climate change impact on animal agriculture essay

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How Much Does Animal Agriculture Contribute to Climate Change?

Animal agriculture is responsible for 16.5 percent of all global greenhouse emissions.

Explainer • Climate • Food

Words by Rachel Graham

Climate change is no longer a crisis of the future. Here’s what we are seeing now — soaring temperatures and extreme drought are killing farmed animals and decimating harvests across the globe. Climate scientists and organizers have long been pleading with world leaders for change. That makes sense — most solutions must come from governments and institutions — but there is also a simple and powerful form of action available for individuals right now — dietary change.

Eating less beef and more plants, along with wasting less food , are some of the most effective forms of household climate action, according to Project Drawdown. It order to appreciate why dietary change can be so effective, it helps to understand the link between animal agriculture and climate change.

Does Animal Agriculture Contribute to Climate Change?

Over 70 billion land animals are raised and killed every year for food in processes that cause extreme environmental damage.

The animal agriculture industry is currently one of the largest producers of greenhouse gases in the world, making it a serious contributor to the environmental destruction we’re now seeing unfold. 

How Does Animal Agriculture Contribute to Climate Change?

Greenhouse gases like carbon dioxide, methane and nitrous oxide trap heat from the sun and prevent it from being released into space, causing damage to the atmosphere.

Methane gas is one of the most potent of these greenhouse gases, as it’s extremely effective at trapping heat. The focus on limiting greenhouse gas emissions tends to be on carbon dioxide emissions, which stay in the atmosphere for longer, but over a timescale of 100 years methane is 28 times more powerful. And one of the largest sources of global methane emissions is farmed animals, specifically cattle. 

Cows, sheep and goats all emit significant amounts of methane gas when they belch, a byproduct of their ruminant digestive system, in a process called enteric fermentation. Methane is released into the atmosphere from these belches (and also from dairy and hog manure), where it adds to already dangerous levels of greenhouse gases.

Another greenhouse gas produced in large quantities by the animal agriculture industry is nitrous oxide. This gas, which is 273 times more effective at trapping heat in Earth’s atmosphere than carbon dioxide over the course of 100 years, is released by the processing of animal manure. With such large numbers of animals being bred and raised for food production, manure is produced on a massive scale. The release of nitrous oxide is even more substantial in more intensive agricultural operations where large amounts of manure are stored within confined, and therefore low oxygen, environments, because the manure produces more of the gas when it breaks down in anaerobic conditions . 

In addition to the harmful emissions it directly produces, animal agriculture also contributes to the climate crisis through deforestation, as increasing global demand for beef and dairy leads to agricultural land expansion. This is particularly evident in the Amazon rainforest, where cattle ranching has been responsible for 80 percent of deforestation . Much of the deforested land is used either for beef or the production of soy, a common ingredient in animal feed. As the number of animals raised by the agriculture industry continues to rise, so does the volume of animal feed harvested, all of which adds to increased deforestation in order to expand farming operations. 

Large-scale deforestation has severe environmental implications. As vast areas of forest are torn down or burned, large quantities of carbon dioxide are released into the atmosphere. The loss of forests also depletes the earth’s capacity to store carbon , meaning that carbon dioxide from other sources is more likely to pollute the atmosphere. The animal agriculture industry is driving this deforestation, cutting down what was once one of our strongest defenses against climate change. 

The impact on biodiversity is another way in which animal agriculture contributes to climate change. One recent report found that the key cause of biodiversity loss is the way in which we produce food, and recommended an urgent move toward more plant-based diets if we are to reduce the rate of biodiversity loss. In recent years it has become apparent that biodiversity is essential in helping us cope with and mitigate climate change . 

Animal agriculture is a significant contributor to climate change. The production of meat, eggs and dairy products is responsible for at least 16.5 percent of the world’s greenhouse gas emissions. To prevent climate disaster while also feeding the earth’s rising population, we will need to produce food more sustainably. This goal is going to be extremely difficult to reach unless we substantially change the way we produce food, with far less reliance on animal agriculture.

Animal Agriculture’s Impact

Although the agriculture industry is not the only sector contributing to the climate crisis, it is one of the most significant. The environmental impact of animal agriculture is severe enough that even if the use of fossil fuels were eliminated entirely, we would not be able to prevent a climate disaster without reducing food-sector climate emissions . 

The scale of the industry’s impact also means that moving away from animal agriculture to a food system more focused on plant-based foods and alternative proteins could make a substantial difference in limiting climate change and protecting our environment.

Although the impact of animal agriculture on climate change has been scientifically recognized, the issue does not often get the same attention as factors such as fossil fuels and transportation. In order to reach net zero goals and have a realistic chance of averting climate disaster, drastic changes need to be made to the way we produce food. 

Facts About Animal Agriculture and Climate Change

• Animal agriculture is far from the efficient and sustainable food source that many believe it to be. Despite the fact that 77 percent of the world’s agricultural land is used for animal agriculture, less than 40 percent of the protein and 18 percent of the calories we consume actually comes from the products it provides. 
• Even if more environmentally friendly production methods were used, animal agriculture would still have a substantial effect on climate change. The largest source of emissions in the industry is the animals themselves, with cattle producing extremely large quantities of methane.
• Methane gas, known to be even more potent than carbon dioxide, is produced on a very large scale by the industry. Because of the number of cattle being raised, animal agriculture is responsible for around 32 percent of methane emissions.
• Changing the way we eat has the potential to really make a difference in the fight against climate change. Scientists estimate that a food system with a greater focus on plant-based food sources could get us one-fifth of the way to holding global warming below 2 degrees Celsius.

Could Replacing Animal Agriculture and Shifting to a Plant-Based Diet Curb Greenhouse Gas Emissions?

To prevent a disastrous level of climate change we need to drastically cut our greenhouse gas emissions, and our dietary choices have the potential to help us do this. One 2018 study published in Nature found that a global shift toward flexitarian plant-based diets could mean that emissions in 2050 were 52 percent lower than otherwise projected . 

Shifting to a more plant-based food system would significantly reduce methane production which could go a long way to meeting environmental goals. A drastic reduction in the number of animals raised for food would also significantly reduce our production of nitrous oxide, the most powerful of all greenhouse gases.

In order to see the fall in greenhouse gas emissions that we need, dietary changes will of course need to be significant and widespread. This means that before we see substantial change, more investment likely needs to be made in the production of alternative proteins, and dietary change needs to be accorded the same importance as other methods of mitigating climate change. On an individual basis though, switching even a single meat-based product for a plant-based alternative really does make a difference. One study found that plant-based burgers, for example, are responsible for up to 98 percent less greenhouse gas emissions than burgers made of beef. Another found that replacing beef with plants would reduce the greenhouse gas emissions of the average American diet by 96 percent .

What You Can Do 

The substantial effects that dietary choices have on the environment gives us an opportunity to make a difference in our individual impacts on climate change. By reducing or eliminating animal-based food sources from our diets, we can make a significant contribution to limiting food-sector greenhouse gas emissions.

A global shift towards a more plant-rich food system would spare land and limit climate pollution. For this to happen, we need to eat less beef and increase public investment in meat alternatives. Because animal agriculture is a key contributor to climate change, drastic changes need to be made to limit global warming and feed the planet’s growing population.

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The worldwide phase out of animal agriculture, combined with a global switch to a plant-based diet, would effectively halt the increase of atmospheric greenhouse gases for 30 years and give humanity more time to end its reliance on fossil fuels, according to a new study by scientists from Stanford University and the University of California, Berkeley.

A new model suggests that phasing out animal agriculture over the next 15 years would have the same effect as a 68 percent reduction of carbon dioxide emissions through the year 2100. (Image credit: Getty Images)

“We wanted to answer a very simple question: What would be the impact of a global phase-out of animal agriculture on atmospheric greenhouse gases and their global-heating impact?” said Patrick Brown , a professor emeritus in the department of biochemistry at Stanford University. Brown co-authored the paper with Michael Eisen, a professor of genetics and development at UC Berkeley.

Based on the model, published in the open-access journal PLoS Climate , phasing out animal agriculture over the next 15 years would have the same effect as a 68 percent reduction of carbon dioxide emissions through the year 2100. This would provide 52 percent of the net emission reductions necessary to limit global warming to 2 degrees Celsius above preindustrial levels, which scientists say is the minimum threshold required to avert disastrous climate change.

The changes would stem, the authors say, from the spontaneous decay of the potent greenhouse gases methane and nitrous oxide, and the recovery of biomass in natural ecosystems on the more than 80 percent of humanity’s land footprint currently devoted to livestock.

“Reducing or eliminating animal agriculture should be at the top of the list of potential climate solutions,” Brown said. “I’m hoping that others, including entrepreneurs, scientists and global policymakers, will recognize that this is our best and most immediate chance to reverse the trajectory of climate change, and seize the opportunity.”

Brown is also the founder and CEO of Impossible Foods, a company developing alternatives to animals in food production. Eisen is an advisor to the company. Both Brown and Eisen stand to benefit financially from the reduction of animal agriculture.

Unlocking negative emissions

Brown and Eisen are not the first to point out that ongoing emissions from animal agriculture are contributing to global warming. But what has not been recognized before, they say, is the much more impactful “climate opportunity cost” – the potential to unlock negative emissions by eliminating livestock.

“As the methane and nitrous oxide emissions from livestock diminish, atmospheric levels of those potent greenhouse gases will actually drop dramatically within decades,” Brown said. “And the CO 2 that was released into the atmosphere when forests and wild prairies were replaced by feed crops and grazing lands can be converted back into biomass as livestock are phased out and the forests and prairies recover.”

Brown and Eisen used publicly available data on livestock production, livestock-linked emissions and biomass recovery potential on land currently used to support livestock to predict how the phaseout of all or parts of global animal agriculture production would alter net anthropogenic, or human-caused, emissions from 2019 levels. They then used a simple climate model to project how these changes would impact the evolution of atmospheric greenhouse gas levels and warming for the rest of the century.

They examined four dietary scenarios: an immediate replacement of all animal agriculture with a plant-only diet; a more gradual and, the authors say, more realistic, 15-year transition to a global plant-only diet; and versions of each where only beef was replaced with plant-only products.

For each hypothetical scenario, the scientists assumed that non-agricultural emissions would remain constant and that the land formerly used for livestock production would be converted to grasslands, prairies, forests and the like that will absorb atmospheric carbon dioxide.

“The combined effect is both astoundingly large, and – equally important – fast, with much of the benefit realized by 2050,” Brown said. “If animal agriculture were phased out over 15 years and all other greenhouse-gas emissions were to continue unabated, the phase-out would create a 30-year pause in net greenhouse gas emissions and offset almost 70 percent of the heating effect of those emissions through the end of the century.”

While the complete phase out of animal-based agriculture was projected to have the largest impact, 90 percent of the emission reductions could be achieved by only replacing ruminants such as cattle and sheep, according to the model.

While their paper does not explore the particulars of what a global phaseout of animal agriculture would entail, the authors acknowledge that “the economic and social impacts of a global transition to a plant-based diet would be acute in many regions and locales” and that “it is likely that substantial global investment will be required to ensure that people who currently making a living from animal agriculture do not suffer when it is reduced or replaced.”

But, they write, “in both cases, these investments must be compared to the economic and humanitarian disruptions of significant global warming.”

Changing attitudes

Many will scoff at the idea that billions of people can be convinced to switch to a plant-only diet within 15 years. To these skeptics, Eisen points out that other revolutions have happened in less time. “We went from having no cellphones to cellphones being ubiquitous in less time than that. Electricity, cars, solar panels – all became common in a relatively short period of time,” Eisen said.

Moreover, Brown added, societal attitudes toward food are far from fixed. “Five hundred years ago, nobody in Italy had ever seen a tomato. Sixty years ago, nobody in China had ever drunk a Coke. Mutton was once the most popular meat in America,” he said. “People around the world readily adopt new foods, especially if they are delicious, nutritious, convenient and affordable.”

The scientists have made all of the raw data they used, as well as their calculations and the computer code used to carry out the calculations, publicly available so that others can make up their own mind.

“The great thing about science is that, in the end, it all comes down to whether the conclusions are supported by the evidence,” Brown said. “And in this case, they are.”

To read all stories about Stanford science, subscribe to the biweekly  Stanford Science Digest .

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Ker Than, Stanford News Service: (650) 723-9820, [email protected]

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Article Contents

Introduction, impact of livestock on climate change, livestock mitigation strategies, literature cited.

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Livestock and climate change: impact of livestock on climate and mitigation strategies

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Giampiero Grossi, Pietro Goglio, Andrea Vitali, Adrian G Williams, Livestock and climate change: impact of livestock on climate and mitigation strategies, Animal Frontiers , Volume 9, Issue 1, January 2019, Pages 69–76, https://doi.org/10.1093/af/vfy034

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The livestock sector requires a significant amount of natural resources and has an important role in global greenhouse gas emissions. The most important greenhouse gases from animal agriculture are methane and nitrous oxide.

Mitigation strategies aimed at reducing the emission intensity of this sector are needed to meet the increasing demand for livestock products driven by population growth.

To increase the effectiveness of mitigation strategies, the complex interactions among the components of livestock production systems must be taken into account to avoid environmental trade-offs.

According to the United Nations ( UN, 2017 ), the world population increased by approximately 1 billion inhabitants during the last 12 years, reaching nearly 7.6 billion in 2017. Although this growth is slower than 10 years ago (1.24% vs. 1.10% per year), with an average increase of 83 million people annually, global population will reach about 8.6 billion in 2030 and 9.8 billion in 2050. Population growth, urbanization, and income rise in developing countries are the main driver of the increased demand for livestock products ( UN, 2017 ). The livestock sector requires a significant amount of natural resources and is responsible for about 14.5% of total anthropogenic greenhouse gas emissions (7.1 Gigatonnes of carbon dioxide equivalents for the year 2005; Gerber et al., 2013 ). Mitigation strategies aimed at reducing emissions of this sector are needed to limit the environmental burden from food production while ensuring a sufficient supply of food for a growing world population. The objectives of this manuscript are to 1) discuss the main greenhouse gas emissions sources from the livestock sector and 2) summarize the best mitigation strategies.

The most important greenhouse gases from animal agriculture are methane and nitrous oxide. Methane, mainly produced by enteric fermentation and manure storage, is a gas which has an effect on global warming 28 times higher than carbon dioxide. Nitrous oxide, arising from manure storage and the use of organic/inorganic fertilizers, is a molecule with a global warming potential 265 times higher than carbon dioxide. The carbon dioxide equivalent is a standard unit used to account for the global warming potential ( IPCC, 2013 ).

Figure 1 was adapted from the Global Livestock Environmental Assessment Model (GLEAM) developed by FAO ( FAO, 2017 ) and shows in carbon dioxide equivalents the greenhouse gas incidences that enteric fermentation and manure storage have across the main livestock species raised worldwide.

Greenhouse gases incidence of enteric fermentation and manure storage by animal type, expressed as Gigatonnes of carbon dioxide equivalents. Data referred to 2010 ( FAO, 2017 ).

In addition to greenhouse gases arising from enteric fermentation and manure storage, feed production together with the related soil carbon dioxide and nitrous oxide emissions is another important hot spot for the livestock sector. Soil carbon dioxide emissions are due to soil carbon dynamics (e.g., decomposing plant residues, mineralization of soil organic matter, land use change, etc.), the manufacturing of synthetic fertilizers and pesticides, and from fossil fuel use in on-farm agricultural operations ( Goglio et al., 2018 ). Nitrous oxide emissions are emitted when organic and inorganic fertilizers are applied to the soil.

As shown in Figure 2 , feed production and processing contribute about 45% of the whole sector (3.2 Gigatonnes of carbon dioxide equivalents). Enteric fermentation producing about 2.8 Gigatonnes (39%) is the second largest source of emissions. Manure storage with 0.71 Gigatonnes accounts for about 10% of the total. The remaining 6% (0.42 Gigatonnes of carbon dioxide equivalents) is attributable to the processing and transportation of animal products ( Gerber et al., 2013 ).

Livestock emissions by source (adapted from Gerber et al., 2013 ). Direct livestock emissions are shown in red.

Feed production ( Figure 2 ) includes all the greenhouse gas emission arising from 1) land use change, 2) manufacturing and use of fertilizers and pesticides, 3) manure excreted and applied to fields, 4) agricultural operations, 5) feed processing, and 6) feed transport. Although these processes result in a large share of the livestock supply chain, in this article, we mainly focus on direct livestock emissions enteric fermentation, manure storage, and manure excreted/applied to the soil. All other emissions are outside the scope of this article.

Enteric fermentation

Enteric fermentation is a natural part of the digestive process of ruminants where bacteria, protozoa, and fungi contained in the fore-stomach of the animal (rumen), ferment and break down the plant biomass eaten by the animal. Plant biomass in the rumen is converted into volatile fatty acids, which pass the rumen wall and go to the liver through the circulatory system. This process supplies a major part of the energy needs of the animal and enables the high conversion efficiency of cellulose and semi-cellulose, which is typical of ruminants. The gaseous waste products of enteric fermentation, carbon dioxide and methane, are mainly removed from the rumen by eructation. Methane emission in the reticulorumen is an evolutionary adaptation that enables the rumen ecosystem to dispose hydrogen, which may otherwise accumulate and inhibit carbohydrate fermentation and fiber degradation ( McAllister and Newbold, 2008 ). The emission rate of enteric methane varies according to feed intake and digestibility.

Manure storage

Manure acts as an emission source for both methane and nitrous oxide, and the quantity emitted is linked to environmental conditions, type of management and composition of the manure. Organic matter and nitrogen content of excreta are the main characteristics influencing emission of methane and nitrous oxide, respectively. Under anaerobic conditions, the organic matter is partially decomposed by bacteria producing methane and carbon dioxide. Storage or treatment of liquid manure (slurry) in a lagoon or tank promotes an anaerobic environment which leads to an increase in methane production. Long storage periods and warm and wet conditions can further increase these emissions ( EPA, 2010 ). On the other hand, nitrous oxide emissions need a combination of aerobic and anaerobic conditions to be produced. Therefore, when manure is handled as a solid (dung) or deposited on pastures, nitrous oxide production increases while little or no methane is emitted. Nitrous oxide is generated through both the nitrification and denitrification processes of the nitrogen contained in manure, which is mainly present in organic form (e.g., proteins) and in inorganic form as ammonium and ammonia. Nitrification occurs aerobically and converts ammonium and ammonia to nitrites and then nitrates, while denitrification occurs anaerobically converting nitrates to nitrous oxide and nitrogen gas ( Saggar, 2010 ). The balance between ammonium and ammonia is highly affected by pH, with ammonia increasing as pH increases.

Feed production

Almost 60% of the global biomass harvested worldwide enters the livestock subsystem as feed or bedding material ( Krausmann et al., 2008 ). Greenhouse gas emissions from feed production represent 60–80% of the emission coming from eggs, chicken and pork, and 35–45% of the milk and beef sector ( Sonesson et al., 2009 ). As shown in Figure 2 , emissions from feed production account for about 45% of the livestock sector. The application of manure as fertilizer for feed crops and the deposition of manure on pastures generates a substantial amount of nitrous oxide emissions representing about half of these emissions ( Gerber et al., 2013 ). Although livestock feed production often involves large applications of nitrogen to agricultural soils, good manure management can reduce the need for manufactured fertilizers.

The extreme heterogeneity of the agricultural sector needs to be taken into account when defining the overall sustainability of a mitigation strategy, which can vary across different livestock systems, species, and climates. Generally, no measure in isolation will encompass the full emission reduction potential, while a combination selected from the full range of existing options will be required to reach the best result ( Llonch et al., 2017 ). It is also important to consider the “pollution swapping” effect when evaluating the effectiveness of a mitigation strategy ( Hristov et al., 2013 ). Reduction of methane emissions during enteric fermentation might be counteracted by increased greenhouse gas emissions in applied manure. Reduction of direct nitrous oxide emissions during storage might result in higher nitrate leaching and ammonia volatilization during field application.

Mitigation may occur directly by reducing the amount of greenhouse gases emitted, or indirectly through the improvement of production efficiency. The main strategies to mitigate greenhouse gas emissions in the livestock sector have been investigated and are summarized in Table 1 .

Mitigation potential of various strategies

*High = ≥30% mitigating effect; Medium = 10–30% mitigating effect; Low = ≤10% mitigating effect. Mitigating effects refer to percent change over a “standard practice” according to Newell Price et al. (2011) ; Borhan et al. (2012) ; Hristov et al. (2013) ; Montes et al. (2013) ; Petersen (2013) ; Battini et al. (2014) ; Knapp et al. (2014) ; Llonch et al. (2017) ; Mohankumar Sajeev et al. (2018) .

† Inconsistent/variable results.

‡ Uncertainty due to limited research or lack of data.

Decreasing methane emissions from ruminants is one pressing challenge facing the ruminant production sector. Strategies for reducing this source of emissions focus on improving the efficiency of rumen fermentation and increasing animal productivity. A large number of mitigation options have been proposed (e.g., diet manipulation, vaccines, chemical additives, animal genetic selection, etc.) with different efficiencies in reducing enteric methane as shown in Table 1 .

Forage quality and digestibility affect enteric methane production. Lignin content increases during plant growth, consequently reducing plant digestibility. Therefore, harvesting forage (especially grass) for ensiling at an earlier stage of maturity increases its soluble carbohydrate content and reduces lignification. According to Knapp et al. (2014) practices aimed to increase forage quality have shown a potential enteric methane reduction of about 5% per unit of fat protein corrected milk.

Physical processing of forages, such as chopping, grinding, and steam treatment, also improves forage digestibility and mitigates enteric methane production in ruminants ( Hristov et al., 2013 ). However, the reduction potential of this practice was reported to be less than 2% per unit of fat protein corrected milk ( Knapp et al., 2014 ).

Improving diet digestibility by increasing concentrate feeding is another effective mitigation strategy, reducing by 15% methane emissions per unit of fat protein corrected milk ( Knapp et al., 2014 ). However, the ratio of forage to concentrate has to be carefully taken into account when applying this strategy. Indeed, although a marked reduction of enteric methane can be expected with rates of concentrate inclusion between 35% and 40% ( Gerber et al., 2013 ). A greater proportion of dietary fermentable carbohydrates could increase the risk of metabolic diseases (e.g., rumen acidosis).

Addition of fats or fatty acids to the diets of ruminants can decrease enteric methane emissions by both decreasing the proportion of energy supplied from fermentable carbohydrates and changes in the microbial population of the rumen ( Llonch et al., 2017 ). Although some byproducts (e.g., cottonseed, brewer’s grains, cold-pressed canola meal, etc.) are effective in reducing enteric fermentation ( Moate et al., 2011 ), the mitigation potential of high oil byproducts has not been well-established and in some cases methane production may increase due to increased fiber intake ( Hristov et al., 2013 ). The inclusion of lipids higher than 10% can lead to impairment of ruminal function due to changes to the microbial population which in turn decreases the ability to digest fiber. Lipid diet supplementation between 5% and 8% of the dry matter intake is an effective mitigation strategy ( Grainger and Beauchemin, 2011 ) with a potential enteric methane reduction of about 15% per unit of fat protein corrected milk ( Knapp et al., 2014 ).

Feed additives (electron receptors, ionophoric antibiotics, chemical inhibitors, etc.) have also been tested for their ability to decrease methane emissions ( Beauchemin et al., 2009 ). However, the unknown toxicity and the health risks associated with the use of some of these compounds may severely constrain widespread adoption ( Herrero et al., 2016 ).

Increased animal density together with continuous inflow of nutrients from imported feeds is likely to increase volumes of manure to be managed. Stored manure accounts for a relatively small amount of direct agricultural greenhouse gases ( Figure 2 ), and it is technically possible to mitigate a very high percentage of these emissions ( Hristov et al., 2013 ). In the following section, some of the most effective mitigation strategies are discussed.

As methane production increases with the temperature of stored manure, a reduction of storage temperature has been reported to drop these emissions by 30–50% ( Borhan et al., 2012 ). However, the net greenhouse gas mitigation resulting from this strategy can vary widely, and it is strictly related to the energy used and the cooling system adopted.

Frequent removal of manure to an outside storage facility is an effective practice that can be accomplished using grooved floors combined with regular scraping of manure, especially for pigs and some cattle production systems. Indeed, if the channels underneath the stable are emptied regularly, and the manure/slurry are transported to an outside storage facility, this practice has the potential to reduce methane and nitrous oxide emissions by 55% and 41%, respectively ( Mohankumar Sajeev et al., 2018 ). On poultry farms the litter/manure is usually removed at the end of the crop; however, advanced layer housing using belt scrapers can efficiently remove litter/manure continuously and decrease greenhouse gas emissions ( Fournel et al., 2012 ).

Solid-liquid separation is a processing technology that partially separates the solids from liquid manure using gravity or mechanical systems such as centrifuges or filter presses. As shown in Table 1 , the greenhouse gas mitigation potential of this technique has been reported to be higher than 30% compared with untreated manure ( Montes et al., 2013 ). The organic component with a larger particle size follows the solid stream during the separation process, and it is then stored in stockpiles. The aerated condition of the storage can then limit the potential for methane to be emitted; however, ammonia loss through composting and generating high temperatures can be accelerated. Also, the remaining liquid fraction is still a potential source of indirect nitrous oxide emissions. Indeed, once the fibrous and large pieces of organic material are subtracted, it will not form a crust during storage, leading to increased volatilization of ammonia by increasing the mass transfer coefficient at the surface. Although greenhouse gas mitigation of the solid-liquid separation process can be partially counterbalanced by ammonia emissions, it is important to note that there are many management practices that can overcome these issues, such as covering slurry storage and the use of injection for land application ( Holly et al., 2017 ).

Anaerobic digestion is a biological degradation process, which in the absence of oxygen, produces digestate and biogas (mainly methane and carbon dioxide) from manure. Biogas collected from the system is often used to generate electricity, to fuel boilers or furnaces, or to provide combined heat and power. Taking into account the greenhouse gas emissions arising from the use of the digestate as fertilizer, and the credit for the renewable energy produced, anaerobic digestion has been reported to yield more than 30% reduction in greenhouse gas emissions when compared with traditional manure handling systems ( Battini et al., 2014 ). However, further attention to the management of the digestate leaving the anaerobic digestion is needed. Indeed, mineralization of the organic nitrogen occurring during biological degradation increases the inorganic nitrogen content and pH of the effluent, which in turn may increase ammonia volatilization ( Petersen and Sommer, 2011 ). Combining anaerobic digestion and solid-liquid separation could reduce the amount of ammonia lost following digestion ( Holly et al., 2017 ).

Diet severely affects excretion of nitrogen in most farm animals, therefore grouping livestock on the basis of their feed requirements can help in reducing this source of nitrous oxide in the excreta. Although a low-protein diet could effectively mitigate nitrous oxide emissions from cattle manure storage ( Table 1 ), some attention must be given to manipulating dietary nitrogen ( Montes et al., 2013 ). For example, decreasing protein could lead to an increase of fermentable carbohydrates, which in turn will likely increase methane production.

The diet for all animal species should be balanced for amino acids to avoid a depression in feed intake and a decrease in animal productivity. Manufactured amino acids are routinely used to balance the diet of monogastrics (pigs and poultry), but the environmental impact associated with the manufacturing of these supplements must be considered when including amino acids as a greenhouse gas mitigation strategy. In ruminants, supplementation of free amino acids results in fast degradation in the rumen, without a significant increase in animal productivity. On the contrary, rumen-protected amino acids resist chemical alterations in the rumen and can reach the intestine where they are absorbed, improving milk yield in dairy cows. Overall, feeding protein close to the animal’s requirement is recommended as an effective mitigation strategy to reduce ammonia and nitrous oxide emissions from manure ( Montes et al., 2013 ).

The timing, quantity, and method of fertilizer applications are important factors influencing soil nitrous oxide emissions. The nitrogen fertilizer applied is susceptible to loss by leaching and denitrification before crop uptake. Therefore, ensuring that appropriate amounts of nitrogen get to the growing crop and avoiding application in wet seasons or before major rainfall events, are valuable practices which could help in optimizing biomass production and reduce soil greenhouse gas emissions.

As lower methane emissions occur after manure land application, decreasing storage time can effectively help in reducing greenhouse gas emissions ( Table 1 ). However, the resulting frequent soil applications can have a variable effect on nitrous oxide emissions from field and carbon dioxide emissions from fuel combustion. Avoiding application during prolonged periods with wet soil and periods of low plant nitrogen uptake could help in increasing the effectiveness of this practice ( Hristov et al., 2013 ).

Adequate storage facilities can provide greater flexibility in choosing when to apply manure to fields, while the use of on-farm manure analysis could help the farmer develop a nutrient management plan and minimize environmental impacts ( Newell Price et al., 2011 ).

The use of nitrification inhibitors has the potential to reduce nitrogen leaching by inhibiting the conversion of ammonia to nitrate. However, this beneficial effect is weakened by a reported increase in indirect nitrous oxide emission that can result from increased ammonia volatilization ( Lam et al., 2016 ). This highlights the importance of considering both gases when evaluating the use of nitrification inhibitors as an option to mitigate climate change. Overall, nitrification inhibitors have been demonstrated as an effective practice to reduce nitrous oxide emissions ( Table 1 ).

Intensive rotational grazing systems are being promoted as a good way to increase forage production and reduce nitrous oxide emissions ( Table 1 ). These systems are characterized by multiple smaller fields called paddocks for the rotation of livestock. By subdividing pastures and rotating animals, farmers can manage stocking densities and grazing duration and thereby manage nitrogen excreta distribution and vegetation regrowth. A more uniform distribution of urine throughout the paddock would reduce the effective nitrogen application rate, which could translate into a reduction in nitrous oxide emissions ( Eckard et al., 2010 ). Keeping animals off the paddocks during wet weather will reduce sward damage and soil compaction. In addition, avoiding excreta deposition at these times will reduce nitrous oxide emissions and nitrogen leaching ( Luo et al., 2010 ).

Animal management

There is a direct link between greenhouse gas emission intensities and animal efficiency. The more productive the animal is, the lower the environmental impact will be (on a per unit of product basis). Both management quality and expression of full genetic potential are necessary to increase production efficiency.

Breeding for more productive animals can lead to a reduction of the nutrient requirements needed to reach the same level of production. This is a valuable greenhouse gas mitigation strategy ( Table 1 ). A more efficient animal will retain more dietary nitrogen protein and there will less nitrogen in feces and urine ( Gerber et al., 2013 ). Genetic improvement of daily gain and feed conversion that has been achieved in broilers over the last 20 years has reduced substantially the emissions per unit of weight ( Williams and Speller, 2016 ). Nevertheless, strategies that aim to change animal phenotypes to enhance productivity or efficiency may harm animal health and welfare unless these effects are measured and controlled ( Llonch et al., 2017 ). Animals of a particular genotype selected for increased production will only be able to realize this potential on a high input system in which resources are adequately supplied. In other words, new breeds and crosses can lead to substantial greenhouse gas reduction, but they need to fit within production systems and climates that may be characterized by limited resources and other constraints.

Poor fertility means that more breeding animals are required in the herd to meet production targets, and more replacements are required to maintain the herd size, which in turn increases greenhouse gas emissions. Improved fertility in dairy cattle could lead to a reduction in methane emissions by 10–24% and reduced nitrous oxide by 9–17% ( Table 1 ). Nevertheless, increasing reproductive pressure may increase the metabolic demands associated with pregnancy and lactation that could negatively affect animal health and increase the risk of metabolic diseases, reduce immune function and in turn reduce fertility ( Llonch et al., 2017 ).

Poorer livestock health and welfare are associated with behavioral and metabolic changes, which can effect greenhouse gas emissions in several ways. Animals fighting an infection will need more energy for maintenance. A recent study in the United Kingdom investigated cost-effective ways to reduce greenhouse gas emissions by improving cattle health. These studies found that cattle diseases can increase greenhouse gas emissions up to 24% per unit of milk produced and up to 113% per unit of beef carcass ( Williams et al., 2015 ). A disease that temporarily reduces feed intake or the ability to digest feed, leads to a decline in growth rate, which will result in more time and energy needed to reach the same end point.

Agriculture in general, and livestock production, in particular, contributes to global warming through emissions of methane and nitrous oxide. To meet future needs of an expanding population, animal productivity will need to increase and greenhouse gas emission intensity per unit of product will need to decrease. One of the principal ways to achieve this environmental standard is to adopt effective mitigation strategies. To increase the effectiveness of these strategies, complex interactions among the components of livestock production systems must be taken into account to avoid environmental trade-offs. Unfortunately, there is not a standard procedure to follow. Mitigation practices should not be evaluated individually, but as a component of the entire livestock production system. The majority of these strategies aim to increase productivity (unit of product per animal), which in most cases cannot be achieved without good standards of animal health and welfare. Optimizing animal productivity has a powerful mitigating effect in both developed and developing countries; however, the size of the effect will also depend on factors such as the genetic potential of the animal and adoption of management technologies.

About the Authors

Giampiero Grossi is a PhD student in the Department of Agriculture and Forestry Science (DAFNE) at Tuscia University, Italy. His research is focused on the quantification of greenhouse gases arising from a typical agro-silvo-pastoral system of the Mediterranean area. Giampiero is currently applying life cycle assessment methodology to a case study in Castelporziano, Rome. His background encompasses agri-food environmental certifications, livestock management, and farming practices.

Pietro Goglio is a lecturer in life-cycle assessment and systems modeling at Cranfield University. He has a strong environmental background and has conducted research in the life-cycle analysis of agricultural and bioenergy systems. Currently, Dr Goglio is focusing his research on developing approaches to combine science with life cycle assessment approaches for greenhouse gas removal from the atmosphere and for greenhouse gas accounting for agricultural systems and food systems. These research developments aim to better capture the characteristics of the systems by considering the economic, social, and political factors affecting their performance and implementation.

Andrea Vitali is a lecturer in Sustainable Livestock Production in the master degree of Food Science and Technology at University of Teramo. His research focused on the bidirectional relationships between animals and the environment. He has studied the effects of heat stress on livestock (production, reproduction, and health) and the contribution of animals to global warming. He has expertise in the application of systems based life-cycle assessment to livestock production. He was involved in developing the Italian plan for adaptation to climate change related to agriculture and food production.

Adrian Williams has spent many years working in agri-environmental science. He is a leading expert in the application of systems based life-cycle assessment to agricultural and food production. He has studied the production of all major crop and livestock species in the United Kingdom and abroad (e.g., beef in Brazil). He has applied life-cycle assessment to the greenhouse gas benefits of improved cattle health as well as enhanced welfare in pig and poultry housing. He is responsible for developing the beef sector model in the recently enhanced agricultural greenhouse gas inventory in the United Kingdom.

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Open Access

Peer-reviewed

Research Article

Rapid global phaseout of animal agriculture has the potential to stabilize greenhouse gas levels for 30 years and offset 68 percent of CO 2 emissions this century

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected] (MBE); [email protected] (POB)

Affiliation Department of Molecular and Cell Biology, Department of Integrative Biology, Howard Hughes Medical Institute, University of California, Berkeley, CA, United States of America

Roles Conceptualization, Formal analysis, Writing – original draft, Writing – review & editing

Affiliations Department of Biochemistry (Emeritus), Stanford University School of Medicine, Stanford, CA, United States of America, Impossible Foods, Redwood City, CA, United States of America

• Michael B. Eisen, 
• Patrick O. Brown

• Published: February 1, 2022
• https://doi.org/10.1371/journal.pclm.0000010
• See the preprint
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Animal agriculture contributes significantly to global warming through ongoing emissions of the potent greenhouse gases methane and nitrous oxide, and displacement of biomass carbon on the land used to support livestock. However, because estimates of the magnitude of the effect of ending animal agriculture often focus on only one factor, the full potential benefit of a more radical change remains underappreciated. Here we quantify the full “climate opportunity cost” of current global livestock production, by modeling the combined, long-term effects of emission reductions and biomass recovery that would be unlocked by a phaseout of animal agriculture. We show that, even in the absence of any other emission reductions, persistent drops in atmospheric methane and nitrous oxide levels, and slower carbon dioxide accumulation, following a phaseout of livestock production would, through the end of the century, have the same cumulative effect on the warming potential of the atmosphere as a 25 gigaton per year reduction in anthropogenic CO 2 emissions, providing half of the net emission reductions necessary to limit warming to 2°C. The magnitude and rapidity of these potential effects should place the reduction or elimination of animal agriculture at the forefront of strategies for averting disastrous climate change.

Citation: Eisen MB, Brown PO (2022) Rapid global phaseout of animal agriculture has the potential to stabilize greenhouse gas levels for 30 years and offset 68 percent of CO 2 emissions this century. PLOS Clim 1(2): e0000010. https://doi.org/10.1371/journal.pclm.0000010

Editor: Ana Maria Loboguerrero, Alliance of Bioversity International and CIAT: Alliance of Bioversity International and International Center for Tropical Agriculture, COLOMBIA

Received: July 18, 2021; Accepted: November 29, 2021; Published: February 1, 2022

Copyright: © 2022 Eisen, Brown. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All code and data are available at https://github.com/mbeisen/LivestockClimateImpact .

Funding: There was no formal funding of this work. Michael Eisen is an Investigator with the Howard Hughes Medical Institute which funds all work in his lab. Patrick Brown is CEO of Impossible Foods, Inc.

Competing interests: We have read the journal’s policy and the authors of this manuscript have the following competing interests: Patrick Brown is the founder and CEO of Impossible Foods, a company developing alternatives to animals in food-production. Michael Eisen is an advisor to Impossible Foods. Both are shareholders in the company and thus stand to benefit financially from reduction of animal agriculture. Michael Eisen and Patrick Brown are co-founders and former members of the Board of Directors of the Public Library of Science.

Introduction

The use of animals as a food-production technology has well-recognized negative impacts on our climate. The historical reduction in terrestrial biomass as native ecosystems were transformed to support grazing livestock and the cultivation of feed and forage crops accounts for as much as a third of all anthropogenic CO 2 emissions to date [ 1 , 2 ]. Livestock, especially large ruminants, and their supply chains, also contribute significantly to anthropogenic emissions of the potent greenhouse gases (GHGs) methane and nitrous oxide [ 3 – 5 ].

Solving the climate crisis requires massive cuts to GHG emissions from transportation and energy production. But even in the context of large-scale reduction in emissions from other sources, major cuts in food-linked emissions are likely necessary by 2075 to limit global warming to 1.5°C [ 6 ]. While a reduction of food-linked emissions can likely be achieved by increasing agricultural efficiency, reducing food waste, limiting excess consumption, increasing yields, and reducing the emission intensity of livestock production [ 7 – 12 ], they are not anticipated to have the same impact as a global transition to a plant-rich diet [ 5 , 6 ].

Nutritionally balanced plant-dominated diets are common, healthy and diverse [ 13 – 17 ], but are rarely considered in comprehensive strategies to mitigate climate change [ 18 ], and there is controversy about their viability and the magnitude of their climate benefit [ 19 ]. One source of this discordance is that widely cited estimates of livestock contributions to global warming [ 4 , 5 , 20 ] account only for ongoing emissions, and not for the substantial and reversible warming impact of historical land use change [ 1 , 21 ].

The Food and Agriculture Organization (FAO) of the United Nations estimates that emissions from animal agriculture represent around 7.1 Gt CO 2 eq per year [ 5 ], 14.5% of annual anthropogenic greenhouse gas emissions, although this is based on outdated data and likely now represents and underestimate [ 20 ], and recent estimates [ 1 ] suggest that on the order of 800 Gt CO 2 equivalent carbon could be fixed via photosynthesis if native biomass were allowed to recover on the 30% of Earth’s land surface current devoted to livestock production. Thus, crudely, eliminating animal agriculture has the potential to reduce net emissions by the equivalent of around 1,350 Gt CO 2 this century. To put this number in perspective, total anthropogenic CO 2 emissions since industrialization are estimated to be around 1,650 Gt [ 2 ].

However, a substantial fraction of the emissions impact of animal agriculture comes from methane (CH 4 ) and nitrous oxide (N 2 O), which decay far more rapidly than CO 2 (the half-lives of CH 4 and N 2 O are around 9 and 115 years, respectively), and recent studies have highlighted the need to consider these atmospheric dynamics when assessing their impact [ 22 – 24 ]. Of critical importance, many of the beneficial effects on greenhouse gas levels of eliminating livestock would accrue rapidly, via biomass recovery and decay of short-lived atmospheric CH 4 , and their cooling influence would be felt for an extended period of time.

Our goal here was to accurately quantify the full impact of current animal agriculture on the climate, taking into account the currently unrealized opportunities for emission reduction and biomass recovery together, and explicitly considering the impact of their kinetics on warming. Our approach differs from other recent studies [ 25 , 26 ] in that we did not attempt to predict how global food production and consumption might change with growing populations, economic development, advances in agriculture, climate change and other socioeconomic factors. Nor do we tackle the social, economic, nutrition and agricultural challenges inherent to such a large change in global production.

We used publicly available, systematic data on livestock production in 2019 [ 27 ], livestock-linked emissions [ 3 , 27 ], and biomass recovery potential on land currently used to support livestock [ 1 ] to predict how the phaseout of all or parts of global animal agriculture production would alter net anthropogenic emissions. We then used a simple climate model to project how these changes would impact the evolution of atmospheric GHG levels and warming for the rest of the century.

We calculated the combined impact of reduced emissions and biomass recovery by comparing the cumulative reduction, relative to current emission levels, of the global warming potential of GHGs in the atmosphere for the remainder of the 21st century under different livestock replacement scenarios to those that would be achieved by constant annual reductions in CO 2 emissions.

Modeling the effect of eliminating animal agriculture on GHG levels

We implemented a simple climate model that projects atmospheric GHG levels from 2020 to 2100 based on a time series of annual emissions of CO 2 , CH 4 and N 2 O and a limited set of parameters. We then compared various hypothetical dietary perturbations to a “business as usual” (BAU) reference in which emissions remain fixed at 2019 levels, based on global emissions data from FAOSTAT [ 27 ].

The dietary scenarios include the immediate replacement of all animal agriculture with a plant-only diet (IMM-POD), a more gradual transition, over a period of 15 years, to a plant-only diet (PHASE-POD), and versions of each where only specific animal products were replaced.

We updated estimates of global emissions from animal agriculture using country-, species- and product-specific emission intensities from the Global Livestock Environmental Assessment Model [ 3 ], and country-specific data on primary production of livestock products from the Food and Agriculture Organization (FAO) database FAOSTAT [ 27 ].

Based on this analysis, in 2019 (the most recent year for which full data are available), global production of animal-derived foods led to direct emissions of 1.6 Gt CO 2 , due primarily to energy use (as our model assumes constant overall rates of consumption, we excluded emissions due to land clearing, which are associated with agricultural expansion), 120 Mt CH 4 due primarily to enteric fermentation and manure management, and 7.0 Mt N 2 O due primarily to fertilization of feed crops and manure management ( Fig 1 and S1 Fig ).

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Total CO 2 equivalent emissions (A) assembled from species, product and country-specific production data from FAOSTAT for 2019 and species, product, region and greenhouse-gas specific emissions data from GLEAM [ 3 ], using CO 2 equivalents of 34 for CH 4 and 298 for N 2 O. Land use (B) assembled from species, product and country-specific production data from FAOSTAT for 2019 and species and product specific land use data from [ 12 ].

https://doi.org/10.1371/journal.pclm.0000010.g001

These numbers are broadly consistent with other recent estimates [ 4 , 5 , 26 ], and correspond, respectively, to 4% of CO 2 , 35% of CH 4 and 66% of N 2 O emissions from all human activities, using total human emissions data from FAOSTAT [ 27 ]. Combining the effects of the three gases, using global warming potentials from [ 28 ], results in 6.3 Gt CO 2 eq, with the major difference from the 7.1 Gt CO 2 eq number cited above coming from our exclusion of ongoing land use change.

We modeled the recovery of biomass on land currently used in livestock production using data from [ 1 ] who estimate that the return of land currently used in livestock production to its native state would sequester, over 30 years, 215.5 Gt of carbon (equivalent to 790 Gt of CO 2 ) in plant and non-living biomass. A similar estimate was obtained by [ 21 ].

We assumed in all these hypothetical scenarios that non-agricultural emissions would remain constant; that food from livestock is replaced by a diverse plant based diet; and that, when land is removed from livestock production, the conversion of atmospheric CO 2 into terrestrial biomass occurs linearly over the subsequent thirty years. (We consider alternative assumptions in the “Sensitivity Analysis” section below).

We emphasize that we are not predicting what will happen to global diets. Rather we are projecting simplified scenarios of dietary change forward through time to characterize and quantify the climate impact of current animal agriculture production. Our climate model is intentionally simple, considering only the partition of terrestrial emissions into the atmosphere, and the decay of methane and nitrous oxide, although it replicates the qualitative behavior of widely used MAGICC6 [ 29 ].

Fig 2 shows annual emissions and projected atmospheric levels of CO 2 , CH 4 and N 2 O under BAU and PHASE-POD through the end of the century (projections for IMM-POD and additional scenarios are shown in S2 – S32 Figs).

(A) Projected annual emissions of CO 2 , CH 4 and N 2 O for Business as Usual (red) and PHASEPOD (green) assuming a 15 year transition to new diet and 30 year carbon recovery. (B) Projected atmospheric concentrations of CO 2 , CH 4 and N 2 O under each emission scenario.

https://doi.org/10.1371/journal.pclm.0000010.g002

Rapid phaseout of animal agriculture would freeze increases in the warming potential of the atmosphere for 30 years

The impact of PHASE-POD on CO 2 emissions would be greatest in the period between 2030 and 2060, when biomass recovery on land previously occupied by livestock or feed crops reaches its peak, slowing the rise of atmospheric CO 2 levels during this interval.

Atmospheric CH 4 and N 2 O levels continue to increase in both BAU and PHASE-POD during the transition period, but begin to drop in PHASE-POD as the abatement of animal agriculture-linked emissions accelerates. CH 4 , with a half-life in the atmosphere of around 9 years, approaches a new and lower steady-state level towards the end of the century, while N 2 O, with a half-life of around 115 years, does so over a longer time-scale.

To capture the combined global warming impact of the changing levels of these GHGs, we calculated radiative forcing (RF), the reduction in radiative cooling by GHG absorption of infrared radiation, using the formulae described in [ 30 , 31 ] and used in MAGICC6 [ 29 ].

Fig 3 shows that with PHASE-POD there would effectively be no net increase in RF between 2030 and 2060. And even after that 30-year pause in the previously monotonically increasing global warming potential of the atmosphere, the difference in RF between the POD and BAU scenarios would continue to increase, due to the absence of direct emissions from animal agriculture and the continuing decay of previously emitted CH 4 and N 2 O towards lower steady-state values.

Effect of eliminating emissions linked to animal agriculture and of biomass recovery on land currently used in animal agriculture on Radiative Forcing (RF), a measure of the instantaneous warming potential of the atmosphere. RF values computed from atmospheric concentrations in by formula of [ 30 , 32 ] as modified in MAGICC6 [ 29 ] with adjustment for gases other than CO 2 , CH 4 and N 2 O as described in text.

https://doi.org/10.1371/journal.pclm.0000010.g003

Rapid phaseout of animal agriculture could achieve half of the emission reductions needed to meet Paris Agreement GHG targets

By the end of the century the RF under PHASE-POD would be 3.8 Wm -2 compared to 4.9 Wm -2 for BAU, a reduction in RF equivalent to what would be achieved by eliminating 1,680 Gt of CO 2 emissions ( S33 Fig ), or 46 years of global anthropogenic CO 2 emissions at the current rate of 36 Gt/year.

In 2010, the climate modeling community defined a series of four “Representative Concentration Pathways” that capture a wide range of future warming scenarios, leading to 2100 RF levels of 8.5, 6.0, 4.5 and 2.6 Wm -2 (which is approximately the RF of current atmospheric greenhouse gas levels), respectively [ 33 , 34 ]. These model pathways were extended after the Paris Agreement to include a target of 1.9 Wm -2 . Although the exact relationship between RF and global warming is incompletely understood, 2100 RF values of 1.9 and 2.6 Wm -2 are generally used as targets for limiting warming in this century to 1.5˚C and 2.0˚C, respectively, over the baseline pre-industrial global average temperature [ 18 ].

Reducing 2100 RF from 4.9 Wm -2 under BAU to 2.6 Wm -2 would require a reduction of atmospheric CO 2 levels by 204 ppm, equivalent to 3,230 Gt of CO 2 emissions ( Fig 4 and S33 Fig ), and an additional 47 ppm reduction, equivalent to 750 Gt of CO 2 emissions, would be required to reach 1.9 Wm -2 .

Using projected CH 4 and N 2 O levels in 2100 under business as usual diet as a baseline for RF calculation, we computed the CO 2 reductions necessary to reduce RF from the business as usual diet level of RF = 1.31 to the bovid-free diet level of RF = 4.09 (1300 Gt CO 2 ), the plant-only diet level of RF = 3.83 (1680 Gt CO 2 ), the 2.0° C global warming target of RF = 2.6 (3230 Gt CO 2 ) and the 1.5° C global warming target of RF = 1.9 (3980 Gt CO 2 ). For this analysis we used a corrected RF that accounts for the absence of other gases in our calculation by training a linear regression model on published MAGICC6 output to estimate from CO 2 , CH 4 and N 2 O levels the residual RF impact of other gases.

https://doi.org/10.1371/journal.pclm.0000010.g004

Thus the 1,680 Gt of CO 2 equivalent emissions reductions from the phased elimination of animal agriculture, would, without any other intervention to reduce GHG emissions, achieve 52% of the net GHG emissions reductions necessary to reach the 2100 RF target of 2.6 Wm -2 and 42% of the emissions reductions necessary to reach the 1.9 Wm -2 target [ 18 ].

Eliminating animal agriculture has the potential to offset 68 percent of current anthropogenic CO 2 emissions

While widely used, such single point estimates of radiative forcing tell an incomplete story, as temperature change, and other climate impacts, depend cumulatively on the temporal trajectories of changing atmospheric greenhouse gas levels.

To capture such dynamic effects, we computed, for each dietary scenario, the integral with respect to time of the RF difference between the scenario and BAU, from 2021 (the start of the intervention in this model) to a given year “y”. We designate this cumulative RF difference for year y , CRFD y . We then determined, for each dietary scenario and year y , what level of reduction in annual CO 2 emissions alone, relative to BAU, would yield the same CRFD y , and designate this annual CO 2 equivalent aCO 2 eq y (see S36 and S37 Figs for details of these equivalences).

Critical features of aCO 2 eq are that it operates directly on RF inferred from combined trajectories of atmospheric levels of all GHGs, and thus can directly capture the effects of arbitrarily complex interventions, and that it equates the cumulative RF impact of an intervention over a specified time window to a single number: the sustained reductions in CO 2 emissions that would have the same cumulative impact.

aCO 2 eq is closely related to, and motivated by similar goals as, CO 2 -forcing-equivalent (CO 2 -fe) emissions [ 35 ], which equates an arbitrary emission trajectory of all GHGs to a trajectory of CO 2 emissions that would produce the same trajectory of RF, and GWP* [ 22 – 24 ], which uses various formulae to equate changes in GHG emissions to instantaneous CO 2 pulses.

Fig 5 shows the aCO 2 eq for different scenarios for reference years 2050 (to capture short term impacts) and 2100 ( S38 Fig shows the full dependence of aCO 2 eq on the reference year). The aCO 2 eq 2100 for PHASE-POD is -24.8 Gt/year. As global anthropogenic CO 2 emissions are currently approximately 36 Gt/year, that PHASE-POD would have the same effect, through the end of the century, as a 68% reduction of CO 2 emissions.

Bars show sustained reduction in annual CO 2 emissions necessary to equal cumulative reduction in radiative forcing of the given scenario in 2050 (blue) and 2100 (orange).

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Replacing ruminants achieves over 90 percent of climate benefit of eliminating animal agriculture

We next computed aCO 2 eq 2100 for the 15 year phaseout of individual animal products and product categories (Figs 5 and 6A ; Table 1 ), using the species- and product-specific emissions and land use values described above. Beef alone accounts for 47% of the benefits of phasing out all animal agriculture, and cow milk 24%. Meat and milk from bovids (cattle and buffalo) account for 79% of the climate opportunity. Although they provide less than 19% of the protein in the human diet [ 27 ], ruminants (cattle, buffalo, sheep and goats) collectively account for 90% of the aCO 2 eq 2100 of all livestock.

(A) Total annualized CO 2 equivalents through 2100, aCO 2 eq 2100 , for all tracked animal products, and Emission Intensities based on aCO 2 eq 2100 on a per unit production (B) or per unit protein (C) basis. For (B) and (C) we also convert the values to driving equivalents using a value of 0.254 kg CO 2 eq per km driven of an internal combustion engine fueled sedan in the United States from life cycle analyses described in [ 36 ].

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These product-specific aCO 2 eq’s can be interpreted on a per product unit ( Fig 6B ) or per protein unit ( Fig 6C ) as emissions intensities. Eliminating the consumption of a kilogram of beef, for example, is equivalent to an emissions reduction of 297 kg CO 2 . 38% (113 kg aCO 2 eq) comes from reduced emission, in line with the mean estimate of 99.5 kg CO 2 eq from a systematic meta analysis of GHG emissions from agricultural products [ 12 ], with the remaining 62% from biomass recovery.

As with the total numbers, ruminant meat has the largest emissions intensities, per unit (289 kg CO 2 eq per kg consumer product) and per protein (1,279 kg CO 2 eq per kg protein). The most efficient animal products on a per protein basis are chicken meat (56 kg CO 2 eq per kg protein) and eggs (49 kg CO 2 eq per kg protein), roughly 25 times lower than per protein emissions intensities for ruminant meat.

To connect these numbers to other sources of GHGs, we converted these emissions intensities to distances one would have to drive a typical 2021 model gas-fueled passenger car to produce the same emissions, based on a full life-cycle analysis of auto emissions [ 36 ] ( Fig 6B and 6C ). One kg of beef, for example, has the same emissions impact as driving 1,172 km in a typical US car (or 339 miles per pound).

Sensitivity to assumptions

Our default model assumes a gradual phaseout of animal agriculture over a period of 15 years, producing an aCO 2 eq 2100 of -24.8 Gt/year. If we assume immediate elimination ( S2 Fig ), the aCO 2 eq 2100 is -28.3 Gt/year ( Fig 7A ), a 14% increase in magnitude of the effect. If we assume a phaseout over 30 years ( S3 Fig ), the aCO 2 eq 2100 is -21.3 Gt/year ( Fig 7A ), a 14% reduction.

The grey line in each plot is PHASE-POD, the default scenario of 15 year phaseout, 30 year carbon recovery, livestock emissions from FAOSTAT, and a diverse plant replacement diet based on [ 26 ]. (A) Effect of the immediate elimination of animal agriculture (red line) or a slower, 30 year, phaseout (blue line). (B) Effect of slower carbon recovery reaching completion after 50 years (red line) or 70 years (blue line). (C) Effect of using high (green line) or low (red line) estimates of above ground carbon recovery from [ 1 ]. (D) Effect of reducing either the efficiency or extent of carbon recovery.

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Our default model also assumes that biomass will recover linearly over 30 years, following [ 1 ], but there is considerable uncertainty in the literature, with estimates ranging from 25 to 70 years [ 37 – 39 ]. If we assume recovery takes 50 years ( S4 Fig ), the aCO 2 eq 2100 is -22.4 Gt/year, and if it takes 70 years ( S5 Fig ), the aCO 2 eq 2100 is -20.1 Gt/year, or reductions of 10% and 19% respectively ( Fig 7B ). We also note that passive recovery is not the only option. Further research is required to define optimal management practices for recovery of ecosystems currently impacted by animal agriculture and to estimate the rate and magnitude of their potential impact on climate. But there is evidence that deliberate, active management of ecosystem recovery to optimize for carbon sequestration could accelerate and increase the magnitude of carbon storage on land transitioning from intensive agricultural use [ 40 ].

Estimates of the biomass recovery potential of land currently used for animal agriculture have a high degree of uncertainty. Using the low estimate ( S6 Fig ) of [ 1 ], which addresses uncertainty in above-ground biomass yields an aCO 2 eq 2100 of -21.2 Gt/year ( Fig 7C ), a 14% reduction in magnitude relative to the median value from [ 1 ]. Using the high estimate ( S7 Fig ) of [ 1 ] yields an aCO 2 eq 2100 of -28.1 Gt/year ( Fig 7C ), an increase in magnitude of 13% increase.

A major area of uncertainty not addressed by [ 1 ] is the extent to which the carbon recovery potential of land that transitions away from use in animal agriculture would be realized in the face of other land use pressures. The land needed to replace animal derived foods in the global diet is accounted for in [ 1 ], but not other potential large-scale non-food uses such as biofuel production. While it is beyond the scope of this work to model these uses explicitly, Fig 7D shows the expected RF trajectories if we assume reduced recovery fractions of 25% ( S8 Fig ), 50% ( S9 Fig ), 75% ( S10 Fig ) and 100% ( S11 Fig ), which yield aCO 2 eq 2100 of -21.6, -18.3, -15.0, and -11.6 Gt/year respectively, highlighting the importance of carbon recovery in realizing the climate potential of ending animal agriculture. It is important to note that there is substantial variance in the biomass potential between regions and ecosystems, and recent modeling work by [ 21 ] indicates that half of the biomass recovery potential of land currently used for agriculture could be realized by restoration of 25% of the relevant land.

Our estimate of global emissions due to animal agriculture based on FAO data and analyses of 1.6 Gt CO 2 , 122 Mt CH 4 and 7.0 Mt N 2 O differ in key ways from recent estimates of [ 26 ] of 3.2 Gt CO 2 , 102 Mt CH 4 and 3.9 Mt N 2 O. Using these emissions estimates for livestock ( S12 Fig ) yields an aCO 2 eq 2100 of PHASE-POD of -23.6 Gt/year ( S40 Fig ), a 5% decrease in magnitude.

The models described above assume that the protein currently obtained from animal products would be replaced with a diverse plant based diet, scaled to replace animal products on a protein basis, and agriculture emissions data from FAOSTAT. We considered as an alternative emissions projected from a diverse plant based diet based on data from [ 26 ], scaled to replace animal products on a protein basis. This replacement diet ( S13 Fig ) yields an aCO 2 eq 2100 for PHASE-POD of animal agriculture of -23.7 Gt/year ( S40 Fig ), a 5% decrease in magnitude.

In some areas, the removal of land from use in animal agriculture may lead to an increase in wild ruminant population. Although this is difficult to model globally, this would offset some of the beneficial impacts of reductions in methane emissions from livestock [ 41 ].

This analysis only considered consumption of terrestrial animal products, neglecting emissions and land use (via feed production) associated with seafood capture and aquaculture. While the land and emissions impact of seafood consumption has received comparably little attention, several studies have pointed to at least 500 Mt of CO 2 equivalent emissions per year from seafood [ 12 , 42 , 43 ]. Recent work has also suggested that the disruption of carbon storage due to seafood harvesting via trawling repartitions from 0.58 up to 1.47 Gt CO 2 equivalent carbon per year from sediment into the water column, with the potential to drive atmospheric increases of similar magnitude [ 44 ].

Widely used climate models consider temporal and spatial variation in emissions; feedback between a changing climate and anthropogenic and natural emissions, carbon sequestration, atmospheric chemistry and warming potential; the impact of climate on human social, political and economic behavior. Ours does not. We ran our model on emissions data from the pathways described in [ 45 ] and compared our atmospheric level and RF outputs to theirs, and found them to be in broad qualitative agreement. Thus, while other models could provide more precise estimates, we do not believe they would alter our major conclusions.

Our analysis has provided a quantitative estimate of the potential climate impact of a hypothetical, radical global change in diet and agricultural systems. We have shown that the combined benefits of removing major global sources of CH 4 and N 2 O, and allowing biomass to recover on the vast areas of land currently used to raise and feed livestock, would be equivalent to a sustained reduction of 25 Gt/year of CO 2 emissions.

Crucially eliminating the use of animals as food technology would produce substantial negative emissions of all three major GHGs, a necessity, as even the complete replacement of fossil fuel combustion in energy production and transportation will no longer be enough to prevent warming of 1.5°C [ 6 – 8 ].

The transition away from animal agriculture will face many obstacles and create many challenges. Meat, dairy and eggs are a major component of global human diets [ 27 ], and the raising of livestock is integral to rural economies worldwide, with more than a billion people making all or part of their living from animal agriculture.

Although animal products currently provide, according to the most recent data from FAOSTAT, 18% of the calories, 40% of the protein and 45% of the fat in the human food supply, they are not necessary to feed the global population. Existing crops could replace the calories, protein and fat from animals with a vastly reduced land, water, GHG and biodiversity impact, requiring only minor adjustments to optimize nutrition [ 25 ].

The economic and social impacts of a global transition to a plant based diet would be acute in many regions and locales [ 46 ], a major obstacle to their adoption. It is likely that substantial global investment will be required to ensure that the people who currently make a living from animal agriculture do not suffer when it is reduced or replaced. And, while it is expected that the phaseout of animal agriculture would lead to global increases in food availability as edible crops cease to be diverted for animal feed [ 47 ], investment will also be required to prevent local food insecurity in regions where wide-scale access to a diverse and healthy plant-based diet is currently lacking and to ensure proper nutrition. But, in both cases, these investments must be compared to the economic and humanitarian disruptions of significant global warming [ 48 , 49 ].

Although, as discussed above, there are many uncertainties in our estimates, our assumption that “business as usual” means animal agriculture will continue at current levels was highly conservative, as rising incomes are driving ongoing growth in global animal product consumption [ 50 ]. It is estimated that global demand for animal based foods will increase by nearly 70 percent by 2050 [ 50 ]. For example, using land use data from [ 12 ] and consumption data from FAOSTAT, extending the current diet of wealthy industrialized countries (OECD) to the current global population would require an additional 35 million km 2 to support livestock production—an area roughly equal to the combined area of Africa and Australia.

While such an expansion may seem implausible, even partial destruction of Earth’s critical remaining native ecosystems would have catastrophic impacts not just on the climate, but on global biodiversity [ 51 – 53 ] and human health [ 13 , 21 , 54 – 58 ].

Given these realities, even with the many challenges that upending a trillion dollar a year business and transforming the diets of seven billion people presents, it is surprising that changes in food production and consumption are not at the forefront of proposed strategies for fighting climate change. Although all of the strategies presented as part of the recent Intergovernmental Panel on Climate Change (IPCC) report on steps needed to keep global warming below 1.5˚C [ 18 ] acknowledge the need for significant negative emissions, none propose even a reduction in per capita livestock consumption below current levels ( Fig 8 ).

We downloaded data for the Shared Socioeconomic Pathways (SSPs) [ 45 ] from the SSP database (Version 2.0; last updated December 2018), and plot here the inferred per capita livestock production for scenarios meant to reach an RF target of 1.9 in 2100. While there is widespread acknowledgement of the impact that ongoing animal agriculture has on the climate, it is notable that most of these scenarios, which represent the most aggressive proposed mitigation strategies in this modeling framework, anticipate an increase in per capita livestock consumption, and none anticipate any significant reduction below current levels, in contrast to the complete elimination we propose here.

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Even if the negative emission technology the IPCC anticipates, BECCS (bio-energy combined with carbon capture and storage), proves to be viable at scale, it will require large amounts of land [ 59 ], and the only way to get that land without massive collateral damage is by displacing animal agriculture, primarily land-intensive ruminants. Thus, all potential solutions to the climate crisis likely require some form of large scale dietary change.

It is important to emphasize that, as great as the potential climate impact of ending animal agriculture may be, even if it occurred, and even if all of the benefits we anticipate were realized, it would not be enough on its own to prevent catastrophic global warming. Rather we have shown that global dietary change provides a powerful complement to the indispensable transition from fossil fuels to renewable energy systems. The challenge we face is not choosing which to pursue, but rather in determining how best to overcome the many social, economic and political challenges incumbent in implementing both as rapidly as possible.

Data and code availability

Analyses were carried out in Python using Jupyter notebooks. All data, analyses and results presented here are available at github.com/mbeisen/LivestockClimateImpact .

Updating estimates of emissions from animal agriculture

We obtained country, species, herd and product type specific CO 2 , CH 4 and N 2 O emission data for terrestrial livestock from the public version of GLEAM 2.0 [ 3 ] downloaded from http://www.fao.org/gleam/results/en/ . GLEAM contains data for cattle, buffalo, sheep, goats, pigs and chickens, and attributes emissions to meat, milk and eggs. Although GLEAM further breaks down emissions based on herd type and production system, we used aggregate data for all herds and production types in the country. We did not include CO 2 emissions linked to land-use change, as this is associated with increases in livestock production which are explicitly not considered by our model.

We obtained livestock production data for 2019 (the most recent year available) from the “Production_LivestockPrimary” datafile in FAOSTAT [ 27 ]. We extracted from Production_LivestockPrimary the amount (in tonnes), for all countries, of primary domestic production of meat from cattle, buffalo, sheep, goat, pig, chicken and duck, milk from cows, buffalo, sheep and goat, and eggs from poultry. We computed meat and protein yields from the carcass weight data reported by GLEAM.

We scaled the GLEAM emission data to current production data from FAOSTAT, using GLEAM data for entire herds based on carcass weight for meat, and production weight for milk and eggs. As GLEAM does not provide data for ducks, we used values for chicken. The scaling was done using country-specific livestock production data from FAOSTAT and regional data from GLEAM.

Estimating species-specific land use

We combined livestock production data with average species and product-specific land use data from [ 12 ] to estimate species, product and country-specific land use data associated with animal agriculture. We use data for cattle meat for buffalo meat, and cow milk for milk from buffalo, goat and sheep. The data are reported in m m 2 ( year )(100 g protein ) −1 except for milk which is reported in m 2 ( year )( liter ) −1 which we convert to m 2 ( year )( kg primary production ) −1 using conversion factors inferred from GLEAM, which reports both protein and primary production data.

The total land use for animal agriculture inferred from this analysis is 33.7 million km 2 , almost identical to the 33.2 million km 2 estimated by [ 1 ] from satellite imagery.

Emissions from agriculture

We used the Environment_Emissions_by_Sector_E_All_Data_(Normalized) data table from FAOSTAT, projecting from the most recent year of 2017 to 2019 by assuming that the average annual growth from 2000 to 2017 continued in 2018 and 2019.

Replacement diets

We modeled agricultural emissions under a business as usual (BAU) diet as remaining at 2019 levels. When modeling reductions in livestock consumption, we assumed protein from livestock products would be replaced with the equivalent amount of protein from current food crops, and used per unit protein emission intensities computed from FAOSTAT to infer emissions from this replacement diet. As an alternative we used emission intensities from [ 26 ] as described in the Sensitivity section. For diets involving the removal of one or more specific animal products, we scaled these dietary replacement emissions by the fraction of animal protein obtained from that product, and scaled biomass recovery by the fraction of animal agriculture land attributed to that product.

Replacement scenarios

We also include in the supplemental data a version of the analysis in which the hypothetical transition is instantaneous (IMM-POD).

We assume that, when animal-derived food consumption is reduced in a year by a fraction Δf , that carbon recovery on a corresponding fraction of land begins immediately and continues at a constant rate until it reaches 100% after 30 years [ 1 ] (see also Fig 7 for 50 and 70 year recovery timelines).

Converting between emissions and atmospheric concentrations of GHGs

The total mass of gas in the atmosphere is 5.136 * 10 21 g, at a mean molecular weight of 28.97 g/mole [ 60 ], or 1.77e+20 total moles of gas. Hence 1 ppb is 1.77*10 11 moles and 1 ppm is 1.77 * 10 14 moles.

Estimating global non-anthropomorphic emissions

Both CH 4 and N 2 O decay at appreciable rates, with half-lives of approximately 9 years for CH 4 [ 62 ] and 115 years for N 2 O [ 63 ], although these estimates are being continuously updated [ 64 ]. We balanced the corresponding decay equations against historical emissions and atmospheric levels, inferring unaccounted for and presumably non-anthropogenic sources leading to mole fraction equivalent increases of CH 4 of 25 ppb/year and N 2 O of 1.0 ppb/year.

Projections of atmospheric gas levels

Radiative forcing

We adopt the commonly used formula for radiative forcing (RF) which derives from [ 30 , 32 ] as modified in the climate modeling program MAGICC6 [ 29 ].

C 0 , M 0 and N 0 are the preindustrial levels of the corresponding gases.

Computing emissions and land carbon opportunity cost

The factor of 2 accounts for the half of CO 2 emissions that go to terrestrial sinks.

Computing carbon emissions budgets for RF 2.6 and 1.9

As the RF calculation used in MAGICC6 account for other gases and effects beyond the three gases used here, we used multivariate linear regression as implemented in the Python package scikit-learn to predict the complete RF output of MAGICC6 using data downloaded from the Shared Socioeconomic Pathways (SSPs) [ 45 ]. The model was trained on atmospheric concentrations of CO 2 , CH 4 and N 2 O to predict the difference between the MAGICC6 RF and the RF calculated using only CO 2 , CH 4 and N 2 O. Then, for timepoints in our scenarios we computed RF as above from CO 2 , CH 4 and N 2 O concentrations, and added to this the adjustment from the linear regression model. We use this RF in Figs 3 and 4 .

In the SSP file:

C = Diagnostics|MAGICC6|Concentration|CO 2

M = Diagnostics|MAGICC6|Concentration|CH 4

N = Diagnostics|MAGICC6|Concentration|N 2 O

MAGICC6 RF = Diagnostics|MAGICC6|Forcing

To compute aCO 2 eq y , the annual CO 2 equivalent emission change of each emissions scenario, we first ran scenarios in which annual CO 2 emissions were reduced from 50 Gt/year to 1 Gt/year in increments of 1 Gt/year, then from 1 Gt/year to 10 Mt/year in increments of 10 Mt/year, and then from 1 Mt/year to 100 kT/year in increments of 100 kT/year. For each of these calibration scenarios, and for all years y from 2021 to 2100, we computed the total RF difference between the calibration scenario and BAU, from 2021 to y .

For each multi-gas emissions scenario, we similarly computed CRFD y , and determined what constant level of reduction in annual CO 2 emissions alone by interpolation using the CRFD y of the calibration scenarios, and designate this annual CO 2 equivalent aCO 2 eq y .

Product equivalents

To compute per product unit and per protein emissions equivalents, we divided aCO 2 eq 2100 for immediate elimination of the product (in kg CO 2 eq/year) by the annual production of the product (in kg production/year) yielding a per product unit emission equivalent measured in kg CO 2 eq per kg production.

For example, assuming, as our model does, that emissions and land use scale with consumption, if annual beef production were reduced by one tonne (1,000 kg) per year, it would result in corresponding annual reductions of -3,476 kg CO 2 , -726 kg CH 4 and -36 kg N 2 O, and would initiate 30 year biomass recovery of 6,050,000 kg of CO 2 equivalent carbon on 25.2 ha of land.

The cumulative reduction in RF, through 2100, of such annual emissions reductions and biomass recovery would be equivalent to a CO 2 emission reduction of 199,000 kg/year. The ratio of these two rates, -199,000 kg CO 2 eq/year over 1,000 kg beef/year yields -199 kg CO 2 eq per kg beef as a measure of the warming impact of one kg of beef. Adjusting this for the dressing percentage of beef (the values reported by FAO, and used in these calculations, are carcass weight, of which only approximately ⅔ ends up as a consumer product) yields the values shown in Fig 6 .

For all meat products we scaled the production amount by a typical dressing percentage of ⅔ to convert to consumer product units. For protein unit equivalents we used protein yields from GLEAM. To convert to driving equivalents we used a value of .254 kg CO 2 eq per km driven taken from life cycle analyses reviewed in [ 36 ].

Supporting information

S1 fig. 15yr phaseout vs. elimination..

(A) Projected annual emissions of CO 2 , CH 4 and N 2 O for each scenario. (B) Projected atmospheric concentrations of CO 2 , CH 4 and N 2 O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [ 30 , 32 ] as modified in MAGICC6 [ 29 ]. Only differences between PHASE-POD default assumptions (15yr phaseout, 30yr carbon recovery, 100% carbon recovery, BAU non-agriculture emissions, FAO crop replacement, and FAO animal ag emissions) are given.

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S2 Fig. 15yr phaseout vs. elimination.

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S3 Fig. 15yr vs. 30yr phaseout.

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S4 Fig. 30yr vs. 50yr biomass recovery.

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S5 Fig. 30yr vs. 70yr biomass recovery.

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S6 Fig. Hayek median vs. low recovery potential.

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S7 Fig. Hayek median vs. high recovery potential.

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S8 Fig. 100% vs. 75% biomass recovery.

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S9 Fig. 100% vs. 50% biomass recovery.

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S10 Fig. 100% vs. 25% biomass recovery.

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S11 Fig. 100% vs. 0% biomass recovery.

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S12 Fig. FAO vs. Xu emissions.

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S13 Fig. FAO vs. Xu replacement diet.

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S14 Fig. 15yr phaseout vs. net zero CO 2 .

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S15 Fig. Phaseout of bovids.

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S16 Fig. Phaseout of ruminants.

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S17 Fig. Phaseout of ruminant meat.

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S18 Fig. Phaseout of ruminant milk.

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S19 Fig. Phaseout of poultry.

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S20 Fig. Phaseout of non-ruminants.

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S21 Fig. Phaseout of buffalo meat.

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S22 Fig. Phaseout of cattle meat.

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S23 Fig. Phaseout of chicken meat.

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S24 Fig. Phaseout of duck meat.

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S25 Fig. Phaseout of goat meat.

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S26 Fig. Phaseout of sheep meat.

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S27 Fig. Phaseout of pig meat.

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S28 Fig. Phaseout of buffalo milk.

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S29 Fig. Phaseout of cow milk.

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S30 Fig. Phaseout of goat milk.

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S31 Fig. Phaseout of sheep milk.

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S32 Fig. Phaseout of eggs.

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S33 Fig. Immediate elimination of animal agriculture reduces global warming impact of atmosphere.

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S34 Fig. Similar effects of phaseout of animal ag and drawdown of CO2 emissions.

Comparison of effects of PHASE-POD (a 15 year phaseout of animal agriculture) and a linear drawdown of all anthropogenic CO2 emissions between 2030 and 2050, and the two combined, on Radiative Forcing (RF), a measure of the instantaneous warming potential of the atmosphere. RF values computed from atmospheric concentrations in by formula of [ 30 , 32 ] as modified in MAGICC6 [ 29 ] with adjustment for gases other than CO 2 , CH 4 and N 2 O.

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S35 Fig. Full carbon opportunity cost of animal agriculture.

We define the Emission and Land Carbon Opportunity Cost of animal agriculture as the total CO 2 reduction necessary to lower the RF in 2100 from the level estimated for a business as usual (BAU) diet to the level estimated for a plant only diet (POD). For these calculations we fix the CH 4 and N 2 O levels in the RF calculation at those estimated for the BAU diet in 2100 and adjust CO 2 levels to reach the target RF. We also calculate ELCOC for just bovid sourced foods and determine the emission reductions necessary to reach RF’s of 2.6 and 1.9, often cited as targets for limiting warming to 2.0˚C and 1.5˚C respectively. (A) Shows the results for RF directly calculated from CO 2 , CH 4 and N 2 O, while (B) shows an RF adjusted for other gases using multivariate linear regression on MAGICC6 output downloaded from the SSP database.

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S36 Fig. aCO2eq calibration for PHASE-POD in 2100.

(A) Projected annual emissions of CO 2 , CH 4 and N 2 O for shown scenarios. (B) Projected atmospheric concentrations of CO 2 , CH 4 and N 2 O under each emission scenario. (C) Radiation Forcing. (D) Cumulative difference between scenario and BAU of Radiative Forcing.

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S37 Fig. aCO2eq calibration for IMM-POD in 2100.

(A) Projected annual emissions of CO 2 , CH 4 and N 2 O for shown scenarios. (B) Projected atmospheric concentrations of CO 2 , CH 4 and N 2 O under each emission scenario. (C) Cumulative difference between scenario and BAU of Radiative Forcing.

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S38 Fig. Emissions reduction equivalents of ending animal agriculture.

The equivalent CO 2 emission reductions associated with different interventions in animal agriculture, aCO2eq, vary with the time window over which cumulative warming impact is evaluated. These plots show, for immediate elimination of animal agriculture (IMM-POD) and a 15-year phaseout (PHASE-POD) how aCO 2 eq y which is the aCO 2 eq from 2021 to year y, varies with y. Because all of the changes in IMM-POD are implemented immediately, its effect is biggest as it is implemented and declines over longer time horizons (the decline in the first 30 years, when biomass recovery is occurring at a constant high right, is due to the slowing of annual decreases in atmospheric CH 4 and N 2 O levels as they asymptotically approach new equilibria). In contrast, PHASE-POD builds slowly,reaching a maximum around 2060 when biomass recovery peaks.

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S39 Fig. Emission equivalents of livestock products through 2100.

We calculated the (A) total annualized CO 2 equivalents through 2100, aCO 2 eq 2100 , for all tracked animal products, and the aCO 2 eq 2100 per unit production (B) or per unit protein (C). For (B) and (C) we also convert the values to driving equivalents, assuming cars that get 10.6 km per liter of gas.

https://doi.org/10.1371/journal.pclm.0000010.s039

S40 Fig. Sensitivity of impact of phaseout of animal agriculture to model assumptions.

The grey line in each plot is PHASE-POD, the default scenario of 15 year phaseout, 30 year carbon recovery, livestock emissions from FAOSTAT, and a diverse plant replacement diet based on [ 26 ]. (A) Effect of substituting the default plant based replacement diet from [ 26 ]) with a diet based on all current human consumed crops using data from FAOSTAT, or a soy only replacement diet. (B) Effect of substituting default combined emissions of animal agriculture estimated via GLEAM and FAOSTAT with those from [ 26 ].

https://doi.org/10.1371/journal.pclm.0000010.s040

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U.S. Climate Resilience Toolkit

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• Case Studies

• Climate Change and Agriculture in the United States: Effects and Adaptation

Increases of atmospheric carbon dioxide, rising temperatures, and altered precipitation patterns will affect agricultural productivity. Livestock production systems are vulnerable to temperature stresses. Projections for crops and livestock production systems reveal that climate change effects over the next 25 years will be mixed. Climate change will exacerbate current biotic stresses on agricultural plants and animals. Agriculture is dependent on a wide range of ecosystem processes that support productivity including maintenance of soil quality and regulation of water quality and quantity. The predicted higher incidence of extreme weather events will have an increasing influence on agricultural productivity. Over the last 150 years, U.S. agriculture has exhibited a remarkable capacity to adapt to a wide diversity of growing conditions amid dynamic social and economic changes.

Options Considered

The following resilience actions were considered in the development of this plan. To explore other resilience actions that may be applicable to your community, visit the complete Options Database .

7 Steps to Define Your Business Process by  Stefano Borghi . Used via  CC BY-NC-ND 4.0 DEED , via Google.

How Climate Change Impacts Animals

• First Online: 08 August 2024

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Climate change is causing global temperatures to increase and regional and seasonal changes to become more common. These changing climatic conditions can impact animals in various ways. For example, these changes can influence the number of female sea turtles being born compared to the number of male sea turtles. And summer weather conditions influence how likely honey bees survive the winter. Also, climate change impacts hibernating animals in various ways, including their phenology, condition, reproduction, and survival. Depending on the species, age, and other factors, climate change can have both positive and negative impacts on hibernating animals.

Credit: This chapter is based on four scientific articles by Jacques-Olivier Laloë, Martina Calovi, Erin Wilson Rankin, and Caitlin P. Wells and their colleagues. (Full citations are available at the end of the chapter)

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1.1 This Chapter Is Based On:

Sea turtles:.

Laloë, J. O., Tedeschi, J. N., Booth, D. T., Bell, I., Dunstan, A., Reina, R. D., & Hays, G. C. (2021). Extreme rainfall events and cooling of sea turtle clutches: Implications in the face of climate warming. Ecology and Evolution, 11 (1), 560–565.

Honey Bees:

Calovi, M., Grozinger, C. M., Miller, D. A., & Goslee, S. C. (2021). Summer weather conditions influence winter survival of honey bees (Apis mellifera) in the northeastern United States. Scientific Reports , 11 (1), 1–12.

Wilson Rankin, E. E., Barney, S. K., & Lozano, G. E. (2020). Reduced water negatively impacts social bee survival and productivity via shifts in floral nutrition. Journal of Insect Science , 20 (5), 15.

Hibernating Animals:

Wells, C. P., Barbier, R., Nelson, S., Kanaziz, R., & Aubry, L. M. (2022). Life history consequences of climate change in hibernating mammals: A review. Ecography , 2022 (6), e06056.

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Essays on Climate Change Impacts and Adaptation for Agriculture

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Over the past twenty years economists have developed econometric approaches for estimating the impacts of climate change on agriculture by accounting for farmer adaptation implicitly. These reduced-form approaches are simple to implement but provide little insights into impact mechanisms, limiting their usefulness for adaptation policy. Recently, conflicting estimates for US agriculture have led to research with greater emphasis on mechanisms including renewed interest in statistical crop yield models. Findings suggest US agriculture will be mainly and severely affected by an increased frequency of high temperatures with crop yield suggested as a major driver.

This dissertation is comprised of three essays highlighting methodological aspects in this literature. It contributes to the ongoing debate and shows the preeminent role of extreme temperature is overestimated while the role of soil moisture is seriously underestimated. This stems from issues related to weather data quality, the presence of time-varying omitted weather variables, as well as from modeling assumptions that inadvertently underestimate farmers' ability to adapt to seasonal aspects of climate change. My work illustrates how econometric models of climate change impacts on crop production can be improved by structuring them to admit some basic principles of agronomic science.

The first essay shows that nonlinear temperature effects on corn yields are not robust to alternative weather datasets. The leading econometric studies in the current literature are based on a weather dataset that involves considerable interpolation. I introduce the use of a new dataset to agricultural climate change research that has been carefully developed with scientific methods to represent weather variation with one-hour and 14 kilometer accuracy. Detrimental effects of extreme temperature crucially hinge upon the recorded frequency at the highest temperatures. My research suggests that measurement error in short amounts of time spent at extreme temperature levels has disproportionate effects on estimated parameters associated with the right tail of the temperature distribution. My alternative dataset suggests detrimental temperature effects of climate change over the next 50-100 years will be half as much as in leading econometric studies in the current literature.

The second essay relaxes the prevalent assumption in the literature that weather is additive. This has been the practice in most empirical models. Weather regressors are typically aggregated over the months that include the growing season. Using a simple model I show that this assumption imposes implausible characteristics on the technology. I test this assumption empirically using a crop yield model for US corn that accounts for differences in intra-day temperature variation in different stages of the growing season. Results strongly reject additivity and suggest that weather shocks such as extreme temperatures are particularly detrimental toward the middle of the season around flowering time, which corrects a disagreement of empirical yield models with the natural sciences. I discuss how this assumption tends to underestimate the range of adaptation possibilities available to farmers, thus overstating projected climate change impacts on the sector.

The third essay introduces an improved measure of water availability for crops that accounts for time variation of soil moisture rather than season-long rainfall totals, as has been common practice in the literature. Leading studies in the literature are based on season-long rainfall. My alternative dataset based on scientific models that track soil moisture variation during the growing season includes variables that are more relevant for tracking crop development. Results show that models in the literature attribute too much variation in yields to temperature variation because rainfall variables are a crude and inaccurate measure of the moisture that determined crop growth. Consequently, I find that third of damages to corn yields previously attributed to extreme temperature are explained by drought, which is far more consistent with agronomic science. This highlights the potential adaptive role for water management in addressing climate change, unlike the literature now suggests.

The fourth essay proposes a general structural framework for analyzing the mechanisms of climate change impacts on the sector. An empirical example incorporates some of the flexibilities highlighted in the previous essay to assess how farmer adaptation can reduce projected impacts on corn yields substantially. Global warming increases the length of the growing season in northern states. This gives farmers the flexibility to change planting dates that can reduce exposure of crops during the most sensitive flowering stage of the crop growth cycle. These research results identify another important type of farmer adaptation that can reduce vulnerability to climate change, which has been overlooked in the literature but which becomes evident only by incorporating the principles of agronomic science into econometric modeling of climate change impact analysis.

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Climate Change Impacts on Agriculture and Food Supply

There are over two million farms in the United States, and more than half the nation’s land is used for agricultural production. 1 The number of farms has been slowly declining since the 1930s, 2 though the average farm size has remained about the same since the early 1970s. 3 Agriculture also extends beyond farms. It includes industries such as food service and food manufacturing.

Drought. Since early 2020, the U.S. Southwest has been experiencing one of the most severe long-term droughts of the past 1,200 years. Multiple seasons of record low precipitation and near-record high temperatures were the main triggers of the drought. 37

Wildfires. Some tribal communities are particularly vulnerable to wildfires due to their often-remote locations and lack of firefighting resources and staff. 38 In addition, because wildfire smoke can travel long distances from the source fire, its effects can be far reaching, especially for people with certain medical conditions or who spend long periods of time outside.

Decreased crop yields. Rising temperatures and carbon dioxide concentrations may increase some crop yields, but the yields of major commodity crops (such as corn, rice, and oats) are expected to be lower than they would in a future without climate change. 39

Heat stress. Dairy cows are especially sensitive to heat stress, which can affect their appetite and milk production. In 2010, heat stress lowered annual U.S. dairy production by an estimated $1.2 billion. 40

Soil erosion. Heavy rainfalls can lead to more soil erosion, which is a major environmental threat to sustainable crop production. 41

Agriculture is very sensitive to weather and climate. 4 It also relies heavily on land, water, and other natural resources that climate affects. 5   While climate changes (such as in temperature, precipitation, and frost timing) could lengthen the growing season or allow different crops to be grown in some regions, 6 it will also make agricultural practices more difficult in others.

The effects of climate change on agriculture will depend on the rate and severity of the change, as well as the degree to which farmers and ranchers can adapt. 7 U.S. agriculture already has many practices in place to adapt to a changing climate, including crop rotation and integrated pest management . A good deal of research is also under way to help prepare for a changing climate.

Learn more about climate change and agriculture:

Top Climate Impacts on Agriculture

Agriculture and the economy, environmental justice and equity, what we can do, related resources, the link between agriculture and climate change.

Climate change can affect crops, livestock, soil and water resources, rural communities, and agricultural workers. However, the agriculture sector also emits greenhouse gases into the atmosphere that contribute to climate change. 

Read more about greenhouse gas emissions on the Basics of Climate Change  page.

Learn how the agriculture sector is reducing methane emissions from livestock waste through the AgSTAR program . For a more technical look at emissions from the agriculture sector, take a look at EPA's Greenhouse Gas Emissions Inventory chapter on agriculture activities in the United States . 

Climate change may affect agriculture at both local and regional scales. Key impacts are described in this section.

1. Changes in Agricultural Productivity 

Climate change can make conditions better or worse for growing crops in different regions. For example, changes in temperature, rainfall, and frost-free days are leading to longer growing seasons in almost every state. 8  A longer growing season can have both positive and negative impacts for raising food. Some farmers may be able to plant longer-maturing crops or more crop cycles altogether, while others may need to provide more irrigation over a longer, hotter growing season. Air pollution may also damage crops, plants, and forests. 9  For example, when plants absorb large amounts of ground-level ozone, they experience reduced photosynthesis, slower growth, and higher sensitivity to diseases. 10  

Climate change can also increase the threat of wildfires . Wildfires pose major risks to farmlands, grasslands, and rangelands. 11  Temperature and precipitation changes will also very likely expand the occurrence and range of insects, weeds, and diseases. 12  This could lead to a greater need for weed and pest control. 13  

Pollination is vital to more than 100 crops grown in the United States. 14  Warmer temperatures and changing precipitation can affect when plants bloom and when pollinators , such as bees and butterflies, come out. 15  If mismatches occur between when plants flower and when pollinators emerge, pollination could decrease. 16

2. Impacts to Soil and Water Resources

Climate change is expected to increase the frequency of heavy precipitation in the United States, which can harm crops by eroding soil and depleting soil nutrients. 18  Heavy rains can also increase agricultural runoff into oceans, lakes, and streams. 19  This runoff can harm water quality. 

When coupled with warming water temperatures brought on by climate change, runoff can lead to depleted oxygen levels in water bodies. This is known as hypoxia . Hypoxia can kill fish and shellfish. It can also affect their ability to find food and habitat, which in turn could harm the coastal societies and economies that depend on those ecosystems. 20  

Sea level rise and storms also pose threats to coastal agricultural communities. These threats include erosion, agricultural land losses, and saltwater intrusion, which can contaminate water supplies. 21  Climate change is expected to worsen these threats. 22  

3. Health Challenges to Agricultural Workers and Livestock

Agricultural workers face several climate-related health risks. These include exposures to heat and other extreme weather, more pesticide exposure due to expanded pest presence, disease-carrying pests like mosquitos and ticks, and degraded air quality. 23  Language barriers, lack of health care access, and other factors can compound these risks. 24  Heat and humidity can also affect the health and productivity of animals raised for meat, milk, and eggs. 25   

For more specific examples of climate change impacts in your region, please see the National Climate Assessment .

Agriculture contributed more than $1.1 trillion to the U.S. gross domestic product in 2019. 26  The sector accounts for 10.9 percent of total U.S. employment—more than 22 million jobs. 27  These include not only on-farm jobs, but also jobs in food service and other related industries. Food service makes up the largest share of these jobs at 13 million. 28  

Cattle, corn, dairy products, and soybeans are the top income-producing commodities . 29  The United States is also a key exporter of soybeans, other plant products, tree nuts, animal feeds, beef, and veal. 30

Many hired crop farmworkers are foreign-born people from Mexico and Central America. 31  Most hired crop farmworkers are not migrant workers; instead, they work at a single location within 75 miles of their homes. 32  Many hired farmworkers can be more at risk of climate health threats due to social factors, such as language barriers and health care access.

Climate change could affect food security for some households in the country. Most U.S. households are currently food secure . This means that all people in the household have enough food to live active, healthy lives. 33  However, 13.8 million U.S. households (about one-tenth of all U.S. households) were food insecure at least part of the time in 2020. 34  U.S. households with above-average food insecurity include those with an income below the poverty threshold, those headed by a single woman, and those with Black or Hispanic owners and lessees. 35

Climate change can also affect food security for some Indigenous peoples in Hawai'i and other U.S.-affiliated Pacific islands. Climate impacts like sea level rise and more intense storms can affect the production of crops like taro, breadfruit, and mango. 36 These crops are often key sources of nutrition and may also have cultural and economic importance.

We can reduce the impact of climate change on agriculture in many ways, including the following:

• Incorporate climate-smart farming methods. Farmers can use climate forecasting tools, plant cover crops, and take other steps to help manage climate-related production threats. 
• Join AgSTAR. Livestock producers can get help in recovering methane , a potent greenhouse gas, from biogas created when manure decomposes.
• Reduce runoff. Agricultural producers can strategically apply fertilizers, keep their animals out of streams, and take more actions to reduce nutrient-laden runoff. 
• Boost crop resistance. Adopt research-proven ways to reduce the impacts of climate change on crops and livestock , such as reducing pesticide use and improving pollination.
• Prevent food waste. Stretch your dollar and shrink your carbon footprint by planning  your shopping trips carefully and properly storing food . Donate nutritious, untouched food to food banks and those in need.

See additional actions you can take, as well as steps that companies can take, on EPA’s What You Can Do About Climate Change page.

Related Climate Indicators

Learn more about some of the key indicators of climate change related to this sector from EPA’s Climate Change Indicators :

• Seasonal Temperature
• Freeze-Thaw Conditions
• Length of Growing Season
• Growing Degree Days
• Fifth National Climate Assessment, Chapter 11: “Agriculture, Food Systems, and Rural Communities."
• National Agricultural Center . Provides agriculture-related news from all of EPA through a free email subscription service.
• U.S. Department of Agriculture (USDA) Economic Research Service . Produces research, information, and outlook products to enhance people’s understanding of agriculture and food issues. 
• USDA Environmental Quality Incentives Program . Provides financial and technical assistance to agricultural producers to address natural resource concerns.
• USDA Climate Hubs . Connects farmers, ranchers, and land managers with tools to help them adapt to climate change impacts in their area.
• USDA Rural Development . Promotes economic development in rural communities. Provides loans, grants, technical assistance, and education to agricultural producers and rural residents and organizations.
• National Integrated Drought Information System . Coordinates U.S. drought monitoring, forecasting, and planning through a multi-agency partnership. The U.S. Drought Monitor assesses droughts on a weekly basis.
• Sustainable Management of Food . Provides tools and resources for preventing and reducing wasted food and its associated impacts over the entire life cycle. 
• Resources, Waste, and Climate Change . Learn how reducing waste decreases our carbon footprint and what business, communities, and individuals can do.

1  U.S. Department of Agriculture (USDA), Economic Research Service (ERS). (2022). Ag and food statistics: Charting the essentials. Farming and farm income . Retrieved 3/18/2022.

2  USDA, ERS. (2022). Ag and food statistics: Charting the essentials. Farming and farm income . Retrieved 3/18/2022.

3  USDA, ERS. (2022). Ag and food statistics: Charting the essentials. Farming and farm income . Retrieved 3/18/2022.

4  Walsh, M.K., et al. (2020). Climate indicators for agriculture . USDA Technical Bulletin 1953. Washington, DC, p. 1. 

5  Gowda, P., et al. (2018). Ch. 10: Agriculture and rural communities . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II . U.S. Global Change Research Program, Washington, DC, p. 393. 

6  Walsh, M.K., et al. (2020). Climate indicators for agriculture . USDA Technical Bulletin 1953. Washington, DC, p. 22. 

7  Gowda, P., et al. (2018). Ch. 10: Agriculture and rural communities . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II . U.S. Global Change Research Program, Washington, DC, p. 393.

8  Gowda, P., et al. (2018). Ch. 10: Agriculture and rural communities . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II . U.S. Global Change Research Program, Washington, DC, p. 401. 

9  Nolte, C.G., et al. (2018). Ch. 13: Air quality . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II . U.S. Global Change Research Program, Washington, DC, p. 513. 

10  EPA. (2022). Ecosystem effects of ozone pollution . Retrieved 3/18/2022. 

11  Gowda, P., et al. (2018). Ch. 10: Agriculture and rural communities . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II . U.S. Global Change Research Program, Washington, DC, p. 401.

12  Ziska, L., et al. (2016). Ch. 7: Food safety, nutrition, and distribution . In: The impacts of climate change on human health in the United States: A scientific assessment . U.S. Global Change Research Program, Washington, DC, p. 197.  

13  Ziska, L., et al. (2016). Ch. 7: Food safety, nutrition, and distribution . In: The impacts of climate change on human health in the United States: A scientific assessment . U.S. Global Change Research Program, Washington, DC, p. 197.  

14  USDA. Pollinators . Retrieved 3/18/2022. 

15  Walsh, M.K., et al. (2020). Climate indicators for agriculture . USDA Technical Bulletin 1953. Washington, DC, p. 20.

16  Walsh, M.K., et al. (2020). Climate indicators for agriculture . USDA Technical Bulletin 1953. Washington, DC, p. 40.

17  Gowda, P., et al. (2018). Ch. 10: Agriculture and rural communities . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II . U.S. Global Change Research Program, Washington, DC, p. 405.

18  Gowda, P., et al. (2018). Ch. 10: Agriculture and rural communities . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II . U.S. Global Change Research Program, Washington, DC, p. 409.

19  Gowda, P., et al. (2018). Ch. 10: Agriculture and rural communities . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II. U.S. Global Change Research Program, Washington, DC, p. 409.

20  Gowda, P., et al. (2018). Ch. 10: Agriculture and rural communities . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II. U.S. Global Change Research Program, Washington, DC, p. 405.

21  Gowda, P., et al. (2018). Ch. 10: Agriculture and rural communities . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II . U.S. Global Change Research Program, Washington, DC, p. 405.

22 Gowda, P., et al. (2018). Ch. 10: Agriculture and rural communities . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II . U.S. Global Change Research Program, Washington, DC, p. 405.

23  Gamble, J.L., et al. (2016). Ch. 9: Populations of concern . In: The impacts of climate change on human health in the United States: A scientific assessment . U.S. Global Change Research Program, Washington, DC, pp. 247–286. 

24  Hernandez, T., and S. Gabbard. (2019). Findings from the National Agricultural Workers Survey (NAWS) 2015–2016: A demographic and employment profile of United States farmworkers . Department of Labor, Employment and Training Administration, Washington, DC, pp. 10–11 and pp. 40–45.  

25  Walsh, M. K., et al. (2020). Climate indicators for agriculture . USDA Technical Bulletin 1953. Washington, DC, p. 20. 

26  USDA, ERS. (2022). Ag and food statistics: Charting the essentials . Retrieved 3/18/2022.

27  USDA, ERS. (2022). Ag and food statistics: Charting the essentials . Retrieved 3/18/2022.

28  USDA, ERS. (2022). Ag and food statistics: Charting the essentials . Retrieved 3/18/2022.

29  USDA, ERS. (2022). Farm income and wealth statistics/cash receipts by commodity . Retrieved 3/18/2022. 

30  USDA, ERS. (2022). Farm income and wealth statistics/cash receipts by commodity . Retrieved 3/18/2022. 

31  USDA, ERS. (2020). Farm income and wealth statistics/cash receipts by state . Retrieved 5/11/2022.

32  USDA, ERS. (2020). Farm income and wealth statistics/cash receipts by state . Retrieved 5/11/2022.

33  USDA, ERS. (2020). Farm income and wealth statistics/cash receipts by state . Retrieved 5/11/2022.

34  Coleman-Jensen, A., et al. (2020). Household food security in the United States in 2020 , ERR-298, USDA, ERS, p. v. 

35  Coleman-Jensen, A., et al. (2020). Household food security in the United States in 2020 , ERR-298, USDA, ERS, p. v.

36  Keener, V., et al. (2018). Ch. 27: Hawai‘i and U.S.-affiliated Pacific islands . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II . U.S. Global Change Research Program, Washington, DC, p. 1269. 

37  Mankin, J.S., et al. (2021). NOAA Drought Task Force report on the 2020–2021 southwestern U.S. drought. National Oceanic and Atmospheric Administration (NOAA) Drought Task Force; NOAA Modeling, Analysis, Predictions and Projections Programs; and National Integrated Drought Information System, p 4. 

38  Gowda, P., et al. (2018). Ch. 10: Agriculture and rural communities . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II . U.S. Global Change Research Program, Washington, DC, p. 401.

39  Gowda, P., et al. (2018). Ch. 10: Agriculture and rural communities . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II . U.S. Global Change Research Program, Washington, DC, p. 409.

40  Gowda, P., et al. (2018). Ch. 10: Agriculture and rural communities . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II . U.S. Global Change Research Program, Washington, DC, p. 407.

41  Gowda, P., et al. (2018). Ch. 10: Agriculture and rural communities . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II . U.S. Global Change Research Program, Washington, DC, p. 415.

• Climate Change Impacts Home
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FIGURE 1 Integrated assessment model

FIGURE 2 Likely carbon emissions, years 2000-2100

FIGURE 3 Predicted global temperature changes, years 2000-2100

Latin America

Eastern Europe includes the former Soviet Union.

TABLE 2 Cross-sectional results for Brazil

-47 300 (6.62)

Winter Temp

-12 000 (13.12)

Spring Temp

16 300 (14.82)

Summer Temp

-19 400 (11.19)

-309 (15.53)

10 100 (5.95

715 (11.10)

Winter Temp Squared

1 490 (12.05)

Winter Precip Squared

-0.1 (0.52)

Spring Temp Squared

-3 690 (31.99)

Spring Precip Squared

-5.1 (15.11)

Summer Temp Squared

Summer Precip Squared

Fall Temp Squared

Fall Precip Squared

-0.3 (3.67)

-2 600 (1.41)

-6 200 (1.62)

14 700 (7.53)

-45 000 (8.78)

-42 500 (14.50)

Dependent variable is pooled land values. T-statistics are in parentheses. Source: Sanghi and Mendelsohn, 1999.

TABLE 3 Cross-sectional results for India

4 660 (8.92)

-133 (3.38)

18.5 (6.11)

-372 (16.71)

-14.4 (8.00)

-103 (2.84)

-0.4 (2.11)

-39.3 (11.40)

-0.16 (1.57)

-80.3 (12.48)

0.28 (10.58)

35.0 (4.62)

0.01 (3.89)

-68.1 (6.77)

-0.04 (7.34)

Winter Temp x Precip

-3.62 (4.57)

-0.21 (1.97)

Spring Temp x Precip

8.21 (11.59)

3.01 (5.83)

-153 (4.39)

Cultivators

28 680 (8.98)

Pop. density

-174 (7.83)

Dependent variable is pooled net revenues. T-statistics are in parentheses. A set of dummy variables for each year is also included but not shown. Source: Sanghi and Mendelsohn, 1999.

TABLE 4 Agro-economic results: change in yields

Wheat Maize

Wheat Rice Maize

United States

Source: Reilly et al ., 1996.

TABLE 5 Ricardian results: percent reduction in net income

These estimates do not include carbon fertilization, which is expected to add 30% to crop productivity. Climate scenario assumes a 7% increase in precipitation.

TABLE 6 Agricultural impacts (000 million US$)

Negative numbers imply damages and positive numbers imply benefits. Effects are annual impacts in the year 2100. CO2 is assumed to be 700, 900, and 1000 ppmv in the three respective scenarios. Eastern Europe includes the former Soviet Union. Global agricultural GDP in 2100 is assumed to be 4759 000 million dollars.

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• v.9(1); 2019 Jan

Livestock and climate change: impact of livestock on climate and mitigation strategies

Giampiero grossi.

1 Dipartimento di Scienze Agrarie e Forestali, Università della Tuscia, Viterbo, Italy

Pietro Goglio

2 School of Water, Energy and Environment, Cranfield University, Cranfield, UK

Andrea Vitali

3 Facoltà di bioscienze e tecnologie agro-alimentari e ambientali, University of Teramo, Italy

Adrian G Williams

Implications.

• The livestock sector requires a significant amount of natural resources and has an important role in global greenhouse gas emissions. The most important greenhouse gases from animal agriculture are methane and nitrous oxide.
• Mitigation strategies aimed at reducing the emission intensity of this sector are needed to meet the increasing demand for livestock products driven by population growth.
• To increase the effectiveness of mitigation strategies, the complex interactions among the components of livestock production systems must be taken into account to avoid environmental trade-offs.

Introduction

According to the United Nations ( UN, 2017 ), the world population increased by approximately 1 billion inhabitants during the last 12 years, reaching nearly 7.6 billion in 2017. Although this growth is slower than 10 years ago (1.24% vs. 1.10% per year), with an average increase of 83 million people annually, global population will reach about 8.6 billion in 2030 and 9.8 billion in 2050. Population growth, urbanization, and income rise in developing countries are the main driver of the increased demand for livestock products ( UN, 2017 ). The livestock sector requires a significant amount of natural resources and is responsible for about 14.5% of total anthropogenic greenhouse gas emissions (7.1 Gigatonnes of carbon dioxide equivalents for the year 2005; Gerber et al., 2013 ). Mitigation strategies aimed at reducing emissions of this sector are needed to limit the environmental burden from food production while ensuring a sufficient supply of food for a growing world population. The objectives of this manuscript are to 1) discuss the main greenhouse gas emissions sources from the livestock sector and 2) summarize the best mitigation strategies.

Impact of Livestock on Climate Change

The most important greenhouse gases from animal agriculture are methane and nitrous oxide. Methane, mainly produced by enteric fermentation and manure storage, is a gas which has an effect on global warming 28 times higher than carbon dioxide. Nitrous oxide, arising from manure storage and the use of organic/inorganic fertilizers, is a molecule with a global warming potential 265 times higher than carbon dioxide. The carbon dioxide equivalent is a standard unit used to account for the global warming potential ( IPCC, 2013 ).

Figure 1 was adapted from the Global Livestock Environmental Assessment Model (GLEAM) developed by FAO ( FAO, 2017 ) and shows in carbon dioxide equivalents the greenhouse gas incidences that enteric fermentation and manure storage have across the main livestock species raised worldwide.

Greenhouse gases incidence of enteric fermentation and manure storage by animal type, expressed as Gigatonnes of carbon dioxide equivalents. Data referred to 2010 ( FAO, 2017 ).

In addition to greenhouse gases arising from enteric fermentation and manure storage, feed production together with the related soil carbon dioxide and nitrous oxide emissions is another important hot spot for the livestock sector. Soil carbon dioxide emissions are due to soil carbon dynamics (e.g., decomposing plant residues, mineralization of soil organic matter, land use change, etc.), the manufacturing of synthetic fertilizers and pesticides, and from fossil fuel use in on-farm agricultural operations ( Goglio et al., 2018 ). Nitrous oxide emissions are emitted when organic and inorganic fertilizers are applied to the soil.

As shown in Figure 2 , feed production and processing contribute about 45% of the whole sector (3.2 Gigatonnes of carbon dioxide equivalents). Enteric fermentation producing about 2.8 Gigatonnes (39%) is the second largest source of emissions. Manure storage with 0.71 Gigatonnes accounts for about 10% of the total. The remaining 6% (0.42 Gigatonnes of carbon dioxide equivalents) is attributable to the processing and transportation of animal products ( Gerber et al., 2013 ).

Livestock emissions by source (adapted from Gerber et al., 2013 ). Direct livestock emissions are shown in red.

Feed production ( Figure 2 ) includes all the greenhouse gas emission arising from 1) land use change, 2) manufacturing and use of fertilizers and pesticides, 3) manure excreted and applied to fields, 4) agricultural operations, 5) feed processing, and 6) feed transport. Although these processes result in a large share of the livestock supply chain, in this article, we mainly focus on direct livestock emissions enteric fermentation, manure storage, and manure excreted/applied to the soil. All other emissions are outside the scope of this article.

Enteric fermentation

Enteric fermentation is a natural part of the digestive process of ruminants where bacteria, protozoa, and fungi contained in the fore-stomach of the animal (rumen), ferment and break down the plant biomass eaten by the animal. Plant biomass in the rumen is converted into volatile fatty acids, which pass the rumen wall and go to the liver through the circulatory system. This process supplies a major part of the energy needs of the animal and enables the high conversion efficiency of cellulose and semi-cellulose, which is typical of ruminants. The gaseous waste products of enteric fermentation, carbon dioxide and methane, are mainly removed from the rumen by eructation. Methane emission in the reticulorumen is an evolutionary adaptation that enables the rumen ecosystem to dispose hydrogen, which may otherwise accumulate and inhibit carbohydrate fermentation and fiber degradation ( McAllister and Newbold, 2008 ). The emission rate of enteric methane varies according to feed intake and digestibility.

Manure storage

Manure acts as an emission source for both methane and nitrous oxide, and the quantity emitted is linked to environmental conditions, type of management and composition of the manure. Organic matter and nitrogen content of excreta are the main characteristics influencing emission of methane and nitrous oxide, respectively. Under anaerobic conditions, the organic matter is partially decomposed by bacteria producing methane and carbon dioxide. Storage or treatment of liquid manure (slurry) in a lagoon or tank promotes an anaerobic environment which leads to an increase in methane production. Long storage periods and warm and wet conditions can further increase these emissions ( EPA, 2010 ). On the other hand, nitrous oxide emissions need a combination of aerobic and anaerobic conditions to be produced. Therefore, when manure is handled as a solid (dung) or deposited on pastures, nitrous oxide production increases while little or no methane is emitted. Nitrous oxide is generated through both the nitrification and denitrification processes of the nitrogen contained in manure, which is mainly present in organic form (e.g., proteins) and in inorganic form as ammonium and ammonia. Nitrification occurs aerobically and converts ammonium and ammonia to nitrites and then nitrates, while denitrification occurs anaerobically converting nitrates to nitrous oxide and nitrogen gas ( Saggar, 2010 ). The balance between ammonium and ammonia is highly affected by pH, with ammonia increasing as pH increases.

Feed production

Almost 60% of the global biomass harvested worldwide enters the livestock subsystem as feed or bedding material ( Krausmann et al., 2008 ). Greenhouse gas emissions from feed production represent 60–80% of the emission coming from eggs, chicken and pork, and 35–45% of the milk and beef sector ( Sonesson et al., 2009 ). As shown in Figure 2 , emissions from feed production account for about 45% of the livestock sector. The application of manure as fertilizer for feed crops and the deposition of manure on pastures generates a substantial amount of nitrous oxide emissions representing about half of these emissions ( Gerber et al., 2013 ). Although livestock feed production often involves large applications of nitrogen to agricultural soils, good manure management can reduce the need for manufactured fertilizers.

Livestock Mitigation Strategies

The extreme heterogeneity of the agricultural sector needs to be taken into account when defining the overall sustainability of a mitigation strategy, which can vary across different livestock systems, species, and climates. Generally, no measure in isolation will encompass the full emission reduction potential, while a combination selected from the full range of existing options will be required to reach the best result ( Llonch et al., 2017 ). It is also important to consider the “pollution swapping” effect when evaluating the effectiveness of a mitigation strategy ( Hristov et al., 2013 ). Reduction of methane emissions during enteric fermentation might be counteracted by increased greenhouse gas emissions in applied manure. Reduction of direct nitrous oxide emissions during storage might result in higher nitrate leaching and ammonia volatilization during field application.

Mitigation may occur directly by reducing the amount of greenhouse gases emitted, or indirectly through the improvement of production efficiency. The main strategies to mitigate greenhouse gas emissions in the livestock sector have been investigated and are summarized in Table 1 .

Mitigation potential of various strategies

*High = ≥30% mitigating effect; Medium = 10–30% mitigating effect; Low = ≤10% mitigating effect. Mitigating effects refer to percent change over a “standard practice” according to Newell Price et al. (2011) ; Borhan et al. (2012) ; Hristov et al. (2013) ; Montes et al. (2013) ; Petersen (2013) ; Battini et al. (2014) ; Knapp et al. (2014) ; Llonch et al. (2017) ; Mohankumar Sajeev et al. (2018) .

† Inconsistent/variable results.

‡ Uncertainty due to limited research or lack of data.

Decreasing methane emissions from ruminants is one pressing challenge facing the ruminant production sector. Strategies for reducing this source of emissions focus on improving the efficiency of rumen fermentation and increasing animal productivity. A large number of mitigation options have been proposed (e.g., diet manipulation, vaccines, chemical additives, animal genetic selection, etc.) with different efficiencies in reducing enteric methane as shown in Table 1 .

Forage quality and digestibility affect enteric methane production. Lignin content increases during plant growth, consequently reducing plant digestibility. Therefore, harvesting forage (especially grass) for ensiling at an earlier stage of maturity increases its soluble carbohydrate content and reduces lignification. According to Knapp et al. (2014) practices aimed to increase forage quality have shown a potential enteric methane reduction of about 5% per unit of fat protein corrected milk.

Physical processing of forages, such as chopping, grinding, and steam treatment, also improves forage digestibility and mitigates enteric methane production in ruminants ( Hristov et al., 2013 ). However, the reduction potential of this practice was reported to be less than 2% per unit of fat protein corrected milk ( Knapp et al., 2014 ).

Improving diet digestibility by increasing concentrate feeding is another effective mitigation strategy, reducing by 15% methane emissions per unit of fat protein corrected milk ( Knapp et al., 2014 ). However, the ratio of forage to concentrate has to be carefully taken into account when applying this strategy. Indeed, although a marked reduction of enteric methane can be expected with rates of concentrate inclusion between 35% and 40% ( Gerber et al., 2013 ). A greater proportion of dietary fermentable carbohydrates could increase the risk of metabolic diseases (e.g., rumen acidosis).

Addition of fats or fatty acids to the diets of ruminants can decrease enteric methane emissions by both decreasing the proportion of energy supplied from fermentable carbohydrates and changes in the microbial population of the rumen ( Llonch et al., 2017 ). Although some byproducts (e.g., cottonseed, brewer’s grains, cold-pressed canola meal, etc.) are effective in reducing enteric fermentation ( Moate et al., 2011 ), the mitigation potential of high oil byproducts has not been well-established and in some cases methane production may increase due to increased fiber intake ( Hristov et al., 2013 ). The inclusion of lipids higher than 10% can lead to impairment of ruminal function due to changes to the microbial population which in turn decreases the ability to digest fiber. Lipid diet supplementation between 5% and 8% of the dry matter intake is an effective mitigation strategy ( Grainger and Beauchemin, 2011 ) with a potential enteric methane reduction of about 15% per unit of fat protein corrected milk ( Knapp et al., 2014 ).

Feed additives (electron receptors, ionophoric antibiotics, chemical inhibitors, etc.) have also been tested for their ability to decrease methane emissions ( Beauchemin et al., 2009 ). However, the unknown toxicity and the health risks associated with the use of some of these compounds may severely constrain widespread adoption ( Herrero et al., 2016 ).

Increased animal density together with continuous inflow of nutrients from imported feeds is likely to increase volumes of manure to be managed. Stored manure accounts for a relatively small amount of direct agricultural greenhouse gases ( Figure 2 ), and it is technically possible to mitigate a very high percentage of these emissions ( Hristov et al., 2013 ). In the following section, some of the most effective mitigation strategies are discussed.

As methane production increases with the temperature of stored manure, a reduction of storage temperature has been reported to drop these emissions by 30–50% ( Borhan et al., 2012 ). However, the net greenhouse gas mitigation resulting from this strategy can vary widely, and it is strictly related to the energy used and the cooling system adopted.

Frequent removal of manure to an outside storage facility is an effective practice that can be accomplished using grooved floors combined with regular scraping of manure, especially for pigs and some cattle production systems. Indeed, if the channels underneath the stable are emptied regularly, and the manure/slurry are transported to an outside storage facility, this practice has the potential to reduce methane and nitrous oxide emissions by 55% and 41%, respectively ( Mohankumar Sajeev et al., 2018 ). On poultry farms the litter/manure is usually removed at the end of the crop; however, advanced layer housing using belt scrapers can efficiently remove litter/manure continuously and decrease greenhouse gas emissions ( Fournel et al., 2012 ).

Solid-liquid separation is a processing technology that partially separates the solids from liquid manure using gravity or mechanical systems such as centrifuges or filter presses. As shown in Table 1 , the greenhouse gas mitigation potential of this technique has been reported to be higher than 30% compared with untreated manure ( Montes et al., 2013 ). The organic component with a larger particle size follows the solid stream during the separation process, and it is then stored in stockpiles. The aerated condition of the storage can then limit the potential for methane to be emitted; however, ammonia loss through composting and generating high temperatures can be accelerated. Also, the remaining liquid fraction is still a potential source of indirect nitrous oxide emissions. Indeed, once the fibrous and large pieces of organic material are subtracted, it will not form a crust during storage, leading to increased volatilization of ammonia by increasing the mass transfer coefficient at the surface. Although greenhouse gas mitigation of the solid-liquid separation process can be partially counterbalanced by ammonia emissions, it is important to note that there are many management practices that can overcome these issues, such as covering slurry storage and the use of injection for land application ( Holly et al., 2017 ).

Anaerobic digestion is a biological degradation process, which in the absence of oxygen, produces digestate and biogas (mainly methane and carbon dioxide) from manure. Biogas collected from the system is often used to generate electricity, to fuel boilers or furnaces, or to provide combined heat and power. Taking into account the greenhouse gas emissions arising from the use of the digestate as fertilizer, and the credit for the renewable energy produced, anaerobic digestion has been reported to yield more than 30% reduction in greenhouse gas emissions when compared with traditional manure handling systems ( Battini et al., 2014 ). However, further attention to the management of the digestate leaving the anaerobic digestion is needed. Indeed, mineralization of the organic nitrogen occurring during biological degradation increases the inorganic nitrogen content and pH of the effluent, which in turn may increase ammonia volatilization ( Petersen and Sommer, 2011 ). Combining anaerobic digestion and solid-liquid separation could reduce the amount of ammonia lost following digestion ( Holly et al., 2017 ).

Diet severely affects excretion of nitrogen in most farm animals, therefore grouping livestock on the basis of their feed requirements can help in reducing this source of nitrous oxide in the excreta. Although a low-protein diet could effectively mitigate nitrous oxide emissions from cattle manure storage ( Table 1 ), some attention must be given to manipulating dietary nitrogen ( Montes et al., 2013 ). For example, decreasing protein could lead to an increase of fermentable carbohydrates, which in turn will likely increase methane production.

The diet for all animal species should be balanced for amino acids to avoid a depression in feed intake and a decrease in animal productivity. Manufactured amino acids are routinely used to balance the diet of monogastrics (pigs and poultry), but the environmental impact associated with the manufacturing of these supplements must be considered when including amino acids as a greenhouse gas mitigation strategy. In ruminants, supplementation of free amino acids results in fast degradation in the rumen, without a significant increase in animal productivity. On the contrary, rumen-protected amino acids resist chemical alterations in the rumen and can reach the intestine where they are absorbed, improving milk yield in dairy cows. Overall, feeding protein close to the animal’s requirement is recommended as an effective mitigation strategy to reduce ammonia and nitrous oxide emissions from manure ( Montes et al., 2013 ).

The timing, quantity, and method of fertilizer applications are important factors influencing soil nitrous oxide emissions. The nitrogen fertilizer applied is susceptible to loss by leaching and denitrification before crop uptake. Therefore, ensuring that appropriate amounts of nitrogen get to the growing crop and avoiding application in wet seasons or before major rainfall events, are valuable practices which could help in optimizing biomass production and reduce soil greenhouse gas emissions.

As lower methane emissions occur after manure land application, decreasing storage time can effectively help in reducing greenhouse gas emissions ( Table 1 ). However, the resulting frequent soil applications can have a variable effect on nitrous oxide emissions from field and carbon dioxide emissions from fuel combustion. Avoiding application during prolonged periods with wet soil and periods of low plant nitrogen uptake could help in increasing the effectiveness of this practice ( Hristov et al., 2013 ).

Adequate storage facilities can provide greater flexibility in choosing when to apply manure to fields, while the use of on-farm manure analysis could help the farmer develop a nutrient management plan and minimize environmental impacts ( Newell Price et al., 2011 ).

The use of nitrification inhibitors has the potential to reduce nitrogen leaching by inhibiting the conversion of ammonia to nitrate. However, this beneficial effect is weakened by a reported increase in indirect nitrous oxide emission that can result from increased ammonia volatilization ( Lam et al., 2016 ). This highlights the importance of considering both gases when evaluating the use of nitrification inhibitors as an option to mitigate climate change. Overall, nitrification inhibitors have been demonstrated as an effective practice to reduce nitrous oxide emissions ( Table 1 ).

Intensive rotational grazing systems are being promoted as a good way to increase forage production and reduce nitrous oxide emissions ( Table 1 ). These systems are characterized by multiple smaller fields called paddocks for the rotation of livestock. By subdividing pastures and rotating animals, farmers can manage stocking densities and grazing duration and thereby manage nitrogen excreta distribution and vegetation regrowth. A more uniform distribution of urine throughout the paddock would reduce the effective nitrogen application rate, which could translate into a reduction in nitrous oxide emissions ( Eckard et al., 2010 ). Keeping animals off the paddocks during wet weather will reduce sward damage and soil compaction. In addition, avoiding excreta deposition at these times will reduce nitrous oxide emissions and nitrogen leaching ( Luo et al., 2010 ).

Animal management

There is a direct link between greenhouse gas emission intensities and animal efficiency. The more productive the animal is, the lower the environmental impact will be (on a per unit of product basis). Both management quality and expression of full genetic potential are necessary to increase production efficiency.

Breeding for more productive animals can lead to a reduction of the nutrient requirements needed to reach the same level of production. This is a valuable greenhouse gas mitigation strategy ( Table 1 ). A more efficient animal will retain more dietary nitrogen protein and there will less nitrogen in feces and urine ( Gerber et al., 2013 ). Genetic improvement of daily gain and feed conversion that has been achieved in broilers over the last 20 years has reduced substantially the emissions per unit of weight ( Williams and Speller, 2016 ). Nevertheless, strategies that aim to change animal phenotypes to enhance productivity or efficiency may harm animal health and welfare unless these effects are measured and controlled ( Llonch et al., 2017 ). Animals of a particular genotype selected for increased production will only be able to realize this potential on a high input system in which resources are adequately supplied. In other words, new breeds and crosses can lead to substantial greenhouse gas reduction, but they need to fit within production systems and climates that may be characterized by limited resources and other constraints.

Poor fertility means that more breeding animals are required in the herd to meet production targets, and more replacements are required to maintain the herd size, which in turn increases greenhouse gas emissions. Improved fertility in dairy cattle could lead to a reduction in methane emissions by 10–24% and reduced nitrous oxide by 9–17% ( Table 1 ). Nevertheless, increasing reproductive pressure may increase the metabolic demands associated with pregnancy and lactation that could negatively affect animal health and increase the risk of metabolic diseases, reduce immune function and in turn reduce fertility ( Llonch et al., 2017 ).

Poorer livestock health and welfare are associated with behavioral and metabolic changes, which can effect greenhouse gas emissions in several ways. Animals fighting an infection will need more energy for maintenance. A recent study in the United Kingdom investigated cost-effective ways to reduce greenhouse gas emissions by improving cattle health. These studies found that cattle diseases can increase greenhouse gas emissions up to 24% per unit of milk produced and up to 113% per unit of beef carcass ( Williams et al., 2015 ). A disease that temporarily reduces feed intake or the ability to digest feed, leads to a decline in growth rate, which will result in more time and energy needed to reach the same end point.

Agriculture in general, and livestock production, in particular, contributes to global warming through emissions of methane and nitrous oxide. To meet future needs of an expanding population, animal productivity will need to increase and greenhouse gas emission intensity per unit of product will need to decrease. One of the principal ways to achieve this environmental standard is to adopt effective mitigation strategies. To increase the effectiveness of these strategies, complex interactions among the components of livestock production systems must be taken into account to avoid environmental trade-offs. Unfortunately, there is not a standard procedure to follow. Mitigation practices should not be evaluated individually, but as a component of the entire livestock production system. The majority of these strategies aim to increase productivity (unit of product per animal), which in most cases cannot be achieved without good standards of animal health and welfare. Optimizing animal productivity has a powerful mitigating effect in both developed and developing countries; however, the size of the effect will also depend on factors such as the genetic potential of the animal and adoption of management technologies.

About the Authors

Giampiero Grossi is a PhD student in the Department of Agriculture and Forestry Science (DAFNE) at Tuscia University, Italy. His research is focused on the quantification of greenhouse gases arising from a typical agro-silvo-pastoral system of the Mediterranean area. Giampiero is currently applying life cycle assessment methodology to a case study in Castelporziano, Rome. His background encompasses agri-food environmental certifications, livestock management, and farming practices.

Pietro Goglio is a lecturer in life-cycle assessment and systems modeling at Cranfield University. He has a strong environmental background and has conducted research in the life-cycle analysis of agricultural and bioenergy systems. Currently, Dr Goglio is focusing his research on developing approaches to combine science with life cycle assessment approaches for greenhouse gas removal from the atmosphere and for greenhouse gas accounting for agricultural systems and food systems. These research developments aim to better capture the characteristics of the systems by considering the economic, social, and political factors affecting their performance and implementation.

Andrea Vitali is a lecturer in Sustainable Livestock Production in the master degree of Food Science and Technology at University of Teramo. His research focused on the bidirectional relationships between animals and the environment. He has studied the effects of heat stress on livestock (production, reproduction, and health) and the contribution of animals to global warming. He has expertise in the application of systems based life-cycle assessment to livestock production. He was involved in developing the Italian plan for adaptation to climate change related to agriculture and food production.

Adrian Williams has spent many years working in agri-environmental science. He is a leading expert in the application of systems based life-cycle assessment to agricultural and food production. He has studied the production of all major crop and livestock species in the United Kingdom and abroad (e.g., beef in Brazil). He has applied life-cycle assessment to the greenhouse gas benefits of improved cattle health as well as enhanced welfare in pig and poultry housing. He is responsible for developing the beef sector model in the recently enhanced agricultural greenhouse gas inventory in the United Kingdom.

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• Published: 03 September 2024

Effects of changing farming practices in African agriculture

• Todd S. Rosenstock   ORCID: orcid.org/0000-0002-1958-9500 1 , 2 , 3 , 4   na1 ,
• Peter Steward 3 , 5   na1 ,
• Namita Joshi   ORCID: orcid.org/0009-0006-1473-7718 3 , 5 ,
• Christine Lamanna   ORCID: orcid.org/0000-0002-6773-7352 6 ,
• Nictor Namoi 7 ,
• Lolita Muller 1 ,
• Akinwale O. Akinleye 8 ,
• Erica Atieno 3 , 9 ,
• Patrick Bell 10 ,
• Clara Champalle 11 ,
• William English 12 ,
• Anna-Sarah Eyrich 13 ,
• Angela Gitau 14 ,
• Dorcas Kagwiria 15 ,
• Hannah Kamau 16 ,
• Anna Madalinska 17 ,
• Lucas Manda 18 ,
• Scott McFatridge 13 ,
• Elijah Mumo 5 ,
• Alex Nduah 3 , 5 ,
• Babra Ombewa 5 ,
• Anatoli Poultouchidou 19 ,
• Janie Rioux 20 ,
• Meryl Richards 21 ,
• Julia Shuck 22 ,
• Helena Ström 23 &
• Katherine Tully 4  

Scientific Data volume  11 , Article number:  958 ( 2024 ) Cite this article

Metrics details

• Agroecology
• Plant ecology

Information on the effects of changing agricultural management on crop and livestock performance is critical for developing evidence-based policies, investments, and programs. Evidence for Resilient Agriculture (ERA) v1.0.1 presents a dataset that harmonizes and aggregates 112,859 observations from 2,011 agricultural studies taken place in Africa between 1934 and 2018. The dataset includes information on the effect of 364 combinations of management practices and technologies on 87 environmental, social, and economic indicators of outcomes. Observations are geolocated and temporally tagged and thus can be linked to other datasets such as historical weather, soil properties, and road networks. ERA offers a new resource for understanding the impacts of changing farming practices under diverse environmental contexts, providing data to support strategic interventions aimed to enhance productivity, resilience, and sustainability of African agriculture.

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Background & summary.

Which agricultural management practices or technologies work where in Africa? This question lies at the forefront of development initiatives aimed at eradicating poverty, ensuring food and nutritional security, mitigating and adapting to climate change, and restoring degraded lands 1 , 2 . That is because the management practices or technologies (hereafter ‘technologies’) farmers use to produce crops and livestock determine yield, profitability, environment impacts, and more 2 . Hence, governments, the private sector, and program developers need information on the potential benefits or losses due to changing management practices to develop policies, programs, and products that support African farmers.

Research efforts have generated a significant corpus of literature on agricultural technologies, with more than 500 meta-analyses conducted in the past decade to assess the efficacy of these practices for Africa agriculture 3 , 4 . Despite the volume of research, there remains a gap in the translation of these data into insights for agricultural programming 5 . This gap can be attributed to several factors: a narrow focus on a few technologies 4 , a lack of quantitative estimates of benefits 6 , 7 , publication delays that cause the findings to be released after they are needed for decision-making processes 8 , and/or misalignment with stakeholder questions 9 . Consequently, stakeholders’ information needs are not fully met potentially leading to decision-making that is poorly supported by the available evidence 10 , 11 . Thus, there is a need for datasets that provide quantitative estimates of multiple agricultural technologies that can address a range of stakeholder questions 12 .

Evidence for Resilient Agriculture (ERA) v1.0.1 helps address the gap. Started in 2012, ERA was envisaged to evaluate the evidence base of Climate-Smart Agriculture (CSA)—that is, agriculture that delivers productivity, resilience, and climate change mitigation outcomes simultaneously 13 . However, the technologies included within ERA such as agroforestry, intercropping, and crop rotations, among many others are common features of agroecology, regenerative agriculture, nature-based solutions, ecosystem-based adaptation, sustainable land management, and other approaches as are many of the outcome indicators such as yield, net economic returns, soil organic carbon, land equivalent ratio, labour required, and more 11 , 14 , 15 , 16 , 17 , 18 , 19 . This means that ERA is relevant for various perspectives on development and is flexible to allow users to define ‘effectiveness’ or ‘work’ consistently with their worldview.

ERA is a comparatively large agricultural meta-dataset in terms of number of technologies, outcomes, and studies. ERA v1.0.1 includes data from 2,011 agricultural experiments that took place in Africa between 1934 and 2018. Together, these data compare how changing more than 364 combinations of agronomic, livestock, or tree management technologies affect more than 87 indicators of productivity, resilience, and greenhouse gas emissions and/or carbon stocks. The experiments were identified via Web of Science and Scopus and were evaluated against predetermined inclusion criteria: (i) location, (ii) technology and outcome relevance, (iii) data on both a new and conventional technology, and (iv) inclusion of primary data. Extracted data from the 2,011 studies ( N  = 112,859 observations) include 135 fields describing each study’s context, experimental design, management treatments (i.e., which are combinations of technologies used), and outcomes. This paper releases ERA v1.0.1. Future releases are planned. Meanwhile, we are developing the infrastructure to turn ERA into a living evidence synthesis to facilitate updates, expansion, and the broader research community’s participation.

Development of ERA has aligned to common practice for evidence reviews 9 . The methods described here update and expand on a previously released protocol 1 .

Literature search

Our initial search, conducted in 2014, queried the Web of Science and Scopus Databases for English language articles. Database queries consisted of key terms describing three components: technology, outcome, and geographic location (Table  S1 ). Given the myriad outcomes of interest, we constructed distinct search strings for each category: productivity, resilience, and mitigation outcomes, as well as a key term related to barriers to adoption. The geographical aspect of each search string included low and middle income countries (LMICs) and specific regions of interest (e.g., ‘Africa’,’Sahel’,’Amazon’, etc.). Our methodology employed Boolean operators ‘OR’ and ‘AND’ to ensure the comprehensiveness of the search. This string for each technology category was run in both search engines for each of the outcome categories, ‘productivity,’ ‘resilience,’ ‘mitigation,’ and additionally for ‘barriers.’ This approach returned 144,567 unique articles published between 1965 and 2013 inclusively (Table  1 ).

Initial search and screening

We used a two-stage screening strategy to determine the relevance of articles to our primary research question. Studies were excluded that did not: (1) include data on at least one technology and one outcome of interest identified a priori and covered in our search terms; (2) include data on both the new technology and a control (conventional or farmer’s standard) technology; (3) take place in a LMIC as identified by the World Bank; or (4) report primary data from field trials. Modelling studies, and meta-analyses were excluded. Greenhouse experiments were only included when they were representative of a real-world growing system such as tomato production in polytunnels or presented outcomes related to greenhouse gas emissions due to the paucity of field data on climate change mitigation in Africa.

Title and abstract screening. Our team manually screened the 144,567 articles’ titles and abstracts. To ensure consistency and reliability in reviewer decisions, preliminary rounds of screenings were conducted on subsets of 100 articles. Inter-reviewer agreement was tested and met minimum Cohen’s kappa statistic of 0.6. Post reviewer calibration, each reviewer was allocated a technology theme (e.g., livestock) based on expertise, and proceeded to conduct title and abstract screening according to the inclusion criteria listed above. Of the total articles identified in the search, 12,803 (8.8%) were likely to meet the inclusion criteria.

Full text screening. Articles that passed the title and abstract screening were assessed in their entirety against the same criteria as the earlier abstract and title screening. The full text screening considered all criteria listed above and focused on outcomes, comparators, and primary data, which is less commonly described in titles and abstracts. The full text screening resulted in a final 2014 library of 7,311 references, comprising 57% of articles from Stage 1 and 5.1% of initial search results.

Recursive search

Due to human resource constraints and in response to stakeholder requests, in 2014, we focused efforts on studies conducted within Africa and augmented the library through a recursive search. This ‘recursive’ search—now commonly called ‘citation chasing’—was based on the reference lists from the 785 publications that occurred in Africa and had passed full-text screening. Articles identified through these searches underwent the same two-stage screening described above. This process yielded an additional 394 articles.

2019 update

The search and screening were repeated in 2019 to update the library for the years 2014 to 2018 for African countries adding an additional 972 articles to the corpus. Combining those articles with those identified during the previous search and screening efforts generated today’s ERA library of 2,011 studies published between 1965 and 2018 (Table  1 ).

Data management

Data were extracted from tables, text, and figures. Figures were digitized using available software, such as Graph Click ( http://www.arizona-software.ch/graphclick ) or Web Plot Digitizer ( https://apps.automeris.io/wpd/ ). Each row of the dataset has a unique ID and represents a unique combination of article, site, comparison between new and common technology, commodity, outcome measure, and time period. For example, the same measurement of the same treatments at the same site in consecutive years (e.g., yield of treatment X at site Y in 2004 and 2005) would generate two observations. Studies of multiple treatments (e.g., three quantities of fertilizer) or outcomes measures (e.g., gross returns and soil carbon) contribute multiple observations to the dataset. Factorial studies conducted over multiple years measuring many outcome variables in many locations add large numbers of observations to the dataset. Below we describe the broad categories of variables compiled in the dataset. Descriptions of each variable can be found in Table  S2 .

Record and bibliographic information

Each row included the article’s bibliographic information, geographic location, environmental context, experimental design, treatment comparisons, and outcome indicator effects. In addition, ERA compiled the surname of the first author, the year of publication, and the journal abbreviation.

Geographic location

We collected country, site name paraphrased from study, and spatial coordinates when given. Location’s coordinates were verified in Google Maps, as they were often inaccurately reported. Enumerators also recorded a measure of spatial uncertainty. When authors reported decimal degrees and there was no correction required to the co-ordinates, then uncertainty was measured in terms of the value’s precision. When the location was estimated using Google Maps, the spatial uncertainty value was measured in terms of the precision of the site location description (e.g., a single farm or region) and the enumerator’s visual interpretation of land use at and near the coordinates. Observation’s geographic coordinates were collected to facilitate linking the data compiled in ERA to external databases, for example related to climatic and environmental factors not necessarily reported in the original study.

Environmental context

Information collected describes (i) growing season, (ii) climate, (iii) physical environment (elevation and slope), and (iv) soils. Growing season encoded rainfall as unimodal or bimodal and season start and end dates. Climate was typically reported as mean annual temperature and mean or total annual or seasonal precipitation. All reported were captured. Monthly amounts were summed to total and/or average seasonal and/or annual, as the reported data allowed.

Detailed soil information describing the site was also recorded. Soil classification and texture were recorded as reported. When articles report percentages of sand, silt and clay, soil texture was estimated based on USDA Soil Texture triangle and calculator https://www.nrcs.usda.gov/wps/portal/nrcs/detail/national/home/?cid=nrcs142p2_054167 ). Soil chemistry was recorded as a percentage, either as reported or converted from g/kg. Multiple depths, sites, and seasons were combined to match the detail for the respective outcome of the observation.

Experimental design

We recorded (i) the number of replicates of a treatment, (ii) the plot size harvested for yield measurements, converted to m 2 as the data allows, and (iii) the site type, e.g., research station, farmer field, or survey. When the number of replicates differed between comparisons (see below), the lowest number of replicates was recorded as a conservative measure.

Treatment comparisons

Each observation encodes a comparison between two treatments. Experimental treatments were coded according to the technologies labeled in the concept scheme (Table  S3 , Table  S4 ). The following principles assisted in consistent comparisons of treatment outcomes across studies:

New versus common treatment . A new technology versus a control, which is typically farmers’ conventional technologies.

Additive complexity . An improved technology / set of technologies to a simpler option, e.g., agroforestry + fertilizer vs agroforestry alone.

Agriculture to agriculture . Agricultural systems are never compared to natural systems. For example, we can compare soil organic carbon in mulch versus no mulch systems, but not versus natural or semi-natural vegetation.

Same implementation levels . Comparisons were made within the same ‘level’ of implementation. For example, 40 kg N/ha was compared with other treatments using 40 kg N/ha but not 20 kg N/ha.

All treatment details . All major characteristics of the treatments were coded, including seed variety, tillage type, weed control, tree species, and chemical applications, among others, up to 13 labels per treatment.

In-year comparisons . Only comparisons of treatments and outcomes that occur in the same year or season were included. Residual effects, e.g., of phosphorus applied in year one with yield in year three, were not recorded.

Include all possible treatments . All treatments, and their component technologies, in a study were coded if they formed part of valid comparison.

Outcome measures, units, and products were extracted as reported. Outcome codes differentiated plant parts (grain vs stover) and products from the same species (milk versus meat). Oftentimes authors discussed many treatments but only reported a few. The ways in which the outcomes were reported in the papers, by year or aggregation across practices, dictated which data were extracted and how treatments were coded. Experimental Units (EU) described the species or product that were measured (e.g., maize grain).

Data Records

The ERA v1.0.1 dataset includes 112,859 observations compiled from 2,011 articles found in Web of Science and Scopus (Fig.  1 ). The dataset, publicly available on GitHub, Zenodo 20 and Dataverse 21 consists of an R object in the ERAg package and Microsoft Excel workbook, respectively. It includes 135 fields, described in methods (Table  S2 ). The package and file include the codebooks for technologies, outcomes, and products.

Systematic map of ERA. ( a ) Geographic locations of studies, ( b ) Distribution of products included by number of studies, ( c ) Distribution of technology groups for crop and livestock by number of studies with each square equal to ~40 papers, ( d ) Number of studies including outcome indicators related to farm productivity, resilience, and climate change mitigation.

The dataset provides an extensive resource on the effects of changing crop, livestock, and tree management practices on agricultural, social, and environmental outcomes. Data are available from 42 countries, but research efforts have been unevenly distributed, with notable hotspots in certain countries (Fig.  1a ). The countries most represented are Nigeria ( N  = 397 studies , 19.7%) , Kenya (218, 10.8%), Ethiopia (212, 10.5%), South Africa (151, 7.5%), and Zimbabwe (134, 6.6%). Meanwhile the database contains no or few studies from Angola, Central African Republic, the Democratic Republic of Congo, Liberia, Gabon, Chad, and Namibia.

The ERA dataset contains information on a diverse range of agricultural products including both crops and livestock (Fig.  1b ). Most studies focus on cereals (1262 studies), maize (916), rice (97), wheat (99), sorghum (141), and millet (116). However, there is also a significant amount of information on legumes (504) and other starchy staples such as cassava (108), potatoes (30), and vegetables (117). ERA also include data on livestock. About 25% of the studies describe livestock management (442), including more than 200 studies on goats and sheep and 80 studies on cattle.

ERA categorizes practices at three levels, with increasing specificity (Table  S3 ). At the highest and broadest level of aggregation (‘themes’), ERA includes information on 36 technology categories such as agroforestry, inorganic fertilizers, crop rotation, and improved feeding of animals. Nutrient and soil management practices are most well represented in the dataset, and are the focus of 1284 and 1044 studies, respectively. Other important themes include agroforestry (426 studies), water management (449 studies), and livestock management including practices like changing feeding practices (442 studies). Nearly 500 studies examine crop diversification including intercropping or rotations. However, studies were coded at greater specificity (‘practices’ and ‘sub-practices’) such as alley cropping with nitrogen fixing tree, or improved feeding with leguminous fodder. ERA captures information on 113 and 1,285 of sub-practices used alone and in combination, respectively.

ERA compiles data on the impacts of technology use on 87 indicators of productivity, resilience, and climate change mitigation outcomes (Fig.  1d ). Productivity is studied most extensively (1705 studies) and includes both yield and economic performance indicators such as net returns. Social and environmental proxies for resilience such as resource use efficiency and soil organic carbon are included in 1094 studies. Climate change mitigation, although studied less often, with only 54 studies, includes both greenhouse gas fluxes and soil carbon stock changes. It is important to note that in many cases single studies contain multiple outcomes allowing ERA to be used to look at synergies and trade-offs among outcomes.

The geographic and topic distribution of studies provide valuable insights into historical research efforts and highlight future research needs. This distribution can identify over- and under-researched agroecologies, commodities, practices, or outcomes, particularly when coupled with information on  the importance of commodities to food security, poverty alleviation, or other development and environmental impacts. It is important to note however that a lack of data in any specific location, practice, or outcome does not necessarily indicate a complete absence of information in the literature. Instead, it suggests little or limited available data, and especially data that meets the criteria that required a control in the studies analysed.

Technical Validation

Meta-analyses inherently require decisions regarding search parameters, such as the selection of keywords, the establishment of inclusion criteria, and the choice of analytical methods. These decisions can significantly influence the data compiled and the study’s conclusions, thus necessitating an evaluation of the data compilation to the extent possible to contextualize the results.

The process of search and screening represents a potential source of error, primarily due to variability in human judgement. To mitigate this, we implemented rigorous training and calibration for our enumerators. This process involved pairs of enumerators independently assessing the same articles and reconciling differences under the guidance of a project lead. Calibration was deemed satisfactory when inter-rater reliability, measured by the Kappa statistic, consistently exceeded 0.6—reflective of the standard in meta-analytical research.

Data extraction accuracy is another concern. To mitigate this, enumerators received extensive training over a 4 to 6-weeks period. The training followed a detailed manual that explains the concepts and provides examples of the types of data, experiments, and complexities likely to be encountered. Following training, ongoing support and daily meeting helped reconcile any extraction concerns. Slack was then used to enable rapid feedback for any questions among peers and with project leads. Despite the precautions, errors still occur. Thus, we adopted the Four Eyes Principle. Each article’s extraction was checked by a second enumerator or a project lead, and in some cases both. Errors were rectified to achieve a high standard of data integrity.

This commitment to accuracy in data extraction lays a strong foundation for the subsequent validation of ERA. The validity of ERA can be substantiated through comparative analysis with the results from other meta-analyses. Crop yield, which represent 38.8% of ERA’s data (Fig.  1 ), is also among the most investigated outcomes in meta-analysis. We analysed ERA’s crop yield data using log response ratios and effect sizes 22 , 23 . Critically, the calculated effect sizes were in the range of these other studies (Table 2 ). These results indicate that ERA generates results expected by other, more specific, assessments of the literature and suggest ERA’s approach delivers quality results. Furthermore, since the effect size calculations integrate all potential sources of error together, the validation results provide a robustness check on ERA processes broadly, enhancing confidence in the dataset’s overall reliability.

Exact replication of results across meta-analyses is not expected due to unique protocols, search strategies, and analytical decisions employed by individual research teams. Consequently, the datasets and results can exhibit considerable variability as demonstrated by meta-analyses comparing the effects of organic farming on yield 4 and agroforestry on carbon sequestration 24 . This variability is also reflected in our validation of ERA. For specific technologies, ERA includes more studies than most related meta-analyses but not all. Differences can be attributed to definitional discrepancies, inclusion decisions (e.g., grey literature), and analytic criteria for what constitutes a valid comparison. These observations further underscore the significance that these decisions have on research results.

Usage Notes

Concept scheme.

ERA uses a hierarchical concept scheme to classify practices, outcomes, and products. Related concepts are nested within similar concepts. ERA’s concept scheme is unique and was born from experience with the literature and deep engagement with stakeholders about their needs. This structure allows the data to be aggregated and disaggregated to respond to specific user questions at different levels of specificity and to combine across studies when using dimensionless analysis approaches such as response ratios and effect sizes. Multiple agricultural ontologies have been developed since ERA was conceived in 2014. In 2020, ERA concepts were mapped and aligned to other agricultural semantic ontologies and thesauri, including FAO’s AGROVOC and CGIAR’s AgrO, to increase interoperability and future dataset expansion. The aligned concepts are included in the dataset.

Interoperability

ERA is designed to connect to other publicly available datasets. This allows it to reuse the data contained within to answer new user-specific questions. There are at least four ways to link ERA to other datasets, by: geographic coordinates, growing season dates, species data, and practice data.

ERA was co-designed with various stakeholders, including scientists, policy makers, development partners, and investors. They contributed to identifying the relevant questions, analyses, and data visualization possibilities that provide actionable insights for different users. So far, six primary use cases have been developed or discussed with partners (Table  3 ). Much of the early engagements focused on generating context-sensitive analyses of costs, benefits, and risks for investment design and policy formulation. Yet, additional use cases of ERA’s analytical potential have emerged from discussions with stakeholders. For example, farm management data under different environmental, social, and economic conditions lends itself to other decision and planning processes, including advisory service, design of targeted financial products, monitoring and evaluation of development projects, and prioritization of research agendas. These diverse applications underscore ERA's versatility and its utility in enhancing decision-making across various contexts.

Data analysis

ERA is made available with accompanying R packages (‘ERAg’ and ‘ERAgON’). These packages contain functions for conducting the basic meta-analysis using response ratios and effect sizes as well as extracting treatments with multi-year observations or all the unique treatments in the dataset. The principles described above determine which data were extracted and included in the dataset. For example, ERA v1.0.1 did not encode comparisons that captured substituting one fertilizer source for another. Nor did we extract data to compare among new technologies except in terms of multiple-technology bundles, though subsequent releases will include such information. However, because each treatment was coded separately, extracting individual treatments without considering the comparison between technologies is possible. This unique treatment subset would not capture treatments in the studies where the comparisons were not the target of ERA’s data extraction. These decisions determine ERA’s relevance for some uses. Nevertheless, we expect that the value of the subset extraction and the availability of consistent cross-technology data outweighs imprecision concerns for most research questions and use cases but should be done with caution. The authors welcome inquiries about these or other limitations.

Code availability

The code used to generate the dataset and run basic analyses is compiled in the ERAg R package 25 using R 4.2.1 (R Development Team) and may be downloaded from GitHub https://github.com/EiA2030/ERAg . This beta version package is actively being developed, so new functions and improvements should be expected.

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Acknowledgements

TSR personally thanks A. Jarvis, L. Wollenberg, and B. Campbell for their support for the last decade. We thank J. Porciello and V. Skidan for critical collaborations. ERA v1.0.1 is being released based on financial support from the CGIAR Excellence in Agronomy Initiative for the crop data and the CGIAR Livestock and Climate Initiative for the livestock data. Earlier efforts toward ERA’s development received financial support from the CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS) Flagship Program on Practices and Technologies. Supplemental funding was provided by the Food and Agriculture Organization of the United Nations (FAO), European Union (EU), International Fund for Agricultural Development (IFAD), United States Department of Agriculture-Foreign Agricultural Service (USDA-FAS), CCAFS Flagship Program on Low Emissions Development, and the International Centre for Forestry Research (CIFOR)’s Evidence-Based Forestry. The effort has also benefited from partnership between the Climate Action Team at the Alliance of Bioversity-CIAT and the Agroecology Lab at the University of Maryland.

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These authors contributed equally: Todd S. Rosenstock, Peter Steward.

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Todd S. Rosenstock

Previously: World Agroforestry Centre, UN Avenue, PO Box 30677-00100, Nairobi, Kenya

Todd S. Rosenstock, Peter Steward, Namita Joshi, Erica Atieno & Alex Nduah

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Contributions

T.S.R. conceived the project; T.S.R., P.S., N.J., C.L. developed the methodology; T.S.R., P.S., C.L developed formal analysis; P.S. developed the R packages with support from T.S.R. and N.J.; All authors curated data; T.S.R. wrote the original draft; All authors reviewed and edited the draft; T.S.R. acquired funding.

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Correspondence to Todd S. Rosenstock .

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Rosenstock, T.S., Steward, P., Joshi, N. et al. Effects of changing farming practices in African agriculture. Sci Data 11 , 958 (2024). https://doi.org/10.1038/s41597-024-03805-z

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Animals Are Running Out of Places to Live

By Catrin Einhorn and Lauren Leatherby Dec. 9, 2022

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WILDLIFE IS DISAPPEARING around the world, in the oceans and on land. The main cause on land is perhaps the most straightforward: Humans are taking over too much of the planet, erasing what was there before. Climate change and other pressures make survival harder.

This week and next, nations are meeting in Montreal to negotiate a new agreement to address staggering declines in biodiversity. The future of many species hangs in the balance. Meet some of the animals most affected as humans convert more and more land:

The groups of animals you just scrolled through aren’t the only species that have lost a third or more of their global habitat. They’re just some of the mammals, birds, amphibians and reptiles researchers can currently track. Most live in tropical forests.

“If the forest disappears, they will disappear,” said Walter Jetz, a professor of biodiversity science at Yale University who leads Map of Life , a platform that combines satellite imaging with ecological data to determine how species ranges are changing around the world. Map of Life shared data with The New York Times.

Biodiversity, or all the variety of life on the planet — including plants, invertebrates and ocean species — is declining at rates unprecedented in human history , according to the leading intergovernmental scientific panel on the subject. The group’s projections suggest that a million species are threatened with extinction, many within decades.

Nations are meeting in Montreal to try to chart a different path. Delayed two years because of the pandemic, delegations are working to land a new, 10-year agreement to tackle biodiversity loss under a United Nations treaty called the Convention on Biological Diversity.

“With our bottomless appetite for unchecked and unequal economic growth, humanity has become a weapon of mass extinction,” said António Guterres, the United Nations secretary general, in his opening remarks on Tuesday in Montreal.

The last global biodiversity agreement failed to meet a single target at the global level, according to the Convention on Biological Diversity itself, and wildlife populations continue to plummet.

Take the Honduran white bat.

At first glance, they resemble a cluster of cotton balls stuck under a leaf. But each tiny mound of fluff possesses an even tinier yellow snout and ears. Honduran white bats work together to fashion leaves into tent homes and are known to nurse each other’s young. At night, they fly out in search of a specific species of fig, dispersing its seeds in return.

These bats offer potential benefits to people. Their cuteness makes them an ecotourism draw, and they have an ability that’s rare in mammals to store carotenoids in their skin, which could hold promise for unlocking treatment for conditions like macular degeneration.

But in the last 20 years, Honduran white bats have lost about half their range in Central America as people clear rainforest for pasture, crops and homes. Not yet considered endangered, they are nevertheless in steep decline, one of countless examples in this worsening global crisis.

It’s not only wildlife that will suffer as a result. Biodiversity loss can trigger ecosystem collapse, scientists say, threatening humanity’s food and water supplies. Alarm is growing that the threat is comparable in significance to the climate crisis.

“Climate change presents a nearer-term threat to the future of human civilization,” said Katharine Hayhoe, a prominent climate change researcher who also focuses on biodiversity as chief scientist at the Nature Conservancy. “The biodiversity crisis presents a longer-term threat to the viability of the human species.”

Scientists emphasize that one can’t be solved without the other because they are interconnected.

Source: Map of Life | Photo: Chien C. Lee

This was the habitat of the Abah River flying frog in 2001. It lives in the forests of Malaysia and Indonesia, where it parachutes among the trees with its special webbed feet.

By 2021 it had lost more than a third of its home, Map of Life researchers estimated.

What’s driving the loss?

The human population has doubled since 1970. While the rate of population growth is slowing, the sheer number of people continues to rise. Consumption levels in different parts of the world mean some people put more pressure on nature. In the United States, for example, each person uses the equivalent of eight global hectares on average, according to the Global Footprint Network , a nonprofit research group. In Nigeria, it’s about one hectare per person.

All that is related to the causes of biodiversity loss, which scientists have ranked. First are changes in land and sea use. Then comes the direct taking of species, for example hunting, fishing and wildlife trafficking. Climate change is next, followed by pollution and invasive species. Unfortunately for wildlife, these pressures build on each other.

In the future, scientists expect climate change to become the main driver of biodiversity loss as changes in temperature, rainfall and other conditions continue to transform ecosystems. That shift is expected “some decades down the road,” Dr. Jetz said. “But we might already be looking at a much-reduced set of species at that point.”

For the best chance at adapting to climate change, plants and animals need robust populations and room to migrate. Instead, they are depleted and hemmed in.

Why are people taking over so much land? Mostly for agriculture. In many parts of the world, that means exports driven by booming global trade. In recent decades, for example, Southeast Asia has become a major supplier of coffee, timber, rice, palm oil, rubber and fish to the rest of the world.

“All of that economic expansion has come at the cost of biodiverse habitat,” said Pamela McElwee, an environmental anthropologist at Rutgers University who studies the region.

Some momentum is building for companies to ensure their products are deforestation-free. Reducing meat consumption and food waste are key to freeing up land for other species, Dr. McElwee said.

In many places, poverty, powerful interests and a lack of law enforcement make habitat loss especially hard to address.

Source: Map of Life | Photo: Glenn Bartley/Alamy

EL SALVADOR

In 2001, this was the habitat of the shining honeycreeper, a bird with a range across Central America.

It had lost more than a third of its home by last year, researchers estimated. Because of its large population, it’s still not considered threatened.

In Central America, illegal cattle ranching drives deforestation on protected state and Indigenous lands, said Jeremy Radachowsky, director for Mesoamerica and the Caribbean at the Wildlife Conservation Society. Wealthy individuals, often affiliated with drug cartels, grab land, sometimes through illegal payments. They raise beef, some of which ends up in the United States, he said.

Elsewhere in the region and beyond, desperation sometimes pushes people to find remote areas with little government presence where they can simply take land to make a living.

“They need land in order to feed their families,” said David López-Carr, a professor of geography at the University of California Santa Barbara who studies how people interact with tropical forests in Latin America.

Rainforest countries like Brazil and the Democratic Republic of Congo are known for widespread deforestation. But the species that have lost the largest portions of their habitats tend to be concentrated in places that are geographically isolated in some way, like the isthmus of Central America and Madagascar. Because animals there often have smaller ranges to begin with, habitat loss hits them especially hard.

For example, 98 percent of lemurs, primates that only exist in Madagascar, are threatened. Almost a third are on the brink of extinction. “I don’t want to lose my hope,” said Jonah Ratsimbazafy, a primatologist who leads a nonprofit group on the island that seeks to save lemurs while helping people. Madagascar is among the poorest countries in the world.

Recognition is growing that stanching biodiversity loss requires addressing the needs of local communities.

“There needs to be a way that the people that live close to the forests benefit from the intact forests, rather than clearing the forest for short term gain,” said Julia Patricia Gordon Jones, a professor of conservation science at Bangor University in Wales. “That’s the ultimate challenge of forest conservation globally.”

This is the 2001 habitat of the white fronted brown lemur, a primate that eats fruit and flowers.

Over the last 20 years, it has lost around 40 percent of its habitat, Map of Life researchers estimated. People also hunt these lemurs for meat and they are now threatened with extinction.

A decisive moment in Montreal

While countries in the global south are experiencing the most dramatic biodiversity losses right now, Europe and the United States went through their own severe declines hundreds of years ago.

“We lost pretty much 100 percent of primary forest in most parts of Europe,” Dr. Jetz said.

Now, with negotiations underway in Montreal, countries that are poor economically but rich in biodiversity argue that they need help from wealthier countries if they’re going to take a different route.

Overall, the financial need is daunting: hundreds of billions per year to help poorer countries develop and implement national biodiversity plans, which would include actions like creating protected areas, restoring degraded lands, reforming harmful agricultural, fishing and forestry practices; managing invasive species; and improving urban water quality.

On the other hand, failing to address biodiversity loss carries enormous financial risk. A report by the World Economic Forum found that $44 trillion of economic value generation is “moderately or highly dependent on nature and its services and is therefore exposed to nature loss.”

A vast source of funding could come from redirecting subsidies that presently support fossil fuels and harmful agricultural practices, said David Cooper, deputy executive secretary of the Convention on Biological Diversity.

“Currently, most governments spend far more on subsidies that actually are destroying nature than they do on financing conservation,” Mr. Cooper said. “So, certainly a change in that will be critical.”

The United States is the only country besides the Holy See that isn’t a party to the convention, so although the United States will attend the meeting, it will be participating from the sidelines.

“We can play a very constructive role from the outside,” said Monica Medina, an assistant secretary of state who is also special envoy for biodiversity and water resources. But she acknowledged that being a member would be better. “I hope that someday we will be,” she said.

Of the many targets being negotiated, the one that has gotten the most attention seeks to address habitat loss head on. Known as 30x30, it’s a plan to safeguard at least 30 percent of the planet’s land and oceans by 2030. More than 100 countries back the proposal. While some Indigenous groups fear it will lead to their displacement, other s support the plan as a means to secure stronger land rights.

But experts emphasize that action will have to go further than lines on a map.

“You can set up a protected area, but you’ve not dealt with the fact that the whole reason you had habitat loss in the first place is because of demand for land,” Dr. McElwee said. “You have to tackle the underlying drivers, otherwise you’re only dealing with like half the problem.”

Methodology

All estimates on habitat loss come from Map of Life and its Species Habitat Index . Habitat loss estimates since 2001 run through 2021 and are approximations, based on models of geographic range that incorporate remote sensing and expert research. Map of Life shared data for terrestrial vertebrate species for which the group’s methods can confidently ascertain habitat loss. The researchers estimate many more species are experiencing significant habitat loss than are in the group’s data.

Common names for species used in this article come from Map of Life. In the graphic showing the species that have lost a third or more of their habitat since 2001, they are grouped based on Map of Life’s habitat loss estimates, which the researchers caution may be slightly higher or lower in the real world. Almost all animals in that Map of Life dataset are visually represented in this article, with a few exclusions.

Data used in the maps showing habitat loss also comes from Map of Life.

Map of Life is led by Walter Jetz, professor of ecology at Yale University and scientific chair at the E. O. Wilson Biodiversity Foundation. Other Map of Life contributors to the research shown in this story include Kalkidan Fekadu Chefira, John Wilshire, Ajay Ranipeta, Yanina Sica and Rohan Simkin.

Production and editing by Claire O'Neill and Douglas Alteen Additional concept and development support by Blacki Migliozzi Additional photo support by Matt McCann

Photography credits

Abah River flying frog (Chien C. Lee), Allen's coral snake (Robert Hamilton/Alamy), Andrangoloaka Madagascar frog (Alf Jacob Nilsen/Alamy), Anolis kreutzi (Josiah Townsend), Anolis quaggulus (Peter Janzen), Antsouhy Tomato Frog (Frank Vassen), Ballmann’s malimbe (David Monticelli), Banggai crow (Alpian Maleso), Barton Springs salamander (Timothy R. Burkhardt), Berkenbusch's robber frog (Sean Michael Rovito), Bernier's vanga (Dubi Shapiro/Alamy), Bicolored roundleaf bat (Nick Baker), Bighead anole (Bazzano Photography/Alamy), Black capped fruit bat (Muhammad Imam R), Black forest racer (Josue Ramos Galdamez), Black headed nightingale thrush (Niall Perrins), Black throated shrike tanager (Glenn Bartley/Alamy), Blommers’ Madagascar frog (Devin Edmonds), Blue headed pitta (Chien C. Lee), Blue Mountains tree frog (John Sullivan), Böhme's bright eyed frog (Library Book Collection/Alamy), Bonaparte's nightjar (Bram Demeulemeester), Boophis bottae (John-Yves Grospas/Alamy), Boophis marojezensis (Franco Andreone), Boophis rufioculis (Franco Andreone), Bornean peacock pheasant (Danny Ye/Alamy), Bornean wren babbler (Chien C. Lee), Borneo treefrog (Anton Sorokin/Alamy), Boulenger's digging frog (Franco Andreone), Bourret’s blind skink (Leonid A. Neymark), Bransford's robber frog (Bill Gozansky/Alamy), Brazilian dwarf brocket (Carlos Schmidtutz), Brazilian heart tongued frog (Euvaldo Marciano Jr.), Brown grainy frog (Devin Edmonds), Bullock's Mountains false toad (Edgardo Flores), Bushy crested hornbill (Cheong Weng Chun), Bushy tailed olingo (Michael and Patricia Fogden/Minden), California mountain salamander (Michael Benard/Shutterstock), Calumma marojezense (Chien C. Lee), Cascade torrent salamander (Nature Picture Library/Alamy), Central Madagascar frog (John Sullivan), Chestnut naped forktail (Alex Vargas/Alamy), Chestnut necklaced partridge (Chien C. Lee), Collared nightjar (Markus Lilje), Columbia torrent salamander (Connor Dooley), Common thick thumbed bat (Sergei Kruskop), Compsophis laphystius (Chien C. Lee), Costa Rica water snake (Josiah Townsend), Crested fireback (Chien C. Lee), Crested partridge (Robin Chittenden/Alamy), Crossley's ground roller (Dubi Shapiro/Alamy), Cryptic warbler (Markus Lilje), Deppe's squirrel (Ray Wilson/Alamy), Dusky gliding lizard (Chien C. Lee/Minden), Dwarf anole (derick_gil/iNaturalist), Eastern casquehead iguana (Chris Mattison/Alamy), Eastern woolly lemur (Edward E. Louis Jr.), Fletcher's frog (Ken Griffiths/Alamy), Folohy golden frog (Frank Vassen), Forest bright eyed frog (Devin Edmonds), Four striped forest gecko (John Sullivan), Furry eared dwarf lemur (Terry Whittaker/Alamy), Garnet pitta (Gregory Greene), Gilded tube nosed bat (Needhi K. Thangasamy), Goniurosaurus hainanensis (Nature Picture Library/Alamy), Goodman's mouse lemur (Chien C. Lee), Gould's frogmouth (Cheong Weng Chun), Grandidier's tuft tailed rat (Joel Sartore), Gray barred frog (Ken Griffiths/Alamy), Greater naked bat (Chien C. Lee), Green bright eyed frog (Franco Andreone), Grey bellied bulbul (Pavel Chonya/Alamy), Grey hooded manakin (Lev Frid), Guibé’s mantella (Alexandra Laube/Alamy), Guizhou warty newt (Tim Johnson), Harrisson's flying frog (Anton Sorokin/Alamy), Helmet vanga (Francesco Veronesi), Highland guan (Georges Duriaux), Honduran white bat (Piotr Naskrecki/Minden), Horned Madagascar frog (Devin Edmonds), Imperial Amazon (Joel Sartore), Ithycyphus perineti (Monica Rua/Alamy), Jan’s snake (Christoph Kucharzewski), Java tree toad (Ben Tsai), Keel billed motmot (Francesco Veronesi), Kinkelin graceful brown snake (Josue Ramos Galdamez), Kulambangra white eye (Andrew Cox), Laotian keeled skink (Michael Cota), Large billed niltava (Nobuo Matsumura/Alamy), Large green pigeon (Wich'yanan Limparungpatthanakij), Large wren babbler (Mikael Bauer), Lined flat tail gecko (Antonio Rodríguez Arduengo), Liophidium rhodogaster (Marius Burger), Liopholidophis dolicocercus (Paul Prior), Liopholidophis rhadinaea (Chien C. Lee), Louisiana pine snake (Pete Oxford/Alamy), Louisiana slimy salamander (Evan Grimes), Loveridge's anole (Josiah Townsend), Madagascar grey throated rail (Allan Hopkins), Malayan free tailed bat (Chien C. Lee), Malayan slug snake (Sam Yue/Alamy), Mantidactylus zipperi (Miguel Vences), March’s palm pit viper (Josue Ramos Galdamez), Marojejy leaf chameleon (Antonio Rodriguez Arduengo), Mell's gecko (Yibo Lin), Merendon palm pit viper (Adalberto H. Vega), Nicaragua narrowmouth toad (Brian Kubicki), Nightingale wren (Carlos Sanchez), Noble's robber frog (George Grall/Alamy), Ochraceous bulbul (Oliver Thompson-Holmes), Odorrana bolavensis (Bryan Stuart), Oliver's parrot snake (Alex Shepack), Palenque robber frog (Pedro E. Nahuat-Cervera), Peyrieras' woolly lemur (Edward E. Louis Jr.), Dark throated oriole (Yingyod Lapwong), Pleasing poison frog (Ignacio Yufera/Alamy), Pope's spiny toad (Peter Janzen), Pseudoxyrhopus tritaeniatus (Chien C. Lee), Puerto Rican robber frog (IrinaK/Shutterstock), Pugh's frog (Stephen Mahony), Red headed krait (Marius Burger), Red headed poison frog (Dirk Ercken/Alamy), Reticulate bright eyed frog (Robin Hoskyns/Alamy), Rhadinella anachoreta (Wouter Beukema), Rhinella magnussoni (Albertina Pimentel Lima), Ringed snail sucker (Jen Guyton/Minden), Rocky Mountain salamander (Aleta Quinn), Rufous breasted coua (Dubi Shapiro/Alamy), Rufous winged philentoma (Neil Bowman/Alamy), Rusty anaides (Henk Wallays/Alamy), Satanic leaf tailed gecko (Markus Lilje), Scaly breasted bulbul (Ben Tsai), Scaly ground roller (Carlos Sanchez), Schmidt's spinythumb frog (anfibiosearaucarias/iNaturalist), Scott Bar salamander (Henk Wallays/Alamy), Sculpted Madagascar frog (Chien C. Lee), Seepage salamander (A. Hartl/Alamy), Selangor forest skink (Gc Gan), Shining honeycreeper (Glenn Bartley/Alamy), Short legged ground roller (Frank Vassen), Snowy cotinga (Eric van den Berghe), Speckled day gecko (Chien C. Lee), Sphagnum frog (Stephen Mahony), Spinomantis fimbriatus (Antonio Rodríguez Arduengo), Spinomantis phantasticus (Miguel Vences), Splash backed poison frog (Dirk Ercken/Alamy), Stitchbird (Public domain), Stream anole (George Grall/Alamy), Striped shrew tenrec (Harald Schütz), Stuart's anole (Sune Holt), Stub tailed spadebill (Don Faulkner), Sumatran ground cuckoo (Javier Gonzalez), Sunda robin (Francesco Veronesi), Tanala tufted tailed rat (Markus Lilje), Thai horseshoe bat (Shutterstock), Thamnosophis infrasignatus (Chien C. Lee), Thomas's flying squirrel (Marc Faucher), Tilaran robber frog (Public domain), Two colored snail eater (Petr Dolejsek/Shutterstock), Underwood's long tongued bat (Michael and Patricia Fogden/Minden), Valdivia ground frog (Bert Willaert/Alamy), Weasel sportive lemur (Nigel Dennis/Alamy), Western dwarf gecko (John Sullivan), White footed tamarin (Luiz Claudio Marigo/Minden), White fronted brown lemur (Chien C. Lee), White spotted Madagascar frog (Chris Mattison/Alamy), White throated shrike tanager (Neil Bowman/Alamy), Wightman’s robber frog (Luis J. Villanueva-Rivera)

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The University of Chicago The Law School

Abrams environmental law clinic—significant achievements for 2023-24, protecting our great lakes, rivers, and shorelines.

The Abrams Clinic represents Friends of the Chicago River and the Sierra Club in their efforts to hold Trump Tower in downtown Chicago accountable for withdrawing water illegally from the Chicago River. To cool the building, Trump Tower draws water at high volumes, similar to industrial factories or power plants, but Trump Tower operated for more than a decade without ever conducting the legally required studies to determine the impact of those operations on aquatic life or without installing sufficient equipment to protect aquatic life consistent with federal regulations. After the Clinic sent a notice of intent to sue Trump Tower, the State of Illinois filed its own case in the summer of 2018, and the Clinic moved successfully to intervene in that case. In 2023-24, motions practice and discovery continued. Working with co-counsel at Northwestern University’s Pritzker Law School’s Environmental Advocacy Center, the Clinic moved to amend its complaint to include Trump Tower’s systematic underreporting each month of the volume of water that it intakes from and discharges to the Chicago River. The Clinic and co-counsel addressed Trump Tower’s motion to dismiss some of our clients’ claims, and we filed a motion for summary judgment on our claim that Trump Tower has committed a public nuisance. We also worked closely with our expert, Dr. Peter Henderson, on a supplemental disclosure and on defending an additional deposition of him. In summer 2024, the Clinic is defending its motion for summary judgment and challenging Trump Tower’s own motion for summary judgment. The Clinic is also preparing for trial, which could take place as early as fall 2024.

Since 2016, the Abrams Clinic has worked with the Chicago chapter of the Surfrider Foundation to protect water quality along the Lake Michigan shoreline in northwest Indiana, where its members surf. In April 2017, the U. S. Steel plant in Portage, Indiana, spilled approximately 300 pounds of hexavalent chromium into Lake Michigan. In January 2018, the Abrams Clinic filed a suit on behalf of Surfrider against U. S. Steel, alleging multiple violations of U. S. Steel’s discharge permits; the City of Chicago filed suit shortly after. When the US government and the State of Indiana filed their own, separate case, the Clinic filed extensive comments on the proposed consent decree. In August 2021, the court entered a revised consent decree which included provisions advocated for by Surfrider and the City of Chicago, namely a water sampling project that alerts beachgoers as to Lake Michigan’s water quality conditions, better notifications in case of future spills, and improvements to U. S. Steel’s operations and maintenance plans. In the 2023-24 academic year, the Clinic successfully litigated its claims for attorneys’ fees as a substantially prevailing party. Significantly, the court’s order adopted the “Fitzpatrick matrix,” used by the US Attorney’s Office for the District of Columbia to determine appropriate hourly rates for civil litigants, endorsed Chicago legal market rates as the appropriate rates for complex environmental litigation in Northwest Indiana, and allowed for partially reconstructed time records. The Clinic’s work, which has received significant media attention, helped to spawn other litigation to address pollution by other industrial facilities in Northwest Indiana and other enforcement against U. S. Steel by the State of Indiana.

In Winter Quarter 2024, Clinic students worked closely with Dr. John Ikerd, an agricultural economist and emeritus professor at the University of Missouri, to file an amicus brief in Food & Water Watch v. U.S. Environmental Protection Agency . In that case pending before the Ninth Circuit, Food & Water Watch argues that US EPA is illegally allowing Concentrated Animal Feeding Operations, more commonly known as factory farms, to pollute waterways significantly more than is allowable under the Clean Water Act. In the brief for Dr. Ikerd and co-amici Austin Frerick, Crawford Stewardship Project, Family Farm Defenders, Farm Aid, Missouri Rural Crisis Center, National Family Farm Coalition, National Sustainable Agriculture Coalition, and Western Organization of Resource Councils, we argued that EPA’s refusal to regulate CAFOs effectively is an unwarranted application of “agricultural exceptionalism” to industrial agriculture and that EPA effectively distorts the animal production market by allowing CAFOs to externalize their pollution costs and diminishing the ability of family farms to compete. Attorneys for the litigants will argue the case in September 2024.

Energy and Climate

Energy justice.

The Abrams Clinic supported grassroots organizations advocating for energy justice in low-income communities and Black, Indigenous, and People of Color (BIPOC) communities in Michigan. With the Clinic’s representation, these organizations intervened in cases before the Michigan Public Service Commission (MPSC), which regulates investor-owned utilities. Students conducted discovery, drafted written testimony, cross-examined utility executives, participated in settlement discussions, and filed briefs for these projects. The Clinic’s representation has elevated the concerns of these community organizations and forced both the utilities and regulators to consider issues of equity to an unprecedented degree. This year, on behalf of Soulardarity (Highland Park, MI), We Want Green, Too (Detroit, MI), and Urban Core Collective (Grand Rapids, MI), Clinic students engaged in eight contested cases before the MPSC against DTE Electric, DTE Gas, and Consumers Energy, as well as provided support for our clients’ advocacy in other non-contested MPSC proceedings.

The Clinic started this past fall with wins in three cases. First, the Clinic’s clients settled with DTE Electric in its Integrated Resource Plan case. The settlement included an agreement to close the second dirtiest coal power plant in Michigan three years early, $30 million from DTE’s shareholders to assist low-income customers in paying their bills, and $8 million from DTE’s shareholders toward a community fund that assists low-income customers with installing energy efficiency improvements, renewable energy, and battery technology. Second, in DTE Electric’s 2023 request for a rate hike (a “rate case”), the Commission required DTE Electric to develop a more robust environmental justice analysis and rejected the Company’s second attempt to waive consumer protections through a proposed electric utility prepayment program with a questionable history of success during its pilot run. The final Commission order and the administrative law judge’s proposal for final decision cited the Clinic’s testimony and briefs. Third, in Consumers Electric’s 2023 rate case, the Commission rejected the Company’s request for a higher ratepayer-funded return on its investments and required the Company to create a process that will enable intervenors to obtain accurate GIS data. The Clinic intends to use this data to map the disparate impact of infrastructure investment in low-income and BIPOC communities.

In the winter, the Clinic filed public comments regarding DTE Electric and Consumers Energy’s “distribution grid plans” (DGP) as well as supported interventions in two additional cases: Consumers Energy’s voluntary green pricing (VGP) case and the Clinic’s first case against the gas utility DTE Gas. Beginning with the DGP comments, the Clinic first addressed Consumers’s 2023 Electric Distribution Infrastructure Investment Plan (EDIIP), which detailed current distribution system health and the utility’s approximately $7 billion capital project planning ($2 billion of which went unaccounted for in the EDIIP) over 2023–2028. The Clinic then commented on DTE Electric’s 2023 DGP, which outlined the utility’s opaque project prioritization and planned more than $9 billion in capital investments and associated maintenance over 2024–2028. The comments targeted four areas of deficiencies in both the EDIIP and DGP: (1) inadequate consideration of distributed energy resources (DERs) as providing grid reliability, resiliency, and energy transition benefits; (2) flawed environmental justice analysis, particularly with respect to the collection of performance metrics and the narrow implementation of the Michigan Environmental Justice Screen Tool; (3) inequitable investment patterns across census tracts, with emphasis on DTE Electric’s skewed prioritization for retaining its old circuits rather than upgrading those circuits; and (4) failing to engage with community feedback.

For the VGP case against Consumers, the Clinic supported the filing of both an initial brief and reply brief requesting that the Commission reject the Company’s flawed proposal for a “community solar” program. In a prior case, the Clinic advocated for the development of a community solar program that would provide low-income, BIPOC communities with access to clean energy. As a result of our efforts, the Commission approved a settlement agreement requiring the Company “to evaluate and provide a strawman recommendation on community solar in its Voluntary Green Pricing Program.” However, the Company’s subsequent proposal in its VGP case violated the Commission’s order because it (1) was not consistent with the applicable law, MCL 460.1061; (2) was not a true community solar program; (3) lacked essential details; (4) failed to compensate subscribers sufficiently; (5) included overpriced and inflexible subscriptions; (6) excessively limited capacity; and (7) failed to provide a clear pathway for certain participants to transition into other VGP programs. For these reasons, the Clinic argued that the Commission should reject the Company’s proposal.

In DTE Gas’s current rate case, the Clinic worked with four witnesses to develop testimony that would rebut DTE Gas’s request for a rate hike on its customers. The testimony advocated for a pathway to a just energy transition that avoids dumping the costs of stranded gas assets on the low-income and BIPOC communities that are likely to be the last to electrify. Instead, the testimony proposed that the gas and electric utilities undertake integrated planning that would prioritize electric infrastructure over gas infrastructure investment to ensure that DTE Gas does not over-invest in gas infrastructure that will be rendered obsolete in the coming decades. The Clinic also worked with one expert witness to develop an analysis of DTE Gas’s unaffordable bills and inequitable shutoff, deposit, and collections practices. Lastly, the Clinic offered testimony on behalf of and from community members who would be directly impacted by the Company’s rate hike and lack of affordable and quality service. Clinic students have spent the summer drafting an approximately one-hundred-page brief making these arguments formally. We expect the Commission’s decision this fall.

Finally, both DTE Electric and Consumers Energy have filed additional requests for rate increases after the conclusion of their respective rate cases filed in 2023. On behalf of our Clients, the Clinic has intervened in these cases, and clinic students have already reviewed thousands of pages of documents and started to develop arguments and strategies to protect low-income and BIPOC communities from the utility’s ceaseless efforts to increase the cost of energy.

Corporate Climate Greenwashing

The Abrams Environmental Law Clinic worked with a leading international nonprofit dedicated to using the law to protect the environment to research corporate climate greenwashing, focusing on consumer protection, green financing, and securities liability. Clinic students spent the year examining an innovative state law, drafted a fifty-page guide to the statute and relevant cases, and examined how the law would apply to a variety of potential cases. Students then presented their findings in a case study and oral presentation to members of ClientEarth, including the organization’s North American head and members of its European team. The project helped identify the strengths and weaknesses of potential new strategies for increasing corporate accountability in the fight against climate change.

Land Contamination, Lead, and Hazardous Waste

The Abrams Clinic continues to represent East Chicago, Indiana, residents who live or lived on or adjacent to the USS Lead Superfund site. This year, the Clinic worked closely with the East Chicago/Calumet Coalition Community Advisory Group (CAG) to advance the CAG’s advocacy beyond the Superfund site and the adjacent Dupont RCRA site. Through multiple forms of advocacy, the clinics challenged the poor performance and permit modification and renewal attempts of Tradebe Treatment and Recycling, LLC (Tradebe), a hazardous waste storage and recycling facility in the community. Clinic students sent letters to US EPA and Indiana Department of Environmental Management officials about how IDEM has failed to assess meaningful penalties against Tradebe for repeated violations of the law and how IDEM has allowed Tradebe to continue to threaten public and worker health and safety by not improving its operations. Students also drafted substantial comments for the CAG on the US EPA’s Lead and Copper Rule improvements, the Suppliers’ Park proposed cleanup, and Sims Metal’s proposed air permit revisions. The Clinic has also continued working with the CAG, environmental experts, and regulators since US EPA awarded $200,000 to the CAG for community air monitoring. The Clinic and its clients also joined comments drafted by other environmental organizations about poor operations and loose regulatory oversight of several industrial facilities in the area.

Endangered Species

The Abrams Clinic represented the Center for Biological Diversity (CBD) and the Hoosier Environmental Council (HEC) in litigation regarding the US Fish and Wildlife Service’s (Service) failure to list the Kirtland’s snake as threatened or endangered under the Endangered Species Act. The Kirtland’s snake is a small, secretive, non-venomous snake historically located across the Midwest and the Ohio River Valley. Development and climate change have undermined large portions of the snake’s habitat, and populations are declining. Accordingly, the Clinic sued the Service in the US District Court for the District of Columbia last summer over the Service’s denial of CBD’s request to have the Kirtland’s snake protected. This spring, the Clinic was able to reach a settlement with the Service that requires the Service to reconsider its listing decision for the Kirtland’s snake and to pay attorney fees.

The Clinic also represented CBD in preparation for litigation regarding the Service’s failure to list another species as threatened or endangered. Threats from land development and climate change have devastated this species as well, and the species has already been extirpated from two of the sixteen US states in its range. As such, the Clinic worked this winter and spring to prepare a notice of intent (NOI) to sue the Service. The Team poured over hundreds of FOIA documents and dug into the Service’s supporting documentation to create strong arguments against the Service in the imminent litigation. The Clinic will send the NOI and file a complaint in the next few months.

Students and Faculty

Twenty-four law school students from the classes of 2024 and 2025 participated in the Clinic, performing complex legal research, reviewing documents obtained through discovery, drafting legal research memos and briefs, conferring with clients, conducting cross-examination, participating in settlement conferences, and arguing motions. Students secured nine clerkships, five were heading to private practice after graduation, and two are pursuing public interest work. Sam Heppell joined the Clinic from civil rights private practice, bringing the Clinic to its full complement of three attorneys.