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DISCUSSION 
 

Response to the Comments by C.P. Geevan, Arun 
Dixit and Chandra Shekhar Silori  
 

Mihir Mathur  and Kabir Sharma  

  
In this response, we will first address the general statements made by C.P. 
Geevan, A. Dixit S. Silori (this issue of EES; henceforth GDS) in the 
context of our paper published earlier in EES (M&S), and then move to 
addressing the specific points raised. The model presented in (M&S) indeed 
follows a highly coupled approach, refer Figure 1 (Higher Order Causal 
Loop Diagram of the Simulation Model) (M&S, 37), as is what GDS 
suggest should be the case for such an ecological-economic system, and 
unlike what they claim the model actually is. The sectors of livestock, 
economy and land area are interdependent through multiple feedbacks, 
some of which are described in the section ‘Key Cross-sectorial Feedbacks’ 
(M&S, 44). As is asserted incorrectly by GDS, the computation of livestock 
numbers as well as P. juliflora (PJ) area - referred to as MCA by GDS- is not 
done using a ‘stand alone’ approach, and both the sectors are 
interdependent on each other. PJ area is influenced by livestock numbers 
through an increased spread rate due to livestock acting as vectors in Banni; 
and, livestock numbers depend on PJ area as with increased PJ invasion, 
area available for grass growth comes down, thereby reducing biomass 
production, which leads to fodder deficit - named as such in our model - 
impacting livestock numbers. These impacts manifest themselves through 
three feedbacks: 1) by increasing migration of buffalo and cattle from 
Banni, 2) by increasing fodder and feed input cost, thereby reducing 
livestock profitability, and increasing the stress sale of livestock and 3) 

 
 Founder Director, DESTA Research LLP, New Delhi; mihir@desta.co.in 

 Founder Director, DESTA Research LLP, New Delhi; kabir@desta.co.in 

Copyright © Mathur and Sharma 2019. Released under Creative Commons Attribution-

NonCommercial 4.0 International licence (CC BY-NC 4.0) by the author.  

Published by Indian Society for Ecological Economics (INSEE), c/o Institute of Economic 

Growth, University Enclave, North Campus, Delhi 110007.  

ISSN: 2581-6152 (print); 2581-6101 (web). 

DOI: https://doi.org/10.37773/ees.v2i1.60  

Editorial note: no further discussion on this matter can be published.  

https://doi.org/10.37773/ees.v2i1.60


Ecology, Economy and Society–the INSEE Journal [132] 

Figure 1: Prosopis and Grasslands 

 

Source: Authors 

increase in the death rate of Kankrej cattle from consumption of pods of PJ 
(Bharwada & Mahajan 2012, 90). The results seen in (M&S) are the 
outcome of the tightly coupled dynamics between PJ area, area under 
grassland, economics and livestock management and manifest through the 
multiple feedbacks present in the model. In absence of such feedbacks, the 
model results would be very different from those presented in (M&S). 

Finally, the model described in GDS is very similar to the one in Geevan et 
al. (2003), which was in fact reviewed during the course of our study and is 
also referenced a number of times in our paper. Now, we will present 
responses to the specific points raised.  

a) The spread of PJ in Banni has been rampant. Data from studies 
conducted specifically for Banni (Shah & Somusundaram, 2010) and 
(GUIDE, 1999) –as also tabulated explicitly in (Bharwada & Mahajan, 
2012, 82) –show that the spread of area under dense PJ in Banni grew 
from 16133 ha in 1997(~6% of area) to 72074 ha (27% of area) in 



[133] Mihir Mathur and Kabir Sharma 

 

Figure 2: Livestock 

 

Source: Authors 

 

2008, giving a compounded growth rate of 14.58 % per year. Further, 
as these numbers are derived from satellite imagery, these are the ‘net  

seen’ values, which are effectively the net positive difference between 
the additional land invaded by PJ and land cleared of PJ through 
biomass removal (uprooting) practices. As also explained in (M&S, 34), 
between 2004 and 2008, the ban on chopping of charcoal was lifted 
and a significant portion of PJ area was cleared (Bharwada & Mahajan 
2012). In this light, the actual spread rate would need to be higher than 
that mentioned above, which would then manifest itself as the ‘net 
seen’ value, after the effects of clearing land of PJ is accounted for. 
Thus, the dynamic annual spread rate taken in our study (including the  

 



Ecology, Economy and Society–the INSEE Journal [134] 

Figure 3: Economy 

 

Source: Authors 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



[135] Mihir Mathur and Kabir Sharma 

 

Figure 4: Full model 

 

Source: Authors 

 

 

 

 

 

 



Ecology, Economy and Society–the INSEE Journal [136] 

amplification by livestock), has a possible range of 8.5% to 17% 
(changing with livestock values), and varies between 8.5-12% for most 
part of the simulation run. Further, the model simulation runs begin 
from year 1992, and thus the time between 1992 to year 2015 served as 
back-casting for validating changes in land area in the simulation 
against observed values. This method was used for fine tuning 
parameter values of spread rate in the model such that it reflected area 
change as observed in Banni.  

b) As correctly stated by GDS, there is no way to estimate ‘unassisted 
spread rate’, and it is a proxy number, which needs to be corrected for 
amplification by livestock in order to be comparable with observations. 
Thus, in our study, an estimated base rate is used, which is then 
amplified by livestock, as explained in (M&S, 39). The number of 8.5 % 
was taken from a range provided in (Vaibhav et al. 2012, 3) just in order 
to obtain a plausible reference value. Although the mentioned study 
may have focused on biomass regeneration, as pointed out by GDS, the 
concerned number is of area spread as observed by (Vaibhav et al. 
2012) and not of biomass, as implied by GDS. Curiously, the paper 
cited by GDS (Pasha et al. 2014) in providing a number for spread rate, 
is a study carried out not exclusively for Banni, but for the Greater 
Rann of Kachchh(GRK) region, which has a geographical boundary 
overlapping only marginally with some parts of eastern Banni (Pasha et 
al. 2014, 1482). Since this study area contains regions with very 
different ecological conditions including saline areas and wetlands 
(Pasha et al. 2014, 1483) as well as varying economic use, numbers for 
this region are not representative of Banni. The authors of the study 
themselves (Pasha et al. 2014, 1484-5) note that among the areas within 
the GRK boundary, most (~96%) of the PJ invasion took place in the 
grassland area and only ~4% in other area types. They also go on to 
cite the same studies cited in point a) above, carried out exclusively for 
Banni (which reveal a growth rate of 14.58%), to emphasize the high 
spread of PJ in Banni (Pasha et al. 2014, 1486-7).  

c) Graphical functions have been one of the distinguishing 
characteristics of system dynamics methodology since the inception of 
the field, and are important tools used by system dynamics modellers to 
represent non-linear feedbacks between variables in a system (Forrester 
1973, 34; Sterman 2000, 551; Pruyt 2013, 120). Furthermore, as 
explained earlier on, our model is tightly coupled, and any change in PJ 
area due to livestock numbers reflects in the next time step on the 
livestock numbers through profitability and fodder deficit (causing 
livestock stress sale or migration). Thus, the assertion that this 



[137] Mihir Mathur and Kabir Sharma 

 

approach excludes feedback effects on the livestock sector is also 
unfounded.  

d) PJ spread has indeed been modelled with the rate of change being 
proportional to the area, as has also been done by other studies (Dayal 
2007). However, the effect GDS mention is interesting and we 
acknowledge that along with other approaches for modeling spread of 
PJ area, such as considering biomass as the driver of PJ area change, 
this approach could also be of potential importance to explore further.  

e) Contrary to the claim, the point made here is unclear, and the 
explanation provided does not suffice. The livestock sector is not a 
‘standalone model’, as the system of Banni is tightly coupled as a 
human-nature system. The livestock sector was modelled to closely 
represent the real dynamics of Banni. Both the livestock varieties 
(Banni Buffalo and Kankrej cattle) are modelled using ageing chains as 
is traditionally done in SD models (Sterman 2000, 470; Ford 2009). 
Breeding, selling and migration of buffaloes are all managed based on 
fodder and profitability and are modeled as such. Livestock both 
impacts and is impacted by the economics, shifts between grassland 
and PJ areas and biomass dynamics of Banni (Refer to ‘Key Cross-
sectorial Feedbacks’ (M&S, 44)). Indeed, the death rate of livestock (as 
per their average dying age) is the only exogenously input variable. 
Finally, as explained earlier, back-casting (1992-2015) of the model was 
done for parameter calibration, and the model was found to capture 
observed data in Banni.  

We have responded to each of the specific points raised by GDS as well as 
to the general statements made by them. We have demonstrated how their 
claim of certain formulations being flawed is unfounded, and also provided 
credible validating references for the parameter values they claim as 
inaccurate. We do believe, however, that some of these comments may not 
have arisen if the system dynamics stock-flow diagram had been provided 
along with the text. It is for this reason that the stock-flow diagrams of the 
sectors as well as the overall model are attached herewith (Figure1 to Figure 
4). We hope this response and the model images will help readers to better 
understand our model, and the system dynamics approach of modelling.  

 

REFERENCES 

Bharwada, C., and V. Mahajan. 2012. “Let it be Banni: Understanding and 
Sustaining Pastoral Livelihoods of Banni.” Centre for Economic and Social Studies 
(CESS) Monograph: RULNR Monograph 13. 
http://www.cess.ac.in/cesshome/mono/CESSMonograph-26(RULNR-13).pdf  



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Dayal, V. 2007. “Social diversity and ecological complexity: How an invasive tree 
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Forrester, J.W.1973. World Dynamics. 2nd ed. Cambridge, Massachusetts: Wright-
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Ford, A. 2009. Modeling the Environment: an introduction to system dynamics models of 
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Geevan, C.P., A.M. Dixit, and C.S. Silori. 2003. “Ecological Economic Analysis of 
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http://irade.org/eerc/pdf/CPR_FR_Geevan.pdf 

GUIDE (Gujarat Institute of Desert Ecology). 1999. “Ecorestoration of Banni 
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Pasha, S.V., K.V. Satish, C.S. Reddy, P.P. Rao, and C.S. Jha. 2014. “Satellite image 
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Pruyt, E. 2013. Small System Dynamics Models for Big Issues: Triple Jump towards Real-
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Shah, J. and S. Somasundaram. 2010. “Preliminary GIS and remote sensing analysis 
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Sterman, J.D. 2000. Business Dynamics: Systems Thinking and Modelling for a Complex 
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Vaibhav, V., A.B. Inamdar, D.N. Bajaj. 2012. “Above ground biomass and carbon 
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https://doi.org/10.1017/S1355770X07003695
https://doi.org/10.1007/s12040-014-0486-0

