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. Information 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. Phenomenon, 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:
Crop production forecasting
Crop yield forecast models
Drought assessment
Soil mapping
Soil degradation analysis
8

9. Command area monitoring
Flood damage assessment
Land suitability mapping
Insect 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
Buffer 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
On-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

30. 30