Computer applications in_crop_production

Computer applications in crop production

1. Computer applications in crop production Mohammed Jazeel M 2011-11-111 1

2. Introduction The information age brings the potential for integrating the technological and industrial advances into sustainable agriculture production system . The application of the computer in agriculture research-for the conversion of statistical formula or complex model in digital farm for easy and accurate calculation which are found relatively tedious in manual calculation. 2

3. I nformation retrieval system (IRS) It is an environment of people, technologies, and procedures (software) that help find data, information, and knowledge resources that can be located in a particular library Information about available resources is acquired, stored, searched, and retrieved when it is needed. 3

4. Data Mining : Data mining is the process of discovering potentially useful,interesting , and previously unknown patterns from a large collection of data All most all statistical techniques including bioinformatics we are using data mining either it may be in the field of agriculture, medicine or engineering 4

5. Bioinformatics : Bioinformatics integrates the advances in the areas of Computer Science , Information Science and Information Technology to solve complex problems in Life and plant Sciences . The present role of bioinformatics is to aid agriculture researchers in gathering and processing genomic data to study protein function 5

6. Remote Sensing Remote sensing refers to the process of gathering information about an object, at a distance, without touching the object Remote Sensing techniques have a unique capability of recording data in visible as well as invisible (i.e. ultraviolet, reflected infrared, thermal infrared and microwave etc.) part of electromagnetic spectrum 6

7. P henomenon , which cannot be seen by human eye, can be observed through remote sensing techniques eg : the trees, which are affected by disease , or insect attack These can be detected by remote sensing techniques much before human eyes see them 7

8. The application of remote sensing is useful in: C rop production forecasting C rop yield forecast models D rought assessment S oil mapping S oil degradation analysis 8

9. Command area monitoring Flood damage assessment Land suitability mapping I nsect pest infestation forecasting 9

10. Geographical Information System It is a computer-based information system that can acquire spatial data from a variety of sources, then change the data into useful formats, store the data, and retrieve and manipulate the data for analysis Today , GIS has become part of a basic information infrastructure GIS technology is being employed by agriculture researchers: to create resource database to arrive at appropriate solutions for sustainable development of agricultural resources 10

11. Analytical functions of GIS B uffer zones, neighbourhood characterization , and connectivity measurement A particular feature of GIS is the ability to calculate more realistic distance measures among objects based on actual geometry , travel time, and cost, rather than straight-line distance 11

12. Precision agriculture: Precision Agriculture is conceptualized by a system approach to re-organize the total system of agriculture towards a low-input, high-efficiency, sustainable agriculture. 12

13. This new approach mainly benefits from the emergence and convergence of several technologies, including- Global Positioning System (GPS), Geographic information system (GIS) Miniaturized computer components Automatic control Remote sensing Mobile computing Advanced information processing and telecommunications 13

14. Expert Systems: An expert system is a specific kind of information system in which computer software serves the same function expected of an expert The computer is programmed to mimic the thought processes of experts Provides the decision-maker with suggestions as to the best choice of action for a particular problem situation 14

15. Decision Support Systems: Computer systems that provide users with support to analyze complex information and help to make decisions are called decision support systems (DSSs). 15

16. Crop Growth Simulation Models It is a model that describes processes of crop growth and development as a function of weather conditions, soil conditions, and crop management Such models estimate: times of specific growth stages biomass of crop components (e.g., leaves, stems, roots and harvestable products) as they change over time, changes in soil moisture and nutrient status 16

17. Crop simulation models have been classified into three broad categories Statistical models These typically rely on yield information for large areas (such as counties), and identify broad trends The two main trends identified- gradual increase in crop yield, and variation based on weather conditions. 17

18. Mechanistic models These attempt to use fundamental mechanisms of plant and soil processes to simulate specific outcomes Computationally easier than mechanistic models Often give results that are of less accuracy 18

19. Explanatory models Consist of quantitative descriptions of the mechanisms and processes involved that are responsible for the behaviour of the system The behaviour of a crop growth model can be explained by the basic physiological, physical and chemical processes and the effects of environmental factors on them 19

20. Application of Crop Simulation Modelling Environmental Characterization Optimising Crop Management Pest and Disease Management Impact of Climate Change Yield Forecasting 20

21. InfoCrop It is a crop simulation model used to study the impact and adaptation of climate change on mustard, sorghum and maize to climate change in India Model has been validated for dry matter and grain yields of several annual crops Losses due to multiple diseases and pests, and emissions of carbon dioxide, methane and nitrous oxide in a variety of agro environments can be analysed 21

22. WOFOST: World Food Studies A simulation model for the quantitative analysis of the growth and production of annual field crops It explains crop growth on the basis of processes as photosynthesis, respiration and how these processes are influenced by environmental conditions 22

23. DSSAT The Decision Support System for Agrotechnology Transfer (DSSAT) is a software application program that comprises crop simulation models for over 42 crops (as of Version 4.7) as well as tools to facilitate effective use of the models The tools include database management programs for soil, weather, crop management and experimental data, utilities and application programs 23

24. Uses O n-farm and precision management, regional assessments of the impact of climate variability and climate change, water use, greenhouse gas emissions , and long-term sustainability through the soil organic carbon and nitrogen balances used by more than 14,000 researchers, educators, consultants, extension agents, growers, and policy and decision makers in over 150 countries worldwide 24

25. Inputs Daily weather data, soil surface and profile information, and detailed crop management as input Crop genetic information is defined in a crop species file that is provided by DSSAT Cultivar or variety information that should be provided by the user crop’s vegetative and reproductive development stage also added 25

26. Applications Agronomic Studies Seasonal and Risk Analysis Allows users to evaluate alternate management practices for single growing seasons that account for both weather and economic uncertainty The economic uncertainty can be defined 26

27. CropSyst multi-year multi-crop daily time-step  crop simulation model  being developed by a team at  Washington State University 's Department of Biological Systems Engineering Used to study the effect of cropping systems management on productivity (budgeting ) The model has been parameterised for a wide range of crops such as  potatoes, lentils, tea  and  grapes Management options include rotations,  irrigation, fertilization and  tillage 27

28. CERES Models Comprehensive crop-soil system simulation models Consist of six models: wheat, maize, barley, sorghum, millet and rice The CERES models are currently included in DSSAT Version 3.5 The models are one dimensional along a vertical axis and divide soil into several layers (up to 10) 28

29. Various submodels present NTRANS submodel : Soil N-transformation processes are described submodel SOLT: Soil temperature at the center of each soil layer is predicted NFLUX submodel calculates the rate of nitrate movement between layers as the product of the rate of water movement and the nitrate concentration of a layer 29

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