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Effect of abandoning highland grazing on nutrient balances and economic performance of Italian Alpine dairy farms
Livestock Science 139 (2011) 142–149
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Livestock Science j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / l i v s c i
Effect of abandoning highland grazing on nutrient balances and economic performance of Italian Alpine dairy farms☆ C. Penati a, P.B.M. Berentsen b, A. Tamburini a, A. Sandrucci a, I.J.M. de Boer c,⁎ a b c
Dipartimento di Scienze Animali, Facoltà di Agraria, Università degli Studi di Milano, Milano, Italy Business Economics Group, Wageningen University, Wageningen, The Netherlands Animal Production Systems Group, Wageningen University, Wageningen, The Netherlands
a r t i c l e Keywords: Dairy cattle Highland grazing Sustainability Profitability Nutrient surplus
i n f o
a b s t r a c t In many European mountain areas, such as the Alps, highland grazing is declining. In addition to its effect on natural landscape and biodiversity, abandoning highland grazing may affect dairy-farm profitability and have environmental consequences in the lowland. The objective of this study was to assess economic and environmental effects of abandoning highland grazing of dairy herds in the central Italian Alps. We compared environmental and economic indicators of 12 farms that applied highland grazing (HG) of dairy cows with those of 16 farms that applied no grazing (NG), neither in highland nor in lowland. Environmental indicators used were nitrogen (N) and phosphorus (P) surplus per ha of land or per ton of FCM (fat-corrected milk). Economic indicators used were labor income of the farm family (k€ per farm) or labor income per ton of FCM (€ ton− 1 FCM). Compared with HG farms, NG farms had larger total milk production (370.7 vs 141.4 ton FCM), higher production per ha (13.9 vs 8.4 ton FCM ha− 1) and higher annual milk yield per cow (6.3 vs 4.4 ton FCM). Because of the extensive manner of milk production in highland of HG farms, the NP surplus per ha of highland was negligible (6.4 N and −0.2 P ha− 1 year− 1), and, therefore, not further considered. The N surplus averaged 186 kg N ha− 1 year− 1 for NG farms compared with 137 kg N ha− 1 year− 1 for lowland of HG farms. The P surplus averaged 30 kg P ha− 1 year− 1 for NG farms compared with 24 kg P ha− 1 year− 1 for lowland of HG farms. A high milk production per ha and a low % FSS (feed self-sufficiency) were associated with a high NP surplus, whereas the grazing system did not affect NP surplus per ha. Labor income per farm was lower for HG farms (30.3 k€) than for NG farms (72.2 k€), but expressed in euro per ton FCM, labor income was higher for HG (0.24 €) than for NG farms (0.16 €). A smaller farm size in the lowland and a lower milk production per ha lowland explained the lower labor income for HG farms compared with NG farms. Additional revenues from highland grazing, i.e. high-value cheese and grazing subsidies, caused higher labor income per ton FCM for HG than for NG farms. Hence, farmers tend to increase their net farm income by increasing their milk production per farm, via increasing their area of lowland and/or their milk production per ha lowland, while at the same time they abandon highland grazing. As enlargement of the lowland area is hampered by increasing urbanization of the valleys of the Italian Alps, farmers probably will increase their milk production per ha lowland. A higher milk production per ha lowland, however, will increase the environmental impact in the lowlands. © 2011 Elsevier B.V. All rights reserved.
1. Introduction ☆ This paper is part of the special issue entitled: Assessment for Sustainable Development of Animal Production Systems, Guest Edited by Akke van der Zijpp. ⁎ Corresponding author at: Animal Production Systems Group, Wageningen University, PO Box 338, 6700 AH Wageningen, The Netherlands. Tel.: +31 317 484589; fax: +31 317 485550. E-mail address: [email protected] (I.J.M. de Boer). 1871-1413/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.livsci.2011.03.008
In mountain areas, such as the Alps, traditional dairy farming is characterized by keeping milking cows and young stock indoors in the lowland most of the year, generally without access to pasture. During summer, however, animals
C. Penati et al. / Livestock Science 139 (2011) 142–149
are transferred from the barn in the lowland to pasture in the highland. Highland grazing is of interest to dairy farmers because it offers access to special subsidies for ecosystem services such as maintaining biodiversity and conservation of the landscape in mountain areas. Farmers, furthermore, obtain additional revenues by producing special branded, high-value cheeses from milk produced during highland grazing (Pretolani and Raffaelli, 2007). To maintain profitability, dairy farms in mountain areas have shown in recent decades a trend, shown also in dairy farming in general, towards enlarging milk quota, increasing stocking density, and increasing milk production per cow (Cozzi et al., 2006). Enlargement and intensification, together with harsh living conditions of farmers during the period of highland grazing (Talamucci, 1997) and lack of successors (Bernués et al., 2005), contributed to a gradual reduction in the number of dairy farms that graze cattle in the highlands (Gios and De Ros, 1991). The decrease of highland grazing of cattle is a risk for European mountain areas because livestock plays a central role in the conservation of their natural landscape and biodiversity (Casasùs et al., 2007; Mc Donald et al., 2000). The end of highland grazing, moreover, may affect farm profitability and have environmental consequences in the lowland, where dairy farming is concentrated. The objective of this study was to assess the economic and environmental effects of abandoning highland grazing of dairy farms in Alpine areas. We compared economic and environmental indicators of farms that still apply highland grazing with those of farms that do not apply highland grazing. 2. Material and methods 2.1. Material This study involved a sample of 28 dairy farms, of which 12 applied highland grazing (HG) of dairy cows and 16 applied no grazing (NG), neither in highland nor in lowland areas. All farms were located in the central Italian Alps (i.e. Valtellina and Valchiavenna). Using experts, these 28 farms were selected out of 47 farms that sold their milk produced in the lowland to the same local cheese factory in order to represent typical HG and NG farms. The main breed on HG farms was Brown Swiss (85%), whereas NG farms used mainly Holstein–Friesians (53%) and Brown Swiss (33%). Data were collected for 2006. Data regarding land use (i.e. ha grassland and ha maize land) and number of animals present at the farm were retrieved from the generic agricultural information system of Lombardy (SIARL, 2006). Data required to compute revenues from milk production in the lowland, e.g. milk price, amount and composition of milk produced, were retrieved from the local cheese factory. Furthermore, one interviewer visited each farm between June and July 2007 and collected data needed to evaluate the environmental and economic performance of each farm (for detailed description see Sections 2.2 and 2.3) based on individual administration of each farm (e.g., invoices). Data collected for environmental evaluation were amount of purchased roughages, concentrates, synthetic fertilizers, litter, amount of purchased and sold animals, and number of days of highland grazing. Additional data required for
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economic evaluation were amount of milk produced in the highland, amount of purchased pesticides, energy sources, and prices of purchased inputs. 2.2. Environmental indicators A farm-gate nutrient balance was used to assess the environmental performance of a farm (Oenema et al., 2003; Schröder et al., 2003; Thomassen and De Boer, 2005). Such a nutrient balance measures the difference between nutrient inputs and outputs of a farm, i.e. annual nutrient surplus, which is assumed to be lost into the environment (Berentsen and Tiessink, 2003; Ondersteijn et al., 2002). Nitrogen (N) surplus was assumed to be lost by gaseous emissions to the air (i.e. NH3, N2O and NOx), by leaching and runoff of nitrate to surface or groundwater, or by storage in the soil. Phosphorus (P) surplus was assumed to be lost by run-off to surface water, by storage in the soil or, in case of P-saturated soils, by leaching to groundwater. For each NG farm, we quantified inputs and outputs, and the resulting total annual surplus of N and P. We computed separate N and P balances (i.e. NP balances) for the lowland and for the highland on HG farms because of their different geographical location, and because of the fact that a nutrient balance is mainly directed at the local environmental impact, i.e. leaching of nitrate or phosphate and emission of NH3 (De Boer, 2003). The NP balance for the lowland was expressed per ha lowland, whereas the NP balance for the highland was expressed per ha highland. Most farms rented pastures in the highland individually, and, therefore the highland area was known precisely. Two farmers, however, rented the same pastures in the highland. For these farms, the highland area was allocated by relative livestock numbers. To gain insight into NP efficiency at farm level, we also expressed NP balance for the lowland per ton FCM produced in the lowland, and the NP balance in the highland per ton FCM produced in the highland. The NP balance for the highland was relatively simple to compute because, in addition to grazing, only a small amount of concentrates was fed to dairy cows (1.0 kg/d, on average). No bedding material and no synthetic fertilizer was used, moreover, and no manure was exported. Nutrient output through purchased or culled animals in the whole year was assigned to the balance for the lowland. Relevant inputs and outputs included in the nutrient balance of a dairy farm, and their assumed N and P content, are in Table 1. For each farm, N input through purchased concentrates, for example, was computed by multiplying the amount of each concentrate purchased by its N content. Kjeldahl nitrogen analysis was used to determine the N content of roughages, whereas ICP-MS (inductive coupled plasma mass spectrometry) was used to determine the P content. The N content of concentrates was based on feed labels, whereas the P content of concentrates was based on Cornell-Penn-Miner (2004). NP input via mineral supplements was included in NP of purchased concentrates. A fixed N deposition of 20 kg per ha and a fixed N fixation of 15 kg per ha of grassland was assumed (see Table 1). The N and P outputs were computed similarly by multiplying the amount of milk sold or animals removed by their N and P contents. The N content of milk produced in the lowland was obtained from the local cheese factory, whereas the protein content of milk produced in the highland was
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Table 1 Information required and sources used to compute the nitrogen (N) and phosphorus (P) surplus of a dairy farm. Variable
Information required
Source
Input Purchased feed
Q a and NP content purchased feed
Purchased synthetic fertilizer Purchased bedding material
Q and NP content synthetic fertilizer Q and NP content of straw and sawdust
Purchased animals
Q and NP content purchased animals
Nitrogen fixation Atmospheric deposition
20 kg N ha− 1 grassland (in lowland and in highland) no. of hectares grassland 15 kg N ha− 1 grassland; no. of hectares of grassland
Roughages N: Kjeldahl P: ICP-MS Concentrates N: feed label; P: Cornell-Penn-Miner (2004). CPM Dairy, version 3.0.6. Q: interviews NP fertilizer: Q: interviews NP straw: Baldoni and Giardini (2002). AOAC (Association of Official Analytical Chemists) (1998). NP sawdust: CVB (Centraal Veevoeder Bureau) (1991). AOAC (Association of Official Analytical Chemists) (1998) Q: interviews NP animals: Cornell Nutrient Management Spear Program (2007). Q: interviews Bassignana et al. (2004); Grignani et al. (2003) no. hectares: SIARL (2006) DM 19 April 1999; no. hectares: SIARL (2006)
Output Cow milk sold
Lowland: Q and NP content of milk sold
Highland: Q and NP content of milk processed
Sold/dead animals
Q and NP content of sold animals
Total NP surplus
∑ NP inputs − ∑ NP outputs
a
N: data from local cheese factory (based on Oterholm (1994)) P: Schiavon (2010). Q: cheese factory database N: MIR spectrometry and Oterholm (1994); P: Schiavon (2010) Q: interviews NP animals: Cornell Nutrient Management Spear Program (2007). Q: interviews
Q = quantity of product purchased/sold.
determined using MIR (mid-infrared) spectrometry (Milkoscan TM4000, Foss, Denmark). Subsequently, milk protein content was converted into milk N content based on Oterholm (1994). A fixed P content of milk of 1.05 g per liter was assumed (Schiavon, 2010). 2.3. Economic indicators For each farm, profitability was assessed by quantifying labor income of the family farm (Mollenhorst et al., 2006; Van Calker et al., 2005). Labor income is defined as the difference between revenues and costs, excluding costs of labor supplied by the family and is the equivalent of returns to labor and management (Kay et al., 2004). Information required and sources used to compute the different revenues as well as variable and fixed costs are in Table 2. Most information required was obtained from farm interviews. Fixed costs for housing and machinery (i.e. depreciation, interest, and maintenance) and for interest of livestock were based on actual dimensions, types, and numbers, as given by farmers, and on standards regarding values and percentages of depreciation, interest, and maintenance (Hemmer et al., 2007). The interest rate was assumed to be 4.8%. Costs of land were standardized by assuming that all land was rented by the farmers, using an average rental cost obtained by interviews. 2.4. Statistical analysis Means of normally distributed general farm characteristics were compared between HG and NG farms using
independent T-test, whereas means of non-normally distributed farm characteristics were compared using the nonparametric Mann–Whitney Test. A general linear model (GLM) was used to determine the effect of grazing system G (i.e. HG versus NG) and other relevant farm characteristics on environmental (i.e. NP surplus per ha or per ton FCM) and economic indicators (i.e. labor income per farm or per ton FCM). Farm characteristics chosen as covariates in GLM were based on literature (Fangueiro et al., 2008; Nevens et al., 2005; Thomassen et al., 2009). To analyze environmental indicators, two covariates were included: milk production per ha (X1) and percentage of feed self-sufficiency (% FSS, X2). The % FSS was computed as the ratio of purchased feed (i.e. concentrates and roughages) to purchased and own-produced feed (i.e. roughages and highland grazing), both of them in terms of dry matter (DM). Dry matter intake from highland grazing was based on Vazquez and Smith (2000). For HG farms, NP balances were computed and analyzed separately for the lowland and the highland. GLM analyses of NP balances in the lowland, for example, were based on milk production per ha in lowland and % FSS in the lowland. To analyze economic indicators, three covariates were included: milk production per ha lowland (X1), percentage of feed self-sufficiency in the lowland (% FSS, X2), and total ha of lowland (X3). Total ha of lowland was included as an additional covariate to correct for farm size (Orland, 2004). Economic effects of milk production per ha in highland, total area of highland or % FSS in the highland were all captured in the effect of grazing system G.
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Table 2 Information required to compute revenues and costs, and sources used. Variable
Information required
Source
Revenues Cow milk
Q a and price of milk
Number of animals sold Dairy products summer grazing Common Agricultural Policy grant Other subsidies
Q and sale price of animals Q and price of dairy products Amount of the grant per farm Amount of the grant per farm
Cheese factory (dependent on quality parameters) Interviews Interviews SIARL (2006) Interviews
Q and relative prices
Interviews
Q and rental price Typology and dimension. Substitution value, depreciation, maintenance, insurance and interest. Q and typology. Actual prices, depreciation, maintenance, insurance and interest. Q, substitution value and interest.
SIARL (2006) and interviews Interviews Hemmer et al. (2007).
Variable costs Purchased feed, diesel, electricity, gas, purchased animals, medical expenses (medicines, veterinary, artificial insemination…), hired labor, seeds, synthetic fertilizers, pesticides, external contract work, other costs Fixed costs Land rent Housing and milking machinery Machinery Livestock
a
Interviews: Q, typology, actual prices. Hemmer et al. (2007) Interviews: Q. Substitution value from Camera di Commercio di Mantova (2007). Interest: 4.8% of full value.
Q = quantity.
Because of non-normality of environmental and economic indicators, they were log-transformed to achieve a normal distribution of residuals in the GLM. The GLM had the functional form: Y =μ +G+bX +e where Y is a set of log-transformed environmental or economic indicators; μ is the overall mean; G is the effect of grazing system; b is a set of coefficients for each covariate; X is a set of covariates, and e is a set of residuals. Statistical analyses were performed using SPSS (SPSS, 2007). 3. Results and discussion 3.1. Farm characteristics Mean and standard deviation of general farm characteristics for the 12 farms that applied highland grazing (HG) of milking cows, and for the 16 farms that applied no grazing (NG) are in Table 3. Milking cows on HG farms grazed on average 92 days. For these farms, therefore, general farm characteristics are shown for the farm as a whole, and for lowland and highland separately. For HG farms, use of highland pastures was extensive compared with use of the lowland area. Milk production per ha of highland (0.7 ton), for example, was about 8% of production per ha of lowland (8.4 ton). This distinct difference was the reason that most comparisons between HG and NG farms were done for lowland area only. Compared with HG farms, NG farms had higher total milk production (370.7 vs 141.4 ton FCM; p = 0.095), higher milk production per ha (13.9 vs 8.4 ton FCM ha− 1; p = 0.012) and higher annual milk yield per cow (6.3 vs 4.5 ton FCM; p = 0.000). These differences are in line with the general
trend of enlargement and intensification of dairy farms in mountain areas as described by Cozzi et al. (2006). Similar results were found by Giustini et al. (2007) for farm in Italian mountain areas. Compared with farms located at high altitude and summer grazing, farms located at a low altitude without grazing had a higher total milk production (284.1 vs 136.2 ton year− 1), a higher milk production per ha (5.7 vs 2.6 ton ha− 1) and higher annual milk yield per cow (5.6 vs 4.8 ton per cow). Compared with HG farms, NG farms had higher roughage production per ha (11.5 vs 8.9 ton DM ha− 1; p = 0.038), and a higher concentrate use per ha (6.1. vs 1.2 ton DM ha− 1; p = 0.000). The higher milk yield per ha of NG farms (13.9 ton) required a higher total amount of feed per ha. The higher DM roughage production per ha lowland on NG farms was caused partly by the higher fertilization rate from animal manure due to a higher milk production per ha, since there are no differences in the utilization of synthetic fertilizer. Literature shows that a higher milk yield per ha is associated with a higher NP excretion per ha (Bannink et al., 1999; Valk et al., 2002). Furthermore, the DM roughage production per ha was higher on NG farms because of the higher percentage of land planted with maize for silage compared with HG farms. DM yield per ha of maize land is higher than DM yield per ha of grassland (Bassanino et al., 2008). Concentrate use per kg FCM and % FSS, however, did not differ between NG and HG farms (Table 2). 3.2. Environmental indicators Mean and standard deviation of N balance for all NG farms, and of N balance for the lowland and highland for all HG farms are in Table 4, similar results for P are in Table 5. Because of the extensive manner of milk production in highland of HG farms (i.e. 0.9 ton FCM/ha, see Table 3), the N
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Table 3 Mean (standard deviation) of general farm characteristics for non-grazing (NG) and highland grazing (HG) farms. For HG farms, results are presented separately for lowland and highland area. General farm characteristic (unit)
NG (n = 16) mean (SD)
HG (n = 12)
Milking cows (n) Grazing days (no. d) Total milk production (ton FCM) a Land (ha) Maize land (%) Roughage production (kg dry matter ha− 1) Concentrate use (ton dry matter ha− 1) Concentrate use (kg dry matter kg FCM− 1) Synthetic fertilization (kg N ha− 1) LU b per ha (no. ha− 1 year− 1) Milk yield (ton FCM cow− 1 year− 1) Milk yield/ha (ton FCM ha− 1) Feed self-sufficiency (%)
54.9 (56.6) N.A. a 370.7 (405.5) ⁎ 27.4 (31.1) 21.0 (16.4) ⁎ 11.5 (3.3) ⁎⁎ 6.1 (3.8) ⁎⁎⁎ 0.43 (0.13) 15.0 (18.0) 3.04 (1.15) 6.3(1.2) ⁎⁎⁎ 13.9 (7.4) ⁎⁎⁎ 65.3 (17.1)
Total mean (SD)
Lowland mean (SD)
Highland mean (SD)
31.2.(14.9) 92 (23) 141.4 (75.3) ⁎ 54.6 (27.9) N.A. N.A. N.A. N.A. N.A. N.A. 4.5 (0.7) ⁎⁎⁎ N.A. N.A.
31.2 (14.9) N.A. 116.5 (62.0) 13.7 (5.5) 9.9 (11.8) ⁎ 8.9 (2.7) ⁎⁎ 1.2 (0.9) ⁎⁎⁎
28.1 (16.1) 92 (23) 24.9 (18.3) 40.9 (26.4) 0 N.A. N.A. N.A. 0 0.25 (0.07) 0.9 (0.3) 0.7 (0.3) 95.3 (4.8)
0.42 14.8 2.79 3.7 8.4 62.4
(0.12) (16.9) (1.58) (0.9) (2.5) ⁎⁎⁎ (16.0)
a
N.A. = Not applicable; FCM = fat-corrected milk. b LU = Livestock Units (included young stock). For HG farms LU in lowland, for example, was a weighted average of LU per ha in non-grazing period, and LU in grazing period (i.e. 0), using number of grazing days as weighing factor. ⁎ Means differ using p b 0.10. ⁎⁎ p b 0.05. ⁎⁎⁎ p b 0.01 (means of highland were not tested).
and P surplus per ha was negligible. Comparison of environmental indicators of HG versus NG farms, therefore, focuses on the NP balances in the lowlands only. For both HG and NG farms, the main N input of the lowland N balance was purchased concentrates (i.e. 58 and 65% respectively), whereas milk sold was the main N output (i.e., 80–84% respectively, Table 4). The N surplus averaged 186 kg N ha − 1 year − 1 for NG farms compared with 137 kg N ha− 1 year− 1 for lowland of HG farms. From GLM analyses, we concluded that differences in N surplus per ha among farms were not due to the grazing system (p = 0.92), but rather resulted from differences in milk production per ha (p = 0.002), and in % FSS among individual farms (p = 0.000).
Because % FSS did not differ between NG and HG farms, the higher milk production per ha of NG compared with HG farms, therefore, explained the higher N surplus per ha of NG farms (186 kg N ha− 1 year− 1) compared with HG farms (137 kg N ha− 1 year− 1). For both HG and NG farms, the main P input of the lowland P balance was purchased concentrates (i.e. 82% and 79, respectively), whereas milk sold was the main P output (i.e. 77 and 80%, respectively; Table 4). The P surplus averaged 30 kg P ha− 1 year− 1 for NG farms compared with 24 kg P ha− 1 year− 1 for lowland of HG farms. From GLM analyses, we concluded that difference in P surplus per ha among farms were not due to the grazing system (p = 0.46),
Table 4 Mean (standard deviation) of nitrogen (N) balance of non-grazing (NG) and highland grazing (HG) farms. For HG farms, a separate N balance is presented for the highland and lowland area. HG (n = 12) Variable
NG (n = 16) Mean (SD)
Input (kg N ha− 1 year− 1) Concentrates Forages Synthetic fertilizer Bedding material Animals Nitrogen fixation Deposition Total Output (kg N ha− 1 year− 1) Milk Animals Total Surplus •kg N ha− 1 year− 1 •kg N ton FCM− 1 yr− 1 a a
FCM = fat-corrected milk.
Lowland %
181.8 (132) 39.8 (65.5) 15.1 (18.0) 5.8 (4.9) 3.8 (4.7) 14.3 (7.5) 20.0 (0.0) 280.6 (199.6)
64.8 9.1 5.9 2.6 1.2 6.8 9.6 100
79.4 (42.8) 15.3 (10.4) 94.7 (52.0)
84.4 15.7 100
186.0 (152) 12.5 (4.6)
Mean (SD) 111 29.3 14.8 5.4 2.8 13.5 20.0 196.6
Highland %
Mean (SD)
%
(46) (46.3) (16.9) (4.3) (3.9) (1.8) (0.0) (86.1)
57.7 10.7 6.7 3.3 1.7 7.9 11.9 100
1.1 (1.2) 0 0 0 0 3.8 (0.9) 5.0 (1.2) 9.9 (2.2)
11.0 0 0 0 0 38.1 50.9 100
48.4 (14.6) 11.5 (5.6) 59.9 (14.5)
80.3 19.7 100
3.5 (1.4) 0 3.5 (1.4)
100 0 100
136.8 (75.9) 16.2 (6.3)
6.4 (2.4) 11.5 (7.3)
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Table 5 Mean (standard deviation) of phosphorus (P) balance of non-grazing (NG) and highland grazing (HG) farms. For HG farms, a separate P balance is presented for the highland and lowland area. HG (n = 12) Variable
NG (n = 16)
−1
year Input (kg P ha Concentrates Forages Synthetic fertilizer Bedding material Animals Total −1
%
Mean (SD)
36.5 (22.1) 4.6 (7.6) 5.8 (6.8) 0.8 (0.6) 1.0 (1.2) 48.6 (33.0)
78.7 6.5 11.0 2.1 1.8 100
26.1 3.8 2.0 0.7 0.7 33.3
14.9 (8.2) 3.8 (2.5) 18.7 (10.4)
80.3 19.7 100
9.0 (2.6) 2.9 (1.4) 11.9 (2.7)
Highland %
Mean (SD)
%
−1
) (12.8) (5.5) (5.7) (0.6) (1.0) (20.4)
82.4 9.1 3.3 2.7 2.5 100
0.4 (0.4)
100
0.4 (0.4)
100
0.6 (0.2) 0 0.6 (0.2)
100 0 100
– – – –
−1
Output (kg P ha year ) Milk Animals Total Surplus •kg P ha− 1 year− 1 •kg P ton FCM− 1 year− 1 a a
Lowland
Mean (SD)
29.9 (24.1) 2.1 (1.1)
77.4 22.5 100
21.4 (18.7) 2.4 (1.5)
− 0.15 (0.36) − 0.25 (0.69)
FCM = fat-corrected milk.
but rather resulted from differences in milk production per ha (p=0.07) and in % FSS (p=0.006). The difference in P surplus per ha between HG and NG farms, therefore, can be explained by the difference in milk yield per ha between these farms. The result that the NP surplus of a dairy farm in mountain areas was affected mainly by milk production per ha and % FSS was found also by Giustini et al. (2007). They concluded that differences in NP surplus among farms located in lowland (136 kg N ha− 1 year− 1; 73 kg P ha− 1 year− 1) and farms located in the upland (53 kg N ha− 1 year− 1; 27 kg P ha− 1 year− 1) resulted from differences in milk production per ha, in % purchased feed and in fertilization rate. To gain insight into NP efficiency at farm level, NP surplus of farms was evaluated also per ton of FCM. The N surplus averaged 12.5 kg N ton− 1 FCM year− 1 for NG farms compared with 16.2 kg N ton− 1 FCM year− 1 for HG farms in lowland (Table 4). These results are comparable with those from Segato et al. (2009), obtained in a plain area in the North of Italy, i.e. 15.6 kg N ton− 1 FCM year− 1 for maize-based systems and 17.6 kg N ton− 1 FCM year− 1 for hay-based systems. The P surplus averaged 2.1 kg P ton− 1 FCM year− 1 for NG farms compared with 2.4 kg P ton− 1 FCM year− 1 for HG farms in lowland. From GLM analysis, we concluded that differences in NP surplus per ton FCM among farms were not affected by the grazing system (N: p=0.35; P: p=0.90), but rather resulted from differences in % FSS among individual farms (N: p=0.001; P: p=0.012). As FSS increased NP surplus per ton FCM decreased. Farms can increase their % FSS by increasing own-farm roughage production by optimizing fertilization management, improving conservation strategies or increasing the percentage of land planted with maize (Jonker et al., 2002). Increasing the percentage of maize land in the lowland of the Alps, however, raises concern regarding landscape and biodiversity preservation (Pileri, 2008). Other general measures to reduce NP surplus per ton FCM could be feeding cows in according with their NP requirements (Arriaga et al., 2009) or increasing feed efficiency of dairy cows. Furthermore, exporting manure or outsourcing young stock can contribute to a reduction of the NP surplus per ha of land (Kuipers and Mandersloot, 1999).
3.3. Economic indicators Comparison of cost benefit analysis showed that the main source of revenues was selling milk, which on average accounted for 82.5% of the revenues for NG farms, and for 47% for HG farms (Table 6). For HG farms, the second most important source of revenues originated from production of cheese and other milk products (27.5%) during the period of highland grazing. Moreover, HG farms had higher revenues from “other subsidies” than NG farms (11.9% versus 3.4%). These revenues were mainly subsidies given for grazing in the Alps, which depend on the amount of land used for grazing and on stocking density. On average, the majority of variable costs were for costs for purchased feed (48% for HG and 55% for NG), followed by costs for energy sources, such as diesel, electricity, gas (12% for HG and 15% for NG). Main fixed costs were due to depreciation, maintenance, and insurance of housing structures (38.8% for HG and 41.5% for NG) and machinery (29.1% for HG and 28.5% for NG). The resulting labor income per farm was lower for HG farms (30.3 k€) than for NG farms (72.2 k€). Expressed in euro per ton FCM, however, labor income was higher for HG (0.24 €) than for NG farms (0.16€), which was due mainly to extra revenues from highland cheese and Alpine grazing subsidies. From GLM analyses, we concluded that differences in labor income among farms were explained by the grazing system (p = 0.024), farm size (p = 0.000) and milk production per ha of lowland (p = 0.003), whereas % FSS did not affect labor income per farm (p = 0.41). Differences in labor income per FCM were only due to the grazing system (p = 0.03). Hence, a larger farm size and a higher milk production were associated with a higher labor income per farm. The smaller farm size and lower milk production per ha of HG compared with NG farms (Table 3), therefore, explained the lower labor income per farm for HG farms compared with NG farms. Highland grazing resulted in extra revenues, and explained the higher labor income per kg FCM for HG compared with NG farms. Data did not include any information on family labor input. Such information could have been helpful to place into
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Table 6 Mean (standard deviation) of results from cost benefit analysis of non-grazing (NG) and highland grazing (HG) farms. NG (n = 16)
HG (n = 12)
Mean (SD)
%
Mean (SD)
163.5 (179.3) N.A. a 12.7 (18.0) 15.5 (19.1) 5.5 (6.6) 197.2 (218.8)
82.5 N.A. 6.1 7.8 3.4 100
48.8 (28.9) 28.4 (17.3) 8.3 (4.1) 5.6 (2.6) 12.2 (4.2) 103.3 (48.0)
47.2 27.5 8.0 5.4 11.9 100
37.3 (32.8) 10.3 (10.9) 9.4 (12.0) 6.1 (7.1) 3.9 (5.3) 1.2 (4.6) 4.7 (6.3) 72.8 (73.8) 125.5(150.6)
54.7 14.9 11.0 8.0 3.9 0.4 7.0 100
20.7 (12.1) 4.8 (2.3) 4.2 (5.4) 3.4 (2.3) 0.7 (1.0) 5.2 (13.8) 4.2 (2.9) 43.3 (30.0) 60.0 (23.9)
47.9 11.6 9.8 7.8 1.6 11.9 9.8 100
5.4 (5.9)
11.1
4.5 (1.7)
15.0
Depreciation, maintenance, insurance Housing structures Machinery Milking machinery/tank
22.0 (24.1) 15.2 (16.8) 5.0 (5.8)
41.5 28.5 8.9
11.6 (6.1) 8.7 (5.3) 2.4 (1.6)
38.8 29.1 7.9
Interest Livestock Machinery and housing Total
4.5 (5.4) 1.0 (1.1) 53.3 (57.1)
8.2 1.9 100
Revenues (in k euro year− 1) Cow milk Highland cheese Sold animals Common Agricultural Policy grant Other subsidies Total Variable costs (in k euro year− 1) Purchased feed Diesel, electricity, gas Purchased animals Medical expenses Seeds, fertilizer, pesticides, contract work Hired labor Other variable costs Total Gross margin (in k euro year− 1) Fixed costs (in k euro year− 1) Land rent
Labor income •per farm (in k euro year− 1) •per ton FCM (in euro ton FCM− 1) a
a
72.2 (94.8) 0.16 (0.07)
2.2 (1.3) 0.5 (0.3) 29.8 (148.1)
%
7.3 1.8 100
30.3 (6.4) 0.24 (0.13)
N.A. = Not applicable; FCM = fat-corrected milk.
perspective the difference in labor income between HG and NG farms. As labor input, on average, is higher on larger farms, NG farms, which are larger than HG farms, on average apply more family labor input to run their farms. On the other hand, highland grazing appears very laborious. From the results, it appears that farms that increase their farm size in the lowland or their milk production per ha abandon highland grazing. An increase in milk production increases net farm income and also compensates for the loss of additional revenues from highland cheese and Alpine grazing subsidies. Enlargement of farms, however, is hampered by increasing urbanization of the valley area of the Italian Alps (Varotto, 2002). Due to lack of a successor, some HG farms will stop farming (Bernués et al., 2005), giving remaining farms the opportunity to enlarge and intensify. 4. Conclusion Results show that a high milk production per ha and a low % FSS were associated with a high NP surplus per ha, whereas the grazing system did not affect NP surplus per ha. A high milk production per ha and a large farm size, furthermore, were associated with a high labor income of the farm, whereas highland grazing was associated with a high labor income per kg FCM. These results illustrate the current practice dairy farmers in
the Italian Alps are facing at the moment. Farmers tend to increase their net farm income by increasing their milk production per farm, via increasing their area of lowland and/or their milk production per ha, while at the same time they abandon highland. As enlargement of farm area in lowland is hampered by increasing urbanization of the valleys of the Italian Alps, farmers probably will increase their milk production per ha. A higher milk production per ha, however, will increase the environmental impact in the lowlands, which can be compensated by increasing own-farm roughage production, optimizing fertilization, increasing feed efficiency, exporting of manure or outsourcing of young stock. Conflict of interest Furthermore, we state that the authors or the author's institution have no financial or other relationship with other people or organizations that may inappropriately influence the author's work. Acknowledgments The authors express their gratitude to all farmers who participated in the project and to the director of the cheese factory, Marco Deghi.
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Livestock Science j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / l i v s c i
Effect of abandoning highland grazing on nutrient balances and economic performance of Italian Alpine dairy farms☆ C. Penati a, P.B.M. Berentsen b, A. Tamburini a, A. Sandrucci a, I.J.M. de Boer c,⁎ a b c
Dipartimento di Scienze Animali, Facoltà di Agraria, Università degli Studi di Milano, Milano, Italy Business Economics Group, Wageningen University, Wageningen, The Netherlands Animal Production Systems Group, Wageningen University, Wageningen, The Netherlands
a r t i c l e Keywords: Dairy cattle Highland grazing Sustainability Profitability Nutrient surplus
i n f o
a b s t r a c t In many European mountain areas, such as the Alps, highland grazing is declining. In addition to its effect on natural landscape and biodiversity, abandoning highland grazing may affect dairy-farm profitability and have environmental consequences in the lowland. The objective of this study was to assess economic and environmental effects of abandoning highland grazing of dairy herds in the central Italian Alps. We compared environmental and economic indicators of 12 farms that applied highland grazing (HG) of dairy cows with those of 16 farms that applied no grazing (NG), neither in highland nor in lowland. Environmental indicators used were nitrogen (N) and phosphorus (P) surplus per ha of land or per ton of FCM (fat-corrected milk). Economic indicators used were labor income of the farm family (k€ per farm) or labor income per ton of FCM (€ ton− 1 FCM). Compared with HG farms, NG farms had larger total milk production (370.7 vs 141.4 ton FCM), higher production per ha (13.9 vs 8.4 ton FCM ha− 1) and higher annual milk yield per cow (6.3 vs 4.4 ton FCM). Because of the extensive manner of milk production in highland of HG farms, the NP surplus per ha of highland was negligible (6.4 N and −0.2 P ha− 1 year− 1), and, therefore, not further considered. The N surplus averaged 186 kg N ha− 1 year− 1 for NG farms compared with 137 kg N ha− 1 year− 1 for lowland of HG farms. The P surplus averaged 30 kg P ha− 1 year− 1 for NG farms compared with 24 kg P ha− 1 year− 1 for lowland of HG farms. A high milk production per ha and a low % FSS (feed self-sufficiency) were associated with a high NP surplus, whereas the grazing system did not affect NP surplus per ha. Labor income per farm was lower for HG farms (30.3 k€) than for NG farms (72.2 k€), but expressed in euro per ton FCM, labor income was higher for HG (0.24 €) than for NG farms (0.16 €). A smaller farm size in the lowland and a lower milk production per ha lowland explained the lower labor income for HG farms compared with NG farms. Additional revenues from highland grazing, i.e. high-value cheese and grazing subsidies, caused higher labor income per ton FCM for HG than for NG farms. Hence, farmers tend to increase their net farm income by increasing their milk production per farm, via increasing their area of lowland and/or their milk production per ha lowland, while at the same time they abandon highland grazing. As enlargement of the lowland area is hampered by increasing urbanization of the valleys of the Italian Alps, farmers probably will increase their milk production per ha lowland. A higher milk production per ha lowland, however, will increase the environmental impact in the lowlands. © 2011 Elsevier B.V. All rights reserved.
1. Introduction ☆ This paper is part of the special issue entitled: Assessment for Sustainable Development of Animal Production Systems, Guest Edited by Akke van der Zijpp. ⁎ Corresponding author at: Animal Production Systems Group, Wageningen University, PO Box 338, 6700 AH Wageningen, The Netherlands. Tel.: +31 317 484589; fax: +31 317 485550. E-mail address: [email protected] (I.J.M. de Boer). 1871-1413/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.livsci.2011.03.008
In mountain areas, such as the Alps, traditional dairy farming is characterized by keeping milking cows and young stock indoors in the lowland most of the year, generally without access to pasture. During summer, however, animals
C. Penati et al. / Livestock Science 139 (2011) 142–149
are transferred from the barn in the lowland to pasture in the highland. Highland grazing is of interest to dairy farmers because it offers access to special subsidies for ecosystem services such as maintaining biodiversity and conservation of the landscape in mountain areas. Farmers, furthermore, obtain additional revenues by producing special branded, high-value cheeses from milk produced during highland grazing (Pretolani and Raffaelli, 2007). To maintain profitability, dairy farms in mountain areas have shown in recent decades a trend, shown also in dairy farming in general, towards enlarging milk quota, increasing stocking density, and increasing milk production per cow (Cozzi et al., 2006). Enlargement and intensification, together with harsh living conditions of farmers during the period of highland grazing (Talamucci, 1997) and lack of successors (Bernués et al., 2005), contributed to a gradual reduction in the number of dairy farms that graze cattle in the highlands (Gios and De Ros, 1991). The decrease of highland grazing of cattle is a risk for European mountain areas because livestock plays a central role in the conservation of their natural landscape and biodiversity (Casasùs et al., 2007; Mc Donald et al., 2000). The end of highland grazing, moreover, may affect farm profitability and have environmental consequences in the lowland, where dairy farming is concentrated. The objective of this study was to assess the economic and environmental effects of abandoning highland grazing of dairy farms in Alpine areas. We compared economic and environmental indicators of farms that still apply highland grazing with those of farms that do not apply highland grazing. 2. Material and methods 2.1. Material This study involved a sample of 28 dairy farms, of which 12 applied highland grazing (HG) of dairy cows and 16 applied no grazing (NG), neither in highland nor in lowland areas. All farms were located in the central Italian Alps (i.e. Valtellina and Valchiavenna). Using experts, these 28 farms were selected out of 47 farms that sold their milk produced in the lowland to the same local cheese factory in order to represent typical HG and NG farms. The main breed on HG farms was Brown Swiss (85%), whereas NG farms used mainly Holstein–Friesians (53%) and Brown Swiss (33%). Data were collected for 2006. Data regarding land use (i.e. ha grassland and ha maize land) and number of animals present at the farm were retrieved from the generic agricultural information system of Lombardy (SIARL, 2006). Data required to compute revenues from milk production in the lowland, e.g. milk price, amount and composition of milk produced, were retrieved from the local cheese factory. Furthermore, one interviewer visited each farm between June and July 2007 and collected data needed to evaluate the environmental and economic performance of each farm (for detailed description see Sections 2.2 and 2.3) based on individual administration of each farm (e.g., invoices). Data collected for environmental evaluation were amount of purchased roughages, concentrates, synthetic fertilizers, litter, amount of purchased and sold animals, and number of days of highland grazing. Additional data required for
143
economic evaluation were amount of milk produced in the highland, amount of purchased pesticides, energy sources, and prices of purchased inputs. 2.2. Environmental indicators A farm-gate nutrient balance was used to assess the environmental performance of a farm (Oenema et al., 2003; Schröder et al., 2003; Thomassen and De Boer, 2005). Such a nutrient balance measures the difference between nutrient inputs and outputs of a farm, i.e. annual nutrient surplus, which is assumed to be lost into the environment (Berentsen and Tiessink, 2003; Ondersteijn et al., 2002). Nitrogen (N) surplus was assumed to be lost by gaseous emissions to the air (i.e. NH3, N2O and NOx), by leaching and runoff of nitrate to surface or groundwater, or by storage in the soil. Phosphorus (P) surplus was assumed to be lost by run-off to surface water, by storage in the soil or, in case of P-saturated soils, by leaching to groundwater. For each NG farm, we quantified inputs and outputs, and the resulting total annual surplus of N and P. We computed separate N and P balances (i.e. NP balances) for the lowland and for the highland on HG farms because of their different geographical location, and because of the fact that a nutrient balance is mainly directed at the local environmental impact, i.e. leaching of nitrate or phosphate and emission of NH3 (De Boer, 2003). The NP balance for the lowland was expressed per ha lowland, whereas the NP balance for the highland was expressed per ha highland. Most farms rented pastures in the highland individually, and, therefore the highland area was known precisely. Two farmers, however, rented the same pastures in the highland. For these farms, the highland area was allocated by relative livestock numbers. To gain insight into NP efficiency at farm level, we also expressed NP balance for the lowland per ton FCM produced in the lowland, and the NP balance in the highland per ton FCM produced in the highland. The NP balance for the highland was relatively simple to compute because, in addition to grazing, only a small amount of concentrates was fed to dairy cows (1.0 kg/d, on average). No bedding material and no synthetic fertilizer was used, moreover, and no manure was exported. Nutrient output through purchased or culled animals in the whole year was assigned to the balance for the lowland. Relevant inputs and outputs included in the nutrient balance of a dairy farm, and their assumed N and P content, are in Table 1. For each farm, N input through purchased concentrates, for example, was computed by multiplying the amount of each concentrate purchased by its N content. Kjeldahl nitrogen analysis was used to determine the N content of roughages, whereas ICP-MS (inductive coupled plasma mass spectrometry) was used to determine the P content. The N content of concentrates was based on feed labels, whereas the P content of concentrates was based on Cornell-Penn-Miner (2004). NP input via mineral supplements was included in NP of purchased concentrates. A fixed N deposition of 20 kg per ha and a fixed N fixation of 15 kg per ha of grassland was assumed (see Table 1). The N and P outputs were computed similarly by multiplying the amount of milk sold or animals removed by their N and P contents. The N content of milk produced in the lowland was obtained from the local cheese factory, whereas the protein content of milk produced in the highland was
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Table 1 Information required and sources used to compute the nitrogen (N) and phosphorus (P) surplus of a dairy farm. Variable
Information required
Source
Input Purchased feed
Q a and NP content purchased feed
Purchased synthetic fertilizer Purchased bedding material
Q and NP content synthetic fertilizer Q and NP content of straw and sawdust
Purchased animals
Q and NP content purchased animals
Nitrogen fixation Atmospheric deposition
20 kg N ha− 1 grassland (in lowland and in highland) no. of hectares grassland 15 kg N ha− 1 grassland; no. of hectares of grassland
Roughages N: Kjeldahl P: ICP-MS Concentrates N: feed label; P: Cornell-Penn-Miner (2004). CPM Dairy, version 3.0.6. Q: interviews NP fertilizer: Q: interviews NP straw: Baldoni and Giardini (2002). AOAC (Association of Official Analytical Chemists) (1998). NP sawdust: CVB (Centraal Veevoeder Bureau) (1991). AOAC (Association of Official Analytical Chemists) (1998) Q: interviews NP animals: Cornell Nutrient Management Spear Program (2007). Q: interviews Bassignana et al. (2004); Grignani et al. (2003) no. hectares: SIARL (2006) DM 19 April 1999; no. hectares: SIARL (2006)
Output Cow milk sold
Lowland: Q and NP content of milk sold
Highland: Q and NP content of milk processed
Sold/dead animals
Q and NP content of sold animals
Total NP surplus
∑ NP inputs − ∑ NP outputs
a
N: data from local cheese factory (based on Oterholm (1994)) P: Schiavon (2010). Q: cheese factory database N: MIR spectrometry and Oterholm (1994); P: Schiavon (2010) Q: interviews NP animals: Cornell Nutrient Management Spear Program (2007). Q: interviews
Q = quantity of product purchased/sold.
determined using MIR (mid-infrared) spectrometry (Milkoscan TM4000, Foss, Denmark). Subsequently, milk protein content was converted into milk N content based on Oterholm (1994). A fixed P content of milk of 1.05 g per liter was assumed (Schiavon, 2010). 2.3. Economic indicators For each farm, profitability was assessed by quantifying labor income of the family farm (Mollenhorst et al., 2006; Van Calker et al., 2005). Labor income is defined as the difference between revenues and costs, excluding costs of labor supplied by the family and is the equivalent of returns to labor and management (Kay et al., 2004). Information required and sources used to compute the different revenues as well as variable and fixed costs are in Table 2. Most information required was obtained from farm interviews. Fixed costs for housing and machinery (i.e. depreciation, interest, and maintenance) and for interest of livestock were based on actual dimensions, types, and numbers, as given by farmers, and on standards regarding values and percentages of depreciation, interest, and maintenance (Hemmer et al., 2007). The interest rate was assumed to be 4.8%. Costs of land were standardized by assuming that all land was rented by the farmers, using an average rental cost obtained by interviews. 2.4. Statistical analysis Means of normally distributed general farm characteristics were compared between HG and NG farms using
independent T-test, whereas means of non-normally distributed farm characteristics were compared using the nonparametric Mann–Whitney Test. A general linear model (GLM) was used to determine the effect of grazing system G (i.e. HG versus NG) and other relevant farm characteristics on environmental (i.e. NP surplus per ha or per ton FCM) and economic indicators (i.e. labor income per farm or per ton FCM). Farm characteristics chosen as covariates in GLM were based on literature (Fangueiro et al., 2008; Nevens et al., 2005; Thomassen et al., 2009). To analyze environmental indicators, two covariates were included: milk production per ha (X1) and percentage of feed self-sufficiency (% FSS, X2). The % FSS was computed as the ratio of purchased feed (i.e. concentrates and roughages) to purchased and own-produced feed (i.e. roughages and highland grazing), both of them in terms of dry matter (DM). Dry matter intake from highland grazing was based on Vazquez and Smith (2000). For HG farms, NP balances were computed and analyzed separately for the lowland and the highland. GLM analyses of NP balances in the lowland, for example, were based on milk production per ha in lowland and % FSS in the lowland. To analyze economic indicators, three covariates were included: milk production per ha lowland (X1), percentage of feed self-sufficiency in the lowland (% FSS, X2), and total ha of lowland (X3). Total ha of lowland was included as an additional covariate to correct for farm size (Orland, 2004). Economic effects of milk production per ha in highland, total area of highland or % FSS in the highland were all captured in the effect of grazing system G.
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Table 2 Information required to compute revenues and costs, and sources used. Variable
Information required
Source
Revenues Cow milk
Q a and price of milk
Number of animals sold Dairy products summer grazing Common Agricultural Policy grant Other subsidies
Q and sale price of animals Q and price of dairy products Amount of the grant per farm Amount of the grant per farm
Cheese factory (dependent on quality parameters) Interviews Interviews SIARL (2006) Interviews
Q and relative prices
Interviews
Q and rental price Typology and dimension. Substitution value, depreciation, maintenance, insurance and interest. Q and typology. Actual prices, depreciation, maintenance, insurance and interest. Q, substitution value and interest.
SIARL (2006) and interviews Interviews Hemmer et al. (2007).
Variable costs Purchased feed, diesel, electricity, gas, purchased animals, medical expenses (medicines, veterinary, artificial insemination…), hired labor, seeds, synthetic fertilizers, pesticides, external contract work, other costs Fixed costs Land rent Housing and milking machinery Machinery Livestock
a
Interviews: Q, typology, actual prices. Hemmer et al. (2007) Interviews: Q. Substitution value from Camera di Commercio di Mantova (2007). Interest: 4.8% of full value.
Q = quantity.
Because of non-normality of environmental and economic indicators, they were log-transformed to achieve a normal distribution of residuals in the GLM. The GLM had the functional form: Y =μ +G+bX +e where Y is a set of log-transformed environmental or economic indicators; μ is the overall mean; G is the effect of grazing system; b is a set of coefficients for each covariate; X is a set of covariates, and e is a set of residuals. Statistical analyses were performed using SPSS (SPSS, 2007). 3. Results and discussion 3.1. Farm characteristics Mean and standard deviation of general farm characteristics for the 12 farms that applied highland grazing (HG) of milking cows, and for the 16 farms that applied no grazing (NG) are in Table 3. Milking cows on HG farms grazed on average 92 days. For these farms, therefore, general farm characteristics are shown for the farm as a whole, and for lowland and highland separately. For HG farms, use of highland pastures was extensive compared with use of the lowland area. Milk production per ha of highland (0.7 ton), for example, was about 8% of production per ha of lowland (8.4 ton). This distinct difference was the reason that most comparisons between HG and NG farms were done for lowland area only. Compared with HG farms, NG farms had higher total milk production (370.7 vs 141.4 ton FCM; p = 0.095), higher milk production per ha (13.9 vs 8.4 ton FCM ha− 1; p = 0.012) and higher annual milk yield per cow (6.3 vs 4.5 ton FCM; p = 0.000). These differences are in line with the general
trend of enlargement and intensification of dairy farms in mountain areas as described by Cozzi et al. (2006). Similar results were found by Giustini et al. (2007) for farm in Italian mountain areas. Compared with farms located at high altitude and summer grazing, farms located at a low altitude without grazing had a higher total milk production (284.1 vs 136.2 ton year− 1), a higher milk production per ha (5.7 vs 2.6 ton ha− 1) and higher annual milk yield per cow (5.6 vs 4.8 ton per cow). Compared with HG farms, NG farms had higher roughage production per ha (11.5 vs 8.9 ton DM ha− 1; p = 0.038), and a higher concentrate use per ha (6.1. vs 1.2 ton DM ha− 1; p = 0.000). The higher milk yield per ha of NG farms (13.9 ton) required a higher total amount of feed per ha. The higher DM roughage production per ha lowland on NG farms was caused partly by the higher fertilization rate from animal manure due to a higher milk production per ha, since there are no differences in the utilization of synthetic fertilizer. Literature shows that a higher milk yield per ha is associated with a higher NP excretion per ha (Bannink et al., 1999; Valk et al., 2002). Furthermore, the DM roughage production per ha was higher on NG farms because of the higher percentage of land planted with maize for silage compared with HG farms. DM yield per ha of maize land is higher than DM yield per ha of grassland (Bassanino et al., 2008). Concentrate use per kg FCM and % FSS, however, did not differ between NG and HG farms (Table 2). 3.2. Environmental indicators Mean and standard deviation of N balance for all NG farms, and of N balance for the lowland and highland for all HG farms are in Table 4, similar results for P are in Table 5. Because of the extensive manner of milk production in highland of HG farms (i.e. 0.9 ton FCM/ha, see Table 3), the N
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Table 3 Mean (standard deviation) of general farm characteristics for non-grazing (NG) and highland grazing (HG) farms. For HG farms, results are presented separately for lowland and highland area. General farm characteristic (unit)
NG (n = 16) mean (SD)
HG (n = 12)
Milking cows (n) Grazing days (no. d) Total milk production (ton FCM) a Land (ha) Maize land (%) Roughage production (kg dry matter ha− 1) Concentrate use (ton dry matter ha− 1) Concentrate use (kg dry matter kg FCM− 1) Synthetic fertilization (kg N ha− 1) LU b per ha (no. ha− 1 year− 1) Milk yield (ton FCM cow− 1 year− 1) Milk yield/ha (ton FCM ha− 1) Feed self-sufficiency (%)
54.9 (56.6) N.A. a 370.7 (405.5) ⁎ 27.4 (31.1) 21.0 (16.4) ⁎ 11.5 (3.3) ⁎⁎ 6.1 (3.8) ⁎⁎⁎ 0.43 (0.13) 15.0 (18.0) 3.04 (1.15) 6.3(1.2) ⁎⁎⁎ 13.9 (7.4) ⁎⁎⁎ 65.3 (17.1)
Total mean (SD)
Lowland mean (SD)
Highland mean (SD)
31.2.(14.9) 92 (23) 141.4 (75.3) ⁎ 54.6 (27.9) N.A. N.A. N.A. N.A. N.A. N.A. 4.5 (0.7) ⁎⁎⁎ N.A. N.A.
31.2 (14.9) N.A. 116.5 (62.0) 13.7 (5.5) 9.9 (11.8) ⁎ 8.9 (2.7) ⁎⁎ 1.2 (0.9) ⁎⁎⁎
28.1 (16.1) 92 (23) 24.9 (18.3) 40.9 (26.4) 0 N.A. N.A. N.A. 0 0.25 (0.07) 0.9 (0.3) 0.7 (0.3) 95.3 (4.8)
0.42 14.8 2.79 3.7 8.4 62.4
(0.12) (16.9) (1.58) (0.9) (2.5) ⁎⁎⁎ (16.0)
a
N.A. = Not applicable; FCM = fat-corrected milk. b LU = Livestock Units (included young stock). For HG farms LU in lowland, for example, was a weighted average of LU per ha in non-grazing period, and LU in grazing period (i.e. 0), using number of grazing days as weighing factor. ⁎ Means differ using p b 0.10. ⁎⁎ p b 0.05. ⁎⁎⁎ p b 0.01 (means of highland were not tested).
and P surplus per ha was negligible. Comparison of environmental indicators of HG versus NG farms, therefore, focuses on the NP balances in the lowlands only. For both HG and NG farms, the main N input of the lowland N balance was purchased concentrates (i.e. 58 and 65% respectively), whereas milk sold was the main N output (i.e., 80–84% respectively, Table 4). The N surplus averaged 186 kg N ha − 1 year − 1 for NG farms compared with 137 kg N ha− 1 year− 1 for lowland of HG farms. From GLM analyses, we concluded that differences in N surplus per ha among farms were not due to the grazing system (p = 0.92), but rather resulted from differences in milk production per ha (p = 0.002), and in % FSS among individual farms (p = 0.000).
Because % FSS did not differ between NG and HG farms, the higher milk production per ha of NG compared with HG farms, therefore, explained the higher N surplus per ha of NG farms (186 kg N ha− 1 year− 1) compared with HG farms (137 kg N ha− 1 year− 1). For both HG and NG farms, the main P input of the lowland P balance was purchased concentrates (i.e. 82% and 79, respectively), whereas milk sold was the main P output (i.e. 77 and 80%, respectively; Table 4). The P surplus averaged 30 kg P ha− 1 year− 1 for NG farms compared with 24 kg P ha− 1 year− 1 for lowland of HG farms. From GLM analyses, we concluded that difference in P surplus per ha among farms were not due to the grazing system (p = 0.46),
Table 4 Mean (standard deviation) of nitrogen (N) balance of non-grazing (NG) and highland grazing (HG) farms. For HG farms, a separate N balance is presented for the highland and lowland area. HG (n = 12) Variable
NG (n = 16) Mean (SD)
Input (kg N ha− 1 year− 1) Concentrates Forages Synthetic fertilizer Bedding material Animals Nitrogen fixation Deposition Total Output (kg N ha− 1 year− 1) Milk Animals Total Surplus •kg N ha− 1 year− 1 •kg N ton FCM− 1 yr− 1 a a
FCM = fat-corrected milk.
Lowland %
181.8 (132) 39.8 (65.5) 15.1 (18.0) 5.8 (4.9) 3.8 (4.7) 14.3 (7.5) 20.0 (0.0) 280.6 (199.6)
64.8 9.1 5.9 2.6 1.2 6.8 9.6 100
79.4 (42.8) 15.3 (10.4) 94.7 (52.0)
84.4 15.7 100
186.0 (152) 12.5 (4.6)
Mean (SD) 111 29.3 14.8 5.4 2.8 13.5 20.0 196.6
Highland %
Mean (SD)
%
(46) (46.3) (16.9) (4.3) (3.9) (1.8) (0.0) (86.1)
57.7 10.7 6.7 3.3 1.7 7.9 11.9 100
1.1 (1.2) 0 0 0 0 3.8 (0.9) 5.0 (1.2) 9.9 (2.2)
11.0 0 0 0 0 38.1 50.9 100
48.4 (14.6) 11.5 (5.6) 59.9 (14.5)
80.3 19.7 100
3.5 (1.4) 0 3.5 (1.4)
100 0 100
136.8 (75.9) 16.2 (6.3)
6.4 (2.4) 11.5 (7.3)
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Table 5 Mean (standard deviation) of phosphorus (P) balance of non-grazing (NG) and highland grazing (HG) farms. For HG farms, a separate P balance is presented for the highland and lowland area. HG (n = 12) Variable
NG (n = 16)
−1
year Input (kg P ha Concentrates Forages Synthetic fertilizer Bedding material Animals Total −1
%
Mean (SD)
36.5 (22.1) 4.6 (7.6) 5.8 (6.8) 0.8 (0.6) 1.0 (1.2) 48.6 (33.0)
78.7 6.5 11.0 2.1 1.8 100
26.1 3.8 2.0 0.7 0.7 33.3
14.9 (8.2) 3.8 (2.5) 18.7 (10.4)
80.3 19.7 100
9.0 (2.6) 2.9 (1.4) 11.9 (2.7)
Highland %
Mean (SD)
%
−1
) (12.8) (5.5) (5.7) (0.6) (1.0) (20.4)
82.4 9.1 3.3 2.7 2.5 100
0.4 (0.4)
100
0.4 (0.4)
100
0.6 (0.2) 0 0.6 (0.2)
100 0 100
– – – –
−1
Output (kg P ha year ) Milk Animals Total Surplus •kg P ha− 1 year− 1 •kg P ton FCM− 1 year− 1 a a
Lowland
Mean (SD)
29.9 (24.1) 2.1 (1.1)
77.4 22.5 100
21.4 (18.7) 2.4 (1.5)
− 0.15 (0.36) − 0.25 (0.69)
FCM = fat-corrected milk.
but rather resulted from differences in milk production per ha (p=0.07) and in % FSS (p=0.006). The difference in P surplus per ha between HG and NG farms, therefore, can be explained by the difference in milk yield per ha between these farms. The result that the NP surplus of a dairy farm in mountain areas was affected mainly by milk production per ha and % FSS was found also by Giustini et al. (2007). They concluded that differences in NP surplus among farms located in lowland (136 kg N ha− 1 year− 1; 73 kg P ha− 1 year− 1) and farms located in the upland (53 kg N ha− 1 year− 1; 27 kg P ha− 1 year− 1) resulted from differences in milk production per ha, in % purchased feed and in fertilization rate. To gain insight into NP efficiency at farm level, NP surplus of farms was evaluated also per ton of FCM. The N surplus averaged 12.5 kg N ton− 1 FCM year− 1 for NG farms compared with 16.2 kg N ton− 1 FCM year− 1 for HG farms in lowland (Table 4). These results are comparable with those from Segato et al. (2009), obtained in a plain area in the North of Italy, i.e. 15.6 kg N ton− 1 FCM year− 1 for maize-based systems and 17.6 kg N ton− 1 FCM year− 1 for hay-based systems. The P surplus averaged 2.1 kg P ton− 1 FCM year− 1 for NG farms compared with 2.4 kg P ton− 1 FCM year− 1 for HG farms in lowland. From GLM analysis, we concluded that differences in NP surplus per ton FCM among farms were not affected by the grazing system (N: p=0.35; P: p=0.90), but rather resulted from differences in % FSS among individual farms (N: p=0.001; P: p=0.012). As FSS increased NP surplus per ton FCM decreased. Farms can increase their % FSS by increasing own-farm roughage production by optimizing fertilization management, improving conservation strategies or increasing the percentage of land planted with maize (Jonker et al., 2002). Increasing the percentage of maize land in the lowland of the Alps, however, raises concern regarding landscape and biodiversity preservation (Pileri, 2008). Other general measures to reduce NP surplus per ton FCM could be feeding cows in according with their NP requirements (Arriaga et al., 2009) or increasing feed efficiency of dairy cows. Furthermore, exporting manure or outsourcing young stock can contribute to a reduction of the NP surplus per ha of land (Kuipers and Mandersloot, 1999).
3.3. Economic indicators Comparison of cost benefit analysis showed that the main source of revenues was selling milk, which on average accounted for 82.5% of the revenues for NG farms, and for 47% for HG farms (Table 6). For HG farms, the second most important source of revenues originated from production of cheese and other milk products (27.5%) during the period of highland grazing. Moreover, HG farms had higher revenues from “other subsidies” than NG farms (11.9% versus 3.4%). These revenues were mainly subsidies given for grazing in the Alps, which depend on the amount of land used for grazing and on stocking density. On average, the majority of variable costs were for costs for purchased feed (48% for HG and 55% for NG), followed by costs for energy sources, such as diesel, electricity, gas (12% for HG and 15% for NG). Main fixed costs were due to depreciation, maintenance, and insurance of housing structures (38.8% for HG and 41.5% for NG) and machinery (29.1% for HG and 28.5% for NG). The resulting labor income per farm was lower for HG farms (30.3 k€) than for NG farms (72.2 k€). Expressed in euro per ton FCM, however, labor income was higher for HG (0.24 €) than for NG farms (0.16€), which was due mainly to extra revenues from highland cheese and Alpine grazing subsidies. From GLM analyses, we concluded that differences in labor income among farms were explained by the grazing system (p = 0.024), farm size (p = 0.000) and milk production per ha of lowland (p = 0.003), whereas % FSS did not affect labor income per farm (p = 0.41). Differences in labor income per FCM were only due to the grazing system (p = 0.03). Hence, a larger farm size and a higher milk production were associated with a higher labor income per farm. The smaller farm size and lower milk production per ha of HG compared with NG farms (Table 3), therefore, explained the lower labor income per farm for HG farms compared with NG farms. Highland grazing resulted in extra revenues, and explained the higher labor income per kg FCM for HG compared with NG farms. Data did not include any information on family labor input. Such information could have been helpful to place into
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Table 6 Mean (standard deviation) of results from cost benefit analysis of non-grazing (NG) and highland grazing (HG) farms. NG (n = 16)
HG (n = 12)
Mean (SD)
%
Mean (SD)
163.5 (179.3) N.A. a 12.7 (18.0) 15.5 (19.1) 5.5 (6.6) 197.2 (218.8)
82.5 N.A. 6.1 7.8 3.4 100
48.8 (28.9) 28.4 (17.3) 8.3 (4.1) 5.6 (2.6) 12.2 (4.2) 103.3 (48.0)
47.2 27.5 8.0 5.4 11.9 100
37.3 (32.8) 10.3 (10.9) 9.4 (12.0) 6.1 (7.1) 3.9 (5.3) 1.2 (4.6) 4.7 (6.3) 72.8 (73.8) 125.5(150.6)
54.7 14.9 11.0 8.0 3.9 0.4 7.0 100
20.7 (12.1) 4.8 (2.3) 4.2 (5.4) 3.4 (2.3) 0.7 (1.0) 5.2 (13.8) 4.2 (2.9) 43.3 (30.0) 60.0 (23.9)
47.9 11.6 9.8 7.8 1.6 11.9 9.8 100
5.4 (5.9)
11.1
4.5 (1.7)
15.0
Depreciation, maintenance, insurance Housing structures Machinery Milking machinery/tank
22.0 (24.1) 15.2 (16.8) 5.0 (5.8)
41.5 28.5 8.9
11.6 (6.1) 8.7 (5.3) 2.4 (1.6)
38.8 29.1 7.9
Interest Livestock Machinery and housing Total
4.5 (5.4) 1.0 (1.1) 53.3 (57.1)
8.2 1.9 100
Revenues (in k euro year− 1) Cow milk Highland cheese Sold animals Common Agricultural Policy grant Other subsidies Total Variable costs (in k euro year− 1) Purchased feed Diesel, electricity, gas Purchased animals Medical expenses Seeds, fertilizer, pesticides, contract work Hired labor Other variable costs Total Gross margin (in k euro year− 1) Fixed costs (in k euro year− 1) Land rent
Labor income •per farm (in k euro year− 1) •per ton FCM (in euro ton FCM− 1) a
a
72.2 (94.8) 0.16 (0.07)
2.2 (1.3) 0.5 (0.3) 29.8 (148.1)
%
7.3 1.8 100
30.3 (6.4) 0.24 (0.13)
N.A. = Not applicable; FCM = fat-corrected milk.
perspective the difference in labor income between HG and NG farms. As labor input, on average, is higher on larger farms, NG farms, which are larger than HG farms, on average apply more family labor input to run their farms. On the other hand, highland grazing appears very laborious. From the results, it appears that farms that increase their farm size in the lowland or their milk production per ha abandon highland grazing. An increase in milk production increases net farm income and also compensates for the loss of additional revenues from highland cheese and Alpine grazing subsidies. Enlargement of farms, however, is hampered by increasing urbanization of the valley area of the Italian Alps (Varotto, 2002). Due to lack of a successor, some HG farms will stop farming (Bernués et al., 2005), giving remaining farms the opportunity to enlarge and intensify. 4. Conclusion Results show that a high milk production per ha and a low % FSS were associated with a high NP surplus per ha, whereas the grazing system did not affect NP surplus per ha. A high milk production per ha and a large farm size, furthermore, were associated with a high labor income of the farm, whereas highland grazing was associated with a high labor income per kg FCM. These results illustrate the current practice dairy farmers in
the Italian Alps are facing at the moment. Farmers tend to increase their net farm income by increasing their milk production per farm, via increasing their area of lowland and/or their milk production per ha, while at the same time they abandon highland. As enlargement of farm area in lowland is hampered by increasing urbanization of the valleys of the Italian Alps, farmers probably will increase their milk production per ha. A higher milk production per ha, however, will increase the environmental impact in the lowlands, which can be compensated by increasing own-farm roughage production, optimizing fertilization, increasing feed efficiency, exporting of manure or outsourcing of young stock. Conflict of interest Furthermore, we state that the authors or the author's institution have no financial or other relationship with other people or organizations that may inappropriately influence the author's work. Acknowledgments The authors express their gratitude to all farmers who participated in the project and to the director of the cheese factory, Marco Deghi.
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