Farming Practices Research Articles

Isotopic analysis of modern sorghum and finger millet from different altitudes in Ethiopia: implications for ancient farming practices

C4 crops such as sorghum (Sorghum bicolor) and finger millet (Eleusine coracana) have played a significant role in the economic livelihood in arid and semi-arid zones of tropical and sub-tropical Africa since prehistoric times. However, to date, our knowledge of their past management practices is limited. Stable isotope analysis of archaeobotanical remains has been recognized as a valuable tool for reconstructing past agricultural practices, e.g. water management, and fertilization. Nonetheless, our limited understanding of the isotopic variability of C4 plants calls for further research on modern plant before application to archaeobotanical remains. In this paper, we aim to enhance our understanding of modern C4 botanical remains' isotopic variability by analyzing sorghum and finger millet plants. These crops were cultivated according to traditional local practices and collected from ten villages located in the Konso Zone (South Ethiopia) and Tigray Regional State (North Ethiopia), where they are among the daily ingredients for food, and traditional alcoholic and non-alcoholic drinks. We analyzed carbon and nitrogen stable isotopes of seeds and biosilica content in chaff, as it has been suggested that a relationship can exist between silicon and C:N. Carbon isotope values show significant variability, positively correlated with altitude. By demonstrating the sensitivity of C4 grain carbon stable isotope to altitude variations, which are likely connected to water availability, this study offers invaluable insights for the accurate assessment of isotopic values derived from ancient C4 crops. The absence of significant correlations with δ15N suggests that nitrogen isotope values may be less effective for understanding environmental variations in this kind of context. This highlights the limitations of nitrogen isotope data for interpreting ancient agricultural practices and underscores the importance of relying more on carbon isotopes for insights related to environmental conditions and altitude. Furthermore, we confirm that the amount of assimilated carbon may depend also on the biosilica content, which is in turn modulated by environmental parameters such as water availability or soil silicon levels.

Agrobiodiversity and food security: challenges and sustainable solutions

Objective: Agricultural biodiversity, also known as agrobiodiversity, encompasses the variety of plants, animals, and microorganisms that are directly or indirectly involved in agriculture. This diversity is the result of millennia of selection, management, and domestication of species, which has allowed societies to adapt to different environmental and cultural conditions. However, in recent decades, the loss of agrobiodiversity, accelerated by industrial agriculture, the expansion of monocultures, and the reduction of varieties, has put global food security at risk. This diversity is crucial to ensuring the resilience of agricultural systems in the face of challenges such as climate change, emerging pests, and the depletion of natural resources. This paper examines the importance of conserving agricultural biodiversity from the perspective of food security. It emphasizes how agrobiodiversity not only contributes to the stability of food production but also improves human diets by diversifying crops and providing essential micronutrients. Design/Methodology/Approach: A search was conducted on the following scientific information platforms: Web of Science database and Google Scholar. A systematic search for publications related to agrobiodiversity systems was carried out in the WoS database and Google Scholar over the last 49 years (1975–2024). Results: Genetic erosion is particularly concerning because genetic diversity is essential for crops to face environmental challenges such as climate change, pests, and diseases. The loss of traditional varieties, which are selected by local farmers to adapt to specific conditions, increases agriculture's vulnerability to external disruptions. These landraces, having been cultivated in genetically diverse mosaics, offer protection against catastrophic losses in the event of crop failures due to extreme conditions or diseases. Findings/Conclusions: Genetic diversity allows for the development of sustainable solutions to pests and diseases, reducing dependence on pesticides and promoting more environmentally friendly farming practices. However, challenges related to biodiversity conservation persist, making it essential to implement public policies that promote agrobiodiversity and address the socioeconomic issues that limit its adoption.

Habitat Associated with Ramps/Wild Leeks (Allium tricoccum Ait.) in Pennsylvania, USA: Guidance for Forest Farming Site Selection

Ramps or wild leeks (Allium tricoccum) are a popular foraged non-timber forest product in North America consumed for their edible bulbs and leaves. The agroforestry practice of forest farming is a possible solution to conservation challenges surrounding the wild exploitation of this species, but it requires proper site selection to be successful. In this study, maximum entropy (Maxent) modeling using 163 occurrence points and field data collected at 30 wild populations were combined to determine the characteristics of the ramp habitat in Pennsylvania. Both Maxent modeling and field measurements highlighted moist, lower slope positions with base-rich bedrock types as suitable for ramps. Sites shared 50% of their floristic associates on average, with 252 species documented in total. Forest communities associated with ramps included many species indicative of base-rich mesic soil conditions, but the relative abundance of some indicator species differed by region. The confirmation of model variables by field measurements and forest community types points to the usefulness of these characteristics in identifying suitable forest farming sites. When used in tandem, these results can help to guide site selection for forest farming and other conservation strategies.

Diversity and evenness of birds during seasonal paddy farming practice in southwestern Sarawak

Diversity and evenness of birds during seasonal paddy farming practice in southwestern Sarawak

ANFIS Fuzzy convolutional neural network model for leaf disease detection

Leaf disease detection is critical in agriculture, as it directly impacts crop health, yield, and quality. Early and accurate detection of leaf diseases can prevent the spread of infections, reduce the need for chemical treatments, and minimize crop losses. This not only ensures food security but also supports sustainable farming practices. Effective leaf disease detection systems empower farmers with the knowledge to take timely actions, leading to healthier crops and more efficient resource management. In an era of increasing global food demand and environmental challenges, advanced leaf disease detection technologies are indispensable for modern agriculture. This study presents an innovative approach for detecting pepper bell leaf disease using an ANFIS Fuzzy convolutional neural network (CNN) integrated with local binary pattern (LBP) features. Experiments involve using the models without LBP, as well as, with LBP features. For both sets of experiments, the proposed ANFIS CNN model performs superbly. It shows an accuracy score of 0.8478 without using LBP features while its precision, recall, and F1 scores are 0.8959, 0.9045, and 0.8953, respectively. Incorporating LBP features, the proposed model achieved exceptional performance, with accuracy, precision, recall, and an F1 score of higher than 99%. Comprehensive comparisons with state-of-the-art techniques further highlight the superiority of the proposed method. Additionally, cross-validation was applied to ensure the robustness and reliability of the results. This approach demonstrates a significant advancement in agricultural disease detection, promising enhanced accuracy and efficiency in real-world applications.

AI-Driven Precision Agriculture: Optimizing Crop Yield and Resource Efficiency

This article explores the multifaceted applications of Artificial Intelligence (AI) technologies in precision agriculture, focusing on their potential to significantly enhance crop yields while optimizing resource utilization. The article examines five key areas where AI is making substantial impacts: predictive analytics for crop management, intelligent irrigation systems, automated pest and disease detection, precision fertilizer application, and robotic harvesting. By integrating data from various sources and employing advanced machine learning algorithms, these AI-driven systems demonstrate remarkable improvements in efficiency, accuracy, and sustainability. The article highlights significant advancements, such as a 15% improvement in yield prediction accuracy, up to 30% reduction in water usage, and a 20% decrease in fertilizer use without compromising crop yields. While acknowledging challenges such as data privacy concerns and initial investment costs, the article underscores the long-term benefits of AI adoption in agriculture, including increased profitability, environmental sustainability, and improved food security. This comprehensive analysis provides insights into how AI-driven precision agriculture is reshaping modern farming practices and its potential to address global food production challenges while reducing agriculture's environmental footprint.

Smart technology-based microclimate control system for greenhouse

This study proposes an agricultural management system designed to enhance productivity by leveraging artificial intelligence and Internet of Things (IoT) technologies. It addresses the complex microclimate conditions within greenhouses by integrating microcontrollers and sensors to monitor parameters such as temperature, humidity, and soil moisture, enabling precise environmental management. Web and mobile applications provide interactive interfaces, allowing users to monitor real-time factors influencing the greenhouse environment. The system can execute actions like activating fans or irrigation pumps based on user inputs or predefined settings. In addition to improving yields, the system aims to create a dataset that can be used for assessing the effectiveness of future machine learning models. Important considerations include data privacy, training farmers to effectively use the technology, and integrating the system with traditional farming practices, thereby enhancing sustainability. Overall, this study aspires to continuously improve agricultural practices by combining modern technology with traditional knowledge, ultimately aiming for a more efficient and sustainable agricultural landscape.

Mapping Soil Surface Moisture of an Agrophytocenosis via a Neural Network Based on Synchronized Radar and Multispectral Optoelectronic Data of SENTINEL-1,2—Case Study on Test Sites in the Lower Volga Region

In this article, the authors developed a novel method for the moisture mapping of the soil surface of agrophytocenosis using a neural network based on synchronized radar and multispectral optoelectronic data from Sentinel-1,2. The significance of this research lies in its potential to enhance precision farming practices, which are increasingly vital in addressing global agricultural challenges such as water scarcity and the need for sustainable resource management. To verify the developed method, data from two experimental plots were utilized. These plots were located on irrigated soybean crops, with the first plot situated on the right bank (plot No. 1) and the second on the left bank (plot No. 2) of the lower Volga River. Two experimental soil moisture geodatasets were created through measurements and geo-referencing points using the gravimetric method (for plot No. 1) and the proximal sensing method (for plot No. 2) employing the Soil Moisture Sensor ML3-KIT (THETAKIT, Delta). The soil moisture retrieval algorithm was based on the use of a neural network to predict the reflection coefficient of an electro-magnetic wave from the soil surface, followed by inversion into soil moisture using a dielectric model that takes into account the soil texture. The input parameter of the neural network was the ratio of the microwave radar vegetation index (calculated based on Sentinel-1 data) to the index (calculated based on the data of multispectral optoelectronic channels 8 and 11 of Sentinel-2). The retrieved soil moisture values were compared with in situ measurements, showing a determination coefficient of 0.44–0.65 and a standard deviation of 2.4–4.2% for plot No. 1 and similar metrics for plot No. 2. The conducted research laid the groundwork for developing a new technology for remote sensing of soil moisture content in agrophytocenosis, serving as a crucial component of precision farming systems and agroecology. The integration of this technology promotes sustainable agricultural practices by minimizing water consumption while maximizing crop productivity. This aligns with broader environmental goals of conserving natural resources and reducing agricultural runoff. On a larger scale, data derived from such studies can inform policy decisions related to water resource management, guiding regulations that promote efficient water use in agriculture.

THE ROLE OF NOVEL PROBIOTIC STRAINS IN ENHANCING IMMUNITY AND PREVENTING INFECTIONS IN LIVESTOCK: A COMPREHENSIVE REVIEW

The use of probiotics in animal production has become increasingly important due to growing concerns about antimicrobial resistance and the pursuit of sustainable farming practices. This study aimed to explore the role of novel probiotic strains in enhancing immunity and preventing infections in livestock. We conducted a systematic literature review following PRISMA guidelines and used the PICO model to formulate the research question. The results indicated that various probiotic strains, such as Lactobacillus fermentum, Enterococcus faecium, Lactobacillus rhamnosus, Lactobacillus gasseri, and Bacillus subtilis, showed significant antimicrobial and immunomodulatory activities. These strains improved nutrient digestibility, reduced disease incidence, and increased the production of immunoglobulins and anti-inflammatory cytokines. We conclude that probiotics have significant potential as an alternative to antibiotics in livestock production, promoting safer and more sustainable agricultural practices.

Study on the Greenhouse Gas (Ghg) Emissions from Rice Field Under Various Nutrient Management Practices

Modernizing traditional farming practices and intensive cultivation on limited land has increased the reliance on fertilizers to maintain soil health. The study was conducted on rice crop from 2022 to 2023. The seven treatment combinations in which recommended dose of nitrogen applied through neem coated urea (NCU) and nano urea, viz., T1:Control (No nitrogen), T2: 1/3rd as (NCU) as basal) + 1/3rd NCU at active tillering (AT)) + 1/3rdNCU at panicle initiation (PI)), T3: 1/3rd NCU as basal + 1/3rd NCU at AT + 2sprays of NU at maximum tillering -MT and PI) @ 2 ml / litre), T4: 1/3rd NCU as basal + 1/3rd NCU at AT + 1 spray of nano-urea at PI @ 4 ml / litre), T5: 1/4 NCU as basal + 1/4 NCU at AT +2 sprays of nano-urea (at MT and PI) @ 2 ml / litre), T6 :1/3rd NCU as basal + 2 sprays of nano-urea (at AT& PI) @ 2 ml / litre), and T7: 1/3rd NCU as basal + 3 sprays of nano-urea (at AT, PI, and heading) @ 2 ml / litre). The study revealed significant differences in GHG emissions, particularly nitrous oxide (N₂O) and carbon dioxide (CO₂), among the different fertilizer management practices. The highest (p=??) N₂O emissions were observed in the T2 treatment {1/3rd RDN (NCU as basal) + 1/3rd RDN (NCU at active tillering) + 1/3rd RDN (NCU at panicle initiation)} at 352 kgCO₂eha⁻¹, while the lowest emissions were recorded in the T1 treatment (Control – without nitrogen) at 88.4 kgCO₂eha⁻¹. Similarly, CO₂ emissions were also the highest in the T2 treatment at 1376 kgCO₂eha⁻¹, with the control (T1) recording the lowest at 1165 kgCO₂eha⁻¹. Methane emissions, mainly influenced by water regimes, remained consistent across all treatments, each contributing 2112.3 kgCO₂eha⁻¹, due to similar water management across all treatments. Overall, total GHG emissions were significantly higher in fertilized treatments, with the T2 treatment exhibiting the highest total emissions of 3841 kgCO₂eha⁻¹, while the control had the lowest at 3366 kgCO₂eha⁻¹. These findings highlighted the complex trade-offs between enhancing rice yields through improved nutrient management and the associated increase in GHG emissions, emphasizing the need for strategies that balance productivity with environmental sustainability.

A Cross-Zonal Analysis of Farmers’ Climate Adaptation Strategies in West Bengal

Climatic factors are an essential factor in the production of agricultural products. The present study was undertaken to analyze the farmers’ adaptation strategies for climate variability in three agro-climatic zones of West Bengal. Primary data from 150 farmers (50 from each zone) were collected to analyze the adaptation strategies. The study revealed that ‘Crop diversification’ was the most preferred adaptation strategy among the farmers, followed by ‘changing crop variety’ to mitigate climate vagaries. 43.33 per cent of farmers had a medium adoption of adaptation strategies, followed by 39.33, and 17.33 per cent had a high and low adoption. More experienced farmers and farmers with higher annual incomes are more likely to adopt an adaptation strategy. It is suggested that the government ensure farmers can access credit and subsidies. The government should also ensure that bank credit terms are flexible to maximize farmers’ access to affordable credit, enhancing their ability and flexibility to adjust agricultural practices in response to climate variability. Policies promoting climate adaptation should emphasize the need to provide information on better farming practices and climatic variability.

Practices of Maize Handling and Nixtamalization to Reduce Fungal Toxin Exposure in Rural Guatemala

Fungal toxins in local food supplies are a critical environmental health risk to communities globally. To better characterize hypothesized toxin control points among households, we conducted household surveys across four departments (first administrative division) in Guatemala. Data gathered included maize harvesting, processing, storage, and traditional nixtamalization practices. In total, n=33 households participated in the survey and were from 4 unique departments, 17 unique municipalities, and represented 4 different languages. The results suggested that the majority of households consumed a combination of personally cultivated and purchased maize. There was significant variation in how this maize was stored, in regards to pre-processing (kernel vs whole cob), as well as storage system type. For nixtamalization, the largest differences in practices (e.g., cooking time) were based on household size while the majority of households reported practices that aligned with previously reported best practices. Lastly, all reported maize-based food products produced by households utilized the nixtamalization process except one. Current maize handling and nixtamalization practices reported by a majority of households aligned with best practices, however, with locally tailored and culturally sensitive guidance disseminated by key stakeholders, the prevalence of best practice use among households can be improved. Further community-based research on traditional farming and nixtamalization practices can improve these recommendations.

Unravelling the Significance of Seed Proteomics: Insights into Seed Development, Function, and Agricultural Applications.

Seeds are essential for plant reproduction, ensuring species survival and dispersal while adapting to diverse environments throughout a plant's life. Proteomics has emerged as a powerful tool for deciphering the complexities of seed growth, germination, and stress responses. Advanced proteomic technologies enable the analysis of protein changes during germination, dormancy, and ageing, enhancing our understanding of seed lifespan and vitality. Recent studies have revealed detailed insights into metabolic processes and storage protein profiles across various plant species. This knowledge is crucial for improving seed storage, conserving quality, and maintaining viability. Additionally, it contributes to sustainable agriculture by identifying stress-responsive proteins and signalling pathways that can mitigate stress and enhance farming practices. This review highlights significant advancements in seed proteomics over the past decade, discussing critical discoveries related to storage proteins, protein interactions, and proteome modifications due to stress. It illustrates how these insights transform seed biology, boosting productivity, food security, and environmentally friendly practices. The review also identifies existing knowledge gaps and provides direction for future research, underscoring the need for continued interdisciplinary collaboration in this dynamic field.

Using Unmanned Aerial Systems Technology to Characterize the Dynamics of Small-Scale Maize Production Systems for Precision Agriculture

Precision agriculture (PA) utilizes spatial and temporal variability to improve the sustainability and efficiency of farming practices. This study used high-resolution imagery from UAS to evaluate maize yield variability across three fields in Ghana: Sombolouna, Tilli, and Yendi, exploiting the potential of UAS technology in PA. Initially, excess green index (EGI) classification was used to differentiate between bare soil, dead vegetation, and thriving vegetation, including maize and weeds. Thriving vegetation was further classified into maize and weeds, and their corresponding rasters were developed. Normal difference red edge (NDRE) was applied to assess maize health. The Jenks natural breaks algorithm classified maize rasters into low, medium, and high differential yield zones (DYZs). The percentage of bare spaces, maize, weed coverages, and total maize production was determined. Significant variations in field conditions showed Yendi had 34% of its field as bare, Tilli had the highest weed coverage at 22%, and Sombolouna had the highest maize crop coverage at 73.9%. Maize yields ranged from 860 kg ha−1 in the low DYZ to 4900 kg ha−1 in the high DYZ. Although yields in Sombolouna and Tilli were similar, both fields significantly outperformed Yendi. Scenario analysis suggested that enhancing management practices to elevate low DYZs to medium levels could increase production by 2.1%, while further improvements to raise low and medium DYZs to high levels could boost productivity by up to 20%.

Silicate impedes arsenic release and oxidation from ferrihydrite

Silicate fertilization is a common farming practice and an effective method to mitigate arsenic (As) pollution in paddy. Investigating the interaction between silicate and ferrihydrite on As retention is key to comprehensively understand the mechanism of As sequestration by silicate fertilization. Our results indicated that the transformation of ferrihydrite into goethite and hematite was inversely proportional to Si/Fe ratios. The added silicate impeded the decrease of solution pH from neutral to acidity, and imposed strong inhibitory effect on goethite formation. The aqueous As in silicate-free system was 3.43 times higher than that with Si/Fe ratio at 0.33, but similar results were not observed in those with high-level As pollution due to the inhibitory effect of As on ferrihydrite transformation. Solid characterization showed that silicate was monomerically adsorbed to ferrihydrite through Si-O-Fe bond, which impeded the reductive dissolution, Fe atom exchange, internal atomic rearrangement and dehydration of ferrihydrite. As(III) oxidation weakened in silicate-coprecipitated ferrihydrite due to the lack of Fe(II) catalysis stem from ferrihydrite dissolution. This work demonstrated that As release could be effectively impeded through the inhibitory effect of silicate on ferrihydrite transformation, thereby providing new insights into the understanding of As accumulation reduction in rice by silicate fertilization.

Optimization of Long Line Management in Seaweed Farming: Strategies for Increasing Farmers' Group Income in Pinrang Regency, Indonesia

This research aims to explore the role of the long-line method in increasing the income of seaweed farmers in Pinrang Regency, South Sulawesi, Indonesia, while examining the challenges they face and the strategies they employ to overcome them. The study employs a qualitative case study approach, involving 15 seaweed farmers from various coastal villages within Pinrang Regency, representing diverse demographic backgrounds and experiences. Data collection was conducted through semi-structured interviews, allowing for an in-depth understanding of the participants' farming practices and challenges. The findings indicate that seaweed farmers increase their income by optimizing resources, collaborating within farmer groups, and adapting to environmental and market changes. Strategies such as space optimization, material reuse, and knowledge sharing among farming communities significantly enhance productivity and sustainability. Furthermore, farmers employ adaptive techniques to mitigate environmental risks and market volatility, including using durable materials and processing seaweed into higher-value products. The implications of these findings are crucial for policymakers and development organizations, as they highlight the potential of sustainable farming practices in improving the livelihoods of coastal communities. Policymakers are encouraged to develop support programs that promote resource optimization, foster community-driven farming networks, and train farmers in adaptive strategies. However, the study's limitations, including the small sample size and reliance on self-reported data, may affect the generalizability of the results. Future research should incorporate larger, more diverse samples and objective measures to enhance the robustness of the findings, and explore the long-term impacts of the long-line method on income and sustainability.

Feeding Practices of Pig Farmers in the districts of Perambalur and Ariyalur in Tamil Nadu

Feeding Practices of Pig Farmers in the districts of Perambalur and Ariyalur in Tamil Nadu

IMPACT OF FARMERS PERCPTION OF USING ORGANIC MANURE ON THE ONION PRODUCTION IN KUDAN LOCAL GOVERNMENT AREA OF KADUNA STATE, NIGERIA

This study assessed the impacts of farmers’ perception of using organic manure on the onion production in Kudan Local Government Area (LGA) of Kaduna State, Nigeria. The specific objectives were to: describe the socio-economic characteristics of onion farmers, assess the impacts of using organic manure on income and productivity, and perception of farmers in using organic manure. A multi-stage sampling procedure was employed to select 200 respondents for the study. Primary data were collected using structured questionnaires and analyzed through descriptive and inferential statistics. The findings revealed that the majority of respondents were male (89.5%) with a mean age of 40.76 years, and most households comprised 10-15 members. Compost manure (53%) was the most widely used organic input due to its availability and effectiveness in enhancing soil fertility. The results indicated that the use of organic manure had a positive impact on the productivity and income of onion farmers. Key socio-economic factors influencing productivity and income-included education, access to extension services where farmers received training on organic manure use, farming experience and the quantity of manure applied. For sustainability and continuous use of organic manure in Kudan LGA. Improving access to organic inputs, enhancing transportation infrastructure, and providing targeted education and training on organic farming practices are critical. These recommendations align with recent policies promoting sustainable agriculture in Nigeria.

Al-driven Precision Agriculture: Advancing Crop Yield Prediction

This article explores the integration of Artificial Intelligence (AI) in precision agriculture, focusing on its role in advancing crop yield prediction and improving overall agricultural productivity. It examines three key areas: satellite imagery analysis, soil health monitoring, and weather data integration. The paper discusses how AI techniques such as Convolutional Neural Networks, IoT sensor networks, and ensemble methods are revolutionizing farming practices. It highlights the technical implementations of these technologies, their applications, and their significant impact on yield optimization, resource efficiency, and decision-making in agriculture. The study emphasizes the crucial role of AI in addressing global food security challenges and improving agricultural resilience to climate change.

Evaluation of IPM Module for the Management of Key Insect Pests in Chilli

Chilli is a crop cultivated in tropical and subtropical regions of India, and the crop is attacked by a multitude of pests at different crop stages. The present study revealed that field experiments were laid out with three treatments, viz., IPM module, farmer practice, and untreated control, at Kuttiyagoundanur village of Kolathur block of Salem District, Tamil Nadu. The results of the IPM capsule for the management practices of major pests and diseases, including viral diseases in chillies. The pre-treatment count on thrips/leaf was non-significant in all treatments, and it ranged from 7.20 to 8.40 thrips/leaf. Among the three treatments, farmers practice of chemical control recorded less infestation of 2.93 thrips/leaf, followed by the IPM module recording 3.60 thrips/leaf. Before adoption of treatment, the mite/leaf count was between 17.09 and 19.23 mite/leaf. The IPM module recorded less infestation of 5.32 mites/leaf, followed by farmers practice recording 12.90 mites/leaf. The pre-treatment count of Helicoverpa armigera larvae was between 3.20 and 3.72 larvae/plant. Among the treatments, chemical control recorded less infestation of fruit borer and fruit damage (0.17 larvae/plant) and 1.40 percent, followed by the IPM module, which is 1.17 larvae/plant and 4.91 percent. The leaf curl infection was low in farmers practices. 10.82 percent, followed by the IPM module recorded 15.26 percent. Maximum fruit yield of 11200 kg/ha with a comfortable B:C ratio of 2.96 was obtained in the IPM module, followed by farmer practice with a B:C ratio of 2.91.