Agricultural Sector Research Articles

Analisis Pemasaran Kelapa Sawit Rakyat Desa Perdomuan Nauli Kecamatan Kandis Kabupaten Siak Provinsi Riau

Indonesia, as an agricultural country, has an agricultural sector that contributes significantly to the national economy. Palm oil is the main commodity that plays an important role in increasing state income, especially in Perdomuan Nauli Village, Kandis District, Riau. This research aims to analyze palm oil marketing in the village using descriptive methods. Primary data was obtained through interviews and questionnaires, while secondary data came from literature and related institutions. The research location was selected through purposive sampling involving 35 respondents who used three main marketing channels: Farmers–Collectors–RAM–PKS, Farmers–Collectors–PKS, and Farmers–RAM–PKS. The research results show that the Farmer–RAM–PKS channel provides the highest profits for farmers with a margin of IDR 434.3/kg, followed by the Farmer–Collector–PKS channel with a profit of IDR 234.3/kg. The Farmer–Collector–RAM–PKS channel, even though it is the longest, still provides profits for farmers of IDR 139.3/kg. Each channel has its own advantages and disadvantages, depending on the facilities provided and marketing costs incurred. This research emphasizes the importance of evaluating and improving marketing channels to increase the efficiency and welfare of oil palm farmers in Perdomuan Nauli Village

Rural public investment and wage inequality under small‐scale agriculture modernization

Increasing rural public investment is crucial for sustainable rural development and can be an effective approach for reducing inequality. However, wage inequality has increased in some developing countries (e.g., China) after increasing fiscal support. This study explores how rural public investment influences skilled‐unskilled wage inequality by incorporating agricultural modernization in small‐scale agriculture. Agricultural modernization refers to introducing modern non‐agricultural intermediate inputs in agriculture. Owing to the small scale of agriculture, an intermediate sector, the agricultural producer service sector, is required to facilitate this process. We establish a three‐sector general equilibrium model with a two‐layer vertical production structure and investigate the effects of increased public investment in rural areas on the wage inequality between skilled and unskilled labour. The theoretical results show that while more rural public investment raises the wages of both skilled and unskilled labour, it also increases wage inequality. Overall, this study explains why inequality widens as rural public investment increases.

Climate Change and Heatwaves in India: Review of Impacts on Public Health, Livelihoods, and Policy

This paper presents a literature review studying the impacts of climate change and heatwaves on public health and livelihoods, with a focus on India. As global climate change accelerates, heatwaves are becoming more frequent and intense, necessitating new approaches to mitigate their effects on vulnerable populations, especially in urban areas. The review draws on recent studies (2021-2024) that explore the intersections of climate change, heatwaves, public health, livelihoods, policy interventions, and Heat Action Plans. The main findings reveal that heatwaves in India have significant public health impacts, such as increased mortality and morbidity, and disproportionately affect livelihoods, especially in agriculture and informal labor sectors. Vulnerable populations, including urban dwellers and informal workers, face heightened risks, further exacerbated by inadequate infrastructure and policy gaps. The purpose of this review is to synthesize current research to highlight these vulnerabilities and identify gaps in knowledge and policy response. A comprehensive, localized approach combining detailed risk assessments, nature-based solutions, technological innovations, and strengthened governance is essential for effective heatwave management in India. The findings underscore the urgent need for continued research focused on local spaces and comprehensive policy interventions to address the escalating risks posed by climate-induced heatwaves, setting the groundwork for future research and effective heatwave management strategies.

Exploring farmers’ intention to adopt marketing contracts: empirical insights using the TOE framework

AbstractThe European agricultural sector faces considerable exposure to price risk, which poses a threat to farmers’ income and long-term resilience. Marketing contracts have emerged as a potential risk mitigation strategy by enabling farmers to fix the selling price of their output. Despite their benefits, the adoption of marketing contracts among European farmers remains limited. To investigate the factors influencing farmers’ participation in MCs, this study applies the Technology-Organization-Environment (TOE) framework, marking its first application in this field of research. By analyzing data from an online survey of 84 Italian arable crop producers using a partial least squares structural equation model, this study investigates the factors influencing farmers’ participation in marketing contracts. The results demonstrate the effectiveness of the TOE model in examining farmers’ intentions to adopt marketing contracts. Specifically, the perceived usefulness of marketing contracts, their compatibility with farm characteristics, the positive attitude of farm owners towards these tools, and normative pressure from buyers all exhibit positive associations with farmers’ intention to adopt marketing contracts. These findings offer valuable insights for the development of targeted strategies aimed at promoting the widespread use of marketing contracts among European farmers.

PARADIGM DEVELOPMENT OF LAND RELATIONS AND HIGH-TECHNOLOGICAL LAND USE

The study substantiates that land resources are a strategic resource, the socio-economic development of society depends on the rationality and efficiency of their use. The economic content of the concept of «land relations» is generalized and it is revealed that they are a component of social relations, arise in the process of interaction of society with natural resources and regulate the norms of behavior of business entities of the agricultural sector of the economy within the framework of land legislation. The basis of land relations is the category of land ownership, therefore changes and development of organizational and legal forms of land ownership are perhaps the most important condition. It is worth noting that land relations in agriculture are part of production relations and can be both stimulating and restraining factors for the rational use of agricultural land and high-tech land use. It was found that land legislation in Ukraine remains fragmentary and often inconsistent between all regulatory acts, and changes to individual regulations and legal mechanisms cause risks for investors and landowners, which creates legal uncertainty and complicates the implementation of the efficiency of land resources use. High-tech land use is a direction of modern agrarian development aimed at increasing the efficiency of agriculture, preserving natural resources and ensuring food security. Systematizing the problems and directions of development of land relations and high-tech land use, it was highlighted that the main priority remains the implementation of the social function of land ownership and land use, which ensures the most efficient land use of land plots for their intended purpose. Thus, the main guidelines of land policy are the stability and effectiveness of the functioning of the system of land relations and agricultural land use, aimed at ensuring the country's food security and increasing the well-being of the population. The development of land relations and high-tech land use should be based on maximum consideration of natural, economic and social conditions and environmental requirements for preserving the environment, protecting land resources and restoring their fertility, increasing the productivity of the land fund, as well as limiting the negative impact of economic activity on flora and fauna, etc.

Optimizing Agricultural Labour: An Examination of On-Demand Workforce Solutions for Seasonal Demands

To address the growing challenges in managing on-demand Labour within the agricultural sector, we introduce Agro Finder, an innovative application developed using Figma for design and flutter for frontend development and firebase for backend. This research presents a comprehensive overview of the current state of Labour management in agriculture and proposes Agro Finder as a solution tailored to the sector's unique needs. The app leverages real-time data and sophisticated algorithms to streamline the connection between farmers and Labourers, optimizing Labour allocation based on geographic location, skills, and availability. Further analysis reveals that Agro Finder significantly enhances Labour utilization efficiency, reducing downtime in agricultural operations. The app's intuitive design, supported by Figma, ensures a user-friendly experience, while the fronted, powered by Flutter, provides reliable performance. The integration of these technologies facilitates efficient Labour management, leading to increased productivity and operational efficiency in agriculture. The research findings underscore the potential of applying modern development tools and methodologies to solve traditional problems in agriculture. By harnessing the power of Figma and Flutter, Agro Finder sets a new standard in on- demand Labour management, offering valuable insights and practical solutions. This study provides a solid foundation for future advancements in agricultural Labour management and highlights the importance of technological innovation in enhancing agricultural productivity. Key Words: on-demand Labour, agricultural management, mobile application development, Figma, Flutter, Labour allocation, real-time data processing, Firebase.

Spiral of Silence: Consumer Attention, Media Emotion, and Pork Price Fluctuations

This study utilized web crawling and text analysis to collect weekly data from July 2018 to March 2022. Using the spiral of silence theoretical framework, it explores the relationship between consumer online attention during sudden animal epidemics and pork price volatility. The research found that consumer online attention, influenced by animal disease outbreaks, significantly intensifies pork price volatility both directly and indirectly through changes in media sentiment and consumer expectations. On the one hand, the sentiment in official media reports partially mediates the relationship between consumer attention and pork price volatility. As consumer attention increases, positive sentiment in official media also rises, exerting an indirect stabilizing effect on pork prices. On the other hand, consumer expectations also have a partial mediating role, and the volume of negative news reports from unofficial media significantly amplifies the impact of consumer attention on pork price volatility. These findings provide valuable insights into the relationship between consumer online attention and market fluctuations in the agricultural sector and offer practical recommendations for enhancing demand-side management strategies.

A New Classification Model Using a Decision Tree Generated from Hyperplanes in Dimensional Space

ABSTRACT One of the issues addressed by machine learning, with applications in various disciplines or fields such as the health sector and the agricultural sector among others, involves data classification. For this purpose, various models within supervised learning have been proposed and developed that allow for the classification of these data. However, one of the implications of the No-Free-Lunch theorems is that there is no optimal general-purpose model, i.e. there is no classifier model that achieves the best results for all problems presented. Hence the importance of proposing and implementing new classifier models, evaluating their performance, and comparing them with other classifier models in order to achieve models with good results in specific problems. This work presents a new classifier model that, by constructing hyperplanes from the training set, generates a decision tree and partitions the dimensional space. The proposed model was applied to different problems such as the XOR logical function problem, where the proposed model managed to solve the problem, it was also applied to the Iris Dataset where one of the trees generated by the model managed to classify with 100% accuracy the test set, and finally, the proposed model was applied to the Pima Indians Diabetes Database and compared with other models using the accuracy value. The proposed model obtained an accuracy of 81.81%, achieving the best result in the same way as the Random Forest Classifier. The results obtained in this work show that the proposed model manages to partition the dimensional space adequately from the training set and thus competitively classify the data with other state-of-the-art models.

How to manage regime shifts: insights from a dryland social–ecological system

ABSTRACT Effective water resource management in the Zayandeh-Rud River Basin, a critical region in the Middle East, requires understanding system dynamics and anticipating regime shifts. This study uses system dynamics to model water consumption changes in the basin, analysing various scenarios (10% to 30% increase/decrease) in agricultural, urban, and industrial sectors. Results show that increased consumption harms water systems, while reductions, especially a 30% decrease, stabilize systems and improve water quality. Upstream reductions benefit downstream resources, highlighting the importance of integrated management. Proactive strategies can mitigate adverse impacts, ensuring a sustainable water future for the region’s inhabitants.

Stabilizing food supply chains with Blockchain technology during periods of economic inflation

This study explores the potential of blockchain technology to stabilize food supply chains during periods of economic inflation. Economic inflation poses significant challenges to food security, as rising prices can disrupt supply chain transparency, efficiency, and trust among stakeholders. This research develops a conceptual model demonstrating how blockchain can enhance transparency and stability in food supply chains, ultimately improving resilience against inflationary pressures. By providing a decentralized, immutable ledger, blockchain technology allows for real-time tracking of food products from farm to table, enabling better inventory management and demand forecasting. The proposed model integrates key components, including smart contracts, which automate transactions and ensure compliance with quality and safety standards. This feature not only reduces delays but also fosters trust among supply chain participants, such as farmers, distributors, retailers, and consumers. Additionally, the model emphasizes the role of data analytics in harnessing blockchain data to provide insights into pricing trends and inventory levels, facilitating proactive decision-making in response to inflation. Future research directions are identified, focusing on innovations within blockchain technology that could further mitigate the effects of inflation on food supply chains. Potential areas of exploration include integrating artificial intelligence (AI) for predictive analytics, utilizing Internet of Things (IoT) devices for real-time data collection, and developing decentralized finance (DeFi) solutions to enhance liquidity and investment in the agricultural sector. This study contributes to the growing body of literature on blockchain applications in supply chain management by highlighting its potential as a tool for enhancing food security in inflationary environments. By ensuring transparency, improving stakeholder collaboration, and providing actionable insights, blockchain technology can play a pivotal role in stabilizing food supply chains amidst economic fluctuations. Ultimately, this research advocates for the adoption of blockchain solutions as part of comprehensive strategies to safeguard food systems from the adverse impacts of inflation. Keywords: Blockchain Technology, Food Supply Chains, Economic Inflation, Transparency, Stability, Smart Contracts, Predictive Analytics, Data Analytics, Food Security, Decentralized Finance.

A Mixed Methods Assessment of an Online Mental Health and Resilience Program for Agricultural Sector Students

ABSTRACT Financial, environmental, and socio-cultural challenges affect the mental health and wellbeing of those working and living on-farm. Education programs can help in improving mental health and overall wellbeing, but most of these programs are offered to established farmers in a face-to-face format, leading to a gap in offerings to many subgroups, including young agricultural entrants. To address these gaps, this paper assesses the value of an online mental health and resilience education program for young agricultural entrants. An explanatory sequential mixed methods approach was applied. Firstly, a quantitative survey was conducted with 172 first year agricultural tertiary students, aged 18–20, to assess the knowledge and skills gained due to program participation. Regressions and propensity score matching were used to determine the effect of program participation. Subsequently, seven interviews were conducted with program participants to provide insights into their experiences of the program. Thematic coding was applied to analyze the qualitative data. Findings from the quantitative survey show a significant increase in mental health knowledge and skills amongst program participants compared to participants in the control group. The qualitative interviews show the ability to work through the material in their own time and space, creates a safe environment for online students. Other mechanisms contributing to learning are using “normal language”, a peer voice in communicating wellbeing, and building on learnings in previous education. This paper addresses a gap in literature by being the first study to evaluate online mental health education for agricultural tertiary students. It provides educators and other program designers with valuable information for future program design to support the wellbeing of young agricultural workers.

Early Modeling of the Upcoming Landsat Next Constellation for Soybean Yield Prediction Under Varying Levels of Water Availability

The upcoming Landsat Next will provide more frequent land surface observations at higher spatial and spectral resolutions that will greatly benefit the agricultural sector. Early modeling of the upcoming Landsat Next products for soybean yield prediction is essential for long-term satellite monitoring strategies. In this context, this article evaluates the contribution of Landsat Next’s improved spectral resolution for soybean yield prediction under varying levels of water availability. Ground-based hyperspectral data collected over five cropping seasons at the Brazilian Agricultural Research Corporation were resampled to Landsat Next spectral resolution. The spectral dataset (n = 384) was divided into calibration and external validation datasets and investigated using three strategies for soybean yield prediction: (1) using the reflectance from each spectral band; (2) using existing and new vegetation indices developed based on three general equations: Normalized Difference Vegetation Index (NDVI-like), Band Ratio Vegetation Index (RVI-like), and Band Difference Vegetation Index (DVI-like), replacing the traditional spectral bands by all possible combinations between two bands for index calculation; and (3) using a partial least squares regression (PLSR) model composed of all Landsat Next spectral bands, in comparison to PLSR models using Landsat OLI and Sentienel-2 MSI bands. The results show the distribution of the new spectral bands over the most prominent changes in leaf reflectance due to water deficit, particularly in the visible and shortwave infrared spectrum. (1) Band 18 (centered at 1610 nm) had the highest correlation with yield (R2 = 0.34). (2) A new vegetation index, called Normalized Difference Shortwave Vegetation Index (NDSWVI), is proposed and calculated from bands 19 and 20 (centered at 2028 and 2108 nm). NDSWVI showed the best performance (R2 = 0.37) compared to traditional existing and new vegetation indices. (3) The PLSR model gave the best results (R2 = 0.65), outperforming the Landsat OLI and Sentinel-2 MSI sensors. The improved spectral resolution of Landsat Next is expected to contribute to improved crop monitoring, especially for soybean crops in Brazil, increasing the sustainability of the production systems and strengthening food security in Brazil and globally.

Cultivation of Oyster Mushroom could be a Viable Option for Doubling the Farmer’s Income - An Overview

Our country is celebrating 75th year of Azadi ka Amrit Mahotsava and our Government is very much focused on how to double the farmer’s income, it is the utmost duty of every researcher, educationist and agriculture scientist to explore the ways and means to achieve this target. The cultivation of oyster mushrooms has the potential to significantly increase the income of farmers. Oyster mushrooms are easy to grow, require minimal resources, and have a high market demand. By cultivating oyster mushrooms, farmers can diversify their income streams and increase their profits. This abstract suggests that oyster mushroom cultivation could be a viable option for farmers looking to double their income. Indian economy is primarily agriculture based and where the average income from Agriculture Sector ha-1 is very less in comparison to the other developed nations of the world. This is a call of time to enhance the farmer’s income so they can live their lives in a lucrative manner. Present objective cannot be achieved merely by enhancing the price of the different agricultural produce which the farmers grow on their farm lands, but we should include certain other practical implications to increase the existing productivity level of different agricultural and allied ventures by diversifying farm enterprises. Mushroom culture is one of such enterprises with particular reference to oyster mushroom which has the vast potential to double the existing income level of farmers and this enterprise can easily be adopted by any category of farmers even having marginal landholding.

Human capital, creativity and innovation as pillars of leading technology systems

The relevance of the research is determined by the need of modern agricultural enterprises for innovations. Only on the basis of innovative development they can be competitive and later enter industry 4. The purpose of the article is to analyze and justify an innovative approach to the formation of technological networks in the agricultural sector based on the use of competitive advantages of human capital and strengthening of its creativity. The authors note that in order to attract innovation, project managers must coordinate the company's strategy with the strategy of knowledge management and creativity development. The study summarizes the results of using an innovative model in the Brazilian agricultural sector that promotes human capital development and knowledge generation, as well as environmental sustainability (the UTAUT model). The evaluation of these results showed that a significant factor in the successful implementation of the innovation model is the creation of an appropriate organizational and management culture, focused on the involvement of innovations in the activities of agricultural enterprises. The authors conducted a survey of agricultural workers of large and small enterprises of this industry in order to identify their need and degree of readiness for innovations. The results of the survey showed significant differences between large and small enterprises in the given parameters, which require the choice of different ways and sources of strengthening their innovativeness. The problem raised in the article is also relevant for Ukraine as a country with a traditional agricultural sector.

ML-based Anomaly Detection for CAN Bus Network in Agriculture Machinery

The adoption of advanced automation and next-generation technologies like the Internet of Things (IoT) and modern communication networks has revolutionized the food and agriculture sector, boosting the efficiency and precision of farm machinery. However, this increased inter-connectivity has also exposed significant vulnerabilities, particularly in Controller Area Network (CAN) protocols, widely used in advanced agricultural machinery and equipment. Due to its lack of inherent security features, CAN is susceptible to various cyber-attacks, potentially leading to severe consequences if these attacks remain undetected and unmitigated. This paper introduces a supervised machine learning (ML)-based anomaly detection system (CAN-ADS) designed to detect various cyber-attacks on CAN-based agricultural machinery. The system leverages network traffic augmentation and data balancing techniques to train ML algorithms on CAN-specific datasets. Experimental results show that CAN-ADS achieves high accuracy (approx 98%) and true-positive rates with low false-negative rates (approx 1%).

Effects of Tourism Village on Sustainable Livelihoods and Pandemic Resilience in Serang Village

This research explores the policy approach adopted by the management of Serang Tourism Village for village development, with an emphasis on the five livelihood capitals and the community’s strategies for sustaining livelihoods after development. Employing a mixed-methods sequential exploratory approach, the research identifies the key policy strategy as the creation of BUM Desa. This initiative has boosted PADes contributions to villages, thereby advancing local development. Consequently, this has improved access to the five livelihood capitals, fostering more sustainable livelihoods. The research also underscores the community's susceptibility to the impacts of the Covid-19 pandemic, emphasizing the importance of both the tourism and agriculture sectors in sustaining livelihoods during crises. It concludes that the tourism village strategy implemented by BUM Desa plays a significant role in supporting the community’s well-being through the five capitals, thereby protecting their living conditions from potential vulnerabilities. The results should prompt local authorities to focus on enhancing the capabilities of BUM Desa, aiming to offer more sustainable livelihoods for the community.

An assessment of China’s methane mitigation potential and costs and uncertainties through 2060

China, the world’s largest methane emitter, is increasingly focused on methane mitigation in support of its climate goals, but gaps exist in the understanding of key methane sources, as well as mitigation opportunities and their associated uncertainties. We use a bottom-up modeling approach with updated methane emission projections and abatement cost analysis to account for additional sources, uncertainties, and mitigation measures in China’s energy and agricultural sectors. Here we show the significant cost-effective potential for reducing methane emissions in China by 2030, with 660 million tonnes of carbon dioxide equivalent possible with average negative abatement costs of US$6.40 per tonne CO2e. Most of this potential exists in the energy sector, particularly coal mining, but the greater potential will shift towards agriculture by 2060. Aquaculture and biochar applications in rice cultivation have net economic benefits but need greater support for deployment, while new mitigation measures will be needed for remaining emissions from enteric fermentation, rice cultivation, and wastewater.

Qualitative Insights into Antimicrobial Use on Cow-Calf Operations in Ontario

Antimicrobial use and misuse in the agricultural sector are driving causes of the ongoing antimicrobial resistance public health crisis. To enact meaningful on-farm stewardship and best-practice discussions, we need more research that provides in-depth understandings of producers' beliefs, values, practices, and challenges surrounding the use of antimicrobials. Through a discussion of the experiences of twelve cow-calf operators in Ontario, we demonstrate how qualitative research methods can be mobilized to gain insights into the priorities and concerns that shape farmers’ use of antimicrobials, as well as barriers to their effective use.

Development of National Emission Factor for Rice Production System in Malaysia: A Step Toward Achieving Sustainable Development Goals

Objective: Currently, methane (CH4) emissions from rice cultivation in Malaysia are calculated using the regional’s methane emission factor (EF) of 1.60 kg CH₄ ha⁻¹ day⁻¹, as Malaysia has not yet developed a national emission factor. The objective of this study is to generate a country-specific EF of CH4 emission from rice cultivation in Malaysian rice fields. The EF generated would then be used in future emission estimates and the country’s greenhouse gas (GHG) inventory reports to the United Nations Framework Convention on Climate Change (UNFCCC). Theoretical Framework: The establishment of a national GHG emission factor for rice cultivation in Malaysia is critical for accurate GHG inventory reporting and effective climate change mitigation. This study utilizes the Intergovernmental Panel on Climate Change (IPCC) guidelines to derive a country-specific emission factor, enhancing Malaysia’s compliance with international climate obligations and promoting sustainable agricultural practices. Method: A new national EF was developed from eleven rice planting seasons in rice granary areas (IADA Pulau Pinang, IADA Barat Laut Selangor, MADA and KADA) and non-granary area (Sik, Kedah). The EF was calculated from methane emissions from the new data sets, published journals and unpublished data from MARDI. For the new data set, methane gas (CH4) was measured using a static chamber method (Minamikawa et al., 2015). Sampling of GHG from the gas chamber is carried out in the field every 2 weeks and gas was analysed by a GC System Agilent 7890A gas chromatography. The daily methane flux and methane emission follow the methods by Habib et al. (2007), Fauzi et al. (2023) and the Guidelines for National Greenhouse Gas Inventories for Cropland (IPCC, 2006a). Results and Discussion: The national EF calculated from CH4 gas emissions over eleven rice planting seasons in Malaysia between 2012-2024 was 1.80 kg CH4 ha-1 day-1, which is higher than the current regional EF used (1.60 kg CH4 ha-1 day-1). However, the value is within the range of the default value of 1.30 kg CH4 ha-1 day-1 by the Intergovernmental Panel on Climate Change (IPCC) 2006 and 2.00 kg CH4 ha-1 day-1 (IPCC, 1996). Research Implications: The use of this new EF resulted in 12.49% increase in the national GHG inventory from the rice cultivation sub-sector as compared to using the current regional EF. This initiative is part of a comprehensive plan to enhance and strengthen GHG inventory reporting for Malaysia's agriculture sector, aiming to meet IPCC requirements and progress to Tier 2 status.

The Electrooxidation of Synthetic Bipyridyl Herbicide Wastewaters with Boron-Doped Diamond Electrodes: A Technical and Economic Study to Boost Their Application for Pollution Prevention in the Agricultural Sector

Boron-doped diamond electrodes (BDDEs) offer a highly efficient pathway to mineralize recalcitrant compounds due to their reduced energy requirements, fewer chemical inputs, and mechanical stability. In this work, the electrochemical degradation of paraquat (PQ) and diquat (DQ) was studied using an undivided cell (Condiacell®-type) at circumneutral pH, and under galvanostatic control. The roles of applied current density, volumetric flow rate, and herbicide concentration were systematically studied through a central composite design (CCD) using a closed-flow reaction setup. Under the best operating conditions (i.e., for PQ: 1.6 mA/cm2, 80 mL/min, and 70 mL/min, and 70 mg/L; and for DQ: 1.5 mA/cm2, 80 mL/min, and 73 mg/L), a spectrophotometric analysis evidenced that the herbicides were satisfactorily removed (ca. 100%) while mineralization degrees were above 90%. Furthermore, the produced effluents yielded significant increases in seed germination and root length, which suggest a reduction in toxicity. Energy consumptions of 0.13 and 0.18 kWh/g of TOC are reported with the electrochemical cells for the PQ and DQ treatments, respectively. The PQ and DQ treatments by electrooxidation are estimated to emit nearly 2.7 and 38.9 kg CO2/m3 of water treated, with a cost around USD 250/m3. Carbon emissions could be greatly decreased for PQ (0.28 kg CO2/m3) and DQ (0.40 kg CO2/m3) if electricity were generated from renewable resources. Although this study suggests that the use of BDDE can be considered as a green alternative for agrochemical removal due to lower carbon emissions, the environmental profile of the process is determined by the degree of renewability of the electrical grid of each country or region.