The profitability of early grass silage harvesting on dairy goat farms in mountainous areas of Norway

Small Ruminant Research 103 (2012) 133–142

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The profitability of early grass silage harvesting on dairy goat farms in mountainous areas of Norway O. Flaten a,∗ , L.J. Asheim a , I. Dønnem b , T. Lunnan c a b c

Norwegian Agricultural Economics Research Institute, P.O. Box 8024 Dep., 0030 Oslo, Norway Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, P.O. Box 5003, 1432 Ås, Norway Norwegian Institute for Agricultural and Environmental Research, Arable Crop Division, Løken, 2900 Heggenes, Norway

a r t i c l e

i n f o

Article history: Received 7 December 2010 Received in revised form 9 August 2011 Accepted 2 September 2011 Available online 29 September 2011 Keywords: Goat farming Grassland management Cutting system Forage quality Milk production Linear programming

a b s t r a c t This study evaluates how harvesting regimes (HRs) of grass silage influences economic return and optimal use of inputs, in particular fertilisers and concentrates, in dairy goat farming in mountainous areas of Norway. Goats in such areas are fed indoors with conserved silages (mostly grass silage) and concentrate supplements for up to 9 months a year. A whole-farm linear programming model was developed to examine three HRs: very early (HR1), early (HR2) and normal (HR3), producing silage containing different concentrations of net energy. Linear input/output response relations incorporated into the model were derived from a field experiment with two levels of fertilisation and an animal experiment with two levels of concentrate feeding to supplement silage from each HR. The model maximises total gross margin of a dairy goat farm with 70,000 l milk quota, and stalling capacity for 100 goats. Farmland availability varied from 4 to 10 ha with 6.5 ha as the basis. The study demonstrates that farmland availability profoundly influences the choice of input intensity and the profitability of producing and feeding high quality grass silage to dairy goats. At 6.5 ha, optimal input of fertilisers and concentrates as well as output of milk per goat was highest when fed HR1 and HR2 silage. However, HR3 was most profitable as benefits such as higher milk yield and better milk price due to higher total solids content for HR1 and HR2, did not offset the additional costs and increased shortage of silage at lower yields achieved and the higher consumption of silage. HR1 was particularly unfavourable as it was impossible to fully produce the milk quota. More land than 6.7 ha was necessary for HR2 to outperform HR3. Inputs of fertilisers and concentrates could be reduced as more land became available, however, at the lowest land constraint for HR3. By removing the milk quota, the profitability of HR3 was strengthened at limited land availabilities, at 7 ha HR2 surpassed HR3 in profit and above 8.5 ha HR1 was most competitive. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Statistics Norway (2011) reports that 379 farms kept about 35,000 dairy goats in 2011. Cheese products from milk are the main commodity outputs of dairy goat

∗ Corresponding author. Tel.: +47 62487100; fax: +47 22367299. E-mail addresses: ola.fl[email protected] (O. Flaten), [email protected] (L.J. Asheim), [email protected] (I. Dønnem), [email protected] (T. Lunnan). 0921-4488/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.smallrumres.2011.09.003

farming and goat milk sales generated about D 10 million (D 1 ≈ 8.75 Norwegian kroner) in 2009, accounting for only 0.4% of the market value of agricultural commodities produced in the country during the period. However, dairy goats have an important position due to their ability to forage on marginal range and farmlands, and dairy goat farming in Northern Europe are to a large extent environmentally friendly, socially and culturally compatible, desirable or even necessary for rural development ´ and beneficial to landscape conservation (Dyrmundsson, 2006).

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The cold agro-climatic conditions in mountainous areas of Norway restrict grazing to a short summer season, so dairy goats are fed indoors for up to 9 months a year. The farmland is best used for grass production and concentrate supplements are purchased. Governmental policies have kept prices for concentrate supplements high in order to encourage the use of home-grown forages. The main characteristics of the predominantly indoor dairy goat farming systems in such areas include the nutritive value of conserved forages (mostly silage), level of concentrate feeding, and intensity of fertilisation of grasslands (Morand-Fehr et al., 2007). Goat farmers have to consider all these aspects of the business together when deciding upon their management plans. It is well known that increasing supplies of concentrates increase goat milk yield (e.g., Eik, 1991; Morand-Fehr et al., 2007), and that grass production responds markedly to nitrogen application (e.g., Bonesmo and Belanger, 2002). Feeding properly conserved high quality forages, harvested at an early stage of maturity (i.e., high in digestibility and nutrients), can increase goat milk production and also affect milk composition (e.g., Morand-Fehr et al., 2007). Less concentrates may then be required to produce a given output of milk. Harvesting forages frequently at early stages, however, results in a lower dry matter (DM) yield per hectare (Nissinen and Hakkola, 1995), higher cutting costs and reduced life of the stand. Earlier harvesting leads to increased forage DM intake per goat (Sauvant et al., 1991), however, which increases the forage area required. Farmers must decide whether the gains from improved animal performance and higher nutrient concentration in early-harvested forage DM can justify the additional costs and reduced forage yield following this practice. To the best of our knowledge, no studies have reported on the economic performance of grass silage harvesting regimes (HRs) in dairy goat farming. It also seems that little is known about the inputs of fertilisers in grasslands and of concentrate supplements that optimise economic returns under the various HRs. Earlier studies of pre-quota dairy cow farms in the UK on which cutting and grazing areas were separate, reported lower gross margins/ha for early and more frequent cutting systems compared to later, less frequent cutting systems (Brooke, 1979; Doyle et al., 1983). When grazing and conservation were integrated, less frequent cutting systems were not automatically most profitable (Doyle et al., 1983). A later UK study found medium quality silage to be optimal when milk quotas were in place, whereas high quality silage was preferred without the quota (Valencia and Anderson, 2000). These studies suggest that high forage quality may not result in the best economic performance. As dairy cows and goats have different digestive capacities for silage (Tolkamp and Brouwer, 1993), one should be careful in transferring conclusions from dairy cow studies to dairy goats. Also, winter conditions in Norway are harsher than in the UK, and early harvesting can adversely affect the survival of the swards (e.g., Azzaroli and Skjelvåg, 1981). The great reduction in dairy cow numbers in the last 10–15 years (Kumbhakar et al., 2008) has, however, increased the availability of grassland which may facilitate the use of early HRs in Norway.

The aim in this study is to evaluate how the HR of grass silage influences economic return and optimal use of inputs, in particular fertilisers and concentrates, in dairy goat farming in mountainous areas of Norway. The study is based on interdisciplinary work on crop, livestock and economic aspects of growth and use of forage in Norwegian meat and milk production. Yield responses of grassland to fertiliser use were derived from field experiments with two levels of fertilisation, and milk responses of goats to silage and concentrates came from feeding experiments with two levels of concentrates for each silage HR. These input/output relations were incorporated into a dairy goat farm linear programming (LP) model to compute optimal farming systems and compare annual economic returns of three HRs. 2. Methods and materials 2.1. General structure of the model LP techniques have been applied frequently in farm-level studies to identify optimal farming systems (e.g., Janssen and Van Ittersum, 2007). LP is based on a constrained optimisation procedure that can be said to match the reality of farmers who strive with limited resources to achieve their goals. Several activities, restrictions and production techniques with associated technical specifications and biological responses can be considered simultaneously, and the effects of changing parameters, for example land availability, can easily be assessed. The general structure of the mathematical model has the standard form of a primal LP problem (Luenberger, 1984): Max Z = c  x

subject to

Ax ≤ b, x ≥ 0.

Here Z is the objective value at the farm level; x the vector of levels of activities forming the combined system, to be determined; c the vector of net returns per unit level from each activity; A the matrix of technical coefficients showing per unit resource requirements by the activities; b is the vector of right-hand side values of fixed resources and intermediate produce balances, relating to the constraints of the model. The problem is to find the farm plan with the largest possible objective function value which does not violate any of the resource constraints, or involve any negative activity levels. The model is defined under assumed certainty. The model was applied to a representative dairy goat farm in the mountainous areas of Norway. One version of the single-year LP model was formulated and solved for each of the three HRs to compare their optimal production plans and economic returns. The model activities consist of sward establishment, established leys for grass silage production at two N rates for on-farm use, and renting out of excess farmland. Separate activities for primary growth (PG) and re-growth (RG) were set up with transfer of PG and RG between the feeding periods of the dairy goat herd to match supply of silage qualities with periodical livestock demand. Separate activities for supply of a variety of purchased concentrate feeds in each feeding period, goat milk production processes at two concentrate levels, replacement kids, various government farm payments, and labour supply were also set up. In the prevailing farming system in the area goats are turned out to graze after the start of vegetation growth in early summer. In the model the goats are assumed to graze subalpine rangelands, separated from the cultivated farmland, for 100 days. These pastures primarily consists in native vegetation such as grasses, sedges, willow thickets (Salix spp), various herbs, Betula pubescens, Betula nana, etc. and do not require seeds, fertilisers or any cultural operations. The goats have free access to them; however, supplementary concentrate feed is also provided. No grazing activities or grazing constraints are modelled. Each model activity has its own specific vector of technical coefficients and all vectors together form the matrix A. The constraints link the different activities to the available fixed assets of farmland, milk quota, stalling capacity, and farm labour available. Constraints were also set up to balance the combinations of activities while accounting for rotational

O. Flaten et al. / Small Ruminant Research 103 (2012) 133–142 limitations, herd replacement, government farm payments, and periodical feeding requirements to match feed produced or purchased with animal requirements for protein and DM intakes of concentrates and silages. The model objective is to maximise total gross margin (TGM), that is, total returns from livestock production, government farm payments, and land rented out minus variable costs. Fixed inputs such as payments for fixed labour and costs of land, milk quota, buildings, drainage, fixed machinery, insurances, electricity, administration, etc. are not included since they may be assumed to be equal for all HRs. Thus, differences in profit between the HRs can be obtained from a comparison between their optimal TGM values. The matrices developed each comprised of 30 activities and 31 constraints. The LP model versions and their underlying budgets were specified in a Microsoft Excel spreadsheet and solved using the LINDO (v. 6.1) software (LINDO Systems, 2003). The two experiments on grass HRs and goat feeding are the core data sources of this study. Key features of these experiments will be described before different parts of the model system and its construction are described in more details. 2.2. Experimental data 2.2.1. Grass field experiment A field experiment was conducted to quantify the relationship between the HRs at two N rates (120 and 240 kg N/ha, using an 18:3:15 NPK fertiliser) and the associated DM yields and forage qualities. The fields were established in 2003 and in 2004, and records were kept for the following 4 years. A seed mixture of timothy (Phleum pratense L.), meadow fescue (Festuca pratensis Huds.) and red clover (Trifolium pratense L.) varieties suitable for the climate in the area was used. The plots were located at Løken Research Station (61◦ 8 N, 9◦ 8 E, altitude 525 m) in the mountainous areas of Eastern Norway. The following three HRs were examined: very early (HR1), early (HR2), and normal (HR3). HR1 and HR2 are high-quality three-cut systems, while HR3 represents the traditional two-cut system dominating in the area. The first cuts were taken when timothy reached the following phenological stages: onset of stem elongation (around June 7, HR1); 3–4 days before early heading (around June 16, HR2); and at full heading (around June 30, HR3). Early heading occurs when the top of the inflorescence has appeared on at least 10% of the tillers. Full heading occurs when 50% of the elongated tillers have heads fully emerged from the flag leaf sheath. The second cut was taken 500 (600) accumulated day-degrees after the first cut for HR1 (HR2), that is, around July 15 (August 2). All final cuts coincided around September 1. Near infrared reflectance spectroscopy was used to determine feed quality parameters used in the model, i.e., net energy for lactation (NEL) according to Van Es (1978) and amino acids absorbed from the small intestine (AAT) and protein balance in the rumen (PBV) according to Madsen et al. (1995). More details of the field experiment and feed quality parameters are described in Bakken et al. (2009). 2.2.2. Feeding experiment A feeding experiment was conducted at the Norwegian University of Life Sciences (UMB, 59◦ 40 N, 10◦ 46 E, altitude 85 m). Goats of the Norwegian dairy breed in the 2nd to 8th lactation were used to derive silage intake and milk yield responses. The experiment started on 6 February 2008 after a preparation period lasting for 2 weeks. It was designed as a cyclic changeover experiment (Davis and Hall, 1969) with 4 periods of 4 weeks (112 days) and six treatments. The animals were offered free access to three PG silages supplemented with either a low (0.6 kg/goat/day) or a high (1.2 kg/goat/day) level of concentrate. Energy values of the silages were 7.18, 6.17, and 5.26 NEL/kg DM for HR1, HR2 and HR3, respectively. The silages harvested in the UMB fields were at similar growth stages and with similar energy values as those of the PGs at Løken Research Station. Dønnem et al. (2011a) have given more detailed information about the feeding experiment, including preparation and analyses of the silages and the concentrate. 2.3. Land use, silage production, and purchased feeds In the model, farmland can be used for grass silage production or rented out. Silage production is set equal to the demand for silage on

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the farm, that is, no opportunities to sell or purchase forage are included. Clearly, forages are traded but quality parameters are lacking or not very informative, and the markets are thin, in particular for the best qualities. Therefore, a farmer cannot always presume that silage can be purchased (or sold). We used the data from the field experiment to represent the PG and RG activities of the ley years at the two N rates for each HR model version (Section 3.1.1). We assume that the new grass swards are established in the spring after ploughing and conventional cultivation for seedbed preparation (no companion crop). One herbicide treatment is needed to control annual weeds, 80 kg N/ha is applied, and the fields are harvested only once during the seeding year. The DM yields and quality parameters employed are reported in Section 3.1.1. The silage crop is mown, wilted a few hours (to 25% DM), and baled into round bales weighing 800 kg. A formic acid based additive (GrasAAT Lacto, Addcon Nordic AS, Porsgrunn, Norway) is applied at a rate of 4.4 l/t fresh grass. A 7.5% loss of DM and nutrients at feeding is assumed, quite close to the silage residues measured in Dønnem et al. (2011a). The costs of grassland activities incorporated into the net returns of the activities include nutrients and lime, machinery fuel and repairs related to field operations, silage additives, custom hiring for baling, and, in addition, costs of renewal for the sward establishment activities. The establishment costs are equal for all HRs but a reduced lifetime of the stand will increase annualised costs for early and more frequent cutting systems. Concentrates can be purchased (Table 2) with separate activities and costs for each of the concentrates available for goats (FF70, FF80, FF90, FP45, and FG) per feeding period. 2.4. Livestock production The farm livestock activities in the model comprise dairy goats and kids for replacement. Goats kid in the end of January, represented by January 23 in the model, and 1.5 kids per goat are borne annually. We assume 0.25 replacement kids per goat in the flock, and that surplus kids are culled. Age at first kidding is 1 year. The goat feeding experiment lasted 112 days (February 6–May 28) and the results were used to determine feed intake and milk performance with low and high input of concentrates for the experimental period (Section 3.1.2). Regarding the non-experimental stages of the lactation the goats in the experiment were fed the same amounts of concentrates and given free access to PG silage of HR2 quality in the indoor season. Intake of silage in these stages was recorded only before the start of the experimental period (1.4 kg/day), while individual milk yields were only measured monthly throughout the lactation. Livestock feeding requirements in the model are specified for three distinct periods: experimental (112 days), non-experimental indoors (153 days), and outdoors (100 days). Table 1 summarises the actual input of concentrates and our estimates of intakes of silage and milk yield and milk composition in the non-experimental parts of the year to be employed in the model. These estimates are based on the small number of records for the goats outside the experimental period, comparisons with responses in the experiment and own judgments. Silage intake and milk performance indoors are estimated lower for HR3 than for the earlier HRs at the same input of concentrates. A residual response of approximately half of the direct response to high quality silage and concentrates recorded in the experimental period is assumed in the 16 days period after the experiment (cf. Table 4). Pasture milk yield and composition are taken to be the same, irrespective of the winter feeding (Table 1). For the dry period, net energy requirements for maintenance and late pregnancy are calculated using the Norwegian feeding standards, and rates of concentrates and silage feeding are set according to silage quality. The two dairy goat activities with low and high input of concentrates (in the experimental period) are central elements of the LP model. Milk sold per goat per year is added up in Table 4. The feeding experiment was conducted with multiparous goats. The milk responses are not adjusted for primiparous goats, which are lower in silage intake and milk yield than multiparous goats. Only PG silage was used in the feeding experiment. For HR1 and HR2, the total supplies of PG silage for the experimental period represented in the model were, however, too low. A transfer activity was thus necessary to allow some dairy goats (represented with a

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Table 1 Actual input of concentrates and calculated feed intake and milk performance in the non-experimental periods for dairy goats and feed intake for kids. Concentratesa (kg DM/day) Before experiment (14 days: January 23–February 5) 0.78 Early harvestc 0.78 Normal harvestc After experiment (16 days: May 29–June 13) 0.87 Early harvest Normal harvest 0.87 Pasture (100 days: June 14–September 21) All 0.44 Late lactation (33 days: September 22–October 24) Early harvest 0.78 Normal harvest 0.78 Dry period (90 days: October 25–January 22)d Early harvest 0.13 Normal harvest 0.19

Replacement kidse Early harvest Normal harvest

Silage (kg DM/day)

Milk yield (kg/day)

Milk solidsb (g/kg milk)

1.40 1.20

2.50 2.30

113.4 110.0

1.40 1.25

See Table 4 See Table 4

113.4 110.0

Free-grazing

2.85

112.8

1.30 1.15

2.30 2.12

113.4 110.0

0.92 0.91

– –

Concentratesa (kg DM in total)

Silage (kg DM in total)

131 137

95 92

– –

a

Concentrate mixture FG indoors and FF80 at grazing (Table 2). Average milk solids contents indoors and at pasture as found in the national goat herd control (TINE Rådgiving, 2010), adjusted for effects of silage quality in the experiment (cf. Table 4). c Early harvest: HR1 and HR2. Normal harvest: HR3. Goats following HR1 or HR2 are outside the experimental period fed RG silage having the same quality as PG of HR2. d Goats are culled close to the end of the dry period since the turn of the year is a counting date for dairy goat governmental payments (cf. Table 2). e From birth to kidding. Kids were also given 15 l of milk and 75 l of milk replacer. b

separate activity for each level of concentrates) to be fed silage made from RGs or grass harvested in the seeding year in the experimental period. For HR3, supplies of PG were plentiful and some PG could be transferred from the experimental period to the non-experimental indoors period. Model inputs for goats fed RG silage in the experimental period were determined by the feeding experiment because the digestibility and NEL values of PG of HR2 and RGs of HR1 and HR2 were approximately equal (cf. Section 3.1.1). RG of both HR1 and HR2 were thus assumed to yield similar silage intake and milk production responses as the PG of HR2. PG and RG of HR3 were also assumed to be perfect substitutes of each other. We are however aware of studies of dairy cows suggesting somewhat lower silage intake and milk production potential of RG silage compared to those made from PG (e.g., Kuoppala et al., 2008). In the experimental period, separate minimum DM requirements for silage and concentrates for the dairy goats are specified in the model. Minimum protein requirements in the same period are specified according to the Norwegian AAT–PBV system (Madsen et al., 1995). However, actual protein supply in the experiment is applied as a minimum requirement in cases where less experimental protein than the standard was supplied. The available feeds consist of PG silage (and for HR1/HR2 transferred RG/seeding year silage) and concentrates (FF70, FF80, FF90, and FP45) that differ in protein content (Table 2). The concentrates have similar energy content as the tailored mixture used in the feeding experiment. Feeding requirements in the non-experimental indoors period are specified as one DM limit for RG/seeding year silage (and additionally for HR3 transferred PG silage) to all livestock and one DM limit for concentrates to goats. In the outdoors period only a minimum DM requirement of concentrates for goats is specified. Protein requirements in the nonexperimental periods are assumed always to be met and consequently are not modelled. For kids, one typical feeding plan is developed for each of the HRs (summarised in Table 1). We assume that kids are fed RG silage and concentrates indoors. The returns from the goat activities include sales of milk, cull goats, and the value of manure. The costs include those of minerals, breeding, vet and medicines, interest on the capital invested in the herd and miscellaneous. Additionally, costs of concentrates and milk replacer are included in the kid replacement activity.

2.5. Labour, prices, and other farm premises On dairy goat farms, labour needs throughout the year are quite stable. The labour requirements for many farm tasks are not directly allocable to specific production activities (overhead labour). The supply of family labour available to production activities, or variable labour (1500 h), are set as equal to total family labour (3500 h) less overhead labour (2000 h). The input–output coefficients for variable labour requirements such as field machinery operations (except rental of herbicide spraying, lime spreading, and baling), feeding of silage and concentrates, milking and animal handling are assumed to be constant per unit of each activity, irrespective of the scale of these activities. The prices of farm inputs and outputs, some of which are reproduced in Table 2, are set to reflect 2009-conditions. Operating field machinery costs are not shown as they vary with the type of machine. These costs include repairs (estimated per hour of use, for each D 100 of repurchase price for various types of machines) and costs of diesel fuel and lubricants per hour of use for various field operations. Farmers are paid various premiums per livestock head and per ha of farmland, with rates varying according to type of livestock or crops and in some instruments with a lower rate for higher numbers, as shown in Table 2. These premiums are included in the model. The farm milk production is constrained by an annual quota of 70,000 l. This quota is above the average figure of 56,500 l (TINE Rådgiving, 2010) but is closer to the typical quota of goat farms in mountainous areas of Norway. The model farm is assumed to have stalling capacity for 100 dairy goats and 6.5 ha of cultivated land available. 2.6. Parametric programming Norwegian farm accountancy survey data show a wide diversity across farms with respect to farmland/l milk quota and the model farm has a potential to supply somewhat more silage relative to the milk quota than is typical in the area. We investigated how the economic returns and the optimal use of inputs changed as a function of farmland availability over a rather wide range using the parametric programming routine in LINDO Systems (2003:173–174). Although the size of the milk quota relative to other fixed farm resources varies across farms, we confined the modelling to examining

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Table 2 Economic parameters, prices, and government farm payments. Parameter Receipts Milk pricea Cull goat Manure, fertilising valueb Manure, fertilising valueb Land, rent out Livestock expensesc Kid concentrates FF70 (7.53, 106, 21)d FF80 (7.46, 110, 0)d FF90 (7.46, 116, −24)d FP45 (7.61, 219, 155)d FG (8.09, 99, 22)d Mineralse Milk replacer Misc. livestock, interest Misc. livestock, interest

Value (D ) 0.91/l 33.7/goat 19.1/goat 6.40/kid 171.4/ha 417/t 367/t 360/t 355/t 530/t 296/t 2.30/goat 35.7/kid 17.4/goat 2.86/kid

Parameter Other expenses Seeds and herbicides Fertiliser price Limef Diesel Silage additive Cost of labour Custom baling, incl. wrapping and transport Governmental payments Grassland, 1–20 ha Grassland, >20 ha Dairy goat, 1–125 Dairy goat, 126–250 Dairy goat, structural 1–27 Goats and kids, grazing Goats and kids, mountain grazing

Value (D ) 173.7/ha 371/t 74.3/t 0.66/l 1.49/l 11.4/h 17.4/bale 456/ha 275/ha 128/head 47.1/head 370/head 6.86/head 10.9/head

Source: NILF (2009). a Total mill solids content is paid as a bonus (penalty) of D 13.71 per 1000 l milk per 1/10% above (below) the base value of 10.6%. Other milk quality parameters, such as milk free fatty acids, somatic cell counts, bacterial count and odour/taste, did not influence the milk price paid by harvesting time or concentrate level (Dønnem et al., 2011b). b Value based on what it would cost to provide the same quantities of plant nutrients from fertilisers. Manure handling costs is assumed to be unaffected by HR. c Commercially available concentrate mixtures produced by Felleskjøpet Rogaland Agder, Norway, October 2009. Price per kg feed, 870 g DM/kg feed. d In parentheses: NEL in MJ/kg DM, AAT and PBV in g/kg DM. e D 1.14/goat in addition at low concentrates. f Limestone applied at a rate of 3 t/ha supplied over a 5 year cycle, increasing to 3.9 t/ha at highest N rate to compensate for acidification of N fertilisers.

effects of removing the milk quota constraint while varying the land constraint over an interval.

3. Results 3.1. Experimental results For more details of the experimental results, included tests of statistical significance, see Bakken et al. (2009) for the grass field experiment and Dønnem et al. (2011a) for the feeding experiment. 3.1.1. Grass Table 3 summarises the main findings of the field experiment. In general, it is well known that responses under experimental conditions significantly exceed the responses achieved under workaday farm conditions (Davidson et al., 1967). Consistent with these observations, we have assumed farm DM yields at 60% of the experimental yields. Substantial stand and yield losses occurred in the last ley years of the early HRs, irrespective of fertiliser input, and weeds, especially Taraxacum officinale, invaded these plots. For HR1 we used the average yields from the first two harvesting years of the experiment and in consequence a ley duration of 2 years (the seeding year excluded), whereas we used 3 and 4 year averages for HR2 and HR3, respectively. Compared to HR3 (6986 kg DM/ha across the two levels of fertilisation), average annual DM yield/ha decreased by 25.2% for HR1 and by 21.1% for HR2 (Table 3). Doubling the rate of fertilisation was associated with increased annual DM yields of 7.9%, 11.9% and 13.4% for HR1, HR2 and HR3, respectively, and also with an increase in the PBV content,

whereas the levels of AAT and NEL were little affected. Energy concentration in the PG from HR1 was very high. NEL in RGs of HR1 and in both the PG and RGs of HR2 were almost similar, whereas HR3 produced forage with lower energy concentration in both harvests. Postponing the first cut was accompanied by a pronounced decline in protein concentration. The DM yield in the seeding year is assumed to be of similar quality to that of the RGs (Table 3). For HR1, only 25% of the farm DM yield (the seeding year included) originates from the high quality PG. In comparison, the share of PG is approximately 40% for HR2 and 50% for HR3. 3.1.2. Dairy goats Table 4 summarises the main findings from the 112 days of the feeding experiment. Averaged over the two levels of concentrates, silage intakes were higher for HR1 and HR2 than for HR3 by 32.4% and 11.2%, respectively. Increases in concentrate allowance were associated with decreased voluntary intake of silage, with substitution rates (decrease in intake of silage DM per kg increase in concentrate DM intake) of 0.43, 0.21 and 0.27 for HR1, HR2 and HR3, respectively. Earlier silage harvesting had a positive effect on the milk yield (Table 4). Compared to HR3, milk yields averaged for the two levels of concentrates were higher by 0.69 kg/day for HR1 and by 0.27 kg/day for HR2. The response in milk output to increasing concentrate input was smallest with the silage of very high digestibility. The marginal responses were 53, 85 and 82 g milk/day per 100 g increase in concentrate DM for HR1, HR2 and HR3, respectively. Total milk solids content was the lowest for HR3 (Table 4).

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Table 3 DM yields and feed quality parameters in the primary growth and re-growth of the leys yearsa according to harvesting regime and fertiliser application and in the seeding year.b Farm yield (kg DM/ha) Very early harvest (HR1, 2 years) 1629 PG 120N RGs 120N 3398 PG 240N 1892 RGs 240N 3533 Early harvest (HR2, 3 years) 2596 PG 120N RGs 120N 2606 PG 240N 2788 RGs 240N 3035 Normal harvest (HR3, 4 years) 3843 PG 120N 2705 RG 120N PG 240N 4133 3290 RG 240N Seeding year 2601 (Very) early harvest 3274 Normal harvest

Digestibility (% of DM)

NEL (MJ/kg DM)

AAT (g/kg DM)

81.9 72.4 80.9 72.8

7.11 6.04 7.03 6.10

90.6 80.9 90.1 81.7

−0.1 20.8 16.7 27.9

73.9 72.6 72.4 72.4

6.18 6.07 6.11 6.07

81.6 80.9 81.2 81.2

−19.6 12.1 −0.4 20.1

66.0 69.8 65.0 68.4

5.39 5.69 5.31 5.63

73.8 76.7 72.8 76.3

−31.6 −27.7 −25.5 −11.9

72.5 69.0

6.07 5.66

81.0 76.0

20.0 −15.0

PBV (g/kg DM)

a

Data from the field experiment at Løken Research Station (Bakken et al., 2009). Farm yields are assumed to be 60% of the observed experimental yields. No experimental data available. Yields are assumed to be 50% of the annual yields of HR2 and HR3 (120 kg N/ha), respectively. HR1 followed the same establishment practice as HR2. b

3.2. Optimal solutions Table 5 summarises optimal model results for the three HRs. Under the two early HRs all the land was used to produce silage. The smaller area of established leys under HR1 than under HR2 was due to its shorter duration. For HR3 sufficient silage was produced and 0.6 ha of land was rented out. A combination of lower DM grass yields and increased silage intake in HR1 and HR2 compared to HR3 led to a much greater scarcity of land for silage production and consequently there were higher cost of the silage. The high rate of fertilisation was thus applied under HR1 and HR2. For HR3, the costs of applying more than the low level of fertiliser exceeded its contribution to the marginal value product of silage production. Even with less land used at a lower fertiliser rate than in the other systems, HR3 resulted in the highest total DM yields.

The high price of silage relative to that of concentrates explains the high consumption of concentrates in the early HRs. The lower silage/concentrates price-ratio and the abundant housing made a low input of concentrates profitable in HR3. The use of concentrates richer in protein increased with postponed time of first cut. The milk quota for HR3 (HR2) was produced with 99 (89) goats each yielding 708 (791) l/year. The stalling capacity was thus not fully used. Less milk sold per goat in HR3 compared to HR2 was because of lower silage quality and lower input of concentrates. The amount of milk sold was only 27 l/goat higher in HR1 than in HR2 because the PG of HR1 forage was sufficient for less than 60% of the dairy goats. For HR1, even though the use of inputs was the highest of the alternatives modelled, only 89% of the milk quota was produced. In economic terms, HR1 was particularly unfavourable as it was impossible to fully produce the quota of high-priced milk. The low number of goats also

Table 4 Effects of harvesting regime and concentrate level on primary growth silage intake and on milk production of the goats in the experiment. Harvesting regime HR1 Concentrate levela

HR2

LC

HR3

HC

LC

HC

LC

HC

1.02 1.55 3.92 110.4

0.53 1.44 3.16 112.1

1.01 1.34 3.57 109.3

0.53 1.32 2.89 105.9

1.04 1.18 3.31 107.7

3.55

3.20

3.40

3.00

3.20

b

Experimental period (112 days: February 6–May 28) Concentrate (kg DM/day) 0.53 1.76 Silage (kg DM/day) 3.66 Milk yield (kg/day) 109.8 Milk solids (g/kg milk) After experiment (16 days: May 29–June 13)c Milk yield (kg/day) 3.40 Per goat per year 804 Milk sold (l)c , d a b c d

834

748

795

708

LC = low concentrate level, NC = normal concentrate level. Data from Dønnem et al. (2011a). Own calculations. Milk production in the non-experimental periods are described in Section 2.4 and summarised in Table 1. The unsold milk includes milk fed to kids (15 l), colostrums milk, penicillin milk and waste. The density of goat milk is 1.029 kg per l.

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HR1 Land use Sward establishment (ha) Established leys (ha) Land rented out (ha) N use in leys (kg/ha) Total silage supply (t DM) Livestock Dairy goats (heads) Fed PG silage in trial period (%) Milk sold (l/goat) Milk quota produced (%) Concentrate use (kg DM/goat per day in trial perioda ) (t DM in total) Financial results Gross output (D ) Milk sales Meat sales Value of manure Land rented out Government farm payments Variable costs (D ) Forage Concentrates Miscellaneous, livestock Variable labour Gross margin (D )

HR2

HR3

75000 70000

TGM (€)

Table 5 Model solutions for the three harvesting regimes at 6.5 ha farmland available, 70,000 l milk quota, and 100 goat places.

139

65000 60000 55000 50000

2.17 4.33 0 240 27.1

1.62 4.88 0 240 30.3

1.18 4.73 0.59 120 32.4

76.2 57.6

88.5 94.6

98.9 100

Fig. 1. Optimal TGM functions for the farmland constraint (4–9 ha) with milk quota 70,000 l, and 100 goat places (HR = harvesting regime).

818 89.0

791 100

708 100

1.02

0.97

0.53

19.2

21.9

20.3

87,788 61,145 643 1580 0.0 24,419

97,384 68,546 747 1834 0.0 26,258

97,064 66,522 835 2050 101 27,556

26,556 7241 8357 2232 8726 61,232

29,741 7707 9568 2599 9868 67,643

28,671 6230 9095 3010 10,336 68,393

For HR3, the milk quota was filled at the lowest breakpoint of only 4.7 ha (Table 6). Simultaneously, the use of fertilisers started to decline gradually and the low rate was reached at 5.3 ha. Then, the input of concentrates began to decrease and a low rate was attained at 5.9 ha. From there, excess land was rented out, with no changes in the farming system itself. For the earlier HRs, the inputs of fertilisers and concentrates were also lowered as land became more abundant. These extensifications, however, started at land areas above the maximum that could possibly be used by HR3 farms (Table 6). In fact, for high input/output systems a 55% (37%) increase in the silage area for HR1 (HR2) farms was required compared to HR3. In low input/output systems the required land increments were 44% and 32% for HR1 and HR2, respectively. Fig. 1 demonstrates the economic advantage of HR3 on farms up to somewhat above normal land availability in the area. Only for a farmland of 6.7 ha and above was HR2 most profitable. This stems from the low opportunity value of excess land in HR3 and the importance of high quality forages for milk yield and price as land availability increases. HR1 with its very high quality PG silage never performed best economically.

a Concentrate mixtures used (% of DM): HR1: FF90 (100%); HR2: FF80 (75%), FF90 (25%); HR3: FF70 (96%); FP45 (4%).

resulted in reduced livestock-related payments. The loss in profit for HR1 compared to the best alternative, HR3, was D 7160. HR2 compared to HR3 had benefits such as a higher milk-yield, lower total costs of fixed inputs per head, and better milk price due to higher content of total solids (additional milk sales of D 2024, Table 5). However, these benefits did not offset the losses of livestock-related income and government payments and the higher costs of producing silage due to lower DM yields, increased silage intake, and more frequent cuts and sward renewals for HR2. In total, HR3 was found to be D 750 better than HR2. 3.3. Parametric analysis of farmland availability The effect on the relative performance of the three HRs was investigated using parametric programming, varying the farmland constraint from 4 to 9 ha. Fig. 1 shows that the optimal TGM functions for the three HRs are all piecewise linear and concave. At a certain value or breakpoint the slopes of the functions change with an associated change in activities in the optimal solution (Table 6). Breakpoints including entries or exits of concentrate mixtures in the optimal solutions are not shown. When land was constrained to 4 ha the optimal numbers of goats were 47, 55 and 79 for HR1, HR2 and HR3, respectively. High inputs of fertilisers and concentrates were then observed for all HRs, due to the very strict land constraint.

45000

HR1

HR2

HR3

40000 4

5

6

7

8

9

Farmland available (ha)

3.4. Removal of the milk quota constraint In Fig. 2 the optimal TGM functions are drawn for the land constraint varying from 4 to 10 ha while assuming no milk quota restriction, ceteris paribus. At the first breakpoint of each HR, the stalling capacity was fully utilised and the input of fertilisers then started to decline gradually. The first breakpoints occurred at 8.5, 7.3 and 5.1 ha for HR1, HR2 and HR3, respectively. The low level of fertiliser input was achieved at 9.0, 8.1 and 5.7 ha for HR1, HR2 and HR3, respectively, beyond which excess land started to be rented out as more farmland became available. Table 6 Breakpoints in ha of the optimal solutions when land is constrained (4–9 ha), the milk quota is 70,000 l, and 100 goat places.

Milk quota filled Less use of concentrates Less use of fertiliser Low input of fertiliser Low input of concentrates Additional land rented out

HR1

HR2

HR3

7.3 7.3 7.6 8.1 8.5 8.5

6.5 6.5 7.1 7.8 7.1 7.8

4.7 5.3 4.7 5.3 5.9 5.9

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80000 75000

TGM (€)

70000 65000 60000 55000 50000 45000

HR1

HR2

HR3

40000 4

5

6

7

8

9

10

Farmland available (ha) Fig. 2. Optimal TGM functions for the land constraint (4–10 ha) with 100 goat places and no milk quota restriction (HR = harvesting regime).

For all HRs, the high rates of concentrate feeding were always beneficial. This finding is because of a high marginal value of goat milk compared to the marginal feed input cost. In comparison, under a quota constraint where average costs of production should be minimised, the input of concentrate feed declined when land became less constrained, and more of the stalling capacity and land resources were then kept in use. At the original 6.5 ha farm area, removal of the milk quota increased milk sold by 5560 l and profit by D 3628 for the HR3 farm. For HR2 the quota was just filled and quota removal resulted in minor changes in optimal solutions and profit, whereas for HR1 the quota was not filled at that area so its removal had no effect. The gains for HR3 over HR1 and HR2 at removal of the quota were D 10,789 and 4183, respectively. The advantage of high quality forage for milk yield improvements, however, emerged at abundant land availabilities, where the earlier HRs performed better than with a restricting milk quota. The improved land availability made room for more high-yielding goats to be fed high quality silage. At 7 ha HR2 surpassed HR3 in profit and above 8.5 ha HR1 was most competitive. 4. Discussion This study of improving the quality of forage by earlier and more frequent cutting has been undertaken to examine opportunities for producing goat milk using less concentrates. We have examined management and financial effects of three HRs producing silage of different qualities on dairy goat farms in mountainous area of Norway, using a mathematical modelling framework. First, it is important to emphasise that mathematical models are idealised representations of actual decision problems and that numerical results depend on the assumptions on which the model has been constructed. Optimisation, therefore, should be regarded as a tool of conceptualisation and analysis rather than as yielding the philosophically ‘correct’ solution (Luenberger, 1984). For example, the stability of the LP models within certain ranges result in some unresponsiveness of the production system to changes in input parameters. Moreover, there may be alternative solutions to the optimal, that yield

closely similar TGMs but which possibly will be more preferred by some farmers for other reasons. One weakness of the model is that the grass and feeding experiments both included only two treatments per HR. An optimal solution will then contain either of the two activities alone, or any linear combinations of the two. By increasing the number of experimental input levels, piecewise linear approximations to concave input/output relations can be included in the LP model and more finetuned specifications of management practices could have been revealed. Moreover, decision variables such as kidding period, or alternatives to carrying out a full reseeding operation were not examined. There was also a lack of goat feeding experiments using RG silage, which may have a lower milk production potential than PG silage. However, at low to more than typical land availabilities and a restricting milk quota, the study has demonstrated economic advantages of a normal two-cut system of silagemaking (HR3) compared to the high-quality three-cut systems (HR1 and HR2) explored. The feeding experiment clearly confirmed previous studies (e.g., Morand-Fehr et al., 2007) of the importance of high quality silage for improving goat milk yield and composition. However, silage DM intake also increased with silage quality, whereas the field crop experiment showed that more frequent harvesting produced lower DM yields (cf., Nissinen and Hakkola, 1995). The net result was a 44–55% (32–37%) increase in the silage area required to fill the milk quota at HR1 (HR2) compared to HR3. The high quality silages were obtained at too high costs due to lower grass yields, increased cutting costs, more frequent sward establishment, and farms with HR1 and HR2 produced less milk than those with HR3. With more land available, high quality silages emerged as more suitable, partly because of better milk production performances, but also due to the rather low opportunity value of surplus land (influencing HR3-profit most). If other farm enterprises can make profitable use of excess land, the competitive position of HR3 relative to HR1 and HR2 would further improve. The optimal TGM functions of the three HRs without the milk quota generally showed the same features as with the quota, although with more sensitivity. The advantages of HR3 were strengthened at limited land availabilities, since this was the only HR capable to produce more milk. On the other hand, with more land HR1 and HR2 performed better compared to the lower milk-yielding HR3 system with no quota. High-quality forage requires less concentrate supplement for the same output of milk as lower quality forage. The economic optimisations with a milk quota, however, demonstrated that HR1 and HR2 increased the input of concentrates per l milk sold as a consequence of the greater scarcity of land for silage production and associated higher cost of silage. (The lower experimental response in milk output to increasing concentrate input for silages of very high rather than lower digestibility worked in the opposite direction.) Without milk quotas, a high input of concentrates was always beneficial. However, our sparse specification of milk response to increasing concentrate supplementation may have hidden more precise input adjustments. With or without a milk quota, the input of

O. Flaten et al. / Small Ruminant Research 103 (2012) 133–142

fertiliser decreased as the land constraint was relaxed but the extensification occurred at a much lower land constraint for HR3 compared to HR2, and HR1 in particular, cf., again, the cost of the silage. Comparisons with other dairy goat studies are hampered by the apparent lack of published research in the area. However, similar conclusions about the economic advantage of less frequent cutting systems at restricted land availabilities have been reported in UK dairy cow studies (e.g., Brooke, 1979; Doyle et al., 1983). Our research supports Valencia and Anderson’s (2000) finding that a higher forage quality may be optimal without rather than with a restricting milk quota. Although milk is produced under a wide range of environmental and socio-economic conditions and the production systems vary across livestock species, our study suggests some relevant insights and lessons for wider dissemination across grassland based dairy systems relying upon winter feeding indoors. On the other hand, the particular climatic conditions in mountainous areas that severely limit the range of crops grown and require a long indoor season, coupled with the specific policy measures supporting the farming sector in the study area, do detract to some extent from the general applicability of our results. Risk related factors to evaluate in grass harvesting schedule decisions were not included in the model. Grass fields are continuously producing biomass in the growing season and are occasionally harvested but are exposed to widely varying and uncontrollable factors such as weather, diseases, and poor winter survival. The result is variability and unpredictability in grass growth, cutting times and possibly number of cuts across years. Opportunities to make sequential decisions and adjust activities (e.g., level of fertilisation and phenological stage at cutting) as the season progresses and more information become available therefore characterise grassland management within a single growing season (e.g., Blank et al., 2001). Further, there is a risk of harvest delays and timeliness costs due to wet weather, which may not be similar across HRs with different numbers of cuts. Also, the harvest may be hastened or postponed due to weather forecast. There is therefore scope to extend the model developed to allow for some of these uncertainties. That would be a matter for a further study demanding additional information. However, no model can incorporate all the highly complex realities of farming and the current model has proved robust enough to generate some understandings of the system that appear logically sound. 5. Conclusions Our LP analysis demonstrated that no single silage harvest regime is always best on dairy goat farms in mountainous areas of Norway. The most profitable approach depends on the availability of land compared to other fixed farm resources such as milk quota and stalling capacity. Availability of relatively large areas of land was, however, necessary for the high quality three-cut systems to outperform the dominating two-cut system. Optimal inputs of fertilisers and concentrates decrease as more land becomes

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available, but at a much lower land constraint for the twocut system than for the three cut systems. Acknowledgements This work was funded by the Foundation for Research Levy on Agricultural Products, the Agricultural Agreement Research Fund and the companies TINE BA, Felleskjøpet TINE BA, Felleskjøpet Fôrutvikling BA, Animalia, Addcon Nordic AS and Yara Norge AS through signed contract with the Research Council of Norway. The authors are grateful to J. Brian Hardaker, Anne Kjersti Bakken, Helge Bonesmo, Åshild Randby, the journal editor and two anonymous reviewers for helpful suggestions and comments, and wish to thank all the others who participated in the larger project. References Azzaroli, M., Skjelvåg, A.O., 1981. Influences of fertilization and cutting times on the freezing tolerance of four grass species. Sci. Rep. Univ. Norway 60 (23), 1–8. Bakken, A.K., Lunnan, T., Höglind, M., Harbo, O., Langerud, A., Rogne, T.E., Ekker, A.S., 2009. Mer og bedre grovfôr som basis for norsk kjøttog mjølkeproduksjon. Resultater fra flerårige høstetidsforsøk i blandingseng med timotei, engsvingel og rødkløver [More and better forage as basis for Norwegian meat and milk production. Results from perennial field experiments in mixed swards of timothy, meadow fescue and red clover]. Rapport nr. 38/2009. Bioforsk, Ås/Stjørdal. Blank, S.C., Orloff, S.B., Putnam, D.H., 2001. Sequential stochastic production decisions for a perennial crop: the yield/quality tradeoff for alfalfa hay. J. Agric. Resour. Econ. 26, 195–211. Bonesmo, H., Belanger, G., 2002. Timothy yield and nutritive value by the CATIMO model: I. Growth and nitrogen. Agron. J. 94, 337–345. Brooke, M.D., 1979. Silage: quantity or quality? Farm Manage. 3, 520–529. Davidson, B.R., Martin, B.R., Mauldon, R.G., 1967. The application of experimental research to farm production. J. Farm Econ. 49, 900–907. Davis, A.W., Hall, W.B., 1969. Cyclic change-over designs. Biometrika 56, 283–293. Doyle, C.J., Corrall, A.J., Thomas, C., Le Du, Y.L.P., Morrison, J., 1983. The integration of conservation with grazing for milk production: a computer simulation of the practical and economic implications. Grass Forage Sci. 38, 261–272. ´ Ó.R., 2006. Sustainability of sheep and goat production in Dyrmundsson, North European countries—from the Arctic to the Alps. Small Rumin. Res. 62, 151–157. Dønnem, I., Randby, Å.T., Eknæs, M., 2011a. Effects of grass silage harvesting time and level of concentrate supplementation on nutrient digestibility and dairy goat performance. Anim. Feed Sci. Technol. 163, 150–160. Dønnem, I., Randby, Å.T., Eknæs, M., 2011b. Effect of grass silage harvesting time and level of concentrate supplementation on goat milk quality. Anim. Feed Sci. Technol. 163, 118–129. Eik, L.O., 1991. Effects of feeding intensity on performance of dairy goats in early lactation. Small Rumin. Res. 6, 233–244. Janssen, S., Van Ittersum, M., 2007. Assessing farm innovations and responses to policies: a review of bio-economic farm models. Agric. Syst. 94, 622–636. Kumbhakar, S., Lien, G., Flaten, O., Tveterås, R., 2008. Impacts of Norwegian milk quotas on output growth: a modified distance function approach. J. Agric. Econ. 59, 350–369. Kuoppala, K., Rinne, M., Nousiainen, J., Huhtanen, P., 2008. The effect of cutting time of grass silage in primary growth and re-growth and the interactions between silage quality and concentrate level on milk production of dairy cows. Livest. Sci. 116, 171–182. LINDO Systems, 2003. LINDO User’s Manual. LINDO Systems, Chicago. Luenberger, D.G., 1984. Linear and Nonlinear Programming, 2nd ed. Addison-Wesley, Reading. Madsen, J., Hvelplund, T., Weisbjerg, M.R., Bertilsson, J., Olsson, I., Spörndly, R., Harstad, O.M., Volden, H., Tuori, M., Varvikko, T., Huhtanen, P., Olafsson, B.L., 1995. The AAT/PBV protein evaluation system for ruminants. A revision. Norwegian J. Agric. Sci. Suppl. 19, 1–37.

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