Agricultural Management Systems Research Articles

Fire ant nests: Abundance and size in crops under different agricultural management systems and landscape compositions

Abstract The genus Solenopsis is widely distributed in the Neotropical region, mainly in South America. In Brazil, the species S. invicta and S. saevissima, known as fire ants, cause significant impacts, especially in agricultural areas in open environments with little vegetation. Soil cover and environmental variables can influence the dimensions and occurrence of nests of these species. Thus, this study assessed the effect of management (conventional or organic) and the dominant landscape (agricultural areas or native vegetation) on the occurrence and dimensions of S. invicta and S. saevissima nests on rural properties in the “Cinturão Verde Paulista” region, in southeastern Brazil. Our results indicate a higher abundance of S. invicta nests in agricultural areas with conventional management, while S. saevissima nests are more abundant in areas with organic management. The morphological study revealed that the tallest nests of S. saevissima are predominantly found in agricultural landscapes, whereas management practices and landscape type appear to have no significant influence on S. invicta nesting dimensions. These findings contribute to our understanding of the ecological dynamics of S. saevissima. They also provide insights into potential strategies for population control and highlight the importance of considering both management systems and landscape factors when studying invasive species, such as S. invicta.

Artificial intelligence-based smart galliformes farm management system

Artificial intelligence-based smart galliformes farm management system

Biochar Amendment as a Mitigation Against Freezing–Thawing Effects on Soil Hydraulic Properties

Seasonal freeze–thaw cycles compromise soil structure, thereby increasing hydraulic conductivity but diminishing water retention capacity—both of which are essential for sustaining crop health and nutrient retention in agricultural soils. Prior research has suggested that biochar may alleviate these detrimental effects; however; further investigation into its influence on soil hydraulic properties through freeze–thaw cycles is essential. This study explores the impact of freeze–thaw cycles on the soil water retention and hydraulic conductivity and evaluates the potential of peanut shell biochar to mitigate these effects. Peanut shell biochar was used, and its effects on soil water retention and unsaturated hydraulic conductivity were evaluated through evaporation tests. The findings indicate that freeze–thaw cycles predominantly affect clay’s ability to retain water and control hydraulic conductivity by generating macropores and fissures; with a notable increase in conductivity at high matric potentials. The impact lessens as matric potential decreases below −30 kPa, resulting in smaller differences in conductivity. Introducing biochar helps mitigate these effects by converting large pores into smaller micro- or meso-pores, effectively increasing water retention, especially at higher content of biochar. While biochar’s impact is more pronounced at higher matric potentials, it also significantly reduces conductivity at lower potentials. The total porosity of the soil increased under low biochar application rates (0% and 1%) but declined at higher application rates (2% and 3%) as the number of freeze–thaw cycles increased. Furthermore, the characteristics of soil deformation during freeze–thaw cycles shifted from frost heaving to thaw settlement with increasing biochar application rates. Notably, an optimal biochar application rate was observed to mitigate soil deformation induced by freeze–thaw processes. These findings contribute to the scientific understanding necessary for the development and management of sustainable agricultural soil systems.

First report of Anaplasma marginale and Anaplasma ovis in goats in Kelantan, Malaysia.

Anaplasma species are obligate rickettsial intraerythrocytic pathogens that cause an important tick-borne disease of economic importance in livestock production in many countries. Anaplasma species have been detected from farm animals worldwide, there is a paucity of information on Anaplasma infections in goats from Malaysia. Thus, this study aimed to assess the infection rate and identify Anaplasma species and some selected risk factors in goats across selected districts in Kelantan, Malaysia. A total of 411 blood samples were collected from goats and analysed for Anaplasma species targeting the msp4 gene using conventional PCR and sequencing. The infection risk was determined by breed, age, management system and location. Our results indicate an overall infection rate of 30.9% for Anaplasma species detected. Interestingly, sequencing of selected amplicons revealed the presence of Anaplasma ovis and A. marginale. Data analysis revealed a marked statistically significant association between Anaplasma infection and some variables such as location (district), farm management system, breed, and age (P < 0.05). Specifically, goats raised on intensive management had the highest prevalence of 46.25% (37/80) compared to other management types. Also, with regards to district, goats raised in the coastal region had a higher prevalence of 39.23% (71/181) compared to those raised in inland region 24.35% (56/230). Regarding breed, goats that were of the pure breed had a higher prevalence of Anaplasma species infection 38.19% (97/254) compared to crossbreeds with a prevalence of 19.11% (30/157). Lastly, goats 3 years had the least prevalence 18.99% (34/179). To the best of our knowledge, this is the first report of A. marginale and A. ovis in goats from northeastern Peninsular Malaysia. The infected goats were clinically healthy; this revealed the role of goats as a potential reservoir for A. marginale and the presence of A. ovis in goats in Malaysia. Continuous efforts towards tick control must be sustained to ensure high productive yield and reduced disease burden associated with TBPs of goats in the study area.

Organic management shapes AM fungal community structure and function, partially mitigating the negative effects of conventional agriculture

Abstract Arbuscular mycorrhizal (AM) fungi are important plant symbionts that provide plants with nutrients and water as well as support plant defences against pests and disease. Consequently, they present a promising alternative to using environmentally damaging and costly fertilisers and pesticides in agricultural systems. However, our limited understanding of how agricultural practices impact AM fungal diversity and functions is a key impediment to using them effectively in agriculture. We assessed how organic and conventional agricultural management systems shaped AM fungal communities. We also investigated how AM fungal communities derived from these agricultural management systems affected crop biomass and development. Six soil samples from five organically and five conventionally managed agricultural sites were used to cultivate Sorghum bicolor. Plant growth, plant nutrient concentrations and AM fungal colonisation rates were analysed alongside DNA metabarcoding of community composition. We observed that soil from conventional agricultural fields resulted in a pronounced reduction in sorghum biomass (−53.6%) and a significant delay in flowering compared to plants grown without AM fungi. Sorghum biomass was also reduced with soil from the organic system, but to a lesser extent (−30%) and without a delay in flowering. Organic systems were associated with a large proportion of AM fungal taxa (50.5% of VTs) not found in conventional systems, including Diversispora (r2 = 0.09, p &lt; 0.001), Archaeospora (r2 = 0.07, p &lt; 0.001) and Glomus (r2 = 0.25, p &lt; 0.001) spp., but also shared a large proportion of taxa with conventional systems (42.3% of VTs). Conventional systems had relatively few unique taxa (7.2% of VTs). Our results suggest that conventional agricultural practices selected against AM fungi that were, in this context, more beneficial for host plants. In contrast, organic management practices mitigate this negative effect, likely due to the presence of specific key AM fungal taxa. However, this mitigation is only partial, as less beneficial AM fungal taxa still persist, probably due to abiotic factors associated with agricultural management and the sensitivity of AM fungi to these factors. This persistence explains why the effect is not entirely eradicated. Read the free Plain Language Summary for this article on the Journal blog.

Design and Application of Agricultural Integrated Management System Based on TLINK

With the continuous advancement of science and technology, the era of the Internet of Everything has arrived. Agricultural management is becoming more and more intelligent and refined. Agricultural Internet of Things technology is vital in data collection, transmission, user management, etc. To achieve a highly integrated and low-cost smart agriculture with data display. This paper designs an intelligent agricultural management system based on low-cost ESP8266 WIFI Internet of Things technology, aiming to transform traditional agricultural management into a smart, remote, and data-user collaborative mode. The system integrates sensor nodes such as temperature, humidity, light, and carbon dioxide to replace traditional industrial sensors for real-time agricultural environmental data collection, which can realise the automation of irrigation, ventilation, temperature control, lighting, etc. It can start the ecological control system through local voice control. WIFI data transmission, data management through the TLINK cloud platform, PC or mobile phone APP, online remote data viewing and control at any time for agricultural management. The experimental results show that the system has good stability, reliability, high integration, strong environmental adaptability, accurate data, and less packet loss. It dramatically reduces the cost of intelligent agricultural management and construction difficulty, bringing users higher economic benefits.

Effects of a Multifunctional Cover Crop (LivinGro®) on Soil Quality Indicators in Zaragoza, Spain

Soil degradation is a significant threat to agricultural systems and contemporary societies worldwide, especially in the context of climate change. Proper management of agricultural systems is a priority for maintaining food security and achieving sustainable development. It is therefore important to assess the efficacy of different interventions that are designed to improve the quality of agricultural soils. Measurements of physical, chemical, and biological indicators of soil quality can be used to examine the efficacy of strategies or methods that were designed to prevent soil degradation. We measured seven physicochemical indicators of soil quality at a representative experimental plot of nectarines in the province of Zaragoza (Spain) over three years (2020–2023) and compared the effect of a multifunctional cover crop (LivinGro® MCC, Basel, Switzerland) with conventional treatment (control) on soil quality. Soil samples were collected every two months from the treelines and inter-rows (paths for farming vehicles). In general, the MCC zones in the treelines and inter-rows had better soil health, especially in key indicators such as basal soil respiration, organic matter, nitrogen, and porosity. Climatic variability, especially seasonal differences in rainfall, also affected multiple soil indicators. During many sample periods, the MCC zones of the treelines and inter-rows had significantly increased soil organic matter, basal respiration, total nitrogen, nitrate, total porosity, and available water content, but the MCC and control zones had no significant differences in bulk density. The differences between the MCC zones and control zones, especially in basal soil respiration, were greater during the wet seasons. Our results indicate that the LivinGro® MCC prevented degradation of agricultural soils in a region with a continental Mediterranean climate.

The Unsung Champions of Evolution: Weeds and Their Management in Agricultural Systems

The history of weeds is closely intertwined with the development of agriculture. As early farmers began to select crops, weeds emerged as formidable competitors, evolving alongside them in a continuous struggle for survival. Over the past 23,000 years, while crops have been bred for human purposes, weeds have continuously adapted, thriving despite rigorous weed management practices. This coevolution highlights the remarkable ability of weeds to evolve rapidly, establishing them as true champions of adaptation. Herbicide resistance, in particular, exemplifies this phenomenon, with weeds developing both target-site resistance (TSR) through genetic mutations and non-target-site resistance (NTSR) through metabolic changes. Often, these mechanisms combine, resulting in heightened levels of resistance, especially within herbicide-resistant crop systems. The evolutionary success of weeds not only underscores their adaptability but also emphasizes their critical role in supporting biodiversity and ecosystem resilience, particularly in simplified agricultural landscapes. This chapter focuses on the evolution of weed management strategies and emphasizes the urgent need to transition to ecologically based weed management (E-bWM) approaches that balance productivity with sustainability, recognizing the ecological value of weeds in fostering resilient agricultural systems.

Analisis Permeabilitas Tanah Berpasir Dan Tanah Lempung Dalam Hubunganya Dengan Manajemen Irigasi

This research examines the permeability characteristics of sandy and clay soils and their implications for irrigation management in agricultural systems. Soil permeability is a key factor in determining water use efficiency and optimal irrigation strategies. The research methodology includes laboratory analysis of sandy and clay soil samples from various agricultural locations, measuring physical soil characteristics, infiltration rates, water storage capacity, and hydraulic conductivity. The results show significant differences between the two soil types, where sandy soil has high permeability ranging from 10^-3 to 10^-2 cm/second, low water storage capacity, and rapid infiltration rates, thus requiring more frequent irrigation with smaller volumes. In contrast, clay soil has low permeability ranging from 10^-7 to 10^-6 cm/second, high water storage capacity, and slow infiltration rates, requiring less frequent irrigation with larger volumes. Based on these results, specific irrigation management recommendations were developed that include irrigation schedule adjustments, water volume regulation, selection of appropriate irrigation methods, and water conservation techniques. This research provides important contributions to optimizing irrigation water use based on soil permeability characteristics, which can improve water use efficiency and agricultural productivity.

Large-Scale Maize Condition Mapping to Support Agricultural Risk Management

Crop condition mapping and yield loss detection are highly relevant scientific fields due to their economic importance. Here, we report a new, robust six-category crop condition mapping methodology based on five vegetation indices (VIs) using Sentinel-2 imagery at a 10 m spatial resolution. We focused on maize, the most drought-affected crop in the Carpathian Basin, using three selected years of data (2017, 2022, and 2023). Our methodology was validated at two different spatial scales against independent reference data. At the parcel level, we used harvester-derived precision yield data from six maize parcels. The agreement between the yield category maps and those predicted from the crop condition time series by our Random Forest model was 84.56%, while the F1 score was 0.74 with a two-category yield map. Using a six-category yield map, the accuracy decreased to 48.57%, while the F1 score was 0.42. The parcel-level analysis corroborates the applicability of the method on large scales. Country-level validation was conducted for the six-category crop condition map against official county-scale census data. The proportion of areas with the best and worst crop condition categories in July explained 64% and 77% of the crop yield variability at the county level, respectively. We found that the inclusion of the year 2022 (associated with a severe drought event) was important, as it represented a strong baseline for the scaling. The study’s novelty is also supported by the inclusion of damage claims from the Hungarian Agricultural Risk Management System (ARMS). The crop condition map was compared with these claims, with further quantitative analysis confirming the method’s applicability. This method offers a cost-effective solution for assessing damage claims and can provide early yield loss estimates using only remote sensing data.

Assessing Ammonia (NH₃) Emissions, Precursor Gas (SO2, NOx) Concentrations, and Source Contributions to Atmospheric PM2.5 from a Commercial Manure Composting Facility

Increased ammonia (NH3) emissions from intensive agriculture negatively affect environmental and ecosystem health, contributing to formation of particulate matter (PM) and the potent greenhouse gas, N2O. Better understanding NH3 emissions from the manure composting process and their behavior as a constituent of the atmospheric aerosol load is a crucial element in creating better farm management systems, improving public health outcomes, and mitigating the broader environmental and climatic impacts of agriculture. Retarded generation of PM with a major constituent source of NH3 is a primary mechanism for evaluating the effects of agricultural contribution to PM. This study aimed to quantify NH3 emissions, examine the influence of environmental factors, and investigate the relationship between precursor gases (SO2, NOx, NH3) and PM2.5 at a modern manure composting facility in Paju, South Korea. Over 35 days, average internal concentrations of NH3, SO2, and NOx were significantly higher than external levels. NH3 concentrations reached 3.64 ± 0.06 mg m−3 at 3 m height and 2.43 ± 0.16 mg m−3 at ground level, while the total NH3 flux from the facility was 24.47 ± 1.39 NH3-N kg d−1. Internal PM2.5 concentrations (36.9 ± 2.6 µg m−3) were about 50% higher than external levels (23.7 ± 2 µg m−3), with a moderate correlation (r = 0.341) suggesting some contribution of external PM2.5 to internal levels. Despite large quantities of internal emissions, the facility’s sealed design with a negative pressure ventilation system effectively minimized external emissions. These results suggest that while manure composting facilities are significant sources of NH3 and PM2.5, advanced systems like high-volume ventilation and scrubbing technologies can effectively reduce their impact on regional air pollution, contributing to better environmental management in agriculture.

Toward a transdisciplinary and unifying definition of legacy phosphorus.

Legacy phosphorus (P) is a concept advanced by Dr. Andrew Sharpley and colleagues that was originally applied to the persistence of anthropogenic signatures in watersheds, and it has since been adopted in a diversity of settings to help guide the science and management of P. Following Sharpley's example to develop consensus-based science, we considered contrasting perspectives on legacy P and defined legacy P as those stores within the environment that arise from historic human activity excluding "natural" or "background" geogenic sources. Legacy P is not restricted to one system or setting; it may reside in soils, sediments, biota, and water bodies. Legacy P has been estimated by fluxes (inputs minus outputs of P to a system) or, equivalently, by mass stocks (total minus geogenic). Because the origin of P in the environment cannot currently be directly quantified, we recommend that researchers report "total P" to track wider watershed P stocks and fluxes of P that include legacy P. We recognize that the definition of legacy P will continue to evolve as we continue to work toward consensus. Ultimately, the final definition of legacy P has consequences for the implementation and success of regulatory and voluntary strategies for legacy P management in agricultural systems. We support continued progress toward a consensus-backed, research-grounded definition for legacy P that is widely applicable yet useful for guiding management and policy.

AI Driven Animal Farming and Livestock Management System

AI-Driven Animal Farming and Livestock Management System represents a pivotal advancement in agricultural technology, promising to revolutionize traditional farming practices by integrating artificial intelligence (AI) into livestock management. This abstract presents an overview of the system's multifaceted capabilities, emphasizing its role in guiding animal farmers towards optimal livestock care, enhancing marketing strategies, and offering a suite of advanced functionalities. At its core, this system employs sophisticated AI algorithms like Natural Language Processing (NLP) and Convolutional Neural Networks (CNNs) to provide personalized guidance to animal farmers, ensuring they adhere to best practices in livestock care. Through real-time monitoring and data analysis, the system offers actionable insights into nutrition, health, and reproduction management, thereby maximizing the well-being and productivity of livestock. Furthermore, the System incorporates innovative features tailored to streamline marketing efforts. By analyzing market trends, consumer preferences, and supply chain dynamics, the system enables farmers to make informed decisions regarding product positioning, pricing strategies, and distribution channels, thereby enhancing market competitiveness and profitability

Exploring the thermal dynamics of mango leaf hopper (Cicadellidae: Homoptera) populations and evaluating insecticide susceptibility

Effective pest management in agricultural systems requires understanding environmental conditions and chemical solutions. The efficacy of lambda cyhalothrin, imidacloprid, and acephate for mango leaf hopper control was examined in relation to temperature. A controlled laboratory setup with a Bio-Oxygen-Demand (B.O.D) incubator maintained temperatures of 25 ºC, 22 ºC, and 29 ºC for the experiment. The investigated pesticides' toxicity varied with temperature. March 2022, October 2022, and July 2023 lambda cyhalothrin LD50 values were 0.524, 0.412, and 0.992 ppm at 25 ºC. In the same timeframes, imidacloprid showed LD50 values of 0.797, 1.187, and 1.774 ppm. Acephate has 0.890, 1.572, and 1.927 ppm LD50s. Piperonyl butoxide (PBO), N-(Propargyloxy) phthalimide (PP), diethyl maleate (DEM), and S,S,S-tributyl phosphorotrithioate (DEF) were also tested for synergy. DEM and the studied insecticides had synergistic ratios, suggesting improved pest control tactics. The data showed that temperature, pesticide effectiveness, and synergistic interactions promote sustainable mango pest management.

A technical survey on practical applications and guidelines for IoT sensors in precision agriculture and viticulture

Climate change pose significant challenges to modern agriculture management systems, threatening food production and security. Therefore, tackling its effects has never been so imperative to attain sustainable food access and nutrition worldwide. In the case of viticulture, besides jeopardizing grape production, climate change has severe impact in quality, which has becoming more challenging to manage, due to the increasingly frequent fungal contamination, with consequences for relevant quality parameters such as the aromatic profiles of grapes and wines and their phenolic compounds. This has been leading to a reconfiguration of the wine industry geostrategic landscape and economy dynamics, particularly in Southern Europe. To address these and other emerging challenges, in-field deployable proximity-based precision technologies have been enabling real-time monitoring of crops ecosystems, including climate, soil and plants, by performing relevant data gathering and storage, paving the way for advanced decision support under the Internet of Things (IoT) paradigm. This paper explores the integration of agronomic and technological knowledge, emphasizing the proper selection of IoT-capable sensors for viticulture, while considering more general ones from agriculture to fill gaps when specialized options are unavailable. Moreover, advisable practices for sensor installation are provided, according to respective types, data acquisition capabilities and applicability.

Understanding Cassava Ethnovarieties in the Brazilian Amazon: Harnessing Traditional Knowledge For Food Security and Sustainable Agriculture

Objective: To characterize ethnovarieties of cassava (Manihot esculenta Crantz) in traditional communities on the border between Juruti (Pará) and Parintins (Amazonas), focusing on the diversity and management practices associated with the cultivation of this plant in the Brazilian Amazon. Theoretical Framework: Cassava is vital for food security and the economy in the Brazilian Amazon. The loss of traditional varieties or ethnovarieties in border communities in Amazonas and Pará is a reality. By focusing on morphological descriptors, ancestry and management practices, we can identify the use of these varieties and thus highlight ecological and diverse adaptations in the region. Method: With ethical approval and community consent, ethnobotanical interviews, farm walks and participatory analysis were carried out in four communities. Morphological descriptors were used, such as leaf shape and root pulp color, as well as climatic variations, to understand the relationship between traditional knowledge and the adaptation of local varieties to the conditions. Results and Discussion: The data was analyzed using PCA (Principal Component Analysis) and a statistical dendrogram, which revealed high similarity between areas 2 and 3, followed by area 1, indicating similar agricultural practices and environments. This diversity of ethnovarieties reflects an ancestral and consolidated agricultural management system, showing a deep cultural identity and knowledge about the adaptability of varieties to the region. Research Implications: In this research, the importance for community members to conserve ethnovarieties in situ for social justice resistance, income and culinary quality, important factors for the conservation and sustainable use of agrobiodiversity, can be seen in the localities. Originality/Value: In order to give greater value to the use and conservation of these ethnovarieties, this research shows the potential for genetic improvement programs and public policies in these locations aimed at food security and strengthening family farming in the Amazon.

Predictive Modeling in IoT-Driven Agriculture: A Comparative Study of Regression and Classification with LIME Interpretability

Abstract. This study aims to compare the performance of regression and classification machine learning models in crop prediction by analyzing data from IoT-driven agriculture. Linear regression and random forest regression models were used to predict the percentage of root growth dry matter, while logistic regression and support vector machines were employed to classify crop production regions. To enhance model interpretability, the LIME tool was applied to analyze feature importance. The experimental results demonstrate that the models perform well in terms of prediction accuracy, and LIME provided clear feature explanations, helping identify the variables with the greatest impact on prediction outcomes. This research offers data-driven insights for optimizing resource management in smart agricultural systems.

The Development of Bioindustry Technology in Realizing IoT-Based Sustainable Agricultural Industry Waste Management in West Java

The threat of climate change is increasing, especially the damage to nature caused by pollution in the agricultural sector. The impact of agricultural waste that is not managed optimally can reduce ecosystem function and disrupt food security stability. This research/research aims to mapping, analyzing, and developing Internet of Things (IoT)-based bioindustry technology in order to create an efficient, green and sustainable agricultural industry. This research uses quantitative methods with agroecology theory related to the development of agricultural waste management systems. The benefits of anaerobically processing agricultural waste such as in the West Java region is to create an optimal, efficient and land-saving food production system. Biotechnology based on the IoT automation system is a technology that can control agricultural food production waste by composting waste into new product that can be used. Therefore, we conducted research on agricultural waste, namely husk waste and cow blood water from slaughterhouses. The end result of the waste treatment is Bokashi Fertilizer and Liquid Organic Fertilizer by using biotechnology technology.

Research on the Development and Management of Ecological Agriculture Characteristic Tourism Based on Artificial Intelligence Technology for Water Resources

In order to improve the effectiveness of ecological agriculture tourism development and management of water resources, this article combines artificial intelligence technology to study the development and management of ecological agriculture tourism. This article conducts a hierarchical analysis based on the characteristics of tourism symbol aggregation and spatial distribution patterns within a certain period of time, determines the threshold for extracting the optimal tourism behavior boundary, and strives to solve the problems caused by the complexity and dynamism of tourism behavior. In addition, this article also developed an intelligent water resources agricultural characteristic tourism management system to improve the development effect of agricultural characteristic tourism and promote precise control of water conservancy irrigation systems in ecological agricultural tourism areas. Finally, this article combines the proposed algorithm model to construct an ecological agriculture characteristic tourism development and management system. Simulation evaluation shows that the ecological agriculture characteristic tourism development and management model based on artificial intelligence technology proposed in this paper has good effects.

Farmer Management System using Integrated Chatbot

The main aim of developing “Farm Management System Project” application is to help farmers by providing all kinds agriculture related information in the site. “Farm Management System Project” is web application which helps farmers to share best- practice farming processes. It helps farmers to improve their productivity and profitability. It enables farmers to sell their products online and farmers can purchase tools and seeds directly from seller. Farmers can view their profile and they can register, edit and delete data. The farmers can sell their productions online and the buyer can purchase various agricultural products online. Buyer can send purchase request to check the quality of the Agricultural product through mails.