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Livestock production systems can be defined based on feed source, as grassland-based, mixed, and landless. , 30% of Earth's ice- and water-free area was used for producing livestock, with the sector employing approximately 1.3 billion people. Between the 1960s and the 2000s, there was a significant increase in livestock production, both by numbers and by carcass weight, especially among beef, pigs and chickens, the latter of which had production increased by almost a factor of 10. Non-meat animals, such as milk cows and egg-producing chickens, also showed significant production increases. Global cattle, sheep and goat populations are expected to continue to increase sharply through 2050. Aquaculture or fish farming, the production of fish for human consumption in confined operations, is one of the fastest growing sectors of food production, growing at an average of 9% a year between 1975 and 2007. During the second half of the 20th century, producers using selective breeding focused on creating livestock breeds and crossbreeds that increased production, while mostly disregarding the need to preserve genetic diversity. This trend has led to a significant decrease in genetic diversity and resources among livestock breeds, leading to a corresponding decrease in disease resistance and local adaptations previously found among traditional breeds.
Grassland based livestock production relies upon plant material such as shrubland, rangeland, and pastures for feeding ruminant animals. Outside nutrient inputs may be used, however manure is returned directly to the grassland as a major nutrient source. This system is particularly important in areas where crop production is not feasible because of climate or soil, representing 30–40 million pastoralists. Mixed production systems use grassland, fodder crops and grain feed crops as feed for ruminant and monogastric (one stomach; mainly chickens and pigs) livestock. Manure is typically recycled in mixed systems as a fertilizer for crops. Landless systems rely upon feed from outside the farm, representing the de-linking of crop and livestock production found more prevalently in Organization for Economic Co-operation and Development member countries. Synthetic fertilizers are more heavily relied upon for crop production and manure use becomes a challenge as well as a source for pollution. Industrialized countries use these operations to produce much of the global supplies of poultry and pork. Scientists estimate that 75% of the growth in livestock production between 2003 and 2030 will be in confined animal feeding operations, sometimes called factory farming. Much of this growth is happening in developing countries in Asia, with much smaller amounts of growth in Africa. Some of the practices used in commercial livestock production, including the usage of growth hormones, are controversial.
Production practices. Tillage is the practice of breaking up the soil with tools such as the plow or harrow to prepare for planting, for nutrient incorporation, or for pest control. Tillage varies in intensity from conventional to no-till. It can improve productivity by warming the soil, incorporating fertilizer and controlling weeds, but also renders soil more prone to erosion, triggers the decomposition of organic matter releasing CO2, and reduces the abundance and diversity of soil organisms. Pest control includes the management of weeds, insects, mites, and diseases. Chemical (pesticides), biological (biocontrol), mechanical (tillage), and cultural practices are used. Cultural practices include crop rotation, culling, cover crops, intercropping, composting, avoidance, and resistance. Integrated pest management attempts to use all of these methods to keep pest populations below the number which would cause economic loss, and recommends pesticides as a last resort. Nutrient management includes both the source of nutrient inputs for crop and livestock production, and the method of use of manure produced by livestock. Nutrient inputs can be chemical inorganic fertilizers, manure, green manure, compost and minerals. Crop nutrient use may also be managed using cultural techniques such as crop rotation or a fallow period. Manure is used either by holding livestock where the feed crop is growing, such as in managed intensive rotational grazing, or by spreading either dry or liquid formulations of manure on cropland or pastures.
Water management is needed where rainfall is insufficient or variable, which occurs to some degree in most regions of the world. Some farmers use irrigation to supplement rainfall. In other areas such as the Great Plains in the U.S. and Canada, farmers use a fallow year to conserve soil moisture for the following year. Recent technological innovations in precision agriculture allow for water status monitoring and automate water usage, leading to more efficient management. Agriculture represents 70% of freshwater use worldwide. However, water withdrawal ratios for agriculture vary significantly by income level. In least developed countries and landlocked developing countries, water withdrawal ratios for agriculture are as high as 90 percent of total water withdrawals and about 60 percent in Small Island Developing States. According to 2014 report by the International Food Policy Research Institute, agricultural technologies will have the greatest impact on food production if adopted in combination with each other. Using a model that assessed how eleven technologies could impact agricultural productivity, food security and trade by 2050, the International Food Policy Research Institute found that the number of people at risk from hunger could be reduced by as much as 40% and food prices could be reduced by almost half.
Payment for ecosystem services is a method of providing additional incentives to encourage farmers to conserve some aspects of the environment. Measures might include paying for reforestation upstream of a city, to improve the supply of fresh water. Agricultural automation. Different definitions exist for agricultural automation and for the variety of tools and technologies that are used to automate production. One view is that agricultural automation refers to autonomous navigation by robots without human intervention. Alternatively, it is defined as the accomplishment of production tasks through mobile, autonomous, decision-making, mechatronic devices. However, FAO finds that these definitions do not capture all the aspects and forms of automation, such as robotic milking machines that are static, most motorized machinery that automates the performing of agricultural operations, and digital tools (e.g., sensors) that automate only diagnosis. FAO defines agricultural automation as the use of machinery and equipment in agricultural operations to improve their diagnosis, decision-making or performing, reducing the drudgery of agricultural work or improving the timeliness, and potentially the precision, of agricultural operations.
The technological evolution in agriculture has involved a progressive move from manual tools to animal traction, to motorized mechanization, to digital equipment and finally, to robotics with artificial intelligence (AI). Motorized mechanization using engine power automates the performance of agricultural operations such as ploughing and milking. With digital automation technologies, it also becomes possible to automate diagnosis and decision-making of agricultural operations. For example, autonomous crop robots can harvest and seed crops, while drones can gather information to help automate input application. Precision agriculture often employs such automation technologies. Motorized machines are increasingly complemented, or even superseded, by new digital equipment that automates diagnosis and decision-making. A conventional tractor, for example, can be converted into an automated vehicle allowing it to sow a field autonomously. Motorized mechanization has increased significantly across the world in recent years, although reliable global data with broad country coverage exist only for tractors and only up to 2009. Sub-Saharan Africa is the only region where the adoption of motorized mechanization has stalled over the past decades.
Automation technologies are increasingly used for managing livestock, though evidence on adoption is lacking. Global automatic milking system sales have increased over recent years, but adoption is likely mostly in Northern Europe, and likely almost absent in low- and middle-income countries. Automated feeding machines for both cows and poultry also exist, but data and evidence regarding their adoption trends and drivers is likewise scarce. Measuring the overall employment impacts of agricultural automation is difficult because it requires large amounts of data tracking all the transformations and the associated reallocation of workers both upstream and downstream. While automation technologies reduce labor needs for the newly automated tasks, they also generate new labor demand for other tasks, such as equipment maintenance and operation. Agricultural automation can also stimulate employment by allowing producers to expand production and by creating other agrifood systems jobs. This is especially true when it happens in context of rising scarcity of rural labor, as is the case in high-income countries and many middle-income countries. On the other hand, if forcedly promoted, for example through government subsidies in contexts of abundant rural labor, it can lead to labor displacement and falling or stagnant wages, particularly affecting poor and low-skilled workers.
Effects of climate change on yields. Climate change and agriculture are interrelated on a global scale. Climate change affects agriculture through changes in average temperatures, rainfall, and weather extremes (like storms and heat waves); changes in pests and diseases; changes in atmospheric carbon dioxide and ground-level ozone concentrations; changes in the nutritional quality of some foods; and changes in sea level. Global warming is already affecting agriculture, with effects unevenly distributed across the world. In a 2022 report, the Intergovernmental Panel on Climate Change describes how human-induced warming has slowed growth of agricultural productivity over the past 50 years in mid and low latitudes. Methane emissions have negatively impacted crop yields by increasing temperatures and surface ozone concentrations. Warming is also negatively affecting crop and grassland quality and harvest stability. Ocean warming has decreased sustainable yields of some wild fish populations while ocean acidification and warming have already affected farmed aquatic species. Climate change will probably increase the risk of food insecurity for some vulnerable groups, such as the poor.
Crop alteration and biotechnology. Plant breeding. Crop alteration has been practiced by humankind for thousands of years, since the beginning of civilization. Altering crops through breeding practices changes the genetic make-up of a plant to develop crops with more beneficial characteristics for humans, for example, larger fruits or seeds, drought-tolerance, or resistance to pests. Significant advances in plant breeding ensued after the work of geneticist Gregor Mendel. His work on dominant and recessive alleles, although initially largely ignored for almost 50 years, gave plant breeders a better understanding of genetics and breeding techniques. Crop breeding includes techniques such as plant selection with desirable traits, self-pollination and cross-pollination, and molecular techniques that genetically modify the organism. Domestication of plants has, over the centuries increased yield, improved disease resistance and drought tolerance, eased harvest and improved the taste and nutritional value of crop plants. Careful selection and breeding have had enormous effects on the characteristics of crop plants. Plant selection and breeding in the 1920s and 1930s improved pasture (grasses and clover) in New Zealand. Extensive X-ray and ultraviolet induced mutagenesis efforts (i.e. primitive genetic engineering) during the 1950s produced the modern commercial varieties of grains such as wheat, corn (maize) and barley.
The Green Revolution popularized the use of conventional hybridization to sharply increase yield by creating "high-yielding varieties". For example, average yields of corn (maize) in the US have increased from around 2.5 tons per hectare (t/ha) (40 bushels per acre) in 1900 to about 9.4 t/ha (150 bushels per acre) in 2001. Similarly, worldwide average wheat yields have increased from less than 1 t/ha in 1900 to more than 2.5 t/ha in 1990. South American average wheat yields are around 2 t/ha, African under 1 t/ha, and Egypt and Arabia up to 3.5 to 4 t/ha with irrigation. In contrast, the average wheat yield in countries such as France is over 8 t/ha. Variations in yields are due mainly to variation in climate, genetics, and the level of intensive farming techniques (use of fertilizers, chemical pest control, and growth control to avoid lodging). Investments into innovation for agriculture are long term. This is because it takes time for research to become commercialized and for technology to be adapted to meet multiple regions’ needs, as well as meet national guidelines before being adopted and planted in a farmer's fields. For instance, it took at least 60 years from the introduction of hybrid corn technology before its adoption became widespread.
Agricultural innovation developed for the specific agroecological conditions of one region is not easily transferred and used in another region with different agroecological conditions. Instead, the innovation would have to be adapted to the specific conditions of that other region and respect its biodiversity and environmental requirements and guidelines. Some such adaptations can be seen through the steadily increasing number of plant varieties protected under the plant variety protection instrument administered by the International Union for the Protection of New Varieties of Plants (UPOV). Genetic engineering. Genetically modified organisms (GMO) are organisms whose genetic material has been altered by genetic engineering techniques generally known as recombinant DNA technology. Genetic engineering has expanded the genes available to breeders to use in creating desired germlines for new crops. Increased durability, nutritional content, insect and virus resistance and herbicide tolerance are a few of the attributes bred into crops through genetic engineering. For some, GMO crops cause food safety and food labeling concerns. Numerous countries have placed restrictions on the production, import or use of GMO foods and crops. The Biosafety Protocol, an international treaty, regulates the trade of GMOs. There is ongoing discussion regarding the labeling of foods made from GMOs, and while the EU currently requires all GMO foods to be labeled, the US does not.
Herbicide-resistant seeds have a gene implanted into their genome that allows the plants to tolerate exposure to herbicides, including glyphosate. These seeds allow the farmer to grow a crop that can be sprayed with herbicides to control weeds without harming the resistant crop. Herbicide-tolerant crops are used by farmers worldwide. With the increasing use of herbicide-tolerant crops, comes an increase in the use of glyphosate-based herbicide sprays. In some areas glyphosate resistant weeds have developed, causing farmers to switch to other herbicides. Some studies also link widespread glyphosate usage to iron deficiencies in some crops, which is both a crop production and a nutritional quality concern, with potential economic and health implications. Other GMO crops used by growers include insect-resistant crops, which have a gene from the soil bacterium "Bacillus thuringiensis" (Bt), which produces a toxin specific to insects. These crops resist damage by insects. Some believe that similar or better pest-resistance traits can be acquired through traditional breeding practices, and resistance to various pests can be gained through hybridization or cross-pollination with wild species. In some cases, wild species are the primary source of resistance traits; some tomato cultivars that have gained resistance to at least 19 diseases did so through crossing with wild populations of tomatoes.
Environmental impact. Effects and costs. Agriculture is both a cause of and sensitive to environmental degradation, such as biodiversity loss, desertification, soil degradation and climate change, which cause decreases in crop yield. Agriculture is one of the most important drivers of environmental pressures, particularly habitat change, climate change, water use and toxic emissions. Agriculture is the main source of toxins released into the environment, including insecticides, especially those used on cotton. The 2011 UNEP Green Economy report stated that agricultural operations produced some 13 percent of anthropogenic global greenhouse gas emissions. This includes gases from the use of inorganic fertilizers, agro-chemical pesticides, and herbicides, as well as fossil fuel-energy inputs. Agriculture imposes multiple external costs upon society through effects such as pesticide damage to nature (especially herbicides and insecticides), nutrient runoff, excessive water usage, and loss of natural environment. A 2000 assessment of agriculture in the UK determined total external costs for 1996 of £2,343 million, or £208 per hectare. A 2005 analysis of these costs in the US concluded that cropland imposes approximately $5 to $16 billion ($30 to $96 per hectare), while livestock production imposes $714 million. Both studies, which focused solely on the fiscal impacts, concluded that more should be done to internalize external costs. Neither included subsidies in their analysis, but they noted that subsidies also influence the cost of agriculture to society.
Agriculture seeks to increase yield and to reduce costs, often employing measures that cut biodiversity to very low levels. Yield increases with inputs such as fertilizers and removal of pathogens, predators, and competitors (such as weeds). Costs decrease with increasing scale of farm units, such as making fields larger; this means removing hedges, ditches and other areas of habitat. Pesticides kill insects, plants and fungi. Effective yields fall with on-farm losses, which may be caused by poor production practices during harvesting, handling, and storage. The environmental effects of climate change show that research on pests and diseases that do not generally afflict areas is essential. In 2021, farmers discovered stem rust on wheat in the Champagne area of France, a disease that had previously only occurred in Morocco for 20 to 30 years. Because of climate change, insects that used to die off over the winter are now alive and multiplying. Livestock issues.
Land and water issues. Land transformation, the use of land to yield goods and services, is the most substantial way humans alter the Earth's ecosystems, and is the driving force causing biodiversity loss. Estimates of the amount of land transformed by humans vary from 39 to 50%. It is estimated that 24% of land globally experiences land degradation, a long-term decline in ecosystem function and productivity, with cropland being disproportionately affected. Land management is the driving factor behind degradation; 1.5 billion people rely upon the degrading land. Degradation can be through deforestation, desertification, soil erosion, mineral depletion, acidification, or salinization. In 2021, the global agricultural land area was 4.79 billion hectares (ha), down 2 percent, or 0.09 billion ha compared with 2000. Between 2000 and 2021, roughly two-thirds of agricultural land were used for permanent meadows and pastures (3.21 billion ha in 2021), which declined by 5 percent (0.17 billion ha). One-third of the total agricultural land was cropland (1.58 billion ha in 2021), which increased by 6 percent (0.09 billion ha).
Eutrophication, excessive nutrient enrichment in aquatic ecosystems resulting in algal blooms and anoxia, leads to fish kills, loss of biodiversity, and renders water unfit for drinking and other industrial uses. Excessive fertilization and manure application to cropland, as well as high livestock stocking densities cause nutrient (mainly nitrogen and phosphorus) runoff and leaching from agricultural land. These nutrients are major nonpoint pollutants contributing to eutrophication of aquatic ecosystems and pollution of groundwater, with harmful effects on human populations. Fertilizers also reduce terrestrial biodiversity by increasing competition for light, favoring those species that are able to benefit from the added nutrients.
Pesticides. Pesticide use has increased since 1950 to 2.5 million short tons annually worldwide, yet crop loss from pests has remained relatively constant. The World Health Organization estimated in 1992 that three million pesticide poisonings occur annually, causing 220,000 deaths. Pesticides select for pesticide resistance in the pest population, leading to a condition termed the "pesticide treadmill" in which pest resistance warrants the development of a new pesticide. An alternative argument is that the way to "save the environment" and prevent famine is by using pesticides and intensive high yield farming, a view exemplified by a quote heading the Center for Global Food Issues website: 'Growing more per acre leaves more land for nature'. However, critics argue that a trade-off between the environment and a need for food is not inevitable, and that pesticides can replace good agronomic practices such as crop rotation. The Push–pull agricultural pest management technique involves intercropping, using plant aromas to repel pests from crops (push) and to lure them to a place from which they can then be removed (pull).
Contribution to climate change. Agriculture contributes towards climate change through greenhouse gas emissions and by the conversion of non-agricultural land such as forests into agricultural land. The agriculture, forestry and land use sector contribute between 13% and 21% of global greenhouse gas emissions. Emissions of nitrous oxide, methane make up over half of total greenhouse gas emission from agriculture. Animal husbandry is a major source of greenhouse gas emissions. Approximately 57% of global GHG emissions from the production of food are from the production of animal-based food while plant-based foods contribute 29% and the remaining 14% is for other utilizations. Farmland management and land-use change represented major shares of total emissions (38% and 29%, respectively), whereas rice and beef were the largest contributing plant- and animal-based commodities (12% and 25%, respectively). South and Southeast Asia and South America were the largest emitters of production-based GHGs. Effects of climate change on agriculture.
Climate change put significant part of crops in danger already at 1.5 degrees of warming. While in North Anerica, Europe and central Asia the share of endangered crops is relatively little at this level of warming, in the Middle east and North Africa region for example, close to 50% of cropland is in danger. With further temperature rise the risk increase in all regions, in some more, in some less. Globally the cropland area in safe climatic zone decrease for all the major crop groups as warming exceed 1.5 degrees. Sustainability. Current farming methods have resulted in over-stretched water resources, high levels of erosion and reduced soil fertility. There is not enough water to continue farming using current practices; therefore how water, land, and ecosystem resources are used to boost crop yields must be reconsidered. A solution would be to give value to ecosystems, recognizing environmental and livelihood tradeoffs, and balancing the rights of a variety of users and interests. Inequities that result when such measures are adopted would need to be addressed, such as the reallocation of water from poor to rich, the clearing of land to make way for more productive farmland, or the preservation of a wetland system that limits fishing rights.
Technological advancements help provide farmers with tools and resources to make farming more sustainable. Technology permits innovations like conservation tillage, a farming process which helps prevent land loss to erosion, reduces water pollution, and enhances carbon sequestration. Agricultural automation can help address some of the challenges associated with climate change and thus facilitate adaptation efforts. For example, the application of digital automation technologies (e.g. in precision agriculture) can improve resource-use efficiency in conditions which are increasingly constrained for agricultural producers. Moreover, when applied to sensing and early warning, they can help address the uncertainty and unpredictability of weather conditions associated with accelerating climate change. Other potential sustainable practices include conservation agriculture, agroforestry, improved grazing, avoided grassland conversion, and biochar. Current mono-crop farming practices in the United States preclude widespread adoption of sustainable practices, such as 2–3 crop rotations that incorporate grass or hay with annual crops, unless negative emission goals such as soil carbon sequestration become policy.
The food demand of Earth's projected population, with current climate change predictions, could be satisfied by improvement of agricultural methods, expansion of agricultural areas, and a sustainability-oriented consumer mindset. Energy dependence. Since the 1940s, agricultural productivity has increased dramatically, due largely to the increased use of energy-intensive mechanization, fertilizers and pesticides. The vast majority of this energy input comes from fossil fuel sources. Between the 1960s and the 1980s, the Green Revolution transformed agriculture around the globe, with world grain production increasing significantly (between 70% and 390% for wheat and 60% to 150% for rice, depending on geographic area) as world population doubled. Heavy reliance on petrochemicals has raised concerns that oil shortages could increase costs and reduce agricultural output. Industrialized agriculture depends on fossil fuels in two fundamental ways: direct consumption on the farm and manufacture of inputs used on the farm. Direct consumption includes the use of lubricants and fuels to operate farm vehicles and machinery.
Indirect consumption includes the manufacture of fertilizers, pesticides, and farm machinery. In particular, the production of nitrogen fertilizer can account for over half of agricultural energy usage. Together, direct and indirect consumption by US farms accounts for about 2% of the nation's energy use. Direct and indirect energy consumption by U.S. farms peaked in 1979, and has since gradually declined. Food systems encompass not just agriculture but off-farm processing, packaging, transporting, marketing, consumption, and disposal of food and food-related items. Agriculture accounts for less than one-fifth of food system energy use in the US. Plastic pollution. Plastic products are used extensively in agriculture, including to increase crop yields and improve the efficiency of water and agrichemical use. "Agriplastic" products include films to cover greenhouses and tunnels, mulch to cover soil (e.g. to suppress weeds, conserve water, increase soil temperature and aid fertilizer application), shade cloth, pesticide containers, seedling trays, protective mesh and irrigation tubing. The polymers most commonly used in these products are low- density polyethylene (LPDE), linear low-density polyethylene (LLDPE), polypropylene (PP) and polyvinyl chloride (PVC).
The total amount of plastics used in agriculture is difficult to quantify. A 2012 study reported that almost 6.5 million tonnes per year were consumed globally while a later study estimated that global demand in 2015 was between 7.3 million and 9 million tonnes. Widespread use of plastic mulch and lack of systematic collection and management have led to the generation of large amounts of mulch residue. Weathering and degradation eventually cause the mulch to fragment. These fragments and larger pieces of plastic accumulate in soil. Mulch residue has been measured at levels of 50 to 260 kg per hectare in topsoil in areas where mulch use dates back more than 10 years, which confirms that mulching is a major source of both microplastic and macroplastic soil contamination. Agricultural plastics, especially plastic films, are not easy to recycle because of high contamination levels (up to 40–50% by weight contamination by pesticides, fertilizers, soil and debris, moist vegetation, silage juice water, and UV stabilizers) and collection difficulties . Therefore, they are often buried or abandoned in fields and watercourses or burned. These disposal practices lead to soil degradation and can result in contamination of soils and leakage of microplastics into the marine environment as a result of precipitation run-off and tidal washing. In addition, additives in residual plastic film (such as UV and thermal stabilizers) may have deleterious effects on crop growth, soil structure, nutrient transport and salt levels. There is a risk that plastic mulch will deteriorate soil quality, deplete soil organic matter stocks, increase soil water repellence and emit greenhouse gases. Microplastics released through fragmentation of agricultural plastics can absorb and concentrate contaminants capable of being passed up the trophic chain.
Disciplines. Agricultural economics. Agricultural economics is economics as it relates to the "production, distribution and consumption of [agricultural] goods and services". Combining agricultural production with general theories of marketing and business as a discipline of study began in the late 1800s, and grew significantly through the 20th century. Although the study of agricultural economics is relatively recent, major trends in agriculture have significantly affected national and international economies throughout history, ranging from tenant farmers and sharecropping in the post-American Civil War Southern United States to the European feudal system of manorialism. In the United States, and elsewhere, food costs attributed to food processing, distribution, and agricultural marketing, sometimes referred to as the value chain, have risen while the costs attributed to farming have declined. This is related to the greater efficiency of farming, combined with the increased level of value addition (e.g. more highly processed products) provided by the supply chain. Market concentration has increased in the sector as well, and although the total effect of the increased market concentration is likely increased efficiency, the changes redistribute economic surplus from producers (farmers) and consumers, and may have negative implications for rural communities.
National government policies, such as taxation, subsidies, tariffs and others, can significantly change the economic marketplace for agricultural products. Since at least the 1960s, a combination of trade restrictions, exchange rate policies and subsidies have affected farmers in both the developing and the developed world. In the 1980s, non-subsidized farmers in developing countries experienced adverse effects from national policies that created artificially low global prices for farm products. Between the mid-1980s and the early 2000s, several international agreements limited agricultural tariffs, subsidies and other trade restrictions. However, , there was still a significant amount of policy-driven distortion in global agricultural product prices. The three agricultural products with the most trade distortion were sugar, milk and rice, mainly due to taxation. Among the oilseeds, sesame had the most taxation, but overall, feed grains and oilseeds had much lower levels of taxation than livestock products. Since the 1980s, policy-driven distortions have decreases more among livestock products than crops during the worldwide reforms in agricultural policy. Despite this progress, certain crops, such as cotton, still see subsidies in developed countries artificially deflating global prices, causing hardship in developing countries with non-subsidized farmers. Unprocessed commodities such as corn, soybeans, and cattle are generally graded to indicate quality, affecting the price the producer receives. Commodities are generally reported by production quantities, such as volume, number or weight.
Agricultural science. Agricultural science is a broad multidisciplinary field of biology that encompasses the parts of exact, natural, economic and social sciences used in the practice and understanding of agriculture. It covers topics such as agronomy, plant breeding and genetics, plant pathology, crop modeling, soil science, entomology, production techniques and improvement, study of pests and their management, and study of adverse environmental effects such as soil degradation, waste management, and bioremediation. The scientific study of agriculture began in the 18th century, when Johann Friedrich Mayer conducted experiments on the use of gypsum (hydrated calcium sulphate) as a fertilizer. Research became more systematic when in 1843, John Lawes and Henry Gilbert began a set of long-term agronomy field experiments at Rothamsted Research Station in England; some of them, such as the Park Grass Experiment, are still running. In America, the Hatch Act of 1887 provided funding for what it was the first to call "agricultural science", driven by farmers' interest in fertilizers. In agricultural entomology, the USDA began to research biological control in 1881; it instituted its first large program in 1905, searching Europe and Japan for natural enemies of the spongy moth and brown-tail moth, establishing parasitoids (such as solitary wasps) and predators of both pests in the US.
Policy. Agricultural policy is the set of government decisions and actions relating to domestic agriculture and imports of foreign agricultural products. Governments usually implement agricultural policies with the goal of achieving a specific outcome in the domestic agricultural product markets. Some overarching themes include risk management and adjustment (including policies related to climate change, food safety and natural disasters), economic stability (including policies related to taxes), natural resources and environmental sustainability (especially water policy), research and development, and market access for domestic commodities (including relations with global organizations and agreements with other countries). Agricultural policy can also touch on food quality, ensuring that the food supply is of a consistent and known quality, food security, ensuring that the food supply meets the population's needs, and conservation. Policy programs can range from financial programs, such as subsidies, to encouraging producers to enroll in voluntary quality assurance programs.
A 2021 report finds that globally, support to agricultural producers accounts for almost US$540 billion a year. This amounts to 15 percent of total agricultural production value, and is heavily biased towards measures that are leading to inefficiency, as well as are unequally distributed and harmful for the environment and human health. There are many influences on the creation of agricultural policy, including consumers, agribusiness, trade lobbies and other groups. Agribusiness interests hold a large amount of influence over policy making, in the form of lobbying and campaign contributions. Political action groups, including those interested in environmental issues and labor unions, also provide influence, as do lobbying organizations representing individual agricultural commodities. The Food and Agriculture Organization of the United Nations (FAO) leads international efforts to defeat hunger and provides a forum for the negotiation of global agricultural regulations and agreements. Samuel Jutzi, director of FAO's animal production and health division, states that lobbying by large corporations has stopped reforms that would improve human health and the environment. For example, proposals in 2010 for a voluntary code of conduct for the livestock industry that would have provided incentives for improving standards for health, and environmental regulations, such as the number of animals an area of land can support without long-term damage, were successfully defeated due to large food company pressure.
Aldous Huxley Aldous Leonard Huxley ( ; 26 July 1894 – 22 November 1963) was an English writer and philosopher. His bibliography spans nearly 50 books, including non-fiction works, as well as essays, narratives, and poems. Born into the prominent Huxley family, he graduated from Balliol College, Oxford, with a degree in English literature. Early in his career, he published short stories and poetry and edited the literary magazine "Oxford Poetry", before going on to publish travel writing, satire, and screenplays. He spent the latter part of his life in the United States, living in Los Angeles from 1937 until his death. By the end of his life, Huxley was widely acknowledged as one of the foremost intellectuals of his time. He was nominated for the Nobel Prize in Literature nine times, and was elected Companion of Literature by the Royal Society of Literature in 1962. Huxley was a pacifist. He grew interested in philosophical mysticism, as well as universalism, addressing these subjects in his works such as "The Perennial Philosophy" (1945), which illustrates commonalities between Western and Eastern mysticism, and "The Doors of Perception" (1954), which interprets his own psychedelic experience with mescaline. In his most famous novel "Brave New World" (1932) and his final novel "Island" (1962), he presented his visions of dystopia and utopia, respectively.
Early life. Huxley was born in Godalming, Surrey, England, on 26 July 1894. He was the third son of the writer and schoolmaster Leonard Huxley, who edited "The Cornhill Magazine", and his first wife, Julia Arnold, who founded Prior's Field School. Julia was the niece of poet and critic Matthew Arnold and the sister of Mrs. Humphry Ward. Julia named him Aldous after a character in one of her sister's novels. Aldous was the grandson of Thomas Henry Huxley, the zoologist, agnostic, and controversialist who had often been called "Darwin's Bulldog". His brother Julian Huxley and half-brother Andrew Huxley also became outstanding biologists. Aldous had another brother, Noel Trevenen Huxley (1889–1914), who took his own life after a period of clinical depression. As a child, Huxley's nickname was "Ogie", diminutive for "Ogre". He was described by his brother, Julian, as someone who frequently contemplated "the strangeness of things". According to his cousin and contemporary Gervas Huxley, he had an early interest in drawing.
Huxley's education began in his father's well-equipped botanical laboratory, after which he enrolled at Hillside School near Godalming. He was taught there by his own mother for several years until she became terminally ill. After Hillside he went on to Eton College. His mother died in 1908, when he was 14 (his father later remarried). He contracted the eye disease keratitis punctata in 1911; this "left [him] practically blind for two to three years" and "ended his early dreams of becoming a doctor". In October 1913, Huxley entered Balliol College, Oxford, where he studied English literature. He volunteered for the British Army in January 1916, for the Great War; however, he was rejected on health grounds, being half-blind in one eye. His eyesight later partly recovered. He edited "Oxford Poetry" in 1916, and in June of that year graduated BA with first class honours. His brother Julian wrote: Following his years at Balliol, Huxley, being financially indebted to his father, decided to find employment. He taught French for a year at Eton College, where Eric Blair (who was to take the pen name George Orwell) and Steven Runciman were among his pupils. He was mainly remembered as being an incompetent schoolmaster unable to keep order in class. Nevertheless, Blair and others spoke highly of his excellent command of language.
Huxley also worked for a time during the 1920s at Brunner and Mond, an advanced chemical plant in Billingham in County Durham, northeast England. According to an introduction to his science fiction novel "Brave New World" (1932), the experience he had there of "an ordered universe in a world of planless incoherence" was an important source for the novel. Career. Huxley completed his first (unpublished) novel at the age of 17 and began writing seriously in his early twenties, establishing himself as a successful writer and social satirist. His first published novels were social satires, "Crome Yellow" (1921), "Antic Hay" (1923), "Those Barren Leaves" (1925), and "Point Counter Point" (1928). "Brave New World" (1932) was his fifth novel and first dystopian work. In the 1920s, he was also a contributor to "Vanity Fair" and British "Vogue" magazines. Contact with the Bloomsbury Group.
Works of this period included novels about the dehumanising aspects of scientific progress, (his magnum opus "Brave New World"), and on pacifist themes ("Eyeless in Gaza"). In "Brave New World", set in a dystopian London, Huxley portrays a society operating on the principles of mass production and Pavlovian conditioning. Huxley was strongly influenced by F. Matthias Alexander, on whom he based a character in "Eyeless in Gaza". During this period, Huxley began to write and edit non-fiction works on pacifist issues, including "Ends and Means" (1937), "An Encyclopedia of Pacifism", and "Pacifism and Philosophy", and was an active member of the Peace Pledge Union (PPU). Life in the United States. In 1937, Huxley moved to Hollywood with his wife Maria, son Matthew Huxley, and friend Gerald Heard. Cyril Connolly wrote, of the two intellectuals (Huxley and Heard) in the late 1930s, "all European avenues had been exhausted in the search for a way forward – politics, art, science – pitching them both toward the US in 1937." Huxley lived in the U.S., mainly southern California, until his death, and for a time in Taos, New Mexico, where he wrote "Ends and Means" (1937). The book contains tracts on war, inequality, religion and ethics.
Heard introduced Huxley to Vedanta (Upanishad-centered philosophy), meditation, and vegetarianism through the principle of ahimsa. In 1938, Huxley befriended Jiddu Krishnamurti, whose teachings he greatly admired. Huxley and Krishnamurti entered into an enduring exchange (sometimes edging on debate) over many years, with Krishnamurti representing the more rarefied, detached, ivory-tower perspective and Huxley, with his pragmatic concerns, the more socially and historically informed position. Huxley wrote a foreword to Krishnamurti's quintessential statement, "The First and Last Freedom" (1954). Huxley and Heard became Vedantists in the group formed around Hindu Swami Prabhavananda, and subsequently introduced Christopher Isherwood to the circle. Not long afterwards, Huxley wrote his book on widely held spiritual values and ideas, "The Perennial Philosophy", which discussed the teachings of renowned mystics of the world. Huxley became a close friend of Remsen Bird, president of Occidental College. He spent much time at the college in the Eagle Rock neighbourhood of Los Angeles. The college appears as "Tarzana College" in his satirical novel "After Many a Summer" (1939). The novel won Huxley a British literary award, the 1939 James Tait Black Memorial Prize for fiction. Huxley also incorporated Bird into the novel.
During this period, Huxley earned a substantial income as a Hollywood screenwriter; Christopher Isherwood, in his autobiography "My Guru and His Disciple", states that Huxley earned more than $3,000 per week (approximately $50,000 in 2020 dollars) as a screenwriter, and that he used much of it to transport Jewish and left-wing writer and artist refugees from Hitler's Germany to the US. In March 1938, Huxley's friend Anita Loos, a novelist and screenwriter, put him in touch with Metro-Goldwyn-Mayer (MGM), which hired him for "Madame Curie" which was originally to star Greta Garbo and be directed by George Cukor. (Eventually, the film was completed by MGM in 1943 with a different director and cast.) Huxley received screen credit for "Pride and Prejudice" (1940) and was paid for his work on a number of other films, including "Jane Eyre" (1944). He was commissioned by Walt Disney in 1945 to write a script based on "Alice's Adventures in Wonderland" and the biography of the story's author, Lewis Carroll. The script was not used, however.
Huxley wrote an introduction to the posthumous publication of J. D. Unwin's 1940 book "Hopousia or The Sexual and Economic Foundations of a New Society". On 21 October 1949, Huxley wrote to George Orwell, author of "Nineteen Eighty-Four", congratulating him on "how fine and how profoundly important the book is". In his letter, he predicted: In 1953, Huxley and Maria applied for United States citizenship and presented themselves for examination. When Huxley refused to bear arms for the U.S. and would not state that his objections were based on religious ideals, the only excuse allowed under the McCarran Act, the judge had to adjourn the proceedings. He withdrew his application. Nevertheless, he remained in the U.S. In 1959, Huxley turned down an offer to be made a Knight Bachelor by the Macmillan government without giving a reason; his brother Julian had been knighted in 1958, while his brother Andrew would be knighted in 1974. In the fall semester of 1960 Huxley was invited by Professor Huston Smith to be the Carnegie Visiting professor of humanities at the Massachusetts Institute of Technology (MIT). As part of the MIT centennial program of events organised by the Department of Humanities, Huxley presented a series of lectures titled, "What a Piece of Work is a Man" which concerned history, language, and art.
Robert S. de Ropp (scientist, humanitarian, and author), who had spent time with Huxley in England in the 1930s, connected with him again in the U.S. in the early 1960s and wrote that "the enormous intellect, the beautifully modulated voice, the gentle objectivity, all were unchanged. He was one of the most highly civilized human beings I had ever met." Late-in-life perspectives. Biographer Harold H. Watts wrote that Huxley's writings in the "final and extended period of his life" are "the work of a man who is meditating on the central problems of many modern men". Huxley had deeply felt apprehensions about the future the developed world might make for itself. From these, he made some warnings in his writings and talks. In a 1958 televised interview conducted by journalist Mike Wallace, Huxley outlined several major concerns: the difficulties and dangers of world overpopulation; the tendency towards distinctly hierarchical social organisation; the crucial importance of evaluating the use of technology in mass societies susceptible to persuasion; the tendency to promote modern politicians to a naive public as well-marketed commodities. In a December 1962 letter to brother Julian, summarizing a paper he had presented in Santa Barbara, he wrote, "What I said was that if we didn't pretty quickly start thinking of human problems in ecological terms rather than in terms of power politics we should very soon be in a bad way."
Huxley's engagement with Eastern wisdom traditions was entirely compatible with a strong appreciation of modern science. Biographer Milton Birnbaum wrote that Huxley "ended by embracing both science and Eastern religion". In his last book, "Literature and Science", Huxley wrote that "The ethical and philosophical implications of modern science are more Buddhist than Christian..." In "A Philosopher's Visionary Prediction", published one month before he died, Huxley endorsed training in general semantics and "the nonverbal world of culturally uncontaminated consciousness", writing that "We must learn how to be mentally silent, we must cultivate the art of pure receptivity... [T]he individual must learn to decondition himself, must be able to cut holes in the fence of verbalized symbols that hems him in." Spiritual views. For much of his life, Huxley described himself as agnostic, a word coined by his grandfather Thomas Henry Huxley, a scientist who championed the scientific method and was a major supporter of Darwin's theories. This is the definition he gave, “…it is wrong for a man to say that he is certain of the objective truth of any proposition unless he can produce evidence which logically justifies that certainty.” Aldous Huxley's agnosticism, together with his speculative propensity, made it difficult for him fully embrace any form of institutionalised religion. Over the last 30 years of his life, he accepted and wrote about concepts found in Vedanta and was a leading advocate of the Perennial Philosophy, which holds that the same metaphysical truths are found in all the major religions of the world.
In the 1920s, Huxley was skeptical of religion, "Earlier in his career he had rejected mysticism, often poking fun at it in his novels [...]" Gerald Heard became an influential friend of Huxley, and since the mid-1920s had been exploring Vedanta, as a way of understanding individual human life and the individual's relationship to the universe. Heard and Huxley both saw the political implications of Vedanta, which could help bring about peace, specifically that there is an underlying reality that all humans and the universe are a part of. In the 1930s, Huxley and Gerald Heard both became active in the effort to avoid another world war, writing essays and eventually publicly speaking in support of the Peace Pledge Union. But, they remained frustrated by the conflicting goals of the political left – some favoring pacifism (as did Huxley and Heard), while other wanting to take up arms against fascism in the Spanish Civil War. After joining the PPU, Huxley expressed his frustration with politics in a letter from 1935, “…the thing finally resolves itself into a religious problem — an uncomfortable fact which one must be prepared to face and which I have come during the last year to find it easier to face.” Huxley and Heard turned their attention to addressing the big problems of the world through transforming the individual, "[...] a forest is only as green as the individual trees of the forest is green [...]" This was the genesis of the Human Potential Movement, that gained traction in the 1960s.
In the late 1930s, Huxley and Heard immigrated to the United States, and beginning in 1939 and continuing until his death in 1963, Huxley had an extensive association with the Vedanta Society of Southern California, founded and headed by Swami Prabhavananda. Together with Gerald Heard, Christopher Isherwood and other followers, he was initiated by the Swami and was taught meditation and spiritual practices. From 1941 until 1960, Huxley contributed 48 articles to "Vedanta and the West", published by the society. He also served on the editorial board with Isherwood, Heard, and playwright John Van Druten from 1951 through 1962. In 1942 "The Gospel of Ramakrishna" was published by the Ramakrishna-Vivekananda Center in New York. The book was translated by Swami Nikhilananda, with help from Joseph Campbell and Margaret Woodrow Wilson, daughter of US president Woodrow Wilson. Aldous Huxley wrote in the foreword, "...a book unique, so far as my knowledge goes, in the literature of hagiography. Never have the small events of a contemplative's daily life been described with such a wealth of intimate detail. Never have the casual and unstudied utterances of a great religious teacher been set down with so minute a fidelity."
In 1944, Huxley wrote the introduction to the "Bhagavad Gita – The Song of God", translated by Swami Prabhavananda and Christopher Isherwood, which was published by the Vedanta Society of Southern California. As an advocate of the perennial philosophy, Huxley was drawn to the "Gita", as he explained in the Introduction, written during WWII, when it was still not clear who would win: As a means of personally realizing the "divine Reality", he described a "Minimum Working Hypothesis" in the Introduction to Swami Prabhavananda's and Christopher Isherwood's translation of the "Bhagavad Gita" and in a free-standing essay in "Vedanta and the West", a publication of Vedanta Press. This is the outline, that Huxley elaborates on in the article: For Huxley, one of the attractive features of Vedanta is that it provided a historic and established philosophy and practice that embraced the Perennial Philosophy; that there is a commonality of experiences across all the mystical branches of the world's religions. Huxley wrote in the introduction of his book "The Perennial Philosophy":
Huxley also occasionally lectured at the Hollywood and Santa Barbara Vedanta temples. Two of those lectures have been released on CD: "Knowledge and Understanding" and "Who Are We?" from 1955. Many of Huxley's contemporaries and critics were disappointed by Huxley's turn to mysticism; Isherwood describes in his diary how he had to explain the criticism to Huxley's widow, Laura: Psychedelic drug use and mystical experiences. In early 1953, Huxley had his first experience with the psychedelic drug mescaline. Huxley had initiated a correspondence with Doctor Humphry Osmond, a British psychiatrist then employed in a Canadian institution, and eventually asked him to supply a dose of mescaline; Osmond obliged and supervised Huxley's session in southern California. After the publication of "The Doors of Perception", in which he recounted this experience, Huxley and Swami Prabhavananda disagreed about the meaning and importance of the psychedelic drug experience, which may have caused the relationship to cool, but Huxley continued to write articles for the society's journal, lecture at the temple, and attend social functions. Huxley later had an experience on mescaline that he considered more profound than those detailed in "The Doors of Perception".
Huxley wrote that "The mystical experience is doubly valuable; it is valuable because it gives the experiencer a better understanding of himself and the world and because it may help him to lead a less self-centered and more creative life." Having tried LSD in the 1950s, he became an advisor to Timothy Leary and Richard Alpert in their early-1960s research work with psychedelic drugs at Harvard. Personality differences led Huxley to distance himself from Leary, when Huxley grew concerned that Leary had become too keen on indiscriminately promoting the drugs. Eyesight. Differing accounts exist about the details of the quality of Huxley's eyesight at specific points in his life. Circa 1939, Huxley encountered the Bates method, in which he was instructed by Margaret Darst Corbett. In 1940, Huxley relocated from Hollywood to a "ranchito" in the high desert hamlet of Llano, California, in northern Los Angeles County. Huxley then said that his sight improved dramatically with the Bates method and the extreme and pure natural lighting of the southwestern American desert. He reported that, for the first time in more than 25 years, he was able to read without glasses and without strain. He even tried driving a car along the dirt road beside the ranch. He wrote a book about his experiences with the Bates method, "The Art of Seeing", which was published in 1942 (U.S.), 1943 (UK). The book contained some generally disputed theories, and its publication created a growing degree of popular controversy about Huxley's eyesight.
It was, and is, widely believed that Huxley was nearly blind since the illness in his teens, despite the partial recovery that had enabled him to study at Oxford. For example, some ten years after publication of "The Art of Seeing", in 1952, Bennett Cerf was present when Huxley spoke at a Hollywood banquet, wearing no glasses and apparently reading his paper from the lectern without difficulty: Brazilian author João Ubaldo Ribeiro, who as a young journalist spent several evenings in the Huxleys' company in the late 1950s, wrote that Huxley had said to him, with a wry smile: "I can hardly see at all. And I don't give a damn, really." On the other hand, Huxley's second wife Laura later emphasised in her biographical account, "This Timeless Moment": "One of the great achievements of his life: that of having regained his sight." After revealing a letter she wrote to the "Los Angeles Times" disclaiming the label of Huxley as a "poor fellow who can hardly see" by Walter C. Alvarez, she tempered her statement: Laura Huxley proceeded to elaborate a few nuances of inconsistency peculiar to Huxley's vision. Her account, in this respect, agrees with the following sample of Huxley's own words from "The Art of Seeing": Nevertheless, the topic of Huxley's eyesight has continued to endure similar, significant controversy.
American popular science author Steven Johnson, in his book "Mind Wide Open", quotes Huxley about his difficulties with visual encoding: Personal life. Huxley married on 10 July 1919 Maria Nys (10 September 1899 – 12 February 1955), a Belgian epidemiologist from Bellem, a village near Aalter, he met at Garsington, Oxfordshire, in 1919. They had one child, Matthew Huxley (19 April 1920 – 10 February 2005), who had a career as an author, anthropologist, and prominent epidemiologist. In 1955, Maria Huxley died of cancer. In 1956, Huxley married Laura Archera (1911–2007), also an author, as well as a violinist and psychotherapist. She wrote "This Timeless Moment", a biography of Huxley. She told the story of their marriage through Mary Ann Braubach's 2010 documentary, "Huxley on Huxley". Huxley was diagnosed with laryngeal cancer in 1960; in the years that followed, with his health deteriorating, he wrote the utopian novel "Island", and gave lectures on "Human Potentialities" both at the UCSF Medical Center and at the Esalen Institute. These lectures were fundamental to the beginning of the Human Potential Movement.
Huxley was a close friend of Jiddu Krishnamurti and Rosalind Rajagopal, and was involved in the creation of the Happy Valley School, now Besant Hill School, of Happy Valley, in Ojai, California. The most substantial collection of Huxley's few remaining papers, following the destruction of most in the 1961 Bel Air Fire, is at the Library of the University of California, Los Angeles. Some are also at the Stanford University Libraries. On 9 April 1962 Huxley was informed he was elected Companion of Literature by the Royal Society of Literature, the senior literary organisation in Britain, and he accepted the title via letter on 28 April 1962. The correspondence between Huxley and the society is kept at the Cambridge University Library. The society invited Huxley to appear at a banquet and give a lecture at Somerset House, London, in June 1963. Huxley wrote a draft of the speech he intended to give at the society; however, his deteriorating health meant he was not able to attend. Death. In 1960, Huxley was diagnosed with oral cancer and for the next three years his health steadily declined. On 4 November 1963, less than three weeks before Huxley's death, author Christopher Isherwood, a friend of 25 years, visited in Cedars Sinai Hospital and wrote his impressions:
At home on his deathbed, unable to speak owing to cancer that had metastasized, Huxley made a written request to his wife Laura for "LSD, 100 μg, intramuscular." According to her account of his death in "This Timeless Moment", she obliged with an injection at 11:20 a.m. and a second dose an hour later; Huxley died aged 69, at 5:20 p.m. PST on 22 November 1963. Media coverage of Huxley's death, along with that of fellow British author C. S. Lewis, was overshadowed by the assassination of John F. Kennedy on the same day, less than seven hours before Huxley's death. In a 2009 article for "New York" magazine titled "The Eclipsed Celebrity Death Club", Christopher Bonanos wrote: This coincidence served as the basis for Peter Kreeft's book "Between Heaven and Hell: A Dialog Somewhere Beyond Death with John F. Kennedy, C. S. Lewis, & Aldous Huxley", which imagines a conversation among the three men taking place in Purgatory following their deaths. The main theme of the book is a philosophical debate on the nature and identity of Jesus Christ.
Huxley's memorial service took place in London in December 1963; it was led by his elder brother Julian. On 27 October 1971, his ashes were interred in the family grave at the Watts Cemetery, home of the Watts Mortuary Chapel in Compton, Guildford, Surrey, England. Huxley had been a long-time friend of Russian composer Igor Stravinsky, who dedicated his last orchestral composition to Huxley. What became "" was begun in July 1963, completed in October 1964, and premiered by the Chicago Symphony Orchestra on 17 April 1965. Commemoration. In 2021, Huxley was one of six British writers commemorated on a series of UK postage stamps issued by Royal Mail to celebrate British science fiction. One classic science fiction novel from each author was depicted, with "Brave New World" chosen to represent Huxley.
Aberdeen (disambiguation) Aberdeen is a city in Scotland. Aberdeen may also refer to:
Algae Algae ( , ; : alga ) is an informal term for any organisms of a large and diverse group of photosynthetic eukaryotes, which include species from multiple distinct clades. Such organisms range from unicellular microalgae such as "Chlorella", "Prototheca" and the diatoms, to multicellular macroalgae such as the giant kelp, a large brown alga which may grow up to in length. Most algae are aquatic organisms and lack many of the distinct cell and tissue types, such as stomata, xylem, and phloem that are found in land plants. The largest and most complex marine algae are called seaweeds. In contrast, the most complex freshwater forms are the "Charophyta", a division of green algae which includes, for example, "Spirogyra" and stoneworts. Algae that are carried passively by water are plankton, specifically phytoplankton. Algae constitute a polyphyletic group since they do not include a common ancestor, and although their chlorophyll-bearing plastids seem to have a single origin (from symbiogenesis with cyanobacteria), they were acquired in different ways. Green algae are a prominent example of algae that have primary chloroplasts derived from endosymbiont cyanobacteria. Diatoms and brown algae are examples of algae with secondary chloroplasts derived from endosymbiotic red algae, which they acquired via phagocytosis. Algae exhibit a wide range of reproductive strategies, from simple asexual cell division to complex forms of sexual reproduction via spores.
Algae lack the various structures that characterize plants (which evolved from freshwater green algae), such as the phyllids (leaf-like structures) and rhizoids of bryophytes (non-vascular plants), and the roots, leaves and other xylemic/phloemic organs found in tracheophytes (vascular plants). Most algae are autotrophic, although some are mixotrophic, deriving energy both from photosynthesis and uptake of organic carbon either by osmotrophy, myzotrophy or phagotrophy. Some unicellular species of green algae, many golden algae, euglenids, dinoflagellates, and other algae have become heterotrophs (also called colorless or apochlorotic algae), sometimes parasitic, relying entirely on external energy sources and have limited or no photosynthetic apparatus. Some other heterotrophic organisms, such as the apicomplexans, are also derived from cells whose ancestors possessed chlorophyllic plastids, but are not traditionally considered as algae. Algae have photosynthetic machinery ultimately derived from cyanobacteria that produce oxygen as a byproduct of splitting water molecules, unlike other organisms that conduct anoxygenic photosynthesis such as purple and green sulfur bacteria. Fossilized filamentous algae from the Vindhya basin have been dated to 1.6 to 1.7 billion years ago.
Because of the wide range of types of algae, there is a correspondingly wide range of industrial and traditional applications in human society. Traditional seaweed farming practices have existed for thousands of years and have strong traditions in East Asian food cultures. More modern algaculture applications extend the food traditions for other applications, including cattle feed, using algae for bioremediation or pollution control, transforming sunlight into algae fuels or other chemicals used in industrial processes, and in medical and scientific applications. A 2020 review found that these applications of algae could play an important role in carbon sequestration to mitigate climate change while providing lucrative value-added products for global economies. Etymology and study. The singular is the Latin word for 'seaweed' and retains that meaning in English. The etymology is obscure. Although some speculate that it is related to Latin , 'be cold', no reason is known to associate seaweed with temperature. A more likely source is , 'binding, entwining'.
The Ancient Greek word for 'seaweed' was (), which could mean either the seaweed (probably red algae) or a red dye derived from it. The Latinization, , meant primarily the cosmetic rouge. The etymology is uncertain, but a strong candidate has long been some word related to the Biblical (), 'paint' (if not that word itself), a cosmetic eye-shadow used by the ancient Egyptians and other inhabitants of the eastern Mediterranean. It could be any color: black, red, green, or blue. The study of algae is most commonly called phycology (); the term algology is falling out of use. Classifications. One definition of algae is that they "have chlorophyll as their primary photosynthetic pigment and lack a sterile covering of cells around their reproductive cells". On the other hand, the colorless "Prototheca" under "Chlorophyta" are all devoid of any chlorophyll. Although cyanobacteria are often referred to as "blue-green algae", most authorities exclude all prokaryotes, including cyanobacteria, from the definition of algae.
The algae contain chloroplasts that are similar in structure to cyanobacteria. Chloroplasts contain circular DNA like that in cyanobacteria and are interpreted as representing reduced endosymbiotic cyanobacteria. However, the exact origin of the chloroplasts is different among separate lineages of algae, reflecting their acquisition during different endosymbiotic events. The table below describes the composition of the three major groups of algae. Their lineage relationships are shown in the figure in the upper right. Many of these groups contain some members that are no longer photosynthetic. Some retain plastids, but not chloroplasts, while others have lost plastids entirely. Phylogeny based on plastid not nucleocytoplasmic genealogy: Linnaeus, in "Species Plantarum" (1753), the starting point for modern botanical nomenclature, recognized 14 genera of algae, of which only four are currently considered among algae. In "Systema Naturae", Linnaeus described the genera "Volvox" and "Corallina", and a species of "Acetabularia" (as "Madrepora"), among the animals.
In 1768, Samuel Gottlieb Gmelin (1744–1774) published the "Historia Fucorum", the first work dedicated to marine algae and the first book on marine biology to use the then new binomial nomenclature of Linnaeus. It included elaborate illustrations of seaweed and marine algae on folded leaves. W. H. Harvey (1811–1866) and Lamouroux (1813) were the first to divide macroscopic algae into four divisions based on their pigmentation. This is the first use of a biochemical criterion in plant systematics. Harvey's four divisions are: red algae (Rhodospermae), brown algae (Melanospermae), green algae (Chlorospermae), and Diatomaceae. At this time, microscopic algae were discovered and reported by a different group of workers (e.g., O. F. Müller and Ehrenberg) studying the Infusoria (microscopic organisms). Unlike macroalgae, which were clearly viewed as plants, microalgae were frequently considered animals because they are often motile. Even the nonmotile (coccoid) microalgae were sometimes merely seen as stages of the lifecycle of plants, macroalgae, or animals.
Although used as a taxonomic category in some pre-Darwinian classifications, e.g., Linnaeus (1753), de Jussieu (1789), Lamouroux (1813), Harvey (1836), Horaninow (1843), Agassiz (1859), Wilson & Cassin (1864), in further classifications, the "algae" are seen as an artificial, polyphyletic group. Throughout the 20th century, most classifications treated the following groups as divisions or classes of algae: cyanophytes, rhodophytes, chrysophytes, xanthophytes, bacillariophytes, phaeophytes, pyrrhophytes (cryptophytes and dinophytes), euglenophytes, and chlorophytes. Later, many new groups were discovered (e.g., Bolidophyceae), and others were splintered from older groups: charophytes and glaucophytes (from chlorophytes), many heterokontophytes (e.g., synurophytes from chrysophytes, or eustigmatophytes from xanthophytes), haptophytes (from chrysophytes), and chlorarachniophytes (from xanthophytes). With the abandonment of plant-animal dichotomous classification, most groups of algae (sometimes all) were included in Protista, later also abandoned in favour of Eukaryota. However, as a legacy of the older plant life scheme, some groups that were also treated as protozoans in the past still have duplicated classifications (see ambiregnal protists).
Some parasitic algae (e.g., the green algae "Prototheca" and "Helicosporidium", parasites of metazoans, or "Cephaleuros", parasites of plants) were originally classified as fungi, sporozoans, or protistans of "incertae sedis", while others (e.g., the green algae "Phyllosiphon" and "Rhodochytrium", parasites of plants, or the red algae "Pterocladiophila" and "Gelidiocolax mammillatus", parasites of other red algae, or the dinoflagellates "Oodinium", parasites of fish) had their relationship with algae conjectured early. In other cases, some groups were originally characterized as parasitic algae (e.g., "Chlorochytrium"), but later were seen as endophytic algae. Some filamentous bacteria (e.g., "Beggiatoa") were originally seen as algae. Furthermore, groups like the apicomplexans are also parasites derived from ancestors that possessed plastids, but are not included in any group traditionally seen as algae. Evolution. Algae are polyphyletic thus their origin cannot be traced back to single hypothetical common ancestor. It is thought that they came into existence when photosynthetic coccoid cyanobacteria got phagocytized by a unicellular heterotrophic eukaryote (a protist), giving rise to double-membranous primary plastids. Such symbiogenic events (primary symbiogenesis) are believed to have occurred more than 1.5 billion years ago during the Calymmian period, early in Boring Billion, but it is difficult to track the key events because of so much time gap. Primary symbiogenesis gave rise to three divisions of archaeplastids, namely the Viridiplantae (green algae and later plants), Rhodophyta (red algae) and Glaucophyta ("grey algae"), whose plastids further spread into other protist lineages through eukaryote-eukaryote predation, engulfments and subsequent endosymbioses (secondary and tertiary symbiogenesis). This process of serial cell "capture" and "enslavement" explains the diversity of photosynthetic eukaryotes.
Recent genomic and phylogenomic approaches have significantly clarified plastid genome evolution, the horizontal movement of endosymbiont genes to the "host" nuclear genome, and plastid spread throughout the eukaryotic tree of life. Relationship to land plants. Fossils of isolated spores suggest land plants may have been around as long as 475 million years ago (mya) during the Late Cambrian/Early Ordovician period, from sessile shallow freshwater charophyte algae much like "Chara", which likely got stranded ashore when riverine/lacustrine water levels dropped during dry seasons. These charophyte algae probably already developed filamentous thalli and holdfasts that superficially resembled plant stems and roots, and probably had an isomorphic alternation of generations. They perhaps evolved some 850 mya and might even be as early as 1 Gya during the late phase of the Boring Billion. Morphology. A range of algal morphologies is exhibited, and convergence of features in unrelated groups is common. The only groups to exhibit three-dimensional multicellular thalli are the reds and browns, and some chlorophytes. Apical growth is constrained to subsets of these groups: the florideophyte reds, various browns, and the charophytes. The form of charophytes is quite different from those of reds and browns, because they have distinct nodes, separated by internode 'stems'; whorls of branches reminiscent of the horsetails occur at the nodes. Conceptacles are another polyphyletic trait; they appear in the coralline algae and the Hildenbrandiales, as well as the browns.
Most of the simpler algae are unicellular flagellates or amoeboids, but colonial and nonmotile forms have developed independently among several of the groups. Some of the more common organizational levels, more than one of which may occur in the lifecycle of a species, are In three lines, even higher levels of organization have been reached, with full tissue differentiation. These are the brown algae,—some of which may reach 50 m in length (kelps)—the red algae, and the green algae. The most complex forms are found among the charophyte algae (see Charales and Charophyta), in a lineage that eventually led to the higher land plants. The innovation that defines these nonalgal plants is the presence of female reproductive organs with protective cell layers that protect the zygote and developing embryo. Hence, the land plants are referred to as the Embryophytes. Turfs. The term algal turf is commonly used but poorly defined. Algal turfs are thick, carpet-like beds of seaweed that retain sediment and compete with foundation species like corals and kelps, and they are usually less than 15 cm tall. Such a turf may consist of one or more species, and will generally cover an area in the order of a square metre or more. Some common characteristics are listed:
Physiology. Many algae, particularly species of the Characeae, have served as model experimental organisms to understand the mechanisms of the water permeability of membranes, osmoregulation, salt tolerance, cytoplasmic streaming, and the generation of action potentials. Plant hormones are found not only in higher plants, but in algae, too. Symbiotic algae. Some species of algae form symbiotic relationships with other organisms. In these symbioses, the algae supply photosynthates (organic substances) to the host organism providing protection to the algal cells. The host organism derives some or all of its energy requirements from the algae. Examples are: Lichens. Lichens are defined by the International Association for Lichenology to be "an association of a fungus and a photosynthetic symbiont resulting in a stable vegetative body having a specific structure". The fungi, or mycobionts, are mainly from the Ascomycota with a few from the Basidiomycota. In nature, they do not occur separate from lichens. It is unknown when they began to associate. One or more mycobiont associates with the same phycobiont species, from the green algae, except that alternatively, the mycobiont may associate with a species of cyanobacteria (hence "photobiont" is the more accurate term). A photobiont may be associated with many different mycobionts or may live independently; accordingly, lichens are named and classified as fungal species. The association is termed a morphogenesis because the lichen has a form and capabilities not possessed by the symbiont species alone (they can be experimentally isolated). The photobiont possibly triggers otherwise latent genes in the mycobiont.
Trentepohlia is an example of a common green alga genus worldwide that can grow on its own or be lichenised. Lichen thus share some of the habitat and often similar appearance with specialized species of algae ("aerophytes") growing on exposed surfaces such as tree trunks and rocks and sometimes discoloring them. Coral reefs. Coral reefs are accumulated from the calcareous exoskeletons of marine invertebrates of the order Scleractinia (stony corals). These animals metabolize sugar and oxygen to obtain energy for their cell-building processes, including secretion of the exoskeleton, with water and carbon dioxide as byproducts. Dinoflagellates (algal protists) are often endosymbionts in the cells of the coral-forming marine invertebrates, where they accelerate host-cell metabolism by generating sugar and oxygen immediately available through photosynthesis using incident light and the carbon dioxide produced by the host. Reef-building stony corals (hermatypic corals) require endosymbiotic algae from the genus "Symbiodinium" to be in a healthy condition. The loss of "Symbiodinium" from the host is known as coral bleaching, a condition which leads to the deterioration of a reef.
Sea sponges. Endosymbiontic green algae live close to the surface of some sponges, for example, breadcrumb sponges ("Halichondria panicea"). The alga is thus protected from predators; the sponge is provided with oxygen and sugars which can account for 50 to 80% of sponge growth in some species. Life cycle. Rhodophyta, Chlorophyta, and Heterokontophyta, the three main algal divisions, have life cycles which show considerable variation and complexity. In general, an asexual phase exists where the seaweed's cells are diploid, a sexual phase where the cells are haploid, followed by fusion of the male and female gametes. Asexual reproduction permits efficient population increases, but less variation is possible. Commonly, in sexual reproduction of unicellular and colonial algae, two specialized, sexually compatible, haploid gametes make physical contact and fuse to form a zygote. To ensure a successful mating, the development and release of gametes is highly synchronized and regulated; pheromones may play a key role in these processes. Sexual reproduction allows for more variation and provides the benefit of efficient recombinational repair of DNA damages during meiosis, a key stage of the sexual cycle. However, sexual reproduction is more costly than asexual reproduction. Meiosis has been shown to occur in many different species of algae.
Numbers. The "Algal Collection of the US National Herbarium" (located in the National Museum of Natural History) consists of approximately 320,500 dried specimens, which, although not exhaustive (no exhaustive collection exists), gives an idea of the order of magnitude of the number of algal species (that number remains unknown). Estimates vary widely. For example, according to one standard textbook, in the British Isles, the "UK Biodiversity Steering Group Report" estimated there to be 20,000 algal species in the UK. Another checklist reports only about 5,000 species. Regarding the difference of about 15,000 species, the text concludes: "It will require many detailed field surveys before it is possible to provide a reliable estimate of the total number of species ..." Regional and group estimates have been made, as well: and so on, but lacking any scientific basis or reliable sources, these numbers have no more credibility than the British ones mentioned above. Most estimates also omit microscopic algae, such as phytoplankton.
The most recent estimate suggests 72,500 algal species worldwide. Distribution. The distribution of algal species has been fairly well studied since the founding of phytogeography in the mid-19th century. Algae spread mainly by the dispersal of spores analogously to the dispersal of cryptogamic plants by spores. Spores can be found in a variety of environments: fresh and marine waters, air, soil, and in or on other organisms. Whether a spore is to grow into an adult organism depends on the species and the environmental conditions where the spore lands. The spores of freshwater algae are dispersed mainly by running water and wind, as well as by living carriers. However, not all bodies of water can carry all species of algae, as the chemical composition of certain water bodies limits the algae that can survive within them. Marine spores are often spread by ocean currents. Ocean water presents many vastly different habitats based on temperature and nutrient availability, resulting in phytogeographic zones, regions, and provinces.
To some degree, the distribution of algae is subject to floristic discontinuities caused by geographical features, such as Antarctica, long distances of ocean or general land masses. It is, therefore, possible to identify species occurring by locality, such as "Pacific algae" or "North Sea algae". When they occur out of their localities, hypothesizing a transport mechanism is usually possible, such as the hulls of ships. For example, "Ulva reticulata" and "U. fasciata" travelled from the mainland to Hawaii in this manner. Mapping is possible for select species only: "there are many valid examples of confined distribution patterns." For example, "Clathromorphum" is an arctic genus and is not mapped far south of there. However, scientists regard the overall data as insufficient due to the "difficulties of undertaking such studies." Ecology. Algae are prominent in bodies of water, common in terrestrial environments, and are found in unusual environments, such as on snow and ice. Seaweeds grow mostly in shallow marine waters, under deep; however, some such as "Navicula pennata" have been recorded to a depth of . A type of algae, "Ancylonema nordenskioeldii", was found in Greenland in areas known as the 'Dark Zone', which caused an increase in the rate of melting ice sheet. The same algae was found in the Italian Alps, after pink ice appeared on parts of the Presena glacier.
The various sorts of algae play significant roles in aquatic ecology. Microscopic forms that live suspended in the water column (phytoplankton) provide the food base for most marine food chains. In very high densities (algal blooms), these algae may discolor the water and outcompete, poison, or asphyxiate other life forms. Algae can be used as indicator organisms to monitor pollution in various aquatic systems. In many cases, algal metabolism is sensitive to various pollutants. Due to this, the species composition of algal populations may shift in the presence of chemical pollutants. To detect these changes, algae can be sampled from the environment and maintained in laboratories with relative ease. On the basis of their habitat, algae can be categorized as: aquatic (planktonic, benthic, marine, freshwater, lentic, lotic), terrestrial, aerial (subaerial), lithophytic, halophytic (or euryhaline), psammon, thermophilic, cryophilic, epibiont (epiphytic, epizoic), endosymbiont (endophytic, endozoic), parasitic, calcifilic or lichenic (phycobiont).
Cultural associations. In classical Chinese, the word is used both for "algae" and (in the modest tradition of the imperial scholars) for "literary talent". The third island in Kunming Lake beside the Summer Palace in Beijing is known as the Zaojian Tang Dao (藻鑒堂島), which thus simultaneously means "Island of the Algae-Viewing Hall" and "Island of the Hall for Reflecting on Literary Talent". Uses. Agar. Agar, a gelatinous substance derived from red algae, has a number of commercial uses. It is a good medium on which to grow bacteria and fungi, as most microorganisms cannot digest agar. Alginates. Alginic acid, or alginate, is extracted from brown algae. Its uses range from gelling agents in food, to medical dressings. Alginic acid also has been used in the field of biotechnology as a biocompatible medium for cell encapsulation and cell immobilization. Molecular cuisine is also a user of the substance for its gelling properties, by which it becomes a delivery vehicle for flavours. Between 100,000 and 170,000 wet tons of "Macrocystis" are harvested annually in New Mexico for alginate extraction and abalone feed.
Energy source. To be competitive and independent from fluctuating support from (local) policy on the long run, biofuels should equal or beat the cost level of fossil fuels. Here, algae-based fuels hold great promise, directly related to the potential to produce more biomass per unit area in a year than any other form of biomass. The break-even point for algae-based biofuels is estimated to occur by 2025. Fertilizer. For centuries, seaweed has been used as a fertilizer; George Owen of Henllys writing in the 16th century referring to drift weed in South Wales: Today, algae are used by humans in many ways; for example, as fertilizers, soil conditioners, and livestock feed. Aquatic and microscopic species are cultured in clear tanks or ponds and are either harvested or used to treat effluents pumped through the ponds. Algaculture on a large scale is an important type of aquaculture in some places. Maerl is commonly used as a soil conditioner. As food. Algae are used as foods in many countries: China consumes more than 70 species, including "fat choy", a cyanobacterium considered a vegetable; Japan, over 20 species such as "nori" and "aonori"; Ireland, dulse; Chile, cochayuyo. Laver is used to make laverbread in Wales, where it is known as . In Korea, green laver is used to make .
Three forms of algae used as food: The oils from some algae have high levels of unsaturated fatty acids. Some varieties of algae favored by vegetarianism and veganism contain the long-chain, essential omega-3 fatty acids, docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA). Fish oil contains the omega-3 fatty acids, but the original source is algae (microalgae in particular), which are eaten by marine life such as copepods and are passed up the food chain. Pollution control. Agricultural Research Service scientists found that 60–90% of nitrogen runoff and 70–100% of phosphorus runoff can be captured from manure effluents using a horizontal algae scrubber, also called an algal turf scrubber (ATS). Scientists developed the ATS, which consists of shallow, 100-foot raceways of nylon netting where algae colonies can form, and studied its efficacy for three years. They found that algae can readily be used to reduce the nutrient runoff from agricultural fields and increase the quality of water flowing into rivers, streams, and oceans. Researchers collected and dried the nutrient-rich algae from the ATS and studied its potential as an organic fertilizer. They found that cucumber and corn seedlings grew just as well using ATS organic fertilizer as they did with commercial fertilizers. Algae scrubbers, using bubbling upflow or vertical waterfall versions, are now also being used to filter aquaria and ponds.
Polymers. Various polymers can be created from algae, which can be especially useful in the creation of bioplastics. These include hybrid plastics, cellulose-based plastics, poly-lactic acid, and bio-polyethylene. Several companies have begun to produce algae polymers commercially, including for use in flip-flops and in surf boards. Bioremediation. The alga "Stichococcus bacillaris" has been seen to colonize silicone resins used at archaeological sites; biodegrading the synthetic substance. Pigments. The natural pigments (carotenoids and chlorophylls) produced by algae can be used as alternatives to chemical dyes and coloring agents. The presence of some individual algal pigments, together with specific pigment concentration ratios, are taxon-specific: analysis of their concentrations with various analytical methods, particularly high-performance liquid chromatography, can therefore offer deep insight into the taxonomic composition and relative abundance of natural algae populations in sea water samples. Stabilizing substances. Carrageenan, from the red alga "Chondrus crispus", is used as a stabilizer in milk products.
Analysis of variance Analysis of variance (ANOVA) is a family of statistical methods used to compare the means of two or more groups by analyzing variance. Specifically, ANOVA compares the amount of variation "between" the group means to the amount of variation "within" each group. If the between-group variation is substantially larger than the within-group variation, it suggests that the group means are likely different. This comparison is done using an F-test. The underlying principle of ANOVA is based on the law of total variance, which states that the total variance in a dataset can be broken down into components attributable to different sources. In the case of ANOVA, these sources are the variation between groups and the variation within groups. ANOVA was developed by the statistician Ronald Fisher. In its simplest form, it provides a statistical test of whether two or more population means are equal, and therefore generalizes the "t"-test beyond two means. History. While the analysis of variance reached fruition in the 20th century, antecedents extend centuries into the past according to Stigler. These include hypothesis testing, the partitioning of sums of squares, experimental techniques and the additive model. Laplace was performing hypothesis testing in the 1770s. Around 1800, Laplace and Gauss developed the least-squares method for combining observations, which improved upon methods then used in astronomy and geodesy. It also initiated much study of the contributions to sums of squares. Laplace knew how to estimate a variance from a residual (rather than a total) sum of squares. By 1827, Laplace was using least squares methods to address ANOVA problems regarding measurements of atmospheric tides. Before 1800, astronomers had isolated observational errors resulting
from reaction times (the "personal equation") and had developed methods of reducing the errors. The experimental methods used in the study of the personal equation were later accepted by the emerging field of psychology which developed strong (full factorial) experimental methods to which randomization and blinding were soon added. An eloquent non-mathematical explanation of the additive effects model was available in 1885. Ronald Fisher introduced the term variance and proposed its formal analysis in a 1918 article on theoretical population genetics, "The Correlation Between Relatives on the Supposition of Mendelian Inheritance". His first application of the analysis of variance to data analysis was published in 1921, "Studies in Crop Variation I". This divided the variation of a time series into components representing annual causes and slow deterioration. Fisher's next piece, "Studies in Crop Variation II", written with Winifred Mackenzie and published in 1923, studied the variation in yield across plots sown with different varieties and subjected to different fertiliser treatments. Analysis of variance became widely known after being included in Fisher's 1925 book "Statistical Methods for Research Workers".
Randomization models were developed by several researchers. The first was published in Polish by Jerzy Neyman in 1923. Example. The analysis of variance can be used to describe otherwise complex relations among variables. A dog show provides an example. A dog show is not a random sampling of the breed: it is typically limited to dogs that are adult, pure-bred, and exemplary. A histogram of dog weights from a show is likely to be rather complicated, like the yellow-orange distribution shown in the illustrations. Suppose we wanted to predict the weight of a dog based on a certain set of characteristics of each dog. One way to do that is to "explain" the distribution of weights by dividing the dog population into groups based on those characteristics. A successful grouping will split dogs such that (a) each group has a low variance of dog weights (meaning the group is relatively homogeneous) and (b) the mean of each group is distinct (if two groups have the same mean, then it isn't reasonable to conclude that the groups are, in fact, separate in any meaningful way).
In the illustrations to the right, groups are identified as "X"1, "X"2, etc. In the first illustration, the dogs are divided according to the product (interaction) of two binary groupings: young vs old, and short-haired vs long-haired (e.g., group 1 is young, short-haired dogs, group 2 is young, long-haired dogs, etc.). Since the distributions of dog weight within each of the groups (shown in blue) has a relatively large variance, and since the means are very similar across groups, grouping dogs by these characteristics does not produce an effective way to explain the variation in dog weights: knowing which group a dog is in doesn't allow us to predict its weight much better than simply knowing the dog is in a dog show. Thus, this grouping fails to explain the variation in the overall distribution (yellow-orange). An attempt to explain the weight distribution by grouping dogs as "pet vs working breed" and "less athletic vs more athletic" would probably be somewhat more successful (fair fit). The heaviest show dogs are likely to be big, strong, working breeds, while breeds kept as pets tend to be smaller and thus lighter. As shown by the second illustration, the distributions have variances that are considerably smaller than in the first case, and the means are more distinguishable. However, the significant overlap of distributions, for example, means that we cannot distinguish "X"1 and "X"2 reliably. Grouping dogs according to a coin flip might produce distributions that look similar.
An attempt to explain weight by breed is likely to produce a very good fit. All Chihuahuas are light and all St Bernards are heavy. The difference in weights between Setters and Pointers does not justify separate breeds. The analysis of variance provides the formal tools to justify these intuitive judgments. A common use of the method is the analysis of experimental data or the development of models. The method has some advantages over correlation: not all of the data must be numeric and one result of the method is a judgment in the confidence in an explanatory relationship. Classes of models. There are three classes of models used in the analysis of variance, and these are outlined here. Fixed-effects models. The fixed-effects model (class I) of analysis of variance applies to situations in which the experimenter applies one or more treatments to the subjects of the experiment to see whether the response variable values change. This allows the experimenter to estimate the ranges of response variable values that the treatment would generate in the population as a whole.
Random-effects models. Random-effects model (class II) is used when the treatments are not fixed. This occurs when the various factor levels are sampled from a larger population. Because the levels themselves are random variables, some assumptions and the method of contrasting the treatments (a multi-variable generalization of simple differences) differ from the fixed-effects model. Mixed-effects models. A mixed-effects model (class III) contains experimental factors of both fixed and random-effects types, with appropriately different interpretations and analysis for the two types. Example. Teaching experiments could be performed by a college or university department to find a good introductory textbook, with each text considered a treatment. The fixed-effects model would compare a list of candidate texts. The random-effects model would determine whether important differences exist among a list of randomly selected texts. The mixed-effects model would compare the (fixed) incumbent texts to randomly selected alternatives.
Defining fixed and random effects has proven elusive, with multiple competing definitions. Assumptions. The analysis of variance has been studied from several approaches, the most common of which uses a linear model that relates the response to the treatments and blocks. Note that the model is linear in parameters but may be nonlinear across factor levels. Interpretation is easy when data is balanced across factors but much deeper understanding is needed for unbalanced data. Textbook analysis using a normal distribution. The analysis of variance can be presented in terms of a linear model, which makes the following assumptions about the probability distribution of the responses: The separate assumptions of the textbook model imply that the errors are independently, identically, and normally distributed for fixed effects models, that is, that the errors (formula_1) are independent and formula_2 Randomization-based analysis. In a randomized controlled experiment, the treatments are randomly assigned to experimental units, following the experimental protocol. This randomization is objective and declared before the experiment is carried out. The objective random-assignment is used to test the significance of the null hypothesis, following the ideas of C. S. Peirce and Ronald Fisher. This design-based analysis was discussed and developed by Francis J. Anscombe at Rothamsted Experimental Station and by Oscar Kempthorne at Iowa State University. Kempthorne and his students make an assumption of "unit treatment additivity", which is discussed in the books of Kempthorne and David R. Cox.
Unit-treatment additivity. In its simplest form, the assumption of unit-treatment additivity states that the observed response formula_3 from experimental unit formula_4 when receiving treatment formula_5 can be written as the sum of the unit's response formula_6 and the treatment-effect formula_7, that is formula_8 The assumption of unit-treatment additivity implies that, for every treatment formula_5, the formula_5th treatment has exactly the same effect formula_11 on every experiment unit. The assumption of unit treatment additivity usually cannot be directly falsified, according to Cox and Kempthorne. However, many "consequences" of treatment-unit additivity can be falsified. For a randomized experiment, the assumption of unit-treatment additivity "implies" that the variance is constant for all treatments. Therefore, by contraposition, a necessary condition for unit-treatment additivity is that the variance is constant. The use of unit treatment additivity and randomization is similar to the design-based inference that is standard in finite-population survey sampling.
Derived linear model. Kempthorne uses the randomization-distribution and the assumption of "unit treatment additivity" to produce a "derived linear model", very similar to the textbook model discussed previously. The test statistics of this derived linear model are closely approximated by the test statistics of an appropriate normal linear model, according to approximation theorems and simulation studies. However, there are differences. For example, the randomization-based analysis results in a small but (strictly) negative correlation between the observations. In the randomization-based analysis, there is "no assumption" of a "normal" distribution and certainly "no assumption" of "independence". On the contrary, "the observations are dependent"! The randomization-based analysis has the disadvantage that its exposition involves tedious algebra and extensive time. Since the randomization-based analysis is complicated and is closely approximated by the approach using a normal linear model, most teachers emphasize the normal linear model approach. Few statisticians object to model-based analysis of balanced randomized experiments.
Statistical models for observational data. However, when applied to data from non-randomized experiments or observational studies, model-based analysis lacks the warrant of randomization. For observational data, the derivation of confidence intervals must use "subjective" models, as emphasized by Ronald Fisher and his followers. In practice, the estimates of treatment-effects from observational studies generally are often inconsistent. In practice, "statistical models" and observational data are useful for suggesting hypotheses that should be treated very cautiously by the public. Summary of assumptions. The normal-model based ANOVA analysis assumes the independence, normality, and homogeneity of variances of the residuals. The randomization-based analysis assumes only the homogeneity of the variances of the residuals (as a consequence of unit-treatment additivity) and uses the randomization procedure of the experiment. Both these analyses require homoscedasticity, as an assumption for the normal-model analysis and as a consequence of randomization and additivity for the randomization-based analysis.
However, studies of processes that change variances rather than means (called dispersion effects) have been successfully conducted using ANOVA. There are "no" necessary assumptions for ANOVA in its full generality, but the "F"-test used for ANOVA hypothesis testing has assumptions and practical limitations which are of continuing interest. Problems which do not satisfy the assumptions of ANOVA can often be transformed to satisfy the assumptions. The property of unit-treatment additivity is not invariant under a "change of scale", so statisticians often use transformations to achieve unit-treatment additivity. If the response variable is expected to follow a parametric family of probability distributions, then the statistician may specify (in the protocol for the experiment or observational study) that the responses be transformed to stabilize the variance. Also, a statistician may specify that logarithmic transforms be applied to the responses which are believed to follow a multiplicative model. According to Cauchy's functional equation theorem, the logarithm is the only continuous transformation that transforms real multiplication to addition.
Characteristics. ANOVA is used in the analysis of comparative experiments, those in which only the difference in outcomes is of interest. The statistical significance of the experiment is determined by a ratio of two variances. This ratio is independent of several possible alterations to the experimental observations: Adding a constant to all observations does not alter significance. Multiplying all observations by a constant does not alter significance. So ANOVA statistical significance result is independent of constant bias and scaling errors as well as the units used in expressing observations. In the era of mechanical calculation it was common to subtract a constant from all observations (when equivalent to dropping leading digits) to simplify data entry. This is an example of data coding. Algorithm. The calculations of ANOVA can be characterized as computing a number of means and variances, dividing two variances and comparing the ratio to a handbook value to determine statistical significance. Calculating a treatment effect is then trivial: "the effect of any treatment is estimated by taking the difference between the mean of the observations which receive the treatment and the general mean".
Partitioning of the sum of squares. ANOVA uses traditional standardized terminology. The definitional equation of sample variance is formula_12, where the divisor is called the degrees of freedom (DF), the summation is called the sum of squares (SS), the result is called the mean square (MS) and the squared terms are deviations from the sample mean. ANOVA estimates 3 sample variances: a total variance based on all the observation deviations from the grand mean, an error variance based on all the observation deviations from their appropriate treatment means, and a treatment variance. The treatment variance is based on the deviations of treatment means from the grand mean, the result being multiplied by the number of observations in each treatment to account for the difference between the variance of observations and the variance of means. The fundamental technique is a partitioning of the total sum of squares "SS" into components related to the effects used in the model. For example, the model for a simplified ANOVA with one type of treatment at different levels.
formula_13 The number of degrees of freedom "DF" can be partitioned in a similar way: one of these components (that for error) specifies a chi-squared distribution which describes the associated sum of squares, while the same is true for "treatments" if there is no treatment effect. formula_14 The "F"-test. The "F"-test is used for comparing the factors of the total deviation. For example, in one-way, or single-factor ANOVA, statistical significance is tested for by comparing the F test statistic formula_15 formula_16 where "MS" is mean square, formula_17 is the number of treatments and formula_18 is the total number of cases to the "F"-distribution with formula_19 being the numerator degrees of freedom and formula_20 the denominator degrees of freedom. Using the "F"-distribution is a natural candidate because the test statistic is the ratio of two scaled sums of squares each of which follows a scaled chi-squared distribution. The expected value of F is formula_21 (where formula_22 is the treatment sample size) which is 1 for no treatment effect. As values of F increase above 1, the evidence is increasingly inconsistent with the null hypothesis. Two apparent experimental methods of increasing F are increasing the sample size and reducing the error variance by tight experimental controls.
There are two methods of concluding the ANOVA hypothesis test, both of which produce the same result: The ANOVA "F"-test is known to be nearly optimal in the sense of minimizing false negative errors for a fixed rate of false positive errors (i.e. maximizing power for a fixed significance level). For example, to test the hypothesis that various medical treatments have exactly the same effect, the "F"-test's "p"-values closely approximate the permutation test's p-values: The approximation is particularly close when the design is balanced. Such permutation tests characterize tests with maximum power against all alternative hypotheses, as observed by Rosenbaum. The ANOVA "F"-test (of the null-hypothesis that all treatments have exactly the same effect) is recommended as a practical test, because of its robustness against many alternative distributions. Extended algorithm. ANOVA consists of separable parts; partitioning sources of variance and hypothesis testing can be used individually. ANOVA is used to support other statistical tools. Regression is first used to fit more complex models to data, then ANOVA is used to compare models with the objective of selecting simple(r) models that adequately describe the data. "Such models could be fit without any reference to ANOVA, but ANOVA tools could then be used to make some sense of the fitted models, and to test hypotheses about batches of coefficients." "[W]e think of the analysis of variance as a way of understanding and structuring multilevel models—not as an alternative to regression but as a tool for summarizing complex high-dimensional inferences ..."
For a single factor. The simplest experiment suitable for ANOVA analysis is the completely randomized experiment with a single factor. More complex experiments with a single factor involve constraints on randomization and include completely randomized blocks and Latin squares (and variants: Graeco-Latin squares, etc.). The more complex experiments share many of the complexities of multiple factors. There are some alternatives to conventional one-way analysis of variance, e.g.: Welch's heteroscedastic F test, Welch's heteroscedastic F test with trimmed means and Winsorized variances, Brown-Forsythe test, Alexander-Govern test, James second order test and Kruskal-Wallis test, available in onewaytests R It is useful to represent each data point in the following form, called a statistical model: formula_23 where That is, we envision an additive model that says every data point can be represented by summing three quantities: the true mean, averaged over all factor levels being investigated, plus an incremental component associated with the particular column (factor level), plus a final component associated with everything else affecting that specific data value.
For multiple factors. ANOVA generalizes to the study of the effects of multiple factors. When the experiment includes observations at all combinations of levels of each factor, it is termed factorial. Factorial experiments are more efficient than a series of single factor experiments and the efficiency grows as the number of factors increases. Consequently, factorial designs are heavily used. The use of ANOVA to study the effects of multiple factors has a complication. In a 3-way ANOVA with factors x, y and z, the ANOVA model includes terms for the main effects (x, y, z) and terms for interactions (xy, xz, yz, xyz). All terms require hypothesis tests. The proliferation of interaction terms increases the risk that some hypothesis test will produce a false positive by chance. Fortunately, experience says that high order interactions are rare. The ability to detect interactions is a major advantage of multiple factor ANOVA. Testing one factor at a time hides interactions, but produces apparently inconsistent experimental results.
Caution is advised when encountering interactions; Test interaction terms first and expand the analysis beyond ANOVA if interactions are found. Texts vary in their recommendations regarding the continuation of the ANOVA procedure after encountering an interaction. Interactions complicate the interpretation of experimental data. Neither the calculations of significance nor the estimated treatment effects can be taken at face value. "A significant interaction will often mask the significance of main effects." Graphical methods are recommended to enhance understanding. Regression is often useful. A lengthy discussion of interactions is available in Cox (1958). Some interactions can be removed (by transformations) while others cannot. A variety of techniques are used with multiple factor ANOVA to reduce expense. One technique used in factorial designs is to minimize replication (possibly no replication with support of analytical trickery) and to combine groups when effects are found to be statistically (or practically) insignificant. An experiment with many insignificant factors may collapse into one with a few factors supported by many replications.
Associated analysis. Some analysis is required in support of the "design" of the experiment while other analysis is performed after changes in the factors are formally found to produce statistically significant changes in the responses. Because experimentation is iterative, the results of one experiment alter plans for following experiments. Preparatory analysis. The number of experimental units. In the design of an experiment, the number of experimental units is planned to satisfy the goals of the experiment. Experimentation is often sequential. Early experiments are often designed to provide mean-unbiased estimates of treatment effects and of experimental error. Later experiments are often designed to test a hypothesis that a treatment effect has an important magnitude; in this case, the number of experimental units is chosen so that the experiment is within budget and has adequate power, among other goals. Reporting sample size analysis is generally required in psychology. "Provide information on sample size and the process that led to sample size decisions." The analysis, which is written in the experimental protocol before the experiment is conducted, is examined in grant applications and administrative review boards.
Besides the power analysis, there are less formal methods for selecting the number of experimental units. These include graphical methods based on limiting the probability of false negative errors, graphical methods based on an expected variation increase (above the residuals) and methods based on achieving a desired confidence interval. Power analysis. Power analysis is often applied in the context of ANOVA in order to assess the probability of successfully rejecting the null hypothesis if we assume a certain ANOVA design, effect size in the population, sample size and significance level. Power analysis can assist in study design by determining what sample size would be required in order to have a reasonable chance of rejecting the null hypothesis when the alternative hypothesis is true. Effect size. Several standardized measures of effect have been proposed for ANOVA to summarize the strength of the association between a predictor(s) and the dependent variable or the overall standardized difference of the complete model. Standardized effect-size estimates facilitate comparison of findings across studies and disciplines. However, while standardized effect sizes are commonly used in much of the professional literature, a non-standardized measure of effect size that has immediately "meaningful" units may be preferable for reporting purposes.
Model confirmation. Sometimes tests are conducted to determine whether the assumptions of ANOVA appear to be violated. Residuals are examined or analyzed to confirm homoscedasticity and gross normality. Residuals should have the appearance of (zero mean normal distribution) noise when plotted as a function of anything including time and modeled data values. Trends hint at interactions among factors or among observations. Follow-up tests. A statistically significant effect in ANOVA is often followed by additional tests. This can be done in order to assess which groups are different from which other groups or to test various other focused hypotheses. Follow-up tests are often distinguished in terms of whether they are "planned" (a priori) or "post hoc." Planned tests are determined before looking at the data, and post hoc tests are conceived only after looking at the data (though the term "post hoc" is inconsistently used). The follow-up tests may be "simple" pairwise comparisons of individual group means or may be "compound" comparisons (e.g., comparing the mean pooling across groups A, B and C to the mean of group D). Comparisons can also look at tests of trend, such as linear and quadratic relationships, when the independent variable involves ordered levels. Often the follow-up tests incorporate a method of adjusting for the multiple comparisons problem.
Follow-up tests to identify which specific groups, variables, or factors have statistically different means include the Tukey's range test, and Duncan's new multiple range test. In turn, these tests are often followed with a Compact Letter Display (CLD) methodology in order to render the output of the mentioned tests more transparent to a non-statistician audience. Study designs. There are several types of ANOVA. Many statisticians base ANOVA on the design of the experiment, especially on the protocol that specifies the random assignment of treatments to subjects; the protocol's description of the assignment mechanism should include a specification of the structure of the treatments and of any blocking. It is also common to apply ANOVA to observational data using an appropriate statistical model. Some popular designs use the following types of ANOVA: Cautions. Balanced experiments (those with an equal sample size for each treatment) are relatively easy to interpret; unbalanced experiments offer more complexity. For single-factor (one-way) ANOVA, the adjustment for unbalanced data is easy, but the unbalanced analysis lacks both robustness and power. For more complex designs the lack of balance leads to further complications. "The orthogonality property of main effects and interactions present in balanced data does not carry over to the unbalanced case. This means that the usual analysis of variance techniques do not apply. Consequently, the analysis of unbalanced factorials is much more difficult than that for balanced designs." In the general case, "The analysis of variance can also be applied to unbalanced data, but then the sums of squares, mean squares, and "F"-ratios will depend on the order in which the sources of variation are considered."
ANOVA is (in part) a test of statistical significance. The American Psychological Association (and many other organisations) holds the view that simply reporting statistical significance is insufficient and that reporting confidence bounds is preferred. Generalizations. ANOVA is considered to be a special case of linear regression which in turn is a special case of the general linear model. All consider the observations to be the sum of a model (fit) and a residual (error) to be minimized. The Kruskal-Wallis test and the Friedman test are nonparametric tests which do not rely on an assumption of normality. Connection to linear regression. Below we make clear the connection between multi-way ANOVA and linear regression. Linearly re-order the data so that formula_25-th observation is associated with a response formula_26 and factors formula_27 where formula_28 denotes the different factors and formula_29 is the total number of factors. In one-way ANOVA formula_30 and in two-way ANOVA formula_31. Furthermore, we assume the formula_32-th factor has formula_33 levels, namely formula_34. Now, we can one-hot encode the factors into the formula_35 dimensional vector formula_36.
The one-hot encoding function formula_37 is defined such that the formula_4-th entry of formula_39 is formula_40 The vector formula_36 is the concatenation of all of the above vectors for all formula_32. Thus, formula_43. In order to obtain a fully general formula_29-way interaction ANOVA we must also concatenate every additional interaction term in the vector formula_36 and then add an intercept term. Let that vector be formula_46. With this notation in place, we now have the exact connection with linear regression. We simply regress response formula_26 against the vector formula_46. However, there is a concern about identifiability. In order to overcome such issues we assume that the sum of the parameters within each set of interactions is equal to zero. From here, one can use "F"-statistics or other methods to determine the relevance of the individual factors. Example. We can consider the 2-way interaction example where we assume that the first factor has 2 levels and the second factor has 3 levels. Define formula_49 if formula_50 and formula_51 if formula_52, i.e. formula_53 is the one-hot encoding of the first factor and formula_32 is the one-hot encoding of the second factor. With that, formula_55 where the last term is an intercept term. For a more concrete example suppose that formula_56 Then, formula_57
Alkane In organic chemistry, an alkane, or paraffin (a historical trivial name that also has other meanings), is an acyclic saturated hydrocarbon. In other words, an alkane consists of hydrogen and carbon atoms arranged in a tree structure in which all the carbon–carbon bonds are single. Alkanes have the general chemical formula . The alkanes range in complexity from the simplest case of methane (), where "n" = 1 (sometimes called the parent molecule), to arbitrarily large and complex molecules, like pentacontane () or 6-ethyl-2-methyl-5-(1-methylethyl) octane, an isomer of tetradecane (). The International Union of Pure and Applied Chemistry (IUPAC) defines alkanes as "acyclic branched or unbranched hydrocarbons having the general formula , and therefore consisting entirely of hydrogen atoms and saturated carbon atoms". However, some sources use the term to denote "any" saturated hydrocarbon, including those that are either monocyclic (i.e. the cycloalkanes) or polycyclic, despite them having a distinct general formula (e.g. cycloalkanes are ).
In an alkane, each carbon atom is sp3-hybridized with 4 sigma bonds (either C–C or C–H), and each hydrogen atom is joined to one of the carbon atoms (in a C–H bond). The longest series of linked carbon atoms in a molecule is known as its carbon skeleton or carbon backbone. The number of carbon atoms may be considered as the size of the alkane. One group of the higher alkanes are waxes, solids at standard ambient temperature and pressure (SATP), for which the number of carbon atoms in the carbon backbone is greater than about 17. With their repeated – units, the alkanes constitute a homologous series of organic compounds in which the members differ in molecular mass by multiples of 14.03 u (the total mass of each such methylene-bridge unit, which comprises a single carbon atom of mass 12.01 u and two hydrogen atoms of mass ~1.01 u each). Methane is produced by methanogenic bacteria and some long-chain alkanes function as pheromones in certain animal species or as protective waxes in plants and fungi. Nevertheless, most alkanes do not have much biological activity. They can be viewed as molecular trees upon which can be hung the more active/reactive functional groups of biological molecules.
The alkanes have two main commercial sources: petroleum (crude oil) and natural gas. An alkyl group is an alkane-based molecular fragment that bears one open valence for bonding. They are generally abbreviated with the symbol for any organyl group, R, although Alk is sometimes used to specifically symbolize an alkyl group (as opposed to an alkenyl group or aryl group). Structure and classification. Ordinarily, the C–C single bond distance is . Saturated hydrocarbons can be linear, branched, or cyclic. The third group is sometimes called cycloalkanes. Very complicated structures are possible by combining linear, branched, cyclic alkanes. In alkanes, the number of C–C bonds ("k") is given by: "k = n - 1 + r" , where "n" is the number of carbon atoms and "r" the number of rings or cycles. The number of C-H ("h") bonds is then: "h = 2n + 2 - 2r" "," this gives a total bond count ("b") of: "b = 3n + 1 - r" . For a homologous series of (unbranched) linear alkanes, the topology and/or connectivity of the carbon atoms in the molecules (implicitly considering the hydrogen atoms) can be described by an adjacency matrix. The enumerations of carbon atoms are equivalent by symmetry (left to right or viceversa), and therefore the adjacency matrix for linear alkanes can be written as follows:
formula_1 The determinant of this matrix alternates between -1, 0 and 1 for successive members of the series. The adjacency matrix for the homologous series of (unbranched) monocyclic cycloalkanes can be written as: formula_2 where formula_3 is the number of carbon atoms (order of the matrix). The determinant of this matrix alternates between 2, 0, 2, and -4 for successive members of the series. Isomerism. Alkanes with more than three carbon atoms can be arranged in various ways, forming structural isomers. The simplest isomer of an alkane is the one in which the carbon atoms are arranged in a single chain with no branches. This isomer is sometimes called the "n"-isomer ("n" for "normal", although it is not necessarily the most common). However, the chain of carbon atoms may also be branched at one or more points. The number of possible isomers increases rapidly with the number of carbon atoms. For example, for acyclic alkanes: Branched alkanes can be chiral. For example, 3-methylhexane and its higher homologues are chiral due to their stereogenic center at carbon atom number 3. The above list only includes differences of connectivity, not stereochemistry. In addition to the alkane isomers, the chain of carbon atoms may form one or more rings. Such compounds are called cycloalkanes, and are also excluded from the above list because changing the number of rings changes the molecular formula. For example, cyclobutane and methylcyclopropane are isomers of each other (C4H8), but are not isomers of butane (C4H10).
Branched alkanes are more thermodynamically stable than their linear (or less branched) isomers. For example, the highly branched 2,2,3,3-tetramethylbutane is about 1.9 kcal/mol more stable than its linear isomer, "n"-octane. Nomenclature. The IUPAC nomenclature (systematic way of naming compounds) for alkanes is based on identifying hydrocarbon chains. Unbranched, saturated hydrocarbon chains are named systematically with a Greek numerical prefix denoting the number of carbons and the suffix "-ane". In 1866, August Wilhelm von Hofmann suggested systematizing nomenclature by using the whole sequence of vowels a, e, i, o and u to create suffixes -ane, -ene, -ine (or -yne), -one, -une, for the hydrocarbons C"n"H2"n"+2, C"n"H2"n", C"n"H2"n"−2, C"n"H2"n"−4, C"n"H2"n"−6. In modern nomenclature, the first three specifically name hydrocarbons with single, double and triple bonds; while "-one" now represents a ketone. Linear alkanes. Straight-chain alkanes are sometimes indicated by the prefix "n-" or ""n"-"(for "normal") where a non-linear isomer exists. Although this is not strictly necessary and is not part of the IUPAC naming system, the usage is still common in cases where one wishes to emphasize or distinguish between the straight-chain and branched-chain isomers, e.g., ""n"-butane" rather than simply "butane" to differentiate it from isobutane. Alternative names for this group used in the petroleum industry are linear paraffins or "n"-paraffins.
The first eight members of the series (in terms of number of carbon atoms) are named as follows: The first four names were derived from methanol, ether, propionic acid and butyric acid. Alkanes with five or more carbon atoms are named by adding the suffix -ane to the appropriate numerical multiplier prefix with elision of any terminal vowel ("-a" or "-o") from the basic numerical term. Hence, pentane, C5H12; hexane, C6H14; heptane, C7H16; octane, C8H18; etc. The numeral prefix is generally Greek; however, alkanes with a carbon atom count ending in nine, for example nonane, use the Latin prefix non-. Branched alkanes. Simple branched alkanes often have a common name using a prefix to distinguish them from linear alkanes, for example "n"-pentane, isopentane, and neopentane. IUPAC naming conventions can be used to produce a systematic name. The key steps in the naming of more complicated branched alkanes are as follows: Saturated cyclic hydrocarbons. Though technically distinct from the alkanes, this class of hydrocarbons is referred to by some as the "cyclic alkanes." As their description implies, they contain one or more rings.

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