Sustainable Food Security Research Articles

First report of Erwinia papayae associated with papaya mushy canker disease in Guam.

Carica papaya (papaya) in Guam, USA may experience soft rot symptoms, often referred to as mushy canker disease. Disease symptoms first appear as expanding water-soaked dark-green stem lesions or leaf spotting with chlorotic halos. Defoliation at petiole-stem junctions and crown necrosis leads to plant death. Papaya diseases caused by Erwinia spp. are documented in nearby tropic regions such as the Northern Mariana Islands (Trujillo and Schroth 1982), the Philippines (Dela Cueva et al. 2017), Japan (Hanagasaki et al. 2020), and Indonesia (Suharjo et al., 2021). The pathogen was isolated from symptomatic papaya stem sections (cv. Red Lady) from a nursery at the University of Guam Agriculture and Life Sciences building in April 2023. Approximately 20% of seedlings collapsed from stem soft rot, with nearly all plants showing varying degrees of water-soaked lesions on leaves or stems. Stem tissue from lesion margins was excised, surface sterilized with 70% EtOH, and macerated in sterile water. Macerate was plated onto nutrient agar (NA) and incubated at 28°C, yielding colonies that were clear to white in color, smooth, circular and mucoid on NA plates for five suspect isolates (JGD231-235). Strains produced blue diffusible pigment on King's B (KB) media, were Gram-negative rods, and exhibited swimming motility on semi-solid (0.5% agar) NA plates. Crown stab inoculation of ten papaya plants (cv. Red Lady) with isolates resulted in mushy canker symptoms within seven days, while negative control plants stabbed with a sterile probe remained asymptomatic. Koch's postulates were fulfilled by drench-inoculating spontaneous rifampicin-resistant (100µg/ml) mutants, JGD233r and JGD235r, onto ten papaya plants (cv. Solo Sunrise). Nine days post-inoculation, bacterial strains were recovered from symptomatic stem tissue macerate plated on rifampicin (100µg/ml) NA and incubated at 28°C. No symptoms or bacterial cells were recovered from the tissue of negative control plants. Cell morphology, culture phenotypes, and disease symptoms suggested the isolates were Erwinia spp., and blue pigment production on KB further suggested E. papayae (Gardan et al. 2004). Partial 16S rDNA sequences of Guam strains JGD231-235 (sequenced using PCR forward primer 5' - AGAGTTTGATCMTGGCTCAG - 3' and reverse primer 5' - GGTTACCTTGTTACGACTT - 3', GENEWIZ (South Plainfield, NJ)) were deposited into GenBank (OR577627- 631). Highest NCBI BLAST results for all strains showed a 16S rDNA sequence identity of 98.17-98.91% with those of Erwinia sp. I-leaf (LC590218) and E. mallotivora BT-MARDI (HQ456230). A maximum likelihood phylogenetic tree based on concatenated partial atpD, infB, and rpoB sequences of strains JGD232 (PP669340, PP669346, PP669343), JGD233 (PP669341, PP669347, PP669344), and JGD235 (PP669342, PP669348, PP669345) (Brady et al. 2008) constructed using MEGA11 (Tamura et al. 2021) showed all strains formed a monophyletic group with Erwinia sp. I-leaf (Hanagasaki et al. 2020) and E. papayae NCPPB 4294T (Gardan et al. 2004), supported with 98% bootstrap. This note documents the first occurrence of E. papayae as a papaya pathogen in Guam. Papaya cultivation supports sustainable food security for Guam (Bevacqua and Sayama 2023), and Erwinia spp. pathogens threaten papaya on other Pacific islands like Hawaii. These findings convey the need for effective quarantine practices, local disease management, and further research on this pathogen.

Assessing the Role of Agroforestry-Livelihood-Food Security Nexus in the Madhupur Sal Forest of Bangladesh

Agroforestry practices play an inevitable role in reinforcing the livelihood of millions of small-scale farmers while ensuring sustainable food security throughout the world. The significance of practicing various strategies of agroforestry models in densely populated countries like Bangladesh is highly recognized according to livelihood functionalities. To achieve the sustainable goals of the United Nations, identifying potential agroforestry practices along with their impacts towards socio-economic, and ecological demands is crucial. Socio-economic interactions are highly influenced by livelihood improvement and assurance of food security for the participants. Madhupur Sal forest is one of the major sources of diversified combination agroforestry systems practiced by the people around it. Age, education, annual income, and agroforestry knowledge had a significant relationship with the farmers' opinions on socio-economic development through agroforestry practices. Sustainable livelihood capitals (Human, Social, Physical, Financial, and Natural) were improved in each model we determined, and agroforestry multi-functionally increased food security ensuring 94% yearly food sufficiency, and 6% sufficiency for two-thirds of the year according to the response variables. This study depicted various prospects for livelihood enhancement and food security concerning diverse agroforestry models.

Monitoring wheat area using sentinel-2 imagery and In-situ spectroradiometer data in heterogeneous field conditions

Crop statistics are crucial for developing a demand-based export and import strategy to ensure a country’s sustainable food security. Remote sensing efficiently generates essential crop statistics, while ground-based supplementary sensor data offers sufficient information for crop delineation. This study explored the multispectral satellite imagery using in-situ ground-based hyperspectral reflectance phenology information as training data to delineate wheat from other competitive winter crops in Northwestern Bangladesh as a case study. Wheat spectral signatures were primarily obtained through a hand-held Spectroradiometer at various phenological stages, aligned with Sentinel-2 data availability. Five vegetation indices (VIs), namely Normalized Difference Vegetation Index (NDVI), Red-edge NDVI (RENDVI), Enhanced Vegetation Index (EVI), Greenness Chromatic Coordinate (GCC) and Soil-Adjusted Vegetation Index (SAVI), were derived from Spectroradiometer-data across six wheat growth stages: seedling, tillering, booting, flowering, grain development, and maturity. Maximum and minimum threshold values for the VIs at those six growth stages were determined from regression analysis of the values collected from Spectroradiometer and Sentinel-2. A rule-based classification technique was then used to categorize Sentinel-2 for wheat crop delineation based on those threshold values. The results revealed that maps based on NDVI, EVI, and SAVI showed overall accuracies of 83.33%, 85.18%, and 81.48%, respectively. These accuracies were found to be statistically acceptable (p < 0.05) outcomes. A positive agreement was observed when comparing the remotely sensed area at the union (4th tier administrative level) with the officially reported data of Bangladesh. This innovative method has the potential to be extended for developing phenology and area delineation for other major crops locally and globally.

Footprints of drought risk on Africa’s agricultural, water and nutritional security

Abstract Prolonged and recurrent droughts seriously threaten Africa’s food and water security. This threat frequently coexists with human-induced calamities, such as domestic and international conflicts and civil unrest, which could exacerbate the socio-economic instability already present in the region. Using a novel data-driven approach, we evaluated how drought risk in Africa affects the security of various crucial sectors of sustainable development, such as agriculture, water, and food nutrition/health (referred here as ‘nutritional’). Our findings show that different sectors and geographical areas exhibit distinct risk footprints. In Central African countries, for instance, we found that social instability linked to higher nutritional risk is more prevalent than that resulting from the agriculture and water sectors. Socio-economic volatility rather than uncertainty in the climate is the primary driver of this elevated nutritional risk. However, most Northern African countries are at risk of considerable agriculture and water insecurity because of extreme water stress and unstable climate trends. We indicate that the risk is majorly driven by recurrent drought events in Southern Africa, which significantly affect inclusive sectoral securities. The cause of higher risks in Eastern and Western Africa has been found to be an unfavorable interaction of all the risk components—vulnerability, exposure, and hazard. Notably, basic amenities, climate stability, and access to sustainable and renewable water sources are often missing from Africa’s sectoral risk hotspots. Our results emphasize the necessity of maximizing the efficacy of bottom-up initiatives to achieve sustainable food and water security, by integrating socio-economic policies and climate change at the granular level through observation.

Beyond environmental sustainability: Low‐carbon land use policies can contribute to the realization of comprehensive sustainable development

AbstractIn the context of climate change and sustainable development, there has been numerous researches studying the influence of land use policies on carbon sequestration. However, most of them focus on specific experimental area to explore the impact of land use policies on carbon fixation, or their impacts on a certain aspect, lacking the comprehensive explanation of both the internal influence mechanism and external effects. Therefore, this article presents a comprehensive review and the results show that scientific land use policies have the capacity of increasing net carbon sinks and soil organic carbon to realize environmental sustainability. In addition, these low‐carbon land use policies can not only bring out this climate change mitigation influence, but also have external effects on cities sustainable development, food security, ecological protection, and economic disparities to promote comprehensive sustainable development. However, if these policies fail to adapt to local natural and socio‐economic conditions, or overlook the potential negative impacts they could pose, these policies cannot facilitate the achievement of comprehensive sustainable development and could even impede this progress.

The Optimal Zoning of Non-Grain-Producing Cultivated Land Consolidation Potential: A Case Study of the Dujiangyan Irrigation District

Non-grain-producing cultivated land (NGPCL) greatly affects sustainable agricultural development and food security, and its consolidation is important. With the Dujiangyan irrigation district as an example, an empirical study of NGPCL consolidation zoning was performed following the idea of “connotation definition and classification—potential identification—consolidation zoning”. On the basis of expert evaluation, NGPCL was classified into three levels according to the degree of damage to cultivated land by crop type. NGPCL was common in the study area, accounting for 53.8% of the total area. The spatial pattern of NGPCL was characterized as “continuous in the south and scattered in the north”. The assessment of theoretical and realistic NGPCL consolidation potentials suggested that areas with medium consolidation potential exhibited a contiguous distribution in the southern part of the study area, whereas it was dispersed in other regions. The proportion of area suitable for consolidation exceeded 40%. Finally, through a multiobjective optimization algorithm, a potential zoning scheme for NGPCL consolidation was constructed. The final experimental results revealed that the areas with medium or high consolidation potential accounted for 97.54% of the total area. This study is useful for supporting the governance of NGPCL.

Soil volatilomics uncovers tight linkage between soybean presence and soil omics profiles in agricultural fields

Securing a stable food supply and achieving sustainable agricultural production are essential for mitigating future food insecurity. Soil metabolomics is a promising tool for capturing soil status, which is a critical issue for future sustainable food security. This study aims to provide deeper insights into the status of soybean-grown fields under varying soil conditions over three years by employing comprehensive soil volatile organic compound (VOC) profiling, also known as soil volatilomics. Profiling identified approximately 200 peaks in agricultural fields. The soil of soybean-presented plots exhibited markedly higher VOC levels than those of non-soybean plots during the flowering season. Pentanoic acid, 2,2,4-trimethyl-3-carboxyisopropyl, isobutyl ester, a discriminative soil VOC, was identified through multivariate data analysis as a distinctively present VOC in fields with or without soybean plants during the flowering period. Soil VOC profiles exhibited strong correlations with soil-related omics datasets (soil ionome, microbiome, metabolome, and physics) and no significant correlations with root microbiome and rhizosphere chemicals. These findings indicate that soil VOC profiles could serve as a valuable indicator for assessing soil status, thereby supporting efforts to ensure future global food security.

Maintaining environmental context and geoprivacy protection in agriculture

To achieve sustainable agriculture and food security there is an urgent need to share agricultural data with a range of relevant stakeholders; however, to reduce the risk of identification, spatial data must be obfuscated prior to sharing. To-date, most obfuscation methods that have been developed do not consider a) the areal nature of field-level data and b) the differing environmental conditions at the original and obfuscated sites. To address these issues, we developed the Polygon-based Environmental Similarity Obfuscation Method (PESOM) to provide geoprivacy protection and guarantee that obfuscated data will retain the same environmental conditions as the original data. PESOM was developed using an unsupervised clustering algorithm and seasonal climate data, before being applied to the Nutrient Management Plan (NMP) online in Ireland. PESOM satisfied high level of geoprivacy protection and absolute environmental clustering preservation, with no false-identification and non-unique obfuscation risk. It provided a low level of distribution preservation and correlation preservation, large location displacement and subsequently low local analytical accuracy. PESOM is a significant advance on existing obfuscation techniques in agriculture data and will allow the sharing of data to be used widely for agri-environmental purposes, a current limitation of existing methods. The results of this research should be of wide interest to those working in agri-environmental research and computer science, and be of relevance to researchers, data managers, and practitioners.

LOSS OF BIODIVERSITY AND FOOD SECURITY IN NIGERIA

Nigeria is rich in biodiversity, boasting of diverse ecosystems and numerous species. The country's ecosystems range from semi-arid savannas to montane forests, floodplains, rainforests, freshwater swamps, and coastal vegetation. However, Nigeria's rich biodiversity, essential for agriculture and food security, is under severe threat, impacting adversely on food security. This study investigates the impact of oil pollution, deforestation, and desert encroachment on biodiversity loss within Nigeria's agricultural ecosystems. This study employed a comprehensive review of existing literature on the assessment of biodiversity loss in Nigeria's agricultural ecosystems due to pollution from oil spill, deforestation and desert encroachment. By analyzing the correlation between these environmental stressors and food security challenges, this study highlight the critical need for integrated conservation and agricultural strategies. The findings indicate that the loss of biodiversity has far-reaching implications, including malnutrition, food price volatility, and the erosion of rural livelihoods. The study emphasizes the pressing necessity of tackling these challenges in order to guarantee sustainable food security and ecological equilibrium in Nigeria. To address these interconnected issues, the study recommends a holistic approach that prioritizes sustainable practices, robust policies, and community engagement. By safeguarding Nigeria's biodiversity, we can ensure a sustainable food future for generations to come.

Integrating Maize Yield and Agricultural Drought Analysis for Sustainable Food Security: A Provincial Study in South Africa (1993–2022)

ABSTRACTExtreme climatic events, such as droughts, hinder progress toward achieving the sustainable development goal of food security. South Africa is vulnerable to drought‐related agricultural losses, which have led to food insecurity. However, few studies have focused on the long‐term impacts of drought on crop production at a regional scale. Therefore, we aimed to examine the intensity, magnitude, and trend of rainfall‐based short‐term agricultural drought at the provincial scale in South Africa based on the Standardized Precipitation Index (SPI). Additionally, we analyzed the impact of agricultural drought on maize yield by calculating the Standardized Yield Residual Series (SYRS) and Crop Drought‐Resilience Factor (CDRF). To this end, we collected rainfall data from 29 stations across nine provinces along with maize yield data for the period of 1993–2022. Agricultural drought analyses based on the three‐month (SPI‐3) and six‐month (SPI‐6) SPIs demonstrated dynamic variations in occurrence, with Sen's slope indicating that 10 stations exhibited a significant increase in drought events across South Africa. Notably, SPI‐6 analysis showed that Gauteng, Free State, and North West provinces experienced the highest percentages of severe to extreme drought events during the study period, at 4.17%, 3.89%, and 3.61%, respectively. Furthermore, the majority of provinces in South Africa experienced an extreme SPI‐6 magnitude ranging from −46.03 in Western Cape Province to −61.6 in Free State Province. The dynamic effects of agricultural drought on maize yield revealed that the maximum yield loss of 13% occurred in 1993 in Eastern Cape Province, while some provinces experienced no yield loss during certain years. However, CDRF analyses identified Western Cape (CDRF [SPI‐3] = 0.52, CDRF [SPI‐6] = 0.62) and Mpumalanga (CDRF [SPI‐6] = 0.7) provinces as the most vulnerable to food insecurity due to the severe non‐resilience of maize to drought in these regions. This study reveals the complex interplay between climatic extremes and maize yield variability, providing valuable insights for managing regional food production systems and ensuring future food security in South Africa.

Empowering Widowed Women with Sustainable Food Security in Tunyai Location, Tharaka Sub-County, Tharaka Nithi County, Kenya: A Qualitative Analysis of Their Roles, Challenges, and Resilience

Food security, a fundamental global concern, is intrinsically linked to the pivotal roles, women play in agricultural and food production. This qualitative research, conducted in Tunyai Location, Tharaka Sub-County, Tharaka Nithi County, Kenya, delves into the significant contributions of widowed women to food security by exploring their roles in agricultural and food production. The research aims to understand the experiences, challenges, and strategies of widowed women in enhancing the local food supply. Using a purposive sampling method, 50 widowed women actively engaged in agriculture were selected for participation in Key Informant Interviews. NVivo software facilitated the data analysis, enabling systematic exploration and thematic identification of patterns, challenges, and strategies employed by these women. The study findings underscore the profound contributions of widowed women to food security, with 56% actively engaged in agricultural activities. These women represent a cornerstone in sustaining local food security through diverse roles encompassing crop cultivation, livestock management, and food processing. Furthermore, the research underscores the need to address the 44% of widowed women not actively involved, emphasizing potential gender disparities and barriers. In conclusion, this study highlights the integral role of women in enhancing food security and the imperative to address gender-related challenges to ensure a resilient and sustainable local food supply in Tunyai Location.

How does blockchain impact sustainable food security? Insights from literature review

Blockchain technology can enhance sustainable food security because of its distinct characteristics such as traceability, decentralized and unchangeable databases, and smart contract protocols. Nevertheless, blockchain technology in agricultural applications is still in the early stages of development. Therefore, this study aims to ascertain the efficiency and cognitive framework of blockchain technology for attaining long-term food security. This study used a review-based approach to ascertain the intellectual framework. A literature search was conducted using the "Scopus" database to locate research articles published between 2017 and 2023. A “systematic literature review” was performed using the PRISMA framework on the 52 eligible publications. The study results indicated that traceability, real-time information availability, and immutably distributed databases were the most influential factors. The results showed that blockchain technology has benefits beyond facilitating reliable data dissemination and establishing intimate bonds between manufacturers and clients. Furthermore, blockchain technology may pave the way for less food waste, improved supply chains and agricultural working environments, and more environmentally responsible eating practices. This study is the first of its kind to assess the intellectual structure of food security and augment the cognition of the in-depth examination of the benefits of blockchain technology that might ultimately provide a way to achieve zero-hunger goals.

Ameliorative effects of humic acid and L-tryptophan onenzyme activity, mineral content, biochemical properties, and plant growth of spinach cultivated in saline conditions.

Salinity poses a significant abiotic stress that limits plant productivity, thereby posing a serious threat to agricultural sustainability and worldwide food security. Techniques that can overcome this problem are needed. Recent focus has been placed on employing organic substances like humic acid (HA) and amino acids, including L-tryptophan (L-TRP), to mitigate the negative effects of salt stress on cultivated plants. Accordingly, in this research, the impact of foliar applications of HA and L-TRP, both separately and combined, on the growth parameters and biochemical properties of spinach subjected to salt stress was investigated. In the present study, eight treatments (1. control, 2. salt (NaCl), 3. HA, 4. L-TRP, 5. HA + NaCl, 6. L-TRP + NaCl, 7. HA + L-TRP, and 8. HA + L-TRP + NaCl) were investigated. The study showed that salt stress markedly reduced several growth properties in spinach, including plant height, number of leaves, leaf dimensions, and both fresh and dry weight. Additionally, it significantly lowered contents of chlorophyll (a, b, and total), carotenoid, polyphenol, lutein, anthocyanin, polyphenol oxidase, glycine betaine, relative water content, and the antioxidant enzyme activities (ascorbate peroxidase, catalase, peroxidase, and superoxide dismutase). On the other hand, significant increases were observed in sodium, chlorine, potassium, sulfur, zinc, nickel, proline, malondialdehyde, and hydrogen peroxide levels of spinach with salinity. Individual and combined applications of HA and L-TRP positively influenced plant growth, relative water content, activities of antioxidant enzyme, chlorophyll, and mineral contents of spinach under both normal and saline conditions. In conclusion, the combined use of HA and L-TRP under salt stress conditions is promising in mitigating the negative impacts of salinity and can be suggested as an effective alternative approach for cultivating spinach in saline environments.

A comprehensive review on impact of post-harvest management and treatment practices on the quality of garlic (Allium sativum L) during storage

Garlic (Allium sativum L.) plays a crucial role in global agriculture due to its culinary and medicinal uses, but it faces significant post-harvest losses due to moisture, temperature fluctuations, and pest infestations. Proper storage and handling practices can mitigate these losses, yet conventional methods like curing, dehydration, cryopreservation, and vacuum sealing still result in 25–40 % losses during storage. This review investigates the types, factors, and causes of garlic losses, focusing on biological factors such as microbial infections pest infestations, and premature sprouting which cause decay and deformities. Physical factors such as mechanical damage, inadequate curing, temperature fluctuations, humidity, and prolonged light exposure also contribute to deterioration. This review explores practices and treatments to minimize losses, detailing their mechanisms, benefits, and commercial potential. It compares thermal and non-thermal technologies such as irradiation, ozone treatment, nanotechnologies, edible coatings, and films. Irradiation is effective against pathogens but may lead to nutrient loss, ozone treatment provides microbial control with minimal residue. Nanotechnologies, edible coatings, and films help extend shelf life by reducing microbial growth and moisture loss, with considerations for safety and consumer acceptance. The review highlights the need for innovative solutions to reduce losses and preserve the nutritional value and safety, contributing to sustainable garlic production and food security. Additionally, it evaluates the implications of these management practices for sustainability and circular economy. The findings highlight the need for further research to enhance the efficiency, cost-effectiveness, and environmental sustainability of post-harvest technologies to ensure the long-term viability and profitability of garlic production.

Regulatory Dynamics of Plant Hormones and Transcription Factors under Salt Stress.

The negative impacts of soil salinization on ion homeostasis provide a significant global barrier to agricultural production and development. Plant physiology and biochemistry are severely affected by primary and secondary NaCl stress impacts, which damage cellular integrity, impair water uptake, and trigger physiological drought. Determining how transcriptional factors (TFs) and hormone networks are regulated in plants in response to salt stress is necessary for developing crops that tolerate salt. This study investigates the complex mechanisms of several significant TF families that influence plant responses to salt stress, involving AP2/ERF, bZIP, NAC, MYB, and WRKY. It demonstrates how these transcription factors (TFs) help plants respond to the detrimental effects of salinity by modulating gene expression through mechanisms including hormone signaling, osmotic stress pathway activation, and ion homeostasis. Additionally, it explores the hormonal imbalances triggered by salt stress, which entail complex interactions among phytohormones like jasmonic acid (JA), salicylic acid (SA), and abscisic acid (ABA) within the hormonal regulatory networks. This review highlights the regulatory role of key transcription factors in salt-stress response, and their interaction with plant hormones is crucial for developing genome-edited crops that can enhance agricultural sustainability and address global food security challenges.

The Dynamics of Urbanization and Its Effects on Rural Livelihoods: Evidence from Adama City and Adama Rural District, Ethiopia

Rapid urbanization in cities brings about both opportunities and challenges for national development. This study focuses on investigating the dynamics of urbanization and its impact on livelihoods using land use/land cover change in Adama City, located in the Oromia Regional State. Various datasets from the U.S. Geological Survey (USGS), including Landsat Thematic Mapper (LTM), Landsat Enhanced Thematic Mapper Plus (ETH[Formula: see text]), and Operational Land Imager (OLI), were utilized. Geographic Information System (GIS) and remote sensing tools were employed to extract relevant information from these datasets for years from 1986 to 2022. In addition, findings pertain to the impact of urbanization on income diversification and the overall welfare of farm households near Adama City was aligned with the study. The findings reveal that built-up areas and agricultural land exhibit the most significant changes over the examined period. However, these changes occur in opposite directions. The built-up area has experienced a substantial increase of 60% from 1986 to 2022, while agricultural land has decreased by 40% during the same timeframe. The conversion of farmland to built-up areas negatively impacts farm households by reducing farming activities and increasing reliance on non-farm activities, resulting in lower consumption expenditures for rural households near urban areas compared to those farther from the urban center. The result underscores the need for policies that promote appropriate land management and a well-planned urbanization process to ensure sustainable urban development. Furthermore, it is vital to enhance understanding of the spatial and temporal dynamics of urban growth and its impacts on farm households residing near urban areas. By addressing these issues, sustainable food security and poverty reduction objectives can be better achieved in peri-urban areas of the region.

Random forest machine learning for maize yield and agronomic efficiency prediction in Ghana

Maize (Zea mays) is an important staple crop for food security in Sub-Saharan Africa. However, there is need to increase production to feed a growing population. In Ghana, this is mainly done by increasing acreage with adverse environmental consequences, rather than yield increment per unit area. Accurate prediction of maize yields and nutrient use efficiency in production is critical to making informed decisions toward economic and ecological sustainability. We trained the random forest machine learning algorithm to predict maize yield and agronomic efficiency in Ghana using soil, climate, environment, and management factors, including fertilizer application. We calibrated and evaluated the performance of the random forest machine learning algorithm using a 5 × 10-fold nested cross-validation approach. Data from 482 maize field trials consisting of 3136 georeferenced treatment plots conducted in Ghana from 1991 to 2020 were used to train the algorithm, identify important predictor variables, and quantify the uncertainties associated with the random forest predictions. The mean error, root mean squared error, model efficiency coefficient and 90 % prediction interval coverage probability were calculated. The results obtained on test data demonstrate good prediction performance for yield (MEC = 0.81) and moderate performance for agronomic efficiency (MEC = 0.63, 0.55 and 0.54 for AE-N, AE-P and AE-K, respectively). We found that climatic variables were less important predictors than soil variables for yield prediction, but temperature was of key importance to yield prediction and rainfall to agronomic efficiency. The developed random forest models provided a better understanding of the drivers of maize yield and agronomic efficiency in a tropical climate and an insight towards improving fertilizer recommendations for sustainable maize production and food security in Sub-Saharan Africa.

Baculoviruses in Integrated Pest Management of Fall Armyworm, (Spodoptera frugiperda) (Lepidoptera: Noctuidae): Structure, Classification and Application

Baculoviruses are crucial biological agents in integrated pest management (IPM), particularly for controlling lepidopteran pests in agriculture. These are highly specific, eco-friendly viruses characterized by rod-shaped nucleocapsids containing a protein-encased genome. The Baculoviridae family comprises four genera: Alphabaculovirus, Betabaculovirus, Gammabaculovirus, and Deltabaculovirus, each targeting specific insect orders. Nucleo polyhedron virus (NPVs) and granuloviruses (GVs) are extensively used in pest management due to their high virulence and specificity, ensuring safety for non-target organisms. Ascoviruses are dsDNA viruses that infect Spodoptera frugiperda larvae, transmitted primarily by parasitoids. Despite high virulence, their use in pest control is limited due to ineffective oral transmission. Combining ascoviruses with other pathogens, such as Bacillus thuringiensis kurstaki (Btk), may enhance their pest control potential. The infection process begins when insect larvae ingest viral occlusion bodies, leading to systemic infection and host death. Despite their narrow host range and slower action compared to chemical insecticides, baculoviruses are invaluable in sustainable agriculture. Their application has expanded beyond pest control to include gene therapy and vaccine production. This review examines the structure, classification, infection process, and current status of baculoviruses in controlling the fall armyworm, Spodoptera frugiperda, a significant agricultural pest. By exploring their potential as a cornerstone in biological pest management, we highlight the importance of baculoviruses in developing sustainable and environmentally friendly pest control strategies. The integration of baculoviruses into IPM programs offers a promising approach to reduce chemical pesticide use, preserve biodiversity, and enhance crop protection, ultimately contributing to more sustainable agricultural practices and food security.

Organic Agriculture for Sustainable Food Systems: A Comprehensive Review of Benefits and Constraints

The global population is increasing at a rapid pace, posing significant challenges to sustainable development and food security. This review paper examines the advantages and challenges of organic farming as a potential solution to address the pressing issues in modern agriculture and food production. Organic farming offers a range of benefits, including improved soil fertility, higher profitability, reduced external input usage, land reclamation, improved market access, and enhanced farmer capacity and self-reliance. Organic farming practices prioritize environmental sustainability by reducing chemical usage and reliance on biological methods of pest control, as well as enhancing biodiversity, which strengthens ecological balance and resilience against pests and diseases. However, organic farming also faces challenges that need to be addressed for its widespread adoption. These challenges include uncertainty surrounding legislative environments, psychological and sociological costs of conversion, financial risks during the transition period, securing marketing channels for organic produce, and diminishing profit margins. Efforts should be made to provide farmers with the necessary support, including technical assistance, subsidies, and access to markets, to overcome these challenges. Despite the challenges, the potential of organic farming in promoting sustainable agriculture and ensuring a secure food system cannot be overlooked. It offers a natural and environmentally friendly approach to food production, prioritizing the health of both humans and the ecosystem.

Optimizing protein content prediction in rice bean (Vigna umbellata L.) by integrating near-infrared reflectance spectroscopy, MPLS, deep learning, and key wavelengths selection algorithms

Rice bean, a potential protein-rich underutilized crop, can serve as a valuable source for food and feed, contributing to sustainable food and nutrition security. However, traditional methods for measuring protein content are laborious, tedious, and expensive. This study evaluates the performance of Modified Partial Least Squares (MPLS) and 1D Convolutional Neural Network (1D CNN) models in predicting protein content in rice bean using Near-Infrared Reflectance Spectroscopy (NIRS) data. Both models were validated with whole spectra, key wavelengths, and spectral segments derived from Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Competitive Adaptive Reweighted Sampling (CARS). The 1D CNN model demonstrated superior predictive performance, with an 11 % increase in R² and an 18 % increase in RPD using the whole spectra compared to the MPLS model. Specifically, the 1D CNN model with CARS-selected wavelengths achieved an R² of 0.82 and an RPD of 2.15, while the MPLS model achieved an R² of 0.84 and an RPD of 2.25. The segmented spectra approach further enhanced the 1D CNN model's performance, achieving the highest R² of 0.84 and an RPD of 2.28 with CARS-selected segments. These results highlight that the 1D CNN model with CARS-selected wavelengths provides the best predictive performance for protein content in rice bean. The optimized method can lead to rapid early-phase breeding practices and improved quality assessments in leguminous crops, emphasizing the importance of integrating advanced modeling techniques with NIRS for agricultural applications.