eChapters

Precision farming involves the collection of data by observation and measurement, and the subsequent response. Sensing is key to the collection of data; whether this is remote from space by satellites, by aircraft or drones, or by proximal sensors used on the ground, mounted on vehicles or otherwise. Responses are increasingly automated and data are often integrated into wholefarm management systems. Early commercial applications were directed towards improving crop nutrition, however, utilising precision technologies to control weeds, pests and diseases is now a reality.

Precision crop protection requires detailed data on the occurrence and distribution of problems at the earliest possible stage of detection. An early detection is the key for successful management of pests in precision farming. Some of the key aspects are:

Identification: Identifications of crops, targets and beneficial; temporal and spatial distribution: presence of the target before, during and after the crop growth period;

Sampling techniques: statistically valid sampling regimes must be used to permit an accurate view of any problem

Thresholds: under particular circumstances, the point at which action to ensure levels of infestation or damage are kept to those deemed to be acceptable;

Prediction: the speed and pattern of development of a problem must be capable of being modelled, e.g. spread and severity of insect pests and fungal diseases; weed emergence according to species, dormancy and environmental conditions.

The development of a new drug begins typically in the chemistry laboratory and is followed by a wide range of biological studies in vitro (cells/tissues) and in vivo (animals), which forecast what the agent would do in humans. The focus of these studies moves from evaluating the safety of a drug for use in humans to looking at its clinical relevance. Pharmacodynamics involves a set of biological studies both in vitro and and in vivo to get a better understanding of the mechanism of action of a drug. Preclinical studies mainly include safety and toxicity to ensure its safety for use in humans. Typically, preclinical in vitro, in vivo, and Phase I testing may not be necessary if preliminary data on safety are available. However, preclinical data or clinical observations are still necessary to justify a rationale for use and safety in initial clinical studies.

Three main screening approaches are generally used in drug development. Simple Screening evaluates substances that have a particular property by one or two tests. There is no need for a series of tests in which the interpretation of results of one test may depend on others. Blind Screening is used for a new plant extract where no adequate pharmacological information is available. Blind screening may give clues for potential activity and worthiness to probe further for pharmacological activities. Programmed screening involves a new plant extract or substance with known pharmacological effects and results in more precise information.

Radiation combined injury (RCI) is complex condition that arises from simultaneous exposure to ionizing radiation and other types of physical trauma, such as blunt force injuries, thermal burns, or chemical exposure. This combination significantly complicates diagnosis and treatment, leading to increased morbidity and mortality. The past experiences of Hiroshima, Nagasaki, and Chernobyl have revealed that a large fraction of victims of such nuclear accidents or attacks suffer from combined radiation injuries. The possibility of a nuclear attack seems very far-fetched, but the destruction that would occur in such events will be massive, with a huge loss in terms of lives. Therefore, preparedness for the same should be done beforehand. Ionizing radiation damages cellular structures, primarily through direct ionization of DNA, leading to a range of acute and chronic health effects.

The severity of radiation damage is influenced by the dose, type of radiation, and duration of exposure. Simultaneously, traumatic injuries can result from various mechanisms such as combat, accidents, explosions that may involve blunt trauma, lacerations, or burns. These injuries can lead to significant blood loss, organ failure, and infections, which may be aggravated by the immune suppression caused by radiation exposure. The interaction between radiation effects and traumatic injuries can create a unique clinical challenge, as each type of injury can exacerbate the other’s impact on the body. The pathophysiology and prognosis of RCI are more intricate and challenging to manage compared to simple burns, wound or radiation injuries. Although RCI is uncommon, it poses significant risks during unfortunate events like nuclear or radiological terrorist attacks and nuclear accidents. Historical nuclear incidents have shown that 65-70% of victims experienced varying degrees of thermal burns and other traumatic injuries alongside radiation exposure. These victims often sustain damage to nearly every organ system, particularly the gastrointestinal, cardiovascular, neurological, and hematopoietic systems, all crucial for survival.

The domestication of oxen in Mesopotamia and the Indus Valley Civilization, perhaps as early as the 6th millennium B.C. provided mankind with the draft power necessary to develop the early plough into the larger, animal-drawn true ard (or scratch plough). Some of the earliest evidence of ploughing from around 2800 B.C.was found at the Indus Valley site of Kalibangan. Subsequent archaeological finds in Prague, Czech Republic, push the oldest known ploughed field even further back to 3500-3800 B.C. All food produce was planted and harvested by hand: seeds were scattered by hand and planted grains were brought from the field to be threshed on the dooryard. Milking a small herd of cows or goats was also done by hand. This continued for centuries. Then man discovered electricity.

In 1746, just a few years before Benjamin Franklin of Philadelphia sent a kite to catch lightning in a storm, a Doctor von Maimbray gave talk to the Royal Society of London where he described his experiment of electrifying the myrtle bush (Morella cerifera) which then put forth new branches in October, something which had never happened before. This news inspired others in Europe to carry out similar experiments. In 1749, Abbot Jean Antoine Nollet of the Royal Academy of Sciences in Paris France, inventor of the electroscope, published a pamphlet on “The Harmful Effects or Advantageous of Electrical Phenomena on Plants.”¹Isolated experiments continued until in 1783 Father Pierre Bertholon de Saint-Lazare of Montpellier published “About the Electricity of Plants”, a work in which he dealt with the effect of atmospheric electricity on plants, taking in their medicinal and nutritive-electrical virtues and but in particular the practical means of applying it usefully to agriculture.²He even built an electro-vegetometer, a device for collecting atmospheric electricity to distribute it in the soil and then to the plants, but he met with little success. Bertholon also conceived of subjecting plants to “electric rain” by connecting a large syringe to an electric source. A gardener, standing on a slab of insulating material in a wagon, carried a sprinkling can electrified by means of a wire leading to a static machine. “By means of this process, strange for the times, the good Abbé Bertholon, who was considered something of a sorcerer, obtained lettuces of an extraordinary size.”3

Abstract
 
The definition of what drought is and what drought is not, has profound implications for the environment and all segments of society, yet it may be different for each. Many attempts have been made to develop a comprehensive and meaningful definition. A generic definition provides a starting point:  Drought is a persistent and abnormal moisture deficiency having adverse impacts on vegetation, animals, and people. The public perceives “drought” as a serious departure from normal water conditions, a departure that requires a public response to reduce negative impacts. About two thirds of the geographic area of India receives low rainfall (less than 1000 mm), which is also characterized by uneven and erratic distributions.

Dry matter intake (DMI) is crucial for animal performance, especially in cattle. It ensures that animals receive the necessary nutrients and energy for metabolic functions. Cattle have complex digestive systems with unique metabolic functions. Variations in feed intake are essential throughout the growth cycle to maintain a balance in response to metabolic and environmental challenges.
Limitations in feed intake can lead to nutrient deficiencies, impacting growth rates and overall profitability. Feed stress can also result in health issues and digestive disturbances.
Importance of Predicting Dry Matter Intake (DMI) in Cattle Using NRC Guidelines
Predicting Dry Matter Intake (DMI) in cattle is crucial for managing livestock health, productivity, and economic efficiency. The National Research Council (NRC) offers guidelines to estimate DMI, aiding producers in optimizing feeding strategies. Here’s why predicting DMI is essential:
1. Optimizing Nutrient Intake
• Meeting Nutritional Needs: Accurate DMI prediction ensures cattle receive sufficient energy, protein, vitamins, and minerals for maintenance, growth, lactation, or reproduction.
• Avoiding Deficiencies: Inadequate feed can result in poor health, reduced production, and reproductive issues.
• Preventing Overfeeding: Excessive intake can lead to health problems like acidosis, obesity, or metabolic disorders.
2. Improving Productivity

Despite the tremendous advances in agriculture, the agrarian output is still weather and climate-dependent. Frost or low-temperature stress is a single weather-related phenomenon that causes losses greater than any other environmental or biological hazard. As the plants are immobile, they are bound to undergo this stress and suffer damage under natural condition. For avoiding and managing the adverse effects of frost-induced freezing in plants, a reliable early warning/prediction of intensity and duration of frost is necessary.

Abstract

Gastrointestinal parasitic infections particularly Haemonchus contortus infection in goats is very common and found as major nematode infections in farm condition. To chalk out the control programme and to prevent the initiation of anthelmintic resistant parasites, it is essential to know well in advance the prevalence of H. contortus infection. During the year 2009, a study was undertaken on H. contortus infection in the goat farm of ICAR Research Complex for NEH Region, Umiam, Meghalaya, which is a subtropical hill area and bioclimatograph was prepared to predict the prevalence of H. contortus infection in goats of this area. Meteorological data of this area was collected to visualize the effect of temperature, rainfall and humidity. Bioclimatographs were prepared in which total rainfall (TRF) or relative humidity (RH) is plotted against the maximum temperature (T-max) or minimum temperature (T-min) for the months and the resultant points are joined by a closed curve. The lines constituted the windows on these bioclimatographs, indicating the limit of the climatic conditions most suitable for development and survival of exogenous stages of gastrointestinal nematodes.

According to Fereshteh et al. (2020) microorganisms use biofilm to protect themselves against the harmful effects of the host body immune system and the surrounding environment. This helps them to survive against the anti-microbial agents. Inhibition of bacterial biofilm formation is very time consuming and expensive. Newer computational tools have been found to be a good option to predict the inhibition of biofilm peptides. In a computational prediction tool a large number of peptide sequences can be screened to select the peptides for validation. Vectors are extracted from the peptide primary structure and classification algorithms are carried out which includes SVM, random forest and k-nearest neighbour to evaluate their performance. Using a combination of features, helps in good evaluation. National Institute of Health (NIH) has found out that 80% of bacterial pathogens form biofilms and their multicellular structure makes them resistant to the hosts’ defense systems and traditional antimicrobials than planktonic bacteria. Recently, the use of Antimicrobial peptides

Predictive analytics estimates future health risks and outcomes, while prescriptive analytics recommends appropriate interventions and resource-allocation strategies. This chapter explains how statistical models, machine learning and healthcare data can support disease-risk assessment, prognosis and personalized treatment. Applications involving cardiovascular disease, diabetes, cancer, chronic-disease management and early-warning systems are examined. The integration of clinical, genomic, lifestyle and electronic health-record data is discussed alongside survival analysis, disease-progression modelling and treatment-response prediction. The chapter also addresses bias, fairness, transparency, privacy, regulation and interoperability. Future developments involving real-time analytics, IoT-enabled monitoring and precision medicine illustrate the transition toward proactive, preventive and data-driven healthcare.

Predictive modelling of plant growth can help farmers and researchers improve crop management, resource utilization and production planning. This chapter examines machine learning approaches for predicting important plant growth characteristics. It introduces plant growth fundamentals and the environmental and management factors that influence development. The chapter covers the machine learning workflow, including data acquisition, preprocessing, feature engineering, model development and evaluation. Supervised learning, unsupervised learning and deep learning approaches are considered for predicting plant height, biomass, canopy development, leaf area and yield-related characteristics. Data from IoT devices, drones and satellite imagery can strengthen prediction models. The chapter also discusses precision agriculture applications, limitations and emerging technologies such as explainable AI, digital twins and edge AI.

Abstract

The study of computational toxicity prediction is very crucial and necessary, as it is quite time consuming and expensive to measure the toxicity via experimental techniques (Quan Liao et al., 2006). The use of computational approaches particularly to reduce the time and cost for the evaluation of the kinetic and toxic behaviour of any drug has been increased tremendously in recent time period (Richard Judson et al., 2004). Machine learning is a powerful tool to discover new complex combinatorial relationships in various in vitro and in vivo data information (Richard Judson et al., 2004) and has been successfully used in many diverse fields with numerous applications such as pharmacodynamics properties prediction (Czerminski et al., 2001; Livingstone et al., 2003), protein function prediction (Cai et al., 2003), medical decision making (Veropoulos, 2001), spam categorization (Drucker et al., 1999), detection of oil spills (Kubat et al., 1998), and speech recognition (Nuttakorn et al., 2001).

6.1 Age

Age is one of the most important risk factors for developing mammary tumors in dogs. A comprehensive study carried on dogs has found that the risk of mammary tumors become significant by 8 years of age and increases linearly thereafter. The age of onset of tumors, however, may vary depending on the life span of the dog breed. An average age of CMT onset is 8-11 years, indicating as a condition of the middle-aged or older dogs. Mammary tumors are generally rare in dogs under 3 years of age, unless the dogs have been given exogenous hormones such as progestins

6.2 Breed

Most consistently listed breeds in which mammary tumor occur include, English springer spaniel, Brittany, Cocker spaniel, English setter, Pointer, Afghan hound and German shepherd, in addition to the smaller breeds such as Poodle, Maltese, Chihuahua, Beagle, Dachshund and Terriers. These studies clearly show that the risk for mammary tumors has some breed association and, therefore, having a genetic component. Pure breeds of dogs are generally at a greater risk i.e., known to account for 80% of the submitted cases. Some studies have further shown that certain lines within specific breeds are at more risk than others.

SUMMARY

Multiplicity of cropping systems has been one of the main features of Indian agriculture and it is attributed to rain-fed agriculture and prevailing socio-economic situations of farming community. It has been estimated that more than 250 double cropping systems are followed throughout the country and based on rationale of spread of crops in each district in the country, 30 important cropping systems have been identified. Cropping systems of a region are decided by and large, by a number of soil and climatic parameters which deter mine overall agro ecological setting for nourishment and appropriateness of a crop or set of crops for cultivation. Much of the Madhya Pradesh area also coincides with the rainfed semi-arid agro-ecological region and sub-regions which are upland, forested with low agricultural productivity and predominately tribal population. It would appear that over the centuries – including in recent history tribals have been inexorably moved off the better lands, into areas which from an agricultural perspective have low productive capacity and little potential for growth. Low agricultural productivity is one of the main causes of persistently high poverty rates in the state and the poor masses not only depends upon the farming system but also chosen different alternatives for their sustanability.

The present study was carried out to assess the role of endogenous cannabinoids in regulating aortic vascular response during early and late phases of sepsis. Polymicrobial sepsis was induced by caecal ligation and puncture (CLP) in mice. Besides, recording the isometric tension in arterial rings, estimation of haemato-biochemical parameters, histopathological examinations of vital organs, mRNA expression of CB1 receptor and MAGL enzyme and their role in the vasoconstrictor response to noradrenaline in the aorta of septic mouse were studied. Sepsis significantly reduced RBC, Hb and WBC counts during both early (CLP-6h) and late (CLP-20h) phases of sepsis whereas neutrophil count was increased during early phase. There was also a marked fall in lymphocyte count during late phase of sepsis indicative of immunosuppressive state. Significant rise in the plasma ALT, AST, BUN and creatinine levels during early and late phases of sepsis were suggestive of liver and kidney dysfunctions which were further substantiated byhistopathological examinations of these vital organs. Sepsis produced a state of hypoproteinaemia with significant reduction in plasma albumin level.

Accurate early diagnosis of pregnancy is essential for successful breeding programme.

Ability is required for successful practice.

Skill is to be developed to indicate the duration within weeks in early and months in late pregnancy.

The diagnosis of pregnancy (Cyesiognosis) was desired earlier by farmers for curiosity, but currently early pregnancy diagnosis is considered essential for economically viable and profitable dairy farming. Clinical methods are commonly used for pregnancy diagnosis in cattle with recto genital palpation and trans-rectal ultrasonography as the methods of choice for an accurate and early pregnancy diagnosis.

Q.1 An experiment is a means of getting an answer to the question which arises in the mind of an experimenter (reseacher).

Q.2 An experiment may be planned for not to compare the yield of several varieties of wheat.