Simulating the effects of zero tillage and crop residue retention on water relations and yield of wheat under rainfed semiarid Mediterranean conditions

Field Crops Research 132 (2012) 40–52

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Simulating the effects of zero tillage and crop residue retention on water relations and yield of wheat under rainfed semiarid Mediterranean conditions R. Sommer ∗ , C. Piggin, A. Haddad, A. Hajdibo, P. Hayek, Y. Khalil International Center for Agricultural Research in the Dry Areas (ICARDA), P.O. Box 5466, Aleppo, Syria

a r t i c l e

i n f o

Article history: Received 8 July 2011 Received in revised form 26 January 2012 Accepted 22 February 2012 Keywords: Crop modeling Conservation agriculture Planting date Residue management Frost Drought

a b s t r a c t Many studies have shown that zero tillage (ZT) in combination with a surface crop residue layer – two components of conservation agriculture (CA) practice – can improve the agronomic water balance by increasing the amount of water that is readily plant available. However, no account has yet been published in which this effect had been fully quantified under rainfed semiarid Mediterranean conditions. To tackle the issue, in the 2009/2010 cropping season we studied the soil water dynamics of wheat grown after barley in northern Syria under two contrasting tillage regimes (zero tillage vs. conventional tillage, CT), two levels of surface residue retention (partial and full) and early and late planting. For a comprehensive quantification of the water balance, we applied the crop-soil simulation model CropSyst for the season under study and for the period 1980–2010 (30 years). Results showed that planting date had a notable impact on crop performance and yield (30-year average, early: 2.68 Mg/ha; late: 2.30 Mg/ha). Simulations indicated that planting wheat immediately after the first sufficient rainfall in autumn bears little risk of crop failure due to early season droughts, and more should be done to encourage farmers to do so. ZT and residue management changed yields only very little, even though in 25 out of 30 years, ZT yields were higher than CT yields. About 55% of the seasonal precipitation (∼150 mm) was lost by unproductive soil evaporation, whereas ZT and residue retention had only a minor mitigating impact; too little to be clearly distinguishable by field observations. A potential obstacle for meticulous simulation of CA with CropSyst is the model’s inability to simulating the dynamic nature of tillage, i.e. its decreasing impact over time, and the beneficial effect of ZT and residue retention on soil water infiltration. However we argue that such impact may be limited on soils with self-mulching characteristics that are common in the region of this study. © 2012 Elsevier B.V. All rights reserved.

1. Introduction Dry areas of the developing world occupy some 3 billion hectares. They are characterized by water scarcity related to generally low precipitation and considerable year-to-year variability in rainfall. In the dry areas of the Mediterranean region, which include North Africa, Southern Europe and West Asia, winter rainfall predominates, and rainfed cropping is practiced during the rainy season from late October to April. Wheat (Triticum aestivum) is the most important staple food crop grown in this region (Pala et al., 1996). In Syria in 2008, wheat occupied some 1.49 million ha or 32% of the arable land (FAOSTAT, 2011); at least half of this was grown under rainfed conditions (MAAR, 2009). Barley (Hordeum vulgare) and food and forage legumes are other dominant crops in Syria and West Asia and North Africa in general. Livestock, mainly sheep and goats, are an integral part of the agricultural system in

∗ Corresponding author. E-mail address: [email protected] (R. Sommer). 0378-4290/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.fcr.2012.02.024

these regions. Given the importance of maintaining food security, as well as the potentially negative impact of climate change on crop production, increasing the yield of the major crop wheat remains a challenge these days, even after some decades of intensive research on the issue. In comparison to the wheat growing area under similar Mediterranean climate conditions in many countries, rainfed wheat productivity in West Asia and North Africa is low. In Syria, for example, rainfed wheat productivity on-farm on average in the last decade did not surpass 1 Mg/ha/yr with high year-to-year variation. In Western Australia, on the other hand, on average 1.67 Mg/ha was produced annually by farmers during the period 1996–2010 (ABARES, 2012). Assuming that in West Asia and North Africa wheat productivity could be similar to that of Western Australia, a considerable yield gap exists. There is evidence that besides differences in cultivars used, agronomic aspects also contribute greatly to the observed gaps in yield (Anderson et al., 2005; Sadras and Angus, 2006). These may comprise yield losses in response to late planting (Musick and Dusek, 1980; Blue et al., 1990; Coventry et al., 1993; Oweis

R. Sommer et al. / Field Crops Research 132 (2012) 40–52

et al., 1999, 2000; El-Gizawy, 2009), as well as suboptimal soil/crop management. The latter, in the dry areas of West Asia, may involve conventional, often excessive, soil tillage and a lack of appropriate (or any) crop residue management. Deterioration of the soil physical structure and loss of soil fertility are well known consequences of such management (Lal et al., 2007). With rising awareness of these production and degradation problems, serious efforts have been undertaken recently in West Asia to find better, resource conserving alternatives to the conventional resource-depleting way of tillage and residue management. Conservation agriculture (CA), which focuses – as the name implies – on conservation of the natural resource base, is seen as a promising way to address the issue. It comprises three vital components: no tillage or zero tillage, crop residue management to retain as much residue as possible in the field, and crop diversification/rotation. There are numerous reports on the success of CA under a wide range of climatic conditions (Hobbs et al., 2008; Kassam et al., 2009; for a recent review see Kienzler et al., 2012, in this issue). CA has been adopted over 100 million hectares worldwide, mainly in the USA, Brazil, Argentina, Canada and Australia, but there is limited awareness and almost no adoption in West Asia and North Africa (Derpsch and Friedrich, 2009). Given the potential of CA to improve rainfed crop production, and narrow the apparent yield gap, a series of experiments and participatory on-farm demonstrations have been undertaken over the last 7 years by the International Center of Agricultural Research in the Dry Areas, ICARDA (Piggin et al., 2011). Some of the more in-depth on-station research has investigated the impact of zero tillage and surface crop residue retention on the agronomic water balance and crop performance. It had been repeatedly reported that CA increases the amount of water that is readily plant available, by increasing soil water infiltration and reducing runoff (Scopel et al., 2005; Thierfelder et al., 2005) and/or by reducing soil evaporation (Bescansa et al., 2006). However, no account has yet been published in which this effect had been fully quantified under rainfed semiarid Mediterranean conditions. Wider-scale assessment of the potentials of CA across various agro-ecosystems, regions and climate can be facilitated by application of quantitative, system-dynamic tools such as cropsoil simulation modeling (in short “crop modeling”). For over two decades, crop models have proven useful to support agronomic assessment and integrated decision making (see Sommer et al., 2010, p. 9–12, for an overview). Crop model application can complement ongoing CA research by assessing the integrated impact of important variables on productivity and resource conservation. However, crop models were originally developed for conventional agriculture systems. Depending on the particular model chosen, only a few of the bio-physical aspects that are important under CA may have been considered and put in the source code (Sommer et al., 2007). Among the major crop models that consider the most important features of CA, the Cropping System Simulation Model, CropSyst (Stöckle et al., 2003), has been applied successfully under a range of climatic conditions and for a variety of crops, such as maize, barley, rice, sorghum, potato, alfalfa and cotton. Crop modeling of wheat accounts for most of the published simulation studies with CropSyst. This comprises studies on durum wheat in northern Syria (Pala et al., 1996), winter and spring wheat in Washington (USA) in response to different fallow and tillage management practices (including no-till; Pannkuk et al., 1998), as well as winter wheat in Italy (Bechini et al., 2006), the Turkish Central Anatolia Plateau (Benli et al., 2007) and northwest Uzbekistan (Djumaniyazova et al., 2010). CropSyst had also been applied for the assessment of potential impacts related to climate change (Tubiello et al., 2000; Giannakopoulos et al., 2009; El Afandi et al., 2010; Ouda et al., 2010; Temani, 2010; Vano et al., 2010; Sommer, 2011), which may be

41

Fig. 1. Long-term (1980–2010) and 2009/10 cropping season monthly precipitation and mean monthly maximum and minimum temperature at Tel Hadya (36.011◦ N 36.931◦ E, 285 m asl).

an indication for a proven reliability of the model to realistically capture the yield limiting (stress) factors water and heat. In this study we applied CropSyst to simulate the effects of zero tillage and crop residue retention on water relations and yield of wheat under the rainfed Mediterranean conditions of northern Syria. The basis of model application is data from a long-term CA crop rotation trial that had been initiated at ICARDA headquarters in 2006 with the aim to assess the impact of time of planting and different tillage and residue management practices on the cropping system’s performance. The aim of the study was, with the help of crop-soil simulation modeling, to detect treatment-related differences in water dynamics and explore to what extent these are responsible for the productivity of the different treatments in general. Furthermore, the study served as an example to discuss current crop modeling limits to describe some further water related aspects of CA. 2. Materials and methods 2.1. Location The experimental site is located at the main station of the International Center for Agricultural Research, ICARDA at Tel Hadya, 30 km south of Aleppo in northern Syria (36.011◦ N 36.931◦ E, 285 m above sea level). The climate of this region is semiarid. Rainfall pattern is typical Mediterranean (winter rain), with an average rainfall of 334 mm yr−1 (Fig. 1). The average annual temperature is 17.8 ◦ C. Frost occurs 32 nights per year on average, in very rare cases until late March/early April, i.e. until a week or two before flowering of wheat. The lowest temperature ever measured at Tel Hadya was −10.3 ◦ C on 15 January 2008. Maximum temperatures above 40 ◦ C are observed on average 13 days during June to August. Relative humidity is low in summer, and wind speed is moderate with an annual average of 3.1 m s−1 with higher wind speed generally occurring in summer. The rainfed cropping season usually begins in November and extends to May-June, with frequent terminal drought. Soils of the region have been classified as Vertisols, Inceptisols or Aridisols (Ryan et al., 1997). They are inherently low in soil organic matter (SOM) and nutrients, especially nitrogen and phosphorus. Soils of the lowland cropping areas are mostly deep (<2 m), have a clay texture (60–70%), and are highly calcareous (∼20% CaCO3 ). Limestones may occupy as much as 20 vol.% of the soil depending very much on the micro-location in the field. Stone content in the deeper lowland soils of Tel Hadya are usually around 5% or less. The soil at Tel Hadya has been classified as a very fine, montmorillonitic, thermic, Chromic Calcixerert (Ryan et al., 1997). Soil water infiltration rates and saturated hydraulic conductivity are moderate to low. The amount of potentially plant available water (PAW)

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R. Sommer et al. / Field Crops Research 132 (2012) 40–52

Table 1 Soil physical and chemical parameters of the seven soil layers distinguished in the CropSyst simulations. Soil layer (cm) 0–5 5–30 30–45 45–60 60–75 75–90 90–150

Sand (g/kg) 92 111 97 75 71 68 60

Clay (g/kg)

Silt (g/kg)

Soil organic matter (g/kg)

Permanent wilting point (m3 /m3 )

Field capacity (m3 /m3 )

Bulk density (g/cm3 )

Sat. hydr. conductivity (cm/day)

597 601 619 635 651 668 690

311 288 284 290 278 264 250

9.1 8.1 7 4 4 4 3

0.180 0.185 0.190 0.194 0.229 0.251 0.263

0.305 0.310 0.315 0.320 0.341 0.335 0.350

1.25 1.35 1.40 1.41 1.42 1.43 1.43

25 20 20 20 15 15 15

defined as the difference between field capacity (FC) and permanent wilting point (PWP), is moderate to high (Table 1). Depending on the stone content, the upper meter of soil may hold between 100 and 150 mm of PAW.

2.2. Experiment A long-term experiment at Tel Hadya was established in 2006 to study the impact of conventional tillage (CT) and zero tillage (ZT) and planting date (Early, Late) on growth and yield of common crops in the region. Conventional tillage comprises mouldboard plowing after cereals and disk plowing after legumes soon after harvest in June or July, and a second tillage by a tine cultivator before sowing in autumn. Planting of all plots is done with a zero-tillage seeder, either Early, i.e. as soon as soil moisture conditions/early rainfall are sufficient for good seed germination, or Late, which is about 4–6 weeks after Early seeding. A residue management treatment was added to the experiment in 2008, when plots were further split into R+ and R− sections. Since then, on R+ plots at harvest cereal plants are cut as high as possible and stubble and loose residues are fully retained. On R− plots cereals are cut as low as possible and loose residues are baled and removed from the field, with the short stubbles and the non-baled fraction of loose straw also retained in the field, which makes R− a partial residue removal treatment. The plot size is 0.5 ha. The experiment was laid out in a split-split plot design with three reps and 24 plots in total. The crop rotation so far has been wheat (season 06/07), lentil (07/08) barley (08/09), wheat (09/10) and lentil (10/11). This paper focuses on the wheat cropping season in 2009/2010. Wheat was planted 9 November (Early) and 2 December 2009 (Late). Wheat had fully emerged 22 November and 27 December, respectively. After establishment it was discovered that through a seed mix-up the early planting was in fact durum (Triticum turgidum ssp. durum, ICARDA cultivar Fadda-98), whereas bread wheat (Triticum aestivum, ICARDA cultivar Babaga-3) was used as planned for the late planting. Related complications of interpreting research results are discussed. At planting, 100 kg/ha of diammonium phosphate (18% N and 46% P2 O5 ) was banded below the seeds. N-fertilizer, as urea (40 kg N/ha) was applied 15 February 2010 at stem elongation. Weed control comprised glyphosate application (1 L/ha) to all plots 1 day before planting and application of 250 mL/ha of 22.3% clodinafop-propargyl and 15 g/ha 75% tribenuron-methyl on 16 February 2010 for grass and broadleaf weed control. To study soil water dynamics in more detail and to facilitate crop model evaluation, small sub-plots, 5 m × 10 m in size, were installed on selected ZT, Early plots. On sub-plots placed in the three ZT, Early, R− plots, barley crop residues were completely removed before planting by manual raking (henceforth called ZT-). Crop management otherwise remained unchanged in these subplots. Additional subplots within the ZT, Early, R+ and ZT, Early, R− treatments were kept fallow (no crop or weeds) by repeated glyphosate application (henceforth called ZT, Fallow, R+ and ZT, Fallow, R−).

2.3. Measurements In these sub-plots and directly next to them in the ZT, Early, R− and ZT, Early, R+ treatments as well as in the CT, Early, R− treatment (n = 18 sub-plots in total), 1.5 m long aluminum access tubes were installed for biweekly monitoring of soil moisture content by neutron probe at 15 cm intervals from 0.15 m to 1.5 m depths. Soil moisture at 0–15 cm depth was measured by soil sampling and gravimetric determination. Independent calibration of the neutron probe against gravimetrically determined soil moisture and subsequent linear regression analysis of back-scattered neutrons (counts) vs. soil moisture content provided for a standard error of the estimate of 0.034 m3 /m3 . Soil moisture data together with corresponding rainfall (P) data were used to calculate actual evapotranspiration (ETa ) by mass balance calculation, whereas it was assumed – proven by the absence of any change in deep soil moisture content over time – that water did not drain below the rooting zone. The corresponding equation is: t  t−1

ETa =

t  t−1

P+

z 

(t−1 − t )

(1)

z=1

where t = time, z = soil layer, and  = soil moisture (mm). Soil bulk density (Table 1) was determined in October 2008 by undisturbed ring core sampling (100 cm3 ) in 10 cm intervals (n = 3 per depth) to a depth of 1.1 m in three temporarily excavated soil pits located diagonally across the experimental site. SOM (Walkley Black wet oxidation method), soil texture (hydrometer method; results in Table 1) and mineral N content (NO3 and NH4 ) were determined in July 2009. Soil samples were taken from 0 to 25 cm (unified sample of 10 sub-samples per plot), 25 to 60 cm (unified sample of 5 sub-samples per plot) and 60 to 100 cm (n = 3) by manual augering on all R+ plots. Half of the samples were oven dried at 45 ◦ C for subsequent SOM and texture analysis. The other half were stored field-fresh in the fridge until mineral N analysis following standard methods as outlined in Ryan et al. (2001). The latter results were corrected for the sample’s water content. Soil sampling for mineral N determination was repeated 4 January and 10 March 2010 (0–25 cm only). Wheat grain yield was measured by harvesting whole (0.5 ha) plots with a combine harvester, whilst harvest index was determined by manual sub-sampling of 6 one meter long rows, bulking and separation and weighing of straw and grain. The baled straw removed from the R− plots was also weighed. 2.4. Crop model simulation 2.4.1. Basic settings CropSyst version 4.14.04 was used. CropSyst is a multi-crop, daily time step, mechanistic cropping system simulation model. Details are explained in Stöckle et al. (2003). The simulated – early and late planted – wheat was the durum wheat variety Cham-1. Related crop-phenological and physiological model settings were taken from an intensive calibration exercise (Sommer

R. Sommer et al. / Field Crops Research 132 (2012) 40–52

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Table 2 CropSyst crop phenological and physiological parameters. Parameters

Value

Base temperature (◦ C) Cutoff temperature (◦ C) Accumulated growing degree days from seeding to Emergence (◦ C day) Reach maximum rooting depth (◦ C day) End of vegetative growth Flowering (◦ C day) Beginning grain filling (◦ C day) Maturity (◦ Cvday) Adjustment factor for phenological response to stress (0–1) Leaf area duration (◦ C day) Leaf area duration sensitivity to water stress (0–3) Maximum expected LAI (m2 m−2 ) Specific leaf area, SLA (m2 kg−1 ) Leaf/stem partition coefficient, SLP Leaf water potential that begins reduction of canopy expansion (J kg−1 ) Leaf water potential that stops canopy expansion (J kg−1 ) Canopy extinction coefficient (0–1) Evapotranspiration crop coefficient at fully canopy Maximum water uptake (mm day−1 ) Leaf water potential at the onset of stomatal closure (J kg−1 ) Wilting leaf water potential (J kg−1 ) Act. to pot. transpiration ratio that limits root growth (0–1) Transpiration use efficiency when VPD is at 1 kPa (g BM/kg H2 O) Scaling coefficient of TUE regression (power function) PAR use efficiency (g MJ−1 ) Mean daily temperature that limits early growth (◦ C) Unstressed harvest index (HI) Sensitivity to water and N stress during flowering (0–1.5) Sensitivity to water and N stress during grain filling (0–1.5) Duration of grain filling period (unstressed; days) Sensitivity to temperature stress during flowering (0–1.5) Maximum rooting depth (m) Root length per unit root mass (km kg−1 ) Max. surface root density at full rooting depth (cm cm−3 ) Curvature of root density distribution (0.0001–6) Nitrogen demand adjustment (0–1) Max N concentration of chaff and stubble (kg kg−1 DM) Standard root N concentration (kg kg−1 DM) Maximum N uptake during rapid linear growth (kg ha−1 day−1 ) Residual soil N not available for uptake (mg kg−1 ) Soil N at which uptake starts decreasing (mg kg−1 ) Plant avail. water at which N uptake start decreasing (0–1) Cold temperature that begins to damage plant (◦ C) Cold temperature which is lethal to plant (◦ C) Conditional planting Earliest planting date (DOY) Significant rainfall amount (lower threshold; mm) Number of days to delay planting (days) Sowing when PAW in layer 1 is less than (m3 /m3 ) Sowing when PAW in layer 2 is greater than (m3 /m3 )

0 18 150 520 520 540 570 1050 1 700 0.5 5 18 1.8 −800 −1200 0.59 1 10 −700 −1800 0.5 5.5 0.35 3 12 0.46 0.1 0.1 27 1 1.3 90 6 0.1 0.65 0.006 0.006 5 2 10 0.5 −5 −11 289 15 1 0.29 0.20

Source C C O C C O O O D C C C C C D D C D D D D D C C C/D C O C C C C C D D D C C C D C C D D C User-defined

C, calibrated; O, observed; D, default.

Table 3 Grain yield, straw yield and harvest index for tillage, planting date and residue management treatments in the 2009–10 wheat crop; NS = not significant. Treatment Tillage ZT CT LSD Planting date Early Late LSD Residue R+ R− LSD a b d *

From whole 0.5 ha plots. Adjusted (early/durum × 0.94). From six 1 m long rows. Significant at p < 0.05.

Yielda (Mg/ha)

Adjusted yielda,b (Mg/ha)

HId (–)

1.71 1.66 NS (0.16)

1.61 1.66 NS (0.14)

0.294 0.307 NS (0.031)

1.64 1.73 0.08*

1.53 1.73 0.07*

0.283 0.318 0.029*

1.69 1.68 NS (0.08)

1.64 1.63 NS (0.07)

0.301 0.301 NS (0.029)

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R. Sommer et al. / Field Crops Research 132 (2012) 40–52

unpublished) of Cham-1 using the comprehensive multi-year data set that had been described by Oweis et al. (1999). In summary, the photosynthetically active radiation use efficiency of Cham-1 had been adjusted to 3 g/MJ. The transpiration use efficiency (TUE) was described with a TUE-curve model that only recently had been introduced into CropSyst. This model comprises TUE (g biomass/kg H2 O) when atmospheric vapor pressure deficit is 1 kPa and a scaling coefficient for the TUE regression power function as detailed in Kemanian et al. (2005). Canopy growth was simulated based on leaf area index (LAI) development. Sensitivity to cold temperatures (potential frost damage) was enabled with −5 ◦ C as the onset threshold and a lethal temperature of −11 ◦ C. CropSyst default values would be −5 ◦ C and −10 ◦ C. The latter would have led to a complete crop failure in the 2007/2008 season, and thus was lowered by 1 ◦ C, as such complete failure had neither been observed in 2008 nor in the whole 32-year history of ICARDA. Cham-1 was simulated to be photoperiod sensitive (long-day plant). Enabling this setting allowed for a closer simulation of the observed key phenological stages of flowering and crop maturity as compared to considering Cham-1 to be photoperiod insensitive. Further crop phenological and physiological parameters are listed in Table 2. It should be noted that all above-mentioned calibration was done at an earlier stage, and apart from adjusting the Sensitivity to water and N stress during flowering and the Sensitivity to water and N stress during grain filling parameters, no further crop-phenology or -physiology related calibration was necessary for the experiment described in this paper. Water movement in the soil was simulated with the hourly cascade module, and water infiltration/surface water runoff with the USDA curve number approach. Observed soil moisture data were used for a site-specific calibration of the two soil physical properties PWP and FC. The FAO-56 Penman–Monteith method, built into CropSyst, was chosen for estimating reference crop evapotranspiration (Allen et al., 1998). As has been demonstrated by McAneney and Itier (1996), this method is much preferable over the Priestley–Taylor method to determine potential evapotranspiration (ETpot ), as the Priestley–Taylor method is error-prone during times when the aerodynamically driven evaporative demand contributes a major share to ETpot . In the semiarid environment of northern Syria this is consistently the case during late spring and summer times. The (soil) organic matter and N turnover was simulated with CropSyst’s Single organic matter, straw and manure residue pool with carbon decomposition module (Stöckle et al., 2007; Kemanian and Stöckle, 2010) using default settings. Furthermore, simulation of soil freezing was enabled. The simulation period was from 1 July 2009 to 30 June 2010. The initial state variables soil moisture, nitrate, ammonium and SOM were set equal to observed values. To initialize surface residue amounts, i.e. stubble and loose (flat) straw of the preceding barley crop, data on average straw production and withdrawal of straw bales of the barley season were used. In detail, from the 4.44 Mg/ha straw available after harvest in 2009, in the R+ treatment roughly 50% (2.22 Mg/ha) remained in the field as loose straw and the other 50% as standing stubble. For R− this was 50% and 17% (0.74 Mg/ha), respectively, while the remaining 33% (1.49 Mg/ha) was removed.

2.4.2. Multi-year simulations In order to test the impact of the tillage management (ZT, CT), planting date (Early, Late) and residue management (R+, R−) in response to varying climatic conditions, namely the year-to-year variability in rainfall and temperature, these (eight) treatments were simulated for a period of 30 years, from 1980 to 2010. These are years for which ICARDA has reliable daily weather data

necessary for the computation of the Penman–Monteith reference crop evapotranspiration. Crop management of each of the treatments was kept identical to that outlined above. For example, total N-fertilizer application (split in two) was 58 kg/ha throughout, and partial residue removal followed field observations of 2009. Simulated Early planting dates in each year were not fixed but related to rainfall and soil moisture conditions using CropSyst’s conditional planting module with details listed in Table 2. This was done to reflect real decision making, namely to start planting only when germination of crops without delay can be assured. Simulated Late planting, on the other hand, was determined to be exactly 4 weeks after Early planting; again following management practices used in our long-term trial. All multi-year simulations were set up to be continuous simulations, i.e. with residual effects of one season (soil moisture, mineral N content, surface residue retention) affecting the following season. The risk of post-emergence early droughts was assessed for a period of 2 months after emergence by calculating the number of days for which the percentage of PAW in all the 5 cm sub-soil layers (that CropSyst automatically creates) which contained roots dropped below a certain value. Frost damage was evaluated by counting the days in each season for which night-time temperatures fell below −5 ◦ C. 2.5. Statistical analysis Statistical analysis of the results was undertaken with GenStat (version 12) applying ANOVA (multi-year simulated data set) or ANOVA for split-spit-plot design (observed data). For the ANOVA of the simulated data, individual year data served as reps and the three treatments as factors. To account for the interdependency of soil mineral N contents measured at progressing depths, the different soil depths were considered as repeated measurements in the ANOVA. For determining the fit between the observed and simulated model results, the root mean squared error (RMSE) and relative root mean squared error (RRMSE) was calculated, where



n (observedi i=1

RMSE =

− simulatedi )2

n

(2)

and RRMSE =

RMSE × 100 Average (observed)

(3)

3. Results 3.1. Observed data 3.1.1. Wheat grain yield, and yield components Wheat production was constrained by the amount and distribution of rainfall, with yields ranging from 1.59 to 1.76 Mg/ha (Table 3). Constraints included 5 days with minimum temperatures from −1 to −5 ◦ C between 26 January and 6 February, late moisture stress with only 26 mm of rain after the end of February, and hot conditions during grain filling with 7 days of temperatures up to 35–37 ◦ C on 10 to 16 May and 7 days up to 35–42 ◦ C on 30 May to 6 June. There were no significant differences in yield or yield components between ZT and CT. Early planting did not improve crop performance, with late planting actually giving a significantly higher average yield and HI (Table 5). The grain yields from large plots (0.5 ha) were generally a little less and less variable (CV 5.2%) than those from yield component sampling (CV 14.7%). The time of planting effects should be viewed with caution. As described earlier, different wheats were inadvertently used

R. Sommer et al. / Field Crops Research 132 (2012) 40–52

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Table 4 Soil nitrate and ammonium contents beginning of July 2009. Treatment

CT, Early, R+ ZT, Early, R+ CT, Late, R+ ZT, Late, R+

Nmin (kg/ha)

NO3 -N NH4 -N NO3 -N NH4 -N NO3 -N NH4 -N NO3 -N NH4 -N

Soil layer (cm) 0–25

25–60

60–100

0–100

8.6 (1.0) 10.5 (3.4) 7.2 (1.1) 7.0 (3.0) 7.0 (1.8) 7.5 (0.4) 7.4 (2.4) 8.8 (4.0)

11.4 (2.9) 6.9 (2.4) 12.1 (2.5) 3.5 (2.4) 8.0 (0.4) 11.5 (12.3) 10.6 (6.3) 6.1 (1.2)

18.2 (15.5) 5.2 (1.6) 14.6 (3.7) 6.6 (0.4) 10.3 (1.9) 4.2 (2.1) 17.5 (9.8) 10.2 (3.4)

38.2 (9.4) 22.6 (3.9) 33.8 (5.1) 17.0 (5.3) 25.3 (3.6) 23.2 (14.0) 35.6 (18.5) 25.1 (3.9)

n = 3; values in parentheses are standard deviations of the means.

3.1.2. Mineral N in the soil Soil mineral N (Nmin ) measured beginning July 2009 in the R+ treatments amounted to 49–61 kg/ha in the upper 1 m of soil, with no significant difference between treatments for the total profile or the three sampled layers individually (Table 4). Similarly, there were no significant differences between treatments regarding Nmin contents measured later during the season, 4 January and 15 March 2010. The Nmin data from July 2009 were used for initializing the soil N routine of CropSyst, and it was assumed that these would also apply to the R− treatments in which Nmin was not measured. 3.1.3. Actual evapotranspiration Total rainfall from 1 September 2009 until 30 June 2010 was 272 mm (Fig. 1). Of this, 17 mm was received 19–21 September. Soil moisture measurements started 18 October and ended 3 June. Slightly more water than the 255 mm rainfall received during this observation period, namely in total 261 mm was transferred back into the atmosphere either by transpiration or evaporation in the CT, Early, R− treatment (Fig. 2). There was no significant difference between total (3 June 2010) cumulative ETa in this treatment and that of ZT, Early, R+ and ZT, Early, R− or ZT, Early, R−−. Significant differences, however, were observed 28 March, 15 April and 12 May 2010, when CT R− had a higher cumulative ETa than at least one of the other three treatments. The vegetation-free treatments ZT, Fallow, R− and ZT, Fallow, R+ had significantly lower cumulative ETa than all other treatments from 28 March onwards. At the end of the observation period, cumulative actual evaporation of these two treatments amounted to 172 and 164 mm, respectively, leaving less than 91 mm (36%) of the total rainfall in the soil. 3.2. Simulation data, season 2009/2010 The Sensitivity to water and N stress during flowering and the Sensitivity to water and N stress during grain filling – two parameters which adjust the harvest index (HI) in CropSyst – had been adjusted to 0.25 for Cham-1 by Sommer et al. (forthcoming) for an optimal simulation of the comprehensive multi-year (1990s)

data set of Oweis et al. (1999). For the 2009/2010 data set (variety Fadda-98 and Babaga-3) presented in this paper, the two parameters had to be reduced to 0.1 (Table 2), for best matching observed HI and thus grain yield, which in CropSyst is calculated as the product of total aboveground biomass (AGB) production and HI. Considering above-mentioned 6% difference in yield between sideby-side early-planted strips of bread and durum wheat, the best fit of simulated to observed bread wheat yield was achieved by slightly increasing these two stress parameters to 0.12 (data not shown). AGB, on the other hand, was very well simulated with the model calibrated to the 1990s data set (Fig. 3). The RMSE between observed and simulated yield, AGB and HI was 0.10 Mg/ha, 0.27 Mg/ha and 0.013, corresponding to a RRMSE of as low as 6%, 5%, and 4%, respectively. The RMSE of yield increased to 0.16 Mg/ha (RRMSE = 10%) when simulated yields were compared against observed Early planted yields lowered by 6% to account for the seed mix-up (light symbols in Fig. 3). Similar to observations (Table 3), simulations of the eight treatments resulted in only very minor differences between treatments in terms of yield, straw, harvest index (HI), and the various pathways of water evaporation or transpiration (Table 5). Following observed trends, HI was higher for the Late than Early planted treatments, and higher for ZT than CT treatments. For AGB or yield, all treatments were close, and actual transpiration tended to be slightly higher for ZT. There was a slight diminishing effect of a surface residue layer on soil evaporation. However, residues also intercepted water, which evaporated and, according to the model, more than counterbalanced savings from reduced soil evaporation. The overall water balance of the eight considered treatments was not clearly discernible in the 2009/2010 season. This also meant that measured as well as observed soil

300 *

250

cumulaveETact (mm)

for early planting (Triticum turgidum ssp. durum, ICARDA cultivar Fadda-98) and late planting (Triticum aestivum, ICARDA cultivar Babaga-3), caused by an unfortunate mislabeling of seed, which confounded planting date with wheat variety. We were able to make some comparison of early-planted durum and bread wheat on several plots where 4-m wide strips of the two wheats were sown side-by-side because the seed box was filled on each side by the different wheats. In these strips, grain yield of bread wheat (1.59 Mg/ha) was 6% less than of the durum yield (1.70 Mg/ha). Adjusting the whole plot yields of the early-sown treatments with this correction factor widened the gap between early and late planting but did not alter significant effects.

*

*

CTR ZTR+ ZTR

200

ZTR LSD

ZTFallowR

150

ZTFallowR+

100 50 0 1/11/09

1/1/10

1/3/10

1/5/10

Fig. 2. Cumulative observed actual evapotranspiration under conventional tillage (CT) and zero tillage (ZT), with either retaining (R+), partially (R−) or fully (R−−) removing residues under early planted wheat or fallow (weeds controlled) conditions during 2009/10 cropping season; dates of measurement exemplarily indicated for ZT, Fallow, R+; LSD provided for periods when significant differences were detected, with asterisk indicating periods with significant differences between wheat plots (upper four lines).

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R. Sommer et al. / Field Crops Research 132 (2012) 40–52

Table 5 Simulated yield, straw, harvest index (HI), total actual soil evaporation, transpiration, residue evaporation, crop water interception and N-uptake in the 2009/10 cropping season. Treatment

Yield (Mg/ha)

Straw (Mg/ha)

HI (–)

Soil evap. (mm)

Transp. (mm)

CT, Early, R+ CT, Early, R− CT, Late, R+ CT, Late, R− ZT, Early, R+ ZT, Early, R− ZT, Late, R+ ZT, Late, R− Avg. all Avg. Early Avg. Late Avg. CT Avg. ZT Avg. R+ Avg. R−

1.64 1.73 1.60 1.60 1.78 1.82 1.75 1.78 1.71 1.74 1.68 1.64 1.78 1.69 1.73

4.05 4.17 3.67 3.66 4.26 4.30 3.85 3.89 3.98 4.19 3.77 3.89 4.07 3.95 4.01

0.288 0.293 0.304 0.304 0.295 0.297 0.313 0.314 0.301 0.293 0.308 0.297 0.305 0.300 0.302

146 142 155 155 131 133 140 142 143 138 148 149 137 143 143

103 107 103 103 110 111 110 111 107 108 107 104 111 106 108

Residue evap. (mm) 0 0 0 0 11 8 12 9 5 5 5 0 10 6 4

Crop water intercept. (mm)

N-uptake (kg/ha)

18 18 11 11 18 18 11 11 15 18 11 15 15 15 15

72 70 59 59 52 53 70 70 63 62 64 65 61 63 63

Fig. 3. 2009/10 observed versus simulated yield (left), aboveground biomass (mid) and harvest index (right); light gray symbols in the yield figure denote 6%-lowered yields.

water contents did not differ, omitting judgment about the viability of the system only by comparing soil moisture dynamics. CropSyst simulations of the cumulative ETa reflected wellobserved patterns (Fig. 4). Only a minor overestimation of midseason ETa was visible for ZT, Early, R+. Noteworthy was the ability of CropSyst to correctly simulate the cumulative evaporation under no-crop/fallow conditions, with only some moderate underestimation at the end of the observation period 3 June 2010 by 16 mm for ZT, Fallow, R+ and 23 mm for ZT, Fallow, R−. Though not a primary focus of this paper, but nevertheless important in view of a correct simulation of N-uptake as well as potential (intermediate) N-deficiency affecting growth and yield, is the ability of the model to simulate correctly soil mineral Ndynamics. As far as the limited observations allowed judgment, CropSyst and its Single organic matter, straw and manure residue pool with carbon decomposition module reflected observed mineral N dynamics well. Simulated data were mostly within the standard deviation range of observations.

3.3. Multi-year simulations 3.3.1. Conditional planting The simulations of conditional Early planting, with day-of-year 289 as the earliest possible planting date and requirements for good soil moisture conditions for seed germination and field access by tractor, resulted in planting dates that varied considerably (Table 6). Planting date differences existed between CT and ZT, and in the case of ZT between R+ and R−. Residue management is likely to have little effect on conditional planting dates under CT when residues are plowed under.

The 30-year average conditional Early planting date was 31 October, 3 and 5 November for ZT, Early, R+, ZT, Early, R− and CT, Early, respectively. Latest conditional planting under CT, Early and ZT, Early, R− was simulated to be the 26 December 2008, whilst under ZT, Early, R+, some early precipitation allowed thresholds for conditional planting to be reached by 15 October. In 16 out of the 30 years (53.3%) under simulation, soil moisture conditions under ZT, Early, R+ allowed planting as early as 16 October (Fig. 5). Under ZT, Early, R− this was 14 out of 30 years (46.7%), and under CT, Early only 10 out 30 years (33.3%). Early planting bears the risk of lack of subsequent rainfalls and post-emergence dry spells that could severely affect young wheat plants. Depending on how well adapted the wheat is to early drought stress and the soil water retention characteristics of the particular soil, different thresholds of plant available water in the rooting zone apply. It should be noted that this threshold is not equal to the average percent PAW in the rooting zone, but rather its maximum. This is to account for the fact that, for instance, the top 10 cm of soil might contain sufficient soil moisture to sustain crop survival/growth, while the subsequent 10 cm of soil might still Table 6 Earliest, latest and average planting dates of wheat under the premises of sufficient soil moisture for germination and good field access conditions (Table 2) for the years 1980–2010 under conventional tillage (CT) and zero tillage (ZT), early planting and full (R+) or partial (R−) residue retention; SD = standard deviation of the mean expressed in days.

Earliest Latest Avg. SD (days)

CT, early

ZT, early, R+

ZT, early, R−

15/Oct 26/Dec 05/Nov 20.6

15/Oct 17/Dec 31/Oct 19.7

15/Oct 26/Dec 03/Nov 21.5

R. Sommer et al. / Field Crops Research 132 (2012) 40–52

300

300

CT,Early,R-, observed

cumulaveETact (mm)

250

ZT,Early,R+, observed

250

simulated

200

simulated

200

150

150

100

100

50

50

0

0

1/11/09

1/1/10

1/3/10

1/5/10

300

cumulaveETact (mm)

47

1/11/09

1/1/10

1/3/10

1/5/10

1/3/10

1/5/10

300

ZT,Fallow,R+

250

simulated

200

ZT,Fallow,R-

250

simulated

200

150

150

100

100

50

50

0

0

1/11/09

1/1/10

1/3/10

1/5/10

1/11/09

1/1/10

Fig. 4. Observed and simulated cumulative actual evapotranspiration under conventional tillage (CT), zero tillage (ZT), retaining (R+) or fully (R−−) removing residues under wheat (above figures) or fallow conditions (below figures) during 2009/10 cropping season.

be completely dry; the average would not reflect such a situation correctly. Under ZT, Early, R+, 4 out of 30 years (13.3%) experienced a postemergence dry spell for a period of 5 or more days, with the PAW of all sub-layers – CropSyst by default distinguishes 5 cm thick sublayers – dropping below 20% (Fig. 6). Only in two of these years the dry spell lasted 10 or more days, and only in 1 year (2008/2009) it lasted 20 days or more. The post-emergence dry spell risk was similar for the other three Early planting treatments and, as expected, considerably diminished if planting was (4 weeks) Late. With a threshold temperature of −5 ◦ C and a lethal temperature of −11 ◦ C for the simulation of frost damage, leaf damage of varying degrees occurred in 23 (ZT, Early, R+ = 24) out of 30 years, on average 5.1 (ZT, Early, R+ = 5.4; range: 0–16) days per year under Early Planting. For Late planting this was 22 out of 30 years and on average

4.4 days per year (Fig. 7). ZT, Early, R+ deviated from the rest of the Early planted treatments, because only this treatment was affected by eight frost days below −5 ◦ C in late December and beginning of January 2008/2009, when crops of this treatment (only) had already emerged (see conditional planting results above). 3.3.2. Yield Time of planting had the comparably biggest influence on simulated crop yield. Across all years and tillage and residue treatments Early planting yielded on average 2.68 Mg/ha grain. Yields reduced significantly (ANOVA with the three factors tillage, planting and residue management) in response to Late planting by 0.38 Mg/ha to 2.30 Mg/ha. In 25 out of 30 years (same for all treatments), early planting resulted in higher yields than late planting, while only in 5 years was it the other way round.

Frequency(no.oimes/30years)

Cumulaveperecentage

100% 80% 60%

CT,Early, R+ ZT,Early, R+ ZT,Early, R

40% 20%

29/Oct

12/Nov

26/Nov

10/Dec

24/Dec

Fig. 5. Cumulative percentage probability of conditional planting after day-of-year 288 (=14 October in a leap year, 15 October otherwise) during 1980–2010 under the requirements of sufficient soil moisture for germination and good field access conditions under conventional tillage (CT) and under zero tillage (ZT), early planting and with full (R+) or partial (R−) residue retention.

66% 5days or more 10days or more 20days or more

15

50%

10

33%

5

17%

0%

0 100%

0% 15/Oct

20

80%

60%

40%

20%

0%

Plant available water in root zone Fig. 6. Risk of dry spells as a function of plant available water in the root zone (maximum of all considered sub-layers), lasting at least 5, 10 or 20 days during the first 2 months after emergence under conditional Early planting during 30 years of wheat cropping (1980–2010) under zero tillage and full residue retention (ZT, Early, R+).

48

R. Sommer et al. / Field Crops Research 132 (2012) 40–52 18

300

16

12

Late

10 8

*

6

2 0 1980/1981

1985/1986

1990/1991

1995/1996

2000/2001

2005/2006

Fig. 7. Number of days with a simulated impact of frost on leaf development; *ZT, Early, R+ only.

Cum.precipitaon(mm)

Number of days

Early

4

2006/07 200 150 100 50 0 01/Sep

Comparing ZT, Early, R+, which is the most resource conserving form of agricultural management, with farmer’s practice CT, Late, R−, the average yield increase was almost 0.5 Mg/ha or 22% (CT, Late, R−: 2.27 Mg ha−1 ; ZT, Early, R+: 2.77 Mg ha−1 ). However, on average across all years, neither tillage nor residue management had a statistically significant impact on crop yields. Simulations showed that in 67% (Late, R+) to 87% (Late, R−) of the years, ZT had higher yields than CT. The year-to-year variability in yield was low under ZT, Early, R+ (average yield: 2.78 Mg/ha). This treatment had the highest 25% percentile (1.98 Mg/ha; lower end of the gray box in Fig. 8) as well as the highest 10% percentile (1.42 Mg/ha; lower whisker in Fig. 8). In comparison, the 10% percentile of both CT, Late treatments was only 0.80 Mg/ha. This is an indication that ZT Early R+ may perform better under severe drought conditions. The impact of drought could be seen for the season 1999/2000 with a total rainfall of only 260 mm (1 September to 30 June), a very late onset of the rainy season (Fig. 9), and thus late conditional “early” planting, on 16–17 December. Under such conditions, only the Early planted wheat could make good use of the limited water, while main biomass development of the Late planted crops occurred in late spring when evaporative demand was high and water use efficiency consequently was low. AGB of the Late planted crops (1.44 Mg/ha) was less than a third of the Early planted crops (5.18 Mg/ha). Consequently, grain yield of ZT, Early, R+ was 1.37 Mg/ha, while CT Late, R+ was only 0.44 Mg/ha. The 2006/07 season, on the other hand, is an example where total seasonal rainfall (263 mm) was comparable, but distribution

01/Nov

01/Jan

01/Mar

01/May

01/Jul

Fig. 9. Cumulative precipitation of the cropping season 1999/2000 and 2006/07.

much different from 1999/2000 (Fig. 9). Conditional Early planting took place 16 October; late planting as per definition 4 weeks later. Both treatments, Early and Late, could make similar use of limited available water, and yield differences between treatments were small.

4

3.3.3. Water relations, HI and N-uptake Averaged across 30 simulated years, ZT, Early, R+ performed best in terms of actual soil evaporation, transpiration, water use efficiency, harvest index, and the share of transpiration on total ETa (Table 7). ZT, Early, R+ had on average 33 mm – or 24 mm when subtracting residue evaporation – less water losses by evaporation than the CT, Late, R− treatment, which is the usual farmer practice in West Asia and North Africa. Cultivation during summer/autumn, when humidity is low, can deplete soil moisture through exposing moist soil on the surface. Further comparing these two treatments, transpiration was 14 mm higher under ZT, Early, R+, but water loss by crop water interception was also 9 mm higher. Higher transpiration resulted in less crop water stress and consequently a higher HI and water use efficiency. The N-uptake differed only slightly between treatments. Intermediate N-stress was largely absent in all treatments with the exception for some days in a few years with exceptionally heavy winter rainfall and downward movement of Nitrate below the shallow rooting zone (data not shown). Total N-uptake was slightly higher than yearly N-fertilizer application (58 kg N/ha), but the balance would be even if corrected for the N retained in the field as straw residues.

3

4. Discussion

5

B

A

Yield (Mg/ha)

1999/2000

250

14

2 1 0

R+

R

R+

CT Early planng

R ZT

R+

R CT

R+

R ZT

Late planng

Fig. 8. Box-Whisker plot of grain yield for the years 1980–2010 (n = 30) in response to conventional tillage (CT) or zero tillage (ZT), early or late planting and partial (R−) residue removal or full retention (R+); + indicate average values; Early planting and Late planting significantly different at p < 0.05 (indicated by A and B above the boxes).

In this long-term trial and in other ZT versus CT comparisons at ICARDA headquarter in northern Syria over the last 6 years, yields were often higher and rarely less with ZT than CT and with early than late planting (Piggin et al., 2011). Our multi-year simulations support these findings, with the season 2009/10 being an exception to the rule. Further, the simulations indicated that the gain in yield in response to ZT alone was minimal (not significant), but nevertheless quite consistent throughout the years. It is the combination of Early planting and ZT that led to the notable (but still statistically not significant) yield increase of on average 0.5 Mg/ha or 22% if comparing farmer’s practice (CT, Late, R−) with the best CA management (ZT, Early, R+). Residue management as implemented in the study, namely retaining all residues or removing on average 33% of loose straw, did not have any notable impact on grain or straw yield or observed

R. Sommer et al. / Field Crops Research 132 (2012) 40–52

49

Table 7 Simulated average total soil and residue evaporation (1 September to 30 June), transpiration, crop water interception, percent share of transpiration on total ETa (T/ETa ), water use efficiency (WUE), harvest index (HI) and N-uptake during 30 years of wheat cropping; standard deviation of the mean in parenthesis; WUE = grain yield/precipitation (1 September to 30 June). Treatment

Soil evap. (mm)

Transpiration (mm)

Resid. evap. (mm)

Crop water intercept. (mm)

CT, Early, R+ CT, Early, R− CT, Late, R+ CT, Late, R− ZT, Early, R+ ZT, Early, R− ZT, Late, R+ ZT, Late, R−

150 (21) 151 (21) 164 (21) 165 (21) 132 (20) 139 (20) 147 (21) 156 (21)

145 (53) 145 (53) 138 (44) 138 (44) 152 (51) 150 (53) 143 (40) 140 (42)

1 (1) 1 (1) 1 (3) 1 (3) 10 (3) 8 (2) 12 (3) 9 (3)

20 (9) 20 (9) 13 (6) 13 (6) 22 (9) 22(10) 13 (6) 13 (6)

Avg. all Avg. Early Avg. Late Avg. CT Avg. ZT Avg. R+ Avg. R−

150 (23) 143 (22) 158 (22) 157 (22) 143 (22) 148 (24) 153 (23)

144 (47) 148 (52) 140 (42) 142 (48) 146 (46) 145 (47) 143 (48)

5 (5) 5 (5) 6 (6) 1 (2) 10 (3) 6 (6) 5 (4)

17 (9) 21 (9) 13 (6) 17 (8) 18 (9) 17 (9) 17 (9)

ETa . This is in contrast with simulation studies of Verburg and Bond (2003), who detected a notable impact of residue coverage on soil evaporation under dryland agriculture in southeastern Australia. Our simulation results revealed only a very minor increase in unproductive water loss by soil evaporation (9 mm), when removing 33% residues, and not plowing under the remaining fraction. The reason for little impact of residue management in the simulations is likely because in both residue treatments the same amount of residues remained as loose, flat straw on the surface (50%), while treatments differed only in the amount of standing stubble (50% vs. 17%). These fractions, standing and flat, differ significantly in their impact on mitigating soil evaporation, as described by Steiner (1989, 1994) and Steiner et al. (1994), and implemented analogously in the CropSyst routines. Flat residues contribute the major share to reducing evaporation, by covering the soil more efficiently (higher area-mass ratio). Cutting straw high and keeping tall stubbles in the ZT, R+ treatments provides for easier planting into residues (less risk of clogging of the zero-tillage seeder by loose residues), and may be beneficial in terms of lowering wind speed at the soil surface and maintaining a more favorable micro-climate for (young) plants (Aase and Siddoway, 1990; Cutforth and McConkey, 1997). But further research has to show whether this is the best way of reducing soil evaporation, in terms of protecting the soil from direct irradiation. French and Schultz (1984) – henceforth referred to as F&S – developed an empirical relationship between seasonal rainfall and attainable wheat yield for the Mediterranean-type environment of South Australia, where Yield [kg/ha] = 20 [kg/ha/mm] × (rainfall110[mm]). Following this model, with 272 mm seasonal rainfall in 2009/10, 3.24 Mg/ha should potentially be achievable; however, this is far above yields achieved in years with average seasonal rainfall. The factor 20 in the F&S equation constitutes bulk maximum transpiration use efficiency with a unit comparable to WUE. The 110 mm – i.e. the x-intercept of the curve – is considered the inevitable loss of water from evaporation, amounting to about 1/3 of total ETa according to French and Schultz (1984). Our simulation results provide evidence that in the Mediterranean-type environment of northern Syria, evaporation losses amount to 50–55% of total ETa or about 150 mm. Modifying the French and Schultz equation accordingly, with 272 mm of rainfall, attainable yield should be around 2.44 Mg/ha grain. This means actual yields of 2009/10 fell short 0.7–0.85 Mg/ha. Analogously, the potentially attainable yield for a year with an average amount of seasonal rainfall would be 3.3–3.5 Mg/ha, which is 0.5–1.2 Mg/ha higher than 30-year average yields in our simulations.

WUE (kg/m3 )

HI (–)

N-uptake (kg N/ha)

46% 46% 44% 44% 48% 47% 45% 44%

0.85 (0.21) 0.84 (0.23) 0.71 (0.20) 0.71 (0.21) 0.85 (0.21) 0.84 (0.23) 0.71 (0.20) 0.71 (0.21)

0.375 (0.067) 0.375 (0.067) 0.359 (0.061) 0.359 (0.061) 0.393 (0.055) 0.387 (0.061) 0.366 (0.051) 0.361 (0.055)

65 (15) 65 (15) 61 (18) 61 (18) 65 (14) 67 (15) 57 (13) 61 (16)

45% 47% 44% 45% 46% 46% 45%

0.78 (0.23) 0.85 (0.23) 0.71 (0.21) 0.78 (0.23) 0.78 (0.22) 0.78 (0.23) 0.77 (0.23)

0.372 (0.060) 0.382 (0.062) 0.361 (0.057) 0.367 (0.064) 0.377 (0.057) 0.373 (0.059) 0.370 (0.061)

63 (16) 65 (14) 60 (16) 63 (17) 63 (15) 62 (15) 64 (16)

T/ETa (%)

Following the French and Schultz (1984) approach, Sadras and Angus (2006) assessed the water use efficiency of wheat grown in a range of global dry environments. They proposed 60 mm (rather than 110 mm) for the x-intercept and a factor 22 (rather than 20) kg/ha/mm for the transpiration use efficiency, which, applied to our study environment, would widen even further the gap between actual on-station yield and potentially attainable yield. Oliver et al. (2009) re-evaluated the F&S model and tested several improved empirical models against actual long-term regional yield data (Western and South Australia) and crop model (APSIM) simulated yields. One of their suggested improvements was increasing the x-intercept to 130 mm for years with seasonal rainfall above 180 mm, and lowering the WUE to 12 kg/ha/mm for heavy clay soils (model number 9 in their paper). Omitting soil water stored – in response to summer rainfall, which may occur in Western Australia but is an absolute rarity in Northern Syria – this model would predict 1.70 Mg/ha grain yield for the 2009/10 cropping season, which is very close to observed yields. The model would however underestimate 1980–2010 simulated yields by on average 0.54 Mg/ha (CT, Early, R+) to 0.72 Mg/ha (ZT, Early, R+), thus leaving further room for improvement. On the other hand, as our comparison of the cropping seasons 1999/2000 and 2006/07 revealed, depending on the onset and distribution of seasonal rainfall, AGB accumulation, water stress and yield vary significantly. French and Schultz (1984) considered that “the spread of data below this line [i.e. sites or seasons where the potential yield was not achieved] indicates sites where yield was limited by factors such as extremes of temperature, agronomic deficiencies, the effect of pests and diseases and possibly soil erosion” – our data suggest that rainfall distribution should also be included in this critical risk factor list. Because of the complexity of seasonal rainfall (pattern) and its impact on yield formation, sophisticated (empirical) models, such as those presented by Bobojonov and Sommer (2011), are required for a realistic assessment of water-yield relationships in semi-arid environments. CropSyst simulations of the durum wheat variety Fadda-98 (Early planting) and the bread wheat variety Babaga-3 (Late planting) were based on an earlier calibration on the performance of the durum wheat variety Cham-1 under varying planting dates and levels of fertilizer and supplemental irrigation. Of the phenological or physiological model settings, only the Sensitivity to water and N stress during flowering and the Sensitivity to water and N stress during grain filling parameters were changed. These two parameters describe the diminishing effect of water and N stress

50

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on harvest index. They were reduced from 0.25 to 0.1 for a more precise simulation of yield under the prevailing conditions of terminal drought in 2010. To account for the mix-up of (durum and bread wheat) seeds, the parameters would have to be increased to 0.12. As these differences were small and the RRMSE still low (10%) even when using 6%-reduced Early planted durum wheat yields for comparison, we decided to adhere to observed data, although these constituted a mix of durum and bread wheat. In any case, simulations suggest that Fadda-98 and Babaga-3 are less sensitive to water and N stress during flowering and grain filling and can transfer more biomass to the grain under drought conditions than Cham-1. Both Fadda-98 and Babaga-3 are new ICARDA varieties – not yet released – while Cham-1 is the product of early ICARDA breeding activities of the 1980s. As far as conclusions are valid from simulation studies and a single year data set, it seems that ICARDA wheat breeders were successful in making varieties more drought-tolerant. Simulation routines of CropSyst of aboveground biomass (AGB) formation, transpiration, evaporation and soil water dynamics do not use the Sensitivity to water and N stress during flowering and the Sensitivity to water and N stress during grain filling parameters. The fact that we did not change any physiological model parameters and nevertheless could describe well biomass accumulation of Cham1, Fadda-98 and Babaga-3 in the simulations, means that our data did not allow distinguishing differences with regard to these traits between the three varieties. Proof of the robustness of CropSyst is the outstanding accuracy of AGB simulations (RRMSE = 6%) for the season 2009/10. It was higher than Pala et al. (1996) achieved in the same environment (RRMSE ≥10%) or Djumaniyazova et al. (2010) for irrigated conditions in northwest Uzbekistan (RRMSE = 10%). Frost damage was a reoccurring phenomenon in the simulations. This reflects reality, but despite the apparent frequency of frost damage, a systematic evaluation of the impact of frost on growth and yield of wheat in northern Syria has never been carried out. Also, we could not find any publication in which threshold temperatures as well as the impact of winter hardening and/or dehardening of wheat grown in Mediterranean environments were studied in more detail, so as to allow variety-specific model calibration. The study of Eberbach and Pala (2005) carried out on-station at ICARDA is one of the few accounts where the impact of frost was identified and documented. They reported that in response to 12 days of frost below −5 ◦ C in early 1997, green leaf area of wheat (variety not given) grown at regular row spacing (17 cm) was reduced by about 25%. The authors also did not rule-out damage to reproductive organs. The current version of CropSyst does not allow simulating the latter. The primary focus of the study of Eberbach and Pala (2005) was the quantification of partitioning of actual evapotranspiration (ETa ) of wheat into evaporation (Ea ) and transpiration (Ta ). Depending on the row spacing (regular: 17 cm; wide: 30 cm) the share of Ta on ETa ranged between 46% (wide) and 50% (regular), which is in the range shown by our multi-year simulation. The 30-year simulation assessment highlights that following the strategy of waiting for sufficient early autumn rainfall and then planting as soon as field conditions allow tractor access, bears only little risk of complete crop failure and the need for reseeding because of early season drought. However, simulated conditional early planting dates were much earlier than farmer practice in the region. A survey carried out in 2008/2009 in northern Syria revealed that only 20% of the interviewed farmers would plant wheat in November, 60% in December, 10% the first half of January, and the rest even later (El-Shater et al., 2009). This means that there is scope for farmers in the region to decrease the yield gap by making an effort to plant earlier. Late planting may occur in cases where wheat is preceded by irrigated summer cropping of potato, which is only harvested in late November/beginning of December. Another

reason for late planting is the common practice of undertaking plowing and final seed-bed preparation, which takes time, only after the onset of the first rains. There is also a lack of appreciation of the negative effects of delays on grain filling and yield. Timely planting, on the other hand, is facilitated where pre-planting tillage is omitted, as is the case under ZT (Wang et al., 2006). Our simulation result that in the semiarid Mediterranean climate of northern Syria in most years late planting results in a yield penalty, is in line with earlier studies from the region (Oweis et al., 1999, 2000) and from other environments (Ortiz-Monasterio et al., 1994; Ortiz-Monasterio and Lobell, 2007). Winter and Musick (1993) and Hammon et al. (1999) argued that root development of early planted wheat (in Colorado, USA) in comparison to late planted wheat is more vigorous which enables the early-planted crop to fully utilize deep soil moisture, grow bigger and be less susceptible to drought and winter injury. This, however, does not apply to northern Syrian conditions, because deep soil moisture does not play an important role under conditions of continuous cropping, as plant available soil moisture is usually fully depleted by the end of the growing season, unless it has been extraordinarily wet or the crop demanded little water because of pests or diseases. Although early planted crops usually cannot benefit from any deep soil moisture, less evaporation and higher transpiration under ZT may also mean better growth and less susceptibility to droughts and winter injury. On the other hand, if such conditions applied, CropSyst simulations would not reveal them, as the underlying routine for frost damage does not built on crop water status but merely on ambient air temperature. Comparing model predictions with the observations of the soil mineral N status, it can be concluded that CropSyst’s Single organic matter, straw and manure residue pool with carbon decomposition module is well suited to describe SOM and N turnover in the highclay, high-pH, low SOM soils prevailing in northern Syria. This is not only important when the focus of research is an assessment of soil fertility, but required in general for a precise simulation of crop growth, water relations and yield when nitrogen may be growth limiting. In this study we did not address the impact of improved residue and tillage management by application of CA on other important soil physical characteristics. There is some evidence from ongoing research at ICARDA (data not shown) that, under ZT and residue retention, SOM increases and with it soil aggregate stability. An increased aggregate stability leads to improved soil porosity, better soil water infiltration and less surface water runoff (Whitbread et al., 2000; Blair et al., 2006; Soil Quality for Environmental Health, 2011). CropSyst currently has no routine that addresses this aspect, meaning that an increase in SOM does not affect soil water infiltration via, for instance, changing the saturated hydraulic conductivity in the model. Also, the impact of tillage on temporarily decreasing soil bulk density and thus increasing porosity and the capacity of the soil to store water is not considered in CropSyst. This has for instance recently been introduced into the DSSAT modeling framework (Jones et al., 2003; White et al., 2009). The reduction in surface water runoff has been identified as a crucial component of CA in environments where torrential rains are common (Sommer et al., 2007; Verhulst et al., 2011). Surface water runoff however is quite rare in the 250–400 mm rainfall area of northern Syria, and often only observed on parts of the field where the soil has been compacted (e.g. by tractor wheels). Otherwise, the montmorillonitic, thermic, Chromic Calcixererts, which are common soils of the region, have self-mulching characteristics which by their very nature improve/maintain soil water infiltration at moderate levels. CT, at least for a limited time, loosens the soil further and improves water infiltration. Therefore, we assume that the beneficial effect of ZT in this regard is rather limited. Further research on this aspect is necessary and, for the sake of providing a

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comprehensive insight, should also address the risk of increased soil evaporation with increased soil porosity and facilitated water vapor movement into the top soil under ZT, and any interactions with residue levels. 5. Conclusion The beneficial effect of early planting on grain and straw yields of wheat under rainfed semiarid Mediterranean conditions could be clearly identified by crop model simulation and by field observations. A positive effect of ZT on crop yield was visible in the simulation in 25 out of 30 years, but given the high year-to-year variability, could statistically not be proven. On the other hand, ZT undoubtedly reduced the cost of agronomic management as compared to CT, and provides an incentive for adoption by farmers, as long as there is no yield penalty or a disproportionate increase in costs related to weed control by herbicides. The two tested levels of residue management did not show any impact on yield, which may have been related to the fact that both treatments only differed in the amount of standing residues (stubbles), while loose residue left on the soil surface after harvest was the same. About 55% of the seasonal precipitation was lost by unproductive soil evaporation, whilst ZT and residue retention had only minor mitigating impacts. Recurrent frosts affect crop growth in the region, but to what extent remains to be unveiled, especially in relation to effects of planting time and crop duration on establishment, flowering and grain filling. Planting after the first sufficient rains in autumn bears little risk of crop failure due to early season droughts, and more should be done to encourage farmers to do so. A potential obstacle for meticulous simulation of CA with CropSyst is the model’s inability of simulating the dynamic nature of tillage, i.e. its decreasing impact over time, and the beneficial effect of ZT and residue retention on soil water infiltration. It remains to be investigated, both by field studies and by crop model application (once related routines are implemented), whether these effects play an important role in the considered agricultural system with generally low crop productivity, and on soils with self-mulching properties. References Aase, J.K., Siddoway, F.H., 1990. Stubble height effects on seasonal microclimate, water balance, and plant development of no-till winter wheat. Agric. Forest Meteorol. 21, 1–20. R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop Allen, Evapotranspiration—Guidelines for Computing Crop Water Requirements. FAO Irrigation and Drainage Paper 56, Food and Agriculture Organization of the United Nations, Rome. ABARES, 2012. Australian Bureau of Agricultural and Resource Economics and Sciences. Commodity Statistics 2010, http://adl.brs.gov.au/data/ warehouse/pe abares99001762/ACS 2010 Wheat Tables.xls (last accessed Jan. 2012). Anderson, W.K., Hamza, M.A., Sharma, L., D’Antuono, M.F., Hoyle, F.C., Hill, N., Shackley, B.J., Amjad, M., Zaicou-Kunesch, C., 2005. The role of management in yield improvement of the wheat crop—a review with special emphasis on Western Australia. Aust. J. Agric. Res. 56, 1137–1149. Bechini, L., Bocchi, S., Maggiore, T., Confalonieri, R., 2006. Parameterization of a crop growth and development simulation model at sub-model components level. An example for winter wheat (Triticum aestivum L.). Environ. Modell. Softw. 21, 1042–1054. Benli, B., Pala, M., Stöckle, C., Oweis, T., 2007. Assessment of winter wheat production under early sowing with supplemental irrigation in a cold highland environment using CropSyst simulation model. Agric. Water Manage. 93, 45–53. Bescansa, P., Imaz, M.J., Virto, I., Enrique, A., Hoogmoed, W.B., 2006. Soil water retention as affected by tillage and residue management in semiarid Spain. Soil Tillage Res. 87, 19–27. Blair, N., Faulkner, R.D., Till, A.R., Korschens, M., Schulz, E., 2006. Long-term management impacts on soil C, N and physical fertility: Part II: bad Lauchstadt static and extreme FYM experiments. Soil Tillage Res. 91, 39–47. Blue, E.N., Mason, S.C., Sander, D.H., 1990. Influence of planting date, seeding rate and phosphorus rate on wheat yield. Agron. J. 82, 762–768.

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