Farming Practices Research Articles

Progress and Innovations in Hydrogels for Sustainable Agriculture

The growing global demands for food security, efficient water use, and environmentally resilient agricultural strategies call for a sustainable agricultural revolution. Water scarcity, the excessive use of pesticides, and soil degradation are viewed as threats to food security. Hydrogels, three-dimensional hydrophilic polymer networks, appear to be revolutionary solutions for the future. Hydrogels have emerged as a promising solution for enhancing crop resilience, enhancing crop yields, and agribusiness productivity. The development and prospects of hydrogels bring about a revolution in sustainable agriculture by focusing on their unique properties, including excellent water absorption capacity, biodegradability, and controlled nutrient/agrochemical delivery. Hydrogels have the potential to transform traditional farming practices into novel crop varieties with improved traits like disease resistance, insect resistance, and drought resistance. Hydrogels enhance soil moisture retention, thereby facilitating seed germination and establishment. Thus, it has been demonstrated that hydrogel application has a significant impact on soil quality and resilience under challenging conditions. With this in mind, this review emphasizes that hydrogels are the way forward for sustainable agriculture, taking into consideration the economic benefits like reduced irrigation and increased crop yields while highlighting the need for regulatory considerations in terms of their safety, biodegradability, environmental impact, and long-term soil effects.

Driving transformation: the role of institutions in shaping ecological farming adoption in Kiambu County, Kenya

ABSTRACT Institutions promote ecological farming through various interventions. However, there is a dearth of thorough empirical research on which institutional interventions influence the adoption intensity of ecological farming. Therefore, this paper aims to fill this gap by determining the role of institutional intervention in the adoption intensity of ecological farming in Kiambu County. We used cross-sectional survey data obtained from a multi-staged sample of 320 households. The data were collected using semi-structured questionnaires and analysed using a generalised Poisson regression model. Based on inferential statistics at a significant level of p ≤ 0.05, the results revealed that access to eco-friendly markets, training, information sources, and extension contacts increased the likelihood of a farmer adopting ecological farming practices at a higher intensity by 50.1 per cent, 50.9 per cent, 4.3 per cent, and 38.7 per cent, respectively. This study recommends policy interventions to promote multi-stakeholder partnerships to develop eco-friendly markets and enhance knowledge dissemination through extension services and training forums for farmers.

Comment on "Socio-economic aspects and farming practices of goats in Southern Tunisia" by Chniter et al. (2024).

The following comment is addressed to Chniter et al. (Trop Anim Health Prod 56:220,2024) related to "Socio-economic aspects and farming practices of goats in Southern Tunisia". In their review of socio-economic aspects of goats growing in southern Tunisia, the authors have overlooked or missed some important issues that, in my view, can turn in incomplete (or sometimes confusing) interpretation of the performed data analyses. The following comment is intended to draw attention to (1) the threat and the impact of goat raising on land degradation and the spread of desert-like conditions in a region already prone to desertification, (2) whether goat raising is considered a direct cause or a consequence of desertification and prolonged drought in arid and semi-arid areas located in the northern fringes of the Sahara such as southern Tunisia (Medenine and Tataouine), and (3) the difficulties of implementing proper grazing management in these areas.

Cloud and IoT Technologies Revolutionizing Precision Agriculture

Integrating Cloud Computing and Internet of Things (IoT) technologies is revolutionizing the agricultural sector by enabling precision farming practices and data-driven decision-making. This comprehensive article explores the transformation of traditional farming through smart agriculture technologies, examining the technological infrastructure, key applications, and measurable benefits. The article discusses how IoT sensor networks, cloud-based analytics, and automated systems enhance resource optimization, environmental sustainability, and operational efficiency. The article demonstrates that these technological implementations significantly impact water conservation, crop health monitoring, field mapping, equipment management, and environmental protection while delivering substantial economic benefits through improved yields and reduced operational costs. The article provides insights into the current state of smart agriculture adoption and its implications for future farming practices.

Integrated Rice-Snail-Crayfish Farming System Shapes Soil Microbial Community by Enhancing pH and Microbial Biomass in South Subtropical China

Within the framework of sustainable agriculture, the integrated rice-snail-crayfish farming system has been recognized as a highly efficient agroecological approach that enhances crop production while minimizing the application of chemical fertilizers and pesticides. Nonetheless, the mechanisms by which this system influences soil microbial community composition to achieve these benefits remain unknown. In this study, we focused on traditional rice farming (TR), the integrated rice-snail-crayfish (R-S-C) farming system, and mono-rice farming (CK), and systematically examined the impacts of these farming systems on soil chemical properties, microbial biomass, enzyme activity, and microbial community composition. Our results showed that the R-S-C significantly increased soil pH, microbial biomass carbon (MBC), and the MBC/microbial biomass phosphorus (MBP) ratio compared to TR, as well as the peroxidase activity. Moreover, the R-S-C significantly increased soil total phospholipid fatty acid (PLFA), bacterial PLFAs, Gram-negative bacterial (GN) PLFAs, anaerobic bacteria PLFAs, arbuscular mycorrhizal fungi (AMF) abundances, and the bacteria/fungi ratio compared to the other two systems. However, the soil microbial α-diversity indices, including Shannon–Wiener index (H), Simpson index (D), and Pielou evenness index (J), were significantly lower in the R-S-C system than in the other two systems. Further exploration suggested that soil pH, microbial biomass nitrogen (MBN), the MBN/total nitrogen (TN) ratio, and the MBC/MBP ratio were critical factors governing microbial community composition under the three farming practices. Notably, soil pH alone accounted for 64.5% of the observed variation in microbial community composition. Path analysis using partial least squares structural equation modeling further revealed the pathways by which the R-S-C system enhanced total PLFAs, AMF, and gram-positive bacteria by regulating the soil pH and MBN/TN ratio. This study provides insights into the regulatory mechanisms driving soil microbial communities in the R-S-C system and offers a theoretical foundation for developing sustainable agricultural management practices.

Leveraging Robotics for Sustainable Agriculture: A Comparative Analysis of Africa and the EU’S Food Security Strategies

The intensifying demand for food production, driven by population growth and climate pressures, has placed a strain on agricultural systems worldwide, particularly in Africa and the European Union (EU). This paper presents a comparative analysis of the adoption of robotics in agriculture across these regions, exploring the current practices, limitations, and advancements shaping the future of sustainable farming. In Africa, limited infrastructure, high costs, and technological barriers hinder the integration of robotics, challenging smallholder farmers and reducing productivity. Conversely, the EU demonstrates more advanced adoption, supported by robust policy frameworks and technology infrastructure, although it faces challenges including workforce aging and the need for ethical guidelines in AI applications. This study highlights significant case studies within the EU, such as those in the Netherlands and Germany, showcasing the economic and environmental impacts of robotics in diverse farming models. The analysis extends to the benefits of robotics in increasing productivity and resource efficiency while reducing labor dependency, contributing to precision farming practices and environmental sustainability. The findings underscore the critical role of robotics in future agricultural systems, suggesting that while Africa faces more immediate barriers to adoption, targeted investments and policy adaptations could bridge these gaps. The study concludes by advocating for tailored, region-specific strategies to achieve sustainable agriculture through robotics, underscoring the technology's potential to address global food security challenges in Africa and the EU.

The Impact of Applying Different Fertilizers on Greenhouse Gas Emissions and Ammonia Volatilization from Northeast Spring Corn

Reducing greenhouse gas (GHG) emissions and ammonia (NH3) volatilization by improving fertilization methods to increase crop yield is beneficial for the green and sustainable development of agriculture. This study evaluated the effects of farmer practice fertilization (FP), nutrient expert optimized fertilization (NE—optimized fertilizer usage and time), the application of stable compound fertilizer (SF), and the application of controlled-release coated urea (CRU) on greenhouse gases, NH3 volatilization, and corn yield through field experiments set up in the corn planting area in western Liaoning Province. The results showed that compared with FP treatment, NE could significantly reduce NH3 volatilization by 28% and increase N2O release by 41%. Compared with FP treatment, SF could significantly reduce NH3 volatilization by 48.54%, N2O release by 38.54%, CO2 release by 13.96%, global warming potential (GWP) by 16.60%, and greenhouse gas emission intensity (GHGI) by 27.23%, and could significantly increase corn yield by 15.86%. Compared with FP treatment, CRU could significantly reduce NH3 volatilization by 63.46%, CO2 release by 11.98%, GWP by 10.73%, and GHGI by 13.77%, while increasing N2O release by 6.71%. Overall, NE, SF, and CRU treatments all showed better effects than FP treatment in increasing corn yield or reducing NH3 volatilization and GHG emissions. Among them, SF treatment demonstrated superior performance over NE and CRU treatments in terms of NH3 volatilization, corn yield, and GHGI. Therefore, the application of stable compound fertilizer is the optimal choice for corn planting in western Liaoning, with broad application prospects.

Development of a Knowledge Assessment Tool to Measure Farmers' Understanding of Improved Groundnut Cultivation Practices

This study focused on assessing farmers' knowledge of improved cultivation practices in groundnut farming. An initial pool of 40 knowledge-related items was developed, drawing from various sources such as literature, input from field extension personnel, insights from relevant specialists, and the researchers' own expertise. After applying statistical procedures to ensure the reliability and validity of the knowledge test, the final set was refined to 21 items. This standardized knowledge test serves as a tool for evaluating the extent of farmers' understanding of advanced groundnut cultivation practices.

Impact of Full-Spectrum and Infrared Lighting on Growth, Oxidative Stress, and Cecal Microbiota in Broilers

Lighting is crucial for the development of broilers as it affects their growth performance, oxidative stress, and overall health. This study investigates the impact of full-spectrum light, infrared light, and LED white light exposure on the growth performance, oxidative stress markers, and cecal microbiota of medium-growth yellow-feathered broilers. A total of 216 medium-growth yellow-feathered chicks (Yuhuang No. 5), five days old, were randomly divided into three groups: 72 chicks in each group, with three replicates of 24 chicks. The birds were raised under different lighting conditions, including LED infrared light (II), full-spectrum therapy light (FB), and LED white light (CG) until day 87. This experiment comprised the early growth phase and measured critical hormones such as Melatonin (Mel), Growth Hormone (GH), and Growth Hormone Releasing Hormone (GHRH), as well as Malondialdehyde (MDA), Superoxide Dismutase (SOD), and Catalase (CAT). Additionally, this study examined the differences in microbiota diversity and composition. The results demonstrated that LED infrared and full-spectrum light exposure significantly (p < 0.05) increased broiler body weight. Particularly, full-spectrum light was effective in comb redness and reducing final comb length and oxidative stress. Furthermore, full-spectrum light improved microbial prosperity and diversity compared with the other lighting conditions. Overall, the findings suggest that full-spectrum lighting is more beneficial for broiler growth, reducing oxidative stress, and promoting gut health compared with LED infrared lighting. These insights can be applied to optimizing broiler farming practices, thereby improving productivity and animal welfare.

Assessment of Dairy Farmers' Knowledge Regarding Good Dairy Farming Practices in Uttar Pradesh, India

Dairy farming is essential for households with low and moderate-income, serving as a crucial component of both subsistence and commercial farming. The dairy sector holds significant potential and offers ample opportunities to enhance the socio-economic status of farmers due to its ability to generate higher productivity and profits within a shorter timeframe compared to other businesses. The study was conducted to measure dairy farmers' knowledge level regarding good dairy farming practices in Uttar Pradesh. Being the highest milk-producing state in India, Uttar Pradesh was selected purposively for the study. Further selection of districts, blocks, and villages for two each was done randomly. While selecting respondents, 10 percent of dairy farmers from the total household of each village were considered under a proportionate random sampling method. The criteria of the respondents were the dairy farmers should have at least one lactating dairy animal at the time of investigation. From the study, it was observed that 64.85 percent of respondents had knowledge about how many days is the colostrum-free milk available from animals and 81.21 percent of respondents knew about animals going to repeat their heat cycle. The majority (97.88%) of respondents had knowledge about colostrums feeding to newborn calves and 62.42 percent respondents had knowledge about special bedding for newborn calves. 84.24 percent of respondents had knowledge about ill animals and 56.36 percent of respondents had knowledge about the proper disposal of dung. 94.24 percent of respondents knew how many workers manage 5 animals on their farm. 48.79 percent of respondents had overall knowledge about good dairy farming practices. Study identified require a clear strengthening of extension services through targeted training programs to improve the knowledge of key practices among the farming community. Another low awareness is about schemes by the government on support to farmers in terms of finances and technical means. This can be improved through better communication channels by working with local cooperatives and NGOs.

Integrated Cultivation of Land-Sea Ecosystems That Prioritizes Harmony with Nature and Society

Integrated Cultivation of Land-Sea Ecosystems That Prioritizes Harmony with Nature and Society

Spatial and temporal variation in crop productivity and relation with soil fertility within upland agroforestry

Agroforestry can be an option of sustainable farming practices for upland communities. However, information of spatial and temporal variation in soil fertility and crop productivity along slopes, which can guide e.g. effective management, such as application of fertilizers, is limited. This study evaluated spatial and temporal variability in crop productivity and soil fertility along slopes in two different fruit tree agroforestry systems in upland areas of north-west Vietnam: (1) longan-mango agroforestry integrating longan (Dimocarpus longan L.), mango (Mangifera indica L.), maize (Zea mays L.), and guinea grass (Panicum maximum Jacq.), and (2) plum agroforestry integrating plum (Prunus salicina L.), maize, and guinea grass. The two systems were established on relatively steep slopes, 37 % and 65 % slope, respectively. Crop performance and soil fertility were measured in different positions relative to the fruit tree-grass rows over 4–5 years and compared with sole-crop maize. The results showed that maize height, grain yield, and leaf nitrogen (N) concentrations were significantly higher at the upslope than downslope side of tree-grass rows. For example, the grain yields were 30–35 % higher at the upslope than downslope side. Regarding soil fertility, there was a tendency that SOC, total N, total phosphorus (P), available P, and available potassium (K) were higher at the upslope than downslope side of tree-grass rows. Thus, the forage grass strips played an important role in trapping N and other nutrients, and enhanced nutrient use efficiency within agroforestry at the steeply sloping study sites. The maize performance and soil fertility in the areas mid-way between two tree-grass rows were comparable to those in sole-crop maize. The results of this study can provide guidance for farmers managing fruit tree agroforestry in north-west Vietnam or other regions with similar cropping, climate, and biophysical characteristics to implement more effective plot management practices on sloping land.

Agricultural land use change and its drivers in the Sudanese Ethiopian borderland: the case of Al-Fashaga region

ABSTRACT The Al-Fashaga region, situated along the Sudanese-Ethiopian border, has experienced significant land use and land cover (LULC) changes over the past three decades due to large-scale mechanized agriculture and conflict-related dynamics. This study examines LULC transitions from 1990 to 2020 using multi-temporal Landsat data, Google Earth imagery, field surveys, and key informant interviews. Findings reveal a substantial increase in agricultural land, now covering over 75% of the region, predominantly at the expense of natural vegetation, which has declined to 13.7%. The 2020 Tigray conflict in Ethiopia triggered shifts in land control, enabling Sudanese farmers to reclaim abandoned lands, and illustrating the complex interrelations between conflict, land tenure, and resource use. This dynamic has exacerbated environmental degradation and heightened socio-political tensions over fertile borderlands. The study emphasizes the urgency of conflict-sensitive land management strategies to balance agricultural productivity and environmental sustainability. Recommendations include strengthening land tenure security, adopting sustainable farming practices, and fostering cross-border collaboration to address resource-based conflicts. These findings offer critical insights into how geopolitical tensions influence land use in border regions, contributing to policies aimed at sustainable land management, conflict resolution, and peacebuilding in similarly affected areas.

Potted garden pea grown in presence of pre-emergence herbicides: Impacts on soil enzymes and human health

Pre-emergence herbicide residues in pea cultivation can pose significant health risks to nontarget organisms, including humans. To assess the impact of these residues on soil and human health, garden peas were grown in an agricultural soil (MAT) and potting mix (POM) treated with dimethenamid-P, metazachlor, and pyroxasulfone. Dehydrogenase activity in MAT soil was not significantly affected by these herbicides. However, in POM, herbicide application resulted in significantly higher dehydrogenase levels, likely due to the higher organic matter content promoting microbial activity and moisture retention. In contrast, phosphatase activity levels remained relatively consistent across untreated and treated samples of both MAT and POM. Herbicide residues in pea pods, shoots and roots were measured to evaluate potential human health hazards. The detected residue levels were at or above the European Union’s maximum residue levels. The calculated non-cancer acute health hazard index of (aHI <100 %) and hazard quotient of (HQ <1) suggested that consuming peas grown in potting medium treated with these herbicides at recommended doses does not pose a health hazard to humans. This study is the first to provide insights into the environmental impact of pre-emergence herbicide residues in soil, aiding the development of safer and more sustainable vegetable farming practices.

A Comprehensive Review of Low- and Zero-Residue Pesticide Methods in Vegetable Production

Increasing demand for sustainable vegetable production is leading to low- and zero-pesticide farming practices. This review examines many strategies intended to lower pesticide use without impacting crop quality and production. The use of biopesticides, biological control, integrated pest management (IPM), and organic farming are some of the important techniques that are examined. This investigation also covers cutting-edge technology that improves the efficacy and efficiency of various techniques, such as robots, artificial intelligence (AI), and precision agriculture. A rigorous evaluation of the effects of pesticide residues on the environment and human health emphasises how crucial it is to use fewer pesticides. Market trends and customer preferences are considered, as well as the social and economic effects of implementing these strategies. The paper’s conclusion identifies obstacles to the general adoption of low- and zero-pesticide approaches and makes recommendations for future research topics to overcome these obstacles.

Global Meta-Analysis of Innovation Attributes Influencing Climate-Smart Agriculture Adoption for Sustainable Development

Climate-smart agricultural technologies offer transformative potential for achieving Sustainable Development Goals, especially in mitigating extreme weather impacts and enhancing food security. Despite this potential, adoption rates remain limited due to various factors, with perceived complexity playing a significant role. This study conducted a systematic review and meta-analysis to assess the influence of perceived innovation complexity on adopting climate-smart technologies. Using frameworks of the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology, we systematically reviewed 28 studies and conducted a meta-analysis of 15 studies across diverse geographic contexts. Our findings from the systematic review indicate inconsistent results on the impact of complexity on adoption due to the different items and scales used to measure the concepts of complexity across contexts, suggesting that there is a need for the development of a standardized scale to measure complexity. Results from the meta-analysis generated a summary effect size (r = 0.51, 95% CI = [0.05, 0.72], z = 6.78, p ≤ 0.0001), revealing a significant relationship between perceived complexity and adoption intent. The effect size of 0.51 indicates that higher complexity levels significantly decrease the likelihood of adoption intent for climate-smart technologies. Differences in CSA research trends across geographic regions highlight the need for tailored approaches to technology adoption that take into account the specific capabilities and constraints of each region. These findings provide valuable insights for policymakers, Extension professionals, and technology developers to design interventions to promote ease of use and enhance technology diffusion in sustainable farming practices and food security. These findings contribute to ongoing efforts to foster sustainable agricultural innovations, offering guidance to accelerate the global transition to more resilient farming systems.

AgriFusion: A Low‐Carbon Sustainable Computing Approach for Precision Agriculture Through Probabilistic Ensemble Crop Recommendation

ABSTRACTOptimizing crop production is essential for sustainable agriculture and food security. This study presents the AgriFusion Model, an advanced ensemble‐based machine learning framework designed to enhance precision agriculture by offering highly accurate and low‐carbon crop recommendations. By integrating Random Forest, Gradient Boosting, and LightGBM, the model combines their strengths to boost predictive accuracy, robustness, and energy efficiency. Trained on a comprehensive dataset of 2200 instances covering key parameters like nitrogen, phosphorus, potassium, temperature, humidity, pH, rainfall, and crop type, the model underwent rigorous preprocessing for data integrity. The RandomizedSearchCV method was employed to do hyperparameter tuning, namely improving the number of trees in the Random Forest algorithm and the learning rates in the Gradient Boosting algorithm. This ensemble approach achieves a remarkable accuracy rate of 99.48%, optimizes computer resources, lowers carbon footprint, and responds efficiently to a variety of agricultural situations. The model's performance is confirmed using metrics including cross‐validation, accuracy, precision, recall, and F1 score. This demonstrates how the model might improve agricultural decision‐making, make the most use of available resources, and promote ecologically responsible farming practices.

The Impact of Smart Farming Technology on Agricultural Productivity: Evidence from a Large-scale Database in Thailand

Thailand 4.0 is a national strategy focused on integrating digital technologies and innovation to drive economic development in Thailand. The agricultural sector, a vital part of the economy, plays a crucial role in this strategy. One key initiative is the smart farming project, which aims to enhance agricultural productivity. This study aims to examine the impact of Thailand’s smart farming project on agricultural productivity within the context of this policy. In pursuit of this objective, the study adopts a quantitative research methodology, employing a comprehensive analysis of secondary data. The data utilized in the study is obtained from reliable sources, namely the Office of the National Economic and Social Development Council and the FAOSTAT database. This dataset spans the period from 2006 to 2020 and undergoes meticulous analysis through the application of a specified equation. The study findings demonstrate that higher growth rates of total output relative to total inputs result in noticeable improvements in agricultural total factor productivity. This positive outcome can be attributed to the significant influence exerted by Thailand 4.0 and smart farming policies. Consequently, the adoption of smart farming practices in Thailand leads to significant advancements in agricultural productivity. Based on these results, the study provides valuable insights into the implications of Thailand 4.0 for agricultural development and offers recommendations for policymakers and stakeholders. These recommendations involve strategies to leverage digital technologies in agriculture, promote innovation, enhance digital literacy and skills among farmers, and address challenges that hinder the effective implementation of digital transformation initiatives. Keywords: Thailand 4.0 policy, smart framing, agricultural total factor productivity, innovation, sustainable development

OPTIMIZATION IOT TECHNOLOGY IN WEATHER STATIONS FOR IMPROVE AGRICULTURAL SUCCESS DURING EL NIÑO ERA

The El Niño phenomenon is significant to global weather patterns, particularly in Indonesia, which adversely affects the agricultural sector, especially rice production. El Niño causes drastic changes in rainfall patterns, making it difficult for farmers to determine the right planting time. Limited access to accurate weather information is a major obstacle for farmers in planning their agricultural activities. This research aims to develop an Internet of Things (IoT)-based weather station capable of providing real-time and accurate weather data to support farmers' decision-making in their land management. The research method starts with observation in Babakan Jaya Village, Gabuswetan District, Indramayu Regency, to understand the local weather conditions and specific challenges faced by farmers. Next, the construction and implementation of a weather station equipped with sensors to measure various weather parameters such as temperature, humidity, wind direction and speed, and rainfall. The weather data collected by these stations is then processed and presented in real-time through a cloud platform, which allows access from computer devices and smart phones. The observation results from 1 June to 27 July 2024 showed that the air temperature ranged from 29°C to 35°C, humidity between 55% to 90%, and wind speed ranged from 0 to 7 km/h, with sporadic rainfall patterns. The developed IoT weather station successfully provides relevant and accurate weather data, which can be accessed in real-time by farmers. With this data, farmers can make more informed decisions in their land management, hopefully improving the efficiency and success of farming practices, especially in the midst of erratic weather conditions due to El Niño.

Innovative Service Design of Online Agricultural Product Platforms: Improving User Experience and Trust

The rapid growth of online agricultural product platforms has revolutionized traditional agricultural markets, offering consumers more convenient purchasing options and expanding sales channels for farmers. However, challenges such as the lack of product transparency, inadequate trust mechanisms, and varied user needs hinder the platforms' development. This study explores innovative service design strategies to enhance user experience and build trust in online agricultural product platforms, guided by the Double Diamond Model. By identifying critical obstacles and addressing them through digitalization, AI-driven analytics, and ethical farming practices, this research proposes a user-centered service innovation framework. The study emphasizes intelligent interface design, product traceability mechanisms, and interactive consumer feedback systems to foster trust and engagement. Through iterative prototyping, testing, and optimization, this approach aims to create a sustainable and reliable platform ecosystem that meets diverse user needs. The findings provide actionable insights for online agricultural product platforms to improve competitiveness, user satisfaction, and long-term market sustainability.