An Explanation of Oscillating Cashflows Experienced by Ugandan
Smallholder Farmers: The First Use of a Model to Simulate the
Effects of Trader Business Strategies on Farmer Livelihoods
Katherine R. Picchione
August 6, 2018
Extended Abstract
The agricultural sector is a staple of Uganda’s economy, employing 75% of Ugandans and accounting for 85%
of export earnings (USAID, bout). However, many smallholder farmers, who typically cultivate two hectares
or less, experience volatile cashflows around crop cycles. Before the growing season begins, farmers invest
in seeds, agrochemicals, tools, and field preparation; farmers’ returns on investment come at the harvest,
though not without risk. Financial services are limited in rural Uganda, and crop insuran essentially
non-existent for smallholder farmers. Consequently, farmer livelihoods are vulnerable to uncertain growing
conditions, market price fluctuations, and financial shocks such as hospital bills or school fees. Farmers have
limited cash during the growing season and the most financial stability at harvest, a pattern of behavior
offering a veritable reference mode for system dynamics models.
s knowled
Agribusinesses—commodity traders in particular—are well positioned to help farmers ac
goods, and services necessary for production. This paper contributes to a larger body work that explores
the extent to which trader busin ies might be leveraged to mutually benefit agribusiness growth
and farmer livelihoods (Picchione, 2018 (fort: g)).
s is that oscillations in farmer and trader cash are caused by concentrated
costs surr d resour ‘ained crop prod and changing market prices. To explore
this hypothesis, I designed a system dynamics model to simulate fluctuations in farmer cash. The model
is based on empirical evidence from interviews with Ugandan agribusinesses and subsequent qualitative
. The model is explanatory, elucidating the underlying causal structure of growth modalities in the
It is also a platform that can be used to explore how business strategies affect farmer and trader
This paper describes the model structure, base model behavior, and modifications for testing two
cing of agricultural inputs, and (2) increasing farmer yields.
aoe 7)
_ Farmer Cash.
+ Trader Cash @S
'
f 2 + Purchase Inputs
R R
‘Trader Sales Trader Purchases Parmer Sales - +
f Trader Cash Transactional
Conversion Cycle Relationship
The dynamic hypothes
S
Cost of Living:
Farmer Cash
Conversion Cycle xe
Crop Production
Farmer Stock
Storage Capacity
Figure 1: Causal loop diagram of the transactional relationship between farmers and traders.
EXPLANATION OF OSCILLATING FARMER CASHFLOWS Page 2
The model has two main components: a structure simulating farmer production and sales, and a structure
simulating trader purchases and sales. Stocks include Trader Cash, Trader Inventory, Farmer Cash, Farmer
Crops in Fields, and Farmer Crops in Stock. Both structures are governed by a strong, second order positive
feedback loop called the Cash Conversion Cycle, the main driver of business growth and farmer income.
The model emulates how farmers plant crops, grow crops, harvest them, and sell them to traders. Traders
in turn are able to purchase, store, and sell crops at a profit. Additional variables impose costs (cos'
of production, living expenses), limits on crop production capacity (planting area), limits on crop storage
capacity, growing season r and time constants (time to plant, time to grow crops, transaction
time). The model also includes a construct for Indicated Month, which relies on the Get_Time_Value
function of Vensim_DDS and is used to dictate growing seasons and price seasonality. In the Base Case
h. The Base Case model
simulation (Figure B), the model produces reference mode behavior: oscillating
and simulations are included as supplemental materials. Auxiliary variables were populated with real data
or reasonable estimates based on fieldwork.
Farmer Cash Trader_Cash
750,000 225M
500,000 13M
250,000 750,000
° o
0 6 2 Wo 30 36 42 48 o 6 2 36 428.
‘Time (Month)
Farmer Cash : Base Case_§ —— Trader Cash : Base Case
Figure 2: Farmer and Trader Cash under Base Case conditions
Sensitivity s reveals that, when paired with costs of production and living, crops maturation time
is the major cause of fluctuations in farmer cash. Crops In Fields is the stock responsible for the main
os since Time to Grow Crops is the longest delay. Thus, analysis of the Base Cas
that farmer livelihoods are most vulnerable to financial shocks during the growing season because funds are
tied up in production—and not without high risk.
model confirms
The Base Case model was modified and used to test two trader business strategies:
tural inputs Ids.
(1) financing of agricul-
and (2) increasing farmer y’
Inputs fi ing creates r by ing con ated expenses. Several simulations were
run to test the effects of an Inputs Financing business strategy. Where the cost of seed and input chemicals
is offset by a trader, a farmer facing bankruptcy is able to plant and produce instead. By offering credit for
inputs or, by extension, for other concentrated expenses, the trader builds resilience into the supply chain.
At the same time, the farmer ben ashflow. When traders have lenient payback polic
farmers also benefit from inputs financing as a type of informal insurance against a bad season.
from less volatile
Production efficiency has a strong direct effect on trader and farmer cash. Traders use various
methods to help farmers increase yields. To observe the effect of production efficiency on cash, Farmer
Yield was set to 1%, 20%, 50%, and 100% of the theoretical maximum. Predictably, increased yield leads
to increased income. Of all the causal links and feedback loops explored, the relationship between yields
and cash is perhaps the least complex. As described by traders in interviews, more quantity leads to more
income. When farmers have higher yields, both farmers and traders benefit. Thus, incentives are aligned for
traders to help farmers increase yields
EXPLANATION OF OSCILLATING FARMER CASHFLOWS Page 3
However, the mechanisms by which traders influence farmer yields proved difficult to model. Several un-
s were made to add structure for training, adoption of methods, and provision of quality
While qualitative analysis of methods for increasing yields was quite extensive in pre-
vious work, constraints on data and time made it difficult to discern additional causal structures. Further
al evidence is needed to model how traders provide these benefits to farmers—and how they then
benefit in return. Herein lies a great opportunity for future work in applied system dynamics.
References
Angerhofer, B. J. & Angelides, M. C. (2000). System dynamics modelling in supply chain management:
Research review. In Simulation conference, 2000. proceedings. winter (Vol. 1, pp. 342-351). IEEE.
Feed the Future Knowledge-Driven Agricultural Development. (2015). The facilitation approach at usaid.
US Agency for International Development.
Morecroft, J. D. (1983). System dynamics: Portraying bounded rationality. Omega, 11(2), 131-142.
AL f
MSMA. (2017, March 17). Uganda agricultural ti
em Monitoring Activity. Retrieved from http://huma
Picchione, K. (ois (forthcoming). Dy ics of agribusi ion making in uganda.
Sterman, J. D. [John D.]. (2000). Business dynamics: Systems thinking and modeling for a complex world.
Sterman, J. D. [John D], Henderson, R., Beinhocker, E. D., & Newman, L. I. (2007). Getting big too fast:
Strategic dynamics with increasing returns and bounded rationality. Management Science, 53(4), 683-
696.
UNFAO. (2017, May 9). Drought-tolerant maize varities in uganda. Retrieved from
read/8920
USAID. (2013). Feed the future zone of influence baseline report. US Agency for International Development.
USAID. (2016a). Uganda country development strategy. US Agency for International Development.
USAID, (2016b). Us global food security strategy 2017-2021. Retrieved from https://www.usaid.gov/sites,
default /files /documents/1867/USG-Global- Food-Security-Strategy-2016.pdf
USAID. (2017, October 12), Usaid uganda agriculture and food security. Retrieved from hhttps://www.usaid.
ov /uganda /agriculture-and-food-security
USAID Feed the Future. (2011, March 17). Feed the future uganda fy 2011-2015 multi-year stretegy. US
Agency for International Developme
2017). Global food prices database. Retrieved from https: / /data.humdata.org /dataset / wfp-food4
WEP.
