Agricultural Losses Research Articles

Molluscicidal Activity and Inhibition of Acetylcholinesterase Activity of Azadirachta Indica Extract on Pomacea Canaliculata

The Golden Apple Snail, Pomacea canaliculata, is a notorious pest in Malaysia, leading to substantial agricultural and economic losses, while, the neem tree, Azadirachta indica has been identified as alternative source for bio-pesticides. There is currently limited information on A. indica as natural molluscicide against P. canaliculata. This study aims to evaluate the effects of A. indica on P. canaliculata at various life stages (eggs, juvenile and adult). The bioactive compound, azadirachtin in A. indica extracts was quantified using high-performance liquid chromatography (HPLC). Egg hatchability was determined using spot spraying on egg clusters while the molluscicidal activity of A. indica on juvenile and adult specimens was evaluated using the immersion bioassay approach. The snail's acetylcholinesterase (AChE) inhibition was assessed using Ellman assay. The azadirachtin content in the ethanolic extract of A. indica leaves at a concentration of 1 µg/mL was found to be 45.42%. Increasing the concentration of A. indica extracts resulted in lower egg hatchability and increased mortality of juvenile and adult snails. Azadirachta indica extracts showed the highest effectiveness against adult P. canaliculata with an LC50 value of 0.52 mg/mL, compared to 0.92 mg/mL for juveniles and 0.74 mg/mL for eggs. Additionally, A. indica extract exhibited greater inhibition of AChE in adult specimens compared to juvenile specimens and eggs. The IC50 for adult is 0.60 μg/mL, which is the lowest while the IC50 for juvenile is 0.68 μg/mL, the highest. Taken together, A. indica shows immerse potential as natural pesticide for managing the population of P. canaliculata.

African food system and biodiversity mainly affected by urbanization via dietary shifts

The rapid urbanization in Africa profoundly affects local food and ecological systems. According to earlier research, urbanization may cause food production and biodiversity losses as agricultural or natural lands are absorbed by expanding cities. Land-use displacement effects may buffer agricultural production losses or may lead to additional biodiversity losses but are often overlooked. Moreover, impacts of dietary changes associated with urbanization are rarely considered. To address this, we combined spatially explicit projections of African urban area expansion with observed rice consumption shifts to inform a partial equilibrium model (the Global Biosphere Management Model). We demonstrate the importance of displacement effects to identify potential food production or biodiversity issues until 2050 and argue for their integration in land-use planning and policymaking across spatial scales. We identify that because of agricultural displacement, the impact of urban area expansion on food production losses is probably limited (<1%)—at the cost of additional losses of natural lands by 2050 (up to 2 Mt). We also show that considering dietary shifts associated with urbanization increases rice consumption, production (+8.0%), trade (up to +2 Mt of required import) and agricultural methane emissions (up to +12 MtCO2-equivalent yr–1), thereby underscoring the need for a systems approach in future sustainability studies.

Evaluating the Cost-Effectiveness of Green Infrastructure for Mitigating Diffuse Agricultural Contaminant Losses

Evaluating the Cost-Effectiveness of Green Infrastructure for Mitigating Diffuse Agricultural Contaminant Losses

BUDDHIMAN KRISHI ANUVADHANAM: A New Hope for Better Agriculture System

The Agriculture Monitoring System addresses pressing challenges in contemporary farming, notably ineffective soil moisture management and persistent threats of animal intrusion. Recent statistics indicate a global agricultural loss of 20% due to suboptimal soil moisture practices and animal-related damages. Existing monitoring systems fall short in providing a holistic solution, resulting in significant economic losses for farmers. In response, the proposed system integrates advanced technologies, including precise soil moisture sensors, infrared (IR) sensors for animal detection, and real-time visual monitoring via an ESP32 camera. Farmers can also monitor and control irrigation pumps and electric shock generators through their smartphones. This comprehensive approach aims to minimize yield losses, optimize resource utilization, and empower farmers with actionable insights for informed decision-making. By bridging critical gaps in agricultural monitoring and protection, the Agriculture Monitoring System emerges as a transformative solution with the potential to revolutionize and fortify global farming practices

LOSS MODEL OF CLIMATE INSURANCE BASED ON EFFECT OF GROWING DEGREE DAYS INDEX

Climate change is a threat to agriculture, especially food crops such as rice. Climate index insurance is an alternative to cover the risk of agricultural losses due to crop failure due to climate change factors. The observed climate index is the effect of growing degree days which measures the impact of temperature on plant growth and development. The data used in this study is daily temperature data at Climatology Station Class 1 Darmaga, Bogor and Meteorological Station Class 3 Citeko, West Java, during the gadu (rice that is planted in the Gadu/Dry season) planting period. In determining the amount of loss, the average daily temperature on growing degree days is calculated using a combination of a time series model and a deterministic model. The deterministic model describes the trend and seasonality of the time series at each station. The parameters contained in the model will be estimated using least-square. To see the dependence of temperature at different stations using a normal bivariate distribution. The result show that the amount of loss based on the index of growing degree days per unit rupiah per degree Celsius (℃) for Meteorological Station Class 3 Citeko only occurs for certain percentages, namely 80%, 90%, and 95%, while for Climatology Station Class 1 Darmaga Bogor it can occur for each percentage. This indicates that the amount of losses obtained will depend on determining the strike level by using the mean and standard deviation of the growing degree days index distribution. Furthermore, these findings suggest that Climatology Station Class 1 Darmaga Bogor have higher risk of crop failure due to climate change than Meteorological Station Class 3 Citeko.

An ensemble of deep learning architectures for accurate plant disease classification

A substantial fraction of agricultural produce loss can be attributed to plant diseases. Agricultural yield loss can have far-reaching consequences for a country's economy and contribute to global food insecurity. Early detection of plant diseases can be instrumental in maintaining global health and welfare. A pathologist's visual evaluation is typically used to make an early diagnosis of plant diseases. This technique involves experts or farmers examining plants with the naked eye and classifying the disease depending on their previous experience. This conventional approach includes drawbacks like low accuracy and the need for human expertise. This motivates researchers to investigate automated systems for the early diagnosis of plant diseases.To achieve this goal an ensemble of different deep learning architectures (DenseNet201, efficientNetB0, inceptionresnetV2, efficientNetB3) is introduced to increase the classification accuracy of plant leaf diseases. In this work, a novel image-processing technique is proposed to increase the efficiency of deep-learning models. Also, a data balancing technique is used to solve the problem of the imbalanced dataset. Five different deep-learning models are trained and tested using the largest plant disease dataset; PlantVillage. Ten different ensembles (chosen randomly) of the deep learning models are tested and compared to find the ensemble with the highest accuracy.The proposed ensemble model was able to achieve 99.89% accuracy on the New PlantVillage dataset. PlantVillage is a challenging dataset with 38 classes. Achieving high accuracies on such a dataset proves the ability of the system to generalize on unseen data or real-world scenarios. A comparison with the state-of-the-art is made with other available models from the literature. A section about this is added to show the superior performance of the proposed ensemble model in terms of accuracy and F1-score.

The Genetics and Molecular Biology of Pathogens in Agricultural Crops

Despite advances in science and technology, agricultural yield losses due to plant pathogens and pests currently account for 30% of global production [...]

Hydrological modelling for post-monsoon agricultural drought assessment and implications for the agro-economy

ABSTRACT Reliable drought monitoring is a prerequisite for minimizing potential agricultural losses. Soil moisture is a key variable for monitoring agricultural drought assessment. This study conducted in the Bundelkhand region of Uttar Pradesh uses a macroscale variable infiltration capacity (VIC) hydrological model to simulate soil moisture and calculate soil moisture deficit index (SMDI) for agricultural drought assessment for the Rabi crop growing season, 1998–2016. Crop yield was linked with SMDI and other covariates using a random forest machine learning-based regression technique. The results show that the VIC model effectively simulated root zone soil moisture when compared with the reference data. Major droughts were identified in the years 2000–01, 2007–08, and 2015–16 in the study region. The RF-based crop yield prediction accuracy improved when irrigational factors were added. The study provides a noteworthy reference for drought assessment and prevention, water resource management, and ensuring food security.

A New Agricultural Drought Index to Characterize Agricultural Drought Using Data Mining Techniques

Drought monitoring is a critical task as its occurrence and extent vary according to many factors like drought type, risk, agricultural losses, and impact. Monitoring drought is important because the footprint of this hazard is larger than that of other natural hazards. Many drought indices are developed to monitor complex drought conditions. The intensity and severity of drought in a particular region and at a particular time can be tracked by the drought indicator. In this research, a new agricultural drought index, Yield-Evapotranspiration Drought Index (YEDI) is developed using crop yield, potential, and reference crop evapotranspiration. Data mining and Neural Network techniques have been used to model the drought index. The agricultural and climatic data used is selected from the year 1983 to 2015 (33 years) from the period of June to October (Kharif period) for Maharashtra state in India. The drought index generates the positive values which are further divided into a range of high, medium, and low intensities of drought. SPI and SPEI indices are used for validation against YEDI. Results show that there is a correlation between YEDI and SPEI whereas a low correlation is between YEDI and SPI. YEDI proves to be useful for agricultural drought monitoring.

A Mobile-Based System for Detecting Plant Leaf Diseases Using Deep Convolutional Neural Network

Plant diseases pose a significant challenge to agriculture in India, causing substantial losses in crop production each year. Limited-resource farmers often struggle with diagnosing these diseases accurately, especially when relying on visual observation of leaf symptoms. Hence, there is an urgent need to enhance the detection, monitoring, and prediction of crop diseases to mitigate agricultural losses effectively. To address this challenge, we propose a mobile-based system empowered by Machine Learning (ML) and computer vision tailored for the Indian context. Our system utilizes Convolutional Neural Networks (CNNs) as the core deep learning engine to classify 37 disease categories commonly found in Indian crops. We have curated a comprehensive dataset comprising 96,206 images of plant leaves, encompassing both healthy and infected, for training, validation, and testing purposes. The user interface of our system is designed as an Android mobile application, enabling farmers to capture images of diseased plant leaves effortlessly. Upon image capture, the system swiftly identifies the disease category and presents it to the user along with a confidence percentage. This functionality empowers farmers to take timely action to protect their crops, thereby reducing reliance on incorrect fertilizers that may exacerbate plant stress. Furthermore, we evaluated the performance of our system using various metrics, including classification accuracy and processing time. Our results indicate an impressive overall classification accuracy of 94% across the 37 disease classes prevalent in 14 different crop species commonly cultivated in India. In essence, our ML-powered mobile-based solution offers Indian farmers a robust tool for efficient crop disease diagnosis and management, ultimately contributing to the enhancement of agricultural productivity and sustainability in the country.

MicroRNAs: exploring their role in farm animal disease and mycotoxin challenges.

MicroRNAs (miRNAs) serve as key regulators in gene expression and play a crucial role in immune responses, holding a significant promise for diagnosing and managing diseases in farm animals. This review article summarizes current research on the role of miRNAs in various farm animal diseases and mycotoxicosis, highlighting their potential as biomarkers and using them for mitigation strategies. Through an extensive literature review, we focused on the impact of miRNAs in the pathogenesis of several farm animal diseases, including viral and bacterial infections and mycotoxicosis. They regulate gene expression by inducing mRNA deadenylation, decay, or translational inhibition, significantly impacting cellular processes and protein synthesis. The research revealed specific miRNAs associated with the diseases; for instance, gga-miR-M4 is crucial in Marek's disease, and gga-miR-375 tumor-suppressing function in Avian Leukosis. In swine disease such as Porcine Respiratory and Reproductive Syndrome (PRRS) and swine influenza, miRNAs like miR-155 and miR-21-3p emerged as key regulatory factors. Additionally, our review highlighted the interaction between miRNAs and mycotoxins, suggesting miRNAs can be used as a biomarker for mycotoxin exposure. For example, alterations in miRNA expression, such as the dysregulation observed in response to Aflatoxin B1 (AFB1) in chickens, may indicate potential mechanisms for toxin-induced changes in lipid metabolism leading to liver damage. Our findings highlight miRNAs potential for early disease detection and intervention in farm animal disease management, potentially reducing significant economic losses in agriculture. With only a fraction of miRNAs functionally characterized in farm animals, this review underlines more focused research on specific miRNAs altered in distinct diseases, using advanced technologies like CRISPR-Cas9 screening, single-cell sequencing, and integrated multi-omics approaches. Identifying specific miRNA targets offers a novel pathway for early disease detection and the development of mitigation strategies against mycotoxin exposure in farm animals.

How membership of a farmer organization can support farmers in adapting to climate change in the Pacific Island region

Abstract Climate projections for the Pacific Island region serve to emphasize the climate unpredictability, variability and intensity that farmers will have to adapt to in the future. The impact that climate change is having on agriculture in the Pacific is evident from the data around agriculture loss and damage from extreme weather events such as flooding and cyclones. It is clear that food systems will have to be resilient and farmers will have to adapt, if food security in the region is not to be seriously threatened. The factors considered as essential for building adaptive capacity are: (a) social capital; (b) ability of communities to engage effectively with external agents; (c) access to knowledge including how knowledge is generated, shared and exchanged; (d) merging of local and external knowledge; (e) space for farmers to interact, communicate, experiment and learn from each other; (f) trust in the adaptation measure(s)being promoted; (g) effective capacity building; (h) decentralized research; and (i) supportive policy. This article explores these factors and proposes that membership of a farmer organization (FO) provides an enabling platform for the effective and efficient delivery of these conditions, thereby providing farmers with the tools essential for adaptation to a changing climate. Examples illustrating how farmers around the Pacific Island region are working together to put in place measures that will strengthen the resilience of their farming systems are provided from national and regional farmer organizations in the region.

Research on Predicting Spread of Dandelion Seed Based on PCA and Dandelion Optimizer

Plants such as Taraxacum officinale, as known as dandelion, flood our life and therefore influence our world drastically. They can result in both beneficial outcomes and negative consequences. As a major component of invasive species, the spread of dandelions in the ecosystem could contribute to the broke of ecological balance, causing economic loss in agriculture. Therefore, researching the spread and influencing factors of dandelions has extremely significant importance. To determine the impact factors, we comprehensively consider complicated sub-factors. After quantifying and unifying the data, we utilize PCA for low-dimensional analysis. And, we introduce data from other invasive species to validate the generalization of our model. Based on the results of PCA, we establish a model based on Dispersion Optimization (DO), which predicts the spread of dandelions under different climatic conditions for 1 month, 2 months, 3 months, 6 months, and 12 months by collecting statistical data on climate indicators. We found that the longer the time period, the smaller the differences in dandelion spread across different climates.

A New Dataset and Comparative Study for Aphid Cluster Detection and Segmentation in Sorghum Fields.

Aphid infestations are one of the primary causes of extensive damage to wheat and sorghum fields and are one of the most common vectors for plant viruses, resulting in significant agricultural yield losses. To address this problem, farmers often employ the inefficient use of harmful chemical pesticides that have negative health and environmental impacts. As a result, a large amount of pesticide is wasted on areas without significant pest infestation. This brings to attention the urgent need for an intelligent autonomous system that can locate and spray sufficiently large infestations selectively within the complex crop canopies. We have developed a large multi-scale dataset for aphid cluster detection and segmentation, collected from actual sorghum fields and meticulously annotated to include clusters of aphids. Our dataset comprises a total of 54,742 image patches, showcasing a variety of viewpoints, diverse lighting conditions, and multiple scales, highlighting its effectiveness for real-world applications. In this study, we trained and evaluated four real-time semantic segmentation models and three object detection models specifically for aphid cluster segmentation and detection. Considering the balance between accuracy and efficiency, Fast-SCNN delivered the most effective segmentation results, achieving 80.46% mean precision, 81.21% mean recall, and 91.66 frames per second (FPS). For object detection, RT-DETR exhibited the best overall performance with a 61.63% mean average precision (mAP), 92.6% mean recall, and 72.55 on an NVIDIA V100 GPU. Our experiments further indicate that aphid cluster segmentation is more suitable for assessing aphid infestations than using detection models.

Development and Applicability Evaluation of Damage Scale Analysis Techniques for Agricultural Drought

In recent years, the intensity and frequency of droughts have been increasing with the advent of the climate crisis. Agricultural droughts have a significant economic and social impact. Agricultural drought is not only a natural disaster but also leads to food security threats and reduced economic activities, such as decreased productivity. Therefore, it is very important to specify the scale of agricultural drought and quantitatively estimate the economic damage. In this study, we developed an analytical methodology to quantitatively assess the economic damage of agricultural drought and estimated the damage of agricultural drought in 2018 and 2019 for the Republic of Korea. The 2018 agricultural drought was estimated to have caused USD 4.438 million in damage cost and USD 5.180 million in recovery cost. The 2019 drought was less damaging than the previous year, with an estimated damage cost of USD 286,000 and recovery costs of USD 218,000. The results suggest that the economic impact of agricultural drought varies by region depending on the frequency and intensity of the drought and confirm the importance of regional strategies for effective drought management and response. The impacts of agricultural drought go beyond short-term agricultural losses and lead to long-term economic burdens. Therefore, the results of this study are expected to be used as a basis for understanding the impacts of agricultural drought on national economies and for developing policies and strategies to minimize impacts.

A novel remote sensing-based approach to determine loss of agricultural soils due to soil sealing — a case study in Germany

Soils provide habitat, regulation and utilization functions. Therefore, Germany aims to reduce soil sealing to 30 ha day-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document} by 2030 and to eliminate it by 2050. About 55 ha day-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document} of soil are damaged (average 2018–2021), but detailed information on its soil quality is lacking. This study proposes a new approach using geo-information and remote sensing data to assess agricultural soil loss in Lower Saxony and Brandenburg. Soil quality is assessed based on erosion resistance, runoff regulation, filter functions, yield potential and the Müncheberg Soil Quality Rating from 2006 to 2015. Data from the German Soil Map at a scale of 1:200,000 (BÜK 200), climate, topography, CORINE Land Cover (CLC) and Imperviousness Layer (IMCC), both provided by the Copernicus Land Monitoring Service (CLMS), are used to generate information on soil functions, potentials and agricultural soil loss due to sealing. For the first time, soil losses under arable land are assessed spatially, quantitatively and qualitatively. An estimate of the qualitative loss of agricultural soil in Germany between 2006 and 2015 is obtained by intersecting the soil evaluation results with the quantitative soil loss according to IMCC. Between 2006 and 2015, about 73,300 ha of land were sealed in Germany, affecting about 37,000 ha of agricultural soils. This corresponds to a sealing rate of 11 ha per day for Germany. In Lower Saxony and Brandenburg, agricultural soils were sealed at a rate of 1.9 ha day-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document} and 0.8 ha day-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document} respectively, removing these soils from primary land use. In Lower Saxony, 75% of soils with moderate or better biotic yield potential have been removed from primary land use, while in Brandenburg this figure is as high as 88%. Implementing this approach can help decision-makers reassess sealed land and support Germany’s sustainable development strategy.

The effect of restricting groundwater exploitation on agriculture: Evidence from Huang-Huai-Hai region, China

Food security and groundwater conservation may conflict with each other due to high irrigation demands. This study aims to identify how groundwater conservation policies affect agriculture and the mechanisms of long-term adaptation. Our framework aggregates the stochastic frontier analysis, dynamic panel fixed effects model, and long-difference approach to evaluate the impact of restricting groundwater exploitation on agricultural total factor productivity, agricultural inputs, and outputs in the Huang-Huai-Hai area of China. The results demonstrate that, in the short run, the reduction in groundwater exploitation has a negative impact on agricultural total factor productivity, fertilizer and machines but promotes water input, which results in a more negative effect on agricultural output measured by yield. In the long term, restricting groundwater exploitation still implies a loss of agriculture. However, the long-run adaptation offsets 75% and 64% of the short-run effects on total factor productivity and yield, respectively. Despite more flexible adjustment in fertilizer and machines, long-term adaptation mitigates yield losses to a lesser extent than TFP due to water use constraints.

Developing a Nano Platform for Bovine Brucellosis Diagnostic Product

Background: Brucellosis is one of the most common zoonoses caused by Brucella species. Brucella is an intracellular pathogen that causes abortion in domestic animals and brucellosis in humans. The disease has no specific pathognomonic signs; hence a prompt and accurate diagnosis of the disease plays a very critical role in preventing brucella transmission and treating the disease.Methods: Research was carried out at the Department of Veterinary Science of Kazakh National Agrarian Research University and in the Molecular Biology laboratory of the Institute for Plant Biology and Biotechnology under the Committee of Science of the Ministry of Education and Science of the Republic of Kazakhstan. Studies were carried out on a killed culture of the vaccine strain Brucella abortus RB19, genomic DNA of B. abortus RB19, Grapevine virus A, plasmid vectors for DNA cloning, a fluorescent nano marker, and Brucella membrane proteins.Results: The research demonstrated that brucellosis remained one of the most common zoonotic diseases worldwide. This disease not only resulted in significant economic losses in agriculture but also posed a serious threat to public health.Conclusions: The study successfully established prokaryotic expression vectors for efficient protein production, validated through sequencing and Western blotting, and proposed a promising nano-platform for plant-based synthesis of brucellosis proteins, with potential implications for diagnostic kit development and vaccine research in agriculture.Keywords: Brucellosis; Brucella; Outer membrane proteins; Omp16

IMPACT OF SOIL STERILIZATION FROM DIFFERENT LOCATIONS, WHEAT SEEDS CLEANING AND THEIR INTERACTIONS ON WEED CONTROL, AND YIELD AND YIELD COMPONENTS

IMPACT OF SOIL STERILIZATION FROM DIFFERENT LOCATIONS, WHEAT SEEDS CLEANING AND THEIR INTERACTIONS ON WEED CONTROL, AND YIELD AND YIELD COMPONENTS

Tracing agricultural land carbon loss through transregional food consumption for fairness of responsibility allocation

Agricultural land carbon loss (ALCL) has been increasingly triggered by transregional food consumption in China. Understanding land carbon burden from consumption side is then recognized to be essential for facilitating regional fairness. To this end, we combined physical food-orient multi-regional input-output table and ALCL inventory for 26 kinds of foods in 31 provinces to trace China's ALCL through cross-regional consumption. Results show that ALCL embodied in inter-provincial trade was up to 42 % of the national total ALCL. Food consumption in Guangdong caused the largest ALCL, while Tibet and Qinghai stood out from per capita perspective. Furthermore, consuming foods produced in areas with higher production ALCL intensity would elevate national ALCL, thus shrinking the adverse flows and controlling agricultural land expansion in high-intensity areas may help lessen national ALCL. This study contributes to equitable allocation of responsibility for land carbon loss, and enlightens future land carbon loss risks and restoration opportunities.