Agricultural Food Research Articles

Assessing the 10-Item Food Security Survey Model (FSSM): Insights from College Students in Three US Universities

Background/Objective: Food insecurity remains a significant public health concern that negatively impacts college students’ academic performance and health. One in three college students experiences inconsistent access to food, known as food insecurity, which has attracted significant research interest. This study examined the effectiveness of the 10-item United States Department of Agriculture Food Security Scale Module (USDA-FSSM) in accurately and effectively measuring food security among college students. Methods: A mixed-methods approach was utilized to assess qualitative individual cognitive interviews and survey quantitative data. An online survey was used to collect demographic data and food security status from 462 college students recruited from three public universities in the United States. Qualitative interviews with a subset of participants (n = 26) were conducted to gain further insight into college students’ perceptions and interpretations of the 10-item USDA food security survey. Results: Fourteen (14%) participants were food-insecure, and 12% were at risk of food insecurity. Qualitative data revealed that students misinterpreted some of the language used in the 10-item USDA-FSSM. Participants also indicated difficulty estimating food security experiences over the 12-month reference period in the 10-item USDA-FSSM. Conclusions: This study demonstrates that college students misinterpret food security terms in the 10-item USDA-FSSM, which affects the prevalence rate determined by the measure, emphasizing the need for a validated college student-specific food security survey to inform effective policy and interventions.

Harnessing the multifaceted benefits of probiotics: A sustainable strategy to combat the post-harvest diseases inciting perishable products

Post-harvest management plays a vital role in agricultural food chains, especially in developing countries, as it focuses on maintaining the quality and shelf life of the produce while minimizing losses. Post-harvest loss (PHL) refers to reducing food quantity and quality from harvest until it reaches consumers. Among the various factors contributing to PHL, the losses due to disease are detrimental. They lead to spoilage through symp- toms such as rotting and the production of harmful toxins. Due to its perishable nature, fruit and vegetables are most vulnerable to various post- harvest pathogens. Chemical fungicides are commonly used to manage post-harvest diseases, but they pose risks of environmental pollution, consumer health concerns and pesticide resistance by pathogens. To overcome the negative impact resulting from the use of chemical compounds, there is an urgent need to develop alternate control measures for protecting perish- able produce and human health. Recently, beneficial organisms have gained a significant role in managing these diseases, with probiotic bacteria and yeast as key organisms. They help to maintain the quality of fresh produce by protecting it from harmful pathogens through rapid colonization, competition for space and nutrients, creation of an acidic environment, activation of defence mechanisms and production of antimicrobial compounds such as cell wall-degrading enzymes, bacteriocins and volatile organic compounds. Probiotic-based treatments were applied through edible coatings, sprays, or incorporated into packaging materials as natural and safe ways to extend the shelf life of perishable goods. This review prioritized compiling research findings employing the mechanism of probiotics in disease management and its utilization for managing post-harvest diseases.

The WHEAT MARKER FOR BETTER YIELD AND HEAT STRESS TOLERANCE: A REVIEW

Background Wheat is a staple crop that plays a critical role in global food security, making its yield and resilience to environmental stressors essential for sustaining the growing human population. Improving wheat yield and heat stress tolerance is crucial in the face of climate change, which poses significant challenges to agricultural productivity and food supply. Previous research has identified various genetic markers associated with wheat yield and stress tolerance, but their effectiveness under varying climatic conditions remains inconsistent. Research Method A combination of Literature survey at Google Scholar, Pubmed, Scispace etc to retrieve the data about wheat markers in improving yield and heat stress tolerance. Literature Review A comprehensive review of existing literature on wheat genetics and climate resilience was conducted to inform the research framework. Data Collection Data on wheat yield, heat stress tolerance, and related agronomic traits were collected through on line published article NCBI Google scholar,

Deep Learning for Agriculture: Sweet Corn Disease Detection with Convolutional Neural Networks

Sweet corn crops have a significant detrimental impact on agricultural productivity and food security because of their high vulnerability to several diseases. A timely and accurate illness diagnosis is essential for both disease treatment and yield enhancement. This paper suggests an automated method for classifying and identifying sweet corn diseases from leaf pictures that is based on convolutional neural networks (CNNs). The collection, which is separated into three categories—common rust, northern corn leaf blight, and maize dwarf mosaic virus—includes images of both healthy and damaged sweet corn leaves. Preprocessing techniques including scaling, normalization, and augmentation are applied to the images in order to increase the model's durability. The CNN design uses many convolutional layers, batch normalization, and dropout to maximize feature extraction and reduce overfitting. The categorical cross-entropy loss function and the Adam optimizer are used to train the model in order to achieve high classification accuracy. Performance is evaluated using precision, recall, F1-score, and confusion matrix analysis. Experimental results show that the proposed CNN model outperforms both pre-trained architectures and traditional machine learning methods in the classification of sweet corn illnesses. By facilitating proactive crop management and real-time disease identification, this work represents a major step toward incorporating AI-driven solutions in smart agriculture. Future studies will concentrate on developing a mobile application for field deployment in the actual world, adding attention methods, and growing the dataset.

Time series analysis of Sentinel 1 A SAR data to retrieve annual rice area maps and long-term dynamics of start of season

Rice is a vital staple crop globally, and accurate estimation of rice area was crucial for effective agricultural management and food security. Synthetic Aperture Radar (SAR) data has emerged as a valuable remote sensing tool for rice area estimation due to its ability to penetrate cloud cover and capture backscattered signals from rice fields. The backscatter signature of rice showed a minimum dB value at agronomic flooding indicating the Start of Season (SoS). The parameters viz., the minimum values of −22.03 to −17.69 dB at the start of season, maximum value of −16.10 to −14.20 dB at the peak of season coinciding with heading and corresponding mean increase of 5.07 dB during growing stages were utilized for developing rule-based classification system. Rice area was estimated over the Cauvery Delta Zone of Tamil Nadu, India for the past six years during samba (August–January) season from 2017 to 2023 using Sentinel 1 A Synthetic Aperture Radar satellite data. Rice area maps were generated for the region utilizing parameterization with a classification accuracy of 88.5 to 94.5 per cent with a kappa score of 0.77 to 0.87 during the study period. The total classified rice area during samba season in the Cauvery Delta Zone was 508,581 ha, 456,601 ha, 506,844 ha, 511,714 ha, 524,723 ha and 476,586 ha for the years 2017–18 to 2022–23, respectively. The Start of Season (SoS) maps for samba season revealed that the major planting periods for rice were between the second fortnight of September to first fortnight of November in all the years except 2018 when early planting happened during the first fortnight of September due to favorable weather conditions and assured water supply. Near real-time information on rice area, start of season, and progress of planting derived using SAR satellite data will facilitate the development of decision support systems for sustaining the productivity of rice-based ecosystems.

Discovery of Novel Isoxazoline Derivatives Containing Pyrazolamide Fragment as Insecticidal Candidates.

Lepidopteran species cause significant harm to agricultural yields and food quality. In this study, a series of innovative isoxazoline derivatives incorporating pyrazolamide moieties were carefully designed and synthesized as potential insecticidal agents. Among these, compound F16 demonstrated an LC50 value of 0.01 mg/L against Plutella xylostella, surpassing that of the lead compound fluxametamide (LC50 = 0.15 mg/L). Furthermore, F16 exhibited broad-spectrum insecticidal activity against Pyrausta nubilalis, Spodoptera frugiperda, Chilo suppressalis, Aphis craccivora, and Sogatella furcifera. Notably, F16 possessed low toxicity against Danio rerio, whereas fluxametamide displayed moderate toxicity. Furthermore, molecular docking analysis demonstrated that the potent insecticidal activity of F16 is likely mediated by its specific interactions with γ-GABA receptors primarily through the formation of hydrogen bonds with key residues. Density functional theory calculations and molecular electrostatic potentials were also performed to gain insights into the insecticidal behavior of F16. These findings suggest that F16 is a promising candidate for further investigation as a novel pesticide.

GMIE: a global maximum irrigation extent and central pivot irrigation system dataset derived via irrigation performance during drought stress and deep learning methods

Abstract. Irrigation accounts for the major form of human water consumption and plays a pivotal role in enhancing crop yields and mitigating the effects of drought. Accurate mapping of irrigation distribution is essential for effective water resource management and the assessment of food security. However, the resolution of the global irrigated cropland map is coarse, typically approximately 10 km, and it lacks regular updates. In our study, we present a robust methodology that leverages irrigation performance during drought stress as an indicator of crop productivity and water consumption to identify global irrigated cropland. Within each irrigation mapping zone (IMZ), we identified the dry months of the growing season from 2017 to 2019 or the driest months from 2010 to 2019. To delineate irrigated cropland, we utilized the collected samples to calculate normalized difference vegetation index (NDVI) thresholds for the dry months of 2017 to 2019 and the NDVI deviation from the 10-year average for the driest month. By integrating the most accurate results from these two methods, we generated the Global Maximum Irrigation Extent dataset at 100 m resolution (GMIE-100), achieving an overall accuracy of 83.6 % ± 0.6 %. The GMIE-100 reveals that the maximum extent of irrigated cropland encompasses 403.17 ± 9.82 Mha, accounting for 23.4 % ± 0.6 % of the global cropland. Concentrated in fertile plains and regions adjacent to major rivers, the largest irrigated cropland areas are found in India, China, the United States, and Pakistan, which rank first to fourth, respectively. Importantly, the spatial resolution of GMIE-100 surpasses that of the dominant irrigation map, offering more detailed information essential to support estimates of agricultural water use and regional food security assessments. Furthermore, with the help of the deep learning (DL) method, the global central pivot irrigation system (CPIS) was identified using Pivot-Net, a novel convolutional neural network built on the U-net architecture. We found that there is 11.5 ± 0.01 Mha of CPIS, accounting for approximately 2.90 % ± 0.03 % of the total irrigated cropland. In Namibia, the United States, Saudi Arabia, South Africa, Canada, and Zambia, the CPIS proportion was greater than 10 %. To our knowledge, this is the inaugural study to undertake a global identification of specific irrigation methods, with a focus on the CPIS. The GMIE-100 dataset containing both the irrigated extent and CPIS distribution is publicly available on Harvard Dataverse at https://doi.org/10.7910/DVN/HKBAQQ (Tian et al., 2023a).

Pathogenic Escherichia coli Virulome in Top Soil Improvers and Irrigation Waters Devoted to Leafy Vegetable Production: Hazard Assessment From a Case Study in Italy

ABSTRACTTop soil improvers (TSIs) and reused water from municipal wastewater treatment plants (WWTPs) represent useful and sustainable sources of organic carbon and nutrients for agriculture. However, from their regular use, emerging Escherichia coli enterophatotypes, particularly Shiga toxin‐producing E. coli (STEC), were responsible for severe foodborne outbreaks from contaminated ready‐to‐eat vegetables. We report the hazard characterization of 31 top soil improvers (TSIs) and 15 irrigation water samples of different origins, sampled at the farm level. The increased PCR signals of the marker virulence genes stx1, stx2, aggR, aaiC, ipaH, lt, stp, and sth for enteropathogenic, enteroaggregative, enteroinvasive, enterotoxigenic, and Shiga toxin‐producing E. coli from post‐enrichment samples were considered proof of the presence of viable bacteria. Despite positive signals from TSI‐enriched samples, we isolated one enteropathogenic and one enterotoxigenic strain from the same pig slurry sample and one STEC strain carrying the enterotoxigenic heat‐stable enterotoxin from bovine slurry. From irrigation water samples, the most detected genes were stx1, stx2, and eae, followed by aggR, aaiC, ipaH, lt, stp, and sth, leading to one STEC (stx1+) isolation. E. coli pathotype virulotyping could be proposed to assess if combined TSI and water inputs would potentially lead to hybrid strains with shuffled virulence features to support safe and sustainable ready‐to‐eat vegetable production.

Characterization of Irrigated Rice Cultivation Cycles and Classification in Brazil Using Time Series Similarity and Machine Learning Models with Sentinel Imagery

The mapping and monitoring of rice fields on a large scale using medium and high spatial resolution data (<10 m) is essential for efficient agricultural management and food security. However, challenges such as managing large volumes of data, addressing data gaps, and optimizing available data are key focuses in remote sensing research using automated machine learning models. In this sense, the objective of this study was to propose a pipeline to characterize and classify three different irrigated rice-producing regions in the state of Santa Catarina, Brazil. To achieve this, we used Sentinel-1 Synthetic Aperture Radar (SAR) polarizations and Sentinel-2 optical multispectral spectral bands along with multiple time series indices. The processing of input data and exploratory analysis were performed using a clustering algorithm based on Dynamic Time Warping (DTW), with K-means applied to the time series. For the classification step in the proposed pipeline, we utilized five traditional machine learning models available on the Google Earth Engine platform to determine which had the best performance. We identified four distinct irrigated rice cropping patterns across Santa Catarina, where the northern region favors double cropping, the south predominantly adopts single cropping, and the central region shows both, a flattened single and double cropping. Among the tested classification models, the SVM with Sentinel-1 and Sentinel-2 data yielded the highest accuracy (IoU: 0.807; Dice: 0.885), while CART and GTBoost had the lowest performance. Omission errors were reduced below 10% in most models when using both sensors, but commission errors remained above 15%, especially for patches in which rice fields represent less than 10% of area. These findings highlight the effectiveness of our proposed feature selection and classification pipeline for improving the generalization of irrigated rice mapping in large and diverse regions.

Risk Assessment of Extreme Drought and Extreme Wetness During Growth Stages of Major Crops in China

Climate change has increased the frequency of extreme droughts and floods in China, threatening agricultural production and food security. However, the impacts of these extreme precipitation events on crops (maize, wheat, and rice) during key growth stages remain poorly understood. To address this, we developed a three-step analytical framework: First, we used transpiration data to identify critical crop growth stages across China. Then, we applied a 10-day standardized precipitation evapotranspiration index (SPEI) to quantify drought and extreme wetness conditions during each growth phase. Finally, we integrated these data into an XGBoost model to assess the relationship between extreme weather and crop yield fluctuations. The results show that maize is most sensitive to water variability during both development and mid-season stages, while wheat is particularly vulnerable to drought during development and rice is mainly affected by water stress during the mid-season. Extreme drought risks are highest in the Northeast Plain, North China Plain, and southern China, while extreme wetness risks are concentrated in the middle and lower Yangtze River basin and southeastern coastal regions. Notably, extreme drought risks are significantly more pronounced than those associated with extreme wetness. These findings highlight the urgent need for targeted agricultural strategies to promote sustainable agricultural development.

Research Advances on Identification and Detection Methods for Aflatoxigenic Fungi

ABSTRACTAflatoxin contamination in food and feed is a global concern for food safety, posing great threat to human and environmental health. It is the secondary metabolite mainly produced by Aspergillus flavus (A. flavus) and Aspergillus parasiticus (A. parasiticus). Therefore, it is very urgent to develop rapid and sensitive identification and detection methods for aflatoxigenic fungi to realize early warning the risk of aflatoxin contamination from the source. This article reviews the latest research progress in the strategy of identifying and detecting aflatoxigenic fungi in agricultural food in recent years. The detection principles and applications of different techniques for aflatoxigenic fungi determination, including morphological identification, nucleic acid amplification techniques, spectral analysis technology, and biosensing, are presented in this review. Finally, the challenges and research trends of identification and detection of aflatoxigenic fungi in the future are also discussed. Through rapid and sensitive identification and detection of aflatoxigenic fungi, there will be relatively sufficient time to do corresponding prevention and control measures to reduce or even prevent the production of aflatoxin in the early stage, which will be very beneficial to greatly reduce the risk of aflatoxin contamination in agricultural food and improve food safety.

An analysis of spatial changes in the manufacturing industry in china’s three major urban clusters from 2015 to 2019 using POI data

China’s manufacturing industry has been ranked as the most valuable in the world from 2010 to 2021. However, high-resolution manufacturing datasets are lacking, and this has precluded study of the survival rates and spatial changes in the manufacturing industry in China. Here, we analyzed spatial patterns of the manufacturing industry using point-of-interest (POI) data and a machine learning classification algorithm based on the Naive Bayes classifier. Using 2,780,266 POI data points in 2015 and 3,426,501 POI data points in 2019 covering the three major urban clusters in China, we classified the manufacturing industry data into seven categories: textile and garment (TC), mechatronics and equipment (ME), wood furniture (WF), agricultural and sideline product food processing (AF), metallurgical chemical industry and resource rough processing (MC), pharmaceutical manufacturing (PM), and papermaking culture (PP). The evolution of the manufacturing industry at the scale of 451 districts and counties in the urban clusters and the factors driving new entrants in the manufacturing industry were studied. The main conclusions were as follows. (1) Between 2015 and 2019, manufacturing activities in the three major urban clusters were highly concentrated in provincial capitals, municipalities under direct control of the central government, and their neighboring districts and counties with favorable economic conditions; incremental growth was concentrated in the core cities. (2) The survival rate of enterprises in the Beijing-Tianjin-Hebei urban cluster was relatively high, whereas that in the Pearl River Delta urban cluster was low. Enterprises in the PM industry had a relatively high survival rate, whereas those in the ME industry had a relatively low survival rate. (3) Analysis of the factors driving new entrants in the manufacturing industry indicates that the industrial foundation is the core factor affecting the entry of new manufacturing enterprises. Land transfer policies and high population density promote the development of the manufacturing industry, and regions with high per capita GDP and more research institutions tend to inhibit the development of the manufacturing industry. Further regressions showed that the effects of the proportion of the secondary industry in GDP, the number of development zones, and the number of research institutions on the different urban clusters varied. This paper provides strategic guidance for the future development of China’s manufacturing industry, which will help the government and planning departments optimize the layout of the manufacturing industry, promote the development of the regional economy, and enhance the sustainability of the manufacturing industry.

Nanofertilizers for Sustainable African Agriculture: A Global Review of Agronomic Efficiency and Environmental Sustainability.

As Africa's population continues to grow, the need for sustainable agricultural practices has intensified, sparking greater interest in nanofertilizers This review critically evaluates the agronomic efficiency and environmental sustainability of nanofertilizers in the African context. It combines existing research on nanofertilizers' effectiveness, nutrient-use efficiency, and environmental impact. Nanofertilizers have shown a nutrient-use efficiency boost of up to 30% compared to conventional fertilizers. This review also highlights benefits such as enhanced crop yields (up to 25% increase in maize production), reduced chemical fertilizer requirements (up to 40% reduction in nitrogen application), and improved soil health. The analysis informs policy, research, and practice aimed at optimizing nanofertilizer deployment for sustainable African agriculture. The projected global population of 2.4 billion by 2050 highlights that the need for sustainable agricultural solutions has never been more important. Our review conveys an assessment of nanofertilizers' potential contribution to Africa's agricultural sustainability and food security.

The Impact of Climate Change on Agriculture in the Sichuan Basin over the Past 60 Years

Climate change profoundly affects agriculture, particularly in locations such as the Sichuan Basin, a key grain production region in China. This study analyzes the effects of climate change on Sichuan's agriculture over the past 60 years, focusing on temperature and precipitation trends. Results show that rising temperatures and fluctuating rainfall have directly influenced crop growth cycles, yields, and susceptibility to pests and diseases. Moreover, the phenomenon of climate change has exacerbated the risks associated with extreme weather events, leading to significant ecological disruptions and increased production costs. In response to these challenges, the Sichuan government has implemented a series of adaptive strategies, which include the development of advanced irrigation infrastructure, the establishment of comprehensive agricultural insurance programs, and the promotion of the cultivation of crop varieties that are resilient to changing climatic conditions. These strategies have enhanced the resilience and sustainability of agricultural production. This research emphasizes the importance of integrating macro-level policies with farmer-level innovations to ensure stable agricultural yields and food security under changing climatic conditions.

Deep Learning for Soil Nutrient Prediction and Strategic Crop Recommendations: An Analytic Perspective

Agriculture has been a vital sector for the majority of people, especially in countries like India. However, the increasing need for food production has led to intensive farming practices that have resulted in the deterioration of soil quality. This deterioration in soil quality poses significant challenges to both agricultural productivity and environmental sustainability. To address these challenges, advanced soil nutrient prediction systems that utilize machine learning and deep learning techniques are being developed. These advanced soil nutrient prediction systems utilize various sources of data, such as soil parameters, plant diseases, pests, fertilizer usage, and changes in weather patterns. By mapping and analyzing these data sources, machine learning algorithms can accurately predict the distribution of soil nutrients and other properties essential for precise agricultural practices. A previous study compared machine learning algorithms like SVM and Random Forest with deep learning algorithms CNN and LSTM for predicting crop yields. The most appropriate model is a significant challenge, but several studies have evaluated recommendation system models using deep machine learning techniques. Deep learning models attain accuracy above 90%, while many ML models achieve rates between 90% and 93%. Furthermore, the research seeks to propose specific crop suggestions grounded in soil nutrients for precision agriculture to enhance crop productivity.

Optimizing encapsulation of garlic and cinnamon essential oils in silver nanoparticles for enhanced antifungal activity against Botrytis cinerea pathogenic disease

Optimizing encapsulation of garlic and cinnamon essential oils in silver nanoparticles for enhanced antifungal activity against Botrytis cinerea pathogenic disease

Polyethylene nanoplastics affected morphological, physiological, and molecular indices in tomato (Solanum lycopersicum L.).

Polyethylene nanoplastics affected morphological, physiological, and molecular indices in tomato (Solanum lycopersicum L.).

Soil Organic Carbon Assessment Using Remote-Sensing Data and Machine Learning: A Systematic Literature Review

Soil Organic Carbon Assessment Using Remote-Sensing Data and Machine Learning: A Systematic Literature Review

Drug Discovery Potential of Insect-Derived Compounds: A review.

Insects are the most numerous and diverse collection of organisms on earth. Around the world, human societies have utilised insects and the materials derived from them as medical resources. These arthropods use chemistry, like all other species. According to their survival needs, they make adaptations, including protecting themselves from infection as well as predation, being able to connect with a social life, and carrying out generations with survivability in the environment. The main focus of the study of chemical ecology is the abundance of chemical compounds found in arthropods that are used for these ecological adaptations. This thorough analysis summarises the huge potential for finding new natural compounds with medical benefits from the Arthropods. Insects and their derivatives have a wide range of uses, and their "raw products" have made significant strides in a wide range of industries, including pharmaceuticals, tissue engineering, dentistry, plant and agricultural science, veterinary medicine, cosmetics, and cosmeceuticals, food, and nutraceuticals, among others. Bioactive components derived from insects are emerging as potential therapeutic sources that are beneficial against a variety of disorders. Insects, which have a huge variety of species, are an intriguing and potential source of low molecular biologically active natural compounds that are either produced by the insect itself or by accompanying microbes. The present review work collated the updates of insect-derived compounds, the use of insects worldwide, and drug discovery potential of insect derivatives.

Analysis of the subtype I amidohydrolase responsible for Ochratoxin A degradation in the Sphingomonas genus.

Analysis of the subtype I amidohydrolase responsible for Ochratoxin A degradation in the Sphingomonas genus.