Search Results (7,042)

The aim of this communication article is to present a successful irrigation advisory scheme on the island of Crete (Greece) provided by the Hellenic Agricultural Organization (ELGO DIMITRA), which is well adapted to the different needs of farmers and water management agencies. The motivation to create this advisory scheme stems from the need to save water resources while ensuring optimal production in a region like Crete where droughts seem to occur more and more frequently in recent years. This scheme/approach has three different levels of implementation (components) depending on the spatial level and end-users’ needs. The first level concerns the weekly irrigation bulletins in the main agricultural areas of the island with the aim of informing farmers and local water managers about crop irrigation needs. The second level concerns an innovative digital web-based platform for the precise determination of the irrigation needs of Crete’s crops at a parcel level as well as optimal adaptation strategies in the context of climate change. In this platform, important features such as real-time meteorological information, spatial data on the cultivation type of parcels, validated algorithms for calculating crop irrigation needs, an accurate soil texture map derived from satellite images, and appropriate agronomic practices to conserve water based on cultivation and the geomorphology of a farm are considered. The third level of the proposed management approach includes an open-source Internet of Things (IoT) intelligent irrigation system for optimal individual parcel irrigation scheduling. This IoT system includes soil moisture and atmospheric sensors installed on the field, as well as the corresponding laboratory soil hydraulic characterization service. This third-level advisory approach provides farmers with specialized information on the automated irrigation system and optimization of irrigation water use. All the above irrigation advisory approaches have been implemented and evaluated by end-users with a very high degree of satisfaction in terms of effectiveness and usability. Full article

The present paper aims to quantify the carbon emissions associated with the establishment of 15 walnut orchards (“Juglans californica”) in the greater area of Magnisia, Greece, with the use of a carbon footprint tool interconnected to a Farm Management Information System. The data collection spanned the first five years following the planting of the trees, providing a comprehensive view of the emissions during this critical establishment phase. Over the five-year period examined (02/2019–12/2023), the results revealed net carbon emissions amounting to 13.71 tn CO₂ eq ha⁻¹, with the calculated emissions showing an increasing trend from the first year through the fifth year. Scope 1 (7.38 tn CO₂ eq ha⁻¹) and Scope 2 (3.71 tn CO₂ eq ha⁻¹) emissions emerged as the most significant, while irrigation (drip irrigation) and fertilizing practices were identified as the highest contributors to emissions. This study highlights the significance of using integrated digital tools for monitoring the performance of cultivations rather than standalone tools that are currently widely available. Integrated tools that incorporate various applications simplify data collection, encourage accurate record-keeping, and facilitate certification processes. By automating data entry and calculations, these tools reduce human error during agricultural carbon management and save time; thus, the integration of digital monitoring tools is vital in improving data accuracy, streamlining certification processes, and promoting eco-friendly practices, crucial for the evolving carbon market. Full article

The Gedeo zone agroforestry systems are the main source of Ethiopia’s coffee beans. However, land-use and suitability analyses are not well documented due to complex topography, heterogeneous agroforestry, and lack of information. This research aimed to map the coffee coverage and identify land suitability for coffee plantations using remote sensing, Geographic Information Systems (GIS), and the Analytical Hierarchy Process (AHP) in the Gedeo zone, Southern Ethiopia. Remote sensing classifiers often confuse agroforestry and plantations like coffee cover with forest cover because of their similar spectral signatures. Mapping shaded coffee in Gedeo agroforestry using optical or multispectral remote sensing is challenging. To address this, the study identified and mapped coffee coverage from Sentinel-1 data with a decibel (dB) value matched to actual coffee coverage. The actual field data were overlaid on Sentinel-1, which was used to extract the raster value. Pre-processing, classification, standardization, and reclassification of thematic layers were performed to find potential areas for coffee plantation. Hierarchy levels of the main criteria were formed based on climatological, edaphological, physiographic, and socioeconomic factors. These criteria were divided into 14 sub-criteria, reclassified based on their impact on coffee growing, with their relative weights derived using AHP. From the total study area of 1356.2 km², the mapped coffee coverage is 583 km². The outcome of the final computed factor weight indicated that average annual temperature and mean annual rainfall are the primary factors, followed by annual mean maximum temperature, elevation, annual mean minimum temperature, soil pH, Land Use/Land Cover (LULC), soil texture, Cation Exchange Capacity (CEC), slope, Soil Organic Matter (SOM), aspect, distance to roads, and distance to water, respectively. The identified coffee plantation potential land suitability reveals unsuitable (413 km²), sub-suitable (596.1 km²), and suitable (347.1 km²) areas. This study provides comprehensive spatial details for Ethiopian cultivators, government officials, and agricultural extension specialists to select optimal coffee farming locations, enhancing food security and economic prosperity. Full article

Water quality early warning is a key aspect in industrial recirculating aquaculture systems for high-density shrimp farming. The concentrations of ammonia nitrogen and nitrite in the water significantly impact the cultured animals and are challenging to measure in real-time, posing a substantial challenge to water quality early warning technology. This study aims to collect data samples using low-cost water quality sensors during the industrial recirculating aquaculture process and to construct predictive values for ammonia nitrogen and nitrite, which are difficult to obtain through sensors in the aquaculture environment, using data prediction techniques. This study employs various machine learning algorithms, including General Regression Neural Network (GRNN), Deep Belief Network (DBN), Long Short-Term Memory (LSTM), and Support Vector Machine (SVM), to build predictive models for ammonia nitrogen and nitrite. The accuracy of the models is determined by comparing the predicted values with the actual values, and the performance of the models is evaluated using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) metrics. Ultimately, the optimized GRNN-based predictive model for ammonia nitrogen concentration (MAE = 0.5915, MAPE = 28.95%, RMSE = 0.7765) and the nitrite concentration predictive model (MAE = 0.1191, MAPE = 29.65%, RMSE = 0.1904) were selected. The models can be integrated into an Internet of Things system to analyze the changes in ammonia nitrogen and nitrite concentrations over time through aquaculture management and routine water quality conditions, thereby achieving the application of recirculating aquaculture system water environment early warning technology. Full article

Hydrokinetic arrays, or farms, offer a promising solution to the global energy crisis by enabling cost-effective and environmentally friendly energy generation in locations with water flows. This paper presents research focused on the design and optimization of a Gorlov-type vertical-axis hydrokinetic turbine array for power generation. The study involved (i) numerical simulations using computational fluid dynamics (CFD) software with the six degrees of freedom (6DoF) tool, (ii) optimization techniques such as response surface methodology, and (iii) experimental testing in natural environments. The objective was to develop an efficient system with low manufacturing and maintenance costs. A key finding was that the separation distance between rotors, both along and across the fluid flow, is a critical parameter in designing hydrokinetic arrays. For this study, a triangular array configuration, termed Triframe, was used, consisting of three Gorlov-type turbines with four blades each. The optimization process led to separation distances based on the diameter (D) of the turbines, with 15.9672D along the fluid flow (X) and 4.15719D across the flow (Y). Finally, an experimental scale model of the hydrokinetic array was successfully constructed and characterized, demonstrating the effectiveness of the optimization process described in this study. Full article

Our study aimed to analyse socio-economic sustainability and the drivers of land abandonment in the Kras region of Slovenia, a representative eastern Mediterranean farmland area. We also sought to provide policy recommendations for supporting biodiversity conservation and facilitating the sustainable transition of similar High Nature Value (HNV) farming systems across Europe. The Slovenian Typical Farm Model (SiTFarm) was used to assess the economic performance of representative livestock and wine-growing farm types. Additionally, in-depth interviews with farmers were conducted to understand their perspectives on these farming systems and their preferences for alternative management strategies and policy instruments. Our findings indicate that, due to the introduction of basic income support for sustainability and complementary voluntary coupled payments, budgetary support for the livestock sector in the region is projected to increase by 27–55% in estimated gross margins during the 2023–2027 Common Agricultural Policy (CAP) period, depending on the farm type. Furthermore, farms can enhance their economic performance by converting to organic farming and enrolling in agri-environmental schemes that promote extensive grasslands management, which is crucial for biodiversity conservation. This suggests that Slovenia’s current CAP strategic plan adequately addresses the maintenance of the existing farming systems. However, the region faces significant challenges, particularly in restructuring small farms and adding value to primary farm products. These issues appear to be insufficiently addressed by the current CAP strategic plan, implying that limited progress is expected in mitigating land abandonment in the long term. Comprehensive strategies for the development of feasible HNV farming systems, aligned with biodiversity conservation recommendations, and a well-managed system of supporting institutions and policy instruments is needed to facilitate more market-oriented and sustainable development of agriculture at the local level. Full article

In modern animal husbandry, intelligent digital farming has become the key to improve production efficiency. This paper introduces a model based on improved YOLOv8, Cattle Behavior Recognition-YOLO (CBR-YOLO), which aims to accurately identify the behavior of cattle. We not only generate a variety of weather conditions, but also introduce multi-target detection technology to achieve comprehensive monitoring of cattle and their status. We introduce Inner-MPDIoU Loss and we have innovatively designed the Multi-Convolutional Focused Pyramid module to explore and learn in depth the detailed features of cattle in different states. Meanwhile, the Lightweight Multi-Scale Feature Fusion Detection Head module is proposed to take advantage of deep convolution, achieving a lightweight network architecture and effectively reducing redundant information. Experimental results prove that our method achieves an average accuracy of 90.2% with a reduction of 3.9 G floating-point numbers, an increase of 7.4%, significantly better than 12 kinds of SOTA object detection models. By deploying our approach on monitoring computers on farms, we expect to advance the development of automated cattle monitoring systems to improve animal welfare and farm management. Full article

With the rapid pace of urbanization, a significant number of rural laborers are migrating to cities, leading to a severe shortage of agricultural labor. Consequently, the modernization of agriculture has become a priority. Autonomous picking robots represent a crucial component of agricultural technological innovation, and their development drives progress across the entire agricultural sector. This paper reviews the current state of research on fruit- and vegetable-picking robots, focusing on key aspects such as the vision system sensors, target detection, localization, and the design of end-effectors. Commonly used target recognition algorithms, including image segmentation and deep learning-based neural networks, are introduced. The challenges of target recognition and localization in complex environments, such as those caused by branch and leaf obstruction, fruit overlap, and oscillation in natural settings, are analyzed. Additionally, the characteristics of the three main types of end-effectors—clamping, suction, and cutting—are discussed, along with an analysis of the advantages and disadvantages of each design. The limitations of current agricultural picking robots are summarized, taking into account the complexity of operation, research and development costs, as well as the efficiency and speed of picking. Finally, the paper offers a perspective on the future of picking robots, addressing aspects such as environmental adaptability, functional diversity, innovation and technological convergence, as well as policy and farm management. Full article

In light of the growing plastic waste problem worldwide, including in agriculture, this study focuses on the usefulness of both conventional, non-degradable plastics and environmentally friendly bioplastics in the agricultural sector. Although conventional plastic products are still essential in modern, even ecological agriculture, the increasing contamination by these materials, especially in a fragmented form, highlights the urgent need to search for alternative, easily biodegradable materials that could replace the non-degradable ones. According to the literature, polymers are widely used in agriculture for the preparation of agrochemicals (mostly fertilizers) with prolonged release. They also play a role as functional polymers against pests, serve as very useful super absorbents of water to improve crop health under drought conditions, and are commonly used as mulching films, membranes, mats, non-woven fabrics, protective nets, seed coatings, agrochemical packaging, or greenhouse coverings. This widespread application leads to the uncontrolled contamination of soil with disintegrated polymeric materials. Therefore, this study highlights the possible applications of bio-based materials as alternatives to conventional polyolefins or other environmentally persistent polymers. Bio-based polymers align with the strategy of innovative agricultural advancements, leading to more productive farming by reducing plastic contamination and adverse ecotoxicological impacts on aquatic and terrestrial organisms. On the other hand, advanced polymer membranes act as catching agents for agrochemicals, protecting against environmental intoxication. The global versatility of polymer applications in agriculture will not permit the elimination of already existing technologies involving polymers in the near future. However, in line with ecological trends in modern agriculture, more “green” polymers should be employed in this sector. Moreover, we highlight that more comprehensive legislative work on these aspects should be undertaken at the European Union level to guarantee environmental and climate protection. From the EU legislation point of view, the implementation of a unified, legally binding system on applications of bio-based, biodegradable, and compostable plastics should be a priority to be addressed. In this respect, the EU already demonstrates an initial action plan. Unfortunately, these are still projected directions for future EU policy, which require in-depth analysis. Full article

This paper provides policy recommendations for accelerating the adoption of Fossil-Energy-Free Technologies and Strategies (FEFTS) in the EU agricultural sector. Faster adoption of these technologies and strategies is crucial to achieving the medium- and long-term sustainability targets laid out in EU policy. The prepared policy recommendations originate out of the key outputs and findings of the Horizon 2020 project “AgroFossilFree”, including an assessment and evaluation of the current energy use status in EU agriculture, survey results on farmers’ needs, ideas and interests on the adoption of FEFTS, FEFTS categories identified through an online inventory of FEFTS called the AgEnergy platform, and key innovative processes through national and transnational workshops that combine expertise from hundreds of keys stakeholders (researchers, innovation brokers, policymakers, farmers, and industry representatives). The policy recommendations are synthesized and presented in the form of 19 policy briefs split into three main categories: those that are related to energy issues in farming and can be applied to any farm and FEFTS type; those that are specific to certain agricultural production systems; and those that are necessary for FEFTS integration in agriculture in general. Full article

This study was conducted to investigate and compare the effects of substituting soybean meal (SBM) with untreated cottonseed meal (CSM), fermented CSM (FCSM), or electron beam-irradiated CSM (ICSM) on the growth performance, cecal microbiota, digestive enzyme activity, apparent ileal digestibility (AID), and excreta gas emission of broiler chickens. A total of 384 one-day-old male broiler chickens were randomly assigned to four experimental diets, with eight replicates per diet and 12 birds per replicate, for six weeks. The experimental diets consisted of a control diet based on corn–SBM and three other diets in which 50% of the SBM (control) was substituted with CSM in its raw, irradiated, and fermented forms. The results showed that throughout the entire rearing period, feeding broiler chickens with ICSM significantly increased average daily gain (ADG) and body weight (BW) compared to the CSM diet (p < 0.05). Replacing 50% of SBM with FCSM led to a significant improvement in BW, ADG, and feed conversion ratio (FCR) compared to the CSM and ICSM diets (p < 0.05). Interestingly, no significant differences in BW, ADG, or FCR were observed between birds fed FCSM and those on the control diet (p > 0.05). Birds fed FCSM diets exhibited the lowest pH value in the crop, ileum, and ceca. Substituting SBM with FCSM significantly reduced Escherichia coli and Clostridium spp. counts in the ceca, while enhancing the presence of Lactobacillus spp. (p < 0.05). The AID of protein and ether extract was higher in the FCSM group than in the CSM and ICSM groups (p < 0.05). Compared to the CSM diet, ICSM feeding improved protein digestibility (p < 0.05). Broiler chickens on the FCSM diet exhibited higher intestinal amylase and protease activity than those on the other diets (p < 0.05). Furthermore, feeding diets containing FCSM significantly reduced ammonia emissions compared to the other diets (p < 0.05). Overall, our results indicated that microbial fermentation of CSM is a more effective approach than irradiation for enhancing the nutritional value of CSM. Therefore, FCSM is recommended as a viable alternative protein source that can safely replace up to 50% of SBM in broiler chicken diets, particularly during times of fluctuating SBM prices and availability issues. Full article

Dairy farming is one of the most important sources of greenhouse gas (GHG) emissions in the livestock sector. In order to identify the key emission links and the best emission-reduction strategies for combined dairy farms, this study selected a typical large-scale combined dairy farm in northeast China, constructed a carbon emission model based on the lifecycle assessment concept, and set up different emission reduction scenarios to explore the zero-carbon pathway for combined dairy farms. The results showed that: (1) enteric fermentation and manure management of cows are important sources of carbon emissions from the seeding-integrated dairy farms, accounting for 38.2% and 29.4% of the total, respectively; (2) the seeding-integrated system showed a 10.6% reduction in carbon footprint compared with the non-seeding-integrated system; and (3) scenarios 1–4 reduced carbon emissions by 9%, 20%, 42%, and 61% compared with the baseline scenario, respectively. Therefore, the integrated-farming model is important for the green development of animal husbandry, and as the “net-zero” goal cannot be achieved at present, integrated-farming dairy farms have the potential for further emission reduction. The results of this study provide a theoretical basis for low-carbon milk production. Full article

Tomato is an economically and nutritionally important crop and is vulnerable to drought. Under drought, soil microbes provide beneficial effects to plants and alleviate stress. We suggest a reconstruction of the soil microbiome using biofumigation, an organic farming method, to protect tomatoes. In this study, we treated soil in four ways with varied concentrations: biofumigation (BF0.5, BF1.0, and BF1.5), green manure treatment (GM0.5, GM1.0, and GM1.5), autoclaving (AT), and non-treatment (NT). Tomatoes were grown in each treated soil, subjected to water shortages, and were rewatered. We investigated plant phenotypes and soil properties, focused on microbial communities using the Illumina MiSeq^® System. Relative Water Content and malondialdehyde were measured as plant stress. The results showed that the 1% biofumigation treatment had 105% and 108.8% RWC during drought and after rewatering, compared to the non-treated soil. The highest concentration, the 1.5% treatment, lowered RWC due to an excess of NO₃⁻, K⁺, Ca²⁺, and decreased alpha diversity. Through PLS-PM, bacterial alpha diversity was found to be the largest factor in the increase in RWC (coefficient = 0.3397), and both biofumigant and green manure significantly increased the Shannon index and observed species. In addition, biofumigation increased beneficial functional genes (purine metabolism, pyrimidine metabolism, carbon fixation pathways, and zeatin bio-synthesis) of soil microorganisms (p value < 0.05, <0.01, >0.05, and <0.05, respectively). The 1% biofumigation treatment enriched the core five genera of the fungal network (Enterocarpus, Aspergillus, Leucothecium, Peniophora, and Wallemia) of the fungal network which might suppress the most dominant pathogen, Plectosphaerella. In conclusion, biofumigation-derived soil microbiome alterations have the potential to lower plant stress under drought. Full article

The objective of this study was to determine whether the parity and age of dams affect the longevity of their offspring in dairy cows in pasture-based systems. A total of 12,792 dairy cows born between 2000 and 2017 across five farms were evaluated using records from the Dairy Herd Improvement Database at Instituto Nacional para el Control y Mejoramiento Lechero (Uruguay). Dams were classified as primiparous or multiparous, and parity number and age were considered. The effect of parity status on herd life (HL), the length of productive life (LPL), and the productive life index (PLI) was evaluated using a generalized mixed model. Associations between parity number and dam age with HL, LPL, and PLI were evaluated using regression models. HL, LPL, and PLI were significantly higher for daughters of multiparous cows. Dams with more parities gave birth to longer-living daughters, with an average HL difference of 4.4 months between the first and seventh parity of the dams. The parity number and age of the dam showed a significant association with HL, LPL, and PLI. In conclusion, the parity and age of the dam influence the longevity of dairy cows in pasture-based systems, with older dams and higher parity yielding daughters with greater longevity. Full article

Farmed fish are regularly subjected to various stressors due to farming practices, and their effect in the context of a disease outbreak is uncertain. This research evaluated the effects of unpredictable repeated stress in rainbow trout challenged with the ciliate Ichthyophthirius multifiliis, known to cause white spot disease in freshwater fish. Before and after the pathogen exposure, fish were handled with a random rotation of three procedures. At 7 days post-infection (dpi), the parasite burden was evaluated in fish and in the tank’s water, and the local and systemic immune responses were investigated in the gill and spleen, respectively. The fish mortality was recorded until 12 dpi, when all the fish from the infected groups died. There was no statistical difference in parasite burden (fish and tank’s water) and infection severity between the two infected fish groups. The immune gene expression analysis suggested a differential immune response between the gill and the spleen. In gills, a T helper cell type 2 immune response was initiated, whereas in spleen, a T helper cell type 1 immune response was observed. The stress has induced mainly upregulations of immune genes in the gill (cat-1, hep, il-10) and downregulations in the spleen (il-2, il-4/13a, il-8). Our results suggested that the unpredictable repeated stress protocol employed did not impair the fish immune system. Full article