Agricultural Research Articles

Effects of Climate Changes and Crop Phenological Responses on Soil Organic Carbon of Cultivated Land in Fujian Province

Research on the mechanism of how climate change affects cultivated soil organic carbon is the basis for the management of cultivated land quality in the context of climate change. Crop phenological responses to climate change have an important effect on cultivated soil organic carbon as well. However, previous research primarily focused on the independent effects of climate change or crop phenological responses on the changes in soil organic carbon, and few studies have analyzed the changes in cultivated soil organic carbon under the combined influence of both factors or quantified their contribution rates to the changes in cultivated soil organic carbon. Based on topsoil samples in 2008 and 2021, annual pre-season and mid-season climate data from 2008 to 2021, and the phenological parameters extracted from the enhanced vegetation index （EVI） time series from 2007 to 2022, a soil organic carbon predictive model was constructed using the random forest algorithm. The total change in soil organic carbon from 2008 to 2021, the change in soil organic carbon under climate change alone, and the change in soil organic carbon under the synergistic influence of climate change and crop phenological responses were simulated. Furthermore, the contributions of climate change and crop phenological responses to the changes in cultivated soil organic carbon were distinguished and quantified. Moreover, the dominant influencing factors of soil organic carbon changes and their spatial distributions were identified and analyzed. The results were as follows： ① Under the synergistic influence of climate change and crop phenological responses, a decrease was observed in soil organic carbon in 74.15% of the cultivated land area in Fujian Province during the years 2008-2021, with an average decrease of 2.20 g·kg-1. Additionally, there was an increase in soil organic carbon in 25.85% of the cultivated area, with an average increase of 1.48 g·kg-1. ②The average contribution rates of pre-season climate, crop phenological responses to climate change, mid-season climate, and phenological changes resulting from cultivars shifts or other adjustments of agricultural measures to soil organic carbon changes were 34.08%, 28.56%, 22.75%, and 14.61%, respectively. Overall, climate change had a greater impact on the changes in cultivated soil organic carbon in Fujian Province than the crop phenological response to climate change. ③ The regions where climate change and phenological response jointly acted as dominant influencing factors held the largest area, accounting for 47.06% of the total cultivated land area in Fujian Province, and the regions where climate change was the dominant influencing factor alone held the second-largest area, accounting for 28.64% of the total cultivated land area. ④ Higher contribution rates of pre-season climate factors and phenological changes resulting from cultivar shifts or other adjustments of agricultural measures tended to be distributed in higher-altitude areas, whereas higher contribution rates of mid-season climate factors and phenological responses to climate change tended to be distributed in lower-altitude areas. These research findings can provide a theoretical basis for decision making regarding the management of cultivated land quality and the safeguarding of food security in the context of climate change.

Spatial Heterogeneity of Soil Nutrient in Principal Paddy and Cereal Production Landscapes of Fengtai County within the Huai River Basin, Eastern China

The problem of cultivated land soil quality in the Huaihe River Basin has become increasingly prominent. How to accurately and quantitatively evaluate the soil quality of regional cultivated land and realize its efficient use has become an urgent problem. In order to explore the spatial autocorrelation and variation in soil nutrients in cultivated land in the plain of Fengtai County in the Huaihe River Basin, a total of 306 soil samples and mature wheat samples were collected in the study area to analyze soil pH, total nitrogen (TN), available nitrogen (AN), available phosphorus (AP), available potassium (AK) and slow-release potassium (SK) content and wheat biomass, and combined with geostatistical methods and GIS technology. The Kriging interpolation method and Moram‘s I index method were systematically analyzed. Principal component analysis (PCA) and Pearson correlation analysis were used to establish the minimum data set (MDS) of soil quality, which was used to calculate the soil quality index (SQI) and determine the key factors affecting soil quality. The results showed that the soil pH was in weak variation, and the other nutrient indexes were in medium variation. The spatial variability of soil-available potassium nutrients was affected by random factors such as human activities and structural factors such as soil parent materials. The spatial autocorrelation of organic matter, total nitrogen, alkali-hydrolyzable nitrogen, available phosphorus, available potassium and mitigation potassium was weak, which was mainly affected by random factors such as human activities. An unequivocal positive spatial nexus was discerned across all nutrients. Cumulatively, the nutrient dispersion across the investigated territory was somewhat diffuse, manifesting in a mosaic pattern with pronounced zonal nutrient allocation disparities in the meridional, median, and septentrional segments. An explicit latitudinal dichotomy delineating zones of nutrient opulence and paucity was also observed. These insights can pave the way for tailored fertilization strategies and judicious pedological stewardship in Fengtai County.

Driving Effects and Spatial Spillover of Technological Innovation on the Carbon Balance of Agriculture

<p style="text-align:justify"><span style="font-size:10.5pt"><span style="line-height:150%"><span style="font-family:Calibri,sans-serif"><span lang="EN-US" style="font-size:12.0pt"><span style="line-height:150%"><span style="font-family:"Times New Roman",serif">There is significant controversy over the overall effects of technological innovation on carbon balance. Based on panel data of agriculture from 28 provinces in China from 2009 to 2021, utilizing ArcGIS and spatial Durbin model, the spatial-temporal patterns of agricultural technological innovation and carbon balance are analyzed, and furtherly the spatial effects of technological innovation on carbon balance are examined. The results indicate: (1) the overall agricultural carbon surplus in Chinese provinces has been continuously increasing from 2009 to 2021; (2) the direct effect of technological innovation on the carbon surplus in agriculture is significantly positive, while the spatial spillover effect is significantly negative; (3) the mediating effect results show that technological innovation affects the output of the agricultural sector and thereby influences agricultural carbon balance; (4) the impact of technological innovation on agricultural carbon balance exhibits an asymmetric spatial spillover effect. Advantageous regions suffer from the positive effects of spatial spillover, while non-advantageous regions benefit from the negative impacts of spillover, resulting in an overall negative effect. This study's findings play an important role in addressing the existing controversy over the overall effects of technological innovation on carbon balance and provide important insights for formulating policies on low-carbon technological innovation in agriculture.</span></span></span></span></span></span></p>

Characteristics of Spatial and Temporal Changes in Carbon Stocks in the Middle and Upper Reaches of the Huaihe River Basin and Future Multi-scenario Simulation Prediction

The Huaihe River Basin is located in the north-south climate transition zone of China. The change of carbon storage in this area is of great significance for predicting the future ecological protection, mitigating climate change, and maintaining sustainable development of the Huaihe River Basin. The middle and upper reaches of Huaihe River Basin （above Bengbu station） were taken as the research area. Based on the land use data from 1980 to 2020, the PLUS model was used to simulate and predict the land use types in the study area from 2030 to 2100 under the scenarios of SSP1-2.6, SSP2-4.5, SSP5-8.5, and the continuation of land use status. The carbon module in the InVEST model was used to simulate and predict the carbon storage from 1980 to 2020 and the carbon storage from 2030 to 2100 under various scenarios, and the spatial and temporal changes of carbon storage in the middle and upper reaches of the Huaihe River Basin were compared and analyzed. The results showed that： ① From 1980 to 2020, the basin showed a decrease in both cultivated land and grassland,and the area of forest,water, construction, and unused land all increased, among which the area of cultivated land continued to decrease, with a total decrease of 4 699 km2 in 40 a. Construction land continued to increase, with a total increase of 4 592 km2 in 40 a. ② The carbon storage in the basin showed a downward trend, with a total reduction of 1.05×107 t from 1980 to 2020. ③ In the four scenarios, the area of each land type had different degrees of change, and that of the SSP1-2.6 scenario was relatively small out of the four scenarios. ④ Compared with the carbon storage in 2020, the carbon storage in the SSP1-2.6 scenario increased by 8.7×104 t, the carbon storage in the SSP2-4.5 scenario decreased by 1.42×107 t, the carbon storage in the SSP5-8.5 scenario decreased by 1.34×107 t, and the carbon storage in the current continuation scenario decreased by 1.22×107 t. The study can provide a scientific basis for land use structure management and ecological protection in the middle and upper reaches of the Huaihe River Basin （above Bengbu station） in the future.

Characteristics and Influencing Factors of Soil and Crops Selenium Content in Eastern Sanjiang Plain

Too identify seleniut（Se） content characteristics and influencing factors in soil and crops of Shengli Farm in eastern Sanjiang Plain, statistical analysis and correlation analysis were comprehensively used to analyze the test results of 83 groups of surface soil samples and 34 groups of crop seed samples. The results showed that the Se content in the study area ranged from 0.12 to 0.95 mg·kg-1, with an average value of 0.37 mg·kg-1, and the enrichment degree was stronger with an enrichment coefficient of 3.18. Oxidizable Se was the main Se fraction, accounting for 81%, 79%, 79%, and 80% of T-Se in marsh soil, white soil, dry land, and paddy field, respectively. The content of reducible Se was the lowest, accounting for less than 5%. The effects of soil physicochemical indexes on Se content differences mainly showed that Se was negatively correlated with pH and total potassium （TK） and significantly positively correlated with cation exchange capacity （CEC）, soil organic matter （SOM）, humus （HS）, total nitrogen （TN）, and total phosphorus （TP）. The average content of Se in different land use types was as follows： dryland > irrigated land > grassland > forest land, as the dryland soil with low pH and high SOM was more likely to enrich Se. Among different soil types, the average Se content in gleysols was the highest at 0.45 mg·kg-1, which was higher than the average value in the study area. The average content of Se in the quaternary alluvial layer was the highest at 0.43 mg·kg-1, and its parent material mainly consisted of lacustrine sediments rich in organic matter, which was one of the important factors in forming Se rich soil. The Se content in crops and root soil showed a negative correlation. Se in low pH or high SOM soil was not easily absorbed by crops, and its Se content was also controlled by the form of soil Se, which was positively correlated with available Se content and negatively correlated with oxidizable Se content. Therefore, it is suggested to reduce the amount of artificial fertilizer used in cultivated land as a means of increasing Se bioavailability to change the current situation of crop Se levels in this area.

Land Cover Simulation and Carbon Storage Assessment in Daqing City based on FLUS-InVEST Model

Considering Daqing City as the research area, the impact of land cover change on carbon storage in the future was discussed, and the hot spots of carbon sequestration capacity were identified. The future land use simulation （FLUS） model was used to simulate the land cover pattern of a natural succession scenario, ecological protection scenario, urban development scenario, and comprehensive development scenario in 2030, and the integrated valuation of ecosystem services and trade-offs （InVEST） model was combined to estimate carbon storage in 2010, 2020, and 2030. Finally, the hot spot analysis tool was used to identify the cold hot spots of carbon sequestration capacity. The results showed the following： ① From 2010 to 2020, the area of cultivated land, water, and artificial surface increased, whereas the area of other land cover types decreased, and the total carbon storage decreased by 8.6×105 t. ② The land cover change of the natural succession scenario and urban development scenario in 2030 was similar to that of 2010-2020, with carbon storage decreasing by 1.16×106 t and 1.20×106 t, respectively. The carbon storage of the comprehensive development scenario decreased by 1.00×106 t compared with that in 2020, and carbon storage of the ecological protection scenario was 5.677 7×108 t, which increased by 2.53×106 t compared with that in 2020. ③ The conversion of grassland and wetland to cultivated land was the main cause of carbon storage loss, and the main contributor of carbon storage in the ecological protection scenarios was wetland. ④ The hot spots of carbon sequestration capacity were mainly located in the wetland area, and the cold spots were mainly distributed in the central part of Daqing City. The carbon sequestration capacity of cultivated land was not significant. According to the research results, to realize the urban transformation of Daqing City, we should insist on returning farmland to forest and grass, increase the intensity of returning moisture, improve the utilization rate of urban land, and increase green infrastructure in the main urban area.

Spatio-temporal Evolution of Land Use in Guangdong-Hong Kong-Macao Greater Bay Economic Area, 1985-2022 and Analysis of Influencing Factors

This research employs GIS and remote sensing technology, along with time-series Land cover data, to examine the evolving patterns of various land use categories (such as forest land, cultivated land, and impervious surfaces) within the Guangdong-Hong Kong-Macao Greater Bay Area. The results indicate that the predominant spatial configuration of land use in this region follows a sequence of "forest land-cultivated land-impervious land." Notably, the key transformation observed involves the conversion of cultivated land to impervious surfaces, resulting in a cumulative expansion of impervious areas by 3,346,985 hectares between 1985 and 2022. Moreover, the level of spatial integration among land use types in the Greater Bay Area is progressively standardizing, with notable advancements in scientific, technological, economic, and transportation sectors leading to a high degree of synergy. The system as a whole demonstrates effective coordination and operational efficiency. The methodology section outlines the data sources and analytical techniques employed, encompassing the utilization of the land use transfer matrix, individual land use degree assessment, and spatial coupling function model. Through these methodologies, the study identifies urban economic development as the primary driver behind the proliferation of impervious surfaces, offering valuable insights to inform regional development strategies and environmental governance policies.

Organic Carbon Storage in Waterlogging Soils in Ávila, Spain: A Traditional Agrosilvopastoral Region

Soils play a crucial role in the protection, management, and ecological understanding of the La Moraña region, located in Ávila province, Central Spain, which has a moderate population, traditional agriculture, livestock farming, and low industrial activity, resulting in relatively low environmental degradation. The region’s soils often experience prolonged water stagnation, influencing its agronomy, ecology, and economy. This study aimed to estimate and understand the soil’s role in the C sequestration of an agrosilvopastoral system under conditions of temporary water stagnation and different land uses. The results showed that ryegrass-magaza and Pinus pinaster show more content in soil carbon sequestration storage (98.7 and 92.4 Mg per hectare) compared to the adjacent degraded rangeland (75.8 and 63.9 Mg ha−1). Arenosols exhibited a higher total amount of SOC stocks. The soil profile with ryegrass sequestered more nitrogen (9.7 Mg ha−1) than other land uses; moreover, Arenosols have a lower nitrogen sequestration capacity even in low-forest conditions. The study highlights significant differences in carbon accumulation due to the management practices, temporary water layers, and parent material.

Agricultural terraces in Europe: Geoarchaeological and botanical methods

Agricultural terraces in Europe: Geoarchaeological and botanical methods Tony Brown, Dan Fallu, Sara Cucchiaro, Ben Pears, and the TerrACE Team, highlight agricultural terraces in Europe, providing geoarchaeological and botanical insights. Agricultural terraces exist in all countries in Europe but, particularly those with hilly regions where terraces can significantly increase the area of high-quality cultivatable land. Terraces, which are also common worldwide, are the largest human-made manipulations of the earth’s surface prior to the building of cities and are known to provide beneficial ecosystem services, including long-term carbon storage. Despite this remarkably little is known about their history, and in many cases, what crops they were originally created for. They are also exceptionally resilient, mostly surviving since their first construction to the present day. However, this is changing as since the development of the caterpillar-tracked bulldozer, many have been destroyed.

Is seed aid distribution still justified in South Sudan?

Seed aid—or free distribution of seeds to farmers—is a popular intervention to simultaneously reduce food insecurity and dependency on food aid in fragile countries. However, seed aid distribution also has the potential to hinder or distort the development of local seed markets. In this study we analyze the targeting and impact of seed aid across the green belt (cutting across the southern/equatorial states) of South Sudan. Using a primary and unique dataset on 1,990 farm households, we find that seed aid is widely rather than selectively distributed. Almost a third of farm households receive seed aid despite the general availability of locally recycled seed varieties. Seed aid distribution does not seem to favor particularly poor, vulnerable and food insecure households, but those that are embedded in community networks, organizations and institutions. Using a double robust methodology based on Inverse Probability Weighted Regression Adjustment (IPWRA), we also find that the adoption of seed aid by farm households does not result in increased maize production, as it is neither associated with agricultural intensification nor with the expansion of cultivated land. Seed aid seems to substitute rather than supplement local seed varieties. These findings emphasize a lack of intentionality in seed aid distribution. Still, it must be noted that the effectiveness of seed aid distribution may be greater outside our study area, above the green belt, where conflicts and natural disasters remain more frequent and intense, and where farmers are more likely to be seed insecure. But overall, this study supports the widespread perception that South Sudan is ready for a transition towards a market-based seed distribution system.

An IoT-Enabled Real-Time Crop Prediction System Using Soil Fertility Analysis

Changes in soil fertility have led to a decline in crop production, making it challenging for farmers to select the best crops based on soil conditions. Accurate crop prediction can significantly enhance crop productivity, and machine learning plays a crucial role in this process. Crop forecasting is influenced by soil, geographic, and environmental characteristics, with feature selection being essential for identifying suitable crops. In this study, we developed a real-time soil fertility analyzer to obtain the real-time values of soil parameters such as potassium, phosphorus, nitrogen content, temperature, pH, moisture content, and electrical conductivity. The crops examined were coconut, ginger, plantain, and tapioca. The data collected from this analysis served as the dataset for different training and testing classification algorithms for crop prediction using 100 soil samples. Among the algorithms tested, the k-nearest neighbors (KNN) algorithm demonstrated the highest performance, with an accuracy of 84%, precision of 85%, recall of 88.8%, and specificity of 92.4%. These results indicate that machine learning, combined with real-time soil analysis, can effectively predict suitable crops, enhancing crop productivity and aiding farmers in making informed decisions. This approach can revolutionize traditional farming practices by providing precise, data-driven insights into crop selection, ultimately improving agricultural efficiency and sustainability.

Impact of sewage sludge on growth indices of Sorghum bicolor: an alternative approach to conventional agriculture

Impact of sewage sludge on growth indices of Sorghum bicolor: an alternative approach to conventional agriculture

Effect of Cluster Front Line Demonstrations on Productivity and Profitability of Redgram in Karimnagar District, Telangana, India

Between 2017-18 and 2019-20, Krishi Vigyan Kendra, Jammikunta conducted frontline demonstrations on Redgram during the Kharif season across 90 hectares, encompassing 225 demonstrations in six villages within six clusters in Karimnagar district, Telangana, India. The improved technologies included the use of high-yielding varieties, seed treatment with bio-fertilizers, integrated nutrient and weed management, as well as pest and disease management. The results indicated that the demonstrated plots achieved the highest average grain yield of 1350 kg/ha, compared to 1115 kg/ha from traditional farmer practices. Demonstration plots also yielded higher average net returns of Rs. 49,731/ha, in contrast to Rs. 34,815/ha from the farmers’ practices. Overall, the demonstrations showed a 23.79% increase in yield and a 42.81% increase in net returns compared to conventional methods. The average technology gap was 216 kg/ha, the average extension gap was 235 kg/ha, and the average technology index was 13.54%.

The Impact of Cultivated Land Fragmentation on Farmers’ Ecological Efficiency of Cultivated Land Use Based on the Moderating and Mediating Effects of the Cultivated Land Management Scale

To date, scholars have increasingly focused on the reduction in crop yields caused by cultivated land fragmentation, yet its effects on the ecological efficiency of cultivated land use are often overlooked. This oversight leads to land resource waste and environmental pollution. It is essential to explore this problem to achieve moderate-scale farming operations and promote the green transformation of agricultural land. This study theoretically analyzed the mechanisms by which cultivated land fragmentation and management scales influence the ecological efficiency of cultivated land use. Based on 2023 household data from Changde and Shaoyang, China, empirical tests were conducted using the stochastic frontier analysis method, Tobit model, and structural equation model. The research results indicate that: (1) The mean ecological efficiency of cultivated land use among the total sample households was 0.822, and the eco-efficiency in the plains was slightly lower than that in the hilly areas. (2) The scale of cultivated land management played a moderating role in the impact of cultivated land fragmentation on ecological efficiency, with differences observed between topographical types. The scale of management can offset part of the negative impact of cultivated land fragmentation on the ecological efficiency of cultivated land use. (3) Regarding the impact of cultivated land fragmentation on the ecological efficiency of cultivated land use, cultivated land management scale changes play a complete mediating role. These findings help provide policy implications to improve the ecological efficiency of cultivated land use. Policy support should be strengthened by promoting moderate-scale cultivated land operations, enhancing the comprehensive remediation of cultivated land fragmentation, and developing skilled farmers for long-term environmental sustainability.

CFD Analysis to Optimize Air Quality in Aeroponic Tower Garden

Aeroponic tower gardens represent an innovative approach to urban agriculture, utilizing vertical structures to cultivate plants without soil. Research highlights the efficiency of this method in optimizing resource usage, such as water and space, compared to traditional farming. Studies emphasize increased crop yields, faster growth rates, and reduced environmental impact. Additionally, aeroponic systems enable precise nutrient delivery to plants, fostering optimal growth conditions. While the technology shows promise, further investigations are needed to address potential challenges and optimize its application in diverse agricultural settings. The study presents the simulation of air temperature and humidity distribution in a closed chamber of the vertical farm for optimization purposes. The environmental conditions inside the chamber are optimized for plant growth, to maintain the temperature at a range of 18-24 °C temperature and the relative humidity at a range of 75-85 %. The paper also discusses the challenges, limitations, and future recommendations for improving the system. Simulation of the system, on the other hand, is crucial not only for prototype fabrication but also for the optimization of sensor locations. The simulation of the tower garden system was performed using ANSYS-FLUENT to portray how the airflow inside the tower garden's enclosed environment was distributed. The system parameters such as humidity and temperature were set to meet the plants' growth requirements, and the system was treated as the steady state with an adiabatic boundary. Based on the simulation results, the best design recommended is to use two humidity and temperature sensors, one at the top and the other at the core of the prototype. Hence, a complete mapping of temperature and humidity distribution that is critical for plant growth would be occurred.

Rainfall water collection and irrigation via stone bud and karren on karst rocky desertification slopes: Application and benefit analysis

Lack of surface water is a key factor leading to agricultural drought in karst regions and exacerbates the karst rocky desertification, which is characterized by high rate of exposed bedrock. With the characteristics of impermeability, runoff collection, and wide distribution, stone bud and karren (SBK), a unique karst landscape type composed of exposed bedrock and gullies, has a great potential to solve water shortage in agricultural irrigation, especially in the rain-rich karst regions of Guizhou Province, China. Using unmanned aerial vehicle images to identify SBK and measured rainfall–runoff data for validation, we applied SBK as a basic unit to harvest rainfall water on rocky desertification slopes and analyzed the benefits of rainfall water collection and irrigation via SBK. The results showed the following: (1) Karrens on rocky desertification slopes could be identified using a method coupling terrain opening difference and depression filling, and the simulated runoff on SBK could achieved the average NSE and R2 of 0.82 and 0.86, with RMSE and RSR less than 0.03 m3 and 0.51, respectively. (2) The exposed bedrock utilization rate and rainfall water collection coefficient of SBK were 0.62 and 0.55, respectively, such that the area ratio of exposed bedrock to the cultivated land meeting irrigation water demand reached 1:3.1, 1:3.7 and 1:7.0 under 90 %, 80 % and 50 % possibilities of irrigation. (3) Implementing SBK for rainfall water harvesting and irrigation could efficiently avoid crop failure in the karst regions where crops faced drought within only one week after rainfall, and was at an affordable cost to local farmers with limited environmental damage. The use of SBK to harvest rainfall water could also alleviate surface water shortages in karst regions caused by problems such as uneven temporal distribution of rainfall and engineering water shortages. Consequently, SBK demonstrate a strong application potential to alleviate agricultural drought in karst rocky desertification regions.

The role of biochar nanoparticles in reducing salt stress in tomato (Solanum lycopersicum) plants grown in fields

Soil salinization is a major threat to global food security, affecting over 20 % of cultivated land and reducing yields of important crops like tomato (Solanum lycopersicum). This field study explored the potential of biochar nanoparticles (BNPs) derived from rice straw to mitigate salt stress in tomato plants. The BNPs, prepared by pyrolysis at 350 ℃ and ball milling, had an average size of 130.32 nm. Tomato plants were grown for 12 weeks in soil with 0.2 % NaCl (w/w) and BNPs at 0.5, 1, or 2 ‰ w/w. Controls included unamended soil, soil with NaCl only, soil with BNPs only, and regular biochar with and without NaCl. BNPs significantly improved growth, biomass, and fruit yield of salt-stressed tomato plants. The 2 ‰ BNP treatment increased fruit yield by 115.05 % compared to salt stress alone. Stem fresh and dry weights increased by 35.64 % and 79.71 %, respectively, while root fresh and dry weights increased by 138.27 % and 68.35 %, respectively. BNPs enhanced fruit quality, with higher concentrations showing better effects. BNPs alleviated salt stress by regulating ion homeostasis, limiting Na translocation to leaves and fruits (reducing Na content by 35.15 % in leaves and 16.06 % in fruits), improving K content and Na/K ratio, and influencing Mg and P absorption and translocation. The nanoscale size, high surface area, and enhanced reactivity of BNPs allow them to penetrate plant tissues and interact with ion transport components. This study demonstrates the potential of BNPs as a sustainable approach for enhancing tomato salt tolerance in the field. Using crop residues for BNP production supports circular economy and sustainable agriculture. This approach holds potential for enhancing sustainable crop production under saline conditions, contributing to food security and the development of resilient agricultural systems in challenging environments.

The Immune Defense Response and Immune-Related Genes Expression in Macrobrachium nipponense Infected with Decapod Iridescent Virus 1 (DIV1).

Macrobrachium nipponense is a significant cultivated species in China. However, decapod iridescent virus 1 (DIV1), as a newly discovered crustacean-lethal virus, has resulted in significant financial losses for the M. nipponense industry. In order to examine the immunological response of M. nipponense to DIV1, we conducted transcriptome analysis of the hepatopancreas from M. nipponense infected with DIV1 using RNA-seq. RNA sequencing analysis identified a combined total of 41,712 assembled unigenes, and 7014 genes that showed differential expression were identified in the group infected with DIV1, compared to the control group. Among these DEGs, 3952 were found to be up-regulated, while 3062 were down-regulated; many well-characterized DEGs were involved in innate immune defense, particularly involving the C-type lectin receptor signaling pathway, complement and coagulation cascades, phagosome, lysosome and PPAR signaling pathway. Moreover, the expression levels of well-known immune-related genes (dorsal, wnt6, lectin, caspase, integrin, hsp70) in the hepatopancreas and hemolymph were investigated by Quantitative real-time PCR (qRT-PCR), and the findings demonstrated a significant increase in gene expression in the hepatopancreas and hemolymph at various time points after infection. The results acquired in this study offered further comprehensive understanding of the immunological response of M. nipponense to DIV1 infection.

Transmission of multidrug-resistant tuberculosis in Jiangxi, China, and associated risk factors.

The high prevalence of multidrug-resistant tuberculosis (MDR-TB) in Jiangxi Province highlights the importance of understanding the genetic background and drug resistance patterns of these strains. This knowledge is crucial for developing effective control methods. Furthermore, in light of the significance of preventing transmission among tuberculosis patients, whole-genome sequencing was utilized to investigate the recent transmission of MDR-TB and identify associated risk factors. The findings revealed that individuals in the farming sector, those who are unemployed, and patients with a history of tuberculosis treatment are at elevated risk. Consequently, targeted public interventions for these at-risk groups are imperative.

Agriculture Robot Using Image Processing

Background: Agriculture is a cornerstone of the economy, but traditional farming is labor-intensive and time-consuming. Advances in technology, such as Convolutional Neural Networks (CNNs) and robotics, can help address challenges like disease detection, weed control, and labor shortages. Tomatoes, a key crop, benefit from proper management using these innovations. Objective: This project aims to design and develop an agriculture robot using IoT and Machine Learning to automate farming tasks, detect tomato plant diseases, and manage weeds more efficiently. Methods: The robot is controlled by an ESP32 microcontroller connected via Wi-Fi for real-time monitoring. The system uses CNNs for disease detection in tomato plants, along with a custom-trained dataset using the TensorFlow framework. Mechanical arms perform tasks like seeding, watering, spraying fertilizers, and harvesting. A user interface for disease detection was designed for real time disease detection. Results: The model achieved an accuracy of 95.0% for disease detection, 95.56% for fruit detection, and 95.14% for plant detection. These results highlight the effectiveness of the robot in managing tomato crops. Conclusion: The agriculture robot provides a comprehensive solution to key farming challenges, enhancing efficiency through automation and improving crop health monitoring. The integration of IoT and ML offers a pathway toward sustainable and profitable agriculture, particularly in managing tomatoes. Further improvements and scaling could expand its application to other crops and farming tasks. Keywords: Machine Learning, Internet of Things, Agricultural Robot, Disease Detection, Convolutional Neural Networks, Mechanical Tasks.