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Maize (Zea mays L.), rice (Oryza sativa), and sorghum (Sorghum bicolor L. Moench) are
major staple food crops to the most population in Tanzania. The three crops provide the
primary source of livelihood for the majority of rural farming households. Of the three
crops, maize is the most important, accounting for about 20% of the total agricultural
GDP, followed by rice. Sorghum plays an important role in fighting hunger and food
insecurity in central Tanzania, particularly in Dodoma and Singida regions. Unfortunately,
like any other crops, some uncertainty exists about the future productivity and profitability
of these important food crops. Such uncertainty hinders the implementation of different
agricultural policies, plans and strategies set to achieve an agriculture revolution, hence
impacting the decision of investment in agricultural technologies. The inadequacy of
accurate and timely information on productivity and profitability of crops have a
tremendous impact on farmers' decisions, as well as on policy and planning. Hence, a
complete model to help in forecasting and economic analyses of crucial crop sub-sectors
while including their stochastic nature is essential. In this regard, stochastic risk analysis
models were developed and demonstrated to analyse risk and uncertainty in forecasting
and economic analyses of major cereal crops in Tanzania. Most of the available models in
economic analyses and forecasting yields, prices, and net returns of agricultural systems
are deterministic. These models ignore the inherent risk of random variables and provide
only a point estimate for the key output variables (KOVs) instead of values with
probability distributions. Therefore, this study was conducted to address three specific
objectives. The first objective was to develop and demonstrate a stochastic simulation
model for analysing the future viability of main cereals crops in semi-arid and sub-humid
areas of Tanzania. For this reason, a Maize-Sorghum-Rice Simulation Model
(MASORISIM) was developed to simultaneously forecast yields, prices, and probable net
returns for maize, sorghum, and rice as probability distributions. It utilizes deviations from
historical yields and prices (2008 – 2018) to forecast random variables for seven years
from 2019 – 2025. Since the analysis involved yields and prices of three crops, a
multivariate probability distribution was built in the model to incorporate correlations of
the variables and control their heteroscedasticity. The forecasting results on crop yield
show an increasing trend for maize and rice with a marginal increase for sorghum in the
Dodoma region by 2025. Likewise, the yield for rice is expected to rise in Morogoro with
a slight increase for maize and a decreasing trend for sorghum during the same period.
Meanwhile, the prices for the three crops all are projected to increase in the two regions.
The results on economic feasibility using NPV values revealed a high probability of
success for all crops in both regions except maize in Morogoro. The results for maize in
Morogoro presented a 2.93% probability of negative NPV. Of the three crops, maize
indicated the highest relative risk associated with NPV for both regions and was relatively
higher in Morogoro (55.1%) than in Dodoma (34.2%). Although the results on production
indicate increasing trends for the crops, the increase is relatively small, particularly in
Morogoro, which is one of the food basket regions in the country. The second specific
objective of the study was to develop and illustrate a bio-economic simulation model for
analysing the economic feasibility of improved management practices on maize
production in the Wami Basin of Tanzania. The bio-economic simulation model is an
integrated decision support system (IDSS) developed to link data from two biophysical
models, namely APSIM and DSSAT and econometric model (Simetar) for comprehensive
decision-making. Under this objective, the economic feasibility of two farm management
practices was analysed. These practices included the application of 40 kg N/ha and
adjustment of plant population at a rate of 33 000 plants/ha from the current rate of 18 000
to 20 000 plants/ha. The simulated yield from the two crop models was then entered into
the bio-economic IDSS model along with output prices, and cost for each option to
simulate the probable economic net returns to farmers. The APSIM and DSSAT crop
models were used in this study because the two models are capable of simulating yield as
a function of the soil-plant-atmosphere conditions with and without the proposed farm
management practices. However, crop models normally simulate yields and cannot
simulate other variables like prices and costs of management alternatives to inform
economic decisions. The bio-economic simulation model, therefore, was built to bridge
the gap. The results on the economic viability show that the application of 40 kg N/ha was
more profitable than the plant population of 33 000 plants/ha having a zero probability of
negative returns. Both APSIM and DSSAT models suggest that when plant population is
adjusted from current average of 20,000 plants/ha to 33 000 plants/ha, there is 16% and
27% probability of negative returns in semi-arid part, with a 14% and a 30% probability in
sub-humid area. However, the net return for farms supplemented with the two
management options (40 kg N/ha and the 33 000 plants/ha) has a slight difference from
the farms with additional of 40 kg N/ha alone. However, the results suggest that the
application of either fertilizer alone reduces the risks associated with the annual mean
returns. The increase in plant population at a rate of 33 000 plants/ha without application
of 40 kg N/ha has a high probability of economic failure. The third specific objective was
to demonstrate user-friendly Monte Carlo simulation procedures to simulate the economic
viability of different rice farming system in Tanzania. Production data for three seasons
were used to demonstrate how panel survey data can be made stochastic to include risk
available in the data. In this analysis, the rice farming systems entailing traditional and
improved practices were compared by considering the risk associated with each system,
and the best farming system was identified. The systems were categorized based on the
type of seeds used (local or improved), application of fertilizers, and application of the
systems of rice intensification (SRI) practices (partially or fully). The results of the
economic analysis show a high probability of success for rice farmers using all the
recommended SRI principles. Moreover, rice farms that partially applied the SRI
principles did not realize better returns compared to their counterpart farmers that fully
adopt the SRI package. Rice farms that applied fertilizers plus improved seeds were also
better-off compared to rice farms under traditional practices. The study revealed that
farmers who use SRI partially and fully had 2% and zero probabilities of negative annual
net cash income (NCI), respectively. Meanwhile, farmers using fertilizers and improved
varieties had a 21% probability of negative NCI. The farmers using improved and local
rice varieties had 60% and 66% probabilities of negative returns, respectively. With high
dependence on rain-fed farming, production of main cereal crops is likely to face a high
degree of risk and uncertainty threatening incomes, livelihoods, and food availability to
poor households. However, there is a high chance that such households will be better-off if
improved technologies like the application of recommended fertilizers and SRI are
properly applied. Nonetheless, the adjustment in plant population has demonstrated a
slightly impact on both yield and economic returns, particularly under rain-fed production
system. With evidence from crop models like APSIM and DSSAT, bio-economic
integrated studies are, however, needed to explore the potential of more crop management
practices and technologies for better decision-making. This study forms a basis for more
studies that include risks and uncertainty to improved decision marking for farmers,
government, and stakeholders in the agricultural sector. The methodology used in this
study can be expanded to include more zones and other non-cereals crops and livestock
farming systems. |
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