Importance of PGR in fruit production and quality.pdfAbhishek Pratap
This slide focuses on the importance of plant growth regulators in fruit production and quality improvement. As we know very well that the PGR plays an important role in growth and development of plants. Plant growth regulators are chemicals used to modify plant growth such as increasing branching suppressing shoot growth, increasing return bloom,
removing excess fruit, or alternate fruit maturity. The plant hormones are
extremely important agent in the integration of developmental activities.
Environmental factors often exert inductive effects by evoking changes in
hormones in metabolism and distribution within the plant. Apart from it, they also regulate expression of intrinsic genetic potential of plants. Control of genetic expression has been demonstrated for the phytohormones at both transcriptional and translational levels. Also, hormones receptors and binding proteins have been identified on membrane surface that are specific for some hormones. The use of growth regulators has become an important component of agro-technical procedures for most of the cultivated plants and especially for fruit plants. So far in fruit crops, excessive fruit drop can be controlled by the exogenous application of plant growth regulators. The auxin and gibberellins are widely used to control
the fruit drop and to improve the quality of fruit. Ontogenic development from fruit set to fruit ripening and final reach to customer, several agents are responsible for elimination of some fruits from fruit set to final maturity. In this seminar, I will focus on the major functions of plant growth regulators in fruit production.
Importance of PGR in fruit production and quality.pdfAbhishek Pratap
This slide focuses on the importance of plant growth regulators in fruit production and quality improvement. As we know very well that the PGR plays an important role in growth and development of plants. Plant growth regulators are chemicals used to modify plant growth such as increasing branching suppressing shoot growth, increasing return bloom,
removing excess fruit, or alternate fruit maturity. The plant hormones are
extremely important agent in the integration of developmental activities.
Environmental factors often exert inductive effects by evoking changes in
hormones in metabolism and distribution within the plant. Apart from it, they also regulate expression of intrinsic genetic potential of plants. Control of genetic expression has been demonstrated for the phytohormones at both transcriptional and translational levels. Also, hormones receptors and binding proteins have been identified on membrane surface that are specific for some hormones. The use of growth regulators has become an important component of agro-technical procedures for most of the cultivated plants and especially for fruit plants. So far in fruit crops, excessive fruit drop can be controlled by the exogenous application of plant growth regulators. The auxin and gibberellins are widely used to control
the fruit drop and to improve the quality of fruit. Ontogenic development from fruit set to fruit ripening and final reach to customer, several agents are responsible for elimination of some fruits from fruit set to final maturity. In this seminar, I will focus on the major functions of plant growth regulators in fruit production.
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.
Stability analysis and G*E interactions in plantsRachana Bagudam
Gene–environment interaction is when two different genotypes respond to environmental variation in different ways. Stability refers to the performance with respective to environmental factors overtime within given location. Selection for stability is not possible until a biometrical model with suitable parameters is available to provide criteria necessary to rank varieties / breeds for stability. Different models of stability are discussed.
swingle, tanaka, hodgson, and ranjit singh classification of citrus and also description of acid group, orange group, pummelo and grapefruit group and mandarin group, acidlime, sweet orange, mandarins, lime and lemon.
Crop modeling for stress situations, cropping system , assessing stress through remote sensing, understanding the adaptive features of crops for survival under stress .
Genetic variation is crucial for successful barley improvement. Genomic technologies are improving dramatically and are providing access to the genetic diversity within this important crop species. Diverse collections of barley germplasm are being assembled and mined via genome-wide association studies and the identified variation can be linked to the barley sequence assembly. Introgression of favorable alleles via marker-assisted selection is now faster and more efficient due to the availability of single nucleotide polymorphism platforms. High-throughput genotyping is also making genomic selection an essential tool in modern barley breeding.
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.
Stability analysis and G*E interactions in plantsRachana Bagudam
Gene–environment interaction is when two different genotypes respond to environmental variation in different ways. Stability refers to the performance with respective to environmental factors overtime within given location. Selection for stability is not possible until a biometrical model with suitable parameters is available to provide criteria necessary to rank varieties / breeds for stability. Different models of stability are discussed.
swingle, tanaka, hodgson, and ranjit singh classification of citrus and also description of acid group, orange group, pummelo and grapefruit group and mandarin group, acidlime, sweet orange, mandarins, lime and lemon.
Crop modeling for stress situations, cropping system , assessing stress through remote sensing, understanding the adaptive features of crops for survival under stress .
Genetic variation is crucial for successful barley improvement. Genomic technologies are improving dramatically and are providing access to the genetic diversity within this important crop species. Diverse collections of barley germplasm are being assembled and mined via genome-wide association studies and the identified variation can be linked to the barley sequence assembly. Introgression of favorable alleles via marker-assisted selection is now faster and more efficient due to the availability of single nucleotide polymorphism platforms. High-throughput genotyping is also making genomic selection an essential tool in modern barley breeding.
An ecological assessment of food waste composting using a hybrid life cycle a...Ramy Salemdeeb
A conference paper published at the 8th Conference of the International Society for Industrial Ecology, At University of Surrey, Guildford, UK, At Surrey
BC3 Policy Briefing Videos Series: Reports that synthesise the research work carried out by the team from the Basque Centre for Climate Change (BC3). This content is intended to be of use for the agents involved in decision-making on climate change.
This Policy Briefing was authored by Agustin del Prado, Patricia Gallejones and Guillermo Pardo.
It is strongly felt the need to replace fossil fuels with other renewable and more compatible with the environment. The spinneret of algal crops is
producing different solutions ("open ponds", tubular, bioreactors in greenhouses, etc.). The aim is to obtain concentrations of dry substance such as to
justify the high costs of extraction. Another limitation suffered by the actual plants, derives from the choice to move the algal mass (process
characterized by a high energy consumption), with actions necessary to keep it in suspension as well as to move it, to exchange its positioning in order
to bring it to be conditioned by the light (exhausting its effectiveness after the first 0.2-0.3 m of algal mass depth, or even less if thicker and when it
would need more light for its exponential growth). In particular, it is not possible to bring a specific radiation spectrum, in a pervasive and deep way,
with a drastic cost reduction for the mechanical movement of the culture medium. A limitation derives also from the possibility of biological and
chemical contamination from the environment, because the algal mass is in a large contact with the environment itself (e.g. the "open ponds"
situations) and it is heavily exposed to the prevalent thermal cycles (often not suitable to the processes of growth) inside it. Some problems are often
encountered even in the phase of collection and selection of the algal mass to be forwarded to the following processes, that proceeds through the
massive processing of large volumes (by filtration and concentration) that, due to previous limitations (contamination and uncertain conditions of
growth), remain at low concentrations. The purpose of this article is to present the PBRC (Photo Bio Reactor Continuous) plant, subject of an
Italian patent [Lavanga and Farné, 2014], for the cultivation of microalgae, from which to extract an oleic fraction, which can be destined for
production of biofuels, and a protein fraction, which can be destined for use in the chemical, agri-food, pharmaceutical and cosmetic sectors.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Principal Tools for a Cleaner Chemical Technology, presented at the european ...Patrick VanSchijndel
Principal Tools for a Cleaner Chemical Technology, Process improvements have been tremendous in the last century but production volume increase will overshadow these good results in terms of resource use and environmental impact. It will be important to use the right tools in order to achieve the necessary sustainable development within the industry. These tools should be combinations of exergy analysis, LCA and economic analysis. The focus should be on the development of these combinations and on the teaching of these combinations in engineering curricula.
Fuzzy logic for plant-wide control of biological wastewater treatment process...ISA Interchange
The application of control strategies is increasingly used in wastewater treatment plants with the aim of improving effluent quality and reducing operating costs. Due to concerns about the progressive growth of greenhouse gas emissions (GHG), these are also currently being evaluated in wastewater treatment plants. The present article proposes a fuzzy controller for plant-wide control of the biological wastewater treatment process. Its design is based on 14 inputs and 6 outputs in order to reduce GHG emissions, nutrient concentration in the effluent and operational costs. The article explains and shows the effect of each one of the inputs and outputs of the fuzzy controller, as well as the relationship between them. Benchmark Simulation Model no 2 Gas is used for testing the proposed control strategy. The results of simulation results show that the fuzzy controller is able to reduce GHG emissions while improving, at the same time, the common criteria of effluent quality and operational costs.
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
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.
Presentation of Dr. Raymond Tan, DLSU, on "Sustainable Consumption and Sustainable Production" during the UP Manila Conference on Global Climate Change, October 22-23, 2009, Pearl Garden Hotel, Manila.
The aquaponics term derives from the words aquaculture and hydroponics, which by definition, has the meaning of aquatics organisms culture and plant breeding techniques without soil, respectively. This activity has how the main feature the sustainability, once the modality looks for the production with low water consumption and high exploitation of waste generated. The present study had as objective to describe the construction of the aquaponics pilot system. This way, based on the literature and acquired experience during the work, a step-by-step method was established for the assembly of the system. To verify the process efficiency, were analyzed the presence of total and thermotolerants coliforms, counting of facultative mesophiles and quantification of micro and macronutrients in leaves and roots of Xanthosoma sagittifolium. There was no presence of total and thermotolerants coliforms in leaves and roots of X. sagittifolium. In the count of facultative mesophiles the roots presented 6x104 CFU/g and the leaves 1.7x102 CFU/g. In the foliar analysis, 1430mg/kg of Fe was observed in the roots. It was concluded that the pilot project was successfully built and testing can be continued with new plants.
Micronutrients: role and management in fruit crops (2nd doctoral seminar:Panc...Panchaal Bhattacharjee
Micronutrient deficiency is a key isssue to be addressed for sustainable fruit crop production. Here individual micronutrients are discussed in details regarding their role and mangement in fruit crops.
its a improved presentation about kiwi fruit along with available info in slide share by other authors.
interested to have a copy mail panchaal94@gmail.com
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...Wasswaderrick3
In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
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.
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
2. Crop modeling:
Modeling as a preliminary definition one could think of a model
as an attempt to describe a certain process or system through the
use of a simplified representation, preferably a quantitative
expression, that focuses on a relatively few key variables that
control the process or system.
The development of modern agricultural-horticultural crop
models was closely associated with the advent of computer
programming and faster computers during the last decades of the
20th century that facilitated the many calculations needed in
complex models.
3. Modeling of fruit crops-
In the present days we would like to emphasize carbon-based crop
productivity models with a focus on fruit tree models.
The purpose and orientation of the model may differ according to
the researcher’s interest – a variety of problems, Including –
Water use efficiency
Predicting phenology or fruit ripening
Predicting climate effects, evaluation stress responses and/or
pest management
(Boote et al., 2006)
4. 4
Fruit tree Growth Modeling: a multidisciplinary subject
Bio-climatology and Soil Sciences
Botany
Plant Architecture,Phenology
Agronomy:
Ecophysiology
Applied Mathematics
Stochastic Processes, Dynamical
Systems Optimization
Computer sciences
Simulation visualizationPlant Growth Simulation
6. Prerequisites for construction of a Model
• Conceptual understanding of the physiological processes involved.
• Imaginative, quantitative thinking.
• An extensive agricultural/biological database.
6
• A particular limitation for fruit tree crop models is the limited and incomplete database of good
quantitative data for modeling.
The best data and knowledge bases generally are for
• (1) Phenology
• (2) Leaf Photosynthesis (Light And Temperature
Responses)
• (3) Shoot Growth And Leaf Area Development
• (4) Fruit Growth And Respiration.
Some of the major gaps are
•(1) Root Growth Patterns, Respiration, Root Turnover Rates
•(2) Respiration Rates In General – Available data is almost all
short term measurements; responses to
temperature done in short term, not long-term.
• (3) The Seasonal Demands For Carbon Of Different Organs
• (4) Detailed Understanding Of Fruit Abscission Processes
7. 8
A model combining two approaches
Biomass
water Nutriments
CO2
Organogenesis + empirical Geometry = Plant
Architecture.
Plant development coming from meristem trajectory
(organogenesis)
Biomass acquisition (Photosynthesis, root nutriment
uptake) + biomass partitioning (organ expansion)
Compartment level
Morphological models
=> simulation of 3D development
Process-based models
=>Yield prediction as a function of environmental
conditions
Functional-structural models
8. 9
Flowchart for plant growth and plant development
seed
photosynthesis
H2O
Pool of biomass
leaves
roots
Organogenesis + organs
expansion
transpiration
CHO
fruits
branches
GreenLab Plant
9. Architectural modeling
• Architectural analysis was introduced by Hallé and co-workers (Hallé and Oldeman 1970; Hallé
et al. 1978).
• Based on the concept of “axis differentiation” in five main morphological criteria all related to
the meristem activity they have formed “23 architectural models” and are dedicated to famous
botanists.
• Growth direction : (Plagiotropic or Orthotropic)
• Growth rhythm : (Continuous or Rhythmic)
• Branching mode : (Monopodial or Sympodial)
• Sexual differentiation
of meristems : (Terminal or Auxillary)
• Polymorphism of axes : Short(Brachyblasts),medium (Mesoblasts) and long shoots (auxiblasts)
11. • It consists only one meristem and it is not branched.
• The meristem will converts into inflorescence and plant eventually dies
• Ex: Banana
1.Holttum’s model
12. Trunk is single, monopoidal and orthotropic in nature.
• Growth is indeterminate and auxiliary inflorescence is seen.
• Ex: Papaya, Datepalm
2.Corner’s model
13. • Trunk is monopoidal, orthotropic in nature.
• Lateral flowering is seen.
• Ex: Apple.
3.Rauh model
14. • Sympodial and plagiotropic in nature.
• Ex: Annona squamosa
4.Troll’s model
16. Green- Lab Model
This model requires two input files
• The mean leaf area of the shoot, its distribution along the main axis and
mean leaf size (mean shoot file).
• The other file describing plot layout and the number of shoots per plant
(plot file).
18. Figure 3: Comparison of photographs taken in a real vineyard (veraison, stage 35, Coombe) with the
corresponding simulations made for grape training systems.
Louarn et al. 2008
19. L-Modeling
• This plant model is expressed in terms of modules that represent plant
organs.
• Organs are represented as one or more elementary sources or sinks of
carbohydrates.
• The whole plant is modelled as a branching network of these sources and
sinks, connected by conductive elements.
20
20. • In this analogy, accounts the amount of carbon corresponds to an electric
charge.
• Carbon concentration to electric potential.
• Carbon fluxes to current flow.
Daily photosynthesis of individual leaves is represented as an
accumulation of charge that is calculated from the distribution of light in
canopy.
21
21. 22
A formal grammar for plant development (L-system)
Alphabet = {metamers, buds}
(according to their physiological ages = morphogenetic characteristics)
Production Rules : at each growth cycle, each bud in the structure gives a
new architectural growth unit.
Factorization of the growth grammar factorization of the plant into
« substructures »
Computation time proportional to plant
chronological age and not to the number of
organs !
22. 23
Inter-tree competition at stand level
Isolated tree
Central treeTree on the edge
Trees in competition for light
Method of intersections of projected areas
23. Figure . This figure demonstrates the potential of the model to simulate crop load effects on fruit and tree
growth, and carbon partitioning.
• In the first pair, fruit set is altered such
that the crop load in one tree is twice that
of the other.
• In response to this decrease in initial
fruit set, the model produced the
following results.
• by giving Lower variance in fruit size
within a tree, and greater partitioning of
carbon to vegetative growth.
Example:
26
24. • The tree on the left was simulated under
conditions of full irrigation whereas the tree
on the right experienced mild water stress
during growth.
• In this simulation leaf initiation and
stem elongation rate were both set to be
more sensitive to mild water stress.
Figure . This figure demonstrates the potential of the model to simulate the effects of irrigation frequency
or mild water stress on tree growth.
27
25. Effects of elevated CO2 on grapevine (Vitis vinifera L.): Physiological and
yield attributes
J. MOUTINHO-PEREIRA1, B. GONÇALVES1, E. BACELAR1, J. BOAVENTURA CUNHA1, J. COUTINHO2 and C.
M. CORREIA1
1) CITAB – Centre for the Research and Technology of Agro-Environment and Biological Sciences, University of Trás-os-Montese Alto Douro, Vila Real,
Portugal
2) Centre of Chemistry, University of Trás-os-Montes e Alto Douro, Vila Real, Portugal
Vitis 48 (4), 159–165 (2009)
Objective- The main purpose of this study was to
investigate the pattern of acclimation of
grapevine physiology and yield responses to
elevated [CO2] in Open-Top Chamber
(OTC).
To predict the grapevine performance
in a future scenario of climate change.
Pereira et al., 2009
26. A gs A/gs Ψmd
OTC-C 13.65 b 406.9 b 34.14 b -1.48 b
OTC-E 22.46 a 330.0 a 71.55 a -1.33 a
A= Net photosynthesis ( μmol m-2 s-1)
gs= Stomatal conductance (gs, mmol m-2 s-1)
A/gs=Intrinsic water use efficiency (A/gs, μmol CO2 mol-1 H2O)
Ψmd= Midday leaf water potential (MPa)
Table 2- Grapevines grown in ambient CO2 (OTC-C), elevated CO2 (OTC-E). Means (n=10)
followed by the same letter are not significantly different at P < 0.05 (Duncan’s test).
Grape (Vitis vinifera L.) cv. 'Touriga Franca' is taken for the experiment, which is conducted under ambient
(OTC-C, 365 ± 10 ppm) and elevated carbon dioxide [CO2] (OTC-E, 500 ± 16 ppm) under Open-top
chambers.
Pereira et al., 2009
27. Table 3- Stomatal density (stomata mm-2) and leaf tissue thickness (μm) of grapevines grown in ambient CO2
(OTC-C), elevated CO2 (OTC-E).
Means (n=10) followed by the same letter are not significantly different at P < 0.05 (Duncan’s test).
Stomatal
density
Total
lamina
Palisade
parenchyma
Spongy
parenchyma
OTC-C 168.1 b 150.2 b 46.7 b 65.5 b
OTC-E 130.0 a 171.2 a 56.4 a 78.0 a
Yield
(kg·vine-1)
Cluster
(no’s·vine-1)
Cluster weight
(g)
Shoot
(no’s·vine-1)
OTC-C 2.80 ± 0.26 9.7 ± 1.1 303.7 ± 22.1 18.8 ± 1.7
OTC-E 4.20 ± 0.75 12.1 ± 2.2 367.3 ± 54.2 26.4 ± 2.6
P-value 0.062 0.295 0.047 0.025
Table 4- Yield, cluster number and weight, shoot number, of grapevines grown at
elevated CO2 (OTC-E) and ambient CO2 (OTC-C)
Pereira et al., 2009
28. Existing models or modeling frameworks for fruit crops-
Hi-SAFE and Yield-SAFE
• The Hi-SAFE model was designed in response to the need for a
process-based model that could simulate tree-crop interaction and
management options in a temperate region (Dupraz et al., 2005).
The typical agroforestry systems to be simulated by the model are walnut
(Juglans spp.), wild cherry (Prunus avium)or Mediterranean oaks (Quercus
spp.) with winter and summer annual crops, grass and alfalfa.
In Apple orchard modeling-
29. Conclusion
• Crop modeling in fruits provides knowledge about behavior of fruit
trees to various architectural modifications such as effect of root stock,
training, pruning, thinning and plant growth regulators along with
interaction related with environment.
• These modification helps the fruit growers to optimize their yield,
productivity and quality of produce suitable for export.
32