Traditional Farming Methods Research Articles

Ai-driven innovations in greenhouse agriculture: Reanalysis of sustainability and energy efficiency impacts

In the context of greenhouse agriculture, the integration of Artificial Intelligence (AI) is evaluated for its potential to enhance sustainability and crop production efficiency. This study reanalyzes publicly available datasets, using advanced time series analysis and noise reduction techniques through seasonality detection and removal. This novel approach reveals trends more clearly, providing a detailed comparison between AI-driven methods and traditional agricultural practices. An extensive review of literature on AI applications in agriculture is conducted to establish a broad understanding of its current state and future prospects. The core focus is the Autonomous Greenhouses Challenge, an initiative where research teams apply AI technologies in real-world greenhouse settings. This challenge offers crucial data for a thorough assessment of AI’s practical impact. The analysis reveals that AI significantly reduces heating energy consumption, indicating a notable improvement in energy efficiency. However, reductions in CO2 emissions, along with improvements in electricity and water usage, are only marginal when compared to traditional farming methods. Similarly, enhancements in crop quality and profitability achieved through AI are found to be on par with conventional techniques. These findings highlight the dual nature of AI’s impact in greenhouse agriculture: it shows significant promise in some areas, while its effectiveness in other key sustainability aspects remains limited. The study emphasizes the need for further research and investment in technological advancements, as well as the importance of a robust data infrastructure. It also highlights the necessity of education and training in AI technologies for effective implementation in the agricultural sector. The results of this research aim to inform policymakers, researchers, and industry stakeholders about the mixed impacts of AI on sustainable greenhouse farming. By offering a comprehensive evaluation of the benefits and challenges of AI integration, this study contributes to the ongoing discussion on sustainable agricultural practices and provides insights into the future direction of AI in this field.

In-depth exploration of nanoparticles for enhanced nutrient use efficiency and abiotic stresses management: Present insights and future horizons

Nanotechnology is an innovative method of elevating agricultural output without sacrificing quality due to nanoparticles (NPs) unique characteristics and numerous potential uses. It is also nature-friendly, advantageous to living organisms, and cost-effective. Sustainable agricultural practices are gaining attention on NPs and nanofertilizers (NFs) as practical substitutes for traditional fertilizers and pesticides that nanotechnology could surpass some of the issues with traditional farming methods. There should be an emphasis on cutting-edge studies of NPs applications in agriculture. This article presents a positive perspective on the mechanisms leading to the formation of NPs and their application as NFs for managing nutrients in agriculture. We also share up-to-date findings on NPs interactions with plants, the fate of NPs and potential risks associated with them in plants. The as well as on the role of NPs nanomaterials in decreasing abiotic and heavy metal toxicity stress. NFs help reduce the environmental damage caused by traditional, inorganic fertilizers. Due to their enhanced responsiveness and ability to pierce the epidermis, NFs can decrease nutrient surplus while increasing nutrient usage efficiency. It was also established that NPs are essential for protecting against abiotic stress. However, some studies have shown that NPs are harmful to higher plants because the NPs they are deposited upon the surface of cells or in the cell organelles, leading to oxidative stress symptoms. In this review article, we explore the utilization of NPs for nutrient and abiotic stress management for crop production and protection during the climate change era.

Revolutionizing Agritech with Deep Learning-Enhanced Remote Sensing for Precision Agricultural Management

A new method of farm management that makes use of cutting-edge information technology is known as precision agriculture management. By reducing the waste of water, fertilizers, pesticides, fuel, and other types of agricultural inputs, as well as by optimizing agrarian income and limiting adverse effects on the environment, precision agriculture management seeks to boost agricultural output and quality. Traditional agricultural management methods often lead to inefficient use of resources, higher environmental impacts, and decreased crop yields. Finding efficient, scalable, and accurate ways to track important agricultural variables over large regions is a huge challenge. This paper proposes a new framework called ARS-DLConvNN to handle these issues and enhance the management of agritech and precise agriculture. This framework Integrates High-Resolution Agricultural Remote Sensing (ARS) data and Deep Learning (DL) methods like Convolutional Neural Networks (ConvNN). This approach uses Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) network specifically designed to handle hyperspectral data collected from UAVs and multi-spectral satellite photos. These algorithms can assess crop stress in real-time, provide recommendations on effectively managing water, fertilizer, and insects, and learn to estimate weight and yield. It will be quite easy to see how the proposed method improves agriculture management effectiveness and harvest yields. Improved agricultural management efficiency and crop production were striking when using the suggested deep-learning remote sensing system. On average, crop yields increased by 18% compared to traditional methods, according to field trials in various locations. decrease output. With a 92% success rate in detecting crop illnesses early and an 89% success rate in forecasting water stress, the model allowed for prompt treatments.

Strategies for Achieving High and Sustainable Plant Productivity in Saline Soil Conditions

The accumulation of salt in arable lands is a source of significant abiotic stress, contributing to a 10% decline in the world’s total arable lands and threatening food productivity and the sustainability of agriculture. About 76 million hectares of productive land are estimated to have been affected by human-induced salinization such as extreme salt deposits in soil, which are mainly caused by the actions of humans. For instance, continued irrigation and the frequent use of chemical fertilizers need to be understood. To ensure food availability, it is essential to improve upon traditional farming methods using current technologies to facilitate the reclamation of saline-affected arable lands to achieve high and sustainable food production. This review details current innovative strategies such as the modification of metabolic pathways, manipulation of antioxidant pathways, genetic engineering, RNA interference technology, engineered nanoparticles, arbuscular mycorrhizal fungi (AMF), organic amendments, and trace elements for improving saline marginal lands. These strategies were identified to have contributed to the improvement of plants salinity tolerance in diverse ways. For instance, the accumulation of plant metabolites such as amino acids, sugars, polyols, organic acids, saponins, anthocyanins, polyphenols, and tannins detoxify plants and play crucial roles in mitigating the detrimental effects of oxidative damage posed by salinity stress. Multiple plant miRNAs encoding the up- and down-regulation of single- and multi-ion transporters have been engineered in plant species to enhance salt tolerance. Nanomaterials and plant root system colonized by arbuscular mycorrhizal increase water uptake, photosynthetic efficiency, and biomass allocation in plants exposed to saline stress by excluding 65 percent of the Na+ uptake and enhancing K+ uptake by 84.21 percent. Organic amendments and trace elements reduced salinity concentrations by 22 percent and improved growth by up to 84 percent in maize subjected to salinity stress. This study also discusses how researchers can use these strategies to improve plants growth, development, and survival in saline soil conditions to enhance the productivity and sustainability of agriculture. The strategies discussed in this study have also proven to be promising approaches for developing salinity stress tolerance strategies for plants to increase agricultural productivity and sustainability.

Beyond the hype: using AI, big data, wearable devices, and the internet of things for high-throughput livestock phenotyping.

Phenotyping of animals is a routine task in agriculture which can provide large datasets for the functional annotation of genomes. Using the livestock farming sector to study complex traits enables genetics researchers to fully benefit from the digital transformation of society as economies of scale substantially reduces the cost of phenotyping animals on farms. In the agricultural sector genomics has transitioned towards a model of 'Genomics without the genes' as a large proportion of the genetic variation in animals can be modelled using the infinitesimal model for genomic breeding valuations. Combined with third generation sequencing creating pan-genomes for livestock the digital infrastructure for trait collection and precision farming provides a unique opportunity for high-throughput phenotyping and the study of complex traits in a controlled environment. The emphasis on cost efficient data collection mean that mobile phones and computers have become ubiquitous for cost-efficient large-scale data collection but that the majority of the recorded traits can still be recorded manually with limited training or tools. This is especially valuable in low- and middle income countries and in settings where indigenous breeds are kept at farms preserving more traditional farming methods. Digitalization is therefore an important enabler for high-throughput phenotyping for smaller livestock herds with limited technology investments as well as large-scale commercial operations. It is demanding and challenging for individual researchers to keep up with the opportunities created by the rapid advances in digitalization for livestock farming and how it can be used by researchers with or without a specialization in livestock. This review provides an overview of the current status of key enabling technologies for precision livestock farming applicable for the functional annotation of genomes.

INTENSITY OF GREENHOUSE GAS FORMATION DUE TO LIVESTOCK FARMING ACTIVITIES – AS AN ENVIRONMENTAL FACTOR

According to numerous expert assessments by international organizations and specialists, animal husbandry makes a significant negative contribution to global climate change due to the emission of greenhouse gases (GHG), which are formed at different stages of livestock production as a result of various chemical and biological processes in the body of animals and in livestock waste. The domestic animal husbandry is developing mainly due to the intensification of production in the industry, but traditional farming methods as well as small-scale production in the individual sector also take place. Since the use of various technologies in animal husbandry has different effects on the level of environmental pollution and GHG emissions, the aim of the research was to study the differences in the intensity of GHG gas formation by one animal reared with individual features of animal rearing technologies and business activities. The intensity of gas formation and emission of CH4 and N2O in typical farms for pork production and milk production by one animal reared was determined, analyzed and substantiated. A significant variation in this indicator was found depending on the individual economic and technological features of the studied farms. The average weighted annual intensity of CH4 emission from animal manure in pig farms varied within the range of 0.95–25.71, in cattle farm – 2.74; CH4 from intestinal fermentation of dairy cows – 110.8–148.4; N2O (direct) in pig farms – 0.0–0.106, in cattle farm – 0.229; N2O (indirect) in pig farms – 0.071–0.097, in cattle farm – 0.174. The emission intensity is characterized separately in each age and sex group of animals in the herd structure of farms and the average weighted emission intensity in pork producing farms depending on the season. Based on the research results, it is proposed to use the generalized average annual indicator of greenhouse gas emissions per one average weighted animal reared (kg/head/year) as an indicator of the environmental load of livestock farms on the environment, which will allow planning production volumes with minimal environmental risks in the context of climate change.

From Fields to Futures: Examining the Socioeconomic Shifts from Cardamom Cultivation in Eastern Nepal

Large-scale cardamom cultivation in Eastern Nepal is primarily concentrated in six districts, namely Dhankuta, Taplejung, Panchthar, Ilam, Terhathum and Sankhuwasabha, contributing over 80% of the national production and engaging approximately 60,000–70,000 families. The study aims to investigate how cardamom farming has impacted farmers' livelihoods compared to traditional farming methods and how the volatility of cardamom prices underscores the complex dynamics of capitalist market structures. This study focused on Ward No. 2 of Mahalaxmi Municipality in Dhankuta District and spans a five-year period from 2019 to February 18, 2024. Through census-based data collection from 27 households using interviews and observations, the research examined the impact of cardamom production on local livelihoods. The analysis incorporates insights from Marx's Mode of Production, along with perspectives from Jagannath Adhikari, Mark Liechty, and Ian Carlos Fitzpatrick. The findings reveal that earnings from cardamom cultivation did not only elevate living standards but also encouraged self-sufficiency and reduced reliance on foreign employment through borrowing, highlighting changing economic opportunities, self-esteem, and job prospects within the community. The study reveals that cardamom farming has the potential to significantly influence farmers' economic stability and highlights the need for better market strategies and government policies to address price volatility and enhance cardamom productivity.

Flow prediction of irrigation well pump based on experimental and big data analytics

Abstract Currently, China’s agricultural irrigation consumes a huge amount of water, and traditional agricultural irrigation methods lead to low irrigation efficiency and serious water resource waste. Agricultural irrigation water management is also difficult to achieve refined management due to the lack of accurate monitoring of water use data and information in various irrigation areas. To find a more convenient flow measurement method, this paper proposes using data-driven monitoring of water pump characteristic parameters to predict flow rate. Three big data-based methods for predicting the flow rate of irrigation well pumps were compared, including RBF neural network, Support Vector Machine (SVM), and Extreme Learning Machine (ELM). The results indicate that the Extreme Learning Machine (ELM) model not only has short time consumption but also high prediction accuracy, laying the foundation for the application of big data technology in water management and high-quality irrigation water management that can save water.

Yield and growth duration of Maroon rice landraces measured in traditional settings

AbstractRice, the most consumed cereal worldwide, has two domesticated species: Asian and African rice. Commercial cultivars, almost all Asian rice, are mostly selected for high yields. Traditional landraces are often selected for different traits, such as time to maturity or resistance against common stress factors, including bird attacks and poor soils. Maroons, descendants of enslaved Africans in Suriname and French Guiana, cultivate a rich diversity of rice landraces. They categorize landraces into short, medium, and long-maturation and maintain that fast-ripening crops with moderate yields facilitated their flight from enslavement, while once settled in a safe place, there was time for landraces with longer ripening periods and higher yields. Previous authors assumed that all Maroon landraces had low yields (700–1000 kg/ha), but their performance in traditional farming systems had never been investigated. We followed the growth and harvest of 28 Maroon rice landraces and two commercial cultivars in Maroon farmer fields, using traditional farming methods, at three locations. We show that, in farmer-managed fields, Maroon rice can yield 2600 kg/ha (average 1665 kg/ha), without any agrochemicals or machinery. Contrastingly, the commercial cultivars in Maroon farmer fields had a low yield (625–1205 kg/ha), partly due to bird predation. The maturation time varied between 110 and 183 days, but the three Maroon maturation categories showed significant overlap. Our study indicates that Maroon rice performance can only be fairly evaluated if measurements are taken in traditional Maroon farming systems, since this is the environment in which these landraces were originally selected.

Comparative evaluation of machine learning algorithms for rainfall prediction to improve rice crops production

Rainfall has a huge impact on agriculture because it's one of the key causes of crops devastation. Farmers face a slew of issues when unexpected heavy rains fall, as their planted crops are washed away or damaged. Pakistan is an agricultural country where new methods and techniques are needed to improve the traditional farming methods. This research intends to provide aid in the protection of crops from severe rains using machine learning to accurately anticipate the possibility of rainfall, which is a well-known agricultural problem. Various weather factors such as temperature, humidity, and atmospheric pressure can be used to predict rainfall patterns. Rainfall prediction can be used to identify and furnish future rainfall descriptions for agricultural planning for food security, allowing farmers to take precautionary measures to safeguard rice fields. Naïve Bayes, LogitBoost, RIPPER, Decision Stump, AdaBoost, Random Forest, Artificial Neural network, and K* were evaluated for rainfall prediction based on accuracy, precision, recall, F1-measure, Root Mean Squared Error, area under receiver operating characteristic curve, elapsed training time and elapsed testing time. The results obtained indicate that the best performance is achieved by Random forest with maximum accuracy of 83.2%, followed by ANN (82.5%), LogitBoost (82.2%), RIPPER (82%), naïve Bayes (80.3%), AdaBoost (80.2%), and K*(79.2%) respectively. K* The lazy approach involved a minimum of training time and a maximum of test time. Maximum training time was consumed by Random Forest and minimum testing time was taken by Decision Stump.

IoT Based Irrigation and Fertigation System for Smart Smallholder Farming Application

Integrating Internet of Things (IoT) technology with irrigation and fertigation systems has transformed traditional farming methods by enabling reliable monitoring and control of critical environmental parameters. This project aims to design an automated irrigation and fertigation system as a prototype to reduce water wastage and monitor the pH level in the soil, temperature, soil dryness and detection of intruders. Sensors strategically placed throughout the system monitor and control these elements. This system has several important features, including the ability to automatically control water pumps to maintain proper hydration levels, detect intruders within a range of one to six meters of the plant by using the Blynk app, and monitor pH levels through the same app to ensure appropriate neutral levels. Automation and control features are also assessed to confirm that sensors, pumps, and other components precisely respond in line with predetermined requirements. This evaluation helps to determine the overall health of the farmed plants as well as the effectiveness of the system. Finally, the irrigation and fertilizing system for the targeted smallholder farming area has been developed. The resulting smart irrigation and fertigation system is not only a practical solution but also an effective alternative to modern agricultural methods.

Effect of Raised Flat Bed and Ridge Planting on Wheat Crop Growth and Yield under Varying Soil Moisture Depletions

Crop yields remain significantly low in underdeveloped countries, such as Pakistan, primarily due to the prevalent use of traditional farming methods by most farmers. Resource-saving strategies, like ridge and raised flat bed systems, could boost water use efficiency and production per acre. However, further research is needed to investigate the effectiveness of these methods, along with different levels of soil moisture depletion (SMD), on wheat development in the climatic conditions of Sindh Province. Thus, field trials were undertaken at Sindh Agriculture University’s Tandojam for two years (2022 and 2023) during the wheat growing seasons. The experiment included six treatments, viz., T1 (raised flat bed method under 40% depletion), T2 (ridge method under 40% depletion), T3 (raised flat bed method under 50% depletion), T4 (ridge method under 50% depletion), T5 (raised flat bed method under 60% depletion), and T6 (ridge method under 60% depletion). The bed planter was employed to make raised flat beds, and the ridges were prepared using a ridge planter. Wheat variety (TJ-83) was sown. Under all treatments, the plant population, plant height, and seed index were statistically significant (p < 0.05), although spike length, grain spikes−1, and grain weight spikes−1 were non-significant at different SMD levels. To compute water saving through the ridge and raised flat methods, the recommended delta value for wheat crops under the traditional method was used as a benchmark. Both irrigation technologies conserve water compared to traditional irrigation methods. The T5 treatment conserved the most water, followed by T6. Under a 60% depletion level, the ridge approach produced the highest yield of 2175 kg ha−1, compared to 601 kg ha−1 with the raised flat bed method. The best crop water productivity (CWP) of 1.34 kg m−3 was achieved under T6, whereas raised flat beds attained the lowest CWP of 0.27 kg m−3 under T1. In terms of wheat grain production and CWP, the ridge furrow method outperformed the raised flat bed furrow method. Based on the results, it is recommended that farmers should use the ridge furrow method and allow a 60% depletion of soil moisture to obtain a high yield of wheat crops.

Hepatitis E Virus RNA Detection in Liver and Muscle Tissues Sampled from Home Slaughtered Domestic Pigs in Central Italy

Hepatitis E virus is a worldwide emerging foodborne pathogen; raw or undercooked meats and liver pork products can cause infection through the orofecal route. In Central-Southern Italy, small traditional farming method, associated with the possibility of environmental sharing with wild species, can facilitate HEV diffusion and persistence. The aim of this study was to determine HEV genotype and subtype in Marche region from home slaughtered domestic pigs involved in small and traditional food chains. A total of 236 liver and muscle tissues and 6 pooled salami samples were screened. Laboratory workflow started with homogenization, followed by RNA extraction. Nested reverse transcription PCR and qRT-PCR were used to amplify specific parts of overlapping open reading frames belonging to the HEV genome. A total of 42/236 (17.79%) liver and 8/236 (3.39%) diaphragm specimens were positive; none of the pooled salami specimens showed positive HEV signal. The discovered HEV3c presented high nucleotide similarities with ones amplified from wild boar populations hunted in the same province. Extensive farming methods and environmental sharing with wild animal species support cross-infection infections, as observed in the present study. Although salami resulted negative for HEV RNA detection, the effects of food technologies on viral loads remain unclear. Therefore, further scientific investigations coupled with efficacious standardized laboratory procedures will be the next challenge.

An Integrated Smart Pond Water Quality Monitoring and Fish Farming Recommendation Aquabot System.

The integration of cutting-edge technologies such as the Internet of Things (IoT), robotics, and machine learning (ML) has the potential to significantly enhance the productivity and profitability of traditional fish farming. Farmers using traditional fish farming methods incur enormous economic costs owing to labor-intensive schedule monitoring and care, illnesses, and sudden fish deaths. Another ongoing issue is automated fish species recommendation based on water quality. On the one hand, the effective monitoring of abrupt changes in water quality may minimize the daily operating costs and boost fish productivity, while an accurate automatic fish recommender may aid the farmer in selecting profitable fish species for farming. In this paper, we present AquaBot, an IoT-based system that can automatically collect, monitor, and evaluate the water quality and recommend appropriate fish to farm depending on the values of various water quality indicators. A mobile robot has been designed to collect parameter values such as the pH, temperature, and turbidity from all around the pond. To facilitate monitoring, we have developed web and mobile interfaces. For the analysis and recommendation of suitable fish based on water quality, we have trained and tested several ML algorithms, such as the proposed custom ensemble model, random forest (RF), support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), logistic regression (LR), bagging, boosting, and stacking, on a real-time pond water dataset. The dataset has been preprocessed with feature scaling and dataset balancing. We have evaluated the algorithms based on several performance metrics. In our experiment, our proposed ensemble model has delivered the best result, with 94% accuracy, 94% precision, 94% recall, a 94% F1-score, 93% MCC, and the best AUC score for multi-class classification. Finally, we have deployed the best-performing model in a web interface to provide cultivators with recommendations for suitable fish farming. Our proposed system is projected to not only boost production and save money but also reduce the time and intensity of the producer's manual labor.

EXISTING AGRICULTURAL ENGINEERING INNOVATIONS AND FARM MANAGEMENT PRACTICES BY WOMEN FARMERS IN DAVAO DEL SUR: A LITERATURE REVIEW

Agriculture plays a vital role in the economic development of Davao Del Sur, Philippines. However, traditional farming methods often limit productivity and sustainability. As the sector faces numerous challenges, such as climate change, resource scarcity, and changing market demands, innovative solutions are essential to enhance farm management practices. In recent years, agricultural engineering innovations have gained attention as a means to address these challenges. Further, analysing how innovations benefit women farmers can maximize their positive impact. However, some innovations that enhance efficiency may not adequately address women's unique needs. The literature review evaluated current relevant literature and shed light specifically on the existing agricultural engineering innovations employed by one of the agricultural provinces in the Philippines, Davao Del Sur, and highlighted local government units' attention to employing a top-down approach to a more grassroots-oriented approach and provide significance of collaboration to create public value understanding that innovations will be cascaded down to where it matters, and also learn from the feedback of specific areas as to what their needs are specifically women farmers.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0977/a.php" alt="Hit counter" /></p>

IoT Enabled Mushroom Farm Automation with Machine Learning

Mushroom farming has gained prominence due to its significant contribution to the global market. One major challenge for mushroom cultivation is maintaining optimal environmental conditions, specifically temperature and humidity. Traditional farming methods, prevalent in many parts of the world, lack precise control over these parameters, often leading to poor yield. This paper presents an innovative approach combining the Internet of Things (IoT) and Machine Learning (ML) for mushroom farm automation. The proposed system employs the ESP8266 microcontroller with specific agricultural sensors for smart monitoring. To regulate the farm's environmental conditions, ML algorithms predict mushroom farm weather states: mild, normal, and hot. The ensemble ML model, comprising five classifiers – Decision Tree, Logistic Regression, K-nearest neighbor, Support Vector Machine, and Random Forest – delivers a commendable accuracy of 100% when combining predictions, surpassing the performance of individual classifiers. This integrated IoT and ML approach promises to revolutionize real-time automation and cultivation practices in the mushroom industry.

Internet of Things(IoT) Application for Crop Prediction and Fertilizer Suggestion Using Machine Learning Techniques

Abstract: Agriculture serves as the primary source of income globally, particularly in regions like the Indian subcontinent. However, modern challenges such as unpredictable weather patterns, water scarcity, and market fluctuations necessitate the adoption of advanced farming techniques. Soil fertility depletion, caused by various factors, further exacerbates these challenges, leading to decreased crop yields. To solve these problems, our project utilizes modern technology, specifically leveraging machine learning techniques. By integrating an NPK sensor with a web application, our goal is to give farmers practical insights into their soil's nutrient composition. Upon collecting nitrogen, phosphorus, potassium, and pH levels from the soil, these values are entered into the application. Compared to traditional farming methods, our project offers several advantages. Firstly, it empowers farmers with accurate and timely information about their soil's nutrient levels, giving them the ability to make wise decisions.

An analysis of the social, cultural, and ecological factors that affect the implementation of biosecurity measures on smallholder commercial swine farms in Italy in the context of an emerging African Swine Fever outbreak

African Swine Fever (ASF) is a contagious viral disease that infects wild and domesticated swine. In early 2022, the virus was found in wild boar in the Apennine mountains of mainland Italy.22https://storymaps.arcgis.com/stories/9fe6aa3980ca438cb9c7e8d656358f35 Since then, it has spread from wild boar to domesticated swine. To control the spread of ASF, an effective surveillance system and the implementation of strict biosecurity measures on farms are required yet are unevenly implemented across husbandry systems. Smallholder farms in particular are known to have low levels of biosecurity. In the Apennine mountains of Italy, small commercial farms have been found to have low levels of biosecurity despite being located in areas with high densities of wild boar, and, hence, being high-risk sites for potential ASF incursion and subsequent diffusion. To address the question as to why the level of biosecurity is low, interviews and participant observation were conducted with smallholder commercial farmers. The interviews identified the social, cultural, and ecological factors that affect the implementation of biosecurity measures in small commercial swine farms in the Apennines. Farmers expressed knowledge of priority biosecurity measures and an overall willingness to follow rules and regulations; however, the application of the measures in practice was uneven across farms. Economic, political, and ecological factors as well as farmer beliefs about biosecurity emerged as important factors affecting the implementation of biosecurity measures. These include economic constraints, challenges posed by the mountain environment, a shifting regulatory environment, and ideas about animal welfare. Other important factors include cultural factors such as the use of traditional agricultural methods and norms about customer access to animals, time constraints and the perceived hassle of implementing the measures, farmer age, farmer relationships with government officials and veterinarians, and the role of pigs in reducing farm waste. The study confirmed that wild boar are present in high numbers and in close proximity to smallholder commercial farms in the Apennines.

Indigenous Knowledge and Agricultural Practices of Ifugao Ethno-Farming in Ifugao Village, Diffun, Quirino

This study endeavors to delve into the traditional knowledge and farming methods employed by Ifugao farmers residing in Barangay Ifugao Village, Diffun, Quirino. Despite the proven efficacy of Indigenous farming practices, they confront formidable challenges including climate change, biodiversity loss, and the erosion of Indigenous knowledge. The research aims to meticulously document and safeguard the wealth of knowledge and practices upheld by Ifugao farmers, recognizing their pivotal role in fostering environmental sustainability and cultural enrichment. Utilizing an ethnographic approach, this study endeavors to unravel the intricate interplay between farming techniques, traditional wisdom, and the multifaceted challenges posed by modernization. By employing key informant interviews conducted in the Ilocano dialect, the project seeks to elucidate how Ifugao farmers adapt their agricultural practices in response to evolving environmental and socioeconomic dynamics. Through purposive sampling, experienced Ifugao farmers are selected as key informants, ensuring a comprehensive examination of the methods, beliefs, practices, strategies, and knowledge that have underpinned Ifugao ethno-farming traditions. The study, spanning one month and focusing on seasoned Ifugao practitioners, guarantees a thorough exploration of the adaptive measures and customs intrinsic to Ifugao’s agricultural heritage. Reflexive thematic analysis serves as the analytical framework for interpreting the gathered data, with anticipated results offering valuable insights into the resilient techniques employed by Ifugao farmers to preserve their cultural heritage and navigate contemporary challenges. These findings underscore the imperative for collaborative endeavors aimed at safeguarding, documenting, and integrating Indigenous knowledge into agricultural practices, thereby fostering resilience and sustainability for present and future generations. Keywords: Ifugao, Ethno-farming, Indigenous Knowledge, Sustainability, Preservation, Agricultural Practices, Beliefs, Changes perceived, Adaptive Strategies

Design of intelligent agricultural landscape planning and ecological balance control system based on remote sensing images

Abstract With the modernization of agricultural production and the acceleration of urbanization, agricultural landscape planning and ecological balance control have become important issues in promoting sustainable development. Traditional agricultural production methods have problems such as resource waste, environmental pollution, and ecological damage. This article aimed to achieve scientific planning and management of agricultural landscapes, and promote the efficiency of agricultural production, rational utilization of resources, and protection of the ecological environment, thereby promoting sustainable agricultural development. This study first focused on denoising and feature extraction of agricultural remote sensing data images, using remote sensing technology to obtain agricultural landscape information. After that, artificial intelligence was adopted to achieve intelligent agricultural landscape planning, with the goal of maintaining ecological balance and promoting efficient, eco-friendly, and sustainable agricultural production. This study took the landscape of a certain city as an example to test the processing effect of remote sensing images, and it was proved that the effect was good. We tested the performance of an intelligent agricultural landscape planning and ecological balance control system based on remote sensing images. According to the experimental results, it can be concluded that the resource utilization efficiency of farmland A monitored by the intelligent agricultural landscape planning system using remote sensing image technology was 82%, while the traditional one was only 60%. This indicated that the collection effect of remote sensing image technology was much better than that of traditional technology. This article comprehensively and timely monitored and evaluated farmland through remote sensing image technology, including crop growth status, soil moisture, nutritional status, etc. The system can provide precise planting and management suggestions for farmers based on this information, and help them optimize farmland layout, crop selection, and irrigation management, thereby improving the production efficiency of farmland and crop yield.