a simulation model operating with daily weather data to explore silage and haymaking opportunities...

Agricultural Systems 48 (1995) 3 15-343 0 1995 Elsevier Science Limited

Printed in Great Britain. All rights reserved 0308-521X/95/$9.50 + .OO

0308-521X(94)00019-0

A Simulation Model Operating with Daily Weather Data to Explore Silage and Haymaking Opportunities in

Climatically Different Areas of Scotland

M. B. McGechan & G. Cooper

Scottish Centre of Agricultural Engineering, SAC, Bush Estate, Penicuik, Midlothian, EH26 OPH, UK

(Received 17 December 1993; accepted 24 June 1994)

ABSTRACT

A weather driven whole system model, previously used to study a range of hay and silage conservation methods, practices and mechanisation systems, has been further developed to take account of recent experimental data about parameter values, as well as recent developments in forage conservation technology. These are concerned with crop growth, field drying rates, losses caused by rain on crops in the field, and mat-making as a novel field drying technology. In addition, methods have been devised for synthesising from daily climatological data the hourly values of radiation and rainfall used by the field drying simulation, and this greatly extends the value of the model for the study of forage conservation at a wide range of sites. The model has been exploited to study alternative conservation methods at a range of climatically dyerent sites in the West and East of Scotland, at which daily weather data are available. Conservation methods studied include one silage system and one hay system using conventional equipment, plus one silage and one hay system based on mat-making. Results are expressed in economic terms as forage net value, and also in terms of length offield drying period and extent of rain spoilage occurring during this period. Results confirm that silage is the most appropriate method using conventional technology, but there are substantial potential benefits from the new mat-making technology, including elimination of the environmental problem of silage effluent as well as reduced rain spoilage of the forage. Also, with the mat technology, haymaking may come back into

favour as a method of producing high quality forage.

INTRODUCTION

Weather driven simulation models have been found to be very valuable tools for the study of forage conservation processes and methods, since

315

316 M. B. McGechan, G. Cooper

the field drying of forage crops is a very variable and weather dependent process. Such models can make much more efficient use of research resources (equipment and manpower) to study a much wider range of options (methods, machinery combinations, sites and years) than would be practical in extensive field experiments. Models can also combine con- siderations of the economics of alternative systems with representation of physical processes such as crop growth or field drying. Forage conservation models have previously been developed by McGechan (1990a), Rotz et al. (1989) and Gupta et al. (1990a,b).

Since the original development of the forage conservation system model by McGechan (1990~) and its use in the study of a wide range of hay and silage system options (McGechan, 1990b,c; Savoie et al., 1993; McGechan et al., 1993), experimental work has progressed on a number of processes represented in the model. Also, the original model required hourly weather data for simulation of field drying which is dominated by conditions during the middle part of the day, but availability of data restricted use of the model to a small number of meteorological sites, mainly air- ports. Methods of synthesising hourly weather data from daily data have since been developed, enabling use of the model to be extended to a much larger number of sites, including sites in important areas of pastoral agriculture.

This paper describes a number of recent developments to the Scottish Centre of Agricultural Engineering forage conservation model, including a new grass growth sub-model, representation of field drying and weather related losses with conventional and newly developed harvesting technology, plus the procedure for synthesising hourly weather data. Application of the model is concentrated mainly on the climatic aspects of forage conservation in different areas, exploiting the ability of the model to operate at far more sites than in the past, rather than the very wide range of machinery options, conservation practices, farm sizes, and investment levels covered in past studies (McGechan, 1990b,c; McGechan et al., 1993). Simulations are carried out for just four machine options representing different degrees of weather dependence for field drying, all for one size of farm. These illustrate the interaction between weather dependence of the operation and climatic variation between sites.

WHOLE-SYSTEM MODEL OF FORAGE CONSERVATION

The model, which has been described in detail by McGechan (199Ou), is an extension to represent both silage and haymaking of a previous model developed by Parke et al. (1978) for simulating haymaking systems. The

Silage and haymaking opportunities in climatically different areas 317

GRASS GROWTH

Simulation of dry matter accumulation in

terms of weather parameters, water and

nutrient availability.

FORAGE CONSERVATION EVALUATION AS AND STORAGE RUMINANT FEEDSTUFF

(weather driven simulation)

Swath drying and rewetting Losses in field and store Mechanical operations

LP model to determine cost of bought feedstuffs which can be replaced by forage

Fig. 1. Principal components of forage conservation system model.

model is in effect a series of linked sub-models, each representing one of the constituent physical processes (Fig. 1).

The original grass growth sub-model (McGechan, 19906) based on the work of Hume & Corral1 (1986) has now been replaced by a new weather driven sub-model developed by Topp & Doyle (1994). The procedure for using a T-sum calculation for representing the year to year variation in the date of change from reproductive to vegetative growth has been retained from the earlier sub-model. This date also determines the timing of the quality parameters digestibility (D-value), metabolisable energy (ME), crude protein (CP) and water-soluble carbohydrate (WSC). The T-sum chosen was the sum of weekly mean air temperatures in excess of 5.6” reaching 28.82 for perennial ryegrass for silage with a mean peak growth rate around 1 June, or 42.00 for a later maturing hay grass mixture.

The exponential form of equation representing the hourly change in moisture content of cut grass has been retained from the earlier model, but drying is now related to net radiation alone rather than potential evaporation given by Penman’s (1948) combination equation. A recent study (McGechan et al., 1994) using data from field swath drying experiments carried out in 1984 and 1985 (Lamond et al., 1988) showed that net radiation alone was as good a predictor of drying rate as any form of combination equation. Drying-rate coefficients for tissue moisture are listed in Table 1, with adjustment to these coefficients with variation in yield (swath thickness) listed in Table 2.

Procedures for estimating hourly radiation and rainfall values from daily weather data are described below. Some changes have also been made to representation of the rewetting of swaths by rain, and the resulting leaching losses.

MAT-MAKING AS A NOVEL FORAGE TECHNOLOGY

Mat-making represents a new technology intended to reduce the weather dependence of the forage conservation process. A mat-maker combines

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Sage and haymaking opportunities in climatically d@erent areas 319

TABLE 2 Drying-Rate Coefficient Adjustment Factors for Crop Yield (Swath Thickness)

Yield range (t ha-‘) Yield ajustment to drying rate coeficient

Conventional silage, undisturbed windrow

Conventional hay, spread and tedded

Mats

< 1.5 2.50 1.77 4.00 1.5-2.5 1.50 1.58 2.50 2.5-3.5 1.15 1.44 1.50 3.545 1.05 1.37 1.11 4.5-5.5 1.00 1.26 1.00 5.5-6.5 0.95 1.19 0.90 6.5-7.5 0.90 1.12 0.81 7.5-8.5 0.90 1.05 0.77 8.5-9.5 0.90 1.00 0.74 > 9.5 0.90 0.98 0.70

the operations of cutting, very severe conditioning (maceration) by knurled grinding rollers, compression between smooth rollers into thin mats, and deposition on the stubble. Experimental or pre-production mat- makers, or their components, have been built in the UK, USA, Canada, Germany, Sweden and The Netherlands. Matted forage material dries much faster, (typically two-three times as fast) with no intermediate swath treatments, than conventional swaths produced by a mower conditioner. Matted material can be baled as hay with a conventional baler or picked up as wilted silage by a very simple harvester since it requires no further chopping to achieve satisfactory fermentation. The mat-making technology has the potential to produce very high quality forage with almost no rain spoilage by exploiting much shorter rain-free weather windows than are required when using a conventional mower or mower conditioner.

Modelling studies of the benefits of the mat technology for lucerne hay have been carried out by Rotz et al. (1990) and for wilted grass silage by Savoie et al. (1993) and McGechan et al. (1993). In both these studies, assumptions had to be made about a number of parameter values. For some such parameters, including field drying rates and loss levels, experimental evidence has since been obtained so parameter values have been revised for the current study. Drying-rate coefficients for mats (Table 1) and their adjustments for variation in crop yield (Table 2) have been calculated from the data of Savoie et al. (1994a,b).


DETAILS OF NEW MODEL DEVELOPMENTS

Synthesis of hourly net radiation from daily sunshine hours

Net radiation in the current hour R,,h is estimated in three stages. First, the total daily incoming solar radiation Rsd (in kJ md2) is calculated from hours of bright sunshine (as recorded at 0900 h the following day) according to the well known ‘Angstrom formula which is described in a number of textbooks (e.g. Garg, 1982). Second, this total for the day is partitioned into values of incoming solar radiation for each daytime hour Rsh (from sunrise to sunset, in kW h mp2), assuming that radiation in each hour is related to a combination of solar zenith angle and path length through the atmosphere. Equations for solar zenith angle 8 and path length (1969):

where 1JI = 6=

W=

R= H=

d (see Fig. 2) are taken from Garg (1982) and Kondratyev

cos9 = cos$cosScosw + sin+sinS (1)

d= JR2cos26+2RH+@ - Rcos8

latitude

(2)

declination angle (O-41 radians (23.5”) at summer solstice, O-41 radians at winter solstice, zero at either equinox) hour angle from solar noon radius of earth (6390 km) height the atmosphere would have if its density was invariable with height (assumed to be 7991 km)

Fig. 2. Path of solar beam through the earth’s atmosphere.

Silage and haymaking opportunities in climatically d@erent areas 321

Solar radiation is assumed to be proportional to (cos 13)/d, deceasing with higher values of d due to the longer path length and further decreas- ing with increase in 0, since a beam of radiation is spread out over a larger area of ground surface, giving the incoming solar radiation in each hour as:

&h = &d{ (cos ~)/4/(36OOZ)

where Z = c{ (cos 0)/d) , i.e. the sum of (cosO)/d for all daytime 1

hours of that day

Lastly, the net radiation in each hour is calculated from the Brunt (1932) formula as follows:

&, = R&l - o) - o(?-‘+ 273. 15)4 (0. 56 - 7.79 x 10-3fi)

(0 . 1 + 0 .9nsuIl) (4)

where Rsh = incoming solar radiation in current hour, kW h m-2 T = the mean of the dry bulb temperature at 0900 h and the

maximum temperature on that day as recorded at 0900 h the following day

o! = albedo, fraction g = Stephans constant (567x lo-* W mm2 K4) e = vapour pressure, Pa nsun = hours of bright sunshine, expressed as a proportion of

daylight hours

The albedo (reflectance) varies according to the surface and vegetation, but a value of O-25 is usually assumed for growing grass, so this was assumed to be appropriate to cut grass also. The vapour pressure was calculated from weather parameters at 0900 h, but as it is related to the moisture content of the air it can be assumed to remain roughly constant throughout the day. The mean daytime temperature i=‘, as described above, was the only temperature value which could be assumed on the basis of daily data. The use of the Brunt formula in this way to calculate hourly values is unusual since it is normally used to calculate daily net radiation; however, it was considered preferable to estimating hourly net radiation from incoming solar radiation, according to a simple regression fit to the experimental data of Lamond et al. (1988) (as fitted by Glasbey & McGechan, 1986 and used in previous studies with the model), since it would be more likely to be applicable at other sites. Fits of a swath drying relationship based on net radiation to experimental data were as good


when net radiation was calculated according to the Brunt formula as when it was calculated according to the simple regression equation (McGechan et al., 1994).

Synthesis of hourly rainfall from daily values

Simple model-spekjk synthesis method Because of the importance of drying forage, when possible in fair weather windows between occurrences of rain, and the lack of hourly rainfall data at suitable sites, the method of synthesising hourly rainfall from daily values represents a major advance in development of the forage conservation model compared to the earlier version. A very simple method of synthesising hourly rainfall values was devised (Cooper & McGechan, 1992) which took account of the specific requirements of the forage conservation model, particularly that hourly rainfall values are required only for the nine daytime hours between 0900 and 1800 h GMT.

Development of this procedure was preceeded by a detailed analysis of characteristics of rainfall carried out on hourly rainfall data from two Scottish sites, Dyce (Aberdeen Airport) and Prestwick (Cooper & McGe- than, 1992). Some comparisons with data from two south of England sites, Heathrow and Plymouth, were also made. The analysis concerned rainfall ‘events’, i.e. periods of rain in the hourly data sandwiched between hours with no rain, on days with rain only (referred to as ‘raindays’). Characteristics studied included site, length of event, quantity of rain in the event, month of year and time of day, and it was hoped that this would reveal trends which could be incorporated into a model for synthesising hourly rainfall. In fact, the only useful trend shown was a relationship between the quantity of rain falling on a rainday and the probability of that day being of one of three types:

(a) ‘day only rainday’ - rain occurring only during period 0900 - 1800 h;

(b) ‘night only rainday’ - rain occurring only outside the period 0900 - 1800 h;

(c) ‘day and night rainday’ - rain occurring both during the period 0900 - 1800 h and outside that period.

This trend is illustrated as a stacked histogram of the proportion of days of each type, plotted on a logarithmic scale of daily rainfall, in Fig. 3. Log- linear regression lines were calculated to represent the boundaries between the rainday types, for data from the two Scottish sites pooled, as follows:

upper limit of ‘day only raindays’ P = - 18 - 43 In(r) + 25 - 21 (5)

Silage and haymaking opportunities in climatically diJSerent areas 323

Day h night

.g 60

P a 40

Daily rainfall,mm(bgarithmic scale)

Fig. 3. Proportion of raindays in three categories in relation to daily rainfall. 0 -, upper limit of day only raindays; x --, lower limit of day and night raindays.

lower limit of ‘day and night raindays’ P = 36.95 In(r) + 66 1 36 (6)

where P = probability of rainday type r = rainfall on day, mm.

Other relevant characteristics were the average length of an event of 2.60 h, the average number of rainfall events on a day only rainday of 1.27, and an average of 45% of rain on day and night raindays falling during the period 0900 - 1800 h.

The method of synthesising hourly rainfall from daily values was based on the above characteristics. First, for each day with rain, one of three rainday types was selected using a random number generator but taking account of the relative probability of each rainday type for the quantity of rain falling on that day, as in Fig. 3. The forage conservation model only requires a single value of overnight rain (outside the period 0900 - 1800 h), so for night only raindays this was made equal to the total daily rainfall. Similarly, for day and night raindays, 55% of the daily total was allocated to overnight rain. For day only raindays, rainfall was assumed to fall in one single block of length 3 h (the nearest integral values to 1.27 and 2.60) and the block was positioned at random (using a random number generator) throughout the day, but such that it did not overlap the night-time period. An equal quantity of rain was assumed to fall in each of the three hours. For day and night raindays, 45% of the daily total was allocated during the daytime period by the same procedure as for day only raindays.


TABLE 3 Percentage Distribution of Type of Day, Comparing Real and Synthetically Generated

Rainfall Data

Type of rainfall data

Real Preswick 18.28 3544 46.28 Real Dyce - 22.70 37.56 39.78 Synthetic Prestwick Test 1 18.96 34.60 4644 Synthetic Prestwick Test 2 19.61 35.34 45.05 Synthetic Prestwick Test 3 2044 35.80 43.76

Site Data test file no. Distrubution of type of rainday (%)

Day only Night only Both

Testing simple synthetic hourly rainfall procedure This simple synthetic rainfall procedure was tested in the first instance by analysing the distribution of the three types of rainday for Prestwick (Table 3). The distribution in such test files was very close to the original, bearing in mind small random variations caused by the random number generator. Little loss of accuracy arose because the distributions assumed in the synthesis procedure were based on data pooled across sites. It was therefore decided that it would be reasonable to assume the same distribution, as given by eqns (5) and (6), for synthesising hourly rainfall at any Scottish site.

The most serious potential problem with using synthetic hourly rainfall data with the forage conservation model is likely to arise because rainfall events occur earlier or later in the day than they do with real data. This is unavoidable because of lack of knowledge about the timing of rainfall when only daily values are available. In model simulations, there will be instances when rain occurs before a batch of forage is taken into store, whereas with real data rain occurs later so the batch is removed from the field without rain spoilage. However, these will be offset by other instances when the reverse occurs, i.e. rain occurs earlier in the real data so rain spoilage occurs with the real data and not with the synthetic data. Some model-runs with both real and synthetic hourly rainfall from the same site are described below, with results illustrating the degree of rain spoilage. This shows agreement within acceptable limits.

Alternative hourly rainfall synthesis methods At the start of the current study, there appeared to be a number of previous studies reported in the literature in which daily rainfall figures had been subdivided into individual ‘storms’ or hourly values (a process known as ‘disaggregation’), e.g. Hershenhorn & Woolhiser (1987),


Econopouly et al. (1970) Koutoyiannis & Xanthopoulos (1990), and Ormsbee (1989). An alternative, more complex method was therefore devised and tested using weather data for Turnhouse (Edinburgh Airport), a site for which hourly rainfall data representing both quantity and dura- tion of rain were available. This was based on the method of Rodriguez- Iturbe et al. (1987, 1988) for creating entirely synthetic values of hourly rainfall with certain statistical characteristics, but adapted to synthesise hourly values to add up to a known daily total. This procedure involved very complex mathematical processing and analysis, which is described in detail by Glasbey et al. (1994).

For an alternative test of synthesis procedures, the arithmetic mean deviation of the time (in hours) of each mm of rain in the synthetic data compared with the real data was calculated. This is really a means of testing one synthesis procedure against another, since there will always be some deviation with any method that lacks knowledge about timing of rainfall. For Turnhouse data from 1968-77, the simple synthesis method gave a mean deviation of 2.04 h compared to a mean deviation of 3.26 from the method described by Glasbey et al. (1994). To make valid comparisons, deviations were calculated assuming that all overnight rain (outside 0900-1800 h) occurred in the hour 1800 - 1900 h, The mean deviation was higher with the method of Glasbey et al. (1994) and the procedure made very severe demands on computer time. Both these factors supported the decision to carry out simulation in the current study with data files created with the simple synthesis method.

Effect of rainfall on course of drying

The original sub-model representing the process of rewetting of swaths by rain has been retained for conventional swath material. This was developed by Pitt & McGechan (1990) from an analysis of rainfall events in swath drying trials carried out in 1984, 1985 and 1986 (Lamond et al., 1988; Spencer et al., 1987). It was assumed that all rainfall would adhere to the surface of the grass up to a quantity of water equal to 1.2 times the dry matter weight of the grass, thereafter only a small fraction adhering (the remainder running off), and that fraction being dependent on rainfall intensity. At the time of the exploratory study of mat-making by Savoie et al. (1993) here were no experimental data about runoff of rainfall from mats, so the same model was retained, but assuming that matted material would hold twice as much water as swaths before runoff takes place. More recently, Sundberg & Thylen (1994) have measured runoff of rainfall from both mats and conventional swaths, showing that mats do indeed hold more rain than swaths, and that roughly 2.4 times the dry matter weight of


water is retained before runoff commences. The same assumptions have therefore been retained for the current study.

Drying of surface moisture after rain takes place at a faster rate than drying tissue moisture. The rate of drying of surface moisture is represented by a second coefficient in the drying equation (McGechan et al., 1994), which indicates how much net radiation is required to evaporate a given quantity of surface moisture before normal tissue drying can recommence. The theoretical minimum value of this coefficient is 0.685, representing 100% efficiency of conversion of radiation energy to latent heat of evaporation of water. The assumed values of the coefficient, estimated from the data of Lamond et al. (1988) and Savoie et al. (1994a,b), are listed in Table 1. These represent efficiencies of energy conversion of 51% for swaths and 42% for mats.

Nutrient losses caused by rainfall

Equations representing losses of soluble nutrients from plant material in conventional swaths caused by leaching by rain are formulated as follows, based on recent laboratory experiments reported by McGechan ( 1993):

‘I = 6*0rr (‘:it) % (hay grass in moisture content range 20 _ 770/ wet basis)

(7) 0

4 = 0*12r,% (silage and newly cut hay grass, conditioned) (8)

Lt = O.lOr,% (silage and newly cut hay grass, non-conditioned) (9)

where L1= dry matter loss due to leaching, % rr = runoff rainfall, mm m = wet basis moisture content, %.

Loss levels given by eqns (7)-(g) differ slightly from those assumed in the earlier version of the model where eqn (7), but with a coefficient of 10 rather than 6, was assumed for all hay and silage grass.

For mats, leaching losses are now estimated from the following equation presented by Sundberg & Thylen (1994) based on their recent experiments, the only known experimental loss data for matted grass material.

L1 = (0+00380* - 0.490 + 41) exp(-9.9/rt) (LO)

where D = dry matter content, % rt = total rainfall, mm

Silage and haymaking opportunities in climatically dif)*rent areas 327

Losses given by eqn (10) are somewhat lower than those assumed in the exploratory study of mat-making by Savoie et al. (1993) which were calculated according to eqn (7) with the coefficient raised to 58.

For both swaths and mats, the increased overall moisture content caused by rain increases the rate of respiration, hence, the respiration loss given by an equation presented by Wood & Parker (1971), which is unchanged from the original model. Dry matter losses caused by leaching and respiration deplete only the digestible component of the forage, the fibrous component remaining unchanged, so there is a reduction in the overall digestibility (D-value) of the forage remaining.

SELECTION OF SYSTEM OPTIONS, COSTINGS, AND SITES FOR SIMULATIONS

Simulations were carried out with four machinery combination options representing one hay and one silage system based on conventional equipment, plus one hay and one silage system based on the novel mat-making process, with parameters listed in Tables 4 and 5. These machine options represent different levels of weather dependence for field drying, as illustrated by the cumulative net radiation required to reach the target moisture contents under rain free conditions in Table 1. For silage, the target moisture content selected is equivalent to a dry matter content of 25% for conventional equipment, representing roughly 24 h of wilting, as is usual practice in the UK. This substantially reduces the quantity of effluent produced compared to no wilting. However, for the mat silage system a target dry matter content of 30% was selected, which will eliminate effluent production altogether (Bastiman & Altman, 1985) the major environmental benefit perceived for this system.

The annual cost of machine ownership, repairs and housing was calculated from the capital cost by the method described by Witney & Saadoun (1989), an advance on the earlier method described by Audsley & Wheeler (1978), incorporating the treatment of repair costs described by Rotz (1987). Hourly costs of machines such as tractors and trailers, which are shared between forage conservation and other farm operations, were calculated using an assumed annual usage of 1000 h for tractors and 300 h for other items. Annual or hourly costs calculated by this method assuming an interest rate of 8% and an inflation rate of 2% (the cost depends mainly on the difference between these rates) are listed in Table 5. Costs of 60.18 for tractor fuel (tractor fuel consumption, 0.344 litres (kW ht))‘) and g4.30 h-’ for labour are also assumed. Annual costs of storage structures

TA

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l Fi

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ach

for

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ratio

n Tr

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(sha

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mac

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labo

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f h-

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Wor

krat

e

E

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vent

iona

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lage

Con

vent

iona

l ha

y

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ting

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drow

ing

(sec

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cut

only

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arve

stin

g

Tran

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t Fi

lling

si

lo

Cut

ting

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adin

g,

tedd

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and

row

ing

up

Bal

ing

65

45

75

45

65

55

45

45

2.1

m m

ower

co

nditi

oner

8.

76

1.75

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h-’

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win

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(hay

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7.10

2.

5 ha

h-t

Prec

isio

n ch

op

fora

ge

harv

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lage

tra

iler

(thre

e in

lin

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Seco

nd

sila

ge t

raile

r B

uckr

ate

I

28.8

3 7.

0 km

h-r

(m

ax)

%

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&

2.1

m d

rum

m

ower

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06

2.0

ha h

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aker

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us b

ale

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lat

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bale

for

k Se

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tra

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5.0

t D

M

h-r

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spor

t (tw

o m

en)

45

45

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t (w

hen

third

m

an

avai

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45

5.0

t DM

h-

’ 10

.00

t D

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h-t

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-mak

ing,

si

lage

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ha

y

cutti

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Har

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trans

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85

55

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85

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at

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1 8.

0 km

h-’

(m

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Sim

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mat

ha

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ter

and

sila

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26.5

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.0 k

m h

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max

) tra

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(thre

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lin

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nd

sila

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raile

r B

uckr

ate

I

2.1

m m

at m

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(max

) A

s fo

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l ha

y -


TABLE 5 Costs of Tractors, Machinery and Storage Structures

Item

____ Tractor, 45 kW, 2WD Tractor, 55 KW, 4 WD Tractor, 65 kW, 4WD Tractor, 75 kW, 4WD Tractor, 85 kW, 4WD 2.1 m drum mower 2.1 m mower conditioner 2.1 m mat-maker 4 m two-row rotary haymakerlwindrower Pick-up baler Flat eight bale accumulator Trailer for bales Front end loader Flat eight bale fork Forage harvester, precision chop Mat harvester Tipping trailer with silage sides Buckrate Silage clamp with effluent tank Hay barn

Capital cost Annual cost Operating cost ( f) ( f) (f h-l)

16000 2 804 2.80 21500 3760 3.76 25 500 4456 4.46 29 500 5152 5.15 38 000 6112 6.11

3 800 451 6 250 718

12000 1378 - 3 000 380 6 100 683 2.28 1830 205 0.68 2440 317 1.06 3 050 342 1.13

610 68 0.23 10000 1 180 3 000 335 4 200 470 1.56

900 100 0.40 38 000 2 125 13200 673

estimated by an adaptation of the method of Witney & Saadoun (1989) are also listed in Table 5.

For the silage systems, the crop growth simulation represented perennial ryegrass with a peak growth date around 1 June, but with some year to year variation around this date introduced by the T-sum mechanism. The first cut over an area of 40 ha was assumed to commence when the D- value (digestibility) dropped to 68%, with a second cut over an area of 20 ha commencing 66 days later at a D-value of 66%. The reduced area in the second cut was to allow a larger grazing area with the lower growth rates later in the year. For the hay systems, the growth simulation represented a slightly later maturing perennial ryegrass dominated mixture, with a peak growth date around 12 June, and a single cut over an area of 40 ha assumed to commence at a D-value of 64%. Nitrogen fertiliser application rates (as inputs to the growth simulations) were set at 300 kg ha-’ year? total for the two silage cuts, and 200 kg ha-’ year-’ for the single hay cut, with the soil nitrogen level set at 120 kg ha-’ in both cases. Since hay was cut at a later stage of maturity, the quantity of dry matter in the cut grass produced was larger than in the first cut of silage, although


not quite as large as the total of the two cuts of silage. Determination of the monetary value of forage in terms of the costs of bought-feedstuffs which the forage can replace (according to the procedure described by McGechan (1988, 1990a) which takes account of intake constraints and forage quality parameters) was carried out for a herd of 100 dairy cows for both silage and hay systems.

Simulations were carried out over a 10 year period (1981-90) at eight sites distributed throughout Scotland as follows: Dumfries, Auchincruive, Paisley, Bush House (near Edinburgh), Mylnefield (near Dundee), Dyce (Aberdeen Airport), Kinloss and Wick. The first three are located in the mainly pastoral/dairy farming area of SW Scotland. The remainder are located on the eastern side of Scotland where a mixture of arable and pastoral farming takes place. Daily weather parameters are recorded at all sites, whereas hourly weather is only recorded at two (Dyce and Kinloss).

WEATHER AT SITES INDICATING SUITABILITY FOR FORAGE CONSERVATION

Mean weather parameters

Some mean weather parameters which might indicate the suitability of the sites for field drying of forage crops are listed in Table 6. They are for the 12 week period commencing on 18 May, which covers most of the field drying of hay and silage represented in the simulations. Since field drying should ideally be completed in weather windows between rainfall, separate values are shown for rain-free days. However, while the main theme of this study is field drying of forage crops requiring rain-free weather, it should also be born in mind that the forage conservation process presents a paradoxical competing requirement for weather; lack of rain leads to lack of soil moisture which inhibits growth of the crop, particularly for second or later silage cuts.

Important features of the rainfall parameters in Table 6 include higher rainfall in the West (with Paisley being highest of all) than in the East, suggesting the East has a better climate for field drying, but higher temperature and radiation values in the West, which suggest a better climate in the West. Mylnefield has a markedly lower rainfall quantity and probability on a particular day than any other site. The pattern regarding rainfall probability on a day is not always the same as for rainfall quantity; in particular, Dumfries has high rainfall but a low rainfall probability, and Wick has the highest rainfall probability of all sites. Dumfries is an inland site which receives more heavy, short lasting (and perhaps

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thundery) rain, while Wick has a Highland Scottish climate more prone to long lasting, light, drizzly rain. The sites are listed in order progressing northwards, which might be expected to show a progression towards a more hostile climate for forage conservation. This pattern is broken in particular by Bush (the most southerly site in the East), where a high ele- vation (200 m) leads to high rainfall and low temperature/radiation.

Distribution of net radiation

Histograms of daily total net radiation values over a 12 week period, subdivided into rainy and rain-free days, are shown in Fig. 4 for one West Scotland and one East Scotland site. These show a tendency towards higher totals on rain-free days, in particular with hardly any rain on days with net radiation in the range 3-4 kW h. There is little difference in the pattern between the two sites, other than that there are more days with rain at Paisley than at Mylnefield, also shown in Table 6.

Weather windows for field drying

The number of weather windows (between rainfall events) over a 12 week period, containing certain values of cumulative net radiation, are shown for two sites in Fig. 5. As would be expected, these show smaller numbers of windows with higher radiation totals. Relating this to the required net radiation totals to achieve the target moisture contents in Table 1 suggests frequent opportunities for drying silage without rain spoilage (even with conventional equipment), but far fewer such opportunities for hay. Mat- making almost doubles the number of windows, compared to conventional

Paisley Mylnefield

Daily net radiation kwh Daily net radiation kti

Fig. 4. Daily total cumulative net radiation (mean of 10 years, 18 May-9 August). m-rain free day; ??4ays with rain.

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waiting times as Mylnefield, the drier site. Minimum waiting times are very small, even for conventional hay, showing that the target start date occurs during a window with a high net radiation total in at least one year out of 10. Maximum waiting times are erratic, with the same value for three out of the four conservation methods at Paisley, and a longer wait with mat hay at Mylnefield than Paisley.

RESULTS OF SIMULATIONS

Forage dry matter production and losses

The total quantity of grass dry matter cut, and the conservation losses which take place to give a certain quantity of conserved forage dry matter, are illustrated from the four conservation methods and eight sites in Fig. 7.

Differences between sites in the yield of cut grass reflect different grass growth rates represented by the crop growth sub-model. Yields are highest at the West Scotland sites (highest of all at Paisley), reflecting the high rainfall with minimal constraints from lack of soil moisture, and (perhaps to a lesser extent) higher temperature and radiation levels. Low yields at Mylnefield and Kinloss probably reflect mainly soil moisture constraints due to lack of rain, and at Wick mainly the low temperatures and radiation levels.

Different loss levels between conservation methods reflect the different technologies, with high field losses in hay systems and high storage losses in silage systems. There are small differences in loss levels between mat- making and conventional equipment, particularly higher leaching losses when rain falls on mats rather than on conventional forage material. With the haymaking systems, site differences are noticeable in terms of leaching loss levels; leaching losses are particularly low at Mylnefield, reflecting the low rainfall at this site.

Gross and net forage values

Results of feed evaluations of batches of forage produced by simulation runs are presented in Fig. 8 for the four conservation methods and eight sites. Starting with the gross forage value in terms of costs of bought feedstuffs replaced (with feedstuff prices and parameters listed in Table 7), costs in various categories are subtracted to give the net value of the forage to the farmer.

Sage and haymaking opportunities in climatically dxerent areas 335

At all sites with conventional equipment, the gross value is higher for silage than for hay, partly reflecting the higher total grass yield for two cuts of silage compared to one cut of hay. At most sites, the difference is substantial so although costs are slightly higher for silage than for hay, even when allowing for these costs the net value is higher for silage than for hay. These differences reflect the fact that the feed evaluation procedure,

Paisley

300

Dumfries Auchincruive

Bush Mylnefield Dyce 300

250

g 200 "

% 2 'TO e $ 100 t p 50

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KidOSS Wick 300

grossdrymatterprcduced; st0rage10sss;

lowenergyforage mechanlcallosses; leachmglosses; respimtorylosses; netforagedrymatterproducc

Fig. 7.

s1lage hay sllage hay silage hay sdage hay convenflonal matmakmg conventional matmakmg

Forage dry matter production and losses for 40 ha conservation area, means of 10 years.

stiage hay sdage hay convenflonal mat makmg


as well as allowing for material disappearing as dry matter loss, also takes account of quality losses such as reduced D-value, energy content and crude protein content. However, the differences in gross value between silage and hay is much lower at Mylnefield than at the other sites, so when costs are considered, hay has a higher net value than silage. Hay also has a slightly higher net value at Kinloss and Auchincruive. This arises

25

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8 0 w 15 B $ 0 IO F rr”

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Mylnetield

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silage hay conventional

silage hay mat making

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silage hay sllage hay r1lage b’ silage hay COnvenflOnal mat makmg conventional mat makmg

Fig. 8. Gross value, production costs and net value of forage for 40 ha conservation area, means of 10 years.

Silage and haymaking opportunities in climatically dzferent areas 337

TABLE 7 Details of Bought-in Feedstuffs Included in Dairy Cow Ration

Feed cost (1993) DM Metabolisable energy Crude protein (f t-‘) content (%) (MJ kg-’ DM) (% DM)

Dairy concentrate 250 86 12.5 40 Barley grain 120 85 13.7 10.8 Barley straw 40 86 7.3 1.5 Hay (moderate) 80 85 8.4 6.0

because of the low rainfall at Mylnefield resulting in relatively low rain spoilage losses (both in terms of quantity and quality) in haymaking at this site.

Mat-making gives forage with higher gross and net values compared with forage produced by conventional equipment. Silage has an advantage over hay at only two sites, Bush and Wick. At other sites, the economics of hay are better than silage, most markedly at Mylnefield, Kinloss and Paisley. The results illustrate that, despite the higher costs of purchasing and using the mat-maker compared to a conventional mower, mat-making shows benefits from the shorter field wilting period in the form of lower losses of quantity and quality of forage material, which is reflected in the higher net value to the farmer. In the case of mat-made hay, further cost benefits arise because there is no need to spread, ted and row up the crop, plus the benefits of the absence of mechanical losses incurred by these operations (see Fig. 7) which more than offset the higher mechanical losses during cutting and baling matted forage. The reduction in costs and losses with mat-hay are sufficient to make haymaking a worthwhile option at some sites.

Lengths of simulated field drying periods

Distributions of drying times for batches of forage to reach their target moisture content in the 10 year simulations, with the distinction between rained-on-forage and forage with no rain spoilage, are illustrated in Fig. 9 for the four conservation methods and two sites. Drying times are broadly as would be expected from the required cumulative net radiation values listed in Table 1 and the climatic variables illustrated in Figs 4-6 and Table 6, with more material suffering rain spoilage for hay than for silage, and slightly more rain spoilage with conventional equipment than with mat-making. Drying times are shorter at the drier site at Mylnefield than at Paisley, again as would be expected, but the quantity of rain spoiled material is not noticeably lower at Mylnefield.

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Silage and haymaking opportunities in climatically dtjierent areas 339

TESTING SYNTHETIC HOURLY RAINFALL USING THE SIMULATION MODEL

In addition to the simple tests of the hourly rainfall synthesis procedure described previously, the procedure was tested further by comparing

TABLE 8 Gross Value of Forage from Simulations with Real and Synthetic Hourly Rainfall Data

(Ten Year Average; Dyce; Conventional Equipment)

Type of rainfall data Gross value of forage (f)

Silage Hay

Real 18208

Synthetic 18420

Auchincruive Conventional silage Conventional hay

T

Run 1 15670 Run 2 15426 Run 3 15425 Test 1 15393 Test 2 15183 Test 3 15220

Real = 200 mm12 (28.6h) mcan4.9 (1185h) a^

3 B

150

$ 8

100

2 50

0

250

?I 200 -SZ (1267h)

Synthetic

0 012345 0 1 2 3 4 5 6 7 8 9 >I0 No. of nights in field No. of nights in field

Fig. 10. Length of field drying period comparing runs with real and synthetically generated hourly rainfall data; mean of 10 years, Dyce. m-rain free forage; m-rained on

forage.


simulation runs using synthetic data with runs using real hourly rainfall data from Auchincruive (Cooper & McGechan, 1992). Comparisons were made for both conventional silage and conventional hay systems, and results expressed both as forage gross value (Table 8) and length of field drying period for rain-free and rain-spoiled forage (Fig. 10). Results show reasonably close agreement between runs with real and synthetically generated rainfall data, particularly for silage making. This suggests that instances where rain spoilage occurs when rain occurs earlier in synthetic data rather than in real data, are roughly matched by instances when the reverse is the case, as discussed above. For haymaking, the model uses a random number generator to simulate a farmers decision processes about when to carry out cutting and other operations in relation to the weather forecast, so there is some variation in the results of different simulations with the same data, even with real weather data. Differences in results between simulations with real and synthetic rainfall data are no greater than differences between results in such different runs with real data.

CONCLUSIONS

The weather-driven forage conservation model previously described by McGechan (1990a) has been further developed in a number of important areas. A new grass growth sub-model has been added which is based on mechanistic principles. Field swath drying is now related to net radiation alone, hourly values of which can be estimated from sunshine hours and other daily recorded parameters. A method of synthesising hourly rainfall data from daily values has been devised and tested. Equations representing field drying rates and losses caused by leaching by rain for forage produced by the newly developed mat-making system are now based on experimental data for grass, rather than crude adjustments to lucerne data. Leaching loss equations for conventional forage are also based on recent experimental data.

Results of the simulation study using the forage conservation model bear out the results of a previous similar study (McGechan 1990b) and general farm practice, that silage is a more satisfactory and economically viable conservation method than hay for producing forage without rain spoilage in the Scottish climate, when using conventional equipment. This finding has now been tested over a much wider range of Scottish sites in more climatically different areas of Scotland than previously. The study has also shown that mat silage, once the technology has been adequately developed for the practical field situation, will be an economically viable system which could also eliminate the major environmental pollution

Silage and haymaking opportunities in climatically deferent areas 341

problem of current silage-making practice caused by silage effluent. This bears out the conclusions of an earlier exploratory study (McGechan et al., 1993) but this time the model equations have been based mainly on real data rather than assumed values. The mat technology also raises the prospect that hay-making may again become a viable forage conservation option in some areas of Scotland.

The procedure for synthesising hourly rainfall data from daily values to extend the number of sites at which the model can be run represents a significant improvement to the model, which has been exploited in this study and will be valuable for future applications as well. Highlighting the potential of mat-making technology to radically transform forage conservation practices to avoid environmental pollution represents the most important finding of this study.

ACKNOWLEDGEMENT

The authors wish to thank the Scottish Office Agriculture and Fisheries Department for financial support to carry out the work.

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Silage and haymaking opportunities in climatically deferent areas 343

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a simulation model operating with daily weather data to explore silage and haymaking opportunities...

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