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Majority of the pesticides are applied as sprays. The liquid formulations of pesticide either diluted or directly are applied in small drops to the crop by different types of sprayers. Generally the wettable powder, EC formulations are diluted suitably with water which is a common carrier of pesticides. However, in few cases oil is used as carrier of pesticides.

As the chill of winter fades away, and nature begins to bloom, it’s time to embrace the joys of spring with your furry friend by your side. While spring brings about a sense of renewal and excitement, it also brings some unique challenges for pet owners. Here are some essential tips to ensure your pet dog stays happy, healthy, and safe during the spring season:

1. Regular Grooming Sessions: Springtime means shedding season for many dogs, as they lose their winter coats to make way for lighter ones. Regular grooming sessions, including brushing and bathing, can help reduce shedding and prevent matting. Additionally, grooming helps in checking for any ticks, fleas, or skin irritations that may arise as the weather warms up.

2. Watch Out for Allergies: Just like humans, dogs can suffer from seasonal allergies too. Keep an eye out for signs such as excessive scratching, licking, or sneezing. Consult your veterinarian if you suspect your dog is experiencing allergies, as they can recommend appropriate treatment options or medication.

3. Protect Against Fleas and Ticks: Springtime brings a surge in flea and tick activity. Ensure your dog is up to date on flea and tick prevention medications recommended by your veterinarian. Regularly inspect your pet for any signs of these pests, especially after outdoor activities in grassy or wooded areas.

4. Stay Hydrated: Warmer weather means increased activity levels for both you and your dog. Make sure your pet has access to clean and fresh water at all times, especially during outdoor excursions. Dehydration can be a serious concern, so carry water and a portable bowl when going for walks or hikes.

Sprinkler irrigation is an advanced method of irrigation in which water is sprayed to air and allowed to fall on the ground similar to rainfall. The spraying of water occurs through nozzle connected to a network of pipes with water under pressure. The rate of application and area of coverage under a sprinkler is regulated by suitable selection of nozzle size and pressure in the system.

History of Sprinkler Irrigation

The sprinkler irrigation has started elsewhere in the world in the early part of twentieth century. Before 1920, sprinkler irrigation was limited to orchard, nurseries and intensive vegetable cultivation. The cost of sprinkler reduced considerably by the development of impact sprinkler and lightweight steel pipe with quick couplers in the 1930s. As a result, sprinkler irrigation began to spread in larger area and to the area of field crops. However, sprinkler irrigation developed mainly after the Second World War with the introduction of lightweight portable aluminum pipe.

Sprinkler irrigation has started in India from mid of 1950s. Due to requirement of high initial investment, the average Indian farmers cannot afford the system and therefore the area under sprinkler irrigation in India was 0.66mha (approx.) out of total irrigated area of 87.80mHa in 1995 (INCID). The sprinkler irrigation was first introduced in India in hilly regions such as Western Ghats in Kerala, Tamil Nadu, Karnataka and in the North Eastern States especially by plantation owners for irrigating tea, coffee and cardamom crops. Since the cost of aluminum pipe is high, to replace it, High Density Polyethylene Pipe (HDPE)/PVC with suitable modification have been introduced in sprinkler system. However, the area under HDPE/PVC piping system is below 20% to the total area under sprinkler irrigation. The remaining area is cultivated under aluminum or steel based sets. The indigenous manufacturing of the system has started in India only 35-40 years ago and there are about fifty numbers of manufacturers till 1996 (INCID). The details of the cultivated area under sprinkler irrigation system in the world and in Indian States are given in Table 8.1 & 8.2 respectively.

Farmers Must Always Get Genuine Products

Since its start, the pesticide industry has supported crop productivity and our agricultural economy. It was crucial in bringing about the Green Revolution. By protecting plants from pests and diseases, pesticides let India’s high yielding types and hybrids reach their full potential for productivity. The Integrated Pest Management (IPM) Programme of the Government of India, state governments, and state agricultural institutes is not complete without the use of pesticides. Farmers will receive value-added solutions from the industry via on-farm demonstrations and on-the-spot knowledge-sharing. The emphasis is on creating plans using chemicals for plant protection and attending to the farmers’ urgent requirements.

Pesticides have a direct impact on agricultural economy and food security. The presence of spurious, fake, illegal or duplicate pesticides in the market result in losses of yield and farmers’ income. Over 30% of plant protection chemicals sold in India are not registered, fake, sub-standard, misbranded, counterfeit, unapproved and illegitimate pesticides, bio pesticides and bio-stimulants. They are developed by passing regulations and field trials.

There is a common practice of using the terms spurious, counterfeit, misbranded, illegal, sub-standard and others interchangeably and this often creates confusion among scientist, industrialist and extension officer. Spurious and counterfeit pesticides are the two major words used to describe all kind of anomalies in the pesticides. Hence, there is a need to have a clear understanding of these and differences between them.

SQL, or Structured Query Language, is a fundamental component in the domains of Machine Learning and Artificial Intelligence, serving as a powerful tool for managing, retrieving, and manipulating data stored in relational databases. In an era where data drives intelligent systems, SQL plays a crucial role in preparing and organizing datasets that form the backbone of machine learning models and AI applications. Its ability to efficiently handle large volumes of structured data makes it indispensable for data scientists, analysts, and engineers working in these fields. Originally developed in the 1970s at IBM by researchers Donald D. Chamberlin and Raymond F. Boyce, SQL was designed to provide a standardized way to interact with relational databases. Over time,

SQL, or Structured Query Language, provides a powerful framework for querying, aggregating, and transforming financial data stored in relational databases, which is essential for constructing accurate and dynamic financial models. Unlike spreadsheet-based models, SQL enables automation, repeatability, and scalability, making it particularly useful when dealing with complex datasets or real-time financial information. The first step in financial modeling with SQL is understanding the structure of the database. Financial data is often stored across multiple tables, including transactions, accounts, customers, and market data. Effective financial modeling requires familiarity with relational database concepts such as primary keys, foreign keys, and normalization. By using SQL joins, analysts can merge these tables to create a unified dataset that captures all necessary information, such as revenues, expenses, and cash flows, which are essential for forecasting and valuation exercises. Data extraction in SQL

Financial modeling programs integrated with data analytics using SQL provide a strong foundation for managing, analyzing, and transforming financial data in a structured and scalable manner. SQL, or Structured Query Language, is essential in environments where large volumes of financial data are stored in relational databases, enabling analysts to build reliable and data-driven financial models. Its ability to efficiently query, aggregate, and manipulate data makes it a critical tool in modern financial analytics. The process begins with understanding the database architecture. Financial data is typically distributed across multiple tables such as transactions, accounts, customers, budgets, and market indicators. SQL enables the integration of these datasets through joins, allowing analysts to

This tumour may occur due to specific mutation in the p53 tumor suppressor gene. This is one of the most common epithelial tumor of dog. It is classified into well differentiated, moderately differentiated and poorly differentiated tumour.

Squash: It is a type of fruit beverage containing at list 25% fruit juice or pulp and 40-50% TSS commercially. It’s also containing about 1% citric acid and 350ppm sulphurdioxide or 600ppm sodium benzoate. Generally it is diluted before serving. Mango, orange, pineapple, lemon, lime, guava, litchi etc are commercially used for squash making using KMS as preservative. The coloured fruit like jamun, passion fruit, peach, phalasa, plum, mulberry, strawberry and grapefruits are also used for making squash but due to bleaching effect KMS is not used as preservative. In such types of fruits sodium benzoate used as preservative.

Squashes and Pumpkin (Cucurbita, spp, 2n = 2x = 40) are the words quite often used interchangeably. However, the term squash is more commonly used for C. pepo which is consumed as an immature fruit. The term pumpkin is normally applied to the edible fruit of any species of Cucurbita utilized when ripe as a table vegetable or in pies.
 
Origin:  The genus Cucurbita is native to America.  The center of diversity is the tropics near the Mexico- Gauatamla border. The archaeology evidences indicate that these were widely cultivated in South Western United States, Mexico, and northern South America in Pre-Colombian times i.e. prior to 1492 A.D.

C. pepo : Summer squash : Some varieties belong to winter squash, pumpkin and marrows. It has bushy habit, peduncle hard, sharply angular, grooved, leaves broad, triangular in outline, usually with deep lobes little or no expansion of peduncle at fruit attachment. In North America it is commonly found.

C. mixta : Squash, cymlins, cushaws: It is used as summer and winter squash, leaves shallowly lobed, hard corky peduncle. This is also commonly found in North America.

1. OVERVIEW OF THE NATIONALAGRICULTURE SCENARIO - NATIONAL EXTENSION SYSTEMS, SCOPE AND IMPORTANCE

Like in many developing countries, Agriculture Sector being the backbone of the Sri Lankan economy, contributes significantly (11.3 per cent in the year 2007) to the Gross Domestic Products (GDP) and the economic growth by 7.7 per cent (Central Bank of Sri Lanka, 2007). As to the situation in 2007, 80 per cent of country’s population of 20.01 million is rural and 31.3 per cent of the total Sri Lankan work force is employed in Agriculture sector. Nearly 14.6 per cent of the population still depends on the agriculture as the main livelihood. The country’s GDP has been growing at 6.8 per cent per annum (in 2007), though that of agriculture is growing at only 3.3 per cent.

Country as a whole is constituted by 25 administrative districts all of which is encompassed into nine provinces. Of these, nearly four fifths are predominantly agricultural districts with over 60 per cent of the total population living in them. Majority of the population in these districts constitutes the farming community and they, by and large, are the ones who keep the nation feeding. Apart from plantation agriculture which looks after large scale plantation level crops like tea, rubber, coconut along with other crops like cinnamon, pepper, coffee, cloves, nutmeg etc., grown extensively for the export market, almost all other food crops are grown by farmers, majority of who own land plots less than 2 hectares. On the one hand, this makes agriculture mainstay of a major sector of the population and on the other hand they are spread nearly everywhere in the country rather thinly on the ground.

We, the participants of ITAF2023 visited Sriracha fisheries research station under the guidance of Asst. Prof. Dr. Naphat Panthukumbol and Dr. Somrat Taweedet on 16/10/23 at 08:00am. The research station is under faculty of fisheries, Kasetsart university in Chonburi Province. The purpose of visit was to learn about the mussel aquaculture and Integrated Multi- Trophic Aquaculture (IMTA).

1) Mussel aquaculture

Cultured species was Perna viridis( Green mussel ). Almost all rafts were owned by fishermen and 10 rafts were under the management of sriracha fisheries research station. The cost of production was 100 baht /acre. Simple method of culture is practiced by installing rafts before breeding season and allowing the natural spawn to attach. The harvesting size of mussel is 8 cm. Observed growth rate is 1cm/ month. Other maintenance includes repairing of ropes ,buoys etc. during off-season. Farmers face many constraints like plankton bloom, waste water discharge, predation by sting ray,parasitism during summer months.

Ramya (2012) studied stability of 12 hybrids in three environments. The researcher selected the 12 hybrids based on the standard heterosis. The hybrids are: P₂ × P₅, P₁ × P₅, P₃ × P₅, P₂ × P₇, P₂ × P₃ and P₁ × P₂. Among the twelve hybrids, the cross combinations viz., P₂ × P₅, P₂ × P₇ and P₂ × P₃ showed non-significant reciprocal effects for most of the traits including fruit yield per plant. The direct hybrid P₁ × P₂ showed less than 40 per cent standard heterosis for fruit yield per plant. It also exhibited low standard heterosis for number of fruits per plant. Eight hybrids viz., P₂ × P₅, P₅ × P₁, P₃ × P₅, P₂ × P₇, P₁ × P₅, P₂ × P₃, P₂ × P₁, P₅ × P₃, based on standard heterosis (>40 per cent) and their parents viz., P₁, P₂, P₃, P₅, P₇ were selected and grown in three different locations for finding their stable performance. Several authors have suggested different types of models to identify the stable genotypes. The model most commonly preferred by breeders is Eberhart and Russell (1966) model which was used in the present investigation. Various stability parameters were worked out on the available data and the results were discussed below.

Among the ten traits studied, days to first flowering, fruit weight and fruit girth showed non-significant genotype × environment interaction. Hence, the remaining seven traits were taken for further analysis. ANOVA across environment revealed highly significant mean sum of square for genotypes, indicating the presence of wide genotypic variability among the genotypes involved in the study for all the characters studied. High significant differences were observed for environments for the traits viz., number of branches per plant, fruit length and fruit yield per plant, indicating the divergence among the growing environments and differential response of genotypes to the various environments. The main factors contributing to the divergence of the environments might be temperature differences during crop season, intensity and duration of sun shine hours. Therefore, the genotypes must be tested over an extensive range of environments for proper assessment of stability of genotypes (Salesh Kumar Jindal et al.,2008).

In developing countries, the most important challenges is to increase food productivity for the continuously growing population from limited land area. To great extent, this challenges has been met with good amount of success in cereals but not in case of pulses. Pulses from an indispensable component of Indian agricultural too. Further, it is an excellent source of protein to the growing population who are mostly vegetarians. The high level of lysine in the protein makes them ideal supplement to cereals. Moreover, they are endowed with nitrogen fixation, and thus improve the soil fertility.

India is one of the major pulse growing countries in the world with a total area of 23.31 million hectares, with a total production of 14.50 million tones (Source: FAO, 2011). The area, under pulses has decreased particularly during the last decades as a consequence of intensive diffusion of techniques in green revolution on the high yielding varieties of cereals. This is mainly due to the low yield potential of legumes even under high input conditions.

Among the various pulses, blackgram/urd bean known scientifically as Vigna mungo (L.) Hepper, former known as Phaseolus mungo (L.) belonging to the tribe Paseolea of family Papilionaceae is of immense importance. It is the most widely grown and highly esteemed grain legume. In India it occupies 2.9 million hectares with an annual production of 1.24 million tones (Source: FAO, 2011).

In Asia this crop is widely cultivated in India, Pakistan, and Bangladesh both in kharif and rabi seasons. In India, it is mainly grown in the states of Madhya Pradesh, Maharashtra, Uttar Pradesh, Rajasthan, Karnataka and Bihar. Tamilnadu is placed eleventh and tenth in area and production respectively (FAO, 2011).

A successfully developed new cultivar should have stable performance and broad adaptation over a range of environments, seasons and locations, in addition to high yield potential. Evaluating performance stability and range of adaptations is becoming increasingly important in breeding programs. Since stability and adaptability are important selection criteria in breeding programs, in depth research on stability is needed for a better estimate of crop performance and adaptability (Lin and Binns, 1988).

Having realized the importance of genotype × environment, several biometrical models have been developed to analyse the stability of genotypes. Such models have been discussed, critically reviewed comprehensively by Knight (1970), Freeman (1973), Hill (1975), Brain (1986) and Gautam et al. (1986).

However, the various methods proposed for the statistical analysis of genotype × environment are based on one of following approaches (Jain, 1982). In the first the variance interactions are estimated by equating the observed mean squares in the analysis of variance to their expectations on the random model. The second approach called phenotypic approach by Hill (1975) used the technique of partitioning the genotype _× environment component of variability into its linear and non-linear portions of assessing the stability of genotypes over a range of environments. This approach which is very widely adopted in plant breeding, came to be known as “Joint regression analysis”.

Many workers contributed for the development of this approach, notably Finlay and Wilkinson (1963), Eberhart and Russell (1966), Freeman and Perkins (1971) and Shukla (1972). The third approach called genotypic approach which also suggested by Hill (1975) is based on fitting of general models in which it specify, the contribution of genetic, environmental and genotype × environment to the generation means and variance. The contributions for he development of this approach were from Bucio (1966), Bucio and Hill (1966), Bucio et al. (1969), Perkins and Jinks (1968), Breese (1969), Hill and Perkins (1969) and Perkins (1970). Linear regression and joint regression models were more commonly used due to their simplicity and reliability in biological experiments. In general, these approaches fall conveniently into two parts.

Stability and adaptability studies have been carried out for upland or hill rice varieties also. Amirthadevarathinam (1987) observed significant genotype × environment and reported that both linear and non-linear components were equally important for assessing stability of yield and four yield components in four environments. Both TKM 6 and Poongar were high yielding with adaptability and stability in addition to being especially suitable for unfavourable environments. Kuruvaikalangium was the best performer for all characters except grains per panicle for which PM 1023 was the best.

Dineshkumar (1987) evaluated sixteen early maturing genotypes for their stability in four major agroclimatic zones of the Karnataka state. No genotype was found stable as they observed more of unpredictable variance (pooled deviation) than predictable (genotype × environment linear).

Ganesh and Soundarapandian (1987) observed that stability of number of ear bearing tillers was a major contributor for yield stability Shivaprakash (1987) reported similar results.

Studies by Sinha and Biswas (1987) on yield and yield components of twenty photo insensitive rice cultivars revealed IR-36 and IET-4094 as high yielder with unit ‘bi’ and least ‘S² d_i’ and hence they were recommended to be suitable for general cultivation for all the environments under study. Similarly IET-2881, which had high mean yield with high ‘bi’ was found suitable for high yielding environments.

Genotypes × Season interaction is of major importance to the plant breeder in developing stable high yielding varieties of crop plants. When new varieties are tested over a series of seasons, the relative ranking of the varieties for any given attribute is rarely the same at each season. This increases the difficulty of identifying superior stable genotypes. Comstock and Moll (1963) have shown statistically the effect of large genotype × environment interaction is reducing the progress from selection.

In order to minimize the genotype × season interaction, stratification of the seasons can be practiced. The implification in stratification is that suitable selections could be made for the infavourable and favourable seasons encountered within the sampling units proposed. Stratification of environments has been practicised for a long time to identify suitable selection that could be made for the narrower range of environments encountered within the sampling units proposed. However, even with this refinement of technique, the interactions of genotypes with locations in a sub-region and within environments encountered at the same location in different years, frequently remain too large (Allard and Bradshaw, 1964). Little is known about the environmental factors contributing to such interactions. Sprague (1966) suggested that even if such information are available, the possibility of materially reducing genotype × environment interaction in field experiments would remain questionable.

On line with Allard and Bradshaw’s (1964) statement, the interactions of genotypes with locations in a sub-region with different seasons at the same location in different years, may remain too large. As far as sesame is concerned, practically nothing is known about the factors contributing to such genotype × season interactions. Hence, the progress in breeding a stable variety of sesame has been slow. Since sesame in India is grown essentially as rainfed crop, fluctuations in yield levels closely follow changes in the environment and seasons. Breeding for multiseasonal variety for may small regions within a state is being considered necessary. There is widespread awareness of achieving stability of production as well as improvement in yield of sesame under low and high-in-put conditions as well as for different seasons.

A successfully developed new cultivar should have stable performance and broad adaptation over a range of environments, seasons and locations, in addition to high yielding potential. Evaluating performance stability and range of adaptation is become increasingly important in breeding programmes. Since, stability and adaptability are important selection criteria in breeding programmes, in depth research on stability is needed for a better estimate of crop performance and adaptability.

Genotype × season interaction is of major performance to the plant breeder in developing stable but high yielding varieties of crop plants. When new varieties are tested over a series of seasons, the relative ranking of the varieties for any given attribute is rarely the same at each season. This increases the difficulty of identifying superior stable genotypes. Comstock and Moll (1963) have shown statistically the effect of large genotype × environment interaction in reducing the progress from selection.

In order to minimize the genotype × season interaction, stratification of the seasons can be practiced. The implication in stratification is that suitable selections could be made for unfavourable and favourable seasons encountered within the sampling units proposed. Stratification of the environments has been practiced for a long time to identify suitable selection that could be made for the narrower range of environments encountered within the sampling units proposed. However, even with this refinement of technique, the interactions of genotypes with locations in a sub-region with environments encountered at the same location in different years, frequently remain too large (Allard and Bradshaw, 1964). Little is known about the environmental factors contributing to such interactions. Sprague (1966) suggested that even if such information were available, the possibility of materially reducing the genotype × environment interaction in field experiments would remain questionable.