Modeling economic-environmental decision making for agricultural land use in Argentinean Pampas

Agricultural Systems 143 (2016) 183–194

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Modeling economic-environmental decision making for agricultural land use in Argentinean Pampas Silvina M. Cabrini ⁎, Carlos P. Calcaterra Instituto Nacional de Tecnología Agropecuaria, Argentina

a r t i c l e

i n f o

Article history: Received 14 October 2014 Received in revised form 11 September 2015 Accepted 21 December 2015 Available online 16 January 2016 Keywords: Argentine Pampas Multi-criteria farm models Economic/environmental indicators Compromise programming

a b s t r a c t This research evaluates the economic and environmental impacts of land allocation to crops and extensive livestock production in the Argentine Pampas using compromise programming. Two economic indicators (gross margin, direct costs) and five environmental indicators (organic carbon input to soil, nutrient balances, agrochemicals impact and soil erosion) are considered. The tradeoff between economic and environmental objectives is assessed and the preferred land allocation schemes determined by the multicriteria model are compared to the current land use in the region of Pergamino, North of Buenos Aires, Argentina. Results indicate that it is important to consider not only the environmental-economic tradeoff in farming, but also the conflict across different environmental criteria in order to determine whether it is reasonable to stimulate activities which improve some environmental indicators at the expense of getting less desirable values in others. For instance, an increase of productivity in agriculture enhances soil organic carbon content but it may increase the risk of contamination with agrochemicals and nutrients. Results are consistent with the notion that extensive crop-livestock production systems are more balanced than continuous crop farming. According to model results, optimal land assignment is a combination of crop and livestock activities (62 and 38% of land, respectively) which is associated with lower agrochemical use, greater organic carbon input to soil, better soil protection from erosion and more efficient nutrient cycling. This land assignment presents a decrease of 20% in the economic gross margin compared to the continuous agriculture scheme that maximizes the economic result. The comparison between current land use and the optimal land assignment based on model results show that farmers assign a smaller fraction of land to extensive livestock production (2–4%), than in the most balanced compromise solution (38%). Results also suggest that it would be appropriate to encourage farmers to reduce the area of land assigned to full-season soybeans. © 2016 Elsevier Ltd. All rights reserved.

1. Introduction The agricultural sector plays an important role in Argentina's economy. Argentina produces food for approximately 10 times the country's population and has an important participation in the international agricultural market. In recent years, there has been a large increase in crop production, especially in soybeans. This growth represents an economic success, however, the expansion and intensification of agriculture in Argentina has also raised concerns about the impact of crop production on the environment (e.g., Cabrini et al., 2013; Flores and Sarandón, 2002; Viglizzo et al., 2006). Currently, there is a growing interest for redefining production systems in the search for a balance between high productivity and the protection of the environmental services provided by these agroecosystems (Rossing et al., 2007). However, stakeholders (farmers, environmentalists, consumers and government) have conflicting ⁎ Corresponding author. E-mail address: [email protected] (S.M. Cabrini).

http://dx.doi.org/10.1016/j.agsy.2015.12.016 0308-521X/© 2016 Elsevier Ltd. All rights reserved.

views regarding the definition of the most suitable production system since they attach different values to the economic, social and environmental impacts of farming (Berge et al., 2000). A negotiation process is needed where every party should express their goals clearly, based on objective measurements of relevant farming impacts. The mix of crops, livestock production and the technologies that farm managers select are mostly determined by input and output prices. However, profit maximizing is not the only relevant goal in agricultural land use decisions. It is essential to consider farming effects on soil productivity, contamination, water and energy use efficiency; greenhouse gas emissions, etc. (Viglizzo et al., 2006). Multi-criteria Decision Modeling (MCDM) provides a formal approach in the search of sustainable agro-ecosystems (Elfkih et al., 2009; Diaz-balteiro and Romero, 2004; Bertomeu et al., 2009; Berge et al., 2000; Gómez-Limón and Berbel, 2000). Multi-criteria models are used to assess alternatives based on several criteria. They first find the set of feasible alternatives based on available resources and technical restrictions, and then choose the best alternatives within this feasible set based on the preference of decision makers. This approach has

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numerous applications since it can be applied to solving a variety of agricultural problems as well as economic, financial, management and engineering problems (Romero, 1996). In the Argentine Pampas, some studies have employed these MCDM to analyze land use decisions and the adoption of conservation technologies considering economic, social and environmental criteria. Cisneros et al. (2011) and de Prada (2001) have studied a basin in the eastern area of the Province of Córdoba with emphasis on criteria related to water management and soil erosion, since these are major causes of concern in this area. Angeli et al. (2008) used a model for land allocation in the South of the Cordoba province and also considered soil erosion plus environmental attributes related to input level. The results in these studies show a strong conflict among the criteria because when short-run economic result is maximized the environmental performance of production systems is poor. But these studies have also shown that the opportunity cost to achieve reasonable levels of environmental indicators is relatively low. In this research the economic-environmental impacts of land-use options in the Argentine Pampas were assessed using multi-criteria compromise programming for the Pergamino district, North of Buenos Aires Province. Farmland throughout this area is allocated largely to extensive crop production, and is among the most productive agricultural regions with a higher level of agricultural input use compared to other regions in the Pampas. The economic indicators considered are gross margin and direct expenses. The environmental indicators are organic carbon input to soil, nutrient balances, soil erosion, and level of agrochemical toxicity. These are considered key environmental indicators in farming in the Argentine Pampas (Viglizzo et al., 2006; Cabrini and Calcaterra, 2009). Compromise programming (CP) is a well-known MCDM approach (Ballestero and Romero, 1991; Ballestero, 2007). The basic idea in CP is the identification of an ideal solution as a point where each criterion under consideration achieves their optimum value. Since there is a trade-off among criteria, the ideal point is typically not feasible and it is used as a reference point; alternatives are ranked based on how far they are from it. A characteristic of this type of model compared to other MCDM techniques (as goal programming or lexicographic programming) is that the CP ideal is not a target established by the decision maker based on his views and judgments, but instead, it is the best value that can be obtained from the alternatives available (Ballestero, 2007). The results reported in this study include the identification of a set of land allocation schemes that provide a balance between the economic and the environmental performance. These preferred alternatives are compared to the current use of land. Incentives for farmers to consider not only the economic indicators but also the environmental indicators in land use decisions are discussed. 2. Area of study Argentina's Pampas are a vast plain covering an area of 500,000 km2 located in the central-eastern part of the country. Soils that have developed from deep loess sediments are highly fertile and the climate is temperate-humid with an average annual rainfall that goes from 1000 mm/year in the East to 800 mm/year in the West. The Pampas maintained native grasslands until the beginning of the 1920s, when the establishment of perennial pastures and annual crops started. Since then, and until the mid 1970s, wheat, flax, sunflower and maize alternated soil occupation with alfalfa pastures for beef and milk production (Cascardo and Peretti, 1991). The area of the current study is the Pergamino district in the North of Buenos Aires Province (Fig. 1). This area can be considered representative of The Rolling and Central Subhumid Pampas, the most productive agricultural region in the country. Since the 1970s, a process of continuous expansion of the area planted with annual grain crops, and an even more rapid expansion of soybeans at the expense of other crops and pasture area have occurred in Pergamino, as well as in the rest of the Pampas. In the 1990s the spread of genetically modified herbicide-resistant soybeans enhanced the use of

no-till and glyphosate. The entire production system changed dramatically and herbicide-resistant soybeans are now planted in more than 80% of agricultural land in the Pergamino (SIIA, 2014). Phosphorous fertilization prevails in all annual crops while nitrogen is applied in wheat and maize (Cabrini and Calcaterra, 2008). Irrigation is used only for seed production in a small area (Cataldo and Cabrini, 2014). 3. Compromise programming model In this study a compromise programming (CP) optimization problem is solved to find efficient land allocation alternatives, based on economic and environmental criteria, for crop/livestock farms. The scale of analysis is the farm unit. The model represents a 350 ha crop/livestock farm, representative of farming systems in the region of Pergamino. The variables, restrictions, attributes and objectives are presented and discussed in the following sections. 3.1. Variables and technical restrictions An agricultural system can be modeled as a linear combination of activities. Each activity is characterized by the type and quantity of the output produced, the inputs employed, and the environmental impacts. Fifteen activities corresponding to four different crops and livestock production are considered (Table 1). Several activities with different input levels (fertilizers and agrochemicals) were defined for each crop. Higher quantities of fertilizers and agrochemicals are needed to obtain higher crop yields (input–output relationships are based on data obtained from crop simulation models and a farmers survey1). Decision variables correspond to the area assigned to each activity in hectares. The model is a static one-year steady-state model. Therefore, the model solution implies an optimal land rotation scheme. For instance, a model solution that assigns 75% of land to full-season soybeans and 25% to corn, implies a land rotation scheme of three years of soybeans followed by one year of corn, then three years of soybeans and so on. Activities 1 to 11 and 15 are full-season activities, while activities 12 to 14 are for second-season soybeans planted after wheat in the same cropping year. The last activity in Table 1 (15) refers to cattle beef production, where cattle are fed on perennial grass-legume pastures that last four years (in a model solution that assigns 120 ha to livestock production, for instance, 30 ha of pastures would be replanted every year). It is assumed that crops and pastures are planted no till, since this is the planting system employed in more than 90% of the land in this region (Cabrini and Calcaterra, 2008). The model includes land availability and crop rotation restrictions (equations presented in the Appendix A); these are standard equations in mathematical programming farm models. One of the equations restricts total land availability for first-season activities to 350 ha (Eq. (A.5)), which is the average farm size in Pergamino according to the 2002 National Agricultural Census (Cabrini and Calcaterra, 2008). The model also includes an equation that restricts the area assigned to second-season soybeans to be equal to the wheat area2 (Eq. (A.6)), 1 Nitrogen fertilization rates for every expected yield level in wheat and corn were estimated based on the simulated results generated by the CERES model. Phosphorus fertilization rates are based on the fertilization recommendations of Echeverría and Garcia (1998). Agrochemical combos were defined based on data obtained from farmers (Cabrini and Calcaterra, 2008). 2 Eqs. (A.5) and (A.6) imply that all farmland is assigned to crops or pastures during the summer. Since this assumption may be too restrictive, the model was also run with an alternate form of Eq. (A.6) as AREA_soy2 ≤ AREA_wheat, implying that is possible to plant wheat without 2nd-season soybean afterwards. Main differences in the solutions are reported. However, this set of results should be interpreted with caution because there is no reliable information on productive and environmental coefficients for growing only wheat in the cropping year, a rare situation in the study area. In particular, there is no information about reasonable weed management practices when summer crops are not planted in a cropping year. Also, it is important to note that extensive cattle raising on permanent pastures represents an option of land use in some way comparable to the grassland original ecosystem.

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Fig. 1. Area of study, Pergamino District, Buenos Aires, Argentina, and planted area with each of major crops. Note: Soybeans planted area includes full-season and 2nd season soybeans. For Pergamino district the ratio 2nd-season soybeans/total soybeans area is around 0.15. Source: http://www.laargentinaenmapas.com.ar/(Conicet).

since these two crops are planted in the same crop season. The model includes an equation that restricts high yield soybeans (45 qq ha−1) to be planted only in parcels under rotation with other crops or pastures (Eq. (A.7)). This equation is consistent with the notion that high yielding soybean crops are possible only with good soil physical conditions, associated with land managed under rotations including cereals or pastures (Bacigaluppo et al., 2011; Franzluebbers et al., 2014). Finally, Eq. (A.8) restricts corn area; this restriction is based on the notion that corn monoculture presents plant emergence problems.

3.2. Attributes The concept of attribute is central in multi-criteria models. Attributes are values that the decision maker wants to consider which can be measured as a function of the decision variables. In this study, two

economic and five environmental indicators are included as attributes. While this set of attributes does not completely characterize the economic and environmental performance of production systems, they are key indicators based on previous studies on agricultural environmental impact and population concerns found in the region of study (Cabrini et al., 2013, 2014; Cabrini and Calcaterra, 2008). Economic attributes

Environmental attributes

Total gross margin (GM) Total direct costs (DC)

Organic carbon input to soil (OC) Nitrogen balance (NB) Phosphorus balance (PB) Pesticide environmental impact quotient (EIQ) Soil loss from hydric erosion (SL)

The gross margin (GM) is widely used by Argentinean crop and livestock farmers as an economic indicator for short-run crop rotation

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Table 1 Agriculture and livestock production included in the compromise programming model for land assignment. Fertilizers Crop 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Wheat

Corn

Full-season soybeans

Second-season soybeans

Beef cattle production

Expected yield

Urea

(tn ha-1)

(kg ha-1)

3.0 3.3 3.6 3.8 8.4 9.5 10.0 10.2 2.5 3.5 4.5 1.5 2.5 3.5 0.5

50 100 150 200 50 100 150 200 0 0 0 0 0 0 0

TSF/DAP⁎

35 51 66 78 108 119 130 139 18 57 96 0 18 57 20

Agrochemicals⁎⁎ (units ha-1) seed treatment, gly 3lt, met 6g seed treatment, gly 3lt, met 6g seed treatment, gly 3lt, met 6g, fung in 50% of the area seed treatment, gly 3lt, met 6g, fung in 100% of the area seed treatment, gly 3lt, atra 2.5lt, ace 2lt seed treatment, gly 3lt, atra 2.5lt, ace 2lt seed treatment, gly 3lt, atra 2.5lt, ace 2lt seed treatment, gly 3lt, atra 2.5lt, ace 2lt gly 7lt, cyper 0.1lt gly 7lt, cyper 0.1lt gly 7lt, cyper 0.1lt, fung in 100% of the area gly 5lt, cyper 0.1lt gly 5lt, cyper 0.1lt gly 5lt, cyper 0.1lt, fung in 100% of the area Planting: gly 3lt, 24DB 0.75lt. Annual maintenance: dime 0.43lt, chlor 0.3lt, BT 0.5lt

Note: (*) Phosphorus fertilizers are triple superphosphate (TSF) for crops and diammonium phosphate (DAP) for pastures. (**) Products and concentrations: seed treatment: Thiram 0.35 + Carbendazim 0.15 (wheat), Tefluthrin 0.19 (corn) — gly: glyphosate 0.5 — met: metsulfuron 0.6 — fung: trifloxystrobin 0.5 in part A 1.2lt + Tebuconazole 0.24 in part B 4.8lt (wheat), Trifloxystrobin 0.19 + Cyproconazole 0.08 (corn) — atra: atrazine 0.5 — ace: acetochlor 0.9 — cyper: cypermethrine 0.25 — dime: dimethoate 0.5 — chlor: chlorpyrifos 0.48 — BT: Bacillus thuringiensis 0.54.

planning. The GM for each activity is calculated as the difference between crop income and direct costs. Income is computed multiplying expected crop yields or beef production by their prices. Direct costs (DC) account for the expenditure in fertilizers, agrochemicals, seeds, tillage, harvest and marketing expenses. Planting, harvesting and applications of fertilizers and pesticides are considered customary operations. Cattle beef production DC also include animal health and feed expenses and annual amortization and maintenance of perennial pastures. Output and input prices are average prices in US$ for the years 2008 through 2011. Since farmers are likely to consider the direct costs (DC) related to the different activities in land assignment decisions, this indicator is also included as an economic attribute. Soil organic carbon loss is an important concern in the area of study, and it is mainly attributed to the large proportion of full-season soybeans in crop rotations, (Berhongaray et al., 2013; Milesi Delaye et al., 2013; Caride et al., 2012). Therefore, organic carbon input to soil (OC) is included in the multi-criteria model as an environmental attribute. This environmental indicator is related to both off-farm and on-farm environmental impacts. First, carbon sequestration in soils contributes to climate change mitigation (Stockmann et al., 2013; Bolinder et al., 2007). Also, soil organic carbon level is associated with its productivity, since an adequate level of organic matter in soils is related to better air and water circulation, and nutrient availability (Loveland and Webb, 2003). Organic carbon input to soil in the agro-ecosystem depends on the type of crops or forages planted and the productivity level (Bolinder et al., 2007). In this study, the organic carbon input is computed with a simplified model developed for the major crops in the Argentine Pampas, based on crop yields, harvest indexes and humidification coefficients (Alvarez and Steinbach, 2006). The level of organic carbon input in pastures is estimated based on field measurements done by Bertin et al. (1999). These authors report the evolution of soil organic carbon during five years of pastures in Pergamino district, and estimate the average annual organic carbon input to soil based on the observed changes. Nitrogen and phosphorus balances (NB and PB) are measured as the difference between nutrient inputs from fertilizers and biological nitrogen fixation, and nutrient extraction in crop harvested and beef production exported from the agro-ecosystem. Nutrient balances are key indicators in the assessment of environmental performance of agricultural and livestock production systems, mainly because of the concerns

about soil and water contamination associated to nutrients excess in several regions of the world (Austin et al., 2013; Berge et al., 2000). However, several authors have expressed concerns about negative nutrients balances in Argentina's Pampas and the importance in considering these balances in land use and fertilizing decisions (e.g. Flores and Sarandón, 2002; Manchado, 2010). Therefore, in this study both positive and negative deviations from a neutral nutrient balances are considered as undesirable results for the NP and NB indicators. Fertilization levels used to compute nutrient balances are presented in Table 1. Nutrient exports are computed based on expected yields and nutrient contents in grains, oilseeds and beef (Garcia and Correndo, 2011). The fourth environmental attribute measures the environmental impact of pesticides. This is a relevant indicator since the implementation of the no-till system is associated with an important increase in the use of agrochemicals, mostly herbicides, and there is an increasing concern within the local population about human health and ecosystem impact of agrochemicals employed in food production (Cabrini et al., 2014). The indicator used to quantify the pesticide contamination risk is the environmental impact quotient (EIQ). The EIQ was developed by the Integrated Pest Management program at Cornell University for each active ingredient in the pesticides employed in agriculture. The EIQ is computed based on the three principal components of agricultural production systems: farm worker, consumer, and ecological components (Kovach et al., 1992). Each component in the equation is given equal weight. The EIQ values for each pesticide considered in the farming activities in this study were obtained from the EIQ calculator: http://www. nysipm.cornell.edu/EIQCalc/input.php. Then, based on the dose and the frequency of application of each pesticide, the EIQ field use rating was computed for each activity. The EIQ expresses the contamination risk of different production practices in relative terms. The last environmental indicator, soil loss from hydric erosion (SL), is related to negative impacts in soil productivity in both, the short and long terms. Within the cropping year, erosion effects include planting/emergence problems and water and nutrient loss. In the long term, a decrease in soil depth generates lower radical soil exploration and less capacity for water and nutrients intake (Lal et al., 2003). Currently, because of the widespread use of the no-till system, SL is not a major concern in the area of study. Nevertheless it is considered an important environmental indicator because of its irreversibility and the high potential soil erosion in the study area, due to the high content of silt in superficial soil horizons, long slopes, and Spring and Summer rain

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erosivity. The Universal Soil Loss Equation (USLE) is the method commonly employed to compute soil loss due to hydric erosion (e.g., Clérici and Gracía Préchac, 2001). This equation quantifies soil loss at plot/parcel scale based on data on rain erosivity, soil susceptibility to water erosion, slope, field length and vegetation cover.3 A farm-level value for each attribute j, j = (1,2,…,7),is computed as the sum-product of land assigned to each activity and the attribute's coefficient for each activity (Eq. (1)). The values for the coefficients (aij) are presented in the following sections. 15 X

areai  aij ∀ j :

ð1Þ

i¼1

Multicriteria models do not force the attributes to take certain values; instead ideal values for each attribute are defined and the model's objective is to minimize the deviations from these ideal values. 3.3. Anchor or ideal point The first step in CP is to set an ideal or anchor point; its coordinates are the ideal values for each of the attributes, considering available activities and technical restrictions. To set these coordinates each attribute is optimized separately; maximizing, minimizing or setting the value equal to zero, according to what is desirable for each attribute. The optimum for GM and OC is the maximum values and the optimum for DC, EIQ and SL is the minimum. For nutrient balances, NB and PB, optimal values are the closest to zero, since positive balances are related to contamination risk and negative values are related to lower nutrient availability in the future. A land assignment scheme in which all attributes take optimal values is not possible. Therefore, alternative land uses are ranked based on the distance to the ideal point. Preferred alternatives are those closer to the ideal. To set the distance between each alternative and the ideal point, the degree of discrepancy is measured for each attribute first (Eq. (2)). The variable dj measures the degree of discrepancy for attribute j, this is the difference between the value that takes the attribute under an alternative land assignment and the ideal value. In order to consider both positive and negative deviations from the idealj, dj is defined as = nj + pj (with nj, pj ≥ 0), the term ni is defined as negative deviation and pi as positive deviation. 15 X areai  aij ¼ n j −p j ideal j − i¼1

ð2Þ

d j ¼ n j þ p j ∀ j: To aggregate dj for all the attributes it is necessary to normalize these variables. This is because in the model presented in this study, as in most cases, the units of measurement for the attributes are very different and, therefore, the magnitudes of the deviations from ideal values could differ greatly. Variables dj are normalized to take values between 0 and 1. This is done by dividing dj by the range: best value of the attribute (ideal) minus the worst value of the attribute (anti-ideal). The normalized degree of discrepancy takes a value of 0 when it reaches the best possible value and a value of 1 when it reaches the worst possible value. 3.4. Objective function Compromise programming deals with the search of efficient solutions that are closer to the ideal point. The objective function is defined

3 The USLE model takes into account the characteristics of precipitations, soil, landscape and management to calculate the soil loss. This model does not permit the quantification of the effects of erosion out of the plot due to the fact that the redistribution of sediments within the landscape or the incorporation to the water system is not modeled.

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as a distance equation depending on the chosen metric (Ballestero, 2007), which is not necessarily the usual Euclidean quadratic metric (Eq. (3)). 0 11 π =π 7  X dj @ A min range j j¼i

ð3Þ

where π represents the metrics that define the set of distance functions. With linear programming it is possible to find the solutions with metrics π = 1 and π = ∞. For π = 1 the sum of normalized degrees of discrepancies is minimized, for π=∞ the maximum normalized degree of discrepancy is minimized. The solution for π = 1, is the most efficient one; however, it could be highly unbalanced, since it could combine very small deviations for some of the attributes and large deviations for the others. The solution for π =∞ is the most balanced solution. The linear equation that allows finding model solutions for different π values, is as follows: 0 7 X minð1−λÞ  D þ λ  @

1 dj A range j j¼1

dj ≤D ∀ j λ∈½0; 1: range j

ð4Þ

The parameter λ = 0 corresponds to the minimization of the maximum normalized degree of discrepancy, λ = 1 corresponds to the minimization of the sum of the normalized degrees of discrepancy. Intermediate values of λ allow intermediate solutions. 3.5. Attributes coefficients Table 2 presents the coefficients for each attribute in each activity. Note that attribute values are expressed on an annual basis. The first two columns present economic attributes: GM and DC. Activities with higher GM are wheat/2nd season soybeans (3.3 tn ha−1/3.5 tn ha−1), followed by full-season soybeans (4.5 tn ha−1) followed by corn (10 tn ha−1). Lowest values for GM are for wheat/2nd season soybeans (3.8 tn ha/1–1.5 tn ha−1), followed by livestock production. The economic indicator DC takes higher values for higher yield levels within each activity since, in order to obtain higher yields, higher quantities of inputs are needed. The lowest DC is for low yield full-season soybeans (2.5 tn ha−1). High yield corn (10.2 tn ha−1) has the highest DC. The last five columns in Table 2 present the environmental attributes. The first environmental indicator is OC. Livestock production has the greatest OC input to soil (it is assumed that forage is consumed by cattle in the field). Within agriculture, high yield is related to higher OC values. When comparing agricultural activities, double crop wheat/ soybeans have the highest OC input to soil followed by corn and fullseason soybeans. Columns 5 and 6 in Table 2 present phosphorus and nitrogen balances, respectively. Nitrogen balance is negative for all activities; except for livestock production (due to biological nitrogen fixation in pastures with legumes) and high yield wheat (since high fertilization rates are required to obtain high yields). Nitrogen balances are more negative in corn than in soybeans, since biological nitrogen fixation provides a large proportion of the nitrogen exported in soybeans. For phosphorus, balances are slightly positive in high yield wheat, corn and beef production. Otherwise, phosphorus balances are negative. The fourth environmental indicator in Table 2 is the EIQ. Corn presents the highest value for this indicator, followed by the double crop wheat/soybeans, then full-season soybeans and finally, livestock production. The last environmental indicator presented in Table 2 is SL. The lowest SL is for livestock production, followed by the double crop, then corn and finally soybeans with the worst value for this indicator.

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Table 2 Economic and environmental coefficients for farming activities.

Activity 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Wheat (3tn ha−1) Wheat (3.3 tn ha−1) Wheat (3.6 tn ha−1) Wheat (3.8 tn ha−1) Corn (8.4 tn ha−1) Corn (9.5 tn ha−1) Corn (10 tn ha−1) Corn (10.2 tn ha−1) Full-season soybeans (2.5 tn ha−1) Full-season soybeans (3.5 tn ha−1) Full-season soybeans (4.5 tn ha−1) 2nd-season soybeans (1.5 tn ha−1) 2nd-season soybeans (2.5 tn ha−1) 2nd-season soybeans (3.5 tn ha−1) Beef cattle production (500 Kg ha−1)

Gross margin (GM)

Direct costs (DC)

Organic carbon input to soil (OC)

Nitrogen balance (NB)

Phosphorus balance (PB)

–u$s ha−1–

–u$s ha-1–

–tn ha−1–

–Kg ha−1–

–Kg ha−1–

206 208 186 160 530 607 629 617 406 602 771 202 415 585 401

162 201 260 316 359 394 429 462 163 196 254 140 155 213 307

1.33 1.48 1.61 1.72 2.02 2.27 2.40 2.45 0.93 1.30 1.67 0.56 0.93 1.30 4.50

−31.45 −14.55 3.08 21.45 −87.02 −77.92 −61.97 −41.59 −16.94 −23.72 −30.49 −10.16 −16.94 −23.72 91.65

−2.99 −0.68 1.35 3.11 0.87 0.51 1.27 2.66 −9.46 −6.39 −3.33 −7.92 −9.46 −6.39 0.80

Pesticide environmental impact quotient (EIQ) 28.32 28.32 32.61 36.90 87.71 87.71 87.71 87.71 54.56 54.56 57.17 39.23 39.23 41.84 23.52

Erosion soil loss (SL) –tn ha−1– 2.61 2.61 2.61 2.61 3.28 3.28 3.28 3.28 5.69 5.69 5.69 * * * 0.95

*Wheat and 2nd-season soybeans are planted in the same crop year. The value of soil loss from hydric erosion registered for wheat, is for the double crop wheat/soybeans.

Note that the ranking of activities based on best/worst values is exactly the same for OC and SL..4 3.6. Comparison of model-based preferred land allocation vs. current land use After running the CP model, the solutions were compared to current land use. Current land use is described based on survey data gathered in 2007 from crop producers in Pergamino district. The survey was conducted on a sample of 70 farms, stratified by land operated (Strata: 50–150 ha, 151–500 ha and more than 500 ha). The survey data includes detailed information about quantities of grain and oilseed production, quantities of inputs employed, as well as several characteristics of the firms and managers. The descriptive statistics for the relevant variables that describe farming systems are reported for each strata in Cabrini and Calcaterra (2008). 4. Results and discussion The first result in this study is the pay-off matrix (Table 3). This matrix is computed optimizing each of the seven attributes separately and recording the values for the rest of the attributes in each case. In Table 3 the columns' labels indicate the attribute being optimized while rows show the values for the rest of the attributes. Panel A presents the values in US$ ha−1, the last two columns show the ideal (best value in each row) and anti-ideal (worst value in each row) for each attribute. Panel B shows the normalized values. When the attribute takes a value of 1 it is equal to the ideal value while attributes equal to zero take antiideal values. Values between 0 and 1 indicate how close the values are to the ideal for that attribute. Table 4 shows how land is assigned when each attribute is optimized. The model solution for GM maximization is that all land is assigned to wheat/soybeans (3.3 tn ha−1/3.5 tn ha−1). Other authors reported a similar solution in other regions of Argentina's Pampas (Mosciaro and Iorio, 2013). In this solution DC and EIQ take anti-ideal values. When DC is minimized all land is assigned to low-input soybeans (2.5 tn ha−1). In this case GM, OC, PB and SL take anti-ideal values. DC minimization can be considered as a risk reduction strategy, relevant in particular for rented farming land. When environmental attributes are optimized, ideal values of OC, EIQ and SL are obtained by assigning all land to extensive livestock 4 Even though the table shows one single value of soil loss for each activity it is likely that if yield values increase more biomass will be produced and, as a consequence, the soil will receive more protection. Nevertheless, there are no coefficients for the USLE model to distinguish soil loss levels for different yield rates.

production; however anti-ideal values of GM and NB are obtained under these conditions. When NB and PB are optimized, none of the other attributes take ideal or anti-ideal values. In this case, results include both livestock production and agriculture (corn 8.4 tn ha−1, soybeans 4.5 tn ha−1). Note that when environmental attributes are optimized, extensive livestock production is always part of the solution; instead this activity is not included in the solutions when economic attributes are optimized. There is a conflict between some environmental attributes and GM, since there is a strong reduction in this economic result when OC, EIQ or SL are optimized. The opportunity cost is approximately US$300 ha−1 to get ideal values for these three indicators. The strongest economicenvironmental conflict that can be detected in the pay-off matrix is that when GM is maximized, the highest value of EIQ is obtained. However, the results presented in the following paragraph indicate that it is possible to obtain a balanced environmental/economic solution if a relatively small reduction in the economic results is allowed. Optimization models allow computing efficient frontiers for analyzing the rate of substitution between pairs of attributes. The conflicts between GM and each environmental attribute are assessed based on the maximization of GM for increasing restrictions in the environmental indicators levels. This analysis provides the opportunity costs of reducing negative environmental effects from farming. Efficient frontiers are presented in Fig. 2; note that the slopes of the curves can be interpreted as the opportunity cost of improving the values of the environmental indicators. These figures also show the values for each of the attributes in actual production systems (represented with the three markers in each panel). Each marker shows the average values for the attributes based on surveys data (cropping year 2007) for small (50–150 ha), medium size (151–500 ha) and large farmers (N 500 ha), respectively (Cabrini and Calcaterra, 2008). The first panel in Fig. 2 shows the frontiers for GM vs. OC. The relationship between these two variables is approximately linear: GM decreases by US$230 ha−1 per unit increase in OC. It is interesting to compare the frontiers with the thresholds of OC levels required to keep the land productive. Since there is not a general consensus about these thresholds, two benchmarks are considered: 2 and 3.6 tn CO ha−1 based on results reported in different studies.5 The whole frontier has higher CO levels 5 Alvarez and Grigera (2005) do not find direct effects between the OM content in soil and wheat and corn yields in the Pampas Region with OM values (0–20 cm) between 1.9 and 6% suggesting a threshold of less than 1.9%. Loveland and Webb (2003) mention 3.4% in the first 20 cm of soil as a threshold of OM generally accepted for warm areas though they demonstrate that the threshold could be lower. The necessary input to maintain the OM between 1.9 and 3.4% in the first 20 cm of soil would be of 2 and 3.6 CO ha−1 per year, respectively (Alvarez and Steinbach, 2006).

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189

Table 3 Pay-off matrix from multicriteria farm model with economic and environmental attributes. Panel A. Attributes values presented per unit area (ha−1) – Attribute optimized –

GM DC OC NB PB EIQ SL

u$s ha−1 u$s ha−1 tn ha−1 kg ha−1 kg ha−1 tn ha−1

Gross margin (GM)

Direct Costs (DC)

792.63 414.14 2.78 −38.26 −7.07 70.16 2.61

406.34 163.46 0.93 −16.94 −9.46 54.56 5.69

Organic carbon input to soil (OC) 401.23 306.94 4.50 91.65 0.80 23.52 0.95

Nitrogen balance (NB)

Phosphorus balance (PB)

472.63 331.80 3.21 0.00 0.76 56.25 2.20

540.43 322.17 2.69 −22.38 0.00 62.42 3.07

Pesticide environmental impact quotient (EIQ)

Soil loss (SL)

Ideal

Anti-ideal

401.23 306.94 4.50 91.65 0.80 23.52 0.95

401.23 306.94 4.50 91.65 0.80 23.52 0.95

792.63 163.46 4.5 0 0 24 0.95

401.23 414.14 0.93 91.65 −9.46 70.16 5.69

Panel B. Normalized attributes values (as a porporcion of the ideal values)

--- Attribute optimized ---Organic carbon Gross margin (GM) GM

Direct costs (DC)

1

0.01

Pesticides environment

input to soil

Nitrogen balance

Phosphorus balance

al impact

Erosion soil loss

(OC)

(NB)

(PB)

(EIQ)

(SL)

0

0.18

0.36

0

0

DC

0

1

0.43

0.33

0.37

0.43

0.43

OC

0.52

0

1

0.64

0.49

1

1

NB

0.58

0.82

0

1

0.76

0

0

PB

0.25

0

0.92

0.92

1

0.92

0.92

EIQ

0

0.33

1

0.30

0.17

1

1

0.65

0

1

0.74

0.55

1

1

SL

Note: The color scale in Panel B indicate how close is the attribute value from the ideal value (green closer; red farther).

compared to the first threshold. However, GM is US$200 ha lower than the maximum for the threshold of 3.6 tn CO ha−1. Panels B and C show the frontiers for nutrient balances. The cost of improving these attributes is lower compared to the rest of the environmental attributes. A nitrogen neutral balance (NB = 0) is obtained with a decrease of US$55 ha−1 in GM (and a decrease of US$129 ha−1 for PB = 0). Marginal cost for improving these attributes varies along the frontier from US$1 and US$2 per kg of nitrogen and from US$12 and US$30 per kg. of phosphorus. Panel D shows the trade-off between EIQ and GM, the reduction of 10 points in EIQ implies a reduction in GM of 100 US$ ha−1, approximately. Moving from right to left in the frontier, the cost of reducing EIQ (the curve slope) increases.

The last panel in Fig. 2 shows the relationship between GM and SL, the marginal cost of reducing erosion is US$250 tn−1. However, the whole frontier has reasonable soil loss values with respect to thresholds for soil loss between 4 and 11 tn soil ha−1, reported for soils with depths higher than 50 cm Irurtia et al. (2007). This occurs because the crops with higher GM (double crop wheat-soybeans) are also a good alternative for soil protection. The five panels in Fig. 2 include three markers showing the attributes' values for actual land use in Pergamino for three different farm sizes. In all panels the markers are below the frontier, indicating that it is possible for farmers to improve environmental performance without reducing economic results. Note that each panel of Fig. 2 show the tradeoff between two attributes, a more complete analysis considering

Table 4 Optimization model solutions, proportion of land assigned to each activity. – Attribute optimized –

Wheat

Corn

Full-season soybeans

Second-season soybeans

Beef cattle production

Expected yield (tn ha−1)

Gross margin (GM)

Direct costs (DC)

Organic carbon input to soil (OC)

Nitrogen balance (NB)

Phosphorus balance (PB)

Pesticides environmental impact quotient (EIQ)

Erosion soil loss (SL)

3.0 3.3 3.6 3.8 8.4 9.5 10.0 10.2 2.5 3.5 4.5 1.5 2.5 3.5 0.5

. 1 . . . . . . . . . . . 1 .

. . . . . . . . 1 . . . . . .

. . . . . . . . . . . . . . 1

. . . . 0.50 . . . . . 0.02 . . . 0.48

. . . . 0.50 . . . . . 0.20 . . . 0.30

. . . . . . . . . . . . . . 1

. . . . . . . . . . . . . . 1

190

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Fig. 2. Trade-off frontiers for total gross margin vs. each environmental indicator. Note: The three markers in each panel represent average values for the indicators for three different farm sizes in Pergamino.

all attributes simultaneously based on the multicriteria approach is presented in the following paragraphs. Table 5 presents the central results in this study, the set of compromise solutions for different values of λ (Eq. (4)). The first column shows the results for the model with λ = 0, in this case the maximum normalized deviation (discrepancy degree) is minimized, and the most balanced solution is guaranteed. This solution includes 62% of land assigned to agriculture and 38% to livestock production. Full-season soybeans are planted in 42% of the land and the double crop wheat/ soybeans in 20%. Under this land allocation scheme environmental indicators take reasonable values, with a reduction of 25% from the highest GM.

When the sum of deviations is minimized, by setting λ = 1 (third column, Table 5), most of the land is assigned to extensive livestock production (92%) and 8% is planted with wheat/soybeans. The second column in the table shows an intermediate solution (λ = 0.5). In this case, 47% of land is assigned to livestock production, 46% to fullseason soybeans and the remaining to wheat/soybeans. The last three columns of the table (Crop farming system) present the compromise solutions when livestock production is not considered as an alternative. In this case, crops with higher expected yields are selected. Note that the solutions for crop systems are poorly diversified, with soybeans as the only summer crop. Therefore, it is interesting to compare the values for the attributes in these solutions with the values for the

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191

Table 5 Set of compromise solutions for the farm land use model with economic and environmental attributes. Mixed farming system Expected yield (tn ha−1)

Wheat

Corn

Full-season soybeans

Second-season soybeans

Beef cattle production Attributes GM DC CO NB PB EIQ SL

Crop farming system

Minimization of maximum deviation

Minimization of the sum of deviations

Minimization of maximum deviation

Minimization of the sum of deviations

λ=0

λ = 0.5

λ=1

λ=0

λ = 0.5

λ=1

3.0 3.3 3.6 3.8 8.4 9.5 10.0 10.2 2.5 3.5 4.5 1.5 2.5 3.5 0.5

0.20 . . . . . . . . 0.20 0.22 . . 0.20 0.38

0.07 . . . . . . . . . 0.46 . . 0.07 0.47

0.08 . . . . . . . . . . . . 0.08 0.92

. 0.35 . . . . . . . 0.65 . . 0.20 0.15 .

. 0.34 . . . . . . . 0.66 . . . 0.34 .

. . . 1.00 . . . . . . . . . 1.00 .

u$s ha−1 u$s ha−1 tn ha−1 kg ha−1 kg ha−1

600.54 286.49 2.86 12.47 −3.56 46.41 3.28

599.85 286.93 3.05 24.87 −1.81 42.33 3.27

431.86 312.24 4.35 80.11 0.00 27.19 1.08

634.71 260.82 1.75 −27.47 −7.24 59.51 4.61

666.54 270.06 1.80 −28.66 −6.62 59.86 4.64

744.91 529.43 3.02 −2.27 −3.28 78.74 2.61

tn ha−1

Note: In the crop systems models the area for cattle production is restricted to zero.

attributes under a restriction that ensures certain diversification level. Based on this, a crop-farming model (with λ = 0) is solved including a restriction that forces all crops (wheat, corn, soybeans) to enter the solutions in, at least, 20% of the land. Results in this case show similar values for all the attributes, compared to the case without the diversification restriction. The major difference is obtained with the economic indicators that decline about 10%. Note that a more diversified allocation scheme in agricultural systems is better compared to the solutions presented for crop systems in Table 5 in terms of economic risk reduction and agro-ecosystem diversity protection..6 It is interesting to compare the results of the optimization models with the current land allocation chosen by the farmers in the region (Table 6). It is remarkable that farmers assign a very small proportion of land to livestock production (6–7%), compared to the models' solutions. Many factors contribute to explain this difference. It is likely that farmers make decisions based on the economic performance rather than the environmental performance, choosing agricultural activities which have better economic results compared to livestock production. Also, many farmers do not have infrastructure for cattle production (fences, watering points, etc.) and, therefore, they don't consider livestock production as an alternative in the short term. This issue is important since approximately half of the productive land in the region is managed under short term leasing arrangements (one to three cropping years), which are not compatible with planning a livestock production system. Finally, the complexity in livestock management compared to agriculture could also explain the low relevance of livestock production in farming nowadays.

6

The CP model was also run with Eq. (A.6) as AREA_soy2 ≤ AREA_wheat, and results show differences only with λ = 0. In this case wheat (30 qq ha−1) appears in the solution replacing full-season soybeans and the pasture proportion is almost the same as reported in Table 5 (39%). For the optimal solution, MB and NB are 30% higher, and SL is 40% lower compared to the solution for the model with Eq. (A.6): AREA_soy2 = AREA_wheat. However, as it was mentioned before, these results should be interpreted with caution, since there is very limited information on productive and environmental coefficients for growing only wheat in the cropping year.

In addition, farmers grow full-season soybeans in a higher proportion compared to model solutions; this is especially true for the smallest farms. Note that an average of 77% of the land assigned to full-season soybeans reflects the fact that some farmers plant soybean monoculture and others include a large proportion of soybeans after soybeans in their fields. It is highly likely that small farmers having stronger financial constraints choose full-season soybeans because this crop has the lowest direct costs among all alternatives. On the other hand, in medium-sized and large farms the proportion of soybeans, while it is high compared to the solution for mixed farming systems, is approximately equal or smaller than the solution in the agricultural farm models. Corn does not appear in the solution; however, in the farms between 12 and 19% of land is assigned to this crop. It is likely that farmers in Pergamino select this crop as a risk management strategy. Fig. 3 shows the economic and environmental performance for model solutions and actual land use in Pergamino. These semicircles' radiuses are proportional to the normalized values of the attributes. The first scheme shows a semicircle for the optimal attributes' values. In the other semicircles, the smaller the radius the closer the attributes are to the anti-ideal values.

Table 6 Actual arable land use in Pergamino District, North of Buenos Aires, Argentina. Source: Cabrini and Calcaterra (2008). Percentage of land (average yield) Farm size

Corn Full season soybeans Wheat/2nd season soybeans Beef cattle production

50–150 ha

151–500 ha

N500 ha

12% (7.4 tn ha−1) 77% (3.4 tn ha−1) 8%

14% (8.4 tn ha−1) 69% (3.5 tn ha−1) 15%

19% (8.5 tn ha−1) 55% (3.6 tn ha−1) 22%

(3.2/2.7 tn ha−1) 3%

(3.6/2.8 tn ha−1) 2%

(4/2.6 tn ha−1) 4%

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Fig. 3. Achievement level of economic and environmental attributes in the compromise solutions and actual land use in Pergamino department. Note: GM: gross margin, DC: direct costs, EIQ: environmental impact quotient of agrochemicals, NB: nitrogen balance, PB: phosphorus balance, OC: organic carbon input to soil, SL: erosion soil loss.

Fig. 3 shows that when λ is set to 0 (the most balanced) all attributes differ from the ideal values, but none of them is close to the anti-ideal values. The third semicircle represents the attributes' values for the minimization of the sum of deviations (λ = 1). In this case there are some attributes having values tightly close to the ideal (EIQ, PB, OC and SL), but others take the anti-ideal values (GM and NB). The last tree semicircles show that the economic indicators and nutrient balances for actual land use have values similar to those in the compromise solutions for λ = 0. Instead EIQ, OC and SL have less favorable values, closer to the anti-ideal values. When comparing land use for different farm business sizes (last three semicircles), we can observe that these are similar in terms of which attributes are close and which are far from ideal values. The smallest farms choose activities with lower DC and worse values of OC and SL, compared to larger farms. Also, large farms have high values for EIQ, since these farmers tend to choose higher-input activities. The comparison of the semicircles for actual vs. optimal land use suggests that farmers could improve environmental indicators, especially EIQ, NB, PB and OC, without lowering GM. 5. Conclusions Results indicate that it is important to consider not only the environmental-economic tradeoff in farming, but also the conflict across different environmental criteria in order to determine whether it is reasonable to stimulate activities which improve some environmental indicators at the expense of getting less desirable values in others. For instance, an increase of productivity in agriculture enhances soil organic carbon content but it may increase the risk of contamination with pesticides and nutrients. To determine whether it is reasonable to give incentives to certain activities that improve some environmental indicators but are related to poor values in other indicators, it is

necessary to measure the economic value or relative importance of the environmental effects measured by the indicators. Results are consistent with the notion that extensive crop-livestock production systems are more balanced than continuous agriculture. The combination of crop and livestock activities, when compared against continuous agriculture, is associated with a lower use of agrochemicals, greater organic carbon inputs to soil, better soil protection from erosion, more efficient nutrient cycling, and a decrease of 20% in the economic result, Other authors have also recently reported the advantages of integrated crops — extensive livestock production systems in Argentina's Pampas based on the agronomic and environmental impacts of these systems (Franzluebbers et al., 2014; Peyraud et al., 2014). However, farmers assign a smaller fraction of land to extensive livestock production (2–4%), compared to the more balanced compromise solution (38%). In addition, from the comparison between current land use and the optimal land assignment based on model results, it appears that it would be appropriate to stimulate smaller farmers to reduce the land assigned to full-season soybeans, since on average, these farmers grow this crop in a higher proportion of land than in any of the model solutions. For a comprehensive analysis of the future of agricultural production in Argentina's Pampas it would be necessary to consider innovative alternatives of activities and technologies such as cover crops, irrigation and organic production. In this case, finding the coefficients for economic and environmental indicators related to these alternatives would be challenging. In addition, it would be important to consider in future studies other attributes such as the economic risk level and greenhouse gases emissions. Also, it would be interesting to expand this research with spatial multicriteria analysis which allows studying land allocation schemes at water catchment scales (Cisneros et al., 2011; Zhang and Huang, 2011). In these types of studies land uses for each parcel are evaluated taking into account the position in a basin.

S.M. Cabrini, C.P. Calcaterra / Agricultural Systems 143 (2016) 183–194

Finally, it is important to remark that while MCDM is a valuable tool for supporting land use decisions, for a deeper understanding of the sustainability of agricultural production systems other techniques that allow modeling the dynamics of ecological, economics and social processes should also be considered. Acknowledgments The authors would like to thank the Instituto Nacional de Tecnología Agropecuaria Argentina — for the financial support for this study under de project AAES 301321. We also thank three anonymous referees for their comments and suggestions. Appendix A Land Availability and crop rotation restrictions, CP farm model A.1. Decision variables area (i) land assigned to each activity, hectares i = (1,2,..15). i is the set of activities = (wheat_30, wheat_33, wheat_36, wheat_38, corn_84, corn_95, corn_100,corn_102, soy1_25, soy1_35, soy1_45, soy2_15, soy2_25, soy2_35, cattle_500). Activities are described in Table 1. A.2. Land availability and crop rotation restrictions Total wheat area AREA wheat ¼ areað‘wheat 30’Þ þ areað‘wheat 33’Þ

ðA:1Þ

þ areað‘wheat 36’Þ þ areað‘wheat 38’Þ Total corn area AREA corn ¼ areað‘corn 84’Þ þ areað‘corn 95’Þ þ areað‘corn 100’Þ

ðA:2Þ

þareað‘corn 102’Þ

Total full‐season soybeans area ðA:3Þ AREA soy1 ¼ areað‘soy1 25’Þ þ areað‘soy1 35’Þ þ areað‘soy1 45’Þ Total second‐season soybeans area ðA:4Þ AREA soy2 ¼ areað‘soy2 15’Þ þ areað‘soy2 25’Þ þ areað‘soy2 35’Þ Total land availability for first‐season activities AREA wheat þ AREA corn þ AREA soy1 þ areað‘cattle 500’Þ ¼ 350 ðA:5Þ Area assigned to second‐season soybeans AREA soy2 ¼ AREA wheat

ðA:6Þ

High yield soybeans restriction areað‘soy1 45’Þ≤AREA corn þ areað‘cattle 500’Þ

ðA:7Þ

Corn area restriction AREA corn ≤AREA soy1 þ AREA wheat þ areað‘cattle 500’Þ:

ðA:8Þ

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