scholarly journals Development of the limit values of micronutrient deficiency in soil determined using Mehlich 3 extractant for Polish soil conditions. Part II. Rapeseed

2019 ◽  
Vol 70 (4) ◽  
pp. 324-330 ◽  
Author(s):  
Ewa Stanisławska-Glubiak ◽  
Jolanta Korzeniowska ◽  
Wojciech Lipiński
Keyword(s):  
Soil Samples ◽  
High Yield ◽  
Soil Conditions ◽  
Available Phosphorus ◽  
Regression Equations ◽  
Data Set ◽  
Limit Values ◽  
Plant Samples ◽  
First Case ◽  
Bioaccumulation Coefficient

Abstract The aim of the study was to develop limit values for low microelement concentration in the soil, determined with the use of Mehlich 3 extractant for assessing their deficits in rapeseed crops. The values were prepared on the basis of 1944 fields with rapeseed, covering the whole Poland. In 2017, the samplers of Polish agro-chemical laboratories took soil samples and corresponding plant samples at the BBCH 30/31 stage. In the plant samples, the concentration of microelements was determined, and in the soil samples, apart from microelements, also pH, texture and the concentration of organic carbon and available phosphorus, were determined. Moreover, for each field, data on rapeseed yield were collected. Limit values were determined by two independent methods: 1) the method of regression equations and 2) the so-called high yield method. In the first case, the limit microelement concentration in the soil was calculated from the equation describing the relationship between the R/G bioaccumulation coefficient and a specific soil feature (n=1944). The bioaccumulation coefficient is a quotient of the concentration of a microelement in a plant (R) and its concentration in the soil determined by the Mehlich 3 (G) method. Limit values were calculated after substituting the critical concentration of microelements in the plant (R) to the equation, and subsequently, an appropriate conversion of the equation. The second method was based on the separation of a group of high yields ≥4.0 t ha−1 (n=755) from the whole data set. Then in this group, the lower quintiles (QU1) were calculated for the concentration of individual microelements in the soil determined in Mehlich 3 extract and adopted as limit values. It was found that QU1 is a good indicator of the lowest microelement concentration in the soil at which a yield of at least 4.0 t ha−1 can be obtained. The final limit values were worked out by averaging the values calculated by the equations and high yield method and their appropriate correction. In the combined soil sample collections for wheat and rapeseed (n=3865), the values were checked by evaluating the percentage of soils with microelement shortage separately for rape and wheat. The results of this evaluation were compared with the evaluation using the old system based on the 1 M HCl, which did not take into account the plant species.

scholarly journals Development of the limit values of micronutrient deficiency in soil determined using Mehlich 3 extractant for Polish soil conditions. Part I. Wheat

2019 ◽  
Vol 70 (4) ◽  
pp. 314-323 ◽  
Author(s):  
Jolanta Korzeniowska ◽  
Ewa Stanisławska-Glubiak ◽  
Wojciech Lipiński
Keyword(s):  
Stem Elongation ◽  
Soil Samples ◽  
Bioaccumulation Factor ◽  
High Yield ◽  
Soil Conditions ◽  
Available Phosphorus ◽  
Regression Equations ◽  
Data Set ◽  
Limit Values ◽  
First Case

Abstract To implement the Mehlich 3 method in Polish agro-chemical laboratories, limit values for deficiency of B, Cu, Fe, Mn and Zn in soil for wheat were developed. The values were developed on the basis of 1921 fields with wheat, evenly distributed throughout Poland. Soil samples were collected from these fields in 2016, together with the plants growing on them, at the stage of stem elongation (BBCH 30/31). The concentration of micronutrients was determined in all soil and plant samples. In addition, pH, texture, and the content of organic carbon and available phosphorus were determined in soil samples. Moreover, grain yield after wheat harvest was estimated for all fields. Limit values were developed by two independent methods: 1) the regression equation method and 2) the so-called high yield method. In the first case, the limit microelement concentration in soil was calculated from the equation describing the relationship between the bioaccumulation factor (R/G) and a specific soil feature (n=1921). The bioaccumulation factor is the quotient of the concentration of a micronutrient in a plant (R) and its concentration in the soil (G) determined by the Mehlich 3 method. The equations were constructed using the Stagraphics program. For each micronutrient, 8 models were tested in search for the equation with the highest determination coefficient r2. Limit values were calculated after substituting the critical value of microelements in the plant (R) to the selected model and transforming the equation accordingly. The basis of the second method was to separate the “high yield group” ≥7.0 t ha−1 (n=578) from the entire data set. In this group, lower quintiles for the Mehlich 3-concentration of individual microelements in soil were calculated. The lower quintiles (QU1) were taken as limit values. It was assumed that QU1 is a good indicator of the lowest micronutrient concentration in the soil at which a yield of 7.0 t ha−1 or higher can be obtained. The comparison of the values calculated with the regression equations method and the high yield method showed their similarity, which confirmed the reliability of these values. The proposed values define the limit for low microelements concentration in soil determined with the Mehlich 3 method, below which wheat fertilization with these nutrients is recommended.

scholarly journals Technical Note: Regression Analysis of Proximal Hyperspectral Data to Predict Soil pH and Olsen P

Agriculture ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 55 ◽  
Author(s):  
Miles Grafton ◽  
Therese Kaul ◽  
Alan Palmer ◽  
Peter Bishop ◽  
Michael White
Keyword(s):  
Soil Nutrient ◽  
Large Data ◽  
Technical Note ◽  
Soil Samples ◽  
Hyperspectral Data ◽  
Data Sets ◽  
Regression Equations ◽  
Data Set ◽  
Hill Country ◽  
Olsen P

This work examines two large data sets to demonstrate that hyperspectral proximal devices may be able to measure soil nutrient. One data set has 3189 soil samples from four hill country pastoral farms and the second data set has 883 soil samples taken from a stratified nested grid survey. These were regressed with spectra from a proximal hyperspectral device measured on the same samples. This aim was to obtain wavelengths, which may be proxy indicators for measurements of soil nutrients. Olsen P and pH were regressed with 2150 wave bands between 350 nm and 2500 nm to find wavebands, which were significant indicators. The 100 most significant wavebands for each proxy were used to regress both data sets. The regression equations from the smaller data set were used to predict the values of pH and Olsen P to validate the larger data set. The predictions from the equations from the smaller data set were as good as the regression analyses from the large data set when applied to it. This may mean that, in the future, hyperspectral analysis may be a proxy to soil chemical analysis; or increase the intensity of soil testing by finding markers of fertility cheaply in the field.

PSVI-8 Meta-regression Analysis to Determine the Relationship Between Growing Pig Body Weight and Variation

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 218-219
Author(s):  
Andres Fernando T Russi ◽  
Mike D Tokach ◽  
Jason C Woodworth ◽  
Joel M DeRouchey ◽  
Robert D Goodband ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Analysis ◽  
Sample Size ◽  
Polynomial Regression ◽  
Data Sets ◽  
Regression Equations ◽  
Prediction Equations ◽  
Data Set ◽  
Rate Of Increase ◽  
The Relationship

Abstract The swine industry has been constantly evolving to select animals with improved performance traits and to minimize variation in body weight (BW) in order to meet packer specifications. Therefore, understanding variation presents an opportunity for producers to find strategies that could help reduce, manage, or deal with variation of pigs in a barn. A systematic review and meta-analysis was conducted by collecting data from multiple studies and available data sets in order to develop prediction equations for coefficient of variation (CV) and standard deviation (SD) as a function of BW. Information regarding BW variation from 16 papers was recorded to provide approximately 204 data points. Together, these data included 117,268 individually weighed pigs with a sample size that ranged from 104 to 4,108 pigs. A random-effects model with study used as a random effect was developed. Observations were weighted using sample size as an estimate for precision on the analysis, where larger data sets accounted for increased accuracy in the model. Regression equations were developed using the nlme package of R to determine the relationship between BW and its variation. Polynomial regression analysis was conducted separately for each variation measurement. When CV was reported in the data set, SD was calculated and vice versa. The resulting prediction equations were: CV (%) = 20.04 – 0.135 × (BW) + 0.00043 × (BW)2, R2=0.79; SD = 0.41 + 0.150 × (BW) - 0.00041 × (BW)2, R2 = 0.95. These equations suggest that there is evidence for a decreasing quadratic relationship between mean CV of a population and BW of pigs whereby the rate of decrease is smaller as mean pig BW increases from birth to market. Conversely, the rate of increase of SD of a population of pigs is smaller as mean pig BW increases from birth to market.

scholarly journals Outlier Detection Transilience-Probabilistic Model for Wind Tunnels Based on Sensor Data

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2532
Author(s):  
Encarna Quesada ◽  
Juan J. Cuadrado-Gallego ◽  
Miguel Ángel Patricio ◽  
Luis Usero
Keyword(s):  
Anomaly Detection ◽  
Outlier Detection ◽  
Research Work ◽  
Industrial Sector ◽  
Sensor Data ◽  
Correct Selection ◽  
Wind Tunnels ◽  
Data Set ◽  
First Case ◽  
Anomaly Analysis

Anomaly Detection research is focused on the development and application of methods that allow for the identification of data that are different enough—compared with the rest of the data set that is being analyzed—and considered anomalies (or, as they are more commonly called, outliers). These values mainly originate from two sources: they may be errors introduced during the collection or handling of the data, or they can be correct, but very different from the rest of the values. It is essential to correctly identify each type as, in the first case, they must be removed from the data set but, in the second case, they must be carefully analyzed and taken into account. The correct selection and use of the model to be applied to a specific problem is fundamental for the success of the anomaly detection study and, in many cases, the use of only one model cannot provide sufficient results, which can be only reached by using a mixture model resulting from the integration of existing and/or ad hoc-developed models. This is the kind of model that is developed and applied to solve the problem presented in this paper. This study deals with the definition and application of an anomaly detection model that combines statistical models and a new method defined by the authors, the Local Transilience Outlier Identification Method, in order to improve the identification of outliers in the sensor-obtained values of variables that affect the operations of wind tunnels. The correct detection of outliers for the variables involved in wind tunnel operations is very important for the industrial ventilation systems industry, especially for vertical wind tunnels, which are used as training facilities for indoor skydiving, as the incorrect performance of such devices may put human lives at risk. In consequence, the use of the presented model for outlier detection may have a high impact in this industrial sector. In this research work, a proof-of-concept is carried out using data from a real installation, in order to test the proposed anomaly analysis method and its application to control the correct performance of wind tunnels.

scholarly journals Complementary Use of Magnetometric Measurements for Geochemical Investigation of Light REE Concentration in Anthropogenically Polluted Soils

Minerals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 457
Author(s):  
Piotr Fabijańczyk ◽  
Jarosław Zawadzki
Keyword(s):  
Soil Samples ◽  
Soil Conditions ◽  
Soil Cores ◽  
Light Rare Earth ◽  
Polluted Soils ◽  
Chemical Measurements ◽  
Geochemical Investigation ◽  
Light Rare Earth Elements ◽  
Soil Magnetic Susceptibility ◽  
Geophysical Measurements

The purpose of this study was to use fast geophysical measurements of soil magnetic susceptibility (κ) as supplementary data for chemical measurements of selected light rare earth elements (REEs) in soil. In order to ensure diversity in soil conditions, anthropogenic conditions and types of land use, seven areas were selected, all located in regions subjected to past or present industrial pollution. Magnetometric parameters were measured using a selected magnetic sensor that was specially designed for measurements of soil cores and were used to classify collected soil cores into six distinctive types. The analysis of REEs concentrations in soil was carried out taking into account the grouping of collected soil samples based on the type of study area (open, forested and mountain), and additionally on the measured magnetometric parameters of collected soil cores. A use of magnetometric measurements provided different, but complementary to chemical measurements information, which allowed to obtain deeper insight on REEs concentrations in soils in studied areas.

ASPECTS CONCERNING PEANUTS CROPS ON SANDY SOILS IN SOUTHERN OLTENIA

2021 ◽  
Author(s):  
Milica Dima ◽  
Aurelia Diaconu ◽  
Reta Drăghici ◽  
Drăghici Iulian ◽  
Matei Gheorghe
Keyword(s):  
Proper Time ◽  
Sandy Soils ◽  
Vegetation Period ◽  
Yield Potential ◽  
High Yield ◽  
Soil Conditions ◽  
Growth Type ◽  
Great Production ◽  
Large Production ◽  
Seeding Time

"For the capitalization of the climate and soil conditions for the sandy soil region in Southern Oltenia by cultivating peanuts it is necessary to use varieties with large production abilities and proper technology for the crops. In view of its cultivation on south Oltenia sandy soils, there were carried out in the period 2004-2006, at the Plants Crops Research and Development Station on Sandy Soils Dabuleni, experiments have been set regarding aspects such as: the optimal seeding period, the recommendation varieties with high yield potential and balanced composition. The research was conducted under irrigation conditions, in a three-year rotation of wheat, peanut, maize. Along with erect growth type varieties, known for their short vegetation period, rising and creeping growth type varieties can also be used; these varieties have a great production potential in our country`s conditions. Establishing the proper time for seeding is espe since sandy soils are heating quickly but are also cooling quickly, the best seeding time is between the end of April- the beginning of May, depending on the date when the seeding depth has a steady temperature, minimal required for the seed to germinate."

Agronomic and environmental soil test phosphorus in manured and non-manured Manitoba soils

2007 ◽  
Vol 87 (1) ◽  
pp. 73-83 ◽  
Author(s):  
D. Kimaragamage ◽  
O O Akinremi ◽  
D. Flaten ◽  
J. Heard
Keyword(s):  
Iron Oxide ◽  
Surface Soil ◽  
Single Equation ◽  
Soil Samples ◽  
Soil Test ◽  
Regression Equations ◽  
Soil P ◽  
Soil Test Phosphorus ◽  
Bray 1 ◽  
Water Extractable

Quantitative relationships between soil test phosphorus (STP) methods are needed to guide P management especially in manured soils with high P. Our objectives were: (i) to compare amounts of P extracted by different methods; (ii) to develop and verify regression equations to convert results among methods; and (iii) to establish environmental P thresholds for different methods, in manured and non-manured soils of Manitoba. We analyzed 214 surface soil samples (0–15 cm), of which 51 had previous manure application. Agronomic STP methods were Olsen (O-P), Mehlich-3 (M3-P), Kelowna-1 (original; K1-P), Kelowna-2 (modified; K2-P), Kelowna-3 (modified; K3-P), Bray-1 (B1-P) and Miller and Axley (MA-P), while environmental STP methods were water extractable (W-P), Ca Cl2 extractable (Ca-P) and iron oxide impregnated filter paper (FeO-P) methods. The different methods extracted different amounts of P, but were linearly correlated. For an O-P range of 0–30 mg kg-1, relationships between O-P and other STP were similar for manured and nonmanured soils, but the relationships diverged at higher O-P levels, indicating that one STP cannot be reliably converted to another using a single equation for manured and non-manured soils at environmentally critical P levels (0–100 mg kg-1 O-P). Suggested environmental soil P threshold ranges, in mg P kg-1, were 88–118 for O-P, 138–184 for K1-P, 108–143 for K2-P, 103–137 for K3-P, 96–128 for B1-P, 84–111 for MA-P, 15–20 for W-P, 5–8 for Ca-P and 85–111 for FeO-P. Key words: Phosphorus, soil test phosphorus, manured soils, non-manured soils, environmental threshold

scholarly journals Effect of Tillage System and Organic Matter Management Interactions on Soil Chemical Properties and Biological Activity in a Spring Wheat Short-Time Cultivation

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7451
Author(s):  
Barbara Breza-Boruta ◽  
Karol Kotwica ◽  
Justyna Bauza-Kaszewska
Keyword(s):  
Organic Matter ◽  
Biological Activity ◽  
Organic Carbon ◽  
Spring Wheat ◽  
Chemical Properties ◽  
Soil Samples ◽  
Available Phosphorus ◽  
Organic Carbon Content ◽  
Tillage System ◽  
Short Time

Properly selected tillage methods and management of the available organic matter resources are considered important measures to enable farming in accordance with the principles of sustainable agriculture. Depending on the depth and intensity of cultivation, tillage practices affect soil chemical composition, structure and biological activity. The three-year experiment was performed on the soil under spring wheat (cv. Tybalt) short-time cultivation. The influence of different tillage systems and stubble management on the soil’s chemical and biological parameters was analyzed. Organic carbon content (OC); content of biologically available phosphorus (Pa), potassium (Ka), and magnesium (Mg); content of total nitrogen (TN), mineral nitrogen forms: N-NO3 and N-NH4 were determined in various soil samples. Moreover, the total number of microorganisms (TNM), bacteria (B), actinobacteria (A), fungi (F); soil respiratory activity (SR); and pH in 1 M KCl (pH) were also investigated. The results show that organic matter amendment is of greater influence on soil characteristics than the tillage system applied. Manure application, as well as leaving the straw in the field, resulted in higher amounts of organic carbon and biologically available potassium. A significant increase in the number of soil microorganisms was also observed in soil samples from the experimental plots including this procedure.

scholarly journals Arsenic and heavy metal concentrations in agricultural soils in South Savo province

2002 ◽  
Vol 11 (4) ◽  
pp. 285-300 ◽  
Author(s):  
V. MÄNTYLAHTI ◽  
P. LAAKSO
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Agricultural Soils ◽  
Mineral Soil ◽  
Soil Samples ◽  
Potential Hazard ◽  
Metal Concentrations ◽  
Limit Values ◽  
Heavy Metal Concentrations ◽  
Target Values

Increasing concentrations of arsenic and heavy metals in agricultural soils are becoming a growing problem in industrialized countries. These harmful elements represent the basis of a range of problems in the food chain, and are a potential hazard for animal and human health. It is therefore important to gauge their absolute and relative concentrations in soils that are used for crop production. In this study the arsenic and heavy metal concentrations in 274 mineral soil samples and 38 organogenic soil samples taken from South Savo province in 2000 were determined using the aqua regia extraction technique. The soil samples were collected from 23 farms.The elements analyzed were arsenic, cadmium, chromium, copper, mercury, nickel, lead and zinc. The median concentrations in the mineral soils were:As 2.90 mg kg –1, Cd 0.084 mg kg –1, Cr 17.0 mg kg –1, Cu 13.0 mg kg –1, Hg 0.060 mg kg –1, Ni 5.4 mg kg –1, Pb 7.7 mg kg –1, Zn 36.5 mg kg –1. The corresponding values in the organogenic soils were:As 2.80 mg kg –1, Cd 0.265 mg kg –1, Cr 15.0 mg kg –1, Cu 29.0 mg kg –1, Hg 0.200 mg kg –1, Ni 5.9 mg kg –1, Pb 11.0 mg kg –1, Zn 25.5 mg kg –1. The results indicated that cadmium and mercury concentrations in the mineral and organogenic soils differed. Some of the arsenic, cadmium and mercury concentrations exceeded the normative values but did not exceed limit values. Most of the agricultural fields in South Savo province contained only small amounts of arsenic and heavy metals and could be classified as “Clean Soil”. A draft for the target values of arsenic and heavy metal concentrations in “Clean Soil” is presented.;

scholarly journals Using Model Performance to Assess the Representativeness of Data for Model Development and Calibration in Financial Institutions

Risks ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 204
Author(s):  
Chamay Kruger ◽  
Willem Daniel Schutte ◽  
Tanja Verster
Keyword(s):  
Case Studies ◽  
Model Development ◽  
Model Performance ◽  
Data Set ◽  
Pooled Data ◽  
External Data ◽  
First Case ◽  
Multivariate Prediction ◽  
Credit Data

This paper proposes a methodology that utilises model performance as a metric to assess the representativeness of external or pooled data when it is used by banks in regulatory model development and calibration. There is currently no formal methodology to assess representativeness. The paper provides a review of existing regulatory literature on the requirements of assessing representativeness and emphasises that both qualitative and quantitative aspects need to be considered. We present a novel methodology and apply it to two case studies. We compared our methodology with the Multivariate Prediction Accuracy Index. The first case study investigates whether a pooled data source from Global Credit Data (GCD) is representative when considering the enrichment of internal data with pooled data in the development of a regulatory loss given default (LGD) model. The second case study differs from the first by illustrating which other countries in the pooled data set could be representative when enriching internal data during the development of a LGD model. Using these case studies as examples, our proposed methodology provides users with a generalised framework to identify subsets of the external data that are representative of their Country’s or bank’s data, making the results general and universally applicable.

Sign in / Sign up

Export Citation Format

Share Document