Search Results (47)

Precision application of fertiliser nutrients based on soil-available nutrients is a vital means of increasing castor (Ricinus communis L.) productivity. Fertiliser application based on the targeted yield model under inorganic fertilisers alone and Integrated Plant Nutrition System (IPNS) differ from the blanket recommendation practices. Field experiments were conducted in two locations to validate the Soil Test Crop Response (STCR) targeted yield model developed for hybrid castor on non-calcareous Alfisol. The main objective was to determine the effect of inorganic fertilisers and organic manures on microbial populations, enzyme dynamics in soil, and productivity of castor. Experimental field data revealed that combined application of inorganic fertilisers along with 12.5 t ha⁻¹ farmyard manure increased the soil microbial population and enzyme activity in the rhizosphere soils of castor. Castor responded positively with an increase in highest targeted yield level. The highest yield of 2726 and 2695 kg ha⁻¹ were attained in the treatment T₈ (STCR-IPNS −2.75 t ha⁻¹) in both locations, and Treatment T₅ (STCR-NPK alone −2.75 t ha⁻¹) was on par with T₈. The IPNS treatments showed higher percent achievement than the NPK treatments alone. Root length and dry matter production increased significantly with the application of a higher dose of fertiliser along with farmyard manure. Root dry matter production significantly contributed towards the castor seed yield. More soil-beneficial microorganisms and enzyme dynamics were observed in the IPNS treatment. Full article

Accurate estimation of winter wheat yield is essential for ensuring food security. Recent studies on winter wheat yield estimation based on deep learning methods rarely explore the interpretability of the model from the perspective of crop growth mechanism. In this study, a multiscale winter wheat yield estimation framework (called MultiScaleWheatNet model) was proposed, which was based on time series remote sensing data and further takes into account meteorological and soil factors that affect wheat growth. The model integrated multimodal data from different temporal and spatial scales, extracting growth characteristics specific to particular growth stage based on the growth pattern of wheat phenological phase. It focuses on enhancing model accuracy and interpretability from the perspective of crop growth mechanisms. The results showed that, compared to mainstream deep learning architectures, the MultiScaleWheatNet model had good estimation accuracy in both rain-fed and irrigated farmlands, with higher accuracy in rain-fed farmlands (R² = 0.86, RMSE = 0.15 t·ha⁻¹). At the county scale, the accuracy of the model in estimating winter wheat yield was stable across three years (from 2021 to 2023, R² ≥ 0.35, RMSE ≤ 0.73 t·ha⁻¹, nRMSE ≤ 20.4%). Model interpretability results showed that, taking all growth stages together, the remotely sensed indices had relatively high contribution to wheat yield, with roughly equal contributions from meteorological and soil variables. From the perspective of the growth stages, the contribution of LAI in remote sensing factors demonstrated greater stability throughout the growth stages, particularly during the jointing, heading-filling and milky maturity stage; the combined impact of meteorological factors exhibited a discernible temporal sequence, initially dominated by water availability and subsequently transitioning to temperature and sunlight in the middle and late stages; soil factors demonstrated a close correlation with soil pH and cation exchange capacity in the early and late stages, and with organic carbon content in the middle stage. By deeply combining remote sensing, meteorological and soil data, the framework not only achieves high accuracy in winter wheat yield estimation, but also effectively interprets the dynamic influence mechanism of remote sensing data on yield from the perspective of crop growth, providing a scientific basis for precise field water and fertiliser management and agricultural decision-making. Full article

Soil nutrient monitoring is essential to achieving UN development goals and meeting the projected 70% increase in agricultural production from 2009 values by 2050. This study presents a novel, low-cost impedimetric device for the direct and simultaneous measurement of soil ion bioavailability (Na⁺, K⁺), temperature, and humidity. Designed for Arduino integration, the device offers scalable, cost-effective deployment. Different AI algorithms were trained to interpret signals (Support Vector Machine, Random Forest, XBoost), enabling real-time monitoring. Best performance was achieved for XBoost. Calibration was first performed using solutions of known NaCl and KCl concentrations to establish impedance patterns, and benchmarking against fitted Cole model outputs demonstrated high predictive accuracy (R² = 0.99 for both Na⁺ and K⁺). The system operated across a 1–100 kHz impedance range with environmental resolution of ±0.5 °C, ±3% RH, and ±1 hPa, acquiring data every 10 min during in vivo trials. This affordable, AI-enhanced platform has the potential to empower smallholder farmers by reducing reliance on costly laboratory analyses, enabling precise fertiliser application, and integrating seamlessly into smart farming platforms for sustainable yield improvement. Full article

Spatial variability in vine vigour and yield components is a major challenge for vineyard management and consistent grape quality, particularly in hilly landscapes. This study evaluates the impact of on-the-go variable-rate fertilisation (VRA) in reducing within-vineyard variability in an 8.5 hectares commercial vineyard in the Conegliano Valdobbiadene Prosecco DOCG region (Italy). Over two growing seasons, a proximal NDVI sensor (GreenSeeker) guided real-time fertiliser applications without prescription maps. Vine vigour, yield components, and grape quality were evaluated using geostatistical analysis and coefficient of variation (CV) metrics. VRA reduced total spatial variability (sill) by 55% and erratic variance (nugget effect) by 39% for NDVI measurements. Variability in yield components also decrease (−21.1% for cluster number, −6.25% for cluster weight), while grape composition parameters (total soluble solids, titratable acidity, and pH) was not significantly altered despite a slightly higher variability (in titratable acidity and pH), indicating that fertiliser modulation did not compromise grape quality. Nitrogen input was reduced by 50%, highlighting economic and environmental benefits (−302 kg CO₂). These results show that simplified, sensor-based, on-the-go VRA is a practical and sustainable precision viticulture tool, even in small and heterogeneous vineyards typical of the Conegliano Valdobbiadene Prosecco DOCG area. Full article

The nitrogen (N) requirement of sugarcane (Saccharum spp.) is very high due to the extensive growth of biomass. N fertilisers are applied excessively to ensure the optimum growth of the sugarcane crop. Improper N management causes a decrease in nitrogen utilisation efficiency (NUE) and contributes to N losses via leaching and gaseous emissions in the form of ammonia (NH₃) and nitrous oxide (N₂O), leading to unintended negative consequences. Asynchronous timing between the sugarcane N demand and supply by the N sources exacerbates these losses. Therefore, proper N management strategies need to be implemented to mitigate losses and enhance NUE. This review provides an overview of global sugarcane cultivation and discusses the N requirements for sugarcane crops. Additionally, it summarises the various strategies utilised in N management for sugarcane cultivation and evaluates their effectiveness. Furthermore, it identifies research gaps and outlines future research directions. Full article

Agriculture relies on the widespread application of nitrogen fertilisers to improve crop yields and meet the demands of a growing population. However, the excessive use of these fertilisers has led to significant water quality challenges, posing risks to aquatic life, ecosystems, and human health. This study examines the relationship between synthetic nitrogen fertiliser usage and water pollution while identifying gaps in existing research to guide future studies. A systematic search across databases (Scopus, Web of Science, and Greenfile) identified 18 studies with quantitative data, synthesised using a single-group meta-analysis of means. As the data were continuous, the mean was used as the effect measure, and a random-effects model was applied due to varied study populations, with missing data estimated through statistical assumptions. The meta-analysis found an average nitrate concentration of 34.283 mg/L (95% confidence interval: 29.290–39.276), demonstrating the significant impact of nitrogen fertilisers on water quality. While this average remains marginally below the thresholds set by the World Health Organization (50 mg/L NO₃⁻) and EU Nitrate Directive, it exceeds the United States Environmental Protection Agency limit (44.3 mg/L NO₃⁻), signalling potential health risks, especially in vulnerable or unregulated regions. The high observed heterogeneity (I² = 100%) suggests that factors such as soil type, agricultural practices, application rate, and environmental conditions influence nitrate levels. While agriculture is a key contributor, other anthropogenic activities may also affect nitrate concentrations. Future research should comprehensively assess all influencing factors to determine the precise impact of nitrogen fertilisers on water quality. Full article

The intensification of olive growing has raised environmental concerns, particularly regarding nutrient loss from excessive fertiliser use. In line with the European Union’s Farm to Fork strategy, which aims to halve the soil nutrient losses by 2030, this study evaluates the effectiveness of two sensor-based approaches—proximal sensing with a FLAME spectrometer and remote sensing via UAV-mounted multispectral imaging—compared with foliar chemical analyses as the reference standard, for diagnosing the nutritional status of olive trees. The research was conducted in Elvas, Portugal, between 2022 and 2023, across three olive cultivars (‘Azeiteira’, ‘Arbequina’, and ‘Koroneiki’) subjected to different fertilisation regimes. Machine learning (ML) models showed strong correlations between sensor data and nutrient levels: the multispectral sensor performed best for phosphorus (P) (determination coefficient [$R^2$] = 0.75) and potassium (K) ($R^2$ = 0.73), while the FLAME spectrometer was more accurate for nitrogen (N) ($R^2$ = 0.64). These findings underscore the potential of sensor-based technologies for non-destructive, real-time nutrient monitoring, with each sensor offering specific strengths depending on the target nutrient. This work contributes to more sustainable and data-driven fertilisation strategies in precision agriculture. Full article

Cannabis sativa L. is a dioecious species known to produce over 1600 chemical constituents, including more than 180 cannabinoids classified into 11 structural groups. These bioactive compounds are predominantly synthesised in the glandular trichomes of female inflorescences. However, sex determination in C. sativa is influenced by both genetic and environmental factors, often leading to the development of male flowers on female plants. This unintended fertilisation reduces cannabinoid yield and increases genetic heterogeneity and challenges in medical cannabis production. Haploid and doubled haploid (DH) technologies offer a promising solution by rapidly generating homozygous lines from gametophytic (e.g., unpollinated ovaries and ovules) or sporophytic tissues (e.g., anthers and microspores) via in vitro culture or chromosome reduction during hybridisation. In land plants, the life cycle alternates between a diploid sporophyte and a haploid gametophyte generation, both capable of mitotic division to form multicellular bodies. A single genome regulates this phase transition and encodes the molecular, genetic, and epigenetic mechanisms that precisely control the developmental processes unique to each generation. While the application of haploid technology in C. sativa remains limited, through recent progress in haploid induction (HI) and CRISPR-based genome editing, the direct modification of haploid gametes or embryos enables the creation of null homozygous lines following chromosome doubling, improving genetic uniformity. Understanding the molecular mechanisms of spontaneous chromosome doubling may further facilitate the development of elite cannabis genotypes. Ultimately, enhancing the efficiency of DH production and optimising genome editing approaches could significantly increase the speed of genetic improvement and cultivar development in Cannabis sativa. Full article

One of the most commonly performed environmental explorations is soil sampling to identify soil properties of agricultural fields, which can inform the farmer about the variable rate treatment of fertilisers in precision agriculture. However, traditional manual methods are slow, costly, and yield low spatial resolution. Deploying multiple robots with proximal sensors can address this challenge by parallelising the sampling process. Yet, multi-robot soil sampling is under-explored in the literature. This paper proposes an auction-based multi-robot task allocation that efficiently coordinates the sampling, coupled with a dynamic sampling strategy informed by Kriging variance from interpolation. This strategy aims to reduce the number of samples needed for accurate mapping by exploring and sampling areas that maximise information gained per sample. The key innovative contributions include (1) a novel Distance Over Variance (DOV) bid calculation for auction-based multi-robot task allocation, which incentivises sampling in high-uncertainty, nearby areas; (2) integration of the DOV bid calculation into the cheapest insertion heuristic for task queuing; and (3) thresholding of newly created tasks at locations with low Kriging variance to drop those unlikely to offer significant information gain. The proposed methods were evaluated through comparative simulated experiments using historical soil compaction data. Evaluation trials demonstrate the suitability of the DOV bid calculation combined with task dropping, resulting in substantial improvements in key performance metrics, including mapping accuracy. While the experiments were conducted in simulation, the system is compatible with ROS and the ‘move_base’ action client to allow real-world deployment. The results from these simulations indicate that the Kriging-variance-informed approach can be applied to the exploration and mapping of other soil properties (e.g., pH, soil organic carbon, etc.) and environmental data. Full article

The restrictions imposed by the European Green Deal on Europe are expected to make Europe climate-neutral by 2050. In this context, this article examines the current efforts to reduce emission levels, focusing on available international scientific papers concerning European territory, particularly Poland. The study paid special attention to the sector of agriculture, which is considered a key contributor to greenhouse gas generation. It also analysed the impact of various tillage techniques and the application of organic and inorganic fertilisers, e.g., nitrogen fertilisers, digestate, or compost, on the emissions of greenhouse gases and other environmentally harmful substances. Although there are few scientific articles available that comprehensively describe the problem of greenhouse gas emissions from agriculture, it is still possible to observe the growing awareness of farmers and their daily impact on the environment. The current study demonstrated that agricultural activities significantly contribute to the emissions of three main greenhouse gases: carbon dioxide, nitrous oxide, and methane. The tillage and soil fertilisation methods used play a crucial role in their emissions into the atmosphere. The use of no-tillage (or reduced-tillage) techniques contributes to the sustainable development of agriculture while reducing greenhouse gas emissions. The machinery and fuels used, along with innovative systems and sensors for precise fertilisation, play a significant role in lowering emission levels in agriculture. The authors intend to identify potential opportunities to improve crop productivity and contribute to sustainable reductions in gas emissions. Full article

Mitochondria, as multifunctional and partially independent structures, play a crucial role in determining essential life processes. Recently, their significance in reproductive biology has gained increasing attention. This review aims to comprehensively analyse the role of mitochondrial processes in oocyte maturation and embryo culture. A comprehensive literature review was conducted to highlight the importance of mitochondrial activity in the early stages of life formation. Proper mitochondrial function provides energy, maintains genomic stability, and ensures optimal conditions for fertilisation and embryo progression. Understanding these processes is essential to optimise culture conditions and identify new mitochondrial biomarkers that improve reproductive success and improve assisted reproductive technologies (ARTs). Enhancing mitochondrial function in female reproductive cells is the key to improving oocyte and embryo quality, which can lead to better in vitro fertilisation and embryo transfer. Furthermore, advances in diagnostic techniques, such as mitochondrial genome sequencing, offer a more precise understanding of the relationship between mitochondrial health and oocyte quality. However, fully understanding mitochondrial functions is only part of the challenge. Expanding knowledge of the interactions between mitochondria and other cellular structures is crucial for future advancements in reproductive medicine. Understanding these complex relationships will provide deeper insight into improving reproductive outcomes and embryo development. Full article

On intensive dairy farms, good decision making regarding application of fertilisers and irrigation requires an understanding of soil moisture conditions. Targeted fertiliser application not only contributes to high nutrient use efficiency but reduces the potential for leaching of nutrients and controls emissions from farms. This calls for the development of an improved farm management decision support system focussed on precision agriculture solutions for sustainable agriculture. Knowledge of soil moisture at high resolution at the farm scale can help develop such solutions while at the same time reducing the risk of soil compaction by machinery and/or animals, especially under wet conditions. The objective of this study is to examine and compare two intensive dairy farms, with similar average annual rainfall but contrasting soil (but similar drainage) and topographic characteristics, for their resilience towards extreme conditions (e.g., saturation or drought). Soil moisture thresholds for optimal conditions and corresponding farm area proportions were calculated, identifying areas for targeted farm management. This study addresses the knowledge gap of including high-resolution satellite derived soil moisture as a variable in designing farm management systems targeted towards precision agriculture. Farm 1 was situated in a drumlin belt, whereas Farm 2 had lowland terrain, representing major land cover categories in Ireland. The results showed that Farm 2 was more resilient towards extreme conditions and that the variable topography and soil heterogeneity act as a buffer in regulating moisture regimes on the farm, preventing movement towards the extremes. Across the years, Farm 1 showed less variability in optimal farm area proportions and could be managed better than Farm 2 in terms of overall productivity and resilience towards extreme weather conditions such as droughts, even in a drought year. This study showed that along with variations in soil type, topographic features also dictate water movement and therefore soil moisture regimes on farms. Full article

Nutrient levels in liquid organic manures (LOM) vary greatly, so it is important to determine the concentrations before field application in order to ensure that fertilisation is tailored to the crop requirements. Precise knowledge of the nutrient content in LOMs is a basic prerequisite for the optimum supply of these nutrients to crops and for avoiding environmental problems caused by over-fertilisation. The constituents of LOMs can be determined on site using various methods. One possibility is near infrared spectroscopy (NIRS). This method is already a common procedure for use in the laboratory. This review deals with the suitability of the use of NIRS for the characterisation of LOMs on farm. For on-farm applications, there are many factors such as the ambient temperature or movements and vibrations of the machines which can influence the measurement with the sensors and thus also the measured values. The influencing factors should therefore be taken into account. The reliability of NIRS systems for the on-farm analysis of liquid manure is verified by the German Agricultural Society. For the tests, various LOMs from different farms are measured with NIRS sensors and the quality of the agreement of the NIRS data with laboratory tests is certified for the respective ingredients for each LOM type. In order to exploit the full potential of the NIRS technology in the future, the indispensable calibrations need to be expanded and improved so that the sensors deliver precise and reproducible results for the different LOM types in practical applications. Full article

From fertilisation to delivery, calcium must be transported into and within the foetoplacental unit for intracellular signalling. This requires very rapid, precisely located Ca²⁺ transfers. In addition, from around the eighth week of gestation, increasing amounts of calcium must be routed directly from maternal blood to the foetus for bone mineralisation through a flow-through system, which does not impact the intracellular Ca²⁺ concentration. These different processes are mediated by numerous membrane-sited Ca²⁺ channels, transporters, and exchangers. Understanding the mechanisms is essential to direct interventions to optimise foetal development and postnatal bone health and to protect the mother and foetus from pre-eclampsia. Ethical issues limit the availability of human foetal tissue for study. Our insight into the processes of placental Ca²⁺ handling is advancing rapidly, enabled by developing genetic, analytical, and computer technology. Because of their diverse sources, the reports of new findings are scattered. This review aims to pull the data together and to highlight areas of uncertainty. Areas needing clarification include trafficking, membrane expression, and recycling of channels and transporters in the placental microvilli; placental metabolism of vitamin D in gestational diabetes and pre-eclampsia; and the vascular effects of increased endothelial Orai expression by pregnancy-specific beta-1-glycoproteins PSG1 and PSG9. Full article

The proposed changes in the European Green Deal require the restructuring of the agricultural sector throughout the European Union. Particularly crucial for the agri-food industry are “From Farm to Fork” strategies and new legislation “for biodiversity,” which are an integral part of the European Green Deal. From an environmental perspective, changes are required immediately, but at the same time, they may reduce agricultural production in Poland. Therefore, this study aims to assess the potential consequences of implementing the European Green Deal principles on Polish agriculture’s productivity. The study is based on two key assumptions which involve a 50% reduction in the use of plant protection products and a 20% reduction in fertilisation. The conducted analyses rely on data from the Farm Accountancy Data Network (FADN). The results were obtained by constructing a panel regression model for farms of different economic sizes. Although the most significant reduction in production value may concern the largest farms (using the most fertilisers and plant protection products), the smallest farms may experience the most critical difficulties. The potential decline in crop production in Polish agriculture could be stopped by implementing modern technologies enabling the development of precision and digital agriculture 4.0. Full article