Agricultural Productivity and Sustainability: Evidence from Low Input Farming in Argentina

Jorge D. de Prada1, Boris Bravo-Ureta2 and Farhed Shah3

Paper prepared for presentation at the American Agricultural Economics Association Annual Meeting, Montreal, Canada, July 27-30, 2003

1 Graduate Assistant, Department of Agricultural and Resource Economics, University of Connecticut, 1376 Storrs Road, Storrs CT 06269- 4021, U.S.A. E-mail: [email protected]

2 Executive Director Office of International Affairs, and Professor, Department of Agricultural and Resource Economics, University of Connecticut, 843 Bolton Rd., Storrs, CT 06269-1182, U.S.A. E-mail: [email protected].

3 Associate Professor, Department of Agricultural and Resource Economics, University of Connecticut, 1376 Storrs Road, Storrs CT 06269- 4021, U.S.A. E-mail: [email protected]

Copyright 2003 by Jorge D. de Prada, Boris Bravo-Ureta and Farhed Shah. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies. Agricultural Productivity and Sustainability: Evidence from Low Input Farming in Argentina

Jorge de Prada, Boris Bravo-Ureta and Farhed Shah

University of Connecticut

Abstract: The tradeoff between short-term agricultural productivity and sustainability is examined with a statistical analysis of evidence from low input agriculture in Argentina.

Estimation results show that more intensive land use, corporate leasing of land, and larger farm size are likely to increase current revenues, but at the cost of sustainability.

Keywords: Agricultural Productivity; Sustainable Agriculture; Low Input Farming;

Developing Countries; Argentina.

Acknowledgements: We appreciate the support given by the agreement between

Universidad Nacional de Río Cuarto and University of Connecticut and are especially grateful to Professors Alberto Cantero Gutierrez, Horacio Gil and Liliana Cristina Issaly for discussion of the initial idea. We would also want to thank Kazu Kawashima for his helpful comments.

2 1. Introduction

One of the major challenges facing agriculture worldwide is how to feed an increasing population while improving environmental services and social equity. There is a debate in the literature regarding the role of market institutions in promoting agricultural productivity and sustainability in developing countries. Some authors, following the lead of the International Monetary Fund, are pushing for reforms that would allow market forces to allocate scarce resources and enhance farm productivity (e.g., Hazell, 1998). However, there are also others (e.g., van Dam 1999) who believe that the practice of these recommendations has increased social inequality and moved agricultural practices away from the ideals of sustainability.

Parallel to the international discussion about sustainable development, and partly in response to increasing public pressure on environmental issues, developed countries have also been introducing frameworks for sustainable agriculture (de Koeijer 1995, Gold,

1999). One example from the U.S. is the low input sustainable agriculture (LISA) program, which was subsequently incorporated in the Sustainable Agriculture Research and

Education Act of 1990. Unlike traditional industrialized agriculture, this policy promotes the use of reduced chemical inputs, such as pesticides, fertilizer and fossil energy, and more ecological management to produce food and fiber (Pimentel et al., 1989, Gold 1999).

Nevertheless, there is concern that the use of less inputs may reduce farm productivity and competitiveness.

The experience of Argentina may shed some light on this policy debate since agriculture in this country is currently driven by market forces and has historically been based on low chemical inputs. During the 1990s, a market friendly structural reform in line with the ‘Washington Consensus’ took place (Chisari et al., 1996). For all practical

3 purposes, international prices have been the main force driving agricultural modernization in the country over the last decade (Schnepf et al., 2001). This has resulted in higher agricultural output, but there are some authors who have expressed alarm at the increasing use of pesticides and chemical fertilizers and claimed that these trends are not sustainable.

Furthermore, farming systems are gradually changing from mixed-livestock and crop based systems to more specialized systems involving simple crop rotations and with a relatively greater use of chemical inputs (Viglizzo et al., 2001).

This paper attempts to make an empirical contribution to the above policy debate by reviewing evidence from Córdoba, Argentina, where a transformation of low input agriculture is taking place. We explore how factors that are leading to agricultural modernization in the area, such as land use, land tenure, and farm size, are impacting agricultural productivity and sustainability.

The economics literature relating to the role of the afore-mentioned factors is quite controversial. For example, McConnell (1983) hypothesized that farmers would select land use and management strategies to offset the cost of soil erosion and that land tenure arrangements would be an important determinant of farmer behavior in this regard. This author also assumed that corporation looking at long terms profit would practice soil conservation in the same ways as farmers with similar planning periods. However, the available evidence from U.S. agriculture is somewhat mixed. For example, Miranda (1992) finds no-support for the theory, while Soule et al., (2000) claim that the theory is consistent with empirical evidence if the period of return of soil conservation practices and specific characteristics of land tenure are accounted properly.

4 In developing countries, the controversy is even greater. For example, in Argentina, some authors believe that market driven forces and the internationalization of agriculture have induced farmers to become more efficient and competitive (Ghida Daza, 2000;

Schnepf et al,. 2001); however, in practice, farmers have ignored crop rotation based on pasture to get increased short-term profits, and this can have significant effects on soil erosion and crop productivity in the long-run (Miranda 1992). Some authors also contend that the new type of agriculture has reduced sustainability because many of the land operators are now corporations who rent land and do not care about conservation or environmental issues. Moreover, there is also a higher rate of poverty among some farm groups (e.g. van Dam, 1999, Pengue, 2000). However, the empirical support for these kinds of arguments is mostly anecdotal.

We propose to provide a more rigorous analysis by adopting a production function approach to relate land use, land tenure, and farm size with the total value of crop output and agricultural sustainability. We test directly the impact of those factors on crop production, and make inferences related to sustainability issues by comparing different farming systems and practices. Byiringiro and Reordan (1996) use a similar approach to test the impacts of soil degradation, soil conservation effort, and farm size on marginal products of land and labor.

The rest of the paper is divided into four sections. First, a conceptual model is developed, focusing on the factors that affect total productivity and sustainability. Second, characteristics of the farmers and other aspects of the data set are presented, followed by a discussion of empirical procedures. Third, the results for the models are described. The final section offers some concluding remarks.

5 2. Conceptual framework

The production function is taken to be:

Yi = f(Xij; Lij, Zik, Dl) (1)

th where Yi represents the total value of all crop outputs (i.e., crop revenue) for the i farmer,

Xj is a vector of inputs including land and labor, Lj is a vector that includes land use, land tenancy and farm size which are the variables of particular interest in the analysis; Zk represents the vector of control variables, and Dl are dummy variables that account for agro-ecological regions.

Based on economic theory, we expect that inputs such as cropland1 and labor used for crop productions will have a positive impact on crop revenue. The variables Lij, Zik, can be thought as shifters of crop revenue. Given the fact that there is enough variability among farming systems, land tenancy, and farm size, we can estimate the effects of these factors on crop revenue. At the same time, we can infer how land use and management practices cause different level of soil erosion, water runoff, and water pollution, which are considered the main off-farm damages.

Impact of farming systems on crop revenues

Farming systems have been changing from diversified type to more specialized and intensive land use systems that are based on row crops (Viglizzo et al., 1995 and Viglizzo et al., 2001) and monoculture (for example soybean, or simple rotation, such as soybean wheat and soybean corn). Farming systems that are more diversified, include livestock activities, and rely on perennial pasture and row and small grain crops, usually are

1 Cropland is a type of land that can be used for small grain and row crops, pastures or any other land use with a few limitations.

6 considered more environmentally friendly and sustainable compared with monoculture or two crops rotations (Viglizzo et al., 1995, USDA, 2000). Cropland used for pasture as part of long-term rotation with row crops provide on-site benefits and also reduce off farm damages. Some of the onsite benefits are reduced soil loss (Papendick et al., 1986, Weir and Arce 1999), water runoff (Weir and Arce 1999) and less damage from weeds, insects and diseases (Pimentel et al., 1995, Satorre, 2001). For example, Weir and Arce (1999) developed a six years experiment in Marco Juarez-Cordoba-Argentina measuring soil loss.

This authors report that the land used for row crops has soil erosion rates that are two to nine times higher compared with land used for pasture. It follows that farming systems that include rotation with pasture are likely to have lower rates of soil loss. This allows us to hypothesize that an increase in the proportion of cropland used for pasture, other thing being equal, should result in higher crop revenues. Although it is considered difficult to attach a dollar value to on farm benefits of pasture (Power 1987), we believe that the crop revenue function ought to capture the impact of this variable, and its coefficient may be interpreted as the marginal value of pasture in crop activities.

Land tenure and farm size

Land tenure is another factor that has been changing with agricultural modernization. Corporations and a new type of business, called “pool de siembra”, have become more prominent in production activities. These enterprises reduce risk by cropping in different areas and take advantage of economies of size, thereby paying lower input prices, receiving higher output prices, and having access to financial and technical assistance. Land tenure is also linked to soil erosion and land degradation (e.g. Becerra et al. 1992, de Prada et al., 1994, van Dan 1999). According to van Dan (1999), these new

7 land arrangements related to the modernization of agriculture are larger business operations that use off-farm financial capital. They look for a high short-term profit, and are not concerned with sustainability issues since their businesses are neither linked to the local economies nor to the land and its fertility in the long term.

In this paper we consider three land tenure arrangements: owner operators, renters to other farmers, and renters to corporations. The latter two forms are short-term land tenure arrangements, and the last one is the new arrangement discussed above that emerged during the 1990s. We hypothesize this tenure arrangement may increase crop production either because of better technical know-how related to cropping (efficiency), economies of size, use of more fertile soil (converting long term rotation) or the use of more inputs.

Farm size has been gradually increasing in Argentina since 1970s, reducing the total number of farmers and demand for labor. Some explanation for this phenomenon may be given by the economies of scale. Binswanger et al., (1995) mention three sources of economies of scale: scale of processing plants and timing coordination, lumpy inputs such as farm machinery and management skills, and access to credit and risk diffusion. On the other hand, the inverse relationship between size and productivity of land has been one of the major reasons for land reforms in developing countries. Thus, we are interested in testing whether or not farm size affects total production and if it is negative or positive, and if the marginal product of land changes with the size of farm.

8 3. Area of study, farm characteristics and estimation procedures

Data

The study area is located in the Central Pampas, where the farming activities are predominantly carried out under dryland conditions (Viglizzo et al., 1995). The region has a temperate climate and an average rainfall of 780 mm (Becerra et al., 1992, Cantero et al.,

1998).

A sample 1806 observations from a 1999 Survey implemented by the Ministry of

Production of the Province of Cordoba (SayG-MP, 1999) with the help of the National

University of Rio Cuarto and the National Institute of Agricultural Technology is used.

The observations are from three counties in Cordoba: Tercero Arriba, Juarez Celman and

Río Cuarto. The survey contains detailed information on land tenure, land use, family and hired labor, yields, credit, and livestock activities (for details see SayG-MP, 1999). The basic statistics and variable definitions are presented in Table 1. The row crops grown are peanuts, soybean, corn, sorghum, and wheat. The average crop revenue equals approximately $96000 per farmer. Cropland used for pasture in average is 90 hectares, and the average size of the farm is 432 hectares. Steer fattening and cow-calf operations, supported by perennial and annual pastures, are the main livestock activities. It is important to note that crop revenue does not include the market value for these farm activities.

The data has some limitations. First, land tenancy is difficult to isolate clearly. A farmer may own part of land in operation, and either rent land from a landowner or crops land under contract eventually with corporations, but we cannot specifically recognize the revenue from these three land tenancy arrangements for the same observation. A

9 corporation may rent land to different farm operators even in the same area, so two or more observations may lease land by contract to the same corporations. The result is that from a total of 1806 observations, there are 1482 farm operators who are owners at least of part of the total land in operation, 710 rent at least part of total land from other farmers, and 428 farmers lease land by contracts to someone, possibly corporations or other farmers. The average contract in the last category is for 234 hectares, but we cannot identify how many farms have leasing arrangements with the same corporation. Thus, we use dummy variables for land tenancy categories as control variable in the general equation, and then we conduct an independent regression for each category to compare marginal value of land assign to crops. Secondly, credit and labor cannot be easily assigned to farm activities. We weight these variables by land used for crops and employ the weighted variable as inputs (similar to the procedure used by Byiringiro and Reardom 1996). This means that a farm with 100 hectares that has 50 hectares with crops is going to have total labor reported time 0.5 as input for crops revenue. Finally, some variable inputs such as fertilizers, pesticides are not reported. We assume that farmers would use the optimal amount for each of these variable inputs and control for technologies such as irrigation and non-till that use different amount of such variable input. In addition, we use other control variable short-term credit that may be related to variable input intensity.

Estimation Procedures

Using generalized least squares, we estimate two forms of the production function, namely quadratic and Cobb-Douglas. These functions are estimated for the entire data set, and then individually for each land tenure arrangement. Next, we conduct non-nested hypotheses testing to determine the specification that is most suitable for the data at hand.

10 We follow Byiringiro and Reardom’s (1996) approach but modify it to accommodate our data limitation.

The quadratic form takes crop revenue as function of linear and quadratic expression for inputs, and linear for control and dummy variables. It can be represented as:

2 2 y = βo+ β1 rcland + β2 rcland + β3 labor + β4 labor + β5 pasture + β6 size

+ β7 cl+ β8 rl + Σj βj Zj + Σk βk Dk + ϖ (2)

where y represents crop revenue, and rcland represents cropland used for wheat and row crops measure in hectare. Labor represents labor assign to crops measured in man-day per year. Pasture, measured in hectare, includes perennial pasture based on alfalfa 67% and other 33%. Size measured as total land in operation. cl and rl are the dummies contracted land and rent land respectively. Zj and Dk represent the vectors of control variables and dummy variables respectively. ϖ is the error term. Maximum likelihood estimator correcting heterocesdaticity by dependent variable is used (Shazam 1997)

The second functional form is represented as:

lny = βo+ β1 lnrcland + β3 lnlabor + β5 lnpasture + β6 lnsize

+ β7 cl+ β8 rl +Σj βj Zj + Σk βk Dk + ε (3)

Natural logarithm is used to transform the variable for estimation of the Cob-

Douglas function.

11 The observations that do not have cropland used for crops are removed from the estimations of all equations. The number of observation for each specific equation is reported in the results.

4. Results

Of the two functional forms, the quadratic is chosen to describe the results because it fits better with the data set at hand (see details for both functional form in Table 2 and 3).

The estimation results shows that the presence of land in perennial pasture, land tenure arrangements, and farm size have significant effects on the value of crops production. The results from the regression using all the observations and correcting for heteroskedasticity are shown in Table 2. The squared correlation coefficient between observed and predicted value is 0.84, which represents a very good fit of the model to the data set.

Overall most of the variables are statistically significant and have the expected sign with one exception, namely the estimated parameter for the quadratic variable ‘land used for crops’, which is also not statistically significant. The linear parameter of ‘land used for crops’ has the expected sign and the absolute value looks consistent with average revenue per hectare for the main crops in the area. Labor used as inputs has the expected signs for both linear and quadratic form. Crop revenues increase with respect to labor but at a decreasing rate. The variables Irrigated land, non-till, and short-term credits that we use to control for other input intensity also have the expected signs. However, the value of the estimated parameters should be interpreted with caution since we can not distinguish the effects of different techniques from variable input intensities on crop revenue.

12 Effects of farming systems, land tenure and farm size on crop revenue

The market value of crops is affected by farming systems, land tenure and farm size.

The farming system effect, as captured by the amount cropland used for pasture, is positive.

Thus, farmers that combine livestock activities based on pasture with row crops in a long- term rotation receive both income from the market value of livestock activities and from crop sales. The variable perennial pasture adds an average $ 92 per hectare to crop revenue, which represents 450 kilograms of soybean (Table 2).

This finding is consistent with experiments taken place in different region of the world (e.g. see Power 1987 various experiment of the U.S.). As noted, by Power (1987), one of the reasons for these additional values to crop revenue is the conservation or improvement of soil function for crop developing contrasted with simple rotation or monoculture. Policy makers should consider the on-site benefits of this farming system to promote farming technique that reduce external damage such as soil erosion and water runoff. In fact, this knowledge should persuade farmers to conserve a greater amount of perennial pasture since it is in their best interest to do so.

The other variable closely related to farming system is the number of head of livestock. This is significant and negative at $ 12 per head. The result may be explained by the fact that farming systems that include livestock activities usually use crop residue to feed cows and calves during winter, reducing by surface compaction the ability of soil to retain water and to initially establish the crops. However, in overall terms mixed systems cattle-crop production based on pasture show a better performance on average than simple rotation. Of course, these may still be improved with different cattle management, for example, reducing the impacts of animals eating crop residues.

13 In order to explore further the economics and sustainability of farming systems, and effects of farm size, and technology, we sort the sample for size of farm and divide into three categories: small, medium and large farms, 163, 304 and 994 hectares respectively

(see details in Table 5).

The economic performance can be evaluated directly with the regression results.

First of all, the three types of farms have the same marginal value of land assign to crops,

(approximately $360 per hectare from Table 2). Second, farm size is negatively related with crop revenue. If farm size operations increased, it would reduce crop revenue by $37 per hectare. This implies that small and medium farm perform better in economic terms than large farms.

The sustainability issues can be inferred from two direct components in our analysis: farming system and tillage. First, there exists an important difference in terms of land allocation to pasture and crops. In Table 5, small farms allocate on average 34% of the land to crops (i.e. 56 ha), while large farm size allocate on average 75% of the land to crops. Moreover, there exists a greater specialization in crops on large-farm. Oil crops, such soybean and peanut, account for 62% of the land assigned to crops on large-farms versus 44% on small-farms. In addition, small farms assign proportionally more land to pasture than large farms. The proportion of land in pasture to land in crops is 0.97 for small farms versus 0.23 for large farms. Those characteristics imply that small-farms use the land less intensively and are less specialized than the large farms do. As a result, small farms are more likely to protect the long-term productivity and also may cause less external damage than the large farms. The medium farms are between smaller and larger farms in the land allocation characteristics.

14 On the other hand, large farm size operators have increasingly adopted the non- tillage system (almost 40% of the cropland operations are done with this system). From an economic point of view, there is no proof of the superiority of this technology to either the conventional tillage or reduced till. Our findings show that the non-tillage adds about $5 per hectare to crop revenue, but this result should be interpreted with caution since we cannot differentiate non-tillage system from input intensities. From a sustainability perspective, this technology improves long-term soil conditions, reduces soil erosion and also to some extent reduces water runoff as compared with traditional tillage systems.

Nevertheless, this technology usually increases the use of fertilizer and pesticides that are considered an important source of water pollution.

Taking together farming systems and tillage practices, we infer that small size farms perform better from economic and environmental perspective than large size farms. This is mainly due to land use basically, but the large farms reduce external damage of more aggressive systems by some extent due to non-tillage practices. Moreover, our findings suggest that both types of farmers can do better. Small size farms can improve by adopting more conservation tillage, while larger farmers should integrate farming system with more diversified rotation base on pasture.

Land tenure

Regression analysis is conducted for each land tenure category: owner operators, land rented or operated by tenants-sharecroppers, and land under contract (which represents contracts with corporation, industries and others). Due to the data limitations mentioned

Section 3, these types of land operators are not totally identifiable. A farmer who rents land may also own or contract land; however, a farmer who does not rent land is excluded from

15 the rented land category. The same criterion is applied to the other types. There are 1239 observations with landowner operators, 630 observation with tenant-sharecroppers, and 383 observations with leasing to corporation, industries or other farmers. The results for these regressions are reported in Table 4.

Most of the estimated coefficients are statistically significant and the squared correlation coefficients between predicted and observed value show a good fit of data with the model even though some of the coefficients do not show a consistent pattern among categories. The estimated linear coefficients of cropland used for crops, pasture, share of high value crops, farm size, irrigated land shows the expected sign and the pattern is consistent among different types, while the estimated coefficients for labor (linear and quadratic) and for short run credits shows unexpected signs for tenant-sharecroppers. The speculation for these unexpected results is that the procedure used for imputing labor and credit to crops may not be appropriate for this category.

The estimation results show that land tenure causes a big difference in crop revenue.

A joint test was carried out to test the hypothesis that three types of tenure arrangements have the same coefficient, which is rejected at the 99% level of significance. Farmers who own land have less short-term land productivity than farmers who rent or contract land.

The results regarding land tenure indicate that farmers who rent land and other services to corporations have a marginal value product of land2 equal to $ 530 per ha versus

$ 355 per ha for farmers who own their land or rent to other farmers. The other category is in the middle. In addition, the return to pasture is significantly higher for the land used under contract than in the other two categories. This finding may explained by the fact that

2 From Equation 2, the marginal value product of land in crops, MVP, can be calculated as MVP= ∂y/∂ rcland

= b1 + 2 b2 rcland. If b2 is not statistically significant, then MVP = b1 is used.

16 due to higher short-term land productivity, corporations can provide higher payment to convert land from pasture to crops, which is land with relative higher quality.

To explore sustainability issues we refer to Table 6, which shows the main characteristics of farming systems, tillage systems and size of the farm. Land use is somewhat different for owner farmers who rent to corporations and those who do not. On average, corporations allocate 10% more land to oil crops and 50% less to pasture than owners. The tillage systems do not show clear difference among land tenure. Thus, from sustainability perspective the land used under contract is slightly less environmentally friendly than in the other categories.

These results suggest that corporations may be more responsible for the transformation from perennial pastures to continuous cropping or simple rotation. If so, one of the comparative advantages of Argentine agriculture, namely high soil fertility, will tend to disappear since the short term benefits will potentially allow these land tenure agreements to dominate. In addition to this on-site damage, farmers who convert to continuous cropping have greater soil erosion, water runoff, and pesticide contamination.

5. Concluding Remarks

This paper examines the tradeoff between short-term agricultural productivity and sustainability with a statistical analysis of evidence from low input agriculture in Argentina.

Our empirical results suggest a number of factors that should be considered to address the issue of sustainability and agricultural growth from a policy perspective. First, market forces may increase short-term agricultural productivity but are likely to have a negative impact on sustainability of agriculture. Agricultural modernization at the hands of

17 corporations have increased crop revenue, but it may also more responsible for adopting simple mono-cropping operation that reduce long-term productivity and increase off-farm damage.

Second, we find support for policies that promote mixed-production systems (crops and livestock activities). Adoption of such systems can have positive effects in terms of both economic efficiency and environmental quality. Thus, government policy should favor small and medium size farms since these operations are relatively more efficient and friendly towards the environment than larger units.

Third, our analysis suggest that government policy should promote longer-term land contracts to induce industry and corporation to increase their time horizon for decision-making. This would improve their incentives for making on-site investment in soil conservation and also make them more sensitive to reducing environmental damage.

Finally, even though our results are consistent with the findings of other studies, the reader should be aware of data limitations mentioned in Section 3. In the interest of helping future researchers, we recommend that survey design and data collection procedures be improved to accounts for the emerging trends related to land tenure and farming systems.

18 References

Becerra, V., et al. "Sintesis del anteproyecto preliminar: ordenamiento y manejo integral de aguas y tierras para la cuencas de general deheza, Cordoba, Argentina." Universidad Nacional de Río Cuarto, 1992.

Binswanger, H.-P., K. Deininger, and G. Feder (1995) Chapter 42. Power, distortions, revolt and reform in agricultural land relations, ed. J. Behrman, and T. N. Srinivasan, vol. III, pp. 2659-2772.

Byiringiro, F. and T. Reordan. “Farm Productivity in Rwanda: Effects of Farm Size, Erosion, and Soil Conservation Investments.” Agricultural Economics 15(1996):2 127-136.

Cantero G., A., et al. Las Tierras y Aguas del Sur de Córdoba: Propuesta para un Manejo Sustentable. 1ra ed. Río Cuarto, Cba. Argentina: UNRC, 1998.

Chisari, O. O., J. M. Fanelli, and R. Frenkel. "Argentina: Growth resumption, sustainability and environment." World Development 24, no. 2(1996): 227-240. de Koeijer, T. J., J. A. Renkema, and J. J. M. van Mensvoort. "Environmental-Economic Analysis of Mixed Crop-Livestock Farming." Agricultural Systems 48(1995): 515- 530. de Prada, J., et al. "Proyecto de conservación y ordenamiento de tierras - General Deheza." Master of Art, Universidad Nacional de Cordoba, 1994.

Ghida Daza, C., “Eficiencia económica agrícola en el sudeste de Córdoba: Cambios durante la década del ‘90”. INTA EEA Marcos Juarez, 2000. [http://mjuarez.inta.gov.ar/econo/daza/ganaderia/gan2000.htm]

Gold, M. V. (1999) Sustainable Agriculture: Definitions and Terms, Special Reference Briefs Series no. SRB 99-02 Updates SRB 94-05. http://www.nal.usda.gov/afsic/AFSIC_pubs/srb9902.htm

Hazell, P. “Equity and Environmental "Modifiers" for Rural Development Policies.” Agriculture and the Environment: Perspective on Sustainable Rural Development / Edited by Ernst L. Washington, D.C.: World Bank, 1998; 92-94

McConnell, K. E. "An Economic Model of Soil conservation." American Journal of Agricultural Economics 65(1983): 83-89.

Miranda, M. L. "Landowner Incorporation of Onsite Soil Erosion Costs: An Application to the Conservation Reserve Program." American Journal of Agricultural Economics 74(1992): 434-43.

19 Papendick, R. I., L. F. Elliot, and R. B. Dahlgren. "Environmental consequences of modern production agriculture : how can alternative." American Journal of Alternative Agriculture 1, no. 1(1986): 3-10.

Pengue, W. A., “Agricultura Argentina Sustentables, hasta cuando?” 2000 [http://www.campus-oei.org/salacttsi/walterp.htm]

Pimentel, D., et al. "Environmental and Economic Costs of Soil Erosion and Conservation Benefits." Science 267(1995): 1117-1123.

Pimentel, D., et al. (1989) Low-input sustainable agriculture using ecological management practices, ed. M. G. Paoletti, B. R. Stinner, and G. G. Lorenzoni. Amsterdam, Elsevier Science, pp. 3-24.

Power, J. F. "Legumes: their potential role in agricultural production." American Journal of Alternative Agriculture 2, no. 2(1987): 69-73.

Secretaría de Agricultura y Ganadería-Ministerio de la Producción de la Provincia de Córdoba. “Relevamiento Agropecuario 1999” [http://sayg.cba.gov.ar/paginas/reag.html]

Satorre, E. (2001) Siembra directa, biotecnologia y los sistemas productivos en la zona Norte de Santa Fe.

Schnepf, R. D., E. N. Dohlman, and C. Bolling. "Agriculture in Brazil and Argentina Developments and prospects for major field crops." USDA-ERS Outlook.ERS- USDA. WRS-01-3, 2001.

Shaban, R. A. (2000) Testing between competing models of sharecropping, Ed. P. Bardhan, and C. Udry. Cambridge-Massachusetts, USA, The MIT press.

SHAZAM User’s Reference Manual Version 8.0, Ed. McGraw-Hill, 1997.

Soule, M. J., A. Tegene, and K. D. Wiebe. “Land Tenure and the Adoption of Conservation Practices” American Journal of Agricultural Economics 82(2000): 993-1005.

U.S. Department of Agriculture, E. R. S., Resource Economics Division. Agricultural Resources and Environmental Indicators, 2000. Edited by R. M. William Anderson, Ralph Heimlich. Washington, DC 20036-5831, 2000. van Dam, C. “La Tenencia de la Tierra en América Latina El Estado del Arte de la Discusión en la Región Unión Mundial para la Naturaleza.” (UICN) Oficina Regional para Sud América/SUR, 1999.

Viglizzo, E. F., et al. "Climate variability and agroecological change in the Central Pampas of Argentina." Agricultural and Resource Economics Review 55(1995): 7-16.

20 Viglizzo, E. F., et al. "Ecological lessons and applications from one century of low external-input farming in the pampas of Argentina." Agriculture Ecosystems & Environment 83(2001): 65-81.

Wainio, J., and T. Raney. "Argentina's economic reforms expand growth potential for agriculture." Agricultural Outlook (1998) 24-30.

Weir, E., and J. Arce (1999). Parcelas de escurrimiento para medir la perdida de agua y suelo, EEA INTA Marco Juarez. Online at [http:/mjuarez.inta.gov.ar].

21 Table 1. Variables and Basic Statistics Variable name Mean Standard Deviation Crops revenue, measured in pesos (using constant prices 1999) 95693 180640 Crop land, cropland used for crops measured in hectare 285 451 Labor, labor used for crops production, measured in days 343 378 Owned land, land operated by the owner, measured in hectare 273 492 Rent land, land lease to tenant (other farmers), measured in hectare 116 288 Contracted land, land leased to corporations, measured in hectare 63 243 Size, total land under operation, measured in hectare. 433 627 Short run credit, measured in pesos. 7667 19115 Pasture land, cropland used for perennial pasture, measured in hectare 90 243 Share high value crops (% of crop land use for oil-crops) 55 30 Cattle (# of head) 222 414 Irrigated land, measured in hectare 6.5 63 Non-till, measured in hectare. 89 292 Dummy zone homogeneous 1 0.45 0.49 Dummy zone homogeneous 2 0.43 0.49 Dummy zone homogeneous 3 0.11 0.32

Data Source: SayG-MP (1999)

22 Table 2. Estimated Parameters of Quadratic Revenue Function. Variable name Estimated Asym. Stand. Coefficient Error Constant -11207.0*** 183.9 Crop land 361.6*** 4.32 Squared crop land 0.0064 0.0067 Labor 12.85*** 0.57 Squared labor -0.015*** 0.0012 Rent land -4003.5*** 61.14 Contracted land -692.5*** 104.8 Size -36.6*** 0.96 Short Run Credit 0.28*** 0.032 Pasture 92.0*** 1.17 Share high value crops 161.28*** 1.68 Cattle -12.11*** 0.47 Irrigated land 502.94*** 83.8 Non till land 4.86*** 1.8 Dummy zh1 4081.1*** 154.7 Dummy zh2 -5425.0*** 145.9 R2 0.84 Number of observation 1477 Variance equation Alpha 0.35*** 0071

Note: R2 is the squared correlation coefficient between observed and predicted values. ’ Alpha can be interpreted as the estimate of the standard deviation of the ratio εt/(Xt β)” Shazam, 1997. Statistically significant at *** 1%, ** 5%, and * 10%.

23 Table 3. Estimated Parameters for Cob-Douglas Revenue Function Variable name Estimated As. Stand. Coefficient Error Constant 4.55*** 0.39 Ln crop land 0.95*** 0.09 Ln labor 0.011 0.047 Owned land -0.00027 0.017 Rent land -0.041 0.13 Contracted land 0.023 0.16 Ln size 0.000009 0.00017 Ln short Run Credit -0.00027 0.016 Ln pasture 0.015 0.048 Ln share high value crops 0.12*** 0.048 Ln cattle 0.0015 0.044 Ln irrigated land 0.017 0.10 Ln non till land 0.0019 0.035 Dummy zh1 0.19 0.22 Dummy zh2 0.016 0.2 R2 0.24 Number of observation 1477 Variance equation Alpha 0.27*** 0.0053 R2 is the squared correlation coefficient between observed and predicted values. Alpha can ’ be interpreted as the estimate of the standard deviation of the ratio εt/(Xt β)” Shazam, 1997. Statistically significant at *** 1%, ** 5%, and * 10%.

24 Table 4. Different Land Tenure Arrangements with Quadratic Revenue Function Owner operators Rented land Land under contract Variable name Estimated As. Stand. Estimated As. Stand. Estimated As. Stand. Coefficient Error Coefficient Error Coefficient Error Crop land 355*** 4.84 453*** 8.8 529*** 12.30 Squared crop land 0.00021 0.007 0.013 0.01 0.034*** 0.011 Labor 27.5*** 0.59 -57.2*** 2.22 87.5*** 3.25 Squared labor -0.0276*** 0.005 0.017*** 0.0027 -0.058*** -.0032 Short run credit 0.109** 0.054 -0.16* 0.053 0.47*** 0.04 Pasture 95.2*** 1.63 114.1*** 3.14 289.4*** 7.7 Share high value crops 149.2*** 2.01 195.5*** 4.6 380.2*** 9.39 Cattle -4.0*** 0.58 -30.2*** 1.56 89.9*** 4.20 Dumzh1 3589.3*** 173.2 -4678.7*** 345.2 -2563*** 727 Dumzh2 -8082.6*** 116.6 -13059*** 266.3 -13623*** 785.3 Size -41.9*** 0.93 -43.8*** 2.92 -240*** 6.88 Irril 445*** 99.8 22.6 34.3 614.4** 250.6 Nontilll -6.5*** 3.0 11.38*** 1.86 10.1 15.1 Constant -11859.0*** 169.6 -5851.0*** 32.12 -33569*** 1133 R2 0.84 0.87 0.88 Nº 1239 630 383 Variance equation Alpha 0.40*** 0.008 0.30*** 0.009 0.269*** 0.01

25 Table 5. Land Use, Size of Farm, Crops Choice and Pasture Allocation Categories Nº Land with Size Non-till Proportion of oil Pasture / land with crops (ha) (ha) (ha) crops (1) crops Small farm 574 56 (34%) 163 6 44% 0.97 Medium farm 560 187 (61%) 304 27 62% 0.35 Large farm 403 748 (75%) 994 294 62% 0.23

Note: Nº is the number of observations in the different categories. (1) Proportion of oil crops with respect total cropland used for crops

Table 6. Land Tenure and Land Allocation Categories Nº MVP Crops land Non-till Size Oil crops (1) Pasture / land ($/ha) (ha) (ha) (ha) with crops Owner operators 1287 355 291 (64%) 91 454 54% 0.34 Rented land 709 423 306 (66%) 92 467 55% 0.32 Contracted land 428 529 336 (76%) 86 456 60% 0.20 Note: MVP = marginal value products of land, Nº = number of observations in each categories. (1) Proportion of oil crops with respect total cropland used for crops