Crop modelling uses simulations and algorithms to predict crop growth and yields under various environmental conditions. It helps farmers, researchers, and policymakers make informed decisions. There are three main types of crop models: statistical models which use historical data, process-based models which simulate physiological processes, and machine learning models which analyze patterns in large datasets. Crop modelling software is used with data from remote sensing, weather, and soils to optimize crop management, assess climate change impacts, and predict yields. Popular software includes CropSyst, APSIM, DSSAT, CROPGRO, and CropScape.

1. Crop Modelling

2. Introduction to Crop Modelling
Purpose
Crop modelling is a powerful tool used in agriculture
to simulate and predict crop growth, development, and
yield under different environmental conditions. It helps
farmers, researchers, and policymakers make informed
decisions and optimize crop management practices.
Benefits
• Crop modelling offers several benefits, including:
• Improved crop management and resource
allocation.
• Enhanced decision-making for planting, irrigation,
and fertilization.
• Increased productivity and yield.
• Reduced environmental impact through optimized
resource use.
• Risk assessment and mitigation strategies
• Insights into the effects of climate change on crop
production.

3. Types of Crop Models
Statistical Models
•Utilize historical data and
statistical techniques to
analyze and predict crop
yield based on factors such as
weather conditions, soil
quality, and crop
management practices.
•Examples include linear
regression models and time
series analysis.
Process-Based Models
•Simulate the physiological
processes of crop growth and
development, taking into
account factors such as
photosynthesis, respiration,
and nutrient uptake.
•Examples include crop growth
models and crop water use
models.
Machine Learning Models
•Utilize algorithms and
computational techniques to
analyze large datasets and
make predictions based on
patterns and relationships.
•Examples include decision
trees, random forests, and
neural networks.

4. Data Collection and Analysis
Remote Sensing
•Remote sensing techniques,
such as satellite imagery and
aerial photography, are used
to collect data on crop growth
and health.
•These images provide
valuable information on
vegetation indices, land
cover, and crop yields.
Weather Data
•Weather data, including
temperature, precipitation,
and humidity, is collected and
analyzed to understand the
impact of climate on crop
growth.
•This information helps in
predicting crop yields and
identifying potential risks.
Soil Data
•Soil data, such as soil type,
nutrient content, and moisture
levels, is collected to assess
the suitability of the soil for
different crops.
•This data is used to optimize
irrigation, fertilization, and
other agronomic practices.

5. Applications of Crop Modelling
Crop modelling is a powerful tool that can be used in various applications related to agriculture and crop management.
Application Description
Yield Prediction Crop models can be used to predict crop yields based
on various factors such as weather conditions, soil
fertility, and management practices.
Crop Management Optimizing crop management practices such as
irrigation scheduling, fertilizer application, and pest
control.
Climate Change Impact Assessment To assess the potential impact of climate change on
crop production. By simulating future climate
scenarios, researchers can evaluate how changes in
temperature, precipitation, and CO2 levels may affect
crop yields and identify strategies to mitigate the
negative effects of climate change on agriculture.

6. Crop Modelling
Softwares

7. Crop Modelling Softwares
Software Developer Features
CropSyst Agricultural Research Service (ARS) of the
United States Department of Agriculture
(USDA).
Crop growth simulation model.
APSIM (Agricultural
Production System
sIMulator)
Agricultural Production Systems Research
Unit of the Commonwealth Scientific and
Industrial Research Organisation (CSIRO)
in Australia.
Allows simulation of a wide range of
agricultural systems (crops, pastures, trees,
and livestock).
DSSAT (Decision Support
System for
Agrotechnology Transfer)
Developed by a collaborative effort led by
the University of Florida with partners
worldwide.
Includes crop simulation models for over 40
different crops.
CROPGRO United States Department of Agriculture
(USDA) Agricultural Research Service.
Crop growth simulation, Yield prediction,
Water and Nutrient management, Climate
impact assessment.
CropScape National Agricultural Statistics Service
(NASS) of the USDA.
Provides detailed maps of cropland cover and
land use.

8. • AquaCrop is available as a mobile app designed to assist farmers, extension
workers, and researchers in optimizing irrigation management for various crops.
The app provides users with easy access to AquaCrop's functionality and
features, allowing them to simulate crop growth, yield, and water productivity
under different management and environmental conditions directly from their
mobile devices.
• Some Indian origin softwares are also there but they provide solutions not
purely focused on crop modelling but encompass broader agricultural
management, advisory, and decision support systems. Some of these Indian
origin agricultural software solutions include:

9. Fasal: Fasal is an AI-
powered platform
developed by Wolkus
Technology Solutions,
an Indian agri-tech
startup. It provides real-
time crop health
monitoring, pest and
disease detection, and
personalized advisory
services to farmers.
Agribolo: Agribolo is
an Indian agricultural
platform that offers
services such as soil
health testing, crop
advisory, market
information, and weather
forecasts to farmers
through a mobile
application.
Kisan Suvidha: Kisan
Suvidha is a mobile app
developed by the Indian
government's Ministry of
Agriculture and Farmers
Welfare. It provides
farmers with information
on weather forecasts,
market prices, agro-
advisories, and other
relevant agricultural
services.

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