crop modeling in intercrops and intercropping

1. PRESENTED BY
DEBASIS DAS
DEPARTMENT OF AGRONOMY

2. HOW A CROP MODEL POSSIBLY CAN
HELP ?
• Problem identification of expected agronomic and
economic impact at farmer level.
• Determination of crop responses to the several
major limiting factors.
• Choice of crops and cultivars.
• Evaluation of a new intercropping system.

3. WHY IT IS IMPORTANT TO ASSESS
INTERCROPPING THROUGH CROP MODEL ?
• Complexities in interspecies interaction
,research in intercropping is still lagging behind
in comparison to monoculture systems.
• Most intercropping studies have focused on
issues of yield, economy and food value of
crops, basing conclusions on measures of final
yield.
• To enhance interest in intercropping research
and possible farmer adoption.

4. Resource utilisation in intercrop systems
• The success of an intercrop has often been
attributed to compatibility of component crops in
resource utilization.
• These resource interactions can be classified as
competitive or non-competitive.
• Water, radiation and nutrients are major limiting
resources affects intercropping system

5. Approaches for modelling resource use
in intercropping systems
Intercrop models can be divided into three groups
depending on spatial compartmentalization -:
1. de Wit approach
2. Discrete crop-based approach
3. Dual-species canopy approach

6. de Wit approach
• Initial models includes the static photosynthesis
model and crop respiration to measure the
biomass and growth rate per day basis.
• This model mainly focuses at the canopy level and
these models often assume as ‘big leaf’ structure
(Malézieux et al. 2009).
• It includes ELCROS and BACROS comprehensive
models.

7. • Later in 1980’s development of advanced models
were used for diverse applications from
determining yield potentials to improving pest,
nutrient and water management e.g. SUCROS,
WOFOST, ORYZA, PAPRAN and INTERCOM.
• In 1990’s more advanced model were developed
such as in wide applications at farm level (e.g. In
SARP), regional yield forecasting (CGMS-Europe),
and in regional land use studies (e.g SOLUS,
SysNet and RMLA)

9. Discrete crop-based approach
• This approach to modelling is used by describing the
intercrop as a series of discrete crop-based points
with flow of energy and mass between each
component.
• For each layer, biophysical attributes are calculated
and canopy scale fluxes are obtained by integrating
them over the depth of the canopy.
• Here variables like solar radiation, wind speed that
are sensitive to canopy features are taken while less
sensitive variables e.g. carbon dioxide concentration
are held constant throughout the depth of the
canopy

10. • An advantage of the multilayer method is that
spatial discretion within a heterogeneous canopy
and we can estimate even a point variations in the
field.
• But this approach is complex, requires a large
quantities of data, making it time consuming and
costly.
• Examples of such models include the Water
Nutrient and Light Capture in Agroforestry Systems
(WaNulCas)

11. Dual-species canopy approach(3D)
• This approach gives a realistic description of the
complex dual-species canopy in 3 dimensional
way.
• These models estimates variation in light
attenuation inside canopy in horizontal , vertical
and diagonal directions.
• The main function of this model is to simulate
processes involved in plant growth and
development, such as photosynthesis, ET and
photomorphogenesis.

12. DIFFERENT MODELS FOR
INTERCROPPING
It is divided into two types -:
1. Those which are able to model plant growth
within a field by taking soil, atmosphere and
management information into account in a more
dynamic and mechanistic way.
2. Those which are more specified to
intercropping phenomenon and are more static
and empirical.

13. 1. ALMANAC
 Maize and soybean
 Dynamic, process-oriented plant growth, water
balance and nutrient balances model
2. APSIM
 Maize with leguminous shrub
 Simulating agricultural production systems,
different soil, biological and managerial
processes that are arising from interactions
between different crops grown in rotation

14. 3. AUSIM
 Maize and cowpea
 Morphological, physiological and phenological
model by linking the respective sole crop models
4. GAPS
 Dynamic simulation of interspecies competition
in agricultural systems
 Dynamic model of the soilplant- atmosphere
systems where multiple plant species are grown
in competition

15. 5. GROWIT
 Millet-cowpea
 Stochastic model use to estimates plant
growth by integrating over a continuous growth
function depending on air temperature
6. INTERCOM
 Celery and ALLIUM sps
 Process-based eco-physiological model,
simulates dynamically competition processes
based on physiological, morphological and
phenological processes

16. 7. STICS
 Pea and barley
 Whole-field soil-plant atmosphere model
over one or several crop cycles.
8. WATERCOMP
 Maize and sorghum
 Transpiration partitioning model with special
regard to spatial aspects of root competition
between intercrops

17. 2. 1. Canopy photosynthesis model in plant
Populations
 Calculate canopy photosynthetic rate as a sum of
leaf photosynthetic rate.
 It also estimates leaf area index, light extinction
coefficient, leaf photosynthetic capacity and
nitrogen allocation between leaves.
2. 2 Radiation transmission model
 Imitates interaction between the radiation in field
and plant structures by the influence of vegetation
on the surface water and energy balance.

18. 0
Source: Knoerzer et al.
Grain yield of maize crop in intercropping and sole cropping

19. Source: Graeff et al.

20. Observed and predicted effects of application rate (t ha–1) of heaped
and pitted manure on maize yields
Source: Chivenge et al.

21. Measured and simulated values of sorghum and maize in intercropping of total crop biomass
for rabi crops of different nutrient treatments.
Source: Dimes et al.

22. Thank you