J. agric. Engng Res. (1990)45, 117-136

Operational Research Study of Forage Conservation Systems for Cool, Humid Upland Climates. Part 1: Description of

Model

M. B. MCGECHAN*

An improved operational research model is described for studying the economics of forage conservation systems, particularly for cool, humid, upland climates. This covers the whole process, from growth of grass, through all the conservation operations, to evaluation of conserved forage for feeding ruminants. Sub-models simulate growth of grass, drying and rewetting of swaths in the field after cutting, mechanical field operations, crop losses in the field and in storage, and drying in a barn, in relation to historical weather data. Forage is evaluated using a standard dairy cow ration formulation procedure, adapted to take account of variations in intake with quality of forage. Conservation can be represented as field-dried hay, barn-dried hay, wilted silage and direct-cut silage.

1. Introduction Parallel programmes of experimental and modelling work in the field of forage

conservation are currently being carried out at the Scottish Centre of Agricultural Engineering (SCAE). Their ultimate purpose is to provide a sound research basis for recommendations to farmers and their advisers about the benefits of alternative conservation methods and mechanization systems for on-farm production of winter feed for ruminant livestock from grass. This is particularly important for the upland areas of Scotland, Wales and northern England where the climate is unfavourable for silage wilting or field hay drying.

The experimental programme is focused particularly on the process of drying grass swaths in the field in relation to weather and machine treatments (Lamond et al.1). A parallel programme of modelling the physical process of swath drying and rewetting is also being undertaken (Smith et al.,2 Glasbey and McGechan, 3 Pitt and McGechan*) and modelling studies of other aspects of forage conservation are in progress elsewhere. The purpose of the current study, however, is to put the detailed studies in particular areas into the context of the economics of the wider animal production system. The particular value of this study is that it represents a synthesis of items from a range of separate research programmes, to provide answers to important questions of agricultural econom- ics which cannot emerge from each research programme alone.

This series of papers describes an operational research (OR) or "systems" study of the economics of the whole process of forage conservation, from cutting grass to feeding to animals. The model on which this is based has the principal components illustrated in Fig. 1; the model is in effect a series of linked sub-models, each sub-model representing one of the physical processes. This paper describes all the components of the model, and subsequent papers will describe exploitation of the model to study alternative conserva- tion practices with details of the costs of machinery, fuel, labour and chemicals.

* Scottish Centre of Agricultural Engineering, Bush Estate, Penicuik, Midlothian, EH26 0PH Scotland

Received 11 February 1988; accepted in revised form 10 September 1989

Paper presented at AG ENG 86 International Conference, Nordwijkerhout, Netherlands, 1-4 September,

1986 117

0021-8634/90/020117 + 20 $03.01)/0 (~ 1990 The British Society for Research in Agricultural Engineering

118 FORAGE CONSERVATION SYSTEMS

f

G

H,, H2,~

a swath drying rate constant

b yield adjustment for variation in cutting interval

C concentrate dry matter intake, kg dm d-

c constant in swath rewetting model equation

CF crude fibre content, % dm

CP crude protein content, % dm

D D value, % Dc D value at time of

cutting, % De D value at heading date,

% d dry matter content (dm),

% E evaporation predicted by

Penman's equation, mm e coefficient in flail and

double chop forage harvester speed equation rainwater evaporation factor mass of tractor plus trailed load, t rates of herbage production in periods 1, 2 and 3, kg ha-~ d-

h~, h2 daily sunshine hours one day and two days prior to cutting, h

I specific rainfall intensity, h - I

Io constant value of specific rainfall intensity, h-

if intake potential of silage or hay as sole feed, kg dm d- 1

im intake potential of silage or hay in mixed diets, k g d m d -l

i, maximum dry matter intake, kg dm d-

J time past peak in grass

N o t a t i o n

m M A D F

ME

N

t l a

l l t

P Q

growth curve, weeks k barn drier efficiency, %

Lh hay storage loss, % dm L~ dry matter loss due to

leaching by rain, % dm Lp pick-up loss of area

cleared, t dm ha- Lr rate of loss of dry matter

due to respiration, % dm h -1

Ls shatter loss, % dm (per mechanical operation)

l silage chop length, mm M m.c.d.b., fraction

Mo m.c.d.b, when cut, fraction

Mc equilibrium m.c.d.b., fraction

Mmax maximum m.c.d.b. during rewetting, fraction

AM gain in m.c.d.b, due to rain, fraction

AMc critical value of gain in m.c.d.b, due to rain, fraction m.c.w.b., % modified acid detergent fibre, % metabolizable energy, MJ kg dm- l

proportion of nitrogen in the form of ammonia, % ammonia concentration, % dm nitrogen concentration, % dm tractor rated power, kW water soluble carbohydrate (WSC) concentration, % dm

Rn net radiation, W m -2 Rs solar radiation, W m -2 r~ cumulative adhered

rainfall, mm rr runoff rainfall, mm rs specific rainfall, kg water

kg dm- 1 RDP lumen degradable

M. B. McGECHAN 119

protein, % dm w pick up width, m S tractor forward speed, x constant in shatter loss

km h -1 equation T temperature, °C Ym milk yield, kg cow-~ d-1 tw throughput of wet y crop yield (wet

material, t h -~ material), t ha -~ u wind speed 2 m above tr angle of uphill slope, °

ground, m s-~ A slope of saturation U D P undegraded dietary vapour

protein, % dm pressure/temperature V vapour pressure deficit, curve, mbar °C -1

mbar q~ relative humidity, % W cow live weight, kg T cutting interval, d

The model carries out simulations using recorded historical weather data. This means that many alternative conservation methods and mechanization systems can be investi- gated over a much wider range of weather conditions (both in terms of sites and years) than would be practical in field experiments. The model can represent the main conservation methods employed in northern Britain, field-dried hay, barn-dried hay, wilted and direct-cut silage, with a range of machines for each. Alternative strategies, such as cutting grass at different stages of maturity, different numbers of cuts or use of additives can also be investigated.

There have been a number of previous attempts at developing whole system models of forage conservation, in particular a model of silage systems developed by Corrail et al. ~ and a model developed mainly to study hay systems by Parke et al.* However, the value of both these earlier models was limited somewhat by lack of information on which to base some of the component sub-models. There have recently been substantial advances in knowledge in all the important areas such that it is now worthwhile developing a new model to exploit current knowledge.

2. Crop growth sub-model

2.1. Categories o f crop growth m o d e l

The earlier forage conservation model developed by Parke et al., e simulated swath drying in relation to historical weather, but grass growth was assumed to follow the same

Fig.

Crop g row th ICut t ing (S imula t ion

model ) / i f

Wea th er ~-~-~-~ . . . . . . . . .

Forage conservat ion and s torage

(Simulat ion model )

..Drying and rewett in(

Losses /Mechanical operation

~8 ~o ~o

Feeding [ Eva luat ion of conserved lforage as ruminan t

forage / feedstuff (LP model',

I. Principle components o f forage conservation system model. . . . . . , environmental inputs

" T ~ -

0~ "0 0

- - , economic inputs;

120 F O R A G E C O N S E R V A T I O N S Y S T E M S

course in relation to calendar date in every year. However, an examination of published information about grass growth showed a substantial variation in timing and pattern of growth from year to year and site to site. On-going work on the development of grass growth models was reviewed by McGechan 7 with a view to selecting one which could be used as a sub-model for a whole system model. These included "empirical" models, in which experimental grass growth data are related by simple equations to various recorded weather parameters; and "mechanistic" models which attempt to explain the mechanism of endothermic conversion of raw material to plant tissue, with experimental data used to determine coefficients at a much more detailed level. A mechanistic model of the late season "vegetative" phase of grass growth unconstrained by lack of water or nitrogen has been developed by Johnson and Thornley. 8 In the long term, this type of model would be ideally suited as a sub-model for a whole system model since it would give an accurate representation of growth in areas and seasons different from the source data. However, it cannot be used until it is extended to represent the early season mainly reproductive phase of growth and the effects of water and nitrogen shortage. For the current study therefore, an empirical model developed by Corrall 9 was selected and adapted as the growth sub-model, to give grass dry matter yields for first and regrowth cuts from historical daily weather data.

2.2. Empirical model equations for grass yield In order to represent a typical herbage production curve throughout the season (Fig. 2)

the grass growth sub-model divides the season into three periods.

1. Up to the peak in the growth curve (7 days before the 50% ear emergence or "heading" date), growth dominated by seed tillers ("reproductive" growth); rate of growth rises as temperature and radiation rise.

2. Eight weeks following the peak in the growth curve, some fertile and some vegetative tillers; negative regression arising from declining rate of herbage production as temperature and radiation rise, reflecting the change from mainly reproductive to mainly vegetative growth.

T x~ 100 T N ~" 9 0

E ~ 8 0

- 7 0 i- 0

~ 6o 3 "O ~ 5o

~ 4 0

¢1

"~ 3 0

T 2 0

1 0

0

P e r i o d 1 I P e r i o d 2 P e r i o d 3

I I

, ,I i ~l , , i March April May June July August September

Fig. 2. Typical rate o f herbage production measured at cutting (Corrallg). Four plots cut one week apart, each plot cut eoery four weeks. - - . , irrigated; , not irrigated

M. B. M c G E C H A N 121

3. After 8 weeks following the peak in the growth curve, "vegetative" growth consisting mainly of leaf material.

The peak in the growth curve occurs each year when the cumulative sum of weekly mean temperatures above a base level of 5.6°C reaches a value of 18.6. This value is an example of a "T-sum", a parameter first described by Jagtenberg, TM and now commonly used to predict a particular stage of grass growth which occurs at a different date in each year.

To represent irrigated grass growth i.e., with no constraint caused by lack of water, the rate of herbage production in each period is related to radiation and air temperature as follows:

in period 1 Hi = 417"6e ~'-'~~7+~-~?R,~ (1)

in period 3 Ha = 68"32e ('~3-~T+~-~R~ (2)

and

in period 2 //2 = {1 - 1.125(1 - e-°'2751J)}H I + 1.125(1 - e-°275~)H3 (3)

The rate of irrigated grass growth is then multiplied by a correction factor to represent growth constrained by lack of water. The non-irrigation correction factor is calculated from the difference between rainfall and potential transpiration, where transpiration is calculated from wet and dry bulb temperatures, wind speed, sunshine hours and radiation by an adaptation of the Penman 11 formula (see Section 3). Where radiation has not been recorded, it is estimated from sunshine hours. Since the above rates of herbage production are based on data for multiple cuts at 4 week intervals, starting in mid-March, for a different cutting interval, r, and start time these production rates are adjusted by multiplying by a factor, b, calculated according to the following formula presented by Doyle et al. TM

Early season (up to 15 June)

1.91 b = 1 + e -°'046(r-26"10) (4)

Mid-season (16 June to 31 August)

1.30 b = 1 + e -°'13(r-18"87) (5 )

Late season (1 September onwards)

1.31 b - 1 + e -°'t3Cx-18"9°) (6)

Also, all the parameters in Eqns (1)-(3) plus the T-sum value were based on source data for an early perennial ryegrass ($24 or Cropper); as an alternative a late perennial ryegrass ($23 or Talbot) can be represented by increasing the T-sum to 29.3 and adjusting the rate of reproductive growth, H~, by a factor of 0.8, which gives similar mean annual yield to that from the basic model (McGechan13).

2.3. Further grass growth parameters

The moisture content, D value and crude protein of a first cut crop are related to the peak in the growth curve as shown in Fig. 3, based on data for perennial ryegrass

122 F O R A G E C O N S E R V A T I O N S Y S T E M S

40

~" 30

C o 2o

E 10

60

70 Y

8 0 U

E 90

100 0

10o

90

dm yield adjustment fo r

80 ~ imiting nitrogen 70 ~ / f e r t i l i za t ion

~ 6 0 E ~

"6 5o

4O

Maximum level of WSC with c lear skies 30 \ . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2O ~ \ B a s e level of WSC with ~ C# zero sunshine

10 ~ Heading (50°1o ear emergence) date

021 -1'4 -7 C) "7 1'4 2'1 ;28 15 42 4'9 16 Co3 70 Days past peak in growth curve

Fig. 3. Crop growth parameters in relation to peak in growth curve, first cut

presented by Green et a1.14 Water soluble carbohydrate (WSC) content is also related to the stage of growth; from experimental work with perennial ryegrass carried out by Henderson, is the base level of WSC in cloudy conditions is given by the curve in Fig. 3, to which is added a supplement in terms of the sunshine history as follows:

0"27(hl + 0"16h2)%

Eqns (1)-(6) represent grass growth unlimited by lack of nitrogen. A further adjustment of the crop yield for a typical farm level of nitrogen fertilization, in relation to the peak growth date (Fig. 3), is similar to that used by Corrall et al., s based on data presented by Morrison et al.lS

3. Swath drying in the field

3.1. Alternative models of swath drying and their current state of development Four approaches to relating the drying of cut grass in swaths to historical weather data

have been considered for the swath drying sub-model:

M. B. M c G E C H A N 123

1. relating rate of drying to vapour pressure deficit (as Parke et aL e and Luder17); 2. relating rate of drying to pan evaporation (as Pitt~a); 3. relating rate of drying to potential evaporation according to the Penman ~ formula; 4. using a detailed mechanistic swath drying model as proposed by Thompson ~a and by

Smith et al.2

Work currently in progress on development of a mechanistic model in conjunction with the programme of experimental swath drying work at SCAE will lead to a comprehensive and universally applicable swath drying sub-model. This will require only one parameter to be determined experimentally, a tissue resistance characterizing the grass species, variety, maturity and whether it has been conditioned; this resistance is measured on a laboratory thin layer drying rig. 1 The effects of other swath parameters such as structure, porosity and thickness (arising from crop yield) will be taken into account by the physical principles embodied in the model. To date, only a "bulk" model has been developed (Smith et al.2), which gives a good representation of drying swaths for hay, where frequent tedding maintains reasonably homogeneous conditions. However, common practice for silage making in the UK is to leave a swath undisturbed between mowing and picking up; hence the next phase of this work to develop a multilayer model representing a swath, which is substantially drier in its upper than its lower layers, must be awaited. In the meantime a simple swath drying model is being used based on the well established Penman ~ equation for evaporation from vegetative surfaces. This has been shown to give a substantially better fit to experimental swath drying data in dry periods and after rain than the relationships with either vapour pressure deficit or pan evaporation (Glasbey and McGechana).

3.2. Formulation o f swath drying sub-model based on Penman's equation

The equations currently being used to represent swath drying are as follows:

M = (Mo - M~)e -a(e-;ra) + M e (7)

where Mc = 0.033 + 0.095{ - In (1 - 0-01q~)} 15 (8)

~ 0-0015R,A + 0.0072(1 + 0-54u)V E = A + 0-66 (9)

o

R, = -20 + 0-63R~ (10)

A = 0.443 + 0-0295T + 0-000665T 2 + 0-0000188T 3 (11)

and f = 1.5. Eqn (8) was derived by Dumont and Parke a° to fit data reported by Waite. 21 Suitable

values of the constant, a, in Eqn (7) are listed in Table 1; these are based on experimental data for drying silage and hay grass in windrowed and spread swaths, measured by Lamond et al. ~ and analysed by Glasbey and McGechan. s The silage grass was left

Table 1

Experimentally determined values of coefficient in swath drying equation based on the Penman equation n

Coefficient a in Eqn (7) Yield, Windrow Spread

Crop t dm h a - i Unconditioned Conditioned Unconditioned Conditioned

Silage grass 4.76 0-080 0.096 0-108 0.128 Hay grass 8-3 0.110 0-132 0-148 0.176

124 F O R A G E C O N S E R V A T I O N SYSTEMS

u 2 2

E

2

E

~T .<

I I I l I I

S i l a g e m i x t u r e

I I I I I I

I I I I I I

H a y m i x t u r e

I I I I I I I 4 6 t5 10

Y i e l d , t d m ha -1

Fig. 4. Adjustment factors for coefficient a in swath drying equation, in relation to yield. - - windrow; . . . . . , spread swath

undisturbed between mowing and picking up, while the hay grass was tedded at regular intervals. Crop yield, and hence swath thickness, is another factor influencing drying, but drying rates are known only for the yields of the crops in which they were measured (Table 1). For current work with the model, the values of a [in Eqn (7)] shown in Table 1 are adjusted for different yields (as determined in the crop growth model) by multiplying by the factor shown in F/~. 4; these adjustment factors are based on runs of the bulk mechanistic model (Smith z') with a range of yield values.

3.3. Es t imat ion o f solar radiation

Simulations using the detailed mechanistic swath drying sub-model, or the simpler model based on the Penman equation, require hourly data for five weather parameters, namely, rainfall, dry bulb temperature, relative humidity (r.h.), wind speed and solar radiation. Of these, radiation data have been recorded by the Meteorological Office at only a very small number of sites, whereas the other parameters are available for several sites near to the main agricultural areas of Scotland. For sites where all weather parameters are available except radiation, a procedure has been developed for estimating hourly solar radiation from daily sunshine hours (McGechan and Glasbey23).

Using estimated radiation data, the Penman formula still gives a better fit to experimental swath drying data than either vapour pressure deficit or pan evaporation. Furthermore, a better representation of experimental swath drying data is given by

M. B. M c G E C H A N 1 2 5

considering evaporat ion over the whole 24 h period rather than assuming all evaporat ion takes place between 0900 and 1800 h as did Parke et aL e

3.4. Drying o f baled forage

There is no known experimental data for drying of baled forage, although it is reasonable to assume drying rates are lower than for swaths. Bales are assumed to dry according to Eqn (7) with a value of 0-0315 for the constant a. This is based on the value assumed by Parke et al. s (described in detail by Parke and Dumont24), but adjusted for a drying equation based on the Penman formula rather than vapour pressure deficit.

4. R e w e t t i n g o f swaths and bales by rain

4.1. Alternative swath rewetting models

As with swath drying, a large scale p rogramme of experimental and modelling work on rewetting of swaths by rain is currently taking place at SCAE, which will lead to a detailed sub-model of the process. A new "dis t r ibuted" model with three categories of moisture: absorbed moisture (which includes tissue moisture) , adhered surface moisture and loosely held moisture which can run off has been proposed by Pitt and McGechan. 4 However , before this can be used as a rewetting sub-model , more experimental work will be required to measure some of its parameters . In the meant ime, an alternative single category model has been developed and is currently being used as the sub-model. This has been found to give a good representat ion of swath moisture content after rainfall has ceased and run-off has taken place, as measured in the laboratory by Van Eideren et al. 2s and in the field by Lamond et al. 1 (Pitt and McGechan*).

4.2. Formulat ion o f single category swath rewetting model

The single category model assumes all rain water is initially retained until a certain critical amount of rain has fallen, after which the fraction retained decreases to some value dependent on rainfall intensity such that higher intensities reduce retention:

{ 1 ,O<~AM < ~ A M o M < M . . . .

d M = k ( l ) , A M > A M o M < M .... (12) dr,

0, M/> M . . . .

where

and

1 k(1) = 1------------ ~ . (13)

AMc = 1-2

M . . . . = 6.5

1,, = 0.220

c = 0-765

Windrowed swaths are assumed to cover half the ground area, intercept half the rainfall and have an areal density double the crop yield; spread swaths are assumed to cover the ground completely; these are close to average values measured in experiments at SCAE.

126 F O R A G E C O N S E R V A T I O N S Y S T E M S

4.3. Bale rewetting model

In the absence of any known experimental work on the effect of rainfall on baled forage awaiting carting from the field, the same set of assumptions has been made as described by Parke and Dumont. a4 This assumes that 80% of intercepted rainfall is retained, the remainder running off the sides; small bales contain 20 kg and big round hay bales 400 kg of dry matter; the area over which rainfall is intercepted is 0-418 m 2 for small bales and 3.72 m 2 for big round bales.

5. Barn hay drying

A simple sub-model is included to represent the drying of baled hay in a storage barn with a porous floor, in which air is forced by an electric fan to flow upwards through the hay (McGechan2S); this is one of several typical barn drying options described by the Electricity Council. 27 Simulation of the drying process in relation to weather data is based

8 on principles outlined by Spencer a and is similar to a model of grain drying developed by Smith. The electrical energy consumed by the fan and any heaters is assumed to raise the temperature of the incoming air at constant moisture content. As air passes upwards through the hay it picks up moisture until it reaches the r.h. value such that it is in equilibrium with the hay, according to Eqn (8). However, since bales in the top layer are the last to dry in a drier of this type, the hay moisture content value used to calculate this equilibrium r.h. is that at which bales were loaded into the drier, a similar assumption has been found satisfactory for simulations of grain drying by Smith. aa Changes in temperature and r.h. are assumed to follow the constant wet bulb temperature line on the psychometric chart; this is almost the same as following the constant enthalpy line, the same simplifying assumption as was made by Brooker a° in similar circumstances. A problem which arises when drying baled hay in a barn is that some air finds easy paths along the boundaries between bales, reducing the efficiency of the drying process; to represent this phenomenon, the fan air flow is multiplied by a factor k, to give the effective air flow for drying purposes.

Test simulations using this model with a value of 70% for k are in reasonable agreement with results of barn drying experiments carried out in 1981 and 1982 at SCAE, drying baled hay from about 30% to about 16% m.c.w.b. (Ferguson, al Ferguson and Fisheraa).

The bale density (typically about 125 kg m -3 wet) and drier floor area must be specified as input data, and should be selected so that a typical crop from the growth model will load the barn to a depth of no more than 5-5 m. The power consumption of the fan and any additional heaters must be specified as input data, as well as the flow rate; the Electricity Council recommend a flow rate of 0-23 m 3 s-t m-2 of floor area for this type of drier.

6. Losses during conservation and storage

Losses of dry matter and nutrient components of forage occur during field operations, and also for silage in particular, during storage. Extensive published information about losses in various categories has been reviewed by McGechan. aa-a5 Equations or tabulated values representing losses in each category have been selected for the forage conservation simulation model.

M. B. M c G E C H A N 127

6.1. Field losses

6.1.1. Respiratory losses

Respiratory losses in swaths and bales in the field are related to moisture content, ambient temperature, WSC content and stage of maturity by the following equation:

0-1O Lr 1.0 +-------Q (1 - 0.05(De - D)}e°C~gr(0-000128 m 2 - 0.00588 m + 0.10576) (14)

where De = 70. Coefficients in this equation have been chosen to give losses in relation to temperature

and moisture content similar to those of Wood and Parker 36 and of Honig. a? Since respiration oxidizes sugars to CO2 and water, dry matter, digestible dry matter and WSC are depleted by the same amount, representing a reduction in D value for this type of loss. If a partially dried swath is rewetted by rain, the respiration rate rises, as shown by Pizzaro and James. 38

6.1.2. Loss due to rain

This is one of the most difficult categories of loss to measure, and there is very little data available. Laboratory equipment for accurately determining losses of nutrients from swaths due to leaching by rain is currently being developed at SCAE; however until experimental results become available, the following relationship is being assumed between loss, rainfall and swath moisture content.

r [90 - m'~ (15) Li = r\ 70 ]

This is the same relationship as used by Parke et aL e based on experiments reported by Dernedde and Wilmschen 39 and by Fleischmann, 40 but multiplied by a factor of 10; also, r was taken to be the run-off rainfall derived from the rewetting model, rather than actual precipitation as used by Parke et aL e This relationship gives loss values similar to those measured by M¢ller and Skovborg. 41 Furthermore, simulation runs with the model gave values of respiration loss plus leaching loss similar to those reported by Riicker and Knabe ~'4a and by Kormos and Chestnutt. ~ Like respiratory loss, leaching loss represents a loss of digestible WSC only, resulting in a reduction in D value.

For baled hay, if sufficient rain occurs to raise the m.c.d.b, by 0.135, the forage is assumed to be unusable and a total loss, as assumed by Parke and Dumont . 24

6.1.3. Mechanical loss

Losses occur during all mechanical operations, i.e. mowing, spreading, tedding, turning or windrowing and during picking up a baler or forage harvester. They take the form of small fragments of crop material which become detached and blow away or drop into the stubble, so they are not picked up by the baler or forage harvester.

Losses during mechanical operations are represented as two components , "shat ter loss", and where relevant, "pick-up loss". Shatter loss for tedding spread swaths and spreading windrows are represented by the following bilinear relationship with forage moisture content, based on laboratory experiments into the susceptibility of drying grass to mechanical loss reported by McGechan: aa'45

= ~0.6 + 0-25x, m >~236-3Dc (16) Ls I. 0.6 + 0.25x + (1-42 - 0.02Dc)(236 - 3Dc - m), m < 236 - 3De

where x = 1 for conditioned grass, 0 for non-condit ioned grass.

128 F O R A G E C O N S E R V A T I O N S Y S T E M S

A lower level of shatter loss for turning windrows, windrowing two or more windrows into a larger windrow, rowing up, and picking up by a baler or forage harvester, is represented as half that given by Eqn (16). This variation in loss according to the level of abuse imposed on forage material in different operations is based on information reported by Overvest '~ and Honig. 37

In addition, for most crops, pick-up losses of 0.15 t dm ha- ~ of stubble area cleared are assumed, independent of yield or moisture content, for rowing up, windrowing, and picking up by a baler or forage harvester; this is based on the work of Honig a7 and Klinner and Wood. 47 However , for an unscrambled crop, i.e. cut with a drum mower without a conditioner and subjected to no tedding, turning or windrowing treatments before picking up, the following formula is assumed, also from Klinner and Wood. 47

L v = 0-1 + 0.0025tw (17)

6.2. Storage losses

6.2.1. Hay storage loss

For hay, storage losses tend to be low, and the following simple relationship with moisture content assumed by Parke et al.,n assembled from various sources by Klinner, 48 is also used in the current model:

Lh =0.15 m - 0 - 2 5 (18)

6.2.2. Silage storage losses

Compared with hay, losses which take place during the storage of silage are large, typically 20% or more, and the subject is very complex. Losses depend on various factors including grass maturity, chop length, density and whether or not an additive has been used, as well as moisture content. It has been found convenient to subdivide silage storage losses according to the mechanism by which they arise, into loss arising due to fermentation, losses due to air infiltration, and effluent loss. Air infiltration losses arise

m . c w . b , °/o

7 8 5 . . . . 8 p . . . . 7,5 . . . . p , ,

6

g

c

o

015 ' 2 0 2 5 3 0 ' '

d rn c o n t e n t , °/o

Fig. 5. Effluent loss against dry matter and moisture content

M. B. McGECHAN 129

Table 2

Invisible silage storage losses, and surface waste

Inuisible losses, ~ dm drn

Hart,esting content, D No Additive at Surface method ~ value additive standard rate waste, q~ dm

Precision 20% 70 19 13 8 chopped (direct 65 17 14 8

cut) 6(.) 19 17 g 25% 70 17 13 12

65 16 14 12 60 18 17 12

30% 70 16 14 12 65 15 14 12 60 17 17 12

Flail 20% 70 25 17 6 harves- (direct 65 22 16 6 ted cut) 6(.) 24 19 6

25% 70 23 16 10 65 20 16 I 0 611 19 18 10

3(.)% 70 20 16 10 65 18 16 lO 60 19 18 10

during filling of silos, during the storage period and during feeding out; during the storage period, these include totally spoilt unusable material, often called surface waste, and losses which take place without being apparent, sometimes grouped with other categories of loss under the title "invisible losses".

For loss due to production of effluent, a simple relationship with moisture content has been assumed (Fig. 5); this represents the mean of reasonably consistent data from Bastiman and Altman, *s Peters and Weissbach, S° Weissbach and Peters, sl Mayne and Gordon.Sa

It is proposed to adopt a two-phased approach to representation of the remaining categories of silage storage loss. For the first phase, suggested values of surface waste and invisible losses in relation to various factors have been assembled by McGechan 33"3s (Table 2); these are based on experimental data reported by Bastiman and Altman ~ and by Mayne and Gordon, Sa plus information from studies of the mechanisms of loss occurrence (e.g. McDonald, s3 Rees et a l .~ ) .

Development of a simulation model of silage fermentation, reported by Pitt et al. 5s and by Leibensperger and Pitt, ss is well advanced. Preliminary experimental and modelling work on the mechanisms of air infiltration into silage has been reported by Rees et a l . ,~ 's7 Pitts8 and Weise et al. ss'~° For the later phase, it is proposed to determine loss levels from work with models of silage storage.

7. Field conservation operations

7.1. Operat ions considered

A series of routines simulate the field operations associated with conservation of forage--mowing, spreading, tedding, turning and windrowing swaths, baling (standard or big bales) and forage harvesting.

132 F O R A G E C O N S E R V A T I O N S Y S T E M S

forage, M E content is calculated according to the following equations (MAFFT°),

M E = 0-185D - 1.88 (hay) (21)

M E = 0.11D + 3.2 (silage) (22)

If the M E content of a batch of farm produced forage has dropped to below 8-0 MJ kg-I dm as a result of excessive field losses, it is considered unsuitable for inclusion in a dairy cow ration and is excluded from the evaluation procedure. Crude protein content (CP) comes from the growth sub-model less losses which occur during the simulated conservation operations, with degradability of protein calculated from the "modified acid detergent fibre" ( M A D F ) , as follows (Lewis71):

R D P = 0-918(CP - 0.0632 M A D F ) (23)

U D P = CP - R D P (24) where

M A D F = (92.8 - D)/0-82 (25)

8.3. Dry matter intake constraints

Four dry matter intake constraints are included, maximum total dry matter, minimum total dry matter, maximum straw and maximum roughage (total of farm produced forage, bought hay and straw). Total maximum dry matter intake in each period is derived by the following equation, as suggested by Corrall et al. s and Neal et al., ~2 with a further reduction of 10% in period 1 as assumed by Doyle and Edwards. 73

it = 0-022W + 0"2Ym (26)

The minimum dry matter constraint ensures that the cow's appetite limit is satisfied, and is assumed to be 90% of the maximum dry matter, the maximum straw intake constraint is set at 30% of the total dry matter intake.

The maximum roughage constraint is of considerable importance in the current study, since it is well established that intakes of these feedstuffs increase considerably with increases in quality. Also, even for the highest quality forages, they can only supply a proportion of the requirements of dairy cows in periods 1 and 2. A wide range of information from literature sources about variations in forage intake with quality factors such as degree of wilting, weather conditions during wilting or drying, additives, and silage chop length, has been analysed by McGechan. ~ For silage, a useful set of equations relating intake to such factors has been presented by Lewis, 74 recommended after further tests against intake data by Neal et al., 72 and used in another model by Doyle and Edwards. 7s

(1.03d + 0.516D - 0.5N + 45)W °75 it = 1000 (silage) (27)

where d takes a maximum value of 30 and N takes a maximum value of 20.

(2-47if + 3.37C z) _ 0.0109W,) 75 (28) im = 1"0681if- W~).75

For this study, silage intake is calculated according to Eqns (27) and (28), with ammonia nitrogen calculated according to Eqn (29) from Leibensperger and Pitt, $6 Eqn (30) and Eqn (31)

n,, = 0.405 - 0-00564d (29)

n, = CP/6 .25 (30)

N = 82-35 n;,/nt (31)

M. B. M c G E C H A N 133

This has been shown by McGeechan , ~ in an analysis of a range of experimental data, to explain variations in silage intake with dry mat ter content, concentrate intake and additive, but does not fully account for variations with chop length. Intakes of precision chopped silage are therefore assumed to be 10% greater than determined by Eqns (27) and (28), while for flail or double chop similar approach is adopted but using (28), as suggested by Lewis. 71

harvested silage no adjustment is made. For hay, a Eqn (32) in place of Eqn (27), together with Eqn

4.0W °.75 ir = (hay) (32)

(100- D)

This gives intake values similar to those in the few reported studies of hay intakes (Demarquilly and Jarrige, 75 Bertilsson and Burstedt?S). Since the concentrate intake is both part of the solution to the LP problem, and, from Eqn (28), influences one of the constraints, an iterative approach must be adopted to obtain the least cost ration.

A further constraint imposes a minimum crude fibre (CF) constraint of 10% in period 1 and 15% in periods 2 to 4, as r ecommended by SAC. 77

9. Conclusion

Development work on all the sub-models has been taken to a point where a cautious start can be made with the exploitation of the whole system model , with some confidence in its assessments of forage conservation practices. However , it is anticipated that some of these assessments will act as sensitivity studies indicating areas where there is a need for more detailed information or further development of the sub-models , while in other areas they will be shown to be adequate. Also, since work is continuing on modelling some of the physical processes, the model has been designed so that alternative sub-models can be incorporated for these processes as they become available.

References

1 Lamnnd, W. J.; Spencer, H. B.; Glasbey, C. A.; Haughey, D. P. Field wilting and drying of grass in a cool moist climate. Research and Development in Agriculture 1988, 5(1): 23-28

2 Smith, E. A.; Duncan, E. J.; McGechan, M. G.; Haughey, D. P. A model for the field drying of grass in windrows. Journal of Agricultural Engineering Research 1988, 41:251-274

3 Glasbey, C. A.; McGechan, M. B, A relationship between weather and drying rates in grass swaths. Departmental Note SIN/480, Scottish Institute of Agricultural Engineering, Penicuik, 1986 (unpublished)

* Pitt, R. E.; McGechan, M. B. The rewetting of partially dried grass swaths by rain. Departmental Note 1, Scottish Centre of Agricultural Engineering, Penicuik, 1987

s Corrall, A. J.; Neal, H. D., St. C; Wilkinson, J. M. Silage in milk production; a simulation model to study the economic impact of management decisions in the production and use of silage in a dairy enterprise. Technical Report 29, Grassland Research Institute, Hurley, January 1983 (unpublished)

6 Parke, D.; Dumont, A. G.; Boyce, D. S. A mathematical model to study forage conservation methods. Journal of the British Grassland Society 1978, 33:261-273

7 McGechan, M. B. A crop growth sub-model for a forage conservation system model--an exploratory study Departmental Note SIN/444, Scottish Institute of Agricultural Engineering, Penicuik, July 1985 (unpublished)

8 Johnson, I. R.; Thornley, J. H. M. Vegetative crop growth model incorporation leaf area expansion and senescence, and applied to grass. Plant, Cell and Environment 1983, 6:721-729

s Corrall, A. J. Grass growth and seasonal pattern of production under varying climatic conditions. 10th General Meeting of the European Grassland Federation, As, Norway, 1984

134 FORAGE CONSERVATION SYSTEMS

lo Jagtenberg, W. D. Predicting the best time to apply nitrogen to grassland in spring. Journal of the British Grassland Society 1970, 25:266-271

11 Penman, H. L. Natural evaporation from open water, bare soil and grass. Proceedings of the Royal Society, London, 1948, A193:120-146

la Doyle, C. J.; Ridout, M. S.; Morrison, J.; Edwards, C. Predicting the response of perennial ryegrass to fertiliser nitrogen in relation to cutting interval, climate and soil. Grass and Forage Science 1986, 41:303-310

13 McGechan, M. B. Further development of a crop growth sub-model for a forage conservation system model. Departmental Note, Scottish Centre of Agricultural Engineering, Penicuik, 1988

14 Green, J. O.; Corrall, A, J.; Terry, R. A. Grass species and varieties: relationships between stage of growth, yield and forage quality. Technical Report 8, Grassland Research Institute, Hurley, 1971 (unpublished)

is Henderson, A. R. Chemical changes during the ensilage of grass with particular reference to carbohydrates. PhD thesis, University of Edinburgh, 1973

le Morrison, J.; Jackson, M. F.; Sprarrow, P. E. The response of perennial ryegrass to fertilizer nitrogen in relation to climate and soil: Report of joint ADAS/GRI grassland manuring trial--GM20. Technical Report 27, Grassland Research Institute, Hurley, 1980 (unpublished)

17 Luder, W. Ermittlung der Erntegelegenheiten und des Verlustrisikos aufgrund von Klimadaten, dargestellt am Beispiel der Raufutterernte. (Determination of harvesting opportunities and loss risks on the basis of climatic data, demonstrated by the example of forage conservation.) Doctoral Dissertation No. 6981, Federal Technical College, Ziirich, 1982.

is Pitt, R. E. Forage drying in relation to pan evaporation. Transactions of the American Society of Agricultural Engineers 1984, 27:1933-1944

19 Thompson, N. Modelling the field drying of hay. Journal of Agricultural Science (Cambridge) 1981, 97: 241-260.

2o Dumont, A. G.; Parke, D. Effect of barn drying on hay value. Departmental Note 868, National Institute of Agricultural Engineering, Silsoe, 1978 (unpublished).

al Waite, R. The relation between moisture content and moulding in cured hay. Annals of Applied Biology 1949, 36:496-503

22 Smith, E. A. A bulk model for the field drying of grass in windrows. Departmental Note SIN/451, Scottish Institute of Agricultural Engineering, Penicuik, June 1985 (unpublished).

a3 McGechan, M. B.; Glasbey, C. G. Estimates of solar radiation based on other meteroiogical parameters for use in simulations of swath drying. Departmental Note 5, Scottish Centre of Agricultural Engineering, Penicuik, 1988

a4 Parke, D.; Dumont, A. G. A simulation model to study forage conservation systems. Departmental Note 920, National Institute of Agricultural Engineering, Silsoe, 1979

2s Van EIderen, E.; De Feijter, J.; Van Hoven, S. P. J. H. Moisture in a grass swath. Journal of Agricultural Engineering Research 1972, 17:209-218

2e MeGeehan, M. B. A simple barn drying routine for a forage conservation model. Departmental Note, Scottish Centre of Agricultural Engineering, Penicuik, 1989

a7 Electricity Council. Hay drying--a guide to the practical design of installations. National Agricultural Centre, Stoneleigh, 1985

aa Spencer, H. B. A simple method of estimating drying rates in deep beds of agricultural produce. Departmental Note SIN/37, Scottish Institute of Agricultural Engineering, Penicuik, August 1969 (unpublished)

29 Smith, E. A. Microcomputer program for interactive evaluation of the control options for near ambient drivers. Departmental Note SIN/393, Scottish Institute of Agricultural Engineering, Penicuik, April 1984 (unpublished)

3o Brooker, D. E. A mathematical model of the psychrometric chart. Transactions of the American Society of Agricultural Engineers 1967:558-569

31 Ferguson, W. E. Solar-assisted drying of medium density hay bales in the SIAE solar barn: 1981 experiment. Departmental Note SIN/366, Scottish Institute of Agricultural Engineering, Penicuik,.February 1983 (unpublished)

aa Ferguson, W. E.; Fisher, R. Comparison of barn drying by solar-assisted drying and by drying with conventional air heating: 1982 solar barn experiment. Departmental Note SIN/387, Scottish Institute of Agricultural Engineering, Penicuik, November 1983 (unpublished)

M. B. McGECHAN 135

• a McGechan, M. B. Losses during forage conservation--a review of available information for use in a forage conservation system model. Departmental Note SIN/487, Scottish Institute of Agricultural Engineering, Penicuik, March 1987 (unpublished)

a, McGechan, M. B. A review of losses arising during conservation of grass forage: Part 1, Field losses. Journal of Agricultural Engineering Research 1989, 44:1-21

as M c G e c h a n , M. B. A review of losses arising during conservation of grass forage: part 2, storage losses, Journal of Agricultural Engineering Research 1990, 45:1-30

an Wood, J. G. M.; Parker, J. Respiration during the drying of hay. Journal of Agricultural Engineering Research 1971, 16:179-191

aT Honig, H. Mechanical and respiratory losses during pre-wilting of grass. Proceedings of Occasional Symposium 11, European Grassland Federation, Brighton, May 1979

Pizzaro, E. A.; James, D. B. Estimates of respiratory rates and losses in cut swards of Lolium perenne (S-321) under simulated haymaking conditions. Journal of the British Grassland Society 1972, 27(17): 17-21

Dernedde, W.; Wilmschen, R. Heuwerbungsverluste dutch Niederschlage bei gequetschten Gras. (Haymaking losses due to rain on lacerated grass.) Landbauforschung V61kenrode 1969, 19(1): 39-42

4o F i e i s c h m a n n , F. Ver~inderungen, welche bei der Diirrheubereitung im Grase vor sich gehen. (Losses of dry matter in haymaking under various conditions.) Landw, Versuchs-Stationen, 1912, 76:237-447

41 Meller, E.; Skovborg, E. B. Sk~rlaegnin og sk~rbehandling af graesmarksafgroder til fortorring. (Methods of harvesting and mechanical treatment during pre-wilting of grassland crops.) Baretning fra Statens Fors0gsvirksomhed i Plantikultur, Copenhagen, 1971, 968:483-497

42 Riicker, G.; Knabe, R. Non-mechanical field losses in wilted grasses as influenced by different factors. Proceedings of the 13th International Grassland Congress, Leipzig, 1977

Riicker, G.; Knabe, O. Massnahmen zur Verlustsenkung und Qualit~itserhaltung beim Welkung yon Gr~isern. (Measures for reducing losses and maintaining quality during wilting of grasses.) Feldwirtschaft 1983, 24(4): 174-176

44 Kormos, J.; Chestnutt, J. M. B. Measurement of dry matter losses in grass during the wilting period. 1. The effects of length of the wilting period. Record of Agricultural Research 1967, 1 6 : 1 4 5 - 1 5 0

McGechan, M. B. Susceptibility to losses during mechanical silage and haymaking operations in relation to grass dry matter content. Grass and Forage Science 1988, 43:387-393

Overvest, J. Droge-stofverliezen tijdens de veldperiode. (Dry matter losses during wilting.) Report 49, Proefstation voor de rundveehouderij, Lelystad, 1977

47 Kl inner , W. E.; Wood, G. M. An experimental field rig for collecting forage crops and separating out stones and metal objects. Departmental Note 1954, National Institute of Agricultural Engineering, Silsoe, 1981 (unpublished)

'~ Klinner, W. E. Mechanical and chemical field treatment of grass for conservation. Paper 2. Joint Conference of Institution of Agricultural Engineers and British Grassland Society, 1976

'~ Bastiman, B.; AItman, J. B. V. Losses at various stages of silage making, Research and Development in Agriculture 1985, 2(1): 19-25

5o Peters, G.; Weissbach, F. Factors influencing the formation of effluent in forage ensiling. Proceedings of 13th International Grassland Congress, Leipzig 1977, 1133-1134

sl Weissbach, F.; Peters, G. Anfall chemische Zusammemsetzung und Futterwert von Silosickers- aft. (Quantity, chemical composition and feed value of silage effluent.) Feldwirdschaft 1983, 24(2): 78-81

s2 Mayne, C. S.; Gordon, F. J. The effect of harvesting system on nutrient losses during silage making. 2. In-silo losses. Grass and Forage Science 1984, 41:341-351

McDonald, P. The biochemistry of silage. Chichester: John Wiley and Son, 1981 Rees, D. V. B.; Audsley, E.; Neale, M. A. Some physical properties that affect the rate of

diffusion of oxygen into silage. Journal of Agricultural Science (Cambridge) 1983, 100(3 ) : 601-605

• s Pitt, R. E.; Muck, R. E.; Leibensperger, R. V. A quantitative model of the ensilage process in lactate silages. Grass and Forage Science 1985, 40:279-303

Leibenspe~er, R. V.; Pitt, R. E. A model of clostridial dominance in ensilage. Grass and Forage Science 1987, 42(3): 297

136 F O R A G E C O N S E R V A T I O N S Y S T E M S

s7 Rees, D. V. H.; Audsley, E.; Neale, M. A. An apparatus for obtaining an undisturbed core of silage and measuring porosity and gas diffusion in the sample. Journal of Agricultural Engineering Research 1983, 28:107-114

sa Pitt, R. E. Dry matter losses due to oxygen infiltration in silos. Journal of Agricultural Engineering Research 1986, 35:193-205

s9 Weise, G.; Rettig, H.; Suckow, G. Untersucbungen zur Quantifizierung des Lufteinflusses bei der Silierung. (Studies on the quantization of effects of air in the process of silage making.) Archiv ftir Tierernabrung 1975, 25:69-82

s o Weise, G.; Rettig, H.; Suckow, G. Losses and stability of silage in dependence on the degree of air influence. Proceedings of 13th International Grassland Congress, Leipzig, 1977

61 ADAS. Silage, Liscombe grass bulletin 2, Liscombe EHF, Dulverton 1984 s2 McGechan, M. B. Mechanization aspects of forage conservation models--A review of alternative

systems and their parameters. Departmental Note SIN/462. Scottish Institute of Agricultural Engineering, Penicuik, January 1986 (unpublished)

ea O'Dogherty, M. J. A review of research on forage chopping. Journal of Agricultural Engineering Research 1982, 27(3): 267-289

Catt, W. R. Commercial harvesters now. Occasional Symposium 17, British Grassland Society, 1985

8s Pascall, J. A. A comparison between the performance of simple and "'double-chop'" flail forage harvesters. Journal of Agricultural Engineering Research 1962, 7(3): 241-247

Ward, S. M.; Cunney, M. E.; McNulty, P. B. Power and chop length analyses of trailed forage harvesters. Energy in Agriculture 1985, 4 :15-23

e7 ADAS. Forage harvesting systems survey 1983. Agricultural Development and Advisory Service, 1983

68 McGeehan, M. B. A procedure for evaluating farm produced forage for use with a forage conservation model. Departmental Note 4, Scottish Centre of Agricultural Engineering, Penicuik, 1988

69 ARC. Nutrient requirements of ruminant livestock. CAB, Slough, 1980 70 MAFF. Energy allowances and feeding systems for ruminants. Report No. 433 (revised edn),

1976 71 Lewis, M. Personal communication, 1987 72 Neal, H. D. Stc.; Thomas, C.; Cobby, J. A. Comparison of equations for predicting voluntary

intake by dairy cows. Journal of Agricultural Science (Cambridge) 1984, 103:1-10 7a Doyle, C.; Edwards, C. A. A model for evaluating grassland management decisions on dairy

farms in the UK. Agricultural Systems 1986, 21:243-246 74 Lewis, M. Equations for predicting silage intake by beef and dairy cattle. Proceedings of 6th

Silage Conference, Edinburgh, 1981 7s DemarquiUy, C.; Jardge, R. The effect of method of forage conservation on digestibility and

voluntary intake. Proceedings of 11th International Grassland Congress, Surfers Paradise, Australia, 1970

?e Bertilsson, J.; Burstedt, E. Effects of conservation method and stage of maturity upon the feeding value of forages to dairy cows. 1. Forage intake and effects of concentrate/forage ratio. Swedish Journal of Agricultural Research 1983, 13:189-200

77 SAC. Complete diets for dairy cows. Technical Note 18, Scottish Agricultural Colleges, 1977