Statistical Model
ii Phonological Model
iii Mechanistic Model
iv Deterministic Model
v Stochastic Model
Dynamic Model
vii Static Model
viii Crop Simulation Models
ix Descriptive Model
x Explanatory Model
contact: dhota3@gmail.com
Global climate change and increasing climatic variability are recently considered a huge concern worldwide due to enormous emissions of greenhouse gases to the atmosphere and its more apparent effect on fruit crops because of its perennial nature. The changed climatic parameters affect the crop physiology, biochemistry, floral biology, biotic stresses like disease-pest incidence, etc., and ultimately resulted to the reduction of yield and quality of fruit crops. So, it is big challenge to the scientists of the world.
Climate change effect on abiotic stress in fruit crops Parshant Bakshi
A change of climate, which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods.
Role of new generation plant bioregulators in fruitSindhu Reddy
In order meet out the emerging consumer demand and challenges towards fruit production, there is the need to explore new interventions. One among that is use of new generation plant growth regulators in fruit crops. Plant growth regulators (PGR), recently name has been changed to plant bio-regulators (PBR’s) are defined as organic compounds, other than nutrients, that in small concentrations, affect the physiological processes of plants. There are five classical growth hormones which have the specific function in growth and development were already commercially exploited in fruit crops, but use of new generation growth regulators in fruit crops are recent and emerging trend. New generation PBR’s includes brassinosteroids, Jasmonate, salicylic acid, polyamines, karrikins and strigolactones and retardants such as 1-MCP and prohexodione-Ca. These are utilized in fruit crops starting from propagation to improving quality also including biotic and abiotic stress resistant. Hence, new generation plant growth regulators are an effective alternative for future fruit production combating major production challenges.
Crop modeling for stress situations, cropping system , assessing stress through remote sensing, understanding the adaptive features of crops for survival under stress .
Global climate change and increasing climatic variability are recently considered a huge concern worldwide due to enormous emissions of greenhouse gases to the atmosphere and its more apparent effect on fruit crops because of its perennial nature. The changed climatic parameters affect the crop physiology, biochemistry, floral biology, biotic stresses like disease-pest incidence, etc., and ultimately resulted to the reduction of yield and quality of fruit crops. So, it is big challenge to the scientists of the world.
Climate change effect on abiotic stress in fruit crops Parshant Bakshi
A change of climate, which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods.
Role of new generation plant bioregulators in fruitSindhu Reddy
In order meet out the emerging consumer demand and challenges towards fruit production, there is the need to explore new interventions. One among that is use of new generation plant growth regulators in fruit crops. Plant growth regulators (PGR), recently name has been changed to plant bio-regulators (PBR’s) are defined as organic compounds, other than nutrients, that in small concentrations, affect the physiological processes of plants. There are five classical growth hormones which have the specific function in growth and development were already commercially exploited in fruit crops, but use of new generation growth regulators in fruit crops are recent and emerging trend. New generation PBR’s includes brassinosteroids, Jasmonate, salicylic acid, polyamines, karrikins and strigolactones and retardants such as 1-MCP and prohexodione-Ca. These are utilized in fruit crops starting from propagation to improving quality also including biotic and abiotic stress resistant. Hence, new generation plant growth regulators are an effective alternative for future fruit production combating major production challenges.
Crop modeling for stress situations, cropping system , assessing stress through remote sensing, understanding the adaptive features of crops for survival under stress .
Crop is defined as an “Aggregation of individual plant species grown in a unit area for economic purpose”.
Growth is defined as an “Irreversible increase in size and volume and is the consequence of differentiation and distribution occurring in the plant”.
Simulation is defined as “Reproducing the essence of a system without reproducing the system itself”. In simulation the essential characteristics of the system are reproduced in a model, which is then studied in an abbreviated time scale.
High Density Planting is a method of densely planting plant with plant population more than the optimum to get higher productivity in terms of quality and yield by manipulating the tree architecture and planting systems such as use of dwarfing rootstock, interstocks, scions, spurs; intensive use of growth regulators, training and pruning, cultural practices and reducing the spacing. The main principle is to improve efficiency of horizontal and vertical space utilisation per unit time, and resources and input utilisation. There is a balance between the vegetative and fruiting structures without affecting the plant health. Advantages include increased productivity, high income, efficient use of resources and mechanisation and operational efficacy
The presentation highlighted about its impact on temperate fruit production and also the suggestion to mitigate its effect. It was presented in a National Seminar on Climate change held at Amity University, Noida, India
CROP MODELING IN VEGETABLES ( AABID AYOUB SKUAST-K).pptxAabidAyoub
crop modeling is future in agriculture to tackle changing environment conditions and increase food security in the world. These models incorporate various factors such as climate, soil characteristics, agronomic practices, and crop physiology to predict crop yields, water usage, nutrient uptake, and other important parameters. Crop modeling helps in understanding the complex interactions between different variables affecting crop growth and assists farmers, researchers, and policymakers in making informed decisions related to crop management, resource allocation, and risk assessment.
Role of AI in crop modeling: Artificial Intelligence (AI) plays a significant role in enhancing crop modeling by leveraging advanced computational techniques to improve model accuracy, efficiency, and scalability. One of the most important aspects of precision farming is sustainability. Using artificial neural networks (ANNs), a highly effective multilayer perceptron (MLP) model. The most common type in crop modeling is DSSAT , DSSAT (Decision Support System for Agro-technology Transfer).The Decision Support System for Agro-technology Transfer (DSSAT) is a software application program that comprises crop simulation models for over 42 crops (as of Version 4.8.2) 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. The crop simulation models simulate growth, development and yield as a function of the soil-plant-atmosphere dynamics.DSSAT and its crop simulation models have been used for a wide range of applications at different spatial and temporal scales. This includes on-farm and precision management, regional assessments of the impact of climate variability and climate change, gene-based modeling and breeding selection, water use, greenhouse gas emissions, and long-term sustainability through the soil organic carbon and nitrogen balances.In conclusion, crop modeling stands as a crucial tool in modern agriculture, offering a systematic approach to understanding and predicting crop growth dynamics in diverse environmental conditions. By simulating the complex interactions between various factors influencing crop development, including climate, soil properties, agronomic practices, and genetic traits, crop models provide valuable insights for farmers, researchers, and policymakers.
‘Crop Modeling for Stress Situation , Assessing Stress through Remote Sensing’AmanDohre
‘Crop Modeling for Stress Situation , Assessing Stress through Remote Sensing’
Crop modeling for stress situations involves utilizing mathematical models to simulate plant growth, development, and responses under various stress conditions. These models integrate data on environmental factors, soil properties, and crop physiology to predict crop performance and yield potential. By simulating stress scenarios such as drought, heat, or salinity, crop models help assess the impact of stress on crop growth and yield, enabling proactive management decisions and adaptation strategies.
Assessing stress through remote sensing involves using satellite or aerial imagery to monitor crop health, stress levels, and productivity. Remote sensing techniques, such as multispectral or thermal imaging, detect subtle changes in plant reflectance and temperature associated with stress-induced physiological responses. These data are processed using advanced algorithms to generate stress indices and maps, providing valuable insights into spatial and temporal patterns of stress distribution across agricultural landscapes. Integrating crop modeling with remote sensing enables more accurate and timely assessments of stress impacts, facilitating targeted interventions and resource allocation for stress mitigation and crop management.
Crop is defined as an “Aggregation of individual plant species grown in a unit area for economic purpose”.
Growth is defined as an “Irreversible increase in size and volume and is the consequence of differentiation and distribution occurring in the plant”.
Simulation is defined as “Reproducing the essence of a system without reproducing the system itself”. In simulation the essential characteristics of the system are reproduced in a model, which is then studied in an abbreviated time scale.
High Density Planting is a method of densely planting plant with plant population more than the optimum to get higher productivity in terms of quality and yield by manipulating the tree architecture and planting systems such as use of dwarfing rootstock, interstocks, scions, spurs; intensive use of growth regulators, training and pruning, cultural practices and reducing the spacing. The main principle is to improve efficiency of horizontal and vertical space utilisation per unit time, and resources and input utilisation. There is a balance between the vegetative and fruiting structures without affecting the plant health. Advantages include increased productivity, high income, efficient use of resources and mechanisation and operational efficacy
The presentation highlighted about its impact on temperate fruit production and also the suggestion to mitigate its effect. It was presented in a National Seminar on Climate change held at Amity University, Noida, India
CROP MODELING IN VEGETABLES ( AABID AYOUB SKUAST-K).pptxAabidAyoub
crop modeling is future in agriculture to tackle changing environment conditions and increase food security in the world. These models incorporate various factors such as climate, soil characteristics, agronomic practices, and crop physiology to predict crop yields, water usage, nutrient uptake, and other important parameters. Crop modeling helps in understanding the complex interactions between different variables affecting crop growth and assists farmers, researchers, and policymakers in making informed decisions related to crop management, resource allocation, and risk assessment.
Role of AI in crop modeling: Artificial Intelligence (AI) plays a significant role in enhancing crop modeling by leveraging advanced computational techniques to improve model accuracy, efficiency, and scalability. One of the most important aspects of precision farming is sustainability. Using artificial neural networks (ANNs), a highly effective multilayer perceptron (MLP) model. The most common type in crop modeling is DSSAT , DSSAT (Decision Support System for Agro-technology Transfer).The Decision Support System for Agro-technology Transfer (DSSAT) is a software application program that comprises crop simulation models for over 42 crops (as of Version 4.8.2) 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. The crop simulation models simulate growth, development and yield as a function of the soil-plant-atmosphere dynamics.DSSAT and its crop simulation models have been used for a wide range of applications at different spatial and temporal scales. This includes on-farm and precision management, regional assessments of the impact of climate variability and climate change, gene-based modeling and breeding selection, water use, greenhouse gas emissions, and long-term sustainability through the soil organic carbon and nitrogen balances.In conclusion, crop modeling stands as a crucial tool in modern agriculture, offering a systematic approach to understanding and predicting crop growth dynamics in diverse environmental conditions. By simulating the complex interactions between various factors influencing crop development, including climate, soil properties, agronomic practices, and genetic traits, crop models provide valuable insights for farmers, researchers, and policymakers.
‘Crop Modeling for Stress Situation , Assessing Stress through Remote Sensing’AmanDohre
‘Crop Modeling for Stress Situation , Assessing Stress through Remote Sensing’
Crop modeling for stress situations involves utilizing mathematical models to simulate plant growth, development, and responses under various stress conditions. These models integrate data on environmental factors, soil properties, and crop physiology to predict crop performance and yield potential. By simulating stress scenarios such as drought, heat, or salinity, crop models help assess the impact of stress on crop growth and yield, enabling proactive management decisions and adaptation strategies.
Assessing stress through remote sensing involves using satellite or aerial imagery to monitor crop health, stress levels, and productivity. Remote sensing techniques, such as multispectral or thermal imaging, detect subtle changes in plant reflectance and temperature associated with stress-induced physiological responses. These data are processed using advanced algorithms to generate stress indices and maps, providing valuable insights into spatial and temporal patterns of stress distribution across agricultural landscapes. Integrating crop modeling with remote sensing enables more accurate and timely assessments of stress impacts, facilitating targeted interventions and resource allocation for stress mitigation and crop management.
CROP SIMULATION MODELS AND THEIR APPLICATIONS IN CROP PRODUCTION.pptxSarthakMoharana
CROP SIMULATION MODELS AND THEIR APPLICATIONS IN CROP PRODUCTION
Crop growth is a very complex phenomenon and a product of a series of complicated interactions of soil, plant and weather.
Crop growth simulation is a relatively recent technique that facilitates quantitative understanding of the effects of these factors and agronomic management factors on crop growth and productivity.
These models are quantitative description of the mechanisms and processes that result in growth of crop. The processes could be physiological, physical and chemical processes of crop.
MAJOR & POPULAR CROP SIMULATION MODELS:
DSSAT (Decision Support System for Agrotechnology Transfer)
Aqua Crop
Info Crop
APSIM (Agricultural Production System Simulator
HIGH-THROUGHPUT PHENOTYPING METHODS FOR ECONOMIC TRAITS and DESIGNER PLANT TY...Komal Kute
A growing world population is expected to cause a "perfect storm" of food, feed, and biofuel. Under the climate change scenario, it is a challenge for agricultural scientists to ensure food and nutritional security for an ever-increasing population with limited and rapidly depleting resources. However, researchers are now observing that conventional breeding methods will not be sufficient to meet projected future demands for foods. To overcome these constraints, plant breeding has evolved over the past two decades towards a much closer integration of high-throughput phenotyping (HTP) tools and technologies.
The "phenotyping revolution" targets extremely precise and accurate measurements of very specific traits in large populations in the field. Sorghum breeding is not new to this advancement, which obviously implies significant shifts in the breeding programs. First, it indicates breeders integrate trait assessment with traditional yield and agronomic evaluation, emphasising that breeding programmes are opened up to new or other disciplines. It additionally requires that these new or other disciplines think about and conceptualise their own actions and orientations from the perspective of how they may fit into a breeding methodology. In this instance, the four primary sorghum breeding domains—staying green and transpiration limitation under high vapour pressure deficit (VPD); nodal root angle and depth; grain mineral content (Fe, Zn); and grain and stover quality traits—are tightly correlated with HTP. These ongoing initiatives focus on value of the particular trait and why it is considered by breeders; how it is measured with HTP approaches (method, throughput, cost, simplicity) and finally, how these traits are currently being embedded in the breeding program. Through various research, it became evident there are several other avenues of technology that, although not yet routinely implemented, could bring about a major benefit to the breeding programme’s endeavour to increase the rate of genetic gains. Here, we discuss the use of drone imaging for yield trial quality control and pinpoint plot heterogeneity, the integration of quality analysis into the assessment of agronomic traits in the field, and the use of X-ray spectroscopy to assess grain or crop architecture traits.
Author: Norman Uphoff
Title: Opportunities to Raise Agricultural Production with Water-Saving and with Climate-Change Resilience for Diverse Crops and CountriesOpportunities to Raise Agricultural Production with Water-Saving and with Climate-Change Resilience for Diverse Crops and Countries
Presented at: The Brown Bag Lunch with Foreign Agricultural Service, USDA
Date: November 6, 2017
Venue: FAS/USDA, Washington D.C.
Presented by: Norman Uphoff, CIIFAD, Cornell University, USA
Presented at: BioVision Alexandria 2010 New Life Sciences: Future Prospects
Date Presented: 04/14/2010
Authors: Norman Uphoff, Vasilia Fasoula, Iswandi Anas, Amir Kassam and A.K. Thakur
Title: Improving the Phenotypic Expression of Rice Genotypes: Reasons to Rethink Selection Practices and ‘Intensification’ for Rice Production Systems
Oral presentation at: The 4th International Rice Congress
Venue: Bangkok International Trade and Exhibition Center, Bangkok, Thailand
Date: October 31, 2014
Presented by: Norman Uphoff, CIIFAD, Cornell University, USA
Presented at: 12th European Rice Millers Convention. Venice
Presented on: September 18, 2009
The effect of leguminous cover crops on growth and yield of tomatoAI Publications
Tomato (Lycopersicon esculentum L.) is one of the vegetable fruit crops commonly cultivated around the globe and used mostly as a flavour in cuisines. Cover cropping is a form of sustainable agriculture which helps to maintain soil fertility and reduces the need and the amount of inorganic fertilizer and thus helps the farmer to increase profitability. The objective of this study was to find the effect of the cover crops on growth and yield of tomato. In this experiment legume cover crops were grown in five treatment plots and these were Bare soil, inorganic fertilizer (NPK 15:15:15), Vigna unguiculata (Cowpea), Mucuna pruriens (Mucuna) and Canavalia ensiformis (Canavalia) in 3 blocks. The results showed that tomato plants grown on Canavalia ensiformis plots showed earlier flowering and fruiting than the other treatments. It also showed significantly higher yield than the other treatments (P= 0.006). The study shows that cover crops especially Canavalia ensiformis could be considered as part any farming system that wants to use sustainable farming to improve soil nutrients and reduce cost of farming.
Presented by: Norman Uphoff, CIIFAD, Cornell University, USA
Presented at: ECHO Conference on Asian Agriculture Chiangmai, Thailand
Presented on: September 21, 2009
Effect of Different Sources of Nutrient on Growth and Yield of Okra (Abelmosc...Agriculture Journal IJOEAR
The experiment was carried out at Nepal Polytechnic Institute field, Bharatpur, Chitwan, Nepal to study the effect of different nutrient sources on growth and yield of okra (Abelmoschus esculentus L Monech). Five different treatments; poultry manure, FYM, goat manure, chemical (as per N equivalent) and no fertilizer (control) were replicated four times. The experiment was arranged in Randomize Complete Block Design (RCBD). The okra variety ArkaAnamika was used for experiment. The data were collected on the growth and yield parameters including plant height (cm), canopy (cm), numbers of leaves per plant, numbers of branches per plant, fruit length, diameter and yield. Results indicated that different nutrient sources had significant (P<0.05) affected on plant height, canopy, leaf number, branches and also in yield parameters. Based on the findings of the experiments, it can be concluded that application of poultry manure significantly increased the growth and yield performances on Abelmoschus esculentus L. Monech (okra) compared to other types of fertilizers. As the study reflected the use of no fertilizer results in the lowest vegetative growth and yield performances which indicates to use some nutrient sources for better growth and production of okra.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
2. i)Defination- Crop, Modeling and Stress
ii)Stress- Brief Introduction
iii)Crop Modeling
Defination
Need
Applications
Impact
Types of Models
Popular Models
Limitation
iv) Cropping System
v) Remote Sensing
vi) Case Studies
vii) Conclusion
3. Crop :
Aggregation of individual plant species grown
in a unit area for economic purpose.
Modeling :
It is an act of mimicry or a set of equations,
which represents the behaviour of a system.
Stress:
A phenomenon that limits crop productivity or
destroys biomass.
7. ABIOTIC STRESS
Any adverse factor acting on physiological processes/
biochemical activity of the plants is called as Abiotic stress.
Air pollution
Mechanical damage
Cold stress
Light stress
High temperature stress
Drought
salt stress
7
8.
9.
10.
11.
12.
13. Research on Interaction of Plant, Soil, Weather and Management Practices.
Prediction of Crop Growth as well as Limiting factors
On farm decision Making and Agronomic management.
Optimizing management using climatic predictions.
Precision Farming and Site Specific Experimentation.
Weather Based agro advisory services.
Yield analysis and Forecasting.
Introduction and Breeding of New Varieties.
Policy Management.
14.
15. i Statistical Model
ii Phonological Model
iii Mechanistic Model
iv Deterministic Model
v Stochastic Model
vi Dynamic Model
vii Static Model
viii Crop Simulation Models
ix Descriptive Model
x Explanatory Model
Murthy, Hyderabad
16. 1. Statistical Model:
These models rely on Statistical techniques such as
Correlation and Regression of the appropriate plant and
environment variable.
Example of Such model is response of crop yield to
fertilizers application.
17. 2. Phenological models:
These models predict the crop development from one
crop growth stage to another. The Prediction is generally
based on accumulated Heat units.
3.Mechanistic Model:
These models explains not only the relationship
between weather parameters and yield, but also the
mechanism of these models (explains the relationship of
influencing independent variable)
18. 4. Deterministic Model:
These models estimate the exact value of yield. It make
definite predictions for quantities without any probability,
variance or random element.
5.Stochastic Model:
When Variation and Uncertainty reaches a high level, it
becomes advisable to develop a Stochastic Model.
For each set of Inputs ,different outputs are given along
with probabilities. It Defines status of dependent variable
at a given rate.
19. 6. Dynamic Model
Time is included as a variable. Both dependent and
independent variables are having values which remain
constant over a given period of time. After which these
variables changes due to change in independent
variable.
7. Static Model
Time is not included as a variable. The dependent and
independent variable having values remain constant.
20. 8. Crop Simulation Model
These models predict the final yield and also provide
quantitative information on intermediates steps like daily
weight of plant parts.
It estimate agriculture production as a function of
weather and soil conditions as well as crop
management.
This model uses one or more differential equation over
time normally from planting until harvest.
21. 9. Descriptive Model
A descriptive model defines the behaviour of a system in
a simple manner. The model reflects little or none of the
mechanisms that are the causes of phenomena. But,
consists of one or more mathematical equations.
10. Explanatory Model
This consists of quantitative description of the
mechanisms and processes that cause the behaviour of
the system such as leaf area expansion, flowering, fruiting
etc. as crop growth is a consequence of these processes.
22.
23.
24.
25. The term cropping system refers to the crops, crop sequences and
management techniques used on a particular agricultural field over
a period of years.
1. Mono-species orchards: Mono-species also referred as
monoculture.
In this, fruit trees of a single species are planted in the field.
This system is common in modern horticulture, where trees are
planted densely, using dwarf or semi-dwarf trees with modified
canopy to ensure better light interception and distribution and ease
of mechanization .
26. 2. Multi-storied cropping : Growing plants of different height in the
same field at the same time is termed as multi-storeyed cropping
Examples of some multi-storied cropping
i Coconut+ banana + pineapple
ii. Coconut+ banana
iii. Coconut+ pasture
iv. Mango+ pineapple
v. Mango+ papaya+ pineapple
vi. Coconut+ jackfruit+ coffee+ papaya+ pineapple
vii. Coconut+ papaya+ pineapple
27. 3. Intercropping:
Intercropping, as one of the multiple cropping systems,
has been practiced by farmers for many years in various
ways and most areas, and has played a very important role
in India.
Care should be taken that there should be no competition
between main crop and intercrop.
28. Mixed Intercropping: Growing two or more crops simultaneously with
no distinct row arrangement .
Row Intercropping: Growing two or more crops simultaneously where
one or more crops are planted in rows.
Strip Intercropping: Growing two or more crops simultaneously in
different strip wide enough to permit independent cultivation but narrow
enough for the crops to interact agronomically.
Relay Intercropping: Growing two or more crops simultaneously in
which second crop is planted after the first crop has reached its
reproductive stage.
Ref: Cropping System in the Tropics: SP Palaniappan & K.Sivaraman
29. Mango Based Intercropping System
Intercrop Treatment
(Kg/ha.)
Net Return
Elephant Foot Yam 80:60:80
107493
Elephant Foot Yam 40:30:40
106271
Sweet Potato 60:40:60 43480
Sweet Potato 30:20:30 42766
Cassava 75:50:75 39000
Cassava 37.5:25:37.5 38500
http://www.krishisewa.com/crop_system/369-fruit-crop-intercropping. html
(Prof. R.K. Bhoyar,Prof. Sevak A. Dhenge and Prof. V. Swami.,CoA,Tiwsa,Amravati (M.H.)
Three root tuber crops are planted in a Mango Orchard with full and
half doses of RDF.
30. Litchi Based Intercropping System
Intercrop Treatment Net Return
Sweet Potato (30:20:30 kg/ha.) 20046
Sweet Potato (60:40:60 kg/ha.) 27527
Elephant Foot Yam (40:30:40 kg/ha.) 108001
Elephant Foot Yam (80:60:80 kg/ha.) 140000
Colocassia (40:30:40 kg/ha.) 41749
Colocassia (80:60:80 kg/ha.) 47833
Turmeric (40:30:40 kg/ha.) 28750
Turmeric (80:60:80 kg/ha.) 32583
http://www.krishisewa.com/crop_system/369-fruit-crop-intercropping. html
(Prof. R.K. Bhoyar,Prof. Sevak A. Dhenge and Prof. V. Swami.,CoA,Tiwsa,Amravati (M.H.)
33. It is a technique used to collect information about an object or
area without actually being in contact with that object or area.
Remote Sensing can be done through Aerial photography or by
satellite imaging.
It may be of two types i.e. active and passive remote sensing
“Passive" remote sensing (i.e., when the reflection of sunlight is
detected by the sensor)
“Active" remote sensing (i.e., when a reflection by the object is
detected by the sensor).
34. Every material on the earth absorbs and reflect the
solar energy. In addition they emit certain amount of
Internal energy.
The absorbed, reflected and emitted energy is
detected by remote sensing instruments or sensors
which are carried by Aircraft or Satellites.
The detection are made by the characteristics term
called “Spectral Signature” and “Images”
35.
36.
37. Spectral indicators of plant chlorophyll content
Chlorophyll pigment content, in particular, is directly associated with
photosynthetic capacity and productivity (Gaussman, 1977; Curran et al., 1992).
Reduced concentrations of chlorophyll are indicative of plant stress (Curran et
al., 1992).
In stressed vegetation, leaf chlorophyll content decreases, thereby changing the
proportion of light-absorbing pigments, leading to a reduction in the overall
absorption of light (Murtha, 1982; Zarco-Tejada et al., 2000).
These changes affect the spectral reflectance signatures of plants through a
reduction in green reflection and an increase in red and blue reflections,
resulting in changes in the normal spectral reflectance patterns of plants
(Murtha, 1982; Zarco-Tejada et al., 2000).
Thus, detecting changes from the normal (unstressed) spectral reflectance
patterns is the key to interpreting plant stress.
40. Journal of Experimental Botany,
Volume 58, Issue 4, 1 March 2007, Pages 869–880
https://doi.org/10.1093/jxb/erl231
Quantification of plant stress using remote sensing observations and
crop models: the case of nitrogen management
F. Baret, V. Houle`s and M. Guerif
INRA-CSE, Site Agroparc, F-84914 Avignon, France
Remote sensing techniques offer a unique solution for mapping stress and
monitoring its time-course.
This article reviews the main issues to be addressed for quantifying stress
level from remote sensing observations, and to mitigate its impact on crop
production by managing cultural practices.
The case of nitrogen fertilization is used here as a paradigm.
It is used for nitrogen stress evaluation by comparison with a reference
unstressed situation.
The combination of remote sensing observations with crop models provides
an elegant solution for stress quantification.
41. International Journal of Agriculture Science
Volume 8 , Issue 1,Januaray 2012: 174-178.
Effect of intercropping systems on growth, yield, fruit quality and leaf
nutrient status of mango under rainfed situation
S.C. SWAIN, S.C. SAHOO AND P.J. MISHRA
College of Agriculture, Orissa University of Agriculture and Technology,
Bhawanipatna, KALAHANDI,Odisha
An intercropping experiment comprised of nine treatments such as mango
ginger, turmeric, tomato, cowpea, French bean, ragi, niger, upland paddy and
control (without intercrop) was laid out in RBD with three replications to assess the
effect of various intercrops on the performance of mango in the rainfed uplands of
Odisha.
Among different intercropping systems tried, mango + guava +cowpea exhibited
better performance which has been reflected in the form of panicle production, fruit
retention, fruit weight and fruit yield of mango.
The leaf analysis result after completion of the study revealed that the N and P
content of mango leaf were found to be maximum under mango + guava + cowpea
intercropping system ;whereas the K content was estimated maximum in the
mango + guava + French bean system.
42. Crop growth model is a very effective tool for predicting possible
impacts of climatic change on crop growth and yield.
Proper cropping system will have the benefit of increased yield and
thus improve the economics of a grower.
Remote sensing (RS) data has become an important tool for yield
modeling as the satellites are taking continuous images which give
an prediction of a crop situation and status for yield estimation and
for adopting suitable management practices.