the scope for efficiency-enhancing transfers of land to operators with higher levels of ability. Data on estate performance could allow assessing the extent and the incidence of such insecurity and policy implications.

# **3.2 Assessing overlapping rights using spatial data**

Beyond the textual information discussed above, complete digitization allows us to use spatial data to assess record quality by exploring overlaps among records. The most basic way of doing so is to check for overlaps in the data, which, if records are correct, would imply that land was simultaneously transferred to two different owners. Figure 2 illustrates this by displaying (in black lines) recorded boundaries for all estates as per the registry in one district. Even cursory inspection reveals a large number of substantial overlaps that are unlikely to be due to limited precision of the survey technology used when issuing leases.

District-level figures from analysis of the spatial part of estate leases in table 3 show that 28% of agricultural estates have at least 20% of their area registered to two different owners. Such double-registration affects 10.2% of the area under agricultural estates or 137,064 ha.<sup>18</sup> The share of double-registration varies across districts: figures are highest in Balaka (55%), Kasungu (18%), and Mzimba (9%). The table also highlights cross-district variation in the share of leased area that has expired, a figure that is highest in Dowa (84%), Mzimba (70%), and Mangochi (43%), with a national average of 48%. Double-registration of agricultural estates by lease validity suggests that the problems are slightly more frequent for expired leases (appendix table 2). If this reduces tenure security and incentives for investment or effective land use, a systematic process of ground verification may be needed.

An expanding literature highlights the potential of using remotely sensed imagery for crop forecasting and early warning (Basso _et al._ 2013), including the assessment of cultivation status and possibly yields at field level based on machine learning (Lobell 2013). To capitalize on these advances, medium resolution SPOT imagery from 2013-14 was used to obtain an estimate of the share of registered estate land under different types of land cover (Van Setten _et al._ 2014).<sup>19</sup> Subject to the caveats regarding quality of spatial data noted earlier, these estimates suggest that a sizeable share of estate land seems to be not used for crop production.

The figures in table 4 show that, with some 42% of land under crops in the aggregate, intensity of land use in the estate sector has not increased compared to what had been found by ELUS, implying that farms fail to comply with original plans of full land utilization but at the same time fail to lease out their land. Only about 18% of estates are estimated to use 70% or more of their land for crops. Intensity of land use is highest in the size group below 20 ha, lowest in the 50-500 ha group, and then again increases slightly in the above 500 ha group, similar to what was found by ELUS and in line with the narrative of significant amounts of

> 18 We chose the 20% cutoff to exclude small and non-substantive overlaps that may be due more to the accuracy of mapping. 19 Categories used were maize, other crops, grassland, savannah/shrubs, forest, and built up area including bare land and waterbodies.

10