Improved manure management refers to the use of impermeable covers and physical or chemical treatments applied during the storage and processing of wet manure. These techniques can reduce methane emissions under anaerobic storage conditions and nitrous oxide emissions under aerobic conditions. They offer multiple environmental benefits, including reduced air pollution, reduced nutrient leaching and eutrophication of downstream aquatic systems, and reduced demand for energy-intensive synthetic fertilizers. Disadvantages include a relatively small climate impact and, except for covers, high costs. Even at an optimistic level of adoption, the climate impact is unlikely to be globally meaningful (<0.1 Gt CO₂‑eq/yr ). Despite this modest climate impact, we conclude that Improve Manure Management is a “Worthwhile” solution.
Based on our analysis, improved manure management using impermeable covers and physical or chemical treatments will reduce emissions, although not by a globally meaningful amount. However, because these manure management techniques are broadly available, we conclude this climate solution is “Worthwhile.”
| Plausible | Could it work? | Yes |
|---|---|---|
| Ready | Is it ready? | Yes |
| Evidence | Are there data to evaluate it? | Yes |
| Effective | Does it consistently work? | Yes |
| Impact | Is it big enough to matter? | No |
| Risk | Is it risky or harmful? | No |
| Cost | Is it cheap? | ? |
Manure generated from industrial livestock production contains significant quantities of organic carbon and nitrogen. Under low-oxygen conditions, bacteria convert organic material in manure to methane through anaerobic decomposition. Liquid manure, particularly from pigs and cows, produces significant quantities of methane. In oxygen-rich conditions, organic nitrogen in manure undergoes chemical reactions to produce nitrous oxide. Once produced, these GHGs diffuse towards the surface of the manure storage tank, where they are emitted into the atmosphere.
Improved manure management interrupts the production or release of methane and nitrous oxide through a structural barrier, or physical or chemical treatment processes. Manure storage covers made from impermeable synthetic materials effectively prevent the release of GHGs, and can be utilized in conjunction with biogas systems for energy generation. Chemical treatments, such as acidification and the addition of additives, suppress microbial activity, thereby inhibiting methane and nitrous oxide production. Physical processes, such as aeration and temperature reduction, similarly limit optimal conditions for microbial growth. Separating the solids and liquids from manure can also reduce the potential for methane production, enabling more effective solutions such as composting and anaerobic digestion.
Available technologies for manure management are mature and market-ready. However, empirical evidence of their effectiveness for reducing methane emissions is limited. Pilot studies indicate high effectiveness of manure acidification, moderate effectiveness of impermeable synthetic covers, and low effectiveness of manure additives. Except for the use of natural and synthetic impermeable covers, the overall adoption of these techniques is low.
Improved manure management can provide environmental benefits by reducing air pollution, preventing nutrient leaching from organic solids that settle into sludge, mitigating eutrophication in downstream aquatic ecosystems, and preventing soil acidification. In the food system, manure management allows for better alignment between crop needs and natural fertilizer characteristics. Since hauling liquid manure is expensive, manure storage and treatment methods promote efficient nutrient cycling and reduce the need for energy-intensive synthetic fertilizers. Abated methane in manure also limits ground-level ozone production upon application, thereby improving crop yields.
At the farm scale, the wide range of treatment options allows for a high level of customization in the manure management process to achieve joint goals of nutrient management, revenue generation, and emission reductions. Covers also directly mitigate risks to farmworker health and safety from manure handling, and manure treatment can further limit exposure to irritants and noxious gases, improving the health of surrounding communities.
Compared to no treatment and other manure-related solutions, such as composting and anaerobic digesters, evidence for the effectiveness of impermeable covers and manure treatment technologies is limited. At realistic levels of adoption, improving manure management is unlikely to have a globally meaningful climate impact (<0.1 Gt CO₂‑eq/yr ). High costs are also a key barrier to wider adoption, ranging from US$110–145/t CO₂‑eq for synthetic covers to US$500–3,000/t CO₂‑eq for other treatments.
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Rice production is a significant source of methane emissions and a minor source of nitrous oxide emissions. Most rice production occurs in flooded fields called paddies, where anaerobic conditions trigger high levels of methane production. This solution includes two related practices that each reduce emissions from paddy rice production: noncontinuous flooding and nutrient management. Noncontinuous flooding is a water management technique that reduces the amount of time rice paddy soils spend fully saturated, thereby reducing methane. Unfortunately, noncontinuous flooding increases nitrous oxide emissions. Nutrient management helps to address this challenge by controlling the timing, amount, and type of fertilization to maximize plant uptake and minimize nitrous oxide emissions.
Rice is a staple crop of critical importance, occupying 11% of global cropland (FAOstat 2025). Rice production has higher GHG emissions than most crop production, accounting for 9% of all anthropogenic methane and 10% of cropland nitrous oxide (Wang et al., 2020). Nabuurs et al. (2022) found methane emissions from global rice production to be 0.8–1.0 Gt CO₂‑eq/yr and growing 0.4% annually.
Rice paddy systems are fields with berms and plumbing to permit the flooding of rice for the production periods, which helps with weed and pest control (rice thrives in flooded conditions, though it does not require them). Paddy rice is the main source of methane from rice production. Upland rice is grown outside of paddies and does not produce significant methane emissions, so we excluded it from this analysis. Irrigated paddies are provided with irrigation water, while rain-fed paddies are only filled by rainfall and runoff (Raffa, 2021). For this analysis, we considered both irrigated and rain-fed paddies.
Flooded rice paddies encourage the production of methane by microbes. Conventional paddy rice production uses continuous flooding, in which the paddy is flooded for the full rice production period. Several approaches can reduce methane, with the most widespread being noncontinuous flooding. This is a collection of practices (such as alternate wetting and drying) that drain the fields one or more times during the rice production period. As a result, the paddy spends less time in its methane-producing state. This can be done without reducing rice yields in many, but not all, cases, and also significantly reduces irrigation water use (Bo et al., 2022). Impacts on yields depend on soils, climate, and other variables (Cheng et al., 2022).
A major drawback to noncontinuous flooding is that it increases nitrous oxide emissions from fertilizer compared to continuous flooding. High nitrogen levels in flooded paddies encourage the growth of bacteria that produce methane, reduce the natural breakdown of methane, and facilitate emissions of nitrous oxide to the atmosphere (Li et al., 2024). The effect is small compared to the mitigated emissions from methane reduction (Jiang et al., 2019), but remains serious. Use of nutrient management techniques, such as controlling fertilizer amount, type (e.g., controlled-release urea), timing, and application techniques (e.g., deep fertilization), can reduce these emissions. This is in part because nitrogen fertilizers are often overapplied, leaving room to increase efficiency without reducing rice yields (Hergoualc’h et al., 2019; Li et al., 2024).
Other practices also show potential but were not included in our analysis. These include the application of biochar to rice paddies and the use of rice cultivars that produce fewer emissions (Qian et al., 2023). Other approaches include saturated soil culture, System of Rice Intensification (“SRI”), ground-cover systems, raised beds, and improved irrigation and paddy infrastructure (Surendran et al., 2021).
Note that some practices, such as incorporating rice straw or the use of compost or manure, can increase nitrous oxide emissions (Li et al., 2024).
There is also evidence that, under some circumstances, noncontinuous flooding can sequester soil organic carbon by increasing soil organic matter. However, there are not enough data available to quantify this (Qian et al., 2023). Indeed, one meta-analysis found that noncontinuous flooding can actually lead to a decrease in soil organic carbon (Livsey et al., 2019). One complication is that many production areas plant rice two or even three times per year, and data are typically presented on a per-harvest or even per-flooded day basis. To overcome this challenge, we use data on the percentage of global irrigated rice land in single, double, and triple cropping from Carlson et al. (2016) to create weighted average values as appropriate.
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Eric Toensmeier
Ruthie Burrows, Ph.D.
James Gerber, Ph.D.
Yusuf Jameel, Ph.D.
Daniel Jasper
Alex Sweeney
Aiyana Bodi
James Gerber, Ph.D.
Hannah Henkin
Zoltan Nagy, Ph.D.
Ted Otte
Paul C. West, Ph.D.
Methane Reduction
We calculated per-hectare methane emissions using Intergovernmental Panel on Climate Change (IPCC) methodology (Ogle et. al, 2019). To develop regional emissions per rice harvest, we multiplied standard regional daily baseline emissions by standard cultivation period lengths, then multiplied by the mean scaling factor for noncontinuous flooding systems. However, the total number of rice harvests per year ranged from one to three. Carlson et al. (2016) reported a global figure of harvests on rice fields: 42% were harvested once, 50% were harvested twice, and 8% were harvested three times. We used this to develop a weighted average methane emissions figure for each region. National effectiveness ranged from 1.55 to 3.29 t CO₂‑eq /ha/yr (Table 1a).
Nitrous Oxide Reduction
Using data from Adalibieke et al. (2024) and Gerber et al. (2024), we calculated the current country-level rate of nitrogen application per hectare and a target rate reflecting improved efficiency through nutrient management. For a full methodology, see the Appendix.
In noncontinuously flooded systems, nitrous oxide emissions are 1.66 times higher per t of nitrogen applied (Hergoualc’h et al., 2019). Using the different emissions factors, we calculated total nitrous oxide emissions for 1) flooded rice with current nitrogen application rates, and 2) noncontinuously flooded rice with target nitrogen application rates.
The effectiveness of nutrient management for each country with over 100,000 ha of rice production ranged from –0.48 to 0.11 t CO₂‑eq /ha/yr (Table 1).
Combined Reduction
Combined effectiveness of methane and nitrous oxide reduction was 1.49–3.39 t CO₂‑eq /ha/yr (Table 1).
Table 1a. Combined effectiveness at reducing emissions, by country, for noncontinuous flooding with nutrient management.
Unit: t CO₂‑eq /ha/yr
| Afghanistan | 1.63 |
| Argentina | 2.70 |
| Bangladesh | 1.63 |
| Benin | 2.30 |
| Bolivia (Plurinational State of) | 2.70 |
| Brazil | 2.70 |
| Burkina Faso | 2.30 |
| Cambodia | 2.13 |
| Cameroon | 2.30 |
| Chad | 2.30 |
| China | 2.48 |
| Colombia | 2.70 |
| Côte d'Ivoire | 2.30 |
| Democratic People's Republic of Korea | 2.48 |
| Democratic Republic of the Congo | 2.30 |
| Dominican Republic | 2.70 |
| Ecuador | 2.70 |
| Egypt | 2.30 |
| Ghana | 2.30 |
| Guinea | 2.30 |
| Guinea-Bissau | 2.30 |
| Guyana | 2.70 |
| India | 1.63 |
| Indonesia | 2.13 |
| Iran (Islamic Republic of) | 3.29 |
| Italy | 3.29 |
| Japan | 2.48 |
| Lao People's Democratic Republic | 2.13 |
| Liberia | 2.30 |
| Madagascar | 2.30 |
| Malaysia | 2.13 |
| Mali | 2.30 |
| Mozambique | 2.30 |
| Myanmar | 2.13 |
| Nepal | 1.63 |
| Nigeria | 2.30 |
| Pakistan | 1.63 |
| Paraguay | 2.70 |
| Peru | 2.70 |
| Philippines | 2.13 |
| Republic of Korea | 2.48 |
| Russian Federation | 3.29 |
| Senegal | 2.30 |
| Sierra Leone | 2.30 |
| Sri Lanka | 1.63 |
| Thailand | 2.13 |
| Turkey | 3.29 |
| Uganda | 2.70 |
| United Republic of Tanzania | 2.30 |
| United States of America | 1.55 |
| Uruguay | 2.70 |
| Venezuela (Bolivarian Republic of) | 2.70 |
| Vietnam | 2.13 |
Unit: t CO₂‑eq /ha/yr
| Afghanistan | 0.03 |
| Argentina | 0.07 |
| Bangladesh | 0.06 |
| Benin | 0.03 |
| Bolivia (Plurinational State of) | 0.00 |
| Brazil | 0.00 |
| Burkina Faso | –0.02 |
| Cambodia | 0.01 |
| Cameroon | 0.00 |
| Chad | 0.01 |
| China | 0.01 |
| Colombia | –0.07 |
| Côte d'Ivoire | 0.02 |
| Democratic People's Republic of Korea | 0.02 |
| Democratic Republic of the Congo | 0.01 |
| Dominican Republic | –0.16 |
| Ecuador | –0.08 |
| Egypt | –0.15 |
| Ghana | 0.05 |
| Guinea | 0.01 |
| Guinea-Bissau | 0.01 |
| Guyana | –0.06 |
| India | –0.02 |
| Indonesia | 0.11 |
| Iran (Islamic Republic of) | –0.05 |
| Italy | 0.00 |
| Japan | 0.07 |
| Lao People's Democratic Republic | 0.02 |
| Liberia | 0.02 |
| Madagascar | 0.00 |
| Malaysia | –0.01 |
| Mali | –0.03 |
| Mozambique | 0.01 |
| Myanmar | 0.04 |
| Nepal | 0.04 |
| Nigeria | 0.01 |
| Pakistan | –0.04 |
| Paraguay | 0.01 |
| Peru | 0.09 |
| Philippines | 0.00 |
| Republic of Korea | 0.00 |
| Russian Federation | 0.04 |
| Senegal | –0.04 |
| Sierra Leone | 0.02 |
| Sri Lanka | 0.02 |
| Thailand | –0.03 |
| Turkey | 0.10 |
| Uganda | 0.00 |
| United Republic of Tanzania | 0.04 |
| United States of America | –0.05 |
| Uruguay | 0.03 |
| Venezuela (Bolivarian Republic of) | –0.48 |
| Vietnam | 0.00 |
Unit: t CO₂‑eq /ha rice paddies/yr
| Afghanistan | 1.67 |
| Argentina | 2.77 |
| Bangladesh | 1.69 |
| Benin | 2.34 |
| Bolivia (Plurinational State of) | 2.70 |
| Brazil | 2.70 |
| Burkina Faso | 2.28 |
| Cambodia | 2.15 |
| Cameroon | 2.30 |
| Chad | 2.32 |
| China | 2.48 |
| Colombia | 2.63 |
| Côte d'Ivoire | 2.32 |
| Democratic People's Republic of Korea | 2.50 |
| Democratic Republic of the Congo | 2.31 |
| Dominican Republic | 2.54 |
| Ecuador | 2.62 |
| Egypt | 2.16 |
| Ghana | 2.35 |
| Guinea | 2.32 |
| Guinea-Bissau | 2.32 |
| Guyana | 2.63 |
| India | 1.61 |
| Indonesia | 2.24 |
| Iran (Islamic Republic of) | 3.24 |
| Italy | 3.29 |
| Japan | 2.54 |
| Lao People's Democratic Republic | 2.15 |
| Liberia | 2.32 |
| Madagascar | 2.31 |
| Malaysia | 2.13 |
| Mali | 2.28 |
| Mozambique | 2.32 |
| Myanmar | 2.17 |
| Nepal | 1.67 |
| Nigeria | 2.32 |
| Pakistan | 1.59 |
| Paraguay | 2.71 |
| Peru | 2.79 |
| Philippines | 2.14 |
| Republic of Korea | 2.47 |
| Russian Federation | 3.33 |
| Senegal | 2.27 |
| Sierra Leone | 2.32 |
| Sri Lanka | 1.65 |
| Thailand | 2.10 |
| Turkey | 3.39 |
| Uganda | 2.31 |
| United Republic of Tanzania | 2.35 |
| United States of America | 1.49 |
| Uruguay | 2.72 |
| Venezuela (Bolivarian Republic of) | 2.22 |
| Vietnam | 2.13 |
Table 1b. Combined effectiveness at reducing emissions, by country, for noncontinuous flooding with nutrient management.
Unit: t CO₂‑eq /ha rice paddies/yr
| Afghanistan | 4.75 |
| Argentina | 7.85 |
| Bangladesh | 4.75 |
| Benin | 6.71 |
| Bolivia (Plurinational State of) | 7.85 |
| Brazil | 7.85 |
| Burkina Faso | 6.71 |
| Cambodia | 6.21 |
| Cameroon | 6.71 |
| Chad | 6.71 |
| China | 7.20 |
| Colombia | 7.85 |
| Côte d'Ivoire | 6.71 |
| Democratic People's Republic of Korea | 7.20 |
| Democratic Republic of the Congo | 6.71 |
| Dominican Republic | 7.85 |
| Ecuador | 7.85 |
| Egypt | 6.71 |
| Ghana | 6.71 |
| Guinea | 6.71 |
| Guinea-Bissau | 6.71 |
| Guyana | 7.85 |
| India | 4.75 |
| Indonesia | 6.21 |
| Iran (Islamic Republic of) | 9.57 |
| Italy | 9.57 |
| Japan | 7.20 |
| Lao People's Democratic Republic | 6.21 |
| Liberia | 6.71 |
| Madagascar | 6.71 |
| Malaysia | 6.21 |
| Mali | 6.71 |
| Mozambique | 6.71 |
| Myanmar | 6.21 |
| Nepal | 4.75 |
| Nigeria | 6.71 |
| Pakistan | 4.75 |
| Paraguay | 7.85 |
| Peru | 7.85 |
| Philippines | 6.21 |
| Republic of Korea | 7.20 |
| Russian Federation | 9.57 |
| Senegal | 6.71 |
| Sierra Leone | 6.71 |
| Sri Lanka | 4.75 |
| Thailand | 6.21 |
| Turkey | 9.57 |
| Uganda | 6.71 |
| United Republic of Tanzania | 6.71 |
| United States of America | 4.51 |
| Uruguay | 7.85 |
| Venezuela (Bolivarian Republic of) | 7.85 |
| Vietnam | 6.21 |
Unit: t CO₂‑eq /ha rice paddies/yr
| Afghanistan | 0.03 |
| Argentina | 0.07 |
| Bangladesh | 0.06 |
| Benin | 0.03 |
| Bolivia (Plurinational State of) | 0.00 |
| Brazil | 0.00 |
| Burkina Faso | 0.02 |
| Cambodia | 0.01 |
| Cameroon | 0.00 |
| Chad | 0.01 |
| China | 0.01 |
| Colombia | –0.07 |
| Côte d'Ivoire | 0.02 |
| Democratic People's Republic of Korea | 0.02 |
| Democratic Republic of the Congo | 0.01 |
| Dominican Republic | 0.16 |
| Ecuador | –0.08 |
| Egypt | –0.15 |
| Ghana | 0.05 |
| Guinea | 0.01 |
| Guinea-Bissau | 0.01 |
| Guyana | –0.06 |
| India | –0.02 |
| Indonesia | 0.11 |
| Iran (Islamic Republic of) | –0.05 |
| Italy | 0.00 |
| Japan | 0.07 |
| Lao People's Democratic Republic | 0.02 |
| Liberia | 0.02 |
| Madagascar | 0.00 |
| Malaysia | –0.01 |
| Mali | –0.03 |
| Mozambique | 0.01 |
| Myanmar | 0.04 |
| Nepal | 0.04 |
| Nigeria | 0.01 |
| Pakistan | –0.04 |
| Paraguay | 0.01 |
| Peru | 0.09 |
| Philippines | 0.00 |
| Republic of Korea | 0.00 |
| Russian Federation | 0.04 |
| Senegal | –0.04 |
| Sierra Leone | 0.02 |
| Sri Lanka | 0.02 |
| Thailand | –0.03 |
| Turkey | 0.10 |
| Uganda | 0.00 |
| United Republic of Tanzania | 0.04 |
| United States of America | –0.05 |
| Uruguay | 0.03 |
| Venezuela (Bolivarian Republic of) | –0.48 |
| Vietnam | 0.00 |
Unit: t CO₂‑eq /ha rice paddies/yr
| Afghanistan | 4.78 |
| Argentina | 7.93 |
| Bangladesh | 4.81 |
| Benin | 6.74 |
| Bolivia (Plurinational State of) | 7.85 |
| Brazil | 7.85 |
| Burkina Faso | 6.68 |
| Cambodia | 6.22 |
| Cameroon | 6.71 |
| Chad | 6.72 |
| China | 7.21 |
| Colombia | 7.21 |
| Côte d'Ivoire | 6.73 |
| Democratic People's Republic of Korea | 7.23 |
| Democratic Republic of the Congo | 6.71 |
| Dominican Republic | 7.69 |
| Ecuador | 7.77 |
| Egypt | 6.56 |
| Ghana | 6.76 |
| Guinea | 6.72 |
| Guinea-Bissau | 6.72 |
| Guyana | 7.79 |
| India | 4.73 |
| Indonesia | 6.31 |
| Iran (Islamic Republic of) | 9.52 |
| Italy | 9.57 |
| Japan | 7.27 |
| Lao People's Democratic Republic | 6.23 |
| Liberia | 6.72 |
| Madagascar | 6.71 |
| Malaysia | 6.20 |
| Mali | 6.20 |
| Mozambique | 6.72 |
| Myanmar | 6.25 |
| Nepal | 4.79 |
| Nigeria | 6.72 |
| Pakistan | 4.71 |
| Paraguay | 7.86 |
| Peru | 7.95 |
| Philippines | 6.21 |
| Republic of Korea | 7.20 |
| Russian Federation | 9.61 |
| Senegal | 6.67 |
| Sierra Leone | 6.73 |
| Sri Lanka | 4.77 |
| Thailand | 6.18 |
| Turkey | 9.67 |
| Uganda | 6.71 |
| United Republic of Tanzania | 6.75 |
| United States of America | 4.45 |
| Uruguay | 7.88 |
| Venezuela (Bolivarian Republic of) | 7.38 |
| Vietnam | 6.20 |
For conventional paddy rice, we assumed an initial cost of US$0 because many millions of hectares of paddies are already in place (Table 2). We used regional per-hectare average profits from Damania et al. (2024) as the source for net profit per year. Because the initial cost per hectare is US$0, the net cost per hectare is the negative of the per-hectare annual profit.
Table 2. Net cost and profit of conventional paddy rice by region in 2023.
Unit: US$/ha rice paddies
| Africa | 0.00 |
| East Asia | 0.00 |
| Europe | 0.00 |
| North America | 0.00 |
| South America | 0.00 |
| South Asia | 0.00 |
| Southeast Asia | 0.00 |
Unit: US$/ha rice paddies/yr
| Africa | 457.34 |
| East Asia | 543.67 |
| Europe | 585.43 |
| North America | 356.27 |
| South America | 285.69 |
| South Asia | 488.85 |
| Southeast Asia | 322.13 |
Unit: US$/ha rice paddies/yr
| Africa | -457.34 |
| East Asia | -543.67 |
| Europe | -585.43 |
| North America | -356.27 |
| South America | -285.69 |
| South Asia | -488.85 |
| Southeast Asia | -322.13 |
For noncontinuous flooding, we assumed an initial cost of US$0 because no new inputs or changes to paddy infrastructure are required in most cases. Median impact on net profit was an increase of 17% based on nine data points from seven sources. National results are shown in Table 3.
We assumed nutrient management has an initial cost of US$0 because in many cases, nutrient management begins with reducing the overapplication of fertilizer. Here we used the mean value from Gu et al. (2023), a savings of US$507.8/t nitrogen. We used our national-level data on overapplication of nitrogen to calculate savings per hectare. National results are shown in Table 3.
Combined Net Profit per Hectare
Net profit per hectare varies by country due to regional and some country-specific variables. Country-by-country results are shown in Table 3.
Net Net Cost Compared to Conventional Paddy Rice
Net net cost varies by country. Country-by-country results are shown in Table 3.
Table 3. Net cost and profit of noncontinuous flooding with nutrient management by region.
Unit: US$/ha rice paddies
| Afghanistan | 0.00 |
| Argentina | 0.00 |
| Bangladesh | 0.00 |
| Benin | 0.00 |
| Bolivia (Plurinational State of) | 0.00 |
| Brazil | 0.00 |
| Burkina Faso | 0.00 |
| Cambodia | 0.00 |
| Cameroon | 0.00 |
| Chad | 0.00 |
| China | 0.00 |
| Colombia | 0.00 |
| Cote d'Ivoire | 0.00 |
| Democratic People's Republic of Korea | 0.00 |
| Democratic Republic of the Congo | 0.00 |
| Dominican Republic | 0.00 |
| Ecuador | 0.00 |
| Egypt | 0.00 |
| Ghana | 0.00 |
| Guinea | 0.00 |
| Guinea–Bissau | 0.00 |
| Guyana | 0.00 |
| India | 0.00 |
| Indonesia | 0.00 |
| Iran (Islamic Republic of) | 0.00 |
| Italy | 0.00 |
| Japan | 0.00 |
| Lao People's Democratic Republic | 0.00 |
| Liberia | 0.00 |
| Madagascar | 0.00 |
| Malaysia | 0.00 |
| Mali | 0.00 |
| Mozambique | 0.00 |
| Myanmar | 0.00 |
| Nepal | 0.00 |
| Nigeria | 0.00 |
| Pakistan | 0.00 |
| Paraguay | 0.00 |
| Peru | 0.00 |
| Philippines | 0.00 |
| Republic of Korea | 0.00 |
| Russian Federation | 0.00 |
| Senegal | 0.00 |
| Sierra Leone | 0.00 |
| Sri Lanka | 0.00 |
| Thailand | 0.00 |
| Turkey | 0.00 |
| Uganda | 0.00 |
| United Republic of Tanzania | 0.00 |
| United States of America | 0.00 |
| Uruguay | 0.00 |
| Venezuela (Bolivarian Republic of) | 0.00 |
| Vietnam | 0.00 |
Non-continuous flooding and nutrient management.
Unit: US$/ha rice paddies/yr
| Afghanistan | 573.4 |
| Argentina | 354.8 |
| Bangladesh | 576.7 |
| Benin | 535.1 |
| Bolivia (Plurinational State of) | 354.1 |
| Brazil | 363.4 |
| Burkina Faso | 553.3 |
| Cambodia | 377.8 |
| Cameroon | 543.7 |
| Chad | 535.1 |
| China | 675.1 |
| Colombia | 397.7 |
| Cote d'Ivoire | 535.8 |
| Democratic People's Republic of Korea | 654.6 |
| Democratic Republic of the Congo | 535.6 |
| Dominican Republic | 428.4 |
| Ecuador | 390.3 |
| Egypt | 802.2 |
| Ghana | 535.5 |
| Guinea | 538.5 |
| Guinea–Bissau | 539.2 |
| Guyana | 382.0 |
| India | 607.9 |
| Indonesia | 382.3 |
| Iran (Islamic Republic of) | 726.7 |
| Italy | 567.9 |
| Japan | 636.0 |
| Lao People's Democratic Republic | 377.0 |
| Liberia | 535.3 |
| Madagascar | 535.0 |
| Malaysia | 401.2 |
| Mali | 561.0 |
| Mozambique | 535.5 |
| Myanmar | 380.7 |
| Nepal | 575.2 |
| Nigeria | 537.1 |
| Pakistan | 610.0 |
| Paraguay | 385.9 |
| Peru | 351.7 |
| Philippines | 399.5 |
| Republic of Korea | 678.2 |
| Russian Federation | 475.2 |
| Senegal | 569.9 |
| Sierra Leone | 535.1 |
| Sri Lanka | 591.1 |
| Thailand | 407.7 |
| Turkey | 694.5 |
| Uganda | 543.3 |
| United Republic of Tanzania | 537.4 |
| United States of America | 490.4 |
| Uruguay | 377.6 |
| Venezuela (Bolivarian Republic of) | 546.2 |
| Vietnam | 416.6 |
Non-continuous flooding and nutrient management.
Unit: US$/ha rice paddies/yr
| Afghanistan | -573.4 |
| Argentina | -354.8 |
| Bangladesh | -576.7 |
| Benin | -535.1 |
| Bolivia (Plurinational State of) | -354.1 |
| Brazil | -363.4 |
| Burkina Faso | -553.3 |
| Cambodia | -377.8 |
| Cameroon | -543.7 |
| Chad | -535.1 |
| China | -675.1 |
| Colombia | -397.7 |
| Cote d'Ivoire | -535.8 |
| Democratic People's Republic of Korea | -654.6 |
| Democratic Republic of the Congo | -535.6 |
| Dominican Republic | -428.4 |
| Ecuador | -390.3 |
| Egypt | -802.2 |
| Ghana | -535.5 |
| Guinea | -538.5 |
| Guinea–Bissau | -539.2 |
| Guyana | -382.0 |
| India | -607.9 |
| Indonesia | -382.3 |
| Iran (Islamic Republic of) | -726.7 |
| Italy | -567.9 |
| Japan | -636.0 |
| Lao People's Democratic Republic | -377.0 |
| Liberia | -535.3 |
| Madagascar | -535.0 |
| Malaysia | -401.2 |
| Mali | -561.0 |
| Mozambique | -535.5 |
| Myanmar | -380.7 |
| Nepal | -575.2 |
| Nigeria | -537.1 |
| Pakistan | -610.0 |
| Paraguay | -385.9 |
| Peru | -351.7 |
| Philippines | -399.5 |
| Republic of Korea | -678.2 |
| Russian Federation | -475.2 |
| Senegal | -569.9 |
| Sierra Leone | -535.1 |
| Sri Lanka | -591.1 |
| Thailand | -407.7 |
| Turkey | -694.5 |
| Uganda | -543.3 |
| United Republic of Tanzania | -537.4 |
| United States of America | -490.4 |
| Uruguay | -377.6 |
| Venezuela (Bolivarian Republic of) | -546.2 |
| Vietnam | -416.6 |
Non-continuous flooding and nutrient management.
Unit: US$/ha rice paddies/yr
| Afghanistan | -1,062 |
| Argentina | -640.5 |
| Bangladesh | -1,065 |
| Benin | -992.4 |
| Bolivia (Plurinational State of) | -639.8 |
| Brazil | -649.0 |
| Burkina Faso | -1,010 |
| Cambodia | -699.9 |
| Cameroon | -1,001 |
| Chad | -992.5 |
| China | -1,219 |
| Colombia | -683.4 |
| Cote d'Ivoire | -993.2 |
| Democratic People's Republic of Korea | -1,198 |
| Democratic Republic of the Congo | -992.9 |
| Dominican Republic | -714.1 |
| Ecuador | -676.0 |
| Egypt | -1,387 |
| Ghana | -992.8 |
| Guinea | -995.8 |
| Guinea–Bissau | -996.5 |
| Guyana | -667.7 |
| India | -1,096 |
| Indonesia | -704.5 |
| Iran (Islamic Republic of) | -1,312 |
| Italy | -1,053 |
| Japan | -1,179 |
| Lao People's Democratic Republic | -699.1 |
| Liberia | -992.6 |
| Madagascar | -992.4 |
| Malaysia | -723.3 |
| Mali | -1,018 |
| Mozambique | -992.8 |
| Myanmar | -702.8 |
| Nepal | -1,064 |
| Nigeria | -994.5 |
| Pakistan | -1,098 |
| Paraguay | -671.6 |
| Peru | -637.4 |
| Philippines | -721.6 |
| Republic of Korea | -1,221 |
| Russian Federation | -865.9 |
| Senegal | -1,027 |
| Sierra Leone | -992.4 |
| Sri Lanka | -1,080 |
| Thailand | -729.8 |
| Turkey | -1,279 |
| Uganda | -1,000 |
| United Republic of Tanzania | -994.7 |
| United States of America | -846.7 |
| Uruguay | -663.3 |
| Venezuela (Bolivarian Republic of) | -831.9 |
| Vietnam | -738.8 |
Non-continuous flooding and nutrient management.
Unit: US$/t CO₂‑eq
| Afghanistan | -222.1 |
| Argentina | -80.82 |
| Bangladesh | -221.5 |
| Benin | -147.2 |
| Bolivia (Plurinational State of) | -81.49 |
| Brazil | -82.60 |
| Burkina Faso | -151.2 |
| Cambodia | -112.5 |
| Cameroon | -149.3 |
| Chad | -147.7 |
| China | -168.9 |
| Colombia | -87.77 |
| Cote d'Ivoire | -147.6 |
| Democratic People's Republic of Korea | -165.8 |
| Democratic Republic of the Congo | -147.9 |
| Dominican Republic | -92.82 |
| Ecuador | -86.99 |
| Egypt | -211.5 |
| Ghana | -146.9 |
| Guinea | -148.1 |
| Guinea–Bissau | -148.2 |
| Guyana | -85.72 |
| India | -232.1 |
| Indonesia | -111.5 |
| Iran (Islamic Republic of) | -137.8 |
| Italy | -110.0 |
| Japan | -162.2 |
| Lao People's Democratic Republic | -112.2 |
| Liberia | -147.6 |
| Madagascar | -147.9 |
| Malaysia | -116.6 |
| Mali | -152.2 |
| Mozambique | -147.7 |
| Myanmar | -112.4 |
| Nepal | -222.2 |
| Nigeria | -148.0 |
| Pakistan | -233.3 |
| Paraguay | -85.41 |
| Peru | -80.22 |
| Philippines | -116.1 |
| Republic of Korea | -169.7 |
| Russian Federation | -90.08 |
| Senegal | -154.0 |
| Sierra Leone | -147.5 |
| Sri Lanka | -226.3 |
| Thailand | -118.1 |
| Turkey | -132.3 |
| Uganda | -149.1 |
| United Republic of Tanzania | -147.3 |
| United States of America | -190.1 |
| Uruguay | -84.18 |
| Venezuela (Bolivarian Republic of) | -112.7 |
| Vietnam | -119.1 |
Non-continuous flooding and nutrient management.
Cost per unit climate impact
The cost per t CO₂‑eq varies by country. Country-by-country results are shown in Table 3. The global weighted average is a savings of US$175.0/t CO₂‑eq (Table 4). Note that this is the same for both 100- and 20-yr results.
Table 4. Weighted average cost per unit climate impact.
Unit: US$/t CO₂‑eq
| Weighted average | -175.0 |
Learning curve data are not available for improved rice cultivation.
Speed of action refers to how quickly a climate solution physically affects the atmosphere after it is deployed. This is different from speed of deployment, which is the pace at which solutions are adopted.
At Project Drawdown, we define the speed of action for each climate solution as gradual, emergency brake, or delayed.
The noncontinuous flooding component of Improve Rice Production is an EMERGENCY BRAKE climate solution. It has a disproportionately fast impact after implementation because it reduces the short-lived climate pollutant methane.
The nutrient management component is a GRADUAL climate solution. It has a steady, linear impact on the atmosphere. The cumulative effect over time builds as a straight line.
Caveats like additionality and permanence do not apply to improve rice production as described here. If its carbon sequestration component were included, those caveats would apply.
Noncontinuous Flooding
Rigorous, up-to-date country-level data about the extent of noncontinuous flooding in rice production are in short supply. We found five sources reporting adoption in seven major rice-producing countries. We used these to create regional averages and applied them to all countries that produce more than 100,000 ha of rice (paddy and upland). The total estimated current adoption is 48.65 Mha, or 47% of global rice paddy area (Table 5). This should be considered an extremely rough estimate.
The available sources encompass different forms of noncontinuous flooding, including alternate wetting and drying (Philippines, Vietnam, Bangladesh), mid-season drainage (Japan), or both (China).
Table 5. Current adoption level (2025).
Unit: Mha
| mean | 48.65 |
Noncontinuous flooding, ha installed.
Nutrient Management
We based nutrient management adoption on our analysis of the overapplication of nitrogen fertilizer on a national basis. Rather than calculate adoption in a parallel way to noncontinuous flooding, this approach provided a national average overapplication rate (the amount of nitrogen fertilizer which is applied that is not needed for crop growth and ends up as nitrous oxide emissions). We assume that every hectare of noncontinuous flooding is also using nutrient management.
We assume the adoption of both noncontinuous flooding and nutrient management for each hectare.
Adoption trend information here takes the form of annual growth rate (%), with a median of 3.76% (Table 6). Adoption rate data are somewhat scarce.
Table 6. Adoption trend.
Unit: %
| 25th percentile | 3.00 |
| median (50th percentile) | 3.76 |
| 75th percentile | 4.25 |
Percent annual growth rate.
There are barriers to adoption of these techniques and practices. Not all paddy rice is suitable for improved water management, and under certain conditions, undesirable yield reductions are possible (Bo et al., 2022). Other challenges include water access, coordinating water usage between multiple users, and ownership of water pumps (Nabuurs et al., 2022).
There are many challenges in estimating paddy rice land. Food and Agriculture Organization (FAO) statistics can overcount because land that produces more than one crop is double or triple counted. Satellite imagery is often blocked by clouds in the tropical humid areas where rice paddies are concentrated.
A comprehensive effort to calculate total world rice paddy land reported 66.00 Mha of irrigated paddy and 63.00 Mha of rain-fed paddy (Salmon et al., 2015). Our own calculation of the combined paddy rice area of countries producing over 100,000 ha of rice found 104.1 Mha of paddy rice.
We summed high-resolution maps of paddy rice area appropriate for noncontinuous flooding (Bo et al., 2022) over maps of irrigated and rain-fed rice areas (Salmon et al., 2015) to determine a maximum adoption ceiling for each country. Several countries have already exceeded this threshold, and we included their higher adoption in our calculation. The sum of these, and therefore, the median adoption ceiling, is 77.53 Mha (Table 7).
Table 7. Adoption ceiling: upper limit for adoption level.
Unit: Mha
| median | 77.53 |
Mha of improved rice production installed.
Table 8. Range of achievable adoption levels.
Unit: Mha
| Current Adoption | 48.65 |
| Achievable – Low | 49.56 |
| Achievable – High | 77.53 |
| Adoption Ceiling | 77.53 |
Mha of improved rice production installed.
Given that both China and Japan have already attained adoption rates above our adoption ceiling (Bo et al., 2022; Zhang et al., 2019), we selected for our adoption ceiling our Achievable – High adoption level, which is 77.53 Mha (Table 8).
In contrast, the countries with the lowest adoption rates had rates under 3%. In the absence of a modest adoption example, we chose to use current adoption plus 10% as our Achievable – Low adoption level. This provides an adoption of 49.56 Mha.
As described under Adoption Ceiling above, adoption of nutrient management is already weighted based on regional or national adoption and should not be overcounted in the achievable range calculations.
We calculated the potential impact of improved rice, on a 100-yr basis, at 0.10 Gt CO₂‑eq/yr from current adoption, and 0.10, 0.16, and 0.16 from Achievable – Low, Achievable – High, and Adoption Ceiling, respectively (Table 9). On a 20-yr basis, the totals are 0.29, 0.29, 0.46, and 0.46, respectively.
Table 9. Climate impact at different levels of adoption.
Unit: Gt CO₂‑eq/yr
| Current Adoption | 0.10 |
| Achievable – Low | 0.10 |
| Achievable – High | 0.16 |
| Adoption Ceiling | 0.16 |
Unit: Gt CO₂‑eq/yr
| Current Adoption | 0.29 |
| Achievable – Low | 0.29 |
| Achievable – High | 0.46 |
| Adoption Ceiling | 0.46 |
The IPCC estimated a technical potential at 0.3 Gt CO₂‑eq/yr, with 0.2 Gt CO₂‑eq/yr as economically achievable at US$100/t CO₂ (100-yr basis; Nabuurs et al., 2022). Achieving the adoption ceiling of 76% of global flooded rice production could reduce rice paddy methane by 47% (Bo et al., 2022). Applying this percentage to the IPCC reported total paddy methane emissions of 0.49–0.73 Gt CO₂‑eq/yr yields a reduction of 0.23–0.34 Gt CO₂‑eq/yr (Nabuurs et al., 2022). Roe et al. (2021) calculated 0.19 Gt CO₂‑eq/yr. Note that these benchmarks only calculate methane from paddy rice, while we also addressed nitrous oxide from nutrient management.
The additional benefits of improved rice production arise from both practices (noncontinuous flooding and improved nutrient management) that form this solution.
Noncontinuous flooding can reduce the accumulation of arsenic in rice grains (Ishfaq et al., 2020). Arsenic is a carcinogen that is responsible for thousands of premature deaths in South and Southeast Asia (Jameel et al., 2021). The amount of arsenic reduced can vary by 0–90% depending upon the timing of the wetting and drying periods (Ishfaq et al., 2020).
Better nutrient management improves soil fertility and health, increasing resilience to extreme heat and droughts. Noncontinuous flooding also slows down the rate of soil salinization, protecting soil from degradation (Carrijo et al., 2017).
Rice irrigation is responsible for 40% of all freshwater use in Asia, and rice requires two to three times more water per metric ton of grain than other cereals (Surendran et al., 2021). Field studies across South and Southeast Asia have shown that noncontinuous flooding can typically reduce irrigation requirements 20–30% compared to conventional flooded systems (Suwanmaneepong et al., 2023; Carrijo et al., 2017) without adversely affecting rice yield or grain quality. This reduction in water usage alleviates pressure on water resources in drought-prone areas (Alauddin et al., 2020).
Adoption of noncontinuous flooding up to the adoption ceiling of 76% would reduce rice irrigation needs by 25%.
Both noncontinuous flooding and improved nutrient management reduce water pollution. Nitrogen utilization is generally poor using existing growing techniques, with two-thirds of the nitrogen fertilizer being lost through surface runoff and denitrification (Zhang et al., 2021). While noncontinuous flooding is primarily a water-efficiency and methane reduction technique, it can improve nitrogen use efficiency and reduce nitrogen runoff into water bodies (Liang et al., 2017; Liang et al., 2023). Improved nutrient management also reduces the excess fertilizers that could end up in local water bodies. Both mechanisms can mitigate eutrophication and harmful algal blooms, protect aquatic ecosystems, and ensure safer drinking water supplies (Bijay-Sing and Craswell, 2021).
Not all paddies are suitable, with variables including soil type, irrigation infrastructure and ownership, community partitioning and scheduling of water resources, field size, and more (Nabuurs et al., 2022; Enriquez et al., 2021).
Many rice farmers in Asia do not directly control irrigation access, but instead use a municipal system, which is not always available when needed for noncontinuous flooding production. In addition, they may not actually experience cost savings, as pricing may be based on area rather than amount of water. An additional change is that multiple plots owned or rented by multiple farmers may be irrigated by a single irrigation gate, meaning that all must agree to an irrigation strategy. Generally speaking, pump-based irrigation areas see the best adoption, with poor adoption in gravity-based irrigation system areas. Improved irrigation infrastructure is necessary to increase adoption of noncontinuous flooding (Enriquez et al., 2021).
Continuously flooded paddies have lower weed pressure than noncontinuous paddies, so noncontinuous flooding can raise labor costs or increase herbicide use. Not all rice varieties grow well in noncontinuous flooding (Li et al., 2024). In addition, it is difficult for farmers, especially smallholders, to monitor soil moisture level, which makes determining the timing of the next irrigation difficult (Livsey et al., 2019).
We did not identify any aligned or competing interactions with other solutions.
ha rice paddies
CH₄ , N₂O
In some cases, rice yields are reduced (Nabuurs et al., 2022). However, this has been excluded from our calculations because we worked from the adoption ceiling of Bo et al. (2022), which explicitly addresses the question of maximum adoption without reducing yields.
Long-term impacts on soil health of water-saving irrigation strategies have not been widely studied, but a meta-analysis by Livsey et al. (2019) indicates a risk of decreases in soil carbon and fertility.
Rice is the third most widely grown crop in terms of cultivated area and provides more calories directly to people than any other crop. It also is an important source of methane emissions. Here we show pixels in which at least 1% of the area is devoted to paddy (flooded) rice. Upland (unflooded) rice is included in the Improve Nutrient Management solution.
Cao, P., Bilotto, F., Gonzalez Fischer, C., Mueller, N. D., Carlson, K. M., Gerber, J.S., Smith, P., Tubiello, F. N., West, P. C., You, L., & Herrero, M. (2025). Mapping greenhouse gas emissions from global cropland circa 2020 [Data set, PREPRINT Version 1]. In review at Nature Climate Change. Link to source: https://doi.org/10.21203/rs.3.rs-6622054/v1
Tang, F. H. M., Nguyen, T. H., Conchedda, G., Casse, L., Tubiello, F. N., & Maggi, F. (2024). CROPGRIDS: A global geo-referenced dataset of 173 crops [Data set]. Scientific Data, 11(1), 413. Link to source: https://doi.org/10.1038/s41597-024-03247-7
Rice is the third most widely grown crop in terms of cultivated area and provides more calories directly to people than any other crop. It also is an important source of methane emissions. Here we show pixels in which at least 1% of the area is devoted to paddy (flooded) rice. Upland (unflooded) rice is included in the Improve Nutrient Management solution.
Cao, P., Bilotto, F., Gonzalez Fischer, C., Mueller, N. D., Carlson, K. M., Gerber, J.S., Smith, P., Tubiello, F. N., West, P. C., You, L., & Herrero, M. (2025). Mapping greenhouse gas emissions from global cropland circa 2020 [Data set, PREPRINT Version 1]. In review at Nature Climate Change. Link to source: https://doi.org/10.21203/rs.3.rs-6622054/v1
Tang, F. H. M., Nguyen, T. H., Conchedda, G., Casse, L., Tubiello, F. N., & Maggi, F. (2024). CROPGRIDS: A global geo-referenced dataset of 173 crops [Data set]. Scientific Data, 11(1), 413. Link to source: https://doi.org/10.1038/s41597-024-03247-7
Improved rice production has its greatest potential in regions where there is substantial paddy rice production and adequate water availability to allow farmers to implement drain/flood cycles throughout the growing season (noncontinuous flooding). Rice production is dominated by Asia, so the greatest potential for solution uptake is there. Brazil and the United States rank 9th and 11th for rice production, and each has regions where this solution would have multiple benefits. Because improved rice production solution may not decrease yields, not all paddy rice-growing areas are suitable. There are regions of great potential throughout Southeast Asia, particularly in Vietnam and Thailand.
Other factors besides biophysical factors govern the suitability of noncontinuous flooding. For example, farmers are more likely to release water in their fields if they are confident that water will be available for subsequent irrigation, which often depends on community structures.
There is very scarce information on adoption of noncontinuous flooding, although Bangladesh, China, Japan, and South Korea have relatively high uptake.
There is high consensus on the effectiveness and potential of noncontinuous flooding and nutrient management (Jiang et al., 2019; Zhang et al., 2023; Nabuurs et al., 2022; Qian et al., 2023).
Hergoualc’h et al. (2019) describe methane reduction and associated nitrous oxide increase from noncontinuous flooding in detail(2019). Bo et al. (2022) calculate that 76% of global rice paddy area is suitable to switch to noncontinuous flooding without reducing yields. Carlson et al. (2016) provide emissions intensities for croplands, including rice production. Ludemann et al. (2024) provide country-by-country and crop-by-crop fertilizer use data. Qian et al. (2023) review methane emissions production and reduction potential.
The results presented in this document summarize findings from 12 reviews and meta-analyses and 26 original studies reflecting current evidence from countries across the Asian rice production region. We recognize this limited geographic scope creates bias, and hope this work inspires research and data sharing on this topic in underrepresented regions.
In this analysis, we calculated the potential for reducing crop nitrogen inputs and associated nitrous oxide emissions by integrating spatially explicit, crop-specific data on nitrogen inputs, crop yields, attainable yields, irrigated extent, and climate. Broadly, we calculated a “target” yield-scaled nitrogen input rate based on pixels with low yield gaps and calculated the difference between nitrous oxide emissions under the current rate and under the hypothetical target emissions rate, using nitrous oxide emissions factors disaggregated by fertilizer type and climate.
We used Tier 1 emissions factors from the IPCC 2019 Refinement to the 2006 Guidelines for National Greenhouse Gas Inventories, including direct emissions factors as well as indirect emissions from volatilization and leaching pathways. Direct emissions factors represent the proportion of applied nitrogen emitted as nitrous oxide, while we calculated volatilization and leaching emissions factors by multiplying the proportion of applied nitrogen lost through these pathways by the proportion of volatilized or leached nitrogen ultimately emitted as nitrous oxide. Including both direct and indirect emissions, organic and synthetic fertilizers emit 4.97 kg CO₂‑eq/kg nitrogen and 8.66 kg CO₂‑eq/kg nitrogen, respectively, in wet climates, and 2.59 kg CO₂‑eq/kg nitrogen and 2.38 kg CO₂‑eq/kg nitrogen in dry climates. We included uncertainty bounds (2.5th and 97.5th percentiles) for all emissions factors.
We classified each pixel as “wet” or “dry” using an aridity index (AI) threshold of 0.65, calculated as the ratio of annual precipitation to potential evapotranspiration (PET) from TerraClimate data (1991–2020), based on a threshold of 0.65. For pixels in dry climates that contained irrigation, we took the weighted average of wet and dry emissions factors based on the fraction of cropland that was irrigated (Mehta et al., 2024). We excluded irrigated rice from this analysis due to large differences in nitrous oxide dynamics in flooded rice systems.
Using highly disaggregated data on nitrogen inputs from Adalibieke et al. (2024) for 21 crop groups, we calculated total crop-specific inputs of synthetic and organic nitrogen. We then averaged over 2016–2020 to reduce the influence of interannual variability in factors like fertilizer prices. These values are subsequently referred to as “current” nitrogen inputs. We calculated nitrous oxide emissions under current nitrogen inputs as the sum of the products of nitrogen inputs and the climatically relevant emissions factors for each fertilizer type.
Next, we calculated target nitrogen application rates in terms of kg nitrogen per ton of crop yield using data on actual and attainable yields for 17 crops from Gerber et al., 2024. For each crop, we first identified pixels in which the ratio of actual to attainable yields was above the 80th percentile globally. The target nitrogen application rate was then calculated as the 20th percentile of nitrogen application rates across low-yield-gap pixels. Finally, we calculated total target nitrogen inputs as the product of actual yields and target nitrogen input rates. We calculated hypothetical nitrous oxide emissions from target nitrogen inputs as the product of nitrogen inputs and the climatically relevant emissions factor for each fertilizer type.
The difference between current and target nitrogen inputs represents the amount by which nitrogen inputs could hypothetically be reduced without compromising crop productivity (i.e., “avoidable” nitrogen inputs). We calculated avoidable nitrous oxide emissions as the difference between nitrous oxide emissions with current nitrogen inputs and those with target nitrogen inputs. For crops for which no yield or attainable yield data were available, we applied the average percent reduction in nitrogen inputs under the target scenario from available crops to the nitrogen input data for missing crops to calculate the avoidable nitrogen inputs and emissions.
This simple and empirically driven method aimed to identify realistically low but nutritionally adequate nitrogen application rates by including only pixels with low yield gaps, which are unlikely to be substantially nutrient-constrained. We did not control for other factors affecting nitrogen availability, such as historical nutrient application rates or depletion, rotation with nitrogen fixing crops, or tillage and residue retention practices.
We define the Improve Nutrient Management solution as reducing excessive nitrogen use on croplands. Nitrogen is critical for crop production and is added to croplands as synthetic or organic fertilizers and through microbial activity. However, farmers often add more nitrogen to croplands than crops can use. Some of that excess nitrogen is emitted to the atmosphere as nitrous oxide, a potent GHG.
Agriculture is the dominant source of human-caused emissions of nitrous oxide (Tian et al., 2020). Nitrogen is critical for plant growth and is added to croplands in synthetic forms, such as urea, ammonium nitrate, or anhydrous ammonia; in organic forms, such as manure or compost; and by growing legume crops, which host microbes that capture nitrogen from the air and add it to the soil (Adalibieke et al., 2023; Ludemann et al., 2024). If more nitrogen is added than crops can use, the excess can be converted to other forms, including nitrous oxide, through microbial processes called denitrification and nitrification (Figure 1; Reay et al., 2012).
Figure 1. The agricultural nitrogen cycle represents the key pathways by which nitrogen is added to croplands and lost to the environment, including as nitrous oxide. The “4R” nutrient management principles – right source, right rate, right time, right place – increase the proportion of nitrogen taken up by the plant, therefore reducing nitrogen losses to the environment.
Illustrations: BioRender CC-BY 4.0
Farmers can reduce nitrous oxide emissions from croplands by using the right amount and the right type of fertilizer at the right time and in the right place (Fixen, 2020; Gao & Cabrera Serrenho, 2023). Together, these four “rights” increase nitrogen use efficiency – the proportion of applied nitrogen that the crop uses (Congreves et al., 2021). Improved nutrient management is often a win-win for the farmer and the environment, reducing fertilizer costs while also lowering nitrous oxide emissions (Gu et al., 2023).
Improving nutrient management involves reducing the amount of nitrogen applied to match the crop’s requirements in areas where nitrogen is currently overapplied. A farmer can implement the other three principles – type, time, and place – in a number of ways. For example, fertilizing just before planting instead of after the previous season’s harvest better matches the timing of nitrogen addition to that of plant uptake, reducing nitrous oxide emissions before the crop is planted. Certain types of fertilizers are better suited for maximizing plant uptake, such as extended-release fertilizers, which allow the crop to steadily absorb nutrients over time. Techniques such as banding, in which farmers apply fertilizers in concentrated bands close to the plant roots instead of spreading them evenly across the soil surface, also reduce nitrous oxide emissions. Each of these practices can increase nitrogen use efficiency and decrease the amount of excess nitrogen lost as nitrous oxide (Gao & Cabrera Serrenho, 2023; Gu et al., 2023; Wang et al., 2024; You et al., 2023).
For this solution, we estimated a target rate of nitrogen application for major crops as the 20th percentile of the current rate of nitrogen application (in t N/t crop) in areas where yields are near a realistic ceiling. Excess nitrogen was defined as the amount of nitrogen applied beyond the target rate (see Adoption and Appendix for more details). Our emissions estimates include nitrous oxide from croplands, fertilizer runoff, and fertilizer volatilization. They do not include emissions from fertilizer manufacturing, which are addressed in the Deploy Low-Emission Industrial Feedstocks and Boost Industrial Efficiency solutions. We excluded nutrient management on pastures from this solution due to data limitations and address nutrient management in paddy rice systems in the Improve Rice Production solution instead.
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Avery Driscoll
Ruthie Burrows, Ph.D.
James Gerber, Ph.D.
Yusuf Jameel, Ph.D.
Daniel Jasper
Alex Sweeney
Eric Toensmeier
Aiyana Bodi
Hannah Henkin
Ted Otte
We relied on the 2019 Intergovernmental Panel on Climate Change (IPCC) emissions factors to calculate the emissions impacts of improved nutrient management. These are disaggregated by climate zone (“wet” vs. “dry”) and by fertilizer type (“organic” vs. “synthetic”). Nitrogen use reductions in wet climates, which include ~65% of the cropland area represented in this analysis (see Appendix for details), have the largest impact. In these areas, a 1 t reduction in nitrogen use reduces emissions by 8.7 t CO₂‑eq on average for synthetic fertilizers and by 5.0 t CO₂‑eq for organic fertilizers. Emissions savings are lower in dry climates, where a 1 t reduction in nitrogen use reduces emissions by 2.4 t CO₂‑eq for synthetic fertilizers and by 2.6 t CO₂‑eq for organic fertilizers. While these values reflect the median emissions reduction for each climate zone and fertilizer type, they are associated with large uncertainties because emissions are highly variable depending on climate, soil, and management conditions.
Based on our analysis of the adoption ceiling for each climate zone and fertilizer type (see Appendix), we estimated that a 1 t reduction in nitrogen use reduces emissions by 6.0 t CO₂‑eq at the global median (Table 1). This suggests that ~1.4% of the applied nitrogen is emitted as nitrous oxide at the global average, which is consistent with existing estimates (IPCC, 2019).
Table 1. Effectiveness at reducing emissions.
Unit: t CO₂‑eq /t nitrogen, 100-yr basis
| 25th percentile | 4.2 |
| Median (50th percentile) | 6.0 |
| 75th percentile | 7.7 |
Improving nutrient management typically reduces fertilizer costs while maintaining or increasing yields, resulting in a net financial benefit to the producer. Gu et al. (2023) found that a 21% reduction in global nitrogen use would be economically beneficial, notably after accounting for increased fertilizer use in places that do not currently have adequate access. Using data from their study, we evaluated the average cost of reduced nitrogen application considering the following nutrient management practices: increased use of high-efficiency fertilizers, organic fertilizers, and/or legumes; optimizing fertilizer rates; altering the timing and/or placement of fertilizer applications; and use of buffer zones. Implementation costs depend on the strategy used to improve nutrient management. For example, optimizing fertilizer rates requires soil testing and the ability to apply different fertilizer rates to different parts of a field. Improving timing can involve applying fertilizers at two different times during the season, increasing labor and equipment operation costs. Furthermore, planting legumes incurs seed purchase and planting costs.
Gu et al. (2023) estimated that annual reductions of 42 Mt of nitrogen were achievable globally using these practices, providing total fertilizer savings of US$37.2 billion and requiring implementation costs of US$15.9 billion, adjusted for inflation to 2023. A 1 t reduction in excess nitrogen application, therefore, was estimated to provide an average of US$507.80 of net cost savings, corresponding to a savings of US$85.21 per t CO₂‑eq of emissions reductions (Table 2).
Table 2. Cost per unit of climate impact, 100-yr basis.
Unit: 2023 US$/t CO₂‑eq
| Mean | -85.21 |
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., & Hegewisch, K. C. (2018). TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific Data, 5(1), 170191. https://doi.org/10.1038/sdata.2017.191
Adalibieke, W., Cui, X., Cai, H., You, L., & Zhou, F. (2023). Global crop-specific nitrogen fertilization dataset in 1961–2020. Scientific Data, 10(1), 617. https://doi.org/10.1038/s41597-023-02526-z
Gerber, J. S., Ray, D. K., Makowski, D., Butler, E. E., Mueller, N. D., West, P. C., Johnson, J. A., Polasky, S., Samberg, L. H., & Siebert, S. (2024). Global spatially explicit yield gap time trends reveal regions at risk of future crop yield stagnation. Nature Food, 5(2), 125–135. https://doi.org/10.1038/s43016-023-00913-8
IPCC, 2019: Summary for Policymakers. In: Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems [P.R. Shukla, J. Skea, E. Calvo Buendia, V. Masson-Delmotte, H.-O. Pörtner, D. C. Roberts, P. Zhai, R. Slade, S. Connors, R. van Diemen, M. Ferrat, E. Haughey, S. Luz, S. Neogi, M. Pathak, J. Petzold, J. Portugal Pereira, P. Vyas, E. Huntley, K. Kissick, M. Belkacemi, J. Malley, (eds.)].
Mehta, P., Siebert, S., Kummu, M., Deng, Q., Ali, T., Marston, L., Xie, W., & Davis, K. F. (2024). Half of twenty-first century global irrigation expansion has been in water-stressed regions. Nature Water, 2(3), 254–261. https://doi.org/10.1038/s44221-024-00206-9
The improved nutrient management strategies considered for this solution are already well established and widely deployed (Fixen, 2020). Large nitrogen excesses are relatively easy to mitigate through simple management changes with low implementation costs. As nitrogen use efficiency increases, further reductions may require increasingly complex mitigation practices and increasing marginal costs. Therefore, a learning curve was not quantified for this solution.
Speed of action refers to how quickly a climate solution physically affects the atmosphere after it is deployed. This is different from speed of deployment, which is the pace at which solutions are adopted.
At Project Drawdown, we define the speed of action for each climate solution as emergency brake, gradual, or delayed.
Improve Nutrient Management is a GRADUAL climate solution. It has a steady, linear impact on the atmosphere. The cumulative effect over time builds as a straight line.
Emissions reductions from improved nutrient management are permanent, though they may not be additional in all cases.
As this solution reduces emissions rather than enhancing sequestration, permanence is not applicable.
Additionality requires that the emissions benefits of the practice are attributable to climate-related incentives and would not have occurred in the absence of incentives (Michaelowa et al., 2019). If they are not contingent on external incentives, fertilizer use reductions implemented solely to maximize profits do not meet the threshold for additionality. However, fertilizer reductions may be additional if incentives are required to provide access to the technical knowledge and soil testing required to identify optimal rates. Other forms of nutrient management (e.g., applying nitrification inhibitors, using extended-release or organic fertilizers, or splitting applications between two time points) may involve additional costs, substantial practice change, and technical expertise. Thus, these practices are likely to be additional.
Given that improved nutrient management takes a variety of forms and data on the adoption of individual practices are very limited, we leveraged several global datasets related to nitrogen use and yields to directly assess improvements in nitrogen use efficiency (see Appendix for details).
First, we calculated nitrogen use per metric ton of crop produced using global maps of nitrogen fertilizer use (Adalibieke et al., 2023) and global maps of crop yields (Gerber et al., 2024) for 17 major crops (see Appendix). Next, we determined a target nitrogen use rate (t nitrogen/t crop) for each crop, corresponding to the 20th percentile of nitrogen use rates observed in croplands with yield gaps at or below the 20th percentile, meaning that actual yields were close to an attainable yield ceiling (Gerber et al., 2024). Areas with large yield gaps were excluded from the calculation of target nutrient use efficiency because insufficient nitrogen supply may be compromising yields (Mueller et al., 2012). Yield data were not available for a small number of crops; for these, we assumed reductions in nitrogen use to be proportional to those of other crops.
We considered croplands that had achieved the target rate and had yield gaps lower than the global median to have adopted the solution. We calculated the amount of excess nitrogen use avoided from these croplands as the difference in total nitrogen use under current fertilization rates relative to median fertilizer application rates. As of 2020, croplands that had achieved the adoption threshold for improved nutrient management avoided 10.45 Mt of nitrogen annually relative to the median nitrogen use rate (Table 3), equivalent to 11% of the adoption ceiling.
Table 3. Current (2020) adoption level.
Unit: t nitrogen/yr
| Estimate | 10,450,000 |
Global average nitrogen use efficiency increased from 47.7% to 54.6% between 2000 and 2020, a rate of approximately 0.43%/yr (Ludemann et al., 2024). This increase accelerated somewhat in the latter decade, from an average rate of 0.38%/yr to 0.53%/yr. Underlying this increase were increases in both the amount of nitrogen used and the amount of excess nitrogen. Total nitrogen additions increased by approximately 2.64 Mt/yr, with the amount of nitrogen used increasing more rapidly (1.99 Mt/yr) than the amount of excess nitrogen (0.65 Mt/yr) between 2000 and 2020 (Ludemann et al., 2024). Although nitrogen use increased between 2000 and 2020 as yields increased, the increase in nitrogen use efficiency suggests uptake of this solution.
We estimated the adoption ceiling of improved nutrient management to be 95.13 Mt avoided excess nitrogen use/year, not including current adoption (Table 4). This value reflects our estimate of the maximum potential reduction in nitrogen application while avoiding large yield losses and consists of the potential to avoid 62.25 Mt of synthetic nitrogen use and 32.88 Mt of manure and other organic nitrogen use, in addition to current adoption. In total, this is equivalent to an additional 68% reduction in global nitrogen use. The adoption ceiling was calculated as the difference between total nitrogen use at the current rate and total nitrogen use at the target rate (as described in Current Adoption), assuming no change in crop yields. For nitrogen applied to crops for which yield data were not available, the potential reduction in nitrogen use was assumed to be proportional to that of crops for which full data were available.
Table 4. Adoption ceiling.
Unit: t nitrogen/yr
| Estimate | 105,580,000 |
We estimated that fertilizer use reductions of 69.85–91.06 Mt of nitrogen are achievable, reflecting current adoption plus nitrogen savings due to the achievement of nitrogen application rates equal to the median and 30th percentile of nitrogen application rates occurring in locations where yield gaps are small (Table 5).
This range is more ambitious than a comparable recent estimate by Gu et al. (2023), who found that reductions of approximately 42 Mt of nitrogen are avoidable via cost-effective implementation of similar practices. Differences in target nitrogen use efficiencies underlie differences between our estimates and those of Gu et al., whose findings correspond to an increase in global average cropland nitrogen use efficiency from 42% to 52%. Our estimates reflect higher target nitrogen use efficiencies. Nitrogen use efficiencies greater than 52% have been widely achieved through basic practice modification without compromising yields or requiring prohibitively expensive additional inputs. For instance, You et al. (2023) estimated that the global average nitrogen use efficiency could be increased to 78%. Similarly, cropland nitrogen use efficiency in the United States in 2020 was estimated to be 71%, and substantial opportunities for improved nitrogen use efficiency are still available within the United States (Ludemann et al., 2024), though Lu et al. (2019) and Swaney et al. (2018) report slightly lower estimates. These findings support our slightly more ambitious range of achievable nitrogen use reductions for this solution.
Table 5. Range of achievable adoption levels.
Unit: t nitrogen/yr
| Current adoption | 10,450,000 |
| Achievable – Low | 69,850,000 |
| Achievable – High | 91,060,000 |
| Adoption ceiling | 105,580,000 |
We estimated that improved nutrient management has the potential to reduce emissions by 0.63 Gt CO₂‑eq/yr, with achievable emissions reductions of 0.42–0.54 Gt CO₂‑eq/yr (Table 6). This is equivalent to an additional 56–76% reduction in total nitrous oxide emissions from fertilizer use, based on the croplands represented in our analysis.
We estimated avoidable emissions by multiplying our estimates of adoption ceiling and achievable adoption by the relevant IPCC 2019 emissions factors, disaggregated by climate zone and fertilizer type. Under the adoption ceiling scenario, approximately 70% of emissions reductions occurred in wet climates, where emissions per t of applied fertilizer are higher. Reductions in synthetic fertilizer use, which are larger than reductions in organic fertilizer use, contributed about 76% of the potential avoidable emissions. We estimated that the current implementation of improved nutrient management was associated with 0.06 Gt CO₂‑eq/yr of avoided emissions.
Our estimates are slightly more optimistic but well within the range of the IPCC 2021 estimates, which found that improved nutrient management could reduce nitrous oxide emissions by 0.06–0.7 Gt CO₂‑eq/yr.
Table 6. Climate impact at different levels of adoption.
Unit: Gt CO₂-eq/yr, 100-yr basis
| Current adoption | 0.06 |
| Achievable – Low | 0.42 |
| Achievable – High | 0.54 |
| Adoption ceiling | 0.63 |
Balanced nutrient concentration contributes to long-term soil fertility and improved soil health by enhancing organic matter content, microbial diversity, and nutrient cycling (Antil & Raj, 2020; Selim, 2020). Healthy soil experiences reduced erosion and has higher water content, which increases its resilience to droughts and extreme heat (Rockström et al., 2017).
Better nutrient management reduces farmers' input costs and increases profitability (Rurinda et al., 2020; Wang et al., 2020). It is especially beneficial to smallholder farmers in sub-Saharan Africa, where site-specific nutrient management programs have demonstrated a significant increase in yield (Chivenge et al., 2021). A review of 61 studies across 11 countries showed that site-specific nutrient management resulted in an average increase in yield by 12% and increased farmer’s’ income by 15% while improving nitrogen use efficiency (Chivenge et al., 2021).
While excessive nutrients cause environmental problems in some parts of the world, insufficient nutrients are a significant problem in others, resulting in lower agricultural yields (Foley et al., 2011). Targeted, site-specific, efficient use of fertilizers can improve crop productivity (Mueller et al., 2012; Vanlauwe et al., 2015), improving food security globally.
Domingo et al. (2021) estimated about 16,000 premature deaths annually in the United States are due to air pollution from the food sector and found that more than 3,500 premature deaths per year could be avoided through reduced use of ammonia fertilizer, a secondary particulate matter precursor. Better agriculture practices overall can reduce particulate matter-related premature deaths from the agriculture sector by 50% (Domingo et al., 2021). Nitrogen oxides from fertilized croplands are another source of agriculture-based air pollution, and improved management can lead to decreased respiratory and cardiovascular disease (Almarez et al., 2018; Sobota et al., 2015).
Nitrate contamination of drinking water due to excessive runoff from agriculture fields has been linked to several health issues, including blood disorders and cancer (Patel et al., 2022; Ward et al., 2018). Reducing nutrient runoff through better management is critical to minimize these risks (Ward et al., 2018).
Nutrient runoff from agricultural systems is a major driver of water pollution globally, leading to eutrophication and hypoxic zones in aquatic ecosystems (Bijay-Singh & Craswell, 2021). Nitrogen pollution also harms terrestrial biodiversity through soil acidification and increases productivity of fast-growing species, including invasives, which can outcompete native species (Porter et al., 2013). Improved nutrient management is necessary to reduce nitrogen and phosphorus loads to water bodies (Withers et al., 2014; van Grinsven et al., 2019) and terrestrial ecosystems (Porter et al., 2013). These practices have been effective in reducing harmful algal blooms and preserving biodiversity in sensitive water systems (Scavia et al., 2014).
Although substantial reductions in nitrogen use can be achieved in many places with no or minimal impacts on yields, reducing nitrogen application by too much can lead to yield declines, which in turn can boost demand for cropland, causing GHG-producing land use change. Reductions in only excess nitrogen application will prevent substantial yield losses.
Some nutrient management practices are associated with additional emissions. For example, nitrification inhibitors reduce direct nitrous oxide emissions (Qiao et al., 2014) but can increase ammonia volatilization and subsequent indirect nitrous oxide emissions (Lam et al., 2016). Additionally, in wet climates, nitrous oxide emissions may be reduced through the use of manure instead of synthetic fertilizers (Hergoualc’h et al., 2019), though impacts vary across sites and studies (Zhang et al., 2020). Increased demand for manure could increase livestock production, which has high associated GHG emissions. Emissions also arise from transporting manure to the site of use (Qin et al., 2021).
Although nitrous oxide has a strong direct climate-warming effect, fertilizer use can cool the climate through emissions of other reactive nitrogen-containing compounds (Gong et al., 2024). First, aerosols from fertilizers scatter heat from the sun and cool the climate (Shindell et al., 2009; Gong et al., 2024). Moreover, other reactive nitrogen compounds from fertilizers shorten the lifespan of methane in the atmosphere, reducing its warming effects (Pinder et al., 2012). Finally, nitrogen fertilizers that leave farm fields through volatilization or runoff are ultimately deposited elsewhere, enhancing photosynthesis and storing more carbon in plants and soils (Zaehle et al., 2011; Gong et al., 2024). Improved nutrient management would reduce these cooling effects.
Improved nutrient management will reduce emissions from the production phase of biomass crops, increasing their benefit.
Improved nutrient management will reduce the GHG production associated with each calorie, so it will reduce the impacts of the Improve Diets and Reduce Food Loss and Waste solutions.
Each of these solutions could decrease emissions associated with fertilizer production, but improved nutrient management will reduce total demand for fertilizers.
t avoided excess nitrogen application
N₂O
The world’s agricultural lands can emit high levels of nitrous oxide, the third most powerful greenhouse gas. These emissions stem from overusing nitrogen-based fertilizers, especially in regions in China, India, Western Europe, and central North America (in red). While crops absorb some of the nitrogen fertilizer we apply, much of what remains is lost to the atmosphere as nitrous oxide pollution or to local waterways as nitrate pollution. Using fertilizers more wisely can dramatically reduce greenhouse gas emissions and water pollution while maintaining high levels of crop production.
Project Drawdown
The world’s agricultural lands can emit high levels of nitrous oxide, the third most powerful greenhouse gas. These emissions stem from overusing nitrogen-based fertilizers, especially in regions in China, India, Western Europe, and central North America (in red). While crops absorb some of the nitrogen fertilizer we apply, much of what remains is lost to the atmosphere as nitrous oxide pollution or to local waterways as nitrate pollution. Using fertilizers more wisely can dramatically reduce greenhouse gas emissions and water pollution while maintaining high levels of crop production.
Project Drawdown
Improved nutrient management will have the greatest emissions reduction if it is targeted at areas with the largest excesses of nitrogen fertilizer use. In 2020, China, India, and the United States alone accounted for 52% of global excess nitrogen application (Ludemann et al., 2024). Improved nutrient management could be particularly beneficial in China and India, where nutrient use efficiency is currently lower than average (Ludemann et al., 2024). You et al. (2023) also found potential for large increases in nitrogen use efficiency in parts of China, India, Australia, Northern Europe, the United States Midwest, Mexico, and Brazil under standard best management practices. Gu et al. (2024) found that nitrogen input reductions are economically feasible in most of Southern Asia, Northern and Western Europe, parts of the Middle East, North America, and Oceania.
In addition to regional patterns in the adoption ceiling, greater nitrous oxide emissions reductions are possible in wet climates or on irrigated croplands compared to dry climates. Nitrous oxide emissions tend to peak when nitrogen availability is high and soil moisture is in the ~70–90% range (Betterbach-Bahl et al., 2013; Elberling et al., 2023; Hao et al., 2025; Lawrence et al., 2021), though untangling the drivers of nitrous oxide emissions is complex (Lawrence et al., 2021). Water management to avoid prolonged periods of soil moisture in this range is an important complement to nutrient management in wet climates and on irrigated croplands (Deng et al., 2018).
Importantly, improved nutrient management, as defined here, is not appropriate for implementation in areas with nitrogen deficits or negligible nitrogen surpluses, including much of Africa. In these areas, crop yields are constrained by nitrogen availability, and an increase in nutrient inputs may be needed to achieve target yields. Additionally, nutrient management in paddy (flooded) rice systems is not included in this solution but rather in the Improve Rice Production solution.
There is high scientific consensus that reducing nitrogen surpluses through improved nutrient management reduces nitrous oxide emissions from croplands.
Nutrient additions to croplands produce an estimated 0.9 Gt CO₂‑eq/yr (range 0.7–1.1 Gt CO₂‑eq/yr ) of direct nitrous oxide emissions from fields, plus approximately 0.3 Gt CO₂‑eq/yr of emissions from fertilizers that runoff into waterways or erode (Tian et al., 2020). Nitrous oxide emissions from croplands are directly linked to the amount of nitrogen applied. Furthermore, the amount of nitrous oxide emitted per unit of applied nitrogen is well quantified for a range of different nitrogen sources and field conditions (Reay et al., 2012; Shcherbak et al., 2014; Gerber et al., 2016; Intergovernmental Panel on Climate Change [IPCC], 2019; Hergoualc’h et al., 2021). Tools to improve nutrient management have been extensively studied, and practices that improve nitrogen use efficiency through right rate, time, place, and type principles have been implemented in some places for several decades (Fixen, 2020; Ludemann et al., 2024).
Recently, Gao & Cabrera Serrenho (2023) estimated that fertilizer-related emissions could be reduced up to 80% by 2050 relative to current levels using a combination of nutrient management and new fertilizer production methods. You et al. (2023) found that adopting improved nutrient management practices would increase nitrogen use efficiency from a global average of 48% to 78%, substantially reducing excess nitrogen. Wang et al. (2024) estimated that the use of enhanced-efficiency fertilizers could reduce nitrogen losses to the environment 70–75% for maize and wheat systems. Chivenge et al. (2021) found comparable results in smallholder systems in Africa and Asia.
The results presented in this document were produced through analysis of global datasets. We recognize that geographic biases can influence the development of global datasets and hope this work inspires research and data sharing on this topic in underrepresented regions.
In this analysis, we calculated the potential for reducing crop nitrogen inputs and associated nitrous oxide emissions by integrating spatially explicit, crop-specific data on nitrogen inputs, crop yields, attainable yields, irrigated extent, and climate. Broadly, we calculated a “target” yield-scaled nitrogen input rate based on pixels with low yield gaps and calculated the difference between nitrous oxide emissions under the current rate and under the hypothetical target emissions rate, using nitrous oxide emissions factors disaggregated by fertilizer type and climate.
We used Tier 1 emissions factors from the IPCC 2019 Refinement to the 2006 Guidelines for National Greenhouse Gas Inventories, including direct emissions factors as well as indirect emissions from volatilization and leaching pathways. Direct emissions factors represent the proportion of applied nitrogen emitted as nitrous oxide, while we calculated volatilization and leaching emissions factors by multiplying the proportion of applied nitrogen lost through these pathways by the proportion of volatilized or leached nitrogen ultimately emitted as nitrous oxide. Including both direct and indirect emissions, organic and synthetic fertilizers emit 4.97 kg CO₂‑eq/kg nitrogen and 8.66 kg CO₂‑eq/kg nitrogen, respectively, in wet climates, and 2.59 kg CO₂‑eq/kg nitrogen and 2.38 kg CO₂‑eq/kg nitrogen in dry climates. We included uncertainty bounds (2.5th and 97.5th percentiles) for all emissions factors.
We classified each pixel as “wet” or “dry” using an aridity index (AI) threshold of 0.65, calculated as the ratio of annual precipitation to potential evapotranspiration (PET) from TerraClimate data (1991–2020), based on a threshold of 0.65. For pixels in dry climates that contained irrigation, we took the weighted average of wet and dry emissions factors based on the fraction of cropland that was irrigated (Mehta et al., 2024). We excluded irrigated rice from this analysis due to large differences in nitrous oxide dynamics in flooded rice systems.
Using highly disaggregated data on nitrogen inputs from Adalibieke et al. (2024) for 21 crop groups (Table S1), we calculated total crop-specific inputs of synthetic and organic nitrogen. We then averaged over 2016–2020 to reduce the influence of interannual variability in factors like fertilizer prices. These values are subsequently referred to as “current” nitrogen inputs. We calculated nitrous oxide emissions under current nitrogen inputs as the sum of the products of nitrogen inputs and the climatically relevant emissions factors for each fertilizer type.
Next, we calculated target nitrogen application rates in terms of kg nitrogen per ton of crop yield using data on actual and attainable yields for 17 crops from Gerber et al., 2024 (Table S1). For each crop, we first identified pixels in which the ratio of actual to attainable yields was above the 80th percentile globally. The target nitrogen application rate was then calculated as the 20th percentile of nitrogen application rates across low-yield-gap pixels. Finally, we calculated total target nitrogen inputs as the product of actual yields and target nitrogen input rates. We calculated hypothetical nitrous oxide emissions from target nitrogen inputs as the product of nitrogen inputs and the climatically relevant emissions factor for each fertilizer type.
The difference between current and target nitrogen inputs represents the amount by which nitrogen inputs could hypothetically be reduced without compromising crop productivity (i.e., “avoidable” nitrogen inputs). We calculated avoidable nitrous oxide emissions as the difference between nitrous oxide emissions with current nitrogen inputs and those with target nitrogen inputs. For crops for which no yield or attainable yield data were available, we applied the average percent reduction in nitrogen inputs under the target scenario from available crops to the nitrogen input data for missing crops to calculate the avoidable nitrogen inputs and emissions.
This simple and empirically driven method aimed to identify realistically low but nutritionally adequate nitrogen application rates by including only pixels with low yield gaps, which are unlikely to be substantially nutrient-constrained. We did not control for other factors affecting nitrogen availability, such as historical nutrient application rates or depletion, rotation with nitrogen fixing crops, or tillage and residue retention practices.
Table S1. Crops represented by the source data on nitrogen inputs (Adalibieke et al., 2024) and estimated and attainable yields (Gerber et al., 2024). Crop groups included consistently in both datasets are marked as “both,” and crop groups represented in the nitrogen input data but not in the yield datasets are marked as “nitrogen only.”
| Crop | Dataset(s) |
|---|---|
| Barley | Both |
| Cassava | Both |
| Cotton | Both |
| Maize | Both |
| Millet | Both |
| Oil Palm | Both |
| Potato | Both |
| Rice | Both |
| Rye | Both |
| Rapeseed | Both |
| Sorghum | Both |
| Soybean | Both |
| Sugarbeet | Both |
| Sugarcane | Both |
| Sunflower | Both |
| Sweet Potato | Both |
| Wheat | Both |
| Groundnut | Nitrogen only |
| Fruits | Nitrogen only |
| Vegetables | Nitrogen only |
| Other | Nitrogen only |
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