timeliness cost grass and forage

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A method of estimating timeliness costs in forageharvesting illustrated using harvesting systems inSweden

C. Gunnarsson*, R. Spo rndly, H. Rosenqvist, A. de Toro and P-A. Hansson*Swedish Institute of Agricultural and Environmental Engineering, Uppsala, Sweden, Swedish Institute of Agricultural and Environmental Engineering, Uppsala, Sweden, Department of Animal Nutrition andManagement, SLU, Kungsangen Research Centre, Uppsala, Sweden and Department of Energy andTechnology, Swedish University of Agricultural Sciences, Uppsala, Sweden

AbstractA method for estimating timeliness costs, depending ondry-matter yield and nutritive value of forage ispresented, and used to estimate timeliness costs, toexamine different harvesting systems and to presentconclusions on machinery selection when harvestingsilage for dairy cows in Sweden. Timeliness costs (in ha

) 1 d) 1 ) of forage for silage were signicantly higher

for the rst cut compared with the second or third cutsin the season. It is, therefore, important to avoiddelaying the rst cut. The timeliness costs also variedgreatly between years. Harvesting costs decreased withincreasing forage area up to a certain threshold area beyond which decreasing machinery costs were out-

weighed by increasing timeliness costs due to a longerduration of harvest. At increasing transport distances,the difference in cost between different harvestingsystems and different sizes of machinery increased.Harvesting of forage by contractors decreased harvest-ing costs, particularly for small forage areas, sinceincreased annual use of the machinery lowered themachinery costs and enabled larger machines withhigher capacity to be used. To avoid high timelinesscosts it is important to avoid delays in harvesting.

Keywords: grassland, forage evaluation, forage quality,harvesting date, machinery capacity, mechanizationcosts

Introduction

High nutritive value of silage for dairy cows is promoted by a high harvesting capacity since it is important thatharvesting is performed when all the forage has such ahigh value. When the harvesting of forage is delayed beyond the optimal harvest date, the value of theoverall harvest is affected through dry-matter (DM)yield increasing with time and the nutritive valuedecreasing with time. Weather is one of the mostimportant constraints on nutritive value, e.g. rain onwilting herbage leads to a decrease in nutritive value(Orosz et al., 2008). The relatively mild winters ofnorth-western Europe and the relatively even distribu-tion of rainfall throughout the year favour herbage

production but it may vary considerably between yearsand also within a growing season due to weatherconditions (Herrmann et al., 2005a).

Timeliness costs, describing the cost for each day thatharvesting is delayed beyond the optimal day, can becalculated for each cut of forage by using informationon the change in nutritive value and yield over thedelay period. Since forage is usually harvested morethan once per season, the time of the rst cut hasimplications not only for the nutritive value and yield ofthe rst cut but also on the nutritive value and,depending on choice of cultivar, the DM yield ofregrowths (Hall et al., 2005). Adding timeliness coststo the machinery and labour costs of forage harvesting

takes into consideration the costs arising when theforage is not harvested at its optimal value or whenthe harvest is extended in time due to low capacity ofthe harvesting equipment or interruptions and delaysdue to weather. According to Forristal and O Kiely(2005), a model considering changes over time in yieldand nutritive value of forage would be valuable inthe optimal selection of machinery systems andcapacities. Ward et al. (1986a,b) developed a model

Correspondence to: Carina Gunnarsson, Swedish Instituteof Agricultural and Environmental Engineering, Box 7033,SE 750 07 Uppsala, Sweden.E-mail: [email protected]

Received 4 December 2008; revised 5 May 2009

2009 Blackwell Publishing Ltd. Grass and Forage Science, 64, 276291 doi: 10.1111/j.1365-2494.2009.00693.x276


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that determines the optimal machinery and labourrequirements for systems of silage mechanization andcalculates the costs both for private ownership andmachinery ring membership, while also consideringtimeliness costs. Timeliness costs were calculated as the

additional costs of concentrates required to offset anyreductions in the nutritive value of the forage.

A number of harvesting and machinery systems existfor harvesting of forage for silage and, according toForristal and O Kiely (2005), the choice of system has amajor inuence on the demand for machinery, labourand management. The most relevant harvesting systemfor a particular farm is dependent on farm-specicfactors such as existing equipment, labour availability,transport distance and forage area. In continuousharvesting systems using a forage harvester, foragecollection in the eld, transport and ensiling mustfollow in direct conjunction with each other, whereaswith a round baler harvesting in the eld and transportcan be separated in time (Schick and Stark, 2002).Continuous systems generally place higher demands onlabour and planning of harvesting to match capacitiesand avoid costly delays and waiting times (Schick andStark, 2002). Because contractors typically prefer con-tinuous harvesting systems, the use of the forage wagonhas been in decline (Forristal and O Kiely, 2005). Inhumid temperate areas, however, baled silage is themost common system on farms (Orosz et al., 2008).

In Sweden, many dairy farms use more than onemachinery system for harvesting silage and round balingis the most commonsystem,usedto some extenton morethan 0 80 of farms (Pettersson et al., 2009). With increas-

ing area of forage, the round bale system gives way tosystems conserving silage in bunker or tower silos. Theprecision-chop forage trailer (PCFT), common in Swe-den, and the precision-chop forage harvester with sep-arate transport trailer, are used to some extent on 0 25ofSwedishfarms(Pettersson et al., 2009).The average dairyfarm in Sweden has 55 cows (SJV, 2008) but there is awide range of herd size with many herds over 100 cows.The typical winter feeds of dairy cows on a DM basis are0550 60 grass/clover silage and 0 400 45 cereal-basedconcentrates. A 55-cow farm typically harvests 220metric tonnes of silage DM from 30 ha with two or threecuts per year. Since all cows are required to graze insummer in Swedendue to animal welfare legislation,theclosest areas of grassland to farm buildings are used forgrazing. Most farms have had their grasslands


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inputs and a set of model parameters specic for thesward and site (Table 1). Daily increase in DM yield is

calculated as the product of the current herbage massand the relative growth rate, Rs. The model is summa-rized by Equation 1, where W t describes the harvestedherbage mass in g m

) 2 on day t.

W t W t 1 W t 1 R s AGE GI 1

In this study, Rs was modied by a growth index (GI)and an age-function (AGE), where GI summarized theeffects on plant growth of temperature, radiationand plant-available soil water and the AGE-function

accounted for the impact of crop ageing quantied as afunction of the leaf area index (LAI). The Age-functionis described by Equation 2.

AGE 1

1 LAI=LAIh k1

h i 2

LAI is the leaf area index, LAI h half the maximumLAI (50 for spring growth and 4 0 in regrowth) and k1 aconstant equalling 4 0 for spring growth and 3 0 forregrowth. Radiation index, RI is described by Equation3 where R i is the daily incoming global radiation, R maxthe insolation at light saturation of the stand and k2 aconstant determining the curvature of the radiationresponse curve.

RI

1 ek2 x Ri =Rmax " #

1 e k2 3

The initial herbage mass and the initial Rs at the startof each growth period take into account the inuenceof botanical composition and management practices,such as the application of nitrogen fertilizer, number ofcuts per season and geographical location. The initialvalues of Rs (Table 2) and the initial herbage mass usedin this study were taken from Fagerberg et al. (1990).

The swards used consisted of a mixture of timothy(Phleum pratense L. ) and red clover ( Trifolium pratense L. )and fertilizer applications were made following thestrategies described in Ho glind (1997) and Jordbruks-verket (2006). The proportion of red clover of theindividual cuts was estimated from eld experimentsinitiated in 1995 in southern and central Sweden(Stenberg et al., 2001) and was dependent on the ageof the sward and the level of fertilizer applied (Table 2).

Feed value of forage

The feed value of the forage was calculated using amethod developed by Gunnarsson et al. (2005). The MEand CP concentrations of forage at the rst and last day

Table 1 Daily input variables and model parametersrequired for estimation of yield of dry matter of herbagefrom Torssell et al. (1982).

Daily input variables Model parameters

Averagetemperature ( C)

050 of maximum leafarea index (LAI h )

Average incomingradiation [cal (cm 2 d) ) 1]

Curvature ofAGE-function

Precipitation (mm) Radiation for maximumgrowth rate [cal (cm 2 d)

) 1]Potential

evapo-transpiration (mm)Curvature of the response

curve of radiationMean daily temperature for

maximum growth rate andzero growth rate ( C)

Curvature of response oftemperature*

Initial value of herbage massin the spring (g m ) 2 )

Initial value of relativegrowth rate ( Rs) [g (g d)

) 1]Maximum plant-available

soil water (mm)

*The function describing the temperature index is symmetricand calculated based on mean daily temperature, mean dailytemperature for maximum growth rate and for zero growthrate.

Table 2 Values used for N-fertilizer application rates (manure + chemical fertilizer), proportion of red clover in the sward and theresulting relative growth rate (R s) value used for each cut in the model of herbage growth.

Age of sward(years)

Fertilizer rate (kg N ha) 1 )

Proportion of redclover Rs [g (g d)

) 1 ]

1 2 3 1 2 3 1 2 3

Cut number1 0 + 50 20 + 50 20 + 50 0 24 0 15 0 11 0 192 0 209 0 2092 0 + 40 20 + 40 20 + 40 0 49 0 23 0 07 0 218 0 229 0 2263 20 + 0 20 + 0 20 + 0 0 60 0 32 0 17 0 194 0 201 0 198

278 C. Gunnarsson et al.

2009 Blackwell Publishing Ltd. Grass and Forage Science, 64, 276291


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of the possible harvesting period was identied from theregression of Fagerberg et al. (1990). The harvestingdays chosen are shown in Table 3. In rst cut the MEconcentrations of the rst and last day of the possibleharvesting period were 11 0 and 10 4 MJ kg ) 1 respec-tively and in the second and third cuts were 10 6 and10 1 MJ kg ) 1 respectively. The corresponding concen-trations of CP are also given in Table 3.

A ration was then constructed using the nutritivevalue of the forage at the rst cutting day at each

harvest. By using prices relevant for Swedish conditionsfor concentrates, milk and the forage harvested at therst possible day of the rst cut, a resulting sum of milkprice minus feed costs was established. A new rationwas then constructed using the nutritive value of the

forage at the last cutting day at each harvest based onthe ME and CP concentrations given in Table 3. Inconstructing the new ration, the objective was toachieve the same milk production by replacing theforage with a lower nutritive value with more concen-

trate. By using the same prices of concentrates and milkas for the rst ration, a new price (feed value) wascalculated for the forage to keep the sum of milk priceminus feed costs constant. Due to the fact that cowsconsume less silage when harvested at a later growthstage (Bertilsson and Burstedt, 1983), it was not fullypossible to achieve the same high peak milk production.The milk production at the last cutting day of eachharvest was, therefore, somewhat lower than at the rstcutting day. It was also necessary to alter the concen-trate in the third harvest due to the higher CPconcentration in the forage.

The resulting amounts of feeds used per cow per yearwith the different feed qualities are presented inTable 4. The DM contents of barley and straw were870 and 850 g kg

) 1 respectively (Spo rndly, 2003). Theconcentrates all had a DM content of 880 g kg

) 1 andwere commercial concentrates available in Sweden in2007. Common prices for Sweden in 2007 were used forfeed components and milk. The rate of exchange usedwas 10 = SEK 9 3.

By keeping the sum of milk price minus feed costsconstant for all cuts, the feed value of the forage ateach cut was calculated in relation to the nutritivevalue of the forage at the rst possible day of the rstcut. These values are presented in the last column inTable 3. The daily change in feed value, calculated

from the difference in feed value between the twoharvesting days in each cut and the number of days between the two harvesting dates, was used togetherwith the DM yield in further calculations of thetimeliness costs.

Table 3 Metabolizable energy (ME) and crude protein (CP)concentration of silage and calculated feed value of the foragefrom the rst and last harvesting day in cut numbers 1, 2 and 3.

Cutno.

Harvestingday

MEconc.

(MJ kg ) 1

DM)

CPconc.

(g kg ) 1

DM)

Feedvalue

( kg ) 1

DM forage)

1 165* 11 0 147 0 01451 175* 10 4 123 5 01062 57 10 6 156 6 01232 72 10 1 131 7 01003 53 10 6 187 2 01303 69 10 1 161 1 0102

The ME and CP concentrations of herbage were calculatedaccording to Fagerberg et al. (1990) and the feed value of theforage was calculated from the rations in Table 4.*Day number of the calendar year starting from 1 January.

Number of days after rst cut. Number of days after second cut.

Table 4 Metabolizable energy (ME) concentration of silage, amounts of silage, straw, concentrate (C1 containing 290 g crudeprotein kg

) 1 DM; C2 containing 260 g crude protein kg) 1 DM) and minerals in the rations and the calculated milk yield cow

) 1 yr ) 1

for rst, second and third cuts of forage.

ME conc. of silage(MJ kg

) 1 DM)Silage

(kg DM yr) 1 )

Barley(kg yr

) 1 )Straw

(kg yr) 1 )

C1(kg yr

) 1 )C2

(kg yr) 1 )

Minerals,(kg yr

) 1 )Milk yield

(kg ECM yr) 1 )

First cut11 0 3660 1674 137 1562 15 1006510 4 3355 1937 137 1549 15 9669

Second cut10 6 3508 1845 137 1629 15 991310 1 3203 2050 137 1604 15 9669

Third cut10 6 3508 1845 137 1562 15 991310 1 3203 2050 137 1604 15 9669

Timeliness of forage harvesting 279



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Calculation of daily timeliness costs per hectare

The value of the forage as a functionof dateof harvestingwas calculated by multiplying the DM yield by the feedvalueof theforage.The optimal time toharvestthe forage

was determined by nding the dates for each cut thatmaximized the total forage value of all cuts (in ha

) 1).The objective function describing the total forage value(FVtot ) of the three cuts is described as:

FVtot M 1 V 1 M 2 V 2 M 3 V 3 4

where M denotes the yield in kg DM ha) 1 , V denotes

the feed value of forage in kg) 1 DM, and the numbers

1, 2 and 3 denote rst, second and third cuts. Choosingthe date for every cut that resulted in the highest valueof the sum of all three cuts allowed account to be takenof the fact that delays in a cut also affected the followingcuts. It was assumed that all cuts were performed on thedates calculated as optimal, i.e. when the dates of thesecond and third cuts were optimized using the Excelsolver, and it was assumed that the preceding cut/soccurred on the optimal day. The method used to ndthe optimal harvest dates was as follows:

(i) for different cutting days of the rst (T1) cut, thecutting dates of the second (T2) and third (T3) cutswere optimized with the aim of maximizing FV tot ,the total forage value of all three cuts;

(ii) the T1 resulting in maximum FV tot was set as T1 opt ;(iii) for different cutting dates of T2 the cutting dates of

the third (T3) cut were optimized with the aim ofmaximizing FV tot ;

(iv) the T2 that maximized FV tot was set as T2 opt ;(v) FV tot was calculated for different dates of the thirdcut (T3); and

(vi) T3 opt was set as the date that resulted in the highestvalue of FV tot .

The two harvesting days for each cut used forcalculating the change in feed value (Table 3) wereset as boundary conditions when nding the optimalharvesting days. To avoid the solution being limited bythe boundary conditions, the optimization of harvestingdates for the rst cut was carried out for an extendedrange of harvesting days corresponding to ME concen-trations between 11 4 and 10 0 MJ kg ) 1 DM. For thesecond and third cuts the corresponding extensions ofthe boundary conditions for harvesting days were madewith ME concentrations of 11 0 and 9 7 MJ kg ) 1 DM.

Finally, the daily timeliness costs per ha, expressing both quantity and quality losses in economic terms in ha

) 1 d) 1 , were calculated. The result of the harvesting

day optimisations were curves for the forage value ofthe rst, second and third cuts. The daily timelinesscosts per ha for each cut, i.e. daily change in monetaryvalue ( ha

) 1 d) 1), were calculated as the difference in

forage value at the optimal date and the forage value 7d after the optimal date, divided by 7. A delay of up toone week was considered an appropriate time range onwhich to base the calculations. The calculations wererepeated for 10 years. For the same sward type the

model of DM yield of forage was run with daily weatherdata for southern Sweden for the years 19841993 and,for central Sweden, for the years 19781987.

While the DM yield was calculated for each year based on daily weather data, the feed value, whichdepended upon change in nutritive value of forage, was based on average values for a number of years. Thechange in CP and ME concentration with time was,therefore, the same for each of the 10 years for whichcalculations were made. Consequently, the timelinesscosts accounted for differences in DM yield betweenyears but not for annual variation in change in feedvalue.

Repeated measurements ANOVAANOVA , as implemented inthe STATISTICA ver. 8 0 software package, was used toinfer the timeliness effects of geographic location andcutting occasion. With measurements repeated annu-ally, the effect of last years yield was compensatedthroughout the material, sigma-restricted parameteri-sation was used, and type VI sum of squares calculated.

Calculation of harvesting costs

Harvesting systems

Calculations of costs were made for each of the threecuts per season in southern (approx. 56 N) and central

(approx. 60 N) Sweden based on the DM yields ofthe individual harvests arising from the results of theoptimizations of harvest days. Three different machin-ery systems, each with three different sizes of machin-ery, small, medium and large, for mowing, harvestingand transport, were studied.

The three machinery systems studied were a preci-sion-chop forage harvester (metered-chop) with sepa-rate transport trailers (PCFH/T), a PCFT and a round baler with integral wrapping (RBI). The PCFH/T and thePCFT systems used the same technique for choppingthe grass using a high-speed chopping cylinder, but thePCFT system fed the herbage into an integral trailerinstead of into a separate trailer towed by the chopperas in the PCFH/T system. The round baler is of the typewith separate baling and wrapping units on the samechassis, resulting in only a short stop to discard thewrapped bale. The RBI system required 12 persons, thePCFT system 23 persons and the PCFH/T system 35persons, depending on whether mowing was carriedout in parallel with harvesting.

Harvesting costs included costs for machines, labourand timeliness. In addition, ensiling costs, such as




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bale plastic and the investment, interest and mainte-nance cost for the bunker silo, or the storage area forround bales, were included when comparing theharvesting systems. Specications for the machinesused are summarized in Table 5. For the three sizes ofeach harvest system, maximum theoretical capacity intonnes DM h

) 1 , working speed and price weredecided according to specications and power require-ment. The theoretical capacity of each machine wascalculated for each harvest considering the DM yieldand the working speed. If the maximum theoreticalcapacity of the machine was exceeded, the speed wasreduced until the capacity was no longer exceeded.After that, the gross harvest capacity of each machine

was calculated by reducing the theoretical capacityaccording to a eld efciency factor considering timefor non-productive time in the eld (turnings, over-lapping, shorter stops and adjustments). Time fortransport to storage was calculated separately depend-ing on the transport distance and was not included inthe eld efciency factor. The same eld efciencywas assumed for the PCFT and the PCFH/T systems.Calculations were based on a 1-km transport distance.The capacity for harvest in the eld and for transportwere compared and the limiting gross capacity of eachof the harvest systems (Table 6) determinedthe duration of the harvest, and was used for thecalculations of cost.

Table 5 Specications of small, medium and large sizes of harvesting machinery mower, precision-chop forage trailer, precision-chop forage harvester with separate transport trailers and round baler with integral wrapping.

Small Medium Large

Mower-conditioner

Mower-conditioner working width (m) 2 8 32 40Tractor for mower-conditioner (kW) 70 80 100Working speed* (km h ) 1 ) 10 10 10Field efciency 080 0 80 0 80

Precision-chop forage trailer (PCFT)Trailer volume (m 3) 30 40 50Tractor for PCFT (kW) 90 110 140Maximum working speed* (km h ) 1) 12 12 12Transport speed (km h ) 1) 25 25 25Maximum theoretical capacity 3(tonne DM h ) 1) 12 14 16Field efciency 075 0 75 0 75

Precision-chop forage harvester with separate transport trailers (PCFH/T)Tractor for PCFH/T (kW) 80 110 140Maximum working speed* (km h ) 1) 12 12 12Maximum theoretical capacity (tonne DM h ) 1) 10 14 18Field efciency 075 0 75 0 75Transport trailer volume (m 3) 20 25 30Tractor for transport (kW) 70 80 90Transport speed (km h

) 1) 25 25 25Round baler with integral wrapping (RBI)

Tractor for round baler (kW) 90Maximum working speed* (km h ) 1) 9Maximum theoretical capacity (tonne DM h

) 1) 14Field efciency 070Plastic use (kg tonne

) 1 DM) 6 4Transport trailer 10 tonne (number of bales per trailer) 14Tractor for transport (kW) 80

Transport speed (km h) 1

) 15*Source: ASABE (2006b) and experience-based values.Source: Field efciency factors from ASABE (2006b) considering time for non-productive activities such as turnings, overlapping,shorter stops and adjustment.Source: Theoretical machine capacity decided by the tractor power, details from machine manufacturers and from the calculationprogramme Agrimach (2000).Source: Experience-based values.




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Mowing is separated in time from the harvestingoperations that follow and was therefore assumed notto limit the harvesting capacity. It was assumed that theherbage was left to dry in the swath formed by themower-conditioner for one day and then ensiling in a bunker silo at a DM content of 300 g kg

) 1 and left todry in the swath for 2 d when ensiling in round balesat a DM content of 450 g kg

) 1 (Eldelind et al., 1974).

Dry-matter losses in the eld are mainly dependent onthe DM content of herbage and were set as 0 08 forround bale silage and 0 06 for bunker silage (Belotti,1990).

Harvesting, using the PCFT system, included twoparallel machine operations: 1) harvesting, loading andtransport of the herbage to the bunker silo; and 2)loading and packing into the bunker silo. The rstoperation was performed using the PCFT and thesecond using a wheeled loader. After transport, theherbage was unloaded onto a concrete area in front ofthe bunker silo where it was picked up by the wheeledloader. The same chopper was mounted on the PCFT forall machinery sizes and, as a consequence, only thetrailer volume varied between the machinery sizes.

Harvesting using the PCFH/T system included threeparallel operations, each performed with separatemachines: (i) harvesting in the eld using the PCFH/Ttowed by a tractor; (ii) transport of the grass to the bunker silo using two transport trailers; and (iii) loadingand packing into the bunker silo using a wheeledloader. During the eld work the transport trailers weretowed by the forage harvester. The chopper size varied

for all three PCFH/T machinery sizes, with the mediumchopper size being the same as that mounted on thePCFTs (Table 5).

A wheeled loader loaded the herbage into the bunkersilo and packed the herbage to a density of 250 kg DM

m) 3

. The packing capacity was calculated using aprogramme developed by Holmes and Muck (2006).With a silage height of 2 7 m after packing, a DMcontent of 300 g kg

) 1 , a thickness of each layer put intothe silo of 0 15 m and a 9000-kg wheeled loader,maximum loading capacity was calculated to be9000 kg DM h

) 1 . The wheeled loader was assumed tospend 0 25 of its working time loading the herbage intothe silo and 0 75 of its working time packing.

The bunker silo consisted of sections, each 40 m long,6 m wide and 3 m high. Each section was estimated tocontain 600 m 3 or 150 ton DM. The size of the sectionschosen is common in Sweden and was applicable to allof the forage areas studied. However, for larger forageareas it would have been more applicable to have widersections. The costs of building the bunker silo weredetermined according to Agriwise (2007) and includedthe costs for the silo, the area for unloading in frontof the silo and equipment for collection of the efuent.The cost of covering the bunker silo included the cost ofplastic covers and labour costs. The calculated invest-ment costs were 61 m ) 3 for the bunker silo consistingof one section and 48 m ) 3 for more than one section.The annual maintenance cost was set at 0 005 of theinvestment cost (Agriwise, 2007). The depreciation timewas set at 20 years and the capital interest at 5%.

For harvesting with a round baler (RBI), the same

round baler was assumed for all three machinery sizesand only the mower-conditioner varied between themachinery sizes (Table 5). The bales were assumed tohave a diameter of 1 25 m and a weight of 250 kg DM.The bales were picked up with a tractor and transporttrailer and transported to storage. The bale transportwas assumed to be carried out at a time when it did notlimit the harvesting capacity, i.e . after the harvestperiod. The bales were stored on an area paved withtarmac and sand. The investment cost for the storagearea was set at 15 m

) 2 (Lindgren and Benfalk, 2003).Depreciation time was set at 15 years and the annualmaintenance cost at 0 07 of the investment cost. Thecapital interest was set at 5%.

Machinery and labour costs

Machinery costs were calculated according to ASABE(2006a,b) and included depreciation, capital interest(5%), maintenance, tax and insurance, housing andfuel. Depreciation was based on the machine list pricefrom Maskinkalkylgruppen (2007) with 20% discount(due to the sale price being normally lower than the list

Table 6 Limiting gross harvest capacity (ha h) 1) at 1 km

transport distance for small, medium and large sizes of machinery of the harvest systems using a precision-chop forage trailer (PCFT), a precision-chop forage harvester with separate transport trailers (PCFH/T) and a round baler with integral

wrapping (RBI) in southern and central Sweden for each of three cuts in a season.

Cut no.

Southern Sweden Central Sweden

Small Medium Large Small Medium Large

PCFT1 17 20 24 18 21 262 14 17 20 18 21 263 17 20 24 18 21 27PCFH/T1 19 25 31 22 26 322 15 21 28 21 26 323 19 25 31 22 26 32

RBI1 18 20 25 18 20 252 18 20 22 18 20 253 18 20 25 18 20 25




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price), on the residual value, on age and on annual use.Total life repair and maintenance costs were calculatedas a proportion of the list price (ASABE, 2006b) and,since machines in Sweden generally have a relativelyshort annual use, reduced for shorter total use

compared with estimated life according to ASABE(2006b). A constant replacement age was assumed(Maskinkalkylgruppen, 2007) irrespective of annualharvested area. Two tractors of 70 and 130 kW, neededfor harvesting operations, were assumed to also be usedfor operations other than harvesting and theircosts ha

) 1 were calculated based on estimates fromMaskinkalkylgruppen (2007) for shorter annual use(455 h for the tractors and 420 h for the wheeledloader). An extra tractor was needed for an additionaltransport trailer at longer distances with the PCFH/Tsystem and this extra tractor was assumed to be olderand only used for 200 h annually. Its depreciation andinterest costs were, therefore, reduced.

Fuel consumption was calculated using a machinery-cost estimator described by Cardoso et al. (2009), basedon ASABE (2006a,b) but adapted to Swedish condi-tions. Fuel consumption was dependent on specicenergy requirement for cutting per tonne and adjustedfor the yield and cutting length. The fuel price was set at 068 L ) 1 .

Machinery and labour costs were calculated for thenumber of hours that the machines were used in theeld or for transport work. Time for breaks was notincluded in the annual use on which calculations of themachinery cost were based. However, labour costsincluded idling time when one operation was delayed

due to another operation. Labour costs were calculatedat 19 h) 1 . The length of the working day was set at 9 h

including lunch and breaks, resulting in a working timeof 75 h.

Harvesting with the machines owned by the farmerwas also compared with harvesting by contractor. Thecontractor costs for the machinery, including driver,tractor and fuel, were set at 102 h

) 1 for the mowerconditioner with 4 m working width, 140 h

) 1 for the50-m 3 PCFT and 52 h

) 1 for the wheeled loader. Thecost for an additional tractor used, when the packingcapacity of the wheeled loader was exceeded, was 50 h

) 1 (Maskinring Sjuharad, 2007). Since list pricesfor the contractor rates were used without possiblereductions due to price negotiations, contractor costswere also reduced by 0 20.

Timeliness costs

The daily timeliness costs per ha resulting from changesin feed value with harvest date were used to calculatetimeliness costs for different harvesting systems usingthe same method as in the study of Gunnarsson et al.

(2005). It was assumed that, due to low bre concen-trations and high CP concentrations in the forage,harvesting did not start before the optimal day. Time-liness costs during the harvest were calculated using thefollowing equation (Gunnarsson and Hansson, 2004):

S Xm

i 1

n i 12 ki l i ni 5

where n i is the average number of days to perform theharvest (including days not workable) of crop i , m arethe number of crops, ki is the average area harvestedeach day of crop i in ha day

) 1 , l i is the timeliness costs in ha

) 1 d) 1 for crop i . Parameter n i was calculated from

Equation 6:

n i Ai

B P C days 6

where Ai represents the total area in ha of crop i, Brepresents the number of working hours d

) 1 , P repre-sents the work-day probability and C represents thegross capacity of the harvesting system in ha h

) 1

(Table 6). The work-day probability, i.e. the probabilitythat harvesting can be performed on a certain day in theharvest period, was decided from weather data and ispresented in Table 7. If the harvest start was delayedfrom the optimal day to start, as assumed when usingcontractors, timeliness costs were calculated for thewhole forage area Ai until the harvest started usingEquation 7 (Gunnarsson and Hansson, 2004):

d i l i Ai d 1 7

Timeliness costs are dependent on the length of theharvesting and interruptions due to weather increasethe duration of the harvest. Available eld days weredescribed as a work-day probability showing how largea part of the days in a given time period on whichharvest could be performed. The work-day probability

Table 7 Work-day probability, i.e. probability that harvestingis possible on a certain day in the harvest period, calculatedfrom weather data from Malmo for three cuts in a season in theperiod 19841993 for southern Sweden and from Uppsala for the period 19801994 for central Sweden.

Cut

SouthernSweden Central Sweden

PeriodBunker

siloRoundbales

Bunkersilo

Roundbales

1 070 0 64 0 71 0 65 May-June2 064 0 56 0 62 0 52 July-August3 058 0 51 0 62 0 53 Sept-Oct




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prolonged the duration of harvesting (Equation 6). Forensiling in bunker silos, it was assumed that one daywithout precipitation was sufcient to achieve a DMcontent of 300 g kg

) 1 (Eldelind et al., 1974). The work-day probability was, therefore, calculated from dailyweather data on the number of days without precipi-tation in each harvest period. When the forage waspreserved in round bales after drying in the eld to aDM content of 450 g kg

) 1 , the work-day probabilitywas calculated from the occurrence of two days with amaximum of 1 mm cumulative precipitation (Table 7).

Results and discussion

Daily timeliness costs per hectare

The daily timeliness costs per ha calculated for 10 yearsfor each harvest in southern and central Sweden are

shown in Figure 1. Average values for the daily time-liness costs for the studied years are summarized inTable 8. The optimal day of each harvest that resultedin the highest forage value varied between years andTable 8 also shows the average optimal harvesting dayfor the rst, second and third cut, as well as the averageDM yield on the optimal harvest day. These yields,reduced for eld losses of DM, were used in thecalculations of harvesting costs.

The daily timeliness costs varied greatly between the10 years for which the calculations were made in thisstudy (Figure 1). Similarly, de Toro (2005) foundconsiderable annual variations in timeliness costs in astudy of grain crops. Since average values for changes infeed value of forage were used, this limits the usefulnessof the results for individual years. However, themachinery systems were compared using average val-ues of timeliness losses over 10 years, not values forindividual years. As Ward (1989) has pointed out,machinery planning must be based on a long timeperiod due to annual variations in weather. Usingaverage values of feed value of the forage, derived fromregression analysis of experiments from different years,results in lower changes in feed value with respect totime compared with the results for individual years(Witney, 1995). More rapid changes in feed value offorage would have resulted in higher timeliness costs.

Timeliness costs were signicantly higher for the rstcut compared with the second and third cut ( P < 005)in both regions of Sweden but there was no signicantdifference between the second and third cuts. Aprevious study (Gunnarsson et al., 2005) also showedthat timeliness costs were highest in the rst harvest. Inaddition, there was no signicant difference betweentimeliness cost between southern and central Sweden.One reason for this could be that differences in nutritive

Figure 1 Daily timeliness costs per ha( ha

) 1 d) 1) for southern Sweden for

the period 19841993 (left) and centralSweden for the period 19781987 (right)for cuts 1 ( j ), 2 ( ) and 3 (h ).

Table 8 The daily timeliness costs ( ha) 1 d

) 1 and kg) 1 DM d

) 1), optimal harvest day (T opt , calendar day number) andcorresponding forage yield (M, kg DM ha

) 1), as mean values (standard deviation in brackets with n = 9) for three cuts in the seasonfor the period of 19841993 for southern Sweden and for the period of 19781987 for central Sweden.

Cut

Southern Sweden Central Sweden

1 2 3 1 2 3

Daily timeliness costs( ha ) 1 d) 1) 87 (5 5) 3 0 (1 4) 2 1 (0 9) 6 4 (3 5) 2 5 (1 1) 1 5 (0 7)( kg ) 1 DM d ) 1) 00024 0 00064 0 00054 0 0020 0 00075 0 00054Topt (day no.) 157 (5) 214 (6) 267 (7) 160 (4) 211 (3) 263 (5)M (kg DM ha ) 1) 3658 (624) 4331 (608) 3699 (390) 3109 (712) 3169 (444) 2948 (490)




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value of forage between the two regions were notaccounted for as the ME and CP concentrations of theforages were calculated from experimental data pre-sented as average values for southern and centralSweden. However, the DM yields were calculated

separately for southern and central Sweden.The data used here for calculation of DM yields

of herbage came from eld experiments performed between 1960 and 1980 (Belotti, 1987). The data onnutritive value the forage were also obtained fromolder eld experiments (Jo nsson, 1981). To considerpossible changes in DM yield of modern plant material,it would be necessary to update the forage productionmodel with data from more recent eld experiments.However, the data used in this study were based on alarge number of experiments and it is uncertainwhether newer eld data are available to the sameextent.

When calculating timeliness costs, it was assumedthat the whole forage area had the same optimalharvesting date but in reality there may be differences between elds and due to the age of a sward. The Rsvalues used to calculate DM yield were average valuesfor rst-, second- and third-year swards. If differentoptimal harvesting dates were set for rst-, second- andthird-year swards, timeliness costs would decrease. Astudy of grain harvesting has shown that timelinesscosts can be determined more exactly if they arecalculated individually for smaller areas with differentmaturity dates instead of the whole area as one unit(de Toro and Hansson, 2004).

Harvesting costs

The harvesting costs are presented in detail for thePCFH/T system in central Sweden, with a transport

distance from eld to farm of 1 km (Figure 2). Whenthe forage area yr

) 1 (rst, second and third cuts)increased from 20 ha to 150 ha, timeliness costsincreased proportionately from 0 01 to 0 19 of thetotal harvesting costs for the medium-size machinery

(Figure 2). Table 9 summarizes the minimum harvest-ing costs for all harvesting systems and machinery sizesin both southern and central Sweden.

For small forage areas the capital cost of the machin-ery was the dominant harvesting cost, as noted previ-ously by de Toro and Rosenqvist (2005), who concludedthat large farms usually have lower machinery costsha

) 1 since the xed costs can be divided over a largerarea. Machinery costs ha

) 1 (or kg harvested DM)decreased with increasing forage area since the annualuse of the machines increased. Timeliness costsincreased with increasing forage area since the harvesttook a longer time to carry out. Labour costs ha

) 1 wereindependent of the harvested area. This resulted in aforage area for each machinery system where theharvesting costs had a minimum value (Figure 2). Thisarea was reached when the increasing timeliness costsoutweighed the decreasing machinery costs. The reasonfor the relatively large range of forage areas with aboutthe same minimum cost was that, at small forage areas,the xed machinery costs (depreciation, interest andhousing) decrease faster when the forage area increases but the larger the forage area the smaller the decrease inxed costs. The at curve in Figure 2, due to a range offorage areas with only minor differences in costs aroundthe minimum cost for the system, shows a robustnessof the harvesting systems for small changes in forage

area. The constant replacement age for machinery thatwas assumed may have overestimated the depreciationand interest costs for small forage areas since lowannual use increases the residual value of the machine.Figure 2 also illustrates that, if timeliness losses areignored, harvesting costs are underestimated and thereis a risk that the harvesting capacity chosen will be toolow. This is conrmed by Ward et al. (1986b) whofound that timeliness costs are of major importance athigh levels of annual use outside the recommendedoperations band. Similar results were obtained by deToro (2005) for grain harvesting.

The area where the harvesting costs kg) 1 DM was at a

minimum was smaller in southern Sweden than incentral Sweden. The reason for this was that the grossharvest capacity in tonneDM h

) 1 washigher in southernSweden due to higher DM yields using the theoreticalcapacity of the machine to a larger extent. Timelinesscosts also constituted a larger proportion of thetotal costsin southern Sweden. Higher DM yields resulted in lowermachinery and labour costs kg

) 1 DM, which increasedthe proportion of total costs as timeliness costs. Further-more, timeliness costs were inuenced by the length of

Figure 2 Harvesting costs ( ha) 1 yr

) 1) for timeliness (j ),labour ( ) and machinery ( h ) for medium-sized machinery in the precision-chop forage harvester with separate transport trailers (PCFH/T) system in central Sweden for a range of annual forage areas.




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the harvest and by the daily timeliness costs per ha,which were higher forsouthern Sweden. Although grossharvest capacity in tonnes DM h

) 1 was lower in centralSweden, the lower DM yields resulted in higher capacityin ha h

) 1 compared with southern Sweden (Table 6).Sensitivity analyses were performed for some input

variables with effects on the harvesting costs. As can beseen from Figure 1, daily timeliness costs per ha varied between years. A 0 20 increase or decrease in the dailytimeliness cost per ha affected harvesting costs by 0 03when harvesting 100 ha in central Sweden withmedium-size machinery in the PCFT system. Thecalculations of harvest costs were made for the averageDM yield at optimal harvesting resulting from thecalculation of daily timeliness costs per ha. A 0 10change in DM yield changed the total harvestingcosts by 0 014 for harvesting of 100 ha forage using

medium-size machinery in the PCFH/T system incentral Sweden.

One way to shorten the duration of the harvest andthereby reduce timeliness costs is to work longer days.In this study a 9 h working day was assumed in thecalculations. When the length of the working day wasincreased to 12 h (10 h working time excluding lunch breaks etc.) timeliness costs decreased by 0 28 and totalcosts (machine, labour and timeliness) decreased by0035 when 100 ha forage was harvested using med-ium-size machinery in the PCFT system in centralSweden. The number of workable days also varied withyear and inuenced the timeliness costs. Increasing thework-day probability by 0 10 decreased harvesting costs by 0015 for the harvesting of 100 ha forage usingmedium-size machinery in the PCFT system in centralSweden.

Table 9 Area, capacity and total harvesting costs for small, medium and large sizes of machinery and the area at which eachmachinery size of the harvest systems, using a precision-chop forage trailer (PCFT), a precision-chop forage harvester with separate transport trailers (PCFH/T) and a round baler with integral wrapping (RBI), had its minimum harvesting costs in southern and centralSweden.

Harvestsystem

Area(ha)

Capacity(ha h

) 1 )

Total costs Machine Labour Timeliness

( ha) 1 yr

) 1 ) ( kg) 1 DM) Prop. ( ton

) 1 DM) Prop. ( ton) 1 DM) Prop. ( ton

) 1 DM)

Southern SwedenSystem PCFT

Small 80 1 6 347 0 031 0 53 16 8 29 9 1 18 5 7Medium 90 1 9 341 0 031 0 57 17 6 26 8 1 17 5 3Large 110 2 2 333 0 030 0 60 18 1 22 6 8 18 5 6

System PCFH/TSmall 80 1 7 365 0 033 0 48 15 9 37 12 1 15 5 2Medium 100 2 4 325 0 030 0 53 15 8 31 9 2 16 4 6Large 130 3 0 333 0 030 0 60 18 3 24 7 2 16 4 7

System RBISmall 70 1 8 406 0 038 0 60 22 6 27 10 1 13 5 0

Medium 80 2 0 381 0 035 0 59 21 1 26 9 3 14 5 0Large 90 2 4 352 0 033 0 60 19 7 26 8 5 14 4 6

Central SwedenSystem PCFT

Small 90 1 8 306 0 035 0 54 19 0 30 10 7 16 5 5Medium 110 2 1 299 0 034 0 57 19 5 27 9 3 16 5 6Large 140 2 6 282 0 032 0 59 19 3 23 7 5 18 5 7

PCFH/TSmall 100 2 2 301 0 034 0 49 17 0 37 12 9 14 4 8Medium 120 2 6 290 0 033 0 52 17 5 33 11 0 15 4 9Large 150 3 2 288 0 033 0 59 19 3 26 8 8 15 4 9

RBISmall 70 1 8 371 0 044 0 62 27 3 27 11 6 11 4 8Medium 80 2 0 347 0 041 0 62 25 3 26 10 7 12 4 8Large 100 2 5 313 0 037 0 61 22 6 26 9 5 13 4 8

Machinery, labour and timeliness costs together with their proportion (prop.) of total costs are also included.




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Choice of machine size

A comparison of the harvesting costs for the small,medium and large sizes of machinery for the PCFH/Tharvesting system in central Sweden (Figure 3) showsthat the small size gave the lowest costs up to an area of

60 ha, while above 100 ha the large size gave thelowest costs. In contrast, in southern Sweden themedium size of machinery gave the lowest costs for awide range of areas for harvesting using the PCFH/Tsystem (Figure 4). The main reason for this differencewas that the higher yields in southern Sweden resultedin higher harvesting capacity in tonnes DM h

) 1 for thePCFH/T harvesting system and required an extra pack-ing tractor to be used in the bunker silo for the large size

of machinery. The extra tractor increased the costs butthe harvesting capacity was not increased to the same

extent, since the packing capacity in the bunker siloagain limited the overall harvesting capacity. When themedium size of machinery was used, the packingcapacity of the silo was not exceeded and, althoughharvesting capacity was somewhat lower, the costs

were also lower. To minimize harvesting costs it is,therefore, important to utilize the capacity of themachines involved in harvesting in order to avoidcostly over-capacity and idle times.

Figure 4 shows the forage area for which the small,medium and large sizes of machinery resulted in thelowest harvesting costs. Where the machinery sizes inFigure 4 overlap, the difference in cost between sizeswas < 00005 kg ) 1 DM. For round baling this studyshowed that, irrespective of forage area, the lowestharvesting costs were achieved with the largest size ofmachinery, i.e. the largest mower-conditioner, sincethe same round baler was used for all sizes ofmachinery (Figure 4). The cost of machinery for themower-conditioner increased with increasing size ofmachinery but the difference in cost between mower-conditioner sizes was small, and the larger mowerincreased the harvesting capacity and thereby reducedharvesting costs. One potential reason why themachinery sizes of the PCFT system overlapped eachother more than the machinery sizes of the PCFH/Tsystem is that the difference in theoretical capacity between the three sizes of machinery was smaller forthe PCFT system than for the PCFH/T system(Table 5). Furthermore, the difference in machineinvestment costs between the three machinery sizeswas less for the PCFT system.

Although not as pronounced as the results reported by Ward et al. (1986b), the results showed that when

Figure 3 Harvesting costs ( kg) 1 DM) for the precision-chop

forage harvester with separate transport trailers (PCFH/Tsystem) using small ( ), medium ( ) and large ( ) sizesof machinery in central Sweden as a function of the annualforage area in hectares.

Figure 4 Area for which small ( ), medium ( ) and large (j ) sizes of machinery resulted in the lowest harvesting costs for the precision-chop forage trailer system (PCFT), the precision-chop forage harvester with separate trailers system (PCFH/T) and theround baler with integral wrapping (RBI) system.




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the forage area increased above the area correspond-ing to the minimum total costs, timeliness costsincreased more slowly for the larger machinery sizes.This means that systems with higher capacity havegreater possibilities to withstand timeliness costs atincreased annual use.

Comparison of harvesting systems

Table 10 shows the resulting harvesting and ensilingcosts for the different systems. In order to compareensiling in a bunker silo with ensiling in round bales,costs associated with the silo were included. In a widerperspective, losses occurring during storage and han-dling of the silage after the forage is placed in the silocan be included. When harvesting 70 ha of forage, theRBI system had slightly lower harvesting and ensilingcosts at a 1-km transport distance compared with silagein bunker silos due to lower losses.

The harvesting and ensiling costs for the differentharvesting systems reacted differently to changes intransport distance (Figure 5). With the PCFH/T system,the transport capacity started to limit the entireharvesting capacity at a transport distance of 3 km.When the option of adding another transport trailer wasinvestigated, it was found that using three transporttrailers resulted in lower costs compared with using twotrailers for transport distances longer than 3 km. Asmentioned by Ward et al. (1986a), Harrigan (2003) and

Amon et al. (2007), transport trailers should be inadequate supply to make sure that the harvester canoperate to maximum capacity. However, when study-ing large-scale harvesting of biomass for biogas produc-tion, Amon et al. (2007) suggest that, due to longwaiting times for the transport trailers in some situa-tions, it may be more economical to accept somewaiting time for the harvester.

Harvesting with the RBI system was most expensiveat transport distances shorter than 34 km and leastexpensive at distances longer than 78 km becausetransport is a separate operation that does not interfere

with the eld operations. The harvesting capacity of theRBI system, therefore, did not decrease when thetransport distance increased. The PCFT system couldonly compete on costs up to a transport distance of1 km. According to Schick and Stark (2002), for longtransport distances it is better from a cost point-of-viewto separate harvesting and transport, i.e. to use differentmachines for the two operations. For long transportdistances, it is suggested that the PCFT will spend a largepart of the time on transport instead of harvesting in theeld (DLZ, 1996). This was conrmed in the presentstudy, where the PCFT and the PCFH/T systems hadcomparable harvesting costs at a transport distance of1 km but lost in cost-competitiveness when the trans-port distance increased due to idle machine and labourtime in the eld.

Sensitivity analyses showed that, due to slightlylower total fuel consumption, the RBI system was lessaffected by changes in fuel consumption. A change of020 in fuel consumption changed total costs by 0 020for the RBI system, compared with 0 025 for both thePCFT and PCFH/T systems harvesting 90 ha forage incentral Sweden.

Table 10 Harvesting and ensiling costs [ per kg dry matter (DM)] using medium-sized machinery for different harvestingsystems (PCFT, precision-chop forage trailer; PCFH/T, preci-sion-chop forage harvester with separate transport trailers; andRBI, round baler with integral wrapping) on 70 ha of forage in

central Sweden.System

PCFT PCFH/T RBI

Machinery costs 0 024 0 021 0 026Labour costs 0 0093 0 011 0 011Timeliness costs 0 0033 0 0027 0 0042Total harvesting costs 0 037 0 035 0 041Plastic and netting costs 0 017Silo/storage area costs 0 020 0 020 0 0064Covering silo costs 0 0032 0 0032 Total harvesting +

ensiling costs0060 0 059 0 064

Storage and ensiling losses*as proportion of DM yield

017 0 17 0 07

Harvesting + ensiling costsincluding losses

0072 0 071 0 070

*Sources: Honig (1977); Lingvall and Spo rndly (1996).

Figure 5 Harvesting and ensiling costs ( kg) 1 DM) for the

three harvesting systems [ precision-chop forage trailer (PCFT,), precision-chop forage harvester with separate transport

trailers (PCFH/T) and round baler with integral wrapping(RBI, )] at varying transport distance for harvest of 90 haforage with medium-sized machinery in central Sweden.PCFH/T-2 ( ) and PCFH/T-3 ( ) refer to using two or three transport trailers.




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Contractor harvesting

The analysis thus far has considered farm-ownedmachinery. An alternative approach to decrease costsis to hire contractors to carry out harvesting. Onecommon perception about the use of contractors is thatharvesting may not start on the optimal day, leading toincreased timeliness losses, in particular duringyears with difcult weather conditions (de Toro andRosenqvist, 2005). On the other hand, harvestingcapacity may be higher due to the larger machinesoften used by contractors.

For contractor harvesting the cost ha) 1 was xed and

the reason for the costs increasing with forage area(Figure 6) was the increasing timeliness costs. The

timeliness costs occurring at a delayed start of theharvest are illustrated in Figure 6 as the difference between the parallel lines showing costs for contractorharvest starting on the optimal day or with a delay of 3or 7 d.

Figure 6 shows that the smaller the forage area, thegreater the benets of machine contractors, a ndingalso reported by Ward et al. (1986b) for silage harvest-ing and by de Toro and Rosenqvist (2005) in a study ofmachine cooperation in grain production. As Figure 6demonstrates, hiring contractors resulted in lowerharvesting costs compared with farm-owned machineryfor forage areas less than about 100 ha. However, whenthe start of harvesting was delayed by 3 d, thetimeliness costs increased and this made farm-ownedmachinery a cheaper alternative even at forage areasabove about 70 ha. The corresponding forage area for adelay of 1 week was about 45 ha. A result conrmed byWard et al. (1986b) was that it was not economically justiable to have farm-owned machinery for smallforage areas, regardless of contractor reliability. Fig-ure 6 also illustrates the importance of nding theoptimal day for harvesting. If the contractors use higher

capacity machines than normal for farm-ownedmachinery, the timeliness costs, occurring due to delaysto the start of harvesting, could be compensated for bylower timeliness costs during the harvest when com-pleted in a shorter time. The cost of using a contractorvaries between different contractors. A 0 20 change incontractor charges for the large-size PCFT systemresulted in a 0 16 change in harvesting costs forharvesting of 100 ha in central Sweden.

ConclusionsThe method presented here for valuing forage andcalculating timeliness costs could be used in otherregions by adapting the calculations on DM yield and

feed value to the prevailing conditions and by selectingappropriate machinery systems and work rates. Foragefor other uses, such as for biogas, could also be valuedusing this method. The daily timeliness costs per hacalculated in this study are suitable for use in futurestudies of timeliness and harvesting costs for forage inSweden.

It is important to know when the harvest has itsoptimum value with respect to both DM yield andfeed value since delaying the start of harvestingincreased timeliness costs, irrespective of harvestingcapacity. Moreover, because timeliness costs werehighest in the rst cut, it is important to avoid delaysin this cut.

Harvesting costs decreased with increasing foragearea up to a certain threshold area beyond whichdecreasing machinery costs were outweighed byincreasing timeliness costs due to a longer durationof harvest. After an initial rapid decrease in costs withincreasing forage area, however, the harvesting sys-tems showed an robustness where smaller changes inforage area did not affect the total costs to any greatextent.

Figure 6 Harvesting costs ( kg) 1 DM)

for small. medium and large sizes of machinery in the precision-chop forage trailer (PCFT) system in central Sweden

compared with using a contractor with thelarge size of machinery (L). Small, farm-owned ( ), Medium, farm-owned ( );Large, farm-owned ( );Large, contractor = harvesting starts on the optimal day ( ); Large, contractor + 3days =harvesting starts 3 d after the optimal day ( ); Large, contractor + 7days =harvesting starts 7 d after the optimal day ( ).




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At increasing transport distances, the difference in cost between different harvesting systems and different sizesof machinery systems increased. It proved to be moreimportant to choose the most appropriate machinerysystem at large transport distances. In contrast to the

harvesting capacity of the PCFH/T and PCFT systems,round bales can be transported to storage after harvest-ing and, therefore, harvesting capacity of this system wasnot affected by changes in transport distance.

The use of contractors for harvesting forage decreasedharvesting costs, particularly for small forage areas,since increased annual use of the machinery lowersmachine costs and allows larger machines with highercapacity to be used. To avoid high timeliness costs,however, it is important to avoid delays in harvestingfrom the optimum date for feed value.

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