com ppt.pptx .

Computer ppt

1. Introduction to computer based agricultural models

2. MODEL A model is a schematic representation of the conception of a system or an act of imitation or a set of equations, which represents the behavior of a system Model' is expressed as a computer program that can be repeatedly run several times for computing several designed mathematical or statistical expressions (equations) governing crop growth environment relations, given appropriate input data. Act of building a model is Modelling . In other words,it is the process of representing a model which includes its construction and working

3. SIMULATION Simulation of a system is the operation of a model in terms of time or space, which helps analyze the performance of an existing or a proposed system. • In other words, simulation is the process of using a model to study the performance of a system. It is an act of using a model for simulation. Ex-growth of biomass with time; water use by a growing crop etc

4. SYSTEM ADVANTAGE MODEL Modeling with several parameters such as soil-plant-atmosphere- water interactions which mutually dependent on each other resulting in crop growth, popularly known as the SPAW-system. System models also include economic factors such as operating costs, cost-benefit ratios from the time land is prepared, till transport and marketing of the produce Examples - Oryza model for rice, , DSSAT models etc., which have several component sub-systems

5. SUB-SYSTEM • These are parts of a complex ‘whole’ which themselves could be viewed independently where needed.

• Rainfall-yield model,

• Soil moisture distribution model,

• rainfall-run off model,

⚫ root growth model etc., are all sub-systems.

6. Mechanistic process models • A Mechanistic process model is an depiction of the mechanism involved in a process e.g., photosynthesis, green or dry matter production, soil water uptake and transport by the root system etc.,

Models for crop growth are designed to simulate daily growth of a plant including all known processes in the soil-plant-water- environment system. They include water-fertilizer uptake and their transport, effect of flood and water logging, effect of pest-disease incidence etc., popularly known as the dynamic crop growth simulation models.

7. Operational models Operational models which are for day-to-day field operations in relation to the SPAW system can be developed to simulate crop growth using known relations such as statistical, empirical, mathematical or graphical models, based on data availability, regional and local crop-environmental conditions.
For example, an operational model can be developed to answer a question such as:
How many days would it take for the field to be free from water logging after a heavy rainfall for a couple of days?

8. CROP WEATHER MODELING-TYPES

Crop-weather modelling is of two types. They are

( i ) Statistical simulation modeling

(ii) Dynamic simulation modeling.

9. STATISTICAL MODELS • Statistical simulation modeling approach is used as research tools for yield forecasting rather than for field operations.

• Statistical models are developed using long term (say 20-30-year series) average values over a long period between two or more parameters, say rainfall and crop yield.

• Statistical functions like linear, curvilinear, multiple regression, orthogonal polynomials etc., are used for modelling.

• Their variability and significance are tested using accurate procedures.

10. LIMITATION • These could assist in making long-term assessment of crop performance on an average over a couple of decades but given the variations in monsoon rainfall, such regressions, more often fail in an individual year.

• In practice, it becomes unusable except to understand the extent of association between rainfall, temperature etc., and yield in general in a locality over a long period. This is a limitation of such regression models in the tropical or sub-tropical region like ours.

Often the experience in the All India Coordinated field trials, is that one year the crop-weather parameter association comes out as significant while the very next year it could be non-significant association leading to erroneous interpretation.

11. DYNAMIC MODEL • Dynamic simulation model computes growth values on a day to day basis using the relations between crop growth parameters and weather parameters.

• It rebuilds the day to day crop growth in mathematical or mechanistic terms (simulation) depending on the magnitude of rainfall (or any other parameters) on a particular day and magnitude of a crop parameter (or other parameters like physiological, soil, biological parameters) representing crop growth till that day.

• Such simulation is continued till harvest time. “Growing the crop on the computer” is a popular phrase.

12. OBJECTIVE ( i ) for academic understanding(research purpose) of crop growth dynamics

(ii) for monitoring crop growth for any possible field action including prediction of crop growth and yield

(iii) for solving field level (extension) problems

(iv) for crop planning in relation to climate change or climate variation, for introduction and assessment of new varieties etc.,

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13. LIST OF AGRICULTURAL MODELS • Sub models

• Graphical and Checklist models

• Crop Environment models

14. SUB MODELS • Sub-model is geared to provide quantitative relationship between the parameters involved.

• For example, root growth subroutine provides information on root growth rates with time, soil depth and moisture for a particular crop and soil type, which are of practical utility in working out water balance or irrigation depth and needs of a growing crop.

• Rainfall-runoff sub-model can provide information on how much of the rain received on a day (a heavy shower) would infiltrate into the soil and get redistributed depending on the rainfall intensity, antecedent soil wetness and root growth.

15. GRAPHICAL AND CHECKLIST MODELS • Besides simulation models, graphical, parametric or checklist models are also useful in day-to-day work in field operational decisions.

• These are developed from thumb rules from past experience and simple relations between crop growth and related environmental parameters.

• For example, at a particular growth stage of crop, afternoon humidity

more than 60percent, a brief rainfall of 3mm or more, temperature between 25 to 30°C is known to initiate a pest/disease development, Such information can be displayed in a graphical form everyday and marked ‘ favourable ’ or ‘ unfavourable ’ using weather data.

• A mere glance at the chart would reveal the situation. No computer model need be run. The country needs such simple models, easy to develop into “EXPERT” systems without much sophistication.

16. CROP ENVIRONMENT MODELS • Crop weather models is designed as operational models that needs weather and agronomic data with no genetic coefficients involved, or not always requiring potential conditions of moisture or nutrients etc.,

• Rainfed agriculture being a dominant practice in the country, with potential conditions being absent in several seasons, rainfall driven models are needed.

17. A few models are listed below that can be written as statistical or dynamic simulation models.
• Rainfall- yield model (atmospheric drought, flood)

• ET-biomass-yield model ---(Yield potential) Rainfall-soil moisture distribution model – (ET, Water balance)

Rainfall soil water balance-yield model

• Rainfall intensity/ surface run-off model ----- (water harvesting)

• Water-nutrient uptake ---yield models Yield potential models with constraints like drought, water logging, pest/disease etc.

18. MODELING – ADVANTAGES Easy to understand – Allows to understand how the system really operates without working on real-time systems. • Easy to test test-Allows – Allo to make changes into the system and their effect on the output without working on real-time systems. Easy to upgrade – Allows to determine the system requirements by applying different configurations • Easy to identifying constraints – Allows to perform bottleneck analysis that causes delay in the work process, information, etc.
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19. MODELING-DISADVANTAGES • Designing a model is an art which requires domain knowledge, training and experience.

Operations are performed on the system using random number, hence difficult to predict the result.

• Simulation requires manpower and it is a time-consuming process.

• Simulation results are difficult to translate. It requires experts to understand.

• Simulation process is expensive

20. Thank you