Global Food Research Articles

From trade-off to synergy: microbial insights into enhancing plant growth and immunity.

The reduction in crop yield caused by pathogens and pests presents a significant challenge to global food security. Genetic engineering, which aims to bolster plant defence mechanisms, emerges as a cost-effective solution for disease control. However, this approach often incurs a growth penalty, known as the growth-defence trade-off. The precise molecular mechanisms governing this phenomenon are still not completely understood, but they generally fall under two main hypotheses: a "passive" redistribution of metabolic resources, or an "active" regulatory choice to optimize plant fitness. Despite the knowledge gaps, considerable practical endeavours are in the process of disentangling growth from defence. The plant microbiome, encompassing both above- and below-ground components, plays a pivotal role in fostering plant growth and resilience to stresses. There is increasing evidence which indicates that plants maintain intimate associations with diverse, specifically selected microbial communities. Meta-analyses have unveiled well-coordinated, two-way communications between plant shoots and roots, showcasing the capacity of plants to actively manage their microbiota for balancing growth with immunity, especially in response to pathogen incursions. This review centers on successes in making use of specific root-associated microbes to mitigate the growth-defence trade-off, emphasizing pivotal advancements in unravelling the mechanisms behind plant growth and defence. These findings illuminate promising avenues for future research and practical applications.

De novo transcriptome assembly database for 100 tissues from each of seven species of domestic herbivore

Domesticated herbivores are an important agricultural resource that play a critical role in global food security, particularly as they can adapt to varied environments, including marginal lands. An understanding of the molecular basis of their biology would contribute to better management and sustainable production. Thus, we conducted transcriptome sequencing of 100 to 105 tissues from two females of each of seven species of herbivore (cattle, sheep, goats, sika deer, horses, donkeys, and rabbits) including two breeds of sheep. The quality of raw and trimmed reads was assessed in terms of base quality, GC content, duplication sequence rate, overrepresented k-mers, and quality score distribution with FastQC. The high-quality filtered RNA-seq raw reads were deposited in a public database which provides approximately 54 billion high-quality paired-end sequencing reads in total, with an average mapping rate of ~93.92%. Transcriptome databases represent valuable resources that can be used to study patterns of gene expression, and pathways that are related to key biological processes, including important economic traits in herbivores.

Crop Yield Prediction using Machine Learning

Weather profoundly impacts agricultural outcomes, making accurate crop prediction vital for farmers' decision-making. This abstract presents a comprehensive overview of weather-based crop prediction, emphasizing its significance, key components, and methodologies. The process begins with the collection and analysis of historical weatherdata encompassing variables such as temperature, precipitation, humidity, and sunlight. Utilizing Python programming and data visualization libraries like Pandas and Matplotlib facilitates the exploration and visualization of this data, revealing trends and patterns. Machine learning algorithms, including regression and ensemble methods, are employedto develop predictive models. These models leverage historical weather data to forecast future crop yields accurately. Python's extensive libraries, such as Scikit-learn and TensorFlow, offer robust tools for model development and evaluation. Incorporating advanced technologies like remote sensing and satellite imagery further refines the prediction process. These tools provide real-time insights into crop health and growth, enhancing the precision of forecasts. Ultimately, weather-based crop prediction serves as a valuable decision support tool for farmers, enabling informed choices regarding planting, irrigation, and harvesting practices. By harnessing historical weather data, machine learning algorithms, and innovative technologies, stakeholders can optimize agricultural productivity, mitigate risks, and contribute to global food security

Global food security estimation: Refining the IFSA caloric threshold using population estimates and projections by age and sex

Global food security estimation: Refining the IFSA caloric threshold using population estimates and projections by age and sex

A Dynamic Evolutionary Analysis of the Vulnerability of Global Food Trade Networks

The global food trade network (FTN) is a critical infrastructure for achieving the Sustainable Development Goals (SDGs). The FTN’s vulnerability to geopolitical conflicts, public health crises, and climate change events directly impacts food security and the ability to meet the SDGs. This study aims to analyze the dynamic evolution of the vulnerability of FTN, focusing on the period from 2000 to 2022, to aim for strategies for enhancing the resilience and sustainability of the global food system. Based on complex network analysis, we examine the structural characteristics and evolution of FTN for four major crops: soybeans, wheat, rice, and maize. We identify a trend towards increased network density and regionalization, with a decline in average shortest path length (ASPL) and an increase in the average clustering coefficient (ACC). These changes indicate a shift towards a more interconnected and resilient FTN in response to various shocks, including the COVID-19 pandemic and the Russia–Ukraine conflict. The findings suggest that the global FTN has adapted to increase resilience, which is essential for achieving the SDGs related to food security and sustainable development. The study’s insights can guide policy interventions to further strengthen the network against future shocks and promote global food security.

Sustainable Cultivation of Millets

Millets, once considered as neglected crops, have gained significant attention due to their nutritional value, climate resilience, and suitability for sustainable agriculture. This review paper synthesizes current research on sustainable cultivation practices for millets, focusing on key aspects such as soil health, water management, pest and disease control, and socio-economic implications. It explores innovative approaches such as intercropping, organic farming techniques, and precision agriculture methods to enhance millet yields while minimizing environmental impact. Furthermore, the paper discusses the role of millets in enhancing food security, promoting biodiversity, and mitigating climate change. By critically evaluating existing literature, this review aims to provide insights for policymakers, researchers, and farmers to promote the adoption of sustainable millet cultivation practices, thereby contributing to global food security and environmental sustainability.

Metagenomic sequencing and reconstruction of 82 microbial genomes from barley seed communities

Barley (Hordeum vulgare) is essential to global food systems and the brewing industry. Its physiological traits and microbial communities determine malt quality. Although microbes influence barley from seed health to fermentation, there is a gap in metagenomic insights during seed storage. Crucially, elucidating the changes in microbial composition associated with barley seeds is imperative for understanding how these fluctuations can impact seed health and ultimately, influence both agricultural yield and quality of barley-derived products. Whole metagenomes were sequenced from eight barley seed samples obtained at different storage time points from harvest to nine months. After binning, 82 metagenome-assembled genomes (MAGs) belonging to 26 distinct bacterial genera were assembled, with a substantial proportion of potential novel species. Most of our MAG dataset (61%) showed over 90% genome completeness. This pioneering barley seed microbial genome retrieval provides insights into species diversity and structure, laying the groundwork for understanding barley seed microbiome interactions at the genome level.

Spatial and temporal-trend assessment of desertification-sensitive land using the desertification sensitivity index in the provincial Ninh Thuan, Vietnam.

Desertification is a specific land-degrading process, reducing soil productivity and potentially threatening global food security. Therefore, spatially and temporally identifying and mapping desertification-sensitive areas is essential for better management. The current study aimed to (1) assess spatial areas sensitive to desertification and (2) examine the changing tendency of the desertification-sensitive areas over the past 25years in the provincial Ninh Thuan. The desertification sensitivity index (DSI) was computed based on the Medalus model using 10 quantitative parameters, grouped into the soil, climate, and vegetation quality indexes, computed for the years 1996, 2005, 2010, and 2016. GIS was used to map desertification-sensitive areas associated with five DSI classes. Results showed that classes II and III had the highest area percentage, followed by classes IV and V, and class I. The classes most sensitive to desertification (classes IV and V) covered around 13 to 17%, and classes II and III were 25 to 32% of the total study area, respectively. The coastal areas located in the southeastern parts were more sensitive to desertification than the other parts. Over the four examined periods, the areas of classes IV and V increased while those of classes II and I decreased. These indicated that the study province tended to increase in its desertification sensitivity with a severe increase in the coastal areas over the past 25years. The key factors involved in these changes could be related the human activities and climate variation, which could be more serious in southeastern areas than in the other areas.

Organic Farming vs. Integrated Nutrient Management: A Comparative Review of Agricultural Productivity and Sustainability

In an era where sustainable agriculture is crucial for global food security and environmental preservation, organic farming and Integrated Nutrient Management (INM) have emerged as leading practices. This review paper offers a comprehensive comparison of these two approaches, focusing on their impact on agricultural productivity and sustainability. Organic farming, with its emphasis on reducing the use of chemical fertilizers, pesticides, growth hormones, and feed additives, aligns closely with the principles of sustainability. It enhances food quality, promotes soil health, and mitigates adverse environmental effects by encouraging natural recovery cycles. However, organic farming's limitations, such as lower yields and a more labour-intensive approach have prompted interest in innovative solutions that blend organic practices with modern technologies. Integrated Nutrient Management (INM) presents a compelling alternative. By combining organic, inorganic, and biological nutrient sources, INM seeks to optimize nutrient use efficiency and maintain soil health. This approach offers a flexible nutrient management strategy, balancing organic and inorganic inputs to maintain crop productivity while reducing environmental impact. Studies have shown that INM can improve soil properties, such as bulk density, porosity, and water-holding capacity, leading to enhanced crop yields and lower greenhouse gas emissions. The paper explores how INM's integrated approach not only stabilizes crop production but also supports the growth of soil microbes, providing a source of energy and organic carbon. By using a combination of specific microorganisms, organic matter, and minimal doses of inorganic fertilizers, INM can achieve a harmonious balance that reduces environmental pollution and ensures long-term soil fertility. Ultimately, this review underscores the potential of combining organic and INM practices to create a sustainable and productive agricultural system. Through a comparative analysis, the paper aims to guide researchers, policymakers, and farmers towards adopting strategies that foster agricultural sustainability without compromising productivity.

Enhancing Sensitivity and Selectivity in Pesticide Detection: A Review of Cutting-Edge Techniques.

The primary goal of our review was to systematically explore and compare the state-of-the-art methodologies employed in the detection of pesticides, a critical component of global food safety initiatives. New approach methods in the fields of luminescent nanosensors, chromatography, terahertz spectroscopy, and Raman spectroscopy are discussed as precise, rapid, and versatile strategies for pesticide detection in food items and agroecological samples. Luminescent nanosensors emerge as powerful tools, noted for their portability and unparalleled sensitivity and real-time monitoring capabilities. Liquid and gas chromatography coupled to spectroscopic detectors, stalwarts in the analytical chemistry field, are lauded for their precision, wide applicability, and validation in diverse regulatory environments. Terahertz spectroscopy offers unique advantages such as noninvasive testing, profound penetration depth, and bulk sample handling. Meanwhile, Raman spectroscopy stands out with its nondestructive nature, its ability to detect even trace amounts of pesticides, and its minimal requirement for sample preparation. While acknowledging the maturity and robustness of these techniques, our review underscores the importance of persistent innovation. These methodologies' significance extends beyond their present functions, highlighting their adaptability to meet ever-evolving challenges. Environ Toxicol Chem 2024;00:1-17. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Identification of Anaplasma marginale adhesins for entry into Dermacentor andersoni tick cells using phage display.

Anaplasma marginale is an obligate, intracellular, tick-borne bacterial pathogen that causes bovine anaplasmosis, an often severe, production-limiting disease of cattle found worldwide. Methods to control this disease are lacking, in large part due to major knowledge gaps in our understanding of the molecular underpinnings of basic host-pathogen interactions. For example, the surface proteins that serve as adhesins and, thus, likely play a role in pathogen entry into tick cells are largely unknown. To address this knowledge gap, we developed a phage display library and screened 66 A. marginale proteins for their ability to adhere to Dermacentor andersoni tick cells. From this screen, 17 candidate adhesins were identified, including OmpA and multiple members of the Msp1 family, including Msp1b, Mlp3, and Mlp4. We then measured the transcript of ompA and all members of the msp1 gene family through time, and determined that msp1b, mlp2, and mlp4 have increased transcript during tick cell infection, suggesting a possible role in host cell binding or entry. Finally, Msp1a, Msp1b, Mlp3, and OmpA were expressed as recombinant protein. When added to cultured tick cells prior to A. marginale infection, all proteins except the C-terminus of Msp1a reduced A. marginale entry by 2.2- to 4.7-fold. Except OmpA, these adhesins lack orthologs in related pathogens of humans and animals, including Anaplasma phagocytophilum and the Ehrlichia spp., thus limiting their utility in a universal tick transmission-blocking vaccine. However, this work greatly advances efforts toward developing methods to control bovine anaplasmosis and, thus, may help improve global food security.

Unlocking value: circular economy in ngos' food waste reduction efforts in Brazil and Togo

This article delves into the intriguing realm of food waste valorization conducted by nongovernmental organizations (NGOs) in two distinct locales—Brasilia, Brazil, and Lomé, Togo. The primary focus of this exploration is the lens of the Circular Economy (CE), a concept that emphasizes the sustainable utilization of resources within closed loops. The discourse is further enriched by introducing the notion of upcycling, a complementary force to the Circular Economy, particularly within the intricate web of food supply chains. The ReSOLVE framework, a strategic approach for managing food waste, is a guiding light in this research. Upon dissecting the outcomes, noteworthy limitations come to the forefront, shedding light on the challenges of aligning NGOs from both countries with the principles of the Circular Economy in their food waste management endeavors. These challenges assume heightened significance against the backdrop of the intricate global food security landscape, effectively underlining the intricacies of harmonizing the Circular Economy's ambitions with the pragmatic goal of diminishing food waste. The overarching objective of this study is to galvanize circular-oriented initiatives that can effectively curtail and prevent food wastage on a global scale, with a particular emphasis on developing nations. This pursuit is underscored by the triad of benefits such initiatives confer: economic advancement, social amelioration, and a positive ecological footprint. In an era where sustainability stands as a paramount concern, this research advocates for concerted efforts to harmonize the noble ideals of the Circular Economy with the pressing need for food waste reduction, especially within regions striving for holistic growth and development.

Genome-wide association study in Chickpea (Cicer arietinum L.) for yield and nutritional components

The increase in the global human population and the accompanying challenges in meeting nutritional needs amidst climate change are a worldwide concern. Widespread protein and micronutrient deficiencies contribute to a significant number of individuals experiencing malnutrition, leading to severe health repercussions. This issue can be addressed through genomics-assisted breeding, particularly in enhancing the nutritional profile of vital staple crops like chickpea (Cicer arietinum L.). Chickpea, beyond being a rich source of protein, provides a diverse nutritional spectrum encompassing carbohydrates, fats, and minerals. To explore and improve the genetic basis of nutritional traits in chickpea, a study was conducted using 93 kabuli-type single plant derived lines and five cultivars in 2018, 2022, and 2023. Genotyping by sequencing revealed a total of 165K single nucleotide polymorphisms (SNPs) within this kabuli chickpea mini-core collection. After filtering for a minor allele frequency greater than 5%, 113,512 SNPs were utilized, distributed across eight chromosomes of the chickpea genome. Marker-trait associations were analyzed using genome wide association study, leading to the identification of 27 significantly associated SNPs from across all eight chromosomes linked to three seed nutritional concentrations and 100-seed weight. To unravel the molecular mechanisms governing seed protein, fiber, fat concentrations, and 100-seed weight, 31 candidate genes were determined within a 30 kb window size. This comprehensive approach holds promise for advancing crop breeding strategies to combat malnutrition and improve global food security.

Cross Domain Early Crop Mapping with Label Spaces Discrepancies using MultiCropGAN

Abstract. Mapping target crops before the harvest season for regions lacking crop-specific ground truth is critical for global food security. Utilizing multispectral remote sensing and domain adaptation methods, prior studies strive to produce precise crop maps in these regions (target domain) with the help of the crop-specific labelled remote sensing data from the source regions (source domain). However, existing approaches assume identical label spaces across those domains, a challenge often unmet in reality, necessitating a more adaptable solution. This paper introduces the Multiple Crop Mapping Generative Adversarial Neural Network (MultiCropGAN) model, comprising a generator, discriminator, and classifier. The generator transforms target domain data into the source domain, employing identity losses to retain the characteristics of the target data. The discriminator aims to distinguish them and shares the structure and weights with the classifier, which locates crops in the target domain using the generator’s output. This model’s novel capability lies in locating target crops within the target domain, overcoming differences in crop type label spaces between the target and source domains. In experiments, MultiCropGAN is benchmarked against various baseline methods. Notably, when facing differing label spaces, MultiCropGAN significantly outperforms other baseline methods. The Overall Accuracy is improved by about 10%.

Genomic Advancement in Wheat (Triticum aestivum L.): Harnessing Technological Breakthroughs for Future Strategies

Wheat can be greatly improved by identifying genes that are significant to agronomy. Although progress in wheat genetics and genomics has been limited by the genome’s vast size and complexity. Wheat has a high-quality genome sequence in light of recent developments in genome sequencing and sequence assembly. Here, we propose that wheat biology can benefit from the same approaches that have been used to define biological systems in model species, such as the generation and characterization of mutants, methods for gene cloning, and enhanced transgenic technology. These tactics will encourage the establishment of wheat breeding programs and hasten the field of wheat biology. We also summarize current developments in functional genomics of wheat. In order to contribute to global food security, we conclude by talking about the future of wheat functional genomics and the sensible design-based molecular breeding of new wheat varieties. We suggest that researchers studying wheat should embrace the methods utilized for functional genomic analysis in other model species. One method for doing this is using gene cloning to find the gene causing an intriguing phenotype, deciphering the biological role of genes through the analysis of their corresponding mutants, and creating a related wheat mutant from the proposed wild-type gene to confirm the target gene’s functionality.

Calcium signaling: Unveiling its role in disease control in plants

<p>Calcium signaling is a fundamental regulatory mechanism in plants, governing a myriad of physiological processes crucial for growth, development, and responses to environmental stimuli, including biotic stresses such as pathogen attack. Over the past decades, research into the intricate network of calcium signaling pathways has revealed its pivotal role in orchestrating plant defense mechanisms against a diverse array of pathogens. This review comprehensively examines the multifaceted roles of calcium ions in modulating plant immune responses, elucidating the molecular mechanisms underlying calcium-mediated signal transduction and its integration with other defense signaling pathways. We delve into the intricate crosstalk between calcium signaling and various hormonal pathways, reactive oxygen species (ROS) signaling, and secondary metabolite biosynthesis, highlighting the interconnectedness of these regulatory networks in shaping plant defense strategies. Furthermore, we explore the potential applications of calcium-based strategies for sustainable disease management in agriculture, including the use of calcium channel blockers, calcium chelators, and calcium-rich compounds as novel antimicrobial agents and priming agents for enhancing plant immunity. Challenges and future directions in the field are also discussed, aiming to advance our understanding of calcium signaling in plant-pathogen interactions and harness its potential for developing innovative approaches to combat plant diseases and ensure global food security. This review provides a comprehensive overview of the current state of knowledge regarding calcium signaling in plant disease control, offering valuable insights and perspectives for future research endeavors in this rapidly evolving field.<strong></strong></p>

Machine Learning-Based Fine Classification of Agricultural Crops in the Cross-Border Basin of the Heilongjiang River between China and Russia

The transboundary region along the Heilongjiang River, encompassing the Russian Far East and Northeast China, possesses abundant agricultural natural resources crucial for global food security. In the face of the challenge of disruptions in the global food supply chain, the precise monitoring and exploitation of agricultural resources in the Heilongjiang Basin becomes imperative. This study employed deep learning to classify crop status in 2023 in the Heilongjiang Basin using Sentinel-2 satellite remote sensing images at a 10 m resolution. Various vegetation indices, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), the Enhanced Vegetation Index (EVI), the Modified Soil Adjusted Vegetation Index (MSAVI), and others, were computed and analyzed for different crops. The Google Earth Engine (GEE) platform was utilized for validation point sampling based on plot objects. The random forest (RF) classification method was successfully employed to classify and identify major crops in the study area (wheat, maize, rice, and soybean), as well as wetlands, tree cover, grassland, water, and constructed land, with an overall classification accuracy of 86%. Tree cover dominated the land cover, constituting 62%, while wheat, maize, rice, and soybeans accounted for 7% of the total area. Of these, soybeans occupied the largest area (57,646.60 hectares), followed by rice (53,209.53 hectares), maize (39,998.37 hectares), and wheat (8782.31 hectares). This study demonstrated that sample selection based on plot objects facilitates efficient sample labeling, providing insights into crop classification in other, potentially larger, areas. This method simultaneously distinguishes wetland, cultivated land, and forest features, supporting further integrated investigations for more natural resources.

Efficiency and sustainability: Evaluating and optimizing energy use and environmental impact in cucumber production

This study assesses the energy-economic-environmental aspects of cucumber production in the face of environmental issues and resource scarcity, which threaten global food security and demand sustainable agriculture solutions. Data were collected by questionnaire and analyzed for inputs from planting to harvesting. The results indicated that diesel fuel (37.41%) and nitrate fertilizer (37.22%) constitute the primary inputs. The energy ratio, energy efficiency and net energy gain were 0.43, 0.54 kgMJ−1 and -30,153.31 MJha−1, respectively. Input optimization by Data Envelopment Analysis (DEA) reduced the carbon footprint from 178.433 to 168.900 kg CO2 per ton, the abiotic depletion from 1228.291 to 964.305 MJ and the acidification from 0.838 to 0.749 kg SO2 eq. Also, the results of DEA showed that 52.5% and 35% of the units were fully efficient under Variable Return to Scale (VRS) and Constant Return to Scale (CRS) models, respectively, while the others were inefficient to varying degrees. The average of technical, pure technical and scale efficiencies were 92.88%, 97.64%, and 95.07%, respectively. The results of economic evaluation showed that production cost is 6058.84 $ha−1 with a benefit-cost ratio of 1.71. Based on the primary results, it is suggested to use precision agriculture techniques and the exploration of alternative energy sources, such as biofuel for machinery. Additionally, using slow-release fertilizers or organic substitutes can decrease nitrogen fertilizer dependency while maintaining crop yields and improve the sustainability in this production.

Analysis of Characteristics and Driving Mechanisms of Non-Grain Production of Cropland in Mountainous Areas at the Plot Scale—A Case Study of Lechang City

With the continuous advancement of urbanization and industrialization, non-grain production on cropland (NGPCL) is gradually becoming more widespread. This phenomenon will compress the space for grain production and trigger a global food crisis. How to scientifically understand and effectively control NGPCL has become a scientific issue. This study, conducted at the plot scale, establishes a measurement index for NGPCL and further explores the characteristics and driving mechanisms of NGPCL in mountainous areas. The results indicate the following: (1) Compared to plots for food-growing, plots for non-grain production tend to be more dispersed, with terraces showing the most significant dispersion, while plains exhibit the highest degree of aggregation. (2) In terms of irrigation conditions, irrigated land and dry land are more likely to undergo NGPCL. In terms of topography, slope croplands have the highest probability of being used for NGPCL. (3) Regions with steeper slopes, higher elevations, greater differences in altitude from the settlement, farther distances from settlements and roads, yet closer proximity to forests, are more likely to engage in NGPCL. (4) Different plot characteristics have varying impacts on NGPCL. Plot characteristics primarily affect the costs and returns of grain production, driving farmers to change their production patterns and triggering NGPCL.

An Exploratory Study of Food Waste in the Hospitality Industry of Pokhara

One-third of the global food production is wasted globally. Food production is a resource intensive process and safe disposal of food wasted requires additional resources. In tourism industry food is just more than fulfillment of basic needs. Food is linked with the experience. Food wastage in hospitality is accepted as a major challenge with multifaced consequences and awareness to address the issue of food waste is also increasing. In the context of developing countries like Nepal the topic is still under-studied. Therefore, this study is focused on owners and managers perspective on food wastage in tourism industry of Pokhara with the objectives of identifying current composition and causes of food waste and established approaches to mitigate it. This study was carried out with key informants’ in-depth interview and on-site observations. Unrealistic expectations, unsustainable consumption patterns, lack of general public awareness and management limitations on wastage controlling were major themes identified by the study. The findings call for quantitative study in mass scale to know the exact situation of the food waste in hospitality industry of Pokhara.