Development Of Modern Agriculture Research Articles

Assessment of the Coupling Degree between Agricultural Modernization and the Coordinated Development of Black Soil Protection and Utilization: A Case Study of Heilongjiang Province

Agricultural management encompasses various processes, including agricultural modernization and land protection and utilization; however, these aspects are seldom considered simultaneously. This study focuses on Heilongjiang Province, a major grain-producing region in China, in efforts to address this gap. The objective is to facilitate the mutual sustainable development of agricultural modernization as well as the protection and utilization of black soil through research and coordination. Statistical data from a case study conducted in Heilongjiang Province serve as the basis for this investigation, aiming to identify contradictions in the coordinated development of agricultural modernization and black soil protection and utilization in the province, design mechanisms to sustain this development, and ensure the mutually supportive progress of both aspects. This research delves into an infrequently explored dimension of the current policies surrounding black soil imposed by the Chinese authorities, offering significant insights into agricultural modernization.

Automatic fruit picking technology: a comprehensive review of research advances

In recent years, the fruit industry has become an important part of agricultural development, and fruit harvesting is a key stage in the production process. However, picking fruits during the harvest season is always a major challenge. In order to solve the challenges of time-consuming, costly, and inefficient fruit picking, researchers have conducted a lot of studies on automatic fruit picking equipment. Existing picking technologies still require further research and development to improve efficiency and reduce fruit damage. Aiming at the efficient and non-destructive picking of fruits, this paper reviews machine vision and mechanical fruit picking technology and the current research status, including the current application status, equipment structure, working principle, picking process, and experimental results. As a promising tool, machine vision technology has been widely researched and applied due to its low hardware cost and rich visual information. With the development of science and technology, automated fruit picking technology integrates information technology, integrates automatic perception, transmission, control, and operation, etc., saves manpower costs, and continuously promotes the development of modern agriculture in the direction of refinement of equipment technology, automation, and intelligence. Finally, the challenges faced by automated fruit picking are discussed, and future development is looked forward to with a view to contributing to its sustainable development.

Security analysis of agricultural energy internet considering electricity load control for dragon fruit cultivation

With the increasing emphasis on sustainable energy and the advancements in modern agriculture, flexible agricultural power loads present challenges to the reliable operation of the agricultural energy internet. However, research on the coupling of energy system security with agricultural security is insufficient and fails to consider the impacts of meteorological elements on agricultural power loads. To address these gaps, this paper establishes load models for irrigation and light supplementation based on the actual cultivation demands of winter dragon fruit in Guangxi province. A static security index system is developed to analyze the security, considering the unique features of agricultural power demands. The condition of the distribution network is assessed by comparing the indexes with predefined limits, using a China 41-bus distribution network. Finally, the optimal scheme for nocturnal supplemental lighting treatment and irrigation is determined based on the method for maintaining secure operation of the distribution network. This study serves as a guide for simulating current farming power loads and demonstrates how security analysis of the agricultural energy internet contributes to the large-scale and sophisticated development of modern agriculture.

Professional standard «Agronomy» in the context of employer's requests

Introduction. In the article, the authors drew attention to the fact that the development of modern agriculture is aimed at finding new ways and methods to increase land productivity and raise farm animals. In the current situation, the interaction between real production and science comes to the fore. Science should provide domestic enterprises with high technologies. However, there is a large gap between universities and the real sector of the economy.Purpose setting. Compliance of the competencies of graduates with the requirements of employers is becoming a modern trend in the development of agricultural education. In the conditions of innovative economic development, the labor market needs specialists who can work in various professional environments and have a high motivation for retraining.Methodology and methods of the study. The secondary analysis of sociological research data on the compliance of the level of professional training of graduates of agricultural universities with the production requirements of today is compared with the results of a sociological study of students of Nizhny Novgorod Agrotechnological University.Results. The researchers not only identified a set of problems inherent in modern agricultural education, but also described specific competencies (according to the federal state educational standard in the area of training «Agronomy» (FGOS 3++), which do not meet the needs of the leading stakeholders of agricultural production, revealed the inconsistency of the subjects of the educational process in understanding and broadcasting professional qualifications.Conclusion. The authors believe that the results obtained determine the relevance of the rights of agricultural universities to make their own adjustments to the formation of the qualifications of graduates.

Evolutionary game analysis of stakeholders’ decision-making behavior in agricultural data supply chain

The significance of agricultural information sharing in fostering agricultural development cannot be overstated. This practice plays a pivotal role in disseminating cutting-edge agricultural technologies, cultivation methods, and pest control strategies, empowering farmers with valuable knowledge to enhance crop yield and quality. Moreover, it aligns with government objectives of resource sharing and addressing gaps, contributing to the advancement of agricultural modernization and the development of the industry chain. Despite its inherent benefits, the practical implementation of agricultural information sharing faces challenges. Stakeholders engaged in information sharing often prioritize individual benefits, potentially leading to a decline in agricultural information quality and the inefficient use of experimental resources. To confront this issue, the present research establishes a three-party evolutionary game model comprising an agricultural product data sharing platform, agricultural data providers, and agricultural data consumers. Leveraging dynamic system theory, the model analyzes the evolutionary stable strategies of stakeholders and investigates the critical factors influencing the strategic choices of these three parties. Experimental findings underscore the pivotal role of participants’ initial strategies, regulatory intensity, reward and punishment mechanisms, and information feedback in shaping stakeholder decision-making behavior. Implementation of measures such as heightened scrutiny of information on the sharing platform and fostering consumer trust in data emerges as imperative for enhancing system stability. These actions are essential for constructing an efficient and reliable information-sharing ecosystem, thereby facilitating the sustainable development of modern agriculture.

Estimation of Soil Moisture Content Based on Fractional Differential and Optimal Spectral Index

Applying hyperspectral remote sensing technology to the prediction of soil moisture content (SMC) during the growth stage of soybean emerges as an effective approach, imperative for advancing the development of modern precision agriculture. This investigation focuses on SMC during the flowering stage under varying nitrogen application levels and film mulching treatments. The soybean canopy’s original hyperspectral data, acquired at the flowering stage, underwent 0–2-order differential transformation (with a step size of 0.5). Five spectral indices exhibiting the highest correlation with SMC were identified as optimal inputs. Three machine learning methods, namely support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN), were employed to formulate the SMC prediction model. The results indicate the following: (1) The correlation between the optimal spectral index of each order, obtained after fractional differential transformation, and SMC significantly improved compared to the original hyperspectral reflectance data. The average correlation coefficient between each spectral index and SMC under the 1.5-order treatment was 0.380% higher than that of the original spectral index, with mNDI showing the highest correlation coefficient at 0.766. (2) In instances of utilizing the same modeling method with different input variables, the SMC prediction model’s accuracy follows the order: 1.5 order > 2.0 order > 1.0 order > 0.5 order > original order. Conversely, with consistent input variables and a change in the modeling method, the accuracy order becomes RF > SVM > BPNN. When comprehensively assessing model evaluation indicators, the 1.5-order differential method and RF method emerge as the preferred order differential method and model construction method, respectively. The R2 for the optimal SMC estimation model in the modeling set and validation set were 0.912 and 0.792, RMSEs were 0.005 and 0.004, and MREs were 2.390% and 2.380%, respectively. This study lays the groundwork for future applications of hyperspectral remote sensing technology in developing soil moisture content estimation models for various crop growth stages and sparks discussions on enhancing the accuracy of these different soil moisture content estimation models.

Preserving traditional systems: Identification of agricultural heritage areas based on agro‐biodiversity

Societal Impact StatementWith the rapid development of modern agriculture and its reliance on high‐yielding and genetically uniform varieties, many traditional agricultural systems are gradually being abandoned. The genetic diversity contained in landraces is crucial for modern eco‐agriculture. An indicator evaluation model combined with machine learning could help to locate and conserve these existing traditional agricultural systems, called agricultural heritage systems (AHS). Here, this method provided the first map of potential areas of Tea‐AHS in China. These results could help policymakers to confirm priorities and rationally allocate conservation resources based on the distribution status and endangerment of AHS. This could also help local people to receive additional support for the transfer of germplasm resources and indigenous knowledge.Summary Modern agriculture is overly dependent on high‐yielding and genetically uniform varieties, whereas traditional agricultural systems contain a large number of genetically diverse landraces and the indigenous knowledge associated with them. We call traditional agricultural systems that survive to the present‐day agricultural heritage systems (AHS). Under the impact of modernization, AHS are gradually disappearing. Identifying these systems is the first step towards conserving them, but the potential areas of AHS related to agro‐biodiversity are not yet clear. Using Chinese tea as an example, this paper provides the first universal method for identifying potential areas of AHS based on agro‐biodiversity and the first map of potential areas of Tea‐AHS in China. The map is constructed based on the maximum entropy model (Maxent) of tea germplasm resources and related indicator functions and has been validated by existing Tea‐AHS in China. The study identified 54 potential areas of Tea‐AHS. These potential areas are mainly concentrated in the southern region, in 15 provinces, including Anhui, Fujian, Guangdong, Yunnan, Guizhou, Guangxi, Hubei, and Hunan. Mangshi, Qimen County, and Chaisang District are among the high potential areas for Tea‐AHS and are the next priority for exploration and conservation work. We have verified the validity of the proposed method, which can help conserve the germplasm resources and traditional wisdom in the global AHS in a timely manner, and contribute to the development of modern and eco‐agriculture.

Isolation, identification, and mechanism analysis of plant growth-promoting rhizobacteria in tobacco.

Plant growth, crop yield, and pest and disease control are enhanced by PGPR (Plant growth promoting rhizobacteria), which are beneficial microorganisms found in a close symbiosis with plant roots. Phytohormones are secreted, nutrient uptake is improved, and soil properties along with the microbiological environment are regulated by these microorganisms, making them a significant focus in agricultural research. In this study, the efficient PGPR strain T1 was isolated and screened from tobacco inter-root soil, and identified and confirmed by ITS sequencing technology. Tobacco growth indicators and soil property changes were observed and recorded through potting experiments. The activities of key enzymes (e.g., sucrase, catalase, urease) in soil were further determined. High-throughput sequencing technology was utilized to sequence the soil microbial community, and combined with macro-genomics analysis, the effects of T1 strain on soil microbial diversity and metabolic pathways were explored. Following the application of T1, significant improvements were observed in the height, leaf length, and width of tobacco plants. Furthermore, the physical and chemical properties of the soil were notably enhanced, including a 26.26% increase in phosphorus availability. Additionally, the activities of key soil enzymes such as sucrase, catalase, and urease were significantly increased, indicating improved soil health and fertility. Comprehensive joint microbiomics and macrogenomics analyses revealed a substantial rise in the populations of beneficial soil microorganisms and an enhancement in metabolic pathways, including amino acid metabolism, synthesis, and production of secondary metabolites. These increase in beneficial microorganisms and the enhancement of their metabolic functions are crucial for plant growth and soil fertility. This study provides valuable references for the development of innovative microbial fertilizers and offers programs for the sustainable development of modern agriculture.

Improvement of agricultural productivity and optimization of rural economic structure from a digital perspective

Abstract This paper first analyzes and researches the current practice status of digital transformation of Chinese agricultural production from production, credit, and management aspects. Then, it analyzes the measurement of agricultural production efficiency and the factors influencing agricultural production efficiency in the context of digital transformation by using DEA measurement tools. The impact of digitalization level enhancement on rural economic structure is also analyzed and explained from the perspective of technological progress bias through spatial econometric models. The results show that the level of digitalization promotes the development of agricultural modernization in terms of the change in the ranking of scores between 2014 and 2023. The correlation coefficients of the level of digital economy development, financial support for agriculture, and urbanization rate are 0.483, 1.163, and 0.764, which significantly promote the efficiency of agricultural production in China. There is a significant spatial dependence relationship between economic structure and digital transformation (P<0.001).

Comprehensive evaluation of green development level of agriculture in Guiyang

Green agriculture is a new mode of modern agricultural development, and measuring the green agricultural development level appropriately can provide some reference for the realization of agricultural modernization in Guiyang City. The estimate index system of the green agricultural development level in Guiyang City was established and using analytic hierarchy process (AHP), the index of green agricultural development leve was calculated. The results show that the green level of production process was highest with the weight value of 0.633, and the weight value of green level of agricultural economy was lower, which was only 0.107. The overall green agricultural development level in Guiyang City was 0.259, showing a low green agricultural development level. Therefore, it has occasion to further reform the factors affecting the green agricultural development.

SCIENTIFIC SUBSTANTIATION OF THE PRODUCTIVITY OF SOWING ALFALFA DEPENDING ON SOIL AND CLIMATIC CONDITIONS OF THE FOREST-STEPPE OF UKRAINE

The article presents a review of literature sources on the study of alfalfa productivity depending on the influence of sowing dates, mineral phosphorus-potassium nutrition, soil liming, pre-sowing seed treatment with bacterial preparations and modes of grass stand use when grown for fodder purposes. It was found that the change of photoperiod in the year of sowing was a signal in alfalfa plants to pass through the microstages of growth and development, which was determined by the length of daylight hours and sowing dates, regardless of hydrothermal resources. Observations showed that the crop reached the beginning of flowering phase (code 61-62 of the BBSN) at daylight hours of 14:53-16:12 h, while at 12:48 h it reached the budding phase (code 49 of the BBSN). Regardless of climate change, which has been observed for more than a decade, sowing alfalfa provided the highest productivity indicators with early spring sowing and long-term use of the grass stand. Over the three years of vegetation, its leaf mass yield averaged 47.03 t/ha, dry matter yield was 11.09 and 2.07 t/ha of crude protein, and gross and metabolizable energy were 204.72 and 104.52 GJ/ha, respectively. At the same time, with the recommended summer sowing date (20 July), the indicators decreased by 9.1, 11.5, 13.1, 12.2 and 11.7%, respectively. Analysing the agro-ecological resources of Ukraine, given the natural soil fertility, moisture, temperature and light conditions, they are quite favourable for the maximum realisation of the biological potential of the fodder productivity of sowing alfalfa within the yield of fodder units of 10-12 t/ha and 1.8-2.2 t/ha of digestible protein. Among the elements of intensifying the cultivation of seed alfalfa for the production of plant raw materials and harvesting of various types of fodder, fertilisation is the most effective factor influencing their quantity and quality. To calculate the amount of mineral fertiliser, we take into account the biological characteristics of the crop, its cyclical growth and development during the growing season, the method of sowing and the duration of grassland use. Regardless of the geographical origin of the seed alfalfa, for two years of using the grass stand and observing technological measures of cultivation (lime, phosphorus-potassium mineral fertilisers, seed inoculation), alfalfa provided the highest yield of crude protein of 5.83-5.97 t/ha when mowing the first mowing in the flowering phase, the second - at the beginning of flowering and the third in the budding phase. Mowing of three mowings of sowing alfalfa at the beginning of the flowering phase contributes to the effective use of the light regime of the growing season as the main factor in increasing productivity. Innovative technologies for growing seed alfalfa use growth-regulating agents of various origins, microfertilisers and biological seed treatment products to increase yields and dry matter quality for foliar feeding. The complex application of these preparations provides an increase in the yield of leaf mass by 16.6-18.2%. Thus, in the context of modern agricultural development, alfalfa is one of the components of reducing the anthropogenic load on the agro-ecological system and increasing the production of environmentally friendly high-protein feed and livestock products for the purpose of efficient use of hydrothermal resources and environmental protection.

Research on application of intelligent robot based on AI technology in agriculture

With the continuous progress of science and technology, artificial intelligence has profoundly changed various industries, and the agricultural field is no exception. As an important part of modern agriculture, smart agriculture is rapidly emerging, creating brand-new formats and opportunities. At present, the development of agricultural industry is also developing in the direction of high efficiency, intensification, precision and intelligence. As an important branch of modern computer science, AI plays an increasingly high role in the development of modern agriculture. The research and development of intelligent robots based on AI technology is becoming more and more mature, and the application fields are becoming more and more extensive. Under this background, this paper analyzes the development of intelligent robots in the field of agricultural automation and its main applications.

The Modern Agriculture Development of Arid and Semi-Arid Areas in China

It was very important to study the agricultural development of arid and semi-arid areas in China for national food security. This area accounts for about 1/3 of China's land area. However, due to long-term water scarcity, strong sandstorms, and severe soil salinization, local agricultural development was limited.Nowadays, with the continuous development of agricultural technology in arid and semi-arid areas, the agricultural development in arid and semi-arid areas of our country had undergone earth-shaking changes. In this paper, from the initial unification of plastic film cover in arid and semi-arid areas to the normalization of greenhouses and solar greenhouses, and with the development of electrification technology and the gradual maturity of information technology, agricultural development has gradually moved from traditional to the era of modernization, information and intelligence, the above four aspects to discuss the agricultural development process in arid and semi-arid areas of our country.

Negative Factors Influencing the Policy of Public Administration Development of the Agriculture in the Region: The Exampleof Poland

The main purpose of the article is to determine the level of importance and influence of the main negative factors influencing the formation of an effective policy for the development of agriculture in the region. And also, to formulate appropriate recommendations for overcoming or adapting modern agricultural development policies based on these factors. The set purpose is achieved through the use of a specific methodology, which includes the method of expert survey and analysis of hierarchies. As a result, seven key negative factors were identified that have the greatest negative impact on the formation of an effective policy for the development of agriculture in the Poland region. For a better understanding of the proposed methodology, we chose a separate region of Poland-Podkarpackie Voivodeship. Ultimately, the most appropriate recommendations were proposed to improve agricultural development policies for the region. The study has a limitation because it includes an analysis of region-specific negative factors. Given this, their level of influence and importance, as well as the prevailing recommendations, are highly specific to this region. In the future, it is planned to expand the analysis of factors and the formation of recommendations across the entire EU sector.

A deep learning-based algorithm for crop Disease identification positioning using computer vision

Food security is fundamental to a country. As the main risk factors, pests and diseases seriously restrict the normal growth of crops and the quality and safety of agricultural products. With the intensification of climate change and the continuous adjustment of farming methods, crop diseases and pests have become more frequent in recent years. Therefore, the agricultural production mode has gradually moved from family production to large-scale agricultural planting, and the production equipment has become more automated and intelligent. Agricultural intelligent robots can reduce labor costs in the process of agricultural production and improve the standardization of agricultural production. The application of computer vision in agriculture is rapidly becoming an important aspect of modern agricultural technology, especially in crop positioning and management. Through the use of advanced image processing algorithms and pattern recognition technology, computer vision systems are able to accurately identify and locate various crops in the field, enabling automated and precise management. This technology shows great potential for crop health monitoring, pest identification, and maturity assessment. For example, by analyzing images of plants, computer vision systems can spot signs of lesions or nutrient deficiencies in time and guide farmers to treat them accordingly. In addition, this technology can also be used to guide automated agricultural machinery, such as driverless tractors and harvesters, to improve the efficiency of crop harvesting and reduce labor costs. In general, the combination of computer vision and crops provides new technical means for the development of modern precision agriculture, which helps to improve the efficiency and sustainability of agricultural production.

Microagroecosystem models and their landscape ecological risk evolution in karst mountainous areas

Micro-spatial agroecosystem (AES) changes in karst mountain areas are a response to the human-land relationship of the human activity center. However, the changes and linkages between AESs and landscape ecological risks (LER) from a micro perspective in karst mountain areas (KMA) have not yet attracted in-depth attention from the academic community. This paper proposes the concept of microagroecosystems (MAESs) and constructs the MAES model based on the landscape development intensity (LDI) to evaluate the systematic landscape evolution rules and the LER intensity, which reflects the human-land relationship in KMA. We used the buffer ring with a 200 m cultivation range interval to categorize the MAES model into six models, namely, High-Mid-Low, High-Low-Medium, Medium-High-Low, Medium-Low-High, Medium-High-High, Low-Medium-High and Low-Medium-High, and the different models not only reflect the regional conditions but also represent the different stages of the evolution of AESs in KMAs. We found that AESs in karst mountainous areas are transforming from traditional food crop systems to economic fruit forest ecosystems, and at the same time, the gravity center of human activities is migrating from high to low altitude areas, and the ecological vitality of MAESs is showing a trend of enhancement. In addition, the regions with high altitudes and steep slopes in karst mountainous areas have a single landscape structure and are dominated by traditional food cultivation, while the regions with low altitudes and gentle slopes have a diverse landscape structure and are dominated by modern and sustainable agricultural development. Finally, we reveal that the LER in KMAs shows a trend of initial increase followed by subsequent decrease, the influence of the human-land relationship on the LER in the micro perspective is significant, and the coordination and trade-off between agricultural development and environmental protection is the key direction of development in the future. The study results can provide a reference for the rational allocation of landscape, ecological environment restoration, and optimization of regional human-land relationships in KMAs worldwide.

Retracted: The Index Data System of Agricultural Modernization Development Based on Internet Big Data

Retracted: The Index Data System of Agricultural Modernization Development Based on Internet Big Data

Automatic Plastic Greenhouse Extraction from Gaofen-2 Satellite Images with Fully Convolution Networks and Image Enhanced Index

Plastic greenhouses (PGs) play a vital role in modern agricultural development by providing a controlled environment for the cultivation of food crops. Their widespread adoption has the potential to revolutionize agriculture and impact the local environment. Accurate mapping and estimation of PG coverage are critical for strategic planning in agriculture. However, the challenge lies in the extraction of small and densely distributed PGs; this is often compounded by issues like irrelevant and redundant features and spectral confusion in high-resolution remote-sensing imagery, such as Gaofen-2 data. This paper proposes an innovative approach that combines the power of a full convolutional network (FC-DenseNet103) with an image enhancement index. The image enhancement index effectively accentuates the boundary features of PGs in Gaofen-2 satellite images, enhancing the unique spectral characteristics of PGs. FC-DenseNet103, known for its robust feature propagation and extensive feature reuse, complements this by addressing challenges related to feature fusion and misclassification at the boundaries of PGs and adjacent features. The results demonstrate the effectiveness of this approach. By incorporating the image enhancement index into the DenseNet103 model, the proposed method successfully eliminates issues related to the fusion and misclassification of PG boundaries and adjacent features. The proposed method, known as DenseNet103 (Index), excels in extracting the integrity of PGs, especially in cases involving small and densely packed plastic sheds. Moreover, it holds the potential for large-scale digital mapping of PG coverage. In conclusion, the proposed method providing a practical and versatile tool for a wide range of applications related to the monitoring and evaluation of PGs, which can help to improve the precision of agricultural management and quantitative environmental assessment.

Effects of Temperature on Growth and Grain Maturity of Spring Maize in Northeast China: A Study of Different Sowing Dates

Situated at middle-to-high latitudes with limited thermal resources, Northeast China is the primary maize-producing region in China. It is also one of the regions most significantly impacted by climate change. Given the persistent impact of climate change, it is crucial to elucidate the effects of the varying thermal conditions and low temperatures for different sowing dates on the growth, development, and grain maturity of spring maize. To ensure secure maize production and disaster prevention, choosing the optimal sowing time for spring maize holds significant implications for the judicious utilization of climatic resources, risk mitigation, and the provision of meteorological guidance. Moreover, it can serve as a technical reference for relevant departments to conduct climate evaluation, disaster monitoring, prediction, and assessment, as well as impact analysis of corn production safety. Additionally, it can provide meteorological evidence to ensure food security and promote the sustainable development of modern agriculture. An interval sowing experiment of spring maize was conducted in Harbin in the north of Northeast China. Two varieties were used in the experiment. Four sowing dates were set, and the interval between adjacent sowing dates was 10 days. The local perennial sowing time, 5 May, was set as the second sowing date, with one date set later and two dates set earlier. During the experiment, the growth process, grain dry matter, seed moisture content, yield components, and temperature of spring maize were observed. The impact of temperature conditions on maize growth and yield formation was analyzed in this paper through mathematical statistics, which further led to the establishment of a monitoring and evaluation model for assessing the effect of thermal conditions and temperature on maize. The results showed that the growth rate of spring maize was closely related to temperature. When the average temperature, minimum temperature, and maximum temperature increased by 1 °C, the average emergence rate increased by 1.05%, 0.99%, and 1.07%, respectively, and the average vegetative growth rate increased by 0.16%, 0.16%, and 0.09%, respectively. The change rate of ≥10 °C active accumulated temperature was significantly correlated with the change rate of the dry weight of the grain kernel, which conformed to the quadratic equation of one variable. The temperature influence coefficients of different sowing dates varied from 1.0% to 1.7%. The relationship between the accumulated values of 10 ℃ active accumulated temperature and the grain moisture content of spring maize was a logarithmic function. From 10 to 50 days after anthesis, the effect of temperature can explain about 95% of the change in grain moisture content. After physiological maturity, the effect of thermal conditions can only explain 56–83%. The temperature influence coefficient ranges from 1.3% to 13.8%. Comparatively speaking, the second sowing date is the most suitable sowing date. Early sowing is prone to encounter low temperatures, resulting in underutilization of the early heat, while late sowing is prone to less heat. Both conditions are not conducive to better improve the yield of spring maize.

Investigation of the Impact of Castor Biofuel on the Performance and Emissions of Diesel Engines

Fossil fuel is a non-renewable fuel, and with the development of modern industry and agriculture, the storage capacity of fossil fuels is constantly decreasing. In this study, a systematic study and analysis were conducted on the combustion characteristics, engine performance, and exhaust emission characteristics of castor biodiesel–diesel blends and pure diesel fuel in different proportions at different speeds of a single-cylinder four-stroke diesel engine under constant load. The castor biodiesel required for the experiment is generated through an ester exchange reaction and mixed with diesel in proportion to produce biodiesel–diesel blends. The experimental results show that as an oxygenated fuel with a higher cetane number, the CO, HC, and smoke emissions of diesel and B80 blend fuel at 1800 rpm were reduced by 16.9%, 31.6%, and 68%, respectively. On the contrary, the NOx and CO2 emissions increased by 17.3% and 34.6% compared to diesel at 1800 rpm. In addition, due to its high viscosity and low calorific value, the brake thermal efficiency and brake-specific fuel consumption of the biodiesel–diesel blends are slightly lower than those of diesel, but the biodiesel–diesel blends exhibit lower exhaust gas temperatures. Comparing B80 and diesel fuel at 1800 rpm, the BSFC of diesel at 1800 rpm is 3.12 kg/W·h, whereas for B80 blended fuel, it increases to 4.2 kg/W·h, and BTE decreases from 25.39% to 21.33%. On the contrary, B60 blended fuel exhibits a lower exhaust emission temperature, displaying 452 °C at 1800 rpm. Based on the experimental results, it can be concluded that castor biodiesel is a very promising clean alternative fuel with low waste emissions and good engine performance.