Simulation of soil water storage and sowing day probabilities with fallow and no-fallow in southern New South Wales: I. Model and long term mean effects

Agricultural Systems 33 (1990) 215-240

Simulation of Soil Water Storage and Sowing Day Probabilities with Fallow and No-Fallow in Southern New South Wales: I. Model and Long Term Mean Effects R. A. Fischer,* J. S. Armstrong & M. Stapper CSIRO, Division of Plant Industry, GPO Box 1600, Canberra City, ACT 2601, Australia (Received 30 December 1988; revised version received 9 August 1989; accepted 15 September 1989)

A BS TRA C T In southern New South Wales, wheat is sown in the autumn (April-June), usually after a period of fallow to conserve soil water. In order to predict the effect of fallow and of new tillage systems, in particular no-fallow followed by direct drilling, on total available soil water (A S W) at sowing and on moisturebased sowing day probabilities, a simulation model combined with historical daily weather data was used. The model comprised a fallow subroutine with no plants, a no-fallow component involving weed germination, growth and water use, both followed by a wheat crop phase for continuous simulation. The model was calibrated and satisfactorily validated using data from several tillage experiments condueted in the region over the period 1979 84. It was then used to compare long fallow ( LF, 1 September start), short fallow ( SF, post-wheatmaturity start, November-December), late short fallow (SFt, 15 February start) and grazed no-fallow ( NoFo) over 41 years at Yass (684 mm rainfall), Wagga Wagga (566 mm) and Griffith (420 mm). On average, A S W on 1 May was 88 mm (LF), 61 mm (SF), 52 mm (SFt) and 33 mm (NoFo), with greater fallowing gains the wetter the site. Sowing probability in May averaged 77%~day for allfallow treatments and 54% /day .for grazed no-fallow. In a number of years, especially following no-fallow, there were few sowing days in May. The average green herbage (weeds) consumption by sheep (at 10 animals~ha) on NoFgfrom harvest to 1 May was only 33 g/m z. Results were insensitive to the assumed starting weed leaf area * Present address: Wheat Program, CIMMYT, Apartado Postal 6-641, 06600 Mexico, DF, Mexico. 215 Agricultural Systems 0308-521X/90/$03"50 © 1990 Elsevier Science Publishers Ltd, England. Printed in Great Britain

216

R. A. Fischer, J. S. Armstrong, M. Stapper index (range 0.001-0.025) after weed-germinating rain, and to stocking rate (range 0-40 per hectare), but were moderately sensitive to the upper and lower limits of seed-bed water content for sowing. Model assumptions are discussed and it was concluded that quantification of the likely cost of not fallowing in terms of reduced A S W and reduced number of sowing days was possible.

INTRODUCTION In southeastern Australia, dryland wheat, occupying 5-7 m ha, is usually sown in the autumn-winter (April-June). Rainfall is almost evenly distributed around the year (mean annual total 350-650mm) and grain yields are usually limited by inadequate rainfall during the cropping season. Stored water at sowing is therefore important, as is timely sowing. The traditional practice of fallowing prior to sowing, amongst other things, increases total stored water and also improves the chance of achieving an optimal sowing date through maintenance of soil water in the seeding zone. Most farms carry livestock and fallowing reduces the forage supply. More recently, the proportion of wheat sown after long cultivated fallow (typically early spring commencement) has declined and that after no prior tillage (direct drilling) is increasing (Fischer, 1987). Nevertheless, long fallowing is still widespread in the drier portion of the southern New South Wales (NSW) wheat belt, and short (typically late summer commencement) fallowing still remains the commonest form of land preparation for wheat in the region. This study emerged as part of a tillage research project (Mason & Fischer, 1986; Fischer el al., 1988) in which no-fallow direct drilling was compared at sites across the southern NSW wheat belt with the traditional strategies of long (September start) and short (December-April start) fallow, kept weedfree by cultivation or through use of herbicides. The total amount of extra water conserved by fallowing varied widely from year to year, and may not have been as important as the generally desirable influence of fallowing on soil water level in the seeding zone at sowing time, and hence on sowing day probabilities. These effects of fallowing were influenced by the pattern of rainfall in any year. This is likely to be the case with other studies on the influence of fallow on soil water in this and related regions (see Fischer, 1987); also, nothing is reported on effects on sowing date. It was therefore desirable to better quantify these influences of fallow through use of a simulation model for soil water in combination with historical weather records from key sites in the region. The processes controlling soil water storage under fallow are well understood and have been modelled at various levels of detail and empiricism for fixed fallow-wheat rotations (Nix & Fitzpatrick, 1969; Baler,

Effect of fallow on soil water and sowing day probabilities

217

1971/72; Greacen & Hignett, 1976), and for opportunistic fallow-crop strategies using soil water-based sowing date rules (Berndt & White, 1976). The WATBAL subroutine of SIMTAG (a simulation model for Triticum aestivum genotypes; Stapper, 1984) was chosen here, based largely on principles of Ritchie (1981). In order to simulate the no-fallow strategy, a weed-germination and growth model (WEEDGRO) was developed. WATBAL and W E E D G R O were calibrated and validated, before being combined with the wheat crop portion of SIMTAG (Stapper, 1984), in order to permit continuous simulation of wheat/pasture-fallow systems over a long enough period to give representative results. Mostly, daily weather data from three sites spanning the southern NSW wheat belt were used to predict the consequences of various fallow strategies (including no-fallow) for mean soil water storage prior to sowing, and for average sowing day probabilities for the sowing years 1943 to 1983 inclusive. A second paper (Fischer & Armstrong, 1990) reports on the variability of the simulated response from year to year and implications for tactical management. METHODS

Experimental Tillage experiments were carried out in the period 1979-84 near the towns of Murrumbateman, Wagga Wagga, Lockhart and Yanco on a 250 km eastwest transect across the wheat belt of southern New South Wales. At Murrumbateman, the soil was a gradational red earth (Stace et al., 1968) or chromic luvisol and at the three last-mentioned sites, a red-brown earth (Stace et al., 1968) or calcic luvisol. The soil surface (0-10 cm) was of medium texture, having 14-20% clay. Each experiment examined continuous cropping (for 3-6 years) with various tillage treatments (Mason & Fischer, 1986) after an annual grass-subterranean clover pasture phase. Soil water changes during fallow periods were monitored by the neutron-scattering technique with permanently located access tubes. For model calibration and validation, water changes under fallow or otherwise weed-free plots were used; some small weed-free plots (4 m 2) were maintained manually when fallow plots were unavailable (e.g. after wheat sowing). Soil water was similarly monitored in no-fallow plots, but weed growth was only assessed by scoring the green ground cover. Standard weather information (daily rainfall, maximum and minimum temperature, relative humidity or wet bulb, and total solar radiation) was recorded at each site. A line source sprinkler system was used on two occasions in the autumn at Ginninderra Experiment Station, near Canberra, to generate data on seed-bed soil water limits for wheat germination.

R. A. Fischer, J. S. Armstrong, M. Stapper

218

TABLE 1 Historical Weather Files (1942-83) Used in Model Location

Rain[all

Temperature, max and r a i n

Solar radiation

Source

Yass (34" 51' S, 148'~56' E, 495 m)

Daily

Long term weekly mean"

Long term weekly mean a

Aust. Bureau Meteor. (Stn 070091)

Wagga Wagga (35 ° 10' S, 147° 02' E, 220m)

Daily

Daily

Long term weekly mean and daily

Aust. Bureau Meteor. (Stn 072150) b (Forest Hill)

Griffith (34 ° 17'S, 146°02'E, 126 m)

Daily

Daily

Long term weekly mean

Aust. Bureau Meteor. (Stn 75628)c

"Calculated by H. A. Nix and colleagues from ABM sources. hCSIRO Building Research for daily radiation values (1968-80), from which long radiation term means were calculated. cCSIRO Centre for Irrigation Research for daily radiation values (1962-83), from which radiation long term means were calculated.

TABLE 2 Average Rainfall, Mean Temperature [(max+min)/2] and Solar Radiation at Key Locations: Yass (Y), Wagga Wagga (WW) and Griffith (G); Period 1942-83 Inclusive" Month

January February March April May June July August September October November December Total

Rain/bll (mm)

Temperature mean (°C)

Solar radiation (M Jim z per day)

Y

WW

G

Y

WW

G

Y

WW

G

54 58 51 50 57 49 55 58 59 79 57 58

41 38 46 43 53 48 52 52 49 62 41 41

27 34 42 36 40 33 31 36 33 47 30 41

20'9 20.4 17.6 13"6 9'4 6-9 6"3 7-5 9"8 13'0 15"6 18.8

23-5 23.5 20.5 15"7 11'4 8.6 7"5 8"8 10-9 14'1 17.4 20.9

23"8 23.7 20.7 16"3 12.2 9'4 8"5 10'1 12.6 16"1 19.1 21.9

25'8 22.1 17.9 13"7 10.0 8.4 9'1 11-6 15"2 19'5 23'6 26.7

27"4 24.4 19.9 14"5 9"8 7.9 8"7 11"4 15"8 20"5 26-0 28.9

27"1 24.7 20'5 15"2 10"8 9"0 10"0 12"4 16.8 21"5 25.9 28-4

684

566

420

"Except solar radiation (see Table 1).

Effect of fallow on soil water and sowing day probabilities

219

Simulation--general approach Historical weather records (1942-83) were obtained for Yass (near Murrumbateman), Wagga Wagga, and Griffith (near to but somewhat drier than Lockhart and Yanco). The weather data which were used are summarized in Table 1; monthly means appear in Table 2. Both Wagga Wagga and Griffith simulations used Wagga Wagga soil properties (see Table 3) since this soil was considered a more typical red-brown earth and better characterized than the Lockhart and Yanco profiles. Yass simulations used Murrumbateman soil properties (see Table 3) representing a gradational red earth, the main soil type cropped in that district. Simulation--tillage strategies The simulations examined six strategies: (1) Long fallow with bare surface (LF) (2) Short fallow with bare surface (SF) (3) Short fallow with stubble surface (SFm) (4) Late short fallow with bare surface (SF 0 (5) No-fallow with weeds according to rains (NoF) (6) As for (5) but grazed as described below (NoFg) The LF strategy was assumed to follow annual pasture, simulated by weeds TABLE 3 Key Soil Moisture Contents a as Determined by Field Measurement under Wheat for the Gradational Red Earth Profile at Murrumbateman and the Red-Brown Earth Profile at Wagga Wagga

Depth (cm)

0-20 20-40 40-60 60-80 80-100 100-120 120-140 140-160

Wagga Wagga (% vol.)

Murrumbateman (% vol.) AD

LL

DUL

SAT

AD

LL

DUL

SAT

6.5 15'2 20'4 22'2 20'4 20.6 19.1 19.6

8.5 15.2 20.4 22'2 20.4 20'6 19.1 19.6

26.0 26.5 30"0 31'5 27.0 25.5 22.5 21.0

35.0 35"0 35"0 36"0 35'0 29.0 26.0 24.0

5.0 16.0 20'2 22'0 25'0 27.5 31.2 31.1

7.5 17.5 20.2 22"0 25.0 27.5 31.2 31.1

26.0 27.0 32"0 32.8 32.7 32.9 33.0 32.5

30.0 30.0 35.0 35"0 35.0 35'0 35-0 35.0

aAD, Air dry; LL, lower limit of extraction; DUL, drained upper limit; SAT, saturated content. Air dry moisture contents are nominal, being the lowest moisture contents recorded at each level at each site.

R. A. Fischer, J. S. Armstrong, M. Stapper

220

Wheat mature weeds can germinate in stubble

Feb 15 start SF I A SF,SF m

B

SFI L,

c

I-ilFg, N!--i

I tO nextcycle to next cycle

°o°o°oo,=o°o°oo°

Sept 1 start LF

First germinating rain, start SF

May15sowwheatonSF fornext cycle

t l l l l l t I I I I I I I I M J J A S 0 N D J F M A M J J MONTH OF YEAR ..... -- fallow ........ weeds on no-fallow .... wheatcrop

I

Fig. 1. Diagram showing fallow, no-fallow and wheat crop sequences simulated by model: LF, long fallow; SF, short fallow (same for SFm, short fallow no burn); SF~,late short fallow; and NoF~,grazed weeds (same for NoF, ungrazed weeds). Broken lines refer to situations with growing plants; streams A, B, C and D are followed closely for total ASW and seed-bed moisture during March July; first weed-germinating rain for weeds assumed to be late December in this example. (Fig. 1), and began with destruction of all vegetation on 1 September. All other strategies were assumed to succeed a wheat crop and their simulation commenced at the date of maturity of the wheat (November-December): while weed control could not normally commence until after harvest, weed germination could, and for simplicity the after-maturity germination and control was adopted for all. Only for SF m were the effects of stubble considered: the above-ground straw amount was kept constant throughout the fallow period and equal to the final above-ground biomass of the wheat crop minus 15 times the grain yield. On treatments (2) and (4) stubble was assumed burnt when fallow commenced, and on (5) and (6) it was ignored. In the late short fallow (SFI), weeds were allowed to germinate, grow and be grazed in the period between maturity of the wheat crop and 15 February, when the fallow commenced with destruction of any weeds present. This strategy should be closer to the regional practice of delaying onset of short fallow until harvest is complete and stubble-burning safe. It was assumed that fallows were maintained weed-free throughout by cultivation while nofallow was never cultivated and was sown by direct drilling.

Effect of fallow on soil water and sowing day probabilities

221

To start the model run, 45 m m available soil water on 20 November 1941 was assumed. Strategy NoFg (grazed weeds) was allowed to proceed on to 1 September 1942 as if it represented a winter pasture, in order to establish the soil water profile for commencement of LF for 1942-43. To establish the other fallow strategies, the initial NoFg was converted to a wheat crop, sown May 1942, in order to determine at maturity of the wheat, the date and soil water profile for the onset of these strategies for 1942-43. In subsequent years of the run, soil water at the commencement of any LF or S F / N o F period was provided directly from the NoF~ or wheat simulations, respectively (Fig. 1); thus, the simulation of each strategy was continuous.

Simulation--sowing decisions A major purpose of the simulation study was to determine the number of days in the autumn and winter when wheat sowing was possible (sowing day probabilities). Primary emphasis was given to the adequacy of seed zone soil water for assured emergence even in the absence of further rain. The lowest soil water content at which this occurred experimentally was determined both in the field (including sowing across a rainfall gradient established with the line source sprinkler system) and in the laboratory (with soil of graded moisture content). With good tilt and low evaporation rates (1-3 per mm per day), wheat emerged from seeding depths of 3-7 cm when the soil water matric potential (0-10cm) initially exceeded - 0 . 3 MPa. For the medium textured soils at the sites, this meant gravimetric soil water contents greater than about 11%, or 15% volumetric in the 0-20cm layer (since only volumetric moisture and 20 cm layers were modelled); this key water content is abbreviated to 0. A sowing day was not registered, however, until this dry threshold had been exceeded for five consecutive days, in which case the sixth day became the first sowing day. This temporal limit was applied in order to allow for a number of activities which usually take place between a substantial rain and sowing: cultivating and/or spraying of pre-emergence herbicide for cultivated fallow fields, application of knockdown herbicide and waiting for weed roots to release their hold on the soil in the case of herbicides used ahead of N o F direct drilling. Along with the further restriction that no sowing took place on days with greater than 10 mm of rain, this constituted criterion 1 for sowing (i.e. moisture-limited sowing). It was assumed, from consideration of Baier (1973) and Danh & WingateHill (1978), that cultivated surfaces could not be sown if volumetric soil water content (0-20cm) exceeded the drained upper limit minus 1% volumetric. Since direct drilling is known to improve trafficability for such surfaces (i.e. all N o F strategies), the upper threshold for sowing was set at the drained upper limit. Application of this restriction in addition to criterion 1

222

R. A. Fischer, J. S. Armstrong, M. Stapper

constituted sowing criterion 2 (i.e. moisture- and trafficability-limited sowing).

Model description---WATBAL subroutine WATBAL is fully documented by Stapper (1984). For each soil layer, it requires four in-situ-determined soil-specific limits, these being in ascending order the lowest value ever recorded (nominally air dry), lower limit of plant extraction, drained upper limit and saturation (Ritchie, 1981). Available soil water (ASW, mm) for plants is that which occurs above the lower limit of extraction (see Table 3). A profile having eight layers each 20 cm thick was worked with, since these corresponded to the neutron-scattering readings. A daily-time scale is used in WATBAL, along with instantaneous infiltration of daily rainfall (the runoff provision was suppressed) and instantaneous distribution according to a tipping bucket model and the proviso that no layer can exceed its saturated water content. Whenever the drained upper limit is exceeded in any layer, redistribution downwards occurs from the lowest such layer according to a drainage factor (set at 0.75 m m / m m per day). Water flowing from the lowest layer of the profile is accumulated as drainage. Potential evapotranspiration (EO) is calculated from a combination-type function with a bare soil albedo set at 0.14. As in Ritchie (1972), bare soil evaporation during stage 1, potential soil evaporation (EOS), equals EO and continues until cumulative evaporation reaches a threshold value (U, mm). In the ensuing stage 2, or falling rate stage of evaporation, cumulative evaporation (mm) is proportional to the square root of lapsed time in days. The proportionality constant has been altered by Stapper (1984) to range from 1.5 to 3-5 mm/day ° 5 as a linear function of EO. The withdrawal of evaporated water from successively deeper soil layers has also been modified by Stapper (1984) using a method of van Keulen (1975) which involves a fitted soil water withdrawal factor (SWWF). Soil evaporation is assumed not to be affected by cultivation per se, in line with pilot studies at Murrumbateman (Fischer, R.A., unpublished). However, it can be reduced in stage 1 by shading from dead plant residue (such as wheat stubble in SFm): EOS = EO(1 - MULCH)

(1)

and M U L C H = 0-67 × 10-6(3930S - 6"83S 2 + 0"0039S 3)

(2)

where M U L C H is a reduction factor varying between 0 and 1, and S is the amount of straw in g/m 2. Equation (2) was derived from Bond & Willis

Effect of fallow on soil waterand sowing day probabilities

223

(1970), plus an assumed factor of 0.67 to allow for the fact that field straw is largely oriented vertically and not horizontally as in their study; 250 g/m 2 of straw reduces EOS 50 per cent, compared to bare soil according to eqns (1) and (2).

Model description--WEEDGRO subroutine Weed growth in the fallow period involves (i) maturing winter annual pastures and cereals during the spring, (ii) summer (and some winter) annuals germinating when rain falls after maturity of the pasture or crop during the warm period up until March, and finally (iii) winter annuals germinating with rain later in the fallow period when it is cooler. The major summer-germinating weeds observed in the tillage experiments were heliotrope (Heliotropium europaeum L.), scented goosefoot (Chenopodium multifidum), subterranean clover (Trifolium subterraneum) and volunteer wheat. After about mid-March the major germinating species, besides subclover and wheat, were annual ryegrass (Lolium rigidurn), barley grass (Hordeum leporinum) and capeweed (Arctotheea calendula). Many other weeds were important in certain situations and it is clearly unrealistic to attempt to model such weed diversity. The option of modelling-all germinating weeds (situations (ii) and (iii) above) was taken, assuming a single representative C 3 weed species, as are all of the above species. Spring weed growth, either pasture or crop (situation (i) above), was separately modelled assuming it was represented by a typical wheat crop sown in midMay and simulated by the SIMTAG model (Stapper, 1984) without modification. In the W E E D G R O subroutine, weeds germinate on the first rain event which exceeds, over two consecutive days, 25 mm (November-February), 2 0 m m (March, April) or 15mm (May onwards); weeds may germinate repeatedly if earlier germinations die from water stress. Emergence takes place 50 day-degrees of mean temperature (above 2°C) later, at which time weeds are given a specified starting leaf area index (LAIWS) and a fixed starting root zone depth (50mm). In the standard case, LAIWS is set at 0-005, equivalent to 25 wheat plants per m 2 at 2 cm 2 each, or 200 subclover plants per m 2 at 0.25 cm 2 each. Subsequent total dry weight increase or growth is directly related to photosynthetically active radiation (50% of total solar irradiance) absorbed by green leaf area. The efficiency ratio was fixed at 2.7g/M J, as for wheat (e.g. Stapper, 1984), and absorption was governed by a reflectance coefficient of 0-1 and an extinction coefficient of 0.45, as for cereals (e.g. Gallagher & Biscoe, 1978). A fixed 70% of growth is allocated to tops and this produces 200cm2/g of new leaf area. Leaf area

224

R. A. Fischer, J. S. Armstrong, M. Stapper

senesces only with water stress, as described below, and weeds are assumed to remain vegetative throughout. After emergence, the weed root zone extends downwards at a constant 1.4 m m per day-degree (2°C base), an average value for wheat taken from SIMTAG, but is reduced in proportion to fractional available water (the ratio of [water content minus lower limit] to [drained upper limit minus lower limit]) in the soil layer encountered by the root tip if this faction is below 0-3. As in Ritchie (1972) and SIMTAG, the transpiration of nonstressed weeds (potential transpiration) as a ratio of EO is curvilinearly related to weed leaf area index (LAIW) until the index exceeds 3.0 when the ratio is fixed at 1.0. Procedures of S I M T A G are used to calculate the pattern of water extraction in the root zone and the effect of soil water stress. Actual transpiration falls below potential transpiration if the fractional available water falls below 0.4. The fractional available water in the total root zone determines the soil water stress factor for dry weight accumulation and determines leaf senescence. As in SIMTAG, the stress factor equals 1"0 (no stress) until this fraction falls below 0.4, dropping to zero at zero available water; it is used as a multiplier to reduce the efficiency of conversion of absorbed photosynthetically radiation to plant dry weight. Determination was made by calibration of a leaf area senescence rate due to water stress which rises sharply from zero at 0.4 fractional available soil water to 50% of the leaf area per day at 0.1 and 100% per day at zero available water; when zero green leaf area index is reached, the vegetation dies. The grazing option is controlled by the above-ground green biomass of weeds (all tops less senesced leaf, in g/m2). When this exceeds 20 g/m 2 (LAI > 0.4), sheep are introduced (10 per hectare in the standard case). The consumption of green biomass (and hence reduction in leaf area) rises linearly from 250g/head per day at this level of green biomass to 1250 g/head per day at 100g/m 2 green biomass or greater (Mulholland et al., 1976). Sheep are removed whenever green biomass falls below 20 g/m 2.

Model description--calibration and validation The four soil water limits, by layers at each site, were obtained by measurement during the tillage study, the lower limit of extraction referring to extraction by the wheat crop (Table 3). The parameters U and SWWF were obtained by fitting the model to replicated measurements (n = 4-12) of bare soil water content change, obtained for m a n y intervals, approximately two weeks in length and free of runoff, between 1982 and 1985 at the M u r r u m b a t e m a n site. Model parameter values of U = 4, 6 and 8 mm and S W W F = 6 and l0 were tested. Validation was attempted using independent

Effect of fallow on soil water and sowing day probabilities

225

longer duration sets of bare soil evaporation data from earlier years at Murrumbateman (1979-82), and from the Wagga Wagga, Lockhart and Yanco sites; in these cases, the model was given only the initial soil moisture measurement for each set. U and SWWF were not altered because the topsoils were basically similar to that at Murrumbateman. Evaporation predictions for soil with stubble was not validated. The W E E D G R O submodel was not calibrated (sensitivity to some inputs affected by management will be examined later). Weed emergence and growth predictions could be validated using information gathered in the autumn from no-fallow (NoF~) plots during the course of the tillage experiments. Observations were made in the Murrumbateman, Lockhart and Yanco experiments (giving 12 site-years), and these were supplemented by observations of pasture growth in the autumn at Murrumbateman on four other occasions. However, only data on weed emergence, species and green ground cover (scored by observation, scale 0-10 = 0-100%), and on soil water (gravimetric sampling or neutron-scattering meter), were collected, and then only on an irregular basis. Also, it was not possible to quantify the actual grazing pressure operating in each instance, or to validate predicted herbage consumption; the standard stocking density of 10 sheep/ha was assumed to be appropriate. For the purposes of converting predicted LAIW to a predicted green ground cover score, an extinction coefficient of 0"45 was used. The factors affecting the sowing decision in the model have been defined and need no further calibration. An approximate validation was possible from visits to the tillage experiments in the autumn-winter when it was noted whether sufficient moisture was present to permit sowing. This critical judgment was largely subjective, although it was checked at three sites on different occasions by bringing 0-10 cm soil samples back to the laboratory for water potential and germination determination. Due to the limited number and brevity of periods of poor trafficability, it was not possible to validate the trafficability restrictions in the model.

Sensitivity analysis of management assumptions The sensitivity of modelled outcome to independent variation in LAIWS, no-fallow stocking rate and soil water limits to sowing was tested. As well as appearing to be critical assumptions, these aspects could be influenced by management and hence their examination seemed desirable. Since results with the standard model differed little between the 1943-62 and 1963-83 periods, these sensitivity studies were confined to the latter period, using Wagga Wagga weather.

226

R. A. Fischer, J. S. Armstrong, M. Stapper

RESULTS Calibration of WATBAL under fallow The WATBAL calibration under bare soil conditions at Murrumbateman gave the most satisfactory fit when using U = 4 m m and SWWF--10. Results are shown in the top line of Table 4. The mean deviations (predicted minus measured) at 10cm, 30cm and over the whole 0-160cm profile were close to and never significantly different from zero. Inspection of the calibration results revealed consistent underprediction, at least of the total profile moisture content, in the data set covering January-July 1984 when the profile was mostly very wet and the model may have overpredicted deep soil (> 100 cm) drainage. A temporary water table developed on top of the underlying granite under such conditions, and this presumably slowed drainage which was normally rapid at this hilltop site. Validation of model components Fallow soil water

Validation of the model against soil water changes in the absence of vegetation was provided largely by independent data sets similar to those described for calibration (remainder of Table 4). With the exception of the prediction of moisture content at 10cm at Lockhart, none of the mean deviations shown in Table 4 was significantly different from zero. There was, however, a tendency for overestimation of moisture content at 10cm (the overall average deviation, including Lockhart, was 1.0% volumetric) but this bias tended to be counterbalanced by underestimations further down the profile, so that for the total profile the overall average deviation was negligible (+ 1.7 ram). Inspection of the Lockhart simulations did not reveal the cause of the consistent overestimations at 10 cm; given the tendency for underestimation at 30 cm, the result could arise from an overestimate of the SWWF factor, obtained from calibration at Murrumbateman which governs upwards flow from layer 2 (30cm) to layer 1 (10cm). The root mean square (RMS) deviations obtained with calibration (Table 4) were as low as could be expected given model assumptions and measurement errors; the total profile RMS deviation (4.6 mm) was similar to that achieved by Johns (1982) in a more precise study of soil evaporation. In validation, most RMS deviations were greater, as would be expected from much longer simulation runs and the inclusion of possible runoff errors. Since validation runs were already of 100-200 days duration, it is unlikely that even longer runs, as will be used in model application, would have

Effect o f fallow on soil water and sowing day probabilities

227

TABLE 4 Summary o f the Soil Water Deviation (predicted minus measured) at 0 - 2 0 cm (10 cm) and 2 0 - 4 0 c m (30cm), and Over the W h o l e Profile ( 0 - 1 6 0 c m ) a for Various Sources o f Measurements o f Soil Water Change in the Absence o f Transpiring Plants Dale source Site and period

Calibration Murrumbateman,

Mean duration of Number of simulation i n d e p e n d e n t (days) data sets

Mean deviation (% vol.) 10 cm

Mean deviation (ram)

30 cm

Mean

RMS

Mean

0-160 cm a

RMS

Mean

RMS

44

16

0.2

2.4

-0.2

0.8

-0.32

4.6

1982 85

Validation Murrumbateman,

8

105

1.2

2.5

1.2

2.4

1.7

15.7

1979-82 Wagga Wagga, 1979 83

6

201

0.6

2.1

0.6

2.3

7.9

13.1

Lockhart,

4

210

2.5*

3.6

1.6

2.6

-3.3

10.1

1980 84 Yanco, 1981-85

10

165

0.7

1.8

- 1.4

3.2

- 1.7

7.9

The first line refers to calibration of the model over intervals defined by pairs of consecutive measurements, while the remainder refer to validation through repeated measurements at given sites over 2-12-month periods. ~0-140cm for Lockhart and Yanco. * Significantly different from zero at P<0-05.

further increased the RMS deviations. On the basis of low bias as well as low deviations, it was concluded that the model of soil water was satisfactory.

Weed emergence and growth During the summer-autumn period in the 12 site-years of tillage experiments at Murrumbateman, Lockhart and Yanco, 18 waves of weed emergence were observed; 14 of these were predicted in the validation run of W E E D G R O . All four emergences not predicted arose from multiple rain events, too small individually to trigger germination in the model; seed priming may be one neglected mechanism favoring germination and emergence under such rainfall sequences. One predicted emergence (following a 27 mm storm on dry soil in mid-summer) was not observed, presumably because the soil dried too rapidly. It is concluded that the germination routine in W E E D G R O is satisfactory, especially as nonpredicted emergences would not have altered water use much without follow-up rains, which in turn brought on simulated emergence in the four cases where it had been missed earlier.

228

R. A. Fischer, J. S. Armstrong, M. Stapper

With weed growth validation against observations over the six site-years (a total of 25 comparisons with subclover the dominant weed), it became obvious that the standard starting leaf area index of weeds (LAIWS = 0"005) was often too low: the emergence of up to 1000 m E plants of subclover was observed on several occasions with autumn rains (LAIWS -- approximately 0.025). However, weed growth after the first few weeks, especially if moisture stress arose, was not particularly sensitive to LAIWS (see sensitivity of results to assumptions relating to management), so that the standard parameters in the model were not altered. Excluding those situations in which standard LA|WS was obviously too low and no soil water stress arose subsequently (7 out of the 25 comparisons), there was a reasonable relationship between observed and predicted green ground c o v e r ( r E - - 0"51), without obvious bias (predicted mean 37%, observed mean 35%) but with a high RMS deviation (25%). The main deviations appeared to arise under water stress, when sometimes modelled weeds died from drought when weeds were observed to be only severely wilted, but not dead, or alternatively on one occasion when modelled weeds maintained leaf area while weeds were observed to senesce under drought. Overprediction of the weed root zone depth appeared to be the cause of the last-mentioned discrepancy. During two of the autumn drying periods at Murrumbateman, observations of soil water extraction patterns under subclover indicated that the soil was not dried deeper than about 50cm; however, wire weed (Polygonum aviculare) plus volunteer wheat growth caused extraction to 100 cm under a similar circumstance at Lockhart. Clearly, there is room for refining W E E D G R O in this regard. Despite poor prediction of weed growth, prediction of topsoil (0-20 cm) volumetric content (0) under weeds (no-fallow) was quite accurate. Twentyone comparisons around sowing time (April-May) were available from the 12 site-years (r2= 0"85, mean predicted value 17.5%, mean observed value 16"0%, RMS deviation 2.9%). Obviously, predictions are aided by those occasions when 0 was close to either the lower or upper limits; however, even confining predictions to whenever observed 0 was between 10% and 20% (n = 12) gave mean values of 16-3% (predicted) and 15-2% (observed). Since 0 under fallow was also accurately predicted (Table 4), it follows that the important difference in 0 between fallow and weeds (no-fallow) around sowing time was successfully predicted. The correlation between predicted difference (mean 1.9%) and measured difference (mean 1.9%) was highly significant (n=21, r=0"66, RMS deviation 2.4%). Four out of the six occasions when the fallow was observed to have a 0 value more than 4"0% greater than the weedy surface were successfully predicted. Also, prediction of the difference between fallow and weeds in total soil moisture at sowing was reasonably accurate: there were 17 comparisons available from the

Effect of fallow on soil water and sow&g day probabilities

229

tillage experiments, the predicted difference (mean 15.6 mm) was correlated (r=0.74) with the measured difference (mean 13.3mm), with a RMS deviation of 13 mm. Sowing judgments Whilst 0 predictions seemed accurate, validation of sowing moisture criteria (lower threshold of 15% for all treatments), and hence sowing day predictions, was difficult since no objective measurements of sowing days could be made (they were attempted with sowings under borderline moisture conditions created with the line source sprinkler but these provided little useful data being thwarted by rain). Soil moisture was judged to be too low for sowing the NoFg treatment in May in five of the 12 site-sowings. These five occasions were clearly predicted by the model, with predicted values of 0 on 16 May ranging between 6.7% and 12-3%, and with only 0-6 (mean 2) predicted sowing days in May. These values compare with the NoFg treatment on the other seven occasions, having a 0 range on 16 May from 17-7% to 27.1% and an average of 26 sowing days in May. For three of the above five years when May was dry the model also predicted sowing difficulties for fallow treatments, with 0 on 16 May ranging from 12.2% to 13.9% and only 0-7 (average 4) sowing days in May. However, field observation and judgment on these three occasions indicated that fallow plots (at least cultivated ones) could be sown with satisfactory germination and emergence. This discrepancy suggests that either the judgment was wrong or that the lower 0 threshold for sowing cultivated fallows should be less than 15%. If the latter case is true, the model as defined somewhat underestimates the difference in sowing days between fallow and no-fallow arising due to dryness; in the interests of simplicity the lower limit for sowing was not altered for the standard runs.

Comparison of tillage strategies using the model Emphasis is placed upon the major contrasting tillage strategies, namely long fallow (LF), short fallow (SF) and no-fallow grazed (NoFg); no-fallow ungrazed (NoF) was little different from NoFg (see later). Total available soil water ( A S W ) Water in the soil profile was decreased by SF compared to LF (mean reduction across dates and sites in Table 5 was 32mm) and by NoFg compared to SF (mean reduction 26 mm). The effects of tillage strategy on ASW were smaller at the driest site, and the total amount of stored water was lower the drier the site and the earlier the considered date of sowing, as might

R. A. Fischer, J. S. Armstrong, M. Stapper

230

TABLE 5 Summary of Simulated Effect of Fallow Period Tillage Strategy on Stored Available Soil Water (ASW) and Through Profile Drainage at Three Sites in Southern NSW; the Simulation Used Historic Weather at Each Site for the Period 1942-83

Tillage strategy

Mean available water in soil profile (mm) 1 March

Yass LF SF m SF S~ NoF,

1 April

1 May

I June

Mean drainage loss since commencement o f SF (ram) Mean 1 May

1 July

96 65 56 35 26

100 78 68 50 32

104 88 75 61 38

112 104 90 77 56

103 84 72 56 38

48 24 17 9

75 58 35 26 19

84 44 37 22 16

89 59 50 38 24

97 73 62 53 35

113 98 83 74 54

96 69 58 47 32

21 9 6 4 2

39 26 17 12 8

44 27 26 20 14

55 44 37 32 22

63 54 46 43 27

74 69 59 56 38

69 49 42 38 25

11

WaggaWagga LF SF m SF SF I NoF~

Griffith LF SF m SF SF I NoFg

4 4 3 2 0-4

8 10 5 4 1

be expected. Short fallow with retained straw (SFm) was intermediate between LF and SF, giving on average a 9 mm water storage advantage over SF. Delayed short fallow (SF 0 was on average only 10 mm lower than SE These mean figures disguise large annual variation, which is examined in the second paper. Since long fallow is most widely practiced in the Griffith region, water conservation was examined for different fixed dates of long fallow commencement there. Data from the SF (average start date, 15 November), SF I and NoF, simulations (Table 5) complete the picture for the response of ASW on 1 May to duration of fallow (Fig. 2). Sensitivity to date of initiation of LF is greatest in August-September. There seems little justification on the grounds of conserving extra water for commencement before 1 August, for example 1 July fallow giving on average only 3 mm extra storage. Delaying fallow until 1 September led to 6 mm less storage, and a further month delay cost another 9mm. Commencement on 1 September therefore seems a

Effect of fallow on soil water and sowing day probabilities

231

A

%

60

40

5 20 "~

<

Fig.

2.

0

I

J

i

A

i

I

J

i

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I

I

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I

i

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Mean total ASW on 1 May as a function of date of commencement of the prior fallow period; simulated results for Griffith, 1943-83.

reasonable compromise, given the greater difficulty of killing weeds with cultivation in the cooler, damper August conditions. The generally wetter soil profiles with fallow were associated with more through drainage, especially in the case of LF (last two columns, Table 5). Also the LF drainage figure in Table 5 does not include drainage recorded between the onset of long fallow (1 September) and the commencement of SF in November-December, a period when other strategies are unlikely to have shown any losses: the additional losses with LF were 31, 9 and 0-5 mm at Yass, Wagga Wagga and Griffith, respectively. The increase in drainage losses with strategies giving initially greater storage should reduce total soil water differences between tillage strategies as one moves into the winter. That the differences between fallow and NoF~ did not actually narrow (Table 5) must have been due to the counteracting effect of an increased weed transpiration component in NoFg as weed cover increased. Nevertheless, greater drainage losses with fallow mean that even with seeding on 1 May not all the extra ASW predicted with fallow for that date (Table 5) will become available to a crop due to additional drainage when crop LAI is low, i.e. through to at least 1 July, as shown in Table 5. The ASW with fallowing can be compared with experimental results summarized by Fischer (1987). On similar soils with fine-textured subsoils in southeastern Australia (seven separate studies comprising 38 site-years), long fallowing (August-September start) conserved at sowing 32 mm more water than April fallow. For a more recent study in northern Victoria, the average difference was 29 mm (Cooke et al., 1985). These differences are best compared to the 1 June difference between LF and SF, in Table 5: 35, 39 and 19 mm at Yass, Wagga Wagga and Griffith, respectively, which amounts to satisfactory agreement considering that Wagga Wagga and Griffith are more representative of the rainfall regimes of the cited experiments.

R. A. Fischer, J. S. Armstrong, M. Stapper

232

TABLE 6 Summary of the Simulated Effect of Fallow Period Tillage Strategy on Sowing Date Probabilities at Three Sites in Southern NSW, Using Historic Weather at Each Site for the Period 1943-82

Tillage strategy

Mean probability (%) of day being suitable for sowing March

Probability (%) o[ < 10 sowing days in May

April

May

June

Yass----criterion 1 LF 60 SF 57 NoFg 28

71 70 37

90 90 63

93 92 76

Yass----criterion 2 LF 59 SFm 60 SF 56 SF, 51 NoFg 28

69 67 68 66 36

83 72 82 79 58

84 69 83 80 67

Wagga Wagga--criterion 1 LF 42 SF 39 NoF~ 21

63 62 39

87 87 62

92 91 80

Wagga Wagga--criterion 2 LF 42 SFm 46 SF 39 SF I 36 NoF~ 20

60 59 59 57 38

75 63 75 75 56

73 62 73 73 69

Griffith----criterion 1 LF 34 SF 34 NoFg 21

60 60 38

80 78 51

90 88 64

Griffith--criterion 2 LF 34 SFm 37 SF 33 SF I 31 NoF, 21

59 59 58 55 38

75 72 75 71 49

83 72 8! 79 61

2 7 2 5 24

m

m

7 20 7 7 29

m

7 7 7 12 37

Criterion 1 =adequate moisture for germination; criterion 2 = 1+ trafficability limits.

Effect of fallow on soil water and sowing day probabilities

233

Finally, it is relevant to calculate the efficiency of storage of fallow rainfall in treatments SF and SF m, which were assumed to commence with maturation of a preceding wheat crop and therefore a relatively dry profile. For example, at Wagga Wagga, starting ASW averaged only 16 mm on the average date of maturation, this date being 23 November. From that date until 1 May, on average 220 mm of rain was recorded. The increase in ASW seen with SF and SF m on 1 May (Table 5) represents 21% and 26% of this rainfall, respectively. These values are close to those measured for dry-start short fallows in the northern Australian wheat belt (Fischer, 1987). Sowing day probabilities Under sowing criterion 1, which ignores possible trafficability differences between tillage strategies, sowing probabilities, while increasing steadily from Griffith to Yass and from March to May, were not substantially different between fallow strategies (Table 6, only LF and SF shown). By the same criterion, probabilities with no-fallow (NoFg) were substantially lower than all fallow strategies except at the two wettest sites in June; over all sites and months it averaged 48% whereas SF averaged 71%. The absence of differences between fallow strategies contrasts with the differences in total soil water (Table 5), and reflects the dominant influence of rainfall after 15 February when all fallows were managed identically. Introducing trafficability restrictions as outlined in methods (criterion 2) reduced sowing probabilities less than 5% in March and April (Table 6). However, greater effects were seen in May and June, especially on fallow treatments and at Wagga Wagga, and in particular with treatment SF m (Table 6), where the straw mulch presumably kept the surface layer wet for longer. This fallow treatment actually showed slightly higher probabilities for sowing than other fallows in March. Despite trafficability considerations, the sowing probability difference between no-fallow and fallow was still substantial; for example, overall under criterion 2 NoFg averaged 45% probability and SF 65%. There are no published data against which the predictions of Table 6 can be checked. The mean probabilities cannot be used to calculate directly the duration or median date of sowing since sowing days are not randomly distributed in any one year, presumably because of persistence in soil water levels which leads to skewed variability around the mean probability. The fact that NoFg induced a substantial increase in the probability of years with less than 10 sowing days in May (final column in Table 6) is another indication of this phenomenon, which will be explored in Part II. Herbage production and consumption Herbage production under no-fallow increased with increased summer plus

234

R. A. Fischer, J. S. Armstrong, M. Stapper

autumn rainfall, both across sites and across years. Average production from the start of SF to 1 May under NoFg was 386, 212 and 121 g/m 2 at Yass, Wagga Wagga and Griffith, respectively. At Wagga Wagga, production (P, g/m 2) ranged from 0 to 871 g/m 2 across years and was closely related to summer plus autumn rainfall (RAINsA, mm): P = l'85(RAINsA - 96)

(r = 0"82, n = 41)

Simulated weed emergence date was recorded. Again at Wagga Wagga, the first wave of emergence usually occurred in December or January (73% of years) and sometimes in February or March (10% each). Often the early germinations of weeds died through lack of moisture, and the model, as outlined earlier, predicted a subsequent second emergence of weeds either in February (7% of years), March (10%), April (7%) or May (12%). All large amounts of herbage production ( > 500 g/m 2) arose from early germinations. At the defined standard stocking rate of 10 sheep per hectare for strategy NoFg, only a small amount of total herbage produced was consumed as green herbage by sheep: up until 1 May the average quantity was 54, 30 and 16 g/m 2 at Yass, Wagga Wagga and Griffith, respectively. The sensitivity to stocking rate is considered in the next section.

Sensitivity of results to assumptions relating to management There was surprisingly little difference in the outcome of no-fallow tNoFg) at Wagga Wagga (1962-83) as LAIWS values increased from 0.001 to 0"025 (0"005 was standard): ASW on 1 May decreased 3 mm and mean sowing day probability in May decreased 1%. However, weed biomass produced to 1 May increased from 182 to 240 g/m 2. In other words, the differences between N o F and SF shown in Tables 5 and 6 are relatively insensitive to LAIWS unless we are dealing with very small LAIWS values (i.e. < 0-001 or less than 40 subclover plants per m2). This insensitivity arose because during the common dry periods following weed emergence in the summer and autumn, weeds tended to exhaust all available soil water regardless of LAIWS (an example is 1975-76 shown in Fig. 3). Also, evapotranspiration was not sensitive to weed LAI when the soil surface was wet or once LAI exceeded 3.0. Again at Wagga Wagga (1962-83), modelled outcome with NoFg was also relatively insensitive to stocking density. This was especially so with ASW on 1 May which rose only 2 mm as stocking density was increased from 0 to 40/ha (10/ha is standard). May sowing probability increased linearly from 55% to 61% over the same range. April sowing probability was increased somewhat less (36-39%) and June probability somewhat more (68-77%). Total weed biomass produced (standing and consumed to 1 May) decreased

Effect of fallow on soil water and sowing day probabilities

-(a)

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50

40

40

30

30

20

20

~

E

QJ

.~

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o I Dec

,,J, Jan

Feb

'"° Mar

Aprl

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e Jun

1976

Fig. 3. Simulation of the no-fallow grazed (NoF,) condition during the period December 1975 June 1976 at Wagga Wagga using a LAIWS of 0"00! (open symbols, broken lines) or 0.025 (closed symbols, solid lines): (a) leaf area index (triangles) and grazing time (histogram); (b) total ASW (squares) and daily rainfall.

steadily from 245 g/m 2 (no grazing = NoF) to 239 g/m 2 (10 sheep/ha) to 193 g/m z (40sheep/ha), while forage consumed increased from 34g/m 2 (10 sheep/ha) to 101 g/m 2 (40sheep/ha). The explanation for this lack of sensitivity to stocking rate lies largely in the factors mentioned above with respect to insensitivity of soil water to LAIWS, in addition to the fact that grazing was only permitted when LAI exceeded 0.4. Whilst there may appear to be some gain with the higher stocking rates tested, it should be remembered that more alternative feed needs to be found whenever, as was c o m m o n in the simulation, the stock exhaust the forage on the no-fallow field. Since it is possible that changed machinery design could alter the thresholds of topsoil moisture for sowing, this effect for SF and NoFg was tested by raising and lowering by 3% the dry threshold to sowing (standard value 15%), and separately varying the wet ones around their standard values (25% and 26%, respectively). As might be expected, sowing

236

R. A. Fischer, J. S. Armstrong, M. Stapper TABLE 7

Sensitivity of Sowing Day Probabilities to the Dry and Wet Soil Moisture Thresholds for Sowing (% vol., 0-20cm) at Wagga Wagga 1962-83 Soil water threshold (% vol.) Dry

Probability o f a sowing day (%)

Wet

April

May

June

SF

NoFo

SF

NoFg

SF

NoFo

Changed dry threshold 13 Standard a 15 Standard s 17 Standard s

70 58 42

45 36 23

72 68 62

60 55 46

78 75 69

74 70 61

Changed wet threshold Standard b 26 Standard b 25 Standard ~ 24 Standard b 23

59 58 56 54

36 34 32 30

76 68 63 56

55 44 39 34

84 75 67 59

70 54 43 32

The standard upper threshold is 25% (fallow), 26% (no-fallow). b The standard lower threshold is 15%. probability was very sensitive to the dry limit in both tillage strategies in April (Table 7), but less sensitive in M a y and June. If the dry threshold was between 2% and 4 % less for NoFg compared to SF then sowing date probabilities would not differ between the two strategies; in fact, the threshold is likely to be slightly higher for NoFg. Sowing probabilities in April were quite insensitive to the wet (trafficability) threshold as it was decreased by up to 3% (Table 7), but probabilities became moderately sensitive in M a y and June. In the latter months, a decrease in the wet limit for SF by between 2% and 3% from its standard value (which is already 1% less than that for NoFg) would reduce SF sowing probabilities to a level approximately equal to those with NoFg.

GENERAL DISCUSSION The objective of this study was to compare soil water at sowing and sowing date probabilities of alternative tillage strategies, namely fallow and nofallow. Apart from limited calculations on forage (weed) production under no-fallow, no attempt was made to quantify other important aspects of the fallow versus no-fallow comparison (effects on soil nitrification, early crop vigor and root growth, root diseases and in-crop weeds, crop yield, soil structure and erodibility, fuel and labour consumption, etc.) which must be

Effect of fallow on soil water and sowing day probabilities

237

considered in an economic assessment of tillage systems. This is cleariy beyond the scope of the present paper, although Part II does look at optimum tactics for fallow initiation by weighing soil water advantages against tillage costs. Validation of the model was extensive and satisfactory, and sensible results, relatively insensitive to most central assumptions in the model, were obtained. Nevertheless, it is useful here to consider briefly some of the possibly critical assumptions involved in the soil water balance (i-iv), and in the tillage logistics (iv-vii). (i) Runoff was assumed to be zero. While runoff was an important component of the fallow water balance in the model of Berndt & White (1976) applied in southern Queensland, runoffis likely to be considerably less in southern NSW. It is not clear which treatment would be favored by the neglect of runoff: greater runoff with cultivated fallow has been measured with a rainfall simulator in our experiments (Burch et al., 1986; Fischer, R. A., unpublished) and others in the region (Packer et al., 1984), but similar soils in northern Victoria (Cooke, 1985) gave greater runoff with no cultivation, as in the case of no-fallow or fallow maintained entirely by herbicide application. The retained-stubble cultivated fallow treatment (SFm) is likely to have shown least runoff. (ii) We have assumed that cultivation per se has no effect on soil evaporation. Overseas results (e.g. Pacific northwest of the USA) have shown that a cultivation-induced dry soil mulch can reduce soil evaporation during long dry spells (e.g. Lindstrom et al., 1974). In southern NSW, the relatively frequent occurrence of rains sufficient to compact any cultivation mulch, the relatively shallow depth of cultivation employed, and the virtual disappearance of the practice of cultivating to recreate such a mulch means that the effect is likely to be small, as our pilot studies at Murrumbateman indicated. (iii) It was assumed that there were never any weeds on the fallow treatments. In practice, weed control is usually not effected until a certain ground cover of weeds is reached. A reasonable upper limit of weed ground cover would be 5% or 8 g/m z of weed dry matter. Calculations show this would have a negligible effect on water use. Therefore, the zero weeds assumption for fallow treatments is unlikely to lead to a significant discrepancy relative to c o m m o n good fallowing practice. In fact, it should be possible to use the model to decide the optimum weed ground cover at which to cultivate a fallow. (iv) Just as fallows can in reality be weedy (iii), no-fallow could conceivably be maintained in an almost weed-free condition if enough grazing pressure is applied. The assumption made here of not introducing sheep until there is more than 20g m -z green material available (16% green cover) could be

238

R. A. Fischer, J. S. Armstrong, M. Stapper

considered too conservative for effective weed control. On the other hand, some weeds are toxic (e.g. Heliotropiumeuropaeum)and/or unpalatable, and there is no doubt that grazing to keep fields virtually weed-free imposes stress on the livestock and increases erosion risk. (v) The model assumed that with fixed fallowing strategies the fallow always commenced on the specified date. In reality, this implied weed control at that date if weeds were already present (e.g. almost always the case with long fallow commencing on 1 September) or at the date of the first subsequent weed emergence if weeds were not present. With the former situation, it is possible for the soil on 1 September to have been too dry for mechanical cultivation. Weed control in such cases would have to wait until the first moderate rain event (say > 15 mm). No estimation has been made as to the extent to which the modelled fallow is favored by this assumption but it is not believed to be a significant effect. (vi) The trafficability or wet limit to sowing was, with some guidance from the literature, rather arbitrarily defined, yet sowing date probability was sensitive to this limit. Also, the possibility of runoff, and hence runon, even of small amounts of water means that when considering a whole field sowing can in reality be limited by the poor trafficability of the wettest, often lowest, portions of the field. (vii) The rule requiring five consecutive days of topsoil moisture above 15% for all strategies substantially simplifies the logistics of the operations involved in sowing (direct drilling) a no-fallow surface (direct drilling). In reality, the weeds need to be stock- and stress-free for several days before spraying with knockdown herbicide, an operation which cannot take place if rain is threatening, and after which several days must elapse, depending on herbicide, before the root-soil contact is released and satisfactory sowing can take place. This is probably a more weather-sensitive procedure than that imposed by the necessary operations preceding the sowing of a cultivated fallow surface. Obviously, assumptions which may be critical to the modelled outcome, in particular (i), (% (vi) and (vii) above, can be replaced in the model by more realistic routines. However, given the very limited data on the key issues of runoff (i) and trafficability (vi) for the soils of the region, it would seem inappropriate to attempt more detailed modelling at this stage.

CONCLUSIONS Notwithstanding the limitations just discussed it can be concluded that the replacement of cultivated fallow with no-fallow involves a moderate cost in terms of reduced stored soil water at sowing and reduced sowing

Effect of fallow on soil water and sowing day probabilities

239

opportunities at the optimum time of sowing, which could lead to delays in sowing. The simulation exercise has provided a more accurate quantitative basis for similar responses seen in the tillage experiments over the 1979-84 period. The simulation results also indicate that reductions in stored water will be greater at the wetter sites and sowing date penalties more c o m m o n at the drier sites. Responses also showed high annual variability, which will be examined in the second paper (Fischer & Armstrong, 1990). Effects of nofallow were quite insensitive to weed density (above a low threshold) and stocking rate (within reasonable bounds). Effects on sowing day probabilities, however, were sensitive to seed-bed soil water content limits for sowing. REFERENCES Baier, W. (1971/72). An agroclimatic probability study of the economics of fallowseeded and continuous spring wheat in southern Saskatchewan. Agric. Meteorol., 9, 305-21. Baier, W. (1973). Field work day calculations using versatile soil water budget. Canada Agric. Eng., 15, 84-7. Berndt, R. D. & White, B. J. (1976). A simulation-based evaluation of three cropping systems on cracking-clay soils in a summer-rainfall environment. Agric. Meteorol., 16, 211-29. Bond, J. J. & Willis, W. O. (1970). Soil water evaporation: First stage drying as influenced by surface residue and evaporation potential. Soil Sci. Soc. Amer. Proc., 34, 924 8. Burch, G. J., Mason, I. B., Fischer, R. A. & Moore, I. D. (1986). Tillage effects on soils: Physical and hydraulic responses to direct drilling at Lockhart, NSW. Aust. J. Soil Res., 24, 377 91. Cooke, J. W. (1985). Effect of fallowing practices on runoffand soil erosion in southeastern Australia. Aust. J. Exp. Agric., 25, 628-35. Cooke, J. W., Ford, G. W., Dumsday, R. G. & Willatt, S. T. (1985). Effect of fallowing practices on the growth and yield of wheat in south-eastern Australia. Aust. J. Exp. Agric., 25, 614-27. Danh & Wingate-Hill (1978). Number of field work days available for tillage. Proc. Agric. Eng. Conference, Toowoomba, 29-31 August 1978. Aust. Soc. Ag. Eng., Melbourne, pp. 187-90. Fischer, R. A. (1987). The responses of soil and crop water relations to tillage. In Tillage Monograph, ed. P.S. Cornish & J.E. Pratley. Australian Society of Agronomy, InKata Press, Melbourne, pp. 194-221. Fischer, R. A. & Armstrong, J. S. (1990). Simulation of soil water storage and sowing day probabilities with fallow and no-fallow in southern New South Wales: II. Stochasticity and management tactics. Agric. Systems, 33(3) 241-55. Fischer, R. A., Mason, I. B. & Howe, G. N. (1988). Tillage practices and the yield of wheat in southern New South Wales: Yanco in a 425 mm rainfall region. Aust. J. Exp. Agric., 28, 223-36. Gallagher, J. N. & Biscoe, P. B. (1978). Radiation absorption, growth and yield of cereals. J. Agric. Sci. Cambridge, 91, 47-60.

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Greacen, E. L. & Hignett, C. T. (1976). A water balance model and supply index for wheat in South Australia. CSIRO Div. of Soils Tech., Paper No. 27, 33 pp. Johns, G. G. (1982). Measurement and simulation of evaporation from a red earth. II. Simulation using different evaporation functions. Aust. J. Soil Res., 20, 179-91. Lindstrom, M. J., Kochler, E E. & Paperdick, R. I. (1974). Tillage effects on fallow water storage in the eastern Washington dryland region. Agron. J., 66, 312-16. Mason, I. B. & Fischer, R. A. (1986). Direct drilling and wheat growth and yield in southern New South Wales. I. Lockhart, a 450mm rainfall site. Aust. J. Exp. Agric., 26, 457-68. Mulholland, J. G., Coombe, J. B., Freer, M. & McManus, W. R. (1976). An evaluation of cereal stubbles for sheep production. Aust. J. Agric. Res., 27, 881-93. Nix, H. A. & Fitzpatrick, E. A. (1969). An index of crop water stress related to wheat and grain sorghum yields. Agric. Meteorol., 6, 321-37. Packer, I. J., Hamilton, G. J. & White, I. (1984). Tillage practices to conserve soil and improve soil conditions. J. Soil Conc. N.S.W., 40, 78 87. Ritchie, J. T. (1972). Model for predicting evaporation from a row crop with incomplete cover. Water Resources Res., 8, 1204~12. Ritchie, J. T. (1981). Water dynamics in the soil-plant atmosphere system. In Soil Water and Nitrogen in Mediterranean-Type Environments, ed. J. Monteith & C. Webb. Martinus Nijhoff/Dr W. Junk, The Hague, pp. 81-96. Stace, H. C. T. et al. (1968). A Handbook of Australian Soils. Rellim Tech. Publications, Glenside, S. Australia. Stapper, M. (1984). SIMTAG: A simulation model of wheat genotypes. Model Documentation. ICARDA, Aleppo, Syria, and University of New England, Armidale, NSW, Australia. van Keulen, H. (1975). Simulation of Water Use and Herbage Growth in Arid Regions. Simulation Monographs. Pudoc, Wageningen, The Netherlands.