846 research outputs found

    Remote Sensing for Precision Nitrogen Management

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    This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment

    Spring wheat yield assessment under drought conditions using vegetation health index: case of Canadian prairies

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    Non-Peer ReviewedAgricultural drought is a major climate concern which occurs frequently on Canadian prairies. It acts negatively on crop production, which directly affects the Canadian economy. The Normalized Difference Vegetation Index (NDVI) has been widely used to assess crop yield losses related to drought events. However, this index suffers from some shortcomings such as the apparent time lag between drought impact due to rainfall deficit and NDVI response. This study was undertaken to investigate the effectiveness of the integrated Vegetation Health Index (iVHI) for the assessment of spring wheat yield across Canadian prairies. A time series of five years from the Advanced Very High Resolution Radiometer (AVHRR) sensor were used to develop a spring wheat yield model for three agroclimatic regions: subarid, semiarid and subhumid. The results demonstrated that spring wheat yield assessment is feasible through the use of iVHI, especially in subarid and semiarid regions where it reached a correlation coefficient of 0.75 and 0.61, respectively. This finding shows that iVHI can be used to estimate spring wheat yield losses due to agricultural drought across the Canadian prairies. However, in subhumid regions where spring wheat growing conditions are favourable because of adequate water supply, the integrated NDVI (iNDVI) outperforms iVHI with a correlation coefficient of 0.44 compared to 0.34. Consequently, to develop an efficient tool, it suggested coupling the iVHI with iNDVI to better estimate spring wheat yield in the Canadian prairies

    2021 Nebraska Beef Cattle Report

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    Cow-Calf Nutrition and Management: Metabolic Profile Associated with Pre-Breeding Puberty Status in Range Beef Heifers 5 • Milk Production Impacts on Cow Reproductive and Calf Growth Performance 8 • Genetic Selection Tools: Using Pooling to Capture Commercial Data for Inclusion in Genetic Evaluations 11 • Categorization of Birth Weight Phenotypes for Inclusion in Genetic Evaluations Using a Deep Neural Network 14 • Genetic Parameter Estimates for Age at Slaughter and Days to Finish in a Multibreed Population 16 Growing Calf and Yearling Management: Effects of Monensin and Protein Type on Performance of Yearling Steers Grazing Smooth Bromegrass Pastures 18 • Impact of Masters Choice Corn Silage on Nutrient Digestion in Growing Cattle 21 • Winter Growth Rate and Timing of Marketing on Economics of Yearling Systems 24 • Alternative Heifer Development Systems Utilizing Corn Residue and Cover Crops 28 • Impacts of Biochar Supplementation in Growing Diets on Greenhouse Gas Emissions 31 • Growing Calf Intake of Hay or Crop Residue Based Diets 33 • Evaluation of Models Used to Predict Dry Matter Intake in Forage- Based Diets 36 Forage Resource Management: Mineral Concentrations of Forages for Livestock in Nebraska and South Dakota 38 Animal Behavior: Training Improves the Reliability of Temperament Assessment in Cattle 41 Finishing Nutrition and Management: Evaluating Finishing Performance of Cattle Fed High- Moisture Corn and Steam-Flaked Corn Blends with Modified Distillers Grains 44 • Evaluation of Processing Technique for High- Moisture and Dry Corn Fed to Finishing Cattle 46 • Impact of Feeding Aspergillus Subspecies Blend and Different Corn Processing Methods on Finishing Beef Cattle Performance and Carcass Characteristics 50 • Evaluation of Wheat Blended with Corn in Finishing Diets Containing Wet Distillers Grains 53 • Evaluation of Condensed Algal Residue Solubles as an Ingredient in Cattle Finishing Diets 56 • Effects of Butyrate in Finishing Cattle Diets 59 • Impact of Days Fed on Holstein Bull and Steer Performance and Cutability of Cattle Pen- Fed Organic Diets 62 • Effect of Increasing Corn Silage Inclusion in Finishing Diets with or without Tylosin on Performance and Liver Abscesses 66 • Economic Analysis of Increased Corn Silage Inclusion in Beef Finishing Cattle 69 Beef Products: Fate of Generic Escherichia coli in Beef Steaks during Sous Vide Cooking at Different Holding Time and Temperature Combinations 72 • Proteomic Analysis of Oxidized Proteins in Beef 74 • The Relationship of Liver Abscess Scores and Early Postmortem Meat Tenderness 81 • The Impact of Oxidative Stress on Postmortem Meat Quality 83 • Accelerated Dry Aging under Anaerobic Conditions 88 • Pseudomonas Survive Thermal Processing and Grow during Vacuum Packaged Storage in an Emulsified Beef System 91 Nutrient Management: Evaluation of Biochar on Nutrient Loss from Fresh Cattle Manure 93 • Using Coal Char from Sugar Production in Cattle Manure Management 95 • Transforming Manure and Cedar Mulch from “Waste” to “Worth” 99 • Predicting Nitrogen and Phosphorus Flows in Beef Open Lots 105 • Perceptions of Barriers and Benefits of Manure Use in Cropping Systems 109 • Dietary Impact on Antibiotic Resistance in Feedlot Manure 112 • Antibiotic Resistance in Manure-Amended Agricultural Soils 114 Explanation of Statistics: Statistics Used in the Nebraska Beef Report and Their Purpose 11

    Handheld near-infrared spectroscopy: state-of-the-art instrumentation and applications in material identification, food authentication, and environmental investigations

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    This present review article considers the rapid development of miniaturized handheld near-infrared spectrometers over the last decade and provides an overview of current instrumental developments and exemplary applications in the fields of material and food control as well as environmentally relevant investigations. Care is taken, however, not to fall into the exaggerated and sometimes unrealistic narrative of some direct-to-consumer companies, which has raised unrealistic expectations with full-bodied promises but has harmed the very valuable technology of NIR spectroscopy, rather than promoting its further development. Special attention will also be paid to possible applications that will allow a clientele that is not necessarily scientifically trained to solve quality control and authentication problems with this technology in everyday life.info:eu-repo/semantics/publishedVersio

    Development of Landsat-based Technology for Crop Inventories: Appendices

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    There are no author-identified significant results in this report

    Nondestructive Multivariate Classification of Codling Moth Infested Apples Using Machine Learning and Sensor Fusion

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    Apple is the number one on the list of the most consumed fruits in the United States. The increasing market demand for high quality apples and the need for fast, and effective quality evaluation techniques have prompted research into the development of nondestructive evaluation methods. Codling moth (CM), Cydia pomonella L. (Lepidoptera: Tortricidae), is the most devastating pest of apples. Therefore, this dissertation is focused on the development of nondestructive methods for the detection and classification of CM-infested apples. The objective one in this study was aimed to identify and characterize the source of detectable vibro-acoustic signals coming from CM-infested apples. A novel approach was developed to correlate the larval activities to low-frequency vibro-acoustic signals, by capturing the larval activities using a digital camera while simultaneously registering the signal patterns observed in the contact piezoelectric sensors on apple surface. While the larva crawling was characterized by the low amplitude and higher frequency (around 4 Hz) signals, the chewing signals had greater amplitude and lower frequency (around 1 Hz). In objective two and three, vibro-acoustic and acoustic impulse methods were developed to classify CM-infested and healthy apples. In the first approach, the identified vibro-acoustic patterns from the infested apples were used for the classification of the CM-infested and healthy signal data. The classification accuracy was as high as 95.94% for 5 s signaling time. For the acoustic impulse method, a knocking test was performed to measure the vibration/acoustic response of the infested apple fruit to a pre-defined impulse in comparison to that of a healthy sample. The classification rate obtained was 99% for a short signaling time of 60-80 ms. In objective four, shortwave near infrared hyperspectral imaging (SWNIR HSI) in the wavelength range of 900-1700 nm was applied to detect CM infestation at the pixel level for the three apple cultivars reaching an accuracy of up to 97.4%. In objective five, the physicochemical characteristics of apples were predicted using HSI method. The results showed the correlation coefficients of prediction (Rp) up to 0.90, 0.93, 0.97, and 0.91 for SSC, firmness, pH and moisture content, respectively. Furthermore, the effect of long-term storage (20 weeks) at three different storage conditions (0 °C, 4 °C, and 10 °C) on CM infestation and the detectability of the infested apples was studied. At a constant storage temperature the detectability of infested samples remained the same for the first three months then improved in the fourth month followed by a decrease until the end of the storage. Finally, a sensor data fusion method was developed which showed an improvement in the classification performance compared to the individual methods. These findings indicated there is a high potential of acoustic and NIR HSI methods for detecting and classifying CM infestation in different apple cultivars

    International conference on science, technology, engineering and economy

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    Analysis Into Artificial Intelligence And Its Developing Dynamic And Relationship In Agricultural Supply Chains

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    The thesis explores artificial intelligence (AI) in agricultural (Ag) supply chains (SCs) and presents a new typology to understand artificial intelligence-based solutions in agricultural SCs. The thesis was performed utilizing a research-based review to investigate the current uses of artificial intelligence-based solutions in agricultural SCs. The AI-based solutions were found in case studies that reviewed AI operations in different areas internationally. The typology was formed on the foundation of two dynamics, the location of AI applications in Ag SCs and the driving values to integrate the AI applications. In order to develop the typology, the AI applications were studied in a series of different analyses. The analyses helped to critique and scrutinize the AI applications to gain new perspectives. The series of analyses consists of exploring the AI applications’ location within the supply chain, the value additions to the supply chain from integrating the AI applications, and the resulting depth of the effect of AI application has on the supply chain. Each additional evaluation of the AI applications examining another parameter further exposed more insight and started to build a structured ideology of AI. The proposed typology aims to create a tool of measurement to infer AI technology’s relation in the SCs and create a new viewpoint that will lead investigation and provide insight for predictions of AI’s future in agricultural SCs. In addition, the new typology should aid agriculture firms in understanding and capturing the potential synergies stemming from the driving values of innovation. The study found that AI applications with a strong relationship in the supply chain provide the greatest beneficiary relationship between technology value creation and supply chain logistics. Furthermore, AI applications will have the strongest relationship and implementation when operating in collaboration with other supply chain locations and AI integrated firms. Concluding the thesis, relevant policy and business practice recommendations are proposed

    Artificial Neural Networks in Agriculture

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    Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible

    An integrated, probabilistic model for improved seasonal forecasting of agricultural crop yield under environmental uncertainty

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    We present a novel forecasting method for generating agricultural crop yield forecasts at the seasonal and regional-scale, integrating agroclimate variables and remotely-sensed indices. The method devises a multivariate statistical model to compute bias and uncertainty in forecasted yield at the Census of Agricultural Region (CAR) scale across the Canadian Prairies. The method uses robust variable-selection to select the best predictors within spatial subregions. Markov-Chain Monte Carlo (MCMC) simulation and random forest-tree machine learning techniques are then integrated to generate sequential forecasts through the growing season. Cross-validation of the model was performed by hindcasting/backcasting and comparing forecasts against available historical data (1987–2011) for spring wheat (Triticum aestivum L.). The model was also validated for the 2012 growing season by comparing forecast skill at the CAR, provincial and Canadian Prairie region scales against available statistical survey data. Mean percent departures between wheat yield forecasted were under-estimated by 1–4% in mid-season and over-estimated by 1% at the end of the growing season. This integrated methodology offers a consistent, generalizable approach for sequentially forecasting crop yield at the regional-scale. It provides a statistically robust, yet flexible way to concurrently adjust to data-rich and data-sparse situations, adaptively select different predictors of yield to changing levels of environmental uncertainty, and to update forecasts sequentially so as to incorporate new data as it becomes available. This integrated method also provides additional statistical support for assessing the accuracy and reliability of model-based crop yield forecasts in time and space
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