Environmental Monitoring and Assessment (2006) 117: 109–134
A RAPID METHOD FOR ASSESSING THE ENVIRONMENTAL
PERFORMANCE OF COMMERCIAL FARMS IN THE PAMPAS
E. F. VIGLIZZO
, F. FRANK
, J. BERNARDOS
, D. E. BUSCHIAZZO
and S. CABO
INTA Centro Regional La Pampa, La Pampa, Argentina;
Facultad de Ciencias
Exactasy Naturales, Universidad Nacional de La Pampa
author for correspondence, e-mail: firstname.lastname@example.org)
(Received 25 October 2004; accepted 25 May 2005)
Abstract. The generation of reliable updated information is critical to support the harmonization
of socio-economic and environmental issues in a context of sustainable development. The agro-
environmental assessment and management of agricultural systems often relies on indicators that
are necessary to make sound decisions. This work aims to provide an approach to (a) assess the
environmental performance of commercial farms in the Pampas of Argentina, and (b) propose a
methodological framework to calculate environmental indicators that can rapidly be applied to prac-
tical farming. 120 commercial farms scattered across the Pampas were analyzed in this study during
2002 and 2003. Eleven basic indicators were identiﬁed and calculation methods described. Such
indicators were fossil energy (FE) use, FE use efﬁciency, nitrogen (N) balance, phosphorus (P) bal-
ance, N contamination risk, P contamination risk, pesticide contamination risk, soil erosion risk,
habitat intervention, changes in soil carbon stock, and balance of greenhouse gases. A model named
Agro-Eco-Index was developed on a Microsoft-Excel support to incorporate on-farm collected data
and facilitate the calculation of indicators by users. Different procedures were applied to validate
the model and present the results to the users. Regression models (based on linear and non-linear
models) were used to validate the comparative performance of the study farms across the Pampas.
An environmental dashboard was provided to represent in a graphical way the behavior of farms.
The method provides a tool to discriminate environmentally friendly farms from those that do not
pay enough attention to environmental issues. Our procedure might be useful for implementing an
ecological certiﬁcation system to reward a good environmental behavior in society (e.g., through
tax beneﬁts) and generate a commercial advantage (e.g., through the allocation of green labels) for
Keywords: ArgentinePampas, commercial farms,ecologicalcertiﬁcation, environmentalassessment,
In recent years, current and potential environmental problems are frequent cause of
concern in Argentina. It is increasingly accepted that the healthy administration of
the rural environment is essential to maintain or improve the productivity of land,
the economic income, the social condition and the integrity of bio-physical systems
that preserve the quality of life. The generation of reliable updated information is
110 E. F. VIGLIZZO ET AL.
critical to support the harmonization of socio-economic and environmental issues
in a context of sustainable development.
The agro-environmental assessment and management of agricultural systems
often relies on indicators that are necessary to make sound decisions. The purpose of
agro-ecological indicators is to facilitate the diagnosis and interpretation of critical
processes in order to improve later the decision capacity (Girardin et al., 1999).
Normally, they are developed by scientists as quantitative expressions that measure
a particular condition of the system in relation to accepted ecological threshold
values (Schiller et al., 2001). Users normally calculate indicators to orient decisions
and communicate ideas to different actors in the society (Walter and Wilkerson,
Ideally, scientists, decision-makers and the public should agree on what eco-
logical indicators are needed. But agreement may be difﬁcult because interests
may diverge and communication among parties may fail. The selection of univer-
sally accepted, cost-effective indicators is necessary to overcome such constraint
(Shaeffer et al., 1988; Munasinghe and Shearer, 1995). Frequently, models that
simulate complex agricultural systems are considered better than simple indicators
to describe a changing reality. But a well-selected set of indicators still appears to
be irreplaceable as a tool to synthesize information and orientate farmers’ decisions
in a rapid way (Girardin et al., 1999).
The use of indicators is neither simple nor automatic. The process of selection,
developing and validation of indicators may impose a practical trouble. There is
not a unique way to select, develop and validate environmental indicators in agri-
culture (Smith et al., 2000). Very frequently, indicators arise more in response to
occasional local or temporal needs than to universally accepted criteria. But beyond
selection, validation is another important step in indicators adoption. Validation is
not common because indicator developers tend to consider that a long-term ac-
ceptance by users guarantee reliability. But this does not necessarily guarantee
usefulness as diagnostic- or decision-support tool. Bockstaller and Girardin (2003)
have demonstrated the importance of validation before adoption, and proposed a
methodological framework to do it.
The potential provision of technical coefﬁcients for agro-ecological certiﬁcation
should not be underestimated as a by-product of indicators adoption (Viglizzo et al.,
2001). Many agricultural ﬁrms in the world are considering the environmental con-
sequences of their activities and how can they affect their competitive advantage
and social credibility (Wall et al., 2001). The development of reliable indicators can
speed-up the willingness of farmers to be monitored by outside, independent orga-
nizations (Teisl et al., 1999) and to prove their environmentally friendly behavior.
Nowadays, it is widely accepted that indicators can provide to outside, third parties
a tool to monitor and certify the application of good-practice principles through
standardized environmental codes, such as those of ISO 14000.
Accepting the need and potential beneﬁt of having reliable agro-environmental
indicators, the objectives of this work were to provide an approach to (a) assess
A RAPID METHOD FOR ASSESSING THE ENVIRONMENTAL PERFORMANCE 111
the environmental performance of commercial farms in the Pampas of Argentina,
and (b) propose a methodological framework to calculate environmental indicators
that can rapidly be applied to practical farming. As a by-product of this study,
we also aimed at discussing the potential application of such indicator to assess
the environmental performance of farms and guide ﬁrst steps to the ecological
certiﬁcation of agricultural processes.
2. Materials and Methods
HE PAMPAS REGION AND THE STUDY FARMS
The Pampas region is a vast, ﬂat region of Argentina that comprises more than 50
million hectares of arable lands for crop and cattle production (Hall et al., 1992).
Given that the region is not homogeneous in soils and rainfall patterns (INTA,
1990; Satorre, 2001), it can be divided into ﬁve agro-ecologically homogeneous
areas (Figure 1) as follows: (1) Rolling Pampas, (2) Central Pampas, which can
be subdivided in Sub-humid on the East and Semiarid on the West, (3) Southern
Pampas, (4) Flooding Pampas, and (5) Mesopotamian Pampas. Rainfall regimes
Figure 1. Geographical location of study farms in the Pampas region.
112 E. F. VIGLIZZO ET AL.
vary across time and space, causing occasional droughts and ﬂood episodes that
transitorily can affect both crop and cattle production (Viglizzo et al., 1997).
According to FAO (1989) criteria, deep and well-drained soils predominate on
the Rolling Pampas, which provides conditions for continuous cropping (INTA,
1990). Despite increased wind erosion sensitivity towards the West, most of the
lands are suitable for mixed cultivation and livestock production in the Central
Pampas (Buschiazzo et al., 1999). The Flooding and Mesopotamian Pampas are
mostly devoted to cattle production on native and introduced perennial pastures.
Limitations for crop production in these areas are normally associated with shallow
soil depth, frequent ﬂooding, soil salinity, poor drainage, and water erosion.
120 real farms scattered across the ﬁve ecological areas in the Pampas (see
dots location in map of Figure 1) were analyzed in this study during years 2002
and 2003. The selection of farms depended strongly on the willingness of farmers
to be assessed and to provide requested ﬁeld data. The geographical spread of
farms guarantees the analysis of contrasting production methods and systems. The
assessment was not focused on single products but on the whole-farm integrated
production process. A standardized procedure was designed to pick up data at the
farm level, and to load such data into a model that automatically calculates eleven
NDICATORS, MODELS, DATA COLLECTION AND STATISTICAL ANALYSIS
As driving criteria, we assumed that indicators need to be sound, simple to calcu-
late, and easy to interpret and use by decision-makers. The selection of indicators
was driven by environmental issues that are frequent cause of concern in agricul-
tural production in the Pampas. The rapid land use change and the increasing use
of external inputs during the 1990s have arisen concerns about the consumption
of fossil energy, the nitrogen and phosphorus input-output relationship in farms,
the risk of contamination by fertilizers and pesticides, the impact on the habitat,
the loss of organic matter and soil sediments due to increasing cultivation, and the
emission of greenhouse gases (Solbrig and Viglizzo, 1999; Viglizzo et al., 2001,
2002b; Ghersa et al., 2002; Ferraro et al., 2003). Thus, eleven basic indicators were
calculated: (1) fossil energy (FE) use, (2) fossil energy use efﬁciency, (3) nitrogen
(N) balance, (4) phosphorus (P) balance, (5) nitrogen contamination risk, (6) phos-
phorus contamination risk, (7) pesticide contamination risk, (8) soil erosion risk,
(9) habitat intervention, (10) changes in soil organic carbon (C) stock, and (11)
balance of greenhouse gases (GHG). Methods for calculating this set of indicators
were described by Viglizzo et al. (2003).
A software named Agro-Eco-Index, based on the above mentioned indicators,
was developed on an Excel-Microsoft magnetic support in order to facilitate the
incorporation of farm data and the calculation of indicators by users. The model
was divided into ﬁve screens: three for data loading, one for showing the calcu-
lated numerical coefﬁcients, and the last one to show the 11 estimated indicators
A RAPID METHOD FOR ASSESSING THE ENVIRONMENTAL PERFORMANCE 113
through an understandable graphical device. This magnetic support, developed and
calibrated only for the Pampas region at the moment, has already been patented in
The assessment comprises a method for data collection at the farm level. At least
one visit to each voluntary farmer is unavoidable. Detailed data on land use and land
cover (annual crops, annual and perennial pastures, natural pastures, forests, waste
areas, etc.), farming activities, use of inputs (fuels, electricity, fertilizers, pesticides
and concentrates), livestock categories, stocking rates, local rainfall, evapotranspi-
ration, and the water holding capacity of soils must be carefully recorded. Given
that not always farmers have ﬁeld data on evapotranspiration and water holding
capacity of their soils, default data on the study area that we have previously in-
corporated into the model were used. Later, collected data must be uploaded to the
model to calculate the indicators.
Land use, a major driver of change in farming, is a common factor for the
calculation of the complete set of indicators. It is well known that land use is
highly relevant in environmental assessments because of its direct impact on the
environment (van Latesteijn, 1993; Rabbinge et al., 1994; Viglizzo et al., 2001).
In our case, the main impact of land use was referred to the proportional allocation
(%) of the land to (a) native rangeland, (b) introduced perennial pastures, or (c)
The statistical methods were restricted to the use of simple regression analysis
through linear and non-linear models. Statistics include the corresponding best-ﬁt
equation, the determination coefﬁcient (R
) and the standard error (SE).
2.2.1. Fossil Energy Use (Indicator No. 1) and Fossil Energy User Efﬁciency
(Indicator No. 2)
The use of fossil energy (FE) correlates well with intensiﬁcation in agriculture.
Being usually linked to fuel use for farming activities and to the use synthetic inputs
like fertilizers and pesticides, the increasing use of FE is frequently associated with
environmental threats like greenhouse gases emission and soil/water contamination
episodes (Agriculture and Agri-Food Canada, 2000).
The calculation of this indicator comprised the energy cost (Mj ha
of predominant inputs (fertilizers, seeds, concentrates, pesticides) and practices
(tillage, planting, weeding, harvesting, etc.). The fossil energy cost of inputs and
practices were obtained from different sources (Reed et al., 1986; Stout, 1991;
Conforti and Giampietro, 1997; Pimentel, 1999). Although it was not possible to
check the original sources and procedures used by those authors, we assume that
they provide a reliable, peer-reviewed estimation of all involved fossil energy costs.
The fossil energy use efﬁciency was calculated by considering the amount of
Mj of FE used to get one Mj of product. Calculations were made on annual basis
taking into account the proportional participation of each analyzed activity. Under
this scheme, the larger the amount of FE used to produce one unit of energy, the
less efﬁcient the production process was.
114 E. F. VIGLIZZO ET AL.
2.2.2. Input–Output Relationship of Nitrogen (Indicator No. 3) and Phosphorus
(Indicator No. 4)
Among other nutrients, the adequate supply of nitrogen (N) and phosphorus (P)
is essential to the plant growth and development. If extraction exceeds supply
over the years, the accumulation of negative balances may cause nutrient depletion,
declining crop yield and lowering of economic return. Conversely, if supply exceeds
extraction, the accumulation of residues can overload the soil with nutrients and
increase the risk of soil and water contamination.
A simplistic input-output relationship procedure was used to estimate a farm
gate balance. The annual average of N and P in soil was estimated as the annual
difference between inputs and outputs per hectare. The ways of nutrient gain in the
case of N were (a) precipitation, (b) fertilizers, (c) biologic ﬁxation by legumes,
and (d) purchased feed, later excreted and returned to the ﬁeld as cattle urine and
manure. The predominant ways of P gain are fertilizers and purchased feed. The
only one way for N and P export in this work is the nutrient extracted by the
agricultural product. Because of the unavoidable calculation difﬁculty, issues of
the nutrient cycle like accumulation, depletion, leaching, volatilization, and other
pathways in the nutrient dynamics were not considered.
2.2.3. Contamination Risk by Nitrogen (Indicator No. 5) and Phosphorus
(Indicator No. 6)
The assessment of contamination risk by N and P (expressed as mg l
water) is essential for assessing threats in intensive agriculture. The risk of N
contamination was calculated by taking into account the residual N when the N
balance was positive. The N concentration in drainage water was estimated by
dividing the amount of residual N by the amount of water available for nitrogen
dilution (water excess). The water excess was calculated on annual basis from a
water balance estimation, which takes into account the water gain by rainfall (mm
) less the real evapotranspiration in the same period. The contamination
risk calculation proceeded only when the excess of water has exceeded the water
holding capacity of soils. Where ﬁeld data were not available, we utilized default
values for water holding capacity of average soils according to McDonald (2000).
They were: (a) 100 mm for a sandy or a sandy loam soil, (b) 150 mm for a loam
soil, (c) 200 mm for a loam clay soil, and (d) 250 mm for a clay soil. Therefore, if
the excess of water (rainfall less real evapotranspiration) was less than the water
holding capacity of the soil, saturation did not happen, leaching was absent and the
water contamination risk did not occur.
A similar procedure was used to calculate the P contamination risk. This was
done even when the process involved in P contamination is quite different from
that related to N contamination. Such difference can be explained by the opposite
mobility of both elements in the soil: while P immobility determines that runoff is
the main way of contamination, lixiviation is the main contamination way in the
case of N. Both, leaching and runoff coexist in the ﬂat plain of the study region.
A RAPID METHOD FOR ASSESSING THE ENVIRONMENTAL PERFORMANCE 115
2.2.4. Pesticide Contamination Risk (Indicator No. 7)
Worldwide, the pesticide contamination risk is cause of concern due to (a) wa-
ter and soil degradation by pesticides residues, (b) air quality degradation by the
volatile fraction of pesticides, and (c) negative impacts on biodiversity. The pesti-
cide contamination risk was quantiﬁed by a relative index because absolute values
are not meaningful for between farms comparison. Calculation included the most
common insecticides, herbicides and fungicides that farmers have extensively used
during the assessment period. This indicator was based on the estimation of the
relative toxicity of predominant pesticides used in different farming activities. The
actual toxicity values (LD-50) were provided by manufacturers, and obtained from
a well-known current pesticides guide (CASAFE, 1997). Land use allocation was
the base layer of information on which pesticides, and their corresponding tox-
icity values, were later incorporated to the calculation. Data on application rate
of active products were provided by farmers for each crop. The relative index for
pesticide contamination was the result of summing-up each pesticide contribution
per hectare, after multiplying the proportion (%) of land allocated to each analyzed
crop, by the toxicity of each product.
Although our estimation of the contamination risk cannot be expressed in abso-
lute terms, farms can be compared in terms of their relative contribution to toxicity
generation by pesticide use.
2.2.5. Soil Erosion Risk (Indicator No. 8)
Soil quality was deﬁned here as the soil capacity to sustain agricultural activities
over time without affecting its productivity and the quality of the environment.
Erosion has negative effects at the farm level, and also can impact larger areas
outside the farm. Unsuitable land use schemes and tillage practices can be cause of
erosion in fragile lands.
Different tillage operations have different effects on soil conservation. No-till
consists of a weed control with herbicides and the seeding of crops without previous
tillage operations. This is considered the less aggressive operation on soil. Although
conservation tillage minimizes the number of tillage operations, it is more a ag-
gressive alternative than no-tillage. The conventional tillage, the most aggressive
one, consists of a mechanical weed control with diskers and harrow disk, repeating
the same operation two, three or more times before seeding the crops.
Depending on soil stability, different tillage methods have different effect on
sediment production by soil erosion. Accepted criteria (Agriculture and Agri-Food
Canada, 2000) classify erosion risk into ﬁve categories: tolerable (less than 6 met-
rictons of sediment loss per hectare and year), low (6 to 11 tons), moderate (11 to
12 tons), high (22 to 33 tons), and heavy (more than 33 tons).
Two methods were utilized for estimating the soil erosion risk: (a) the Wind Ero-
sion Equation (WEQ) for wind erosion, and (b) the Universal Soil Loss Equation
(USLE) for water erosion. The ﬁrst one (Woodruff and Siddo way, 1965; Hagen,
1991) contemplates soil properties (texture, organic matter and CaCO
116 E. F. VIGLIZZO ET AL.
historical climate characteristics (mean wind speed, mean prevailing wind direc-
tion, annual precipitation and mean temperature), and management characteristics
(ﬁeld length, tillage system and vegetation cover). The latter (Wischmeyer and
Smith, 1978) corresponds to water erosion and requires data on rain erosivity, soil
susceptibility to water erosion, slope, ﬁeld length and vegetation cover informa-
tion. In our model, a unique wind and water erosion indicator was calculated and
expressed in terms of t ha
of soil loss.
2.2.6. Human Intervention of the Habitat (Indicator No. 9)
It is accepted that over the centuries agriculture has transformed the natural habitat
into a human-designed one. Despite the fact that agriculture has historically bene-
ﬁted from biodiversity, habitat intervention by agriculture greatly reduces natural
biodiversity. Land use change seems to be the most relevant factor that impacts on
biodiversity (Sala et al., 2000), but tillage operations and pesticides use are causes
of habitat aggression as well.
Under the assumption that human action affects habitat and biodiversity, a
method was designed in this work to calculate a relative index that estimates the
degree of human intervention on the habitat. Intervention occurs through factors
like (a) land use change, (b) tillage operations, and (c) pesticide applications. In our
case, land use was the proportion (%) of land cultivated annually with annual crops.
The tillage impact factor was obtained from the method to estimate soil erosion risk
(t of sediments ha
). Likewise, the corresponding relative coefﬁcient was
obtained and used from the already estimated indicator on pesticide contamination
risk. The emerging combined factor was the result of the simple multiplication of
those three intervention factors. Thus, the higher the proportion of annual crops,
the aggressiveness of tilling practices, and the toxicity of pesticides, the greater the
detrimental effect of humans on the habitat. Thus, farms were compared in terms
of their estimated relative impact on the habitat.
2.2.7. Change in Soil Organic Carbon Stock (Indicator No. 10)
Organic carbon (C) is the main component of soil organic matter, and therefore
a factor that strongly determines soil quality. Organic matter decay is associated
with soil fertility and soil structure losses, and also with higher soil erosion risk.
Depending on organic matter gain or loss, soils can respectively act as a sink or a
source of atmospheric C. Thus, C stocks are dynamic and highly sensitive to human
The procedure followed in this work to estimate changes in C stock (expressed
as t ha
) was based on the IPCC (1996) revised guideline methodology.
An initial C stock (obtained from ﬁeld data or default ﬁgures) that varied accord-
ing to the study area was necessary to initiate the calculation, and 20 years was
the basic period considered to estimate changes. Factors associated with crops and
pastures vary: while perennial pastures improved the C stocks, annual crops de-
pleted such stock. Imported manure is not incorporated in agricultural systems of
A RAPID METHOD FOR ASSESSING THE ENVIRONMENTAL PERFORMANCE 117
the Pampas. Tillage practices were associated with coefﬁcients that represented
different C oxidation rates. While conservation- and no-till operations favored soil
C sequestration, conventional tillage was cause of C emission. Furthermore, the
IPCC method has also provided default coefﬁcients for organic matter enrichment
of soils via accumulation of crop residues.
2.2.8. Greenhouse Gases (GHG) Balance (Indicator No. 11)
Atmospheric gases, including carbon dioxide (CO
), methane (CH
), nitrous oxide
O), ozone (O
) and water vapor are normal components of the atmosphere that
have, at the same time, a greenhouse effect. CO
account for 90% of the
human-driven greenhouse effect (Desjardins and Riznek, 2000). Grain crops and
cattle are both signiﬁcant sources of greenhouse gases emission.
The GHG balance was estimated following the standard guidelines of IPCC
(1996). All gases were converted into CO
equivalent (ton ha
). The cal-
culation has included emission and sequestration of carbon in response to land use
change, grain cropping and cattle production activities.
The use of fossil fuels was a major source of CO
. The method included fuels
used in rural activities and fuels used for manufacturing fertilizers, herbicides and
machinery. Because of the heterogeneity of cases and procedures, the emission
due to rural transportation was not included in our calculations. Ruminants were
a signiﬁcant source of GHG. Ruminants emit methane from enteric fermentation
and fecal losses. Methane has a greenhouse power that is 21 times greater than
. This ﬁgure was used to convert CH
equivalents. Nitrogen excreted
in feces and distributed with fertilizers was another signiﬁcant source of nitrous
O) emission. N
O has a greenhouse power 310 times greater than CO
(IPCC, 1996). Losses of N
O occur via volatilization, leaching and runoff. Arable
soils were also a direct source of greenhouse gases through fertilizers, biological
N ﬁxation and crop residues.
When data from direct ﬁeld measurements were unavailable, default values
suggested by the IPCC (1996) were used for estimating gains and losses of carbon.
The methodology proposed by IPCC (1996) estimated C emission or sequestration
through the following components: (1) CO
stock exchange in soils over time
-SC), (2) CO
stock exchange in timber biomass (CO
-BL), (3) conversion of
forests and prairies into arable land (CO
-CTBP), (4) abandonment of intervened
-aband), and (5) emission of CO
from fossil fuels burning (CO
in different agricultural activities. The procedure estimates CH
3 sources: (1) enteric fermentation (CO
-FE) from domestic animals, (2) fecal
-EF), and (3) rice crop emissions (CO
-EA). The emission of N
was the most difﬁcult to estimate because of the complexity of determinations.
Emission sources are: (1) Feces and urine (CO
-EDHO) from domestic animals,
(2) volatilization, runoff and inﬁltration (CO
-EIVLI) from synthetic fertilizers and
animal excrements (urine and feces), and (3) arable soils (CO
chemical fertilizers, biological N ﬁxation and crop residues. Therefore, the ﬁnal
118 E. F. VIGLIZZO ET AL.
equation for estimating the CO
balance = (CO
-SC + (CO
-BL + CO
-CTBP + CO
-CF) + ((CO
-FE + CO
-EF) × 21)
-EDHO + CO
-EIVLI + CO
-EDSA) × 310)
Inspired in validation procedures used in simulation models, Bockstaller and
Girardin (2003) have recently proposed a methodological framework to validate in-
dicators. Three types of validation procedures have been identiﬁed by these authors:
(1) design validation, (2) output validation, and (3) end-use validation. Validation in
the ﬁrst case occurs when the indicator design was supported by the best scientiﬁc
information available. Output validation is common in modeling science, and it is
based on the soundness of results or, at least, on the low probability of refuting
them. End-use validation, on the other hand, is based on the usefulness that indica-
tors have demonstrated to provide reliable diagnosis and sound decision-support in
practical farming. Given that the third validation involves a long-term period that
cannot be fulﬁlled in our case because our development was very recent, we will
focus on the two ﬁrst validation types.
Within the Bockstaller and Girardin (2003) scheme, the design soundness of
our indicators lies on the fact that most indicators were based on peer reviewed
procedures and results from well-founded scientiﬁc publications. Likewise, the
design of some indicators was subjected to the scrutiny of regional researchers that
provisionally validate the calculation procedure.
On the other hand, the output validation was done by comparing the outcomes of
the model with empirical data from research, experimentation, and ﬁeld measure-
ments. We consider that this procedure allows a well-founded empirical validation.
The only one exception was related to indicators that provide relative indexes that
are not commonly used. Model outputs were validated through two main proce-
dures: (a) by comparing the ranges of variability of empirical data with the corre-
sponding range of model outputs, and (b) by analyzing the performance of farms
across the region in response to land use change. Land use has a demonstrable
and predictable effect (Rabbinge et al., 1994) on the environmental behavior of
farms. Then, the rationality of indicators response to land allocation (% of annual
crops) in the collection of study farms was used as a means for model validation. A
simple regression analysis using linear and non-linear models was applied in order
to detect statistically signiﬁcant relations.
UTPUT REPRESENTATION AND THRESHOLD LEVELS
Different procedures were used for presenting results to the users. For the whole
sample of farms, trend lines were used to describe the environmental performance
A RAPID METHOD FOR ASSESSING THE ENVIRONMENTAL PERFORMANCE 119
of such farms across the whole Pampas in response to a varying proportion of annual
For individual farms, on the other hand, a dashboard that shows the performance
of each environmental indicator was designed to facilitate the farmer interpretation
of its own performance. Each indicator was represented by a ruler which is ranked
into three different areas with colors that resemble the colors of trafﬁc lights. Dif-
ferent tones of green, yellow and red were used to represent the environmental risk
in an understandable way. But in this paper we have used different tones in grey to
represent the same effect: light grey represents low risk on the left side (equivalent
to green), mid-grey represents moderate risk on the middle part of the rule (equiv-
alent to yellow), and dark grey indicates high risk (equivalent to red) on the right
side of the rule (Figure 2).
No doubt that determining the environmental performance of farms through
color ranges that represent different risk conditions was a trouble that had to be over-
come. The ﬁrst approach to deﬁne ranges and threshold limits was to collect quan-
titative data from scientiﬁc literature. But normally, ﬁgures from other agricultural
areas in the world were rather different from ﬁgures that we had been getting from
the Pampas. We reached the conclusion that comparisons were not useful in a strict
sense because outside data did not adapt well to the agro-environmental conditions
of the Pampas. So we ﬁnally decided to identify, for each indicator, speciﬁc envi-
ronmental ranges and thresholds that are applicable only to the Pampas. Therefore,
Figure 2. Detail of the dashboard diagram that is used to assess the environmental performance at
the farm-scale in the Argentine Pampas. Details about indicators and their units are provided in the
text (see Sections 2.2.1–2.2.8).
120 E. F. VIGLIZZO ET AL.
despite it may be considered subjective, the local adaptation to do between-farm
comparisons became useful and reliable.
Once speciﬁc ranges suitable for the Pampas were estimated, threshold limits
were calculated to identify tolerance limits. For each indicator, tolerance limits
were estimated through the variability range of ﬁgures within the already study
set of 120 farms in the Pampas. Farms with good performance provided ﬁgures to
set the range and the tolerance limits for the light-grey area. The same procedure
was followed to set ﬁgures for the mid- and the dark-grey areas, respectively. A
clock-hand was used, for each farm, to indicate the exact position of each indicator
in the ranked range. Eleven small rectangular boxes were located in the middle
of each farm dashboard to set the color determined for each indicator. Thus, the
performance of different farms can be compared by the dominance of colors that
represent low, medium or high environmental risk. For example, when light grey
predominates in a given farm, we assume that such farm has been performing with
low environmental risk. On the contrary, when dark colors predominate, a warning
signal suggests that corrective actions might be required.
3. Results and Discussion
ALIDATION THROUGH THE COMPARISON OF VARIABILITY RANGES
A general comparison of outputs from Agro-Eco-Index indicators with empirical
evidence from different sources is presented in Table I. Although mean values and
variability coefﬁcients of outputs easily arise in our model, the comparison with
empirical ﬁgures from other sources cannot easily be done. Our ﬁgures can be
compared with ﬁgures from outside sources only for a limited number of indicators
(e.g., fossil energy use, use efﬁciency of fossil energy, N balance and greenhouse
gases balance), but not for the remaining indicators. Therefore, our outputs are
meaningful only for people living in the Pampas, but not necessarily for people
living outside the Pampas. Besides, it must be taken into account that the output
range can vary widely both in the case of Agro-Eco-Index and other outside sources.
Unavoidably, this conspires against the rigorous validation of the model.
Accepting such constraints, we have assumed that our model would behave
acceptably well if the variability range of our estimations falls within the variability
range of scientiﬁc data from different sources. As it can be appreciated from Table I,
the variability range of results from the analysis of 120 farms in the Pampas has
fallen within the variability range of data from different scientiﬁc sources.
Beyond the already pointed out constraints, some other limitations in indicators
calculation must be taken into account: (1) data on fossil energy consumption
providedby literature were not necessarily updated in relation to technology change.
Occasionally, imprecise description of calculation has raised the risk of double
accounting in the case of some inputs, (2) the dynamics of nutrient pools and ﬂuxes
A RAPID METHOD FOR ASSESSING THE ENVIRONMENTAL PERFORMANCE 121
Comparison of Agro-Eco-Index estimations with research and experimental results, and ﬁeld measurements
Research and experimental results, and ﬁeld measurements
Agro-Eco-Index model estimations in different countries and areas in the Pampas
indicator ±SD Range of results Range of values Sources Data from
Fossil energy (FE) use
7.15 ±5.97 0.04–41.20 3.99–100.40 (1)–(4) Argentina, UK, Nigeria
Use efﬁciency of FE (Gj FE
0.69 ±1.15 0.05–9.47 0.18–5.56 (1)–(4) Argentina, UK, Nigeria
N balance (kg ha
)21.82 ±28.65 −76.62–(−102.87) −22.00–115.00 (4)–(6) Argentina, Brazil, France,
Netherlands, Nigeria, UK
P balance (kg ha
) −8.50 ±7.99 −3.18–1.34 −12.71–(−0.20) (4) (7) Whole Pampas
N contamination risk (mg l
)8.43 ±22.57 0.00–22.51 0.00–136.00 (8)–(13) Rolling, Southern,
Mesopotamian and Central
P contamination risk (mg l
)0.01 ±0.07 0.00–0.66 0.00–7.60 (9) Rolling Pampas
Pesticide contamination risk
1.34 ±4.57 0.00–44.55 0.02–39.40 (14) Whole Pampas
Soil erosion risk (ton
5.66 ±8.25 −7.22–27.81 0.00–69.00 (15) (16) Rolling and Central Pampas
involving both, conventional
(Continued on next page)
122 E. F. VIGLIZZO ET AL.
Research and experimental results, and ﬁeld measurements
Agro-Eco-Index model estimations in different countries and areas in the Pampas
indicator ±SD Range of results Range of values Sources Data from
Habitat intervention (relative
1.23 ±1.52 0.00–15.46 1.0–30.20 (14) Whole Pampas
Change in soil C stock (ton
−0.13 ±0.15 −0.47–0.50 −2.11–(−0.1 5) (17) (18) Rolling Pampas under
Greenhouse gases balance
0.87–2.93 (14) (19) (20) Argentina, Brasil, Bolivia,
Chile, Paraguay and
High values of greenhouse gases emissions obtained from various farms analyzed through the Agro-Eco-Index model can be explained by the
use of ﬁre as common practice to manage rangelands and cultivated pastures. Source references: (1) Giampietro et al. (1999); (2) Spedding
(1979); (3) Spedding and Walsingham (1975); (4) Viglizzo et al. (2003); (5) Frissel (1978); (6) Stoorvogel and Smiling (1990); (7) Vazquez
(2002); (8) Andriulo et al. (2002a); (9) Andriulo et al. (2002b); (10) Costa et al. (2002); (11) Jouli et al. (2002); (12) Weir (2002); (13) Papparotti
and Machiori (2002); (14) Viglizzo et al. (2002a); (15) Bernardos et al. (2001); (16) Michelena and Irurtia (1995); (17) Alvarez et al. (1999);
(18) Andriulo et al. (1999); (19) Gonzalez (2000); (20) W. R. I. (1996).
A RAPID METHOD FOR ASSESSING THE ENVIRONMENTAL PERFORMANCE 123
was ignored because of practical reasons; then, simple input-output relations do
not accurately reﬂect nutrient cycling in real farming, (3) estimations of nutrient
contamination risk carry away some uncertainty emerging from (2). The same
happens with respect to the hydrological dynamics (rainfall, evapotranspiration
and soil water holding capacity), (4) in relation to the pesticide contamination
risk, aggregated ﬁgures at the farm level ignores potential interactions between
pesticides. Likewise, we have set aside the speciﬁc effect of pesticides on non-
target species, (5) although the estimation of soil erosion risk was based on the
application of universally accepted models, more ﬁeld data measurements in the
region should had been necessary to validate the calculation, (6) the method used to
estimate human intervention on the habitat has resulted from previously calculated
indicators, saying nothing about the real impact on biodiversity. Ideally, the method
should be focused on habitat fragmentation and its impact on key species, (7)
the proposed method to estimate changes in soil organic carbon stock is rather
simplistic in relation to the complex dynamics of C in real farming. Land use,
climate variability, soil and tillage interactions, plus agronomic practices are sources
of uncertainty in our estimations, (8) the revised procedure suggested by IPCC
(1996) to estimate greenhouse gases balance is relatively new, and therefore carries
away some degree of uncertainty. Ideally, on-farm and ﬁeld measurements should
support such estimations.
ALIDATION THROUGH THE ANALYSIS OF FARMS PERFORMANCE
ACROSS THE STUDY REGION
Beyond the evidence from the comparison of output variability ranges, the sound-
ness of results was also checked through between-farm analysis. This validation
was based on the selection of one driving factor that has a well-known environ-
mental impact on farms. Scientiﬁc evidence (Viglizzo et al., 1997, 2001, 2002a;
Bernardos et al., 2001) from the Pampas suggests that land use is an outstanding
factor that has affected the functionality (energy ﬂows, carbon dynamics, nutrient
cycles and hydrological processes) of ecosystems during the last century.
The results from the regression analysis in which the % of crops (as land use
factor) was associated with various dependent variables (X) are presented in Ta-
ble II. The best ﬁtted model was a second degree equation in all cases. With the
exception of phosphorus input-output relation, which showed a statistically signiﬁ-
cant relation (P < 0.05), determination (R
) and correlation (R) coefﬁcients were
highly signiﬁcant (P < 0.01) for the rest of dependent variables.
When the environmental performance of farms was checked against land use
change, it was appreciated that indicators behaved according to the expected re-
sponse (Figure 3). The energy productivity (Figure 3a) and the fossil energy use
(Figure 3b) have increased in response to increasing cultivation. In our case, the en-
ergy productivity represents the energy yield of edible products that were exported
from the farm. To get such expression, all products were previously converted into
124 E. F. VIGLIZZO ET AL.
Results from regression analysis using a binomial model to assess the relation between the percentage
of crops as independent variable (Y ) and various dependent variables (X) in 120 farms scattered across
the Pampas region
% crops (Y ) vs. Intersection abR
Energy productivity 10186.10 −462.93 10.40 0.494 0.702 <0.01 22247.50
Fossil energy consumption 4657.07 2.62 0.54 0.099 0.315 <0.01 5716.19
N input–output relation 19.56 0.62 −0.008 0.124 0.351 <0.01 27.05
P input–output relation 3.12 −0.14 0.0007 0.069 0.263 <0.05 7.78
Pesticide contamination 0.77 −0.054 0.0009 0.095 0.309 <0.01 4.39
Soil erosion risk 5.03 0.42 −0.0026 0.392 0.626 <0.01 6.03
Change in soil −0.0015 −0.0065 0.00005 0.123 0.351 <0.01 0.14
Greenhouse gases 18.02 −0.2198 0.0009 0.090 0.300 <0.01 11.85
energy by taking into account their caloric value (Viglizzo et al., 2003). Figures 3a
and b show that responses agree with data from different sources (Spedding and
Walsingham, 1975; Spedding, 1979; Viglizzo and Roberto, 1998; Giampietro et al.,
1999; Pervanchon et al., 2002; Viglizzo et al., 2002b) that assessed the effect of in-
tensiﬁcation on farming systems. Such evidences have demonstrated that the higher
the percentage of annual crops in the study farms the larger their energy productiv-
ity and their consumption of fossil energy. This relation is conﬁrmed in Figures 3a
The input-output relation of N and P deserves special attention. As it was men-
tioned above, cultivation in the Pampas during the 1990’s comprised an increasing
allocation of land to annual crops, and this was particularly noticeable in the Rolling
Pampas. While N fertilization was usual, fertilization with P remained rather low
across the region. However, a crop-pasture rotation scheme involving N-ﬁxing
legumes still persists in many areas of the Pampas providing signiﬁcant amounts
of N. As a result of this doubled source of N, the model outputs have detected
positive N input-output relations all across the region. Higher N surpluses agreed
with an intermediate % of crops and a high percentage of legume-based pastures
(Figure 3c). However, given that surpluses were not extremely high, they did not
represent a signiﬁcant threat in terms of N contamination. On the other hand, the
predomination of negative P balances across the region, and particularly in the
Rolling Pampas, was the result of low levels of P fertilization (Figure 3d). Similar
trends were found in previous works (Viglizzo et al., 1995, 2001). Given that not
all N-surplus is dissolved in water excess (part is lost through gaseous pathways),
we admit that these estimations are only a rough and simplistic approach of real-
ity. This constraint was accepted in our model in order to preserve the speed and
A RAPID METHOD FOR ASSESSING THE ENVIRONMENTAL PERFORMANCE 125
Figure 3. Response of energy productivity (3a), fossil energy use (3b), nitrogen (3c) and phosphorus input-output relations (3d) to the increasing percentage
of cultivated land in the study farms. Response of pesticides contamination risk (3e), soil erosion risk (3f), change of the organic carbon stock in soils (3g)
and greenhouse gases balance (3h) to the increasing percentage of cultivated land in the study farms. Thick full line: average trend. Thin full line: trend
under conventional tillage; thin dashed line: trend under no-tillage. (Continued on next page)
126 E. F. VIGLIZZO ET AL.
A RAPID METHOD FOR ASSESSING THE ENVIRONMENTAL PERFORMANCE 127
simplicity of calculation under practical farming. The detection of contamination
risk episodes due to N and P overloading was rare in this study, and this agreed
with ﬁeld measurements and experimental results from Andriulo et al. (2002) in
the Rolling Pampas. On the other hand and as it was expected, the contamination
risk by pesticide use (Figure 3e) and the soil erosion risk (Figure 3f) were higher in
areas where annual crops predominate. The different impact of conventional- and
no-tillage on soil erosion risk can be appreciated in Figure 3f. These results were
conﬁrmed through experimental evidence and ﬁeld measurements (Aimar et al.,
1996; Buschiazzo et al., 1998, 1999). This demonstrates that the erosion risk in-
creases in response to the relative expansion of the cropping area. However, such
risk tends to be lower in farming systems which have already adopted no-till prac-
tice. Counter-balancing this, the risk of pesticide contamination tended to increase
in no-tilled cropping systems.
The impact of no-till can also be appreciated in the analysis of changes in the
stock of soil organic carbon stock (Figure 3g). While C depletion can largely be
explained by increasing cropping under conventional tillage, no-till not only seems
to prevent C depletion but also to boost a long-term C gain in soils. This behavior
can be conﬁrmed through experimental results (Alvarez et al., 1999, 2001; Alvarez,
2001; Chagas et al., 1995; Miglierina et al., 2000; Studdert and Echeverr´ıa, 2000).
Greenhouse gases (GHG) emission in response to land use change is also in
line with scientiﬁc information. GHG emission tends to decrease as far as the area
devoted to crop production increases (Figure 3h). This emission has two main
sources: (a) methane (CH
) emission by cattle digestion and (b) GHG emission by
rangeland burning in extensive beef cattle production. The last source was particu-
larly relevant in the case of cow-calf farming systems. These results are conﬁrmed
through literature information (Viglizzo et al., 2002a; Gonz´alez, 2000; W.R.I.,
It may be argued that this part of the validation process is supported by a circular
argumentation. For example, given that speciﬁc technical coefﬁcients were used to
represent no-till, conservation or conventional tillage operations, the model out-
comes will reﬂect this effect and validation might turn invalid. But not necessarily
is this argumentation true because the independent variable was the percentage of
annual crops, but not the area allocated to a particular tillage operation.
HARACTERIZATION OF THE ENVIRONMENTAL PERFORMANCE
OF SINGLE FARMS THROUGH A DASHBOARD ANALYSIS
As mentioned above, an indicators dashboard was designed to better visualize the
environmental performance of individual farms. Three examples that respectively
correspond to a dairy (Figure 4a), a mixed crop-beef (Figure 4b) and a continuous
cropping (Figure 4c) real system in the Southern Pampas are showed in Figure 4.
According to the position of the clock-hand in the ranked rule, the comparative be-
havior of each farm in relation to each environmental indicator can be interpreted.
128 E. F. VIGLIZZO ET AL.
Figure 4. The environmental dashboard applied to three typical farm systems in the Pampas to
facilitate the interpretation by users. LG (4a), EA (4b) and ES (4c) are the initial letters of three selected
systems in the sample of 120 study farms. The different performance of farms can be appreciated by
comparing similar indicators.
A warning signal was detected when the clock-hand tends to the right side. Thus,
farms failing in environmental management can be identiﬁed and eventual techno-
logical solutions can be driven. For example, the application of no-till operations
can help to resolve problems of high fossil energy consumption and soil erosion
when such problems are detected through the dashboard analysis.
After analyzing a farm sample, between-farm comparisons are possible in order
to contrast their respective environmental performance. As an example, and based
on the information provided by their respective dashboards, the comparative be-
havior of nine different farms in the Southern Pampas are represented in Figure 5
by taking into account the central set of colored boxes of each dashboard. Farms
identiﬁed as LL and ET where light colors predominate (9 over 11) showed a better
performance than farms where dark colors dominate.
A RAPID METHOD FOR ASSESSING THE ENVIRONMENTAL PERFORMANCE 129
Figure 5. Comparative environmental behavior of nine farms (see initial letters on the left column) in
the Southern Pampas. The accumulation of light boxes on total boxes (11) indicates better behavior
(for reference of this, see ﬁgures on the right column).
Beyond limitations in our approach, methods and outcomes, the scheme we pro-
pose can help to discriminate among farms in the Pampas of Argentina, separating
those that show a friendly behavior from those that do not pay enough attention
to the surrounding environment. This discrimination is not a minor challenge in
an environmentally sensitive society. An organized community should not ignore
the effort of farms and other commercial ﬁrms to improve the environmental status
of the area where they locate. It sounds sensible to think that such effort should
be encouraged by recognizing and rewarding the environmental commitment of
Based on the principle that a continuous environmental improvement is essential
in modern societies, we believe that ecological certiﬁcation can help to confer social
recognition (for example, through tax beneﬁts) and commercial advantage (for
example, through the allocation of “green labels”) to the outstanding environmental
The use of indicators to assess and monitor the agricultural environment can
represent a ﬁrst step in a program aiming at implementing the ecological certiﬁ-
cation of commercial farms in the region. Despite uncertainties around indicators
calculation, this does not invalidate a useful comparison of farms. If we consider
that any methodological failure equally affects all farms in the study sample, this
still allows a useful inter-farm comparison.
Individual farms can be characterized by means of the dashboard scheme. In-
dicators in dark grey allow the identiﬁcation of speciﬁc environmental problems,
130 E. F. VIGLIZZO ET AL.
providing a viable way to later drive technical solutions. Looking at a potential eco-
logical certiﬁcation program, farms which show most indicators within the light
grey range are in theory demonstrating a better potential to get advantage from
“green labeling” than farms in which dark colors predominate. At the same time,
farms showing a poor performance can also beneﬁt from dashboard indicators. It
provides a rudimentary diagnosis guide of environmental problems that demand at-
tention. No doubt that the predomination of dark colors in critical indicators could
be demanding the support of specialized technological knowledge in speciﬁc ﬁelds.
For example, if the indicator of soil erosion in a farm falls within the dark zone,
a soil scientist or soil technician can provide a better diagnosis of the problem
and suggest a technological way to overcome such constraint. The Agro-Eco-Index
model does not provide a reﬁned diagnosis, but a crude picture of environmental
problems in the farm that can potentially become critical.
No doubt that the calculation of our indicators needs to be improved,especially in
the case of those that arise doubt around the method applied or the validationprocess.
A continuous review and revision is probably the best way to improve the quality of
indicators. However, such improvement should pay special attention to the tradeoffs
between the calculation strictness and the calculation complexity. A more strict
calculation is needed to make indicators more reliable for representing the reality.
But on the other hand, complexitymay impose an economic and intellectual cost that
might turn indicators unsuitable to practical users. In this case, we have decided
that calculation simplicity should be preserved in the model at the expense of a
We want to thank the ﬁnancial support of the National Institute for Agricultural
Technology (INTA), the National Research Institute (CONICET) and the National
Agency for the Promotion of Science and Technology (FONCYT) in Argentina.
Members of two farmer organizations (CREA and Cambio Rural) are specially
acknowledged for their willingness to support this study providing on-farm data.
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