www.elsevier.com/locate/agee

Agriculture, Ecosystems and Environment 122 (2007) 282–294

Nitrogen balances at the crop and farm-gate scale

in livestock farms in Italy

Monica Bassanino *, Carlo Grignani, Dario Sacco, Erica Allisiardi

Department of Agronomy, Forest and Land Management, University of Turin, 44, via L. da Vinci, 10095 Grugliasco, Italy

Received 23 January 2006; received in revised form 3 January 2007; accepted 26 January 2007

Available online 23 March 2007

Abstract

Nutrient balances are often used to represent nutrient flows and to produce sustainability indicators. A soil surface nutrient budget (at the

crop scale) and a farm-gate budget (at the farm scale) were calculated over 41 commercial Italian livestock farms. The objectives were to

estimate the N use efficiency of the main farm types using the two balances independently, and to assess and discuss the relationship between

the two different budget methods. The N surpluses calculated as a farm-gate balance (FGBS) or at the soil surface scale (CBS) ranked

livestock farms in a similar manner. The suckling cow farms (SC) showed the best sustainability, BB (beef breeding) and DC (dairy cow)

farms were intermediate, while PB (pig breeding) farms were the worst due to their weaker link between breeding activities and farm crops.

The CBS was mainly influenced by the manure input, while the FGBS was mainly influenced by the purchased animal feeding in the PB, BB

and DC farms, and by the mineral fertiliser in the SC farms. Other information can be derived from a combination of the N flow quantified in

the farm-gate balance and the crop balance; two examples are given concerning an estimation of gaseous losses and of animal N excreta for the

different animal categories. It has been concluded that even though N balances cannot be directly used to estimate the actual environmental

impact of different farming systems, they remain reliable indicators to help discriminate between different farm types.

# 2007 Published by Elsevier B.V.

Keywords: Nitrogen; Livestock farms; Farm-gate balance; Crop balance; Nutrient surplus

1. Introduction

Agriculture is considered the main nitrogen (N) source of

pollution in water bodies and air (e.g., Carpenter et al., 1998;

OECD, 2001). Intensive farming systems, from high-

yielding agriculture and animal breeding, have led to a

serious imbalance of the main nutrients, above all N, but also

phosphorus (P) (Oenema et al., 1998; Aarts et al., 2000; van

Keulen et al., 2000). Therefore, over the last decade,

environmental policy measures in Europe have been

directed towards trying to protect water quality, and have

mainly been focused on the means of preventing the

excessive use of nutrients, and N in particular. According to

these strategies, nitrate vulnerable zones (NVZs) and

specific action programmes (EEC 91/676) have been

* Corresponding author. Tel.: +39 011 6708776; fax: +39 011 6708798.

E-mail address: monica.bassanino@unito.it (M. Bassanino).

0167-8809/$ – see front matter # 2007 Published by Elsevier B.V.

doi:10.1016/j.agee.2007.01.023

defined and are now in the process of being re-defined.

Therefore, the efficacy of the local agro-environmental

legislations has to be periodically reconsidered. Scientific

data are needed to lead the implementation of these policies.

Moreover, stakeholders and agronomists are today increas-

ingly interested in the environmental performance of the

different farming systems: they need easy-to-apply and

reliable indicators that are useful to produce improvements

in the farmers technical skills, to represent N flows as

sustainability indicators and to evaluate and possibly

enhance the local application of environmental legislation

(e.g., Brouwer, 1998; van der Molen et al., 1998; Halberg

et al., 2005).

Among the different types of nutrient budgets described

by van Eerdt and Fong (1998) and Oenema et al. (2003), the

soil surface nutrient budget (at the crop scale) and the farm-

gate budget (quantifying whole-farm inputs and outputs) are

the most commonly used. There are several examples of the

M. Bassanino et al. / Agriculture, Ecosystems and Environment 122 (2007) 282–294 283

application of these types of nutrient balances throughout

Europe, according to various scales, but most frequently at

the field scale (e.g., Bengtsson et al., 2003; Buciene et al.,

2003; Holmqvist et al., 2003; Kutra and Aksomaitiene,

2003; Sieling and Kage, 2006) or the farm-gate scale (e.g.,

Simon et al., 2000; Beegle et al., 2002; Gustafson et al.,

2003; Hedlund et al., 2003; Grignani et al., 2005), and only

seldom at the territorial scale (Keller and Schulin, 2003;

Sacco et al., 2003). They are usually adopted as policy tools

to reduce N leaching risks and to monitor the effectiveness

of environmental measures (e.g., van der Meer and van der

Putten, 1995; van Beek et al., 2003; Koelsch, 2005), also at

the long-term scale (Smaling and Oenema, 1997), but they

have been recognised as being important even by the farmers

themselves (Chavanes, 1991; Beegle et al., 2000) especially

if not compulsory (Halberg et al., 2005). For instance, farm

scale budgets have been adopted in The Netherlands in the

MINAS N budget system (Ondersteijn et al., 2002). In

Piedmont, North Italy, where animal husbandry and

intensive agriculture are widespread, the Nitrate Directive

has been applied using the soil surface budget approach

(Regione Piemonte, 2002).

The soil surface budget approach potentially produces

interesting information for a more efficient use of fertilizers

and animal manure on the different crops (e.g., Bechmann

et al., 1998). However, it also has some weak points, such as

the difficulty in predicting the real inputs of animal manure

N or the off-field ammonia volatilisation losses, and it does

not give any information on animal feeding efficiency

(Grignani and Bassanino, 2000; Oenema et al., 2003). The N

balance at a farm scale avoids these problems, allows

comparisons of alternative nutrient management options

(Dou et al., 1998; Koelsch and Lesoing, 1999) and it is easier

to calculate, but it does not give information on the N

management of the single crops.

In this context, the objectives of this research were: (a) to

compare N use efficiency of the main livestock farms in NW

Italy at a given time using the two N budgets, a soil surface

crop balance and a farm-gate balance, independently; (b) to

assess and discuss the relationships between the two

different budget methods when applied to commercial

farms.

2. Materials and methods

2.1. Description of the farming system

The study was performed in 2005 in the western Po river

plain (Piedmont, NW Italy), between 458000N and 458500Nand 88000E and 88500E. In the plane area of Piedmont the

climate is temperate sub-continental, characterized by two

main rainy periods in spring (April–May) and autumn

(September–November), with an annual mean precipitation

of 850 mm and an annual mean temperature of 11.8 8C. The

alluvial plain soil types are Inceptisols (47% of the

agricultural land), Entisols (26%) and Alfisols (24%),

followed by Mollisols and Vertisols (3%). The ploughed

horizon has average values of organic matter (2.24� 0.97%),

is poor in K (0.23 � 0.21 mequiv. 100 g�1), but rich in P

(36.9 � 30.4 ppm Olsen P) (Average data 1983–2005,

Regione Piemonte, 2006). Agriculture is characterized by

maize-based intensive farming. On average, maize covers

more than a half of the farm area, either in livestock or non-

livestock farms. Cut grasslands and winter cereals are also

widespread. About 350 mm year�1 of irrigation is generally

supplied in summer (Merlo and Allavena, 2001). NVZs cover

15% of the agricultural land.

According to local advisors, 41 farms were selected as

they were considered typical of the main livestock farms in

the area: beef breeding (BB), dairy cows (DC), suckling

cows (SC) and pig breeding (PB). Only farms located

outside the NVZs were chosen, in order to avoid farms in the

process of reorganization after the local application of the

Nitrate Directive in 2002. The BB farms in Piedmont breed

Garronaise and Limousine beef bulls, bought at 300 kg live

weight (l.w.) and bred to 650 kg l.w. The DC farms breed

highly productive Holstein–Friesian cows. Both farm types

adopt a zero-grazing, totally indoor breeding system, and the

animals are mainly fed with maize silage and concentrates,

which allow very high livestock rates. The SC farms are

small and medium-sized traditional farms; Garronaise,

Limousine or Piemontese herds have suckler cows and

calves, which are born on the farm and bred until

slaughtering weight (600 kg l.w.). Some of the SC farms

interrupt continuous housing with pasture feeding for some

weeks in autumn and spring. The PB farms breed sows, or

buy piglets at 25–30 kg l.w.; in both cases, fattening pigs are

bred until they reach a slaughtering weight of 165 kg, and

these are used for Parma ham production.

The main characteristics of the four different farm types

are described in Table 1. The BB and DC farms have similar

farm areas (a mean value of 80 ha) but different livestock

rates (3.6 and 9.0 LSU ha�1 of farm area, respectively). The

SC is the smallest (57 ha) but the most extensive

(2.3 LSU ha�1) farm type. The PB farms have the largest

total area (farm area plus the extra-land acquired for manure

spreading), and they are very intensive (21.4 LSU ha�1 of

farm area, which decreases remarkably to 6.9 with extra-

land).

As the pedoclimatic conditions do not differ substantially,

the crops are mainly chosen on the basis of animal feeding

requirements. Maize (Zea mays L.) is the most frequently

cultivated crop, with up to 50% of the farm area being

devoted to this cultivation. Maize is used for grain or silage

production. It can also be cropped in DC and SC farms in

combination with Italian ryegrass (Lolium multiflorum

Lam.) during winter, if farm roughage is needed. Where

maize is not a profitable crop, farms have grassland (cut for

silage and/or hay) and winter cereals such as winter wheat

(Triticum aestivum L.) and barley (Hordeum vulgare L.).

Winter cereals are used in cattle farms to produce litter, as

M. Bassanino et al. / Agriculture, Ecosystems and Environment 122 (2007) 282–294284

Table 1

Main characteristics of the different farm types

Beef breeding Dairy cow Suckling cow Pig breeding

Farms (no.) 5 9 16 11

Farm area (ha) 85 (66) 75 (75) 57 (40) 53 (40)

Extra-land for manure spreading

ha 0 4 (7) 0 62 (49)

% of total area – 9 (13) – 50 (35)

Maize

% of farm area 45 (26) 47 (24) 32 (15) 48 (22)

Grain (t DM ha�1) 7.8 (2.1) 7.8 (2.1) 8.5 (2.2) 9.1 (1.4)

Silage (t DM ha�1) 17.3 (1.5) 16.7 (3.0) 16.1 (3.2) 16.6 (2.0)

Grassland

% of farm area 9 (11) 22 (23) 48 (23) 0

Hay (t DM ha�1) 10.4 (0.9) 9.0 (2.8) 8.3 (3.2) –

Winter cereals

% of farm area 31 (19) 17 (17) 11 (10) 19 (13)

Winter wheat (t DM ha�1) 4.5 (0.4) 4.0 (1.3) 4.7 (0.6) 4.5 (0.7)

Poplar trees (% of farm area) 0 0 0 17 (31)

Other crops (% of farm area) 15 (17) 14 (16) 9 (13) 16 (10)

Stocking rate

LSU ha�1 of farm area 3.6 (1.4) 9.0 (7.6) 2.3 (1.2) 21.4 (15.5)

LSU ha�1 of total area 3.6 (1.4) 7.0 (4.1) 2.3 (1.2) 6.9 (2.7)

N load

kg ha�1 of farm area 161 (59) 219 (202) 92 (62) 621 (367)

kg ha�1 of total area 161 (59) 173 (120) 92 (62) 213 (69)

Liquid:solid manure (%) 0:100 40:60 15:85 100:0

Milk production

kg cow�1 – 7700 (3100) – –

t ha�1 of farm area – 22.7 (20.6) – –

Meat production (t ha�1 of farm area) 2.1 (1.0) 0.5 (0.5) 0.3 (0.2) 11.7 (12.5)

Total area: farm area + extra-land acquired for manure spreading. DM: dry matter. LSU: livestock standard units, according to Eurostat (2004). Standard

deviation in brackets.

well as grain, whereas in PB farms they are used as crops for

summer distributions of liquid manure. The grassland areas

reach a significant value mainly in the DC and SC farms.

Poplar trees (Populus ssp.) cover 17% of the PB farm areas;

this crop is mainly used as a practical soil cover for

spreading liquid manure throughout most of the year.

The manure management strategies differ according to

the animal housing system: the DC farms produce either

liquid or solid manure, whereas the BB and SC farms usually

breed animals on litter; the small quota of liquid manure, if

produced, is traditionally redistributed over the solid

manure. The PB farms produce only liquid manure.

The specialized beef farms produce 2.1 t meat ha�1

year�1; a small quantity of meat or live animals are also

sold from the DC and SC farms (0.5 and 0.3 t ha�1 year�1,

respectively). The PB farms reach a high meat productivity,

around 12 t ha�1 year�1. The average milk production of the

DC farms is 7700 kg cow�1 year�1, close to the Holstein

standards. At the farm scale, instead, the milk production

(25.9 t ha�1 year�1 on average) is higher than Northern

Europe values. This is due to a higher farm livestock density,

based on the total indoor breeding system and on the high

consumption of maize silage and purchased concentrates. In

Italy, specialized dairy farms house 2.4–3.1 dairy cows ha�1

on average (Grignani et al., 2003b), but 3.3 dairy cows ha�1

is not uncommon (Xiccato et al., 2005); in Northern Europe,

1.5–1.7 cows ha�1 is the usual stock rate (Aarts et al., 2000;

Bos et al., 2003; Jarvis et al., 2003; Humphreys et al., 2003;

Nevens et al., 2006).

2.2. Data collection

The farmers, through direct interviews, were guided to fill

in a stated questionnaire with the following data concerning

the different farm N management compounds:

� f

arm characteristics (farm type, farm area, extra-land for

manure spreading);

� a

nimal production (livestock rate, live weight for each

animal category, animal housing, manure storage systems,

type of manure produced);

� c

rop production and management (crop rotation and crop

yield, crop residue management, organic and mineral

fertilization);

M. Bassanino et al. / Agriculture, Ecosystems and Environment 122 (2007) 282–294 285

� f

arm inputs (live animals, purchased feed, roughage, litter

and mineral fertilizers);

� f

Table 2

N content for the main crops and animal products in NW Italy

Cropsa N (% DM)

Wheat Grain 2.3

Residues 0.8

Barley Grain 2.1

Residues 0.8

Maize Grain 1.7

Residues 0.8

Silage 1.2

Sugar beet Beet 1.0

Residues 2.3

Soybean Grain 6.6

Residues 1.9

Pea Grain 2.2

Residues 2.0

Bean Grain 6.2

Lucerne Hay 2.8

Temporary grassland Hay 2.4

Permanent grassland Hay 2.2

Animal productsb N (%)

Meat Beef 2.4

Pork 3.0

a Grignani et al. (2003a).b Grignani (1996).

arm outputs (cash crops, meat, milk, live animals and

manure to extra-land).

A first farm visit was necessary to collect all the farm

data through: (a) direct observations (farm type, housing

and manure storage structures, manure type); (b) farm

accounts and official databases (all sold and purchased

products, farm initial and final stocks, farm land and

extra-land for manure spreading, milk protein content); (c)

each farmer’s personal evaluation (live weight, crop

rotation, animal diet, fertilizers management and crop

yields). On average 1.4 visits and 4.5 h per farm were

necessary.

Desk work was then performed in order to cross-verify

the data, and to compare all the information collected from

the farm accounts and sale receipts, as well as the animal

diet and the herd size and performance. As official controls

and a public register exist in Piedmont for the land acquired

from other farms for manure spreading, any farm data

concerning the acquisition of extra-land were verified in this

public register. As already stated (e.g., Grignani and

Bassanino, 2000; Mulier et al., 2003; Powell et al., 2006),

part of the information supplied by farmers in commercial

farms results to be inaccurate, or unknown, especially if the

farm products are not sold but recycled for animal feeding

and bedding. Further contact with the farmers and their feed

and crop consultants was therefore necessary (on average

2.9 h per farm, mainly through phone calls but also through

other farm visits) and this helped to solve most of the

problems.

When the farmers did not know the N content in the crops

and animal products, it was estimated according to standard

figures (Table 2). Standard data were always adopted for the

biological N fixation and the atmospheric N deposition, as

other authors have done (e.g., Domburg et al., 2000). All the

collected data were expressed as N fluxes (kg ha�1 year�1)

referring to the year before the interview, 2004, and were

expressed on a per-year basis. However, it was not possible

to relate the estimation of the internally recycled forage

production to any specific year. In this case, the estimation

referred to the average data of the previous 5-year period.

The change in the animal feed and forage stocks, from the

beginning to the end of the considered year, was specifically

quantified in each farm, but it was negligible as in all

instances a large amount of concentrates and forages was

purchased.

Losses during hay and silage storage were considered

negligible. Losses during animal feeding were included in

the manure production.

2.3. The soil surface indicator: the crop N balance

A simplified soil surface budget, the crop balance (CB),

was adopted according to Grignani et al. (2003a), and all

ingoing and outgoing fluxes were computed at the field level,

as follows:

CBS ¼ ðFcþ Foþ Adþ BfxÞ � ðYbÞ (1)

where CBS is the crop balance surplus; Fc the N from

mineral fertilizers; Fo the N from liquid and solid manure;

Ad the N from wet and dry atmospheric depositions (an

average figure of 30 kg N ha�1 was assumed, according to

Grignani et al., 2003a); Bfx the N biologically fixed by

legumes; Y the crop yield (after field losses) and b the N

content of the crop removed from the field.

As far as Bfx is concerned, 15 kg N ha�1 were considered

to be fixed in grassland areas if the legumes covered 1–5% of

the total surface, 40 kg if they covered 5–15%, 80 kg if it

was 15–25%. In pure legume crops (e.g., bean, soybean,

lucerne), the biologically fixed N was calculated as

follows:

Bfx ¼ ðYbÞ � Fo� Fc� Ad (2)

This assumption derives from the simplified ideas that

these crops tend to use N from fertilizers before fixing

atmospheric N (Meisinger and Randall, 1991) and that their

balance is equal to zero.

This CB method assumes a steady-state condition for soil

organic matter. For these reasons, only farms where no

important changes in soil and/or in fertilization management

had occurred in recent years were chosen (Kohn et al., 1997;

Domburg et al., 2000).

M. Bassanino et al. / Agriculture, Ecosystems and Environment 122 (2007) 282–294286

The farms were divided into subunits with a homogenous

rotation. The crop balance surplus (CBS) was defined for

each subunit.

According to Jarvis and Aarts (2000), each transfer of

nutrients provides a risk of inefficiency. Two efficiency

coefficients were therefore calculated, in a similar way to

Schroder et al. (2003). In order to evaluate the efficient

conversion of all the inputs into the products removed from

the field, the output/total input ratio was calculated, as

follows:

Yb

Fcþ Foþ Adþ Bfx(3)

In order to evaluate the efficient conversion of the

fertilizers into the products removed from the field, the ratio

between the purchased fertilizers or farm manure and the

crop was calculated as an output/fertilizers ratio, as follows:

Yb

Fcþ Fo: (4)

2.4. The farm scale indicator: the farm-gate balance

The farm-gate balance (FGB) is a useful method to

predict potential N losses from different farms (e.g., Barry

et al., 1993; Grignani and Acutis, 1994; van Faasen and

Lebbink, 1994; Weissbach and Ernst, 1994; Argenti et al.,

1996). The input data are all the N fluxes that enter the farm,

whereas the output data are all the N fluxes that leave the

farm. As it does not account for any internal N fluxes, this

balance is only suitable for steady-state soil conditions

(Barry et al., 1993; Watson and Atkinson, 1999).

As for the CB, the FGB adopts all the information

available on the farm, and only when this is lacking, does it

refer to bibliographic data. The FGB was calculated using

the criteria proposed by Simon and Le Corre (1992), as

follows:

FGBS ¼ ðFeþMain þ Liþ AFþ LAin þ Bfxþ AdÞ

� ðAPþ CPþ LAout þMaoutÞ (5)

where FGBS is the farm-gate balance surplus; Fe the

purchased mineral fertilizers; Ma the manure; Li the pur-

chased litter; AF the purchased animal feed and forages; LA

the live animals; Bfx the biological N fixation; Ad the

Table 3

N excretion, gaseous losses in the farm (during housing, removal and storage) a

N from the animal

(kg t�1 l.w. year�1)

Pigs 190

Dairy cows, >7000 kg milk per year 189

Dairy cows, 5000–7000 kg milk per year 171

Dairy cows, <5000 kg milk per year 145

Suckling cows 88

Other beef 124

atmospheric deposition; AP the sold animal products

(cow milk; pork and beef meat) and CP the sold crop

products.

A N use efficiency coefficient was calculated, according

to Ondersteijn et al. (2002) and Børsting et al. (2003), in

order to evaluate the efficient conversion of the total inputs

into sold products (meat, milk and cash crops), as follows:

APþ CPþ LAout

FeþMain þ Liþ AFþ LAin þ Bfxþ Ad: (6)

The manure output to extra-land was not included in this

coefficient, as it cannot be considered an efficient way of

exporting N (Kohn et al., 1997; Halberg, 1999).

In order to evaluate the efficient conversion of the

purchased inputs into the sold products, the following

indicator was also calculated:

APþ CPþ LAout

FeþMain þ Liþ AFþ LAin

: (7)

2.5. The estimation of manure N availability

Farm manure N availability can be calculated in two

ways: (a) according to animal N excretion official standards

(e.g., Schroder et al., 1996; Poulsen and Kristensen, 1997;

Brouwer, 1998; Domburg et al., 2000; van Beek et al., 2003;

Saam et al., 2005); (b) on the basis of a balance between

animal feeding and herd production (e.g., Watson and

Atkinson, 1999; Swensson, 2003; Powell et al., 2006). In the

crop balance, the manure N availability was calculated

according to official standards. In this way, the crop balance

was independent of the data collected for the farm-gate

balance. The adopted official manure standards (Table 3)

take into consideration the animal type, the animal live

weight, the housing system and the type of manure

produced. The average farm availability of manure N is

shown in Table 1 for the different farm types. The N load is

lower in the extensive SC farms (only 92 kg N ha�1),

whereas it increases in the intensive farm types: the BB, DC

and PB farms have N loads of 161, 219 and 621 kg ha�1,

respectively. As the DC and PB farms use extra-land for

manure distribution (9% and 50% of their final total areas,

respectively), their N load is reduced to 173 and

213 kg ha�1.

nd N available for the fields (after Regione Piemonte, 2002)

s Losses on

the farm (%)

N for the fields

(kg t�1 l.w. year�1)

41 112

45 104

45 94

45 80

33 59

33 83

M. Bassanino et al. / Agriculture, Ecosystems and Environment 122 (2007) 282–294 287

The manure N excretion was also calculated in the second

manner, using data collected both for the crop balance and

the farm-gate balance, as follows:

excreted N ¼ ðYbÞ þ LAin þ AF� AP� CP� LAout:

(8)

This estimation was then compared to the official manure

standards shown in Table 3.

2.6. The estimation of gaseous N losses

According to van Beek et al. (2003) and to Oenema et al.

(2003), the FGBS is expected to be the sum of the CBS and

the gaseous N losses (GL) at a farm scale (during manure

removal, storage and distribution in the field):

FGBS ¼ CBSþ GL: (9)

Theoretically, the difference between the FGBS and the

sum of the GL + CBS is zero (van Eerdt and Fong, 1998;

Koelsch, 2005). If the GL are known, the FGBS and the CBS

can be compared.

An independent evaluation of the farm GL was performed

based on the local official standard coefficients (Table 3), in

a similar way to Watson and Atkinson (1999), Aarts et al.

(2000) and Sheldrick et al. (2002). It was therefore possible

to verify Eq. (9).

2.7. Statistical analysis

The experimental unit of the work was the single

farm. All the data were reported as one observation per

farm and analysed with SPSS (2004), according to

Townend (2002).

All the data concerning the CB components were

averaged after having been weighted on the different crop

areas within each farm. The effect of the farm type on the

CB components and CBS was tested through standard

ANOVA procedures. Multiple comparisons between the

means were analysed with Bonferroni’s protected LSD test.

The FGB components and the FGBS could not be analysed

with standard ANOVA procedures, because they did not

Table 4

CB components (kg N ha�1 of farm area) for the different farm types

Beef breeding Dairy cow

Total input 334 311

Manure 198 ab 210 ab

Mineral fertilizer 78 57

Biological N fixation 29 ab 14 ab

Atmospheric deposition 30 30

Crop output 162 157

CBS 172 ab 154 ab

Output/total input ratio 0.48 0.50

Output/fertilizers ratio 0.59 0.59

Different letters show differences at P < 0.05.

result to be normally distributed on the basis of the

Kolmogorov–Smirnov test. As data transformation could

not solve this problem, the non-parametric Kruskal–Wallis

test was used. Multiple comparisons of the means were

analysed with the non-parametric Fisher’s protected LSD

test. A comparison between the FGBS and the sum of the

CBS and GL, and a comparison between the manure N farm

availability through standard coefficients and animal

balances were carried using a paired sample t test. The

hypothesis of the difference equal to zero was verified. The

relationship between the different parameters was tested

using regression analysis, while the bivariate correlation

between the FGB components was tested using the non-

parametric Spearman’s coefficient.

3. Results and discussion

3.1. Crop N balance (CB)

The CBS was significantly different for the different farm

types (Table 4). The PB farms reached the highest soil

surface surplus (230 kg N ha�1), whereas the BB and DC

farms showed lower values (172 and 154 kg N ha�1,

respectively), and the SC farms had the lowest surplus

(78 kg N ha�1). This result is mainly due to the differences

in manure input: the PB farms supplied the highest amount

of manure (266 kg N ha�1) and the SC farms the lowest

(118 kg N ha�1). The BB and DC farms showed similar and

intermediate manure inputs (198 and 210 kg N ha�1,

respectively). No significant differences were shown for

mineral fertilizer. The biological N fixation depended on the

different abundance of grasslands, showing higher values in

cattle breeding farms. The total N input at the soil surface

level did not show any significant difference for the different

farm types: where manure input was lower, farms tended to

increase other inputs.

The differences in crop output reached a 0.051 probability

level. The lower crop removal in PB farms was due to a larger

amount of farm land devoted to maize for grain production

and to poplar trees. The higher crop removal in cattle breeding

Suckling cow Pig breeding Significant F

241 334 0.163

118 b 266 a 0.013

63 36 0.378

30 a 2 b 0.007

30 30

163 104 0.051

78 b 230 a 0.003

0.68 0.31

0.90 0.34

M. Bassanino et al. / Agriculture, Ecosystems and Environment 122 (2007) 282–294288

Fig. 1. Variation in the crop balance surplus (CBS, kg N ha�1) as a function

of animal stocking density (LSU ha�1 of total area) for each farm for the

four different farm types (BB: beef breeding farms; DC: dairy cow farms;

SC: suckling cow farms; PB: pig breeding farms).

farms was due to a higher proportion of maize for silage

production, grassland and maize–Italian ryegrass double

cropping.

Apart from the SC, all the other farm types would be able

to satisfy their crop requirements through the manure N

input alone. A better management of crop nutrition strategies

is therefore possible, both within each farm type (reducing

mineral input) and between farm types. As an example, if the

SC farms reduce their mineral input, they could receive a

manure quota from other livestock farms. Sacco et al.

(2003), working at a larger scale on a southern area of

Piedmont characterized by very intensive animal husbandry,

found that a similar strategy was already being applied:

many SC farms accepted excess liquid manure from either

pig or cattle intensive livestock farms, while being paid for

it. Koelsch (2005) proposed the export of manure to off-farm

users as a valuable Best Management Practice alternative to

beef cattle feedlots. Bos and van de Ven (1999) tested the

Table 5

FGB components (kg N ha�1 of farm area) for the different farm types

Beef breeding Dairy cow

Total input 347 b 517 ab

Fertilizers 64 61

Live animals 34 a 0 b

Litter 12 ab 31 a

Animal feeding 178 381 ab

Atm. deposition 30 30

Biological N fixation 29 a 14 ab

Total output 90 b 199 ab

Milk and meat 83 b 150 ab

Cash crops 6 19

Manure 0 b 29 ab

FGBS 257 ab 318 ab

Sold/total input 0.26 0.33

Sold/purchased 0.31 0.36

Different letters show differences at P < 0.05.

hypothesis of a ‘‘mixed’’ farming system between arable and

dairy-specialized farms in order to exchange land, manure

and crop products.

The lowest efficiency in converting all N inputs into crop

N was shown for the PB farms (31%, which is similar to the

35% reported by Halberg, 1999; Børsting et al., 2003); the

DC and BB farms had intermediate efficiency, similar to that

reported by Aarts et al. (2000) (50% and 48%, respectively),

but lower than the 67% found by Oenema et al. (2003) for

the De Marke experimental dairy farm. The SC farms

showed the highest efficiency (68%).

When biological fixation and atmospheric deposition

were not considered, the output/fertilizer ratio in the SC

farms reached 90%.

The relationships between the CBS and some structural

farm data were also investigated. A significant relationship

between the CBS and the livestock rate was found (Fig. 1).

This was true for cattle farms (where R2 ranged from 0.41 in

the SC farms to 0.79 in the BB farms), but not for the PB

farms (R2 = 0.14), due to an insufficient manure export to

extra-land.

3.2. Farm-gate N balance (FGB)

The mean N data for all the farm-gate balance

components and the FGBS are shown in Table 5 for each

farm type. In a similar way to the CB, the FGB showed the

highest total inputs in the PB farms (1351 kg N ha�1), and

then, in a decreasing order, in the DC (517 kg N ha�1), BB

(347 kg N ha�1) and SC farms (152 kg N ha�1). The

differences between the farm types were larger in the

FGB than in the CB. When each input component was

analysed, it was interesting to note that the more intensive

the farm type, the larger the share of total inputs due to

purchased animal feeding (from 91% in PB to 25% in SC

farms). Therefore, this was the largest N input in the PB, DC

and BB farm types. Mineral fertilizer was instead the largest

Suckling cow Pig breeding Significant F

152 b 1351 a 0.000

50 42 0.535

0 b 44 a 0.000

5 ab 0 b 0.008

38 b 1233 a 0.000

30 30

30 a 2 b 0.000

52 b 864 a 0.000

16 b 520 a 0.000

35 34 0.112

1 b 310 a 0.000

100 b 486 a 0.000

0.34 0.41

0.57 0.42

M. Bassanino et al. / Agriculture, Ecosystems and Environment 122 (2007) 282–294 289

Fig. 2. Variation in the farm-gate balance surplus (FGBS, kg N ha�1) as a

function of animal stocking density (LSU ha�1 of total area) for each farm

for the four different farm types (BB: beef breeding farms; DC: dairy cow

farms; SC: suckling cow farms; PB: pig breeding farms).

Fig. 3. Variation in the farm-gate balance surplus (FGBS) as a function of

animal stocking density (LSU ha�1 of farm area) for each farm for the four

different farm types (BB: beef breeding farms; DC: dairy cow farms; SC:

suckling cow farms; PB: pig breeding farms). Data in kg N ha�1.

input in the SC farms. The N inputs for litter was important

only for DC farms, where farmers prefer to have a larger

maize area than to grow winter cereals.

The output data also differentiated substantially for the

pig and cattle breeding farms: the former reached

864 kg N ha�1 (520 kg N ha�1 due to the sold animal

products), while in the most intensive cattle farm type,

DC, the output was only 199 kg N ha�1 and the sold animal

products covered only 150 kg N ha�1. The amount of

exported manure (310 kg N ha�1 in PB) was another

important difference between pig breeding and other farm

types. If the three cattle farm types are considered, only in

the extensive SC farms do the cash crops export more N than

the animal products.

The PB farms had the highest FGBS (486 kg N ha�1 on

average, but the farm data varied between 153 and 1270). As

usual, the DC and BB farms had an intermediate surplus, and

the SC farms showed the best equilibrium (100 kg N ha�1,

with one farm with a negative FGBS). As expected, the

FGBS ranked the farm types roughly in the same order as the

CBS. Higher FGBS were assessed in Scotland by Domburg

et al. (2000) (520 kg N ha�1) and in France by Simon et al.

(2000) (542 kg ha�1), but showing similar proportions

among the different components.

The DC farms showed a FGBS similar to other

specialized dairy systems in Northern Europe (e.g.,

Verbruggen et al., 1994; Oenema et al., 1998; Ondersteijn

et al., 2002; Buysse et al., 2005). Previous research in Italy

reported similar FGBS (between 338 and 300 kg N ha�1 in

Grignani et al., 2003b). Milk, however, could be produced

more efficiently, as in the ‘‘De Marke’’ experimental dairy

farm in The Netherlands, which shows a FGBS of around

140 kg N ha�1 (Aarts et al., 2000; Oenema et al., 2003). The

BB farms showed a higher FGBS compared to other

European beef systems; however, housed beef breeding is

not common in other countries, and a real comparison is

therefore difficult. The SC farms were in the same range as

the FGBS reported by Simon et al. (2000) for similar

livestock systems. It is interesting to note that a previous

research in Italy (Grignani, 1996) found slightly higher

FGBS values for both the BB and SC farms.

The sold/total input ratio classified the four farm types in

a different way from the FGBS: the PB farms are the most

efficient (41%) in transforming N. Among the cattle farms,

the high milk production in the DC or the high proportion of

cash crops in the SC farms produced intermediate

coefficients of 34% and 33%. The BB farms yielded the

lowest coefficient (26%). The DC farm values were close to

the 35% efficiency calculated for the ‘‘De Marke’’

experimental dairy farm (Aarts et al., 2000; Oenema

et al., 2003).

Another classification was produced to evaluate the

conversion of acquired N into sold N: the SC farms were the

most efficient (57%), whereas the intensive farm types had

lower values (from 31% in the BB to 42% in the PB farms).

Grignani (1996), Simon et al. (2000) and Swensson (2003)

found a lower conversion efficiency for groups of

commercial farms, whereas the ‘‘De Marke’’ experimental

dairy farm reached 47%, which is higher than what was

found here for dairy farming. However, a wide inter-farm

variability is common, when analysing commercial farm

data (e.g., Domburg et al., 2000; Simon et al., 2000;

Ondersteijn et al., 2002).

As for the CBS, it is interesting to compare the FGBS

with some structural farm data. If all the farms are

considered, the FGBS is related to the livestock rate, when

expressed in terms of total area (Fig. 2), only in the DC

(R2 = 0.72) and BB farms (R2 = 0.96). Some PB farms show

a high FGBS with respect to their animal stocking density.

These are the farms that rely heavily on the extra-farm land

for manure spreading, but their manure export is too low.

The FGBS, in fact, appears to be in a closer relationship

when the livestock rate is expressed in terms of farm area

(Fig. 3). It is interesting to note that, in this figure, a unique

M. Bassanino et al. / Agriculture, Ecosystems and Environment 122 (2007) 282–294290

Fig. 4. Variation in the farm-gate balance surplus (FGBS) as a function of

the purchased feed for each farm for the four different farm types (BB: beef

breeding farms; DC: dairy cow farms; SC: suckling cow farms; PB: pig

breeding farms). Data in kg N ha�1.

Fig. 5. Regression of the farm-gate balance surplus (FGBS) and of the crop

balance surplus (CBS) for each farm for the four different farm types (BB:

beef breeding farms; DC: dairy cow farms; SC: suckling cow farms; PB: pig

breeding farms). Data in kg N ha�1.

relationship seems to exist for all the intensive farm types

(PB, DC and BB), as previously stated by Nielsen and

Christensen (2001) and Børsting et al. (2003).

When N fluxes of cash crops are important (as in the SC

farms) neither the animal density on the farm area nor the

animal density on the total area can be efficient predictors of

the FGBS.

The purchased animal feed is a good indicator of the

FGBS (R2 = 0.82), as it is the main N input (Fig. 4). This has

often been found in other research (e.g., Rougoor et al.,

1997; Halberg, 1999; Simon et al., 2000) and could explain

Table 6

Spearman’s correlation between the FGB components of each farm

Fertilizers Live animals Litter Animal

Live animals

RS

Significance n.s.

Litter

RS

Significance n.s. n.s.

Animal feeding

RS 0.636

Significance n.s. 0.000 n.s.

Biol. N fixation

RS �0.498 0.407 �0.480

Significance n.s. 0.001 0.008 0.002

Animal products

RS 0.669 0.941

Significance n.s. 0.000 n.s. 0.000

Crop products

RS �0.454

Significance n.s. n.s. 0.003 n.s.

Exported manure

RS �0.366 0.379 0.661

Significance 0.019 0.015 n.s. 0.000

Significant at P < 0.05.

why the relationship between the FGBS and livestock rate

holds true in DC and BB farms.

A detailed analysis of the correlation between the

different FGB components (Table 6) highlights that many of

them are indicators of the farm livestock intensity and more

generally of the farming system. For example, the greater the

N input with concentrates, the lower the N fixation, but the

higher the animal production and the amount of manure

exported. No correlation exists between the amount of N

input as fertilizers, the N output as cash crops, or most of the

other FGB components. The same components explain the

feeding Biol. N fixation Animal products Crop products

�0.575

0.000

n.s. n.s.

�0.436 0.660

0.004 0.000 n.s.

M. Bassanino et al. / Agriculture, Ecosystems and Environment 122 (2007) 282–294 291

Fig. 6. Regression of the farm-gate balance surplus (FGBS) and of the sum

of the crop balance surplus (CBS) and gaseous losses (GL) for each farm for

the four different farm types (BB: beef breeding farms; DC: dairy cow

farms; SC: suckling cow farms; PB: pig breeding farms). Data in kg N ha�1.

Table 8

Comparison between animal N excretion estimated using standard coeffi-

cients, and that one estimated using an animal input–output balance, for the

different farm types (kg N t�1 l.w. year�1)

N excretion Beef

breeding

Dairy

cow

Suckling

cow

Pig

breeding

Standard coefficients (A) 124 166 105 190

Animal balance (B) 134 165 128 158

A � B �10 1 �23 32

P(t)a 0.508 0.971 0.155 0.004

a Reported t tests the null hypothesis of A � B = 0.

FGBS variability within each farm type at a given animal

stocking density: the farms that rely more on purchased feed

and forages and that have the largest share of extra-land

show the highest FGBS.

3.3. Relationship between the two N balances and the

internal flows

A highly significant correlation (R2 = 0.78 in all the 41

farms) has been found between the two parameters, the CBS

at a field scale and the FGBS at a farm scale (Fig. 5). As the

two methods are computed independently, this correlation

offers the possibility of using both of them to estimate the

potential N load that contributes to the losses from an

agricultural system. A comparison between the two methods

also gives other information. For example, in the studied

region the method used to apply the Nitrate Directive is

based on the CB, despite the fact that the FGB is normally

considered a more robust method to assess the N balance

(e.g., van Eerdt and Fong, 1998; Oenema et al., 2003). The

reported correlation shows the possibility of using both

indexes in the benchmarking of animal stocking farms

within each farm group. However, the absolute value of the

index varies quite remarkably in the different groups. The

Table 7

Comparison between the farm-gate balance surplus (FGBS), the crop

balance surplus (CBS) and the expected gaseous losses (GL) for the

different farm types (kg N ha�1)

Beef

breeding

Dairy

cow

Suckling

cow

Pig

breeding

FGBS (A) 257 318 100 486

CBS (B) 172 154 78 230

GL (C) 79 170 45 363

A � B � C 6 �6 �23 �107

P(t)a 0.886 0.835 0.262 0.007

a Reported t tests the null hypothesis of A � B � C = 0.

FGBS/CBS ratio is much higher in the intensive farms (2.1

in PB and DC farms), intermediate (1.5) in the BB farms and

lower (1.3) in the SC farms.

The relationship between the FGBS and the sum of the

GL + CBS of all the farms yielded a highly significant

relationship (R2 = 0.89; Fig. 6) and the slope of the linear

regression was very close to 1 for all the farm types at any

level of N surplus. Therefore, it was possible to compare the

average data for the different farm groups, as reported in

Table 7. The average difference between the FGBS and the

sum of the GL + CBS was not different from 0 in the SC, BB

and DC farms, whereas the FGBS significantly exceeded the

sum of the GL + CBS of 107 kg N ha�1 in the PB farms.

The difficulty of quantifying high animal feeding inputs

in the PB farms, the more inaccurate evaluation of the crop

balance, where farmers pay less attention to crop cultivation,

and the relevant manure exports to extra-land could explain

this difference, as pointed out by Oenema et al. (1998),

Halberg (1999) and Koelsch (2005).

Further information can be derived from a comparison of

the two N balance types: the estimation of the internal flow

related to manure N excretion. A comparison between the

manure N excretion evaluated through standard coefficients

or through animal balances is shown in Table 8 for the

different farming systems. The reported data refer to the same

farm herd composition. The difference is not statistically

significant for any of the cattle farms, and the average values

for the BB and DC farms are in very close agreement. Instead,

official data referring to the PB farms are higher than the

balance-calculated excreta. Xiccato et al. (2005) have

recently studied N excretion in the main animal categories

in northern Italy. They have confirmed the findings of this

work for cattle farms, and suggest an average excretion of

159 kg N t l.w.�1 for growing pigs, which is closer to that

obtained here with animal balances. These results would

suggest that the difficulty in predicting gaseous losses in PB

farms (Table 7) might be related to an excessive CBS, due to

an excessive manure input in the official dataset.

4. Conclusions

The N surpluses calculated as the farm-gate balance

(FGBS) or at the crop scale (CBS) rank livestock farms in a

M. Bassanino et al. / Agriculture, Ecosystems and Environment 122 (2007) 282–294292

similar manner. The SC farms show the best sustainability,

the BB and DC are intermediate, while the PB are the worst

due to their weaker link between breeding activities and

farm crops. If all the farms are considered together, the CBS

is mainly driven by the manure input, while the FGBS is

mainly driven by animal feeding in the PB, BB and DC

farms, and by the mineral fertiliser in the SC farms.

The analysis of the correlation between the FGB

components helps to describe the most intensive farms.

These farms sell a large quantity of meat or milk, buy a large

quantity of concentrates, do not rely on biological N fixation

and are obliged to export a great deal of manure. The use of

the sold/total N input index seems more interesting than the

use of the sold/purchased N index. The first index measures

the real transformation of N inputs into sold products, thus it

produces information that is complementary to the FGBS.

Composing balance results at the two different scales

enhances the possibility of evaluating different N flows in

livestock farming, such as GL and manure N excretion. Such

evaluations can be used to verify official standard values. It

has been shown that the adoption of official standards for

cattle breeding farms could close the overall budgets, and the

‘‘unaccounted for’’ N was not different from zero. In the PB

farms, a possible error of more than 100 kg N ha�1 was

detected. An overestimation of standard values for N

excretion and for volatilisation losses was hypothesized.

Even though N balances cannot be directly used to

estimate the actual environmental impact of different

farming systems, these results show that they remain

reliable indicators to help discriminate between different

types of farms.

Acknowledgement

This research was funded by Regione Piemonte-

Assessorato Agricoltura e Qualita.

References

Aarts, H.F.M., Habekotte, B., van Keulen, H., 2000. Nitrogen management

in the ‘‘De Marke’’ dairy farming system. Nutr. Cycl. AgroEcosyst. 56,

231–240.

Argenti, G., Pardini, A., Sabatini, S., Talamucci, P., 1996. Rapporti tra

tipologie d’allevamento ed eccessi di azoto e fosforo stimati attraverso il

metodo del bilancio apparente in aziende del Mugello. Riv. Agron. 30

(4), 547–554.

Barry, D.A.J., Goorahoo, D., Goss, M.J., 1993. Estimation of nitrate

concentrations in groundwater using a whole farm nitrogen budget.

J. Environ. Qual. 4, 767–775.

Bechmann, M., Eggestad, H.O., Vagstad, N., 1998. Nitrogen balances and

leaching in four agricultural catchments in south-eastern Norway.

Environ. Pollut. 102, 493–499.

Beegle, D.B., Carton, O.T., Baily, J.B., 2000. Nutrient management plan-

ning: justification, theory, practice. J. Environ. Qual. 29 (1), 72–79.

Beegle, D.B., Lanyon, L.E., Sims, J.T., 2002. Nutrient balances. In:

Haygarth, P.M., Jarvis, S.C. (Eds.), Agriculture, Hydrology and Water

Quality. CABI Publishing, Wallingford, UK, pp. 171–192.

Bengtsson, H., Oborn, I., Jonsson, S., Nilsson, S.I., Andersson, A., 2003.

Field balances of some mineral nutrients and trace elements in organic

and conventional dairy farming—a case study at Ojebyn, Sweden. Eur.

J. Agron. 20, 101–116.

Børsting, C.F., Kristensen, T., Misciattelli, L., Hvelplund, T., Weisbjerg,

M.R., 2003. Reducing nitrogen surplus from dairy farms. Effects of

feeding and management. Livest. Prod. Sci. 83, 165–178.

Bos, J.F.F.P., van de Ven, G.W.J., 1999. Mixing specialized farming systems

in Flevoland (The Netherlands): agronomic, environmental and socio-

economic effects. Neth. J. Agric. Sci. 47, 185–200.

Bos, J.F.F.P., Aarts, F., Schroder, J., Biewinga, E., Schils, R., Velthof, G.,

Willems, J., 2003. Nutrient management at farm scale. Attaining

policy objectives in regions with intensive dairy farming: The Nether-

lands. In: Bos, J.F.F.P., Pflimlin, A., Aarts, F., Vertes, F. (Eds.),

Nutrient Management at Farm Scale, Workshop Proceedings,

Quimper, France, pp. 18–204.

Brouwer, F., 1998. Nitrogen balances at farm level as a tool to monitor

effects of agri-environmental policy. Nutr. Cycl. AgroEcosyst. 52, 303–

308.

Buciene, A., Svedas, A., Antanaitis, S., 2003. Balances of the major

nutrients N, P and K at the farm and field level and some possibilities

to improve comparisons between actual and estimated crop yields. Eur.

J. Agron. 20 (1–2), 53–62.

Buysse, J., van Huylenbroeck, G., Vanslembrouck, I., Vanrolleghem, P.,

2005. Simulating the influence of management decisions on the nutrient

balance of dairy farms. Agric. Syst. 86, 333–348.

Carpenter, S.R., Caraco, N.F., Correll, D.L., Howarth, R.W., Sharpley, A.N.,

Smith, V.H., 1998. Non-point pollution of surface waters with phos-

phorus and nitrogen. Ecol. Appl. 8 (3), 559–568.

Chavanes, M.P., 1991. L’utilisation de l’azote dans une exploitation cer-

ealiere marnaise. In: Girardin, P., Jolivet, G. (Eds.), Azote en question,

APRIA, Paris, pp. 65–70.

Domburg, P., Edwards, A.C., Sinclair, A.H., Chalmers, N.A., 2000.

Assessing nitrogen and phosphorus efficiency at farm and catch-

ment scale using nutrient budgets. J. Sci. Food Agric. 80, 1946–

1952.

Dou, Z., Lanyon, L.E., Ferguson, J.D., Kohn, R.A., Boston, R.C., Chalupa,

W., 1998. An integrated approach to managing nitrogen on dairy farms:

evaluating farm performances using the dairy nitrogen planner. Agron.

J. 90, 573–581.

EUROSTAT, 2004. Eurostat’s concepts and definitions database. Glossary:

Livestock Unit (LU). Available at http://forum.europa.eu.int/irc/dsis/

coded/info/data/coded/en/gl009931.htm.

Grignani, C., 1996. Influenza della tipologia di allevamento e dell’ordina-

mento colturale sul bilancio di elementi nutritivi di aziende padane. Riv.

Agron. 3, 414–422.

Grignani, C., Acutis, M., 1994. Assessment of mineral and organic nitrogen

balance in North-Western Italy milk and beef cattle farms. In: Proceed-

ings of the 3rd ESA Congress, European Society for Agronomy, Abano,

Italy, pp. 700–701.

Grignani, C., Bassanino, M., 2000. A fertilization balance sheet: limits and

quality of information required. In: Sangiorgi, F. (Ed.), Recycling of

Agricultural, Municipal and Industrial Residues in Agriculture, Work-

shop Proceedings, Gargnano (I), pp. 55–60.

Grignani, C., Bassanino, M., Sacco, D., Zavattaro, L., 2003a. Il bilancio

degli elementi nutritivi per la redazione del piano di concimazione. Riv.

Agron. 37, 155–172.

Grignani, C., Sacco, D., Bassanino, M., Mantovi, P., Bonazzi, G., Cumino,

P., 2003b. Nutrient management at farm scale. Attaining policy objec-

tives in regions with intensive dairy farming: Italy. In: Bos, J., Pflimlin,

A., Aarts, F., Vertes, F. (Eds.), Nutrient Management at Farm Scale,

Workshop Proceedings, Quimper, France, pp. 37–54.

Grignani, C., Bassanino, M., Allisiardi, E., Sacco, D., 2005. Nitrogen

balances at the crop and at the farm-gate scale in NW Italy stocking

farms. In: N Management in Agro-systems in Relation to the Water

Framework Directive, Workshop Proceedings, Wageningen, NL, pp.

38–40.

M. Bassanino et al. / Agriculture, Ecosystems and Environment 122 (2007) 282–294 293

Gustafson, G.M., Salomon, E., Jonsson, S., Steineck, S., 2003. Fluxes of K,

P, and Zn in a conventional and an organic dairy farming system through

feed, animals, manure, and urine—a case study at Ojebyn, Sweden. Eur.

J. Agron. 20, 89–99.

Halberg, N., 1999. Indicators of resource use and environmental impact for

use in a decision aid for Danish livestock farmers. Agric. Ecosyst.

Environ. 76, 17–30.

Halberg, N., Verschuur, G., Goodlass, G., 2005. Farm level environmental

indicators; are they useful? An overview of green accounting systems

for European farms. Agric. Ecosyst. Environ. 105, 195–212.

Hedlund, A., Witter, E., Bui Xuan, A., 2003. An assessment of N, P and K

management by nutrient balances and flows on peri-urban smallholder

farms in Southern Vietnam. Eur. J. Agron. 20, 71–87.

Holmqvist, J., Øgaard, A.F., Oborn, I., Edwards, A.C., Mattsson, L.,

Sverdrup, H., 2003. Application of the profile model to estimate

potassium release from mineral weathering in Northern European

agricultural soils. Eur. J. Agron. 20, 149–163.

Humphreys, J., Casey, I.A., Carton, O.T., 2003. Nutrient management at farm

scale. Attaining policy objectives in regions with intensive dairy farming:

Ireland. In: Bos, J., Pflimlin, A., Aarts, F., Vertes, F. (Eds.), Nutrient

Management at Farm Scale, Workshop Proceedings, Quimper, France,

pp. 145–164.

Jarvis, S.C., Aarts, H.F.M., 2000. Nutrient management from a farming

system perspective. Grassl. Sci. Eur. 5, 363–373.

Jarvis, S.C., Withers, P.J.A., Chadwick, D.R., 2003. Nutrient management at

farm scale. Attaining policy objectives in regions with intensive dairy

farming: United Kingdom. In: Bos, J., Pflimlin, A., Aarts, F., Vertes, F.

(Eds.), Nutrient Management at Farm Scale, Workshop Proceedings,

Quimper, France, pp. 55–76.

Keller, A., Schulin, R., 2003. Modelling regional-scale mass balances of

phosphorus, cadmium and zinc fluxes on arable and dairy farms. Eur. J.

Agron. 20 (1–2), 181–198.

Koelsch, R., 2005. Evaluating livestock system environmental performance

with whole-farm nutrient balance. J. Environ. Qual. 34, 149–155.

Koelsch, R., Lesoing, G., 1999. Nutrient balance on Nebraska livestock

confinement systems. J. Anim. Sci. 77 (2/J), 63–71.

Kohn, R.A., Dou, Z., Ferguson, J.D., Boston, R.C., 1997. A sensitivity

analysis of nitrogen losses from dairy farms. J. Environ. Manage. 50,

417–428.

Kutra, G., Aksomaitiene, R., 2003. Use of nutrient balances for environ-

mental impact calculations on experimental field scale. Eur. J. Agron.

20, 127–135.

Meisinger, J.J., Randall, G.W., 1991. Estimating nitrogen budgets for soil–

crop systems. In: Follett, R.F., Kenney, D.R., Cruse, R.M. (Eds.), Ma-

naging Nitrogen for Groundwater Quality and Farm Profitability. Soil

Science Society of America, Madison, WI, pp. 85–124.

Merlo, C., Allavena, L., 2001. Metodologia di verifica dei fabbisogni lordi

nei comprensori irrigui della Regione Piemonte. Regione Piemonte

(Ed.), collana Ambiente no. 22.

Mulier, A., Hofman, G., Baecke, E., Carlier, L., de Brabander, D., de

Groote, G., de Wilde, R., Fiems, L., Janssen, G., van Cleemput, O., van

Herck, A., van Huylenbroeck, G., Verbruggen, I., 2003. A methodology

for the calculation of farm level nitrogen and phosphorus balances in

Flemish agriculture. Eur. J. Agron. 20, 45–51.

Nevens, F., Verbruggen, I., Reheul, D., Hofman, G., 2006. Farm gate

nitrogen surpluses and nitrogen use efficiency of specialized dairy

farms in Flanders: evolution and future goals. Agric. Syst. 88,

142–155.

Nielsen, A.H., Christensen, J.O., 2001. Element budgets as a management

tool on dairy farms in Denmark. In: Element Balances as a Sustain-

ability Tool, Workshop Proceedings, Uppsala, March 16–17, pp. 33–36.

OECD, 2001. Environmental Indicators for Agriculture: Methods and

Results. OECD, Paris.

Oenema, O., Boers, P.C.M., van Eerdt, M.M., Fraters, B., van der Meer,

H.G., Roest, C.W.J., Schroder, J.J., Willems, W.J., 1998. Leaching of

nitrate from agriculture to groundwater: the effect of policies and

measures in The Netherlands. Environ. Pollut. 102, 471–478.

Oenema, O., Kros, H., de Vries, W., 2003. Approaches and uncertainties in

nutrient budgets: implications for nutrient management and environ-

mental policies. Eur. J. Agron. 20, 3–16.

Ondersteijn, C.J.M., Beldman, A.C.G., Daatselaar, C.H.G., Giesen, G.W.J.,

Huirne, R.B.M., 2002. The Dutch mineral accounting system and the

European nitrate directive: implications for N and P management and

farm performance. Agric. Ecosyst. Environ. 92, 283–296.

Poulsen, H.D., Kristensen, V.F., 1997. Standard values for farm manure. A

revaluation of the Danish standard values concerning the N, P and K

content of manure. Danish Institute of Animal Science, Report no. 7.

Powell, J.M., Jackson-Smith, D.B., McCrory, D.F., Saam, H., Mariola, M.,

2006. Validation of feed and manure data collected on Wisconsin dairy

farms. J. Dairy Sci. 89, 2268–2278.

Regione Piemonte, 2002. Regolamento regionale recante: Designazione

delle zone vulnerabili da nitrati di origine agricola e relativo programma

di azione. D.P.G.R. 18/10/2002 no. 9/R.

Regione Piemonte, 2006. Banca dati regionale dei terreni agrari. Available

at: http://www.regione.piemonte.it/agri/ita/agriservice/ricerca/bdrta/

index.htm.

Rougoor, C.W., Dijkhuizen, A.A., Huirne, R.B.M., Mandersloot, F., Schuk-

ken, Y.H., 1997. Relationship between technical, economic and envir-

onmental results on dairy farms: an explanatory study. Livest. Prod. Sci.

47, 235–244.

Saam, H., Powell, J.M., Jackson-Smith, D.B., Bland, W.L., Posner, J.L.,

2005. Use of animal density to estimate manure nutrient recycling

ability of Wisconsin dairy farms. Agric. Syst. 84 (3), 343–357.

Sacco, D., Bassanino, M., Grignani, C., 2003. Developing a regional

agronomic information system for estimating nutrient balances at a

larger scale. Eur. J. Agron. 20 (1–2), 199–210.

Schroder, J.J., van Asperen, P., van Dongen, G.J.M., Wijnands, F.G., 1996.

Nutrient surpluses on integrated arable farms. Eur. J. Agron. 5, 181–191.

Schroder, J.J., Aarts, H.F.M., ten Berge, H.F.M., van Keulen, H., Neeteson,

J.J., 2003. An evaluation of whole-farm nitrogen balances and related

indices for efficient nitrogen use. Eur. J. Agron. 20 (1–2), 33–44.

Sheldrick, W.F., Keith Syers, J., Lingard, J., 2002. A conceptual model for

conducting nutrient audits at national, regional and global scale. Nutr.

Cycl. AgroEcosyst. 62, 61–72.

Sieling, K., Kage, H., 2006. N balance as an indicator of N leaching in an

oilseed rape–winter wheat–winter barley rotation. Agric. Ecosyst.

Environ. 115, 261–269.

Simon, J.C., Le Corre, L., 1992. Le bilan apparent de l’azote a l’echelle de

l’exploitation agricole: methodologie, exemples de resultats. Fourrages

129, 79–94.

Simon, J.C., Grignani, C., Jacquet, A., Le Corre, L., Pages, J., 2000.

Typologie des bilans d’azote de divers types d’exploitations agricole:

recherche d’ indicateurs de fonctionnement. Agronomie 20, 175–195.

Smaling, E.M.A., Oenema, O., 1997. Estimating nutrient balances in

agro-ecosystems at different spatial scales. In: Lal, R. (Ed.), Methods

for the Assessment of soil Degradation. CRC Press, New York, pp.

229–252.

SPSS for Windows, 2004. Version 12.0.1. SPSS Inc., Chicago, IL.

Swensson, C., 2003. Analyses of mineral element balances between 1997

and 1999 from dairy farms in the south of Sweden. Eur. J. Agron. 20,

63–69.

Townend, J., 2002. Practical Statistics for Environmental and Biological

Scientists. Wiley & Sons, Chichester, England.

van Beek, C.L., Brouwer, L., Oenema, O., 2003. The use of farmgate

balances as estimator for nitrogen leaching to surface waters. Nutr. Cycl.

AgroEcosyst. 67, 233–244.

van der Meer, H.G., van der Putten, A.H.J., 1995. Reduction of nutrient

emissions from ruminant livestock farms. In: Pollott, G.E. (Ed.),

Grassland into the 21st Century: Challenges and Opportunities, Pro-

ceedings of the 50th Meeting of the British Grassland Society,

Harrogate, December 4–6, pp. 118–134.

van der Molen, D.T., Breeuwsma, A., Boers, P.C.M., 1998. Agricultural

nutrient losses to surface waters in The Netherlands: impacts, strategies

and perspectives. J. Environ. Qual. 27, 4–11.

M. Bassanino et al. / Agriculture, Ecosystems and Environment 122 (2007) 282–294294

van Eerdt, M.M., Fong, P.K.N., 1998. The monitoring of nitrogen surpluses

from agriculture. Environ. Pollut. 102, 227–233.

van Faasen, H.G., Lebbink, G., 1994. Organic matter and nitrogen dynamics

in conventional versus integrated arable farms. Agric. Ecosyst. Environ.

51, 209–226.

van Keulen, H., Aarts, H.F.M., Habekotte, B., van der Meer, H.G., Spiertz,

J.H.J., 2000. Soil–plant–animal nutrient cycling: the case of dairy

farming system ‘‘De Marke’’. Eur. J. Agron. 13, 245–261.

Verbruggen, I., Carlier, L., van Bockstaele, E., 1994. Surplus of nutrients on

dairy farms in Belgium. In: ’t Mannetje, L., Frame, J. (Eds.), Pro-

ceedings of the 15th Meeting of the European Grassland Federation on

Grassland and Society, Wageningen, The Netherlands, pp. 463–465.

Watson, C.A., Atkinson, D., 1999. Using nitrogen budgets to indicate

nitrogen use efficiency and losses from whole farm systems: a compar-

ison of three methodological approaches. Nutr. Cycl. AgroEcosyst. 53,

259–267.

Weissbach, F., Ernst, P., 1994. Nutrients budgets in farm management to

reduce nutrient emissions. In: ’t Mannetje, L., Frame, J. (Eds.),

Proceedings of the 15th Meeting of the European Grassland Federa-

tion on Grassland and society, Wageningen, The Netherlands, pp.

343–360.

Xiccato, G., Schiavon, S., Gallo, L., Bailoni, L., Bittante, G., 2005. Nitrogen

excretion in dairy cow, beef and veal cattle, pig and rabbit farms in

Northern Italy. Ital. J. Anim. Sci. 4 (3), 103–111.