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| In-depth Analyses |
| The Agro-Ecological Zones (AEZ) methodology. |
The
crop cultivation potential is certainly one of the most important factors for China's food
security. It describes the upper limit for the production of crops under given
agro-climatic and soil conditions on a specific level of agricultural technology. Various
methods have been used to calculate this upper limit (see for instance: Luyten / Qinghua /
de Vries, 1996).
A most detailed and mature methodology is the so-called agro-ecological zones (AEZ)
approach, which was originally developed by FAO and IIASA with support from UNFPA in the
early 1980s (FAO / IIASA / UNFPA 1982) and was then repeatedly improved in several global
and national studies (FAO / IIASA, 1993).
The most recent version of a global AEZ analysis is currently under development in a
collaborative project by IIASA and FAO (see: Fischer / van Velthuizen /
Nachtergaele, 1999). The following discussion is based on preliminary results from the LUC
AEZ study, which investigates the cultivation potential of China, Mongolia, and the Former
Soviet Union on the basis of recently updated soil, terrain, and climate databases. |
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| The AEZ model algorithm |
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understand the basic idea of the AEZ approach let us imagine a hypothetical farmer who has
the task to evaluate the suitability of China's various land areas for crop production. He
would use a whole range of criteria to assess each unit of land, such as the quality of
the soil, the local climate conditions, and the possibilities of using different types of
agricultural input (fertilizers, pesticides, machinery). The farmer would also consider
various kinds of crops, because a particular area might be very suitable for one
particular crop (for instance rice), while only moderately suitable for any other. The AEZ
algorithm proceeds in the same way. However, the model systematically tests the
growth requirements of 154 major crop types (including 83 types of grains)
against a very detailed set of agro-climatic and soil conditions. For China the
model operates on a 5 by 5 kilometer grid; so the total grid matrix has 810 by 970 cells,
of which some 374,814 grid cells cover the Mainland of China. Water bodies are
automatically excluded. In each of these land-related grid cells the AEZ model performs
the following (principal) steps: |
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The
algorithm first evaluates the climate conditions. Obviously, crop
cultivation is only possible, if temperature and precipitation are within a certain range.
From an agronomic point of view the key concept is the "potential
evapotranspiration". Plants need a constant supply of water for their metabolism, in
which they loose moisture due to evapotranspiration - especially during the growth period.
In rain-fed agriculture the moisture supply to plants depends on the precipitation and the
water-holding capacity of the soil. Some soils (such as Andosols or Chernozems) can store
water much better than others. A given amount of rainfall might be sufficient for a
particular crop production on a soil with high water-holding capacity, while it might be
insufficient, when the soil lets the water seep away or evaporate. To take into account
these principal differences between soils the AEZ algorithm evaluates the climate
conditions for 6 soil classes of water-holding capacity - from a water-holding capacity of
150 mm to15 mm, depending on soil characteristics and depth. For each soil class the
algorithm calculates a soil moisture balance under local climate conditions. So far, we
have only mentioned temperature and precipitation as climate parameters, but the AEZ
algorithm actually uses a much more detailed set of climate indicators, which include (a)
monthly precipitation, (b) minimum / maximum temperature, (c) relative humidity, (d)
sunshine fraction, and (e) wind speed. For each grid cell the algorithm uses these climate
parameters to calculate a crop-specific potential reference evapotranspiration.
In that procedure the thermal and water requirements during the typical growing periods of
all crops are matched against the actual temperature and precipitation profile of the
current grid cell. Basically, in this first step the algorithm determines how much water
the various crop plants would need under the climate conditions of a particular grid cell.
These crop water requirements vary with croptype, soil class, and climate parameters. |
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The
algorithm uses the results from step one to estimate location-specific potential
biomass and yields for each crop. For this calculation
the program applies a crop model and the Length of Growing Period (LGP)
concept. The algorithm simulates a series of growth cycles on a daily basis for each crop
- with starting-days covering a complete 365-day period from January to December. In other
words, the model tries to match crop-specific growth cycles into the Length of Growing
Period of a particular grid cell, which is determined by its climate conditions. For each
growth cycle the algorithm calculates a crop-specific photosynthesis response in a
two-step procedure: |
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First, the thermal
and radiation conditions of each grid cell (that is temperature profile, day
length, cloudiness, and sunshine duration) are compared with the thermal and radiation
requirements of the crops during their growth cycles. For this calculation the crops are
grouped into four classes (so called adaptability groups), because in some crops
photosynthesis is more sensitive to changes in thermal and radiation conditions than in
others. Crops are also adapted to different temperature ranges. |
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Second, the
actual moisture supply in the particular grid cell is compared with the water
requirements of the crop. The AEZ model essentially calculates - on a day-by-day basis - a
crop-specific soil moisture balance. If the water supply is less than the water demand of
a particular crop, empirical yield-loss factors are applied, which reduce the potential
biomass yield. On the other hand, the water balance also shows the crop-specific
irrigation demand for each grid cell. |
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purpose of this two-step procedure is to determine the starting-date for a growth cycle
that produces maximum potential yield. Under some climate conditions it is
obviously better for a particular crop to start cultivation early in the year, while under
other conditions it might be better to wait for a rainy period. This procedure is repeated
for both rain-fed and irrigated conditions and for three levels of agricultural inputs. To
quantify potential yields, the program basically uses three characteristics: (a) the
so-called Maximum Leaf Area Index (LAI), which is the ratio of leaf area as compared to
the crop cultivation area. (b) The model also uses a so-called Harvest Index, which is the
proportion of the primary produce (e.g. grain) to total biomass. (c) And the crop
adaptability group defines the relationship between maximum rate of photosynthesis and the
day-time temperature. The first two measures vary with the type of crop and level of
input. |
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So
far, the algorithm has only dealt with hypothetical yields; in this step the
model tries to determine the level of attainable production per grid cell. For
that purpose the model applies three types of constraints: (a)
agro-climatic constraints, (b) agro-edaphic (soil) constraints, and (c) terrain
constraints: |
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Agro-climatic
constraints: In addition to the temperature profile and the other climatic
factors, which the model already takes into account in step 1, two climate-related aspects
affecting crop management are taken into account here: (a) workability constraints; and
(b) wetness-related constraints. For instance, if a particular grid cell has a very high
level of soil moisture (as determined by step one), harvest operations with machinery can
become difficult, so that a high-input level cultivation may become impossible. On very
dry and hard soils, on the other hand, ploughing is more difficult, which also limits the
workability. The procedure also takes into account that humid conditions typically reduce
yields through more frequent pests and crop diseases. |
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Agro-edaphic
constraints: These deal with soil-chemical and -physical constraints to crop
production (in addition to the soil-moisture factors, which the model includes in the
calculations of step 2). The AEZ model uses an agro-edaphic suitability classification,
which was developed by FAO and other organizations and provides additional information on
soil types, soil texture and soil phase. This soil rating scheme defines the suitability
of each soil unit for each individual crop at defined levels of inputs and management
circumstances. For instance, some soils may be very stony, others may have chemical
problems, which will reduce the attainable crop production. |
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Terrain
constraints: These are limitations of crop production due to
landform characteristics. For instance, soils on steep slopes are much harder to
cultivate than soils in flood plains. They are also more susceptible to erosion and,
consequently, fertility loss. For each grid cell the AEZ model takes into account a number
of these constraints (which are specified by a terrain slope suitability classification)
to further reduce the attainable grain production if necessary. |
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In the
calculation above the algorithm has only considered one crop per year. Obviously,
this would be unrealistic in many grid cells, because often multiple crops can be grown in
one season. To take into account the possibility for multicropping the
AEZ model assigns each grid cell to one of 10 cropping zones for both irrigated and
non-irrigated conditions - from single cropping zones (for cryophilic crops) to triple
cropping zones (for thermophilic crops). Several climatic parameters are used to determine
the cropping zone for a particular grid cell, such as the LGP, the days with minimum
temperature above 5 degrees, the accumulated temperature during the growing period, and
others.
Now everything is prepared for the actual selection of an optimal crop
for each grid cell (or a sequence of up to three optimal crops in the case of
multicropping): (a) The model has calculated the potential yields of all
83 grains under the specific climatic, soil and landform conditions of a particular grid
cell; and (b) the algorithm has assigned each grid cell to a cropping zone. Now
the algorithm has to select those grains among the 83, which maximize production
in that particular grid cell. In the case of multicropping, the algorithm has to combine
up to three grains to find the maximum potential yield. |
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The
selection of the "best" grain for a particular grid cell can be described as the
task of matching the requirements of the grains with the characteristics of a particular
grid cell in such a way that a maximum grain production can be achieved. For
this, all crops are grouped into adaptability classes. Some crops have long growing
periods (more than 120 days) others have short ones (less than 120 days). Some grains are
typically sown before the winter (such as winter wheat), others are adapted to hot
temperatures. The AEZ algorithm uses a scheme of 8 generic crop groups to specify the
typical growth requirements of the crops. Each crop type belongs to one of these 8 groups.
In the case of a single cropping zone the selection of the best grain is
easy. The algorithm compares the grid cell characteristics with the requirements of all
83 grains. Among those grains that fit, the algorithm selects the one that produces the
highest potential production. In the case of multicropping the selection is more
difficult. Of course, the algorithm cannot test all possible combinations of the 83
available crops - this would multiply the time needed for the calculations. Instead only
those grains are tested as a second or third grain, that have the
highest yield in each of the 8 adaptability classes. A number of rules are applied to
guide this selection process (for details see: Fischer / van Velthuizen / Nachtergaele,
1999). They have been designed to make sure that the algorithm uses typical crop sequences
in cultivation cycles. For instance, in the typical double-cropping areas around Shanghai,
the algorithm would select a long-cycle rice or maize crop as the most productive summer
crop, and winter wheat or barley (depending on which is more productive) as the winter
crop - if the combination of both grains match the Length of Growing Period of the
particular grid cell. In a triple-cropping zone either three short-cycle crops or two
long-cycle crops are permitted. |
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With
the calculations above the AEZ algorithm can now compare the potential yields of the
selected crop in a particular grid cell with the overall maximum potential yield
of that crop in all other grid cells of China. For instance, the algorithm might find that
in a particular grid cell the selected wheat has a potential yield of 9 tons per hectare.
This potential yield is now compared with the overall maximum yield for
wheat in China (which might be 10 tons per hectare). Now the algorithm "knows"
that in this grid cell one can potentially produce 9 / 10 = 90% of the maximum yield,
which would be equivalent to a grid cell that is "very suitable" for wheat
production. In other words, the potential yields of the primary crop are used to classify
each grid cell into one of the following suitability classes:
very suitable = yields are equivalent to 80% or more of the overall maximum yield,
suitable = yields between 60% and 80%,
moderately suitable = yields between 40% and 60%,
marginally suitable = yields between 20% and 40%,
not suitable = yields between 0% and 20%. |
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As a
result the AEZ model provides the suitability class for each individual grid cell
and the potential maximal yield estimates by level of input. These are maximal crop
yields, which could be expected, if the land would be cultivated under given
climate and soil conditions. However, not all suitable land can be cultivated.
Some arable land must be set aside for settlements and for the water and transportation
infrastructure. China has a large network of open irrigation canals, which take up a
considerable amount of land suitable for cultivation. Some of the potentially suitable
land is also needed for mining and industrial production sites. Finally, some land with
cultivation potential should not be used for agriculture, because it is still covered by
valuable ecosystems, such as natural forests or wetlands. How much of that land can be
reserved for nature depends on the increase in overall food demand and on agricultural
productivity.
In a final step, the AEZ methodology takes into account non-agricultural land use
within arable areas. First, the digital land-cover map of the Chinese Institute
for Remote Sensing Applications (IRSA) is used to determine the size of the
"usable" land area. The "usable" land area is the total land area,
minus water bodies and (almost) unused land, such as rocky mountains, deserts, forests,
and grassland. This actually usable land is used as the denominator for calculating the
percentages of land areas that are used for settlements, infrastructure, and mining (which
are taken from province-level statistics). Second, this province-specific percentage of
infrastructure and settlements is multiplied by 1.33 to account for future urban and
infrastructure expansion. Third, the potential arable land area from the AEZ
model is reduced according to this province-specific correction factor.
Basically, this procedure reduces the number of grid cells that are suitable for crop
cultivation by a province-specific correction factor for infrastructure. |
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result of this seven-step algorithm the AEZ model produces a database with some 375,000
records that specify for each grid cell the maximal crop-specific yields that can be
expected under given agro-climatic conditions. For each grid cell the AEZ model gives the
distribution of suitability classes, from "very suitable" to "not
suitable". |
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| Discussion |
| The AEZ
approach uses a detailed, spatially explicit methodology that is closely linked
to established agronomic concepts, such as the Length of Growing Period concept. It is
also based on widely available soil, terrain, and climate databases. The modeling results
for China certainly represent the most detailed estimate of the potentially
arable land on the basis of currently available data sets. However, to avoid
misinterpretation of the modeling results, it is important to be aware of the following
restrictions: |
Other
crops
The assessment of China's potentially arable land is based on grain suitability.
While the AEZ model can handle 154 different crops - including non-grain crops such as
oilcrops or cotton - only the 83 grains were used in the China assessment. This
restriction was mainly applied to reduce the time requirements for modeling. The
consequence of this restriction is a more "conservative" estimate for the
potentially arable land. There are a few crops which can be cultivated in areas where
grain production is impossible. For instance some tropical crops, such as bananas or
oilpalms, can be cultivated under climate and soil conditions, which would not be suitable
for grain production. Therefore the model might slightly underestimate the areas
that can be cultivated by restricting the crop range to grains. |
Fallow
requirements
We have used the estimates of potentially arable land to calculate China's maximal
attainable grain production. These estimates are based on the typical grain yields for
low, medium, and high input levels. While there is no problem with the yield estimates for
the high and medium input level, the estimates for the low input level are certainly too
optimistic. They are possible for a few years, but cannot be achieved in the long
run. Sustainable agriculture at low input levels (that is without chemical
fertilizers) needs frequent fallow periods so that the natural soil fertility can recover.
Fallow requirements vary depending on the specific soil and agro-climatic conditions; but
we must assume that a sustainable yield over long periods is between 10% (for
high-input conditions) and 30% (for medium-input conditions) lower than the typical yield
during the cultivation years. Fortunately, this restriction does not strongly affect the
modeling results, because most of the arable land in China is suitable for high or medium
input agriculture, where fallow restrictions are only minimal. |
Current
land cover and land use
The AEZ algorithm assesses the potential cultivation suitability of a particular
land area, depending on its soil, terrain and climate conditions - without consideration
of its current land cover or actual land use. This was misunderstood by some critics of
previous AEZ models as a weakness of the method, while, in fact, it is one of its
strengths. The objective of the method is to assess whether a particular land area could
be used for cultivation, not to explain why the land is currently used in a particular
way. The method might find a particular land area to be suitable for crop production,
while, in fact, people have used it for a settlement or for recreation. This also applies
for the natural land cover. The AEZ method might find potential cropland areas,
which are currently covered by natural forests or wetlands. The model does not suggest
that these areas should be used for agriculture, it only indicates whether they could
be used for cultivation.
To get a realistic estimate for the maximum cropland area in China the potential
areas have to be reduced by the land requirements for habitation, water and transportation
infrastructure, recreation, and non-agricultural production. A correction procedure was
outlined above. Some areas should also be set aside to preserve valuable ecosystems. The
AEZ approach could be used to find areas for these non-agricultural land requirements outside
of highly suitable cropland areas. It can also show that only about 10% of the area
suitable for crop cultivation is currently covered by non-agricultural ecosystems, such as
grasslands, forests, and wetlands.
Some readers might feel that a 10% reserve for "natural" ecosystems is not
enough. However, the percentage refers to the land that is suitable for crop production.
Some 80% of China's total land area is not suitable for crop production; so there
is abundant space for "natural" ecosystems in China. Most of these largely
unmanaged ecosystems are located in the central, northern, and western parts of the
country. |
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| Some technical details of the AEZ model |
| The AEZ
algorithm is implemented in a series of FORTRAN programs for DOS-compatible personal
computers. However, the complexity of the computations and the huge volume of the spatial
databases that are required prevent easy portability. For instance, one modeling cycle for
China includes the calculations for 31 years of climate data (1958-1988) plus one run for
the average climate - multiplied by 6 levels of water retention capacity of the
soils, plus one run for irrigated land. Each run tests 83 grains against the agro-climatic
conditions of some 374,000 grid cells. The whole procedure would take some 2,300 hours (or
about 96 days) on a 450 MHz Pentium II PC. At IIASA, 6 PCs were used in parallel to run
the China model, which reduced the time for one complete modeling exercise to about 16
days. The run also required several dozen MB of input data and produced some 12 GB of
output files. |
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| revision Heilig, G.K. (2004): RAPS-China. A Regional Analysis and Planning System. Laxenburg, Austria |
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