Farm-level Data Research Articles

Improved cattle farm classification: leveraging machine learning and linked national datasets

While many countries have registries of livestock farms, it can be challenging to obtain information on their primary production type. For example, for Swiss farms registered as keeping cattle, a distinction can only be made between milk-producing and non-milk-producing farms. The Swiss cattle industry consists of beef and dairy farms, with a strong predominance of small to medium-sized farms. A better differentiation of cattle production types would be beneficial for the planning and evaluation of surveillance programmes for cattle diseases and for the benchmarking antibiotic consumption. The aim of this study was to outline cattle production types of interest and to allow the classification of Swiss cattle farms according to production type in order to optimize surveillance. We collaborated with experts to define the five primary cattle production types: calf fattening, dairy cattle, cattle fattening, rearing cattle and suckler cows. In collaboration with the cantonal Veterinary Offices, we collected production types from 618 reference farms across 14 cantons and defined a total of 24 features by combining information from three national databases. Using farm-level data on milk production, age and sex distribution, cattle breeds, calving, births, slaughter, animal movements and antibiotic use, we trained three different machine learning models capable of classifying the five production types. Among these models, the Random Forest model demonstrated the highest level of performance, achieving an accuracy of 0.914 (95% CI: 0.890, 0.938) and an F1-Score of 0.879 (95% CI: 0.841, 0.913). In conclusion, together with experts, we have outlined five primary production types on cattle farms in Switzerland and developed a model that allows a reproducible, year-to-year classification of cattle farms using national datasets. Our flexible methodology could be adapted to other countries and datasets, enabling veterinary authorities to conduct more efficient and targeted disease surveillance in the future.

Evaluating Producer Welfare Benefits of Whole-Farm Revenue Insurance

Agricultural insurance is by far the most popular risk management tool used in Iran. Despite many years of experience, Iran’s current insurance policy has not managed to protect all producers in the sector. The basic principle of whole-farm insurance consists of pooling all the insurable risks of a farm into a single policy and overcoming most of the major impediments to existing policies. This study aimed to evaluate the benefits of whole-farm insurance (WFI) in Zanjan province of Iran. This study employed historical farm-level and county-level data from 1982 to 2021 to estimate yield and price density functions and predict future values. Parametric and non-parametric approaches were utilized to calculate farmers’ expected compensation and guaranteed and simulated revenues. The premium rates were then calculated using the PQH simulation and Cholesky decomposition and compared under three scenarios: the single-crop, double-crop, and triple-crop options. Finally, farmers’ welfare benefits were compared under the three scenarios with the no-insurance case. The results demonstrate that WFI provides lower loss ratios compared to yield insurance and crop-specific insurance. Furthermore, producer welfare can be improved when they insure at least one crop compared to no-insurance. For example, the welfare benefits of insuring wheat, barley, alfalfa, wheat–barley, wheat–alfalfa, barley–alfalfa, and barley–alfalfa in terms of cost reduction to producers at 75% coverage are 8.8, 1.8, 2.9, 1.2, 0.9, and 1.8, respectively. Therefore, we recommend that the Iranian Agricultural Insurance Fund adopts WFI as a new risk management tool. This policy has the potential to decrease insurance premiums and administrative costs while improving the certainty equivalents and benefits to farmers through crop insurance.

Comparing market instruments for forest conservation in Brazil using farm-level census data

Abstract Almost 40 per cent of Brazil's native vegetation is located on over five million private properties. This study assesses the potential of agricultural land taxes and tradable forest certificates for conserving Brazil's fragmented native vegetation across commercial farms, using micro census data from 2006 and 2017. We explore the variability of optimal tax rates and market prices for forest certificates, revealing a supply-demand imbalance in the Amazon and high sensitivity of conservation outcomes to changes in farmland opportunity costs, especially in productive areas. Despite a more positively skewed distribution of opportunity costs by 2017, market outcomes remained unaffected. Notably, expanding the market to include the Amazon's agricultural frontier microregions could achieve 45 per cent of the conservation target. Our analysis underscores the interplay between market-based conservation mechanisms and regional agricultural economics, highlighting the need for tailored approaches to optimize conservation efforts.

Effects of Rising Rural Labor Prices on Land Use Pattern: Evidence from Grain Production in China

As rural labor prices have risen constantly over the last two decades, Chinese grain production that relies heavily on manual labor has been subjected to considerable challenges and has experienced profound changes in land use patterns. Using a fixed effect model and translog profit function model, this paper investigates the effects of rising rural labor prices on land use patterns in Chinese grain production. The empirical results from 2004–2022 province-level panel data showed that the rising rural labor prices provided significant incentives to adjust the land use patterns of three staple grain crops. The increase in labor prices had a negative effect on the share of the planting area of rice and maize, while wheat experienced a substantial increase in its proportion of planting area in the context of rising labor prices. A further mechanism test based on 2004–2012 farm-level panel data revealed that the factor substitutions, especially labor substitution with fertilizer and machinery, were a significant contributor to the changes in land use patterns. In the spatial–temporal analysis, changes in land use patterns were found to be more pronounced in regions with more rolling terrain conditions but remained relatively stable across years. These findings highlight the importance of the development and promotion of labor-saving technologies in grain production, especially enhanced-efficiency fertilizer and small-sized agricultural machinery. In addition, agricultural subsidies targeted at farmers in hilly and mountainous regions might be a good way to mitigate potential land abandonment in the context of rising labor prices.

Integrated assessment of farm-level mitigation measures for gaseous emissions

ContextSome gaseous emissions continue to pose a serious threat to human health and the environment locally, regionally and globally. This has resulted in several studies advocating for the implementation of mitigation measures to reduce the emissions of harmful gases. ObjectiveWhile the vast majority of studies focus on a single type of gas, much less attention has been paid to the complementary or conflicting effects of mitigation measures across multiple harmful gaseous emissions dimensions. MethodsTo address this research gap, this study uses Irish farm-level data to assess the holistic costs and benefits of a suite of mitigation measures that have the potential to abate greenhouse gases, ammonia or both. A cost-benefit analysis framework is employed to assess the impact of the mitigation measures across five different farm system types. Results and conclusionsResults indicate that the relative effectiveness of the mitigation measure varies depending on the gaseous emission dimension being examined. SignificanceAnalyses that fail to account for such synergistic and antagonistic relationship impacts may lead to flawed policy decisions.

Compliance with food safety measures and their economic impact on smallholder dairy farmers: Evidence from the Indo-Gangetic plains of India

This study examines the adoption and economic impact of compliance with food safety measures (FSM) using cross-sectional farm-level data from three key states in the Indo-Gangetic Plains, Bihar, Punjab, and Uttar Pradesh, in 2023. The majority of the farmers were operating on marginal and small plots. A Food Safety Index (FSI) derived from 71 distinct practices was used as a quantitative measure of adoption of compliance with FSM. The study assigns 0.60, 0.23, and 0.17 wt to microbiological, physical, and chemical safety measures, respectively, to derive FSI. Determinants of compliance with FSM were analysed using multiple linear regression and ordered logistic model, while Generalized propensity score (GPS) was used to assess the economic impact of compliance with FSM on farm-level performance indicators. The findings indicate that farmers are adopting a moderate level (0.48–0.58) of the FSI. Various socio-economic and demographic factors, such as education, income, marketing channel, training exposure, awareness level, and infrastructure, significantly influence the adoption of FSM. The impact assessment reveals that compliance with FSM correlates with milk prices, productivity, and profitability. However, a lower level of compliance may not yield significant improvements in milk productivity. The study suggests incentivization through pricing reforms, improving infrastructure, strengthening formal marketing channels, and raising awareness through training.

A Farming System Approach to Exploring Drivers of Food Insecurity Among Farm Households in Developing Countries: The Case Study of Mozambique

Farm households, especially in developing countries, are among the most vulnerable to food insecurity and poverty. A farming system approach is largely recognized to play an important role in supporting strategies to improve food security and alleviate poverty among farm households. This paper aims at exploring the drivers of food insecurity among farm households in developing countries, using a farming system approach. It also aims to explore farm households’ perceptions regarding the causes of food shortages and the adopted coping strategies, and how these vary across farming systems. This analysis was based on data extracted from an agricultural census, which were analyzed through scatter plots and plot means with 95% confidence intervals. The results indicate that the factors analyzed (level of specialization, market integration, use of yield-raising and labor-saving inputs, farm size, population density, and rainfall) are important drivers explaining food insecurity among farm households, highlighting important differences across farming systems. The analysis also showed that farm households’ perceptions regarding the causes of food shortages are related with the abovementioned drivers of food insecurity. Our findings suggest that less specialized farming systems with better access to markets, using yield-raising and/or labor-saving inputs, are more likely to be food secure. The farming system approach proved to be a useful approach to explore food insecurity drivers and coping strategies as well as to support policymaking. Moreover, the use of an agricultural census data provides an easily attainable tool to access and analyze farm-level data for policy analysis and to explore and understand a range of issues affecting farm households in developing countries.

The adoption of cotton combine services and farm technical efficiency: evidence from Kazakhstan and Uzbekistan

PurposeThis study examines the adoption of cotton combine services and its impact on farm technical efficiency in Kazakhstan and Uzbekistan. The research aims to determine whether mechanisation influences productivity and economic output at the farm level.Design/methodology/approachUsing farm-level data from 511 cotton growers in Kazakhstan and Uzbekistan collected in 2019, this study employs stochastic frontier analysis to measure potential output and technical inefficiency among cotton farmers. The analysis includes a translog functional form to account for the use of cotton combine services and other farming variables.FindingsThe findings indicate that while mechanisation through cotton combines can potentially increase technical efficiency by optimising the harvesting process, the benefits are not uniformly experienced across all farms. Variations in farm characteristics, such as labour availability and existing agricultural practices, influence the efficiency of technology adoption. Institutional factors and historical legacies also play a significant role in the adoption and impact of mechanisation.Research limitations/implicationsThe study is based on cross-sectional data from 2019, and the findings may not capture longer-term trends or recent developments in mechanisation policies in the study countries.Originality/valueThis research provides a nuanced understanding of the conditions under which cotton combine services enhance or hinder technical efficiency. It highlights the necessity for carefully tailored policies for mechanisation, especially in Uzbekistan, where rural labour is abundant and predominantly female. The study contributes to the broader discourse on agricultural mechanisation in developing countries by focusing on the specific context of Central Asia.

Selected farm-level crop protection practices in Europe and Argentina: Opportunities for moving toward sustainable use of pesticides

Extensive use of plant protection products (PPP) in the last decades contributes to negative impacts on ecosystems, animals and humans. For the strategies of PPP reduction and replacement of hazardous pesticides, farm-level data on agronomic management practices and crop protection applications are crucial. In this study, we strategically collected data for the 2021 season at the SPRINT project case study sites (CSS) in 10 European countries and Argentina, on perennial, arable and vegetable crops. Data collection included strategically selected farm and field data, pesticide records and farming practices. Results involved more than 1700 recorded PPP applications across various crops with more than 170 different active substances from PPP in organic, integrated pest management and conventional farming practices. We explored differences in application patterns (fungicides, insecticides, herbicides and non-PPP, e.g. adjuvants, growth regulators, and fertilizers) between and within crops, countries and farming systems and calculated the costs of PPP use. The pesticide dosages applied during the crop season varied up to a factor of 20 around recommended doses. Regarding the costs of PPPs use perennial crops had the highest costs per ha crop production area. Finally, we analysed the active substances applied in different farming systems in terms of their hazard statements. Our results shed light on how PPPs are used across different crop and farming types and will help elucidate how pesticide application patterns can be changed in the future. Finally, we highlighted non-PPP use practices which help to reduce dependency on PPP use. This might be used to support decision-making and policies within agricultural advisory/support systems.

Federating ‘Aquaculture 4.0’ for data-driven social and environmental sustainability

Aquaculture is undergoing a revolution of data-driven innovation. The rapid uptake of digital technologies in the sector contributes ever larger amounts of data that hold potential for improving decision making for aquatic food system sustainability. Realising the potential of digital tools and new data streams is, however, not inevitable. The current ecosystem of global and production-level digital aquaculture technologies and platforms is highly fragmented with a strong focus on private farm-level data for improving production efficiency, and limited publicly available data that can be used for social and environmental sustainability. Three scenarios for the future development of the aquaculture data ecosystem are possible (1) continued fragmentation; (2) centralisation, and (3) coordination in a federated ecosystem. Recognising a federated ecosystem as having the most potential for contributing to food system sustainability we provide three priority areas for improving data coverage, sharing and integration between and among farm-level and global platforms.

Modelling High Resolution Agricultural Nitrogen Budgets: A Case Study for Germany

Water pollution with nitrogen (N) from agriculture constitutes a persisting environmental problem in intensive farming regions worldwide. Understanding the spatio-temporal interconnection between agricultural N emissions and N inputs to water bodies is key to evaluating and improving existing mitigation policies. Nitrogen flux models are an indispensable tool for addressing these complex research questions in the land use–water nexus, requiring adequate data on agricultural N surpluses. However, high-resolution farm management data are not readily available to the scientific community. We develop a municipality-level agricultural N budget model for Germany based on farm-level administration data from the Integrated Administration and Control System (IACS) and regional expert knowledge. We estimate a total agricultural N surplus of 58 kg N ha−1 of utilised agricultural area as the three-year average for 2014–2016. About 90% of municipalities exhibit N surpluses between 21 and 99 kg N ha−1. Evaluation with collected farm accountancy data revealed a good fit of the modelled (with observed) mineral N quantities applied. Our results highlight the potential of farm-level data for N flux models. Due to the ubiquitous reporting of land use and farming structures in the IACS, our approach can be adapted in other countries of the EU to serve as a harmonised backbone of monitoring and policy impact assessments.

Seroprevalence and associated risk factors for Neospora caninum infection in dairy cattle in South Africa

Bovine neosporosis is a widespread parasitic disease associated with significant economic losses. Its effects on the reproductive performance of cows have resulted in losses that run into the hundreds of millions of US dollars in dairy industries in various countries (Reichel et al., Int J Parasitol 43:133–142, 2013). Due to outdated and scant information on the occurrence of Neospora caninum infection in South Africa, the study aimed to determine the seroprevalence and risk factors associated with infection in dairy cattle in South Africa. A total of 1401 blood samples were randomly collected from cattle on 48 dairy farms in seven of the nine provinces in South Africa. A close-ended questionnaire was used in a cross-sectional study to obtain farm-level and animal-level data. Serological testing was done using a commercial IDvet Screen® Neospora caninum Indirect ELISA. An overall seroprevalence, adjusted for test sensitivity and specificity, of 2.3% (95% CI, 1.3–4.1) was detected and 48% (23/48) of sampled farms had at least one animal testing positive. The highest seroprevalence of N. caninum was in the KwaZulu-Natal province with 7.5% (95% CI, 3.8–14.3), and the lowest in Western Cape with 0.1% (95% CI, 0–1.2). The highest within-farm seroprevalence of 25% was detected on a farm in the North West Province. In a multivariable logistic regression model, the odds of N. caninum seropositivity were higher in Holstein–Friesian cattle when compared to other breeds. Good hygiene was identified as a protective factor. Cattle left out on pasture had increased odds of testing positive for N. caninum compared to those that were penned. The odds of testing seropositive for N. caninum was higher on farms that practised segregation of cattle into different age groups. The purchase of replacement animals was a significant risk factor, as open herds had increased odds of N. caninum seropositivity. Cattle on farms that did not have a specific calving location were more likely to be seropositive. This is the first such study in South Africa and shows that N. caninum is widely distributed in the country at a low seroprevalence, but it may be a cause of concern on certain farms.

An accuracy assessment of satellite-based cotton yield estimation using panel data regression: a case study of Uzbekistan

AbstractSatellite-based yield estimation is crucial for spotting potential deficits in crop yields at an early stage, supports farm-level decision-making and early-warning systems, and is a prerequisite for index insurance markets. Precise satellite-based yield estimations are already established for important food crops like maize and wheat. However, for many cash crops like cotton, the accuracy of satellite-based yield estimation has not been scientifically tested, mainly due to their low biomass-yield correlation. This paper contributes to exploring the suitability of multiple vegetation indices based on Sentinel-2 imagery to estimate farm-level yields for one of these cash crops, cotton. We estimated various vegetation indices conjugated with the cotton crop phenology for the selected study area and compared them with farm-level panel data (n = 232) for the years 2016–2018 obtained from a statistical agency in Uzbekistan. Overall, we tested the suitability of the Normalized Difference Vegetation Index, the Modified Soil Adjusted Vegetation Index 2, the Red-Edge Chlorophyll Index and the Normalized Difference Red-Edge Index (NDRE). Among these indices, the NDRE index shows the highest fit with the actual cotton yield data (R2up to 0.96, adj R2 = 0.95 and RMSE = 0.21). These results indicate that the NDRE index is a powerful indicator for determining cotton yields. Based on this approach, farmers can monitor crop growth, which in turn avoids crop loss and thereby increases productivity. This research highlights that a satellite-based estimate of crop production can provide a unique perspective which should improve the possibility of identifying management priorities to improve agriculture productivity and mitigate climate impacts.

Participation in biodiversity schemes and environmental performance: overall farm-level impact and spillover effects on non-enrolled land

Abstract We evaluate how the share of farmland enrolled in agri-environmental schemes affects the biodiversity friendliness of management practices both on the overall farmland as well as on the enrolled and non-enrolled plots separately. To this end, we prepare a unique dataset for Switzerland that links farm-level accountancy data to plot-level data on management practices and their impact on organismal biodiversity. Our estimates allow us to calculate bounds for potential spillovers on non-enrolled farmland. We find that these are positive but small in magnitude. The effect on the overall farmland is also positive but again rather small.

Landscape features support natural pest control and farm income when pesticide application is reduced

Future trajectories of agricultural productivity need to incorporate environmental targets, including the reduction of pesticides use. Landscape features supporting natural pest control (LF-NPC) offer a nature-based solution that can serve as a partial substitute for synthetic pesticides, thereby supporting future productivity levels. Here, we introduce a novel approach to quantify the contribution of LF-NPC to agricultural yields and its associated economic value to crop production in a broad-scale context. Using the European Union as case study, we combine granular farm-level data, a spatially explicit map of LF-NPC potential, and a regional agro-economic supply and market model. The results reveal that farms located in areas characterized by higher LF-NPC potential experience lower productivity losses in a context of reduced synthetic pesticides use. Our analysis suggests that LF-NPC reduces yield gaps on average by four percentage points, and increases income by a similar magnitude. These results highlight the significance of LF-NPC for agricultural production and income, and provide a valuable reference point for farmers and policymakers aiming to successfully invest in landscape features to achieve pesticides reduction targets.

Assessing the Environmental Performance of Agricultural Production Using a Parametric Approach: An Application for Crop Producers in Ukraine

ABSTRACT Agriculture is characterized not only by the production of desirable outputs but also by generating environmental pressures. This article emphasizes the necessity of accounting for “bad” outputs when assessing the performance of agricultural producers. It provides a first attempt to empirically evaluate the environmental performance of crop producers in Ukraine employing farm-level panel data. Our results suggest that the average environmental efficiency for crop producers in Ukraine was 0.84 over the period 2017–2019, meaning a reasonable scope exists for farmers to improve their environmental performance and contribute to meeting carbon reduction targets, as well as help achieve a green postwar economic recovery.

Changes in productive, socio-economic, and environmental performance of field crop farming in the Argentine Pampas, 2007–2018

This study fills important gaps in research by analyzing the evolution over time of productive, environmental, and socio-economic aspects of agricultural production in the Argentine Pampas, utilizing farm-level data. A longitudinal study was conducted to examine the changes that occurred in farming systems during the period 2007–2018. The study evaluated the changes in 30 farms, examining modifications in the structure and management of each farm, as well as in productive, economic, and environmental performance. Canonical correlation analysis was used to relate the changes that occurred in performance to farms' characteristics at the beginning of the study period. The results indicated that, among the farms that stayed in business, there were no significant changes in land tenure and the amount of labor employed. There was a significant increase in the average age of farmers by 7 years, along with a decrease in the percentage of farmers expecting growth, dropping from 70% to 42% over the period. Canonical correlation analysis revealed that smaller farms, with a higher number of workers at the beginning of the period, were more likely to expand their farming area during the analysis period. The findings also indicate a substantial turnover of producers, with leaving farms being succeeded by larger-scale operations. The yields of the main crops and the direct production costs increased by 16% and 48% respectively, during the period. The environmental indicators for the main crops present a mixed picture: soil organic carbon input increased by 12%, while environmental impact quotient decreased on average, by 6% for cereals but increased by 40% for soybeans, and nutrient imbalances rose. The significance of this study resides in its application of a comprehensive approach to analyze the transformation of farming systems over time.

Impact of participatory guarantee systems on sustainability outcomes: the case of vegetable farming in Vietnam

ABSTRACT Sustainable food systems require mechanisms that assure consumers about the sustainability of agricultural production. Building on the existing literature on the impact of sustainability standards, this study is the first to assess the effectiveness of participatory guarantee systems (PGS) for the certification of organic produce. The study uses representative farm-level data on local vegetable value chains in northern Vietnam and uses a broad set of sustainability outcomes as well as counterfactual analysis, including systematic robustness checks. The results show that PGS significantly improves farm profitability (+117%), agroecology performance (+40%), and gives farmers more choice of sales channels (+23%). However, PGS had no significant effect on returns to labour and reduced the average crop yield. Capacity development on nursery practices, transplanting of healthy seedlings rather than direct seeding, reduced tillage, and collective crop planning and management are some of the innovations that can counter adverse effects on crop yields, increase soil health as well as improve returns to labour, and thus attract more youth to farming. Overall, the study shows that organic PGS can make vegetable production more economically viable and more agroecologically sustainable.

Prevalence and antimicrobial resistance profiles of mastitis causing bacteria isolated from dairy goats in Mukurweini Sub-County, Nyeri County, Kenya.

Ruminant mastitis continues to be a cause of economic losses in the dairy industry and remains a major public health hazard globally. This cross-sectional study was carried out in Mukurweini Sub-County of Nyeri County, Kenya, to investigate the prevalence of bacteria causing mastitis, risk factors associated with goat mastitis and the antibiotic resistance profiles of bacteria isolated from the goat milk. Farm level data on risk factors for mastitis was obtained from 56 farmers using a semi structured questionnaire. A total of 189 goat milk samples were collected. The goat's udder was observed for signs of clinical mastitis and the California Mastitis Test (CMT) used to test the milk for sub-clinical mastitis. All samples were then cultured for morphological identification of bacteria and strain typing by Matrix Assisted Laser Desorption/Ionization (MALDI)-Time of Flight (ToF) technique. Antimicrobial susceptibility of the isolated Staphylococcus aureus, coagulase-negative Staphylococcus (CoNS), Escherichia coli, Klebsiella oxytoca, Pseudomonas spp., Enterobacter spp., Proteus vulgaris and Escherichia vulneris to eight commonly used antibiotics was done by the disc diffusion method and validated by determining the presence of antibiotic resistance genes (mecA and blaTEM) using polymerase chain reaction method. The prevalence of clinical mastitis was 1.1% (2/189) while that of sub-clinical mastitis was 84.7% (160/189). Higher (p<0.05) prevalence of mastitis was observed in goats whose houses were cleaned fortnightly and in cases where farmers used same towel to dry different does' udders during the milking process. Thirteen different bacterial species were isolated from the milk samples and identified by MALDI-ToF, and these included S. aureus (22.0%), CoNS (20.3%), E. coli (18.1%), Pseudomonas spp. (14.3%), Enterobacter spp. (10.4%), K. oxytoca (6.0%), E. vulneris (1.7%), P. vulgaris (1.7%), Raoutella ornithinolytica (1.7%), Stenotrophomonas maltophilia (1.1%), Pantoea agglomerans (1.1%), Serratia marcescens (1.1%) and Cedeceas spp. (0.6%). One hundred pathogenic bacterial isolates were randomly selected and tested for antibiotic sensitivity to eight antibiotics out of which S. aureus were 97.5% resistant to Oxacillin and 100% sensitive to Ciprofloxacin. The CoNSs were 100% resistant to Oxacillin and 100% sensitive to Ciprofloxacin. E. coli were 93.9% resistant to Oxacillin, 69.7% sensitive to Ciprofloxacin and 87.9% sensitive to both Amoxicillin/Clavulanic acid and Meropenem. The antimicrobial resistant genes detected in S. aureus and E. coli were mecA [66.7%, 0%], and blaTEM [20% and 78.3%], respectively. In conclusion, the study showed that most of the does were affected by subclinical mastitis with the main causative bacteria being Staphylococci spp. and coliforms. Farmers need to be trained on improved control of mastitis by adoption of good milking practices and use of CMT kit for early detection of mastitis. Occurrence of multidrug resistance by key mastitis causing pathogens was shown to be prevalent and therefore there is need for development of intervention strategies.

Empowering women in sustainable agriculture

The agricultural and rural development policy seeks to facilitate the transition towards environmentally sustainable and climate-neutral agricultural practices, with a focus on human capital, knowledge, and innovation. Gender equality can play a significant role in promoting environmentally sustainable practices in the agricultural sector, particularly through the adoption and implementation of agri-environment-climate schemes (AECS) in the context of farm, agricultural, and rural development. We examine the presence of gender bias in the adoption intensity of AECS by utilising farm-level data from Slovenia. We find that women on Slovenian farms engage in the adoption of AECS and receive subsidies, despite the presence of a gender gap in various agricultural factor endowment variables that typically favour men. The results of this study provide evidence in favour of promoting greater involvement and empowerment of women in the fields of green technology applications and green entrepreneurship, particularly with AECS practices.