204 G. Hudson, R.V. Birnie Agricultural and Forest Meteorology 101 2000 203–216
ities derived from measurements of dynamic variables e.g. temperature are converted to static variables for
the purposes of land evaluation. For example, tem- perature measurements are converted to land qualities
such as length of growing period or accumulated temperature summed over a growing period. These
land qualities, derived for a single seasonal cycle, are then summarised over a sequence of consecutive
years, generally using robust statistics e.g. median. Long-term summaries are used to construct empirical
land evaluation systems, exemplified by Bibby et al. 1982 and the FAO 1996. A key weakness in using
summarised land qualities is that by treating dynamic variables in a static way much of the variability that is
an essential property of the land is removed. Farmers do not farm average landscapes under average cli-
matic conditions. So, whilst land evaluation methods based on this approach are of value in land use plan-
ning, for land management decision making it may be more useful to have information on variability from
which risk may be assessed.
There are few examples of land evaluation where dynamic variables have been explicitly included. van
Lanen et al. 1992 showed that it is possible to de- velop a mixed approach for incorporating weather
variability. In their method, land is first classified qual- itatively using biophysical or socio-economic data to
form land evaluation units. Then dynamic variables are re-incorporated uniformly within each land evalu-
ation unit using a representative climate station. They have applied this approach using simulation modelling
with data from single climate stations. This attempt to incorporate dynamic elements is rare and a recent
review by Rossiter 1996, in which he explored the theoretical basis that could underpin land evaluation,
highlighted the lack of internationally accepted meth- ods for incorporating dynamic variables.
This paper explores the effects of long-term decadal and short-term annual weather variability
on the classifications derived from land evaluation systems. The aim is to develop a robust and repro-
ducible method for incorporating weather variability into land evaluation to make it more relevant to land
management problems. The method is developed us- ing the Land Capability for Agriculture LCA clas-
sification system as applied in Scotland Bibby et al., 1982 as a case study. The effects of using weather
data from two different periods on the LCA classifica- tion are described. In addition, a method is developed
to enable inter-annual variability in weather to be quantified in terms of the mean return time to a land
category based on the LCA classes. The mean return time is derived from analysis of transition sequences
between categories, and uses concepts developed from formal risk assessment procedures. Finally, regional
estimates of the mean return times are mapped using interpolated weather data to show how the risk can
be expressed geographically.
2. Methods
2.1. The Land Capability for Agriculture System The LCA system Bibby et al., 1982 was devel-
oped for Great Britain by an expert group but was applied only in Scotland. It is based on the concept
of flexibility of use of land and was inspired by the work of Klingebiel and Montgomery 1961 in Amer-
ica and the FAO 1984. The LCA system comprises seven classes with some sub-divisions, described in
Table 1, based on a wide range of land qualities de- rived from topographic, soil and climatic variables. In
this study, only the climatic component of the classifi- cation, based on temperature and moisture supply, was
considered. There are seven climatic LCA classes, of which two are sub-divided — 1, 2, 3
1
, 3
2
, 4
1
, 4
2
, 5, 6 and 7. The class boundaries in relation to accumu-
lated temperature and maximum potential soil mois- ture deficit are shown in Fig. 1.
The climatic LCA classes were constructed using weather data to derive annual values of accumulated
temperature above 0
◦
C AT0 and median maximum potential soil moisture deficit in mm MaxPSMD.
AT0 was defined as the accumulated sum of de- grees above 0
◦
C for each day from 1 January to 30 June. MaxPSMD is the theoretical maximum mois-
ture deficit under a complete cover of short grass with no limit on water supply, computed by accu-
mulating the daily moisture deficit between rainfall and evapotranspiration. The maximum value of the
accumulated deficit during the year was used for the general climate classification considered here.
For the published LCA classification, weather data from 96 stations throughout Great Britain were used
G. Hudson, R.V. Birnie Agricultural and Forest Meteorology 101 2000 203–216 205
Table 1 Description of Land Capability for Agriculture Classes
a
Class Category
Description 1
Prime Land capable of producing a very wide range of crops, including winter harvested vegetables.
Yields are consistently high. 2
Prime Land capable of producing a wide range of crops, but not suitable for winter harvested crops.
3
1
Prime Land capable of producing a moderate range of crops. Land in this division is capable of producing
consistently high yields of a narrow range of crops principally cereals and grass or moderate yields of a wider range potatoes, field beans or other vegetables.
3
2
Non-Prime Land capable of producing a moderate range of crops. The land is capable of average production
but high yields of barley, oats and grass are often obtained. 4
1
Non-Prime Land capable of producing a narrow range of crops with high yields of grass. Harvesting may be
difficult. 4
2
Non-Prime Land capable of producing a narrow range of crops with high yields of grass. Harvesting difficulties
may be severe. 5
Non-Prime Land capable of use as improved grassland. Mechanised surface treatments are possible ranging
from ploughing to surface seeding. 6
Non-Prime Land capable of use only as rough grazings. Climatic, soil or site factors generally prevent the use
of tractors-drawn machinery. 7
Non-Prime Land of very limited agricultural value.
a
Bibby et al., 1982.
Fig. 1. Partition of median maximum potential soil moisture deficit and lower quartile accumulated temperature above 0
◦
C into climatic Land Capability for Agriculture LCA classes. The points show the long term LCA for 12 stations in 1958–1978 and 23 stations in
1961–1980. Points depicting stations with climatic data from both periods are connected with a solid line.
206 G. Hudson, R.V. Birnie Agricultural and Forest Meteorology 101 2000 203–216
to compute AT0 and MaxPSMD for each station and year combination. The data were from a non-standard
period, 1958–1978. The annual AT0 and MaxPSMD values were summarised for each station, using quar-
tile statistics to reduce the impact of extreme values. The lower quartile value of AT0 and the median value
of MaxPSMD were used in devising the empirical climatic LCA classification. A plot of lower quartile
AT0 against median MaxPSMD for these climate sta- tions was used to draw climatic LCA class boundaries.
The class boundaries are shown on Fig. 1, together with the positions of 23 Scottish stations used in this
case study. This subdivision of warmth and moisture into LCA classes provides an empirical classification
of Great Britain, in terms of the climatic conditions influencing flexibility of cropping.
In Scotland, a simplification of the LCA system for planning purposes was developed in National Planning
Guidelines for agricultural land Scottish Develop- ment Department, 1987. In these guidelines, ‘Prime’
P land comprises LCA Classes 1, 2 and 3
1
and ‘Non-Prime’ NP land comprises the remaining LCA
classes, which have less flexibility in land use. The intention was to identify Prime land and protect it
from sterilisation by irreversible development e.g. building. Although a range of other soil and topo-
graphic factors influence the designation of Prime or Non-Prime land, only the climatic limitations are con-
sidered in this study.
2.2. Climate data There are no conventions about the length of pe-
riod used when summarising climatic data for land evaluation, and in theory any period could be used. In
addition, there are no definitive methods for obtaining spatial estimates of weather variables for mapping
land classes. The published LCA classification was developed and applied using daily weather data from
the period 1958–1978. For the comparison carried out in this case study, limited to Scotland, the LCA
classes derived by Bibby et al. 1982 were compared to those derived from the nearest available 20-year
reference period, 1961–1980. There are 23 Meteo- rological stations in Scotland for which comparable
data were available Fig. 2. These data are provided by the Meteorological Office within their Rainfall
and Evaporation Calculation System MORECS, and comprise measured and modelled values for
meteorological variables at individual climate sta- tions. As an additional part of the MORECS data, a
standard procedure developed by the Meteorological Office Field, 1983 provides values interpolated from
the stations for 40 km × 40 km grid squares, on a monthly or weekly basis Fig. 2.
The 1961–1980 MORECS data used in this study were summarised over three different time steps. The
station data comprised daily totals of rainfall and evapotranspiration and weekly means of tempera-
ture, whereas the 40 km × 40 km squares comprised monthly values. The latter are, however, computed
by the Meteorological Office from daily observations. At the 23 MORECS stations, we used weekly tem-
perature data to compute AT0 Table 2 and Fig. 2. To compute MaxPSMD, PSMD was computed first,
from daily rainfall and evapotranspiration, with evap-
Table 2 Accumulated temperature AT0 in degrees C, maximum Poten-
tial Soil Moisture Deficit MaxPSMD in mm and climatic Land Capability for Agriculture LCA at the climate stations having
Meteorological Office Rainfall and Evaporation Calculation Sys- tem MORECS data for 1961–1980
Climate Elevation
AT0 MaxPSMD
Climatic station
m LCA
Haddington 49
1190 −
258 1
Turnhouse 35
1196 −
183 1
Kinloss 5
1151 −
227 1
Leuchars 10
1132 −
166 2
Stirling 46
1228 −
134 2
Paisley 32
1352 −
125 2
Auchincruive 48
1259 −
124 2
Mylnefield 30
1160 −
135 2
Dumfries 49
1217 −
99 3
1
Faskally 94
1098 −
117 3
1
Dyce 65
1060 −
125 3
1
Fortrose 5
1189 −
100 3
1
Bush House 184
1032 −
123 3
1
Wick 36
1034 −
104 3
2
Fort Augustus 21
1154 −
79 4
1
Blyth Bridge 253
976 −
91 4
1
Dundeugh 119
1051 −
63 4
2
Penwhirn 166
1038 −
64 4
2
Kirkwall 26
1057 −
71 4
2
Braemar 339
846 −
111 4
2
Stornoway 15
1147 −
54 5
Prabost 67
1168 −
49 5
Eskdalemuir 242
951 −
50 5
G. Hudson, R.V. Birnie Agricultural and Forest Meteorology 101 2000 203–216 207
Fig. 2. The locations in Scotland of the 23 climate stations and of the Meteorological Office Rainfall and Evaporation Calculation System MORECS squares.
otranspiration modelled using the standard method Penman, 1948 that was used in the published cli-
matic LCA classification. Any residual deficit at the end of December was carried forward into the next
year. The accumulated daily PSMD values were then used to obtain the MaxPSMD for each year. In the
MORECS squares, monthly values of temperature were used to compute AT0 directly, by allocation of
the month mean to each day and accumulating as for daily values. The PSMD values for the end of each
month did not adequately represent the annual Max- PSMD. Using the daily values for the 23 stations, a
regression was derived to predict daily MaxPSMD from end-of-month MaxPSMD. The regression was
used to predict daily MaxPSMD in each MORECS square from the end-of-month MaxPSMD.
208 G. Hudson, R.V. Birnie Agricultural and Forest Meteorology 101 2000 203–216
2.3. Risk assessment In this paper, the interpretation of risk follows that
outlined by Warner 1993, who defines risk as the probability that a particular adverse event occurs dur-
ing a stated period of time. An adverse event or haz- ard is defined as an occurrence that produces harm,
i.e. loss consequent on damage, where damage is the loss of some inherent quality suffered by a physical
or biological entity.
Following Warner’s terminology, risk assessment is a general term used to describe the study of decisions
subject to uncertain consequences and comprises three stages — risk estimation, risk evaluation and risk man-
agement. In this study only risk estimation is covered. To do this, we identified hazard with the Non-Prime
land category and estimated the probabilities of oc- currence of the categories Prime and Non-Prime over
time. There are a number of ways that such a time series of annual switches between climatic Prime and
Non-Prime land can be summarised. For example, the proportion of time spent, or the mean number of con-
secutive years in the climatic Prime category could be quoted. In this study, probabilities of occurrence were
estimated from the transitions between climatic Prime and Non-Prime land in consecutive years. This is a
simple form of Markov-chain analysis and has, for ex- ample, been applied to daily rainfall Gabriel and Neu-
mann, 1962. The transitions between climatic Prime and Non-Prime land are calculated using a sequence
of years rather than days as used for rainfall, but the technique is applied in identical fashion. The principal
result is a matrix of transition probabilities. From this matrix, assuming stationarity of the series no trend,
we can estimate two forms of risk: the probability of continuous sequences of years that land is in the cli-
matic Prime category and the mean return period in years to climatic Prime land from Non-Prime land.
3. Analyses
