Agricultural Change Research Articles

Wind Lore as Environmental Knowledge in Southern Vanuatu

Wind lore constitutes an important domain of environmental knowledge in eight cultures of southern Vanuatu (Aneityum, Futuna, Aniwa, Nafe, Naka, Netwar, Nanu, and Nahual). Our study reviews previous studies in Oceania which document wind systems as used primarily for long-distance navigation. The named winds of southern Vanuatu are not merely abstract directional markers, nor primarily for use in navigation, but are closely tied to the local geography, agriculture, health, and seasonal changes. Winds are part of everyday, embodied experience and cognitive-spiritual understanding of the environment. Wind lore is gradually being forgotten, but can still be described in detail by experts. We conclude by situating wind lore within a culture complex that includes knowledge of agriculture, fishing, time-reckoning, and weather magic. Our documentation of these systems is ongoing, and in collaboration with local experts.

Participatory development of Shared Socioeconomic Pathways for Austria’s agriculture and food systems

Agriculture and food systems are central to managing biodiversity loss and climate change. Future changes in agriculture and food systems are highly uncertain and depend on underlying socio-economic developments. Scenarios allow scientists, policy and industry decision-makers to consider such uncertainties and describe a spectrum of plausible futures. The Shared Socioeconomic Pathways for European agriculture and food systems (Eur-Agri-SSPs) semi-quantitatively describe pathways along future challenges for climate change mitigation and adaptation. However, additional or adapted drivers (i.e. scenario elements), like national policies or adaptations of European policies, are relevant to frame plausible future developments at national scales. We aim to set the standard for developing country and sector-specific SSPs in a participatory process by developing and applying an eleven-step protocol and engaging a diverse group of stakeholders throughout the whole research process. By downscaling the Eu-Agri-SSPs to a national level, we contribute to improve their usability in academia, governments and industry. This is achieved by matching qualitative pathways with the needs of integrated land use models (used to simulate land use scenarios), and by quantifying selected drivers for future developments together with stakeholders. In particular, we estimate 20-year trends (2000–2019) and apply fuzzy set theory to quantify ten scenario elements, which are typically required in integrated land use models. The final scenarios describe the semi-quantitative development for 79 scenario elements along the five topics “Population and urbanization”, “Economy”, “Policy and institutions”, “Technology”, and “Environment and natural resources” for the five AT-Agri-SSPs. Exemplary results reveal that stakeholders expect payments for agri-environment-climate measures to increase to 1200 million € in AT-Agri-SSP1 or to decline to 267 million € in AT-Agri-SSP3, 4 and 5. For direct payments, a complete phase-out was specified for AT-Agri-SSP1 and an increase to 711 million € in AT-Agri-SSP3. We discuss 14 lessons learnt and recommendations for future scenario processes and participatory quantification. Recommendations include: tailoring the assignment of scenario elements for quantification to stakeholders’ expertise, keeping survey complexity low, providing historical and relevant SSP data, and encouraging stakeholders to jointly quantify interdependent elements. The AT-Agri-SSPs can inform policy-making, decision-making in industry, and research on the agriculture and food systems.

Spatial-Temporal Evolution and Influencing Factors of Animal Husbandry Carbon Emissions: A Case Study of Shandong Province, China

To further study the spatial distribution and dynamic evolution of carbon emissions from animal husbandry in Shandong Province, the panel data of 16 prefecture-level cities in Shandong Province from 2001 to 2022 were used to measure the carbon emissions of animal husbandry and the carbon emission intensity of animal husbandry. Based on the combination of space, kernel density estimation, and LMDI decomposition model, the spatial and temporal evolution of carbon emissions from animal husbandry in Shandong Province and its driving factors were investigated. The results show that: (1) The total amount of animal husbandry carbon emissions in Shandong Province showed a fluctuating downward trend, with a decrease of 10.10% during the investigation period, showing a peripheral-agglomeration distribution pattern. The carbon emission intensity showed a gradual downward trend, with an average annual decline of 7.47%, showing stepped distribution characteristics of high in the west and low in the east. (2) The difference in carbon emissions of animal husbandry among cities in Shandong Province increased first and then decreased, and the growth distribution was basically in the form of “bimodal”, showing a polarization pattern. (3) The intensity effect has the most obvious inhibitory effect on the carbon emission of animal husbandry; the effect of agricultural structure changes from a promoting effect to an inhibiting effect. The inhibitory effect of the industrial structure effect is second only to the intensity effect; the economic effect has the greatest promoting effect; and the promotion effect of the population size effect is small.

Machine learning system to assess rice crop change detection from satellite-derived RGVI due to tropical cyclones using remote sensing dataset

Tropical cyclones strike massive devastation of agricultural crops in South Asian countries practically every year. For assessing impacts on primary land use and land cover (LULC) classes of Patuakhali district in Bangladesh, this study utilized the Landsat-8 image dataset to compute change detection indices of a consistent cyclone landfall period in the late rainy season (October and November). Different stage of cyclones based on their wind speed (km/h) were also analyzed to explore agricultural crop change detection due to the tropical cyclone (TC) Bulbul (made landfall on November 9th, 2019). Three years later, cyclone Sitrang made landfall on October 24th, 2022, and was employed for the test results that the Bulbul's rice growth vegetation index (RGVI)-CD trained in the study's machine learning (ML) system. Therefore, the objective of this study was to determine rice crop changes from RGVI due to a cyclone (Bulbul) to evaluate growth change status by another cyclone (Sitrang) using machine learning system. In Patuakhali district, the rice crops were damaged mainly and affected in classification as moderately changed detected (MCD) 26.07%, very changed detected (VCD) 48.83%, and extremely changed detected (ECD) 17.73% in total agricultural lands area (1548.93 km2). It was also significantly monitored for the rice crops to change as rice growth vegetation index (RGVI)-CD related to cyclone wind speed (km/h), DEM, distance from the rivers, and shorelines. From the result, the symmetric regression of coefficient (R2) of (i) linear regression, (ii) fine tree, (iii) support vector machine, (iv) ensemble boosted trees, and (v) trilayered neural network were obtained 0.735, 0.734, 0.737, 0.756 and 0.736 as good-fitted test outcomes respectively. Applying the five machine learning models that were created from cyclone Sitrang, it was discovered that the test set in this system was accurate to the observed forecast with acceptable RMSE and MAE values. Therefore, study can be helpful for researchers creating new damaged and yield loss area assessment policies to help TC-affected farming communities, especially rice growers, prepare an effective emergency response plan for their socioeconomic needs fulfillment in the changing climate perspectives for attaining sustainable development.

Examining the Density Gradient Along the Rhizophora mangle Propagule

Mangroves grow in tropical regions around the globe, but these dense forests have been declining due to agriculture, industry, and climate change. Mangrove propagules are not seeds but the seedlings themselves that grow on the parent tree until they reach maturity and fall. They are water dispersed, floating horizontally in seawater and vertically in brackish water. The purpose of this study was to further investigate whether there is a gradient change in density longitudinally along the Rhizophora mangle propagule that results in this floatation behavior. This study also explored whether there was a difference in density between the medulla and the cortex within the propagule. Propagules were cut into one-centimeter sections and placed in a pycnometer to calculate their density. The average densities of these categories were graphed and had a correlation coefficient of 0.6036 showing a strong positive correlation. The medulla, or inner ring, was removed from the cortex and placed in water to determine if there was a difference in buoyancies. In the plumule end of the propagule, both the medulla and cortex floated. Towards the radicle end of the propagule, the cortex would sink while the medulla would continue to float. The research raises the question if there is a difference between the cortex in the radicle end and the cortex in the plumule end but supports the idea that there is a change in density along the propagule longitudinally that plays a role in the buoyancy of the mangrove propagules.

Nano Fertilizers: New Vistas towards Sustainable Agriculture and Climate Change Mitigation: A Review

To ensure the food and nutritional security of the burgeoning global population, food production needs to be doubled in developing countries by 2050. Currently, 30-40% of agriculture production predominantly relies on chemical fertilizers. Precise crop nutrition, low nutrient efficiency, maintaining soil fertility, less damage to soil flora and minimal environmental footprint are the major challenges in modern agriculture. Recent developments in the application of nanotechnology to produce agriculture inputs emerged as a sustainable solution for addressing the challenges in modern intensive agriculture by replacing synthetic bulk fertilizers with their nanoparticle size ( less than 100 nm), superior properties and smart delivery system. Nano fertilizers had the potential to fulfill the requirements of plant nutrition along with imparting sustainability in crop production without compromising the yield of the crops. Some of the current research studies have been reviewed in this paper with citation and these results showed that nano fertilizers have a substantial effect on plant growth, development and physiological parameters including chlorophyll content and photosynthetic activity, it depends on their composition, method and time of application. Their enhanced nutrient use efficiency, correlated with mitigating greenhouse gas emissions. Hence evolving as a cutting-edge approach for sustainable agriculture in climate change is enlightened in this review.

Remote Sensing and AI-based Monitoring of Legume Crop Health and Growth

Background: For the enhancement of agricultural productivity, while ensuring sustainability, this study delves into the under-explored domain of monitoring legume crop health and growth. Traditional methods of crop assessment encounter limitations, prompting a push for innovation by integrating advanced remote sensing technologies and artificial intelligence (AI). The purpose is to revolutionize crop assessment techniques and overcome existing constraints. Methods: The data was collected using a combination of satellite imagery and ground-based sensors, resulting in a rich repository of multispectral and spatial information. By using the capabilities of AI, a robust model was developed to interpret the gathered data, offering a detailed insight into the health and growth dynamics of legume crops. The AI algorithms not only identify anomalies but also forecast future states, facilitating timely interventions and informed decision-making in agriculture. Result: The findings of this study signify a significant change in precision agriculture, where the synergy of remote sensing and AI optimizes resource allocation, minimizes environmental impact and maximizes crop yields. The research unlocks the potential to transform legume farming practices, promoting sustainability and ushering in an era of data-driven cultivation. The implications extend beyond the legume crop sector, influencing the broader agricultural landscape with the promise of more efficient and sustainable practices.

How has the rural digital economy influenced agricultural carbon emissions? Agricultural green technology change as a mediated variable

Digital economy is being closely integrated with agricultural development and tapping into its unique potential to alleviate agriculture’s carbon emissions To explore the mechanism of how digital economy reduce the agricultural carbon emissions, this paper constructs a systematic evaluation method with extend STIRPAT model and panel data drawn from 29 provinces (or municipalities and autonomous regions) in the Chinese mainland from 2013–2020. The results show that the development of the rural digital economy has a significant negative influence on agricultural CEs, and this result is still valid given robustness tests. Second, the alleviation of CEs based on the rural digital economy is more significant in the higher technological investment zones than that in the lower technological investment zones, and the central and eastern regions also have more significant CEs reduction effect. Third, the influence mechanism analysis shows that agricultural green technology change is an effective means to promote the rural digital economy’s CEs reduction effect. This paper not only provide new empirical evidence for understanding nexus between digital economy and agricultural carbon reduction, but also give constructive policy implication to improve agricultural green development.

Analysis of Endangered Andalusian Black Cattle (Negra Andaluza) Reveals Genetic Reservoir for Bovine Black Trunk.

This comprehensive study on the Andalusian Black cattle breed reveals a substantial population decline, with the average herd size decreasing significantly from 305.54 to 88.28 animals per herd. This decline is primarily attributed to agricultural changes and the introduction of foreign meat-focused breeds. The male-to-female ratio shift is noteworthy, with more cows than bulls, impacting selection intensity for both genders. Inbreeding levels, though relatively low historically (5.94%) and currently (7.23%), raise concerns as 37.08% historically and 48.82% currently of the animals exhibit inbreeding. Positive assortative mating is evident, reflected by the increasing non-random mating coefficient (α). Key ancestors play a crucial role in shaping genetic diversity, with one ancestor significantly influencing the current genetic pool and the top 10 ancestors contributing substantially. Breed maintains a conservation index of 2.75, indicating relatively high genetic diversity. Recent conservation efforts have led to an increase in registered animals. The Cañadas Reales, historical transhumance routes, may have contributed to genetic connections among provinces. Challenges include the historical bottleneck, demographic changes, and potential impacts from reproductive practices. The Andalusian Black breed's conservation necessitates ongoing efforts in genealogical registration, targeted breeding programs, and collaborative initiatives to address the observed demographic shifts and ensure sustainable genetic diversity.

Light recovery after maize harvesting promotes soybean flowering in a maize-soybean relay strip intercropping system.

Moving from sole cropping to intercropping is a transformative change in agriculture, contributing to yield. Soybeans adapt to light conditions in intercropping by adjusting the onset of reproduction and the inflorescence architecture to optimize reproductive success. Maize-soybean strip intercropping (MS), maize-soybean relay strip intercropping (IS), and sole soybean (SS) systems are typical soybean planting systems with significant differences in light environments during growth periods. To elucidate the effect of changes in the light environment on soybean flowering processes and provide a theoretical basis for selecting suitable varieties in various planting systems to improve yields, field experiments combining planting systems (IS, MS, and SS) and soybean varieties (GQ8, GX7, ND25, and NN996) were conducted in 2021 and 2022. Results showed that growth recovery in the IS resulted in a balance in the expression of TERMINAL FLOWER 1 (TFL1) and FLOWERING LOCUS T (FT) in the meristematic tissues of soybeans, which promoted the formation of new branches or flowers. IS prolonged the flowering time (2-7 days) and increased the number of forming flowers compared with SS (93.0 and 169%) and MS (67.3 and 103.3%) at the later soybean flowering stage. The higher carbon and nitrogen content in the middle and bottom canopies of soybean contributed to decreased flower abscission by 26.7 and 30.2%, respectively, compared with SS. Canopy light environment recovery promoted branch and flower formation and transformation of flowers into pods with lower flower-pod abscission, which contributed to elevating soybean yields in late-maturing and multibranching varieties (ND25) in IS.

Preface

The 3rd International Conference on Agriculture and food sustainability (The 3rd ICAFOSY 2023) The 3rd INTERNATIONAL CONFERENCE ON AGRICULTURE AND FOOD SUSTAINABILITY (The 3rd ICAFOSY 2023) marked a significant milestone in the pursuit of global efforts to address the challenges facing food systems and nutritional security in the wake of climate change, particularly in developing countries. With its overarching theme, “Enhancing the resilience of food systems for food and nutritional security under climate change,” The conference united academics, researchers, specialists, policymakers, and practitioners committed to advancing sustainable agricultural practices.At the heart of this collective endeavor, the conference served as a dynamic platform for exchanging ideas, cutting-edge research findings, and innovative solutions. Delegates explored various dimensions of the conference’s focus and scopes, ranging from adaptation, mitigation, and resilience strategies to climate-smart agriculture adoption and the intricate interplay between climate change and agriculture. Participants engaged in fruitful discussions on topics such as bioenergy for sustainable agriculture, climate change education for social transformation, and governance strategies for ensuring food security in the face of climate challenges.A moment of reflection and gratitude is extended to all the colleagues and participants whose dedication and expertise contributed to the success of the 3rd ICAFOSY 2023. The collaborative spirit exhibited during the conference underscored the importance of interdisciplinary approaches in addressing the complex and multifaceted issues surrounding agriculture, food sustainability, and climate change. The collective wisdom and shared experiences of our colleagues have undoubtedly enriched the dialogue and propelled us closer to actionable solutions for a more resilient and sustainable future.The 3rd ICAFOSY held on October 30-31, 2023, the conference found its home in the Main Hall of the Department of Agricultural Socio-economic Building, 3rd Floor, Universitas Brawijaya, Malang, East Java, Indonesia. The hybrid format of the event allowed for both in-person and virtual participation, enabling a global audience to engage in the discussions and contribute to the conference’s goals. The unique blend of physical and virtual interactions fostered a sense of global collaboration, transcending geographical boundaries to address the challenges posed by climate change to agriculture and food sustainability.Finally, the resounding success of the 3rd ICAFOSY 2023 would not have been possible without the unwavering support and commitment of the Department of Agricultural Socio-economic, Faculty of Agriculture, Universitas Brawijaya.List of Editor, Technical Programme Committee are available in this pdf.

The analysis of changes in land management indicators over 30 years considering the food security of the country

The overarching aim of ensuring the country's food security is providing of the country's population with wholesome, quality-assured and available agriproduct, raw materials and food in volumes that ensure rational food consumption criteria. One of the central objectives of ensuring food security, whether external and internal conditions change or not, is the sustainable development of agriproduct, raw materials and food, sufficient to provide food sovereignty predicated on the principle of research-backed planning. Using the example of some regions of the Russian Federation, a look-back study of agricultural land use and changes in land management activities over a 30-year period was carried out, and the measures for rational land use organization.

Environmental and Climate Change Education for The Youth to Foster Social Transformation: Case Study in MTs PAKIS Banyumas, Central Java

This study aims to assess the effectiveness of the school’s environmental education and climate change programs to offer suggestions for enhancement in MTs PAKIS (Islamic Junior High School of PAKIS). The study used interviews and observations to collect data for students, administrators, and parents, which was analyzed using qualitative methods. MTs PAKIS is an educational institution situated in a geographically remote region, with a primary emphasis on imparting knowledge to young individuals on environmental matters and climate change. This is achieved by integrating local wisdom and utilizing available resources within the surrounding hamlet. Students are provided with various academic disciplines encompassing agriculture, agroforestry, animal husbandry, biodiversity, and climate change. This educational experience equips individuals with essential skills and knowledge that may be efficiently applied in their future pursuits. The research found that educational institution has demonstrated efficacy in imparting knowledge and deterring early marriage and urban migration. It serves as a valuable foundation for prospective social transformation, fostering improved lifestyles regarding economic prosperity and environmental sustainability. Despite their achievements, educational institutions must prioritize teacher retention for long-term viability since they need help maintaining a stable roster of volunteer teachers.

Optical flow of temperature reveals climate change patterns for agriculture and forestry

The study of climate change's impact on terrestrial ecosystems is crucial for achieving societal and economic sustainability and human well-being. Domains like agriculture and forestry respond to climate change similarly to other terrestrial natural biomes, experiencing reductions in productivity, migration, and loss of biodiversity. However, there is a research gap in understanding climate velocity for different land cover and land use types, which could reveal vulnerability patterns in agriculture and forestry. To address this gap, we present the first study of climate velocity for major land cover and land use classes with a focus on major crop growing regions and functional forest types at the continental level. To enable this analysis, we introduce a new approach for estimating climate velocity based on optical flow. This approach allows us to conduct a global study, improve ecosystem interconnection representation, and reduce the model's sensitivity to noisy data and parametrization. Our study highlights the significant vulnerability to climate change of agriculture as a land cover type with a climate velocity 15% higher than of other vegetation land cover classes. Furthermore, the analysis of climate velocity for functional tree types reveals the highest expected impact on deciduous and evergreen needleleaf trees primarily concentrated in regions with the highest climate velocity, especially in sub-polar and temperate climates. Conversely, tropical forests dominated by evergreen broadleaf tree types exhibits marked variations in climate velocity with the highest rates in South America and Africa and three times lower in Asia. Additionally, our analysis of climate velocity for agriculture shows that North American croplands experience the highest climate velocity exceeding the global average by 32%. This trend is consistent for major crop types such as soybean that have higher climate velocity than globally for 49%, rapeseed and sunflower for 39%, cotton for 35%, maize for 32%, and wheat for 30%. Notably, areas dedicated to cereals, sugar beet, and sunflowers are located in zones with climate velocities higher than the global average by 15%, 12%, and 9%, respectively. These patterns indicate potential shifts in the agriculture and forestry structures in the near future that coherent with recent climate change studies and will have significant impact on the socio-economic dimension of humanity sustainability.

Examining Spatiotemporal Photosynthetic Vegetation Trends in Djibouti Using Fractional Cover Metrics in the Digital Earth Africa Open Data Cube

The Horn of Africa has sensitive, arid ecosystems, with its vegetation commonly distressed by factors such as climate change, population increase, unstable water resources, and rarely enforced land use management practices. These factors make countries such as Djibouti highly variable locations for the growth of vegetation and agricultural products, and these countries are becoming more vulnerable to food insecurity as the climate warms. The rapid growth of satellite and digital image processing technology over the last five decades has improved our ability to track long-term agricultural and vegetation changes. Data cubes are a newer approach to managing satellite imagery and studying temporal patterns. Here, we use the cloud-based Digital Earth Africa, Open Data Cube to analyze 30 years of Landsat imagery and orthomosaics. We analyze long-term trends in vegetation dynamics by comparing annual fractional cover metrics (photosynthetic vegetation, non-photosynthetic vegetation, and bare ground) to the Normalized Difference Vegetation Index. Investigating Djibouti-wide and regional vegetation trends, we provide a comparison of trends between districts and highlight a primary agricultural region in the southeast as a detailed example of vegetation change. The results of the Sen’s slope and Mann–Kendall regression analyses of the data cube suggest a significant decline in vegetation (p = 0.00002), equating to a loss of ~0.09 km2 of arable land per year (roughly 2.7 km2 over the 30-year period). Overall, decreases in photosynthetic vegetation and increases in both non-photosynthetic vegetation and bare soil areas indicate that the region is becoming more arid and that land cover is responding to this trend.

Predicting the effects of climate and land use changes on small rattlesnakes in central Mexico: Insights for conservation planning

Anthropogenic changes pose a significant threat to global biodiversity, especially in reptiles. Mexico, renowned for its diverse reptile population, is experiencing habitat disturbance and fragmentation, endangering rattlesnakes in particular due to their specific habitat requirements and limited mobility. The loss of structural connectivity further increases the risk of extinction for reptiles. Our study focuses on predicting the distribution and connectivity of nine Crotalus species in the Trans-Mexican Volcanic Belt (TMVB), using environmental modeling. We used climate models (CNRM-CM5 and MPI-ESM-LR) for the year 2050 and Representative Concentration Pathways (RCP 85) alongside changes in vegetation cover and land use. The Maxent modeling method in ENMeval helped assess distribution patterns and to identify key environmental variables influencing these species’ distributions and niche overlap. We also conducted an analysis of the structural connectivity of the different Crotalus species. Our findings indicate reductions in suitable habitats due to changes in agriculture, urbanization, and forest cover. The Maxent models showed high accuracy in predicting species distributions. The most influential variables varied among species and included forest types and climatic factors. We observed limited connectivity among small Crotalus species, although some species exhibited greater connectivity than others. Future models suggest potential distribution reductions for all species, with C. armstrongi facing the greatest reduction (78%) according to the cn85 global climate model. Protected areas do not encompass the majority of potential distribution for these small rattlesnake species. These findings highlight the urgency of conservation efforts and the need to mitigate the impacts of future environmental changes on reptile populations.

Smallholder farmers' vulnerability to climate change and variability: Evidence from three agroecologies in the Upper Blue Nile, Ethiopia

This study delves into the profound impact of climate change on agriculture in Ethiopia, particularly the vulnerabilities faced by smallholder farmers and the resulting implications for poverty. Focusing on three distinct agroecologies, namely: highland, midland, and lowland zones. The study employed a robust methodology, combining a cross-sectional survey, spatial-temporal trend analysis using GIS, and the development of an overall vulnerability index through the balanced weighted average method. The study, encompassing 646 households, combines data from a variety of sources and analytical tools like the vulnerability index, ArcGIS 10.8, and ERDA's IMAGINE 2015. Utilizing the LVI-IPCC scale, the study shows that climate change is an immediate vulnerability in all agroecological zones. It identifies highland areas as the most sensitive and exposed regions, while lowland households are found to be the most vulnerable in terms of overall vulnerabilities. The research reveals specific challenges faced by communities, such as inadequate health facilities and insufficient food and water supplies in both highland and lowland agroecosystems. Additionally, our investigation has observed a significant alteration in land use practices, specifically the shift from communal grazing land to private cultivation and plantations, emphasizing eucalyptus. This alteration enhances the ecosystem's vulnerability to climate disturbances. The study suggests targeted interventions, such as advocating for sustainable land-use practices, afforestation, and adopting climate-smart agriculture practices. It is important to implement policy measures that prioritize conserving and restoring shrubland, grazing land, and natural forests to ensure both long-term socio-economic and ecosystem resilience. The study's nuanced insights are instrumental in understanding the diverse challenges posed by climate change in Ethiopian agriculture, supporting informed policymaking and sustainable interventions.

Evaluation of rapid alert system for food and feed (RASFF) data for mycotoxin contaminated dried figs originating from Türkiye

In this study, the Rapid Alert System for Food and Feed (RASFF) database was analysed to assess mycotoxin hazards in Turkish dried figs during the production years from 2002 to 2019. A comprehensive evaluation of influencing factors, such as weather conditions, agricultural and processing practices, as well as non-product related elements such as export quantities and regulatory changes, was carried out while analysing yearly and monthly notification trends. Years marked by drought conditions recorded the highest number of notifications. Notably, the years 2007 and 2012 corresponded with peaks in notifications (104 and 117, respectively), suggesting a potential relationship between drought conditions and increased notifications – a finding supported by existing literature on mycotoxin contamination. The mean total aflatoxin (AF) concentrations ranged from 13 μg/kg to 44 μg/kg. However, the considerable variability in the annual number of notifications (ranging from 2 to 106) and the presence of outliers in certain years limit the usefulness of mean total AF concentration data for trend analysis. Weather-related factors, along with agricultural practices, processing and storage conditions, can influence mycotoxin contamination of dried figs, highlighting the need for integrated and comprehensive control strategies. The observed correlation between specific weather conditions, such as drought, and RASFF notifications, and, consequently, mycotoxin contamination, warrants further exploration to better understand and predict this relationship. This study significantly contributes to the mycotoxin literature by providing a comprehensive analysis of mycotoxin hazards in Turkish dried figs. By examining the complex interplay of factors, from weather conditions to agricultural and processing practices and changes in export quantities and regulations, it addresses a crucial gap in the current understanding of mycotoxin risks and offers insights on the historical evolution of safety protocols within Turkish dried fig production. It thus provides a solid foundation for improving food safety practices and developing more effective policy frameworks.

Remote sensing-enhanced transfer learning approach for agricultural damage and change detection: A deep learning perspective

With the continuous advancement of science and technology, there has been a growing awareness of safety among people worldwide. Natural disasters such as wildfires, earthquakes, and floods pose persistent threats to both lives and property on our planet, which serves as our fundamental habitat. While it is impossible to prevent or entirely avert these calamities, rapid identification of affected areas and prompt damage assessment post-disaster can significantly aid in the formulation of effective rescue strategies, ultimately saving more lives. This article delves into the application of transfer learning in satellite image damage assessment—a methodology that involves transferring previously acquired knowledge to enhance a model's adaptability to new tasks. Given the limited availability of datasets for satellite image analysis, transfer learning proves to be an effective approach. Specifically, the study proposes a transfer learning method based on YOLOv5 for satellite image damage assessment. Initially, a general convolutional neural network model is trained using a substantial dataset of natural images. Subsequently, the early layers of this model are frozen, while the later layers undergo training to adapt to satellite image data. Fine-tuning is then employed to further enhance the overall model performance. The results demonstrate that this approach yields a high accuracy rate in satellite image damage assessment. Moreover, compared to conventional deep learning methods, the proposed method effectively leverages pre-trained models' knowledge, thereby reducing data dependency. Additionally, it displays robust generalization capabilities across diverse tasks and datasets, underscoring its potential for facilitating transfer learning across various domains.

A fast hyperspectral change detection algorithm for agricultural crops based on low-rank matrix and morphological feature extraction

Crop change detection study is the foundation of agricultural sustainability. The inherent high spectral resolution of hyperspectral images, combined with multi-temporal datasets, facilitates the detection of subtle changes. To enhance the accuracy and applicability of hyperspectral change detection in agricultural scenes, this paper introduces a fast hyperspectral change detection approach for agricultural crops based on low-rank matrix and morphological feature extraction (FLRaMF). The goal is to improve detection precision and computational efficiency of the change detection process. The method initially employs rapid low-rank matrix extraction to separate changing and non-changing pixels in the spectral domain. Subsequently, spatial information is introduced using attribute profiles, restricting spectral anomalies through hyperspectral morphology, which ultimately improves the detection results. This study utilized four hyperspectral change detection datasets in agricultural crop scenarios, optimizing and analyzing parameters. Experimental results and analysis indicate that the FLRaMF method can achieve higher detection accuracy with reduced computation cost in unsupervised, default parameter scenarios when performing agricultural crop change detection tasks.