that the yields are much higher in these ecologies but levels of cultivation are well below the upper bounds, suggesting sub-optimal use.

# _Model validation_

To replicate the base year, equivalent to the 2016 survey data, it was necessary to establish upper bounds on acreage for the two ecologies with the highest yields i.e. ‘riverain’ and ‘mangrove’ and lower bounds for the other three ecologies at the base-year level of cultivation. This was necessary to avoid any misspecification of the model constraint set (See Nugent, 1970). The rationale for doing this is that in the unbounded simulations, more use acreages were allocated to these ecologies as the model ‘optimized’ the objective function. This is already an important finding that suggests that the sector is operating at a sub-optimal level with respect to the maximization of consumer and producer welfare in not expanding the acreage in these two ecologies to take advantage of the higher yields, other things being equal. Lower bounds were established for each of the other ecologies to reflect the respective current base year land usage (area under cultivation).

As table 10 below shows, the model is well validated by the available base year survey data on domestic price, consumption, domestic production and imports. There is currently no export of rice from Sierra Leone, as is reflected in the survey data as well as the model results.

**Table 10: Baseline results of the model compared with survey data**

|Scenario|Consumer &<br>Producer<br>Surplus (US$ Mn)|Domestic<br>Consumption<br>(MT)|Domestic<br>Production<br>(MT)|Imports<br>(MT)|Price<br>(US$/MT)|Trade<br>balance<br>(Rice)<br>(US$Mn)|Employment<br>(Numbers)|
|---|---|---|---|---|---|---|---|
|**Survey data (2016)**||**1,029,709.0**|**695, 255.0**|**334,454.0**|**770.00**|**(257.5)**|**732,461.0***|
|**Model baseline**|1,328.3|1,030,210.9|694,394.4|335,816.5|768.6|(258.6)|669,340.0|
|**% Deviation**||0.05%|0.12%|0.41%|0.18%|0.43%|8.62%|

Note* Number of heads of agricultural households.

In the baseline simulation, all the results show absolute differences of less than 1 percent from the 2016 survey data, except for the employment data. The model simulated employment is 63,121 less (8.62 percent) than the survey data of 732,461. It is not clear whether this is a lower employment bias from the model since the survey data is only recording the number of heads of agricultural households. It may be possible that such household heads may in fact be employed outside of the rice sector while their rice farms remain fallow. Furthermore, since the model is ‘efficient’ in the allocation of labor resources, the difference may be truly reflective of the underemployment in the rice sector.

To allow the model to fully optimize the objective function in a more normative sense, only upper bounds on acreage were set to reflect the physical land availabilities in all five of the rice ecologies as represented in Table 1. This adjustment is equivalent to changing the land constraint to: _{Land constraint}_ Where ∑XXcc cc ∗aa represents the five main ecologies. ttcc ≤ ∑AAEE The results of this normative baseline simulation are shown in Table 11 below. The simulated opening of EE The results of this normative baseline simulation are shown in Table 11 below. The simulated opening of the Riverain and Mangrove ecologies, the two most productive, produced some dramatic shifts in the rice sector. First, the consumer/producer surplus is 5.5 percent higher (US$1,401.6 million) compared to 20