Agricultural Production Research Articles

Protecting farmers or protecting institutions? An analysis of strategies to leverage high-quality development of county-specific agricultural insurance

The lagging development of agricultural insurance in the field of new agricultural and specialty agricultural products has become an inherent cause of structural imbalance in China’s agricultural insurance market. How to leverage the new agricultural products agricultural insurance market with limited financial payment capacity is especially critical to promote high-quality agricultural development. This paper constructs an evolutionary game analysis model of government-farmers-insurance companies and compares the effects of two differential subsidy models, “subsidizing farmers” and “subsidizing institutions”, on the participation and underwriting strategies of new agricultural insurance products. The conclusions show that only when the insurance company has positive returns (E-F > 0) can the insurance company provide insurance services, and the government provides subsidies for operating costs to ensure that the insurance company does not operate at a loss, which is the single strategic support condition for the insurance company to provide insurance products. The “insured farmers” approach, due to the scale effect of the insured farmers, will make the insurance company to ensure the profitability, raise the premium level or compress the market size. The adjustment of financial subsidies from “insuring farmers” to “insuring institutions” can ensure that the business scale of insurance companies can effectively leverage the supply of special agricultural insurance. Finally, this paper puts forward suggestions for the optimization of agricultural insurance policies in new agricultural business fields.

The Molecular Identification of Isolated Fluoride-resistant Plant Growth-promoting Fungi

Background: This study is about finding plant growth-promoting fungi (PGPF) that can resist fluoride, which is important for sustainable farming, especially in areas with fluoride contamination. Fluoride is commonly found in water and soil and it can harm plant health and agricultural productivity. Some fungi have developed ways to grow in high-fluoride conditions, helping plants grow and aiding in cleaning up the environment. We focused on identifying useful PGPF from the soil around plant roots, which is known for having many diverse microbes. Method: We tested the successful fungi isolates for their PGPF capabilities by using PCR to amplify the Internal Transcribed Spacer (ITS) region, a commonly used technique for identifying different fungal species. After amplifying the DNA, we sequenced the PCR products and analyzed the sequences using the BLAST database to identify the species. Result: Our research identified important fluoride-tolerant PGPF species, including Aspergillus flavus, Aspergillus niger, Cladosporium cladosporioides and Penicillium chrysogenum. These fungi showed abilities such as breaking down phosphate, producing growth hormones and stopping pathogens in high fluoride conditions. Adding these PGPF to farming practices has the potential to create strong fungal inoculants that can boost crop growth and productivity in areas affected by fluoride. Overall, this study lays the groundwork for more research and field tests to confirm how these fungi can be used practically, which may help improve sustainable farming and food security.

The multifaceted roles of PP2C phosphatases in plant growth, signaling, and response to abiotic and biotic stress.

The multifaceted roles of PP2C phosphatases in plant growth, signaling, and response to abiotic and biotic stress.

Negative ecological impacts of honeybees begin at densities below recommended levels for crop pollination

Abstract In recent decades, managed honeybee (Apis mellifera L.) use has increased across the globe, primarily motivated by the demand for crop pollination and honey production. As agricultural practices become increasingly dependent on honeybees, concerns have emerged about the potential ecological consequences of their widespread use. High‐density honeybee populations, often maintained near agricultural areas, can influence local biodiversity by altering competition dynamics among pollinators, changing plant–pollinator relationships and affecting the availability of floral resources. While the ecological impacts of managed honeybees have been widely studied, most research has focused on their presence or absence, overlooking how varying hive densities might shape ecosystem dynamics. We conducted a comprehensive review of studies examining the ecological impacts of managed honeybees, emphasizing variations in hive densities and their consequences for pollinator communities, plant–pollinator interactions and crop yields. We synthesized findings to identify density thresholds associated with significant ecological disruptions. Our review shows that increasing hive densities reduces visitation rates and pollinator richness, while the benefits for fruit production appear minimal relative to the damage they can potentially cause. Synthesis and applications: The ecological consequences of high‐density honeybee management are significant, calling for a re‐evaluation of pollination strategies in agricultural landscapes. Setting evidence‐based hive density guidelines is critical to balancing agricultural productivity with the conservation of native pollinator communities and the maintenance of healthy ecosystem dynamics.

Estimation of Friction Coefficients of Soybean Seeds with Soft Computing Approach

Determination of physical and mechanical properties of agricultural products plays an important role in the usage areas of the products and industrial applications. Correct determination and evaluation of physical and mechanical properties of agricultural products is of critical importance in determining the quality, durability and usage potential of the product. In this study, the relationship between moisture content and friction coefficients of Samsoy variety soybean seed, which is a trial material, was determined in order to contribute to making correct decisions in industrial design and material selection. The central aim of this research is to expose with different moisture contents and friction surfaces well-accepted data-driven models to predict friction coefficients for soybean seed using different soft computing techniques. Determination of friction coefficient of agricultural products is important in terms of design and functionality of equipment used in post-harvest technologies and agricultural applications. In the study, 3 different moisture contents and five different friction surfaces (steel, stainless steel, galvanized sheet, PVC, court fabric) were used. Artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), group method of data handling (GMDH) are used to predict of friction coefficients. The best accuracy values were recorded as GMDH 7-7-1 for seven input and 7-15-1 model for five input structures for kinetic and static friction that were calculated performance criteria R2 = 0.99-0.98, RMSE =0.00004-0.00006 , MSE = 0.00009 -0.00011, respectively. These selected the best models predicted which can be used in the soft computing techniques determined different conditions to estimating the friction coefficient for soybean seeds.

Harnessing remote sensing for smart agriculture

Remote sensing technologies are transforming smart agriculture by delivering real-time data for decision-making in several spheres of crop health monitoring, precision irrigation, soil analysis and pest control. Crop growth stage monitoring, disease diagnosis and measurement of soil moisture are all made possible through these technologies which rely on advanced image processing algorithms and machine learning techniques. With this integration, farmers can implement precision agriculture practices, which in turn reduces resource waste and maximizes crop yields. Geographic information system (GIS) is also used to create detailed maps of agricultural areas, enabling the implementation of location-specific management practices. However, there are significant barriers that need to be addressed, including the requirement for high -resolution data, weather dependency and the need for technical capability. Despite these challenges, remote sensing technology has the potential to significantly improve agricultural productivity and sustainability. It is expected that further developments in remote sensing technology will lead to extensive application of the technology as well as tremendous impact on the agriculture sector.

Wpływ scalenia gruntów na poprawę struktury przestrzennej i warunków gospodarowania na obszarach dotkniętych budową autostrad

The article discusses the process of land consolidation as a corrective measure used to minimize the negative effects of highway construction. Linear investments, such as highways, cause the fragmentation of agricultural land, leading to numerous problems related to the use of that land, and to limited transport accessibility. As a result, farmers often lose the ability to manage their plots efficiently, which affects the profitability of agricultural production and hinders the development of farms. Moreover, the lack of access to public roads increases transportation costs and reduces the competitiveness of agriculture in the given area. To mitigate these effects, the land consolidation process is applied, allowing for the reorganization of agricultural space, improvement of land structure, and ensuring access to transport infrastructure. The subject of the analysis is the land consolidation process carried out in the northwestern part of Tarnów, where the construction of the A4 highway caused significant disruption to the layout of land plots (parcels) and road network. The goal of the process was to eliminate excessive land fragmentation, improve access to public roads, and modernize the land layout to enable more efficient use in terms of farming and development. The consolidation work included, among other things, the design of a new network of access roads, the elimination of plots without infrastructure access, and the adaptation of space to the needs of local farms and enterprises. The article emphasizes that similar measures are also applied in other European countries, where land consolidation is an essential element of spatial planning.

ST-CFI: Swin Transformer with convolutional feature interactions for identifying plant diseases

The increasing global population, coupled with the diminishing availability of arable land, has rendered the challenge of ensuring food security more pronounced. The prompt and precise identification of plant diseases is essential for reducing crop losses and improving agricultural yield. This paper introduces the Swin Transformer with Convolutional Feature Interactions (ST-CFI), a state-of-the-art deep learning framework designed for detecting plant diseases through the analysis of leaf images. The ST-CFI model effectively integrates the strengths of the Convolutional Neural Networks (CNNs) and Swin Transformers, enabling the extraction of both local and global features from plant images. This is achieved through the implementation of an inception architecture and cross-channel feature learning, which collectively enhance the information necessary for detailed feature extraction. Comprehensive experiments were conducted using five distinct datasets: PlantVillage, Plant Pathology 2021 competition dataset, PlantDoc, AI2018, and iBean. The ST-CFI model exhibited exceptional performance, achieving an accuracy of 99.96% on the PlantVillage dataset, 99.22% on iBean, 86.89% on AI2018, and 77.54% on PlantDoc. These results underscore the model’s robustness and its capacity to generalize across various datasets and real-world conditions. The high accuracy and F1 scores, in conjunction with low loss values, further validate the model’s efficacy in learning discriminative features. The ST-CFI model signifies a substantial advancement in the early and accurate detection of plant diseases, serving as a valuable instrument for precision agriculture. Its capacity to integrate CNNs and Transformers within a unified framework enhances the model’s feature extraction capabilities, resulting in improved accuracy in the identification of plant diseases. This study concludes that the ST-CFI model effectively addresses plant disease detection challenges, with significant implications for agricultural sustainability and productivity.

Improved YOLOv8n Method for the High-Precision Detection of Cotton Diseases and Pests

Accurate detection of cotton pests and diseases is essential for agricultural productivity yet remains challenging due to complex field environments, the small size of pests and diseases, and significant occlusions. To address the challenges presented by these factors, a novel cotton disease and pest detection method is proposed. This method builds upon the YOLOv8 baseline model and incorporates a Multi-Scale Sliding Window Attention Module (MSFE) within the backbone architecture to enhance feature extraction capabilities specifically for small targets. Furthermore, a Depth-Separable Dilated Convolution Module (C2f-DWR) is designed to replace the existing C2f module in the neck of the network. By employing varying dilation rates, this modification effectively expands the receptive field and alleviates the loss of detailed information associated with the downsampling processes. In addition, a Multi-Head Attention Detection Head (MultiSEAMDetect) is introduced to supplant the original detection head. This new head utilizes diverse patch sizes alongside adaptive average pooling mechanisms, thereby enabling the model to adjust its responses in accordance with varying contextual scenarios, which significantly enhances its ability to manage occlusion during detection. For the purpose of experimental validation, a dedicated dataset for cotton disease and pest detection was developed. In this dataset, the improved model’s mAP50 and mAP50:95 increased from 73.4% and 46.2% to 77.2% and 48.6%, respectively, compared to the original YOLOv8 algorithm. Validation on two Kaggle datasets showed that mAP50 rose from 92.1% and 97.6% to 93.2% and 97.9%, respectively. Meanwhile, mAP50:95 improved from 86% and 92.5% to 87.1% and 93.5%. These findings provide compelling evidence of the superiority of the proposed algorithm. Compared to other advanced mainstream algorithms, it exhibits higher accuracy and recall, indicating that the improved algorithm performs better in the task of cotton pest and disease detection.

The dual role of selenium in mitigating cadmium toxicity and inhibiting trace element uptake in rice highlights the critical trade-off for safe agricultural production.

The dual role of selenium in mitigating cadmium toxicity and inhibiting trace element uptake in rice highlights the critical trade-off for safe agricultural production.

Functional Analysis of Double Bituminous Surface Treatments

Purpose: The purpose of this study is to assess the performance of double bituminous surface treatment on the Malekhu–Dhading Beshi (MDB) Road in Nepal-Asia. Design/Methodology/Approach: The descriptive study was conducted to evaluate the compatibility of the road condition assessment method for analysing results and assessing road conditions between 2012 and 2021. The assessment method consists of the International Roughness Index (IRI), the Surface Distress Index (SDI) and the Pavement Serviceability Rating (PSR). The correlation between SDI and IRI, SDI and Average Annual Daily Traffic (AADT), IRI and AADT, SDI and Age of pavement, and IRI and Age of pavement were obtained from the correlation analysis. Research Limitation: The study lacked adequate data on the quality and availability of the performance of the road projects and the delays in the study area. Findings: The relation between IRI-Traffic and SDI-IRI is positive, with R2 values of 0.0713 and 0.6831, respectively. The relation between IRI and Traffic is poor, and the relation between SDI and IRI is good. The relation between SDI-Traffic and SDI-Age of pavement is logarithmic, with R2 values of 0.4786 and 0.4319, respectively, which is a moderate relationship. The relation between the Ages of pavement and IRI is polynomial with an R2 value of 0.2676, indicating a poor relationship. Pavements in this category (value of PSR between 1.00 and 2.00) have deteriorated to such an extent that they affect the speed of free-flow traffic. Practical Implication: Understanding performance characteristics enables the strategic timing of applications, the selection of appropriate treatment types, and the prediction of maintenance cycles. This leads to a more efficient allocation of public resources and extended pavement life cycles. Social Implication: Enhanced road surfaces facilitate emergency vehicle access, school bus transportation, and agricultural product movement, directly impacting quality of life and social equity. Originality/Value: This pavement deterioration model can be used for the forecast of future values of IRI. This model is the basis for the assessment of Double Bituminous Surface Treatment pavement.

PROMOTING NEW AGRICULTURAL TECHNOLOGY ADAPTATION: A REVIEW OF THEORETICAL AND PRACTICAL ISSUES, AND SOLUTIONS FOR VEGETABLE FARMERS IN SVAY RIENG PROVINCE, CAMBODIA

An idea for sustainable agricultural development, new agricultural technology adaptation (NATA) in agricultural production helps overcome problems in agricultural production by utilizing the best aspects of technology application. In order to identify the obstacles and suggest some solutions for improving the NATA in agricultural production in Cambodia in the future, this research reviewed theoretical and practical difficulties on boosting NATA in Svay Rieng province. The study demonstrated that the NATA application in agricultural production is an unavoidable route for the agriculture sector development in Svay Rieng under the context of global integration and impacts of the industry revolution 4.0. This was based on secondary information and data from published papers and documents. Thus, the Cambodian government has recently given close attention to NATA promotion in agricultural output. Despite the impressive accomplishments, there are currently few businesses and farm households using NATA in agricultural output, and NATA growth in Svay Rieng still confronts many obstacles. Improving the current policy structure is essential for raising the NATA in agricultural output in Svay Rieng. Additionally, in the near future in Svay Rieng province, NATA application implementation, NATA development and planning, human resource training, credit assistance, and propaganda on the NATA's effectiveness in agricultural production should all be strengthened.

Creating American Farmland: Governance Institutions and Investment in Agricultural Drainage

The Corn Belt is famously responsible for the bulk of U.S. corn production, and over half of its production comes from counties that rely on artificial drainage. We trace the history of this extensive investment in farmland and document the importance of a key institutional innovation, the drainage management district, which increased the land value of naturally wet eastern U.S. counties by 20–37 percent ($16.8–18.7 billion in 2020 dollars). While dramatically increasing agricultural productivity, drainage converted more than half of the 215 million acres of wetlands estimated to have existed in the United States at the time of colonization to agriculture.

Subsistence Procurement and Production after the Demise of Teotihuacan

Abstract Macro- and microbotanical remains recovered from post-Teotihuacan occupations in quarry tunnels east of the Sun Pyramid, Teotihuacan, contribute to understanding lifeways in the surrounding valley after the partial abandonment of the city. Plant remains associated with domestic and ritual contexts from the excavations directed by Linda Manzanilla (1993–1996) are relevant to subsistence questions, aspects of surrounding vegetation, landscape exploitation, and the possibility of less-intensive agricultural production during the Epiclassic and Early Postclassic occupations.

Agronomic Characteristics and Kinship Of 10 High Yielding Inbred Rice Varieties (Oryza Sativa L.) In West Java

Background. The success of rice production is influenced by many factors, one of which is the selection of varieties. Varieties, as a supporting factor in increasing agricultural productivity, will be able to show values according to potential yields with optimal farming management. Aims. This study aims to examine the differences in agronomic characteristics and kinship of 10 high-yielding inbred rice varieties (Oryza sativa L.) in West Java. The experiment was conducted at the Variety Display Land, Pamekaran Village, Soreang District, Bandung Regency. The experiment was conducted from April to August 2024. The experimental location was situated at an altitude of 700 m above sea level. Methods. The agronomic characteristic experiment method used a qualitative descriptive approach. In the experimental approach, a Randomized Block Design (RBD) was employed with 10 inbred rice phenotype treatments, repeated three times. The materials used in this experiment were 10 high-yielding inbred rice varieties. Result. Based on the study's results, similarities were observed in morphological characters among several inbred rice varieties, including those in Group I, Inpari 32 HDB, and Situ Bagendit, with a similarity level of 100%. In Group II, the Mekongga, Inpari 42, Inpari 33, and Inpari 30 varieties, and in Group III, the Ciherang and Padjadjaran Cakrabuana Agritan varieties, achieved a coefficient value of 78%. Conclusion. The similarity is 0.10 or 100% with a similarity coefficient level of 100% seen from the morphological character data based on the Rice UVOP Table

GeoAI-based soil erosion risk assessment in the Brahmaputra River Basin: a synergistic approach using RUSLE and advanced machine learning.

Soil erosion is a critical environmental issue in the Brahmaputra River Basin, threatening agricultural productivity, water resources, and ecological balance. This study employs the revised universal soil loss equation (RUSLE) alongside remote sensing, geographic information systems (GIS), and advanced machine learning models like random forest (RF) and gradient boosting (GB) to analyze soil erosion patterns from 2005 to 2024. The analysis revealed that average annual soil loss increased from 15.8 tons/ha/year in 2005 to 25.4 tons/ha/year in 2024, marking a 60.76% rise over two decades. Peak erosion rates were observed in 2020, with localized hotspots recording up to 32,130 tons/ha/year. Spatial analysis from 2005 to 2024 indicated substantial variability, with soil loss values ranging from - 7.024 to 9034 tons/ha in 2005. Topographic influence, quantified using the LS factor, revealed that 47.2% of the basin area has slopes steeper than 16°, significantly contributing to elevated erosion risk. The rainfall erosivity (R-factor) fluctuated throughout the period, peaking at 2305.73MJmm/ha h year in 2015 but declining to 799.21MJmm/ha h year by 2024, indicating a temporal shift in rainfall patterns. Vegetation cover improvements during this time reduced the mean C-factor from 0.52 to 0.34, though 13.8% of the basin (approximately 3.05 million ha) still falls under high to very high erosion risk zones. RF model predictions achieved an R2 of 0.915 and RMSE of 4.82, while GB attained an R2 of 0.952 with RMSE of 3.97, indicating superior predictive performance. These findings underscore the urgent need for targeted soil conservation measures, afforestation programs, and sustainable watershed management. The integration of AI-driven modeling with remote sensing and GIS provides a robust framework for long-term soil erosion monitoring, enabling informed decision-making for climate adaptation in the region.

Policy-driven improvements in cultivated land productivity: Changed determinants in Huang-Huai-Hai Plain, China.

Policy-driven improvements in cultivated land productivity: Changed determinants in Huang-Huai-Hai Plain, China.

Spatiotemporal Analysis of Drought Variation from 2001 to 2023 in the China–Mongolia–Russia Transboundary Heilongjiang River Basin Based on ITVDI

Drought impacts agricultural production and regional sustainable development. Accordingly, timely and accurate drought monitoring is essential for ensuring food security in rain-fed agricultural regions. Alternating drought and flood events frequently occur in the Heilongjiang River Basin, the largest grain-producing area in Far East Asia. However, spatiotemporal variability in drought is not well understood, in part owing to the limitations of the traditional Temperature Vegetation Dryness Index (TVDI). In this study, an Improved Temperature Vegetation Dryness Index (ITVDI) was developed by incorporating Digital Elevation Model data to correct land surface temperatures and introducing a constraint line method to replace the traditional linear regression for fitting dry–wet boundaries. Based on MODIS (Moderate-resolution Imaging Spectroradiometer) normalized vegetation index and land surface temperature products, the Heilongjiang River Basin, a cross-border basin between China, Mongolia, and Russia, exhibited pronounced spatiotemporal variability in drought conditions of the growing season from 2001 to 2023. Drought severity demonstrated clear geographical zonation, with a higher intensity in the western region and lower intensity in the eastern region. The Mongolian Plateau and grasslands were identified as drought hotspots. The Far East Asia forest belt was relatively humid, with an overall lower drought risk. The central region exhibited variation in drought characteristics. From the perspective of cross-national differences, the drought severity distribution in Northeast China and Inner Mongolia exhibits marked spatial heterogeneity. In Mongolia, regional drought levels exhibited a notable trend toward homogenization, with a higher proportion of extreme drought than in other areas. The overall drought risk in the Russian part of the basin was relatively low. A trend analysis indicated a general pattern of drought alleviation in western regions and intensification in eastern areas. Most regions showed relatively stable patterns, with few areas exhibiting significant changes, mainly surrounding cities such as Qiqihar, Daqing, Harbin, Changchun, and Amur Oblast. Regions with aggravation accounted for 52.29% of the total study area, while regions showing slight alleviation account for 35.58%. This study provides a scientific basis and data infrastructure for drought monitoring in transboundary watersheds and for ensuring agricultural production security.

Enhancing Farmer Resilience Through Agricultural Insurance: Evidence from Jiangsu, China

Against the backdrop of evolving global climate patterns, the frequency and intensity of extreme weather events have increased significantly, posing unprecedented threats to agricultural production. This change has particularly profound impacts on agricultural systems in developing countries, making the enhancement of farmers’ capacity to withstand extreme weather events a crucial component for achieving sustainable agricultural development. As an essential safeguard for agricultural production, agricultural insurance plays an indispensable role in risk management. However, a pronounced gap persists between policy aspirations and actual adoption rates among farmers in developing economies. This study employs the integrated theory of planned behavior (TPB) and protection motivation theory (PMT) to construct an analytical framework incorporating psychological, socio-cultural, and risk-perception factors. Using Jiangsu Province—a representative high-risk agricultural region in China—as a case study, we administered 608 structured questionnaires to farmers. Structural equation modeling was applied to identify determinants influencing insurance adoption decisions. The findings reveal that farmers’ agricultural insurance purchase decisions are influenced by multiple factors. At the individual level, risk perception promotes purchase intention by activating protection motivation, while cost–benefit assessment enables farmers to make rational evaluations. At the social level, subjective norms can significantly enhance farmers’ purchase intention. Further analysis indicates that perceived severity indirectly enhances purchase intention by positively influencing attitude, while response costs negatively affect purchase intention by weakening perceived behavior control. Although challenges such as cognitive gaps and product mismatch exist in the intention-behavior transition, institutional trust can effectively mitigate these issues. It not only strengthens the positive impact of psychological factors on purchase intention, but also significantly facilitates the transformation of purchase intention into actual behavior. To promote targeted policy interventions for agricultural insurance, we propose corresponding policy recommendations from the perspective of public intervention based on the research findings.

Awareness and Farmers’ Perception towards Biostimulants in Deesa Taluka of Banaskantha, Gujarat, India

Modernizing the agriculture sector is essential to address current challenges and ensure long-term sustainability. Traditional agricultural practices—particularly the excessive use of chemical fertilizers and pesticides—have significantly contributed to environmental degradation, resulting in issues such as soil erosion, water contamination, and a decline in biodiversity. That’s how focus now shifted towards eco-friendly inputs to revitalize production with sustainable future for generations. Biostimulants plays vital role to fulfil the current demand with quality agriculture production and support sustainability too. This study was carried out to find farmers’ awareness and perception towards biostimulants. The 200 respondence were selected using purposive sampling from the study area with the help of semi-structured scheduled. Ten villages were chosen for the research. Twenty farmers from each village were selected through purposive sampling technique. The study found that the majority of farmers are well aware of biostimulants and use them to enhance crop yields. The study suggests that increased use of extension tools can improve the dissemination of information. It also recommends conducting more demonstrations and farmer training programs to enhance outreach and maximize the benefits, supporting sustainable agricultural production.