NLEAP Computer Model and Multiple Linear Regression Prediction of Nitrate Leaching in Vegetable Systems

2002 ◽  
Vol 12 (2) ◽  
pp. 250-256 ◽  
Author(s):  
Hudson Minshew ◽  
John Selker ◽  
Delbert Hemphill ◽  
Richard P. Dick
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Nitrate Leaching ◽  
Cover Crops ◽  
N Uptake ◽  
N Leaching ◽  
Residual Soil ◽  
Vegetable Crops ◽  
Crop N Uptake ◽  
Mlr Model

Predicting leaching of residual soil nitrate-nitrogen (NO3-N) in wet climates is important for reducing risks of groundwater contamination and conserving soil N. The goal of this research was to determine the potential to use easily measurable or readily available soilclimatic-plant data that could be put into simple computer models and used to predict NO3 leaching under various management systems. Two computer programs were compared for their potential to predict monthly NO3-N leaching losses in western Oregon vegetable systems with or without cover crops. The models were a statistical multiple linear regression (MLR) model and the commercially available Nitrate Leaching and Economical Analysis Package model (NLEAP 1.13). The best MLR model found using stepwise regression to predict annual leachate NO3-N had four independent variables (log transformed fall soil NO3-N, leachate volume, summer crop N uptake, and N fertilizer rate) (P < 0.001, R2 = 0.57). Comparisons were made between NLEAP and field data for mass of NO3-N leached between the months of September and May from 1992 to 1997. Predictions with NLEAP showed greater correlation to observed data during high-rainfall years compared to dry or averagerainfall years. The model was found to be sensitive to yield estimates, but vegetation management choices were limiting for vegetable crops and for systems that included a cover crop.

scholarly journals Potato Yield Response and Seasonal Nitrate Leaching as Influenced by Nitrogen Management

Agronomy ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 2055
Author(s):  
Chedzer-Clarc Clément ◽  
Athyna N. Cambouris ◽  
Noura Ziadi ◽  
Bernie J. Zebarth ◽  
Antoine Karam
Keyword(s):  
Nitrate Leaching ◽  
Environmental Concern ◽  
N Uptake ◽  
Growing Season ◽  
Potato Yield ◽  
Yield Response ◽  
N Leaching ◽  
N Availability ◽  
Residual Soil ◽  
Calcium Ammonium Nitrate

Nitrate leaching is of great environmental concern, particularly with potatoes grown on sandy soils. This 3-year study evaluated the effect of three N rates (100, 150, and 200 kg ha−1) of single applications of polymer-coated urea (PCU) and a 75% PCU + 25% urea mixture, plus a conventional split application of 200 kg N ha−1 of a 50% ammonium sulfate + 50% calcium ammonium nitrate mixture (CONV) on NO3−-N leaching, potato yield, and N uptake under irrigated and non-irrigated conditions on a sandy soil in Quebec (Canada). Fertilizer N application increased growing season NO3−-N leaching only under irrigation. On average, irrigation increased seasonal NO3−-N leaching by 52%. Under irrigated conditions, PCU reduced NO3−-N leaching compared to PCU + urea. However, both PCU and PCU + urea significantly increased NO3−-N leaching compared to the CONV at the equivalent N rate of 200 kg N ha−1. This was attributed to the timing of soil N availability and deep-water percolation. Total (TY) and marketable (MY) yields in the CONV were similar to those in the PCU applied at the equivalent N rate of 200 kg N ha−1. Despite lower plant N uptake, PCU resulted in greater TY and MY compared to PCU + urea. Residual soil inorganic N was greater for PCU and PCU + urea compared to the CONV, providing evidence that PCU products have the potential to increase NO3−-N leaching after the growing season. In this study, PCU was an agronomically and environmentally better choice than PCU + urea. The results also showed that the efficiency of PCU to reduce seasonal NO3−-N leaching may vary according to the timing of precipitation and irrigation.

Effects of the timing of ploughing-in temporary leguminous pastures and two winter cover crops on nitrogen mineralization, nitrate leaching and spring wheat growth

1995 ◽  
Vol 124 (1) ◽  
pp. 1-9 ◽  
Author(s):  
G. S. Francis ◽  
R. J. Haynes ◽  
P. H. Williams
Keyword(s):  
Spring Wheat ◽  
Nitrate Leaching ◽  
Cover Crops ◽  
N Uptake ◽  
First Year ◽  
Mineral N ◽  
Leaching Losses ◽  
Winter Cover ◽  
Winter Cover Crops ◽  
Second Year

SUMMARYTwo field experiments at Canterbury, New Zealand during 1991–93 investigated the effect of the timing of ploughing a 4-year-old ryegrass/white clover pasture and the effect of two winter cover crops on subsequent N mineralization, nitrate leaching and growth and N uptake of the following wheat crops.Net N mineralization of organic N (of plant and soil origin) increased with increased fallow period between ploughing and leaching. The total amount of N accumulated in the profile by the start of winter ranged from 107 to 131 and from 42 to 45 kg N/ha for fallow treatments started in March and May respectively. Winter wheat (planted in May) had no effect on mineral N contents by the start of winter, whereas greenfeed (GF) oats (planted in March) significantly reduced the mineral N content in one year.Cumulative leaching losses over the first winter after ploughing-in pasture varied markedly between years in relation to rainfall amount and distribution. Leaching losses were greater from the March fallow (72–106 kg N/ha) than the May fallow treatments (8–52 kg N/ha). Winter wheat did not reduce leaching losses in either year. GF oats did not reduce losses in 1991/92, but losses in 1992/93, when major drainage events occurred late in the winter, were only c. 40% of those under fallow.Incorporation of a large amount (> 7 t/ha dry matter) of pasture or GF oat residue in spring depressed yield and total N uptake of the following spring wheat, largely due to net N immobilization which could be overcome by the application of fertilizer N.First-year treatments had very little residual effect in the second year. Leaching losses over the second winter (mean 142 kg N/ha) were largely unaffected by the extent of first year leaching losses. Second year leaching losses were greater than first year losses, probably due to the greater amount of mineral N at depth in the soil before the start of the second winter.

scholarly journals Aplikasi Analisis Multivariat Berdasarkan Warna untuk Memprediksi Brix dan pH pada Pisang

2017 ◽  
Vol 37 (1) ◽  
pp. 109 ◽  
Author(s):  
Yohanita Maulina Akbar ◽  
Dr. Rudiati Evi Masithoh ◽  
Nafis Khuriyati
Keyword(s):  
Multivariate Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Conventional Method ◽  
Calibration Model ◽  
Validation Data ◽  
Determination Coefficient ◽  
Mlr Model

In this research, Multiple Linear Regression (MLR) model was used to predict Brix and pH of banana based on RGB and Lab color values. Banana samples varied in color and ripening level from less ripen to ripen. RGB and Lab values were measured non-destructively using colormeter, while Brix and pH were determined using conventional method in laboratory. Multivariate analysis was done using the Unscrambler ® X 10.3 (CAMO, AS, OLSO, Norway, and trial version). Results showed that calibration model using MLR was able to predict Brix and pH of banana based on RGB and Lab color values. Furthermore, validation data were used to test the selected models. MLR model to predict Brix based on RGB and Lab validation resulted in 0.8 and 0.84 of determination coefficient between observation and prediction data. The model was also able to predict pH based on RGB and Lab values with 0.71 and 0.79 of determination coefficient between observation and prediction data. ABSTRAKPada penelitian ini, model Multiple Linear Regression (MLR) digunakan untuk memprediksi Brix dan pH pada buah pisang berdasarkan nilai warna Red Green Blue (RGB) dan Lab. Pisang yang dianalisis mempunyai variasi warna dari kurang masak sampai masak. Parameter warna RGB dan Lab dilakukan secara non-destruktif dengan menggunakan colormeter, sedangkan pengukuran kualitas internal yaitu Brix dan pH ditentukan secara destruktif atau dengan prosedur konvensional di laboratorium. Aplikasi analisis multivariat yang digunakan adalah Unscrambler ® X 10.3 (CAMO, AS, OLSO, Norway, versi trial). Analisis data menunjukkan bahwa model kalibrasi MLR dapat digunakan untuk memprediksi Brix dan pH berdasarkan parameter warna RGB dan Lab pada buah pisang. Selanjutnya, data validasi digunakan untuk menguji model MLR terpilih. Model kalibrasi MLR dapat memprediksi Brix berdasarkan nilai RGB dan Lab dengan nilai koefisien determinasi (R2) sebesar 0,8 dan 0,84, secara berurutan. Sedangkan koefisien determinasi (R2) untuk pH berdasarkan warna RGB dan Lab adalah 0,71 dan 0,79.

scholarly journals Particulate matter (PM10) prediction based on multiple linear regression: a case study in Chiang Rai Province, Thailand

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wissanupong Kliengchuay ◽  
Rachodbun Srimanus ◽  
Wechapraan Srimanus ◽  
Sarima Niampradit ◽  
Nopadol Preecha ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Local Level ◽  
Winter Season ◽  
Summer Season ◽  
Weather Data ◽  
Good Prediction ◽  
Mlr Model

Abstract Background The northern regions of Thailand have been facing haze episodes and transboundary air pollution every year in which particulate matter, particularly PM10, accumulates in the air, detrimentally affecting human health. Chiang Rai province is one of the country’s most popular tourist destinations as well as an important economic hub. This study aims to develop and compare the best-fitted model for PM10 prediction for different seasons using meteorological factors. Method The air pollution and weather data acquired from the Pollution Control Department (PCD) spanned from the years 2011 until 2018 at two stations on an hourly basis. Four different stepwise Multiple Linear Regression (MLR) models for predicting the PM10 concentration were then developed, namely annual, summer, rainy, and winter seasons. Results The maximum daily PM10 concentration was observed in the summer season for both stations. The minimum daily concentration was detected in the rainy season. The seasonal variation of PM10 was significantly different for both stations. CO was moderately related to PM10 in the summer season. The PM10 summer model was the best MLR model to predict PM10 during haze episodes. In both stations, it revealed an R2 of 0.73 and 0.61 in stations 65 and 71, respectively. Relative humidity and atmospheric pressure display negative relationships, although temperature is positively correlated with PM10 concentrations in summer and rainy seasons. Whereas pressure plays a positive relationship with PM10 in the winter season. Conclusions In conclusion, the MLR models are effective at estimating PM10 concentrations at the local level for each seasonal. The annual MLR model at both stations indicates a good prediction with an R2 of 0.61 and 0.52 for stations 65 and 73, respectively.

Seasonal ground level ozone prediction using multiple linear regression (MLR) model

2020 ◽  
Vol 6 (4) ◽  
pp. 1981-1989
Author(s):  
Sarat Kumar Allu ◽  
Shailaja Srinivasan ◽  
Rama Krishna Maddala ◽  
Aparna Reddy ◽  
Gangagni Rao Anupoju
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Ground Level ◽  
Ground Level Ozone ◽  
Ozone Prediction ◽  
Mlr Model

scholarly journals Nitrogen Requirements and N Status Determination of Lettuce

HortScience ◽  
2012 ◽  
Vol 47 (12) ◽  
pp. 1768-1774 ◽  
Author(s):  
Thomas G. Bottoms ◽  
Richard F. Smith ◽  
Michael D. Cahn ◽  
Timothy K. Hartz
Keyword(s):  
N Uptake ◽  
N Fertilization ◽  
Fertilizer Efficiency ◽  
Residual Soil ◽  
N Application ◽  
Fresh Biomass ◽  
N Concentration ◽  
Whole Plant ◽  
Crop N Uptake ◽  
N Rates

As concern over NO3-N pollution of groundwater increases, California lettuce growers are under pressure to improve nitrogen (N) fertilizer efficiency. Crop growth, N uptake, and the value of soil and plant N diagnostic measures were evaluated in 24 iceberg and romaine lettuce (Lactuca sativa L. var. capitata L., and longifolia Lam., respectively) field trials from 2007 to 2010. The reliability of presidedressing soil nitrate testing (PSNT) to identify fields in which N application could be reduced or eliminated was evaluated in 16 non-replicated strip trials and five replicated trials on commercial farms. All commercial field sites had greater than 20 mg·kg−1 residual soil NO3-N at the time of the first in-season N application. In the strip trials, plots in which the cooperating growers’ initial sidedress N application was eliminated or reduced were compared with the growers’ standard N fertilization program. In the replicated trials, the growers’ N regime was compared with treatments in which one or more N fertigation through drip irrigation was eliminated. Additionally, seasonal N rates from 11 to 336 kg·ha−1 were compared in three replicated drip-irrigated research farm trials. Seasonal N application in the strip trials was reduced by an average of 77 kg·ha−1 (73 kg·ha−1 vs. 150 kg·ha−1 for the grower N regime) with no reduction in fresh biomass produced and only a slight reduction in crop N uptake (151 kg·ha−1 vs. 156 kg·ha−1 for the grower N regime). Similarly, an average seasonal N rate reduction of 88 kg·ha−1 (96 kg·ha−1 vs. 184 kg·ha−1) was achieved in the replicated commercial trials with no biomass reduction. Seasonal N rates between 111 and 192 kg·ha−1 maximized fresh biomass in the research farm trials, which were conducted in fields with lower residual soil NO3-N than the commercial trials. Across fields, lettuce N uptake was slow in the first 4 weeks after planting, averaging less than 0.5 kg·ha−1·d−1. N uptake then increased linearly until harvest (≈9 weeks after planting), averaging ≈4 kg·ha−1·d−1 over that period. Whole plant critical N concentration (Nc, the minimum whole plant N concentration required to maximize growth) was estimated by the equation Nc (g·kg−1) = 42 − 2.8 dry mass (DM, Mg·ha−1); on that basis, critical N uptake (crop N uptake required to maintain whole plant N above Nc) in the commercial fields averaged 116 kg·ha−1 compared with the mean uptake of 145 kg·ha−1 with the grower N regime. Soil NO3-N greater than 20 mg·kg−1 was a reliable indicator that N application could be reduced or delayed. Neither leaf N nor midrib NO3-N was correlated with concurrently measured soil NO3-N and therefore of limited value in directing in-season N fertilization.

scholarly journals Modeling of soil shear strength using multiple linear regression (MLR) at Penang, Malaysia

2021 ◽  
Vol 9 (3A) ◽  
Author(s):  
Bala Balarabe ◽  
◽  
Andy Anderson Bery ◽  
Keyword(s):  
Shear Strength ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Friction Angle ◽  
Accurate Estimation ◽  
Near Surface ◽  
Independent Variables ◽  
Refraction Tomography ◽  
Soil Shear Strength ◽  
Mlr Model

This paper presents multiple linear regression (MLR) soil shear strength models developed from electrical resistivity and seismic refraction tomography data. The MLR technique is used to estimate the value of dependent variables of soil shear strength based on the value of two independent variables, namely, resistivity and velocity. These parameters were regressed using regression statistics technique for generating MLR model. The results of MLR model, which is based on the estimation of model dependent parameters (Log10 resistivity and Log10 velocity), calculated for p-value, are less than 0.05 and VIF value less than 10 for cohesion and friction angle models. This result shows that there is a statistically significant relationship between cohesion and friction angle with geophysical parameters (independent variables). The estimation accuracy of the MLR models is also conducted for verification, and the result shows that RMSE value for predicted cohesion and predicted friction angle is 0.77 kN/m2 and 1.73° which is close to zero. Meanwhile, MAPE value was found to be 4.57 % and 7.61 %, indicating highly accurate estimation for the MLR models of predicted cohesion and predicted friction angle. Based on the application of near surface, the study area was successfully classified into two regions, namely, medium and hard clayey sand. Thus, it is concluded that MLR method is suitable in estimating the subsurface characterization that covered more regions compared to the traditional method (laboratory test).

scholarly journals Combining Green Manuring and Fertigation Maximizes Tomato Crop Yield and Minimizes Nitrogen Losses

Agronomy ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 977
Author(s):  
Michela Farneselli ◽  
Paolo Benincasa ◽  
Giacomo Tosti ◽  
Marcello Guiducci ◽  
Francesco Tei
Keyword(s):  
Cover Crops ◽  
N Uptake ◽  
Growth And Yield ◽  
Winter Period ◽  
N Leaching ◽  
Tomato Crop ◽  
N Nutrition ◽  
Green Manuring ◽  
Processing Tomato ◽  
Environmentally Sound

The aim of this experiment was to evaluate the effect of fertilizing processing tomato by coupling the green manuring of fall-winter cover crops with fertigation in spring-summer. In a two-year experiment, seven fertilization treatments were compared: green manuring of pure barley (B100) and pure vetch (V100) sown at 100% of their ordinary seeding rates, green manuring of a barley-vetch mixture at a ratio of 75:25 of their own seed rates (B75V25), fertigation with drip irrigation at a rate of 200 kg ha−1 of nitrogen (N) (Fert_N200), fertigation combined with B100 and B75V25 at a N rate complementary to 200 kg N ha−1 (B100 + Fert and B75V25 + Fert, respectively), and an unfertilized control (N0) with no cover crops for green manuring prior to tomato transplanting or fertigation. The Fert_N200 treatment resulted in maximum tomato N uptake, growth and yield, but caused high N leaching, especially during the no-cover fall-winter period, as was also the case for N0. The V100 treatment promoted quite good tomato N status and yield, but did not reduce N leaching. The B100 and B75V25 treatments reduced N leaching but decreased tomato N uptake, growth and yield. The B100 + Fert and B75V25 + Fert treatments reduced N leaching, likely increased soil N stock, and facilitated optimal tomato N nutrition and maximum yields. Combining fertigation with green manuring of cover crops composed of pure grass or grass-legume mixtures appears to be a very effective and environmentally sound practice for fertilizing high N-demanding spring-summer crops like processing tomato.

Estimating and Modeling Uncertainties Affecting Production Throughput Using ARIMA-Multiple Linear Regression

2012 ◽  
Vol 488-489 ◽  
pp. 1263-1267
Author(s):  
Amir Azizi ◽  
Amir Yazid B. Ali ◽  
Loh Wei Ping ◽  
Mohsen Mohammadzadeh
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Production Line ◽  
Moving Average ◽  
Random Variables ◽  
Modeling Uncertainties ◽  
Break Time ◽  
Demand Changes ◽  
Changes Over Time ◽  
Mlr Model

Throughput of each production stage cannot meet the demand in the real production system because of the disruptions and interruptions of the production line for example break time and scrap. On the other hand, demand changes over time due to volume variation and product redesign as the customers’ needs are changing. This situation leads to planning and controlling under uncertain condition. This paper proposes a hybrid model of autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR) for estimating and modeling the random variables of production line in order to forecast the throughput in presence of production variations and demand fluctuation. The random variables under consideration of this study are demand, break-time, scrap, and lead-time. The random variables are formulated in the MLR model, where the mean absolute percentage of error (MAPE) was 2.53%. Further, nine ARIMA models with different parameters in MLR model are fitted to the data and compared by their MAPE. The best model with the lowest MAPE was when the ARIMA parameters set for p=1, d=0, and q=3. Finally the proposed model using ARIMA-MLR is formulated by MAPE of 1.55%.

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