Agentbased modeling forex anteassessment of tree ...

AGRICULTURAL ECONOMICS Agricultural Economics xx (2010) 1–18

Agent-based modeling for ex ante assessment of tree crop innovations: litchis in northern Thailand Pepijn Schreinemachersa,∗ , Chakrit Potchanasinb , Thomas Bergerc , Sithidech Roygrongd a Department

of Land Use Economics in the Tropics and Subtropics, Universit¨at Hohenheim, 70593 Stuttgart, Germany; The Uplands Program (SFB564), Faculty of Agriculture, Chiang Mai University, Chiang Mai 50200, Thailand b Department of Agricultural and Resource Economics, Faculty of Economics, Kasetsart University, Bangkok 10900, Thailand c Department of Land Use Economics in the Tropics and Subtropics, Universit¨ at Hohenheim, 70593 Stuttgart, Germany d Highland Research and Development Institute, 65 Suthep rd., Moo 1, A. Muang, Chiang Mai 50200, Thailand Received 18 February 2009; received in revised form 9 November 2009; accepted 11 February 2010

Abstract This study uses an agent-based model for ex ante assessment of agricultural innovations. The model builds on whole farm mathematical programming (MP) and extends the methodology with a spatial representation of the system, the heterogeneity of farm households and landscapes, and the interaction between farm households. We apply the model in a northern Thai watershed to study the potential of four innovations to increase the profitability of litchi orchards. Cost-benefit analysis shows that each innovation would increase the profitability of litchi growing; however, the results of the agent-based model show that at current price levels these innovations alone would not be enough to stem the decline in the area under litchis. The model was validated and the sensitivity of the results tested for variations in the irrigated water supply and liquidity. We report on how farmers responded to these results and discuss the implications for other areas in northern Thailand. JEL classifications: C61, O13, O33, Q12, Q16 Keywords: Farming systems research; Multi-agent systems; Technology adoption and diffusion

1. Introduction This study presents an agent-based model, called Mathematical Programming-based Multi Agent Systems (MP-MAS), which combines whole farm programming with an agent-based model design. The literature on agent-based models applied to agriculture and agricultural land use dynamics has expanded rapidly (Bithell and Brasington, 2009; Castella et al., 2005; Filatova et al., 2009; Le et al., 2008; Nolan et al., 2009; Robinson et al., 2007; Tyler et al., 2009; van Oel et al., 2010). Yet only a few models deal with technology adoption, even though the diffusion of new technologies is a main driver of agricultural land use change. This study contributes to filling this gap by using MP-MAS to ex ante assess the impact of agricultural innovations. MP-MAS extends traditional farm-based modeling threefold by including a spatial landscape representation, agent hetero∗ Corresponding

author. Tel.: +66 (0)898 999-548; fax: +66 (0)53 893-099. E-mail address: [email protected] (P. Schreinemachers). The data and model needed for replication are available online at http://mpmas.uni-hohenheim.de

 c 2010 International Association of Agricultural Economists

geneity, and agent interactions. The study positions MP-MAS in the rapidly expanding field of agent-based models of land use change, and shows that the approach has a comparative advantage in ex ante technology assessment. We applied the model to assess the potential impact of various tree crop innovations aimed at keeping erosive hill slopes in northern Thailand covered. The next section gives a brief overview of agent-based modeling of land use systems. It then describes the empirical background and introduces the four innovations aimed at making the growing of litchi more profitable: artificial flower induction (AFI), cooperative fruit drying, improved shelf life, and greater irrigation efficiency. It proceeds with an account of the MP-MAS methodology and the data used to parameterize the model. Subsequent sections show the results, discuss the implications, and formulate a conclusion. 2. Agent-based modeling of land use systems Scholars of various scientific disciplines have developed a great diversity of agent-based models to analyze land use DOI: 10.1111/j.1574-0862.2010.00467.x

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change. For the agricultural economist interested in adding the method to his or her toolbox, this diversity might be bewildering, especially since most agent-based models of land use change have only little footing in economics. This section therefore gives a short overview of the diversity of agent-based models, with the aim of pointing out where our own approach is situated. Several typologies of agent-based models already exist in the literature. Matthews et al. (2007) classified models by the purpose for which they have been developed, Robinson et al. (2007) classified them by the data they use, and Hare and Deadman (2004) divided them into spatially explicit and spatially nonexplicit models, and by type of social interaction. Although agent-based models are typically interdisciplinary in nature, most can be traced back to a certain discipline from the research questions they address, the data they use, the assumptions about human behavior they make, and the type of validation methods applied (see also Verburg et al., 2004). In this section, we structure the diversity of agent-based models by the scientific field from which the models originated, as we believe that this provides a more accessible entry into the literature on agent-based models than other typologies. Our aim is not to review the literature, but to position the contributions of agricultural economists within the larger context of agent-based models.

2.2. Crop and forest ecology (for example, PALM and SORTIE) Agent-based models in forest and crop ecology include crop and forest growth as endogenous functions of biophysical variables (soil nutrients, water, and sunlight) and human actions (fertilization, weeding, and harvesting). Compared to the biophysical dynamics in these models, the behavior of agents is relatively simple and typically based on heuristics. Bithell and Brasington (2009) simulated long-term forest clearance in a subsistence farming system. Their model, called SORTIE, combines a hydrology model and tree growth model, representing each individual tree in a 4.1 km2 catchment, with a relatively undemanding rule-based agent model representing wood gathering and field clearing individuals. The People and Landscape Model (PALM) combines process-based crop models with human decision making, based on decision rules (Matthews, 2006). The model ranks alternative decisions by priority, as based on benefits and urgency (such as closeness to a last possible planting date), and chooses the decisions which have the highest priority. The model is used to evaluate seven nutrient management strategies. One strategy is allocated to each agent at the start of the simulation run. Each agent monitors the revenues of its three nearest neighbors, and imitates a nutrient management strategy if the neighbor gets a higher return.

2.1. Geography (for example, LUCITA) Most agent-based models rooted in geography have a clear focus on human-environment interactions in land use systems. These models combine agents (representing decision makers) with pixels (representing landscapes) while reducing much of the biophysical and socioeconomic complexity by giving relatively simple dynamics to the agents and pixels in the model. Agent behavior usually follows a small set of heuristics, while the landscape has no internal dynamic other than that which results from agent-landscape interactions. Models in this group have a clear focus on the spatial aspects of land use dynamics, with some, but not all, validating their model using spatial validation metrics. Jepsen et al. (2006) studied shifting cultivation systems in Vietnam, and tried to replicate the observed spatial pattern of fields. Agents in their model choose where to locate fields following a simple rule that chooses pixels with the highest output per unit of labor required for cultivating crops, and fencing the fields off from free-roaming livestock. In a similar vein, Deadman et al. (2004) set out to replicate spatial settlement patterns in the Brazilian Amazon using an agent-based model called LUCITA (Land Use Change In The Amazon). Agents in this model select among three land use types (fallow, pasture, or annuals) for each pixel, following a decision tree that evaluates food requirements, available cash, and labor. Within the land use type of annual crops, the model determines what crop to grow from a random assignment of crop preferences to each agent.

2.3. Computer science-hydrology-agronomy (CORMAS/ComMod) The CORMAS School is more difficult to pin down to a certain discipline. It is a teaming-up of computer scientists, agronomists, hydrologists, and anthropologists, and has led to the so-called Companion Modeling (ComMod) approach. (The name CORMAS refers to the software platform.) Researchers using ComMod develop their model in interaction with stakeholders to narrow the gap between them and the human subjects they seek to represent in their model. Agents are mostly rulebased, but these rules tend to be more complex than those used in models rooted in geography. The researchers identify decision rules through observation, role-playing games, or interaction with stakeholders. The ComMod approach has resulted in a variety of model applications to small-scale farming communities in developing countries (Barnaud et al., 2008; Barreteau and Bousquet, 2000; Becu et al., 2003; Boissau et al., 2004; Castella et al., 2005). Several of these applications have integrated agent decision making with biophysical dynamics of water flows and runoff. The aim of most ComMod applications is to support collective decision making at the community level. Frequently, this includes a scenario-based assessment of new technologies. For instance, Becu et al. (2008) used ComMod to facilitate negotiations on water use between an upstream and downstream community faced with occasional water scarcity in northern

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Thailand. In participatory simulation sessions, the local people suggested building a reservoir to store water in the rainy season, for use during the dry season. The scientists included this innovation in the model, simulated the impact on the two communities, and let the participants discuss the results.

2.4. Agricultural economics (for example, AgriPoliS and MP-MAS) Agricultural economics has developed quantitative models of land use decision making over a much longer time frame than any of the above approaches. Farming systems research (FSR), the subdiscipline of farm management research that studies farm decision making from a systems perspective, originated from operations research in the mid 1960s, and became popular as computing power for solving complex optimization models became increasingly available in the form of desktop applications (Keating and McCown, 2001). We can roughly divide FSR into a normative and a positive branch. The normative branch focuses on the design of optimal resource allocations for farms, which gave rise to decisionsupport systems (DSS) in agriculture. The positive branch focuses on actual farm decisions, using constrained optimization to study observed rather than optimal decisions. This positive approach is frequently used to identify constraints to technology adoption, and to ex ante assess the impact of improved technologies or policy interventions. New applications of both approaches, as well as FSR in general, have become less frequent with the rising popularity of econometrics in agricultural economics, and with the apparent lack in farmer adoption of decision-support systems (McCown, 2002). This decline in FSR since the early 1990s coincided with the rise of agent-based modeling approaches in other disciplines, which might explain the relative absence of agricultural economists in the emerging field of land use and land cover change modeling. AgriPoliS (Agricultural Policy Simulator) and MP-MAS are two notable exceptions to this. These are agent-based models with roots in the positive branch of FSR, using intricate MP models to simulate the decision making of actual farm households. AgriPoliS has its strengths in simulating the impact of policy changes on farm structures and farm incomes (Balmann, 1997; Happe et al., 2006, 2008, 2009). AgriPoliS has been applied to agriculture in the European Union, chiefly in Germany. MP-MAS, which originated from an early version of AgriPoliS, has its strength in simulating agricultural technology diffusion and the interaction between economic decision making and biophysical dynamics, such as soil fertility changes and water flows. The model has primarily been applied to developing country agriculture, with applications to study sites in Chile, Uganda, Thailand, Ghana, and Vietnam (Berger 2001; Schreinemachers et al., 2007; Schreinemachers, Berger, et al., 2009).

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AgriPoliS and MP-MAS extend previous farming systems models across three functions: space, agent and landscape heterogeneity, and agent interactions:  Spatial representation: These models explicitly represent the spatial aspects of farming, and thus facilitate the coupling of decision models with spatial biophysical models, such as water flows and soil erosion. The ease of linking crop models to farm-based MP models has been exploited in the bioeconomic modeling literature (Holden and Shiferaw, 2004; Kuyvenhoven et al., 1998; Ruben and van Ruijven, 2001). Recent applications of these bioeconomic models have placed representative farm models within a space, allowing them to capture the spatial dynamics of soil processes and water flows. For instance, Barbier and Bergeron (1999) spatially integrated the EPIC model with MP-based farm models, while Letcher et al. (2006) coupled DSSAT with representative farm models located in space.  Heterogeneity: Instead of modeling one or a couple of representative farms, agent-based models can represent each individual farm. This facilitates the capture of much more of the diversity found in economic and biophysical conditions, as well as the diversity in economic impacts resulting from simulated policy change. The need to capture diversity and distributional effects, rather than the average farm and the average effect, has been increasingly realized in agricultural economics with regard to research on poverty, food insecurity, and inequality. Heterogeneity is also a crucial aspect in the study of technology adoption, as knowledge, risk perceptions, and relative scarcity conditions vary among households. Technology adoption, as a result, is not equally attractive to everyone. Introducing an innovation into a representative MP model gives either an abrupt change or no change at all. Even though this is a realistic outcome at the level of an individual farm, at the level of a population, continuous change is the rule rather than the exception. Berger (2001) and Schreinemachers, Berger, et al. (2009) showed that the aggregate response in a heterogeneous population of agents tends to be smooth, making simulation outcomes much more realistic, while distributional effects can be analyzed.  Human Interaction: AgriPoliS and MP-MAS allow for agents to interact with one another, creating possibilities to model disaggregated resource transactions, communication in the diffusion of innovations, competition for resources, and collective action—all of which are difficult to include in representative farm models. While MP-MAS extends FSR by including space, diversity, and interaction, its use of whole farm MP gives it a comparative advantage over agent-based models of land use change that originated in other disciplines. Agents in MP-MAS are goaloriented, maximizing their expected income or utility, which circumvents the need to define a static set of explicit decision rules and an often-arbitrary sequencing of decisions.

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Table 1 Examples of the potential use of various agent-based models for technology assessment Origin

Technology assessment (example)

Decision-making routine (example)

Geography (e.g., LUCITA, Jepsen et al., 2006) Crop and forest ecology (e.g., PALM, SORTIE)

If a more productive crop is introduced, how will it affect the conversion of forest to cropland? Of seven alternative nutrient management strategies, which are the most promising?

ComMod (e.g., SAMBA, CATCHSCAPE, SHADOC) Agricultural economics (e.g., AgriPoliS, MP-MAS)

How will a new technology affect the negotiation over scarce water resources between competing water users?

Agents choose field crops until their food needs are met and subject to the available amount of capital and labor. Agents adopt the technology used by their immediate neighbors if it gives a higher return than the technology currently used. Agents plant rice on the first pixel and other seasonal crops on subsequent pixels, until all available cash or labor is allocated. Agents allocate land, labor, and cash to farm and nonfarm production by maximizing their expected income, subject to resource and knowledge constraints.

If introducing an improved crop variety, who will adopt and how will this affect soil fertility and the distribution of household incomes?

Table 1 illustrates the decision routines used within the various agent-based models introduced above, and examples of a research question related to technology adoption that these models can address. Agents in most other agent-based models are not economically rational: they choose a land use that maximizes crop yields or labor productivity, or that satisfies consumption needs, but they do so irrespective of relative scarcity conditions. But in reality, a farm household with limited land but much labor will have a greater incentive to aim for high crop yields than a household with opposite endowments. The attractiveness of an innovation, such as a crop variety with a greater yield, is hence not the same for all households. To include such economic behavior in a rule-based design is difficult, as one must pre-define each node at which the decision tree branches into alternative intensities of land use. However, this is relatively straightforward to implement using goal-oriented agents in MP-MAS, because additional rules can be included as constraints (Schreinemachers and Berger, 2006). The following sections will show this using a case study of innovation diffusion in litchi orchards in Thailand. 3. Empirical problem and study area Litchi is the major tree crop grown in the mountainous parts of northern Thailand yet, as shown in Fig. 1, the farm gate price has declined significantly over the last 15 years, as the growth in supply has outpaced the growth in consumer demand for the fruit. In parts of northern Thailand where farmers have good market access and the opportunity costs for litchi growing are high, farmers have reduced the scale of litchi orchard management, or even cut down trees. Yet, in more remote areas just opening up to markets, the areas under litchi are still expanding (for example, as reported by Withrow-Robinson et al., 1999). The change in relative importance of growing litchi in mountainous areas can be seen as a logical and perhaps necessary adjustment in response to relative price changes. However, the loss of fruit trees in economically developed areas has raised environmental concerns. The more profitable alternatives, mostly seasonal crops that require intense tillage, worsen the problems of soil erosion, use greater amounts of agrochemicals, and in-

40

Area (x 1000 ha) Price (baht/kg)

Planted area Average price

30

20

10

0

1994

1997

2000

2003

2006

Source: Office of Agricultural Economics, 2008. Fig. 1. Farm gate price and planted area of litchi in northern Thailand, 1994– 2007.

tensify runoff from the hillsides, a factor which has been linked to the flooding of lowland areas (Delang, 2002; Sidle et al., 2005; Turkelboom et al., 1997). Scientists and extension workers have stressed the importance of keeping hillsides covered with trees and have searched for ways to make litchi growing economically more attractive again (Sruamsiri and Neidhart, 2007). 3.1. The study area The study was conducted within the Mae Sa watershed. This watershed, 140 km2 in size, is located about 40 km northwest of Chiang Mai city, and has an elevation that ranges from 300 to 1,700 m above sea level. The watershed has 20 villages with an estimated number of 1,309 farm households, a quarter of these grow litchi. Most of the litchis are grown in the upper parts of the watershed, at elevations above 1,000 m, by farmers of Hmong ethnic origin. In the seven Hmong villages in the watershed, litchi orchards accounted for 39% of the agricultural area in 2006. Because of the proximity of the area to Chiang Mai and other urban areas, the opportunity costs of growing litchi are

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price varying between 6 and 12 baht/kg, profits range from −7 to 15 thousand baht/ha. At an average daily wage rate of 120 baht, litchi growing is therefore profitable only at prices above 7.7 baht /kg.

160 Price of labor (baht/man-day)

5

140 120 100

4. Innovations to make litchi more profitable

80 60

This study was conducted as part of a large international research program that has the objective of making a scientific contribution to sustainable agriculture and rural development in mountainous areas of Southeast Asia (Heidhues et al., 2007). Scientists in this interdisciplinary program have developed four innovations, introduced in the following section and listed in Table 2, to make litchi cultivation more profitable, so that farmers will keep the hillsides covered with trees. The reason for focusing on litchi in this study was due to the focus of the wider program. However, from an economic perspective it would make sense to see if tree crops other than litchi could be an option, though this is outside the scope of this study.

40 20 0 0

2

4

6

8

10

12

14

16

Farm gate price of fresh litchi (baht/kg) Profit level:

< -10

-10 to 0

10 to 20

> 20

0 to 10 (x 1,000 baht/ha)

Source: Own estimates from farm household survey in 2006. Note: The rectangle indicates the current range of profits. Fig. 2. Profitability of litchi growing by level of farm gate price and wage rate.

4.1. Artificial flower induction (AFI) high. Especially with low litchi prices, many households choose not to manage their orchards, but to perform off-farm work or to intensify their field crop production. The main alternative crops for highland farmers are cabbages, potatoes, carrots, and flowers such as rose and gerbera. The research area might not be representative of all of northern Thailand, but the problems the area faces now may be representative of the problems other areas will face in the future, as the intensification of agriculture continues. 3.2. Average profitability of litchi growing in the study area Average fruit yields for litchi in the study area are about 3.1 tons per hectare. At an average 2003–2007 farm gate price of 9 baht/kg (USD 0.25), this creates revenues of about 28 thousand baht per hectare (778 USD). Since variable input use is relatively low, the profitability of litchi is mostly a function of the fresh fruit price and the valuation of labor, as shown in Fig. 2. At the 2006 wage rate of 100 to 140 baht per man-day and a litchi

Litchi harvests in northern Thailand are presently concentrated in a short period from mid-May to mid-June, with much variation in annual fruit yields—a feature called irregular bearing. The short period of harvest relates to the period of flowering, which is induced by relatively low temperatures in January (Sruamsiri et al., 2007). If flowering could be artificially induced, decoupled from temperatures, then this innovation could be expected to reduce irregular bearing and improve the average farm gate price by spreading the fruit supply more evenly over the year. In 1998, scientists found that the application of potassium chlorate (KClO3 ) can artificially induce flowering in longan (Dimocarpus longan L.), a closely related species in the Sapindaceae family (Hegele et al., 2008; Manochai et al., 2005). The innovation was rapidly adopted and the area under longan trees expanded by 7% annually between 1998 and 2007. However, the innovation did not affect the flowering of litchi trees, and so the search is still on for AFI methods in this crop (Sruamsiri et al., 2007).

Table 2 Four innovations to improve the profitability of litchi growing Innovation

Type innovation

Stage of development

Main opportunity

Main challenges

Artificial flower induction Small-scale cooperative fruit drying Shelf-life extension

Agronomic

Research stage

Might improve farm gate prices

Socioeconomic, mechanical Chemical

Used in some villages

Improves profit margins for fruit growers

Benefits might be short-lived if the litchi area expands Unattractive if fresh fruit prices are high

Research stage

Large price premium on high-quality fruits

More efficient irrigation

Mechanical

Available

Might reduce the competition for water

Benefits could accrue to traders rather than farmers Benefits depend on the relative scarcity of water

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50 40 Price (baht/kg)

Bell pepper Rose Strawberry Chrysanthemum Gerbera Onion White cabbage Potato Baby carrot Brocoli Chayote Litchi (off-season) Chinese cabbage Carrot Litchi (in-season) Lettuce String/Bush bean Longan Paddy rice Ginger Soybean Sweet corn Chinese kale White radish Feed maize

Average Standard deviation 95% Confidence interval

30 20 10 0 1

2

3

4

5

6

7

8

9

10 11 12

Source: Office of Agricultural Economics, 2008. Fig. 3. Average monthly farm gate price of longan in northern Thailand, 2001– 2008.

We used the experience from off-season longan production to approximate the costs and returns of AFI in litchi. Fig. 3 shows the average monthly price of longan fruit. The average in-season fruit price, from July to August, is 12.3 baht/kg (sd = 5.6), while the average off-season price is 21.7 baht/kg (sd = 6.1), which suggests an average premium of about 9.4 baht/kg (76%). Yet, as the area increased, the average fresh fruit prices declined during this period. Based on this experience, we can judge that a realistic price premium for off-season litchi might be 40 to 60%. At a fruit price of 9 baht/kg and an average labor price of 120 baht per person-day, this would translate into an average increase in profits from about 4.18 thousand baht per hectare for in-season harvesting, to 21.06 thousand baht per hectare for off-season harvesting (Table 3). Fig. 4 compares the profitability of litchi, with and without AFI, with that of various other crops grown in the study area, excluding the cost of labor, water, and land. In spite of the additional profits from AFI, the profitability of litchi is low relative to most other crops. Whether farmers choose to grow litchi or not, however, also depends on land, labor, water, and cash constraints not considered in the analysis so far.

500

400

300

200

100

0

Profit margin (1000 baht/ha) Note: Calculations exclude all labor costs and the cost of irrigation infrastructure, except for bell pepper. Fig. 4. Profitability of litchi compared to other crops.

4.2. Small-scale cooperative fruit drying Litchi fruits can only be harvested when ripe, as the fruit does not ripen off the tree. Farmers must sell their harvest immediately, as fresh litchis have a vulnerable rind that is susceptible to decay (Jiang et al., 2006). As a result, farmers have little negotiating power if markets are oversupplied. The drying of fresh litchi fruit in the community could improve profits by adding value to the product and reduce the pressure to sell quickly after

Table 3 Average profitability of litchi growing and four innovations under alternative assumptions about household labor costs and farm gate prices, in thousand baht/ha Innovation

Including the cost of household labor

Current litchi orchards ( = Artificial flower induction2 Cooperative fruit drying3 Improved shelf-life / fruit quality4 Greater irrigation efficiency

baseline)1

Excluding the cost of household labor

6 bt/kg

9 bt/kg

12 bt/kg

6 bt/kg

9 bt/kg

12 bt/kg

−5.19 6.06 1.80 11.49 −5.51

4.18 21.06 1.80 29.21 3.86

13.56 36.06 1.80 46.93 13.24

3.40 14.65 34.12 20.08 3.08

12.77 29.65 34.12 37.80 12.45

22.15 44.65 34.12 55.52 21.83

Notes: 1 Average yield = 3,125 kg/ha, average total costs = 23.94 thousand baht/ha, of which 8.59 thousand baht/ha is own household labor costs (valued at 120 baht/man-day). Profit = (price ∗ yield)—total cost. 2 Assumes a 60% price premium for off-season fruit harvests. 3 Profits per kg of fresh fruit were multiplied by the average yield. This profit is irrespective of the fresh litchi price as the fresh litchi input was valued at the production cost and not at the farm gate price 4 Assumed price premium of 89%.

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Grade 1

we therefore ask what the price premium for farmers should be, to reverse the decline in the litchi area.

Grade 2

4.4. More efficient irrigation

Grade 3

Litchi orchards are able to withstand drought, but to get a good yield they require plenty of irrigation, especially during the fruit setting and fruit development stage from January to May (Menzel, 2005). Conventional sprinklers are the most common type of irrigation in northern Thai litchi orchards, but microsprinkler or drip irrigation systems are more efficient and save water. The attractiveness of this innovation depends on how scarce the water used for irrigation is. The water-use efficiency of traditional irrigation sprinklers is about 70%, while that of microsprinklers is about 80%. Although the price of a microsprinkler head is about double that of a conventional sprinkler, the main cost of the irrigation infrastructure is PVC pipes rather than sprinkler heads. As a result, the total cost per hectare of a microsprinkler system is not much higher than a conventional sprinkler system (Table 3). As the water used incurs no direct cost, the attractiveness of microsprinklers varies with the relative scarcity of irrigation water. In summary, the profitability of the innovations crucially depends not only on the fresh fruit prices, but also on the cost of land, household labor, and irrigation water. These resources have no direct monetary cost, but have a scarcity value and an opportunity cost that differs among households. To include these in the analysis, we turn to the agent-based model. The main reason for choosing this method was to include much of the heterogeneity in resource conditions among farm households as we expected that the innovations would not be equally attractive to everyone. Another reason for choosing the method was because we were interested to see how fast the innovations would diffuse in the farm population.

Grade 4 No grade 0

2

4

6

8

10

12

14

16

baht/kg Source: Office of Agricultural Economics, 2008. Fig. 5. Farm gate price of litchi by grade, 2007.

harvest. Competition for labor might, however, be a constraint as harvesting and processing are labor intensive and must be conducted in parallel to avoid reductions in fruit quality. Researchers in the program collected data about cooperative fruit drying by installing an experimental gas dryer in one community and monitoring the costs and returns. From the total revenues and total costs recorded, we calculate that the break-even point of the drying machine lies at a fresh fruit price of 8.24 baht/kg. At a higher fresh fruit price, selling the fresh fruit brings a greater profit then selling the dry fruit. Because fruit drying is labor intensive, the profits are substantially higher when assuming zero opportunity cost of family labor (Table 3). 4.3. Shelf-life extension of fresh fruits Although Thailand is one of the world’s largest exporters of fresh and processed litchi, only a quarter of its litchi output is exported (Thai Customs Department, 2008). The low quality of fresh litchi and a short shelf life are the main constraints to increasing the level of exports (Neidhart et al., 2007; Revathy and Narasimham, 1997). Chemical treatment of fresh litchi, to maintain the fresh fruit quality, could therefore enhance export opportunities (Cronje, 2008; Jiang et al., 2006). Fig. 5 shows the average farm-gate prices for five grades of fresh litchi in 2007. While the average farm gate price of fresh litchi was 5.8 baht/kg in 2007, the price of graded litchi was 10.8 baht/kg, which is 89% higher. This suggests that improving fruit quality could dramatically improve litchi prices for farmers. Shelf-life extension methods might contribute to this, but it is unclear at what stage in the market chain these benefits would accrue (litchi growers, traders, or exporters). Also unclear at the moment is what the costs of the chemical preservatives would be. The profitability of shelf-life extension methods can therefore not be answered with certainty. In the following analysis

5. Methodology 5.1. The agent-based model The agents in the MP-MAS model represent real-world farm households whose decision making is simulated through constrained optimization of the expected net farm and nonfarm income. The MP-MAS model solves individual MP problems for each farm agent, instead of a limited number of representative ones, and reruns them recursively over the entire simulation period of 15 years. Table 4 shows a concise outline of the MP tableau; the actual tableau has 1,812 activities and 812 constraints. MP-MAS adjusts the available resources of each agent (that is, righthand-side values), matrix and objective function coefficients, and solves the MP problem sequentially. The MP tableau was specified for an annual time step, but included monthly land, labor, and water constraints to capture multiple cropping, peak

+1 +A (+A) (+A) +A

E(−Y)

837

+1 +A

+A

E(−Y)

202

1 2 130

36 2 2

Irrigated

377 12 12 1

Rainfed

Annuals (ha)

1

−A

−C

Invest in fruit dryer

130

+1

1. Leave orchard idle

78

E(−Y)

±1

(+A)

+1 (+A) +A

2. Low input

78

E(−Y)

±1

(+A)

(+1) (+A) +A

3. High input

Invest/produce litchi (ha)

78

E(−Y)

±1

(+A)

+A

(+A)

4. High input + drip

E(−Y) 78

±1

(+A)

(+A) +A

5. High input + AFI

E(−Y) 78

±1

(+A)

+A

(+A)

6. High + drip + AFI

14

±1

±E(C)

Hire labor in/out

33

+1

+E(C)

Sell crops (kg)

1

+1

(+A) +1

(+A)

+E(C)

inseason

+1 1

(+A) +1

(+A)

+E(C)

offseason

1

+1

+E(C)

inseason

+1 1

+E(C)

offseason

Drying litchi (kg) Sell litchi (kg)

5

−1

−E(C)

Buy inputs (baht)

≤0 ≤0 ≤0

≤(R) ≤(R) =(R)

≤(R) ≤(R) ≤(R) ≤(R)

Righthandside

Notes: C = price coefficients; A = technical coefficients; Y = crop yields; R = available resources; I = available innovations; E = expected values. Values in parentheses are adjusted inside the model, of which values in bold are agent-specific. The full MP model can be found in THA205/xlsInput/Matrix.xlsx which can be downloaded from https://www.uni-hohenheim.de/mas/Thailand/THA205.rar.

Objective function Resources (monthly) Land (ha) Labor (man-days) Sprinkler irr. (lit/sec) Drip irr. (lit/sec) Resources (annual) Cash (baht), annual Fruit dryer capacity Litchi orchards (ha) Crop yields (tons) Annual crops Litchi in-season Litchi off-season Number of activities

No. of constraints

Table 4 Part of the MP model showing the decision making about litchi-related activities in simplified matrix format

8 P. Schreinemachers et al. / Agricultural Economics xx (2010) 1–18

P. Schreinemachers et al. / Agricultural Economics xx (2010) 1–18 Simulated by solving MP tableaus for each agent

Outcome indicators

Rainfall & inflows

Updating of liquidity & assets

Investment decisions

Biophysical part

9

Production decisions

Crop yields

- Litchi area - Pesticides - Erosion - Incomes

Updating of expectations (prices, yields, water supply) and liquidity Evaluation of innovation thresholds

Fig. 6. Dynamics of model.

labor needs, and monthly variations in the irrigation water supply. Agents own resources such as household labor, cash, agricultural land, irrigation infrastructure, and litchi fruit orchards, which were quantified from a random survey of farm households. The supply land and labor is constant over the simulation period, while the water supply varied with rainfall. In the present model application, agents cannot exchange land, labor, or water. Agents and agricultural fields were located on a grid of cells representing the physical landscape. Each pixel corresponded to one rai (1,600 m2 ), which is the land area unit used in Thailand. Each agent had a discrete number of pixels proportionate to its agricultural land. Because the location of farm plots was not recorded in the survey, we randomly allocated pixels to agents in the landscape by first delineating the agricultural area of each village on an aerial photograph of the watershed, and then randomly allocating pixels to agents in each village boundary using ArcGIS. We divided the pixels into 12 types of agricultural land, determined by the average slope gradient (less than 8%, 8–19%, 20– 35%, and above 35%) and the average altitude (below 650 m, 650–1,000 m, above 1,000 m). Crop activities in the MP tableau were land-type specific; litchis could be cultivated on land types above 750 m. Though crop yields were assumed equal for each type of land, the two steepest types of slope were assumed to increase the labor needs by 20% and 40%, respectively. 5.2. Model dynamics MP-MAS separates agent decision making into investment and annual production problems, as shown in Fig. 6. Investments include the acquisition of new technologies and assets, such as the expansion of fruit orchards and the purchase of a gas drying machine. Including these in an MP model usually requires a multiperiod setup, though Berger (2001) created a shortcut by separating investment from annual production decisions into two MP tableaus. The investment tableau optimizes the expected net returns averaged over the lifespan of each asset using an annuity cost

approach, while simultaneously optimizing annual production decisions. This tableau captures the trade-off between shortterm income from current production and long-term income from investments. After optimizing investments, right-hand-side values are updated (cash is deducted, assets are added) and the annuity values are replaced with the expected actual costs and revenues. The resulting tableau represents the production decisions for the current year and simulates the allocation of cash, land, labor, and water to a monthly cropping plan (Schreinemachers and Berger, 2006). Expected values for prices, crop yields, rainfall, and irrigated water supply formed the basis for investment and production decisions, following the theory of adaptive expectations (Arrow and Nerlove, 1958). This theory states that people form expectations about what will happen in the future based on what happened in the past. The theory is only realistic if there is no systematic trend that real decision makers can learn from and use to adjust their expectations (that is, to develop foresight). Following the implementation of adaptive expectations in MPMAS by Berger (2001), agents revise their expectations periodically in proportion to the difference between actual (Xt−1 ) and expected values (X∗t−1 ) as ∗ ∗ + λ∗ [Xt−1 − Xt−1 ], 0 < λ ≤ 1, Xt∗ = Xt−1

(1)

where λ is the coefficient of expectations. If λ = 0 then current values are expected to persist (static expectations). If 0