Agricultural Processes Research Articles

Operating Point Optimization of Agricultural Tractor–Implement Combinations as Constraint Optimization Problem

Increasing the process efficiency of agricultural tasks is a key measure to decrease overall costs and CO2 emissions. However, optimizing tractor–implement combinations is challenging due to the variety of processes and implements and the complexity of the powertrains in modern tractors. In addition, overall process efficiency is an ambiguous optimization objective in agricultural processes as it relates resource consumption to harvest yields, which are only known at the end of a harvest season. The presented approach defines process constraints, ensuring optimization does not negatively affect harvest yield. These constraints allow for the formulation of explicit objective functions that are observable during the operation. The method establishes a mathematical foundation for the optimization of agricultural processes. The mathematical principles of the theoretical framework and the techniques used to define control constraints are explored, whereby the applicability to alternative objectives like optimizing the overall process cost is highlighted. To demonstrate the practical utility of the proposed approach, an optimization cycle is applied to a real-world scenario: adapting the working speed during the tillage process using a cultivator to maximize energy efficiency. The approach simplifies the optimization problem by formulation as a constraint optimization problem, allowing for improving the operating point of tractor–implement combinations with respect to observable process objective functions. The results underline the importance of advanced control strategies in agricultural machinery, advancing precision agriculture and promoting sustainable farming practices.

Automating Agriculture: The Impact of AI and IoT on Farming

The agriculture industry is under tremendous pressure to boost food grain productivity and production due to the effects of climate change and population growth. Agriculture automation is now the only viable choice and a pressing necessity in the majority of nations where agricultural growth is just not feasible. Artificial intelligence and the Internet of Things have already begun to make inroads into every sector, including agriculture. The development of these digital technologies has revolutionized agriculture by enabling intelligent systems that are as intelligent as human experts and are capable of real-time monitoring, control, and visualization of a variety of farm operations. The study discusses the possible uses of AI and IoT in the establishment of smart farm equipment, irrigation systems, greenhouse culture, storage buildings, weed and pest control, manure application, drones for crop monitoring, plant health monitoring, etc. This study's main objectives are to give a summary of new studies on agriculture powered by digital technology and to list the most well-known applications of artificial intelligence and the internet of things in agriculture engineering. Scientific databases like Web of Science, PubMed and Scopus have been used to examine research conducted in the fields over the past ten years. It has been found that the utilization of IoT and IoT, from its initial conceptual stage to its implementation stage, the effort to automate agriculture has advanced. The technical features of artificial intelligence and the Internet of Things are also covered, along with the challenges associated with putting these digital technologies into practice. It would aid people in understanding how digitalization may be incorporated into agricultural processes to make it easier for farmers to implement AI and IoT-based solutions.

Development and ergonomic evaluation of hand held vegetable seedling transplanter

Efficient vegetable production is essential to meeting growing food demands, requiring innovative solutions to reduce labor intensity and enhance productivity. Vegetable cultivation, especially for small farmers, demands innovative and affordable solutions to enhance productivity and reduce labor-intensive practices. The mechanization of agricultural processes has become increasingly important in reducing the physical strain and time-intensive nature of traditional farming operations, paving the way for innovative tools that enhance efficiency and user comfort. Transplanting of seedlings, a highly drudgery-oriented operation, is mainly performed by women. A hand held seedling transplanter has been designed and developed for transplanting vegetables seedlings based on the anthropometric dimensions of the user population. The number of seedlings transplanted per min using the handheld seedling transplanter ranged from 19 to 21, compared to 16 to18 using the conventional method. By using the hand held seedling transplanter, a significant reduction in the cost of operation was achieved, with 11.44 to 16.52% savings in cost reported. The average working heart rate while transplanting using the seedling transplanter was 103 bpm, compared to 120 bpm for conventional transplanting. The average energy expenditure while transplanting the seedlings using the seedling transplanter was 7.72 kJ min-1, compared to 10.94 kJ min-1. The performance evaluation of the designed transplanted indicted an increase in human productivity and a decrease in musculoskeletal discomfort during the transplanting process. The introduction of ergonomic and cost-effective solutions, such as the handheld seedling transplanter, not only improves productivity and reduces operational costs but also promotes sustainable practices while prioritizing the well-being of farmers.

АНАЛИЗ СОДЕРЖАНИЯ ВИТАМИНА С В МЕСТНЫХ ПРОДУКТАХ ПИТАНИЯ ЯКУТИИ

he purpose of the study is to analyze the content of vitamin C in the most consumed food products and wild berries by the population of Yakutia. The object of research is products of animal and plant origin, wild berries. The research material was collected in 2016–2019 in Central Yakutia. Selection and preparation of samples were carried out according to the method of A.F. Abramova (2007). The content of vitamin C in food products was determined by infrared spectroscopy on a SpectraStar 2200 analyzer, calibrated on the basis of standard chemical methods, in the laboratory of agricultural products processing and biochemical analyzes of the Yaroslavl Research Institute of Agriculture. The norms for vitamin C requirements per day vary by population group, in accordance with the norms of physiological needs: for children – from 30 to 90 mg/day, for adults – 100 mg/day. Among animal foods, the richest in vitamin C are mare's milk (11.4 mg/100 g), fermented milk product "Ot Uerete" (1.6 mg/100 g), and among plant products – green crops (parsley (150.0 mg/ 100 g), dill (100.0 mg/100 g), onion (95.0 mg/100 g), among wild berries, the highest content of vitamin C was noted in rose hips (3100.0 mg/100 g) and black currants (386.0 mg/100 g). When consuming 250 g of mare's milk, the degree of satisfaction in vitamin C can be 28.5 % according to MP 2.3.1.0253-21. A serving (100 g) of cabbage and potatoes satisfies the daily requirement of an adult for vitamin C by 45 and 10 %.

Bio-mediated synthesis of nanoparticles: A new paradigm for environmental sustainability

The synthesis of nanoscale metals and non-metals is an intriguing subject, and the green synthesis of nanoparticles (NPs) is increasingly utilized across various sectors, including environmental science, agriculture, engineering, and food processing. Traditionally, the production of nanoscale materials relies heavily on physical and chemical processes, which can lead to significant challenges such as high energy consumption and environmental contamination. Poor management of agricultural and industrial waste contributes to greenhouse gas emissions, exacerbates climate change and disrupts ecosystems. Conversely, green nanotechnology offers a safer alternative by leveraging biological materials, that inherently provide capping and reducing agents. This approach is not only more cost-effective but also results in lower pollution levels, thereby enhancing environmental safety. Green synthesis involves the reduction of metallic and non-metallic atoms using plant extracts, microorganisms, and agricultural waste instead of conventional harmful substances. The bioactive compounds, including flavonoids, alkaloids, tannins, and saponins, play a critical role in the bioreduction of metals and the production of nanoparticles. There has been the increasing interest in utilizing these biological sources for green nanoparticle production over the past decade from their potential to serve as economical and environmentally friendly alternatives. Overall, green nanotechnology demonstrates its potential to revolutionize industries and pave the way for a more sustainable and resilient future.

Cytological Characterization of vrnp 1, a Pollen-Free Male Sterile Mutant in Mung Bean (Vigna radiata)

Mung bean (Vigna radiata) plays a significant role in agricultural trade, food processing and utilization, and cropping structure adjustment due to its abundant nutritional components, medicine-food homology, capacity for nitrogen fixation, and soil improvement. The low yield level is a crucial limitation factor in the mung bean industry, while heterosis is an efficient path for increasing crop yields. The flexible utilization of male sterile mung bean materials may solve this pressing demand in the industry. This study identified a completely male-sterile mutant, vrnp 1, in the EMS-mutagenized mung bean cultivar Jilv 10 population, which is controlled by a single recessive nuclear gene. Furthermore, we employed a series of microscopical and histological techniques and observed that the tapetal cells in the vrnp 1 mutant did not perform as expected when reaching stage 8 of anther development, notably exhibiting a delay in entering PCD. This was accompanied by a failure to deposit cell wall components onto the pollen wall, culminating in a complete absence of mature pollen and the manifestation of male sterility. In conclusion, the vrnp 1 mutant could potentially serve as a promising candidate for male sterility in exploiting hybrid vigor in mung bean. Our research may elucidate how the delayed initiation of programmed cell death in tapetal cells contributes to a factor implicated in mung bean male sterility. Furthermore, the phenotypic data collected during pivotal developmental phases may have contributed to a better grasp of mung bean microspores and anther development.

The Environmentally Friendly Practices to Fight Plants’ Diseases: Scope Review

Plant diseases reduce the production and affect the food security. The use of synthetic chemicals affects the environment and individual health quality. The environmentally friendly practices were found the solution to minimize the side effects of pesticides and herbicides. This review aims to investigate the impact of environmentally friendly practices on plant disease protection. The results showed that environmentally friendly practices are very effective in controlling plant diseases and the same time reducing the environmental impacts and the individual’s health. These practices were found to be effective in improving the plant product and maximizing the economic profitability of the agricultural practices. These practices will increase the sustainability of the agricultural processes. Keywords: Environmentally friendly practices, plant diseases, cultural practices, biocontrol practices, economic profitability

Study on Mechanical Properties of Concrete through the Combined Use of Pozzolonic & Fibrous Materials from Agricultural Waste

The mechanical properties of concrete using pozzolanic and fibrous materials through agricultural waste. In this, the focus was on enhancing the performance of concretes made possible by incorporating by-products of agricultural processes into the mix. The key objective was to investigate the use of rice husk ash, coconut shell powder, and other product - waste streams sourced from agricultural activities in getting the best possible concrete benefits, especially regarding strength, durability, and sustainability.

ХАРАКТЕРНЫЕ ОСОБЕННОСТИ ВОСПРОИЗВОДСТВЕННОГО ПРОЦЕССА МТП В УСЛОВИЯХ ФОРМИРОВАНИЯ ТЕХНИКО-ТЕХНОЛОГИЧЕСКОГО СУВЕРЕНИТЕТА

The machine and tractor fleet, with its qualitative and quantitative characteristics, is a fundamentally important factor in production, reducing the complexity of technological processes and increasing efficiency in agriculture. Modern vectors of development of the agricultural industry are aimed at supporting national goals and lead to the modernization of the industry with modern and high-tech domestic equipment, including through a flexible system of state support, stimulating investment activity of agricultural producers, including by ensuring access to preferential credit resources and preferential leasing, which is ensured by the effective operation of JSC Russian Agricultural The Bank and JSC Rosagroleasing. However, the imposed economic sanctions, the forced change in logistics for the supply of components for agricultural machinery and spare parts for the repair and maintenance of agricultural machinery, high inflation and an increase in the key rate of the Central Bank of the Russian Federation, and the increasing disparity in prices do not contribute to accelerating reproduction processes in agriculture. In this regard, there is a need to develop a set of scientifically based approaches to solve urgent problems and create effective conditions for the reproduction of the agricultural machinery and tractor fleet in compliance with the principles of rational resource use and in order to achieve technical and technological sovereignty. The article presents the results of scientific research based on the fundamental principles of scientists and economists who studied the evolution of the reproductive process and identify the characteristic features of the stages of the reproductive process of the machine and tractor park (MTP) agriculture in the context of the formation of technical and technological sovereignty.

ЦИФРОВАЯ ТРАНСФОРМАЦИЯ СЕЛЬСКОГО ХОЗЯЙСТВА: ОТ ТЕОРИИ К ПРАКТИКЕ

Digital agriculture allows not only to change the principles of agricultural producers, but also to transform every element of the production chain of raw materials and agricultural products, has an impact on the movement of capital and reproduction processes in agriculture. In addition, digital transformation can significantly affect the relationships of agricultural producers with resource suppliers, procurement, processing and trading organizations, making them more transparent and economically sound. Digital solutions provide the ability to control the entire cycle of product movement from the field to the counter. Their practical use makes it possible to increase the efficiency of resource management, machinery, equipment and technological processes, and to make optimal management decisions at all stages of the technological process of product production. They fundamentally change the usual technologies and provide increased economic efficiency. The article summarizes and systematizes the economic categories "digitalization", "digital transformation", and "digital agriculture", examines the experience of practical application of digital technologies in agriculture, and identifies certain factors constraining the large-scale digital transformation of the Russian agricultural sector.

Комплексная оценка мультипликативного эффекта внедрения зеленых технологий в агропромышленном комплексе на региональную экономику

This article examines the impact of green technologies on the agricultural sector and the multiplier effects that these technologies have on the regional economy. The article examines the role of innovative and sustainable methods in agriculture, as well as their impact on the economic development of related industries, such as agricultural processing, logistics and energy. Particular attention is paid to the analysis of the socio-ecological consequences of the introduction of green technologies, such as changes in employment, improved quality of life and ecosystem sustainability. The article provides an overview of modern methods for assessing multiplier effects, such as inter-industry analysis and econometric modeling, and includes examples of the application of these methods to data from Russian regions. Based on the analysis of current trends in the field of green technologies in the agro-industrial complex, the main barriers to their implementation are identified, as well as recommendations for overcoming them. In conclusion, strategies and measures are proposed that can enhance the positive impact of green technologies on the regional economy, and prospects for further research in this area are outlined.

Development of a Sustainable Flexible Humidity Sensor Based on Tenebrio molitor Larvae Biomass-Derived Chitosan.

This study presents the fabrication of a sustainable flexible humidity sensor utilizing chitosan derived from mealworm biomass as the primary sensing material. The chitosan-based humidity sensor was fabricated by casting chitosan and polyvinyl alcohol (PVA) films with interdigitated copper electrodes, forming a laminate composite suitable for real-time, resistive-type humidity detection. Comprehensive characterization of the chitosan film was performed using Fourier-transform infrared (FTIR) spectroscopy, contact angle measurements, and tensile testing, which confirmed its chemical structure, wettability, and mechanical stability. The developed sensor exhibited a broad range of measurements from 6% to 97% relative humidity (RH), a high sensitivity of 2.43 kΩ/%RH, and a rapid response time of 18.22 s with a corresponding recovery time of 22.39 s. Moreover, the chitosan-based humidity sensor also demonstrated high selectivity for water vapor when tested against various volatile organic compounds (VOCs). The superior performance of the sensor is attributed to the structural properties of chitosan, particularly its ability to form reversible hydrogen bonds with water molecules. This mechanism was further elucidated through molecular dynamics simulations, revealing that the conductivity in the sensor is modulated by proton mobility, which operates via the Grotthuss mechanism under high-humidity and the packed-acid mechanism under low-humidity conditions. Additionally, the chitosan-based humidity sensor was further seamlessly integrated into an Internet of Things (IoT) framework, enabling wireless humidity monitoring and real-time data visualization on a mobile device. Comparative analysis with existing polymer-based resistive-type sensors further highlighted the superior sensing range, rapid dynamic response, and environmental sustainability of the developed sensor. This eco-friendly, biomass-derived, eco-friendly sensor shows potential for applications in environmental monitoring, smart agriculture, and industrial process control.

Chinese consumers’ dish value: a best–worst scaling approach

PurposeThis paper aims to identify Chinese consumers’ dish value systems and investigate whether and to what extent their dish values vary by type of consumers.Design/methodology/approachWe used a best–worst scaling (BWS) developed based on previous literature on food values and attributes of Chinese dishes to elicit Chinese consumers’ dish value structure. Then, we estimated each participant’s share of preferences for dish values and applied Ward’s linkage cluster analysis to identify the heterogeneity of their dish values.FindingsOur results indicate that food safety values, including the safety of agricultural products, additives and food processing, are valued most by Chinese consumers. They rate health-nutrition values as the second most important dish values. There is significant heterogeneity in Chinese consumers' dish values. Their dish values tend to drive their dish consumption patterns.Originality/valueThe dish is the mainstay of the Chinese meal. This study is among a few studies that examine Chinese consumers’ dish values that underlie consumer dish preferences and choices. The results provide essential information to promote a healthy and sustainable diet among Chinese consumers by understanding their values and motivations for making dish choices.

Biotechnology for Global Sustainability: Innovations, Applications, and Challenges

Biotechnology, from a global perspective, involves applying biological systems, organisms, or derivatives to develop innovative products and processes that address global challenges and enhance the quality of life. As an interdisciplinary field bridging biology, technology, and engineering, biotechnology significantly impacts critical sectors, including healthcare, agriculture, environmental management, and industrial processes. In healthcare, biotechnology drives advancements in vaccines, gene therapy, personalized medicine, and diagnostic tools, improving disease prevention and treatment. Agricultural biotechnology focuses on developing genetically modified (GM) crops with higher yields, resistance to pests, and adaptability to climate change, ensuring food security. Additionally, it enhances livestock breeding and productivity. Environmental applications of biotechnology include bioremediation to combat pollution, development of biofuels to reduce reliance on fossil fuels, and creation of biodegradable materials to mitigate waste. In industrial processes, biotechnology optimizes manufacturing efficiency through the use of enzymes and microorganisms, contributing to sustainable production. Globally, biotechnology addresses pressing challenges like food insecurity, climate change, health inequality, and environmental degradation. Its role in sustainable development is critical, fostering eco-friendly solutions and improving the resilience of economies and societies. However, the field raises ethical and regulatory concerns, such as the risks of genetic engineering and the equitable distribution of benefits. International collaboration among governments, industries, and scientists is essential to ensure responsible innovation and maximize biotechnology’s potential to improve global well-being. As a transformative science, biotechnology continues to shape the future, balancing innovation with sustainability and equity.

Ethnomathematics in Traditional Farming Systems in Pegalongan Village, Patikraja

Pegalongan village is located in the south of Purwokerto city and the majority of the population work as farmers. Rice farming in this area is an important part of local culture and tradition, as well as a major source of livelihood for the local community. The purpose of this study is to find the mathematical elements hidden in the operation of rice farming and the philosophical value contained in it. To understand how rice farming communities use mathematics in their cultural context, this study used a descriptive qualitative ethnographic approach. The research involved farmers and farm labourers. The focus of the research includes the planting land, and the process of planting rice, the cultivation of the farmers in the form of mimiti and mbawoni. Data was collected through direct observation of farmers, documentation, and in-depth interviews. Data were analysed thematically to find key themes related to ethnomathematics in rice cultivation. These themes included the use of mathematics in agricultural activities, the use of traditional tools and units of measurement, and agricultural processes rooted in local culture. The study also emphasised that ethnomathematics should be part of the education curriculum to improve students' understanding. The results showed that mathematical concepts such as arithmetic, and measurement can be applied in land cultivation. The results show that local knowledge is critical to agricultural practices, and how ethnomathematics can be incorporated into the education curriculum to improve students' understanding of mathematics in a cultural context.

Data-driven analysis of climate impact on tomato and apple prices using machine learning.

Machine learning has been used in various areas, but there are few studies on price prediction for agricultural products. Here, a machine learning technique for the price prediction of tomato and apple fruits was attempted based on environment and price data for 12 years. The goal of this study is to discover 1) how much can we accurately predict the product prices with the environmental factors and 2) how much each environmental factor affects to the product prices. This study assumes that the environmental factors directly affect crop growth and thus indirectly determine fruit production and accompanying price. In addition, it is assumed that there are two kinds of time lags, between the change in the environmental factors and their effects on the crop growth, and between the change in the crop growth and its effect on the price. In the process, machine learning techniques were used instead of econometric models commonly used in agricultural economics. The relationship between the environmental factors and fruit price with varying time lags in data-driven manner using long short-term memory (LSTM) was modeled in this study. The study empirically revealed that there are suitable time lags between the environmental factors and fruit price in the price prediction, and taking these time lags into the prediction improves the accuracy. Moreover, the importance of each of the environmental factors on the price using shapely additive explanations (SHAP) was demonstrated though this study, which assists the decision-making process in agriculture against the climate change.

Enhancing deep convolutional neural network models for orange quality classification using MobileNetV2 and data augmentation techniques

This study introduces significant improvements in the construction of deep convolutional neural network models for classifying agricultural products, specifically oranges, based on their shape, size, and color. Utilizing the MobileNetV2 architecture, this research leverages its efficiency and lightweight nature, making it suitable for mobile and embedded applications. Key techniques such as depthwise separable convolutions, linear bottlenecks, and inverted residuals help reduce the number of parameters and computational load while maintaining high performance in feature extraction. Additionally, the study employs comprehensive data augmentation methods, including horizontal and vertical flips, grayscale transformations, hue adjustments, brightness adjustments, and noise addition to enhance the model's robustness and generalization capabilities. The proposed model demonstrates superior performance, achieving an overall accuracy of 99.53%∼100% with nearly perfect precision, recall of 95.7%, and F1-score of 94.6% for both “orange_good” and “orange_bad” classes, significantly outperforming previous models which typically achieved accuracies between 70% and 90%. While the classification performance was near-perfect in some aspects, there were minor errors in specific detection tasks. The confusion matrix shows that the model has high sensitivity and specificity, with very few misclassifications. Finally, this study highlights the practical applicability of the proposed model, particularly its easy deployment on resource-constrained devices and its effectiveness in agricultural product quality control processes. These findings affirm the model in this research as a reliable and highly efficient tool for agricultural product classification, surpassing the capabilities of traditional models in this field.

Enhancing Cybersecurity Attack Detection Using Multiplayer Battle Game Optimizer with Hybridization of Deep Learning Models

Cybersecurity is advancing and the rate of cybercrime, which is always rising. Advanced attacks are measured as the novel normal as they are one of the more normal and extensive. Cybersecurity threats have risen promptly in many areas like healthcare, smart homes, energy, automation, agriculture, and industrial processes. An intrusion detection system (IDS) discovers intrusions by analyzing attack designs or mining signatures from system packets. To assess an IDS model, use Machine Learning (ML) and deep learning (DL) approaches for recognizing data traffic into malicious and healthy. ML and DL techniques has earned an extensive interest on countless applications and domains of study, mostly in Cybersecurity. With computing power and hardware becoming more available, ML and DL systems can be employed in order to classify and analyze corrupt actors from a massive group of accessible data. This manuscript presents an Enhancing Detection of Cybersecurity Attack Using Multiplayer Battle Game Optimizer with Hybrid Deep Learning (EDCA-MBGOHDL) technique. The main intention of the EDCA-MBGOHDL technique is to provide a robust framework for cyberattack detection using deep learning integrated with a hyperparameter tuning approach. At first, the feature selection process is implemented by applying improved Harris hawk optimization (IHHO) algorithm for ensuring that only the most relevant features are fed into the model. Furthermore, the hybrid of convolutional neural network, bidirectional long short-term memory and attention mechanism (CNN-BiLSTM-AM) model is employed for the classification of cybersecurity threats. Eventually, the multiplayer battle game optimizer (MBGO) algorithm adjusts the hyperparameter values of the CNN-BiLSTM-AM classifier optimally and outcomes in greater classification performance. The wide range of analysis of the EDCA-MBGOHDL technique takes place using a benchmark dataset. The outcomes pointed out the superior performance of the EDCA-MBGOHDL system across existing models

COMMERCIAL PROPOSAL TO AN AGRICULTURAL MANUFACTURER REGARDING THE PRODUCTION OF BIODIESEL FUEL WITH THE CALCULATION OF ECONOMIC EFFICIENCY

The market of petroleum products in Ukraine, in particular diesel fuel, over the past three years has been analysed. A decrease in the use of the main types of fuel in 2022 compared to 2021 has been determined across the country due to shortages and price increases due to market reorientation in wartime conditions. The year 2023 showed an increase in the use of petroleum products due to the stabilization of supply and demand, although these volumes are still lower than in 2021. The amount of fuel used for growing of the main agricultural crops in Ukraine using various technologies is given, taking into account the productivity indicators and the area of sowing. A specific assessment of the component of production costs for fuel was carried out, on the basis of which, taking into account the price of diesel fuel and the area of arable land in Ukraine, the market shares of diesel fuel accounted for by agricultural production was estimated. The forecasts of scientists regarding the need for diesel fuel on the scale of the country, which will grow every year, increasing the demand for biodiesel fuel, are considered. A reasoned position is presented regarding the relevance of biodiesel production in Ukraine for the needs of agriculture. The measures and regulations that stimulate the production of biodiesel fuel in the countries of the European Union and the state of state support for it in our country, as well as the oil products market for 2023 from the point of view of sales volumes and fuel prices, are analysed. It is proposed to start the production of biodiesel fuel on the basis of agricultural farms or processing enterprises on the order of producers of agricultural products from their raw materials. A methodology for assessing the economic efficiency of biodiesel production has been developed, based on the price of raw materials, which involves primary and secondary processing (raw material - rapeseed), or only secondary processing (raw material – rapeseed oil). During the primary and secondary processing, the added value obtained from the by-products is taken into account, which reduces the cost price of the produced biofuel. This methodology also takes into account such a factor of increasing the cost of production of biodiesel fuel as an excise tax. When evaluating the economic efficiency by comparing it with the market value of standard diesel fuel, the concept of energy coefficient, which takes into account the ratio of the lower heat of combustion of two types of fuel, was introduced. This scientific work presents the results of the initiative research work 0122U002187.

Greenhouse gas (GHG) emissions in fruit production-I: berries, nuts and citrus

The greenhouse gas emission values obtained from agricultural activities such as tillage, pruning, spraying, fertilizing, harvesting and transporting etc. are not at a negligible level when compared to other sectors. Each practice has an energy input amount. There have been studies on many agricultural products all over the World. Therefore, the carbon dioxide equivalent (CO2-eq) emissions associated with agricultural processes were compared in this study with selected berry, nut and citrus fruits. As a result; when the greenhouse gas emission equivalent values are examined on a fruit basis, strawberry fruit has 34517.75 kg CO2-eq per ha amount has the highest input. Wolfberry fruit also follows strawberry fruit with a value of 20718.66 kgCO2-eq per ha. The minimum greenhouse gas emission equivalent is in tangerine fruit with 399.89 kg CO2-eq per ha.