Demand For Agricultural Products Research Articles

Artificial Intelligence in Water Management for Sustainable Farming: A Review

Artificial Intelligence (AI) is capable of enhancing water management for sustainable farming. The growing demand for agricultural productivity and sustainability in the context of finite water resources and climate change drives the necessity for more efficient water management practices. AI technologies, through automated and precision irrigation systems, AI-based predictive models, and AI-driven water quality monitoring, offer significant improvements in water efficiency and agricultural output. These systems optimize irrigation scheduling based on real-time data, enhance the precision of water application, and ensure water quality, thus supporting sustainable agricultural practices. However, the implementation of AI in water management is not without challenges. Technical difficulties in adapting AI to diverse agricultural environments, data privacy and security concerns, ethical considerations, and barriers to adoption among small-scale farmers are critical issues that need addressing. This study addresses both the transformative impacts and the inherent challenges of integrating AI technologies. Furthermore, the review identifies a gap in research regarding AI’s adaptability to variable climates and its integration with socio-economic data, suggesting that future studies focus on these areas. Policy recommendations are also discussed, emphasizing the need for developing standards and best practices, increasing funding and incentives for AI research, promoting training and capacity building, and establishing robust regulatory frameworks for data management. By tackling these challenges and leveraging AI’s full potential, water management in agriculture can be significantly improved, leading to enhanced global water security and sustainability in farming practices. The review concludes that while AI presents a promising future for agricultural water management, strategic and thoughtful approaches are required to overcome obstacles and fully realize the benefits of this technology.

Potential Use of Andean Tuber Waste for the Generation of Environmentally Sustainable Bioelectricity.

The growing demand for agricultural products has increased exponentially, causing their waste to increase and become a problem for society. Searching for sustainable solutions for organic waste management is increasingly urgent. This research focuses on considering the waste of an Andean tuber, such as Olluco, as a fuel source for generating electricity and becoming a potential sustainable energy source for companies dedicated to this area. This research used Olluco waste as fuel in single-chamber microbial fuel cells using carbon and zinc electrodes. An electric current and electric potential of 6.4 ± 0.4 mA and 0.99 ± 0.09 V were generated, operating with an electrical conductivity of 142.3 ± 6.1 mS/cm and a pH of 7.1 ± 0.2. It was possible to obtain a 94% decrease in COD and an internal resistance of 24.9 ± 2.8 Ω. The power density found was 373.8 ± 28.8 mW/cm2 and the current density was 4.96 A/cm2. On day 14, the cells were connected in earnest, achieving a power of 2.92 V and generating enough current to light an LED light bulb, thus demonstrating the potential that Olluco waste has to be used as fuel in microbial fuel cells.

Impact of Climate Change on Regional Water Availability and Demand for Agricultural Production: Application of Water Footprint Concept

Impact of Climate Change on Regional Water Availability and Demand for Agricultural Production: Application of Water Footprint Concept

Promoting sustainable agriculture by exploiting plant growth-promoting rhizobacteria (PGPR) to improve maize and cowpea crops.

Maize and cowpea are among the staple foods most consumed by most of the African population, and are of significant importance in food security, crop diversification, biodiversity preservation, and livelihoods. In order to satisfy the growing demand for agricultural products, fertilizers and pesticides have been extensively used to increase yields and protect plants against pathogens. However, the excessive use of these chemicals has harmful consequences on the environment and also on public health. These include soil acidification, loss of biodiversity, groundwater pollution, reduced soil fertility, contamination of crops by heavy metals, etc. Therefore, essential to find alternatives to promote sustainable agriculture and ensure the food and well-being of the people. Among these alternatives, agricultural techniques that offer sustainable, environmentally friendly solutions that reduce or eliminate the excessive use of agricultural inputs are increasingly attracting the attention of researchers. One such alternative is the use of beneficial soil microorganisms such as plant growth-promoting rhizobacteria (PGPR). PGPR provides a variety of ecological services and can play an essential role as crop yield enhancers and biological control agents. They can promote root development in plants, increasing their capacity to absorb water and nutrients from the soil, increase stress tolerance, reduce disease and promote root development. Previous research has highlighted the benefits of using PGPRs to increase agricultural productivity. A thorough understanding of the mechanisms of action of PGPRs and their exploitation as biofertilizers would present a promising prospect for increasing agricultural production, particularly in maize and cowpea, and for ensuring sustainable and prosperous agriculture, while contributing to food security and reducing the impact of chemical fertilizers and pesticides on the environment. Looking ahead, PGPR research should continue to deepen our understanding of these microorganisms and their impact on crops, with a view to constantly improving sustainable agricultural practices. On the other hand, farmers and agricultural industry players need to be made aware of the benefits of PGPRs and encouraged to adopt them to promote sustainable agricultural practices.

Impact of Young Farmers Supports on the Young Farmers’ Willingness to Continue Farm Activities: A Case of TR52 Region in Türkiye

There has been an increase in the demand for agricultural products and different support programs are implemented by countries to keep a sufficient, economically active population in agriculture. The aim of this study was to examine the impacts of the Young Farmer Support Program (YFP) in Türkiye. The primary data for this study was obtained from face-to-face surveys conducted with 155 young farmers. The logit model was used to analyse the factors affecting the willingness of young farmers to continue agricultural activities. The results showed that about half of the young farmers were not satisfied with the provided support, and 23.7% of them considered exiting the agricultural sector. The results of the Logit model showed that the satisfaction from the Program, the presence of social facilities in the rural areas, crop diversity, agricultural insurance, and investments in the farms were statistically significant and had an impact on the willingness of young farmers to continue their farm activities. Diversifying and expanding the scope of support policies for young farmers could make significant contributions to keeping young farmers in the agricultural sector and rural areas.

Concentration-dependent effects of spinetoram on nontarget freshwater microalgae: A comparative study on Chlorella vulgaris and Microcystis aeruginosa

The rising global demand for agricultural products is leading to the widespread application of pesticides, such as spinetoram, resulting in environmental pollution and ecotoxicity to nontarget organisms in aquatic ecosystems. This research focused on assessing the toxicity of spinetoram at various concentrations (0, 0.01, 0.1, 0.5, 1.0, and 3.0 mg L−1) on two common freshwater microalgae, Chlorella vulgaris and Microcystis aeruginosa, to shed light on the ecotoxicological effects of insecticides. Our findings demonstrate that M. aeruginosa is more sensitive to spinetoram than is C. vulgaris, with a concentration-dependent reduction in the growth rate observed for M. aeruginosa, whereas only the highest concentration of spinetoram adversely affected C. vulgaris. At a concentration of 0.01 mg L−1, the growth rate of M. aeruginosa unexpectedly increased beginning on day 7, indicating a potential hormetic effect. Although initial exposure to spinetoram improved the photosynthetic efficiency of both microalgae strains at all concentrations, detrimental effects became apparent at higher concentrations and with prolonged exposure. The photosynthetic efficiency of C. vulgaris recovered, in contrast to that of M. aeruginosa, which exhibited limited recovery. Spinetoram more significantly inhibited the effective quantum yield of PSII (EQY) in M. aeruginosa than in C. vulgaris. Although spinetoram is not designed to target phytoplankton, its toxicity can disrupt primary productivity and modify phytoplankton-consumer interactions via bottom-up control mechanisms. This study enhances our understanding of spinetoram's ecotoxicity and potential effects on aquatic ecosystems.

Segmentation and detection of crop pests using novel U-Net with hybrid deep learning mechanism.

In India, agriculture is the backbone of economic sectors because of the increasing demand for agricultural products. However, agricultural production has been affected due to the presence of pests in crops. Several methods were developed to solve the crop pest detection issue, but they failed to achieve better results. Therefore, the proposed study used a new hybrid deep learning mechanism for segmenting and detecting pests in crops. Image collection, pre-processing, segmentation, and detection are the steps involved in the proposed study. There are three steps involved in pre-processing: image rescaling, equalized joint histogram based contrast enhancement (Eq-JH-CE), and bendlet transform based De-noising (BT-D). Next, the pre-processed images are segmented using the DenseNet-77 UNet model. In this section, the complexity of the conventional UNet model is mitigated by hybridizing it with the DenseNet-77 model. Once the segmentation is done with an improved model, the crop pests are detected and classified by proposing a novel Convolutional Slice-Attention based Gated Recurrent Unit (CS-AGRU) model. The proposed model is the combination of a convolutional Neural Network (CNN) and a Gated Recurrent Unit (GRU). In order to achieve better accuracy outcomes, the proposed study hybridized these models due to their great efficiency. Also, the slice attention mechanism is applied over the proposed model for fetching relevant feature information and thereby enhancing the computational efficiency. So, pests in the crop are finally detected using the proposed method. The Python programming language is utilized for implementation. The proposed approach shows a better accuracy range of 99.52%, IoU of 99.1%, precision of 98.88%, recall of 99.53%, F1-score of 99.35%, and FNR of 0.011 compared to existing techniques. Identifying and classifying pests helps farmers anticipate potential threats to their crops. By knowing which pests are prevalent in their region or are likely to infest certain crops, farmers can implement preventive measures to protect their crops, such as planting pest-resistant varieties, using crop rotation, or deploying traps and barriers. © 2024 Society of Chemical Industry.

Функціональний базис аграрного маркетингу

The article is examined key principles, func­tions, systems and methods that allow ensuring market orientation of the production and sales activities of the enterprise, where effective management decisions are based not only on the capabilities of the manufacturer, but also prima­rily the needs of the agro-industrial market, existing and potential needs of consumers and buyers of agricultural products. It was the first time to propose a definition of the functional basis of agricultural marketing – it is a system of organizational, economic, technical, financial and commercial functions of enterprises, united by a synergistic effect, and aimed at optimal and adaptive satisfaction of consumer demand for agricultural products and food security. It has been established that agricultural marketing can be implemented in mechanisms of mutually beneficial exchange between various market subjects, create conditions for identifying con­sumer requirements and determining the possibi­lities of their satisfaction, which will provide an opportunity to study the agricultural market and forecast its dynamics, form demand for the products of this market and stimulate it distribution The main aim of the research is to offer the author’s interpretation of the concept of "agricultural marketing" and to determine of the functional basis of agricultural marketing in the conditions of martial law. The main hypothesis of the research is to prove the effectiveness of the functional basis of agricultural marketing in the conditions of a full-scale invasion of the Russian Federation on the territory of Ukraine. It has been proven that for effective planning and management of agrarian business of the national economy in the conditions of a full-scale invasion, as well as taking into account the existing conditions (alienation of territories, damage and occupation of part of the land), entrepreneurs are forced to effectively and quickly implement agricultural marketing in its economic activity for researching consumer needs, forecasting demand, studying assortment policy and developing a production program, studying channels of goods movement and organizing safe sales.

Development of production potential of agro-industrial complex of the Republic of Belarus in the context of trends in global food market

The paper presents the results of analysis of trends in global agricultural production in the context of impact on the development of agro-industrial complex of the Republic of Belarus, including: determining impact of the state of the world grain market on balance of markets and prices, increasing demand for agricultural products and, in particular, livestock products, stimulating consumption of feed, strengthening the negative impact of barriers in global trade in food and resources for its production. The directions and opportunities for the domestic agro-industrial complex are determined due to the internal potential of sustainability, increasing the efficiency of strategic forecasting, intensifying the processes of technical and technological re-equipment of production, and developing the market infrastructure. A set of measures has been proposed, grouped in key areas, which will increase production and export potential of the domestic agro-industrial complex and implement a strategy of sustainable economic growth, including: strengthening the feed base of animal husbandry as an export-oriented industry, increasing production efficiency, optimizing costs, improving product quality, ensuring impeccable compliance with technological, technical and labor discipline, technical regulations, creating sustainable models of product promotion in domestic and foreign markets.

CNN-based plant disease recognition using colour space models

ABSTRACT The growing population increases the demand for agricultural production. Owing to some plant diseases, this production is continuously decreasing. Detecting and recognising these diseases at the initial stage is crucial for the sake of high yields from crops and plants. The conventional methods involve manual detection, which can be error-prone and laborious. Automatic recognition paves a way for the regular monitoring of huge areas of crops in less time. The deep learning-based approaches have entered in a big way in the detection of diseases in plants and crops because of their ability to overcome the limitation of regular manual monitoring. The use of colour space models helps demarcate the affected area ofimages of plants and crops prior to inputting them to the CNN model so as to improve the detection accuracy. For this, different colour space models that include RGB, HSL, HSV, LAB, LUV, XYZ and YUV are investigated. However, HSL-CNN joint model performs the best by achieving 98.97% accuracy, 98.61% precision, and 99.28% F1-score.

Environmental Life Cycle Assessment of Silage Maize in Relation to Regenerative Agriculture

The demand for agricultural products is growing and is resulting in significant environmental impacts due to the overuse of fertilizers (and pesticides in some cases). There is a continued need to find sustainable methods in agricultural systems without harming the environment. Regenerative agriculture can be considered as one of the best methods of sustainable agriculture. The aim of this comparative life cycle assessment (LCA) study was to quantify the environmental impacts associated with the production of silage maize at different doses of fertilizers and pesticides under conventional agriculture and without the use of fertilizers and pesticides under regenerative agriculture. The input data were obtained from the experimental fields and supplemented by background process databases of Ecoinvent, World Food Live Cycle Assessment Database (WFLCD), and the French database AGRIBALYSE. The results of the study were related to six midpoint impact categories: global warming, marine eutrophication, freshwater eutrophication, freshwater ecotoxicity, marine ecotoxicity, and terrestrial ecotoxicity. Although the variant of growing silage maize without the use of fertilizers and pesticides according to the principle of regenerative agriculture showed the lowest burden on the environment, the yields of the cultivated silage maize were 43–55% lower than those of the fertilized variants.

Glolocal Value Chains: A Core-Periphery Framework

A distinction is drawn in a developing economy between ‘town’ and ‘country’, or between ‘agriculture’ and ‘industry’. The connections between the two are the supply of agro-inputs, notably food, from the countryside to the town and the supply of capital goods from the town to the countryside. The distinction mirrors the division between the ‘core’ and the ‘periphery’ or between the ‘North’ and the ‘South’ internationally. The Global Value Chain (GVC) that operates here is the demand for agricultural products like food and raw materials by both the core and the periphery and the demand for land by the former. We use the framework of Marxian political economy to appraise GVCs. The latter are embedded in a model of the economy which consists of capitalists and workers. A metric used is the ratio of profits to the wage bill or exploitation. On a constructive note, we make the case for state-induced technological change in the countryside.

Features of modeling in agricultural production with risks

For the modern economy, the agricultural sector is an important link. In a market economy, agricultural management processes have become significantly more complex, as they develop under conditions of uncertainty. This leads to the need to take into account not only traditional factors of economic activity, but also supply and demand for agricultural products, weather conditions, which largely determine the economic efficiency of the agricultural sector. The uncertainty inherent in the agro-industrial complex is the main difficulty in scientific research into the structural and functional organization of this complex. Uncertainty is generated by many factors, the most important for agricultural production are weather and market conditions, scientific and technological progress. The need to take into account and manage weather risk arises from the objective requirements of a market economy, which requires enterprises to find ways to better adapt to external and internal conditions of operation and development of production. Agricultural production is constantly being reformed in market conditions. In this regard, there is a need to develop new methodological approaches to planning the safety and development of agricultural enterprises, associations and the agro-industrial complex as a whole. The study of these issues is possible through the use of economic-mathematical modeling and computers. The article discusses the concept of planning the use and development of agricultural enterprises under conditions of uncertainty. The concept is based on the development of organizational, economic and methodological approaches to the formation of the planning process, which provides agricultural enterprises with the prerequisites for better adaptation of production in conditions of weather risk, increasing the competitive level of enterprise sustainability.

Analisis Eksternal Sebagai Upaya Pengembangan Bisnis Perdagangan Internasional Baru (Studi Kasus: PT. GFA)

Indonesia is known as an agricultural country with abundant natural wealth and the majority of its population work as farmers. Agricultural products in Indonesia are in excess supply, while market demand for agricultural products abroad is very large. PT. GFA is a company that exports agricultural products from local farmers and SMEs which exports on HS07 and HS09. From this research it was found that the government has a very large role in the export business, for example through international agreements. From economic factors, there was an increase in vegetable exports but a decrease in coffee exports. From a socio-cultural perspective, PT. GFA can be sourced from local farmers. The existence of technological factors makes it easier for PT. GFA in marketing and control. Furthermore, PT. GFA has moderate buyer bargaining power, as evidenced by the Indonesian market penetration of trade value in Asia. However, the bargaining power of suppliers at PT. GFA is high, this is because the company does not have the goods but takes them from suppliers. On the other hand, the threat of small substance products is because HS07 and HS09 are commodities that cannot be replaced. Lastly, PT.GFA has a low threat of new entrants because to become an exporter requires quite a large amount of capital.

Development of an applicability and performance evaluation tool based on energy simulation for smart greenhouse cooling packages

Rapid population growth and a surge in the demand for agriculture products have necessitated improvements in food security and sustainable food production. Shortening the fallow period due to high temperature through cooling in a smart greenhouse during summer, extending the crop harvest period, and improving productivity can help achieve this goal. Cooling during summers requires excessive cooling energy for maintaining optimal crop growth conditions. Therefore, for effective decision-making in introducing and controlling the cooling system, a greenhouse model that accurately predicts the process of mass and energy change, including the effect of crops, and an adequate evaluation tool are necessary. In this study, three cooling packages applicable to smart greenhouses were proposed, and a greenhouse energy model realizing plant transpiration was developed using EnergyPlus. Subsequently, 1,296 simulation datasets were constructed based on the developed model and a combination of four design variables: installation area, scale, crops, and covering materials. Therefore, a cooling package applicability and performance evaluation tool was developed based on the simulation datasets through three evaluation indices: temperature control, humidity control, and energy cost. The developed evaluation tool can enable to select an efficient cooling package for sustainable crop production.

Research on the Management Model and Performance Evaluation of Guangxi ASEAN Cross border Agricultural Product Supply Chain

With the proposal of the "Belt and Road Initiative", China's cross-border trade has been further developed. In order to comply with the development trend of "double cycle", the trade of agricultural products can make full use of "two resources and two markets" both at home and abroad. However, the supply chain of cross-border trade of agricultural products involves many links and has a wide range of chain coverage, showing relatively complex characteristics. In order to realize the sustainable development of cross-border agricultural products trade, an empirical study on the management model and performance evaluation of Guangxi-ASEAN cross-border agricultural products supply chain was carried out. On the basis of defining the concept of Guangxi-ASEAN cross-border agricultural product supply chain, this study first analyzed the management mode of Guangxi-ASEAN cross-border agricultural product supply chain, designed the performance evaluation model of Guangxi-ASEAN cross-border agricultural product supply chain according to the importance of cross-border logistics and demand of agricultural products, and then set evaluation indicators on the basis of target level and criterion level through the selection rules of evaluation indicators. Principal component analysis was used to evaluate the scores of each index, so as to obtain the evaluation level of Guangxi-ASEAN cross-border agricultural supply chain performance, and complete the empirical analysis of performance evaluation. The empirical analysis results are as follows: The logistics system of Guangxi-ASEAN cross-border trade is gradually improved, the delivery timeliness and accuracy of cross-border trade are constantly increasing, the quality of cross-border agricultural products is gradually enhanced, and the information exchange of cross-border trade is becoming more transparent. This improves the satisfaction of consumers, increases the profits of enterprises, reduces the customs clearance time and finished products of agricultural trade, and strengthens the close trade cooperation between Guangxi and ASEAN countries.

Weed–Crop Segmentation in Drone Images with a Novel Encoder–Decoder Framework Enhanced via Attention Modules

The rapid expansion of the world’s population has resulted in an increased demand for agricultural products which necessitates the need to improve crop yields. To enhance crop yields, it is imperative to control weeds. Traditionally, weed control predominantly relied on the use of herbicides; however, the indiscriminate application of herbicides presents potential hazards to both crop health and productivity. Fortunately, the advent of cutting-edge technologies such as unmanned vehicle technology (UAVs) and computer vision has provided automated and efficient solutions for weed control. These approaches leverage drone images to detect and identify weeds with a certain level of accuracy. Nevertheless, the identification of weeds in drone images poses significant challenges attributed to factors like occlusion, variations in color and texture, and disparities in scale. The utilization of traditional image processing techniques and deep learning approaches, which are commonly employed in existing methods, presents difficulties in extracting features and addressing scale variations. In order to address these challenges, an innovative deep learning framework is introduced which is designed to classify every pixel in a drone image into categories such as weed, crop, and others. In general, our proposed network adopts an encoder–decoder structure. The encoder component of the network effectively combines the Dense-inception network with the Atrous spatial pyramid pooling module, enabling the extraction of multi-scale features and capturing local and global contextual information seamlessly. The decoder component of the network incorporates deconvolution layers and attention units, namely, channel and spatial attention units (CnSAUs), which contribute to the restoration of spatial information and enhance the precise localization of weeds and crops in the images. The performance of the proposed framework is assessed using a publicly available benchmark dataset known for its complexity. The effectiveness of the proposed framework is demonstrated via comprehensive experiments, showcasing its superiority by achieving a 0.81 mean Intersection over Union (mIoU) on the challenging dataset.

Inversed virtual water flow pattern and its influencing factors in Northwest China

Northwest China faces an imbalance between scarce water resources and increasing water use demands for agricultural production and economic growth. However, the shifts in virtual water flow (VWF) patterns in Northwest China and its the driving factors are still unclear. In this study, we employed the Multiregional Input–Output (MRIO) method to investigate the evolution of VWF patterns among Northwest China’s primary, secondary, and tertiary industries and analyze their driving factors through Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. The results show that (1) Northwest China changed from 7.24 × 108 m3 net inflow to 175.70 × 108 m3 net outflow from 2002 to 2007. (2) More than 90 % of virtual water of the primary industry was used locally, but both secondary and tertiary industries depended more on virtual water inflow in Northwest China. (3) The virtual water outflow from Northwest China is mostly to the Jing-Jin Region, followed by the Northern Coast, and the main inflow is from the Central Region. (4) Population, economy, and technology affect virtual water from inflow to outflow, and technological development helps to decrease virtual water flow volumes. In conclusion, dramatic decrease of second and third industries inflow is the main reason for the changes from net inflow to net outflow of Northwest China. Meanwhile, it is feasible to increase technological support to further reducing virtual water consumption in primary industry, and strengthen virtual water trades of secondary and tertiary industrial virtual water inflows by promoting population and economic conditions.

STUDY OF THE KINEMATICS OF THE POSITIONING MECHANISMS OF WIDE-GRIP MACHINE-TRACTOR UNITS

As stated in the humanitarian food program "Grain from Ukraine", launched by the President of Ukraine Volodymyr Zelenskyi: "The demand for agricultural products in the world is growing by 2-3% every year, and the shortage of grain on the markets of Africa and Asia may reach 10-15% already in 2024". Therefore, the issue of production of agricultural products is relevant today. As the analysis of the market of agricultural machinery shows, one of the directions of the development of the design of agricultural units is to increase the width of grip of tools for soil cultivation and plant care. The use of agricultural units with an increased working width has certain advantages: - reduction of the number of passes of equipment on the ground; - reduction of fuel consumption; - reducing the negative impact of the unit on soil density. Among the disadvantages of this type of aggregates should be attributed the increase in turning radii, as a result of the increase in width. It is possible to eliminate this shortcoming by quickly transferring the unit from the working position to the transport position and vice versa. To fulfill this task, a review of known designs of wide-grip aggregates was carried out. Known hydraulic drives for raising and lowering and folding and unfolding sections are considered. A kinematic diagram of the mechanism of rotation of the extreme section of the cultivator relative to the frame has been developed. Based on this calculation scheme, the reactions in the hinges were determined and the graphical dependencies of the mutual location of individual elements with respect to the angle of rotation φ were calculated. The existing hydraulic positioning mechanisms, which are equipped with chainless wide-grip machines and tools with articulated frames, do not provide the necessary maneuvering properties of MTA during their movement on the turning lane. Equipping hydroficated positioning mechanisms with follower stabilizing devices is a promising direction for improving hydraulic drives of machine-tractor units, which create prerequisites for reducing the turnaround time on the turning lane by selecting the parameters of the specified equipment. The most important factor affecting the stability of the positioning of the sections of wide-grip machine-tractor units is the variable load that occurs on the hydraulic cylinder rod at the moment of turning the sections around the horizontal hinges at an angle of more than 90⸰, which causes inconsistency in the flows of the working fluid entering and leaving the hydraulic cylinder , which leads to the interruption of the flow in the cavity of the hydraulic cylinder, which is connected to the injection hydraulic line.

Optimization of pXRF instrumentation conditions and multivariate modeling in soil fertility attributes determination

The increasing global demand for agricultural products, food, and energy requires a sustainable and secure approach to agricultural production. Soil nutrition is vital for enhancing agricultural productivity, and understanding the spatial variability of fertility attributes is crucial in soil fertility prevention. Traditional soil analysis methods are time-consuming, costly, and generate waste. Spectral-analytical techniques, such as energy dispersive X-ray fluorescence (EDXRF) combined with multivariate analysis, offer a faster, cost-effective, and environmentally friendly alternative. This study aimed to optimize measurement conditions for predicting soil fertility attributes using a commercial routine from a portable EDXRF (pXRF) equipment and compare its performance with benchtop EDXRF using Partial Least Squares Regression (PLS) modeling. The data fusion from different measurement conditions using low- and mid-level fusion approaches was explored to evaluate the models' synergy. 364 soil samples at three different depths were used. The study area covers 12 ha of Red Latosol and Red Nitosol. Ten fertility attributes were evaluated: exchangeable macronutrients (Ca2+, Mg2+, and K+), available phosphorus (PM), pH, potential acidity (H + Al), soil organic carbon (SOC), cation exchange capacity (CEC), sum of exchange bases (SB) and base saturation percentage (BSP). The results indicated that the measurement of the three experimental conditions tested is unnecessary, and the data fusion does not significantly improve the model's performance. The use of 15 kV and 12.15 μA without filters provided the best performance results for all attributes simultaneously. By the established RPD ranges, the SOC, CEC, SB, and Ca2+ models were considered very good for quantitative prediction (RPD >2.0), while the K+ and Mg2+ models were considered good for quantitative predictions (1.8 < RPD < 2.0). The PM, pH, BSP, and H + Al models provided fair predictions, which may be used for evaluation and correlation (RPD <1.8). Furthermore, the pXRF optimized condition provided results equivalent to those modeled with benchtop EDXRF data. These results highlight the potential of pXRF as an economical, rapid, and efficient alternative approach to the conventional method, and may provide valuable insight for decision-making related to the use of fertilizers and other soil correction products.