Unlocking Insights into Crop Growth and Nutrient Distribution: A Geospatial Analysis Approach Using Satellite Imagery and Soil Data

Accurate monitoring of crop growth and nutrient distribution is crucial for optimizing agricultural practices, promoting a sustainable environment, and ensuring long-term food production. In this study, we propose a novel and comprehensive approach to monitor crop growth and nutrient distribution in large-scale agricultural landscapes. Our methodology combines advanced geospatial and temporal analysis techniques, providing valuable insights into the intricate relationships between crop health, soil nutrients, and other essential soil properties. To monitor vegetation dynamics, we obtained data from the IBM EIS (Environment Intelligence Suite) and processed it using our HPC (High-Performance Computing) infrastructure. This is ingested into our CRADLE (Common Research Analytics and Data Lifecycle Environment). The IBM EIS consists of vast amounts of geospatial data curated from diverse sources, readily available for analysis. Leveraging the Normalized Difference Vegetation Index (NDVI) algorithm and MODIS Aqua satellite imagery, we classified vegetation on a daily basis, yielding a detailed assessment of land use and growth. Additionally, by integrating MODIS Aqua data with USDA Historical Crop planting data, we can identify the dominant crops in each region and monitor their growth and health across Texas and Ohio during 2019. To investigate soil properties and their influence on crop health, we utilize prominent soil databases from IBM EIS such as The Soil Survey Geographic Database (SSURGO) and the World Soil Information Service (WoSIS). These databases provide essential information on key soil properties, including pH, texture, water holding capacity, and organic carbon. By correlating these properties with soil nitrogen content, we can assess their interdependencies and infer their impacts on crop health. Furthermore, we analyze the correlation between crop health and nitrogen content, gaining valuable insights into the effects of soil nitrogen on crop well-being. By integrating remote sensing technology, soil science, and data science, this interdisciplinary study contributes to the development of sustainable agricultural management strategies. The findings of this research enhance food production capabilities and provide valuable information for policy decision-making, ultimately promoting environmental conservation within large-scale agricultural systems

Integrating Multiscale Geospatial Analysis for Monitoring Crop Growth, Nutrient Distribution, and Hydrological Dynamics in Large-Scale Agricultural Systems

Monitoring crop growth, soil conditions, and hydrological dynamics are imperative for sustainable agriculture and reduced environmental impacts. This interdisciplinary study integrates remote sensing, digital soil mapping, and hydrological data to elucidate intricate connections between these factors in the state of Ohio, USA. Advanced spatiotemporal analysis techniques were applied to key datasets, including the MODIS sensor satellite imagery, USDA crop data, soil datasets, Aster GDEM, and USGS stream gauge measurements. Vegetation indices derived from MODIS characterized crop-specific phenology and productivity patterns. Exploratory spatial data analysis show relationships of vegetation dynamics and soil properties, uncovering links between plant vigor, edaphic fertility, and nutrient distributions. Correlation analysis quantified these relationships and their seasonal evolution. Examination of stream gauge data revealed insights into spatiotemporal relationships of nutrient pollution and stream discharge. By synthesizing diverse geospatial data through cutting-edge data analytics, this work illuminated complex interactions between crop health, soil nutrients, and water quality in Ohio. The methodology and findings provide actionable perspectives to inform sustainable agricultural management and environmental policy. This study demonstrates the significant potential of open geospatial resources when integrated using a robust spatiotemporal framework. Integrating additional measurements and high-resolution data sources through advanced analytics and interactive visualizations could strengthen these insights

Bacteriophage [phi chi]-174: Its sensitivity to ultraviolet light and growth in starved and irradiated cells

NOTE: Text or symbols not renderable in plain ASCII are indicated by [...]. Abstract is included in .pdf document. Part I Ultraviolet action spectra for inactivation of [phi chi]-174 virus, its infective single-stranded DNA (SS), and an infective, intracellular and presumably double-stranded DNA (RF) have been determined. The biological activity of the irradiated DNA was measured using bacterial protoplasts. The inactivation cross-section of the RF is nearly an order of magnitude less at all wavelengths than that of either the free single-stranded DNA or the intact virus, which have very similar cross-sections. Besides the difference in magnitude, the action spectrum of RF, when compared to that of SS [phi chi] DNA shows several differences in band shape. The similarity of the free SS and virus sensitivities to radiation in the range 240-302 [megamicrons] suggests that energy of these wavelengths which is effective in inactivation of the virus is that absorbed by the nucleic acid. Below 240 [megamicrons] the DNA is less sensitive than the virus; UV inactivation as a consequence of energy absorbed by the virus protein is a likely explanation of the higher viral sensitivity. The quantum yield for inactivation of the single-stranded DNA is a function of wavelength in the range of wavelengths used, 225-302 [megamicrons]. This may be understood as a result of the variable fraction of absorbed energy localized in pyrimidines. This dependence of quantum yield on wavelength is altered in the case of the whole virus, presumably because of the important role of the protein at low wavelengths. The quantum yield of SS [phi chi] DNA increases slightly with salt concentration, reflecting the existence of some process which is enhanced on contraction of the polymer and the resulting stronger interactions between bases. Part II The ability of [phi chi] bacteriophage-infected cells to release progeny after UV irradiation has been examined using both a host possessing host cell reactivation (E. coli C(subscript)N) and one lacking it (E. coli C(subscript s). The UV sensitivity of both free [phi chi] DNA extracted from infected cells and DNA irradiated in situ in the infected cell, as judged by their infectivity to bacterial protoplasts, is sufficient to account for the intrinsic sensitivity of the host-phage complex. Part III The burst of a starved bacterium infected with several (phi chi]-174 bacteriophage is usually found to contain descendants of only one of the parents; less often, two phage may multiply. Unstarved cells, in contrast, can support the growth of at least four phage. The unproductive phage seem to convert their parental single-stranded DNA into intracellular, double-stranded RF. These results are interpreted to mean that some factor required by [phi chi] for the production of progeny is limited in starved cells. Part IV Evidence is presented that starved, UV-irradiated E. coli C which has lost its capacity to support [phi chi] bacteriophage reproduction, has also become unsuitable for the synthesis of infective RF, though the incoming single stranded viral DNA is able to undergo the transition to an RF whose behavior on neutral density gradient analysis is the same as normal RF. Very alkaline conditions appear to activate the inactive RF, releasing infective parental single strands as well as making infective those RF whose strands do not separate

Practical Geostatistics - An Armchair Overview for Petroleum Reservoir Engineers

Distinguished Author Series articles are general, descriptive representations that summarize the state of the art in an area of technology by describing recent developments for readers who are not specialists in the topics discussed. Written by individuals recognized as experts in the area, these articles provide key references to more definitive work and present specific details only to illustrate the technology. Purpose: to inform the general readership of recent advances in various areas of petroleum engineering. Abstract Some engineers are skeptical of statistical, let alone geostatistical, methods. Geostatistical analysis in reservoir characterization necessitates an understanding of a new and often unintuitive vocabulary. Statistical approaches for measuring uncertainty in reservoirs is indeed a rapidly growing part of the best-practice set of methodologies for many companies. For those already familiar with the basic concepts of geostatistics, it is hoped that this overview will be a useful refresher and perhaps clarify some concepts. For others, this overview is intended to provide a basic understanding and a new level of comfort with a technology that may be useful to them in the very near future. Introduction Geoscientists and geological engineers have been making maps of the subsurface since the late 18th century. The evolution of our ability to predict structure beneath the surface of the Earth has been a complex interaction between quantitative analysis and qualitative judgment. Geostatistics combines the empirical conceptual ideas that are implicitly subject to degrees of uncertainty with the rigor of mathematics and formal statistical analysis. It has found its way into the field of reservoir characterization and dynamic flow simulation for a variety of reasons including its ability to successfully analyze and integrate different types of data, provide meaningful results for model building, and quantitatively assess uncertainty for risk management. Additionally, from a management point of view, its methodologies are applicable for both geoscientists and engineers, thereby lending itself to a shared Earth model and a multidisciplinary workforce. Why Geostatistics? Fig. 1 depicts two images of hypothetical 2D distribution patterns of porosity. Fig. 1a shows a random distribution of porosity values, while Fig. 1b is highly organized, showing a preferred northwest/southeast direction of continuity. While this difference is obvious to the eye, the classical descriptive-summary statistics suggest that the two images are the same. That is, the number of red, green, yellow, and blue pixels in each image is the same, as are the univariate statistical summaries such as the mean, median, mode, variance, and standard deviation (Fig. 1c). Intuitively, as scientists and engineers dealing with Earth properties, we know that the geological features of reservoirs are not randomly distributed in a spatial context. The reservoirs are heterogeneous and have directions of continuity in both 2D and 3D space and are products of specific depositional, structural, and diagenetic histories. Strangely, that these two images would appear identical in a classical statistical analysis is the basis of a fundamental problem inherent in all sciences dealing with spatially organized data. Classical statistical analysis inadequately describes phenomena that are both spatially continuous and heterogeneous. Thus, use of classical statistical descriptors alone to help characterize petroleum reservoirs often will result in an unsatisfactory model. </jats:sec

Modeling complex reservoirs with multiple conditional techniques : a practical approach to reservoir characterization

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