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Cuban Journal of Agricultural Science

Print version ISSN 0864-0408On-line version ISSN 2079-3480

Cuban J. Agric. Sci. vol.54 no.3 Mayabeque Sept.-Dec. 2020  Epub Sep 01, 2020



Implementation of agroecological indicators for environmental diagnosis in a livestock productive facility from the southeast of Buenos Aires, Argentina

Brenda A. Larsen1  *

María J. Kristensen2

Adriana E. Confalone3

1 Facultad de Ciencias Humanas, Universidad Nacional del Centro de la Provincia de Buenos Aires (UNCPBA). Campus Universitario Paraje Aº Seco, 7000 Tandil, Argentina

2 Instituto de Geomorfología y Suelos, Facultad de Ciencias Naturales y Museo, Universidad Nacional de La Plata. Calle 1 nº 644, 1900 La Plata, Argentina

3 Núcleo de Investigación en Actividades Agropecuarias y Cambio Climático. Facultad de Agronomía, Universidad Nacional del Centro de la Provincia de Buenos Aires (UNCPBA). Calle República de Italia 780, 7300 Azul, Buenos Aires, Argentina


An environmental diagnosis of a livestock facility that raises reproductive cattle in the southeast of Buenos Aires was carried out, during a productive year, to evaluate the effects of agricultural practices and deepen the analysis of wild species diversity. The model designed for the AgroEcoindex® Pampas region was applied, which uses 19 indicators related to energy, nutrients, pollution and erosion, water and intervention. Wild species on the area were listed. Energy indicators showed a critical demand for fossil energy. Consumption was 13 times superior to the negative threshold of the model. Production reached the optimal threshold, but was inefficient. It required five units of energy per unit of generated product. C reserve in soil was reduced, greenhouse gas balance exceeded the negative threshold, as well as the impact on habitat and water intake. Risk of pesticide contamination was critical. There were favorable trends: N and P balance, with an annual increase that was 20 times superior to the threshold, without generating contamination risks, efficiency of water use, rain/ produced energy ratio, erosion risk, habitat intervention and agro-diversity. A richness of 81 plants and 75 animals was registered, not evaluated by the model.

Key words: agroecosystem; Pampas region; agroecological indicator; sustainable agricultural system

The adoption of scientific-technological tools and inputs to increase yields and incomes of Pampas agroecosystems, stimulate the processes of productive intensification and expand the agricultural and livestock frontier in Argentina (Manuel-Navarrete et al. 2009 and Viglizzo and Jobbágy 2010), has generated a positive impact on the structure and functionality of the ecosystems in which agricultural activities are carried out (Viglizzo et al. 2011 and Andrade 2016). These tasks interfere with other productive activities, which depend on wildlife (Kristensen et al. 2011), and can compromise the ecological services provided by the natural resources of the area (Viglizzo et al. 2012). To reorient the management of agricultural systems towards sustainability, it is invaluable to carry out diagnoses that allow evaluating the complexity of sustainability of activities, recognize problems that move the analyzed system from the desired condition and establish management guidelines to reverse them. This allows producers and agricultural companies to modify their actions, minimize environmental consequences and obtain agroecological certifications that offer competitive advantages and social credibility (Vigglizo et al. 2011).

The models facilitate the analysis of complexity of the agricultural system, but a well-selected set of indicators that allow the various aspects of its complex nature to be translated into clear, objective and general values, constitutes an irreplaceable tool to summarize information and guide decision-making of farmers. An indicator shows not easily detectable trends, summarizes, in numerical or qualitative information, a particular relevant phenomenon associated with a factor, and describes the evolution of a process (Girardin et al. 1999 and Sarandón and Flores 2014). Several models group empirical or semi-empirical indicators, designed for production systems (Vilain 2008, Pérez and Alcaráz (2015) and Trabelsi et al. 2016) different from those of Pampas region.

Agriculture in the Pampas has a group of regional agro-environmental sustainability indicators that allow monitoring and certifying the application of good agricultural practices through standardized ISO 14000 environmental codes (Viglizzo et al. 2006). It is important that producers also have reliable indicators to evaluate and supervise different components of the agroecosystem of their productive facility and encourage them to focus on environmentally friendly management (Gutiérrez et al. 2008).

The AgroEcoindex® (Viglizzo et al. 2006 and Frank 2007) is a model, sensitive to temporal and spatial changes, applicable to the evaluation of the functioning of variables related to sustainable management in agricultural establishments (Gil et al. 2009). It allows the farmer to diagnose and interpret critical processes of the agroecosystem by estimating quantitative indicators related to energy, nutrients, pollution and degradation, water use, habitat and agrobiodiversity (richness of crops), and to make decisions based on data. For its better use, indicators are displayed on a control panel, which indicates the condition of each one with colors: dark and light green (favorable/without problems), light and dark yellow (regular/alert), orange and red (danger/serious).

In order to analyze processes and tendencies of an agricultural and livestock facility, which rears breeding cattle in Buenos Aires province, the objective of this study was perform an environmental diagnosis using a local design model, and to evaluate the effects of agricultural practices on different components of the environment during a productive year, including the diversity of wild species of the area.

Materials and Methods

The study area has favorable edaphic and climatic characteristics for agriculture of cereals and oleaginous plants under dry land (without irrigation) and cattle rearing. Argiudol and Natraqualf (Soil Survey Staff 2014) soil groups are predominant, which are equivalent to the reference soil groups Phaeozems and Solonetz (IUSS Working Group WRB 2015), respectively. The climate is oceanic temperate, with exchange of air masses between sea and continent and low thermal amplitude (SAGyP - INTA 1990).

A livestock and agricultural production facility in the southeast of Buenos Aires province, Argentina, was analyzed during the 2014-2015 production year. This facility has 666.5 ha and has been under the same type of family management for over 45 years. Harvests are destined to commercialization of grains and cattle feeding. The components of the agricultural system were cereals Triticum aestivum L. (wheat), Phalaris canariensis L. (canary grass), Avena sativa L. (oats); oilseeds Glycine max (L.) Merr (soy bean), Zea mays L. (corn), Helianthus annuus L. (sunflower) and forages Sorghum bicolor (L.) Moench (sorghum), Medicago sativa L. (alfalfa), Bromus catharticus Vahl (brome grass), Lolium multiflorum Lam (annual ryegrass). Double annual cultivation is carried out in direct sowing of seasonal species. Wheat and sunflower are marketed, and the rest of crops contribute to the nutritional management of the animals. The entire area has cattle at some point. In pastures (associated, perennial or multi-annual of five years) and some forage crops (sorghum), rotational grazing is performed in the field. Winter and summer greens (species of annual cycle and seasonal production) are grazed. Chopped corn is ensiled for forage. Oats and barley are used for making forage rolls.

The area under analysis is subdivided into plots with different surfaces, limited by fences (fences with posts and wires). In the model, they were grouped into 15 units, depending on the use or sequence of uses during the productive year (table 1). A plot of 50 ha, where flood and salinity limit cultivation, is covered by a natural pasture grazed by cattle. This is located next to a shallow body of water, with a variable surface (± 700 ha) depending on the annual rainfall. Scattered groups of trees over 40 years old, planted for the shelter and shade of livestock, cover four hectares. Eucalyptus spp., Pinus spp. and Tamarix gallica, a little tree adapted to sandy and brackish soils, are predominant.

Table 1 Area occupied by types of use during the productive year 2014-2015, and its destination 

Use (performed activity) ha Product destination
1 Natural grassland- natural grassland 50 Grazing
2 Pasture - crop (corn) 77 Grazing - silage
3 Pasture - pasture 53 Grazing
4 Crop (oats) - summer greens 42 Forage roll - grazing
5 Greens - greens 127 Crop and grazing
6 Crop (wheat) - fallow 81 Grain commercialization- grazing
7 Barley - soy bean of 2nd 26 Forage roll - harvest and silage
8 Oats - soy bean of 1st 14 Forage roll - harvest and silage
9 Oats - corn 36 Forage roll - cut and silage
10 Oats - sorgo de 2ª 44 Forage roll - grazing
11 Wheat - sunflower 22 Grain commercialization
12 Wheat - soy bean of 2nd 38 Grain commercialization-harvest and silage
13 Barely - corn 39 Forage roll - grazing
14 Winter green - restriction for bulls 6.5 Crop and grazing
15 Afforestation - afforestation 4 Shadow for cattle
Total 666.5

Cattle rearing is based on bovine cattle, with 1,012 heads (295 calves, 180 heifers, 107 steers, 130 bulls and 300 cows). The nutritional management of animals was based on a pastoral system with food inputs produced in the establishment and dietary supplements are eventually imported. Cattle was managed in two groups: one of herd (female and male breeders for sale), and another for the sale of steers for meat or rearing. One-year-old breeders are for sale, and two males go to an insemination center, to later commercialize their semen. Sheep cattle, with 222 heads (140 sheep, 8 rams, 74 lambs), are eventually used for consumption.

AgroEcoindex® model (Viglizzo et al. 2006 and Frank 2007) was applied, with information obtained through interviews and surveys to the farmer and professionals who advise him (veterinary doctors and agricultural engineers). Results for each of the 18 indicators were compared with limit values ​​of the model for the type of mixed, agricultural-livestock production (table 2). To analyze the richness of wild species, seven less disturbed areas were distinguished (wire edges, floodplains and lagoons), where plants that grew spontaneously were collected. Species in a cabinet were herborized and identified with a binocular loupe and taxonomic keys (Cabrera 1963-1970) and specific richness was estimated. At dawn and dusk, in Autumn (April, July-August) and in Summer (January-February), birds, mammals and reptiles were sighted, as well as signs of their presence. Binoculars and identification guides were used for this (Narosky and Yzurieta 2010 and Giambelluca 2015). The list was completed with surveys to people who live and work in the area.

Table 2 Indicators applied by AgroEcoindex®. Limit values established by the model for one type of agricultural-livestock production 

Indicator Calculation method Unit Limit value of the model
Dark green Light green Light yellow Dark yellow Red
0. Percentage of annual crops (Annual crops*100)/total of crops % -------- -------- --------- ---------- --------
1. Fossil energy intake (FE) FE of inputs and labors MJ/ha/year 5,000 10,000 15,000 20,000 25,000
2. Energy production (P) E of products (crops and cattle) MJ/ha/year 50,000 40,000 30,000 20,000 10,000
3. Efficient use of FE FE / P Without unit 0.50 1.00 1.50 2.00 2.50
4. N balance (balN) Incomes - expenditure = (rain + biological fixation + fertilizers) - N o P of exported product kg/ha/year 20.00 0.00 -20.00 -40.00 -60.00
5. P balance (balP). kg/ha/year 3.00 0.00 -3.00 -6.00 -9.00
6. Change of C reserve of soil (COS) (current reserve-previous reserve)/20 years t/ha/year 0.10 0.00 -0.10 -0.20 -0.30
7. Change of woody biomass reserve Forest growth- extraction (wood) t/ha/year 0.10 0.00 -0.10 -0.20 -0.30
Contamination risk by:
8. N N and P balance related to rainfall, evapotranspiration, and hydrography mg/L 2.00 4.00 6.00 8.00 10.00
9. P mg/L 0.20 0.40 0.60 0.80 1.00
10. Pesticides Relative index based on mean lethal dose (DL50), persistency, mobility, solubility, degradation rate of active substances IR 17.00 33.00 50.00 67.00 83.00
11. Erosion risk Wind-erosion equation (WEQ) and Universal Soil Loss Equation (USLE), t/ha/year t/ha/year 6.00 12.00 18.00 23.00 30.00
12. Greenhouse gas balance Variation of COS reserve and wooden biomass. Estimation of CO2 emission and sequestration t/ha/year 0.00 10.00 20.00 30.00 40.00
13. Water intake (consH2O) Precipitation/water intake mm/year 100.00 250.00 400.00 550.00 700
14. Efficient use of water H2O intake*100) /annual precipitation % 83.00 67.00 50.00 33.00 17.00
15. Rain-produced energy relation Annual precipitation/E of products L/MJ 170.00 330.00 500.00 670.00 830
16. Habitat intervention risk Anthropic interference (use, type of farming and contamination, pesticides) IR 0.17 0.33 0.50 0.67 0.83
17. Impact on habitat Farmed hectares año-1(use, type of farming and agrochemicals in each paddock) IR 1.70 3.30 5.00 6.70 8.30
18. Agrodiversity Number of different crops IR 3.00 2.50 2.00 1.50 1.00

Results and Discussion

Results of total indicators and per plot (table 3) were analyzed, grouped into five axes and compared with the limits established by the model (table 2).

Indicators related to energy indicate the intensity and frequency of use of energy resources, processes of transformation and conversion to products of agricultural value (Frank 2010).

Fossil energy (FE) consumption showed an unfavorable trend, since total energy entry in inputs (seeds, fertilizers, pesticides, fuel for labor and transport and food supplements) and for work carried out surpassed 13 times the negative threshold established by the model (tables 2 and 3). The 25% of uses exceeded the threshold. The highest inputs (40,000 to 65,000 MJ ha-1y-1) were required by pasture-corn, greens-greens and wheat-fallow. Oats-soy bean, forest and greens-winter were less demanding.


Table 3 (a) Total general results and (b) separated by type of use, obtained with AgroEcoindex®, in an agricultural and livestock productive facility from the SE of Buenos Aires (Argentina) during the productive year 2014-2015 

Indicator n° Value Unit Description
0 37.70 % Percentage of annual crops
1 344373.90 MJ/ha/year Fossil energy intake
2 67023.70 MJ/ha/year Energy production
3 5.14 MJ FE/MJ prod. Efficient use of fossil energy
4 384.80 kg/ha/year N balance
5 63.61 kg/ha/year P balance
6 -0.37 t/ha/year Change of soil C reserve
7 -0.02 t/ha/year Change of C reserve of the woody biomass
8 0.00 mg/L Risk of contamination by N
9 0.00 mg/L Risk of contamination by P
10 55.91 Relative index Risk of contamination by pesticides
11 5.70 t/ha/year Risk of wind and water erosion
12 62.37 t/ha/year Greenhouse gas balance
13 9556.20 mm/year   Water intake
14 1124.30 % Efficient use of water
15 126.82 L/MJ Rain-produced energy relation
16 0.85 Relative index   Risk of habitat intervention
17 1.70 Relative index Impact on habitat
18 5.02 Relative index Agrodiversity


Ind. Pot 1 Pot 2 Pot 3 Pot 4 Pot 5 Pot 6 Pot 7 Pot 8 Pot 9 Pot 10 Pot 11 Pot 12 Pot 13 Pot 14 Pot 15
1 24978.00 40539.00 27191.70 21689.20 64900.00 41591.70 13549.70 7233.00 18853.70 23193.00 11526.80 19789.90 20528.00 3321.10 1996.00
2 280.90 14556.00 297.70 2084.80 713.40 10359.70 3331.00 2196.50 8390.10 2184.10 4414.90 7625.70 9909.10 36.50 22.50
3 88.93 2.79 91.33 10.40 90.97 4.02 4.07 3.29 2.25 10.62 2.61 2.59 2.07 90.96 88.83
4 30.70 41.95 39.87 25.50 81.39 38.11 17.42 7.71 15.99 28.20 8.52 17.94 18.57 4.16 2.46
5 5.72 6.32 6.23 4.36 14.98 6.96 2.13 1.15 2.42 4.76 1.51 2.69 2.22 0.76 0.46
6 -0.034 -0.04 -0.04 -0.02 -0.07 -0.04 -0.01 -0.01 -0.02 -0.03 -0.02 -0.02 -0.021 -0.004 -0.01
7 -0.037 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02
8 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
9 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
10 0.00 3.12 0.91 1.23 1.76 1.73 1.099 0.67 1.68 1.20 0.81 1.77 1.82 0.151 0.00
11 0.43 0.42 0.23 0.35 1.14 1.34 0.20 0.11 0.28 0.37 0.17 0.30 0.29 0.07 0.02
12 1940.80 2988.70 2057.19 1630.20 4929.50 3144.01 1009.20 543.40 1397.30 1707.90 853.93 1474.90 1513.80 252.30 155.26
13 697.30 1109.70 739.10 608.40 1771.00 1165.90 383.00 207.40 534.10 637.50 323.30 560.30 575.10 90.60 55.80
14 82.00 130.60 87.00 71.60 208.40 137.20 45.10 24.40 62.80 75.00 38.00 65.90 67.70 10.70 6.60
15 30262.70 583.90 28549.70 4077.10 11914.40 820.48 2551. 80 3869.80 1013.10 3891.80 1925.30 1114.60 857.80 232789.00 378283.00
16 0.00 0.09 0.05 0.06 0.19 0.12 0.04 0.02 0.05 0.07 0.03 0.06 0.06 0.010 0.00
17 0.00 0.40 0.10 0.10 0.40 0.20 0.10 0.00 0.10 0.10 0.00 0.10 0.10 0.00 0.00
18 0.38 0.58 0.40 0.32 0.96 0.61 0.20 0.11 0.27 0.33 0.17 0.29 0.29 0.05 0.03

Energy production had a positive impact. Energy content of agricultural and livestock products surpassed the most positive threshold of the model. Except wheat and sunflower, the crops served as food input to livestock of the establishment itself.

The efficient use of energy showed a negative impact. An amount of 5 MJ of external energy per MJ of product was used, which doubled the critical threshold of the model. The 80% of the uses were inefficient. Although consumption increases and efficiency decreases, with the increase of annual harvest crops (Viglizzo et al. 2006 and Frank 2007), forages were less efficient.

These three indicators show a production critically subsidized by fossil energy, with high productivity and high inefficiency. It is necessary to review the incorporation of inputs and avoid their reduction does not harm productivity. It is probable that the model, appropriate for agriculture with some livestock, needs some adjustments to fit it in order to assess the rearing of high genetic quality breeding cattle, with more careful management and higher economic value.

Regarding indicators related to nutrients, mean annual balance of N and P was positive. Annual increase of N and P was 20 times superior to the optimal threshold. Reserves are stored in non-exported biomass (plant and animal) and in the soil, where their solubility could cause water contamination (indicators 8 and 9). Organic forms of N and P from biomass are released with necromass and feces, and, when mineralized, they become available to plants, which completes their cycling in the establishment. The estimation of N and P reserve in soil would complete the model analysis. In addition, it would allow knowing the capital that guarantees the functioning of the ecosystem and provides essential ecosystem services in agricultural production (Laterra et al. 2012).

Soil C reserve (COS) decreased in all types of use and had a negative impact. Berhonaraya and Álvarez (2013) pointed out that the IPCC (2014) methodology, used by the AgroEcoindex®, overestimates COS losses and suggests calibrating default parameter values with local data. The relationship between the organic C stock of biomass and that of the soil defines, in part, the intrinsic sustainability of the agroecosystem, because the former is more exposed to degradation, whereas COS reserves are less sensitive to short-term losses (Jarecki and Lal 2003) and are modified by agricultural activities. The negative value of the indicator may be due to the intensive use of land and grazing of the remaining biomass of crops (cuttings) that does not go to the detritus.

The change of C reserve of woody biomass was positive, only in areas with trees. The theoretical growth of biomass was estimated (IPCC 2014), since no wood was commercialized.

Regarding indicators related to contamination and erosion, the model did not show any risk of contamination by N and P. There was no residual N and P in the soil and values ​​of indicators were positive in both cases.

Risk of contamination by pesticides did not reach the maximum threshold, although it was critical. The effect of herbicides and pesticides depends on their toxicity, persistence and mobility of the active substances, solubility and degradation rate (Stoate et al. 2001). Although organo-chlorinated products were not used due to their toxicity and persistence, and many modern pesticides degrade easily under the sun, there is a risk that they will persist for some time in the subsoil or in groundwater (Viglizzo et al. 2011). The 50 ha of native grassland that did not receive agrochemicals may have determined that the indicator was not more negative.

There was no risk of water and wind erosion. In the Pampas area, this risk increases with the proportion of cultivated land, and the positive effects of direct sowing (Álvarez et al. 1998 and INTA 2011) have been evidenced, which is the predominant cultivation modality in the analyzed case.

Greenhouse gas balance had a negative impact. Since 1750, global atmospheric concentrations of CO2 (burning of fossil fuels and deforestation), CH4 and N2O (associated with agricultural activity) have increased markedly (IPCC 2014). In the study area, fertilizers and cattle were the emission sources, which releases CH4 by enteric fermentation, a gas with a greenhouse power 21 times greater than CO2. N in feces, synthetic fertilizers, the result of biological fixation in legumes and harvest residues are indirect sources of N2O emissions, with a greenhouse power 310 times greater than CO2.

In indicators related to water, land use is one of the determining factors of consumption and use efficiency of this resource (Victoria et al. 2005). Consumption had a negative impact and exceeded the model threshold.

Efficient use of water, with precipitation of 900 mm year-1, generated a positive impact. The indicator exceeded the maximum efficiency that the model postulates (83%).

Regarding the rain-produced energy relation, although water cycles on a large spatial and temporal scale, the model considers the agroecosystem as an ecological entity, where water circulates to support production. This indicator was positive. An amount of 126.82 L of water was used to generate 1MJ-1 of product. The water needed for most crops and forage varies between 500-1,000 L/kg of product, and it is 50-100 times more to produce 1 kg of meat or 1L of milk (Pimentel et al. 1997).

Regarding indicators dealing with biodiversity, there was no evidence of risk of habitat intervention, despite the fact that it is accepted that agriculture simplifies the structure of the environment in large areas and replaces natural diversity with few cultivated plants and domestic animals (Fowler and Mooney 1990).

Impact on habitat due to labor was high and annual crops increase this impact. Only natural grassland and afforestation did not reveal negative effects.

Agrodiversity was positive due to the variety of cultivated species. Low diversity of agricultural species and ecological processes, associated with heterogeneous landscapes, was considered as negative. Agricultural intensification is related to biodiversity loss (Altieri 1999, Donald et al. 2001 and Aviron et al. 2018), to the point of considering agricultural land distribution as an indicator that threatens wildlife, more accurate than the distribution of human population (Schalermann et al. 2005). Landscape homogenization leads to losses of wild species of unique traits, and of those that can live or subsist on agricultural or mixed areas (Tscharntke et al. 2005 and Coetzee and Chown 2016). Mixed and rotational production plantations, which provide heterogeneous and variable habitats in space and time, constitute beneficial practices for biodiversity, as well as the reduction of pesticides and inorganic fertilizers to strictly necessary levels, and the strategic management of marginal sites, not cultivated as biodiversity reservoirs (Hole et al. 2005).

Estimating wildlife richness favored the perspective of AgroEcoindex®, which only focuses on the richness of crops when there are methods that evaluate agro-environmental dimension, by equally valuing diversity in agricultural practices and spatial arrangement (Vilain 2008).

In samplings, 81 herb species were collected from 15 botanical families, which grow spontaneously (table 4), and 53% were native. Poaceae and Asteraceae were species-rich families (27 and 21). Several Chenopodiaceae (13) grew in alkaline, brackish environments and modified soils. As a consequence of flooding and salinity, families were reduced to half. Brassicaceae (6) and many exotic weeds increased in the internal roads. Apiaceae (10), Solanaceae (8), forage Fabaceae collected from cultivation (4), Cyperaceae, Lamiaceae, Plantaginaceae, Malvaceae, Polygonaceae and Portulacaceae were collected.

Table 4 Presence (x) of spontaneous plant species in eight sites of the establishment with lower intervention 

Species Origin Sites
1 2 3 4 5 6 7 8
Panicum sp. x
Festuca arundinacea Schreb. E x
Eucaliptus spp. E x
Elymus scabrifolius (Döll) J.H. Hunz. N x
Cynodon dactylon (L.) Pers. var. longiglumis Caro & E.A. Sánchez N x
Undentified 1 x
Hordeum jubatum L. E x
Chenopodium album L. E x
Malvella leprosa (Ortega) Krapov. N x x
Cynodon dactylon (L.) Pers. var. pilosus Caro & E.A. Sánchez N x
Solanum glaucophyllum Desf. N x
Sarcocornia ambigua (Michx.) M.A. Alonso & M.B. Crespo N x x
Apium sellowianum H.Wolf N x x
Agrostis platensis Parodi N x x
Baccharis glutinosa Pers. N x
Conyza sumatrensis (Retz.) E. Walker var. leiotheca (S.F. Blake) Pruski & G. Sancho N x
Distichlis scoparia (Kunt) Arechavaleta N x
Pseudognaphalium leucopeplum (Cabrera) Anderb. N x
Distichlis laxiflora Hack N x
Undentified 2 x
Hydrocotyle modesta Cham. & Schltdl. N x
Cirsium vulgare (Savi) Ten. E x
Undentified 3 E x
Atriplex prostrata Boucher ex DC. E x
Oxybasis macrosperma (Hook. f.) S. Fuentes, Uotila & Borsch N x x
Schoenoplectus americanus (Pers.) Volkart ex Schinz & R. Keller N x x x x
Sporobolus densiflorus (Brongn.) P.M. Peterson & Saarela N D x
Chenopodium hircinum Schrad. ssp. hircinum N x x
Ambrosia tenuifolia Spreng. N x x x
Conyza bonariensis (L.) Cronquist var. bonariensis N x x
Bromus catharticus Vahl var. catharticus N x x x
Senecio madagascariensis Poir. E x x x x
Poligonum aviculare L. E x x
Tagetes minuta L. N x x
Lepidium bonariense L. N x x
Amaranthus hybridus L. hybridus E x x
Taraxacum officinale F.H. Wigg. E x x
Conyza bonariensis (L.) Cronquist var. angustifolia (Cabrera)Cabrera N x x x
Raphanus sativus L. E x x
Solanum chenopodioides Lam. N x x
Cynodon dactylon (L.) Pers var. dactylon E x x
Plantago lanceolata L. E x x
Portulaca aff oleraceae L. E x x
Hydrocotyle leucocephala Cham. & Schltdl. N x x
Capsella bursa-pastoris (L.) Medik. E x
Stellaria media (L.) Cirillo var. media E x
Dactylis glomerata L. E x
Diplotaxis tenuifolia (L.) DC var. tenuifolia E x
Solanum sisymbriifolium Lam. E x
Trifolium pratense L. E x
Matricaria chamomilla L. E x
Eleusine tristachia (Lam.) Lam. N x
Oxalis conorrhiza Jacq. N x
aff. Lactuca x
Undentified 4 x
Undentified 5 x
Undentified 6 x
Polygonum laphatifolium L. E x
Physalis viscosa L. N x
Hypochaeris pampasica Cabr. N x
Hypochaeris petiolaris (Hook. & Arn.) Griseb. N x
Solanum elaeagnifolium Cav. N x
Datura ferox L. N x
Trifolium repens L. E x
Carduus pycnocephalum L. E x
Atriplex patula L. E x
aff Torilis nodosa (L.) Gaertn. E x
Lotus tenuis Waldst. & Kit. ex Willd. E x
Centaurea melitensis L. E x
Bupleurum tenuissimum L. E x
Dysphania chilensis (Schrad.) Mosyakin & Clemants N x x
Sporobolus indicus (L.) R. Br. var. indicus N x
aff.Amelichloa caudata (Trin.) Arriaga & Barkworth N x
Urochloa platyphylla (Munro ex C. Wright) R.D. Webster N x x
Eragrostis japonica (Thunb.) Trin. E x
Echinochloa colona (L.) Link E x
aff Parapholis incurva (L.) C. E. Hubb. E x
Dysphania multifida L. E x
Marrubium vulgare L. E x
Chenopodium dessicatum A. Nelson var. leptophylloides (Murr) Wahl E x
Xanthium spinosum var. spinosum L. E               x
Richness 81 6 11 5 20 31 21 13 7

Mill (1) lagoons (2-4) internal roads and canyons (5-8)



Among the fauna (75), birds (52) were predominant, mammals (17) in smaller number and reptiles (8). Lagoons and flood areas raised biodiversity of the establishment and housed 47% of birds. Several are raised there, as well as a large rodent (Hydrochoeris hydrochaeris). Passerines were numerous and the presence of Falconidae (10%) would be an indicator of the quality of the agroecosystem. Given their terminal position, they indicate a complex trophic network (Zacagnini 2011). Predators and scavengers perform regulatory services, by controlling pests, rodents and removing dead bodies.


Energy indicators showed critical fossil energy demand, since consumption was 13 times superior to the negative threshold of the model. Production reached the optimal threshold, but was inefficient. It required five units of energy per unit of generated product. Other negative tendencies were the reduction of soil C reserve, greenhouse gas balance, which surpassed the negative threshold, as well as the impact on habitat and water intake. The risk of pesticide contamination was critical. Indicators of favorable trends were N and P balance (with an annual increase that was 20 times superior to the model threshold, without causing contamination), efficient use of water, rain/produced energy relation, risk of erosion and habitat intervention and agro-diversity. A richness of 81 plants and 75 wild animals was registered.

The diagnosis allowed to evaluate aspects that distance the establishment of sustainability and point out the issues to improve. From an environmental perspective, it should be considered a reduction of fossil energy consumption and of the emission of greenhouse gases and water (less loss due to runoff and infiltration) and increase of COS, avoiding stubble grazing. Reducing pesticides and promoting ecosystem service of insectivorous birds would increase energy efficiency. Actions that release CO2 (fossil fuels, reduced C reserve in the soil), NH4 (livestock) and NO2 (fertilizers) should be reviewed. Water intake per hectare was high, but its use was efficient due to the high energy of products.

In addition, it would be appropriate to adapt the model according to two issues: analysis of purely livestock activities and evaluation of wildlife of the establishment.


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Received: November 11, 2019; Accepted: December 08, 2019


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