functions 0.0 0

20

40

60

80

100

120

140

Days after emergence Fig. 2. Cotton plant height at the three N levels (top; mean values across all irrigation levels) and at the four irrigation levels (bottom; mean values across all N levels) during the 2006 season at Tel Hadya in northern Syria.

The importance of production functions in simulation modeling and irrigation optimization of agricultural systems is well recognized. Tables 6 and 7 indicate that N has no significant effect on ETc , therefore one may safely average data over different N levels. In this study, an attempt is made to relate cotton lintseed yield with

1246

T.Y. Oweis et al. / Agricultural Water Management 98 (2011) 1239–1248

Table 10 Mean water productivity for cotton (kg lintseed/m3 ) under different levels of water and nitrogen for all years at Tel Hadya, northern Syria. Year

Irrigation level (%)

Nitrogen level (kg/ha)

Mean

100

150

200

2004

40 60 80 100 Mean

0.35 0.39 0.34 0.33 0.35

0.35 0.37 0.36 0.36 0.36

0.39 0.41 0.39 0.42 0.40

0.37 0.39 0.37 0.37 0.37

2005

40 60 80 100 Mean

0.31 0.37 0.42 0.42 0.38

0.34 0.35 0.43 0.45 0.39

0.36 0.39 0.43 0.51 0.43

0.34 0.37 0.43 0.46 0.40

2006

40 60 80 100 Mean

0.23 0.28 0.32 0.28 0.28

0.25 0.29 0.34 0.35 0.30

0.27 0.32 0.37 0.42 0.35

0.25 0.30 0.34 0.35 0.31

Yield = 4.37ETc − 887 r 2 = 0.84

(3)

where W is the sum of seasonal irrigation water and initial available water (above wilting point) stored in the top 1.5 m of the soil profile at sowing time (early May). In these equations, Yield is in kg ha−1 and ETc and W in mm. If a soil depth greater than the maximum depth of the effective root zone is used, a constant value representing the available water (above wilting point) below the actual maximum depth will be equally added to all values of W in Eq. (3). This of course will not change the slope of the line (4.37), but does change the value of the constant (−887). However, the resulting yield values do not change. These functions are useful for irrigation optimization of cotton production as well as forecasting the impact of water rationing and drought on regional water budget and agricultural economies. 4. Discussion Water productivity is computed as yield per ETc (WPET ) and yield per applied irrigation water (WPiw ). The former is more a biological indicator while the latter is influenced by the performance of the irrigation system and the degree of water losses beyond transpiration. Mean WPET decreased with increasing water rationing (from more than 0.39 kg m−3 at 100% irrigation to about 0.32 kg m−3 at 40% irrigation) while WPiw increased with increasing water rationing (from 0.43 at 100% irrigation to 0.48 kg m−3 at 40% irrigation) due to a greater utilization of stored soil water at higher deficits. Results suggest that deficit irrigation does not improve biological water productivity of drip-irrigated cotton. On the surface, this conclusion does not support promoting deficit irrigation, but careful consideration is needed since the core problem is low water productivity because of wasteful irrigation practices under conditions of declining water resources. As discussed previously, productivity of cotton per unit land is at acceptable levels in Syria, but productivity per unit of applied water is very low because of inefficient irrigation systems and improper water management. In Syria, WPiw in surface irrigated cotton (the common practice) is about 0.2 kg m−3 . In this study,

Lintseed yield (kg ha-1)

(2)

Yield = 4.3493 ETc - 622.95 R2 = 0.7989

3500 3000 2500 2000 1500 1000

Year 2004

500 0 0

200

400

600

800

1000

ETc (mm) 4500

Lintseed yield (kg ha-1)

Yield = 5.12ETc − 972 r 2 = 0.79

4000

Yield = 5.1941 ETc - 1068.6 R2 = 0.9031

4000 3500 3000 2500 2000 1500 1000 500

Year 2005

0 0

200

400

600

800

1000

ETc (mm)

Lintseed yield (kg/ha)

seasonal ETc (Fig. 3). These relationships are more accurately determined if the initial water stored in the crop root zone (top 1.50 m) at sowing is taken into account (Fig. 4). The following two production functions are proposed for estimating cotton lintseed yield as influenced by water in northern Syria:

5000 4500 4000 3500 3000 2500 2000 1500 1000 500 0

Yield = 5.8635 ETc - 1258.4 R2 = 0.9282

Year 2006 0

200

400

600

ETc (mm)

800

1000

Fig. 3. The relationships between cotton lintseed yield and seasonal evapotranspiration.

T.Y. Oweis et al. / Agricultural Water Management 98 (2011) 1239–1248

1247

4400 Yield versus initial available water plus irrigation

Cotton Lint plus seed yield - kg ha -1

4000

Yield versus ETc

3600 Yield= 5.12 ETc - 972 r = 0.79

3200 2800 2400 2000

Yield = 4.37 W - 887 r = 0.84

1600 1200 800 400 0 0

200

400

600

800

1000

1200

ETc or Sum of (initial available water and irrigation)- mm Fig. 4. Cotton (lint + seed) yield versus ET or sum of (initial available water in the soil profile at sowing plus irrigation) in northern Syria (2004–2006).

mean WPiw for the well-watered treatments (100% irrigation) was 0.43 kg m−3 . Although this still represents the lower range of values obtained in many countries, it is twice as high as current WPET values in Syria. Obviously, there exist large opportunities to improve WPET in Syrian cotton production – and thus improve resource use and sustainability – through better irrigation water and system management. Doubling of current WPET values to about 0.4 kg m−3 would substantially reduce Syria’s annual water deficit of 1.7 billion m3 . In terms of technology, WPiw in irrigated land can be increased by improving system efficiency and uniformity to reduce irrigation losses of runoff and deep percolation. In 2001, the Syrian government adopted a national irrigation modernization plan with the goal of increased irrigation and network system efficiency and reduced field water use. The project facilitated a change to modern irrigation techniques (drip and sprinkler) by providing technical support and tax-free low-interest loans. Although the adoption of modern irrigation systems is slow (national data show less than 5% of cotton land is drip irrigated), there is an increasing desire among cotton growers to convert to drip irrigation because of its potential to conserve water and reduce pumping cost, labor, and other inputs (Janat, 2004). These benefits have been demonstrated in Syria, with research showing 35–100% irrigation savings using drip and up to 50% increase in yield using drip with fertigation as compared to traditional surface irrigation (Al-Darir, 1998; Janat and Somi, 2001). The calculation of WPiw is explicitly biased towards surface irrigated practices that produce substantial leaching (such as in Syria) and runoff. This is because a portion of the leaching and runoff water from surface irrigated fields is recoverable (or recovered) at some later date, not necessarily depleted. Nonetheless, inefficient irrigation that allows excessive deep percolation and/or runoff should not be rewarded. Excessive deep percolation, even if recovered later, still involves cost for pumping and removes plant nutrients and other chemicals from the soil, thus degrading water resources downstream or in the well. The study revealed the importance of the initial water available in the soil profile, at sowing time, in the seasonal water budget of irrigated cotton in northern Syria. For instance, about one-third of ETc in the 40% deficit irrigation treatment was secured from initial soil water stored as carry-over from previous fallow months (from June of previous year to May planting). Results suggest substantial benefits to cotton production through a greater capture and storage

of off-season rainfall in the soil profile. Practices such as reduced or no-till are known to enhance infiltration of rainwater and limit soil evaporation, collectively promoting greater soil water storage. In the water-scarce countries of West Asia and North Africa, cereal and legume crops are cultivated in winter and in rotation with summer crops like cotton, corn, rice and others. In many of these areas, scarce water resources are currently being used for not only supplemental irrigation of winter crops but also full irrigation of the summer crops. In most of these areas, sustainable irrigation practice, at best, would mean using the water for either supplemental irrigation of winter crops or full irrigation of summer crops, not both. This study measured overall WPET of cotton lintseed as 0.36 kg m−3 ; while the mean WPET for wheat in the same locality is 1.0 kg m−3 (Oweis and Hachum, 2009). One cubic meter of water produces about three times as much wheat as cotton. This should not be interpreted as recommending that it is better to grow more wheat and no cotton if only one of the two crops in the rotation can be irrigated. Rather, the message is that the net return per unit cultivated land for each crop must also be taken into consideration for profitable operation. This, of course, will depend on production costs and sale price for each crop and is best determined for the complete rotation, not for each crop individually. The important question not addressed here is the irrigation optimization of the entire rotation (wheat-cotton) to maximize profit while increasing WP. 5. Conclusions Both yield and water productivity (WPET ) of cotton increase with an increase in irrigation level. This was most pronounced at optimum N fertility levels. From a biological view point, deficit irrigation may not necessarily improve water productivity. However, economically, the conclusion may be different depending primarily on the cost of irrigation and/or water and the sale value of the produce. The WPET values obtained in this study are double the current cotton WP values in Syria, suggesting huge opportunities for improvement via improved irrigation water and system management, leading to enhanced conservation and sustainability in water-scarce regions, not only Syria but also other countries in the eastern and southern Mediterranean. However, this will require detailed irrigation optimization analysis of the entire crop rotation system with the goal of maximizing farm profit while increas-

1248

T.Y. Oweis et al. / Agricultural Water Management 98 (2011) 1239–1248

ing water productivity. The production functions developed in this study are useful for performing such optimization analysis and for forecasting the impact of water rationing and drought on water budgets and farm economics. Acknowledgements The authors wish to thank Pierre Hayek, Jihad Abdullah, Ali Haj Dibo, Issam Halimeh, and other ICARDA field staff for managing the field trials and for carrying out the tedious and labor intensive soil water content and plant measurements. Appreciation is also extended to Dr. Murari Singh for his generous help with statistical design and data analysis. References Al-Darir, A., 1998. Determination of cotton irrigation efficiency in Hama. R J Aleppo Univ Agric. Sc. Series 31, 19–20. Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration. In: Irrig. and Drain. Paper No. 56. United Nations FAO, Rome, Italy. Ayars, J., Phene, C., Hutmacher, R., Davis, K., Schoneman, R., Vail, S., Mead, R., 1999. Subsurface drip irrigation of row crops: a review of 15 years of research at the Water Management Research Laboratory. Agric. Water Manage. 42, 1–27. Dagdelen, N., Yilmaz, E., Sezgin, F., Gurbuz, T., 2006. Water-yield relation and water use efficiency of cotton and second crop corn in western Turkey. Agric. Water Manage. 82, 63–85. DeTar, W.R., 2008. Yield and growth characteristics for cotton under various irrigation regimes on sandy soil. Agric. Water Manage. 95, 69–76. English, M., Musick, J., Murty, V., 1990. Management of Farm Irrigation Systems. American Society of Agricultural Engineers, St. Joseph, MI, 631–663. Farahani, H.J., Oweis, T.Y., Bruggeman, A., 2006. Management of modern irrigation systems for high water productivity. In: Proc. International Symposium on Irrigation Modernization – Constraints and Solutions , FAO-IPTRID, March 28–31, Damascus, Syria.

Falkenberg, N., Piccinni, R., Tom, G., Cothern, J., Leskovar, D.I., Rush, C.M., 2007. Remote sensing of biotic and abiotic stress for irrigation management of cotton. Agric. Water Manage. 87, 23–31. Grismer, M.E., 2002. Regional cotton lint yield, ETc and water value in Arizona and California. Agric. Water Manage. 54, 227–242. Howell, T.A., Evett, S.R., Tolk, J.A., Schneider, A.D., 2004. Evapotranspiration of full-, deficit-irrigated, and dryland cotton on the northern Texas high Plains. J. Irrig. Drain Eng. 130 (4), 277–285. Hunsaker, D.J., Clemmens, A.J., Fangmeier, A., 1998. Cotton response to high frequency surface irrigation. Agric. Water Manage. 37, 55–74. Janat, M., 2004. Assessment of nitrogen content, uptake, partitioning, and recovery by cotton crop grown under surface irrigation and drip fertigation by using isotopic technique. Commun Soil Sci. Plant Anal. 35, 2515–2535. Janat, M., Somi, G., 2001. Performance of cotton crop grown under surface irrigation and drip fertigation I. Seed cotton yield, dry matter production, and lint properties. Commun. Soil Sci. Plant Anal. 32, 3045–3061. Karam, F., Rafic, L., Randa, M., Daccache, A., Mounzer, O., Rouphael, Y., 2006. Water use and lint yield response of drip irrigated cotton to the length of irrigation season. Agric. Water Manage. 85, 287–295. Kijne, J., Tuong, T., Bennett, J., Bouman, B., Oweis, T., 2002. Ensuring food security via improvement in crop water productivity. In: Background Papers; Challenge Program on Water and Food. CGIAR, IWMI, Colombo, Sri Lanka, pp. 3–42. Oweis, T., Hachum, A., 2009. Optimizing supplemental irrigation: tradeoffs between profitability and sustainability. Agric. Water Manage. 96, 511–516. Ryan, J., Masri, S., Garabet, S., Diekmann, J., Habib, H., 1997. Soils of ICARDA’s agricultural experiment stations and sites: climate, classification, physical and chemical properties and land use. ICARDA, Aleppo, Syria. Salman, M., 2004. The Euphrates and Tigris: South Boundary Utilization and Views, International Workshop on Water, Development and Cooperation-Cooperative Perspective: Euphrates-Tigris and Southern Africa, Bonn, Germany March. IPTRID, FAO, Rome, Italy. Wanjura, D., Upchurch, D., Mahan, J., 2002. Cotton yield and applied water relationships under drip irrigation. Agric. Water Manage. 55, 217–237. Wright, J.L., 1990. Comparison of ET measured with neutron moisture meters and weighing lysimeters. In: Harris, S.R. (Ed.), Irrigation and Drainage. Proc. 1990 National Conference. Durango, CO 11–13 July 1990. Am. Soc. Civil Eng., New York, NY, pp. 202–209. Zwart, S., Bastiaanssen, W., 2004. Review of measured crop water productivity values for irrigated wheat, rice, cotton and maize. Agric. Water Manage. 69, 115–133.