Land Use and Production Practices Shape Unequal Labour Demand in Agriculture and Forestry

Abstract

Agriculture and forestry remain vital sources of rural employment; yet, both sectors face challenges of low labour productivity, demographic change, and structural inefficiencies. Modernisation improves productivity but often reduces labour demand, creating a policy dilemma between innovation and job preservation. Therefore, this study aims to quantify labour input across different land use types and farm sizes in agriculture and forestry. Latvia was used as a case region representing a sparsely populated territory suitable for both agricultural activities and forestry. This study develops a multi-stage framework to quantify labour inputs across agricultural and forestry land uses. The research findings suggest that labour use intensity decreases as farm size increases; however, it exhibits greater variation across agricultural production types. Perennial plantations, vegetable and potato cultivation, and dairy farming show the highest labour demands, whereas energy crops and grass-based systems require the least. In forestry, establishment and tending dominate labour needs, while mechanised harvesting reduces input requirements. These findings highlight the strategic role of labour-intensive, high-value activities in sustaining rural employment and the need for targeted rural development policies that recognise this pattern, supporting employment in rural areas without discouraging improvements in labour productivity.

1. Introduction

Employment in agriculture and forestry remains a vital contributor to sustaining rural livelihoods. Agriculture remains a cornerstone of rural economies, serving as a primary source of income and employment through a wide range of activities along the agri-food value chain. Labour-intensive agricultural practices are essential for enhancing household incomes and generating stable employment opportunities [1,2]. Employment diversification within agriculture further strengthens economic resilience, particularly among poorer households, by increasing productivity and wages [3]. The reallocation of labour to diversified agricultural activities and the adoption of cooperative farming structures foster community cohesion, social participation, and institutional collaboration [4,5].

However, the agricultural sector continues to face persistent structural challenges. These include low labour productivity, rising wage costs, and recurrent seasonal or long-term labour shortages [6,7]. These issues are exacerbated by demographic pressures such as an ageing rural population and youth outmigration, as well as by inefficiencies in labour organisation and allocation [8,9]. Additionally, climate change poses a threat to rural labour capacity, with rising temperatures projected to reduce agricultural labour productivity by up to a half in some vulnerable regions by the end of the century [10].

Forestry also provides essential, though often overlooked, employment opportunities in rural regions, particularly where reforestation and ecosystem restoration projects are active. The sustainable use of forest resources contributes to poverty reduction, enhances household income generation, and reinforces rural socio-economic structures [11]. Agroforestry systems have been shown in some cases to require less labour than input-intensive farming systems [12]. Despite a general decline in the forestry workforce and substantial regional disparities, the sector remains a key contributor to rural development [13]. Labour migration undermines the efficiency of forest management and local income generation [14], underscoring the need for a sufficiently skilled and locally available workforce to ensure both ecological sustainability and rural well-being.

Addressing these rural employment challenges requires a deeper understanding of the structural constraints shaping labour demand, including low labour productivity, demographic ageing, and spatially uneven workforce distribution. Previous research has shown that sustainable intensification, improved resource management, and targeted mechanisation can reduce labour input while maintaining or even increasing productivity [15,16]. However, such approaches may also lead to labour displacement if technological adoption progresses more rapidly than workers can adapt [17,18]. Therefore, rural labour policies must focus not only on enhancing efficiency, but also on ensuring workforce adaptability, qualification, and long-term demographic sustainability to reinforce the resilience of agricultural and forestry systems [19,20].

Policymakers in rural labour policy face a fundamental trade-off between technological innovation and employment preservation. On one side, public support for innovation, digitalisation, and competitiveness is expected to improve farm productivity and resilience, contributing to long-term sustainability. On the other side, automation and mechanisation frequently reduce the demand for manual labour, particularly in sectors such as crop cultivation and livestock production [15,21]. Even sustainability-oriented strategies, such as conservation agriculture or precision farming, can lower labour requirements [16]. Consequently, modernisation may unintentionally intensify rural depopulation unless it is accompanied by proactive measures for workforce reskilling and inclusive employment creation.

Within the European Union (EU), the Common Agricultural Policy (CAP) 2023–2027 explicitly links competitiveness and sustainability with rural employment objectives [19]. Complementary policy frameworks, such as the European Green Deal [22], the Circular Economy Action Plan [23], and the Bioeconomy Strategy [20], highlight the potential for job creation in green and circular sectors, provided that appropriate skills and institutional support are in place. Yet, quantitative evidence on how these transitions affect labour input across land use types in developed economies remains limited [24]. Most existing studies examine labour dynamics in developing countries, where structural conditions and labour mobility differ substantially from those in OECD economies [9,16,25]. As Blanco and Raurich (2022) demonstrate, labour productivity levels in agriculture diverge far more between developing and developed countries than in non-agricultural sectors, underscoring the need to investigate labour demand patterns within advanced, high-productivity rural economies [26]. Yet few empirically grounded assessments examine how land use and production practices shape labour demand within such developed contexts, particularly in the Baltic and Nordic regions.

To fill this gap, the present study quantifies labour input across agricultural and forestry land use in Latvia, a representative EU member state with diverse production systems and strong bioeconomy potential. Therefore, this study aims to quantify labour input across different land use types in agriculture and forestry in Latvia. The research question is as follows: “How does labour input vary across different land use types in agriculture and forestry in Latvia?” We introduce a multi-stage framework that systematically accounts for enterprise size, production specialisation, and technology levels, enabling detailed estimation of labour hour requirements per hectare or per animal.

By providing this integrated quantitative evidence, this study contributes to a more precise understanding of structural labour dynamics in developed rural contexts. The findings offer a data-driven foundation for aligning economic, environmental, and social policy goals, helping to balance innovation-driven productivity growth with the preservation of rural employment opportunities.

2. Study Background

2.1. Task- and Sector-Specific Approaches to Quantifying Labour Input

In the literature on agriculture and forestry, measurement of labour input varies mainly according to sectoral focus. A common approach in crop production involves expressing labour input per unit of land, most often as labour time per hectare. Many studies adopt this land-based metric, using terms such as effective working hours per hectare, labour days per hectare, or man-hours per hectare [21,27,28,29]. Measurement approaches further diverge based on farming operations, such as sowing, weeding, fertilising, and irrigation [15,30,31,32,33].

Livestock and dairy systems apply distinct labour input metrics, often defined per animal or herd task. Studies quantify the time spent per cow to carry out activities such as feeding or milking, typically using weekly or daily timeframes [34,35]. In forestry contexts, labour input is recorded as worker days per hectare for tasks like felling, reflecting episodic but labour-intensive activities [36].

Additionally, several sources quantify labour input using standardised constructs such as annual work units or productive work hours [37,38], enhancing comparability across farm types and labour regimes. When labour inputs are expressed relative to other inputs, such as land area or the number of animals, the concept of labour intensity is appropriate [39].

A notable research gap is the absence of standardised labour input metrics, which hinders cross-study comparisons. Diverse units such as days, hours, or annual work equivalents introduce inconsistencies. Measuring labour input in terms of hours per hectare or per animal provides a consistent, scalable, and task-insensitive unit that aligns with land use intensity and livestock management, thereby enabling more accurate benchmarking across various farming systems and operational contexts.

2.2. Mechanisation as a Driver of Labour Reduction in Large-Scale and Specialised Production

Mechanisation plays a central role in reducing labour requirements in arable crop systems. Mechanical planters and dibble bars significantly reduce the time and physical effort associated with sowing operations compared to manual planting techniques [29,40]. Similarly, mechanised weeding and cultivation enhance worker productivity by lowering both the number of labourers needed and the physical burden of field tasks [41,42]. Mechanised irrigation systems, particularly sprinkler and drip systems, are likewise associated with significant labour savings per hectare when compared to furrow-based irrigation [30,43].

In contrast to annual systems, where sowing and weeding dominate labour use, mechanisation is most impactful during pruning and thinning operations in permanent cropping systems. Devices such as blossom thinners and automated thinning machines improve labour productivity by decreasing the time and labour intensity of manual thinning while enhancing crop uniformity [44,45]. Although complete replacement of manual labour is not feasible in many contexts, mechanical thinning substantially reduces the need for intensive follow-up labour and facilitates improved marketability of fruits [46,47]. Mechanical harvesters substantially reduce labour input by performing harvesting tasks more efficiently and with fewer operators [21,27].

Grassland-based production systems, including meadows and pastures, introduce yet another set of labour challenges, which are solved by mechanisation, particularly during short harvesting times. Tools such as chippers and pickup headers expedite formerly manual alignment tasks, contributing to time savings, although these technologies do not eliminate the need for manual adjustment [48]. Mechanisation of these systems allows for quicker completion of seasonal tasks, reducing dependence on large seasonal labour forces and improving time-sensitive harvesting logistics.

Mechanisation is also transformative in livestock systems, where feeding and milking are the most labour-intensive tasks. Mechanised feeding systems distribute feed while maintaining consistency in ration delivery [49]. Automated milking systems reduce the labour required for routine milking and offer consistent labour demands across varying herd sizes, contributing to scalability and management flexibility [35,50]. More broadly, the integration of digital technologies and mechanised equipment into dairy operations has led to considerable gains in labour efficiency and productivity [51].

As this evidence suggests, larger farms often benefit more from the economies of scale associated with mechanisation, reducing labour input per unit area or animal. Specialisation in production increases task complexity, usually resulting in higher labour efficiency gains from mechanisation. Accurate labour input estimation must thus consider both the scale and specialisation of production to reflect realistic workload differences and help optimise resource allocation in diverse agricultural and forestry settings.

2.3. Scale and Technology Boost Labour Efficiency on Larger Farms

Larger operations tend to exhibit greater labour efficiency and increased reliance on hired hands relative to their smaller counterparts. In crop systems, evidence from plantation contexts shows that large estates employing mechanised harvesting cover substantially more area per hour than those relying on manual tools [21]. Mechanised grain farms similarly report improvements in yields, profitability, and food self-sufficiency. Interestingly, the most significant benefits are often observed among the smallest holdings, which face acute labour shortages. At the same time, the largest farms show more modest reductions in wage labour demand following mechanisation [17,25].

A similar pattern emerges in livestock production, where labour productivity rises with herd size. Herds beyond the medium threshold require markedly fewer labour hours per animal than their smaller counterparts [50,52]. Beef enterprises of considerable scale achieved higher output per labour unit, reflecting economies of scale in both land and livestock management [8]. Time devoted to calf care per farm increased with herd expansion, while per-calf attention remained greatest on the smallest holdings [53].

Beyond efficiency measures, structural characteristics also influence labour organisation. Larger and more diversified farms are more likely to employ wage workers, and greater tenancy is associated with higher overall labour inputs [37,54].

Despite these insights, comparative studies explicitly contrasting small and large enterprises across production systems remain limited. Most research focuses either on highly mechanised large-scale operations or on smallholder farms in isolation. Moreover, existing work typically measures labour efficiency in terms of output value rather than land area or animal numbers. These gaps constrain a comprehensive understanding of how scale shapes labour input as a dimension of resource demand.

2.4. Labour Needs Differ by Production Specialisation and Crop Type

Labour requirements in agriculture vary significantly across different production systems and management practices. Specialised, high-value systems, such as cherry and vegetable cultivation, are notably labour-intensive [2,55]. Similarly, organic farming, although it demands more labour per hectare, tends to provide more stable employment compared to conventional methods [1]. Conversely, some less intensive systems, like oil palm cultivation, can lead to a decrease in overall family labour [56].

The adoption of certain agricultural practices also influences labour dynamics. Conservation agriculture, for instance, presents a mixed picture. Some studies indicate an increase in labour input requirements, particularly for women, with instances of child labour observed in Sub-Saharan Africa [57]. However, other findings suggest that conservation agriculture-based sustainable intensification practices can substantially decrease labour use [16,58]. Agroforestry practices have also been shown to require less labour than fertilised maize fields, though more than non-fertilised continuous maize [12].

Furthermore, specific tasks within agricultural systems contribute to varying labour demands. For example, early cleaning activities can require varying labour inputs depending on the season, spanning several productive work hours per hectare [38]. In livestock systems, calf care and milk feeding illustrate how certain stages require disproportionately high labour inputs despite overall efficiency gains from scale [53,59].

Overall, comparative studies examining labour inputs across different agricultural domains remain scarce. Most research tends to focus on specific animals or crops, resulting in fragmented insights that hinder a comprehensive understanding of labour dynamics across farming types. This gap restricts the ability to generalise findings or develop integrated labour-efficient strategies applicable across varied agricultural contexts.

2.5. Integration of Labour Costs into Operational Planning in Forestry

Labour costs are a central component of forestry planning models and a key determinant of operational feasibility. They are often incorporated to evaluate economic efficiency and conduct sensitivity analyses of major cost drivers such as harvesting method, timber volume, and transport distance [60,61,62]. For instance, models developed in Latvia have included labour expenses to assess the sensitivity of total harvesting costs to fluctuations in productivity and transportation parameters [63].

Empirical evidence from the Baltic and Nordic regions suggests that labour efficiency in forestry is strongly influenced by both environmental and operational conditions. In mechanised commercial thinning, productivity depends on stand density, average tree volume and stem form, species composition and stand structure, soil and stand type, harvesting time, and the operator’s skill and technical proficiency [64]. Studies from Finland further show that mechanised planting productivity varies with terrain, site preparation, and machine–operator interaction [18]. Similar patterns have been observed elsewhere, such as in North America, where challenging terrain and dense vegetation significantly reduce worker productivity [65]. More generally, stand type and the nature of silvicultural operations determine the time and effort required for forest work.

Work time studies confirm that production activities such as felling, processing, stacking, and extraction typically account for the majority of labour input, whereas additional tasks such as worksite preparation, maintenance, and supervision form a smaller but non-negligible share [36]. The duration of these operations varies considerably depending on stand characteristics, mechanisation level, and weather conditions, a pattern confirmed in a recent review of forest operations across four European biogeographical regions, including boreal systems [66].

Although existing studies provide valuable insights, comprehensive quantitative assessments of labour input across different forest management types remain limited. Most existing studies address labour costs indirectly through modelling or small-scale time studies, while regionally comparable analyses that integrate diverse operational settings are scarce. Closing this gap is particularly important in countries such as Latvia, where forestry substantially contributes to rural employment and bioeconomy development. Strengthening the empirical basis of forestry labour data enhances both the accuracy of planning models and the broader understanding of structural labour dynamics within northern European forestry systems.

3. Study Area

More than half of the EU’s population lives in predominantly or intermediate rural areas. These regions produce 45% of gross value added (GVA) and account for 53% of employment in the EU-27 [67]. Latvia, located in the northeastern part of the EU, is characterised by a high proportion of rural land use, with agriculture and forestry occupying the majority of its territory. In Latvia, the development of rural territories depends on economic growth in key industries for rural areas—agriculture and forestry [68]. These sectors are the main sources of job creation and the primary drivers of economic development in rural areas [69]. For socioeconomic performance, a rapid rise in value added is projected, with the need to invest in fixed assets to offset workforce losses, resulting in a significant increase in labour productivity [70]. The contribution of primary production of bioresources to the total turnover of the bioeconomy is 40% (agriculture—22%, forestry—17%, fishing and aquaculture—1%) [71].

Forests cover 53% of Latvia’s territory, making it the fifth most forested country in the EU [72]. According to OECD data (2019), agriculture, hunting, forestry, and fishing contributed approximately 4% to Latvia’s economy, 16% to trade, and 7% to employment in 2016—a higher share across all accounts than the average for EU and OECD countries. The significance of agriculture is even greater in rural areas, where it accounts for around 20% of employment. There is potential for UAA growth, possibly in competition with forestry [73]. There is potential for UAA growth, possibly in competition with forestry [73]. In 2024, the added value of agriculture, forestry, and fisheries increased by 7.0% compared to 2023, and its share reached 4.7% of the total GDP added value. The average number of people employed in agriculture, forestry, and fisheries in the 15–74 age group continues to decrease. In 2024, the total dropped to 60.3 thousand, although the proportion remained at 7.3% of the total employed population in the country as the total number of people employment also decreased [74]. Latvia’s land use—particularly in agriculture and forestry—aligns with or deviates from the EU average, depending on the indicators used, such as production intensity [75].

Agricultural activity is concentrated in the central and eastern parts of Latvia, where crop production and livestock farming dominate (Figure 1). The agricultural sector is marked by a fragmented farm structure, with small and medium-sized holdings often engaged in mixed production systems. Labour demand in agriculture tends to be continuous and seasonally variable.

Forestry is particularly prominent in the northern and western parts of Latvia, and productivity and labour demand depend on forest stand age and the main species (Figure 2).

This spatial and sectoral diversity makes Latvia a relevant case for analysing labour intensity, competitiveness, and the dynamics of sustainable rural development.

4. Materials and Methods

4.1. Agriculture

In this study, labour input intensity in agricultural land use is defined as the number of labour hours per hectare of land or per grazing animal. Labour input intensity is quantified for the main crop groups (cereals, oilseeds and pulses; vegetables and potatoes; perennial crops; energy crops; other crops; fallow land; grassland in arable land; meadows and pastures) and main grazing animal groups (dairy cows; other grazing animals; pigs; poultry). The calculation process incorporates a farm size-based classification system, based on [76] as summarised in Appendix A (

Table A1. Farm size groups in different agricultural sectors in Latvia.

Agricultural Sector	Large Farms	Medium Farms	Small Farms	Micro Farms
Cereals, oilseeds, pulses (ha)	>300	>100, ≤300	>20, ≤100	≤20
Potatoes and vegetables (ha)	>30	>10, ≤30	>2, ≤10	≤2
Perennial plantations (ha)	>30	>10, ≤30	>2, ≤10	≤2
Energy crops (ha)	>30	>10, ≤30	>2, ≤10	≤2
Other crops (ha)	>150	>50, ≤150	>10, ≤50	≤10
Fallow land (ha)	>300	>100, ≤300	>20, ≤100	≤20
Grassland (ha)	>300	>100, ≤300	>20, ≤100	≤20
Meadows and pastures (ha)	>300	>100, ≤300	>20, ≤100	≤20
Dairy cows (number)	>200	>30, ≤200	>4, ≤30	≤4
Other grazing animals (number)	>200	>30, ≤200	>4, ≤30	≤4
Horses (number)	–	>30	>4, ≤30	≤4
Goats (number)	–	>50	>5, ≤50	≤5
Sheep (number)	–	>50	>5, ≤50	≤5
Pigs (number)	≥1000	≥100, <1000	≥5, <100	<5
Poultry (number)	≥50 k	≥1 k, <50 k	≥20, <1 k	<20

). The classification was undertaken to examine the effects stemming from the assumption that smaller farms require more intensive labour input than larger farms, primarily due to their reliance on outdated machinery and equipment. The assessment of labour input intensity across agricultural sectors and farm size groups in this study applies multiple methods as follows: ordinary least squares regression was performed on Latvian Farm Accountancy Data Network (FADN) farm-level data for 2021 [77], the average number of labour hours for specialised farms according to FADN data was calculated [77], adjustments were made using the Latvian Rural Consulting and Education Centre (LLKC) Gross Margins calculations for 2021 [78], the calculation result was verified with Eurostat data for 2021, the calculated labour input per hectare and per grazing animal was extrapolated to Rural Support Service and Agricultural Data Centre spatial data for 2024, and finally the results was mapped.

4.1.1. Labour Input Intensity in Agricultural Sectors

To quantitatively assess labour input intensity for various types of agricultural land use, anonymised farm-level data from the Latvian FADN for the year 2021 were utilised [79]. The FADN is a harmonised European system for collecting microeconomic data on farms, providing detailed information on farm incomes, production activities, and structural characteristics for policy analysis and research [77]. From the 1000 farms in the FADN data, a subset of 869 farms (87% of the total) was used for calculations, excluding the following:

• Rabbit farms (very small number of observations);
• Beekeeping farms (large disparities in labour input between medium and large farms);
• Farms where the number of calves is at least double the total number of dairy cows and other cattle (rapidly expanding);
• Cereal, oilseed, and pulse farms with a high share of outsourcing of activities (shows very low labour input figures due to outsourcing to other companies);
• Farms where the extreme difference between the observed values and those predicted by the statistical model (residual) could interfere with accurate calculations.

For the calculations, ordinary least square (OLS) regression was used, where the dependent variable is total labour input (hours), comprising both paid and unpaid labour within farm operations, and the independent variables are land area allocated to different production types and livestock population per farm. To address multicollinearity concerns, grasslands were excluded (they are highly correlated with grazing animals), assuming that labour input for grazing animals includes grassland-related activities. Due to small group sizes, a composite grazing animal group, “other grazing animals”, was created, applying standardised weighting coefficients of 1 for non-dairy cattle and horses, and 0.3 for sheep and goats. The regression outputs are presented in

Table 1. Average labour input (hours) per hectare or per grazing animal, and statistical output parameter estimates and significance levels in different agricultural sectors in Latvia in 2021.

Parameter	Estimate	Std. Error (HC1)	t-Value	Probability (>|t|)	Significance †
Intercept	1163	92.74	12.55	<0.001	***
Cereals, oilseeds, pulses	13.06	0.36	36.09	<0.001	***
Vegetables and potatoes	192.3	13.75	13.98	<0.001	***
Perennial plantations	304.9	26.55	11.49	<0.001	***
Other crops	68.4	6.42	10.66	<0.001	***
Dairy cows	95.62	2.3	41.56	<0.001	***
Other grazing animals	18.27	2.6	7.03	<0.001	***
Pigs	3.44	0.07	48.85	<0.001	***
Poultry	0.84	0.08	10.56	<0.001	***

^† Significance codes: ***: 0. Residual standard error: 2147 on 860 degrees of freedom. Multiple R-squared: 0.893, adjusted R-squared: 0.8921. F-statistic: 897.6 on 8 and 860 DF, p-value: <0.001.

.

The model shows that the Variance Inflation Factors (VIFs) for all independent variables range between 1.00 and 1.17 (<5), indicating that multicollinearity is not a concern.

As is often the case with cross-sectional data involving farms of significantly different sizes, heteroscedasticity is a concern. To account for this issue, and since the analysis focuses primarily on the coefficient estimates rather than their precise inference, robust (HC1) standard errors were used to evaluate coefficient significance.

The model results indicate a significant disparity in labour requirements among agricultural sectors. Vegetable and potato cultivation and plantations of perennials recorded the highest average labour per hectare (192.3 and 304.9 h, respectively), while dairy cow husbandry had the highest average labour input per animal (95.62 h). The poultry and pig sectors, however, required significantly lower amounts of labour (0.84 and 3.44 h, respectively), while other grazing animals had an average of 18.27 h per animal.

4.1.2. Labour Input Intensity Across Farm Sizes

Although the FADN provides detailed data on nearly one thousand farms of different sizes, the data do not allow for an accurate econometric determination of labour input intensity for different specialisations across different farm size groups.

To overcome this challenge, in addition to the analysis of labour input across agricultural specialisations (Section 4.1.1 of this paper), the average labour input was calculated using labour input differences in FADN farms with the narrowest possible specialisation within each size group. However, in many cases within those groups, notable differences in the results were observed between farms. Therefore, the obtained data were adjusted using the LLKC Gross Margins data for 2021 [78], which define standard activities for different crop and grazing animal groups and include analytical assumptions about differences in equipment and, consequently, labour productivity. These data were cross-checked against the results for typical farms in the FADN data. These calculations and adjustments were performed for both the crop groups (cereals, oilseeds, pulses; vegetables and potatoes; perennial plantations; other crops) and animal groups (dairy cows; other grazing animals; pigs; poultry) included in the regression analysis and the crop groups (energy crops; fallow land; grassland; meadows and pastures) and animal groups (sheep; goats; horses) not included in the regression analysis.

As the calculations are conducted analytically, an important part of this exercise is the verification of the results (see Section 4.1.3 of this paper).

4.1.3. Verification of Labour Input Intensity Results

The calculated labour input intensity coefficients (see Section 4.1.2 of this paper) for each specialisation and size group were verified by multiplying each coefficient by the corresponding area or number of animals in the group (see Appendix A,

Table A2. The total land area (ha) and grazing animal number for each crop and grazing animal group, categorised by farm size groups in Latvia in 2021.

Agricultural Sector	Large Farms	Medium Farms	Small Farms	Micro Farms	Total
Cereals, oilseeds, pulses (ha)	526,827	217,347	148,684	77,355	970,213
Potatoes and vegetables (ha)	4459	1894	2530	14,916	23,798
Perennial plantations (ha)	2174	2509	3387	1733	9803
Energy crops (ha)	881	176	138	15	1210
Other crops (ha)	682	1065	2459	9612	13,819
Fallow land (ha)	309	6718	26,564	26,194	59,785
Grassland (ha)	44,568	55,153	75,044	118,223	292,989
Meadows and pastures (ha)	22,100	71,726	173,626	331,180	598,631
Dairy cows (number)	37,049	48,509	34,902	10,783	131,243
Other grazing animals (number)	86,516	131,355	42,255	2648	262,774
Horses (number)	-	62,290	25,226	2466	89,982
Goats (number)	-	3721	5014	2355	11,090
Sheep (number)	-	1857	4177	2430	8464
Pigs (number)	315,972	8298	11,020	3634	338,924
Poultry (number)	4,956,310	609,399	253,395	38,596	5,857,700

). This allowed us to obtain a total labour input close to the total agricultural employment rate reported in the statistics. This verification was performed for overall agricultural employment data (national statistics), but the results are also double-checked for the sectoral distribution of employment in agriculture (FADN-based data).

To verify the results at the national level, Eurostat economic accounts on agricultural labour input were used [80]. According to these data, in 2021, the total labour input in agriculture in Latvia was 61 thousand annual work units (1 annual work unit = 1840 h per year). This is similar to the values calculated using our labour input intensity coefficients for each specialisation and size group multiplied by the corresponding group sizes.

4.2. Forestry

As less detailed data were available for the calculations of labour input from forestry land use, a different approach was applied, although the outcome was the same—labour hours per hectare of land use. In forestry, labour input is dependent on the dominant tree species as well as the silvicultural activity or timber extraction performed in the forest area.

The methodology used to calculate labour input in forestry in this study includes multiple methods as follows: identification and classification of different forestry activities, estimation of labour hours per hectare of silvicultural activities for different dominant tree species based on a previous study [81], assessment of capacities (m³ per hour) and proportion of usage of the different pieces of equipment in logging activities using Latvian State Forest information for 2024, calculation of the total area (hectares) of each forestry activity based on assumptions from Latvian State Forest research results [82,83,84] and national statistics for 2022, calculation result verification with Eurostat data for 2022, extrapolation of the calculated labour inputs per hectare to State Forest Register spatial data for 2024, and mapping of the results.

4.2.1. Labour Input Intensity Across Forestry Activities

To account for the different forestry activities, labour input calculations have been classified into silvicultural and logging activities. For the estimation of labour inputs in forestry land use, silvicultural activities such as soil preparation, planting, forest protection, forest replenishment, tending, young stand tending, underbrush tending, and maintenance of amelioration systems were included. The labour inputs for each silvicultural operation, depending on the dominant tree species, have been determined based on evaluations in the study “Evaluation of land use optimization options in the context of Latvian climate policy” (2019) [81].

As for logging activities, labour input was assessed by determining the capacity of each piece of equipment per hour, depending on the logging activity, in cubic metres per hour (m³/h), as reported in the Latvian State Forest’s 2024 efficiency indicators of logging service providers [85]. Logging activities employ machines such as a harvester and chainsaw (for cutting trees), a forwarder (for timber delivery), and transport (for timber transportation). In addition to capacity, the proportion of chainsaw use in percentage (%) was determined, depending on the dominant tree species and logging activity (see Appendix A,

Table A3. The proportion of chainsaw usage depending on the dominant tree species and logging activity in Latvia in 2024, %.

Dominant Tree Species	Maintenance Felling	Main Felling	Other Types of Felling
Pine	12	9	9
Spruce	12	9	9
Birch	15	12	12
Black alder	18	15	15
Aspen	25	20	20
Grey alder	28	18	18
Other	12	9	9

).

The total land area for silvicultural and logging activities, depending on the dominant tree species, was calculated using data from national statistics in 2022 as follows:

• Soil preparation is employed in natural forest regeneration to enhance the success rate of regeneration through natural processes. However, soil preparation is not universally applied in all instances of natural regeneration. For this analysis, it was assumed that soil preparation, specifically topsoil mineralisation achieved by creating furrows with a disc plough or ridges using an excavator, is implemented in approximately 50% of naturally regenerating forest areas [82]. Consequently, the total area subjected to soil preparation was estimated by summing the total area of forest regeneration achieved through sowing and planting and half of the naturally regenerating forest area, based on national statistical data on forest regeneration [86].
• The total area of forest planting was calculated by combining the total area of forest regeneration (by sowing and planting) with the total area of planted forests as reported in national statistical data on afforestation [87].
• The total area of forest requiring protection (except for dominant tree species—black alder, aspen, and white alder) and young stand tending was determined by aggregating the total area of forest regeneration conducted over the preceding four years.
• It is posited that the area requiring tending is approximately 30% smaller than the total area requiring young stand tending [83].
• The total area subjected to forest replenishment was estimated as half of the total area of regenerated forest [84].
• Data on the total forest area where logging activities—including main felling, maintenance felling, and other types of felling—were conducted, along with corresponding timber stock, were obtained from national statistical records on inventoried forest felling areas and stock volume [88].
• It was assumed that the total area subjected to underbrush tending corresponds to 90% of the total area of logging activities.
• The total area where maintenance of amelioration systems has been performed was obtained from national statistical data on forest land area [89].

The total areas of silvicultural activities are summarised in Appendix A (

Table A4. The total land area of forestry activities, depending on the dominant tree species in Latvia in 2024, ha.

Dominant Tree Species	Soil Preparation	Planting	Forest Protection	Forest Replenishment	Tending	Young Stand Tending	Underbrush Tending	Maintenance of Amelioration Systems
Pine	8337	8486	31,021	4316	31,021	21,715	38,272	19,049
Spruce	9820	9851	38,935	5081	38,935	27,255	37,208	14,581
Birch	5809	2592	42,669	4788	42,669	29,868	31,193	20,265
Black alder	1080	583	0	857	6693	4685	2122	4726
Aspen	2918	25	0	2914	24,712	17,298	5459	6078
Grey alder	2515	168	0	2511	21,102	14,771	8682	7496
Other	63	49	508	54	508	356	374	2271
Total	30,540	21,754	113,133	20,520	165,640	115,948	123,311	74,466

). The total areas of logging activities and their total stock volumes are reported in Appendix A (

Table A5. The total land area (ha) and the total stand volume (m³) of logging activities, depending on the dominant tree species in Latvia in 2024.

Dominant Tree Species	Maintenance Felling		Main Felling		Other Types of Felling
Indicator	Area	Stand Volume	Area	Stand Volume	Area	Stand Volume
Pine	10,110	447,099	11,229	2,949,161	21,185	453,687
Spruce	8689	430,924	5241	1,314,902	27,412	875,109
Birch	11,575	365,271	16,527	3,586,121	6557	165,630
Black alder	803	26,898	990	220,134	565	19,622
Aspen	1588	50,072	3759	924,629	719	24,188
Grey alder	1122	22,094	7778	1,148,901	747	29,950
Other	65	1527	117	12,368	234	7650
Total	33,952	1,343,885	45,641	10,156,216	57,419	1,575,836

).

4.2.2. Verification of Labour Input Intensity Results

The labour input intensity coefficients calculated for each forestry activity and dominant tree species (Section 5.2) were verified by multiplying each coefficient by the corresponding activity area (Appendix A, Table A4). This produced an estimate of the total labour input that closely aligned with the overall forestry employment figures reported in national statistics. To verify the results at the national level, we used Eurostat national account data on employment by detailed industry [90], which reported a total workforce of 13,220 full-time equivalent employees in 2022. By comparing our results with this official statistic, we ensured the methodological robustness of our approach.

4.3. Spatial Analysis

Several data sources are used for spatial identification. Rural Support Service provided data on the area, the crop grown, and the spatial location of each agricultural land parcel for 2024 (on request, not publicly available). Data on the spatial location, the type, and the number of animals in livestock farms in 2024 were prepared by the Agricultural Data Centre, and these data are available on request (not publicly available). For spatial information on all forest stands and their dominant tree species and areas, as well as the last forestry activity performed and the year in which it took place, data from the State Forest Register for 2024 was used (on request, not publicly available).

Based on this information, we calculated the labour input at the individual parcel level, taking into account the characteristics of each plot and management practices (the overall size of the farm managing each specific land parcel, crop type, animal type, dominant tree species). To ensure data protection for individual fields and to make spatial visualisation more straightforward, the parcel-level results were summarised in a grid of 100-hectare square cells [91]. By standardising the data in 100-hectare cells, greater consistency and comparability in the spatial representation of labour intensity across the study area were achieved, and the processing of complex parcel geometries was simplified.

5. Results

5.1. Agriculture

The results reveal substantial variation in labour inputs across different agricultural sectors and farm size groups (

Table 2. Average labour inputs in agricultural land use, categorised by farm size groups and the main crop and grazing animal groups in Latvia in 2021 in hours per hectare or grazing animal.

Agricultural Sector	Large Farms	Medium Farms	Small Farms	Micro Farms
Cereals, oilseeds, pulses	16	20	45	67
Vegetables and potatoes	217	254	509	728
Perennial plantations	323	360	615	834
Energy crops	12	12	24	24
Other crops	72	79	130	174
Fallow land	7	8	15	24
Grasslands	15	28	41	73
Meadows and pastures	3	6	8	15
Dairy cows (with calves)	95	111	245	363
Other grazing animals	23	30	52	130
Sheep	8	8	17	38
Goats	39	39	68	170
Horses	16	16	27	120
Pigs	4	9	42	120
Poultry	0.8	1.4	2.3	4

). The highest labour inputs per hectare were observed in perennial plantations and vegetable and potato cultivation. On micro farms, perennial plantations required an average of 834 h per hectare, followed closely by vegetables and potatoes at 728 h per hectare. Small farms reported slightly lower labour inputs in these categories, with 615 and 509 h per hectare, respectively. Thus, labour intensity decreased substantially with farm size: large farms required 323 and 217 h per hectare in perennial plantations and vegetable production, respectively, while medium farms required 360 and 254 h.

Labour input differences across farm size groups were also notable for grazing animal categories. For dairy cows with calves, micro farms reported the highest labour requirement at 363 h per animal, followed by small farms (245 h), medium farms (111 h), and large farms (95 h). A similar pattern emerged across other grazing animal farms. Goats required 170 h per animal on micro farms, but only 39 on both large and medium farms. Horses required 120 h per animal on micro farms and 16 h on large and medium farms. Sheep and other grazing animals followed the same trend, with labour inputs increasing consistently from large to micro farms.

Among arable crops, cereals, oilseeds, and pulses exhibited moderate labour intensity, with micro farms requiring 67 h per hectare and large farms only 16 h per hectare. Energy crops consistently required the lowest labour inputs across all farm size categories, ranging from 12 h per hectare on large and medium farms to 24 h per hectare on micro farms.

For fallow land, grasslands, and meadows/pastures, labour inputs were uniformly very low but still demonstrated a positive association with decreasing farm size. For example, labour input on fallow land ranged from 7 h per hectare on large farms to 24 h per hectare on micro farms. Similarly, meadows and pastures required just 3 h per hectare on large farms compared to 15 on micro farms (Table 2).

In the category of non-ruminant livestock, pigs and poultry showed markedly low labour requirements on large farms, i.e., 4 and 0.8 h per animal, respectively. However, these values increased considerably on micro farms to 120 h for pigs and 4 h for poultry. Medium farms showed an especially steep increase for pigs, reporting 9 h per animal (Table 2).

The spatial distribution of agricultural labour intensity across Latvia shows considerable regional variation (Figure 3). The highest continuous levels of labour input intensity are concentrated in the western regions of the country, corresponding to the presence of larger and more concentrated farms in this area (Figure 1). Central Latvia records significant labour input, where a mix of medium and large farms contributes to sustained agricultural activity. In contrast, the areas surrounding the capital, Riga, show very low levels of agricultural labour input, matching the sparse distribution of farms and dominance of micro and small holdings. Eastern Latvia maintains high labour input, reflecting a prevalence of medium-sized farms engaged in labour-intensive agriculture. The northern area of Latvia displays a consistent pattern of medium-to-low labour intensity, aligning with a moderate presence of small and medium farms. In general, the map reveals a structured spatial gradient in agricultural labour input intensity, with the most pronounced activity occurring in western and southeastern Latvia.

5.2. Forestry

The average labour input associated with forestry land use shows notable variation across silvicultural operations and dominant tree species (

Table 3. Average labour inputs in forestry categorised by silvicultural operations and dominant tree species in Latvia in 2022 in hours per hectare.

Dominant Tree Species	Soil Preparation	Planting	Forest Protection	Forest Replenishment	Tending	Young Stand Tending	Underbrush Tending	Maintenance of Amelioration Systems
Pine	20.3	24	13.5	22	23	23	25	13
Spruce	20.3	24	13.5	22	23	23	25	13
Birch	20.3	24	13.5	0	23	23	25	13
Black alder	20.3	24	0	22	23	23	25	13
Aspen	0	0	0	0	23	23	25	13
Grey alder	0	0	0	0	23	23	25	13
Other	20.3	24	13.5	22	23	23	25	13

). The most labour-intensive operations are underbrush tending, young stand tending, and general tending, each consistently requiring 23 to 25 h per hectare across nearly all tree species. Specifically, underbrush tending exhibits the highest average input, reaching 25 h per hectare for all species except aspen and grey alder, which have reduced labour requirements in other categories.

Planting and soil preparation are also prominent labour inputs, with the operations requiring 24 and 20.3 h per hectare, respectively, for pine, spruce, birch, black alder, and other species. These operations are absent for aspen and grey alder, suggesting a different silvicultural regime for these species. Forest replenishment requires 22 h per hectare for all species except birch, aspen, and grey alder.

Forest protection labour input is reported at 13.5 h per hectare for pine, spruce, birch, and other species. The maintenance of amelioration systems is uniform across all species, requiring 13 h per hectare, indicating standardised management efforts in this domain regardless of species composition.

Overall, tending operations are the most uniformly applied and labour-intensive activities across species. At the same time, soil preparation, planting, forest protection, and replenishment vary more substantially and are absent for some species, particularly aspen and grey alder.

The highest equipment capacities are observed in timber transport by lorry, with a consistent output of 18 cubic metres (m³) per hour across all logging activities. Harvesters also demonstrate a substantial capacity, particularly in main felling and other types of felling, yielding 18.7 m³/h, more than double the capacity recorded for maintenance felling (7.9 m³/h) (

Table 4. The average capacity of logging equipment, depending on logging activity in Latvia in 2022 in m³ per hour.

Logging Activity	Harvester	Chainsaw	Forwarder	Timber Lorry
Maintenance felling	7.9	0.9	5.8	18
Main felling	18.7	0.9	12.2	18
Other types of felling	18.7	0.9	12.2	18

).

Forwarders exhibit moderate variation, with capacities ranging from 5.8 m³/h during maintenance felling to 12.2 m³/h during main and other types of felling. This increase parallels the pattern observed for harvesters, indicating higher productivity in non-maintenance operations (Table 4).

Chainsaw productivity remains constant at 0.9 m³/h across all logging activities, representing the lowest capacity among all equipment types and the only value that does not vary with logging context.

In summary, harvester and forwarder capacities are markedly higher during main and other types of felling than in maintenance felling, while timber lorry and chainsaw outputs remain unchanged across activities.

Unlike agriculture, where output is annual or more frequent, forestry demands continuous labour over a long rotation cycle. Labour intensity fluctuates widely, with alternating periods of low and high demand, which limits direct comparability with agriculture.

The spatial distribution of forestry labour input intensity in Latvia reveals a notable clustering and regional variation (Figure 4), which can be explained by underlying patterns in forest stand age and dominant tree species (Figure 2). In the south-eastern region of Latvia, forestry labour intensity is low relative to most of the territory, corresponding to large areas with a predominance of young and middle-aged stands, as well as a higher presence of fast-growing deciduous species such as alder and aspen. This area features several delineated clusters of high-intensity labour input, indicating localised concentrations of forestry operations. Northern Latvia exhibits relatively high levels of forestry labour input, coinciding with frequent mature and over-mature stands and a greater presence of coniferous species, namely—spruce and pine. A contiguous band of elevated labour intensity is observed traversing the central region, extending through parts of Latvia. In the coastal region of Latvia, forestry labour intensity is higher compared to adjacent inland territories, paralleling significant mature forest stands and local dominance of pine. Overall, the data indicate that the most labour-intensive forestry activities in Latvia are concentrated in coastal areas of the west and in the central region, characterised by numerous small spatial clusters with high labour input levels.

6. Discussion

6.1. Farm Size and Labour Intensity in Agriculture

The research results consistently suggest that larger farms systematically require fewer labour hours per unit of output, whether measured per hectare or per animal, than their smaller counterparts. Several inter-related factors contribute to this pattern. Larger farms typically possess greater capacity to invest in mechanisation, automation, and optimised infrastructure, thereby reducing reliance on manual labour [21].

Larger holdings often adopt more streamlined workflows, whereas micro and small farms frequently rely on diverse, low-tech, and labour-intensive methods [53]. As a result, micro farms exhibit labour input levels at least three times higher per unit than large farms, reflecting both technological and organisational limitations.

Fixed labour requirements associated with supervision, maintenance, and animal care do not scale linearly with production size. Larger herds or field areas do not necessarily entail proportional increases in labour demand, as evidenced by markedly lower labour hours per animal on larger livestock farms in the results. This nonlinear scaling of labour inputs aligns with previous empirical findings [50,52], reinforcing the interpretation that increased farm size yields significant labour efficiencies.

The findings also support international evidence suggesting that while land productivity may plateau or decline with increasing size, labour productivity typically rises with larger scales of agricultural production [39]. As scale increases, management and operational efficiencies tend to favour labour productivity gains. In contrast, land productivity may stagnate or even decline due to less intensive land use, diminished attention to marginal plots, or reliance on monocultures.

6.2. Complexity of Production as Key Driver of Labour Demand

Although labour intensity declines with increasing farm size across all sectors, this effect is more pronounced in labour-intensive systems. The analysis thus challenges the common assumption that small farms are intrinsically labour-intensive due solely to their size. Instead, the results of this study highlight that production complexity is a more accurate determinant of labour demand.

Micro and small farms growing vegetables or managing grazing livestock require disproportionately high labour per hectare or animal, indicating that the labour burden is elevated not because of their small scale, but due to the complexity of the agricultural systems involved. Highly specialised and high-value systems, such as vegetable cultivation and perennial plantations, consistently require greater labour input due to their complex, labour-intensive tasks, as demonstrated by some earlier studies [2,55]. In contrast, extensive systems such as fallow land and grasslands are considerably less labour-intensive, and this is likely not due to scale but to their low management complexity.

High-value and management-intensive systems involve manual tasks that are difficult to mechanise, yet mechanisation and standardisation play a critical role in reducing labour input. Arable crops benefit from economies of scale and high mechanisation potential, leading to reduced labour requirements on larger farms, as confirmed by the results. Infrastructure and system constraints explain the labour intensity of small-scale livestock operations. For instance, micro farms raising dairy cows or pigs face higher labour requirements, likely due to the lack of automation and limited access to specialised labour-saving equipment.

Substantial variation across production types further reinforces the role of production complexity. Perennial crops, vegetables, and livestock systems exhibit significant disparities in labour inputs across size classes, whereas energy crops, grasslands, and fallow land show uniformly low labour needs. Thus, variation in labour intensity is explained by the activities involved rather than by the size of operations, suggesting that labour requirements are more closely tied to production type than to scale.

These research results align with broader research indicating that the overall structure of land use and the specific mix of activities, rather than farm size alone, are central determinants of employment creation, rural livelihoods, and the trajectory of structural transformation in rural areas [92].

6.3. Silvicultural Regimes Drive Episodic Variation in Forestry Labour Input

The results indicate that labour demand in forestry is more closely associated with the complexity of specific operations than with the category of tree species. Moreover, labour demand in forestry appears to be episodic, with pronounced peaks during establishment and tending phases, and a significant reduction in direct employment needs as mechanisation in logging increases. The most labour-intensive operations, such as underbrush tending, young stand tending, and general tending, required 23 to 25 h per hectare across nearly all tree species. Their uniformity suggests that labour demand in these phases is governed by the intrinsic complexity of managing early stand development, which is relatively unaffected by species differences. Similarly, planting and soil preparation require high labour input for most species, but are absent for aspen and grey alder, indicating differences in silvicultural regimes rather than species.

The analysis of equipment productivity reveals how operational complexity shapes indirect labour requirements. Harvester and forwarder capacities are significantly higher in main and other types of felling than in maintenance felling, indicating that using equipment to save labour works best in large, less fragmented operations. In contrast, chainsaw productivity remains consistently low across all logging contexts, highlighting both the persistence of manual labour in certain operations and its limited scalability. The high labour intensity associated with timber lorry operations, irrespective of felling method, is supported by a study indicating that log transport by truck incurs relatively high labour costs [63].

To make the best use of the existing forestry labour force and attract new labour, the government should introduce a series of targeted measures to support the labour-intensive stages of forest management. This set of measures may include training and certification programmes to enhance workforce skills, and a grant for small, private, or fragmented forest owners for whom mechanisation is not a viable option. An alternative could be to strengthen cooperation in the forest sector. Moreover, investments in rural infrastructure and transport can lower indirect labour costs and increase productivity. Finally, an incentive system for sustainable practices that, on the one hand, supports ecological goals and, on the other hand, creates jobs, would not only help maintain employment levels in the countryside but also contribute to the development of healthier, more resilient forests in the future.

6.4. Land Use Legacies and Spatial Patterns of Labour Intensity

Historical land use legacies and prevailing biophysical constraints strongly shape labour input patterns. The spatial distribution of labour demand closely mirrors the patterns of forested versus agricultural land cover [93], which continues to structure regional labour allocation. In Latvia, a clear spatial inverse relationship exists between forest and agricultural land use, which is reflected in the opposing labour input intensities across the two sectors. Regions characterised by extensive forest cover show higher forestry labour demand, whereas areas dominated by agricultural land exhibit more labour-intensive farming activities. These patterns suggest that the nature and intensity of land use, determined by both ecological suitability and historical land management regimes, remain key drivers of regional labour demand variation.

In and around the metropolitan region of the capital city, Riga, both agricultural and forestry labour inputs are markedly low. This pattern aligns with the urban nature of the area, characterised by a high population density, a dominant non-agricultural employment sector, and limited land availability for primary production. As a result, labour input patterns in the capital region deviate from those observed elsewhere in the country, further underscoring the influence of land use practices and socio-economic context on labour distribution.

Notably, the findings indicate that peripheral regions, such as the eastern regions of Latvia, can exhibit levels of labour intensity that surpass those of more central economic zones. This contradicts the expectation that labour intensity scales uniformly with production size and suggests instead that diversified farming practices, lower degrees of mechanisation, or specific market and environmental constraints may generate heightened labour demands.

These findings carry important implications for regional development policies. Recognising the spatial differentiation of labour intensity can inform more nuanced strategies for rural support, investment, and workforce development. Policies aimed at promoting regional equity should account for the structural constraints and opportunities embedded in local land use systems. For instance, in forest-dominated regions, policy measures might focus on sustainable forest management, ecosystem services, and value-added processing to stabilise employment. Conversely, in agriculturally intensive areas, interventions could prioritise technological innovation, farm diversification, and training initiatives to enhance productivity while maintaining rural livelihoods. Furthermore, in the capital region and other urbanised zones, the integration of peri-urban agriculture and green infrastructure could help reconnect urban economies with surrounding rural areas. Overall, aligning regional development frameworks with these spatial labour dynamics is essential for fostering balanced territorial development, sustainable resource use, and social cohesion across Latvia.

6.5. Limitations, Policy Improvements, and Opportunities for Future Research

Several limitations related to data and methodology warrant consideration. Parcel-level data often represent mixed-specialisation farms, hindering the precise attribution of labour inputs to specific products. While the analysis emphasises specialised farms and incorporates LLKC standard activity data for calibration, some heterogeneity persists. The land areas of micro-farms are frequently underreported, although partially corrected using national statistics. In forestry, the long rotation cycle and uneven temporal distribution of labour complicate annual comparisons with agriculture. Estimates rely on assumptions about the share of land undergoing particular forestry activities. There is a lack of research, evidence-based results, and statistics on the extent of specific forestry activities. It is not easy to determine precisely in which territories, and over what area, various activities are carried out. There is no precise calculation methodology to determine these volumes. The scientific literature indicates general conditions under which forestry activities are carried out (forest types, soils, and other general conditions).

This study demonstrates how scale, production complexity, land use, and technological interactions impact labour demand in agriculture and forestry. The results point to policy and research priorities. Future research should focus on addressing the structural and data-related gaps revealed in this study. Long-term evaluation of mechanisation uptake, particularly among smaller farms, is necessary to understand how capital constraints and access to technology shape labour dynamics over time. Comparative analysis across production systems would help identify tasks and activities that remain resistant to technological improvements and thus continue to demand significant manual labour. In parallel, integrating labour efficiency assessments with environmental and social sustainability frameworks could clarify trade-offs between productivity gains, ecological goals, and rural employment opportunities. More advanced spatial analyses that combine information on land use, mechanisation potential, and demographic factors would also enhance understanding of regional disparities in rural labour intensity. In forestry, further research should investigate how silvicultural practices and climate adaptation strategies influence episodic patterns of labour demand. Finally, better parcel-level labour data and an end to the underreporting of micro-farms, when combined with the use of dynamic models to assess efficiency factors over farm size and production types, would go a long way in refining the evidence base for rural employment and policy design. The elaboration of tangible frameworks to improve data collection methods and strengthen institutional cooperation among statistical agencies, state institutions, research institutions, and agricultural organisations is equally important. The establishment of common data standards, the facilitation of reporting for small and diversified farms, and the encouragement of cross-sectoral data integration would significantly contribute to enhancing the accuracy, comparability, and policy relevance of research outcomes. Many policymakers are paying substantial attention to facilitating employment in rural areas. Moreover, one of the tools used is differentiating support payments based on the size of the farms [26,94]. Research shows that farm size only partially explains labour demand intensity. Furthermore, specialisation plays an even more critical role in creating employment opportunities than size. From this perspective, for employment facilitation, the primary focus of rural employment facilitation policies should not be on size, but specialisation.

7. Conclusions

This study quantified typical labour inputs across agricultural and forestry land uses in Latvia, calculating labour hours per hectare or animal for a wide range of production types, farm sizes, tree species, and tasks. Labour input estimates were derived by calculating average labour hours for specialised farms, further adjusted through LLKC Gross Margins estimates, and complemented by expert evaluations or calculated using equipment productivity indicators. The results demonstrate that labour intensity consistently declines with increasing farm size, yet differences between production types are even more pronounced. The highest labour intensity occurs in perennial plantations, as well as in vegetable and potato cultivation. Among livestock, dairy cows and pigs required exceptionally high labour inputs, with pigs showing one of the most pronounced disparities between micro and large scales of production. Energy crops and grass-based land uses require the lowest labour inputs, though still show increased labour demands on smaller farms. In forestry, tending and establishment operations (planting, soil preparation) dominate labour demand, while mechanised harvesting shows pronounced productivity advantages for felling. The spatial distribution of labour demand closely reflects the longstanding division between forested and agricultural areas, although labour intensity only partly reflects land use.

The empirical evidence generated by this study has several implications for rural development policy, particularly concerning the fostering of employment in the agricultural and forestry sectors. While supporting smaller farms can maximise employment per unit, our findings emphasise that prioritising labour-intensive and high-value crops and activities, such as perennial plantations, vegetable and potato cultivation, dairy farming, and pig farming, is equally, if not more, critical for driving rural job creation and rising incomes. These sectors consistently show the highest labour demands, and are thus strategic targets for employment-focused support. Conversely, energy crops and grass-based systems, while extensive, contribute less to labour demand and should not be prioritised solely for job creation. In forestry, labour demand is concentrated in tending and regeneration operations, suggesting that policy attention should shift to silvicultural workforce development for employment generation. International and European experience demonstrates that rural development policies, such as the CAP, can positively influence farm employment. However, the impact depends strongly on the type of subsidy and its targeting. Investments in labour-intensive sectors, value-added activities, and diversification are more likely to sustain and create jobs than broad-based support based solely on farm size.

Labour dynamics in Latvia’s agriculture and forestry sectors reflect enduring interactions among historical land use legacies, biophysical constraints, and differential levels of mechanisation. Recognising these spatial and structural asymmetries is essential for designing effective rural development policies that promote balanced territorial growth. In agriculture, future priorities should include improving the quality and granularity of labour data, facilitating the adoption of technology among smaller farms, and supporting diversification strategies that sustain employment while enhancing productivity. In forestry, targeted interventions—such as workforce training and certification, financial support for small and fragmented forest owners, and the promotion of cooperative arrangements—could strengthen labour capacity in the most labour-intensive management stages. Broader investments in rural infrastructure and transport would further reduce indirect labour costs and enhance overall efficiency. Finally, the implementation of incentive schemes that align sustainable land management with rural employment creation would help reconcile ecological objectives with socio-economic development, contributing to the long-term resilience and sustainability of Latvia’s primary production landscapes.