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1. The limits in sight for Spanish macro farms

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1. The limits in sight for Spanish macro farms

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https://inspain.news/the-limits-in-sight-for-spanish-macro-farms/

This article was published at the beginning of the COVID-19 pandemic, and highlights the possible problems in Spain's intensive livestock production systems. Problems can arise in these farms, such as limited freshwater that can be used to support a high number of livestock, the ability to monopolize the market because of increased production (which decreases consumer choices), and overspecialization (possibly causing economic issues if the livestock that is intensively produced ever depreciates in value). It also discusses waste disposal from the farms, as it can contain high ammonia and nitrogen content. This also raises concern on the hygiene in these farms because of the high concentration of livestock in a small area, especially with COVID-19 being a zoonotic disease.

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Although it is not widely publicised, the figures are impressive, writes Iñaki Iriarte Goñi in the newspaper ElDiario.es . Some pig farms concentrate 2,000 sows for breeding; a dairy farm in Caparroso (Navarra) has around 5,000 cows and plans to increase this number to over 7,000. This is the same company that presented a building project for over 23,000 cattle at a macro farm in Noviercas (Soria). However, in the case of poultry farms, the numbers shoot up even further. Furthermore, there are cases that manage the production of more than 200,000 animals.

Spain largest pork producer in Europe

According to Greenpeace, the population of these animals in Spain increased by 21.5% between 2015 and 2020. The huge growth is the result of the soaring demand for pork from China. Because this is where the swine fever epidemic decimated the number of farms in the country. Furthermore, in 2020, Spain became the first pig producer in the European Union, with 56 million animals and five million tons of meat. This is according to the Ministry of Agriculture.

See also: How many animals are slaughtered in Spain every year?

Highest possible production per animal

The key to intensive farming is to achieve the highest possible production per animal in a predetermined time. Consequently, the animals are concentrated in prepared facilities where they are provided with the necessary amounts of food, water, supplements, and medicines. The latter includes antibiotics, essential to keep diseases and infections to a minimum. Foods with increasingly sophisticated mixes are also essential.

Animal 'factories'

Ultimately, it is a matter of combining technologies and time to standardise as much as possible the breeding, fattening, or production of meat, milk, or eggs, based on applying to the animals the organic logic typical of large industries. They are called macro farms, but actually have more to do with industrial organisations and logistics systems than farms. Not to mention the welfare of the animals.

Controlled by multinationals

The huge intensive livestock network developed in Spain in recent decades has been controlled by multinationals since the 1960s. They provide the physical production space and especially the work. Their intermediary function is further subordinated to the large companies which provide them with the necessary resources to organise and maintain the activity. On the other hand, the no less large companies control food distribution and set the selling prices of the products.

Decrease in the number of farmers and farms

The increase in livestock production in Spain at the moment, as their size increases, is clearly accompanied by a decrease in the number of farmers and farms. By increasing the number of animals per farm and introducing increasingly complex and automated technical systems, an attempt is made to realise economies of scale that make it possible to maintain a position on the (world) market and to offer products as cheaply as possible. Thus, large companies directly or indirectly promote the macro-farm model that meets these objectives.

However, this commercial interest clearly encounters very divergent limits. The first is of an economic nature. The sharp increase in Spanish livestock production is based on the growth in exports that are not certain to be sustained in the future. This is especially evident in pork production, which is more than twice the national consumption. What is not consumed in Spain mainly goes to China in large quantities. Should China increase its own production, as appears to be the case, it cannot be ruled out that this export boom could turn into a real pig bubble.

But beyond that, there are environmental limits. The Hydrographic Society of the Duero Basin calculated that the 23,000 cows on the proposed Noviercas farm consume as much as 24 litres of water per second. The basin simply does not have these resources and therefore the permit has been suspended for the time being. Pigs also need an average of five thousand litres of water per year. All this in a country where many regions are seeing groundwater levels fall rapidly due to drought.

In addition to the excessive consumption of resources, there is the serious problem of the complex handling of faeces generated by the existing macro farms. "The slurry has a high nitrogen and ammonia content," says Toni Jorge of the association ACEM-Ecologistas en Acción from Cardenete, Cuenca. "All that nitrogen, once the slurry spreads, goes through filtration to the aquifer." Despite what the companies claim, it is highly questionable whether this waste can be recycled without causing a harmful overload to the surrounding area and to the aquifers around the large farms.

See also: Biggest agricultural polluters receive most European subsidies

There are also limits in terms of health and hygiene. Despite the fact that technobiology and veterinary medicine are currently managing the animal epidemics in intensive livestock farms, mad cows, bird flu or swine fever still pose a real danger. It is obvious that the high concentration of animals on macro farms increases the potential hazards. Especially in light of possible zoonoses, infections that can be transmitted from animals to humans. It seems prudent to exercise caution now that we have seen the effects an uncontrolled pandemic can have on our society.

Rural depopulation as an argument

Proponents of macrofarms consistently point to the important role these farms play in countering the worrying depopulation of rural areas. However, according to Ecologistas and Acción, that argument is not convincing. According to a study conducted by this environmental group between 2000 and 2020 in places with less than 5,000 inhabitants, the places with intensive livestock farming lost more population than those where such activities do not take place.

See also: Mega farms are driving away inhabitants of rural villages

Repellent effect

In fact, because macro farms are highly automated, they generate very little local employment. Moreover, the side-effects of these businesses with unavoidable odors or with an abundance of trucks loading and unloading can negatively impact other complementary activities such as rural tourism, and work in exactly the opposite direction.

See also: Again shocking images from Spanish pig farms

As long as current food consumption patterns do not change, eliminating intensive livestock farming is impossible. However, that does not mean that their growth cannot be let go. If one is solely guided by economies of scale and competitiveness, the environmental limits, the hygienic prudence, and also the social prudence is more quickly exceeded, while these must be respected in order to prevent irreparable damage to the environment.

Impending collapse

In addition to the limits mentioned faced by macro farms in Spain, there are other factors that threaten the industry. The enormously increased costs of raw materials, transport, and energy. One example is that the raw material used to feed the animals, mainly imported from Brazil (transgenic and glyphosate riddled soybeans) has risen in price between 20% and 40% due to shortages and speculation.

The skyrocketing energy prices are also heavily impacting these high-tech livestock farms, as is another of their major expenditures, maritime transport (involved in raw material imports and final product exports). The price of renting a container is multiplied by ten.

Demand from China is declining

In addition, the demand from China is now declining as the country has overcome the swine fever epidemic. The national market is saturated. In any case, people are not going to eat more meat. In July, Spain's Consumer Affairs Minister Garzón called on people to eat less meat. They argued that 'eating too much meat is bad for our health and our planet'.

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The limits in sight for Spanish macro farms

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Although it is not widely publicised, the figures are impressive, writes Iñaki Iriarte Goñi in the newspaper ElDiario.es . Some pig farms concentrate 2,000 sows for breeding; a dairy farm in Caparroso (Navarra) has around 5,000 cows and plans to increase this number to over 7,000.

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3. Polyculture Farming: methods, advantages, and disadvantages

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3. Polyculture Farming: methods, advantages, and disadvantages

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https://geopard.tech/blog/polyculture-farming-methods-advantages-and-disadvantages/

This article covers the benefits and disadvantages of not farming monoculturally. Polyculture farming utilizes less resources (fertilizers, water) for each plant, and produces healthier plants because of boosted immune systems from being around other plants (?)

I couldn't find where this idea actually came from though, because the article only says it comes from research :>

Polycultures also benefit the soil quality, as different species of plants have different nutritional needs, decreasing the rate at which nutrients are depleted from the soil when they are planted together. They also have different root structure lengths. As opposed to monocultures, which may increase the risk of drought because the crops all draw water from one layer of soil, plants in polycultures can take water from different layers.

However, a larger investment of both time and capital is needed for polycultures, compared to monocultures.

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Polyculture farming is an agricultural method of planting multiple crops in the same field that falls under the category of crop diversity. 🌻

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2. Monoculture and the Irish Potato Famine: cases of missing genetic variation

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2. Monoculture and the Irish Potato Famine: cases of missing genetic variation

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Case of famine caused by low genetic variation. Potatoes were a common staple food in Ireland because the ease of replanting and growing them in large quantities through cuttings, a type of asexual reproduction, which produces cloned offspring (also they needed only a small plot of land to grow). Since they were genetically identical, they were all heavily affected by a disease that made them inedible, and because these genetically identical potatoes were the main crop in the country, many people starved to death.

I just think this is morbidly cool and really sad, don't think this is really too related tbh :/

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Understanding Evolution - Your one-stop source for information on evolution

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Lack of genetic variation in Irish potatoes contributed to the severity of the Irish potato famine, which devastated Ireland's population and economy. Today, evolutionary theory tells us that relying on crops with low genetic variation can lead to disaster. Heeding the warnings of scientists and history may help us prevent wide-scale crop devastation due to changing environmental conditions.

Lumpers

In the 1800s, the Irish solved their problem of feeding a growing population by planting potatoes. Specifically, they planted the "lumper" potato variety. And since potatoes can be propagated vegetatively, all of these lumpers were clones, genetically identical to one another.

The lumper fed Ireland for a time, but it also set the stage for human and economic ruin. Evolutionary theory suggests that populations with low genetic variation are more vulnerable to changing environmental conditions than are diverse populations. The Irish potato clones were certainly low on genetic variation, so when the environment changed and a potato disease swept through the country in the 1840s, the potatoes (and the people who depended upon them) were devastated.

The importance of diversity

The genetically identical lumpers were all susceptible to a rot caused by Phytophthora infestans, which turns non-resistant potatoes to inedible slime. Because Ireland was so dependent on the potato, one in eight Irish people died of starvation in three years during the Irish potato famine of the 1840s.

Although the famine ultimately had many causes, the disaster would likely not have been so terrible had more genetically variable potatoes been planted. Some potatoes would have carried the right genes to make it through the epidemic, and more of the resistant varieties could have been planted in the years following the first epidemic. Later, scientists identified resistance genes in a potato from South America, where farmers have preserved the genetic variation of potatoes by growing many cultivated varieties alongside the potato's wild cousins.

The image below compares the effect of a blight on diverse and cloned crops.

Ignoring history

Despite the warnings of evolution and history, much agriculture continues to depend on genetically uniform crops. The widespread planting of a single corn variety contributed to the loss of over a billion dollars worth of corn in 1970, when the U.S. crop was overwhelmed by a fungus. And in the 1980s, dependence upon a single type of grapevine root forced California grape growers to replant approximately two million acres of vines when a new race of the pest insect, grape phylloxera ( Daktulosphaira vitifoliae, shown at left) attacked in the 1980s.

Although planting a single, genetically uniform crop might increase short term yields, evolutionary theory and the lessons of history highlight an undesirable side effect. Planting genetically uniform crops increases the risk of "losing it all" when environmental variables change: for example, if a new pest is introduced or rainfall levels drop.

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Monoculture and the Irish Potato Famine: cases of missing genetic variation - Understanding Evolution

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The drawing of a starving family originally appeared in The Illustrated London News, December 22, 1849 Lack of genetic variation in Irish potatoes contributed to the severity of the Irish potato famine, which devastated Ireland's population and economy. Today, evolutionary theory tells us that relying on crops with low genetic variation can lead to disaster.

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1. Advantages and Disadvantages of Monoculture Farming

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1. Advantages and Disadvantages of Monoculture Farming

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https://greentumble.com/advantages-and-disadvantages-of-monoculture-farming/

A monoculture is defined as when only a single species of crop is planted on a plot of agricultural land. The article emphasizes that the desire for economic benefit could be a major motivator for monoculture farming.

Although monocultures are beneficial for farmers due to higher efficiency and easier management, leading to financial gain for farmers, this method of farming leads to issues such as use of fertilizers and freshwater at a faster pace than polycultures and depleted nutrients in soil from lack of crop diversity. This creates an environment that is not sustainable long-term.

Planting the same selectively bred type of crop can also lead to lower variation in genetic makeup, which makes many fields susceptible to the same disease, causing famine when the crop population is largely affected.

Monocultures are also detrimental for pollinators, such as bees 🐝, due to a diet that is not varied enough from only receiving nutrients from a single species of plant.

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Bio food supply project: chemical fertilisers video by Meredith

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This article describes some common methods of weed control, such as biological and chemical control as well as mechanical control methods.

Herbicide application on plants is a chemical method of weed control. It is the most effective and time-efficient method, and numerous types of herbicides are provided for different plants and for weed control, to ensure that the grass does not get injured.

However, misapplication could lead to various consequences. When sprayed or used incorrectly, the herbicides could injure plants that were not targeted or it fails to control the weeds.

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Prevention is the most essential aspect of weed management. Once a noxious weed infestation becomes established, any increase in size and density creates increasingly more expensive management efforts. Awareness of weed seed sources and plant identification is a must. Call the Larimer County Weed District office for help with identification, or to set up a site visit.

Weed seed can be spread from neighboring properties, adjacent road rights-of-way and trails. Direct sources are often livestock, manure, seed, hay, vehicles and equipment.

Disturbed ground is most vulnerable to weed invasion.

Early detection and rapid response save time and money. Aggressive management action on small, newly established infestations can result in eradication. "An ounce of prevention is worth a pound of cure."

Cultural control, the establishment of competitive and desired vegetation, prevents or slows down invasion by weedy species and is a key component of successful weed management. Weeds are typically opportunistic and readily invade disturbed sites. Impacts from road construction, intensive livestock grazing, densely populated prairie dog colonies and other disturbances that damage or remove desirable and competitive vegetation create sites for noxious weed invasion. Controlling weeds on such sites can be futile without vegetative restoration, as weeds will readily re-invade the disturbed area.

Establishment of grassland or pasture can be challenging. Success often depends on proper species selection suitable for a particular soil type, moisture regime and growing season. Other factors, such as soil compaction, seeding depth, time of year and weed control during establishment, can be critical to success.

Whether seeding native plants or pasture grasses, it's important to consult with the Natural Resources Conservation Service, a commercial seeder, CSU Extension or other experts prior to investing time and money.

Herbicide application can provide the most effective and time-efficient method of managing weeds. Numerous herbicides are available that provide effective weed control and are selective so that grasses are not injured. Along with herbicide use, user responsibility and compliance with all product label requirements for herbicide handling, use and cleanup are necessary. Always read the label and keep in mind the label is legally binding.

When using herbicides, be mindful of proximity to water, trees, shrubs and other desirable vegetation. The Herbicide Reference Guide lists products commonly used for control of noxious weeds on small acreage properties. The guide provides information necessary to help decide which herbicide is most appropriate for a particular weed in a particular setting, but is not the complete label. View complete label information

Herbicides are applied by spot spraying (single nozzle application targeting individual plants) or broadcast spraying (multiple nozzles covering an entire area). Whatever method is used, calibration of spray equipment (gallons per acre spray output) is essential for accurate delivery and mixing calculations. Estimating or guessing sprayer output can lead to misapplication, which either injures non-target plants or results in failure to control the target weed species. For help with calibration, read the Sprayer Calibration guide. For professional application, see the applicator list.

The goal of biological control is not eradication but the use of living agents to suppress vigor and spread of weeds. Such agents can be insects, bacteria, fungi or grazing animals, such as sheep, goats, cattle or horses. Grazing produces results similar to mowing. Biological control is most commonly thought of as "insect biocontrol".

The United States Department of Agriculture - Animal and Plant Health Inspection Service (APHIS) is the federal agency responsible for authorizing the screening and importation of biocontrol insects. APHIS conducts intensive multi-year screening programs assuring an insect agent's host specificity (feeds only on target weed species, not other plants) prior to approval for release.

Insect agents, proven to be effective, are utilized in cases where eradication is impractical due to the vastness or inaccessibility of an infestation, and where other methods of management are not feasible. Insect agents typically require 3-5 years for establishment and can limit the spread and density of target weed species by feeding on leaves, stems, roots and/or seed heads. Eradication of a weed cannot be attained through insect biocontrol. The most effective scenario is a weed infestation reduced to a "tolerable level", a level where the insect agents are significantly limiting distribution and abundance of the target weed species and the weed density is no longer considered detrimental to the desired plant community. Some biocontrol insects proven to be successful in certain areas of Larimer County are:

Bindweed mites - field bindweed

Flea beetles - leafy spurge

Mecinus janthiniformus - Dalmatian toadflax

Larinus minutus and Cyphocleonus achates - diffuse knapweed

For more information on insect biocontrol and to obtain insects, check out the Colorado Biological Pest Control Program or call the Colorado Department of Agriculture Insectary in Palisade, Colorado, at (866) 324-2963.

Mechanical control consists of methods that kill or suppress weeds through physical disruption. Such methods include pulling, digging, disking, plowing and mowing. Success of various mechanical control methods is dependent on the life cycle of the target weed species.

Hand pulling and digging are effective on annual and biennial species such as kochia, musk thistle and diffuse knapweed. It is important to remove the upper 2-3 inches of taproot to prevent re-growth. Hand pulling or digging a perennial weed such as leafy spurge can be a futile effort unless one has the time necessary to diligently dig or pull re-growth over several seasons.

Shallow tillage with a disk or sweep is effective for controlling annual species, such as cheatgrass or kochia, but can actually be counter-productive if trying to control perennial weeds, such as Canada thistle, field bindweed, leafy spurge or Russian knapweed. Perennial root systems often have meristematic buds that can set roots and produce a new plant from root segments deposited on the soil surface. Shallow tillage of perennial weeds can result in a larger, denser and more uniform infestation than the initial patch.

Moldboard plowing (complete turnover of the top 10-12 inches of soil) disrupts underground root systems and buries seed from the surface to a depth too deep to germinate. This type of tillage is seldom feasible to practice on a regular basis.

Mowing is a suppression measure that can prevent or decrease seed head production. Mowed weeds will re-grow and set seed from a reduced height, so a combined control method is necessary to be effective. Mowing causes perennial plants to weaken when forced to send up carbohydrates from underground root reserves to nourish re-growth. Mowing a perennial weed such as Canada thistle a couple of times during the summer can significantly weaken the plants and, when combined with a fall herbicide application, provides excellent control.

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Methods of Weed Control (Natural Resources)

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Prevention is the most essential aspect of weed management. Once a noxious weed infestation becomes established, any increase in size and density creates increasingly more expensive management efforts. Awareness of weed seed sources and plant identification is a must. Call the Larimer County Weed District office for help with identification, or to set up a site visit.

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This article briefly explains how weeds can impact crop production and how to use herbicides to manage the issue. It also explains how they determine the most effective time and method to spray the weeds with herbicides.

Weeds can impact crop production in multiple ways, including reducing yields, interfering with harvest efficiency, and contributing to future weed problems. Thus, most farmers decide to use herbicides to tackle these issues and prevent the weeds from growing even more.

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Postemergence herbicides are an important component of today's weed management systems. Herbicides introduced in the past 10 years for use in both corn and soybeans are able to kill much larger weeds than earlier products, thus providing farmers with greater flexibility in application timing. However, this benefit may end up costing farmers money if application is delayed too late into the growing season. This article will discuss the importance of application timing in protecting crop yields.

Critical period of competition Weeds impact yields primarily by competing with the crop for light, water and nutrients. At the start of the growing season the requirement for these resources is small enough that both the crop and weeds can co-exist without significantly affecting each other's growth. As the growing season progresses and plant size increases, weeds begin to compete with the crop for the resources essential for plant growth. Once weeds begin to deprive the crop of these resources the yield potential can be negatively impacted. The time at which competition begins is known as the critical period. Controlling weeds at or before the critical period will protect crop yields from early-season competition, and this should be the goal in systems relying on postemergence herbicides.

While the concept of the critical period is relatively simple, determining when the critical period occurs is difficult due to the many interacting factors involved. The critical period is influenced by the weed community ( weed species, number of weeds, emergence date, etc.), cropping practices ( cultivar, row spacing, population, tillage, etc.) and environment. Applications of preemergence herbicides generally extend the critical period by reducing and delaying weed establishment. Depending on the situation, the critical period may occur as early as two weeks after planting to ten weeks or later after planting.

Research at the University of Nebraska found that row spacing had a large impact on the critical period for early-season competition (Figure 1). The critical period for soybeans planted in 7.5", 15" and 30" rows occurred at the V3, V2 and V1 stage, respectively. Soybean planted in narrow rows were more competitive with weeds and able to delay the onset of competition. Fields with high weed populations that emerge at the same time as the crop will have a relatively short critical periods, whereas clean fields or situations when most weeds emerge several weeks after the crop will have longer critical periods.

Almost all research investing the critical period has been conducted by planting into a weed-free seedbed, therefore providing the crop with an even start with weeds. Skipping the burndown application in no-till when weeds are present at planting provides weeds a competitive advantage with the crop, resulting in a significant reduction in the critical period. The limited application window when burndown applications are omitted greatly increases the risk of reduced profits, and therefore this practice is not recommended except for fields with histories of excellent weed control and low weed pressures.

Determining when to pull the trigger Due to our inability to accurately predict the critical period, subjective evaluations of crop and weed growth are used to determine when to spray. Several criteria can be used to guide this decision, including days after planting, days after crop emergence, crop stage or height, or weed height. Days after planting probably is the least effective method since crop and weed emergence rates vary widely with the environment. This is especially true when relying on herbicides with little or no residual activity, such as glyphosate.

Weed height is commonly used to determine when to apply herbicides. Basing herbicide application on weed height takes into account both weed susceptibility to the herbicide and the competitive potential of the weed. While height guidelines on labels provide information on the ability to kill weeds at specific heights, they do not take into consideration the potential of weeds to impact crop yields. A 10" weed is much more likely to cause yield losses than a 4" weed. Thus, just because a herbicide label states that 12" foxtail can be controlled doesn't mean delaying application until the foxtail reaches 12" tall is a good management decision.

The relationship between weed height at the time of glyphosate application in Roundup Ready corn is shown in Figure 2. The yield response is the mean of 35 experiments conducted across the Corn Belt. The critical period occurred when weeds reached a height of approximately 3", and delaying application beyond this height resulted in rapidly increasing yield losses. Spraying when weeds reached a height of 10" resulted in approximately a 15% yield loss. The critical period and weed competitiveness varied widely in these experiments, with yield losses ranging from 0 to 25% when weeds were controlled at a 6" height.

An important consideration when using weed height to determine herbicide application is weed growth rates. Weeds grow relatively slow initially, but as leaf area expands they enter a phase of logarithmic growth (Figure 3). Giant foxtail and waterhemp were approximately 2" tall 14 days after emergence, but reached a height of 5" 21 days after emergence.

Because of the rapid growth rates of weeds, timing of herbicide application should be set conservatively due to risks associated with application delays. For example, a farmer might decide to target 4" giant foxtail for an application of Steadfast based on the label recommendations. However, if weather delays keep the sprayer out of the field for five days the foxtail could be 8" tall before the conditions allowed the herbicide to be applied. This delay could result in significant yields losses.

Crop stage or height can also be used to determine timing of post applications. This approach is most appropriate in situations when crop tolerance to the herbicide is influenced by stage of development (Clarity or Steadfast on corn), or with herbicides that have significant residual activity (Pursuit in soybean, Callisto in corn). The limitation to this method is that it fails to consider the status of the weed infestation, and therefore ignores emergence time of the weeds in relation to the crop. Weeds frequently emerge significantly before or after the crop, resulting in the growth of the crop and weeds being out of synch. If the weeds emerge ahead of the crop they may reduce yields significantly by the time the crop reaches the stage used to trigger herbicide application. In situations with delayed weed emergence, applications based on crop stage alone may result in the herbicide being applied prior to the majority of the weed population emerging. This could result in problems with late-emerging weeds if the herbicide used has little or no residual activity, such as glyphosate. While crop stage can be used to guide herbicide application timing, in most situations it is also important to take into consideration the development of weeds.

With today's effective herbicides, application timings should be based not only on the ability of a herbicide to kill weeds, but also on the weeds' potential to impact yields early in the growing season. Many factors influence the critical period, making precise predictions of when weeds begin to impact crop yields difficult. However, when weeds emerge with the crop and are present at moderate to high densities ( >5 ft 2 ), delaying application until weeds reach a height of 4 inches or taller may result in significant yield losses. Yield losses accumulate rapidly once the critical period is reached, and losses up to a bushel per acre for each day application is delayed may occur. Because of the risks of reduced yield and profits, farmers should be conservative in determining when to spray fields. Fields having low weed populations, or in which preemergence herbicides are applied, will generally provide a much larger window for application while protecting yields.

What about late-emerging weeds? A primary reason for delaying postemergence applications is the desire to minimize the establishment of weeds that emerge after application. Most weeds emerge over a prolonged period and significant numbers can emerge after postemergence treatments. Delaying application allows the crop canopy to develop sufficiently to smother late-emerging weeds. While there are costs associated with weeds that emerge after early postemergence applications, the competitiveness of these weeds is greatly diminished due to the head start provided the crop.

A competitive crop canopy is one of the most effective weed management tools available. In a competitive situation, the plant that initially captures a resource normally will maintain the competitive edge for the remainder of the growing season. This is why late-emerging weeds are much less injurious to yields than weeds that emerge with the crop. Research at ISU found that the majority of waterhemp plants the emerged at the V4 stage of soybean or corn failed to survive to reproduction stages (Table 1). Biomass of plants that emerged at the V2 stage of soybean was reduced by nearly 80% compared to plants emerging with the crop.
Table 1. Influence of corn and soybean competition on waterhemp emerging at different times
in relation to the crop.¹
¹Hartzler, Battles and Nordby. 1998-2002. Iowa State University.
² Biomass is reported as percentage of dry weight produced by plants emerging at the same time as the crop.

Weeds that emerge at least four or more weeks after the crop are unlikely to impact yields unless they are present at very high densities. While late-emerging weeds should not be ignored since they are capable of producing significant quantity of seeds, protecting yields from weeds that emerge with the crop should take priority in most fields. The yield saved by preventing early season competition by spraying prior to the critical period will cover the cost of a second post application in most situations.

Summary Weeds impact crop production in many ways, including reducing yields, interfering with harvest efficiency, contributing to future weed problems, and detracting from field appearance. Weed management systems relying on postemergence herbicides can produce clean fields at season's end, yet not produce full yield potential due to competition between the crop and weeds prior to herbicide application. The critical period is the point of time when weeds that emerge with the crop first begin to impact yields. Depending upon the situation, the critical period may occur when weeds reach a height of two to ten inches or larger in height. Our inability to precisely predict the critical period requires that conservative judgment be used in determining when to apply postemergence herbicides in order to consistently produce maximum yields.

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Postemergence herbicides are an important component of today's weed management systems. Herbicides introduced in the past 10 years for use in both corn and soybeans are able to kill much larger weeds than earlier products, thus providing farmers with greater flexibility in application timing.

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This article explains the advantages and disadvantages of insecticides, and how they improve the quality and the yield of the crops.

There are both benefits and limitations to the use of pesticides. Some primary benefits include improved crop and livestock quality, and increased crop and livestock yields. It can also prevent the loss of crops to minimise waste and save money.

However, there are also significant disadvantages to insecticide use.  The pesticides could induce immunotoxicity into humans which can lead to allergies and autoimmune diseases. Farmers that are constantly exposed to pesticides are also at greater risk of cancer.

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Food Marketing Awards | ChefsBest | Home : ChefsBest

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Our populous, fast-paced world demands quality food that is affordable - and lots of it. When it comes to agriculture and the need to meet our world's growing needs, pesticide use is often a topic of controversy. In the short term, pesticides greatly assist pest control. In the long term, however, pesticides appear to harm the health of humans and the environment. Whether you're a decision maker in the agricultural industry or a consumer, it is important to weigh the pros and cons of pesticide use.

A pesticide is any substance or mixture of substances whose purpose is to prevent, extinguish, or repel pests or to regulate plants. There are considerable benefits to pesticide use. Cooper & Hans summarize these benefits as primary and secondary. Primary benefits include improved crop and livestock quality and increased crop and livestock yields. Secondary benefits are less immediate, and they include food security, increased export revenues, and reduced international spread of disease. In the short-range, pesticides reduce waste of crops, land, water, time, and other valuable resources. In fact, it has been estimated that investing around $10 billion in pesticides every year saves about $40 billion in crops.

On the other hand, the disadvantages to widespread pesticide use are significant. They include domestic animal contaminations and deaths, loss of natural antagonists to pests, pesticide resistance, Honeybee and pollination decline, losses to adjacent crops, fishery and bird losses, and contamination of groundwater. The fertility of soil is affected by the death or damage to microorganisms caused by pesticides. Further, some pesticides induce immunotoxicity in humans which may lead to immunosuppression, hypersensitivity (allergies), autoimmune diseases, and inflammation; children may be especially susceptible to the adverse effects of being exposed to pesticides. People who work regularly with pesticides, such as farmers, are at greater risk of cancer. Thousands of non-lethal poisonings and cancer cases each year are attributable to pesticides.

There are arguments in support of pesticides and against them. While they improve crop and livestock results, pesticides also damage human and environmental health. To minimize the negative results of pesticides, Arias-Estévez et al. recommend pesticides be very little to not toxic except to the target pest. Developments to pesticide formulation and use, such as microbially derived pesticides and precision band spraying, may also reduce the adverse effects of pesticides. Pesticides are widespread and improve crop quality and yield, thereby saving money to farmers and consumers. Nevertheless, the harmful long-term effects of pesticide use cannot be overlooked.

Sources:

Aktar, Md. Wasim, Dwaipayan Sengupta, and Ashim Chowdhury. "Impact of Pesticides Use in Agriculture: Their Benefits and Hazards." Interdisciplinary Toxicology 2.1 (2009): 1-12. PMC.

Arias-Estévez, Manuel, et al. "The Mobility and Degradation of Pesticides in Soils and the Pollution of Groundwater Resources." Agriculture, Ecosystems & Environment 123.4 (2008): 247-260.

Cooper, Jerry and Dobson, Hans. "The Benefits of Pesticides to Mankind and the Environment." Crop Protection 26 (2007): 1337-1348.

Corsini, E., et al. "Pesticide Induced Immunotoxicity in Humans: A Comprehensive Review of the Existing Evidence." Toxicology 307 (2013): 123-135.

Gilliom, Robert. "Pesticides in U.S. Streams and Groundwater." Environmental Science and Technology (2007): 3409-3414.

Pimentel, David. "Environmental and Economic Costs of the Application of Pesticides Primarily in the United States." Environment, Development and Sustainability 7 (2005): 229-252.

Repetto, Robert, and Sanjay S. Baliga. Pesticides and the Immune System: The Public Health Risks. World Resources Institute, 1996.

"What Is a Pesticide?" US Environmental Protection Agency. 29 Dec. 2015. https://www.epa.gov/minimum-risk-pesticides/what-pesticide

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The Advantages and Disadvantages of Pesticides | ChefsBest

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Our populous, fast-paced world demands quality food that is affordable - and lots of it. When it comes to agriculture and the need to meet our world's growing needs, pesticide use is often a topic of controversy. In the short term, pesticides greatly assist pest control.

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This article explains the importance and benefits of pesticides for edible crops.

Pesticides are used to eliminate pests and diseases that can harm crops. Without the use of pesticides, more than half of the crops will be lost. Thus, crop protection is essential in order to prevent losses.

Pesticides can also be used to ensure that the crops are safe and good quality, by decreasing exposure to food contaminated with harmful toxins and micro-organisms. Yields also rely on crop protection, and without the use of fungicides or pesticides the yield of most fruits and vegetables will fall by 50-90%.

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Without crop protection, including pesticides, more than half of the world's crops would be lost to insects, diseases and weeds.

Pesticide Testing & Regul...

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Importance & Benefits of Pesticides - Pesticide Facts

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Without the use of pesticides, more than half of crops would be lost to insects & diseases. Learn advantages of pesticides in agriculture.

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This article illustrates that agricultural machinery could be a double-edged sword. An increased use of machines could result in higher carbon emission rates, contributing to the major issue of global warming.

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Highlights

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Studies of energy, carbon and water footprint of agricultural machinery are rare.

*

Carbon and water footprint values provided for six types of agricultural machines.

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Data to subsidise further environmental impacts of machinery are presented.

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Carbon steel was material with highest energy, emissions and water footprints.

Agricultural machinery is crucial to agriculture because it enables field activities and production in large scale. Through mechanisation, agriculture requires inputs such as fuel and indirectly demands materials. Its impact can be assessed through energy, water and carbon footprints. Some studies have updated indices for agricultural machinery, showing different results from those of the 1970s and 1980s. This study presents environmental indices based on energy, carbon and water footprints for the life cycle of six types of agricultural machinery. The machines studied were tractors, sugarcane harvesters, coffee harvesters, sprayers, planters and combiners. Inputs used directly (assembly phase) and for the maintenance phase have been taken into account. The total energy, carbon and water footprints were by multiplying the material flows by their respective energy embodiments, consumed water and carbon footprints indices. Carbon steel accounted for the highest share (30-70%) of energy demand, 45-79% of carbon footprint and 19-59% of water footprint. However, the coffee harvester was an exception since it used nylon making it the machinery with the largest water footprint. Repair and maintenance accounted for a smaller share of all footprints, except for the sugarcane harvester. The water footprint was from 21 to 55 m³ for tractors and between 35 and 391 m ³ for other machines. Energy demand varied between 259 and 685 GJ to tractors and between 400 and 3500 GJ for other machines. The carbon footprint varied between 11 and 30 t CO ₂e for tractors and between 27 and 176 tonne [CO ₂ e] for other machines.

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Energy, carbon and water footprints on agricultural machinery

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Studies of energy, carbon and water footprint of agricultural machinery are rare. * Carbon and water footprint values provided for six types of agricultural machines. * Data to subsidise further environmental impacts of machinery are presented. * Carbon steel was material with highest energy, emissions and water footprints.

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This article explains the importance of mechanisation in the process of agricultural crop production. It highlights that not only does agricultural machinery improve efficiency, but it also has benefits farmers financially.

It allows farmers to decrease production costs while making improvements in quality and efficiency.

Rising labour costs are an important cause of the decline in agricultural profitability (Li et al., 2017). The service price of agricultural machinery is generally lower than labour costs (Tian et al., 2020).

If farmers switch from labour intensive production and utilise agricultural machinery, production costs would decrease greatly. With a similar or even higher quality of crops, their total profits would increase. As farmers have more disposable income, they could improve their living standards.

However, some workers may be dismissed as their labour is no longer required in the production process. This would lead to an increased unemployment rate.

Furthermore, agricultural machinery can perform land levelling and land preparation...

Agricultural resources could be put into maximum use while reducing the need for weed and insect pest control, therefore decreasing costs.

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School of Economics, Jiangxi University of Finance and Economics, Nanchang, China

Increasing agricultural operating income is not only an important step in improving agricultural work for farmers in the new era, but is also a powerful way to promote rural revitalization. To improve our understanding of the high-quality development of agriculture in China, the factors limiting agricultural income and the impact of the level of agricultural mechanization on agricultural production and income and its mechanism were analysed. Based on field survey data on farmers, this study analysed the influence of agricultural mechanization level on agricultural production and income by utilizing a sample-modified endogenous merging model and a threshold effect model. The level of mechanization has a significant positive impact on the cost, output value, income and return rate of all types of crops. For every 1% increase in the level of mechanization, the yields of all crops, grain crops and cash crops increase by 1.2151, 1.5941 and 0.4351%, respectively. Heterogeneity analysis shows that the level of mechanization has a certain threshold effect on income, with a greater effect occurring after the threshold. A test of action mechanism shows that the mechanization level can increase income via a factor intensification path and quality improvement path, with the partial mediation effects of the two paths being 28.8 and 27.4%, respectively. It is recommended to increase subsidies to purchase agricultural machinery, research and promote machinery suitable for cash crops, increase the level of socialized agricultural services, and improve the ability of farmers to apply novel agricultural machinery and tools so as to increase their operating profits.

1 Introduction

As China's agriculture is moving towards a high-quality development stage, it is necessary to focus on improving the quality and efficiency of agricultural production ( Huang, 2021). For many years, the limited income obtainable from agriculture has greatly dampened the enthusiasm of agricultural labourers. Although agricultural subsidies, a protective minimum grain price and other relevant policies have guaranteed the income of agricultural workers to a certain extent, they have also aggravated the problems of inverted food prices at home and abroad and weak international competitiveness of agricultural products ( Gao and Wang, 2021). For example, according to The National Data Collection of Cost and Income of Agricultural Products 2009, the average output of the top three grains (rice, wheat and corn) in 2008 was 11891.4 yuan per hectare and its average total cost was 9006.15 yuan per hectare. Hence, the net profit was 2885.25 yuan per hectare, a cost profit rate of 32.04%. Ten years later in 2018, according to The National Agricultural Product Cost and Income.

Data Collection 2019, their average output value was 16175.4 yuan per hectare, the average total cost was 16633.35 yuan per hectare, the net profit was−457.95 yuan per hectare, and the cost profit rate was−2.75%. Hence, the net profit of grain has changed from positive to negative in the past 10 years, and agricultural operating income has been continuously compressed, mainly because the rate of growth in agricultural production costs has been much higher than that of output value. China's present agricultural development situation clearly shows that there is still much agricultural modernization that can be achieved ( Peng et al., 2016; Jiang and Zhang, 2017). In the future, small farmers will still be the basic units of China's agricultural production and operation. Only by strengthening the connection between small farmers and modern agriculture can we achieve better agricultural modernization ( Luo, 2020). Therefore, while improving the quality and efficiency of agricultural development, it is also necessary to continuously improve the confidence and enthusiasm of agricultural workers by increasing their income, which is also the basic aim of China's rural revitalization strategy. The fundamental reason for the backwardness of China's agriculture and rural modernization lies the low use of agricultural machinery, which inhibits the improvement of agricultural production efficiency and leads to unreasonable agricultural production structure and poor circulation of agricultural products market. This series of problems will inevitably affect the pace of agricultural modernization. Therefore, to accelerate the process of agricultural modernization in China, it is necessary to improve the agricultural technology adoption rate of farmers, especially the use of agricultural machinery, so as to improve agricultural production efficiency, create more agricultural surplus economy, increase the possibility of farmers' market participation, and than promot high-quality agricultural development.

2 Literature Review and Theoretical Analysis

2.1 Related Literature Review

The issue of agricultural operating income has always attracted much attention. Previous studies have mainly explored ways to improve agricultural operating income in the following aspects: 1) Agricultural land use. The loss of productivity due to land fragmentation will cause declines in agricultural profits ( Lu and Hu, 2015; Wang and Tan, 2020). Increasing the scale of operation can reduce the cost of agricultural production and, thus, increase income ( Zhang et al., 2018). Some scholars have tried to increase agricultural income by transferring land to expand the scale of farms ( Xu et al., 2020; Yang et al., 2021). Peng et al. (2021a) found that land transfer can effectively lower agricultural production costs. Chen et al. (2014) found that the exogenous land transfer model has greater agricultural benefits than the endogenous land transfer model. Some scholars believe that land transfer cannot increase agricultural operating income but will instead reduce it due to excessively high turnover fees ( Cai et al., 2015). 2) Input of factors of production. Sound agricultural infrastructure can promote the utilization of agricultural factors and income ( Zeng and Li, 2015; Li et al., 2016). The availability of rural public goods and agricultural machinery can alleviate declines in operating income caused by rural aging ( He et al., 2016; Yao et al., 2021). In addition, the use pesticides, agricultural films, fertilizer and division of labor also affect the agricultural per unit yield ( Xiao et al., 2015; Yang and Liu, 2021; Ragasa and Chapoto, 2017), output value and profit, thereby affecting income.

3) Macro policies and institutions. She et al. (2013) found that the land transfer policy can increase the income of farmers, enterprises and the government. Zhou et al. (2017) found that policy subsidies are the main reason for the competitive price advantage of American sorghum. Liu and Wu (2019) found that inappropriate institutional arrangements lead to misallocation of agricultural land resources and reduced agricultural returns. Liu (2020) believes that collective ownership can provide equal land security for collective members, but that long-term contract relationships increase the intergenerational land interest inequality of farmers. Other perspectives also exist; for example, Chen (2019) found that the human capital of farmers increases agricultural operation income, while Lei et al. (2021) found that the internet increases the operating income of such farmers.

There has also been much research on the impact of agricultural machinery on agricultural production ( Deng et al., 2020), which has two main aspects: cost savings and improvements in quality and efficiency. Firstly, rising labour costs are an important cause of the decline in agricultural profitability ( Li et al., 2017). The service price of agricultural machinery is generally lower than labour costs ( Tian et al., 2020). Farmers using agricultural machinery can significantly reduce labour costs ( Yao, 2009; Luo and Qiu, 2021). Furthermore, agricultural machinery can perform land levelling and land preparation, which effectively improve the utilization rate of agricultural resources and reduce the need for weed and insect pest control ( He et al., 2018; Nam et al., 2021). In addition, agricultural mechanization can also carry out combined fertilization and sowing, which not only ensures sowing accuracy but also reduces the cost of seeds and fertilizer ( Liu and Zhou, 2018).

The second aspect is to improve quality and increase efficiency. Such improvements mainly include increased agricultural production and product quality. Agricultural machinery can perform the functions of levelling, land preparation, deep turning and deep scarification ( Aslan et al., 2007), which can improve land quality better than the traditional manual and livestock operation methods, especially in the transformation of medium- and low-yield fields ( Zhou et al., 2019; Peng and Zhang, 2020). Agricultural machinery can increase the degree of multiple cropping of cultivated land to provide the potential for multiple crop cycles per year, thus improving production capacity and land output rates ( Peng et al., 2020; Ji et al., 2021). Mechanical irrigation and drainage, dry farming machinery and mechanical spraying can effectively mitigate risks such as drought, floods, weeds and insect pests ( Berdnikova, 2018). Mechanical sowing and field management can make crop distributions more uniform and promote growth ( Hu and Zhang, 2018), while the use of standardized agricultural machinery can reduce agricultural losses and improve product quality ( Qu et al., 2021). In addition, the scale of land is an important factor restricting the adoption of agricultural machinery. The larger the operating area of farmers, the higher the Frontier of production, and the greater the role of agriculture in boosting agricultural output and increasing income ( Chen, 2015).

Moreover, some studies have shown that the use of agricultural mechanization has an important impact on high-quality agricultural development of agriculture. Liu et al. (2021) used the improved EBM model to measure and analyze the spatial and temporal evolution characteristics of agricultural equipment allocation efficiency in China, and found that the regional differences in agricultural equipment allocation efficiency were obvious, with central and eastern regions of China being higher than the national average and western regions consistently lower than the national average. The high-quality development of agriculture requires continuous improvement of the allocation efficiency of agricultural machinery and equipment, promoting cross-regional coordination and cooperation, and give play to the radiation-driven role of high-efficiency provinces and regions. Chen and Zhang (2021), Xu and Song (2021) believe that strengthening the promotion of advanced agricultural machinery technology is one of the ways to increase the level of green and high-quality agricultural development. Peng et al. (2020) found that agricultural mechanization can improve the level of comprehensive agricultural development by optimizing the agricultural planting structure. Tang et al. (2018) found that the use of agricultural machinery can reduce agricultural production losses, thereby reducing agricultural production costs and promoting the high-quality agricultural development.

There is abundant research on agricultural operating income, some from the perspective of agricultural machinery use. However, it has the following shortcomings. First, although the macro-level data used in existing research can estimate overall agricultural operating income, it is not conducive to analyze its mechanisms. For this, finer-scale farmer household data is more useful. Second, there are different scopes of research on agricultural operating income. Most scholars use indicators of output, output value, income and rate of return, and it is difficult to obtain a complete picture of agricultural operating income by such one-sided analyses. Furthermore, existing analyses do not consider the different effects of agricultural machinery efficiency on cash and food crops. Third, there may be measurement errors in previous analyses, and the threshold effects related to operational scale have not been considered. Based on this, this paper uses data from 1,116 farmers in Hubei Province, China, and IVTobit and threshold models to analyse the impact and mechanisms of agricultural mechanization on agricultural production and income. This provides a reference for the development of agricultural mechanization and income in China.

2.2 Theoretical Analysis and Research Hypothesis

2.2.1 Factor Intensification Path

The use of agricultural machinery can enhance the utilization rate of agricultural factors and reduce the cost of various agricultural production ( Peng et al., 2020). The combined tillage technology with subsoiling as the main part is adopted in the field preparation before sowing, which can replace the traditional operations such as turning, harrowing, ridge raising, stubble planing, and base fertilizer application with only one mechanical operation, thus saving agricultural tools and labor input costs ( He et al., 2018). Mechanical precision sowing technology is adopted in sowing process, which can effectively save seeds and reduce seed cost ( Li et al., 2021). The use of mechanical deep fertilization technology in the fertilization process can apply the fertilizer required for crop production in a fixed proportion, quantitative, fixed position, fixed depth and fixed level to the plough layer soil, so as to avoid the waste of chemical fertilizer application ( Ning et al., 2018). In addtion, higher quality seeds can lead to lower pesticide costs and obtain higher agricultural yields, as well as reduce environmental pollution and lower land restoration costs ( Lu, 2014).

2.2.2 Quality Improvement Path

The functions of agricultural machinery for leveling the ground, deep ploughing and deep loosening can improve the quality of the land more than the traditional human and animal work, thereby maximizing the agricultural output ( Zhou et al., 2019). Moreover, the land management area will affect the use of agricultural machinery, so that there are differences in the income increase of mechanical technological progress in different management areas ( Peng J.Q. and Zhang L.G, 2020). The function of scrambling for planting and harvesting of agricultural machinery will promote the repeated planting of cultivated land and improve the comprehensive agricultural production capacity and land yield rate ( Ji et al., 2021). Furthermore, agricultural machinery has the function of resisting disasters such as droughts and floods, weeds and pests, and mechanical seeding and field management can make crops evenly distributed and grow well ( Hu and Zhang, 2018). Standardized agricultural machinery operation can also reduce agricultural losses and improve product quality ( Li et al., 2019). Agricultural machinery harvesting also allows early access to markets for agricultural products and higher agricultural yields at higher prices, thereby increasing agricultural operating income and contributing to poverty reduction ( Peng et al., 2019). Based on the above theoretical analysis, this paper proposes the following research hypothesis:

Hypothesis 1 Agricultural mechanization can improve agricultural production and income.

Hypothesis 2 Agricultural mechanization has a threshold benefit on agricultural production and income, and has a greater impact on the agricultural operation income of various crops after the threshold.

Hypothesis 3 Agricultural mechanization may promote agricultural production and income through the factor intensification path and the quality improvement path.

3 Materials and Methods

3.1 Sample and Data Source

We used data from a field survey of households in Hubei Province conducted in 2018. The survey obtained the basic information of the household, its natural and physical assets, production and operating conditions, land transfer behaviour, and farmers' knowledge of policy. The research site was in Jianli and Qichun counties of Hubei Province, China, the terrain of the above two counties includes plains, hills and mountains, basically covering all the terrain and crop varieties of Hubei Province. The level of agricultural mechanization in these two counties is 66.75%, which is basically consistent with the comprehensive mechanization level of cultivation, seeding and harvesting of major crops announced in Hubei Province, indicating that these two counties can represent the production of agriculture in Hubei Province to a certain extent. This survey adopted the method of random sampling to select the survey objects, involving 44 villages in 11 towns. There were 26 households investigated in each village, making a total of 1,144 households. After 28 invalid questionnaires were removed, a total of 1,116 valid samples were obtained.

3.2 Definition of Variables

3.2.1 Agricultural Operating Income

In this paper, agricultural operating income refers to the income obtained by farmers by cultivating land. It is mainly measured by the amount of income and rate of return, which are calculated by the net income per hectare and rate of return per hectare, respectively. In order to understand the structure of agricultural operating income sources, the output value and cost of agriculture were also investigated, which were calculated from the average agricultural output per hectare and the average agricultural cost per hectare, respectively. At the same time, considering the different influences of agricultural machinery on different crops, we also considered grain crops and cash crops separately.

3.2.2 Agricultural Mechanization Level

Researchers often measure the level of agricultural mechanization in terms of the total power of the machinery or its net value. These indicators are suitable for measuring the level of agricultural mechanization at the regional level but not at the farmer level. For this, it is more appropriate to use the calculation method of the Ministry of Agriculture and Rural Affairs, which is the weighted average of the machine farming rate, machine seeding rate and machine harvesting rate (with weights of 0.4, 0.3 and 0.3, respectively) at the farmer level ( Peng et al., 2021b). This index is not only easy to obtain but is also more accurate in measuring the machinery usage behaviour of farmers.

3.2.3 Control Variables

Education level per household member ( Xu et al., 2019a). Families with higher education are better at improving their operating income via the rational allocation of resources. Average age of the family labour force ( Xu et al., 2019b). Agriculture is highly technical and requires experience, such that labourers with rich experience in farming gain greater benefits. Percentage of unhealthy people. Unhealthy people reduce the effective supply of family labour and also affect agricultural investment and income due to their high medical expenses. Average working hours of migrant workers. More time taken to travel to work reduces farming time and affects agricultural income ( Liu et al., 2014). Area of farmland. More cultivated land resources allow greater economic benefits from agriculture. Farmland transfer-out area. The more land is transferred out, the lower the economies of scale. Proportion of farmland irrigation. More effectively irrigated farmland allows greater production and better management. Proportion of greenhouse area. As a form of protected agriculture, greenhouses can mitigate environmental risks and guarantee crop production, thereby guaranteeing income. Type of terrain. Plain areas provide better production and operating conditions than other areas, resulting in higher income.

3.3 Descriptive Statistical Analysis

Table 1shows the descriptive statistics of each variable, the total output value of agriculture is higher than the total agricultural cost, and the cost of cash crops is much higher than the cost of grain crops. The output value of grain crops is much higher than the output value of cash crops, which makes the profit and rate of return of grain crops higher than those of cash crops. The level of agricultural mechanization is 66.75%, which is slightly lower than the comprehensive mechanization level of major crops reported for Hubei Province in 2018. The mean education level per household member is 6.3 years, indicating that most rural households have a low education level. The mean age of the family labour force is 40 years, indicating that the current rural labour force is generally comprised of older people. The mean percentage of unhealthy people is 12.73%, which is a significant proportion. The mean working hours of migrant workers is 49 days with a large standard deviation, which indicates that there are large differences in labour force allocation among households. The average farmland area is 0.375 hm ², and the average transfer-out area is 0.027 hm ². The average proportion of farmland irrigation is 35.83%, indicating that the effective irrigated area still needs to be improved. The average proportion of greenhouse area is 0.1, which indicates that built-up agriculture is not yet popular in rural areas. The mean terrain type value is 0.33, indicating that 1/3 of the sample was in a plain area. The mean level of household savings is 9.429, indicating that the average savings of each family is 12,444 yuan.

3.4 Model

The model used to investigate the impact of agricultural mechanization on income can be written as:

Where i denotes farmers and is an explanatory variable that includes the cost, output value, profit and rate of return per hectares for the total crop, grain crop and cash crop. refers to the level of agricultural mechanization, is a control variable, is random perturbation term, and are parameters to be estimated.

There may be a mutual, causal, endogenous relationship between the level of agricultural mechanization and income. Increased mechanization may increase returns, which gives farmers more capital to use machinery. In addition, some unobservable economic variables at the village level may also affect farming income, leading to the problem of model endogeneity due to omitted variables. This article adopts the instrumental variable method to solve this problem. In Eq. 2, the endogenous variable is , and the first stage of estimation by two-stage least-squares (2SLS) is:

Where is a set of instrumental variables and is a random error term.

In this paper, two instrumental variables are selected. One is the level of household savings. As the use of agricultural machinery costs money, households with more savings are better able to use it, and household savings do not affect agricultural income. therefore, the level of household savings satisfies the requirement of exogeneity of the instrumental variable. The second is the level of agricultural mechanization at the village level. When micro-level data analysis is adopted, data aggregated at the regional level can be used to calculate the instrumental variables of the model ( Cadr and Krueger, 1996; Staiger and Stock, 1997). This paper used the level of agricultural mechanization at the village level as the instrumental variable. Generally speaking, this variable will not affect the income of a single family, but may affect the probability of a single farmer using agricultural machinery. When the level of agricultural mechanization in the village is high, individual farmers have greater probability of using agricultural machinery. thus, the level of mechanization at the village level also satisfies the exogeneity of the instrumental variables.

Considering that there are more instrumental variables than endogenous ones, it is necessary to carry out over-identified tests and weak instruments tests on the instrumental variables used in this model to ensure their validity.

A Tobit model was also used to solve the problem of sample selection bias that may appear in the model. Its expression is as follows:

To ensure that the real impact of the level of mechanization on operating income can be estimated, it is necessary to solve both the endogeneity and sample selection bias problems mentioned above. For this, a sample-corrected 2SLS estimation method, the sample-corrected endogenous merging model (IVTobit), can be used with the following steps:

In the first stage, the residual term is estimated using the Tobit model with as the dependent variable and and as the independent variables. Then, it is brought into Eq. 1 to obtain:

In the second stage, the 2SLS regression of Eq. 5 with as the dependent variable is a non-zero sample, and the required estimated parameters can be obtained.

4 Empirical Results

1. Benchmark Regression of the Impact of Agricultural Mechanization Level on Agricultural Operating Income

To analyse the impact of agricultural mechanization level on agricultural operating income in detail, this paper set dependent variables from a cost-benefit perspective. The dependent variables in columns 1) to 3) in Table 2 are total agricultural cost, grain crop cost and cash crop cost, and the dependent variables in columns 4) to 6) are total agricultural output value, grain crop output value and cash crop output value, respectively. The dependent variables in columns 1) to 3) in Table 3 are total agricultural profit, total grain crop profit and total cash crop profit, and the dependent variables in columns 4) to 6) are total agricultural rate of return, rate of return of grain crops and rate of return of cash crops, respectively.

The cost-regression model indicates that the level of agricultural mechanization has a significant positive impact on all types of agricultural costs, indicating that increases in mechanization increase the cost of production, with the increase in the cost of cash crops being the greatest. Each unit increase in the level of agricultural mechanization increases the costs of grain and cash crops by 0.273 and 2.183 units, respectively. The possible reason is that the more complicated operation and higher cost of using machinery for cash crops, and it is more difficult to exert its scale effect than grain crops, resulting in increasing their production cost. In terms of control variables, the education level per member of household, average age of family labour force, area of farmland, proportion of farmland irrigation, proportion of greenhouse area, and type of the terrain all have significant negative impacts on various types of agricultural costs, with a greater impact on the cost of cash crops. The percentage of unhealthy people, average working hours of migrant workers and transfer-out area of farmland all have significant positive impacts on all types of agricultural costs and have the greatest effect on the cost of cash crops.

From the regression model of output value, the level of agricultural mechanization has a significant positive impact on various agricultural output values, indicating that the use of agricultural machinery can indeed improve the production efficiency of crops. For every 1 unit increase in the level of agricultural mechanization, the total agricultural output value, grain crop output value and cash crop output value increase by 1.813, 4.737 and 1.597 units, respectively. The use of agricultural machinery has the greatest impact on grain crop output; it is likely influenced by the economies of scale, since grain crops are planted on a larger scale than cash crops. In terms of control variables, the percentage of unhealthy people, average working hours of migrant workers and transfer-out area of farmland all have significant negative impacts on the output value of each type of agriculture. The education lever per member of household, the average age of family labour force, the area of farmland, the proportion of farmland irrigation, the proportion of greenhouse area, and the type of the terrain all have significant positive impacts on the output value of each type of agriculture, and all of them have the greatest effect on the output value of grain crops.

Table 3 shows the results of the baseline regression of agricultural mechanization level on various types of agricultural profit and rates of return. The dependent variables in columns 1) to 3) are total agricultural profit, grain crop profit and cash crop profit, and in columns 4) to 6) they are total agricultural rate of return, rate of return of grain crops and rate of return of cash crops, respectively.

From the regression model of profit, the level of agricultural mechanization has a significant positive impact on the income of each type of agriculture and, for each unit increase in the level of agricultural mechanization, the total agricultural profit, profit of grain crops, and profit of cash crops increase by 1.597, 2.079 and 1.476 units, respectively, indicating that agricultural machinery can significantly increase the income of agricultural operations, with the greatest increase being for grain crops. This may be due to the differential effects of agricultural machinery on the income obtainable from cash crops and grain crops. The regression above shows that agricultural machinery is more likely to increase the cost of cash crops, but to a lesser degree than grain crops. The combined effect of these two aspects leads to greater income as agricultural machinery allows the expansion of grain crops. The direction of influence of the estimated coefficients of the control variables is also largely consistent with that of the output value regression model. From the regression model of rate of return, the level of agricultural mechanization has a significant positive effect on the income rate of each type of agriculture, with each unit increase in the level of mechanization increasing the total agricultural rate of return, rate of return of grain crops, and rate of return of cash crops by 0.364, 0.389 and 0.279 units, respectively. This indicates that the use of agricultural machinery is more likely to increase the income from grain crops. The direction of influence of the estimated coefficients of the control variables is also basically the same as that of the income value regression model and will not be repeated.

2. Endogeneity Test of the Regression Model

To resolve the possible sample selection errors and endogenous problems in the model, the instrumental variables, level of household savings and level of agricultural mechanization at the village level were brought into the IVTobit model. The results of the regressions are shown in Tables 4, 5; the first stages of the IVTobit regressions are excluded to save space. Considering that agricultural operating income involves multiple dependent variables, there will be many endogenous regression models and would take a lot of space to list the test results for the instrumental variables of all endogenous regression models. Therefore, the paper only reports the results of the instrumental variables when the dependent variable is agricultural profitability. To ensure the exogeneity of the instrumental variables in the model, they were subjected to overidentification tests. The p-value of the transitional identification test is 0.861, which indicates that the model cannot reject the original hypothesis that the instrumental variables are exogenous. A weak instrumental variable test was further conducted. The value of the F-test statistic of the first-stage instrumental variable of the two-stage least squares is 128.37, which meets the standard recommended by Staiger and Stock: that the F-test value is greater than 10 ( Hansen, 2000). This shows that there is no weak identification problem of the instrumental variables in the model, and also proves that the level of household savings and level of agricultural mechanization at the village level are not weak instrumental variables ( Stock and Yogo, 2005). In addition, considering that the number of instrumental variables in the model is greater than the number of endogenous variables, it is necessary to check whether the instrumental variables are redundant. The results of the redundancy test showed that the p-values of household savings level and agricultural mechanization level were 0.001 and 0.000, respectively, indicating that the null hypothesis-that two instrumental variables were redundant instrumental variables-is rejected.

The above tests show that the two selected tool variables meet the requirements of the model. Although the processes of the Tobit and 2SLS estimation of agricultural mechanization level on total agricultural income are not listed in this paper, the estimation results show that the coefficients of agricultural mechanization level in both the Tobit and 2SLS regressions are greater than the estimated coefficients of OLS, indicating that there is endogeneity and sample selection bias in the model and that the IVTobit results are the most reliable. From columns 1) to (3), it can be seen that for each unit increase in the level of agricultural mechanization, the total agricultural cost, grain crop cost and cash crop cost increase by 0.738, 0.548 and 5.51 units, respectively. The increase in cash crop cost is still the most significant and the estimated coefficient of IVTobit is several times greater than that of OLS. From columns 4) to 6), it can be seen that for each unit increase in the level of agricultural mechanization, the total agricultural output value, grain crop output value and cash crop output value increase by 5.479, 8.543 and 2.521 units, respectively. It is found that the grain crop output increases the most and the estimated IVTobit coefficient also increases significantly. This shows that the sample selection bias and endogeneity problems in the model have been resolved.

The IVTobit model was further used to estimate the impact of the level of agricultural mechanization on agricultural profit and rate of return ( Table 5). It can be seen from columns 1)-3) that for each unit increase in the level of agricultural mechanization, the total agricultural profit, grain crop profit and cash crop profit increase by 3.143, 5.479 and 1.694 units, respectively. The increase in grain crop profit is the greatest, and the IVTobit estimation coefficient is also significantly higher than that of OLS. From columns 4) to (6), it can be seen that for every unit increase in the level of agricultural mechanization, the total agricultural rate of return, and those of grain crops and cash crops, increase by 1.215, 1.594 and 0.435 units, respectively, with the greatest effect being on the increase in grain crop rate of return. So far, this paper has largely confirmed that the level of agricultural mechanization increases agricultural profit, more so for grain crops. The possible explanation for this is that grain crops have strong economies of scale and greater availability of suitable agricultural machinery, while cash crops have limited scales of operation and less suitable machinery that is more costly. Therefore, agricultural machinery is likely to increase the production efficiency of grain crops.

3. Heterogeneity Analysis of Agricultural Mechanization Level on Agricultural Operating Income

Due to the relatively high cost of using agricultural machinery, its overall benefits do not always increase with the level of mechanization. When the scale of cultivated land is small, the high cost of using agricultural machinery may weaken its benefits. When farmland reaches a certain scale, the benefits of land-scale operation are sufficient to offset the cost of machinery. Therefore, the effect of agricultural machinery use may vary according to the area of cultivated land. In other words, there is an inflection point in the relationship between farmland area and the influence of mechanization level on operating income. The impact is small before the inflection point and larger after it. The key to solving the above problem is to determine the inflection point and then test the heterogeneity effect before and after the inflection point. The threshold estimation method can accurately determine the threshold value and, considering that the data used in this article are cross-sectional, Hansen's cross-sectional data threshold estimation method can be used to determine the threshold value. This can be combined with graphical analysis to show the trend in inflection point changes ( Hansen, 2000).

4.1 Threshold Analysis

In this study, the area of farmland was taken as the threshold variable. The threshold effect model estimated a threshold value of 0.28 hm ². Then, the LM test method was used to verify whether the model had a threshold effect. The LM statistic was 218.97 after 500 bootstraps with a p-value of 0.000, indicating that there was indeed a threshold effect in the model. In addition, a graphical representation of the inflection point changes is shown in Figure 1. The inflection point of the likelihood rate series curve is obvious at 0.28 hm ², where the likelihood rate is far below the 95% threshold (dashed line in the figure), so it can be judged that 0.28 hm ² is the threshold value of the model.

To ensure the robustness of the threshold estimation, control variables were gradually introduced into the model. Their validity was verified by comparing the threshold values estimated by different models. The result is shown in Table 6. The control variables were divided into three categories: basic household characteristics, production and operation characteristics, and regional variables. The basic household characteristics variables included education level per household member, average age of family labour force, and percentage of unhealthy people, while the production and operation characteristics variables included the average working hours of migrant workers, area of farmland, transfer-out area of farmland, proportion of farmland irrigation, and proportion of greenhouse area. The control variables were added into the threshold model in batches and it was found that with increases in the control variables, the threshold value tended to be more stable and the p-value became more significant. Finally, the threshold value of cultivated land area estimated by the threshold effect model was 0.28 hm ². Therefore, it can be considered that the estimated threshold value is a credible "convergence point" when all control variables are added to the model.

4.2 Estimation of the Threshold Effect of the Level of Agricultural Mechanization on Agricultural Income

After determining the threshold value, the sample was divided into two subsamples according to the threshold value (≤0.28 and >0.28) in order to examine the effect of the level of agricultural mechanization on the agricultural profit and rate of return before and after the threshold value. The results of the IVTobit model are shown in Tables 7, 8. Table 7 shows the estimate of the threshold effect of the level of mechanization on the profits of various crops. From the total agricultural profit model, the regression coefficients of mechanization level before and after the threshold are different. For each unit increase in mechanization level, the total profit before and after the threshold increase significantly, by 1.071 and 4.1 units, respectively. This indicates that increasing the level of agricultural mechanization will increase the total income of farmers with large operating areas, which basically confirms the validity of the threshold value. From the perspective of the profit of grain crops, each unit increase in the level of mechanization significantly increases the profit by 3.047 and 6.002 units before and after the threshold, respectively. For cash crop profit, each unit increase in the level of mechanization significantly increases the profit by 1.043 and 2.002 units before and after the threshold, respectively.

Table 8 shows the estimated threshold effect of agricultural mechanization level on the rate of return of various crops. For the model of the total agricultural rate of return, each unit increase in the level of mechanization significantly increases the total agricultural rate of return before and after the threshold by 0.897 and 1.446 units, respectively. This indicates that machinery is more conducive to increasing the total agricultural rate of return after the threshold. For grain crops, each unit increase in the level of mechanization significantly increases the rate of return by 0.928 and 2.248 units before and after the threshold, respectively. For cash crops, each unit increase in the level of mechanization significantly increases the rate of return before and after the threshold by 0.147 and 0.501 units, respectively. These coefficients are smaller than those of grain crops before and after the threshold. It can be seen that the level of mechanization has heterogeneous effects on operating income. Firstly, in terms of the scale of household farming, the use of machinery is more likely to increase the profit and rate of return of farmers with larger farms. The threshold value of the operating scale tested in this paper is 0.28 hm ²; in other words, farmers who exceed the operating threshold area will have higher incomes. Second, grain machinery is better developed and more available and cost-effective. Therefore, it can more easily increase the profit and rate of return of grain crops.

4. Mechanism of the Effect of Agricultural Mechanization Level on Agricultural Operating Income

The above section confirmed that the level of agricultural mechanization indeed improves agricultural operating income. This section analyses the mechanisms underlying this effect. Table 9 shows the results of the mechanism of action test. Considering the simplicity of the test process, only the total agricultural rate of return was selected as the target variable of agricultural operating income in the model. In general, machinery can effectively improve the yield and quality of crops and this paper also analyses these two paths. The first is the factor-intensification path, in which the use of agricultural machinery can effectively increase labour productivity and land productivity. Obtaining more agricultural products will spread out the cost of agricultural production, thus enhancing the utilization rate of agricultural production factors and increasing income. Therefore, the first mechanism variable chosen is the factor utilization rate, using agricultural output divided by agricultural cost; the second is the quality improvement path. The use of machinery can effectively increase the degree of standardization of agricultural production. Standardized production improves the quality of agricultural products and, hence, their sales price and resulting income. Therefore, the second mechanism variable chosen was the price of agricultural products. Considering the wide variety of agricultural products, this paper uses the average price of agricultural products for measurement.

From the factor-intensification path test, column 2) in Table 9 shows that the level of mechanization has a significant positive effect on the utilization rate of agricultural factors. Column 3) shows that the utilization rate has a significant positive effect on the total agricultural rate of return. Since the estimated coefficients of mechanization level and the utilization rate of agricultural factors in columns 1) to 3) pass the significance test, it indicates that factor utilization rate plays a partial intermediary role in the model, with a 28.8% mediation effect. It also shows that the effect of mechanization in increasing yield improves the utilization rate of agricultural factors, thus improving income. The quality improvement path test in column 4) shows that the level of mechanization significantly improves the quality of agricultural products. After controlling for the level of mechanization, the price of agricultural products still has a significant positive impact on the total income. The estimated coefficients of the level of mechanization and price of agricultural products in columns 4) and 5) both pass the significance test, indicating that the price of agricultural products plays a partially mediating role in the model with an effect size of 27.4%. This also indicates that the effect of mechanization level on improving the quality of agricultural products increases their price and, thus, income.

5 Conclusions and Policy Insight

Improving agricultural operating efficiency is not only an effective means to increase farmers' income and agricultural efficiency, but is also the essence of the Rural Revitalization Strategy. Based on a field survey of 1,116 farmers in Hubei Province in 2018, this paper analysed the impact of the level of agricultural mechanization on agricultural production and income by using a sample-modified endogenous consolidation model (IVTobit) and threshold-effect model. The results show that 1) the level of agricultural mechanization has a significant positive impact on the production cost, output value, income and yield of all kinds of crops. For every unit increase in the level of mechanization, the costs of all crops, grain crops and cash crops increase by 0.738, 0.548 and 5.51 units, the output values increase by 5.479, 8.423 and 2.521 units, the incomes increase by 3.143, 5.479 and 1.694 units, and the rate of return rise by 1.215, 1.594 and 0.435 units, respectively. 2) From the perspective of heterogeneity analysis, there is an obvious threshold effect of mechanization level on income, with a threshold value of 0.28 ha. Subject to scale effects, the mechanization level has a greater impact on the agricultural operation benefits of various crops after the threshold. 3) The action mechanism test showed that mechanization can increase income via the factor intensification path and quality improvement path. The partial mediating effects of the factor utilization rate and agricultural product price on the agricultural total return rate are 28.8 and 27.4%, respectively.

To sum up, this paper found that agricultural mechanization can effectively improve agricultural income. Increasing income is a basic requirement for the high-quality development of agriculture. We should give full play to the positive role of agricultural machinery in increasing agricultural efficiency and farmers' income. Improving the level of mechanization is also an effective way to promote agricultural modernization. First, we should continue to increase subsidies for the purchase of agricultural machinery, improve and optimize the levels of agricultural machinery and equipment, increase subsidies for machines and tools to overcome weak links in grain production and cropping in hilly and mountainous areas, promote green intelligent agricultural machinery, promote the scrapping and upgrading of agricultural machinery, and transform farmland to make it suitable for mechanization. Second, we should accelerate the research and development and popularization of machinery related to major cash crops, reduce the usage cost of cash crop machinery, and gradually mechanize the whole process of cash cropping, from sowing to field management to harvesting. Third, we should promote the development of socialized service organizations for agricultural machinery operations, improve the standard system of socialized service for agriculture, accelerate the improvement of agricultural socialization service levels, form a new pattern of diversified and shared socialized services for agriculture, and improve the utilization rate of agricultural machinery and level of mechanized operation. Fourth, we should give full play to agricultural cooperatives in the promotion of mechanization, deepen the integration of agricultural machinery and agronomy, vigorously promote advanced and second-round exams, promote intelligent, green and novel agricultural machines and tools, improve farmers' ability to apply new agricultural machinery technologies and new machines and tools, and provide strong scientific and technological support for agricultural transformation and upgrades, and quality and efficiency improvement.

This study also has some limitations. Subject to the sample size, this paper did not subdivide the varieties of grain crops and cash crops. The level of mechanization may have different effects on the operating incomes of different grain and cash crops. However, this paper also has some progress, that is, it avoids analyzing only the total cost or total income of agriculture in previous studies, and considers the threshold effect of agricultural machinery use, which is more meaningful for analyzing the mechanism of action and policy making. In addition, this paper did not investigate the impacts of different agricultural machinery operations on income and it may be conducive to policy-making to explore these impacts. Therefore, survey data with a larger sample size is needed to analyse the above problems in future.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author Contributions

PJ conceived of the idea for this study. PJ and ZZ conducted the statistical analysis. LD contributed to the final write-up.

Funding

This research was funded by the National Natural Science Foundation of China (No. 72063012), 2020 ministry of Education Humanities and Social Science Research General Project (No. 20YJC790103) and 2021 Science and Technology Research Project of Jiangxi Provincial Department of Education (No. GJJ210282).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The valuable comments and suggestions of editors and reviewers are gratefully acknowledged.

References

Peng, J., Chen, J., Su, C., Wu, Z., Yang, L., and Liu, W. (2021a). Will Land Circulation Sway "Grain Orientation"? The Impact of Rural Land Circulation on Farmers' Agricultural Planting Structures. PLOS ONE 16, e0253158. doi:10.1371/journal.pone.0253158

PubMed Abstract | CrossRef Full Text | Google Scholar

Stock, J. H., and Yogo, M. (2005). Testing for Weak Instruments in Linear IV Regression. Cambridge: Cambridge University Press. doi:10.1017/CBO9780511614491.006

CrossRef Full Text | Google Scholar

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Impact of Agricultural Mechanization on Agricultural Production, Income, and Mechanism: Evidence From Hubei Province, China

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Increasing agricultural operating income is not only an important step in improving agricultural work for farmers in the new era, but is also a powerful way to promote rural revitalization. To improve our understanding of the high-quality development of agriculture in China, the factors limiting agricultural income and the impact of the level of agricultural mechanization on agricultural production and income and its mechanism were analysed.

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Machinery such as tractors and combine harvesters have made a very big difference. A farmer can now cultivate a much greater area of land in a much shorter time than using manual labor and animals to pull machinery.

Ploughing with oxen and a simple wooden plough is very slow, and the plough cannot go very deep into the soil.

A tractor allows a farmer to plough much more land and to plough more deeply than without machinery

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This article compares chemical and organic fertilisers, specifically defining each of their qualities, nutrients and other aspects.

Chemical fertilisers are inorganic materials of entirely or partially synthetic origin that are added to soil to improve plant growth. Contrastingly, organic fertilisers are substances from remains or by-products of organisms such as manure.

Chemical fertilisers provide the three essential nutrients: nitrogen, phosphorus and potassium. However, organic fertilisers may only have one of the mentioned nutrients, or small amounts of each.

Organic fertilisers are generally more expensive than chemical fertilisers, as chemical fertilisers have a much higher concentration of nutrients per weight. While organic fertilisers can be homemade, it takes more effort and cost.

Chemical fertilisers are often favoured compared to organic fertilisers, due to its lower cost, higher nutrient concentration and immediate supply of nutrients, resulting in higher crop yield. However, high acid content in some chemical fertilisers can harm human skin and change soil fertility, ultimately harming the process of crop growth long-term.

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Comparison chart

Chemical Fertilizer

Organic Fertilizer

current rating is 3.48/5

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(888 ratings)

current rating is 4.17/5

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(961 ratings)

About

A chemical fertilizer is defined as any inorganic material of wholly or partially synthetic origin that is added to the soil to sustain plant growth. Chemical fertilizers are produced synthetically from inorganic materials. Since they are prepared from inorganic materials artificially, they may have some harmful acids, which stunt the growth of microorganisms found in the soil helpful for plant growth naturally. They're rich in the three essential nutrients needed for plant growth. Some examples of chemical fertilizers are ammonium sulphate, ammonium phosphate, ammonium nitrate, urea, ammonium chloride and the like.

Organic fertilizers are substances that are derived from the remains or by products of organisms. Organic fertilizers depend upon the microorganisms found in soil to break them down and release the essential nutrients. Organic nutrients are rich in phosphorous, nitrogen, and potassium, but in unequal proportions. Examples of organic fertilizers are cottonseed meal, blood meal, fish emulsion, and manure and sewage sludge. There are two types of organic fertilizers: first is the synthetic type which is organic compound produced artificially (e.g., Urea, a common organic fertilizer; the other type is natural organic fertilizers because 100% of the ingredients used to create a typical natural organic fertilizer come from nature (e.g., fish extract, seaweed and manure, guano, and compost materials).

Distribution of nutrients

Fertilizers are used to provide nutrients to the plants for their good growth. Soil nutrients' deficiency is the prevalent problem among home-garden owners. One of the distinct advantages of chemical fertilizers over organic fertilizers is that chemical fertilizers are rich equally in all three essential nutrients: nitrogen, phosphorous, and potassium. On the other hand, organic fertilizers may be rich in one of the three nutrients, or may have low levels of all the three nutrients.

Cost

Organic fertilizers are generally much more expensive than chemical fertilizers, mostly because chemical fertilizers have more concentrated levels of nutrients per weight of product than organic fertilizers do. One needs several pounds of organic fertilizer to provide the same soil nutrient levels that a single pound of chemical fertilizer provides, and the higher cost of organic fertilizer is one of the biggest reasons that organic produce is more expensive than non-organic. (The other big reason being lower organic yields, on average.) Although it is possible to make a lot of one's own organic fertilizer as well, once the labor, time, and other resources are accounted for, homemade organic fertilizer is usually more expensive than store-bought chemical fertilizer too.

Supply of nutrients

One aspect of the organic fertilizers is their slow-release capability. Slow-release capability of organic fertilizers has both advantages and disadvantages: Slow-release means there is less risk of over-fertilization but sometimes this slow-release of organic fertilizers is not able to fulfill to needed supply of the nutrients, whenever required. In contrast to organic fertilizer, chemical fertilizers are always there to provide immediate supply of nutrients to plants if situation demands.

Acid content

One of the main disadvantages of chemical fertilizers is that, in contrast to organic fertilizers, several chemical fertilizers have high acid content like sulfuric acid and hydrochloric acid. This high acid content results in the destruction of the nitrogen-fixing bacteria, which is helpful in supplying the nitrogen to a growing plant. In contrast, organic fertilizers support the growth of nitrogen-fixing bacteria..

NPK ratio

Chemical fertilizers always have a high total NPK (nitrogen:phosphorous:potassium), from 20 to 60 percent or more. The total NPK for organic fertilizer blends will always be low. Fourteen percent is about as high as it gets.

Natural fertilizers like manure have been in use for centuries as these were the only form of nutrition that could be provided to crops before the invention of chemical fertilizers. Chemicals were added to natural fertilizers after the second world war. Post the war, with advancement of technology there was an explosive growth in artificial fertilizers due to improved productivity. But of late, there has been mass awareness of the eco friendliness of the use of organic fertilizers and many are using those methods again.

Use

There are two ways to measure fertilizer use in a country. One is by nutrient content - how much nitrogen, phosphate and potash are contained in the fertilizer applied. In fiscal year 2004, 23.4 million tons of nutrients were applied in the US. Another way to measure is in total tonnage - the total tons it takes to deliver the nutrient content. In fiscal year 2004, 57.8 million total tons were used in the United States. The world's largest producers and users of fertilizers are the United States, China, India, Russia and Brazil.

Some reports suggest the US Fertilizer market to be around $40 Billion of which organic fertilizers occupy only about $60 Miliion. The rest of it is the share of the various artificial fertilizers.

References

# data[].attributes.attachment_link.title
Chemical Fertilizer vs Organic Fertilizer

# data[].attributes.attachment_link.description
Chemical Fertilizer vs Organic Fertilizer comparison. A chemical fertilizer is defined as any inorganic material of wholly or partially synthetic origin that is added to soil to sustain plant growth. Organic fertilizers are substances that are derived from the remains or byproducts of natural organisms which...

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This article goes into detail about the need for chemical fertilisers, how they work, its damaging effects and alternatives for improving yield.

Chemical fertilisers are used to treat nutrient deficiency in soil, in order to boost crop quality and raise crop yields. They improve crop yield by increasing the growth and vigor of plants. High inputs of chemical fertilisers are due to the increased demand for food. Crops grown on weak soil may have micronutrient deficiencies and low tissue concentrations, so chemical fertilisers are often used to prevent this.

However, disproportionate use of chemical fertilisers heavily damages the environment. Chemical fertilisers cause plants to develop unhealthy characteristics, and produce by-products that cause air and water pollution. Constant use of chemical fertilisers also cause soil contamination, compaction and degradation. In result, crop yields are actually decreased over time, in contrast to the original purpose of chemical fertilisers.

Alternatives to chemical fertilisers can be used to combat these issues while providing better conditions for crops. Substitutes such as organic manure can offer essential nutrients and improve soil quality for crops, and biofertilizers can also increase crop yield by supplying sufficient nutrients.

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Excessive and Disproportionate Use of Chemicals Cause Soil Contamination and Nutritional Stress

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Open access peer-reviewed chapter Written By Nikita Bisht and Puneet Singh Chauhan DOI: 10.5772/intechopen.94593 From the Edited Volume Edited by Marcelo L. Larramendy and Sonia Soloneski Incredible achievements have been made in agricultural production worldwide, but many daunting challenges remain unresolved to ensure food security and environmental sustainability.

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ii. Disadvantages/ issues of selective breeding--Broiler Chickens

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ii. Disadvantages/ issues of selective breeding--Broiler Chickens

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https://www.chicken.org.au/selective-breeding-why-is-it-important-and-what-does-it-mean/
https://zootecnicainternational.com/poultry-facts/the-broiler-chicken-as-a-signal-of-a-human-reconfigured-biosphere-2/

chickens quickly gain weight--> more livestock

suffer from cardiovascular problems, sores etc.

chickens are put in extreme conditions such as windowless buildings, cramped spaces

University of Guelph, Ontario

16 chicken breeds with different growing rates

generally want faster growing rates for more chicken meat

spends more time sitting--> leg sores, less movement

muscle damage ("wooden breast": muscle hardening and paleness--> "downgraded livestock)

Better Chicken Commitment:

"chickens more space and stimulating environments, raise slower-growing breeds, and use more humane slaughter techniques"

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New Study Confirms Inhumane Breeding of Broiler Chickens

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A new study released in September by the University of Guelph, Ontario, shows decisively that this genetic manipulation comes at a great physical cost to the birds.

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i. Benefits of Selective breeding-- Wheat 

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i. Benefits of Selective breeding-- Wheat 

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487 wheat genotypes are tracked, each with their own special traits.

"hybridisation and gene flow events"

Bread wheat(Triticum aestivum)

10,000 years ago

High yealding varieties available

can be adapted to withstand different climates and weathers

genetically modifying Triticum aestivum today

study of 487 wheat genotypes

allowing scientiests to study the origins, "effects of range expansions"

""pan genome" of wheat -- the description of all the genes and genetic variations within wheat" --> can be used to modify wheat in different environments (yield and disease resistance +global warming)

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Since the Agricultural Revolution about 12,000 years ago, humans have been selectively breeding plants with desirable traits such as high grain yield and disease resistance. Over time, Triticum aestivum, otherwise known as bread wheat, has emerged as one of the world's most important crops. Together with the growing human population and the changing climate, the demand for wheat with a higher yield and additional resilience is increasing.

However, for a few years now the average yield increase of wheat is stagnating. In a new international study, the genetic diversity of 487 wheat genotypes originating from large parts of the world has been catalogued and contextualised with agronomic traits. The map of this rich pool of genetic diversity in bread wheat highlights our current knowledge of the ancestry of wheat and opens new avenues within modern selective wheat breeding.

The evolution of wheat is a complex history of hybridisation and gene flow events, which led to the allohexaploid (with six sets of chromosomes) Triticum aestivum, the species of wheat that we know nowadays as bread wheat. The modern bread wheat originated in the Fertile Crescent about 10,000 years ago and its genepool has been shaped by humans as a result of domestication and cultivation. Today, high-yielding varieties of Triticum aestivum can be found all over the world, each variety adapted to the particular environment it is being grown in, making wheat one of the world's three most important crop species for human calories and protein supply.

The growing demand for wheat, the onset of global warming, and the transitioning of Western farming away from intensive agriculture, are exerting pressure on plant breeders to further adapt and improve modern bread wheat species. However, in order to select and breed new wheat cultivars with new and improved traits, plant breeders require plants with genetic variation for selection and combination during the breeding process. A new international study of bread wheat has now revealed knowledge of an extensive and rich gene pool for future breeding improvements of Triticum aestivum.

In this study, the exomes of 487 wheat genotypes from 68 countries around the world, including landraces, cultivars, as well as modern varieties, were sequenced. The Leibniz Institute of Plant Genetics and Crop Plant Research (IPK Gatersleben) was able to contribute to this by providing wheat samples from the Federal Ex situ Gene Bank. Utilising the Refseqv1.0 reference sequence of the bread wheat landrace "Chinese Spring," which had been published by the "International Wheat Genome Sequencing Consortium" (IWGSC) in 2017, the collaborating researchers were able to compile a comprehensive overview of wheat genomic diversity at the genic, chromosomal and subgenomic levels. This enabled them to refine and expand the model of wheat evolution and to decipher the genetic origins of modern day wheat species. As such, the durum wheat lineage was confirmed as the most likely ancestor of today's bread wheat cultivated germplasm. Moreover, by investigating the selection footprints of wheat, the scientists showcased the effects of range expansion and allelic variants selected since the beginning of wheat domestication.

The reported data is another step towards the assembly of the "pan genome" of wheat -- the description of all the genes and genetic variations within wheat, which will be a valuable resource for plant researchers and wheat breeders alike. However, the study as it stands already reveals a rich genetic data resource, which can be utilised for improving genetic traits in bread wheat, from environmental adaptation to improved yield and disease resistance. Moreover, the results illustrate our current knowledge of the ancestry of bread wheat, highlighting our cultural history as farmers and plant breeders.

Journal Reference:

Caroline Pont, Thibault Leroy, Michael Seidel, Alessandro Tondelli, Wandrille Duchemin, David Armisen, Daniel Lang, Daniela Bustos-Korts, Nadia Goué, François Balfourier, Márta Molnár-Láng, Jacob Lage, Benjamin Kilian, Hakan Özkan, Darren Waite, Sarah Dyer, Thomas Letellier, Michael Alaux, Joanne Russell, Beat Keller, Fred van Eeuwijk, Manuel Spannagl, Klaus F. X. Mayer, Robbie Waugh, Nils Stein, Luigi Cattivelli, Georg Haberer, Gilles Charmet, Jérôme Salse. Tracing the ancestry of modern bread wheats. Nature Genetics, 2019; 51 (5): 905 DOI: 10.1038/s41588-019-0393-z

Cite This Page:

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food and agriculture; food; evolutionary biology; agriculture and food; environmental policy; environmental awareness; exotic species; ecology

# data[].attributes.attachment_link.title
New avenues for improving modern wheat: Ancestry and genetics of bread wheat mapped in a global study

# data[].attributes.attachment_link.description
Since the Agricultural Revolution about 12,000 years ago, humans have been selectively breeding plants with desirable traits such as high grain yield and disease resistance. Over time, Triticum aestivum, otherwise known as bread wheat, has emerged as one of the world's most important crops.