the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Adaptive Behavior of Over a Million Individual Farmers Under Consecutive Droughts: A Large-Scale Agent-Based Modeling Analysis in the Bhima Basin, India
Abstract. Consecutive droughts, becoming more likely, produce impacts beyond the sum of individual events by altering catchment hydrology and influencing farmers' adaptive responses. We use GEB, a coupled agent-based hydrological model, and expand it with the Subjective Expected Utility Theory (SEUT) to realistically simulate farmer behavior and subsequent hydrological interactions. We apply GEB to analyze the adaptive responses of ±1.4 million heterogeneous farmers in India's Bhima basin over consecutive droughts and compare scenarios with and without adaptation. In adaptive scenarios, farmers can either do nothing, switch crops, or dig wells, based on each action’s expected utility. Our analysis examines how these adaptations affect profits, yields, and groundwater levels, considering, e.g., farm size, risk aversion and drought perception. Results indicate that farmers’ adaptive responses can decrease drought vulnerability and impact after one drought (x6 yield loss reduction), but increase it over consecutive due to switching to water-intensive crops and homogeneous cultivation (+15 % income drop). Moreover, adaptive patterns, vulnerability, and impacts vary spatiotemporally and between individuals. Lastly, ecological and social shocks can coincide to plummet farmer incomes. We recommend alternative or additional adaptations to wells to mitigate drought impact and emphasize the importance of coupled socio-hydrological ABMs for risk analysis or policy testing.
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Status: open (until 14 Aug 2024)
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RC1: 'Comment on egusphere-2024-1588', Anonymous Referee #1, 09 Jul 2024
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Thank you for inviting me to comment on the manuscript: “Adaptive Behavior of Over a Million Individual Farmers Under Consecutive Droughts: A Large-Scale Agent-Based Modeling Analysis in the Bhima Basin, India”. I am an expert on agent-based land use change models. This may limit my expetise on the hydrological aspects of the presented work.
The authors use two coupled models (one ABM and one hydrological model) GEB (please spell out what GEB stands for) to model the land use of presumably 1.4 million farmers in the face of consecutive droughts over several decades. Unfortunately, the text is very rich combing description of drought modelling, theory, ABM model, and many more things. Therefore, at least for me it is impossible to understand the model(s) in detail and to put the results into context. Therefore, I am not able to appreciate the model and its results sufficiently, although the topic is timely and I guess the approach is relevant and promising.
Maybe the use of a protocol such as ODD+D (Mueller et al. 2013, Describing human decisions in agent-based models - ODD+D, an extension of the ODD protocol) could help to present the model in a more digestible way. At least the authors should summarize somewhere (maybe as a table) an overview of the properties of the agents and or agent types. The authors present many responses from their model: “Our analysis examines how these adaptations affect profits, yields, and groundwater levels, considering, e.g., farm size, risk aversion and drought perception.” Maybe it would be helpful to reduce the number of responses and/or scenarios? Especially in Figures 5 and 7 I would encourage the authors to present less panels and to focus on a more narrow narrative.
I appreciate that the authors follow a theory to justify their decision model. However, the authors need to guide the reader more carefully, since the SEUT is not a standard theory for all Economists and certainly not for all land use change modellers. Fishburn (1981) is a review of several theories and the list of papers suggested as examples of the application of SEUT needs to be critically revised (Groeneveld (review), Haer and Wens do not mention SEUT and do not cite Fishburn). When SEUT is introduced it also should be mentioned that the authors use imitation and “bounded rationality” (line 215) as well in their decision modelling. Later on also prospect theory is considered.
In general, I am missing an argument why it is useful to model > 1 million agents. Other authors decided to gain knowledge by aggregating actors to agent types in their land use models (e.g. https://landchange.imk-ifu.kit.edu/CRAFTY or the work by Millington et al. https://www.jasss.org/11/4/4.html). It would be great to see an argument developed why this computational demanding approach is seen more appropriate to answer questions of land use change. This is especially critical since the authors argue that it is not computationally feasible to compute for all agents the SEUT for all 300 options (“unique crop rotations”). Would it make sense to compute less agents and therefore consider all 300 options?
I have difficulties to understand the results. To my understanding imitating the strategy of more successful agents in the neighbourhood of an agent is at the heart of the presented dynamics. In the abstract this is not mentioned: “n adaptive scenarios, farmers can either do nothing, switch crops, or dig wells, based on each action’s expected utility.” If my reading is correct the imitation aspect should be mentioned early on and should be discussed in a diffusion of strategy/technology context. What are the updating rules – synchronous or asynchronously? How many neighbours are considered? What is the initial trait distribution of actors. Is there a spatial structure in the inital trait distribution? Is the number of farmers constant over the years? Form Figure 4 it seems that the model does not show much variation between runs. Would have been less agents sufficient? What source/help of A.I. have the authors used for what?
The role of the “spin-up” period (21 years) needs to be explained in more detail. The model is initialized with data from some point in time (when). Given the substantial temporal dynamics of the responses (see Figure 4) the choice of the length of the “spin up” period should have a strong effect on the results? It is written that the calibration has been done in the period after the spin up from 2001 to 2010? It is difficult for me to understand the evolution of Figure 4. The starting point at 2001 is the result of the spin up period? The period from 2001 to 2010 is calibrated and after that it is the model projection? How are small, medium, and large field farmers defined in terms of hectare?
Thus, overall I have the impression that potentially great insights are hidden in the current text. More specific and potentially less research questions could help to narrow down the story to allow easier access to the main highlights of the study. And at least for me it would be necessary to have a clearer motivation why it is beneficial to consider so many agents at the very same time.
Specific comments
Abstract: “realistically simulate” – That is maybe personal but I would avoid phrases like “realistically simulate” since it is a model and the best one can do is to model something useful in respect to the research question.
The models are written in Python?
Lines 73/74 What means “one-to-one scale”?
Lines 78: What “simple assumptions of human behaviour”?
Figure 1: it is not clear that some boxes are empty – please explain.
Line 101: reservoir operators – is this agent type considered in the presented study? “reservoir operators” are never mentioned again.
Figure 2: The land cover map presents less classes (e.g. agricultural land) than used in the results? How did the authors discriminate the different crop types?
Lines 132ff – Can you define in term of your indicator (SPEI) what a severe, moderate and so on drought is? I guess there are thresholds? Please specify.
Line 156: Why 5 km radius – is this decision based on a sensitivity analysis?
Line 165: C_adapt is not considered in equation 4. Does it needs to read “C_input in eq. 4 on current market prices”?
Equations 1-4 please explain subscripts x, d, and m.
Lines 197: crop costs – crop costs C_input are dependent on the type of crop? If so how?
Equation 9: Where is the “crop coefficient” Kc used? The duration of different harvesting stages are not crop-specific?
“Agent initialization”: Spell out IHDS, the authors should give the ranges of the agent properties. Showing boxplots or other useful representation of the distributions of attributes would be useful. I have no intuition for example how net income is initially distributed among the 1.4 million agents and how it is developing over time.
Line 326: (7g)?
Figure 4c: Is it reasonable that one crop is going from 0.05 fraction to the dominating crop?
Figure 5: Very busy graph (6 panels). The authors may want to focus on some panels.
Figure 6 and elsewhere: Specify the unit Rs
Figure 8: Too many panels. Legend unreadable. Please focus on the important aspects.
Technical corrections
The figure labels are too small and therefore hard to read.
Figure 4c and others: colours are too similar – hard to differ crops
Lines 65-66 and elsewhere: Inconsistent in-text citation of Udmale et al. 2014/2015
Line 114 and elsewhere: “95 %” -> “95%”
Line 263: The authors refer to figure 3 in Jun et al. 2014? Jun et al. 2014 is a comment in Nature without Figures to my understanding. Please check.
“Sensitivity Analysis”: What are 300 distinct samples. Sampling from what distribution?
Line 306: Where does stochasticity enters the model(s) and how?
Citation: https://doi.org/10.5194/egusphere-2024-1588-RC1
Data sets
Adaptive Behavior of Over a Million Individual Farmers Under Consecutive Droughts: A Large-Scale Agent-Based Modeling Analysis in the Bhima Basin, India [Data set and Code] Maurice W. M. L. Kalthof and Jens de Bruijn https://doi.org/10.5281/zenodo.11071746
Model code and software
Adaptive Behavior of Over a Million Individual Farmers Under Consecutive Droughts: A Large-Scale Agent-Based Modeling Analysis in the Bhima Basin, India [Data set and Code] Maurice W. M. L. Kalthof and Jens de Bruijn https://doi.org/10.5281/zenodo.11071746
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