eChapters

The yesteryear concept of employing one or two acclaimed anaesthetic drugs to produce sedation and general anaesthesia is now switching over to the era of balanced sedation and balanced anaesthesia. The theory of balanced anaesthesia entails achieving the state of general anaesthesia with the proportionate administration of a mixture of small amounts of several neuronal depressant drugs which summates the advantages of the individual drugs but not their disadvantages (Bettschart-Wolfensberger and Larenza, 2007). Knowledge about balanced anesthesia and targeted effects of drugs on neuronal pathways has started to influence veterinary anaesthetists worldwide. But there are many constraints for a vast majority of veterinary practitioners in India to follow this emerging modality of anaesthetic practice. It is almost impractical for most of them to get the assistance of a veterinary anaesthesiologist every time. So they experience complexities to deal with the pharmacological dosing of multiple component drugs of balanced anaesthetic protocols. Memorising the doses and calculating the volume of injection of multiple component drugs where the doses of some drugs has to be calculated in milligram per kilogram body weight while others have to be calculated in microgram per kilogram body weight becomes a cumbersome task for the practitioner. Moreover, many of these anaesthetic drugs are available in ampoules, and hence multiple ampoules have to be broken for every single surgery; consuming a lot of time. Wastage of the leftover anaesthetic in opened ampoules is another concern. Unavailability of some drugs at some areas is another issue the practitioner faces. These challenges constrain the practitioner from practicing balanced anaesthesia and most of them lean on the verily familiar conventional anaesthetic protocol, based exclusively on one or two frequently used drugs, for almost all the surgical procedures of varying depth. Since only one or two anaesthetic drugs are involved in the protocol, the drugs have to be used at its upper limit of pharmacological doses to achieve the desired endpoints of anaesthesia. This will increase the probable risk of undesired side effects of these drugs. The challenges that restrict our practitioners from advancing beyond the era of sole reliance on one or two drugs for producing the state of anaesthesia has to be addressed and measures to resolve these issues have to be evolved to promote and equip our veterinary practitioners to practice the safer technique of balanced anaesthesia. Encompassing these objectives, and based on evidence and

Introduction 
As we have journeyed through the preceding chapters, meticulously examining the foundational understanding of crop-weather interactions, the sophisticated development of crop simulation models and decision support systems, the invaluable contributions of indigenous technical knowledge, and the systematic formulation of precise and customized agroadvisories, we now arrive at the decisive stage of this entire endeavor of dissemination of Agromet Advisories. The profound scientific insights and meticulously crafted recommendations, no matter how accurate or relevant, remain inert unless they effectively reach the hands, ears, or screens of the farmers who need them most, in a format they can readily understand and apply. This chapter, therefore, is dedicated to unraveling the complex, yet essential, process of ensuring that crucial agrometeorological information successfully traverses the last mile to translate into tangible impact on agricultural practices and outcomes across diverse landscapes. The challenge of effective dissemination extends far beyond a simple broadcast; it necessitates the establishment of a robust, dynamic, and multichannel communication ecosystem. This ecosystem must be meticulously designed to account for the rich tapestry of socio-economic, technological, and literacy disparities prevalent within farming communities. Historically, traditional methods such as the communal gathering around a radio for a farmer‘s bulletin or face-to-face discussions during village meetings formed the bedrock of knowledge transfer, leveraging established social networks and widely accessible, albeit low-tech, communication tools. However, the relentless march of the digital revolution has fundamentally reshaped this landscape, unveiling an unprecedented potential for expansive reach, enhanced personalization, and rapid delivery of information, thereby opening a myriad of new avenues for disseminating critical advisories with remarkable speed and specificity.

Introduction
Having meticulously explored the foundations of crop-weather interactions, the development and validation of crop growth models, the wisdom embedded in Indigenous Technical Knowledge, and the systematic organization of Crop Weather Calendars, we now arrive at the culmination of this knowledge synthesis: the Agroadvisory Based on Weather Forecast. This chapter delves into the critical process of transforming raw meteorological data and scientific understanding into actionable, timely, and location-specific advice for the farming community. An agroadvisory, at its core, is a targeted recommendation provided to farmers, guiding them on various agricultural operations by integrating current weather conditions, short- to medium-range weather forecasts, and the specific needs of their crops at different growth stages. It represents the final, practical output of complex agro-meteorological research and forecasting efforts, designed to empower farmers to make adaptive decisions that can significantly impact their yield, resource efficiency, and resilience against climatic shocks. In the upcoming sections, we will comprehensively examine the multi-faceted nature of weather-based agroadvisories. Our exploration will detail:
• The Conceptual Framework: Understanding the underlying principles and objectives of these advisories, emphasizing their shift from reactive responses to proactive planning.
• Essential Components of an Agroadvisory: Breaking down the key pieces of information typically included, such as weather forecasts (temperature, rainfall, wind, humidity), crop-specific phenological stages, potential risks (pests, diseases, extreme weather), and precise recommendations for farm operations (sowing, irrigation, fertilization, pest control, harvesting).

Introduction 
Building upon our foundational understanding of how weather intrinsically shapes crop development, from the micro-scale physiological responses to the broader patterns revealed by Indigenous Technical Knowledge, we now transition to a chapter dedicated to a highly practical and actionable synthesis of this critical interrelationship: Crop Weather Calendars. These calendars are far more than mere chronological charts; they represent systematically organized and dynamic compilations of meteorological information meticulously integrated with precise crop-specific data, designed to serve as an indispensable guide for optimizing agricultural practices. In this chapter, we will embark on a comprehensive exploration of Crop Weather Calendars, moving beyond the often informal or culturally embedded ecological calendars previously discussed. We will delve into how these powerful tools systematically integrate historical weather patterns, which involves analyzing longterm meteorological data to establish reliable averages and identify probabilities of specific weather events, such as the typical onset of monsoons, average frost dates, or periods of peak solar radiation. They also incorporate real-time meteorological data, bringing in current weather observations to track actual conditions against historical norms and initial forecasts. Furthermore, Crop Weather Calendars utilize future weather forecasts, drawing on short-, medium-, and long-range meteorological predictions to anticipate upcoming conditions (Fig.12.1). This integrated weather information is then critically aligned with the precise phenological stages of a given crop cultivar, recognizing that a crop’s sensitivity and requirements vary significantly throughout its life cycle. Our exploration will detail the essential components that constitute a robust crop weather calendar, including the identification of crucial growth periods, pinpointing the most sensitive and influential developmental stages of the crop like germination, tillering, flowering, and grain filling, where weather impacts are most pronounced. It also involves the establishment of weather thresholds, which means defining the optimal, critical, and damaging ranges of key weather

Introduction
Following our comprehensive exploration of scientific crop modeling and the rigorous processes of model assessment, we now pivot to an equally vital, yet distinct, realm of understanding weather in agriculture: Indigenous Technical Knowledge (ITK). This chapter, Indigenous Technical Knowledge for Weather Forecasts and Its Validity,” delves into the profound and often overlooked wisdom embedded within local communities worldwide, knowledge systems accumulated over generations that offer unique insights into anticipating future weather patterns.
In the upcoming sections, we will embark on an in-depth examination of the diverse forms that ITK related to weather forecasting takes. This includes detailed discussions on:
• Observational Indicators: How traditional communities keenly observe subtle changes in the natural world, such as specific patterns in animal behavior (e.g., bird migration patterns, insect activity, animal calls), plant phenology (e.g., early or late flowering, unusual fruiting patterns of wild plants), and the behavior of flora and fauna as harbingers of upcoming weather shifts.
• Atmospheric and Astronomical Signs: The interpretation of sky conditions (e.g., cloud formations, specific colors of sunrise/sunset, halos around the sun or moon), wind patterns, atmospheric pressure changes (often perceived through bodily sensations or changes in local sound travel), and celestial phenomena (e.g., star brightness, moon phases) as indicators for short-term or seasonal forecasts.
• Traditional Ecological Calendars and Proverbs: The role of traditional calendars that map out expected seasonal weather based on historical observations, and folk proverbs or sayings that encapsulate generations of empirical weather wisdom in concise, memorable forms, guiding agricultural practices.

Introduction 
Weather forecasting stands as a testament to humanity’s enduring quest to understand and anticipate the natural world. Far from being a mere casual prediction, it is a highly sophisticated scientific and technological discipline dedicated to projecting the future state of the Earth’s atmosphere for specific locations and times. This complex endeavor begins with the meticulous collection of vast quantities of real-time data, encompassing everything from surface observations of temperature, pressure, wind, and humidity to upperair measurements gathered by weather balloons, aircraft, radar, and advanced satellite systems. The core of modern weather forecasting lies in numerical weather prediction (NWP) models. These are intricate computer programs that embody the fundamental laws of physics governing atmospheric processes, such as fluid dynamics and thermodynamics. By feeding the enormous datasets of current atmospheric conditions into these models, supercomputers solve complex equations to simulate how the atmosphere will evolve over minutes, hours, and days. The raw output from these models is then further refined by human meteorologists who apply their expertise, integrating local geographical influences, historical patterns (climatology), and statistical techniques to produce the most accurate and actionable forecasts. The importance of accurate weather forecasting cannot be overstated. It serves as a vital tool across countless sectors, guiding daily personal decisions, from what to wear to whether to carry an umbrella. Crucially, for agriculture, precise weather forecasts are indispensable. Farmers rely heavily on these predictions to make informed decisions that directly impact crop yield and livestock health. Knowing when to plant seeds, schedule irrigation, apply fertilizers and pesticides, or harvest crops is directly influenced by anticipated rainfall, temperature extremes, and wind conditions. Timely warnings of frost, hailstorms, or prolonged droughts can enable farmers to take protective measures, minimizing losses and ensuring food security. For example, in regions like Telangana, where agriculture is a dominant sector, understanding

Introduction 
The preceding chapter elucidated the concepts of potential and achievable crop production, highlighting the significant gap that often exists between a crop‘s theoretical maximum yield and what is realized in the field. While genetic potential and optimal climatic conditions define the upper limits, the vast majority of agricultural systems globally operate under less-than-ideal circumstances. Among the most pervasive and impactful constraints on crop productivity are moisture (water) and nutrient limitations. These two factors, either individually or in complex interaction, are frequently the primary drivers of the yield gap, preventing crops from expressing their full genetic potential and significantly impacting farm profitability and regional food security. Water, as the universal solvent and a direct participant in photosynthesis and countless metabolic processes, is indispensable for plant life. However, its availability in agricultural systems is highly variable and often insufficient. From the rainfed fields dependent on increasingly erratic rainfall patterns to irrigated lands facing dwindling water resources, moisture stress – encompassing both drought and, paradoxically, waterlogging – poses an immense challenge. Drought alone accounts for a substantial portion of global crop losses, directly affecting plant physiological functions, growth, and ultimately, yield. Conversely, excessive moisture can lead to anaerobic soil conditions, nutrient leaching, and root damage, equally debilitating for crop performance. Parallel to moisture, the availability and judicious management of essential plant nutrients are foundational to productive agriculture. Plants require a suite of macro- and micronutrients for healthy growth, enzymatic activity, and the synthesis of biomass. Soils, over time and with continuous cropping, often become depleted of these vital elements, necessitating external application through fertilizers or the enhancement of natural nutrient cycling processes. However, nutrient management in the field is rarely perfect. Deficiencies of

Introduction 
The fundamental goal of agriculture is to harness the earth‘s resources to produce food, feed, fiber, and fuel to sustain a growing global population. At the heart of this endeavor lies the concept of crop production, a complex interplay of genetic potential, environmental factors, and human management. This chapter, Potential and Achievable Crop Production, delves into the crucial distinction between what a crop can produce under ideal circumstances and what it actually produces in the farmer‘s field. Understanding this dichotomy is paramount for addressing global food security challenges, optimizing resource utilization, and developing sustainable agricultural practices. We will begin by exploring the theoretical maximum yield, often termed "potential production," which represents the inherent capacity of a crop variety to produce biomass and yield when all limiting factors are eliminated. This potential is primarily dictated by a crop‘s genetic makeup and the prevailing climatic conditions, such as solar radiation, temperature, and carbon dioxide concentration. Subsequently, we will shift our focus to achievable production, the real-world yield obtained by farmers. This actual yield invariably falls short of the potential due to a myriad of biotic (pests, diseases, weeds), abiotic (water scarcity, nutrient deficiencies, extreme temperatures, poor soil health), and socio-economic (lack of access to inputs, knowledge, or markets) constraints. The disparity between potential and achievable yield, known as the "yield gap," represents a significant opportunity for enhancing agricultural output without necessarily expanding cultivated land. By meticulously examining the factors that contribute to this gap, we can identify targeted interventions and innovative strategies to bridge it, thereby moving closer to realizing the full productive capacity of our agricultural systems. This chapter will therefore provide a comprehensive framework for understanding the science behind crop yields and the practical approaches required to optimize them for a more food-secure future.

Introduction 
Having explored the fundamental principles and various types of elementary crop growth models in the previous chapter, we now arrive at a pivotal juncture in their journey from theoretical constructs to practical, dependable tools. It‘s one thing to conceptualize and build a mathematical representation of crop growth; it‘s quite another to ensure that this model accurately reflects realworld plant behavior and can be reliably used for predicting outcomes and informing critical agricultural decisions. This chapter, Calibration, Validation, Verification, and Sensitivity of Elementary Crop Models,” will guide you through the indispensable processes that establish a model‘s credibility and utility. We will delve into the systematic steps involved in calibration, where we meticulously fine-tune a model‘s parameters to accurately reflect a specific crop‘s response within a particular environment, effectively adapting the general model to local conditions. Following this, we will explore validation, a rigorous process of independently testing the model‘s predictive power against datasets not used during calibration, ensuring its robustness and generalizability. Beyond accuracy, we will also cover verification, which confirms the model‘s logical consistency and computational correctness, ensuring that its internal workings are sound. Finally, we will examine sensitivity analysis, a crucial technique for understanding how changes in various input factors—especially weather parameters—or internal model coefficients influence the model‘s outputs, thereby revealing the model‘s critical drivers and potential vulnerabilities. These four processes are far more than mere technical formalities; they form the bedrock upon which the utility, accuracy, and trustworthiness of any crop model, no matter how elementary, are firmly built. By engaging with these essential steps, we can transform theoretical frameworks into powerful instruments capable of supporting sustainable agricultural planning, risk management, and adaptive strategies in a dynamic climate.

Introduction 
Building on our extensive exploration of how individual weather parameters intricately influence every facet of crop life and the diverse methodologies employed to evaluate these responses, we now embark on a journey into the mathematical heart of crop science. This chapter, Elementary Crop Growth Models,” will introduce you to the foundational concepts and simpler mathematical frameworks used to quantify, simulate, and ultimately predict how crops grow under varying environmental conditions. In the upcoming sections, we’ll delve into the fundamental building blocks of crop modeling. You’ll discover how these elementary models simplify the complex biological processes occurring within a plant to create manageable, yet insightful, representations. Unlike highly detailed mechanistic models, which require vast amounts of data and computational power, elementary models focus on capturing the most essential relationships between environmental drivers (like solar radiation, temperature, and water) and key aspects of crop growth, such as biomass accumulation or phenological progression. These simpler models serve as a crucial stepping stone. They allow us to grasp core principles of crop-weather interaction in a quantifiable way, providing practical tools for initial assessments, quick predictions, and conceptual understanding, without getting lost in the overwhelming complexity of every single physiological pathway. By understanding these foundational models, you’ll gain a clearer perspective on how agricultural scientists begin to predict the future performance of our food crops, even as the climate around them continues to evolve

Introduction 
The preceding chapters have laid a robust foundation, introducing the concept of agricultural systems, delving into the architecture and types of crop models, and meticulously detailing the essential data requirements for their operation. We have established that crop models are powerful tools for simulating the complex interactions within the soil-plant-atmosphere continuum, driven by a rich tapestry of environmental and management inputs. Among these inputs, weather stands out as arguably the most dynamic and influential factor, wielding profound control over every aspect of crop life, from the precise timing of germination to the final quantity and quality of the harvest. Chapter 5 shifts our focus to the direct and indirect consequences of this critical environmental driver: the evaluation of crop responses to weather. This chapter is dedicated to understanding, quantifying, and interpreting how variations in meteorological conditions—such as temperature, solar radiation, precipitation, humidity, and wind—directly influence the physiological processes, developmental progression, biomass accumulation, and ultimate yield of cultivated crops. It‘s about moving from simply providing weather data to actually assessing its impact on the plant‘s performance. The ability to accurately evaluate these responses is paramount for several interconnected reasons: Firstly, from a fundamental scientific perspective, it deepens our understanding of plant ecophysiology. By analyzing how crops react to specific weather patterns, we gain invaluable insights into the underlying biological mechanisms that govern growth and development under diverse environmental pressures. This knowledge is crucial for advancing agricultural science itself. Secondly, for agricultural management and decision-making, this evaluation is directly actionable. Understanding optimal weather conditions for specific growth stages, or predicting the impact of adverse events, enables farmers

Introduction 
Crop-weather models are sophisticated constructs, meticulously designed to replicate the dynamic and intricate interplay among atmospheric conditions, complex soil processes, nuanced plant physiology, and the decisive influence of human management interventions. The agricultural system is a prime example of a highly interconnected and non-linear system, where changes in one component can ripple through and affect many others, often with time lags and feedback loops. To truly comprehend, effectively design, rigorously develop, and clearly communicate the inner workings of such multifaceted simulations, relying solely on dense textual descriptions or abstract mathematical equations can prove daunting and often insufficient. This is precisely where relationship diagrams emerge as indispensable tools. At their core, relationship diagrams are powerful visual representations that translate the abstract scientific concepts and mathematical formulations embedded within crop-weather models into intuitive graphical formats. They are essentially the architectural blueprints and flowcharts of these complex systems, allowing us to see, rather than just read, how various components of the agricultural ecosystem interact. Imagine trying to understand the intricate wiring of a skyscraper or the precise movements of a symphony orchestra purely from a written description; diagrams provide the essential visual mapping that brings clarity and coherence to complexity. These diagrams are not mere aesthetic additions; they are fundamentally required because of the inherent complexity and interdisciplinary nature of crop modeling. They bridge critical gaps: Bridging Disciplinary Gaps: Agricultural system modeling brings together experts from diverse fields – agronomists who understand plant responses, meteorologists who provide weather insights, soil scientists who grasp soil physics and chemistry, and computer scientists who implement the models.

Introduction 
The preceding chapters have firmly established the conceptual foundations of the systems approach and introduced the diverse world of crop models, highlighting their intricate mechanisms and their transformative potential for agroadvisory. However, even the most elegantly formulated and sophisticated crop model is ultimately a framework; its predictive power and reliability are inherently tethered to the quality and availability of the information it receives. As the adage in data science goes, Garbage in, garbage out– a truth that resonates profoundly within the realm of agricultural system simulation. The chapter shifts focus from the theoretical constructs of models to the vital practical considerations of data requirements. This chapter will delve into the indispensable role that accurate, comprehensive, and well-structured data plays in every stage of crop modeling, from initial parameterization and rigorous calibration to robust validation and reliable application. Without the right data, even a theoretically perfect model cannot accurately mimic the complex dynamics of the soil-plant-atmosphere continuum or provide trustworthy insights for agricultural decision-making. This chapter will systematically unpack the various categories of data essential for crop modeling. It will meticulously detail the specific characteristics and sources of weather data, which serve as the primary atmospheric drivers. It will then explore the crucial properties and measurement techniques for soil data, underpinning the understanding of water and nutrient availability. The discussion will extend to crop genetic and physiological data, which define the inherent capabilities and responses of specific plant varieties. Furthermore, the chapter will cover management data, illustrating how human interventions are integrated into the modeling framework. Beyond merely listing data types, this section will critically examine the challenges associated with data acquisition, including issues of quality, resolution, consistency, and spatial representativeness.

Introduction 
The preceding chapter introduced the foundational concept of the systems approach, emphasizing the interconnectedness of components within complex entities and the power of simulation to understand their dynamic behavior. Building upon this understanding, This chapter delves into one of the most vital applications of system simulation in agriculture: crop models. These sophisticated computational tools stand as the cornerstone for quantitatively representing the intricate interplay between the soil, the plant, and the atmosphere, providing a dynamic lens through which to examine agricultural productivity and resource use efficiency. Crop models are essentially mathematical descriptions that mimic the growth, development, and yield of agricultural crops under various environmental conditions and management practices. They encapsulate our scientific understanding of how plants absorb light, water, and nutrients, how they convert these resources into biomass, and how they respond to stresses like drought, heat, or nutrient deficiency. Far from being mere statistical regressions, these are often process-based models, meaning they attempt to simulate the underlying biological, physical, and chemical mechanisms that govern crop performance. By integrating knowledge from disciplines such as plant physiology, soil science, meteorology, and agronomy, crop models provide a holistic and dynamic representation of the entire agricultural system. The evolution of crop modeling has mirrored advancements in computational power and scientific understanding. From early empirical models that relied heavily on statistical relationships between yield and a few environmental factors, the field has progressed to complex mechanistic models that simulate detailed physiological processes on a daily or even hourly basis. This evolution has transformed crop models from purely research-oriented tools into powerful instruments for real-world decision-making. Their ability to conduct virtual experiments—simulating hundreds or thousands of growing seasons

Introduction 
A system approach to understand the soil-plant-water continuum fundamentally shifts our perspective from viewing these elements in isolation to recognizing them as an intricately woven, dynamic whole. It’s about appreciating that every change in one part of this natural tapestry—be it the soil, the plant, or the water itself—reverberates through the others, creating a cascade of interconnected effects. This holistic lens allows us to dissect the continuous exchange of water and energy, unraveling the complex feedback loops and emergent behaviors that characterize this vital agricultural system. Instead of merely tracking individual inputs and outputs, a systems view compels us to comprehend how these components influence each other over time, leading to a far more nuanced understanding of their collective behavior. At the heart of this continuum lie three primary, yet inseparable, components: the soil, the plant, and the water itself, all profoundly influenced by the atmosphere. The soil acts as both a foundational anchor and a dynamic reservoir. Its very composition—the proportion of sand, silt, and clay, its structure, and its organic matter content—dictates how readily water infiltrates, how much it can hold against the pull of gravity, and how quickly it can release water to plant roots. Processes like infiltration, percolation, and capillary rise describe water’s journey within the soil matrix, while evaporation from the soil surface represents a significant loss pathway. Crucially, the soil also serves as the medium for nutrient availability, with water acting as the solvent that transports these vital elements to the roots. The plant, in turn, is the primary biological conduit for water’s journey from the soil back to the atmosphere. It actively absorbs water through its roots, moving it upward through the xylem to the leaves, where it is transpired through tiny pores called stomata. This transpiration process is not merely a loss; it’s the driving force behind the plant’s physiological engine, facilitating

“Quality Control” constitutes the very backbone of the success in any industry, in any field of activity, may it be agriculture or spices, since quality is never an accident, but, the result of a conscientious effort, as rightly visualized by John Ruskin. The quality of spices is evaluated by the following three types of techniques:
1. Physical or sensory evaluation
2. Proximate physico-chemical composition and determination of chemical constituents
3. Nutritional assessment
In the following table (Table 61) presentation of the five proximate physicochemical compositions of 5 minerals and 5 vitamins in as many as 35 spices is presented. Since most of the spices are used basically in small quantities in both vegetarian and non vegetarian food as “Food Additives”, they contribute directly very little to human nutrition, but, indirectly spices can play a very valuable role in healthy human nutrition by not only helping to increase the appeal and appreciation of the food dishes, but, also in various other ways as agents with nutraceutical and pharmaceutical properties. For instance, Turmeric has been found to have immense pharmaceutical and nutraceutical properties. Most recently, it has been found to be very beneficial during the covid pandemic. Excepting in most restrictive diets, spices are regularly used in most food preparations and they can make a difference which persuades a patient to stay faithful to an otherwise unappealing diet. The analysis confirm that even the highest calorie spice, Poppyseeds, will add no more than 2 or 3 calories per serving in normal usage. This should be good news, in particular, to weight – watchers. Considering the amounts used, the others typically contribute no more than a calorie per serving, and, usually less than that. Having this definitive data at last, nutritionists and all those interested in good nutition may utilize spices to their full potential in making wholesome foods as excitingly delicious as they are good for us.

Galangal is the dried rhizome or root of a plant, Alpinia galanga Linn. Willd. which grows mainly in the Eastern Himalayas and South-Western India. The plant is 1.8-2.1 meters high, and bears perennial rhizomes which are deep, orange-brown in color, aromatic, pungent and bitter. The fruits are about 13 mm long, constricted in the middle, and contains 3-6 seeds. The seeds are slightly pungent, with an aroma similar to that of the rhizome. Cut pieces of the rhizomes of this species are known as “Greater Galangal”. The rhizome of “Lesser Galangal” is smaller and reddish brown in color, and, has a stronger odor ansd taste. According to Guenther (1973) Galangal root is also known under the names of “Smaller Galangal” and “Radix galangal minoris”. The earliest reference to this spice is that of an Arabian geographer Ibn Khurdabah (869-895 AD), who included Galangal in a list of products from a country he refers to as Sila (which was undoubtedly China). Sometimes later, Plutarch made reference to the use of Galangal as a fumigating spice by the early Egyptians. In 1153, it was mentioned by Edrisi as an incoming spice into Ade, which was then the port used by the Egyptians for all Asiatic products. In his travels, Marco Polo mentions that it was produced fairly extensively in Kachanfu and Kinsai (Tonkin). Galangal is one of the spices which reached European markets relatively early, for it is mentioned along with Black pepper in the literature of the Middle Ages, where it received praise in various writings dealing with drugs, medicines etc. The source of the spice which was reaching the European market remained almost unknown until the last century, when in 1867, a plant of unknown botanical origin was discovered in the extreme south of China. The following year, Hance examined both dried and living specimens of the plant and was able to conclude that it was the same as the “Lesser Galangal” known to the pharmacists.

Long Pepper or Pipli is the dried fruit of Piper longum, which is a slender, aromatic climber with perennial woody roots, occurring in the hotter parts of India, from the Central Himalayas to Assam, Khasi, and Mikir hills and Lower Hills of Bengal, and evergreen forests of the Western Ghats from Konkan to Travancore in Kerala State, India; it has also been recorded from Nicobar Islands. The plant has creeping jointed stems; young shoots are downy; leaves are 5-9 cm long, 3.5 mm wide, ovate, cordate, with broad rounded lobes at the base; female spikes are cylindrical, pedunculate, male spikes are larger and slender; female spikes are 1.3-2.5 cm long and 4.5 mm in diameter; fruits are ovoid; yellowish orange, minute, droop and are sunk in fleshy spike. The female spikes are sharper, sessile and blunt at the tips and erect, are red when ripe; flowers are numerous and packed. Indian Long Pepper is mostly derived from the wild plants, the main sources of supply being Assam State in India, followed by West Bengal, Nepal and Uttar Pradesh. Small quantities are also available from evergreen forests of Kerala State, West Bengal and certain parts of Andhra Pradesh. It is reported to be cultivated at lower elevations in the Annamalai hills in Tamil Nadu, India, and in parts of Assam, in particular, the heaviest (in India) rainfall region, Cherrapunji. It is cultivated in Meghalaya also; India produced about 200 tonnes in 1990-91. The Long pepper, as sold in India, appears to be derived from 2 or 3 species, including one which is Indonesian. Indian Long Pepper is a product of either Piper longum or Piper peepuloides, while the Indonesian or Java Long Pepper imported from Malayasia is from Piper retrofractum. The fruits of these species are used for the same purpose, though they do vary in their comparative effectiveness.

Description and Distribution
A near relative of Turnip, Cabbage and Mustard. It is one of the oldest condiments, and is a well-known, large-leaved, hardy perennial in many long-established gardens. It is the thick, white, fleshy, tasty root of the Horse- Radish, which is highly prized as an appetizing condiment which goes well with many food items. A native of the marshy districts of Eastern Europe, it is grown in the USA, and to some extent in the gardens, both in the northern India and in the hill stations of the southern India. In the USA, about 7 million kilograms of Horse-Radish are processed annually for consumption as food. Cultivated over 1500 acres in the USA, its varieties are classified into two types, namely, Common and Bohemian. The Common type has, broad, crinkled leaves and produces a root of high quality. The Bohemian type has, narrow, smooth leaves, is diseaseresistant, but, yields poorer quality roots. Horse-Radish plant is highly sterile and hence is propagated asexually by the root. The tap root is tuberous and cylindrical, 30 cm long and about 18mm in diameter, possesses an acrid, pungent taste, and, when scraped or bruised, emits a characteristic pungent odor (Guenther, 1973). The root contains a pungent, acrid and vesicating volatile oil. The pungency is due to the presence of “Sinigrin”(potassium myronate), a sulfur-containing glucoside, which, in the presence of water and under the influence of an enzyme myrosinase, yields allyl mustard oil or allyl isothiocyanate, as by mustard seeds (Willstater and Stoll, 1918, Stoll and Seeback, 1948).
Area, Production and Export
Horse-Radish, being a garden crop, so far, there is no recoded data on any of the aspects of area, production and export from India.

Description and Distribution
Sweet Flag or Calamus is reported to be common in Kashmir and Kumaon ranges of the Himalayan forests. The rhizomes are collected from the Himalaya forests and brought to Kathgodan, from where they are despatched to Mumbai, Kolkota and Kanuaj. It is a semi-aquatic perennial herb with a creeping and much branched aromatic rhizome. The rhizome is cylindrical or slightly compressed, about 19-25 mm in diameter and about 10 cm long. Externally, it is light brown or pinkish in color, and, white and spongy within. It is thoroughly washed in water, freed from dirt, and rootlets are removed and then carefully dried at a low temperature. It deteriorates with age and is subject to attack by worms. It is this dried rhizome, which constitutes Calamus or Sweet Flag, which is used as a condiment and as a pharmaceutical product . It has a reputation as a healing herb for its use in medicine. It is found wild as a water weed in shallow margins of creeks, lakes, pools and some rivers, throughout India and Sri Lanka, ascending to 1830 meter above mean sea level in the Himalayas. The plant thrives in marshy places and in most situations, like the edges of lakes and banks of streams. It is cultivated in one district , Kotagere, in Karnataka State, India. Curiously enough, the cultivation of Sweet Flag has not been extended to any other part of the State. It is also cultivated in Europe and USA, more for medicinal purposes.
Area, Production And International Trade
No reliable information is available on any of the above aspects in India, except that it is traded both for medicinal and condiment purposes.