Adaptive self-organization of Bali’s ancient rice terraces

Abstract

Spatial patterning often occurs in ecosystems as a result of a selforganizing process caused by feedback between organisms and the physical environment. Here, we show that the spatial patterns observable in centuries-old Balinese rice terraces are also created by feedback between farmers' decisions and the ecology of the paddies, which triggers a transition from local to globalscale control of water shortages and rice pests. We propose an evolutionary game, based on local farmers' decisions that predicts specific power laws in spatial patterning that are also seen in a multispectral image analysis of Balinese rice terraces. The model shows how feedbacks between human decisions and ecosystem processes can evolve toward an optimal state in which total harvests are maximized and the system approaches Pareto optimality. It helps explain how multiscale cooperation from the community to the watershed scale could persist for centuries, and why the disruption of this self-organizing system by the Green Revolution caused chaos in irrigation and devastating losses from pests. The model shows that adaptation in a coupled human-natural system can trigger self-organized criticality (SOC). In previous exogenously driven SOC models, adaptation plays no role, and no optimization occurs. In contrast, adaptive SOC is a self-organizing process where local adaptations drive the system toward local and global optima.self-organization | criticality | irrigation | evolutionary games | Pareto optimality T he geometric precision of Balinese rice terraces has inspired generations of postcard photographers. Viewed from above, a changing mosaic of colors appears: green when the rice is young, yellow as it nears harvest, silver when the paddies are flooded, and brown when they are drained. These colors are not uniform across the island, because Bali is an equatorial island with only two seasons, wet and dry. Consequently, farmers can plant their crops at any time, although they avoid harvesting in the rainy season. The colored patches that make up the mosaics are visible in Google Earth. Like many natural phenomena, patches show a characteristic power-law distribution of sizes. However, in this case, the patches are created by the farmer's decisions about when to synchronize irrigation schedules with their neighbors: Each patch displays the outcome of these choices. Adjacent patches tend to be correlated. This correlation weakens with distance, a relationship that also follows a specific power law (Fig. 1). To discover why harvests approach a maximum when both the size distribution of patches and the corresponding correlation functions fit power-law distributions, we need a model that relates the decisions of the farmers to the consequences for irrigation flows and rice growth.In Bali, water is regarded as a public good, the gift of the Goddess of the Lakes. Rice is grown in paddy fields fed by irrigation systems dependent on rainfall. Rainfall varies by season and, in combination with groundwater inflow, determines river flow. By virtue of...

From nouns to verbs: How process ontologies enhance our understanding of social‐ecological systems understood as complex adaptive systems

“…When substance‐based concepts are used as basis for the design of solutions to problems, they risk leading to unintended outcomes because an entities’ history or relations to its environment have not been taken into account. Lansing et al () have demonstrated how standardized and decontextualized blueprint solutions such as the introduction of green revolution practices in Bali in the 1970s led to increased pest outbreaks because of a disregard of strongly contextual practices, such as collective and synchronized irrigation schedules and pest management practices which are the result of co‐evolutionary social and ecological processes. Substance‐based concepts correspond to, or ‘mirror’ immutable substances.…”

A History of Possible Futures: Multipath Forecasting of Social Breakdown, Recovery, and Resilience

“…This will enable our models to predict collective dynamics of violence outbreaks, from urban riots to popular insurrections. Thurner has used this fact in numerous previous ABMs of collective human behavior: opinion formation (Lambiotte et al 2007;Klimek et al 2008), financial investments (Thurner et al , 2013, organization of human groups (Szell et al 2010;Klimek et al 2015), emergence of cooperation , voting behavior (Klimek et al 2012), and rice farming in Bali (Lansing et al 2017).…”

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“…Alternatively, synchronization could have emerged “by accident” if several farmers independently copied the cropping pattern of a neighboring farmer who had produced the highest yield the previous year. (see Lansing et al (2017) for a model of such a process) Given the autonomy over their agricultural practices (Lansing 2006; MacRae and Arthawiguna 2011), if synchronized cropping generated greater yields for farmers than they had experienced prior to synchronization, there would have been an incentive to continue synchronizing. The resulting group would have represented the earliest subak.

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“…2e). In fact, Lansing et al (2017) model the emergence of synchronization among farmers and Lansing and Kremer (1993) model a similar process among subaks to illustrate this mechanism. In the latter case, each subak checks the productivity of its four nearest neighboring subaks and adopts the cropping pattern of the neighboring subak with the highest yield.…”