Uruguay: Development of sustainable vegetable farming systems in South Uruguay

Case Study Uruguay: Development of sustainable vegetable farming systems in South Uruguay

Introduction

During the last decades, agriculture in many Latin American countries has followed a path of intensification and specialisation of production systems in response to decreasing economic returns. This process has expelled many farmers and their families from agricultural production and rural areas and it has endangered the maintenance of natural resources such as the soil, water reserves and bio-diversity. Over-exploitation and/or pollution of water resources, soil erosion, loss of nutrients and soil organic matter, increasing impact of weeds and pests in crop yields are common problems in Latin American agriculture. Simultaneously, sources of off farm income or the demand of labour from others sectors of the economy are not enough to compensate the decreasing income of rural families.

The path followed by the Uruguayan vegetable production sector during the last 25 years is not an exception from the above described process. From 1990 to 1998 vegetable production increased by 24%, crop yields increased by 29% and cropped area decreased by 9% (DIEAPREDEG, 1999). Simultaneously, prices of vegetable products from 1992 to 2001 decreased by 34% (constant prices, base year 1992, CAMM, 2002) and 15% more from 2001 to 2004 (constant prices, base year 1996, CAMM, 2005). The South of Uruguay (Departments of Canelones, Montevideo and South East of San José) has the highest concentration in the country of small or family farms (farms where most of the labour force is contributed by the farmer and his/her family). Around 88% of the farms with vegetable production as main source of income are family farms (Tommasino and Bruno, 2005). Between 1990 and 2000 the number of vegetable farms decreased by 20% (DIEA, 2001), and those who stayed in business had to produce more, cheaper and better quality to maintain their family income.

The strategy followed by most farmers was to intensify and specialise their production systems. In the South of Uruguay the average vegetable cropped area per farm increased, while the average total area per vegetable farm stayed approximately the same. The average number of crops per farm also decreased. The observed increase in crop yields was explained by increasing use of irrigation, external inputs (fertilizers, biocides and energy), and higher quality seeds (Aldabe, 2005). The intensification strategy put more pressure in already deteriorated soils and on limiting farm resources. Increasing the crop area and narrowing the crop types without an adequate planning troubled farm operational functioning causing inefficient use of production resources, higher dependence on external inputs and higher impact on the environment.

Consequently, the sustainability in the long term of most of the family farms in South Uruguay is threaten by incomes not enough to cover maintenance of the family and production infrastructure, and/or continued deterioration of the natural resource base. A basic assumption of this project is that the sustainability problems described above cannot be solved by isolated adjustments or modifications in some system components such us pests management or soil tillage. The relevance of the changes occurring in the socio-economic environment and in the quality and availability of production resources demand the adaptation of the farm systems as a whole. Such a re-design of farm systems at the strategic level could be achieved by a participatory, interdisciplinary, systems approach. Involvement of the main stakeholders is particularly important since any intentional change in production systems is always a result of changes in human conduct and therefore it requires an individual and collective learning process (Leeuwis, 1999). Moreover, solutions to problems of this complexity do not come as ‘take it or leave it’ validated packages, they need to be designed within its context of application with the direct involvement of farmers at all stages of the process, from diagnosis to dissemination (Leeuwis, 1999; Masera et al., 2000). This is the only way to ensure relevance, applicability and adoption of the innovations.

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Objective

The objective of this project is to design, implement and evaluate sustainable vegetable farming systems in South Uruguay through a co-innovation process based in a group of pilot farms. Specifically the project aims to:

  1. Adapt a methodological framework for sustainability evaluation of farming systems in South Uruguay
  2. Develop and test a participatory farming systems design approach based on bio-economic quantitative models.
  3. Adapt and evaluate in real farms context innovative techniques of soil and pests management to maintain or improve soil quality and reduce the impact of biocides on environment and human health
  4. Analyse existing farm management systems and develop management tools applicable by farmers and their technical advisers.
  5. Generate a data base with the empirical data from each pilot farm to calculate technical coefficients and to calibrate and validate quantitative simulation models at farm and crop level.

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Project Background

Many of the problems described, constraining socio-economic and bio-physic sustainability of vegetable production systems, deserved the attention of local research from several disciplines. During the last decade several techniques had been designed and experimentally tested in research stations in the region with the aim of reducing the environmental impact of production systems, increase crop yields and vegetables quality.

Research has been done in crop rotations (Do Campo y García, 1999; García y Reyes, 1999; Gilzans et al., 2005), green manures management (García y Reyes, 1999; Peñalva y Calegari, 2000; Do Campo, 2000; Barbazán et al., 2002), animal manures management (Do Campo y Quintana, 1994; Malán y Reyes, 1997), prediction and quantification of soil erosion (García, 1992), biological control and other methods to reduce the use of biocides to control pests, diseases and weeds (González et al., 2005; Maeso, et al., 2005; Paullier, 2005; Perrachón et al., 2005), and breeding of cultivars adapted to local conditions (Galván et al., 2005).

The significant improvement in knowledge obtained in several production system components was not followed by a similar improvement in the sustainability of vegetable production systems. So far, the challenge of integrating new knowledge and techniques generated in research stations into real production systems has been left to the farmer himself. Consequently, the strategy followed by the farmers and their technical advisers to adapt their production systems to the changes in the socio-economic environment and in the availability of production resources has been merely incremental, based on corrections at the operational and tactical level, by trial and error.

Consequently, the task of developing sustainable farming systems demand a shift in paradigm about how innovations in complex systems, in which the human being is an integral part of the system, are developed and adopted (Leeuwis et al., 2002). From a traditional approach, innovations are designed externally to the systems and farmers adopt those innovations by an ‘extension’ process. Extension linearly involves awareness of the problem by the farmer, interest in the solution, evaluation, experimentation and finally adoption. In participatory approaches innovations are developed within the context of application and with direct involvement of those who are taking decisions about production systems structure and functioning, mainly the farmers and their families (Gibbons et al., 1997, Leeuwis, 1999). In this new paradigm, changes in agricultural practices towards more sustainable production systems are seen as a result of a collective learning process of all actors involved in the process of change, including the researchers. This process is called ‘co-innovation’.

Several approaches have been developed with the aim of improving the sustainability of agriculture. Three of them deserve particular attention because we will draw upon them to design the methods of this project. The ‘Prototyping’ approach was developed in the European Union during the 90’s with the aim of designing organic and integrated farming systems, based on pilot groups of farms and interdisciplinary teams of researchers (Vereijken, 1997). This approach allowed relevant improvements in the design and testing of new techniques for organic and integrated production, but did not involve the farmers in the setting of objectives and targets nor in the design of alternatives, and only one ‘alternative’ per farm was proposed, completely designed by the research team without mechanisms to make design criteria transparent or open for discussion (Sterk, 2005). The methodological frameworks to evaluate sustainability of natural resource management systems (for example the MESMIS framework, Masera et al., 2000), involve stakeholders from the beginning of the evaluation process and proved to be very useful tools to identify bottle necks or critical points in the agricultural systems but they do not contribute to the design of solutions to the identified problems (Lopez Ridaura, 2005). Several authors had proposed that bio-economic models, as excellent tools to integrate fragmented information and knowledge from various sources (scientists, experts, farmers), can support the design and evaluation phases of participatory research and development of sustainable farming systems processes (i.e. Mc Cown et al., 2002; Meinke et al., 2001; Dogliotti et al., 2003; Sterk et al., 2005). However, reported applications of such models in real innovation of production systems are still rare.

With the aim of exploring option for sustainable development of vegetable farms in South Uruguay an explorative study was conducted in the region of ‘Canelón Grande’ (Dogliotti et al., 2004, 2005). This study showed that for a large proportion of vegetable farms is possible to significantly increase family income and at the same time reduce soil erosion by a factor 2-4 and reverse soil organic matter decline. These results could be achieved by reducing the area of vegetable crops, implementing crop rotations including green manure, pastures, and forage crops, and in many cases integrating animal production into the farm system. This study developed a bio-economic model able to design farm systems taking into account different viable farm development paths resulting from differences in farm resource endowment or farmers’ strategies, or both. The authors proposed that combining this model-based farming systems design method with participatory approaches could lead to an interactive design process towards implementation of sustainable vegetable production systems in South Uruguay.

To explore this hypothesis, a small, preliminary project was started at the end of 2004 with funding of INIA (National Agricultural Research Institute) and CUDECOOP (Union of production cooperatives) and participation of CNFR (Farmers’ Union), Faculty of Agronomy and INIA. The project involved 6 pilot farms and focused on improving family income without increasing the cropped area with vegetables, and on reducing soil erosion and improving soil physical and biological quality. Introduction of crop rotations and green manure crops, use of animal manures and soil systematisation were the main tools applied. From the bio-economic model designed by Dogliotti et al., (2005), only the components for designing of crop rotations and estimating long term soil erosion and soil organic matter dynamics were used. After one year of evaluation, preliminary results show that family income, soil erosion rate and soil organic matter content are changing in the expected direction (Dogliotti et al., 2006).

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Research strategy

The project is based on pilot farms from the rural areas of Canelones and Montevideo. We selected 12 conventional farms and 4 organic farms. The project will follow 5 basic phases:

  • Selection of pilot farms
  • Diagnosis
  • Design
  • Implementation and evaluation
  • Dissemination

Farmers’ unions (CNFR and APODU) were involved in the selection of pilot farms. Variability among pilot farms in resource endowment, soil quality and distance to the market will be ensured. Attitude of the farmers towards change and to discuss their strategic choices, and involvement in local farmers’ groups will be considered in the selection.

At the diagnosis phase the main objectives and priorities of involved stakeholders (farmers and their families, farmers’ unions, local government) should be identified, the main problems caused by current production systems should be assessed, and available production resources should be quantified. This phase ends with a strategic agreement among participant stakeholders about motivations for design of alternative production systems and about criteria and indicators for evaluation of changes, if possible. Adaptation of a methodological framework for sustainability evaluation of farming systems in South Uruguay should be part of this phase.

At the design phase available knowledge from different sources is combined with the aid of bio-economic models to design and evaluate ‘a priori’ alternative theoretical production systems according to the priorities and problems identified at the diagnosis phase. A reasonable number of alternatives should be discussed, compared and if necessary modified, with the farmers, before going to the next phase.

At the implementation and evaluation phase the operational feasibility, economic benefits and ecological performance of the selected alternatives are evaluated at each farm. Results of this evaluation allow a new cycle of design and testing.

Dissemination of the results and experience achieved in this project will be done by several means: field days with the farmers from the neighbourhood of each pilot farm, training courses with the technical advisers working in the region, presentation of results in technical meetings, publications of articles in local newsletters and papers in international journals.

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Participant stakeholders

The following are the stakeholders involved in the Uruguayan Case Study:

  • Seventeen farmers and their families from the pilot farms
  • Comisión Nacional de Fomento Rural (CNFR) and Asociación de Productores Orgánicos del Uruguay (APODU), as the most important farmers’ unions of conventional and organic vegetable farmers in the South of Uruguay.
  • The local government of Canelones through its Rural Development Office
  • Scientists from The National Agricultural Research Institute (INIA) – Research Station ‘Las Brujas’.
  • Scientists and students from The University of the Republic through its Faculty of Agronomy and its Regional Centre in Canelones.

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Advances on project development

Farm scale determinants of livelihoods

We finished the process of characterization and diagnosis of the 17 farm systems selected. Basic information of each farm is presented in Table 1. Location of the farms in the region is presented in figure 1. Selected farms have vegetable production as main source of income and are located from 15 to 70 km from the main vegetable market in Montevideo. The farm area varies from 4.4 to 59 ha and the irrigated area varies from 0 to 60% of the area of vegetable crops. There are farms highly mechanized and farms with animal traction, and farms where animal production is a very important source of income while others are specialized in vegetable production.

The farm systems characterization was divided in two parts: the management system and the production system. To characterize the management system two rounds of open interviews were planned. We finished the first round and data is being processed before starting the second round. To characterize the production system we carried interviews, field observations and soil survey to gather all relevant data about production system resources, structure and organization, functioning and results.

mapa_ubicacion_predios_piloto

Figure 1. Location of the 16 pilot farms

Tabla 1. Resource availability and main characteristics of pilot farms
Farm Area (ha) Family labour (M.E.) Labour availability (hour ha-1) FL/ TL Mech Level Area Veg (ha) AV/ AT Irrig Area (ha) Irrig Frac Prot cult (ha) Anim load (Std units  ha-1) NºCow/ Tot Forr AF/ AT Nº of crops Type
Crops (ha)
1 15 1.5 1010 0.24 5 12 0.8 7 0.58 0 0 0 0 0 12 C
2 5.5 1.5 790 0.83 3 2.5 0.45 2.5 1 0.15 0 0 0 0 12 C
3 29 3.5 421 0.69 4 14.8 0.51 1 0.07 0 0.38 0.47 4.4 0.15 14 C
4 4.4 1.3 850 0.82 3 2.8 0.64 0.4 0.14 0 0.2 0 0 0 6 C
5 48 2.3 115 1 2 2.3 0.05 1 0.43 0 0.75 0 28 0.58 7 C
6 13 1.2 253 0.87 1 1.5 0.11 0 0 0 1.23 0 10.3 0.79 4 C
7 26 4.5 415 0.96 2 8.9 0.34 2 0.22 0.2 0.85 0.07 3.2 0.12 12 C
8 38 2.5 189 0.83 2 5 0.13 0 0 0 0.53 0.5 2.6 0.11 4 C
9 12 3 617 0.97 2 4.4 0.37 4.4 1 0 0.17 0.5 0.9 0.075 5 C
10 59 6 268 0.9 4 25 0.42 6 0.24 0 0.5 0.38 3 0.05 3 C
11 5.7 2 1084 0.78 1 3.6 0.63 2 0.55 0 0.35 0 0 0 8 C
12 20 2 262 1 1 5 0.25 0 0 0 0.4 0.23 4.5 0.22 3 C
13 25.4 2 577 0.34 3 7.3 0.29 4 0.55 0 1.18 0 15 0.6 9 O
14 19 2.5 316 1 2 3.2 0.17 0 0 0 0.63 0.28 2 0.1 10 O
15 10.5 2 457 1 2 2.7 0.26 0.1 0 0.1 0 0 0 0 12 O
16 7.6 2 716 0.88 1 2 0.26 0.15 0.07 0.05 0 0 0 0 19 O

Area = Total area of the farm in ha
Family labour (ME) = Amount of family members fully occupied in the farm work ( 1 = 2400 hours/year)
Labour availability = total labour availability on the farm in hours per ha per year
FL/TL = fraction of the total labour contributed by family members
Mech Level = mechanization level, 1 = Low: without tractor, 2 – Medium – Low: with tractor, without sprayer machine, 3 – Medium – High: with tractor, with sprayer machine, 4 – High: 2 tractors and sprayer machine, 5 – Very high: 3 or more tractors and sprayer machine.
Area Veg = total area of vegetable crops (ha)
AV/AT = total area of vegetable crops / Total area of the farm
Irrig Area = area of irrigated crops (ha)
Irrig Frac = area of irrigated crops / total area of vegetable crops
Prot Cult = area under protected cultivation (ha)
Animal load = number of standard animal units per ha (1 Std unit = 1 bovine of 380 kg)
NºCow/Tot = number of cows over total number of bovines
Forr crops = area of annual forage crops and pluri-annual pastures (ha).
AF / AT =  area of annual forage crops and pluri-annual pastures / Total area of the farm
Nº of crops = number of vegetable crops
Type = C is conventional farm, O is organic farm

The diagnosis involved the selection of relevant diagnosis criteria in each of the sustainability attributes and dimensions, determination of critical points (positive and negative) of sustainability in each farm, and selection of relevant indicators and target levels to evaluate them. In order to build our list of indicators to evaluate the sustainability of the pilot farms in the Uruguay Case study we draw on the experience of previous applications of the MESMIS approach in Uruguay (Bacigalupe y Salvo 2007; Aguirre, 2007). The learning’s of part of the research team during a previous research and development of farming systems project (FPTA 160) and the common list of indicators of EULACIAS, further improved our case specific list of indicators. We identified indicators within the three dimensions of sustainability (environmental, economic and social) and we classified them in four groups of sustainability attributes:

  1. Productivity: capacity of the system to produce the specific combination of goods and services necessary to realize the objectives and goals of the stakeholders involved.
  2. Stability: presence and effectiveness of negative feedback processes to control the internal positive loops leading to its self-deterioration at a specific level of productivity.
  3. Adaptability, Reliability, Resilience: capability of the system to stand changes in external variables or driving forces.
  4. Self-reliance: capability of the system to regulate or control its interactions with the environment.

 

Bio-physical indicators

Bio-physical indicators were classified in four diagnosis criteria: production efficiency, conservation of natural resources and fragility of the production system (Table 10). Production efficiency indicators reveal the physic productivity of the system compared to attainable levels in the region. Production per unit area and per unit labor is relevant because land and labor are limiting resources in the region. Deterioration of soil quality and loss by erosion are the most relevant environmental impacts of agriculture in the region, and so we found in all but one of the pilot farms. Selected indicators reveal the risk of erosion, soil organic matter loss, and nutrient balance. Due to the slow rate of these processes we selected as evaluation strategy, not only direct measurements (soil analysis), but also simulation models such as RUSLE and ROTSOM (García y Clérici, 1996; Dogliotti et al., 2004).During the diagnosis phase we found in several farms a large fraction of the cropped area that is never harvested or products are never sold due to different causes. High frequency of these problems and a highly variable labor demand distribution during the year are the most important sources of farm system fragility

Table 10. Bio-physical indicators

Attributes

Diagnosis criteria

Indicators

Calculation or measuring procedure

 Productivity  Production efficiency Commercial yield Sold product (Kg) / Cropped area (ha)
Commercial yield per unit of labor Sold product (Kg) / Direct labor (h)
Animal production (Kg meat) per grazed area (Sold animals – bought animals (Kg)) / Grazed area (ha)
Animal production (Kg meat) per unit labor (Sold animals – bought animals (Kg)) / Direct labor (h)
     Stability      Natural Resource conservation Soil erosion Soil erosion estimated with RUSLE model (Mg/ ha yr)
Average soil organic matter rate of change in the top 20 cm Average soil organic matter rate of change in the top 20 cm during a 40 years period estimated with ROTSOM model
Nutrient balance (NPK) Estimated balance based on account of relevant inputs and outputs and SOM change. Checked by standard soil analysis every 6 months.
Evolution of nutrient content in the top soil Standard Chemicals soil analysis in selected fields every 6 months
Evolution of biological activity in the topsoil Soil respiration measures every 6 months on selected fields and undisturbed spots..
Environmental impact of pesticides in soil, water and air Estimated impact using EPIPRE and/or EEP models.
Evolution of weed population Periodical monitoring of weed populations (species and density) in selected fields.
Reliability, Adaptability, Resilience Production system fragility Harvested fraction Harvested area (ha) / Cropped area (ha) of major crops
Commercialized fraction Harvested product (Kg) / Sold product (Kg) of major crops
Labor demand distribution (Ʃ(labor demand in 15 days intervals)2) / (total labor demand)2  (Ginni index)
Biodiversity Fraction of area with ecological infra-structure Area with permanent vegetation / total area (wind brakes, groups of trees and shrubs, etc.)
Rotation length Nº of rotation blocks (years)
Field length / width ratio
Crop diversity Ginni index with cropped area of different species

Economic indicators

Economic indicators were classified in five diagnosis criteria: economic efficiency, income diversification, commercial channels diversification, economic self-sufficiency and dependence of external inputs. The selection of indicators presented in Table 2 is based on the following criteria:

  • Reveal the productivity of the systems in terms of income for the family, and returns of capital and labor.
  • Select widely accepted indicators to be able to compare with other production Systems in the region and abroad.
  • Evaluate the adaptability, reliability and resilience of the pilot farms through the diversification and distribution on time of income, and the quality of links between the farm and the commercial chain.
  • Evaluate pilot farms self-reliance through their use of credit and external inputs.

Table 11. Economic indicators

Attributes

Diagnosis criteria

Indicators

Calculation or measuring procedure

  Productivity   Economic Efficiency Degree of satisfaction of income requirements Family income / Average family income from INE
Family income Gross product – Total costs (without interests and rent) + fixed salaries
Net income Gross Product – Economic costs (with interest and rent)
Labor productivity Family income / Family labor (h)
Input/output relationship Economic Costs / Gross Product
Reliability, Adaptability, Resilience Income Diversification Income distribution among production activities Ginni index
Income distribution along the year Ginni index
Off-farm income relevance Off-farm income / total income
Commercial channels Diversification Income distribution among commercial channels Ginni index
Vertical integration level
Farm gate price fraction Farm gate price/ whole sale market price
Self-reliance Economic resources self-sufficiency Indebtedness level Total requirable debt / Patrimony = leverage
Total requirable debt / Assets (solvency)
Percentage of total costs covered by external funds (Off-farm sources of funds / monetary costs) * 100
External inputs dependency Off-farm inputs to total inputs ratio Off farm inputs costs / total costs of inputs

Social indicators

The family is the main component of farm systems. Life quality and network of relations and knowledge are essential for family well being and consequently to farm system sustainability. Two social diagnosis criteria were distinguished: life quality and human capital accumulation (Table 3). We need to use qualitative and quantitative indicators to reflect the subjective and objective components of life quality (living conditions and perceived degree of satisfaction). (Chiappe, 2002).
We understand human capital as a production factor depending on the quality of knowledge and individual productivity of people involved in the production process. The social capital reflects the quantity and quality of human relationships of a group of people which allow individuals to benefit from the opportunities emerging from those relationships. It considers cooperation and coordination of efforts by individuals for collective benefit.

Table 12. Social indicators

Attributes

Diagnosis criteria

Indicators

Variables to be evaluated

Stability Life quality Housing quality Quality and condition of the house building
Access to services
Quality of house surroundings Quality of the roads to access the farm
Condition of the environment around the house
Leisure time Weekly and annual rest
Health quality Health problems related to the work
Access to social security Persons that have access to social security services and how
Overcrowding level Nº of people living in the house / nº of rooms
Degree of personal satisfaction Family perception about their life quality
Self-reliance Human and social capital accumulation Participation on training or human resource building activities Type of activity
Topics of training
Usefulness according to participant
Link to local groups and networks Organization types
Frequency of contacts or participation
Type of participation

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