The Experts below are selected from a list of 44160 Experts worldwide ranked by ideXlab platform
F E Pardue - One of the best experts on this subject based on the ideXlab platform.
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heat stress and milk production in the south carolina Coastal Plains
Journal of Dairy Science, 1992Co-Authors: D E Linvill, F E PardueAbstract:Abstract A model developed for the South Carolina Coastal Plains relates hours with temperature-humidity index values above 74 and 80 to summer season daily milk production. When tested on an independent production data set for 1985, the root mean square model error was less than 1.3 kg/d per cow. The model can be used to develop expected summer season dairy production climatologies. Realtime milk production forecasts obtained using daily predicted maximum and minimum temperatures can be used in herd management to reduce effects of heat stress on productivity.
P G Hunt - One of the best experts on this subject based on the ideXlab platform.
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simulated soil organic carbon response to tillage yield and climate change in the southeastern Coastal Plains
Journal of Environmental Quality, 2018Co-Authors: P R Nash, Hero T Gollany, J M Novak, Philip J Bauer, P G Hunt, D L KarlenAbstract:: Intensive tillage, low-residue crops, and a warm, humid climate have contributed to soil organic carbon (SOC) loss in the southeastern Coastal Plains region. Conservation (CnT) tillage and winter cover cropping are current management practices to rebuild SOC; however, there is sparse long-term field data showing how these management practices perform under variable climate conditions. The objectives of this study were to use CQESTR, a process-based C model, to simulate SOC in the top 15 cm of a loamy sand soil (fine-loamy, kaolinitic, thermic Typic Kandiudult) under conventional (CvT) or CnT tillage to elucidate the impact of projected climate change and crop yields on SOC relative to management and recommend the best agriculture management to increase SOC. Conservation tillage was predicted to increase SOC by 0.10 to 0.64 Mg C ha for six of eight crop rotations compared with CvT by 2033. The addition of a winter crop [rye ( L.) or winter wheat ( L.)] to a corn ( L.)-cotton ( L.) or corn-soybean [ (L.) Merr.] rotation increased SOC by 1.47 to 2.55 Mg C ha. A continued increase in crop yields following historical trends could increase SOC by 0.28 Mg C ha, whereas climate change is unlikely to have a significant impact on SOC except in the corn-cotton or corn-soybean rotations where SOC decreased up to 0.15 Mg C ha by 2033. The adoption of CnT and cover crop management with high-residue-producing corn will likely increase SOC accretion in loamy sand soils. Simulation results indicate that soil C saturation may be reached in high-residue rotations, and increasing SOC deeper in the soil profile will be required for long-term SOC accretion beyond 2030.
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an analysis of the link between strokes and soils in the south carolina Coastal Plains
Journal of Environmental Science and Health Part A-toxic\ hazardous Substances & Environmental Engineering, 2012Co-Authors: Thomas F Ducey, W J Busscher, Jarrod O Miller, Daniel T Lackland, P G HuntAbstract:The Stroke Belt is a geographical region of the Southeastern United States where resident individuals suffer a disproportionately higher rate of strokes than the rest of the population. While the “buckle” of this Stroke Belt coincides with the Southeastern Coastal Plain region of North and South Carolina and Georgia, there is a paucity of information pinpointing specific causes for this phenomenon. A number of studies posit that an exposure event–potentially microbial in nature–early in life, could be a risk factor. The most likely vector for such an exposure event would be the soils of the Southeastern Coastal Plain region. These soils may have chemical and physical properties which are conducive to the growth and survival of microorganisms which may predispose individuals to stroke. To this aim, we correlated SC stroke mortality data to soil characteristics found in the NRCS SSURGO database. In statewide comparisons, depth to water table (50 to 100 cm, R = 0.62) and soil drainage class (poorly drained, ...
Nilton Curi - One of the best experts on this subject based on the ideXlab platform.
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assessing models for prediction of some soil chemical properties from portable x ray fluorescence pxrf spectrometry data in brazilian Coastal Plains
Geoderma, 2020Co-Authors: Renata Andrade, Sergio Henrique Godinho Silva, David C Weindorf, Somsubhra Chakraborty, Wilson Missina Faria, Luiz Felipe Mesquita, Luiz Roberto Guimaraes Guilherme, Nilton CuriAbstract:Abstract Portable X-ray fluorescence (pXRF) spectrometry is becoming increasingly popular for predicting soil properties worldwide. However, there are still very few works on this subject under tropical conditions. Therefore, the objectives of this study were to use pXRF data to characterize the Brazilian Coastal Plains (BCP) soils and assess four machine learning algorithms [ordinary least squares regression (OLS), cubist regression (CR), XGBoost (XGB), and random forest (RF)] for prediction of total nitrogen (TN), cation exchange capacity (CEC), and soil organic matter (SOM) using pXRF data. A total of 285 soil samples were collected from the A and B horizons representing Ultisols, Oxisols, Spodosols, and Entisols. The pXRF reported elements helped in the characterization of the BCP soils. In general, the RF model achieved the best performances for TN (R2 = 0.50), CEC (0.75), and SOM (0.56) when A and B horizons were combined, although better results have been reported in the literature for soils from other regions of the world. The results reported here for the BCP soils represent alternatives for reducing costs and time needed for assessing such data, supporting agronomic and environmental strategies.
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prediction of soil fertility via portable x ray fluorescence pxrf spectrometry and soil texture in the brazilian Coastal Plains
Geoderma, 2020Co-Authors: Renata Andrade, Sergio Henrique Godinho Silva, David C Weindorf, Somsubhra Chakraborty, Wilson Missina Faria, Luiz Felipe Mesquita, Luiz Roberto Guimaraes Guilherme, Nilton CuriAbstract:Abstract Traditional methods of soil chemical analysis are time consuming, costly, and generate chemical waste. Proximal sensors, such as portable X-ray fluorescence (pXRF) spectrometry, may help to overcome these issues since they have been shown to produce accurate predictions of many soil properties. However, such processes need to be further investigated in Brazilian soils. This work aimed to assess the influence of soil management and mineralogy on elemental composition of soils and predict exchangeable Al3+, Ca2+, Mg2+, and available K+, and P contents from pXRF data alone and associated with soil texture through machine learning algorithms [stepwise generalized linear models (SGLM), and random forest (RF)] in soils of the Brazilian Coastal Plains (BCP). A total of 285 soil samples were collected from the A (n = 123) and B (n = 162) horizons and subjected to laboratory analyses and pXRF scans. Samples were randomly separated into 70% for modeling and 30% for validation. Soil mineralogy and management mainly influenced Al, and Ca and K total content, respectively. In general, the inclusion of the auxiliary input data of soil texture did not change the predictive power of the models. The best results highlight a considerable promise of pXRF technique for rapidly assessing exchangeable Ca2+ (RMSE = 176.3 mg kg−1, R2 = 0.71), Mg2+ (37.7 mg kg−1, 0.60), and available K+ (27.46 mg kg−1, 0.67). The algorithms could not generate reliable models to predict exchangeable Al3+ (30.6 mg kg−1, 0.47) and available P (19.9 mg kg−1, 0.14). In sum, pXRF can be used to reasonably predict soil fertility properties in the BCP soils. Further studies may extend predictions to other soil properties.
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pedotransfer functions for water retention in the main soils from the brazilian Coastal Plains
Ciencia E Agrotecnologia, 2015Co-Authors: Elidiane Da Silva, Nilton Curi, Mozart Martins Ferreira, Margarete Marin Lordelo Volpato, Walbert Junior Reis Dos Santos, Sergio Henrique Godinho SilvaAbstract:Pedotransfer functions (PTFs) are equations used to estimate soil characteristics difficult to determine from other easily obtained ones. Water retention in soil is used in several agronomic and environmental applications, but its direct determination is time consuming and onerous, therefore PTFs are alternatives to obtaining this information more quickly and economically. The aims of this study were to generate a database and develop PTFs for water retention at potentials of -33 kPa (field capacity) and -1500 kPa (permanent wilting point) for Yellow Argisol and Yellow Latosol from the Brazilian Coastal Plains region. The Coastal Plains soils are mostly developed from Barreiras formation (pre-weathered sediments) and their main uses are sugarcane, livestock, forestry and fruticulture. The database to generate the PTFs was composed from the selection of information derived from scientific works and soil survey reports of the region. Specific PTFs were generated for each soil class, in their respective A and B horizons and for solum, through multiple regression by stepwise package of R language programming. Due to the small pedological variability (small number of soil classes containing great geographical expression) and mineralogical uniformity, usually observed in this environment, non-stratification of soil classes to create general PTFs presented similar or superior results compared to equations for each soil class. The adjustment of data demonstrated that water retention values at -33 kPa and -1500 kPa potentials can be estimated with adequate accuracy for the main soils of the Brazilian Coastal Plains through PTFs mainly from particle size distribution and secondarily from organic matter data.
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detailed soil survey of an experimental watershed representative of the brazilian Coastal Plains and its practical application
Ciencia E Agrotecnologia, 2014Co-Authors: Walbert Junior Reis Dos Santos, Elidiane Da Silva, Nilton Curi, Sergio Henrique Godinho Silva, Sebastiao Fonseca, Joao Jose MarquesAbstract:This paper presents a detailed soil survey of an experimental watershed with representative pedoclimatic characteristics of the Coastal Plains in Espirito Santo State and its practical applications. For the pedological survey, 35 observation sites and three soil profiles were sampled and described, which were morphologically characterized and subjected to physical (particle size) and chemical analyses (routine and sulfuric acid digestion). The soil map was made using the geographic information system ArcGIS 9.3. This GIS software was also used to generate the digital elevation model (DEM) for identifying the slope classes. SAGA software was used to calculate the topographic wetness index (WI) which aided in a more accurate separation of Haplic Organosol from other soils. The predominant soil class in the watershed was the dystrophic/dystrocohesive Yellow Argisol (97%), containing morphological, chemical and physical characteristics representative of the most expressive Coastal Plains soils. Geoprocessing tools and techniques aided to make the watershed soil map.
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morphology mineralogy and micromorphology of soils associated to summit depressions of the northeastern brazilian Coastal Plains
Ciencia E Agrotecnologia, 2012Co-Authors: Elen Alvarenga Silva, Joao Bosco Vasconcellos Gomes, Jose Coelho De Araujo Filho, Pablo Vidaltorrado, Miguel Cooper, Nilton CuriAbstract:The scarcity of comprehensive characterizations of soils associated to gentle summit depressions of the Northeastern Brazilian Coastal Plains justifies this work, which had as objective to provide basic information for the more diverse agricultural and non-agricultural uses. For that, representative soils (Spodosols or similar soils) from these environments were selected in Alagoas, Sergipe and Bahia states. This approach included characterization of morphological, mineralogical and micromorphological properties of the soil profiles, employing standard procedures. The morphological characterization corroborated the effect of the podzolization process during the formation of these soils. The mineralogy of the clay fraction of these soils was basically composed of kaolinite and quartz, which, associated to the very sandy texture, helped in the understanding of the obtained data. The soil micromorphological study, besides confirming the field morphology, mainly in regard to the strong cementation, aggregated value to the work in terms of the secure identification of the clay illuviation process (non-identified in the field), in association with the dominant podzolization process.
D E Linvill - One of the best experts on this subject based on the ideXlab platform.
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heat stress and milk production in the south carolina Coastal Plains
Journal of Dairy Science, 1992Co-Authors: D E Linvill, F E PardueAbstract:Abstract A model developed for the South Carolina Coastal Plains relates hours with temperature-humidity index values above 74 and 80 to summer season daily milk production. When tested on an independent production data set for 1985, the root mean square model error was less than 1.3 kg/d per cow. The model can be used to develop expected summer season dairy production climatologies. Realtime milk production forecasts obtained using daily predicted maximum and minimum temperatures can be used in herd management to reduce effects of heat stress on productivity.
Zhuping Sheng - One of the best experts on this subject based on the ideXlab platform.
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assessing aquifer storage and recovery feasibility in the gulf Coastal Plains of texas
Journal of Hydrology: Regional Studies, 2017Co-Authors: Benjamin W Smith, Gretchen R Miller, Zhuping ShengAbstract:Abstract Study region The Gulf Coast and Carrizo-Wilcox aquifer systems in the Gulf Coastal Plains of Texas. Study focus Aquifer storage and recovery is a water storage alternative that is underutilized in Texas, a state with both long periods of drought and high intensity storms. Future water storage plans in Texas almost exclusively rely on surface reservoirs, subject to high evaporative losses. This study seeks to identify sites where aquifer storage and recovery (ASR) may be successful, especially in recovery of injected waters, by analyzing publicly-available hydrogeologic data. Transmissivity, hydraulic gradient, well density, depth to aquifer, and depth to groundwater are used in a GIS-based index to determine feasibility of implementing an ASR system in the Gulf Coast and Carrizo-Wilcox aquifer systems. New hydrological insights for the region Large regions of the central and northern Gulf Coast and the central and southern Carrizo-Wilcox aquifer systems are expected to be hydrologically feasible regions for ASR. Corpus Christi, Victoria, San Antonio, Bryan, and College Station are identified as possible cities where ASR would be a useful water storage strategy.