R 12013(crop weather modeling)

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CROP-WEATHER MODELING AGR-411,4(0+4) : Village Attachment DEPARTMENT OF AGRONOMY, INSTITUTE OF AGRICULTURAL SCIENCES, RAJIV GANDHI SOUTH CAMPUS, BANARAS HINDU UNIVERSITY, BARKACHHA, MIRZAPUR .

Transcript of R 12013(crop weather modeling)

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CROP-WEATHER MODELING

AGR-411,4(0+4) : Village Attachment

DEPARTMENT OF AGRONOMY,INSTITUTE OF AGRICULTURAL SCIENCES,

RAJIV GANDHI SOUTH CAMPUS,BANARAS HINDU UNIVERSITY,

BARKACHHA, MIRZAPUR .

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programming

CROP-WEATHER MODELING

“Growing the crop on the computer”

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1. INTRODUCTION

2. CHRONOLOGY OF CROP WEATHER

MODELING

3. STEPS IN MODELING

4. NEED FOR CROP WEATHER

MODELING

5. SIGNIFICANCE OF CROP WEATHER

MODELING

6. APPROACHES OF CROP WEATHER

MODELING

7. BAR’S CLASSIFICATION OF CROP

WEATHER MODELING

8. RELATION BETWEEN CROP GROWTH

& WEATHER

9. COMPONENTS OF CROP WEATHER

MODELING

10. POPULAR CROP MODELS

11. WHO USES CROP MODELS ?

12. APPLICATION OF CROP MODELING

13. IMPACT OF MODELING ON

AGRICULTURE

14. LIMITATION OF CROP WEATHER

MODELING

15. CONCLUSION

CONTENTS :

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Crop-weather modeling, firstly used by “BAIER” in 1979, refers to the techniques that can be used to determine the likely effects of weather on crop, its growth & production.

According to USDA( 2007), crop models are computer programs that mimic the growth and development of crops .

INTRODUCTION

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YEAR DEVELOPMENTS

1960 Simple water-balance models

1965 Model photosynthetic rates of crop canopies (De Wit )

1970 Elementary Crop growth Simulator construction(ELCROS) by de Wit et al.

1977 Introduction of micrometeorology in the models & quantification of canopy resistance (Goudriaan)

1978 Basic Crop growth Simulator (BACROS) [de Wit and Goudriaan]

1982 International Benchmark Sites Network for Agro-technology Transfer(IBSNAT) began the development of a model (University of Hawaii) Decision Support System for Agro- Technology Transfer (DSSAT)

1992 James reviewed the history of attempts to quantify the relationships between crop yield and water use from the early work on simple water-balance models in the 1960s to the development of crop growth simulation models in the 1980s.

1994 ORYZA1 (Kropff et al., 1994)

1994 India’s Ist crop model WTGROWS followed by the construction of ORYZA1N

1995 INFOCROP model developed that can control 16 crops

Chronology of crop simulation modeling

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Water Management N Application + Organic

Crop(Genetic Coefficients(

Development

Mass of Crop Kg/ha

Duration of Phases

Growth Partitioning

Leaf Stem Root Fruit

Weather

CO2

Photosynthesis

Respiration

TemperaturePhotoperiod

Soil

RELATION BETWEEN CROP-GROWTH & WEATHER(S-W-A-P)

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FIG. : EFFECT OF VARIOUS WEATHER CHANGES ON CROP GROWTH (reflects the need of crop weather modeling)

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STEPS IN MODELING Define goals

Define system and its boundaries

Define key variables in system

Preparation of flowchart

Evaluation

Calibration

Validation Sensitivity analysis

Key variables in system :i. State variables are those which can be

measured. e.g. soil moisture content, crop yield etc

ii. Rate variables are the rates of different processes operating in a system. e.g. photosynthetic rate, transpiration rate.

iii. Driving variables are the variables which are not part of the system but they affect the system. e.g. sunshine, rainfall.

iv. Auxiliary variables are the intermediated products. e.g. dry matter partitioning, water stress etc

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A simplified flowchart of ‘BRASSICA’

model

Source: Crop-weather modeling lecture notes by AICRP on Agrometeorology, CRIDA, Hyderabad

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The main purpose of developing the crop-weather models are:

To understand crop weather interactions, processes involved and their

limitations.

To assess the effect of environment, crop genotype and management

of input resources on crop yields, and to quantify the yield gaps with

existing knowledge.

To undertake strategic and policy decisions to increase the

productivity of resource based efficient cropping systems.

NEED FOR CROP-WEATHER MODELING

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Powerful tools for on-farm management, regional land-use issues, policy planning,

scientific investigation and educational activities.

Quantifies knowledge in a format that can provide scientists with techniques and

methodology for evaluation and additional experiments of related theories.

Development of computer software programs that simplify access to simulations

whose results can be used by both scientists and non-scientists.

Serve as decision support systems for agricultural practitioners

Tool for integrating scientific knowledge on whole plant responses to environment

and management variables

SIGNIFICANCE OF CROP-WEATHER MODELING

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APPROACHES OF CROP-WEATHER MODELING

BASED ON CLIMATE UNDERSTANDING

Climatological model

Water-stress model

Dynamic crop-weather model

BASED ON PURPOSE Statistical model Mechanistic model Deterministic model

Descriptive model

Stochastic model

Simulation model

Dynamic model Static model

Explanatory model

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Fig. - Crop-weather Model Approach for different processes and parameters

Source : Short course on Crop weather modeling 2011 by CRIDA, Hyderabad

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BAR’S CLASSIFICATION OF CROP-WEATHER MODELING(1979)

Empirical-statistical model : One or more variables representing weather/climate, soil water availability, crop’s biological character etc., are related to crop responses such as dry matter yield or seed yields.

Crop growth simulation models : Explanatory modeling approach Dynamic in nature Mimics the crop growth based on

quantitative understanding of the underlying processes, that integrate the effect of soil, weather, crop, and pest and management factor on growth and yield.

Crop weather analysis model : These models are based on the product of two or more factors each representing the functional relationship between a particular plant response i.e., crop yield and the variations in selected weather variables at different crop development stages.

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Fig. : Relational diagram of a simulation model at production level 1 (crop-weather interaction)

Source : Short course on Crop weather modeling 2011 by CRIDA, Hyderabad

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Crop Growth Simulation Model – Input & Output

Inputs Process Output

Weather (Temperature, Rainfall, solar radiation(

Soil Parameters (Texture, depth, soil moisture, soil fertility(

Crop Parameters (Phenology, physiology, morphology(

Management (DOS, irrigation, fertilizer(

Phenological Development

CO2 Assimilation

Transpiration

Respiration

Partitioning

Dry matter Format

Biomass, LAI, Yield

Water Use

Nitrogen Uptake

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File x ExperimentalData

FileFile C

Cultivar Code

File A Crop Data

at Harvest

File TCrop Data during

season

Output Depending on Option Setting and Simulation Application

File w Weather Data

File S Soil Data

Crop Models

INPUTS

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COMPONENTS OF CROP-WEATHER MODELING

Metereological data Crop-growth Soil water balance(SWAP CONTINUM)

FIG. : COMPONENTS OF CROP-WEATHER

MODELING

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POPULAR CROP MODELS USED EXTENSIVELY IN INDIA & WORLD-WIDE

DEVELOPED BY THE IARI IN INDIA

ACQUIRED BY INDIA

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Agronomic Researchers and Extension

Specialists

Policy Makers

Farmers and their Advisors

Private Sector

Educators

WHO USES CROP-WEATHER MODELING ?

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Applications of Crop Models` Understanding of research and plants, soil, weather and management interactions

` Prediction of crop growth, timing (Outputs) and weather

` On farm decision-making and agronomic management

` Optimize Management using Climate Predictions

` Precision Farming and Site-specific experimentation

` Weather based agro-advisory services

` Yield analysis and forecasting

` Plant type design and evaluation

` Policy management

` Breeding and introduction of

a new crop variety

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IMPACT OF MODELING ON AGRICULTURE

Evaluation of optimum management for cultural practice in crop production.

Evaluate weather risk via weather forecasting

Proper crop surveillance with respect to pests, diseases and deficiency & excess

of nutrients.

Yield prediction and forecasting

These are resource conserving tools.

Solve various practical problems in agriculture.

Helps to prepare � adaptation strategies to minimize the negative impacts of

climate change

Identification of the precise reasons for yield gap at farmer’s field

Forecasting crop yields.

Evaluate � cultivar stability under long term weather conditions

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Inaccurate projections of natural processesUnreliable and unrealistic projections of changes in climate variability

Crop models are not universal ( no site specificity). Misuse of modelsInappropriate for Heterogeneous plot

Inherent soil heterogeneity over relatively small distancesModel performance is limited to the quality of input data.Sampling errors also contribute to inaccuracies in the observed data. Rudimentary model validation methodology

Plant, soil and meteorological data are rarely precise and come from nearby

sites.An ideal crop model cannot be developed because of complex biological system

LIMITATIONS OF CROP-WEATHER MODELING

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CONCLUSION

An intensely calibrated and evaluated model can be used to effectively conduct research that in

the end save time and money and significantly contribute to developing sustainable agriculture

that meets the world’s needs for food.

Crop-weather modeling is developed as an excellent research tool.

Crop growth model is a very effective tool for predicting possible impacts of climatic change

on crop growth and yield.

Crop growth models are useful for solving various practical problems in agriculture.

Various kinds of models such as Statistical, Mechanistic, Deterministic, Stochastic, Dynamic,

Static, Simulations are in use for assessing and predicting crop growth and yield.

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