Manure Production Projections for Latvia: Challenges and Potential for Reducing Greenhouse Gas Emissions

Abstract

Manure is a valuable organic resource for sustainable agriculture, enhancing soil fertility and promoting nutrient cycling; however, it also contributes significantly to methane and nitrous oxide emissions. The European Green Deal and Latvia’s National Energy and Climate Plan have set targets for reducing agricultural greenhouse gas (GHG) emissions, including those related to improved manure management. Therefore, this research aims to estimate the future manure production in Latvia to determine the potential for reducing GHG emissions by 2050. Using the LASAM model developed in Latvia, the number of farm animals, the amount of manure, and the associated GHG emissions were projected for the period up to 2050. The calculations followed the Intergovernmental Panel on Climate Change (IPCC) methodology and were based on national indicators and current national GHG inventory data covering the period of 2021–2050. Significant changes in the structure of manure in Latvia are predicted by 2050, with the proportion of liquid manure expected to increase while the amounts of solid manure and manure deposited by grazing animals are expected to decrease. The GHG emission projection results indicate that by 2050, total emissions from manure management will decrease by approximately 5%, primarily due to a decline in the number of farm animals and, consequently, a reduction in the amount of manure. In contrast, methane emissions are expected to increase by approximately 5% due to production intensification. The research results emphasise the need to introduce more effective methane emission reduction technologies and improved projection approaches.

1. Introduction

Manure is a versatile organic fresh material, derived from animal excrement, that is crucial for sustainable farming practices and environmental management. In addition to its traditional use for soil fertilisation, manure contributes significantly to nutrient cycling by improving the fertility and composition of soil, while reducing the potential environmental risks associated with animal waste [1]. The prudent use of organic fertilisers in combination with mineral fertilisers can be a crucial strategy for improving soil quality, thereby increasing microbial activity and efficient nutrient cycling to produce high-quality crops [2]. Such an integrated approach not only increases agricultural productivity but also suggests a viable solution to environmental problems such as soil degradation and pollution [3,4]. This strategy enables a more balanced supply of nutrients to plants, thereby eliminating micronutrient deficiencies that synthetic fertilisers often do not resolve, as well as increasing the overall nutrient use efficiency [5]. In addition, the inclusion of organic fertilisers, such as compost and other organic manure, in agricultural systems is increasingly recognised as an opportunity to increase soil fertility and crop yields by improving basic soil properties [6,7]. Such practices increase the water retention capacity, improve soil aggregation, and enhance the microbial biomass and activity, which are crucial for robust plant growth and effective nutrient cycling. This comprehensive approach also contributes to a significant reduction in the environmental impacts of agricultural practices by reducing nutrient run-off and leaching [8].

Livestock farming is considered to be one of the main contributors to global GHG emissions, and manure management accounts for a significant share of agricultural methane (CH₄) and nitrous oxide (N₂O) emissions [9]. CO₂ is one of the gases released during the fermentation of manure; however, it is mostly of biogenic origin and is quickly taken back into the carbon cycle. In contrast, due to its higher global warming potential, the methane that is also produced as part of this process is a more significant environmental concern [10]. Therefore, the emissions from livestock farming and manure management that are primarily assessed are CH₄ and N₂O, which is due to their greater impacts on the climate. CH₄ emissions primarily originate from the anaerobic decomposition of manure’s organic matter, while N₂O is mainly produced through nitrification and denitrification [11]. Recent studies by the FAO (2023) have shown that the livestock industry produced 6.2 gigatonnes of carbon dioxide equivalent (CO₂ eq) emissions, accounting for approximately 12% of the total anthropogenic GHG emissions, based on data from 2015 [12]. Therefore, countries are increasingly performing and verifying emission calculations for annual inventory reports and seeking to include more industries in their mandatory emission reduction systems [13]. In this context, understanding the nuances and possibilities of manure management becomes extremely important for developing effective emission reduction strategies [14]. Different techniques for manure management, from anaerobic processing to composting and the direct incorporation of manure into soil, have different potential to reduce or increase GHG emissions [15]. The development and application of effective manure management techniques, based on accurate real-world estimates as well as projections, are essential to reduce these GHG emissions and thus tackle climate change-related problems [16].

A review of the existing literature highlights diverse manure treatment solutions, primarily aimed at nutrient recovery and reducing greenhouse gas emissions [17]. These approaches typically involve capturing, handling, storing, treating, and utilising manure through biological, chemical, and physical methods—from fundamental solid–liquid separation to advanced membrane filtration and anaerobic digestion [18,19,20]. Innovative strategies include decentralised manure collection for centralised processing, where solids are composted and marketed as organic fertilisers. At the same time, liquids undergo treatments such as ammonia stripping nitrification–denitrification to recover reusable nutrients, including ammonium salts [21]. Despite being technically and financially viable, the global adoption of these processes remains limited and uneven. Core technologies, such as ammonia stripping, membrane filtration, and solid–liquid separation, enable the targeted treatment of manure fractions, enhancing nutrient recovery and minimising the environmental impacts of intensive livestock farming [18,21,22].

Launched in 2019, the European Green Deal is a comprehensive strategy aimed at achieving climate neutrality by 2050, transforming the European Union (EU) into a modern, resource-efficient, and competitive economy [23]. This ambitious strategy integrates various policies to reduce GHG emissions from all industries [24,25] and involves transitioning to resource-efficient practices and reducing waste, thereby affecting industries ranging from agriculture to manufacturing [26]. Such policy initiatives require significant changes in agricultural practices, particularly regarding manure management [27]. Collective responsibility for reducing GHG emissions and taking climate action, as required by the Paris Agreement, pertains to the national level, thereby encouraging many countries to develop long-term low-emission strategies [28]. This global action underscores the crucial need for governments to adopt comprehensive strategies that are not only aligned with international and EU objectives, but also consider their unique national circumstances and economic structures in the pursuit of decarbonisation [29].

Therefore, as an EU Member State, Latvia, has also integrated these overarching goals into its policy documents, developing specific strategies and instruments to achieve emission reductions in various industries [30]. In 2024, the Latvian government approved the updated National Energy and Climate Plan (NECP) 2021–2030. Latvia’s updated NECP 2021–2030 refers to the revised energy and climate targets agreed under the “Fit for 55” package, which is the EU’s means to achieve a 55% reduction in greenhouse gas emissions by 2030 with respect to the level in 1990. One of the objectives of the NECP is the efficient use of resources and a reduction in GHG emissions from agriculture, which envisages special requirements for the storage and spreading of manure [31]. According to the NECP, the overall target for agriculture is to reduce GHG emissions to 2176.33 kt (kilotonnes) CO₂ eq. by 2030, compared with the real level of 2253.83 kt CO₂ eq. in 2022 (expected decrease of 3.4%) [31]. Livestock emissions in 2022 accounted for almost half (49.8%) of the total [32], with manure management contributing to a portion of these emissions. Additionally, the use of manure is linked to a portion of GHG emissions from crop production. Therefore, it is essential to project GHG emissions from manure management for future periods (e.g., by 2050) to assess the possibilities for their reduction. Therefore, this research aims to estimate the future manure production in Latvian agriculture to determine the potential for reducing GHG emissions by 2050. Therefore, the research hypothesis is that accurate long-term projections of manure production and related GHG emissions, based on national data and calculation models, are essential for assessing Latvia’s agricultural climate impact by 2050.

Overall, such research is crucial for contributing to the development of data-driven policy documents that promote sustainable, environmentally friendly, and climate-neutral agriculture, thereby ensuring food security in the future while striking a balance between economic growth and environmental protection. Assessing the actual situation and obtaining projected data on the amount of manure enables the design of more effective strategies for managing sustainable agriculture, reducing resource waste, and increasing crop productivity, as well as evaluating the potential reduction in greenhouse gas emissions that can be achieved through improved manure management. The obtained results can help policymakers to design appropriate and targeted support policies, while farmers can opt for environmentally friendly agricultural practices. Additionally, the control of emissions and improved resource use efficiency can reduce costs for farmers and create economic benefits.

This study was conducted in Latvia, examining the projected number of farm animals, manure volume, and structure, as well as GHG emissions related to these factors, over the period from 2021 to 2050. By applying the LASAM model developed in Latvia, the IPCC guidelines, and national inventory data, as well as the European Green Deal and Latvia’s Energy and Climate Plan, an analysis was conducted. A key finding is the projected 5% reduction in total manure-related GHG emissions by 2050, attributed to changes in the structure of farming. The results also reflect a need for the development of methane mitigation technologies. Methods such as those applied in this study are expected to provide sophisticated data that contribute to a better understanding of the issue, further promoting environmentally conscious agriculture.

2. Materials and Methods

The present research employed the LASAM model, developed in Latvia, to obtain projection for the agricultural sector until 2050 [33]; in particular, the model was used to simulate the number of farm animals, the amount of manure, and associated GHG emissions up to 2050. The LASAM model projections can be obtained over multiple time periods. It was initially developed in 2016 as an econometric model to provide an overview of the trends in the Latvian agricultural sector up to 2050. It respects the sector’s differences and allows the evaluation of various scenarios, primarily concerning the impacts of climate policy, by varying the parameters of the model, external inputs, and historical data within the system. It should be noted that the LASAM model is updated annually [32], enabling both policymakers and stakeholders to access the most up-to-date historical data on the development of agricultural sectors and to project their long-term development (i.e., up to 2050) [33].

The structure of the LASAM model, workflow, and primary sources for this study, specifically for livestock and manure-related GHG emission projections in Latvia, are shown in Figure 1 which depicts the layout of the LASAM model employed to forecast livestock numbers, the amount of manure produced, and the GHG emissions related to these changes in Latvia over the period 2021–2050. The figure illustrates the process flow, starting from the determination of the number of farm animals and classifying the agricultural systems, through to estimating the volumes of manure and emissions, all based on national data and IPCC guidelines. The entire process is divided into three major blocks: livestock number calculations, manure production calculations, and GHG emission calculations. The numbers in Figure 1 (1–7) indicate the calculation steps.

Figure 1. The structure of the LASAM model, workflow, and primary sources for this study.

The animal numbers used for this study, both current and projected, have been deposited in DataverseLV [34]. The dairy cow projections, which primarily influence total manure production in the country, rely on estimates of raw milk output and milk yield per cow. Raw milk production projections are derived by combining projected raw milk sales (based on the relationship between annual changes in milk sales and the milk production revenue–cost indicator), on-farm food consumption (modelled with a logarithmic trend), and milk used as feed (calculated from the projected ratio of milk used as feed relative to total milk sales and on-farm food consumption, also following a logarithmic trend). Milk yield projections are modelled using a logarithmic function, reaching an average of 10 tonnes by 2050. Other cattle projections include suckler cows, modelled with a logarithmic growth trend, as well as dairy and beef cattle, based on the numbers of dairy and suckler cows, respectively. Further details about the methodology for projecting animal numbers are available in “Projecting Agricultural Development and Developing Policy Scenarios for the Period until 2050” (2024) [32].

To ensure the comparability of the results obtained, the GHG emission calculation in LASAM follows Latvia’s National Inventory Reports (NIR) under the UNFCCC Greenhouse Gas Emissions in Latvia from 1990 to 2022 (2024) [35], which conforms to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC Guidelines) [36]. As noted by Makuteniene et al. (2022), researchers often identify and analyse factors affecting GHG emissions and their evolution, including those related to agriculture [37]. To obtain projections for GHG emissions, the authors used the methodology prescribed by the IPCC Guidelines (2006); nationally developed indicators following Republic of Latvia Cabinet Regulation No. 834 (2014) Requirements Regarding the Protection of Water, Soil and Air from Pollution Caused by Agricultural Activity [38] and Latvia’s NIR (2024) [35], as well as the following research studies conducted in Latvia: Developing a Calculation Methodology for GHG Emissions from the Agricultural Sector and a Simulation Tool for Data Analysis with Climate Change Integrated (2016) [39] and Projecting Agricultural Development and Developing Policy Scenarios for the Period until 2050 (2024) [32].

The research period was 2021–2050, which consisted of two stages: the real situation from 2021 to 2023 (GHG emissions in 2021–2022) and projections of potential development for 2025–2050.

2.1. Manure Production and Management

The estimation of manure production via the LASAM model follows the methodology suggested in studies of manure management systems in Latvia [39], which explored typical animal keeping practices and manure management systems applied in Latvia, including typical grazing periods (days and hours) for the main animal categories. These studies were conducted to support the preparation of the National GHG Inventory, providing a methodology for calculating the distribution of manure management systems in Latvia. The methodology uses manure production values per animal category aligning with the values outlined in the national legislative regulation [38].

First, the present research categorised each type of livestock by farming system: grazing and non-grazing. Next, the types of livestock were categorised by manure management system: solid manure and liquid manure (dairy cows and pigs), as well as manure with litter and manure without litter (laying hens). For grazed farm animals, the proportion of manure remaining on pasture was calculated using the pasture use rate, which considers the number of days and hours that animals graze during the year (((grazing days × grazing hours per day)/(365 × 24)) × 100). The manure management systems for various types of farm animals, including the manure produced per animal category considered in the LASAM model, are presented in Table 1.

Table 1. Livestock manure management systems and the production of various types of manure per animal per year in tonnes [32,38,39].

Category of Farm Animals	Pasture	Solid Manure	Liquid-Manure	Manure Without Litter	Fresh Manure (Pasture)	Solid Manure	Liquid Manure	Manure Without Litter
Dairy cows *	x	x	x	-	9.0	15.0	19.0	-
Dairy cow calves (up to 1 year old)	x	x	-	-	4.2	7.0	-	-
Dairy cow young cattle (1–2 years old)	x	x	-	-	6.6	11.0	-	-
Beef cattle calves (up to 1 year old)	x	x	-	-	3.6	6.0	-	-
Beef young cattle (1–2 years old)	x	x	-	-	6.0	10.0	-	-
Other bovine animals (over 2 years old)	x	x	-	-	5.4	9.0	-	-
Sows and boars	-	x	x	-	-	1.5	2.5	-
Piglets (up to 4 months old)	-	x	x	-	-	0.4	0.65	-
Pigs for fattening (from 4 months old)	-	x	x	-	-	1.2	2.2	-
Sheep	x	x	-	-	1.5	2.4	-	-
Goats	x	x	-	-	1.5	2.4	-	-
Horses	x	x	-	-	5	10.0	-	-
Laying hens	x	x	-	x	0.04	0.05	-	0.03
Broilers	-	x	-	-	-	0.01	-	-
Geese	x	x	-	-	0.03	0.04	-	-
Ducks	x	x	-	-	0.05	0.06	-	-
Turkeys	x	x	-	-	0.12	0.14	-	-
Deer	x	-	-	-	1.2	-	-	-

x—indicates that the livestock manure management system applies to the corresponding animal category. * For dairy cows, the manure amount depends on the milk yield group; here, it is shown for a milk yield of 6–8 tonnes per cow annually. The model uses interpolated data, instead of assigning a single value for the entire 6–8 tonne range.

According to studies of manure management systems in Latvia [39], it was assumed that farms with fewer than 80 dairy cows produce solid manure. In contrast, liquid manure systems are used for dairy herds of 80 or more animals. For pigs, solid manure is obtained in herds with up to 500 animals; beyond that number, liquid manure systems are used. In 2023, according to the assumptions and statistical data on livestock, solid manure was produced by 47.3% of dairy cows and 3.9% pigs while, for laying hens, the share was set at 10%. A projection of the proportion of dairy cows producing solid manure was obtained based on the target equation (assuming that the proportion will be 20% in 2050). Similarly, the proportion of pigs producing litter manure was considered to be below 1% in 2050, and 5% for laying hens.

For livestock that are grazed, a breakdown of stored manure (solid manure) and manure deposited on pasture was obtained according to the pasture use rate (considering the possibilities and practices of pasture use regarding the climatic conditions of Latvia), which was 18.8% for dairy cows, their calves and young cattle (i.e., 18.8% of the total time cows spend on pasture, thereby producing fresh manure). The pasture use rate was assumed to be 86.1% for beef cattle, their calves, and young cattle; 49.9% for sheep; 14.6% for goats; 52.1% for horses; 32.9% for laying hens and turkeys; and 35.6% for ducks and geese [39].

Projections of manure in agriculture are based on projected livestock numbers [34], their distribution between solid and liquid storage systems (projected proportions of the respective animals), the pasture use rate, and the production of manure per animal category (as detailed in Table 1); except for dairy cows, for which the production of manure per cow is estimated based on the projected changes in milk yield [34].

2.2. Calculation of Greenhouse Gas Emissions from Manure Management

The agricultural sector produces CH₄, N₂O, and CO₂ emissions. The following emissions are associated with manure management:

(1)

CH₄ emissions from manure management;

(2)

Direct and indirect N₂O emissions from manure management;

(3)

Direct and indirect N₂O emissions from livestock grazing;

(4)

Direct and indirect N₂O emissions from manure use (incorporation into soil).

According to the IPCC methodology, CH₄ and N₂O emissions from manure management (the first two categories) are included in emissions from livestock and manure management. Therefore, most research studies and national inventory reports on GHG emissions from manure focus only on CH₄ and N₂O emissions from manure management; however, total emissions were estimated in the present study, including those from soil management (i.e., the last two categories), providing a broader and more accurate assessment.

To calculate CH₄ emissions from manure management, emission factors for each farm animal category (EF_(T)) were obtained from Latvia’s NIR (2024) [35], which—for all farm animals except dairy cows, other cattle, and swine—were based on the 2006 IPCC Guidelines’ Tables 10.15 and 10.16, for developed countries in cool climates [36]. EFs for dairy cows, other cattle, and swine are calculated annually by Latvia’s NIR (2024) report compilers, using the Tier 2 approach [35].

To estimate N₂O emissions, the annual amount of N excreted by each farm animal category (Nex_(T)) was also derived from Latvia’s NIR (2024) [35]. Most of the Nex values are obtained from national studies (see Table 5.22 of Latvia’s NIR (2024) [35] for details) with values for dairy cows, other cattle, and swine also being externally calculated each year by Latvia’s NIR team, using Tier 2 approach. Table 2 lists the annual manure management CH₄ emission factors and average N excretions per farm animal.

Table 2. Emission factors for calculating methane emissions from manure management (kg per year per farm animal) and the average amount of N excreted (kg N per year per farm animal) [35].

Category of Farm Animals	CH4 Emission Factor from Manure Management, kg per Year per Animal (EF)	Average N Excretion, kg per Year per Animal (Nex)
Dairy cows *	20.81	120.4
Cattle under 2 years old *	1.13	19.9
Cattle over 2 years old *	2.02	63.3
Pigs *	2.15	10.3
Sheep	0.19	15.30
Goats	0.13	15.80
Horses	1.56	44.00
Laying hens	0.03	0.55
Broilers and others	0.02	0.35
Turkeys	0.09	1.64
Ducks	0.02	0.58
Geese	0.02	1.12
Deer	0.22	12.00

* Values for cattle and pigs are shown for the year 2022.

The distribution of manure management systems used to calculate the actual GHG emissions in 2022 is shown in Table 3. The authors’ estimates of manure type breakdown for future years, obtained from the projected amount of produced manure, with a fixed fraction assumed for anaerobic digestion, were used for GHG emission projections.

Table 3. Manure management system distribution for calculating emissions in Latvia in 2022, fraction (MS) [35].

Category of Farm Animals	Pasture	Solid Manure	Liquid Manure	Anaerobic Digester
Dairy cows	0.05	0.35	0.43	0.18
Dairy cow calves up to 1 year old	0.06	0.80	-	0.14
Dairy cow, young cattle 1–2 years old	0.06	0.80	-	0.14
Beef cattle calves up to 1 year old	0.79	0.21	-	-
Beef young cattle 1–2 years old	0.79	0.21	-	-
Other cattle	0.79	0.21	-	-
Sows and boars	-	0.04	0.46	0.50
Piglets up to 4 months old	-	0.04	0.46	0.50
Fattening and young breeding pigs over 4 months old	-	0.04	0.46	0.50
Sheep	0.38	0.62	-	-
Goats	0.10	0.90	-	-
Horses	0.35	0.65	-	-
Laying hens	0.04	0.45	-	0.51
Broilers	-	1	-	-
Geese	0.29	0.71	-	-
Ducks	0.32	0.69	-	-
Turkeys	0.30	0.70	-	-
Deer	1	-	-	-

CH₄ emissions from manure management in the country are calculated by multiplying the emission factor (EF_(T); kg per year per animal; Table 2) for each livestock category T by the number of farm animals (N_(T)) in the corresponding category (T), according to Equation (1):

$${\mathrm{CH}}_{4\mathrm{manure}}={\Sigma }_{\left(\mathrm{T}\right)}{\mathrm{EF}}_{\left(\mathrm{T}\right)}\times {\mathrm{N}}_{\left(\mathrm{T}\right)}$$

(1)

where CH_4manure denotes CH₄ emissions from manure management in the country, kg of CH₄ per year; EF_(T) is a CH₄ emission factor for a livestock category (T), kg of CH₄ per animal per year; N_(T) is the number of farm animals of the corresponding category in the country; and T denotes the category of farm animal. The emissions are added up for all categories of livestock.

To calculate direct N₂O emissions from manure management, an emission factor (EF_3(S)) for the manure production and storage system was selected based on Table 10.21 of the IPCC Guidelines (2006). For liquid manure and solid litter manure, it was 0.005 while for anaerobic digesters 0 [35,36]. N₂O emissions were calculated based on the number of animals, the fractions of manure management systems (Table 3), and the amount of N excreted per farm animal (Table 2) (Equation (2)):

$${\mathrm{N}}_2{\mathrm{O}}_{\mathrm{MM}}=\left[{\Sigma }_{\left(\mathrm{S}\right)}\right.\left[{\Sigma }_{\left(\mathrm{T}\right)}\right.({\mathrm{N}}_{\left(\mathrm{T}\right)}\times {\mathrm{N}\mathrm{e}\mathrm{x}}_{\left(\mathrm{T}\right)}\times \left.{\mathrm{M}\mathrm{S}}_{(\mathrm{T},\mathrm{S})}\right)]\times {\mathrm{E}\mathrm{F}}_{3\left(\mathrm{S}\right)}]\times 44/28$$

(2)

where N₂O_MM denotes direct N₂O emissions from manure management in the country, kg of N₂O per year; N_(T) is the number of farm animals in the corresponding category (T) in the country; Nex_(T) is the amount of N excreted per farm animal in the corresponding category (T), kg of N per animal per year; MS_{(T, S)} is the fraction of total annual N excretion of farm animals by the corresponding category (T) stored in a manure management system (S); EF_3(S) is the emission factor for direct N₂O emissions from a manure management system (S), kg of N₂O-N per kg of N in the manure management system; S denotes the manure management system; T denotes the category of farm animal; 44/28 is the conversion factor to convert N₂O–N emissions into N₂O emissions. The emissions were summed for all categories of livestock across all manure management systems (S).

Indirect N₂O emissions from manure management arise from: (1) volatilisation of nitrogen, which later enters the soil and water via atmospheric deposition, and (2) nitrogen leaching and run-off.

To calculate nitrogen emissions due to volatilisation, the loss of nitrogen (Frac_GasMS = 12–55%) from storage in each of the manure management systems for a particular category of livestock, which volatilises as ammonia (NH₃) and nitrogen oxides (NO_x) is calculated and then multiplied by an emission factor of 0.01 (EF₄) [36]. The fractions of nitrogen lost in the atmosphere are based on data from the IPCC Guidelines (2006) (Table 10.22) [36] (Equation (3)):

$${\mathrm{N}}_2{\mathrm{O}}_{(\mathrm{ATD},\mathrm{MM})}=\left[{\Sigma }_{\left(\mathrm{S}\right)}\right.\left[{\Sigma }_{\left(\mathrm{T}\right)}\right.({\mathrm{N}}_{\left(\mathrm{T}\right)}\times {\mathrm{N}\mathrm{e}\mathrm{x}}_{\left(\mathrm{T}\right)}\times \left.{\mathrm{M}\mathrm{S}}_{(\mathrm{T},\mathrm{S})}\right)\times ({\mathrm{F}\mathrm{r}\mathrm{a}\mathrm{c}}_{\mathrm{GasMS}}/{100)}_{(\mathrm{T},\mathrm{S})} \left]\right]\times {\mathrm{E}\mathrm{F}}_4\times 44/28$$

(3)

where N₂O_(ATD,MM) denotes indirect N₂O emissions from manure management due to volatilisation of N from manure management, kg of N₂O per year; N_(T) is the number of farm animals in the corresponding category (T) in the country; Nex_(T) is the amount of N excreted of farm animal in the corresponding category (T), kg of N per animal per year; MS_(T,S) is the fraction of total annual N excretion of farm animals of the corresponding category (T) stored in a manure management system (S); Frac_GasMS is the share of N volatilised from manure management system S as NH₃ and NO_x, in %, per livestock category T; S denotes the manure management system; T denotes the category of farm animal; EF₄ is an emission factor for indirect N₂O emissions from atmospheric deposition of N on soil and water surface, kg of N₂O–N per kg of volatilised NH₃–N + NO_x–N; and 44/28 is a conversion factor to convert N₂O–N emissions into N₂O emissions. The emissions are then totalled for all categories of livestock (T) across all manure management systems (S).

Similarly, to calculate nitrogen emissions from leaching and run-off, the loss of nitrogen stored in the management systems due to leaching and run-off was calculated, based on Frac_LeachMS = 5% for solid manure and Frac_LeachMS = 1% for liquid manure (national values identified by Latvian experts) [35], and then multiplied by an emission factor of 0.0075 (EF₅) [36] (Equation (4)):

$${\mathrm{N}}_2{\mathrm{O}}_{(\mathrm{L},\mathrm{MM})}=\left[{\Sigma }_{\left(\mathrm{S}\right)}\right.\left[{\Sigma }_{\left(\mathrm{T}\right)}\right.({\mathrm{N}}_{\left(\mathrm{T}\right)}\times {\mathrm{N}\mathrm{e}\mathrm{x}}_{\left(\mathrm{T}\right)}\times \left.{\mathrm{M}\mathrm{S}}_{(\mathrm{T},\mathrm{S})}\right)\times ({\mathrm{F}\mathrm{r}\mathrm{a}\mathrm{c}}_{\mathrm{LeachMS}}/{100)}_{(\mathrm{T},\mathrm{S})} \left]\right]\times {\mathrm{E}\mathrm{F}}_5\times 44/28$$

(4)

where N₂O_{(L, MM)} denotes indirect N₂O emissions from N leaching and run-off from manure management in the country, kg of N₂O per year; N_(T) is the number of farm animals in the corresponding category (T) in the country; Nex_(T) is the amount of N excreted per farm animal in the corresponding category (T), kg of N per animal per year; MS_{(T, S)} is the fraction of total annual N excretion of farm animals in the corresponding category (T) stored in a manure management system (S); Frac_LeachMS is the share of N lost from leaching and run-off for a category of livestock (T) for a manure management system (S), %; S denotes the manure management system; T denotes the category of farm animals; EF₅ is the emission factor for indirect N₂O emission from N leaching and run-off, kg of N₂O–N per kg of leached N; and 44/28 is a conversion factor to convert N₂O–N emissions into N₂O emissions. The emissions were then summed for all categories of livestock (T) across all manure management systems (S).

Direct N₂O emissions from farm animal grazing are calculated using data on the annual amounts of nitrogen excreted by farm animals (Table 2), the number of farm animals, the fraction of pasture (Table 3) and the emission factor (EF₃), which was 0.02 for cattle (dairy and other), poultry and pigs, and 0.01 [36] for the other categories of farm animals (Equation (5)):

$${\mathrm{N}}_2{\mathrm{O}}_{\left(\mathrm{PRP}\right)}=\left[{\Sigma }_{\left(\mathrm{T}\right)}({\mathrm{N}}_{\left(\mathrm{T}\right)}\times {\mathrm{N}\mathrm{e}\mathrm{x}}_{\left(\mathrm{T}\right)} \right.\times {\mathrm{M}\mathrm{S}}_{(\mathrm{PRP},\mathrm{CPP})}\times {\mathrm{E}\mathrm{F}}_{3(\mathrm{PRP},\mathrm{CPP})})+({\mathrm{N}}_{\left(\mathrm{T}\right)}\times {\mathrm{N}\mathrm{e}\mathrm{x}}_{\left(\mathrm{T}\right) }\times {\mathrm{M}\mathrm{S}}_{(\mathrm{PRP},\mathrm{SO})}\times {\mathrm{E}\mathrm{F}}_{3(\mathrm{PRP},\mathrm{SO})}\left)\right]\times 44/28$$

(5)

where N₂O_(PRP) denotes direct N₂O emissions from urine and dung deposited on pasture in the country, kg of N₂O per year; N_(T) is the number of farm animals in the corresponding category (T) in the country; Nex_(T) is the amount of N excreted per farm animal in the corresponding category (T), kg of N per animal per year; MS _(PRP) is the fraction of total annual N excretion of farm animal category (T) deposited on pasture (CPP applies to cattle, poultry, and pigs; SO to other livestock); EF_{3 (PRP)} is the emission factor for direct N₂O emission from urine and dung deposited on pasture, kg of N₂O–N per kg N; T denotes the category of farm animal; and 44/28—is a conversion factor to convert N₂O–N emissions into N₂O emissions. The emissions were then summed for all categories of livestock (T).

Indirect N₂O emissions from farm animal grazing consist of: (1) N₂O emissions from nitrogen volatilisation, which subsequently enter soil and water from the atmospheric deposition; and (2) N₂O emissions from nitrogen leaching and run-off.

To identify emissions from volatilisation, the loss of nitrogen from pasture fresh manure, which volatilises as NH₃ and NO_x, was calculated and then multiplied by an emission factor of 0.01 (EF4) [36]. According to the 2006 IPCC Guidelines (Table 11.3), the share of urine and dung N deposited by grazing animals that volatilises is 20% (Frac_GasM) [36] (Equation (6)):

$${\mathrm{N}}_2{\mathrm{O}}_{(\mathrm{ATD}, \mathrm{PRP})}={\Sigma }_{\left(\mathrm{T}\right)}({\mathrm{N}}_{\left(\mathrm{T}\right)}\times {\mathrm{N}\mathrm{e}\mathrm{x}}_{\left(\mathrm{T}\right) }\times {\mathrm{M}\mathrm{S}}_{(\mathrm{PRP}, \mathrm{T})}\times ({\mathrm{F}\mathrm{r}\mathrm{a}\mathrm{c}}_{\mathrm{GasM}}/100\left)\right)\times {\mathrm{E}\mathrm{F}}_4\times 44/28$$

(6)

where N₂O_{(ATD, PRP)} denotes indirect N₂O emissions from farm animal grazing due to N volatilisation in the country, kg of N₂O per year; Nex_(T) is the amount of N excreted per farm animal in the corresponding category (T), kg of N per animal per year; MS_{(PRP, T)} is the fraction of total annual N excretion of farm animals in the corresponding category (T) deposited on pasture; Frac_GasM is the share of N from urine and dung deposited on pasture, which volatilises as NH₃–N and NO_x–N; EF₄ is the emission factor for indirect N₂O emission from atmospheric deposition of N on soil and water surfaces, kg of N₂O–N per kg of volatilised NH₃–N and NO_x–N; and 44/28 is a conversion factor to convert N₂O–N emissions to N₂O emissions. The emissions were then summed for all categories of livestock (T).

To obtain emissions from nitrogen leaching and run-off, losses from the N deposited on pasture due to leaching and run–off were calculated based on the national value Frac_LEACH-(H) = 23%, identified in previous Latvian studies [35]. The lost N was then multiplied by an emission factor of 0.0075 (EF₅) [36] (Equation (7)):

$${\mathrm{N}}_2{\mathrm{O}}_{(\mathrm{L}, \mathrm{PRP})}={\Sigma }_{\left(\mathrm{T}\right)}({\mathrm{N}\mathrm{e}\mathrm{x}}_{\left(\mathrm{T}\right) }\times {\mathrm{M}\mathrm{S}}_{(\mathrm{PRP}, \mathrm{T})}\times ({\mathrm{F}\mathrm{r}\mathrm{a}\mathrm{c}}_{\mathrm{LEACH}-\left(\mathrm{H}\right)}/100\left)\right)\times {\mathrm{E}\mathrm{F}}_5\times 44/28$$

(7)

where N₂O_{(L, PRP)} denotes indirect N₂O emissions from N leaching and run-off from manure deposited on pasture in the country, kg of N₂O per year; Nex_(T) is the amount of N excreted per farm animal in the corresponding category (T), kg of N per animal per year; MS_{(PRP, T)} is the fraction of total annual N excretion of farm animals in the corresponding category (T) deposited on pasture; Frac_LEACH-(H) is the share of leached N from urine and dung deposited on pasture; EF₅ is the emission factor for indirect N₂O emissions from N leaching and run-off, kg of N₂O–N per kg of leached N; and 44/28 is a conversion factor to convert N₂O–N emissions into N₂O emissions. The emissions were then summed for all categories of livestock (T).

Direct N₂O emissions from manure applied to soil. First, the total amount of manure available for incorporation into soil was calculated, which involved the number of farm animals, the fractions of the manure management system (pasture excluded) (Table 3), the amount of nitrogen excreted per farm animal (Table 2), and the total nitrogen loss for each of the management systems (Frac_LossMS), based on the data from the IPCC Guidelines (2006), Table 10.23, as well as an emission factor of 0.01 (EF₁) [36] (Equations (8) and (9)):

$${\mathrm{N}}_2{\mathrm{O}}_{\left(\mathrm{AM}\right)}={\mathrm{N}}_{(\mathrm{MMS} \mathrm{Avb})}\times {\mathrm{E}\mathrm{F}}_1\times 44/28$$

(8)

$${\mathrm{N}}_{(\mathrm{MMS} \mathrm{Avb})}=\left[{\Sigma }_{\left(\mathrm{S}\right)}\right.\left[{\Sigma }_{\left(\mathrm{T}\right)}\right.\left.({\mathrm{N}}_{\left(\mathrm{T}\right)} \times {\mathrm{N}\mathrm{e}\mathrm{x}}_{\left(\mathrm{T}\right)} \times \left.{\mathrm{M}\mathrm{S}}_{(\mathrm{T},\mathrm{S})}\right)\times \left((1-({\mathrm{F}\mathrm{r}\mathrm{a}\mathrm{c}}_{\mathrm{LossMS}}/100{)}_{(\mathrm{T},\mathrm{S})})\right]\right]$$

(9)

where N₂O_(AM) denotes direct N₂O emissions from manure application to soil, kg of N₂O per year; N_{(MMS Avb)} is the total amount of N available for application to soil in the country, kg N per year; N_(T) is the number of farm animals in the corresponding category (T) in the country; Nex_(T) is the amount of N excreted per farm animal in the corresponding category (T), kg of N per animal per year; MS_(T,S) is the fraction of total annual N excretion of farm animals in the corresponding category (T) stored in a manure management system (S); Frac_LossMS is the loss of N from the excrement of farm animals in the corresponding category for a manure management system (S), %; EF₁ is the emission factor for direct N₂O emissions from N incorporation into soil, kg of N₂O–N per kg of incorporated N; S denotes the manure management system; T denotes the category of farm animal; and 44/28 is a conversion factor to convert N₂O–N emissions into N₂O emissions. The amounts of N available for application to soil were then summed for all categories of livestock (T) and for all manure storage systems (S).

Indirect N₂O emissions from manure applied to soil consist of (1) N₂O emissions from nitrogen volatilisation, which subsequently enter soil and water from the atmospheric deposition, and (2) N₂O emissions from nitrogen leaching and run-off.

To calculate emissions from nitrogen volatilisation, the loss of N from manure incorporation into soil, which volatilises as NH₃ and NO_x, was calculated and then multiplied by an emission factor of 0.01 (EF4). The share of loss for organic fertilisers is 20% (Frac_GasM) [36] (Equation (10)):

$${\mathrm{N}}_2{\mathrm{O}}_{(\mathrm{ATD}, \mathrm{AM})}={\mathrm{N}}_{(\mathrm{MMS} \mathrm{Avb})}\times ({\mathrm{F}\mathrm{r}\mathrm{a}\mathrm{c}}_{\mathrm{GasM}}/100)\times {\mathrm{E}\mathrm{F}}_4\times 44/28$$

(10)

where N₂O_{(ATD, AM)} denotes indirect N₂O emissions from manure incorporation into soil due to volatilisation in the country, kg of N₂O per year; N_{(MMS Avb)} is the total amount of N available for application to soil in the country, kg pf N per year; Frac_GasM is the share of organic N incorporated into soil, which volatilises as NH₃–N and NO_x–N; EF₄ is the emission factor for indirect N₂O emission from atmospheric deposition of N on soil and water surfaces, kg of N₂O–N per kg of evaporated NH₃–N and NO_x-N; and 44/28 is a conversion factor to convert N₂O–N emissions to N₂O emissions.

To determine emissions from nitrogen leaching and run-off, the loss of N (Frac_LEACH-(H) = 23% [35]) from manure incorporation into soil due to leaching and run-off was calculated, and then multiplied by an emission factor of 0.0075 (EFs) [36] (Equation (11)):

$${\mathrm{N}}_2{\mathrm{O}}_{(\mathrm{L},\mathrm{AM})}={\mathrm{N}}_{\left(\mathrm{MMSAvb}\right)}\times ({\mathrm{F}\mathrm{r}\mathrm{a}\mathrm{c}}_{\mathrm{LEACH}-\left(\mathrm{H}\right)}/100)\times {\mathrm{E}\mathrm{F}}_5\times 44/28$$

(11)

where N₂O_{(L, AM)} denotes indirect N₂O emissions from manure incorporation into soil due to N leaching and run-off in the country, kg of N₂O per year; N_(MMSAvb) is the total amount of N available for application to soil in the country, kg of N per year; Frac_LEACH-(H) is the share of leached N from manure incorporated into soil; EF₅ is the emission factor for indirect N₂O emissions from N leaching and run-off, kg of N₂O–N per kg of leached N; and 44/28 is a conversion factor to convert N₂O–N emissions into N₂O emissions.

CH₄ emissions were converted to CO₂ equivalent emissions using a factor of 28, while N₂O emissions were converted to CO₂ equivalent emissions (Equations (2)–(11)) using a factor of 265 [35].

2.3. Projections of Manure Production and Greenhouse Gas Emissions by 2050

The GHG emission projections were derived from the above equations, based on the projected number of farm animals [34], manure management system projections, and the GHG emission factors and indicators set for 2022, as used in the NIR [32].

An exception was dairy cows, for which the emission factor for CH₄ from manure management and N excreted per cow were projected based on changes in milk yields [34].

The projection of the CH₄ emissions factor from manure management was obtained based on a regression equation reflecting the relationship between the emission factor and milk yield (Equation (12)):

ef_efCH4_cowmi_reg < -LM(ef_efCH4_cowmi~cowmi_yield)

(12)

where ef_mmCH4_cowmi is a factor reflecting CH₄ emissions from manure management for dairy cows, cowmi_yield denotes the milk yield.

The calculated intercept of the regression equation was –1.6940811, the coefficient was 0.0028611, and the p-value was 0.000.

Similarly, the N excretion per dairy cow was projected based on a regression equation which included the relationship between nitrogen (N) excretion and milk yield (Equation (13)):

ef_mmN_cowmi_reg < -LM(ef_mmN_cowmi~cowmi_yield),

(13)

where ef_mmN_cowmi is the N excretion per dairy cow, and cowmi_yield denotes the milk yield.

The calculated intercept of the regression equation was 67.21, the coefficient was 0.00753, and the p-value was 0.000.

3. Results

3.1. Manure Production and Its Projection up to 2050 in Latvia

Figure 2 presents the results of calculations and projections for the amount of manure produced by livestock during the year, categorised by type: solid manure, liquid manure, and fresh manure left on pasture. In particular, we focus on the amount of manure that is available after storage (excluding the loss incurred during storage in each manure storage system) for incorporation into the soil.

Figure 2. Amounts of manure produced for various manure storage systems in Latvia for 2021–2023, with forecasts thereof for the period 2025–2050, in thousand tonnes [32,34,35,38,39].

The calculations assumed a constant output of solid, liquid, and fresh manure deposited on pasture per animal, depending on the category of farm animal and the type of manure, except for dairy cows, for which the manure output varied from year to year, depending on the projected changes in milk yield. Consequently, changes in the total amount of manure from year to year were affected by the changes in the number of farm animals, milk yield for dairy cows, and the share of liquid manure, due to both changes in the ways that livestock are kept and the fact that liquid manure is heavier than litter manure.

Cattle produced the most significant amount of manure in Latvia in 2023, contributing 83% of the total, while pig farming contributed 9%. It is projected that by 2050, the production of cattle manure will increase to 84% of the total, whereas that of pig manure is expected to remain unchanged (Figure 2). This means that to achieve climate objectives, particular attention should be paid to these two categories of livestock to improve the production, management, and use of manure.

For dairy cows, due to the projected modernisation of production, as the proportion of free-range dairy cows increases, the amount of liquid manure will also increase; consequently, the projected amounts of solid manure and fresh manure are expected to decrease. In 2050, the amount of solid manure and fresh manure is projected to decrease by 2.3 times compared with 2023, whereas the amount of liquid manure is projected to increase by 58%.

The rest of the cattle are kept in barns or pasture, and their projected number in 2050 is similar to that in 2023. Therefore, the projected amount of manure also decreases slightly: the amount of solid manure in 2050 is projected to decrease by 5% and that of fresh manure by 11% compared with their levels in 2023.

The total number of pigs, according to the projection for 2050, is similar to that in 2023; yet, the process of intensification of agricultural production and the development of large pig farms is expected to continue. Therefore, in 2050, solid manure production is projected to decrease almost 8 times, while liquid manure production is projected to decrease by only 4%.

The total number of laying hens in 2050 is also projected to be similar to that in 2023; however, the largest number of hens is kept on large farms with intensive production technology. Therefore, the amount of solid manure and fresh manure is projected to decrease (approximately half that in 2023). In contrast, the amount of manure without litter is projected to increase (by 7% compared with 2023). The number of broilers is projected to stabilise at the 2023 level; thus, the amount of manure produced remains similar. For other poultry, litter manure production is projected to increase by 33%, while fresh manure production is projected to increase by 50% in 2050 compared with the 2023 level.

Table 4 shows that in 2023, the total amount of manure reached 4701 thousand tonnes in Latvia. This is projected to increase to 4818 thousand tonnes by 2050, representing a 2% increase. Solid manure accounted for the largest share of total manure production (45%); however, with a projected 24% decrease by 2050, its share is expected to drop to 33%. In contrast, liquid manure production is projected to increase by 45% by 2050, raising its share from 36% in 2023 to 52%. Fresh manure collected from pastures is expected to decline by 18%, with its share decreasing from 17% in 2023 to 13% in 2050. Manure without litter, which is produced exclusively by laying hens, represents a small portion (2%) of total manure and is not expected to change by 2050.

Table 4. Summary of the amounts of manure produced (in thousand tonnes), and the structure thereof (in % of manure groups) by manure storage system in Latvia for 2021–2023, with forecasts for the period 2025–2050.

Indicators	2021	2022	2023	2025	2030	2035	2040	2045	2050	2050/2023, %
Solid manure	2370.4	2331.9	2119.4	2031.4	1982.3	1888.3	1774.4	1672.3	1601.0	76
Liquid manure	1735.0	1742.8	1710.2	1790.9	2051.5	2242.4	2356.2	2425.0	2481.9	145
Fresh manure (pasture)	821.2	808.5	779.7	744.0	723.3	699.5	675.3	654.1	637.4	82
Manure without litter	91.7	90.3	91.4	92.1	93.3	94.5	95.6	96.7	97.9	107
Total	5018.3	4973.5	4700.7	4658.4	4850.4	4924.7	4901.5	4848.1	4818.2	102
Structure of manure groups, in %										2050–2023, %
Solid	47	47	45	44	41	38	36	34	33	−14
Liquid	35	35	36	38	42	46	48	50	52	17
Fresh	16	16	17	16	15	14	14	13	13	−3
Without litter	2	2	2	2	2	2	2	2	2	0

3.2. Amounts of Greenhouse Gases from Manure Management and Their Projections up to 2050 in Latvia

The GHG emissions from manure management for 2021–2022 were calculated, and the projected emissions up to 2050 (Table 5) were obtained using the methodology described in Section 2, based on the current and projected numbers of livestock [32] and amounts of manure (Section 3.1) and GHG emission factors and indicators (Section 2.2 and Section 2.3).

Table 5. GHG emissions from manure management in Latvia in 2021–2022 and their projections for the period 2025–2050, in CO₂ eq. (thousand tonnes) [32,35,36,38,39].

Kinds of Emissions	2021	2022	2025	2030	2035	2040	2045	2050	2050/2022, %
CH4 emissions from manure management	105.2	108.6	105.6	112.0	115.7	116.1	114.9	113.6	105
Direct N2O emissions from manure management	42.7	43.1	39.6	40.6	41.1	40.8	40.2	39.9	92
Indirect N2O emissions from manure management	43.8	42.8	38.8	40.4	41.1	41.0	40.6	40.3	94
Direct N2O emissions from animal grazing	52.4	52.9	49.2	48.4	47.3	46.1	45.1	44.3	84
Indirect N2O emissions from animal grazing	10.4	10.5	9.7	9.5	9.3	9.1	8.9	8.7	83
Direct N2O emissions from manure applied to soil	56.8	56.2	51.4	52.4	52.9	52.5	51.7	51.2	91
Indirect N2O emissions from manure applied to soil	21.2	20.9	19.1	19.5	19.7	19.6	19.3	19.1	91
Total GHG emissions related to animal manure management	332.6	335.1	313.5	322.9	327.2	325.1	320.6	317.1	95
Total GHG emissions from agriculture	2245.3	2242.5	2160.0	2234.0	2269.3	2277.9	2277.2	2281.0	102
Total GHG emissions related to animal manure management, % of the total	14.8	14.9	14.5	14.5	14.4	14.3	14.1	13.9	−1%

Table 5 shows the GHG emissions from manure management in Latvia. In 2022, they accounted for 14.9% of total GHG emissions. It is projected that, by 2050, the total amount of GHG emissions from agriculture will increase by 2%, thereby failing to meet the 3.4% reduction target set by the NECP; however, the amount of GHG emissions from manure management will be reduced by 18,000 tonnes of CO₂ eq., or 5%. The primary reasons for this decrease are changes in the composition of cattle manure and a projected 14% decrease in the number of cattle by 2050 compared with 2022. The number of cows in the analysed period is projected to decrease by 17%, yet the milk yield is projected to increase by 33%. The numbers of sheep and goats are also projected to decrease by 40% and 21% in 2050 compared with 2022 [32]. The projected decreases in the number of grazing livestock also result in the largest decrease in projected direct and indirect N₂O emissions from grazing farm animals—by 16% and 17%, respectively—in 2050. Overall, in 2050, N₂O emissions across all sectors are projected to decrease compared with 2022, whereas CH₄ emissions from manure management are projected to increase by 5000 tonnes CO₂ eq., or 5%. This indicates the need for more efficient methane abatement technologies, especially for the storage and processing of manure.

Figure 3 shows the projected (for 2050) and current (as of 2022) breakdowns of GHG emissions from manure management in Latvia.

Figure 3. Breakdowns of current and projected GHG emissions from manure management in 2022 and 2050 in Latvia (%).

A comparison of GHG emissions from manure management in 2050 and 2022 reveals that the largest changes relate to CH₄ emissions from manure management, as their share in the total is expected to increase by four percentage points. This will occur at the expense of some N₂O emissions, as direct N₂O emissions from animal grazing are projected to decrease by two percentage points, while direct N₂O emissions from manure management and direct N₂O emissions from manure applied to soil are projected to decrease by one percentage point. Therefore, as GHG emissions from manure management are projected to decline in the future, such management practices can contribute to achieving the targets that are set for countries to reduce their total GHG emissions.

4. Discussion

4.1. The Need to Improve the Production, Use, and Management of Manure

The results revealed that considering changes in livestock numbers and farming practices, the amount of manure is projected to remain constant; however, its types and percentage breakdown are expected to change. The most significant trends, according to the projections, are as follows: the amount of liquid manure is projected to increase, whereas the amount of solid manure and fresh manure deposited on pasture is projected to decrease, mainly due to the modernisation of dairy cow production and changes in livestock production. This transition to liquid manure is largely due to the development of manure management strategies, which increasingly prioritise slurry systems due to their efficiency in nutrient recovery and application, despite their impact on greenhouse gas emissions [9]. Furthermore, the modernisation of livestock farms, especially dairy farms, often involves a shift from solid manure production and storage to liquid manure systems to rationalise farm operations and promote nutrient processing [40]. The transition to liquid fertiliser management is also aligned with efforts to increase feed efficiency and integrate sustainable practices, e.g., anaerobic digestion, thus reducing GHG emissions and improving the use of nutrients in agriculture [41]. However, it should be realised that an increase in the production of liquid manure also creates problems associated with its storage, transport, and potential leaching of nutrients into water bodies. Therefore, advanced techniques for manure management are needed to reduce environmental risks [42]. A solution would be to introduce advanced techniques for manure valorisation, e.g., anaerobic decomposition, to convert livestock waste into valuable resources while reducing GHG emissions [43]. The production of digestate, which represents a high-quality organic fertiliser, contributes to circular economy principles in agriculture [44].

It is worth noting that international studies have shown that cattle could become the primary source of nitrogen emissions in the future, thereby underscoring the need for modern manure management strategies to achieve climate objectives [45]. This is particularly important given that the continuous expansion of livestock farming produces a significant amount of livestock manure which, if mismanaged, can cause serious environmental and economic problems [46]. Therefore, effective manure management practices are also relevant, which could significantly reduce GHG emissions [47]. Comprehensive life cycle assessments are therefore necessary to accurately identify the environmental benefits of various manure management strategies and ensure their effectiveness in reducing the total carbon footprint of livestock activities [48]. Such assessments should consider the complexity of nitrogen conversion under various manure systems, taking into account factors such as temperature, humidity, and microbial activity. Given the low efficiency of N absorption by ruminants, unabsorbed N is inevitably excreted through excrement and urine; therefore, appropriate and advanced waste management techniques beyond traditional ones are also needed to reduce environmental impacts [49]. It is thus necessary to continue research into the effectiveness of N absorption in animal feed to reduce its excretion, as well as innovative manure processing technologies that can transform livestock waste into valuable organic fertilisers despite potential greenhouse gas emissions [46,50].

4.2. Potential Reduction in GHG Emissions from Manure Management

The authors found that by 2050, the total amount of emissions from manure management in Latvia would decrease by 5% compared with 2022 due to the projected decrease in the number of cattle, while the milk yield of cows is expected to increase by 33%. There is potential to reduce emissions, primarily attributed to the anticipated widespread implementation of ammonia-reducing technologies and enhanced manure utilisation strategies within agricultural practices [51]. It is expected that emission reductions will be achieved through the improved handling of manure, such as slurry acidification, cooling systems, regular removal in swine operations, and elevated biogas production [52]. The process of anaerobic digestion in biogas plants significantly reduces CH₄ emissions and generates a digestate, which, when applied to soils, can help to lower N₂O emissions [52,53]. Specifically, frequent slurry removal leads to a decrease in CH₄ emissions, while acidification has the potential to reduce NH₃ emissions by up to 49% [52]. Nitrification inhibitors may be capable of lowering N₂O emissions by more than 90% by 2050, provided that their handling is optimised effectively [53]. Such trends are expected to reduce the amount of direct and indirect N₂O emissions from grazing livestock up to 2050. Other researchers have acknowledged that the introduction of optimised fertilisation regimes would improve pasture management, contributing to a significant reduction in N₂O emissions [45]. Romera et al. (2016) found that direct N₂O emissions from nitrogen deposition with animal excrement and indirect N₂O emissions from nitrogen leaching and volatilisation are projected to decrease by 16% and 17%, respectively, by the middle of the 21st century [54]. This projected decrease is significant given that N₂O is a potent greenhouse gas that contributes significantly to global warming [55]. Furthermore, agricultural activities, particularly livestock farming, are recognised as the main contributors to atmospheric N₂O concentrations [56]. In addition, soil compaction, which is common in pastures, alters the physical composition of the soil, thereby affecting N₂O emissions, as well as gas diffusion and microbial activity [57]. Therefore, limiting the density of livestock on pastures could prevent overgrazing and soil degradation [58]. Various pasture management strategies, such as rotational grazing versus continuous grazing, affect soil N dynamics and subsequent N₂O discharge. In addition, it is essential to identify the particular drivers of N₂O emissions from pasture systems [55] for the development of accurate emission reduction strategies.

According to the presented findings, CH₄ emissions from manure management are expected to increase by 5% by 2050. Although this result was obtained based on the milk yield, it may be the case in general; in particular as the methane conversion factor for liquid manure is 10%, compared with 2% for solid manure, and the proportion of liquid manure is projected to increase. It should be recognised that the increasing levels of CH₄ associated with manure management practices cause significant environmental problems, mainly global warming [59]. The growing global demand for food also increases emissions, thereby stressing the need for sustainable manure management practices to reduce the climate impact of the agricultural sector [60,61]. Recognising the critical role of CH₄ in climate change, especially from sources such as manure, requires a deeper understanding of emission sources and the development of effective emission reduction technologies [16]. The complexity of emission sources requires innovative approaches to manure management, focusing on optimising anaerobic processing and improving the conversion of waste into energy to capture methane before it is released into the atmosphere. Furthermore, the development and widespread deployment of advanced manure treatment technologies provide additional opportunities to reduce environmental pollution and increase resource efficiency in agricultural activities [62]. Some scientists have reported that methane emissions from solid manure can be reduced to almost zero by fermenting manure in biogas plants, which would have a positive externality regarding generating renewable energy and is consistent with the principles of organic farming. Meanwhile, the potential to reduce N₂O emissions remains limited for most animals worldwide [63].

Given that the agricultural sector is a significant contributor to greenhouse gas emissions, particularly CH₄ and N₂O, accurate manure projections are crucial for the development of effective emission reduction strategies and achieving national climate objectives [56]. It is therefore necessary to continue researching ways to improve the production, management, and utilisation of manure to reduce all types of GHG emissions in the future. It is also necessary to review the utilised projection models and the methodologies. It is important to incorporate the results of national research during the preparation of inventory reports. For the ever-increasing efforts to mitigate climate change to be successful, a dynamic scientific approach to measuring GHG emissions should be applied. The IPCC GHG emission inventory guidelines are crucial for climate change mitigation and serve as a powerful tool for achieving emission reduction targets. To realise this potential, relevant methodologies should be updated as new information and research studies become available [64].

4.3. Manure Production Trends in EU Member States

Scientists in the EU have extensively analysed issues related to agricultural emissions. In Denmark Lymperatou (2017) examined trends in manure production across the EU, with a focus on the environmental challenges posed by intensive livestock farming in regions with high animal densities [65]. For example, dairy-intensive regions in Italy exhibit elevated eutrophication risks due to excessive nutrient excretion, with manure surpluses often exceeding local crop nutrient requirements, resulting in nitrogen and phosphorus runoff that contributes to eutrophication and water pollution [66]. Hietala-Koivu et al. (2023) analysed regional data on water quality, land use, soil fertility, and manure flows to assess Finland’s agricultural eutrophication strategies and identified key reasons for their limited effectiveness [67]. Intensive livestock farming generates excessive quantities of manure, leading to nitrogen runoff and eutrophication. Detoxifying nitrogen at the farm level would be an advantageous way to reduce the environmental impacts of food production [17]. In France, production intensification often results in surplus manure, reduced profit margins, and larger farm sizes, thereby increasing the environmental burden per unit of land [68,69]. Staniszewski & Muder (2023) [70] stressed that it was particularly striking that, in several countries that joined the EU (e.g., the Baltic states, Slovenia, and Hungary) in 2004, a significant change in sustainable intensification occurred between 2004 and 2018. Therefore, it can be concluded that accession to the EU had a significant positive impact on the sustainable intensification of agricultural production in many member states. However, in Spain, the over-concentration of livestock in pig production has raised concerns about animal welfare and soil nitrogen imbalances. Therefore, sustainable manure management strategies are urgently needed to balance agricultural productivity with environmental protection, especially in regions with concentrated livestock operations [71]. Producers need to be aware of the trade-offs of lowering one emission at the risk of increasing another, and must consider the relative benefits and risks associated with lowering certain emission type.

4.4. Limitations of the Study and Uncertainties

The primary limitations of the present study are the exclusive use of quantitative future scenarios and emissions modelling. Additionally, the utilised methodology does not encompass the technical aspects of manure storage, spreading, or treatment, nor does it take certain mitigation technologies or environmental measures into consideration. The study does not provide a stage-by-stage estimation of GHG emissions throughout the entire cycle of manure management, as its scope is limited to the anticipatory projection of emissions based solely on livestock and national data.

A deep and comprehensive uncertainty analysis serves as the foundation for dependable long-term agricultural projections, as the complex interactions between climate, policy, and economic factors will always result in uncertainties [72,73,74]. Relevant models typically overlook such factors, and the resultant inaccuracies in estimating livestock dynamics and manure-related emissions are inevitable [75]. Furthermore, the uncertainty involved in scaling mitigation from the laboratory to the farm level is compounded by scant data at the regional level [76]. Such an analysis also allows for identification of the sources of uncertainty and shows the effect of spatial resolution [77]. The lack of data in area-specific excretion rates and livestock distribution, among other data gaps, is a significant factor underscoring the continued necessity of system-level approaches to support sustainable development [78]. Therefore, across the EU, managing manure in a way that is both effective and sustainable is a significant challenge that calls for well-coordinated, location-specific measures to not only tackle the environmental impact but also address nutrient imbalances and the lack of reliable data for the future of agriculture.

5. Conclusions

Agriculture is a major contributor to GHG emissions, especially CH₄ and N₂O. Therefore, accurate manure projections are important for developing effective emission reduction strategies and achieving climate objectives.

In Latvia, a 2% increase in manure production is projected by 2050 compared with 2023. In 2023, most of the manure was produced by cattle (83%), with this proportion expected to increase slightly by 2050 (84%); meanwhile, no change is projected for pig farms (9%). This means that in the context of cattle and pig farming, it is essential to improve technologies for producing, managing, and using manure to achieve climate objectives.

By 2050, the percentage breakdown of manure in Latvia is expected to change significantly compared with that is in 2023, with the amounts of litter manure and fresh manure being projected to decrease. In contrast, the amount of liquid manure is projected to increase, especially on dairy (by 54%) and pig farms (by 4%). These changes require the adaptation of manure management strategies, with a particular focus on the more efficient use of liquid manure and GHG emissions reductions.

The projection of GHG emissions up to 2050, based on the data for 2021—2022 and the projected number of livestock and amount of manure, revealed that manure management in Latvia is a significant source of emissions, accounting for 14.9% of the total agricultural GHG emissions in 2022. Although it is projected that the total amount of emissions from agriculture in Latvia will increase by 2% by 2050, emissions from manure management are expected to decrease by approximately 18,000 tonnes of CO₂ eq., or 5%. This decrease is mainly due to expected reductions in the number of herd livestock, including cattle by 14%, dairy cows by 17%, sheep by 40%, and goats by 21%, while cow milk yields are projected to increase by 33%. These trends are expected to have a particular impact on direct and indirect N₂O emissions from grazing livestock, which are expected to decrease by 16% and 17%, respectively, by 2050. However, the projections revealed that CH₄ emissions from manure management would increase by approximately 5000 tonnes CO₂ eq., or 5%, thus increasing their share in the emission composition by four percentage points. These results highlight the need to introduce more efficient methane abatement technologies, especially in the context of manure storage and processing. Therefore, reducing emissions from manure management can significantly contribute to achieving national GHG emission targets; however, this will require targeted technological and management solutions. Further research is needed to improve manure management approaches and enhance the projection models used, as well as to update the methodology by applying the latest scientific approaches and adapting them to real-world conditions; in this manner, the effectiveness of climate change mitigation measures can be ensured.