Search Results (159)

The petiole nitrate–nitrogen concentration (PNNC) has been an industry standard indicator for in-season potato (Solanum tuberosum L.) nitrogen (N) status diagnosis. Leaf sensors can be used to predict the PNNC and other N status indicators non-destructively. The SPAD meter is a common leaf chlorophyll (Chl) meter, while the Dualex is a newer leaf fluorescence sensor. Limited research has been conducted to compare the two leaf sensors for potato N status assessment. Therefore, the objectives of this study were to (1) compare SPAD and Dualex for predicting potato N status indicators, and (2) evaluate the potential prediction improvement using multi-source data fusion. The plot-scale experiments were conducted in Becker, Minnesota, USA, in 2018, 2019, 2021, and 2023, involving different cultivars, N treatments, and irrigation rates. The results indicated that Dualex’s N balance index (NBI; Chl/Flav) always outperformed Dualex Chl but did not consistently perform better than the SPAD meter. All N status indicators were predicted with significantly higher accuracy with multi-source data fusion using machine learning models. A practical strategy was developed using a linear support vector regression model with SPAD, cultivar information, accumulated growing degree days, accumulated total moisture, and an as-applied N rate to predict the vine or whole-plant N nutrition index (NNI), achieving an R² of 0.80–0.82, accuracy of 0.75–0.77, and Kappa statistic of 0.57–0.58 (near-substantial). Further research is needed to develop an easy-to-use application and corresponding in-season N recommendation strategy to facilitate practical on-farm applications. Full article

Simulating potential glacier collapses can provide crucial support for local disaster prevention and mitigation efforts. The Xiaomagou Glacier in the Amnye Machen Mountains, Qinghai–Tibetan Plateau, has experienced five collapses in the past two decades. Field investigation and remote sensing observations indicate that the topography and bedrock characteristics of the Qushi’an No. 22 Glacier, which is 3.5 km south of the Xiaomagou Glacier, are similar to those of the Xiaomagou Glacier. More importantly, the mass movement of the Qushi’an No. 22 Glacier since 2018 closely resembles that of the Xiaomagou Glacier exhibited before its previous collapses. Therefore, in the context of rising temperatures, it is possible that the Qushi’an No. 22 Glacier will collapse in the near future. Based on remote sensing imagery and the glacier’s surface elevation changes, we reconstructed the 2004 collapse process of the Xiaomagou Glacier via numerical simulation. The key parameters of the mass flow model were optimized based on the actual deposition area of the 2004 collapse. The model with optimized parameters was then used to simulate the potential Qushi’an No. 22 Glacier collapse. Two collapse scenarios were set for the Qushi’an No. 22 Glacier. In Scenario 1, the lower half of the tongue detaches; in Scenario 2, the whole tongue detaches. Simulation results show that, in Scenario 1, the maximum mass flow depth is 72 m, the maximum mass flow speed is 51.6 m/s, and the deposition area is 5.40 × 10⁶ km²; in Scenario 2, the maximum mass flow depth is 75 m, the maximum mass flow speed is 59.7 m/s, and the deposition area is 6.32 × 10⁶ km². In both scenarios, the deposition area is much larger than that of the Xiaomagou Glacier 2004 collapse, which had a deposition area of 2.21 × 10⁶ km². The simulation results suggest that the Qushi’an No. 22 Glacier collapse could devastate the pastures and township roads lying in front of the glacier, seriously affecting local transportation and livestock farming; furthermore, it may deposit in the Qinglong River, forming a large, dammed lake. At present, the Qushi’an No. 22 Glacier remains in an unstable state. It is crucial to strengthen monitoring of its surface morphology, flow speed, and elevation. Full article

Significant models predicting Soil Organic Carbon (SOC) and other chemical and biological indicators of soil health in an experimental farm with semi-arid Mediterranean Calcisol have been obtained by partial least squares (PLS) regression, with mid-infrared (MIR) spectra of whole soil samples used as independent variables (IVs). The dependent variables (DVs) included SOC, pH, electric conductivity, N, P₂O₅, K, Ca²⁺, Mg²⁺, Na⁺, Fe, Mn, Cu and Zn. The DVs also included free-living nematodes and microbivores, such as Rhabditids and Cephalobids, and phytoparasitics, such as Xiphinema spp. and other Dorylaimids. More importantly, an attempt was made to determine which spectral patterns allowed each dependent variable (DV) to be predicted. For this purpose, a number of statistical indices were plotted between 4000 and 450 cm⁻¹, e.g., variable importance for prediction (VIP) and beta coefficients from PLS, loading factors from principal component analysis (PCA) and correlation and determination indices. The most effective plots, however, were the “scaled subtraction spectra” (SSS) obtained by subtracting the averages of groups of spectra in order to reproduce the spectral patterns typical in soils where the values of each DV are higher, or vice versa. For instance, distinct SSS resembled the spectra of carbonate, clay, oxides and SOC, whose varying concentrations enabled the prediction of the different DVs. Full article

The increasing integration of sensing devices with smart technologies, deep learning algorithms, and robotics is profoundly transforming the agricultural sector in the context of Farming 4.0. These technological advancements constitute critical enablers for the development of customized, data-driven farming systems, offering potential solutions to the challenges of agricultural intensification while addressing societal concerns associated with the emerging paradigm of “farming by numbers”. The Precision Livestock Farming (PLF) systems enable the continuous, real-time, and individual sensing of livestock in order to detect subtle change in animals’ status and permit timely corrective actions. In addition, smart technology implementation within the housing environment leads the whole farming sector towards enhanced business rentability and food security as well as increased animal health and welfare conditions. Looking to the future, the collection, processing, and analysis of data with advanced statistic methods provide valuable information useful to design predictive models and foster the insight on animal welfare, environmental sustainability, farming productivity, and profitability. This review highlights the significant potential of implementing advanced sensing systems in livestock farming, examining the scientific foundations of PLF and analyzing the main technological applications driving the transition from traditional practices to more modern and efficient farming models. Full article

Our society is facing serious challenges from global warming and environmental degradation. Scientists have identified carbon dioxide as one of the causes. Our society is embracing carbon offset as a way to field the challenges. The purpose of carbon offset is trying to cancel out the large amounts of carbon dioxide by investing in projects that reduce or remove emissions elsewhere. Examples of carbon offset projects are planting trees, renewable energy projects, and capturing methane from landfills or farms. Not all carbon offset projects are equally effective. In stock markets, investors eagerly pursue carbon offset. Namely, investors favor carbon offset in addition to risk and return when investing. Therefore, investors supervise risk, return, and carbon offset. Investors’ pursuits raise the question of how to model carbon offset for investments. The traditional answer is to adopt carbon offset screening and engineer portfolios by stocks with good carbon offset ratings. However, Nobel Laureate Markowitz emphasizes portfolio selection rather than stock selection. Moreover, carbon offset is composed of multiple components, ranging from business, social, economic, and environmental aspects. This multifaceted nature requires more advanced models than carbon offset screening and portfolio selection. Within this context, we systematically formulate multiple-objective portfolio selection models that include carbon offset. Firstly, we extend portfolio selection and treat carbon offset as a whole. Secondly, we separate carbon offsets into different components and build models to monitor each component. Thirdly, we innovate a model to monitor each component’s expectation and mitigate each component’s risk. Lastly, we optimize the series of models and prove the models’ properties in theorems. Mathematically, this paper makes theoretical contributions to multiple-objective optimization, particularly by proving the consistency of efficient solutions during objective classification and model evolution, describing the structure of properly efficient sets for multiple quadratic objectives, and elucidating the optimization’s sensitivity analyses. Moreover, by coordinating the abstract objective function, our formulation is generalizable. Overall, this paper’s contribution is to model carbon offset investments through multiple-objective portfolio selection. This paper’s methodology is multiple-objective optimization. This paper’s achievements are to provide investors with greater precision and effectiveness than carbon offset screening and portfolio selection through engineering means and to mathematically prove the properties of the model. Full article

This study evaluates the safety aspects of a prototype electric vehicle designed to enable wheelchair users to independently perform simple farm tasks in rural settings, like sample collection and crop monitoring. The vehicle, built at CREA, features four in-wheel electric motors, a pneumatic suspension system, and a secure wheelchair anchoring system. Tests at the CREA experimental farm assessed the vehicle’s whole-body vibrations on different surfaces (asphalt, headland, dirt road) using two tyre models and multiple speeds. A triaxial accelerometer on the wheelchair seat measured vibrations, which were analysed in accordance with ISO standards. Frequency analysis revealed significant vibrations in the 2–40 Hz range, with the Z-axis consistently showing the highest accelerations, which increased with the speed. Tyre A generally induced higher vibrations than Tyre B, likely due to the tread design. At high speeds, the effective accelerations exceeded safety thresholds on asphalt and headland. Statistical analysis confirmed speed as the dominant factor, with the surface type also playing a key role—headland generated the highest vibrations, followed by dirt road and asphalt. The results of these first tests highlighted the high potential of the vehicle to improve the agricultural mobility of disabled people, granting safety conditions and low vibration levels on all terrains at speeds up to 10 km h⁻¹. At higher speeds, however, the vibration levels may exceed the exposure limits, depending on the irregularities of the terrain and the tyre model. Overcoming these limitations is achievable through the optimization of the suspensions and tyres and will be the subject of the next step of this study. This technology could also support wheelchair users in construction, natural parks, and urban mobility. Full article

Wheat is a critical economic and food crop in global agricultural production, with changes in wheat cultivation directly impacting the stability of the global food market. Therefore, developing a method capable of accurately estimating carbon flux in wheat is of significant importance for early warning agricultural production risks and guiding farming practices. This study constructs a multimodal model framework to estimate wheat carbon flux using MODIS data products, including the Leaf Area Index (LAI), the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and meteorological data products. The results demonstrate that the constructed carbon flux detection model effectively estimates carbon flux across different growth stages of wheat. Evaluation of the model, using comprehensive accuracy metrics, shows an average adjusted R² of 0.88, an RMSE of 5.31 gC·m⁻²·8d⁻¹, and nRMSE of 0.05 across four growth stages, indicating high accuracy with minimal error. Notably, the model performs more accurately at the green-up stage compared to other stages. Interpretability analysis further reveals key features influencing model estimations, with the top five ranked features being (1) LAI, (2) NDVI, (3) EVI, (4) vapor pressure (Vap), and (5) the Palmer Drought Severity Index (PDSI). Remote sensing indices exhibit a greater influence on carbon flux estimation throughout the whole growth stages compared to meteorological indices. Under water-limiting conditions, the importance of evapotranspiration, precipitation, and drought-related factors fluctuates significantly. This study not only provides an important reference for monitoring wheat carbon flux, but also offers novel insights into the crop carbon cycling mechanisms within agroecosystems under the current environmental context. Full article

Background/Objectives: Understanding beef cattle farmers’ knowledge, attitudes, and practices on infectious disease prevention, antimicrobial use, and antimicrobial resistance (AMR) is important to developing stewardship programs. Methods: A cross-sectional stratified mail or phone survey of beef cattle producers in Illinois was conducted between June and August 2022. Ordinal logistic regression models assessed the impact of having a biosecurity plan on beef cattle farmers’ familiarity with cattle diseases. Logistic regression models evaluated associations between antimicrobial treatment practices and the type of cattle operations. Results: A total of 514 producers responded to all or some of the questions. Only 45% of producers were familiar with AMR, and 11% were concerned about cattle infections with antibiotic-resistant bacteria. Producers agreed or strongly agreed (64%) that inappropriate AMU contributes to the development of AMR. Most producers (70%) thought that antimicrobials were as effective in treating infectious diseases as 5 years ago. Only 50% of farms were visited by a veterinarian in the previous year and 35% had their biosecurity evaluated. Producers were more familiar with infectious diseases if their farm biosecurity was assessed. Treating respiratory infections was the most common reason for antimicrobial use. Compared to cow–calf farmers, whole-cycle farmers had a higher probability of having their farm’s biosecurity evaluated (OR = 1.66) and having a veterinarian visit in the previous year (OR = 2.16). Whole-cycle (OR = 3.92) and stocker/backgrounder (OR = 2.18) farmers had a higher probability of treating their cattle with antibiotics than cow–calf farmers. Conclusions: Antimicrobial stewardship and farm biosecurity programs are needed to raise awareness of disease prevention, AMU, and AMR among Illinois beef cattle producers. Full article

At present, the equivalent modeling method of wind farms mostly adopts single-machine multiplication equivalence, which has the deficiencies of large model error and difficulty in accurately reflecting the fault transient characteristics of wind farms, which imposes limitations on the security and stability analysis of the power system. For this reason, this paper proposes an equivalent modeling method that can accurately reflect the fault ride-through characteristics of wind farms. Based on the control mechanism of direct-drive wind turbines, this method first analyzes the fault ride-through operating characteristics of wind turbines and establishes a single-machine fault transient model; then, taking the fault ride-through control characteristics of wind turbines as the criteria for subgroups, it calculates the relevant parameters of the group through weighted aggregation and QPSO algorithm, and constructs the fault transient equivalent model of each group; finally, combining with the principle of loss conservation, it integrates and obtains the fault transient equivalent model of the whole wind power field. Finally, the equivalent fault transient model of the whole wind farm is obtained by combining the loss conservation principle. Simulation verification shows that the established equivalent model can accurately reflect the dynamic characteristics of the wind farm, and the maximum percentage error of voltage and active power is no more than 10% in comparison with the corresponding detailed model under the same kind of fault perturbation, which not only meets the requirements of China’s wind farm modeling standards, but also shows higher adaptability and accuracy under different working conditions compared with other equivalent modeling methods. Especially under the extreme three-phase zero-passage fault condition, the maximum error of voltage and active power does not exceed 2%, which provides a reliable basic tool for the safety and stability analysis of wind farms. Full article

The rising environmental temperatures and growing global demand for animal protein pose major challenges to sustainable livestock production, highlighting the urgent need for climate change mitigation strategies. The livestock system in different parts of the world, especially in developing and underdeveloped nations, holds a significant role in supporting the livelihoods and nutritional security of millions, yet climate change is jeopardizing its efficiency and exacerbating its carbon footprint. This increase in carbon footprint is an alarming challenge for global sustainability, which needs to be addressed meticulously with fruitful outcomes. As the world’s largest livestock hub, the Indian livestock system can be adopted as a model for understanding the challenges and opportunities within the livestock system to develop sustainable approaches. In 2022, India accounted for approximately 7% of global greenhouse gas emissions (GHGEs), with a total of 3.9 billion metric tons of CO₂e. This review provides updated insights on the livestock-related carbon footprint, sustainability-enhancing technologies, GHG estimation models, and strategies for climate-neutral livestock production. Emission estimation models are categorized into source-based and whole-farm models for a comprehensive assessment of emissions. Mitigation strategies for cattle include rumen modification, nutritional approaches, efficient manure management, and precision livestock farming. India’s commitment to achieving net-zero emissions by 2070 is reflected in various initiatives aimed at promoting sustainable livestock systems. Future perspectives emphasize decision modeling and climate-resilient technologies to address environmental challenges in alignment with the UN’s sustainable development goals. Full article

The Normalized Difference Vegetation Index (NDVI) is an important remote sensing index that is widely used to assess vegetation coverage, monitor crop growth, and predict yields. Traditional NDVI calculation methods often rely on multispectral or hyperspectral imagery, which are costly and complex to operate, thus limiting their applicability in small-scale farms and developing countries. To address these limitations, this study proposes an NDVI estimation method based on low-cost RGB (red, green, and blue) UAV (unmanned aerial vehicle) imagery combined with deep learning techniques. This study utilizes field data from five major crops (cotton, rice, maize, rape, and wheat) throughout their whole growth periods. RGB images were used to extract conventional features, including color indices (CIs), texture features (TFs), and vegetation coverage, while convolutional features (CFs) were extracted using the deep learning network ResNet50 to optimize the model. The results indicate that the model, optimized with CFs, significantly enhanced NDVI estimation accuracy. Specifically, the R² values for maize, rape, and wheat during their whole growth periods reached 0.99, while those for rice and cotton were 0.96 and 0.93, respectively. Notably, the accuracy improvement in later growth periods was most pronounced for cotton and maize, with average R² increases of 0.15 and 0.14, respectively, whereas wheat exhibited a more modest improvement of only 0.04. This method leverages deep learning to capture structural changes in crop populations, optimizing conventional image features and improving NDVI estimation accuracy. This study presents an NDVI estimation approach applicable to the whole growth period of common crops, particularly those with significant population variations, and provides a valuable reference for estimating other vegetation indices using low-cost UAV-acquired RGB images. Full article

Leveraging whole-genome sequencing (WGS) that includes the full spectrum of genetic variation provides a better understanding of the biological mechanisms involved in the economically important traits of farm animals. However, the effectiveness of WGS in improving the accuracy of genomic prediction (GP) is limited. Recent genetic analyses of complex traits, such as genome-wide association study (GWAS), have identified numerous genomic regions and potential genes, which can provide valuable prior information for the improvement of genomic selection (GS). In this study, we applied different genome prediction methods to integrate GWAS results and gene feature annotations, which significantly improved the accuracy of GS for beef production traits. The Bayesian models incorporating genomic features showed the highest prediction accuracy, particularly for average daily gain (ADG) and bone weight (BW). Compared to prediction models based on WGS data, GP including biological prior can optimize the prediction accuracy by up to 11.56% for ADG and 14.60% for BW. Also, GP using GBLUP and Bayesian methods integrating biological priors for single-trait GWAS can significantly increase the prediction accuracy. Bayesian methods generally outperformed GBLUP models, with average improvements of 2.25% for ADG, 5.04% for BW, and 3.44% for live weight (LW). Our results indicate that leveraging biological prior knowledge can significantly refine GS models and underline the potential of combining WGS data with biological prior knowledge to further enhance the breeding process. Full article

The climate change-associated abnormal weather patterns negatively influences the productivity and performance of farm animals. Heat stress is the major detrimental factor hampering production, causing substantial economic loss to the livestock industry. Therefore, it is important to identify heat-tolerant breeds that can survive and produce optimally in any given environment. To achieve this goal, a clearer understanding of the genetic differences and the underlying molecular mechanisms associated with climate change impacts and heat tolerance are a prerequisite. Adopting next-generation biotechnological and statistical tools like whole transcriptome analysis, whole metagenome sequencing, bisulphite sequencing, genome-wide association studies (GWAS), and selection signatures provides an opportunity to achieve this goal. Through these techniques, it is possible to identify permanent genetic markers for heat tolerance, and by incorporating those markers in marker-assisted breeding selection, it is possible to achieve the target of breeding for heat tolerance in livestock. This review gives an overview of the recent advancements in assessing heat tolerance in livestock using such ‘omics’ approaches and statistical models. The salient findings from this research highlighted several candidate biomarkers that have the potential to be incorporated into future heat-tolerance studies. Such approaches could revolutionise livestock production in the changing climate scenario and support the food demands of the growing human population. Full article

Rosmarinic acid (RA) is a phenolic antioxidant naturally occurring in the plants of the Lamiaceae family, including basil (Ocimum basilicum L.). Existing analytical methods for determining the RA content in leaves are time-consuming and destructive, posing limitations on quality assessment and control during cultivation. In this study, we aimed to develop non-destructive prediction models for the RA content in basil plants using a portable hyperspectral imaging (HSI) system and machine learning algorithms. The basil plants were grown in a vertical farm module with controlled environments, and the HSI of the whole plant was captured using a portable HSI camera in the range of 400–850 nm. The average spectra were extracted from the segmented regions of the plants. We employed several spectral data pre-processing methods and ensemble learning algorithms, such as Random Forest, AdaBoost, XGBoost, and LightGBM, to develop the RA prediction model and feature selection based on feature importance. The best RA prediction model was the LightGBM model with feature selection by the AdaBoost algorithm and spectral pre-processing through logarithmic transformation and second derivative. This model performed satisfactorily for practical screening with R²_P = 0.81 and RMSEP = 3.92. From in-field HSI data, the developed model successfully estimated and visualized the RA distribution in basil plants growing in the greenhouse. Our findings demonstrate the potential use of a portable HSI system for monitoring and controlling pharmaceutical quality in medicinal plants during cultivation. This non-destructive and rapid method can provide a valuable tool for assessing the quality of RA in basil plants, thereby enhancing the efficiency and accuracy of quality control during the cultivation stage. Full article

Spotted sea bass (Lateolabrax maculatus) is an important marine economic fish in China, ranking third in annual production among marine fish. However, a declined growth rate caused by germplasm degradation has severely increased production costs and reduced economic benefits. There is an urgent need to develop the fast-growing varieties of L. maculatus and elucidate the genetic mechanisms underlying growth traits. Here, whole-genome resequencing technology combined with extreme phenotype genome-wide association analysis (XP-GWAS) was used to identify candidate markers and genes associated with growth traits in L. maculatus. Two groups of L. maculatus, consisting of 100 fast-growing and 100 slow-growing individuals with significant differences in body weight, body length, and carcass weight, underwent whole-genome resequencing. A total of 4,528,936 high-quality single nucleotide polymorphisms (SNPs) were used for XP-GWAS. These SNPs were evenly distributed across all chromosomes without large gaps, and the average distance between SNPs was only 175.8 bp. XP-GWAS based on the Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (Blink) and Fixed and random model Circulating Probability Unification (FarmCPU) identified 50 growth-related markers, of which 17 were related to body length, 19 to body weight, and 23 to carcass weight. The highest phenotypic variance explained (PVE) reached 15.82%. Furthermore, significant differences were observed in body weight, body length, and carcass weight among individuals with different genotypes. For example, there were highly significant differences in body weight among individuals with different genotypes for four SNPs located on chromosome 16: chr16:13133726, chr16:13209537, chr16:14468078, and chr16:18537358. Additionally, 47 growth-associated genes were annotated. These genes are mainly related to the metabolism of energy, glucose, and lipids and the development of musculoskeletal and nervous systems, which may regulate the growth of L. maculatus. Our study identified growth-related markers and candidate genes, which will help to develop the fast-growing varieties of L. maculatus through marker-assisted breeding and elucidate the genetic mechanisms underlying the growth traits. Full article