What is required to be able to predict land-use intensity?
328 E.F. Lambin et al. Agriculture, Ecosystems and Environment 82 2000 321–331
involution and stagnation. The latter were adverted first in the 1960s by the adoption of high yielding rice
varieties, in the 1980s by a shift to garden crops with high market value, and later with the removal of eco-
nomic and policy barriers to irrigation technologies. Noteworthy are both the socio-economic impediments
or “distortions” Schultz, 1978 in the form of state regulations or institutional structures and similarly
operating environmental constraints Meertens et al., 1996 to the “ideal” trajectory of induced intensifi-
cation. Though the diversity of constraints will be difficult to generalise, the point is made that such
structures can be so significant as to mask the under- lying processes Turner and Ali, 1996, p. 14 986. It
is concluded that it is not the process as a whole that is less developed in conceptual terms, but more the
specifics that divert intensification into the involution and stagnation paths.
In an evaluation of survey results from 67 small farms in the Kiriyaga District of Kenya between
1981 and 1995, Dorsey 1999 provides findings that expand upon induced innovation and intensifica-
tion theory supporting the thesis that — despite claimed negative food production trends in Africa
— farmers are practising highly productive agri- cultural strategies resulting from both subsistence-
and commodity-based production. This rests on the more general observation found in Nigeria by Netting
1993, p. 32 that the main strategy for combining high production per unit area with risk reduction
and sustainability is diversification. The Kenyan case study takes the scale of agricultural production i.e.,
number of hectares under cultivation per farm to ar- rive at a better understanding, and perhaps prediction
of intensification and innovation under the constraints of declining land availability. Indicators of agricul-
tural intensification have been identified as a decrease in grazing land coupled with the effects of a govern-
ment “zero grazing” programme, rising percentage of available land under production, competition for
and fragmentation of land as measured by a decrease in farm size against rising population density, and
widespread capital availability constraints on further production increases. Especially, the latter is consid-
ered to be a crucial indicator. The author states that if the scale at which inputs and credit availability
become constraints can be specified, a more precise conception of induced intensification may be obtained
Dorsey, 1999, p. 181. Strongly correlated are com- mercial specialisation and diversification. They both
have a strong effect on net income per unit area reflecting intensification, while the latter is largely
seen as a function of how much food production goes toward consumption versus sales Dorsey, 1999,
p. 193. Variations in the interconnectedness of the latter may be explained by constraints and behaviour.
Some farmers are risk averse whilst others engage in more risk-taking behaviour. Two other conclusions
seem noteworthy. First, no significant relationship has been found between farm size and intensification and,
secondly, it is stated that the market demand path may have a greater influence on increased production than
population pressure, given that decreases in holding size do not generally lead to declining income per unit
area Dorsey, 1999, p. 192. For other compilations and reviews of studies supporting induced intensifi-
cation, see Brush and Turner, 1987, and Kates et al., 1993.
5. What is required to be able to predict land-use intensity?
Any model prediction can only be based on what is currently known about processes of change. Case
study evidence, however, can highlight the uncer- tainties and surprises inherent in the processes of
land-use intensification. This can both inform model development and reveal a wider range of possible
futures than is evident from modelling alone. More- over, an inductive approach to the understanding of
change processes uses a much larger number of vari- ables than are typically represented in a model. The
understanding of this complexity helps in turn in de- ciding what reductions can be performed in model
development whilst maintaining the validity of the model. Finally, case studies highlight the importance
of decision-making by land managers when facing a range of response options. External driving forces of
land-use change open new andor close old options, but final land management decisions are made by ac-
tors who are influenced by socio-cultural and political factors as well as by economic calculations.
Can the question raised in the title be answered? Inevitably, perhaps, the answer is “partly”, although
there are clear differences in the capability of
E.F. Lambin et al. Agriculture, Ecosystems and Environment 82 2000 321–331 329
different modelling approaches to assess changing levels of intensification. The answer to the question
also depends strongly on the geographic location or socio-economic context of a particular study. It seems
that different modelling approaches are more or less appropriate in tackling the question of intensification
at different locations. One could anticipate, e.g., us- ing different modelling tools or a different mix of
these tools when working in developing rather than developed countries. In general, however, dynamic
simulation models are better suited to predict changes in land-use intensity than empirical, stochastic or
static optimisation models, although some stochastic and optimisation methods may be useful in describ-
ing the decision-making processes that drive land management. Moreover, it appears that an integrated
approach to modelling that is multidisciplinary and possibly cross-sectoral will probably best serve the
objective of improving understanding of land-use change processes including intensification. This is be-
cause intensification is a function of the management of physical resources, within the context of the pre-
vailing social and economic drivers. Because intensifi- cation is driven by management options, the ability to
predict changes in land-use intensity requires models of the process of decision-making by land managers.
Thus, the ability to model decision-making processes is probably more important in land-use intensification
studies then the broad category of model used. For this reason, most landscape change models operating at
an aggregated level have not been used to predict in- tensification. However, there may be opportunities to
develop landscape scale models further that explicitly take account of decision-making processes. Actually,
issues of intensification are also very interesting — and often more policy relevant — at aggregate scales.
A trend toward integrated models is already evident, although the logistical difficulties in developing such
large and complex modelling approaches needs to be recognised. In part, therefore, the future development
of such models may depend as much on the willing- ness of the organisations that fund research as the
researchers themselves because large modelling exer- cises are expensive. Whilst many integrated models
draw heavily on existing approaches, there may also be the need to develop new modelling techniques
that address intensification issues, specifically. The development of new techniques, however, should be
undertaken firmly within the context of the purpose of land-use change models, which is to better understand
land-use change processes. Finally, the development of models of land-use intensification has important
data requirements as one needs spatially-explicit variables on land management, input use, cropping
system, frequency of cultivation, rotations, etc.
Within this context of current model development, there are some simple initiatives that would enhance
the ability of the land-use change modelling commu- nity to improve its understanding and representation
of intensification processes. This includes the devel- opment of models that explicitly incorporate man-
agement and intensification processes. This will need to consider the spatial scale and its influence on the
modelling approach, temporal issues such as dynamic versus equilibrium models, thresholds and surprises
associated with rapid changes, and system feedbacks e.g., the interaction between changes in land manage-
ment and the quality of soil and biological resources. These models should lead to the formulation of sce-
narios which would provide plausible representations of alternative futures where these are unknown, un-
certain or may contain ‘surprises’. Uncertainties and unknowns might include biotechnological change,
social change, or the role of regulation, policy-making and political change. Scenarios would provide an op-
portunity to analyse the sensitivities of land-use and management systems as well as allowing us to explore
different sustainable development strategies and op- tions. Within this context there is a clear need to de-
velop “integrated” scenarios that recognise, in an inter- nally consistent way, that the future will be shaped by
socio-economic as well as biophysical change drivers.