Agricultural Losses Research Articles

Tropical Fruit Virus Resistance in the Era of Next-Generation Plant Breeding

Plant viral diseases constitute a major contributor to agricultural production losses, significantly impacting the economies of exporting countries by more than USD 30 billion annually. Understanding and researching the biology and genomics of viruses is crucial for developing virus-resistant genetically edited or genetically modified plants. Genetic modifications can be targeted to specific regions within genes of target plants which are important or essential for the virus to establish a systemic infection, thus fostering resistance or enabling plants to effectively respond to invading agents while preserving their yield. This review provides an overview of viral incidence and diversity in tropical fruit crops and aims to examine the current state of the knowledge on recent research efforts aimed at reducing or eliminating the damage caused by viral diseases, with emphasis on genetically edited products that have reached the market in recent years.

Smartphone-Based Citizen Science Tool for Plant Disease and Insect Pest Detection Using Artificial Intelligence

In recent years, the integration of smartphone technology with novel sensing technologies, Artificial Intelligence (AI), and Deep Learning (DL) algorithms has revolutionized crop pest and disease surveillance. Efficient and accurate diagnosis is crucial to mitigate substantial economic losses in agriculture caused by diseases and pests. An innovative Apple® and Android™ mobile application for citizen science has been developed, to enable real-time detection and identification of plant leaf diseases and pests, minimizing their impact on horticulture, viticulture, and olive cultivation. Leveraging DL algorithms, this application facilitates efficient data collection on crop pests and diseases, supporting crop yield protection and cost reduction in alignment with the Green Deal goal for 2030 by reducing pesticide use. The proposed citizen science tool involves all Farm to Fork stakeholders and farm citizens in minimizing damage to plant health by insect and fungal diseases. It utilizes comprehensive datasets, including images of various diseases and insects, within a robust Decision Support System (DSS) where DL models operate. The DSS connects directly with users, allowing them to upload crop pest data via the mobile application, providing data-driven support and information. The application stands out for its scalability and interoperability, enabling the continuous integration of new data to enhance its capabilities. It supports AI-based imaging analysis of quarantine pests, invasive alien species, and emerging and native pests, thereby aiding post-border surveillance programs. The mobile application, developed using a Python-based REST API, PostgreSQL, and Keycloak, has been field-tested, demonstrating its effectiveness in real-world agriculture scenarios, such as detecting Tuta absoluta (Meyrick) infestation in tomato cultivations. The outcomes of this study in T. absoluta detection serve as a showcase scenario for the proposed citizen science tool’s applicability and usability, demonstrating a 70.2% accuracy (mAP50) utilizing advanced DL models. Notably, during field testing, the model achieved detection confidence levels of up to 87%, enhancing pest management practices.

Predicting symptom severity in PSTVd-infected tomato plants using the PSTVd genome sequence.

Viroids, one of the smallest known infectious agents, induce symptoms of varying severity, ranging from latent to severe, based on the combination of viroid isolates and host plant species. Because viroids are transmissible between plant species, asymptomatic viroid-infected plants may serve as latent sources of infection for other species that could exhibit severe symptoms, occasionally leading to agricultural and economic losses. Therefore, predicting the symptoms induced by viroids in host plants without biological experiments could remarkably enhance control measures against viroid damage. Here, we developed an algorithm using unsupervised machine learning to predict the severity of disease symptoms caused by viroids (e.g., potato spindle tuber viroid; PSTVd) in host plants (e.g., tomato). This algorithm, mimicking the RNA silencing mechanism thought to be linked to viroid pathogenicity, requires only the genome sequences of the viroids and host plants. It involves three steps: alignment of synthetic short sequences of the viroids to the host plant genome, calculation of the alignment coverage, and clustering of the viroids based on coverage using UMAP and DBSCAN. Validation through inoculation experiments confirmed the effectiveness of the algorithm in predicting the severity of disease symptoms induced by viroids. As the algorithm only requires the genome sequence data, it may be applied to any viroid and plant combination. These findings underscore a correlation between viroid pathogenicity and the genome sequences of viroid isolates and host plants, potentially aiding in the prevention of viroid outbreaks and the breeding of viroid-resistant crops.

Pollen foraging preferences in honey bees and the nutrient profiles of the pollen

Honey bees are important insect pollinators that provide critical pollination services to fruit and nut crops in the US. They face challenges likely due to pressures associated with agricultural intensification related habitat loss. To better understand this, pollen preferences of foraging bees and the nutritional profile of pollen brought into hives by foraging bees in crop fields and nut orchards can provide valuable information. We trained bees to forage on bee-collected pollen from hives placed for pollination services in almond orchards, sunflower fields, or mixed species from inter-row plantings. Using bees trained to a certain kind of hive pollen, we applied a binary scoring system, to test preferences of these preconditioned foragers. We also performed metabolomic analyses of the hive pollen used for training and testing to elucidate their nutritional content. Irrespective of preconditioning, bees collected all the available choice pollen types, predominantly choosing hive-collected mixed species pollen (MSP), followed by almond orchard pollen. The hive-collected MSP was chemically diverse, richest in cholesterol, vitamins, and phytochemicals quercetin, kaempferol, coumarin, and quinine, but was not consistently high for essential amino acids and polyunsaturated fatty acids. Although diversity in chemical profiles may not directly relate to plant species diversity, our results suggest that foragers collect a variety of pollen types when available reiterating the importance of diverse floral resources.

Composition and abundance of midgut plasma membrane proteins in two major hemipteran vectors of plant viruses, Bemisia tabaci and Myzus persicae.

Multiple species within the order Hemiptera cause severe agricultural losses on a global scale. Aphids and whiteflies are of particular importance due to their role as vectors for hundreds of plant viruses, many of which enter the insect via the gut. To facilitate the identification of novel targets for disruption of plant virus transmission, we compared the relative abundance and composition of the gut plasma membrane proteomes of adult Bemisia tabaci (Hemiptera: Aleyrodidae) and Myzus persicae (Hemiptera: Aphididae), representing the first study comparing the gut plasma membrane proteomes of two different insect species. Brush border membrane vesicles were prepared from dissected guts, and proteins extracted, identified and quantified from triplicate samples via timsTOF mass spectrometry. A total of 1699 B. tabaci and 1175 M. persicae proteins were identified. Following bioinformatics analysis and manual curation, 151 B. tabaci and 115 M. persicae proteins were predicted to localize to the plasma membrane of the gut microvilli. These proteins were further categorized based on molecular function and biological process according to Gene Ontology terms. The most abundant gut plasma membrane proteins were identified. The ten plasma membrane proteins that differed in abundance between the two insect species were associated with the terms "protein binding" and "viral processes." In addition to providing insight into the gut physiology of hemipteran insects, these gut plasma membrane proteomes provide context for appropriate identification of plant virus receptors based on a combination of bioinformatic prediction and protein localization on the surface of the insect gut.

CRISPR/Cas9-mediated genome editing technique to control fall armyworm (Spodoptera frugiperda) in crop plants with special reference to maize.

Fall Armyworm imposes a major risk to agricultural losses. Insecticides have historically been used to manage its infestations, but it eventually becomes resistant to them. To combat the pest, a more recent strategy based on the use of transgenic maize that expresses Bt proteins such as Cry1F from the bacteria has been used. Nonetheless, there have been numerous reports of Cry1F maize resistance in FAW populations. Nowadays, the more effective and less time-consuming genome editing method known as CRISPR/Cas9 technology has gradually supplanted these various breeding techniques. This method successfully edits the genomes of various insects, including Spodoptera frugiperda. On the other hand, this new technique can change an insect's DNA to overcome its tolerance to specific insecticides or to generate a gene drive. The production of plant cultivars resistant to fall armyworms holds great potential for the sustainable management of this pest, given the swift advancement of CRISPR/Cas9 technology and its varied uses. Thus, this review article discussed and critically assessed the use of CRISPR/Cas9 genome-editing technology in long-term fall armyworm pest management. However, this review study focuses primarily on the mechanism of the CRISPR-Cas9 system in both crop plants and insects for FAW management.

Agri-Food Management and Sustainable Practices: A Fuzzy Clustering Application Using the Galois Lattice

The objective of this study was to generate groups of agri-food producers with high affinity in relation to their sustainable waste management practices. The aim of conforming these groups is the development of synergies, knowledge management, and policy- and decision-making by diverse stakeholders. A survey was conducted among the most experienced farmers in the region of Nuevo Urecho, Michoacán, Mexico, and a total of eight variables relating to sustainable waste management practices, agricultural food loss, and the waste generated at each stage of the production process were examined. The retrieved data were treated using the maximum inverse correspondence algorithm and the Galois Lattice was applied to generate clusters of highly affine producers. The results indicate 163 possible elements that generate the power set, and 31 maximum inverse correspondences were obtained. At this point, it is possible to determine the maximum number of relationships, called affinities. In general, all 15 considered farmers shared the measure of revaluation of food waste and 90% of the farmers shared affinity in measures related to ecological care and the proper management of waste. A practical implication of this study is the conformation of highly affine clusters for both policy and strategic decision-making.

African Lovegrass Segmentation with Artificial Intelligence Using UAS-Based Multispectral and Hyperspectral Imagery

The prevalence of the invasive species African Lovegrass (Eragrostis curvula, ALG thereafter) in Australian landscapes presents significant challenges for land managers, including agricultural losses, reduced native species diversity, and heightened bushfire risks. Uncrewed aerial system (UAS) remote sensing combined with AI algorithms offer a powerful tool for accurately mapping the spatial distribution of invasive species and facilitating effective management strategies. However, segmentation of vegetations within mixed grassland ecosystems presents challenges due to spatial heterogeneity, spectral similarity, and seasonal variability. The performance of state-of-the-art artificial intelligence (AI) algorithms in detecting ALG in the Australian landscape remains unknown. This study compared the performance of four supervised AI models for segmenting ALG using multispectral (MS) imagery at four sites and developed segmentation models for two different seasonal conditions. UAS surveys were conducted at four sites in New South Wales, Australia. Two of the four sites were surveyed in two distinct seasons (flowering and vegetative), each comprised of different data collection settings. A comparative analysis was also conducted between hyperspectral (HS) and MS imagery at a single site within the flowering season. Of the five AI models developed (XGBoost, RF, SVM, CNN, and U-Net), XGBoost and the customized CNN model achieved the highest validation accuracy at 99%. The AI model testing used two approaches: quadrat-based ALG proportion prediction for mixed environments and pixel-wise classification in masked regions where ALG and other classes could be confidently differentiated. Quadrat-based ALG proportion ground truth values were compared against the prediction for the custom CNN model, resulting in 5.77% and 12.9% RMSE for the seasons, respectively, emphasizing the superiority of the custom CNN model over other AI algorithms. The comparison of the U-Net demonstrated that the developed CNN effectively captures ALG without requiring the more intricate architecture of U-Net. Masked-based testing results also showed higher F1 scores, with 91.68% for the flowering season and 90.61% for the vegetative season. Models trained on single-season data exhibited decreased performance when evaluated on data from a different season with varying collection settings. Integrating data from both seasons during training resulted in a reduction in error for out-of-season predictions, suggesting improved generalizability through multi-season data integration. Moreover, HS and MS predictions using the custom CNN model achieved similar test results with around 20% RMSE compared to the ground truth proportion, highlighting the practicality of MS imagery over HS due to operational limitations. Integrating AI with UAS for ALG segmentation shows great promise for biodiversity conservation in Australian landscapes by facilitating more effective and sustainable management strategies for controlling ALG spread.

Unraveling the enigma of root-knot nematodes: from origins to advanced management strategies in agriculture.

Integrated management strategies, including novel nematicides and resilient cultivars, offer sustainable solutions to combat root-knot nematodes, crucial for safeguarding global agriculture against persistent threats. Root-knot nematodes (RKN) pose a significant threat to a diverse range of host plants, with their obligatory endoparasitic nature leading to substantial agricultural losses. RKN spend much of their lives inside or in contact by secreting plant cell wall-modifying enzymes resulting in the giant cell development for establishing host-parasite relationships. Additionally, inflicting physical harm to host plants, RKN also contributes to disease complexes creation with fungi and bacteria. This review comprehensively explores the origin, history, distribution, and physiological races of RKN, emphasizing their economic impact on plants through gall formation. Management strategies, ranging from cultural and physical to biological and chemical controls, along with resistance mechanisms and marker-assisted selection, are explored. While recognizing the limitations of traditional nematicides, recent breakthroughs in non-fumigant alternatives like fluensulfone, spirotetramat, and fluopyram offer promising avenues for sustainable RKN management. Despite the success of resistance mechanisms like the Mi gene, challenges persist, prompting the need for integrative approaches to tackle Mi-virulent isolates. In conclusion, the review stresses the importance of innovative and resilient control measures for sustainable agriculture, emphasizing ongoing research to address evolving challenges posed by RKN. The integration of botanicals, resistant cultivars, and biological controls, alongside advancements in non-fumigant nematicides, contributes novel insights to the field, laying the ground work for future research directions to ensure the long-term sustainability of agriculture in the face of persistent RKN threats.

Biosynthesis of cupric oxide nanoparticles: its antiviral activities against TMV by directly destroying virion and inducing plant resistance

Tobacco mosaic virus (TMV) is widely recognized as one of the most important plant viruses, causing significant agricultural losses in terms of both quality and yield worldwide each year. This study demonstrated the biosynthesis of copper oxide nanoparticles (CuONPs) using orange peel extract for effective control of TMV infection both in vitro and in vivo. After treatment with CuONPs (100 mg/L) for 2 h, TMV particles exhibited evident fragmentation in vitro, reducing infectivity on tobacco plants. Similarly, the application of CuONPs on Nicotiana benthamiana (N. benthamiana) positively impeded viral replication and accumulation in vivo. Interestingly, the expression of systemic resistance-related genes (PR1, PR2, ERF1, and JAZ3) in the host plant was up-regulated by CuONPs treatment, supporting that CuONPs activated plant immunity to inhibit TMV. Importantly, the application of CuONPs (100 mg/L) did not exhibit any toxic effects on tobacco and, instead, resulted in the promotion of chlorophyll content, as well as an increase in the fresh weight and dry weight of the plant when compared to the control treatment. Overall, we proposed that the appropriate concentration of CuONPs (100 mg/L) can directly break viral particles by passivating, boost plant immunity by stimulating systemic acquired resistance (SAR), and provide nutritional supplements to promote plant growth.

Magnolol inhibits Colletotrichum gloeosporioides via disruption of plasma membrane and induction of reactive oxygen species accumulation

Colletotrichum gloeosporioides caused anthracnose in various tropical fruit, resulting in substantial agricultural loss. Anthracnose diseases are mainly managed by chemical fungicides, which excessive use has led to resistant pathogens and concerns about food safety. In this study, magnolol, a plant-derived active compound from Magnolia officinalis, was investigated. In vivo experiments demonstrated that 4 g L−1 magnolol significantly retarded the expansion of anthracnose lesions in mango fruit. Significant inhibition of C. gloeosporioides growth was observed at magnolol concentration higher than 20 mg L−1, with MIC and IC50 values of 55.98 and 31.0 mg L−1 for mycelial growth, 345.68 and 82.37 mg L−1 for spore germination, respectively. The inhibitory effect was primarily attributed to the disruption of plasma membrane integrity, which led to the leakage of ion, macromolecules, nucleic acids and proteins. Furthermore, magnolol induced the accumulation of reactive oxygen species (ROS), resulting in oxidative stress that perturb cellular functions and even induce cell death. Therefore, magnolol has potential as a feasible substitute for controlling anthracnose.

Modern Breeding Strategies and Tools for Durable Late Blight Resistance in Potato.

Cultivated potato (Solanum tuberosum) is a major crop worldwide. It occupies the second place after cereals (corn, rice, and wheat). This important crop is threatened by the Oomycete Phytophthora infestans, the agent of late blight disease. This pathogen was first encountered during the Irish famine during the 1840s and is a reemerging threat to potatoes. It is mainly controlled chemically by using fungicides, but due to health and environmental concerns, the best alternative is resistance. When there is no disease, no treatment is required. In this study, we present a summary of the ongoing efforts concerning resistance breeding of potato against this devastating pathogen, P. infestans. This work begins with the search for and selection of resistance genes, whether they are from within or from outside the species. The genetic methods developed to date for gene mining, such as effectoromics and GWAS, provide researchers with the ability to identify genes of interest more efficiently. Once identified, these genes are cloned using molecular markers (MAS or QRL) and can then be introduced into different cultivars using somatic hybridization or recombinant DNA technology. More innovative technologies have been developed lately, such as gene editing using the CRISPR system or gene silencing, by exploiting iRNA strategies that have emerged as promising tools for managing Phytophthora infestans, which can be employed. Also, gene pyramiding or gene stacking, which involves the accumulation of two or more R genes on the same individual plant, is an innovative method that has yielded many promising results. All these advances related to the development of molecular techniques for obtaining new potato cultivars resistant to P. infestans can contribute not only to reducing losses in agriculture but especially to ensuring food security and safety.

Quantification of losses in agriculture production in eastern Ukraine due to the Russia-Ukraine war

The ongoing war in Ukraine has seriously impacted the agricultural sector, yet its exact effects on agricultural production are not well understood. Here we combine satellite imagery, machine learning, and statistical regression approaches to present a spatially detailed assessment of agricultural losses for five high-risk provinces in eastern Ukraine (Crimea, Donets’k, Kherson, Luhans’k, and Zaporizhzhya). Our findings indicated that approximately 18.11 ± 2.47% of croplands were left unplanted following the war. Among the cultivated areas, wheat, sunflower, and rapeseed experienced average production losses of 36.39–37.19% in 2022 compared to pre-war levels during 2019–2021. Economically, the indirect losses resulting from decreased production, corresponding to $520.36 ± 22.52, $427.59 ± 24.62, and $205.02 ± 11.53 million USD for wheat, sunflower, and rapeseed crops, respectively, were 1.31–2.16 times higher than direct losses due to unplanted. We also found that reductions in crop production were primarily affected by war intensity indicated by changes in nighttime lights. This methodology offers a comprehensive framework for quantifying agricultural damages from wars, which can be applicable beyond the Russia-Ukraine context.

Potential distribution of three invasive agricultural pests in China under climate change

Invasive pests reduce biodiversity and ecosystem service functions, thereby leading to economic and also agricultural losses. Banana skipper (Erionota torus Evans), red palm weevil (Rhynchophorus ferrugineus), and coconut caterpillar (Opisina arenosella Walker) are invasive insect pests in the palm-growing regions and they have had serious consequences for the planting of bananas (Musa nana), palms (Trachycarpus fortune) and coconut (Cocos nucifera). Based on screened occurrence data, the present research utilized Maximum Entropy model (Maxent) to simulate the distribution dynamics of these three invasive insects in China, under current and future climate (2050s, 2070s, 2090s) in two shared socio-economic pathways (SSPs: 126 and 585) of the newly released coupled model intercomparison project phase6 (CMIP6). The results show that: (1) Under current and future climate conditions, all model groups exhibited an AUC value exceeding 0.92, which shows that the model prediction results are very good;(2) The suitable habitat area of E. torus Evans remains relatively stable with some expansion in the SSP126 of 2090s and some contraction in the SSP585 of 2090s. The suitable habitat area of R. ferrugineus showed an overall contraction, with substantial contraction in the SSP585 of 2090s.The suitable habitat area of O. arenosella has an overall expansion, with the most pronounced expansion in the SSP585 of 2070s; (3) The current centroid of suitable habitats for R. ferrugineus and E. torus Evans is located in Guangxi Province and wholely shift toward the south direction under future climate. The centroid of suitable habitats for O. arenosella is currently located in the northeastern maritime area of Hainan Province and will shift toward the north direction under future climate; (4) Temperature, precipitation and Human disturbance factors (Population density and Human influence index) were crucial variables for describing the distribution of the three species. For E. torus Evans in particular, percentage contributions of Population density was up to 31.4, which is only 0.1 different from ranked first Bio19 (Precipitation of the coldest quarter). The dynamics of habitats of these three species and the correlating driver factors proposed in this work provide essential insights into future spatial management of the three invasive insects in China. Our work is necessary and timely in identifying newly areas at high risk of expansion of the three invasive insects in the future, then suggesting strategic control measures to prevent their spread, and finally providing scientific evidence for the early prevention and rapid response to the three invasive insects.

Sustainable dynamic planning and policy implementation for water, energy and food resources

The current investigation focuses on analyzing dynamic approaches to sustainable water resource management policies, considering the interconnectedness of water, food and energy resources. This analysis takes into consideration changes in demand resulting from population growth and economic development over a twenty-year timeframe, employing a systems dynamics methodology. Initially, the model was developed and simulated using data from the case province as a macro-scale representation of the water-food-energy resource framework, while also factoring in the continuation of current conditions. Through sensitivity analysis utilizing the Monte Carlo model, the study addresses the formulation of sustainable water resource policies across four main categories: water demand management, water supply management, food resource management, and energy resource demand management. Additionally, it explores the integration of policies within an optimal framework. The simulation of proposed solutions revealed that a combination of water demand management and food resource management yielded the most promising outcome. Specifically, the recommended solution entails enhancing water irrigation efficiency by 17 % through the expansion of pressurized irrigation network coverage, adjusting cropping patterns by 12 %, reducing agricultural product losses by 7 % via improved food supply management, minimizing food demand by 7 % due to reduced food consumption losses, and achieving an annual 8 % increase in crop performance. These selected policies form the foundation for sustainable water resource management strategies.

Advances in Migratory Plant Endoparasitic Nematode Effectors.

Unlike sedentary plant-parasitic nematodes, migratory plant endoparasitic nematodes (MPENs) are unable to establish permanent feeding sites, and all developmental stages (except eggs) can invade and feed on plant tissues and can be easily overlooked because of the unspecific symptoms. They cause numerous economic losses in agriculture, forestry, and horticulture. In order to understand the pathogenetic mechanism of MPENs, here we describe research on functions and host targets focused on currently identified effectors from six MPENs, namely Radopholus similis, Pratylenchus spp., Ditylenchus destructor, Bursaphelenchus xylophilus, Aphelenchoides besseyi, and Hirschmanniella oryzae. This information will provide valuable insights into understanding MPEN effectors and for future fostering advancements in plant protection.

Optimization of Financial Flows with the Help of Artificial Intelligence in Public Institutions

Abstract The only technology that is currently mature enough to help us quickly recover losses in agriculture is digital technology. Digital transformation is increasingly referred to as the fourth industrial revolution, aimed at merging the real world with the virtual one, bringing advantages even in traditional sectors such as agriculture. Artificial intelligence (AI) algorithms are used to analyze large amounts of data collected from various sources in the field, as well as from farmers or beneficiaries of European funds for agricultural sector development. Specifically, they can analyze information gathered from soil sensors, weather stations, satellite images, or European funding applications, among others. This analysis provides farmers with valuable information to make informed decisions regarding seed selection, optimal timing, planting techniques, and resource allocation decisions. At the same time, AI-powered systems can learn from historical data and identify patterns, enabling more accurate predictions and recommendations. Machine learning algorithms can anticipate managerial decisions, detect potential problems, and suggest appropriate interventions to improve financial performance for the successful implementation of projects funded by European funds.

Advancements and 4E + Q performance analyses in solar drying for maize kernels preservation: A comprehensive review

AbstractThe world's population is projected to increase, impacting a 60% rise in food production demand by 2050, including the demand for maize. Currently, maize production is estimated to reach 1510 million tons in 2023, necessitating suitable methods for preserving maize kernels to maintain quality and reduce post‐harvest agricultural losses. Although drying methods present a viable solution to address this issue, the majority of farmers in Indonesia still use conventional drying methods, such as open sun drying, which has several drawbacks. Hence, solar drying systems aim to overcome these limitations by harnessing renewable, clean, sustainable, and eco‐friendly energy to support the sustainable development goals (SDGs). This research comprehensively reviews the progress of solar drying technology, providing insights into its working principles, innovative designs, and operational modes across various types of solar dryers. The 4E + Q analysis provides information regarding the performance of solar drying systems, including energy analysis to determine the quantity of energy utilized and exergy analysis to demonstrate irreversibility in thermodynamic processes. Additionally, this analysis highlights environmental impacts such as CO2 emissions and mitigation efforts, while considering economic aspects through lifecycle assessment and payback period methods. The quality assessment of dried maize kernels according to the standards set by the Indonesian National Standard (SNI) is conducted, including proximate analysis, aflatoxin levels, color measurements, and internal cracking analysis.Practical applicationsDrying technology is essential for the food preservation process, enabling the maintenance of product quality and extending storage life by reducing the moisture content to eliminate the activities of microbial agents. The application of solar drying technology harnesses solar radiation as the primary energy source, making it well‐suited for crops like paddy, maize, cassava, etc. The development of solar dryers continues through modifications and the addition of auxiliary systems to achieve more efficient, economical, and sustainable technologies. The hope is that in the future, these technologies can be adopted by farmers on an industrial scale due to low operational costs, ease of operation, and controllable operating conditions. The methodological approach is necessary to evaluate the performance of solar dryers and the quality of dried products through a 4E + Q analysis, it provides information on energy consumption, exergy flow, economic considerations, environmental aspects, and quality assessments against national quality standards (SNI).

Assessing and predicting land cover dynamics for environmental sustainability in Jordan's arid ecosystems using CA-Markov model

Jordan's arid ecosystems face a threat of environmental degradation due to increasing human activities driven by a growing population and economy. This study assesses Land Use and Land Cover (LULC) changes in regional and ecoregional landscapes, particularly in a region with an east-west environmental gradient, spanning 41 years (1990–2030). The study utilizes the Jordan National Land Cover Dataset (JNLCD) for historical data and employs the CA-Markov model to generate a future land cover map. The results revealsignificant changes, particularly the tripling in size of urban areas at the expense of primary croplands. The Steppe ecoregion, known for its fragility, experienced shifts into irrigated agriculture practices and loss of natural rangelands. The period between 2000 and 2015 marked as the most dynamic period as the region landscape underwent significant change due to population growth and influx of refugees. During this period, 123 km2 of rainfed agricultural areas were consumed by urbanization while rainfed agriculture activities expanded into sparsely-vegetated/barren areas (55 km2). Additionally, 33 km2 of sparsely-vegetated/barren areas were transformed into irrigated agriculture. This study offers a comprehensive and innovative analysis of LULC changes in Jordan's arid ecosystems. While it highlights the ecological importance of the region and its potential to enhance environmental resilience, it also underscores the negative impact of human interventions, which have disrupted spatial arrangement, connectivity, stability, and structure. By bridging historical data with future projections, employing detailed landscape metrics, and addressing socio-economic drivers, this work provides valuable insights for sustainable land use planning and environmental conservation in Jordan.

Enhancing water governance and water resources management in Bangladesh

ABSTRACT Bangladesh, a riverine nation with rivers originating beyond its borders, experiences a climatic paradox characterized by excessive monsoon rainfall (June to September) and minimal precipitation during winter (December to February). This climatic duality leads to recurring hydrological disasters, including floods, river erosion, and droughts, causing substantial losses in agriculture, fisheries, navigation, ecology, and biodiversity. Effective water governance is imperative to mitigate these economic and environmental impacts. This study aims to evaluate Bangladesh’s current state of water governance and identify gaps in existing policies and regulations related to water resources management (WRM). It is achieved through a comprehensive review of existing laws, acts, regulations, and policies related to water governance. The study reveals that policy documents developed over the last three decades, aimed at enhancing water governance and reducing vulnerabilities to droughts, floods, and riverbank erosion, have identified only a limited number of critical policy gaps in WRM. The government should prioritize gradually decentralizing management policies to strengthen climate resilience in coastal regions. This research offers insights into improving water governance and WRM and contributes to reducing hydrological disasters in Bangladesh. Additionally, it provides valuable guidance for addressing challenges related to upstream transboundary rivers and formulating future strategies and action plans.