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A Rapid Method for Assessing the Environmental Performance of Commercial Farms in the Pampas of Argentina

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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 practical 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) balance, 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 committed farmers.
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Environmental Monitoring and Assessment (2006) 117: 109–134
DOI: 10.1007/s10661-006-7981-y
c
Springer 2006
A RAPID METHOD FOR ASSESSING THE ENVIRONMENTAL
PERFORMANCE OF COMMERCIAL FARMS IN THE PAMPAS
OF ARGENTINA
E. F. VIGLIZZO
1,2,3,
, F. FRANK
1,3
, J. BERNARDOS
3
, D. E. BUSCHIAZZO
1,2
and S. CABO
1
1
INTA Centro Regional La Pampa, La Pampa, Argentina;
2
CONICET;
3
Facultad de Ciencias
Exactasy Naturales, Universidad Nacional de La Pampa
(
author for correspondence, e-mail: evigliz@cpenet.com.ar)
(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
committed farmers.
Keywords: ArgentinePampas, commercial farms,ecologicalcertification, environmentalassessment,
sustainability indicators
1. Introduction
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,
1994).
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
2.1. T
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
basic indicators.
2.2. I
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
Argentina.
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)
annual crops.
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
2
) 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
1
year
1
)
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
1
of drainage
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
year
1
) 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
3
contents),
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
1
year
1
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
1
year
1
). 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
action.
The procedure followed in this work to estimate changes in C stock (expressed
as t ha
1
year
1
) 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
2
), methane (CH
4
), nitrous oxide
(N
2
O), ozone (O
3
) and water vapor are normal components of the atmosphere that
have, at the same time, a greenhouse effect. CO
2
and CH
4
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
2
equivalent (ton ha
1
year
1
). 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
2
. 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
CO
2
. This figure was used to convert CH
4
into CO
2
equivalents. Nitrogen excreted
in feces and distributed with fertilizers was another significant source of nitrous
oxide (N
2
O) emission. N
2
O has a greenhouse power 310 times greater than CO
2
(IPCC, 1996). Losses of N
2
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
2
stock exchange in soils over time
(CO
2
-SC), (2) CO
2
stock exchange in timber biomass (CO
2
-BL), (3) conversion of
forests and prairies into arable land (CO
2
-CTBP), (4) abandonment of intervened
lands (CO
2
-aband), and (5) emission of CO
2
from fossil fuels burning (CO
2
-CF)
in different agricultural activities. The procedure estimates CH
4
emission from
3 sources: (1) enteric fermentation (CO
2
-FE) from domestic animals, (2) fecal
emissions (CO
2
-EF), and (3) rice crop emissions (CO
2
-EA). The emission of N
2
O
was the most difficult to estimate because of the complexity of determinations.
Emission sources are: (1) Feces and urine (CO
2
-EDHO) from domestic animals,
(2) volatilization, runoff and infiltration (CO
2
-EIVLI) from synthetic fertilizers and
animal excrements (urine and feces), and (3) arable soils (CO
2
-EDSA), through
chemical fertilizers, biological N fixation and crop residues. Therefore, the final
118 E. F. VIGLIZZO ET AL.
equation for estimating the CO
2
balance was:
CO
2
balance = (CO
2
-SC + (CO
2
-BL + CO
2
-CTBP + CO
2
-aband)
+CO
2
-CF) + ((CO
2
-FE + CO
2
-EF) × 21)
+((CO
2
-EDHO + CO
2
-EIVLI + CO
2
-EDSA) × 310)
2.3. M
ODEL VALIDATION
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.
2.4. O
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
crops.
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
3.1. V
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
TABLE I
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
Environmental
indicator ±SD Range of results Range of values Sources Data from
Fossil energy (FE) use
(Gj ha
1
year
1
)
7.15 ±5.97 0.04–41.20 3.99–100.40 (1)–(4) Argentina, UK, Nigeria
Use efficiency of FE (Gj FE
Gj prod
1
)
0.69 ±1.15 0.05–9.47 0.18–5.56 (1)–(4) Argentina, UK, Nigeria
N balance (kg ha
1
year
1
)21.82 ±28.65 76.62–(102.87) 22.00–115.00 (4)–(6) Argentina, Brazil, France,
Netherlands, Nigeria, UK
P balance (kg ha
1
year
1
) 8.50 ±7.99 3.18–1.34 12.71–(0.20) (4) (7) Whole Pampas
N contamination risk (mg l
1
)8.43 ±22.57 0.00–22.51 0.00–136.00 (8)–(13) Rolling, Southern,
Mesopotamian and Central
Pampas
P contamination risk (mg l
1
)0.01 ±0.07 0.00–0.66 0.00–7.60 (9) Rolling Pampas
Pesticide contamination risk
(relative coefficient)
1.34 ±4.57 0.00–44.55 0.02–39.40 (14) Whole Pampas
Soil erosion risk (ton
sediments ha
1
year
1
)
5.66 ±8.25 7.22–27.81 0.00–69.00 (15) (16) Rolling and Central Pampas
involving both, conventional
and no-till
(Continued on next page)
122 E. F. VIGLIZZO ET AL.
TABLE I
(Continued)
Research and experimental results, and field measurements
Agro-Eco-Index model estimations in different countries and areas in the Pampas
Environmental
indicator ±SD Range of results Range of values Sources Data from
Habitat intervention (relative
coefficient)
1.23 ±1.52 0.00–15.46 1.0–30.20 (14) Whole Pampas
Change in soil C stock (ton
ha
1
year
1
)
0.13 ±0.15 0.47–0.50 2.11–(0.1 5) (17) (18) Rolling Pampas under
continuous cropping
Greenhouse gases balance
(ton ha
1
year
1
)
9.07 ±1130
a
2.67–(21. 00)
a
0.87–2.93 (14) (19) (20) Argentina, Brasil, Bolivia,
Chile, Paraguay and
Uruguay
a
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.
3.2. V
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
2
) 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.
TABLE II
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
2
RP SE
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
risk
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
carbon stock
Greenhouse gases 18.02 0.2198 0.0009 0.090 0.300 <0.01 11.85
(GHG) balance
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
and b.
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.
Figure 3.(Continued)
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
4
) 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.,
1996).
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.
3.3. C
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).
4. Conclusions
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
farming firms.
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
behavior.
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
rigorous one.
Acknowledgments
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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... In this sense, a field where great progress has been observed is in the analysis of sustainability (Viglizzo et al., 2006;Jónsdóttir, 2011;Speelman et al., 2007;Sarandón & Flores, 2009). This complex (multidimensional) concept could be addressed through the methodology of indicator development. ...
... There are, however, variations in the methodology across authors. On the one hand, Viglizzo et al. (2006) and Jónsdóttir (2011) propose rigid and/or universal indicators. In these cases, indicators cannot always be applied universally nor are they transferable, since each study site has specific characteristics. ...
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The reductionist and disciplinary paradigm of the Green Revolution coexists with the emerging paradigm of complexity, which values the holistic and the interdisciplinary. Agroecology promotes the need to understand the multiple biophysical relationships that exist in agroecosystems, and this calls for the development of new methodological tools. Sustainability indicators are an example of this. However, their implementation is not simple, as this requires an instrument to simplify the construction of such indicators. The objective of this work is to use the “mental map” as a guide for the development and application of indicators. The graph follows the conceptual path that facilitates the understanding of the variable and its breakdown into smaller and measurable units of analysis, i.e. the indicators. The created mental map has two stages: the development of indicators and their application. Its utility is presented in a case study that addresses local environmental knowledge (LEK). The results of this work show that complexity can be translated into quantifiable, measurable, and comparable variables, without this representing the loss of its characteristics. In addition, it proves that the created tool facilitates the evaluation and understanding of the functioning of agroecosystems, which contributes to decisionmaking
... For both productive orientations, all the losses occurred as emissions of N 2 O-N and NH 3 -N. Therefore, as the surpluses were low and we identified no N available for leaching, the amounts of manure and fertilizer-based N lost through leaching were assumed to be negligible and did not represent a significant threat for N contamination as was also stated by Viglizzo et al. (2006) and Modernel et al. (2013) for beef cattle systems in Argentina and Uruguay respectively. According to this, in the CF calculations, we did not consider the indirect emissions from NO 3 -N. ...
... According to this, in the CF calculations, we did not consider the indirect emissions from NO 3 -N. N fertilizer has been reported as a critical source of N inputs in cattle farms located in Argentina, England, Mexico, and Sweden, accounting for more than 50% of applied N (Carswell et al., 2019;Cederberg and Mattsson, 2000;Cortez-Arriola et al., 2014;Viglizzo et al., 2006), but this was not the case of studied farms. Atmospheric deposition was the primary N input accounting for almost 90%, and N fertilizer plus purchased feeds accounted for the remaining percentage. ...
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In Colombia, the beef production chain accounts for approximately 11.6 million cattle heads and annually produces 933 million kg of the beef carcass. There are no life cycle assessment (LCA) studies that have evaluated the environmental performance of Colombian beef systems. The present study aimed to estimate the carbon footprint (CF), non-renewable energy use, and land use of 251 cow-calf and 275 fattening farms in Colombia. The study also aimed to identify the main hotspots of adverse environmental impacts and propose possible mitigation options and their cost-effectiveness. The impact categories were estimated using the 2006 IPCC, the 2019 Refinement to 2006 IPCC guidelines, databases, and locally estimated emission factors. The functional units used were 1 kg fat and protein corrected milk and 1 kg live weight gain, leaving the farm gate. Three methods of allocating environmental burdens to meat and milk products were applied: economic, energy and mass allocation. The adoption of improved pastures was considered a mitigation measure, and an economic assessment was performed to estimate the relative cost-effectiveness of its establishment. A principal component multivariate analysis and a Hierarchical Clustering on Principal Components were performed. The economic allocation method assigned a greater environmental burden to meat (83%), followed by energy content (80%) and mass production (73%). The largest sources of GHG emissions were enteric fermentation and manure deposited on pasture. Both cow-calf and fattening systems had a cluster of farms with better productivity, pasture and cattle management practices, and environmental performance. The CF for meat could be reduced by 33 to 56% for cow-calf and 21 to 25% for fattening farms, by adopting improved pastures. Therefore, our results suggest that GHG emissions can be reduced by adopting improved pastures, better agricultural management practices, efficient fertilizer usage, using the optimal stocking rate, and increasing productivity.
... Indicators generally simplify a complex reality, and the identification of relevant and valid indicators has considerable potential to guarantee the most effective use of data provided by the systems evaluated (Kosmas et al., 2014). Viglizzo et al. (2006) used eleven indicators to assess environmental performance, and seven of them are directly related to the DBI in this study: 'fossil energy use' with 'natural gas', 'oil (machinery)' and 'electricity'; 'nitrogen balance' and 'P balance' with 'nutrients recovered'; 'nitrogen contamination risk' with 'nitrates', 'phosphorus contamination risk' with 'phosphorus (water)'; 'soil erosion risk' with 'soil quality'; and 'balance of greenhouse gases (GHG)' with 'dinitrogen monoxide', 'methane' and 'carbon footprint'. They also agreed that complex assessments involve an economic and intellectual cost that might make indicators unsuitable for practical users. ...
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The adverse effects of agriculture and livestock production on the environment are well-known and require mitigation in order to achieve sustainability in the food production chain. This study focused on adverse effects related to biogeochemical flows of phosphorus and nitrogen cycles which natural balances have been greatly disturbed by current practices. To assess the potential benefits and detrimental effects of proposed mitigation measures, adequate impact indicators are required. The challenge lies in identifying and providing indicators that cover the important aspects of environmental sustainability and allow a direct comparison of policy alternatives. A review of potential indicators that are also consistent with those used to indicate the performance of agricultural and general sustainability (i.e. the European Green Deal) led to the selection of fifteen agri-environmental indicators covering the main environmental issues in agriculture. The indicators identified offered an effective representation of environmental behaviour and would be useful in communicating a comprehensive ‘dashboard’ for professional end users of solutions to nutrient recovery and nutrient efficiency improvement in arable and livestock systems. The selected dashboard indicators (DBI) covered the dimensions of ‘use of primary resources’, ‘emissions to the environment’ and ‘resilience to climate change’. Five case studies were investigated to test the DBI using an Excel questionnaire applying the qualitative approach of the Delphi method together with expert knowledge. As expected, the results indicated that there were potential benefits of the technologies in terms of improved ‘nutrient recovery’ and decreased ‘nitrate leaching’. Potential disadvantages included increased electricity and oil consumption and greater ammonia volatilisation due to the increased use of organic fertilisers. The indicator ‘water’ received more neutral responses; thus, the specific technology was not expected to consistently affect the indicator. In relation to ‘particulate matter’, the results were indicated to be ‘unknown’ for some solutions due to the difficulty of predicting this indicator. Furthermore, methodologies for estimating quantitative values for the dashboard indicators were proposed, and a quantitative assessment was performed for the solution ‘catch crops to recover nutrients’, confirming the responses in the qualitative assessment. The dashboard indicators selected covered the main aspects of the solutions, identified in more comprehensive studies of environmental impacts, as being suitable for the rapid assessment of technologies for nutrient recovery in agriculture. As such, they can be used as a pre-screening method for technologies designed to improve the environmental sustainability of arable and livestock systems.
... The quantification of nutrient flows through the nutrient balance tool, has been widely used in cattle farms for checking out N surpluses and the possibility of N leaching (Cederberg and Mattsson, 2000;Penati et al., 2011;Viglizzo et al., 2006). Considering this, for each farm, a nitrogen (N) balance at farm level was performed. ...
Article
CONTEXT Colombia has a total of 27.2 million heads of cattle, ranking fourth among the Latin American countries. Identifying sustainable strategies to mitigate greenhouse gas emissions (GHGE) will help the Colombian government meet their goal of a 51% reduction in national emissions by 2030. Estimation of yield gaps for identifying the potential to improve cattle farms productivity and efficiency in Colombia help on reducing the GHGE intensities from the cattle sector. OBJECTIVE This paper aims to calculate the gap between attainable and actual milk and meat yields for specialized dairy, dual-purpose, cow-calf, and fattening production systems in 3 agro-ecological zones (AEZ) in Colombia; to identify the main aspects that restrict the meat and milk yields in these production systems; and analyze how closing yield gaps affect the carbon footprint (CF) of meat and milk production. METHODS The most suitable AEZs for cattle activities were identified by considering environmental, climatic, edaphic, and land characteristics. From a dataset of 1505 surveyed farms, a yield gap benchmarking analysis for estimating the potential to increase meat and milk yields in each of the identified AEZ was applied. The most productive farms were included in the “best farms” while the rest of the farms belonged to the “farms operating below potential”. A “cradle to farm-gate” Life Cycle Assessment was used to calculate the CF. Three scenarios were proposed for closing the yield gaps by 50, 75, and 100%, between the two groups of farms. RESULTS AND CONCLUSIONS Three AEZs likely to support cattle activities in Colombia were identified. Average milk production from the farms operating below potential was 45–50% of potential production, and meat was 34–51%, indicating that a potential to achieve increases in milk and meat productivity exists. CFs of 1 kg milk or meat were lower in the groups of best-performing farms than in the groups of farms operating below potential. Yield gaps for milk and meat production can be closed by improving cattle management practices and better technologies. As a general trend, closing the yield gaps decreases the CFs. SIGNIFICANCE Our findings contribute to understand the farms' current productive performance and provides key insights into the possible technological and managerial changes for improving the productivity of cattle systems in Colombia. In addition, the study showed how milk and meat CFs can be lowered with the adoption of proper cattle management practices, and better technologies.
... The research was taken in: a. lemon cultivation, b. intensive productive system of aromatic, medicinal and condimental greenhouse, c. lemon and blueberry agroindustry and d. wild species management. In the research sustainable indicators were selected using worldwide referents such as Blue Book from the UN [5], OECD's PER Model [4], FAO's FESLM Model [6], MESMIS Model [7] among others, and at a national level Sarandón [18,19], Viglizzo [20] and Blasco [21] methodologies were used. ...
Chapter
Sustainable agriculture is based in the conservation of environmental and productive resources considering the socio-economical dimensions. The cultivation, production and collection of medicinal and aromatic plants (MAPs) are made up by a sector of the agriculture that should follow the concepts of sustainability. Our team researches in the area of sustainability, having developed agro-ecosystems and agro-industry valuation models. The objective was to develop evaluation tools with particular objectives: 1-an assessment model of lemon cultivation sustainability; 2-assessment tools in MAPs greenhouses and agro-industry; 3-introduction of principles for sustainable collection of wild species. Methodologies used by international organizations, like the UN or OECD, and specific models like FESLM (FAO) were taken as a reference. We worked with the construction of a Minimum Sustainable Set of Indicators (MSI) for each case. Sustainable assessment model for lemon cultivation were obtained. MSI for greenhouse's MAPs cultivation and lemon and blueberries packing houses were developed. International principles for sustainable wild collection of MAPs were introduced. Considering the selected indicators, the results obtained are of those analysed systems that should check over and adjust their process, objectives and policies in the search of an improvement tendency towards sustainability. The harvest of wild species should start its way towards sustainability through the approach in the uses of standards.
... Sustainability assessments of agricultural activities have also been reported in a number of Latin American countries. Some studies focused on variables such as soil quality (Arzeno, 2006), the impact of pesticides and tillage practices (Ferraro et al., 2003), or a combination of several environmental parameters (Abbona et al., 2007;Viglizzo et al., 2006). Others included a more diverse mix of aspects related to the production systems analyzed (Manuel-Navarrete et al., 2007;Rigby et al., 2001;Speelman et al., 2007). ...
Article
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... En este punto resulta oportuno expresar que las alternativas mencionadas para derivar indicadores son diferentes a los protocolos para establecer la sustentabilidad, como también a los marcos de evaluación de agroecosistemas. Los primeros consisten en un conjunto de indicadores ya seleccionados que permiten testear el estado o tendencia de la sustentabilidad de un agroecosistema (Agroecoindex©) (6,38,51). Los segundos son propuestas metodológicas flexibles que permiten guiar a un actor social determinado en el proceso de evaluación (ej. MESMIS) (34, 41b). ...
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Los agroecosistemas son ecosistemas construidos por los seres humanos para obtener productos y servicios de su interés. De este modo, los ecosistemas adquieren la forma de socioecosistemas. Por ello, para poder describirlos, comprenderlos o intervenir sobre los mismos, resulta necesario observar propiedades emergentes de tipo biológicas y atributos relacionados con aspectos sociales, económicos, culturales e institucionales. A su vez, si se pretende analizar dichos agroecosistemas en clave de sustentabilidad, además de contemplar las diferentes dimensiones que la integran, se deberá tener un enfoque sistémico en las construcciones conceptuales y operativas de tal modo que sea posible obtener indicadores de sustentabilidad. El presente trabajo repasa y clasifica las diferentes formas expuestas en la bibliografía para obtener indicadores y muestra la escasez de propuestas que operen con un enfoque sistémico para resolver la tensión entre producción y conservación que gira en torno a la idea de sustentabilidad. Por ello, se presenta una propuesta metodológica alternativa para la obtención de indicadores de sustentabilidad de agroecosistemas que opera conceptual y empíricamente desde un enfoque multidimensional y sistémico, mediante la contraposición del estado del agroecosistema, en cada una de sus dimensiones, con las exigencias que dichas dimensiones de la sustentabilidad establece. Finalmente se vierten consideraciones metodológicas sobre su uso y se destacan fortalezas y limitaciones de su aplicación.
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Land-use changes, mainly driven by the advancement of large-scale soybean monocultures over extensive livestock production systems, have fostered land concentration in the agribusiness sector and an increase of grain-finished livestock production systems (feedlots). While it is recognized that both—intensive (grain-finished) and extensive (grass-finished)—livestock production systems are affected by climate change while contributing to it through greenhouse emissions, their distinct socio-environmental impacts must be better understood. Through a mix of literature review and the results of original field research in the grasslands of Argentina and Uruguay, this chapter aims at overcoming the frequently polarizing discourses generated by the “extensive versus intensive” beef production debate by learning from the analysis of fruitful controversies. The authors start by analyzing how feedlots are frequently less vulnerable to climatic change, while they present a greater risk to water quality due to the increased volume of waste and manure management practices. Grain-finished beef systems, on the other hand, may contribute to the reduction of air quality through the emissions of particulate substances and gases, while they may present negative environmental impacts, as they require longer finishing periods (10–12 months) and more animals and land to produce the same quantity (not necessarily quality) of beef. After critically assessing the socio-environmental impacts of both production systems in their complexity, the chapter will end with a series of convergent, evidence-based recommendations to design sustainable and socio-environmentally friendly livestock production solutions.
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As our global crisis on climate, food, fuel and economy continues to aggravate, an advancement into sustainable agriculture has become one of the greatest requirements of this millennium. Despite our knowledge-gap and scepticism around this subject, today’s corporations are demanding for proper assessment methods capable of helping to build business resilience, for the sake of knowing how to well adapt, evolve or transform in the face of future crisis. By reviewing current practices amongst the agrarian sector, findings allowed us to confirm well-established observations from literature and to enlighten the fundamental role of context comprehensiveness when assessing sustainability. This work provides relevant information for micro-level performance evaluations, as results may support decision-makers to recognise, understand or apply any of the analysed 105 assessment tools.
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Ecological assessments and monitoring programs often rely on indicators to evaluate environmental conditions. Such indicators are frequently developed by scientists, expressed in technical language, and target aspects of the environment that scientists consider useful. Yet setting environmental policy priorities and making environmental decisions requires both effective communication of environmental information to decision makers and consideration of what members of the public value about ecosystems. However, the complexity of ecological issues, and the ways in which they are often communicated, make it difficult for these parties to fully engage such a dialogue. This paper describes our efforts to develop a process for translating the indicators of regional ecological condition used by the U.S. Environmental Protection Agency into common language for communication with public and decision-making audiences. A series of small-group sessions revealed that people did not want to know what these indicators measured, or how measurements were performed. Rather, respondents wanted to know what such measurements can tell them about environmental conditions. Most positively received were descriptions of the kinds of information that various combinations of indicators provide about broad ecological conditions. Descriptions that respondents found most appealing contained general reference to both the set of indicators from which the information was drawn and aspects of the environment valued by society to which the information could be applied. These findings can assist with future efforts to communicate scientific information to nontechnical audiences, and to represent societal values in ecological programs by improving scientist-public communication.
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Drriven by changes in agricultural production practices, nitrogen (N) inputs have increased steadily on Canadian farms. An agro-environmental indicator was developed to monitor potential water pollution by N: indicator risk of water contamination by nitrate-nitrogen (IROWC-N). The indicator links the residual soil nitrogen (RSN) indicator to climate and soil conditions to assess the likelihood of N moving through the soil and out of the agricultural system. The results are assessed in terms of N lost via leached water (N lost ) and its concentration in the leached water (N conc ), with the IROWC-N risk classes based on N lost and N conc criteria. The estimated amount of N lost in Canada ranged from 5.1 kg N ha ⁻¹ in 1991 to 6.4 kg N ha ⁻¹ in 2001. N conc values remained fairly constant during the 1981 to 1996 census years (ranging from 3.7 to 4.5 mg N L ⁻¹ ), but increased sharply (27%) to 5.7 mg N L ⁻¹ in 2001 as compared with 1996. During the 1981 to 2001 period, close to 80% of the Canadian farmland area remained in the very low and low IROWC-N risk classes, but over the years 18% shifted to a higher risk class. In 2001, large areas (> 1 million ha) in the high risk IROWC-N class were found in Manitoba, southern and eastern Ontario and in Quebec. Provincial averages of N lost over 5 census years (1981, 1986, 1991, 1996 and 2001) varied from less than 5 kg N ha ⁻¹ in Alberta and Saskatchewan to more than 20 kg N ha ⁻¹ in Ontario, Quebec and the Atlantic provinces. With the exception of Manitoba, provincial N conc values did not exceed the Canadian drinking water guideline of 10 mg NO 3 -N L ⁻¹ . In each of the census years, British Columbia, Alberta and Saskatchewan had more than 70% of the farmland area in the very low and low risk classes for IROWC-N. In Ontario and Quebec, most of the farmland area was either in the low or in the high risk class. More than 50% of the farmland area in New Brunswick, Nova Scotia and Newfoundland was in the very low, low and moderate risk classes, whereas in Manitoba and Prince Edward Island, more than 60% of the farmland was in the moderate and higher level risk classes for IROWC-N. Overall, the 20-yr trend in risk of water contamination by N was worsening. Key words: Water contamination by nitrogen, nitrate, water quality, Soil Landscapes of Canada, Census of Agriculture
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On request of FAO a methodology was developed to assess the state of soil nutrient depletion under agriculture in Sub-Saharan Africa for 1983 and the year 2000. The nutrient balance is described with five input and five output factors, which result in a nutrient loss rate. Production figures and data on fertilizer consumption for 1983 and projections for the year 2000 were provided by FAO. Data on nutrient balances as well as additional country information were collected from literature. Nutrient depletion rates for Sub-Saharan Africa are approximately 20 kg N, 10 kg P2O5 and 20 kg K2O per ha on average up to a maximum of 40 kg N, 20 kg P2O5 and 40 kg K2O per ha in East Africa.
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Sustainability is influenced in many production systems by the variation of soil organic C (SOC) content and dynamics, and crop rotations. We hypothesized that arable layer SOC under conventional tillage can be managed through the amount of residue C (RC) returned to the soil as affected by tillage and fertilization. Soil organic C dynamics of a complex of Typic Argiudoll and Petrocalcic Paleudoll soils under conventional tillage between 1984 and 1995 at Balcarce, Argentina was studied for 16 crop sequences. Crops included were spring wheat (Triticum aestivum L.), soybean [Glycine max (L.) Merr.], sunflower (Helianthus annuus L.), and corn (Zea mays L.). Eleven years of conventional tillage decreased SOC 4.1 to 8.8 g kg-1 without supplemental N and 2.8 to 7.2 g kg-1 when N fertilizer was applied. Soil organic C loss increased when soybean (1.2 Mg RC ha-1 yr-1) was present in the sequence and decreased when corn (3.0 Mg RC ha-1 yr-1) was present. The amount of RC returned by the sequences correlated with SOC in 1995 (r = 0.84, P < 0.01) and with SOC at equilibrium (r = 0.76, P < 0.01), but the sequences with two summer crops (soybean, sunflower, or corn) every 3 yr showed lower SOC in 1995 (28.9-33.8 g kg-1) and at equilibrium (24.0-34.4 g kg-1) than sequences with none or one summer crop (29.7-35.0 g kg-1 either in 1995 or at equilibrium) for the same range of RC (1.4-2.6 Mg RC ha-1 yr-1). The difference between sequences in the relationship between RC and SOC were attributed to tillage timing. Under conventional tillage, arable layer SOC can be managed through the selection of the crops in the rotation and N fertilization, but the timing and intensity of tillage have to be taken into account.
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The wind erosion process on agricultural soils is being modeled as the time-dependent conservation of mass transport of soil moving as saltation and creep. Emission of loose soil and abrasion of clods and crust act as sources, whereas trapping and suspension act as sinks for the moving soil. In this study, an expression for the abrasion source term was derived. Abrasion flux from aggregates or crust was shown to be the product of three variables - fraction of saltation impacting the target, an abrasion coefficient, and saltation discharge. Various aspects of the proposed abrasion source term were then investigated in three wind tunnel studies. First, crusted trays were abraded using a range of windspeeds and sand abrader rates. Regression analysis showed there was no significant relationship between crust abrasion coefficients and fraction of abrader moving below 0.1 m (i.e. abrader trajectories). This result shows practical abrasion coefficients can be developed which depend only on the properties of the target and abrader. Second, a relationship was developed to predict fraction of saltation impacting surface aggregates (or intervening crust) as a function of surface aggregate cover and roughness. The relationship was tested in the tunnel by abrading crusted trays partially covered (0 to 30%) with non-abradable aggregates. Regression analysis showed there was good agreement (R2 = 0.97) between observed and predicted fraction of abrader impacting aggregates. In the third experiment, trays were filled with various mixtures of large- and saltation-size aggregates. The trays were abraded in the wind tunnel by a low saltation discharge from a narrow upwind aggregate bed.
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In this article we show that technological development in agriculture exhibits general trends when assessed on a large scale. These trends are generated by changes in the larger socioeconomic context in which the farming system operates. We characterize agricultural performance by land and farm labor productivity and the pattern of use of technological inputs. By means of a cross-sectional analysis of agricultural performance of 20 countries (at the national level), we show that increases in demographic pressure and socioeconomic pressure (increases in average income and labor productivity) in society are the main driving forces of technological development in agriculture. Further, it is shown that the ecological impact of farming (environmental loading) is linked to the particular combination of land productivity and labor productivity at which the agricultural sector operates (through the particular mix and the level of inputs used in agricultural production). Briefly we discuss the role of international trade in agricultural policies and performance. Special attention is given to the situation of Chinese agriculture.
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This study is the first effort to measure wind erosion in the field in South America. Wind erosion of two bare soils, a loamy sand surface Typic Ustipsamment and a sandy loam surface Entic Haplustoll of the Semiarid Argentinian Pampas, were measured in the field during wind storms that started on June 16 (mean wind speed = 14 km/h; storm duration 103 h) and June 30 (mean wind speed = 21.4 km/h; storm duration 25 h) of 1995, Measurements were made with dust samplers placed at heights of 0.135, 0.54, and 1.47 m within a 1-ha field. Mass nux (amount of eroded material within a given time) was larger in the Ustipsamment soils than in the Haplustoll soils in both storms. A maximum amount of transported dust was found within the limits of the studied field (100 x 100 m(2)) during the June 16 wind storm and outside the studied field during the June 30 wind storm. This was attributed to the variation in wind direction on June 16, The total amount of material eroded from the field during each storm reached 1.82 mt ha(-1) in the Ustipsamment and 0.29 mt ha(-1) in the Haplustoll on June 16 and 0.98 mt ha(-1) in the Ustipsamment and 0.75 mt ha(-1) in the Haplustoll on June 30, Wind velocity was high enough to erode the loosened Ustipsamment but not the better structured Haplustoll on June 16, On June 30, wind velocity was high enough to erode both soils, but a shorter storm duration did not allow the erosion of large amounts of soil.