Crop Modling

8/2/2019 Crop Modling

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Introduction:

Crop modeling can also be useful as a means to help the scientist define research priorities.

Using a model to estimate the importance and the effect of certain parameters, a researcher can

observe which factors should be more studied in future research, thus increasing theunderstanding of the system. The model has also the potential of helping to understand the basic

interactions in the soil-plant-atmosphere system.

, A model isA representation of an object, system or idea in some form

other than that of the entity itself . Its purpose is usually to aid in explaining,

understanding or improving performance of a system. A model is, by definition

Satellite Remote Sensing and GIS Applications in Agricultural Meteorology

TYPES OF MODELS

Depending upon the purpose for which it is designed the models are

classified into different groups or types. Of them a few are :

a. Statistical models: These models express the relationship between

yield

or yield components and weather parameters. In these models relationships

are measured in a system using statistical techniques (Table 1).

Example: Step down regressions, correlation, etc.

b. Mechanistic models: These models explain not only the relationship

between weather parameters and yield, but also the mechanism of these

models (explains the relationship of influencing dependent variables). These

models are based on physical selection.


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c. Deterministic models: These models estimate the exact value of the

yield

or dependent variable. These models also have defined coefficients.

d. Stochastic models: A probability element is attached to each output.

For

each set of inputs different outputs are given alongwith probabilities. These

models define yield or state of dependent variable at a given rate.

e. Dynamic models: Time is included as a variable. Both dependent and

independent variables are having values which remain constant over a given

period of time.

f. Static: Time is not included as a variables. Dependent and independent

variables having values remain constant over a given period of time.

g. Simulation models: Computer models, in general, are a mathematical

representation of a real world system. One of the main goals of crop

simulation models is to estimate agricultural production as a function of

weather and soil conditions as well as crop management. These models

use one or more sets of differential equations, and calculate both rate and

state variables over time, normally from planting until harvest maturity

or final harvest.

WEATHER DATA FOR MODELING

The national meteorological organizations provide weather data for crop


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modeling purposes through observatories across the globe (Sivakumar et al.,

2000). In many European countries weather records are available for over 50

years. In crop modeling the use of meteorological data has assumed a

paramount importance. There is a need for high precision and accuracy of

the data. The data obtained from surface observatories has proved to be

excellent. It gained the confidence of the people across the globe for decades.

These data are being used daily by people from all walks of life. But, the

automated stations are yet to gain popularity in the under developed and

developing countries. There is a huge gap between the old time surface

observatories and present generation of automated stations with reference to

measurement of rainfall. The principles involved in the construction and

working of different sensors for measuring rainfall are not commonly followed

in automated stations across the globe. As of now, solar radiation, temperature

and precipitation are used as inputs in DSSAT.

Weather as an Input in Models

In crop modeling weather is used as an input. The available data ranges

from one second to one month at different sites where crop-modeling work

in the world is going on. Different curve fitting techniques, interpolation,

extrapolation functions etc., are being followed to use weather data in the model

operation. Agrometeorological variables are especially subject to variations in

space. It is reported that, as of now, anything beyond daily data proved

242 Crop Growth Modeling and its Applications in Agricultural Meteorology

unworthy as they are either over-estimating or under-estimating the yield in

simulation. Stochastic weather models can be used as random number

generators whose input resembles the weather data to which they have been


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fit. These models are convenient and computationally fast, and are useful in a

number of applications where the observed climate record is inadequate with

respect to length, completeness, or spatial coverage. These applications include

simulation of crop growth, development and impacts of climate change. In

1995 JW Jones and Thornton described a procedure to link a third-order

Markov Rainfall model to interpolated monthly mean climate surfaces. The

constructed surfaces were used to generate daily weather data (rainfall and solar

radiation). These are being used for purposes of system characterization and

to drive a wide variety of crop and live stock production and ecosystem models.

The present generation of crop simulation models particularly DSSAT suit of

models have proved their superiority over analytical, statistical, empirical,

combination of two or all etc., models so far available. In the earliest crop

simulation models only photosynthesis and carbon balance were simulated.

Other processes such as vegetative and reproductive development, plant water

balance, micronutrients, pest and disease, etc., are not accounted for as the

statistical models use correlative approach and make large area yield prediction

and only final yield data are correlated with the regional mean weather variables.

This approach has slowly been replaced by the present simulation models by

these DSSAT models. When many inputs are added in future the models

become more complex. The modelers who attempt to obtain input parameters

required to add these inputs look at weather as their primary concern. They

may have to adjust to the situation where they develop capsules with the scale

level at which the input data on weather are available.

Role of Weather in Decision Making

Decisions based solely upon mean climatic data are likely to be of limited


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use for at least two reasons. The first is concerned with definition of success

and the second with averaging and time scale. In planning and analyzing

agricultural systems it is essential not only to consider variability, but also to

think of it in terms directly relevant to components of the system. Such

analyses may be relatively straightforward probabilistic analyses of particular

events, such as the start of cropping seasons in West Africa and India. The

principal effects of weather on crop growth and development are well

understood and are predictable. Crop simulation models can predict responses

to large variations in weather. At every point of application weather data are

the most important input. The main goal of most applications of crop models

V. Radha Krishna Murthy 243

is to predict commercial out-put (Grain yield, fruits, root, biomass for fodder

etc.). In general the management applications of crop simulation models can

be defined as: 1) strategic applications (crop models are run prior to planting),

2) practical applications (crop models are run prior to and during crop growth)

and 3) forecasting applications (models are run to predict yield both prior to

and during crop growth).

Crop simulation models are used in USA and in Europe by farmers, private

agencies, and policy makers to a greater extent for decision making. Under

Indian and African climatic conditions these applications have an excellent

role to play. The reasons being the dependence on monsoon rains for all

agricultural operations in India and the frequent dry spells and scanty rainfall

in crop growing areas in Africa. Once the arrival of monsoon is delayed the

policy makers and agricultural scientists in India are under tremendous

pressure. They need to go for contingency plans. These models enable to


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evaluate alternative management strategies, quickly, effectively and at no/low

cost. To account for the interaction of the management scenarios with weather

conditions and the risk associated with unpredictable weather, the simulations

are conducted for at least 20-30 different weather seasons or weather years. If

available, the historical weather data, and if not weather generators are used

presently. The assumption is that these historical data will represent the

variability of the weather conditions in future. Weather also plays a key role

as input for long-term crop rotation and crop sequencing simulations.

CLIMATE CHANGE AND CROP MODELING

Climate change

Climate change is defined as Any long term substantial deviation from

present climate because of variations in weather and climatic elements.

The causes of climate change

1. The natural causes like changes in earth revolution, changes in area of

continents, variations in solar system, etc.

2. Due to human activities the concentrations of carbon dioxide and certain

other harmful atmospheric gases have been increasing. The present level

of carbon dioxide is 325 ppm and it is expected to reach 700 ppm by

the end of this century, because of the present trend of burning forests,

244 Crop Growth Modeling and its Applications in Agricultural Meteorology

grasslands and fossil fuels. Few models predicted an increase in average

temperature of 2.3 to 4.6o

C and precipitation per day from 10 to 32 per

cent in India.


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Green house effect

The effect because of which the earth is warmed more than expected due

to the presence of atmospheric gases like carbon dioxide, methane and other

tropospheric gases. The shortwave radiation can pass through the atmosphere

easily, but, the resultant outgoing terrestrial radiation can not escape because

atmosphere is opaque to this radiation and this acts to conserve heat which

rises temperature.

Effects of climate Change

1. The increased concentration of carbon dioxide and other green house gases

are expected to increase the temperature of earth.

2. Crop production is highly dependent on variation in weather and therefore

any change in global climate will have major effects on crop yields and

productivity.

3. Elevated temperature and carbon dioxide affects the biological processes

like respiration, photosynthesis, plant growth, reproduction, water use etc.

In case of rice increased carbon dioxide levels results in larger number of

tillers, greater biomass and grain yield. Similarly, in groundnut increased

carbon dioxide levels results in greater biomass and pod yields.

4. However, in tropics and sub-tropics the possible increase in temperatures

may offset the beneficial effects of carbon dioxide and results in significant

yield losses and water requirements.

Proper understanding of the effects of climate change helps scientists to

guide farmers to make crop management decisions such as selection of crops,

cultivars, sowing dates and irrigation scheduling to minimize the risks.


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Role of Climate Change in Crop Modeling

In recent years there has been a growing concern that changes in climate

will lead to significant damage to both market and non-market sectors. The

climate change will have a negative effect in many countries. But farmers

V. Radha Krishna Murthy 245

adaptation to climate change-through changes in farming practices, cropping

patterns, and use of new technologies will help to ease the impact. The

variability of our climate and especially the associated weather extremes is

currently one of the concerns of the scientific as well as general community.

The application of crop models to study the potential impact of climate change

and climate variability provides a direct link between models, agrometeorology

and the concerns of the society. Tables 2 and 3 present the results of sensitivity

analysis for different climate change scenarios for peanut in Hyderabad, India.

As climate change deals with future issues, the use of General Circulation

Models (GCMs) and crop simulation models proves a more scientific approach

to study the impact of climate change on agricultural production and world

food security compared to surveys.

Cropgro (DSSAT) is one of the first packages that modified weather

simulation generators/or introduced a package to evaluate the performance of

models for climate change situations. Irrespective of the limitations of GCMs

it would be in the larger interest of farming community of the world that

these DSSAT modelers look at GCMs for more accurate and acceptable weather

generators for use in models. This will help in finding solutions to crop

production under climate changes conditions, especially in underdeveloped

and developing countries.


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23 WHEAT CROP MODEL BASED ON WATER

BALANCE FOR AGROMETEOROLOGICAL CROP

MONITORING

Zahid Rafi

& Rehan Ahmad

Abstract:In this study effort has been made to develop a simple crop model

based on water

balance for crop monitoring. The main distinctive features of the model

are, use of soil

moisture at 30 cm depth, decadal water assessment for the wheat crop,

an index value in

percentage for each decade and for the whole season, which expressed

the performance

of the crop. The maximum cumulative seasonal index value 100 is

apportioned equally

into different decade required by the crop to complete its growth.Different stages are

evaluated on the basis of water deficit/surplus experienced by the crop.

The data used is


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on the basis of experiments conducted on wheat crop in the premises

of the Barani

University, Rawalpindi(33N, 73E), in research farms. The correlation

exhibited

between final yield and soil moisture index is about 98%, which is

considered to be

excellent value. The experiments were conducted from 1999 to 2004.

The years 1999-2000, and 2000-2001 were found generally to be dry

and growth of the crop faced water

stress conditions. No sufficient moisture was available during all

decades and drought

stress occurred at vegetative, flowering and grain filling stages. This

model is useful in

rain fed areas and can give significant result.

Introduction:

Over the last thirty years important progress has been made in the

establishment of the

crop weather model in the world. In one way or another way, these

models are intended

to relate the effect of meteorological parameters e.g. temperature,

pressure, rain, relative

humidity etc to crop yield and production. A big drawback of statistical

model is that


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often they are location specific and they give good results in average or

near average

conditions. It does not work properly during extreme weather

conditions. Crop yield is

the integrated effect of a number of physical and physiological

processes that occur

during the crop-growing period. These processes are influenced by the

characteristics of

the crop, weather, soil and time management factors. Several modelshave been

developed and categorized (Sirotenko, 1994). Models in various fields

like breeding,

soil science, plant physiology, fertilizer response, insect damage and

regional crop

planning are used. A simplified model useful for operational purposes

and able to asses

the crop at any stage of the growth in rain fed areas of the North

Punjab working on

agro-meteorological data and crop data is always desired.

Pakistan Meteorological Department

Pakistan Journal of Meteorology Vol. 2: Issue 3: (March 2005)


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Objectives:

The rain fed areas of Northern Punjab have been relatively neglected bythe research

efforts. Wheat is planted on more than 90% of cropped areas in Rabi

season but average

yields at 1.7t/hac are well below their potential. There is a need for

research in barani

areas of Pakistan to diagnose factors limiting productivity and to

develop

recommendations that can be adopted by formers to improve final

yield.

Importance of Barani Areas:

Barani or rain fed areas make a significant contribution to agriculturalproduction in

Pakistan. Out of total crop areas of twenty million hectors about 5

million hectors

(25%) are rain fed lands with no irrigation. In Punjab 20% of the

cropped areas is

rain fed (PARC/CIMMYT paper 90-2). The main soil in Barani areas have

been

developed from transported material such as alluvium and river

alluvium. They are


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generally medium textured with a fair proportion of clay soils. Crucial

factor in

Barani areas wheat production is moisture conservation (Rain

harvesting technique

may be applied). Weed control is also important when there is

sufficient rain in

early crop stages.

Climatic variations in Research Area:

Rainfall is, of course, the critical factor for crop production; there is

substantial

year-to-year variation in rainfall. The variability in both rainfall and crop

yields is

higher for Rabi season (Nov- Apr) than for the Kharif season (sheikh et

al 1988).

The years 2001-2002, 2002-2003 and 2003-2004 were the best with

regard to

rainfall. The rainfall was evenly distributed throughout the crop season.

The years

1999-2000 and 2000-2001 Rabi seasons were generally poor in terms of

rain fall for

wheat production.

Meteorological Observations:


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Meteorological observations were recorded at 0800,1400 and 1700

(PST) with daily

mean temperature(C), relative humidity (%), rain fall (mm) and also

the soil moisture

at the depths of 5cm, 10cm, 20cm, 50cm,and 100cm were also

observed. In addition to

these pan evaporation (mm), dew duration (hour & minute), rainfall

intensity

(mm/hour), duration of bright sun shine (hour) and solar energy(cal/cm2/day) were also

recorded at Regional Agro meteorological center Rawalpindi, Pakistan.

Research Farms:

This center started agro meteorological observations from 1988 in

experimental

farms of Barani Agricultural University Rawalpindi. Since then the

activity is going

on. The study was conducted on the experimental farms about 300m

west of

meteorological observatory. Field under observation was divided into

four equal

plots and overall ten plants were selected from each plot for

phonological


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observation. These plants were tagged along a row in each plot.

Phonological study

was continued till harvesting.


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Phenological Observations:

The following phases of crop were observed;

1. Emergence

2. Third Leaf

3. Tillering

4. Shooting

5. Heading

6. Flowering

7. Milk Maturity

8. Wax Maturity

9. Full Maturity

10. Harvesting

These phases were observed regularly almost three or four times in a

week and in

case of 50% occurrence of phase of selected plants, it was considered

to be in phase


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and 75% occurrence was considered to be completion of the phase. Soil

moisture

sample were taken from each plots after about every ten days at 5, 10,

20, 30, 40,

50, 70, 90 and 110 cm depths. The soil moisture at 30cm depth is a

critical stage for

crop growth. Hence famous gravimetric method was employed in the

calculation of

soil moisture. This method is laborious but highly reliable.

Details of the model:

This method is based on the cumulative phenological duration crop

water balance,

which gives an index expressing the degree of water requirement

satisfaction index

(WRSI) (Reynold, C.A., 1998). Different components of water balance

have been used.

In this model soil moisture index is taken from the real data and it is not

necessarally100% at the time of sowing. The water balance is the

difference between

precipitation received by the crop and water lost by the crop and soil.

The water retained

by the soil and available to the crop is also taken into account in the

calculation. The


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calculation of the water balance is carried out on special form (FAO

Agro

meteorological rain fed crop monitoring)

Soil Moisture:

The soil moisture in percentage was calculated at the depth of 30cm by

using

gravimetric method.

The Number of rainy days (da)

The number of rainy days to give an idea of the distribution of rainfall

during each

phenological stage.


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Evapotranspiration (ETo)

Climatologically records of temperature, vapors pressure, relative

humidity,

Sunshine duration and wind speed were used for the calculation of

evapotranspiration on the daily basis.

Crop Coefficient (Kc)

To compute crop water requirement during each phenological stage,

crop coefficient


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suitable to the crop stage need to be selected. For initial growth stage it

is 0.3-0.4

and progressively reaches to 1.0-1.2(max) at flowering stages including

roughly 20

days before and after flowering. The water requirements of the crop

progressively

recorded and crop coefficient decreased from 1.0-1.2 to about 0.4-0.5

at maturity

stage.

Crop Water Requirement (WR)

The crop water requirement is computed by multiplying total value of

ETO for each

phenological stage with Kc.To calculate the total water requirements of

the season

from its beginning by summing up the successive water requirements

of each crop

stage.

Water Available (Pa-WR)

If (Pa-WR) is positive, it means sufficient moisture is available to thecrop during

that decade and if it is found negative the crop is considered to be

under water stress


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during that decade.

Water Reserves in the Soil (Rs)

This term expresses the quantity of water existing within the rootingzone of the

crop at a given stage, which can be readily utilized by the crop. The

maximum

quantity of useful water, which may be retained in the soil for a given

crop, is soil

water retention capacity.

Surplus and Deficit (S&D):

The water, more than storage in the soil appears as surplus in the S/D

column. If the

calculation comes to be negative figure, this was shown by minus sign

in S/D

column. Deficit refers to any quantity of water below the zero level of

water

retention capacity of the soil, called permanent wilting point (PWP).

The notion of

water deficit is very important for the calculation of water requirement

satisfaction

index (WRSI).

Cumulative Index (Is):


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Thus considering surpluses and deficits, the actual ten days

contribution were

computed and added to get total cumulative seasonal value (Is) for the

crop

considered. The index (Is) thus expresses the extent to which the water

regimes are

favorable and therefore also indicates the performance of the crop in a

season.


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Seasonal Water Requirement (WR):

The comparison of soil moisture at 30 cm depth and water requirement

calculated from

the ETO (s) and crop coefficient (Kc) for each ten days is shown infigures 2, 3, 4, 5, 6.

In these figures the series 1 indicates the water requirement and series

2 shows actual

water available to the crop during each decade. The crop goes to deficit

in the month of

March and April, which affect the final yield. If at this stage crop is

irrigated by some

artificial means the final yield will be enhanced.

Yield Prediction:


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For wheat, computation of cumulative index (Is) for five seasons (1999

to 2004)

with corresponding yields at different sowing dates, it is possible to

develop a

relationship between Is and yield (Y). This relationship forms a simple

model

capable of predicting yield on the basis of Is value accumulated in a

season.

The new crop water balance model proposed would be tested on thebasis of shifting

sowing date. Five sowing dates were 06Nov, 20 Nov, 30 Oct, 05 Nov, 05

Nov and

crops were harvested after full maturity on 4 May, 30 April, 20 April, 08

May and

26 April respectively. The yields (Y) recorded for these dates were

2000,2025,2400,2694 and 2875 Kg/hac respectively. The soils of

Rawalpindi

(33N, 73E) has clay and its water holding capacity is 32%(mm). The

cumulative

index (Is) five different sowing dates for wheat along with dates ofharvesting,

yields are given in table-1.


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The wheat crop undergoes stress in the first decade of March during

the seasons

1999-2000 and 2000-2001, shown in the figures 2&3. This is the period

of

flowering and grains forming. The years 2001-02 and 2003-04, figures 4

&6, shows

no stress during the whole growth period. The crop observed a little bit

stress during

the year 2002-03, figure 5, in last decade of March, which did not affectthe final

yield. The reduction in the yield is being due to the water stress. The

crop faces

water stress during the flowering and grain filling stages. The graphical

representation between cumulative index (Is) for five different sowing

dates of

wheat and corresponding yield (Kg/hac) is exhibited in figure-1, which is

a straight

line.

Table-1: Seasonal index (Is) according to new model for five different

date of

sowing of wheat with harvesting dates and yield

No Date of sowing and

harvesting


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Cumulative

index (Is)

Yield (Kg/ha)

1 06Nov-04May 50 2000

2 20Nov-30April 47 2025

3 30Oct-20April 60 2400

4 05Nov-08May 69 2694

5 05Nov-26April 76 2875


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Fig1. The linear relation between Index (Is) &

Yield

y = 31.598x + 490.25

R2 = 0.9863

0

500

1000

1500

2000


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2500

3000

3500

0 20406080

Index (Is)

Yield (Kg/ha)

Series1

Linear (Series1)

Conclusion:

The crop water balance at 30 cm depth with various components was

developed. It

exhibited excellent correlation which is r = 0.99 between the yield (Y)

and the seasonal

index (Is). R2 =0.986 the regression equation between the Is and yield is

Y=

490.25+31.59Is, can be used to predict season crop while Is value can

be used to exhibit

crop performance or crop condition in the season. This model was

tested with previous

data and it gives results satisfactorily.

Crop Modling

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